{"cells":[{"cell_type":"markdown","metadata":{},"source":["# Detectron2 + DocLayNet Dataset"]},{"cell_type":"markdown","metadata":{},"source":["## Setup the configuration for training the Detectron2 model"]},{"cell_type":"code","execution_count":1,"metadata":{"id":"z4dDEh-DzRRn"},"outputs":[{"data":{"text/plain":[""]},"execution_count":1,"metadata":{},"output_type":"execute_result"}],"source":["import torch\n","import detectron2\n","\n","# Import common libraries\n","import numpy as np\n","import os\n","import matplotlib.pyplot as plt\n","\n","# Import tools from Detectron2 library\n","from detectron2 import model_zoo\n","from detectron2.engine import DefaultPredictor\n","from detectron2.engine import DefaultTrainer\n","from detectron2.config import get_cfg\n","from detectron2.utils.visualizer import Visualizer\n","from detectron2.utils.logger import setup_logger\n","from detectron2.structures import BoxMode\n","from detectron2.data import DatasetCatalog, MetadataCatalog\n","\n","# Setup Detectron2 logger\n","setup_logger()"]},{"cell_type":"markdown","metadata":{},"source":["## Get the dataset annoatations in COCO format"]},{"cell_type":"code","execution_count":2,"metadata":{"id":"De2IoJ2a7kyG"},"outputs":[{"name":"stdout","output_type":"stream","text":["loading annotations into memory...\n","Done (t=6.60s)\n","creating index...\n","index created!\n","loading annotations into memory...\n","Done (t=0.38s)\n","creating index...\n","index created!\n","loading annotations into memory...\n","Done (t=0.48s)\n","creating index...\n","index created!\n"]}],"source":["from pycocotools.coco import COCO\n","\n","path = \"/home/alex/Datasets/DocLayNet/DocLayNet_core/\"\n","path_to_coco_train = path + \"COCO/train.json\"\n","path_to_coco_test = path + \"COCO/test.json\"\n","path_to_coco_val = path + \"COCO/val.json\"\n","\n","train_coco = COCO(path_to_coco_train)\n","test_coco = COCO(path_to_coco_test)\n","val_coco = COCO(path_to_coco_val)"]},{"cell_type":"code","execution_count":3,"metadata":{},"outputs":[{"data":{"text/plain":["11"]},"execution_count":3,"metadata":{},"output_type":"execute_result"}],"source":["classes = [i[\"name\"] for i in train_coco.loadCats(train_coco.getCatIds())]\n","len(classes)"]},{"cell_type":"markdown","metadata":{},"source":["## Preprocess the data for the Detectron2 model training"]},{"cell_type":"code","execution_count":4,"metadata":{"id":"PPE_PqpdAa4N"},"outputs":[],"source":["def get_dicts(ds_coco):\n"," dataset_dicts = []\n"," for image_id in ds_coco.getImgIds():\n"," record = {}\n","\n"," image_info = ds_coco.loadImgs(image_id)[0]\n"," img_dir = \"/home/alex/Datasets/DocLayNet/DocLayNet_core/PNG/\"\n"," record[\"file_name\"] = os.path.join(img_dir, image_info[\"file_name\"])\n"," record[\"image_id\"] = image_info[\"id\"]\n"," record[\"height\"] = image_info[\"height\"]\n"," record[\"width\"] = image_info[\"width\"]\n","\n"," objs = []\n"," for annotation in ds_coco.loadAnns(ds_coco.getAnnIds(image_id)):\n"," obj = {\n"," \"bbox\": annotation[\"bbox\"],\n"," \"bbox_mode\": BoxMode.XYWH_ABS,\n"," \"category_id\": annotation[\"category_id\"] - 1,\n"," }\n"," # if obj[\"category_id\"] == 11:\n"," # print(image_id)\n"," objs.append(obj)\n"," record[\"annotations\"] = objs\n"," dataset_dicts.append(record)\n"," return dataset_dicts\n","\n","\n","train_dataset = get_dicts(train_coco)\n","test_dataset = get_dicts(test_coco)\n","val_dataset = get_dicts(val_coco)"]},{"cell_type":"code","execution_count":5,"metadata":{"id":"gdHVzNbMB1En"},"outputs":[],"source":["def register_doclaynet(name, dataset_dicts):\n"," DatasetCatalog.register(name, lambda: dataset_dicts)\n"," MetadataCatalog.get(name).set(thing_classes=classes)\n","\n","\n","register_doclaynet(\"train\", train_dataset)\n","register_doclaynet(\"test\", test_dataset)\n","register_doclaynet(\"val\", val_dataset)"]},{"cell_type":"code","execution_count":6,"metadata":{"id":"34iKGKdSCFz_"},"outputs":[],"source":["cfg = get_cfg()\n","cfg.merge_from_file(\n"," model_zoo.get_config_file(\"COCO-Detection/faster_rcnn_R_101_FPN_3x.yaml\")\n",")\n","cfg.DATASETS.TRAIN = (\"train\",)\n","cfg.DATASETS.TEST = (\"test\",)\n","cfg.DATALOADER.NUM_WORKERS = 2\n","cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url(\n"," \"COCO-Detection/faster_rcnn_R_101_FPN_3x.yaml\"\n",")\n","cfg.MODEL.DEVICE = \"cuda\"\n","cfg.SOLVER.IMS_PER_BATCH = 2\n","cfg.SOLVER.BASE_LR = 0.00001\n","cfg.SOLVER.MAX_ITER = 85000\n","cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 128\n","cfg.MODEL.ROI_HEADS.NUM_CLASSES = len(classes)\n","# cfg.MODEL.MASK_ON = True\n","\n","cfg.OUTPUT_DIR = \"./output\"\n","os.makedirs(cfg.OUTPUT_DIR, exist_ok=True)"]},{"cell_type":"markdown","metadata":{},"source":["## Train the model"]},{"cell_type":"code","execution_count":7,"metadata":{"id":"L0eznx3HGKOC"},"outputs":[{"name":"stdout","output_type":"stream","text":["\u001b[32m[08/23 15:52:54 d2.engine.defaults]: \u001b[0mModel:\n","GeneralizedRCNN(\n"," (backbone): FPN(\n"," (fpn_lateral2): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))\n"," (fpn_output2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n"," (fpn_lateral3): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))\n"," (fpn_output3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n"," (fpn_lateral4): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))\n"," (fpn_output4): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n"," (fpn_lateral5): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1))\n"," (fpn_output5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n"," (top_block): LastLevelMaxPool()\n"," (bottom_up): ResNet(\n"," (stem): BasicStem(\n"," (conv1): Conv2d(\n"," 3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n"," )\n"," )\n"," (res2): Sequential(\n"," (0): BottleneckBlock(\n"," (shortcut): Conv2d(\n"," 64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv1): Conv2d(\n"," 64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," )\n"," (1): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," )\n"," (2): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," )\n"," )\n"," (res3): Sequential(\n"," (0): BottleneckBlock(\n"," (shortcut): Conv2d(\n"," 256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n"," )\n"," (conv1): Conv2d(\n"," 256, 128, kernel_size=(1, 1), stride=(2, 2), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n"," )\n"," )\n"," (1): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n"," )\n"," )\n"," (2): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n"," )\n"," )\n"," (3): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n"," )\n"," )\n"," )\n"," (res4): Sequential(\n"," (0): BottleneckBlock(\n"," (shortcut): Conv2d(\n"," 512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," (conv1): Conv2d(\n"," 512, 256, kernel_size=(1, 1), stride=(2, 2), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (1): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (2): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (3): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (4): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (5): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (6): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (7): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (8): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (9): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (10): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (11): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (12): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (13): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (14): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (15): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (16): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (17): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (18): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (19): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (20): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (21): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," (22): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"," )\n"," )\n"," )\n"," (res5): Sequential(\n"," (0): BottleneckBlock(\n"," (shortcut): Conv2d(\n"," 1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n"," )\n"," (conv1): Conv2d(\n"," 1024, 512, kernel_size=(1, 1), stride=(2, 2), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n"," )\n"," )\n"," (1): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n"," )\n"," )\n"," (2): BottleneckBlock(\n"," (conv1): Conv2d(\n"," 2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n"," )\n"," (conv2): Conv2d(\n"," 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n"," )\n"," (conv3): Conv2d(\n"," 512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False\n"," (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n"," )\n"," )\n"," )\n"," )\n"," )\n"," (proposal_generator): RPN(\n"," (rpn_head): StandardRPNHead(\n"," (conv): Conv2d(\n"," 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)\n"," (activation): ReLU()\n"," )\n"," (objectness_logits): Conv2d(256, 3, kernel_size=(1, 1), stride=(1, 1))\n"," (anchor_deltas): Conv2d(256, 12, kernel_size=(1, 1), stride=(1, 1))\n"," )\n"," (anchor_generator): DefaultAnchorGenerator(\n"," (cell_anchors): BufferList()\n"," )\n"," )\n"," (roi_heads): StandardROIHeads(\n"," (box_pooler): ROIPooler(\n"," (level_poolers): ModuleList(\n"," (0): ROIAlign(output_size=(7, 7), spatial_scale=0.25, sampling_ratio=0, aligned=True)\n"," (1): ROIAlign(output_size=(7, 7), spatial_scale=0.125, sampling_ratio=0, aligned=True)\n"," (2): ROIAlign(output_size=(7, 7), spatial_scale=0.0625, sampling_ratio=0, aligned=True)\n"," (3): ROIAlign(output_size=(7, 7), spatial_scale=0.03125, sampling_ratio=0, aligned=True)\n"," )\n"," )\n"," (box_head): FastRCNNConvFCHead(\n"," (flatten): Flatten(start_dim=1, end_dim=-1)\n"," (fc1): Linear(in_features=12544, out_features=1024, bias=True)\n"," (fc_relu1): ReLU()\n"," (fc2): Linear(in_features=1024, out_features=1024, bias=True)\n"," (fc_relu2): ReLU()\n"," )\n"," (box_predictor): FastRCNNOutputLayers(\n"," (cls_score): Linear(in_features=1024, out_features=12, bias=True)\n"," (bbox_pred): Linear(in_features=1024, out_features=44, bias=True)\n"," )\n"," )\n",")\n","\u001b[32m[08/23 15:52:54 d2.data.build]: \u001b[0mRemoved 272 images with no usable annotations. 69103 images left.\n","\u001b[32m[08/23 15:52:55 d2.data.build]: \u001b[0mDistribution of instances among all 11 categories:\n","\u001b[36m| category | #instances | category | #instances | category | #instances |\n","|:----------:|:-------------|:-------------:|:-------------|:-----------:|:-------------|\n","| Caption | 19218 | Footnote | 5619 | Formula | 21167 |\n","| List-item | 161818 | Page-footer | 61313 | Page-header | 47973 |\n","| Picture | 39667 | Section-hea.. | 118590 | Table | 30070 |\n","| Text | 431251 | Title | 4437 | | |\n","| total | 941123 | | | | |\u001b[0m\n","\u001b[32m[08/23 15:52:55 d2.data.dataset_mapper]: \u001b[0m[DatasetMapper] Augmentations used in training: [ResizeShortestEdge(short_edge_length=(640, 672, 704, 736, 768, 800), max_size=1333, sample_style='choice'), RandomFlip()]\n","\u001b[32m[08/23 15:52:55 d2.data.build]: \u001b[0mUsing training sampler TrainingSampler\n","\u001b[32m[08/23 15:52:55 d2.data.common]: \u001b[0mSerializing the dataset using: \n","\u001b[32m[08/23 15:52:55 d2.data.common]: \u001b[0mSerializing 69103 elements to byte tensors and concatenating them all ...\n","\u001b[32m[08/23 15:52:56 d2.data.common]: \u001b[0mSerialized dataset takes 66.68 MiB\n","\u001b[32m[08/23 15:52:56 d2.data.build]: \u001b[0mMaking batched data loader with batch_size=2\n","\u001b[5m\u001b[31mWARNING\u001b[0m \u001b[32m[08/23 15:52:56 d2.solver.build]: \u001b[0mSOLVER.STEPS contains values larger than SOLVER.MAX_ITER. These values will be ignored.\n","\u001b[32m[08/23 15:52:56 d2.checkpoint.detection_checkpoint]: \u001b[0m[DetectionCheckpointer] Loading from https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_101_FPN_3x/137851257/model_final_f6e8b1.pkl ...\n"]},{"name":"stderr","output_type":"stream","text":["Skip loading parameter 'roi_heads.box_predictor.cls_score.weight' to the model due to incompatible shapes: (81, 1024) in the checkpoint but (12, 1024) in the model! You might want to double check if this is expected.\n","Skip loading parameter 'roi_heads.box_predictor.cls_score.bias' to the model due to incompatible shapes: (81,) in the checkpoint but (12,) in the model! You might want to double check if this is expected.\n","Skip loading parameter 'roi_heads.box_predictor.bbox_pred.weight' to the model due to incompatible shapes: (320, 1024) in the checkpoint but (44, 1024) in the model! You might want to double check if this is expected.\n","Skip loading parameter 'roi_heads.box_predictor.bbox_pred.bias' to the model due to incompatible shapes: (320,) in the checkpoint but (44,) in the model! You might want to double check if this is expected.\n","Some model parameters or buffers are not found in the checkpoint:\n","\u001b[34mroi_heads.box_predictor.bbox_pred.{bias, weight}\u001b[0m\n","\u001b[34mroi_heads.box_predictor.cls_score.{bias, weight}\u001b[0m\n"]},{"name":"stdout","output_type":"stream","text":["\u001b[32m[08/23 15:52:56 d2.engine.train_loop]: \u001b[0mStarting training from iteration 0\n"]},{"name":"stderr","output_type":"stream","text":["/home/alex/Projects/Detectron2_DocLayNet/.venv/lib/python3.10/site-packages/torch/functional.py:513: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3609.)\n"," return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]\n"]},{"name":"stdout","output_type":"stream","text":["\u001b[32m[08/23 15:53:04 d2.utils.events]: \u001b[0m eta: 7:14:46 iter: 19 total_loss: 10.6 loss_cls: 2.407 loss_box_reg: 0.4918 loss_rpn_cls: 6.817 loss_rpn_loc: 0.7638 time: 0.3087 last_time: 0.4137 data_time: 0.0108 last_data_time: 0.0045 lr: 1.9981e-07 max_mem: 2221M\n","\u001b[32m[08/23 15:53:13 d2.utils.events]: \u001b[0m eta: 8:59:16 iter: 39 total_loss: 9.985 loss_cls: 2.388 loss_box_reg: 0.6024 loss_rpn_cls: 6.228 loss_rpn_loc: 0.7895 time: 0.3743 last_time: 0.3768 data_time: 0.0047 last_data_time: 0.0045 lr: 3.9961e-07 max_mem: 2224M\n","\u001b[32m[08/23 15:53:21 d2.utils.events]: \u001b[0m eta: 9:41:30 iter: 59 total_loss: 9.876 loss_cls: 2.374 loss_box_reg: 0.5355 loss_rpn_cls: 6.169 loss_rpn_loc: 0.7896 time: 0.3933 last_time: 0.4082 data_time: 0.0047 last_data_time: 0.0050 lr: 5.9941e-07 max_mem: 2236M\n","\u001b[32m[08/23 15:53:30 d2.utils.events]: \u001b[0m eta: 9:46:44 iter: 79 total_loss: 8.903 loss_cls: 2.347 loss_box_reg: 0.5875 loss_rpn_cls: 5.312 loss_rpn_loc: 0.631 time: 0.3993 last_time: 0.4160 data_time: 0.0046 last_data_time: 0.0044 lr: 7.9921e-07 max_mem: 2243M\n","\u001b[32m[08/23 15:53:38 d2.utils.events]: \u001b[0m eta: 9:44:47 iter: 99 total_loss: 8.389 loss_cls: 2.321 loss_box_reg: 0.6732 loss_rpn_cls: 4.901 loss_rpn_loc: 0.6923 time: 0.4002 last_time: 0.3474 data_time: 0.0045 last_data_time: 0.0043 lr: 9.9901e-07 max_mem: 2243M\n","\u001b[32m[08/23 15:53:45 d2.utils.events]: \u001b[0m eta: 9:38:26 iter: 119 total_loss: 7.744 loss_cls: 2.298 loss_box_reg: 0.5782 loss_rpn_cls: 4.227 loss_rpn_loc: 0.6881 time: 0.3985 last_time: 0.4494 data_time: 0.0044 last_data_time: 0.0044 lr: 1.1988e-06 max_mem: 2243M\n","\u001b[32m[08/23 15:53:54 d2.utils.events]: \u001b[0m eta: 9:37:53 iter: 139 total_loss: 6.578 loss_cls: 2.27 loss_box_reg: 0.6707 loss_rpn_cls: 3.129 loss_rpn_loc: 0.6183 time: 0.4002 last_time: 0.4219 data_time: 0.0046 last_data_time: 0.0046 lr: 1.3986e-06 max_mem: 2243M\n","\u001b[32m[08/23 15:54:02 d2.utils.events]: \u001b[0m eta: 9:36:35 iter: 159 total_loss: 5.926 loss_cls: 2.246 loss_box_reg: 0.751 loss_rpn_cls: 2.364 loss_rpn_loc: 0.5705 time: 0.4006 last_time: 0.4441 data_time: 0.0046 last_data_time: 0.0044 lr: 1.5984e-06 max_mem: 2250M\n","\u001b[32m[08/23 15:54:09 d2.utils.events]: \u001b[0m eta: 9:32:02 iter: 179 total_loss: 5.3 loss_cls: 2.215 loss_box_reg: 0.7746 loss_rpn_cls: 1.811 loss_rpn_loc: 0.624 time: 0.3986 last_time: 0.2335 data_time: 0.0046 last_data_time: 0.0044 lr: 1.7982e-06 max_mem: 2250M\n","\u001b[32m[08/23 15:54:16 d2.utils.events]: \u001b[0m eta: 9:26:24 iter: 199 total_loss: 4.377 loss_cls: 2.201 loss_box_reg: 0.7509 loss_rpn_cls: 0.9952 loss_rpn_loc: 0.4763 time: 0.3925 last_time: 0.3759 data_time: 0.0045 last_data_time: 0.0044 lr: 1.998e-06 max_mem: 2250M\n","\u001b[32m[08/23 15:54:24 d2.utils.events]: \u001b[0m eta: 9:26:47 iter: 219 total_loss: 3.846 loss_cls: 2.18 loss_box_reg: 0.8205 loss_rpn_cls: 0.4431 loss_rpn_loc: 0.3941 time: 0.3944 last_time: 0.3924 data_time: 0.0048 last_data_time: 0.0047 lr: 2.1978e-06 max_mem: 2250M\n","\u001b[32m[08/23 15:54:33 d2.utils.events]: \u001b[0m eta: 9:28:39 iter: 239 total_loss: 3.688 loss_cls: 2.138 loss_box_reg: 0.7793 loss_rpn_cls: 0.3706 loss_rpn_loc: 0.4297 time: 0.3961 last_time: 0.4322 data_time: 0.0046 last_data_time: 0.0045 lr: 2.3976e-06 max_mem: 2357M\n","\u001b[32m[08/23 15:54:41 d2.utils.events]: \u001b[0m eta: 9:29:33 iter: 259 total_loss: 3.496 loss_cls: 2.069 loss_box_reg: 0.7301 loss_rpn_cls: 0.2708 loss_rpn_loc: 0.4037 time: 0.3972 last_time: 0.4097 data_time: 0.0048 last_data_time: 0.0046 lr: 2.5974e-06 max_mem: 2357M\n","\u001b[32m[08/23 15:54:49 d2.utils.events]: \u001b[0m eta: 9:29:24 iter: 279 total_loss: 3.605 loss_cls: 2.007 loss_box_reg: 0.7999 loss_rpn_cls: 0.2126 loss_rpn_loc: 0.4816 time: 0.3977 last_time: 0.3875 data_time: 0.0045 last_data_time: 0.0043 lr: 2.7972e-06 max_mem: 2357M\n","\u001b[32m[08/23 15:54:57 d2.utils.events]: \u001b[0m eta: 9:33:01 iter: 299 total_loss: 3.594 loss_cls: 1.955 loss_box_reg: 0.8059 loss_rpn_cls: 0.3698 loss_rpn_loc: 0.4716 time: 0.3988 last_time: 0.4477 data_time: 0.0046 last_data_time: 0.0044 lr: 2.997e-06 max_mem: 2357M\n","\u001b[32m[08/23 15:55:06 d2.utils.events]: \u001b[0m eta: 9:35:42 iter: 319 total_loss: 3.3 loss_cls: 1.881 loss_box_reg: 0.8269 loss_rpn_cls: 0.2463 loss_rpn_loc: 0.4182 time: 0.3998 last_time: 0.4070 data_time: 0.0046 last_data_time: 0.0043 lr: 3.1968e-06 max_mem: 2357M\n","\u001b[32m[08/23 15:55:14 d2.utils.events]: \u001b[0m eta: 9:36:36 iter: 339 total_loss: 3.242 loss_cls: 1.788 loss_box_reg: 0.7701 loss_rpn_cls: 0.2474 loss_rpn_loc: 0.4347 time: 0.4004 last_time: 0.4086 data_time: 0.0046 last_data_time: 0.0045 lr: 3.3966e-06 max_mem: 2357M\n","\u001b[32m[08/23 15:55:22 d2.utils.events]: \u001b[0m eta: 9:36:04 iter: 359 total_loss: 3.115 loss_cls: 1.721 loss_box_reg: 0.7479 loss_rpn_cls: 0.2148 loss_rpn_loc: 0.4013 time: 0.4005 last_time: 0.3831 data_time: 0.0046 last_data_time: 0.0050 lr: 3.5964e-06 max_mem: 2357M\n","\u001b[32m[08/23 15:55:30 d2.utils.events]: \u001b[0m eta: 9:34:52 iter: 379 total_loss: 3.153 loss_cls: 1.615 loss_box_reg: 0.7713 loss_rpn_cls: 0.3002 loss_rpn_loc: 0.4453 time: 0.4003 last_time: 0.3467 data_time: 0.0050 last_data_time: 0.0042 lr: 3.7962e-06 max_mem: 2357M\n","\u001b[32m[08/23 15:55:38 d2.utils.events]: \u001b[0m eta: 9:34:13 iter: 399 total_loss: 2.894 loss_cls: 1.48 loss_box_reg: 0.7596 loss_rpn_cls: 0.2134 loss_rpn_loc: 0.3765 time: 0.4005 last_time: 0.3274 data_time: 0.0047 last_data_time: 0.0050 lr: 3.996e-06 max_mem: 2357M\n","\u001b[32m[08/23 15:55:46 d2.utils.events]: \u001b[0m eta: 9:33:51 iter: 419 total_loss: 2.727 loss_cls: 1.385 loss_box_reg: 0.7593 loss_rpn_cls: 0.2222 loss_rpn_loc: 0.3826 time: 0.4008 last_time: 0.4058 data_time: 0.0048 last_data_time: 0.0042 lr: 4.1958e-06 max_mem: 2357M\n","\u001b[32m[08/23 15:55:54 d2.utils.events]: \u001b[0m eta: 9:33:56 iter: 439 total_loss: 2.644 loss_cls: 1.283 loss_box_reg: 0.7553 loss_rpn_cls: 0.1729 loss_rpn_loc: 0.3993 time: 0.4011 last_time: 0.4154 data_time: 0.0047 last_data_time: 0.0047 lr: 4.3956e-06 max_mem: 2357M\n","\u001b[32m[08/23 15:56:02 d2.utils.events]: \u001b[0m eta: 9:34:45 iter: 459 total_loss: 2.657 loss_cls: 1.247 loss_box_reg: 0.7983 loss_rpn_cls: 0.2104 loss_rpn_loc: 0.4289 time: 0.4015 last_time: 0.4342 data_time: 0.0047 last_data_time: 0.0051 lr: 4.5954e-06 max_mem: 2357M\n","\u001b[32m[08/23 15:56:11 d2.utils.events]: \u001b[0m eta: 9:35:15 iter: 479 total_loss: 2.548 loss_cls: 1.191 loss_box_reg: 0.8132 loss_rpn_cls: 0.1763 loss_rpn_loc: 0.4066 time: 0.4020 last_time: 0.4249 data_time: 0.0047 last_data_time: 0.0044 lr: 4.7952e-06 max_mem: 2357M\n","\u001b[32m[08/23 15:56:19 d2.utils.events]: \u001b[0m eta: 9:35:00 iter: 499 total_loss: 2.441 loss_cls: 1.078 loss_box_reg: 0.7536 loss_rpn_cls: 0.1621 loss_rpn_loc: 0.3764 time: 0.4020 last_time: 0.4348 data_time: 0.0048 last_data_time: 0.0051 lr: 4.995e-06 max_mem: 2357M\n","\u001b[32m[08/23 15:56:27 d2.utils.events]: \u001b[0m eta: 9:35:17 iter: 519 total_loss: 2.409 loss_cls: 1.079 loss_box_reg: 0.8081 loss_rpn_cls: 0.1153 loss_rpn_loc: 0.4179 time: 0.4026 last_time: 0.3741 data_time: 0.0046 last_data_time: 0.0047 lr: 5.1948e-06 max_mem: 2357M\n","\u001b[32m[08/23 15:56:35 d2.utils.events]: \u001b[0m eta: 9:35:31 iter: 539 total_loss: 2.512 loss_cls: 1.06 loss_box_reg: 0.7566 loss_rpn_cls: 0.1812 loss_rpn_loc: 0.4084 time: 0.4030 last_time: 0.4161 data_time: 0.0046 last_data_time: 0.0049 lr: 5.3946e-06 max_mem: 2357M\n","\u001b[32m[08/23 15:56:41 d2.utils.events]: \u001b[0m eta: 9:34:18 iter: 559 total_loss: 2.433 loss_cls: 1.065 loss_box_reg: 0.7848 loss_rpn_cls: 0.1673 loss_rpn_loc: 0.3991 time: 0.3988 last_time: 0.2006 data_time: 0.0046 last_data_time: 0.0045 lr: 5.5944e-06 max_mem: 2357M\n","\u001b[32m[08/23 15:56:46 d2.utils.events]: \u001b[0m eta: 9:31:53 iter: 579 total_loss: 2.461 loss_cls: 1.007 loss_box_reg: 0.8319 loss_rpn_cls: 0.1664 loss_rpn_loc: 0.4349 time: 0.3933 last_time: 0.1818 data_time: 0.0048 last_data_time: 0.0045 lr: 5.7942e-06 max_mem: 2357M\n","\u001b[32m[08/23 15:56:51 d2.utils.events]: \u001b[0m eta: 9:30:09 iter: 599 total_loss: 2.373 loss_cls: 0.9974 loss_box_reg: 0.7971 loss_rpn_cls: 0.1389 loss_rpn_loc: 0.3743 time: 0.3891 last_time: 0.2374 data_time: 0.0049 last_data_time: 0.0052 lr: 5.994e-06 max_mem: 2357M\n","\u001b[32m[08/23 15:56:56 d2.utils.events]: \u001b[0m eta: 9:27:21 iter: 619 total_loss: 2.329 loss_cls: 0.9597 loss_box_reg: 0.7295 loss_rpn_cls: 0.1729 loss_rpn_loc: 0.3929 time: 0.3844 last_time: 0.2319 data_time: 0.0048 last_data_time: 0.0045 lr: 6.1938e-06 max_mem: 2357M\n","\u001b[32m[08/23 15:57:01 d2.utils.events]: \u001b[0m eta: 9:25:32 iter: 639 total_loss: 2.396 loss_cls: 1.006 loss_box_reg: 0.8171 loss_rpn_cls: 0.1762 loss_rpn_loc: 0.3546 time: 0.3798 last_time: 0.2808 data_time: 0.0053 last_data_time: 0.0063 lr: 6.3936e-06 max_mem: 2357M\n","\u001b[32m[08/23 15:57:06 d2.utils.events]: \u001b[0m eta: 9:23:40 iter: 659 total_loss: 2.412 loss_cls: 0.9639 loss_box_reg: 0.7828 loss_rpn_cls: 0.1894 loss_rpn_loc: 0.3893 time: 0.3761 last_time: 0.2845 data_time: 0.0050 last_data_time: 0.0050 lr: 6.5934e-06 max_mem: 2357M\n","\u001b[32m[08/23 15:57:11 d2.utils.events]: \u001b[0m eta: 9:21:54 iter: 679 total_loss: 2.347 loss_cls: 0.9981 loss_box_reg: 0.7855 loss_rpn_cls: 0.1476 loss_rpn_loc: 0.3741 time: 0.3721 last_time: 0.2141 data_time: 0.0046 last_data_time: 0.0049 lr: 6.7932e-06 max_mem: 2357M\n","\u001b[32m[08/23 15:57:16 d2.utils.events]: \u001b[0m eta: 9:18:04 iter: 699 total_loss: 2.354 loss_cls: 0.9847 loss_box_reg: 0.8031 loss_rpn_cls: 0.124 loss_rpn_loc: 0.3904 time: 0.3685 last_time: 0.2741 data_time: 0.0048 last_data_time: 0.0047 lr: 6.993e-06 max_mem: 2357M\n","\u001b[32m[08/23 15:57:21 d2.utils.events]: \u001b[0m eta: 9:13:10 iter: 719 total_loss: 2.334 loss_cls: 0.9605 loss_box_reg: 0.8315 loss_rpn_cls: 0.1301 loss_rpn_loc: 0.3839 time: 0.3649 last_time: 0.2205 data_time: 0.0047 last_data_time: 0.0045 lr: 7.1928e-06 max_mem: 2357M\n","\u001b[32m[08/23 15:57:25 d2.utils.events]: \u001b[0m eta: 9:08:23 iter: 739 total_loss: 2.263 loss_cls: 0.9517 loss_box_reg: 0.7473 loss_rpn_cls: 0.1541 loss_rpn_loc: 0.3506 time: 0.3615 last_time: 0.2307 data_time: 0.0048 last_data_time: 0.0046 lr: 7.3926e-06 max_mem: 2357M\n","\u001b[32m[08/23 15:57:31 d2.utils.events]: \u001b[0m eta: 9:03:11 iter: 759 total_loss: 2.249 loss_cls: 0.9733 loss_box_reg: 0.8167 loss_rpn_cls: 0.1392 loss_rpn_loc: 0.3391 time: 0.3591 last_time: 0.2680 data_time: 0.0050 last_data_time: 0.0047 lr: 7.5924e-06 max_mem: 2357M\n","\u001b[32m[08/23 15:57:36 d2.utils.events]: \u001b[0m eta: 9:00:01 iter: 779 total_loss: 2.269 loss_cls: 0.9499 loss_box_reg: 0.7867 loss_rpn_cls: 0.1529 loss_rpn_loc: 0.3622 time: 0.3566 last_time: 0.3058 data_time: 0.0051 last_data_time: 0.0046 lr: 7.7922e-06 max_mem: 2357M\n","\u001b[32m[08/23 15:57:41 d2.utils.events]: \u001b[0m eta: 8:57:13 iter: 799 total_loss: 2.233 loss_cls: 0.9321 loss_box_reg: 0.7768 loss_rpn_cls: 0.1342 loss_rpn_loc: 0.3695 time: 0.3542 last_time: 0.2251 data_time: 0.0049 last_data_time: 0.0051 lr: 7.992e-06 max_mem: 2357M\n","\u001b[32m[08/23 15:57:47 d2.utils.events]: \u001b[0m eta: 8:53:19 iter: 819 total_loss: 2.211 loss_cls: 0.9301 loss_box_reg: 0.7955 loss_rpn_cls: 0.1167 loss_rpn_loc: 0.3694 time: 0.3520 last_time: 0.2779 data_time: 0.0049 last_data_time: 0.0052 lr: 8.1918e-06 max_mem: 2357M\n","\u001b[32m[08/23 15:57:52 d2.utils.events]: \u001b[0m eta: 8:49:01 iter: 839 total_loss: 2.247 loss_cls: 0.9329 loss_box_reg: 0.7987 loss_rpn_cls: 0.1789 loss_rpn_loc: 0.3338 time: 0.3501 last_time: 0.3011 data_time: 0.0050 last_data_time: 0.0049 lr: 8.3916e-06 max_mem: 2357M\n","\u001b[32m[08/23 15:57:58 d2.utils.events]: \u001b[0m eta: 8:45:37 iter: 859 total_loss: 2.331 loss_cls: 0.9518 loss_box_reg: 0.822 loss_rpn_cls: 0.1257 loss_rpn_loc: 0.4125 time: 0.3484 last_time: 0.2898 data_time: 0.0050 last_data_time: 0.0050 lr: 8.5914e-06 max_mem: 2357M\n","\u001b[32m[08/23 15:58:02 d2.utils.events]: \u001b[0m eta: 8:40:08 iter: 879 total_loss: 2.266 loss_cls: 0.9399 loss_box_reg: 0.8295 loss_rpn_cls: 0.1107 loss_rpn_loc: 0.3671 time: 0.3458 last_time: 0.1918 data_time: 0.0048 last_data_time: 0.0047 lr: 8.7912e-06 max_mem: 2357M\n","\u001b[32m[08/23 15:58:07 d2.utils.events]: \u001b[0m eta: 8:32:58 iter: 899 total_loss: 2.293 loss_cls: 0.9423 loss_box_reg: 0.8006 loss_rpn_cls: 0.1575 loss_rpn_loc: 0.413 time: 0.3432 last_time: 0.2473 data_time: 0.0048 last_data_time: 0.0045 lr: 8.991e-06 max_mem: 2357M\n","\u001b[32m[08/23 15:58:12 d2.utils.events]: \u001b[0m eta: 8:18:20 iter: 919 total_loss: 2.234 loss_cls: 0.9 loss_box_reg: 0.8253 loss_rpn_cls: 0.1321 loss_rpn_loc: 0.3641 time: 0.3408 last_time: 0.2474 data_time: 0.0047 last_data_time: 0.0045 lr: 9.1908e-06 max_mem: 2357M\n","\u001b[32m[08/23 15:58:16 d2.utils.events]: \u001b[0m eta: 8:12:37 iter: 939 total_loss: 2.192 loss_cls: 0.8683 loss_box_reg: 0.827 loss_rpn_cls: 0.115 loss_rpn_loc: 0.3717 time: 0.3385 last_time: 0.2530 data_time: 0.0047 last_data_time: 0.0044 lr: 9.3906e-06 max_mem: 2357M\n","\u001b[32m[08/23 15:58:21 d2.utils.events]: \u001b[0m eta: 8:08:12 iter: 959 total_loss: 2.225 loss_cls: 0.8835 loss_box_reg: 0.7894 loss_rpn_cls: 0.1386 loss_rpn_loc: 0.3538 time: 0.3361 last_time: 0.2237 data_time: 0.0048 last_data_time: 0.0047 lr: 9.5904e-06 max_mem: 2357M\n","\u001b[32m[08/23 15:58:25 d2.utils.events]: \u001b[0m eta: 8:05:12 iter: 979 total_loss: 2.236 loss_cls: 0.9371 loss_box_reg: 0.8331 loss_rpn_cls: 0.1128 loss_rpn_loc: 0.3215 time: 0.3338 last_time: 0.1992 data_time: 0.0048 last_data_time: 0.0047 lr: 9.7902e-06 max_mem: 2357M\n","\u001b[32m[08/23 15:58:30 d2.utils.events]: \u001b[0m eta: 8:02:25 iter: 999 total_loss: 2.188 loss_cls: 0.8709 loss_box_reg: 0.8193 loss_rpn_cls: 0.1259 loss_rpn_loc: 0.35 time: 0.3318 last_time: 0.2435 data_time: 0.0048 last_data_time: 0.0051 lr: 9.99e-06 max_mem: 2357M\n","\u001b[32m[08/23 15:58:35 d2.utils.events]: \u001b[0m eta: 7:59:40 iter: 1019 total_loss: 2.209 loss_cls: 0.9066 loss_box_reg: 0.7903 loss_rpn_cls: 0.1187 loss_rpn_loc: 0.3658 time: 0.3299 last_time: 0.1811 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2357M\n","\u001b[32m[08/23 15:58:39 d2.utils.events]: \u001b[0m eta: 7:30:07 iter: 1039 total_loss: 2.125 loss_cls: 0.8656 loss_box_reg: 0.7824 loss_rpn_cls: 0.1107 loss_rpn_loc: 0.3478 time: 0.3280 last_time: 0.2353 data_time: 0.0053 last_data_time: 0.0063 lr: 1e-05 max_mem: 2357M\n","\u001b[32m[08/23 15:58:44 d2.utils.events]: \u001b[0m eta: 7:15:17 iter: 1059 total_loss: 2.139 loss_cls: 0.8805 loss_box_reg: 0.7687 loss_rpn_cls: 0.1251 loss_rpn_loc: 0.359 time: 0.3262 last_time: 0.2486 data_time: 0.0049 last_data_time: 0.0049 lr: 1e-05 max_mem: 2357M\n","\u001b[32m[08/23 15:58:48 d2.utils.events]: \u001b[0m eta: 6:58:08 iter: 1079 total_loss: 2.169 loss_cls: 0.8582 loss_box_reg: 0.8066 loss_rpn_cls: 0.1253 loss_rpn_loc: 0.3264 time: 0.3243 last_time: 0.2330 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2357M\n","\u001b[32m[08/23 15:58:53 d2.utils.events]: \u001b[0m eta: 6:39:07 iter: 1099 total_loss: 2.157 loss_cls: 0.7992 loss_box_reg: 0.7616 loss_rpn_cls: 0.1247 loss_rpn_loc: 0.3845 time: 0.3227 last_time: 0.2629 data_time: 0.0049 last_data_time: 0.0045 lr: 1e-05 max_mem: 2357M\n","\u001b[32m[08/23 15:58:58 d2.utils.events]: \u001b[0m eta: 6:30:05 iter: 1119 total_loss: 2.135 loss_cls: 0.863 loss_box_reg: 0.8089 loss_rpn_cls: 0.1335 loss_rpn_loc: 0.3732 time: 0.3210 last_time: 0.2475 data_time: 0.0048 last_data_time: 0.0051 lr: 1e-05 max_mem: 2357M\n","\u001b[32m[08/23 15:59:02 d2.utils.events]: \u001b[0m eta: 6:12:41 iter: 1139 total_loss: 2.063 loss_cls: 0.7995 loss_box_reg: 0.8049 loss_rpn_cls: 0.1204 loss_rpn_loc: 0.3244 time: 0.3195 last_time: 0.2128 data_time: 0.0047 last_data_time: 0.0043 lr: 1e-05 max_mem: 2357M\n","\u001b[32m[08/23 15:59:07 d2.utils.events]: \u001b[0m eta: 6:04:16 iter: 1159 total_loss: 2.098 loss_cls: 0.8075 loss_box_reg: 0.8122 loss_rpn_cls: 0.1107 loss_rpn_loc: 0.3425 time: 0.3181 last_time: 0.2450 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2357M\n","\u001b[32m[08/23 15:59:12 d2.utils.events]: \u001b[0m eta: 6:00:50 iter: 1179 total_loss: 2.189 loss_cls: 0.8284 loss_box_reg: 0.8111 loss_rpn_cls: 0.1047 loss_rpn_loc: 0.3593 time: 0.3167 last_time: 0.2445 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2357M\n","\u001b[32m[08/23 15:59:17 d2.utils.events]: \u001b[0m eta: 5:57:39 iter: 1199 total_loss: 2.145 loss_cls: 0.8442 loss_box_reg: 0.8046 loss_rpn_cls: 0.1119 loss_rpn_loc: 0.3537 time: 0.3154 last_time: 0.2566 data_time: 0.0051 last_data_time: 0.0049 lr: 1e-05 max_mem: 2357M\n","\u001b[32m[08/23 15:59:21 d2.utils.events]: \u001b[0m eta: 5:54:17 iter: 1219 total_loss: 2.09 loss_cls: 0.8066 loss_box_reg: 0.7873 loss_rpn_cls: 0.1197 loss_rpn_loc: 0.3541 time: 0.3138 last_time: 0.2155 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2357M\n","\u001b[32m[08/23 15:59:26 d2.utils.events]: \u001b[0m eta: 5:52:06 iter: 1239 total_loss: 2.161 loss_cls: 0.863 loss_box_reg: 0.8062 loss_rpn_cls: 0.1096 loss_rpn_loc: 0.3855 time: 0.3127 last_time: 0.2337 data_time: 0.0049 last_data_time: 0.0050 lr: 1e-05 max_mem: 2357M\n","\u001b[32m[08/23 15:59:31 d2.utils.events]: \u001b[0m eta: 5:49:59 iter: 1259 total_loss: 2.103 loss_cls: 0.7949 loss_box_reg: 0.8023 loss_rpn_cls: 0.1301 loss_rpn_loc: 0.3798 time: 0.3116 last_time: 0.2510 data_time: 0.0048 last_data_time: 0.0050 lr: 1e-05 max_mem: 2357M\n","\u001b[32m[08/23 15:59:36 d2.utils.events]: \u001b[0m eta: 5:48:48 iter: 1279 total_loss: 2.126 loss_cls: 0.8369 loss_box_reg: 0.8174 loss_rpn_cls: 0.1252 loss_rpn_loc: 0.3645 time: 0.3107 last_time: 0.2378 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2357M\n","\u001b[32m[08/23 15:59:41 d2.utils.events]: \u001b[0m eta: 5:48:14 iter: 1299 total_loss: 2.218 loss_cls: 0.8166 loss_box_reg: 0.7962 loss_rpn_cls: 0.1395 loss_rpn_loc: 0.3764 time: 0.3097 last_time: 0.3044 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2357M\n","\u001b[32m[08/23 15:59:46 d2.utils.events]: \u001b[0m eta: 5:47:34 iter: 1319 total_loss: 2.177 loss_cls: 0.8748 loss_box_reg: 0.7998 loss_rpn_cls: 0.1144 loss_rpn_loc: 0.3398 time: 0.3088 last_time: 0.2217 data_time: 0.0048 last_data_time: 0.0044 lr: 1e-05 max_mem: 2357M\n","\u001b[32m[08/23 15:59:51 d2.utils.events]: \u001b[0m eta: 5:47:17 iter: 1339 total_loss: 2.121 loss_cls: 0.8345 loss_box_reg: 0.7965 loss_rpn_cls: 0.1154 loss_rpn_loc: 0.349 time: 0.3080 last_time: 0.2302 data_time: 0.0048 last_data_time: 0.0054 lr: 1e-05 max_mem: 2357M\n","\u001b[32m[08/23 15:59:56 d2.utils.events]: \u001b[0m eta: 5:46:39 iter: 1359 total_loss: 2.139 loss_cls: 0.8118 loss_box_reg: 0.7829 loss_rpn_cls: 0.1335 loss_rpn_loc: 0.3469 time: 0.3071 last_time: 0.2144 data_time: 0.0048 last_data_time: 0.0050 lr: 1e-05 max_mem: 2357M\n","\u001b[32m[08/23 16:00:01 d2.utils.events]: \u001b[0m eta: 5:45:56 iter: 1379 total_loss: 2.185 loss_cls: 0.8898 loss_box_reg: 0.8057 loss_rpn_cls: 0.1081 loss_rpn_loc: 0.352 time: 0.3061 last_time: 0.2672 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2357M\n","\u001b[32m[08/23 16:00:06 d2.utils.events]: \u001b[0m eta: 5:45:32 iter: 1399 total_loss: 2.103 loss_cls: 0.8314 loss_box_reg: 0.8407 loss_rpn_cls: 0.1141 loss_rpn_loc: 0.3426 time: 0.3056 last_time: 0.3247 data_time: 0.0049 last_data_time: 0.0045 lr: 1e-05 max_mem: 2357M\n","\u001b[32m[08/23 16:00:12 d2.utils.events]: \u001b[0m eta: 5:45:27 iter: 1419 total_loss: 2.135 loss_cls: 0.8209 loss_box_reg: 0.806 loss_rpn_cls: 0.1212 loss_rpn_loc: 0.3433 time: 0.3054 last_time: 0.2894 data_time: 0.0050 last_data_time: 0.0046 lr: 1e-05 max_mem: 2357M\n","\u001b[32m[08/23 16:00:17 d2.utils.events]: \u001b[0m eta: 5:44:38 iter: 1439 total_loss: 2.107 loss_cls: 0.8446 loss_box_reg: 0.8103 loss_rpn_cls: 0.119 loss_rpn_loc: 0.3363 time: 0.3047 last_time: 0.2700 data_time: 0.0049 last_data_time: 0.0052 lr: 1e-05 max_mem: 2357M\n","\u001b[32m[08/23 16:00:23 d2.utils.events]: \u001b[0m eta: 5:44:33 iter: 1459 total_loss: 2.007 loss_cls: 0.7968 loss_box_reg: 0.8048 loss_rpn_cls: 0.09874 loss_rpn_loc: 0.3043 time: 0.3044 last_time: 0.3036 data_time: 0.0049 last_data_time: 0.0052 lr: 1e-05 max_mem: 2357M\n","\u001b[32m[08/23 16:00:29 d2.utils.events]: \u001b[0m eta: 5:44:26 iter: 1479 total_loss: 2.099 loss_cls: 0.8244 loss_box_reg: 0.8219 loss_rpn_cls: 0.1255 loss_rpn_loc: 0.3123 time: 0.3043 last_time: 0.3188 data_time: 0.0051 last_data_time: 0.0051 lr: 1e-05 max_mem: 2357M\n","\u001b[32m[08/23 16:00:35 d2.utils.events]: \u001b[0m eta: 5:44:20 iter: 1499 total_loss: 2.072 loss_cls: 0.7873 loss_box_reg: 0.7857 loss_rpn_cls: 0.1109 loss_rpn_loc: 0.323 time: 0.3042 last_time: 0.3110 data_time: 0.0052 last_data_time: 0.0053 lr: 1e-05 max_mem: 2357M\n","\u001b[32m[08/23 16:00:41 d2.utils.events]: \u001b[0m eta: 5:44:14 iter: 1519 total_loss: 2.037 loss_cls: 0.8032 loss_box_reg: 0.8318 loss_rpn_cls: 0.0913 loss_rpn_loc: 0.3331 time: 0.3042 last_time: 0.3144 data_time: 0.0051 last_data_time: 0.0049 lr: 1e-05 max_mem: 2357M\n","\u001b[32m[08/23 16:00:47 d2.utils.events]: \u001b[0m eta: 5:44:09 iter: 1539 total_loss: 2.03 loss_cls: 0.7922 loss_box_reg: 0.7988 loss_rpn_cls: 0.1069 loss_rpn_loc: 0.3252 time: 0.3043 last_time: 0.3339 data_time: 0.0050 last_data_time: 0.0048 lr: 1e-05 max_mem: 2357M\n","\u001b[32m[08/23 16:00:53 d2.utils.events]: \u001b[0m eta: 5:44:46 iter: 1559 total_loss: 2.038 loss_cls: 0.7953 loss_box_reg: 0.8206 loss_rpn_cls: 0.1014 loss_rpn_loc: 0.334 time: 0.3043 last_time: 0.3282 data_time: 0.0049 last_data_time: 0.0053 lr: 1e-05 max_mem: 2357M\n","\u001b[32m[08/23 16:00:59 d2.utils.events]: \u001b[0m eta: 5:45:14 iter: 1579 total_loss: 2.024 loss_cls: 0.7911 loss_box_reg: 0.8068 loss_rpn_cls: 0.0988 loss_rpn_loc: 0.3193 time: 0.3041 last_time: 0.3333 data_time: 0.0050 last_data_time: 0.0049 lr: 1e-05 max_mem: 2357M\n","\u001b[32m[08/23 16:01:05 d2.utils.events]: \u001b[0m eta: 5:45:30 iter: 1599 total_loss: 1.986 loss_cls: 0.7774 loss_box_reg: 0.7998 loss_rpn_cls: 0.09583 loss_rpn_loc: 0.3408 time: 0.3041 last_time: 0.2658 data_time: 0.0049 last_data_time: 0.0049 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:01:11 d2.utils.events]: \u001b[0m eta: 5:45:58 iter: 1619 total_loss: 2.017 loss_cls: 0.7606 loss_box_reg: 0.7976 loss_rpn_cls: 0.09712 loss_rpn_loc: 0.3247 time: 0.3040 last_time: 0.2746 data_time: 0.0049 last_data_time: 0.0048 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:01:17 d2.utils.events]: \u001b[0m eta: 5:46:06 iter: 1639 total_loss: 2.083 loss_cls: 0.7716 loss_box_reg: 0.7874 loss_rpn_cls: 0.1155 loss_rpn_loc: 0.3511 time: 0.3041 last_time: 0.3181 data_time: 0.0054 last_data_time: 0.0052 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:01:23 d2.utils.events]: \u001b[0m eta: 5:46:13 iter: 1659 total_loss: 1.945 loss_cls: 0.7384 loss_box_reg: 0.7889 loss_rpn_cls: 0.09013 loss_rpn_loc: 0.2899 time: 0.3042 last_time: 0.3435 data_time: 0.0053 last_data_time: 0.0055 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:01:28 d2.utils.events]: \u001b[0m eta: 5:46:09 iter: 1679 total_loss: 2.046 loss_cls: 0.7871 loss_box_reg: 0.7806 loss_rpn_cls: 0.1171 loss_rpn_loc: 0.3542 time: 0.3035 last_time: 0.2284 data_time: 0.0049 last_data_time: 0.0045 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:01:33 d2.utils.events]: \u001b[0m eta: 5:46:03 iter: 1699 total_loss: 2.056 loss_cls: 0.7773 loss_box_reg: 0.7866 loss_rpn_cls: 0.09116 loss_rpn_loc: 0.3346 time: 0.3027 last_time: 0.2597 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:01:38 d2.utils.events]: \u001b[0m eta: 5:45:58 iter: 1719 total_loss: 1.994 loss_cls: 0.7517 loss_box_reg: 0.7907 loss_rpn_cls: 0.08898 loss_rpn_loc: 0.3247 time: 0.3018 last_time: 0.3009 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:01:42 d2.utils.events]: \u001b[0m eta: 5:45:53 iter: 1739 total_loss: 2.062 loss_cls: 0.8492 loss_box_reg: 0.8152 loss_rpn_cls: 0.09047 loss_rpn_loc: 0.3262 time: 0.3011 last_time: 0.2500 data_time: 0.0050 last_data_time: 0.0049 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:01:47 d2.utils.events]: \u001b[0m eta: 5:45:34 iter: 1759 total_loss: 2.031 loss_cls: 0.7404 loss_box_reg: 0.7991 loss_rpn_cls: 0.1034 loss_rpn_loc: 0.3325 time: 0.3003 last_time: 0.2596 data_time: 0.0049 last_data_time: 0.0049 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:01:52 d2.utils.events]: \u001b[0m eta: 5:45:11 iter: 1779 total_loss: 2.041 loss_cls: 0.8122 loss_box_reg: 0.8109 loss_rpn_cls: 0.1021 loss_rpn_loc: 0.3105 time: 0.2996 last_time: 0.2250 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:01:56 d2.utils.events]: \u001b[0m eta: 5:44:44 iter: 1799 total_loss: 2.02 loss_cls: 0.7561 loss_box_reg: 0.8047 loss_rpn_cls: 0.1141 loss_rpn_loc: 0.311 time: 0.2988 last_time: 0.2595 data_time: 0.0048 last_data_time: 0.0055 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:02:01 d2.utils.events]: \u001b[0m eta: 5:44:37 iter: 1819 total_loss: 1.958 loss_cls: 0.7482 loss_box_reg: 0.8085 loss_rpn_cls: 0.09337 loss_rpn_loc: 0.2767 time: 0.2982 last_time: 0.2424 data_time: 0.0054 last_data_time: 0.0048 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:02:06 d2.utils.events]: \u001b[0m eta: 5:44:14 iter: 1839 total_loss: 1.914 loss_cls: 0.7394 loss_box_reg: 0.7996 loss_rpn_cls: 0.1057 loss_rpn_loc: 0.289 time: 0.2974 last_time: 0.2265 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:02:11 d2.utils.events]: \u001b[0m eta: 5:43:32 iter: 1859 total_loss: 2.011 loss_cls: 0.7625 loss_box_reg: 0.7874 loss_rpn_cls: 0.1007 loss_rpn_loc: 0.3253 time: 0.2967 last_time: 0.2111 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:02:15 d2.utils.events]: \u001b[0m eta: 5:43:41 iter: 1879 total_loss: 1.971 loss_cls: 0.7617 loss_box_reg: 0.8251 loss_rpn_cls: 0.07721 loss_rpn_loc: 0.2699 time: 0.2961 last_time: 0.2161 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:02:20 d2.utils.events]: \u001b[0m eta: 5:43:39 iter: 1899 total_loss: 2.003 loss_cls: 0.7672 loss_box_reg: 0.8216 loss_rpn_cls: 0.09676 loss_rpn_loc: 0.3147 time: 0.2955 last_time: 0.2281 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:02:25 d2.utils.events]: \u001b[0m eta: 5:43:37 iter: 1919 total_loss: 1.864 loss_cls: 0.6799 loss_box_reg: 0.7799 loss_rpn_cls: 0.0874 loss_rpn_loc: 0.2794 time: 0.2948 last_time: 0.2005 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:02:29 d2.utils.events]: \u001b[0m eta: 5:43:37 iter: 1939 total_loss: 1.989 loss_cls: 0.7177 loss_box_reg: 0.7756 loss_rpn_cls: 0.1052 loss_rpn_loc: 0.365 time: 0.2943 last_time: 0.2216 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:02:34 d2.utils.events]: \u001b[0m eta: 5:43:35 iter: 1959 total_loss: 1.916 loss_cls: 0.7123 loss_box_reg: 0.7673 loss_rpn_cls: 0.1029 loss_rpn_loc: 0.3013 time: 0.2936 last_time: 0.2005 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:02:39 d2.utils.events]: \u001b[0m eta: 5:43:41 iter: 1979 total_loss: 1.925 loss_cls: 0.684 loss_box_reg: 0.7754 loss_rpn_cls: 0.1088 loss_rpn_loc: 0.3225 time: 0.2931 last_time: 0.2622 data_time: 0.0049 last_data_time: 0.0049 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:02:44 d2.utils.events]: \u001b[0m eta: 5:43:36 iter: 1999 total_loss: 1.985 loss_cls: 0.7731 loss_box_reg: 0.8006 loss_rpn_cls: 0.0954 loss_rpn_loc: 0.3348 time: 0.2925 last_time: 0.1978 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:02:48 d2.utils.events]: \u001b[0m eta: 5:43:27 iter: 2019 total_loss: 2.076 loss_cls: 0.7678 loss_box_reg: 0.7973 loss_rpn_cls: 0.1025 loss_rpn_loc: 0.3362 time: 0.2918 last_time: 0.2004 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:02:53 d2.utils.events]: \u001b[0m eta: 5:43:26 iter: 2039 total_loss: 1.941 loss_cls: 0.7193 loss_box_reg: 0.7841 loss_rpn_cls: 0.09408 loss_rpn_loc: 0.3058 time: 0.2912 last_time: 0.2116 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:02:57 d2.utils.events]: \u001b[0m eta: 5:43:11 iter: 2059 total_loss: 1.926 loss_cls: 0.7419 loss_box_reg: 0.7592 loss_rpn_cls: 0.1024 loss_rpn_loc: 0.2837 time: 0.2906 last_time: 0.1969 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:03:02 d2.utils.events]: \u001b[0m eta: 5:43:22 iter: 2079 total_loss: 1.858 loss_cls: 0.7051 loss_box_reg: 0.7305 loss_rpn_cls: 0.08858 loss_rpn_loc: 0.3002 time: 0.2901 last_time: 0.2008 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:03:07 d2.utils.events]: \u001b[0m eta: 5:43:15 iter: 2099 total_loss: 1.865 loss_cls: 0.7016 loss_box_reg: 0.768 loss_rpn_cls: 0.08393 loss_rpn_loc: 0.2912 time: 0.2895 last_time: 0.1828 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:03:11 d2.utils.events]: \u001b[0m eta: 5:43:43 iter: 2119 total_loss: 1.932 loss_cls: 0.7316 loss_box_reg: 0.7907 loss_rpn_cls: 0.09851 loss_rpn_loc: 0.3318 time: 0.2890 last_time: 0.2253 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:03:16 d2.utils.events]: \u001b[0m eta: 5:43:48 iter: 2139 total_loss: 1.999 loss_cls: 0.7328 loss_box_reg: 0.7906 loss_rpn_cls: 0.09652 loss_rpn_loc: 0.3358 time: 0.2886 last_time: 0.1987 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:03:21 d2.utils.events]: \u001b[0m eta: 5:43:36 iter: 2159 total_loss: 1.9 loss_cls: 0.732 loss_box_reg: 0.7625 loss_rpn_cls: 0.09743 loss_rpn_loc: 0.3051 time: 0.2881 last_time: 0.2401 data_time: 0.0051 last_data_time: 0.0057 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:03:26 d2.utils.events]: \u001b[0m eta: 5:43:44 iter: 2179 total_loss: 1.862 loss_cls: 0.7069 loss_box_reg: 0.772 loss_rpn_cls: 0.09431 loss_rpn_loc: 0.2672 time: 0.2875 last_time: 0.2242 data_time: 0.0050 last_data_time: 0.0050 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:03:30 d2.utils.events]: \u001b[0m eta: 5:43:30 iter: 2199 total_loss: 1.921 loss_cls: 0.7224 loss_box_reg: 0.789 loss_rpn_cls: 0.09237 loss_rpn_loc: 0.3328 time: 0.2870 last_time: 0.2565 data_time: 0.0049 last_data_time: 0.0046 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:03:35 d2.utils.events]: \u001b[0m eta: 5:43:53 iter: 2219 total_loss: 1.989 loss_cls: 0.7773 loss_box_reg: 0.7753 loss_rpn_cls: 0.0998 loss_rpn_loc: 0.3292 time: 0.2866 last_time: 0.2288 data_time: 0.0050 last_data_time: 0.0047 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:03:40 d2.utils.events]: \u001b[0m eta: 5:43:33 iter: 2239 total_loss: 1.859 loss_cls: 0.6839 loss_box_reg: 0.787 loss_rpn_cls: 0.0869 loss_rpn_loc: 0.2869 time: 0.2861 last_time: 0.2286 data_time: 0.0049 last_data_time: 0.0049 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:03:44 d2.utils.events]: \u001b[0m eta: 5:43:28 iter: 2259 total_loss: 1.916 loss_cls: 0.7166 loss_box_reg: 0.7886 loss_rpn_cls: 0.09026 loss_rpn_loc: 0.3191 time: 0.2857 last_time: 0.2322 data_time: 0.0048 last_data_time: 0.0043 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:03:49 d2.utils.events]: \u001b[0m eta: 5:42:54 iter: 2279 total_loss: 1.847 loss_cls: 0.7371 loss_box_reg: 0.7541 loss_rpn_cls: 0.1156 loss_rpn_loc: 0.2852 time: 0.2853 last_time: 0.2150 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:03:54 d2.utils.events]: \u001b[0m eta: 5:42:27 iter: 2299 total_loss: 1.909 loss_cls: 0.7558 loss_box_reg: 0.7951 loss_rpn_cls: 0.08361 loss_rpn_loc: 0.3082 time: 0.2848 last_time: 0.1951 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:03:59 d2.utils.events]: \u001b[0m eta: 5:42:12 iter: 2319 total_loss: 1.912 loss_cls: 0.726 loss_box_reg: 0.7657 loss_rpn_cls: 0.09582 loss_rpn_loc: 0.3029 time: 0.2844 last_time: 0.2540 data_time: 0.0049 last_data_time: 0.0045 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:04:03 d2.utils.events]: \u001b[0m eta: 5:41:44 iter: 2339 total_loss: 1.924 loss_cls: 0.7014 loss_box_reg: 0.7751 loss_rpn_cls: 0.1004 loss_rpn_loc: 0.2854 time: 0.2840 last_time: 0.2234 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:04:08 d2.utils.events]: \u001b[0m eta: 5:41:16 iter: 2359 total_loss: 1.863 loss_cls: 0.6711 loss_box_reg: 0.7431 loss_rpn_cls: 0.09006 loss_rpn_loc: 0.3089 time: 0.2836 last_time: 0.2499 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:04:13 d2.utils.events]: \u001b[0m eta: 5:41:11 iter: 2379 total_loss: 1.794 loss_cls: 0.7182 loss_box_reg: 0.7666 loss_rpn_cls: 0.07798 loss_rpn_loc: 0.2276 time: 0.2832 last_time: 0.2552 data_time: 0.0048 last_data_time: 0.0049 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:04:18 d2.utils.events]: \u001b[0m eta: 5:39:37 iter: 2399 total_loss: 1.857 loss_cls: 0.6684 loss_box_reg: 0.7916 loss_rpn_cls: 0.09046 loss_rpn_loc: 0.3032 time: 0.2829 last_time: 0.2180 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:04:23 d2.utils.events]: \u001b[0m eta: 5:37:23 iter: 2419 total_loss: 1.796 loss_cls: 0.6951 loss_box_reg: 0.7172 loss_rpn_cls: 0.08652 loss_rpn_loc: 0.2575 time: 0.2825 last_time: 0.1814 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:04:27 d2.utils.events]: \u001b[0m eta: 5:37:05 iter: 2439 total_loss: 1.977 loss_cls: 0.7735 loss_box_reg: 0.7738 loss_rpn_cls: 0.119 loss_rpn_loc: 0.3263 time: 0.2822 last_time: 0.2247 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:04:32 d2.utils.events]: \u001b[0m eta: 5:35:01 iter: 2459 total_loss: 1.885 loss_cls: 0.731 loss_box_reg: 0.7359 loss_rpn_cls: 0.09281 loss_rpn_loc: 0.3272 time: 0.2818 last_time: 0.2489 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:04:37 d2.utils.events]: \u001b[0m eta: 5:33:20 iter: 2479 total_loss: 1.84 loss_cls: 0.7012 loss_box_reg: 0.759 loss_rpn_cls: 0.1137 loss_rpn_loc: 0.3023 time: 0.2813 last_time: 0.2319 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:04:41 d2.utils.events]: \u001b[0m eta: 5:32:19 iter: 2499 total_loss: 1.896 loss_cls: 0.7125 loss_box_reg: 0.7607 loss_rpn_cls: 0.1024 loss_rpn_loc: 0.3122 time: 0.2810 last_time: 0.2495 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:04:46 d2.utils.events]: \u001b[0m eta: 5:30:58 iter: 2519 total_loss: 1.926 loss_cls: 0.7658 loss_box_reg: 0.7724 loss_rpn_cls: 0.108 loss_rpn_loc: 0.2996 time: 0.2807 last_time: 0.2345 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:04:51 d2.utils.events]: \u001b[0m eta: 5:30:13 iter: 2539 total_loss: 1.798 loss_cls: 0.6629 loss_box_reg: 0.7454 loss_rpn_cls: 0.133 loss_rpn_loc: 0.3216 time: 0.2803 last_time: 0.2321 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:04:56 d2.utils.events]: \u001b[0m eta: 5:29:20 iter: 2559 total_loss: 1.908 loss_cls: 0.6944 loss_box_reg: 0.7508 loss_rpn_cls: 0.1009 loss_rpn_loc: 0.3286 time: 0.2800 last_time: 0.2161 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:05:00 d2.utils.events]: \u001b[0m eta: 5:28:30 iter: 2579 total_loss: 1.735 loss_cls: 0.6489 loss_box_reg: 0.7491 loss_rpn_cls: 0.0912 loss_rpn_loc: 0.291 time: 0.2796 last_time: 0.2356 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:05:05 d2.utils.events]: \u001b[0m eta: 5:27:44 iter: 2599 total_loss: 1.857 loss_cls: 0.6471 loss_box_reg: 0.7351 loss_rpn_cls: 0.12 loss_rpn_loc: 0.343 time: 0.2793 last_time: 0.2403 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:05:10 d2.utils.events]: \u001b[0m eta: 5:27:16 iter: 2619 total_loss: 1.86 loss_cls: 0.6776 loss_box_reg: 0.7528 loss_rpn_cls: 0.08566 loss_rpn_loc: 0.2872 time: 0.2790 last_time: 0.2610 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:05:15 d2.utils.events]: \u001b[0m eta: 5:26:36 iter: 2639 total_loss: 1.945 loss_cls: 0.7121 loss_box_reg: 0.7702 loss_rpn_cls: 0.08088 loss_rpn_loc: 0.2997 time: 0.2787 last_time: 0.2193 data_time: 0.0047 last_data_time: 0.0056 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:05:19 d2.utils.events]: \u001b[0m eta: 5:26:00 iter: 2659 total_loss: 1.956 loss_cls: 0.7261 loss_box_reg: 0.7679 loss_rpn_cls: 0.09949 loss_rpn_loc: 0.3387 time: 0.2783 last_time: 0.2453 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:05:24 d2.utils.events]: \u001b[0m eta: 5:26:02 iter: 2679 total_loss: 1.765 loss_cls: 0.6963 loss_box_reg: 0.7243 loss_rpn_cls: 0.08796 loss_rpn_loc: 0.2905 time: 0.2780 last_time: 0.2741 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:05:29 d2.utils.events]: \u001b[0m eta: 5:26:15 iter: 2699 total_loss: 1.876 loss_cls: 0.742 loss_box_reg: 0.7258 loss_rpn_cls: 0.09402 loss_rpn_loc: 0.2971 time: 0.2777 last_time: 0.2612 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:05:34 d2.utils.events]: \u001b[0m eta: 5:26:17 iter: 2719 total_loss: 1.828 loss_cls: 0.6737 loss_box_reg: 0.7499 loss_rpn_cls: 0.09943 loss_rpn_loc: 0.3353 time: 0.2774 last_time: 0.2421 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:05:39 d2.utils.events]: \u001b[0m eta: 5:26:18 iter: 2739 total_loss: 1.809 loss_cls: 0.6223 loss_box_reg: 0.7535 loss_rpn_cls: 0.1043 loss_rpn_loc: 0.2988 time: 0.2772 last_time: 0.2309 data_time: 0.0047 last_data_time: 0.0050 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:05:43 d2.utils.events]: \u001b[0m eta: 5:26:35 iter: 2759 total_loss: 1.778 loss_cls: 0.6904 loss_box_reg: 0.742 loss_rpn_cls: 0.09709 loss_rpn_loc: 0.2672 time: 0.2769 last_time: 0.2390 data_time: 0.0047 last_data_time: 0.0043 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:05:48 d2.utils.events]: \u001b[0m eta: 5:26:32 iter: 2779 total_loss: 1.936 loss_cls: 0.684 loss_box_reg: 0.7658 loss_rpn_cls: 0.0812 loss_rpn_loc: 0.3008 time: 0.2767 last_time: 0.2344 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:05:53 d2.utils.events]: \u001b[0m eta: 5:26:29 iter: 2799 total_loss: 1.864 loss_cls: 0.6872 loss_box_reg: 0.7363 loss_rpn_cls: 0.09367 loss_rpn_loc: 0.3165 time: 0.2764 last_time: 0.2252 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:05:58 d2.utils.events]: \u001b[0m eta: 5:26:10 iter: 2819 total_loss: 1.794 loss_cls: 0.6207 loss_box_reg: 0.7023 loss_rpn_cls: 0.1001 loss_rpn_loc: 0.3417 time: 0.2761 last_time: 0.2298 data_time: 0.0047 last_data_time: 0.0041 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:06:02 d2.utils.events]: \u001b[0m eta: 5:26:17 iter: 2839 total_loss: 1.784 loss_cls: 0.6675 loss_box_reg: 0.7437 loss_rpn_cls: 0.0989 loss_rpn_loc: 0.3139 time: 0.2758 last_time: 0.2302 data_time: 0.0046 last_data_time: 0.0051 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:06:07 d2.utils.events]: \u001b[0m eta: 5:26:27 iter: 2859 total_loss: 1.818 loss_cls: 0.6814 loss_box_reg: 0.706 loss_rpn_cls: 0.0814 loss_rpn_loc: 0.3138 time: 0.2755 last_time: 0.2501 data_time: 0.0048 last_data_time: 0.0043 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:06:12 d2.utils.events]: \u001b[0m eta: 5:26:14 iter: 2879 total_loss: 1.781 loss_cls: 0.6166 loss_box_reg: 0.701 loss_rpn_cls: 0.1029 loss_rpn_loc: 0.3308 time: 0.2753 last_time: 0.2560 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:06:17 d2.utils.events]: \u001b[0m eta: 5:26:13 iter: 2899 total_loss: 1.802 loss_cls: 0.6438 loss_box_reg: 0.7565 loss_rpn_cls: 0.08275 loss_rpn_loc: 0.2591 time: 0.2750 last_time: 0.2508 data_time: 0.0045 last_data_time: 0.0048 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:06:21 d2.utils.events]: \u001b[0m eta: 5:26:23 iter: 2919 total_loss: 1.801 loss_cls: 0.6879 loss_box_reg: 0.7438 loss_rpn_cls: 0.09001 loss_rpn_loc: 0.2768 time: 0.2748 last_time: 0.2290 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:06:26 d2.utils.events]: \u001b[0m eta: 5:26:13 iter: 2939 total_loss: 1.854 loss_cls: 0.7058 loss_box_reg: 0.7348 loss_rpn_cls: 0.09423 loss_rpn_loc: 0.2846 time: 0.2745 last_time: 0.2568 data_time: 0.0049 last_data_time: 0.0043 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:06:31 d2.utils.events]: \u001b[0m eta: 5:26:18 iter: 2959 total_loss: 1.82 loss_cls: 0.6968 loss_box_reg: 0.7458 loss_rpn_cls: 0.07285 loss_rpn_loc: 0.3078 time: 0.2743 last_time: 0.2136 data_time: 0.0048 last_data_time: 0.0050 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:06:36 d2.utils.events]: \u001b[0m eta: 5:26:18 iter: 2979 total_loss: 1.749 loss_cls: 0.6456 loss_box_reg: 0.7208 loss_rpn_cls: 0.09641 loss_rpn_loc: 0.2586 time: 0.2741 last_time: 0.2692 data_time: 0.0049 last_data_time: 0.0053 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:06:41 d2.utils.events]: \u001b[0m eta: 5:26:13 iter: 2999 total_loss: 1.757 loss_cls: 0.6613 loss_box_reg: 0.7174 loss_rpn_cls: 0.08634 loss_rpn_loc: 0.3116 time: 0.2738 last_time: 0.2356 data_time: 0.0048 last_data_time: 0.0044 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:06:45 d2.utils.events]: \u001b[0m eta: 5:26:13 iter: 3019 total_loss: 1.732 loss_cls: 0.6139 loss_box_reg: 0.6947 loss_rpn_cls: 0.09544 loss_rpn_loc: 0.3275 time: 0.2736 last_time: 0.2680 data_time: 0.0050 last_data_time: 0.0044 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:06:50 d2.utils.events]: \u001b[0m eta: 5:26:11 iter: 3039 total_loss: 1.807 loss_cls: 0.6753 loss_box_reg: 0.718 loss_rpn_cls: 0.1098 loss_rpn_loc: 0.2989 time: 0.2733 last_time: 0.1848 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:06:55 d2.utils.events]: \u001b[0m eta: 5:26:22 iter: 3059 total_loss: 1.658 loss_cls: 0.6106 loss_box_reg: 0.7044 loss_rpn_cls: 0.08602 loss_rpn_loc: 0.2765 time: 0.2730 last_time: 0.2278 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:06:59 d2.utils.events]: \u001b[0m eta: 5:26:35 iter: 3079 total_loss: 1.754 loss_cls: 0.7171 loss_box_reg: 0.7159 loss_rpn_cls: 0.08821 loss_rpn_loc: 0.2943 time: 0.2728 last_time: 0.1993 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:07:04 d2.utils.events]: \u001b[0m eta: 5:26:50 iter: 3099 total_loss: 1.701 loss_cls: 0.5683 loss_box_reg: 0.7038 loss_rpn_cls: 0.08897 loss_rpn_loc: 0.3078 time: 0.2726 last_time: 0.2152 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:07:09 d2.utils.events]: \u001b[0m eta: 5:26:33 iter: 3119 total_loss: 1.845 loss_cls: 0.6899 loss_box_reg: 0.7328 loss_rpn_cls: 0.08433 loss_rpn_loc: 0.3074 time: 0.2723 last_time: 0.2521 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:07:14 d2.utils.events]: \u001b[0m eta: 5:26:18 iter: 3139 total_loss: 1.684 loss_cls: 0.6195 loss_box_reg: 0.7038 loss_rpn_cls: 0.09353 loss_rpn_loc: 0.3223 time: 0.2721 last_time: 0.2178 data_time: 0.0051 last_data_time: 0.0048 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:07:18 d2.utils.events]: \u001b[0m eta: 5:26:16 iter: 3159 total_loss: 1.837 loss_cls: 0.6801 loss_box_reg: 0.7336 loss_rpn_cls: 0.08089 loss_rpn_loc: 0.2932 time: 0.2719 last_time: 0.1992 data_time: 0.0048 last_data_time: 0.0049 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:07:23 d2.utils.events]: \u001b[0m eta: 5:26:11 iter: 3179 total_loss: 1.761 loss_cls: 0.6287 loss_box_reg: 0.7023 loss_rpn_cls: 0.09508 loss_rpn_loc: 0.3142 time: 0.2716 last_time: 0.2156 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:07:28 d2.utils.events]: \u001b[0m eta: 5:26:06 iter: 3199 total_loss: 1.731 loss_cls: 0.6372 loss_box_reg: 0.6838 loss_rpn_cls: 0.09677 loss_rpn_loc: 0.2696 time: 0.2714 last_time: 0.2506 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:07:32 d2.utils.events]: \u001b[0m eta: 5:25:46 iter: 3219 total_loss: 1.687 loss_cls: 0.6001 loss_box_reg: 0.686 loss_rpn_cls: 0.08098 loss_rpn_loc: 0.2919 time: 0.2712 last_time: 0.2515 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:07:37 d2.utils.events]: \u001b[0m eta: 5:25:48 iter: 3239 total_loss: 1.81 loss_cls: 0.686 loss_box_reg: 0.7252 loss_rpn_cls: 0.07257 loss_rpn_loc: 0.3143 time: 0.2709 last_time: 0.2524 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:07:42 d2.utils.events]: \u001b[0m eta: 5:25:16 iter: 3259 total_loss: 1.814 loss_cls: 0.6668 loss_box_reg: 0.702 loss_rpn_cls: 0.09009 loss_rpn_loc: 0.3035 time: 0.2707 last_time: 0.2333 data_time: 0.0046 last_data_time: 0.0050 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:07:46 d2.utils.events]: \u001b[0m eta: 5:25:23 iter: 3279 total_loss: 1.832 loss_cls: 0.6565 loss_box_reg: 0.6874 loss_rpn_cls: 0.1027 loss_rpn_loc: 0.329 time: 0.2705 last_time: 0.2424 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:07:51 d2.utils.events]: \u001b[0m eta: 5:25:43 iter: 3299 total_loss: 1.717 loss_cls: 0.6878 loss_box_reg: 0.712 loss_rpn_cls: 0.09136 loss_rpn_loc: 0.2655 time: 0.2703 last_time: 0.2182 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:07:56 d2.utils.events]: \u001b[0m eta: 5:25:29 iter: 3319 total_loss: 1.768 loss_cls: 0.6548 loss_box_reg: 0.7027 loss_rpn_cls: 0.077 loss_rpn_loc: 0.3128 time: 0.2701 last_time: 0.2350 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:08:01 d2.utils.events]: \u001b[0m eta: 5:25:17 iter: 3339 total_loss: 1.72 loss_cls: 0.5975 loss_box_reg: 0.6784 loss_rpn_cls: 0.08009 loss_rpn_loc: 0.2904 time: 0.2699 last_time: 0.2072 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:08:05 d2.utils.events]: \u001b[0m eta: 5:25:13 iter: 3359 total_loss: 1.762 loss_cls: 0.6468 loss_box_reg: 0.7051 loss_rpn_cls: 0.1087 loss_rpn_loc: 0.318 time: 0.2697 last_time: 0.2537 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:08:10 d2.utils.events]: \u001b[0m eta: 5:24:43 iter: 3379 total_loss: 1.752 loss_cls: 0.6072 loss_box_reg: 0.6657 loss_rpn_cls: 0.08522 loss_rpn_loc: 0.3281 time: 0.2695 last_time: 0.2364 data_time: 0.0046 last_data_time: 0.0051 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:08:15 d2.utils.events]: \u001b[0m eta: 5:24:10 iter: 3399 total_loss: 1.79 loss_cls: 0.6496 loss_box_reg: 0.6955 loss_rpn_cls: 0.08059 loss_rpn_loc: 0.2855 time: 0.2693 last_time: 0.2223 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:08:20 d2.utils.events]: \u001b[0m eta: 5:23:44 iter: 3419 total_loss: 1.709 loss_cls: 0.6461 loss_box_reg: 0.6665 loss_rpn_cls: 0.09761 loss_rpn_loc: 0.243 time: 0.2691 last_time: 0.2362 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:08:24 d2.utils.events]: \u001b[0m eta: 5:23:23 iter: 3439 total_loss: 1.71 loss_cls: 0.6284 loss_box_reg: 0.7006 loss_rpn_cls: 0.08467 loss_rpn_loc: 0.2718 time: 0.2689 last_time: 0.1829 data_time: 0.0046 last_data_time: 0.0051 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:08:29 d2.utils.events]: \u001b[0m eta: 5:23:04 iter: 3459 total_loss: 1.725 loss_cls: 0.654 loss_box_reg: 0.7153 loss_rpn_cls: 0.0898 loss_rpn_loc: 0.3116 time: 0.2686 last_time: 0.1963 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:08:33 d2.utils.events]: \u001b[0m eta: 5:23:13 iter: 3479 total_loss: 1.731 loss_cls: 0.6782 loss_box_reg: 0.6874 loss_rpn_cls: 0.07174 loss_rpn_loc: 0.2941 time: 0.2684 last_time: 0.2119 data_time: 0.0047 last_data_time: 0.0043 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:08:38 d2.utils.events]: \u001b[0m eta: 5:22:40 iter: 3499 total_loss: 1.707 loss_cls: 0.625 loss_box_reg: 0.7049 loss_rpn_cls: 0.08021 loss_rpn_loc: 0.2795 time: 0.2682 last_time: 0.2328 data_time: 0.0048 last_data_time: 0.0054 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:08:43 d2.utils.events]: \u001b[0m eta: 5:22:35 iter: 3519 total_loss: 1.794 loss_cls: 0.7066 loss_box_reg: 0.7012 loss_rpn_cls: 0.08444 loss_rpn_loc: 0.2923 time: 0.2680 last_time: 0.2509 data_time: 0.0046 last_data_time: 0.0049 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:08:47 d2.utils.events]: \u001b[0m eta: 5:22:28 iter: 3539 total_loss: 1.728 loss_cls: 0.6538 loss_box_reg: 0.6917 loss_rpn_cls: 0.09322 loss_rpn_loc: 0.3042 time: 0.2678 last_time: 0.2329 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:08:52 d2.utils.events]: \u001b[0m eta: 5:21:50 iter: 3559 total_loss: 1.729 loss_cls: 0.649 loss_box_reg: 0.7098 loss_rpn_cls: 0.09042 loss_rpn_loc: 0.2979 time: 0.2676 last_time: 0.2045 data_time: 0.0047 last_data_time: 0.0053 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:08:57 d2.utils.events]: \u001b[0m eta: 5:22:12 iter: 3579 total_loss: 1.713 loss_cls: 0.6363 loss_box_reg: 0.7162 loss_rpn_cls: 0.08652 loss_rpn_loc: 0.3204 time: 0.2674 last_time: 0.2375 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:09:01 d2.utils.events]: \u001b[0m eta: 5:21:51 iter: 3599 total_loss: 1.728 loss_cls: 0.6699 loss_box_reg: 0.7054 loss_rpn_cls: 0.07536 loss_rpn_loc: 0.2865 time: 0.2672 last_time: 0.2195 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:09:06 d2.utils.events]: \u001b[0m eta: 5:21:30 iter: 3619 total_loss: 1.685 loss_cls: 0.6302 loss_box_reg: 0.7004 loss_rpn_cls: 0.08056 loss_rpn_loc: 0.2682 time: 0.2670 last_time: 0.2854 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:09:11 d2.utils.events]: \u001b[0m eta: 5:21:12 iter: 3639 total_loss: 1.67 loss_cls: 0.6106 loss_box_reg: 0.6778 loss_rpn_cls: 0.09966 loss_rpn_loc: 0.3 time: 0.2668 last_time: 0.2226 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:09:15 d2.utils.events]: \u001b[0m eta: 5:21:00 iter: 3659 total_loss: 1.615 loss_cls: 0.5983 loss_box_reg: 0.6715 loss_rpn_cls: 0.06951 loss_rpn_loc: 0.2806 time: 0.2666 last_time: 0.2335 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:09:20 d2.utils.events]: \u001b[0m eta: 5:20:53 iter: 3679 total_loss: 1.672 loss_cls: 0.6624 loss_box_reg: 0.7009 loss_rpn_cls: 0.06643 loss_rpn_loc: 0.2638 time: 0.2664 last_time: 0.2340 data_time: 0.0047 last_data_time: 0.0054 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:09:25 d2.utils.events]: \u001b[0m eta: 5:20:30 iter: 3699 total_loss: 1.709 loss_cls: 0.6375 loss_box_reg: 0.701 loss_rpn_cls: 0.08213 loss_rpn_loc: 0.2662 time: 0.2662 last_time: 0.2460 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:09:29 d2.utils.events]: \u001b[0m eta: 5:20:23 iter: 3719 total_loss: 1.72 loss_cls: 0.6069 loss_box_reg: 0.6798 loss_rpn_cls: 0.07864 loss_rpn_loc: 0.2682 time: 0.2661 last_time: 0.2347 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:09:34 d2.utils.events]: \u001b[0m eta: 5:19:50 iter: 3739 total_loss: 1.649 loss_cls: 0.5993 loss_box_reg: 0.6692 loss_rpn_cls: 0.09227 loss_rpn_loc: 0.2921 time: 0.2659 last_time: 0.2328 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:09:38 d2.utils.events]: \u001b[0m eta: 5:19:24 iter: 3759 total_loss: 1.665 loss_cls: 0.6141 loss_box_reg: 0.6719 loss_rpn_cls: 0.09443 loss_rpn_loc: 0.2704 time: 0.2656 last_time: 0.2243 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2363M\n","\u001b[32m[08/23 16:09:43 d2.utils.events]: \u001b[0m eta: 5:19:17 iter: 3779 total_loss: 1.669 loss_cls: 0.6061 loss_box_reg: 0.6737 loss_rpn_cls: 0.09062 loss_rpn_loc: 0.2789 time: 0.2654 last_time: 0.2747 data_time: 0.0048 last_data_time: 0.0051 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:09:47 d2.utils.events]: \u001b[0m eta: 5:18:55 iter: 3799 total_loss: 1.599 loss_cls: 0.5759 loss_box_reg: 0.6375 loss_rpn_cls: 0.08859 loss_rpn_loc: 0.301 time: 0.2652 last_time: 0.2504 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:09:52 d2.utils.events]: \u001b[0m eta: 5:18:56 iter: 3819 total_loss: 1.696 loss_cls: 0.5908 loss_box_reg: 0.6793 loss_rpn_cls: 0.07431 loss_rpn_loc: 0.3188 time: 0.2651 last_time: 0.2495 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:09:57 d2.utils.events]: \u001b[0m eta: 5:19:03 iter: 3839 total_loss: 1.752 loss_cls: 0.649 loss_box_reg: 0.6624 loss_rpn_cls: 0.08955 loss_rpn_loc: 0.3124 time: 0.2649 last_time: 0.2376 data_time: 0.0049 last_data_time: 0.0047 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:10:01 d2.utils.events]: \u001b[0m eta: 5:18:27 iter: 3859 total_loss: 1.631 loss_cls: 0.554 loss_box_reg: 0.6683 loss_rpn_cls: 0.07944 loss_rpn_loc: 0.2706 time: 0.2647 last_time: 0.2300 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:10:06 d2.utils.events]: \u001b[0m eta: 5:18:14 iter: 3879 total_loss: 1.638 loss_cls: 0.6307 loss_box_reg: 0.6921 loss_rpn_cls: 0.08946 loss_rpn_loc: 0.3162 time: 0.2645 last_time: 0.2510 data_time: 0.0047 last_data_time: 0.0043 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:10:11 d2.utils.events]: \u001b[0m eta: 5:18:03 iter: 3899 total_loss: 1.654 loss_cls: 0.5963 loss_box_reg: 0.6635 loss_rpn_cls: 0.09222 loss_rpn_loc: 0.2729 time: 0.2643 last_time: 0.2231 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:10:15 d2.utils.events]: \u001b[0m eta: 5:17:56 iter: 3919 total_loss: 1.663 loss_cls: 0.6776 loss_box_reg: 0.6376 loss_rpn_cls: 0.09452 loss_rpn_loc: 0.2568 time: 0.2641 last_time: 0.2487 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:10:20 d2.utils.events]: \u001b[0m eta: 5:17:23 iter: 3939 total_loss: 1.711 loss_cls: 0.6454 loss_box_reg: 0.6861 loss_rpn_cls: 0.09646 loss_rpn_loc: 0.2577 time: 0.2639 last_time: 0.2250 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:10:24 d2.utils.events]: \u001b[0m eta: 5:17:15 iter: 3959 total_loss: 1.648 loss_cls: 0.6015 loss_box_reg: 0.679 loss_rpn_cls: 0.08422 loss_rpn_loc: 0.2536 time: 0.2638 last_time: 0.2318 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:10:29 d2.utils.events]: \u001b[0m eta: 5:17:01 iter: 3979 total_loss: 1.829 loss_cls: 0.6725 loss_box_reg: 0.6791 loss_rpn_cls: 0.09864 loss_rpn_loc: 0.3145 time: 0.2636 last_time: 0.2124 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:10:33 d2.utils.events]: \u001b[0m eta: 5:16:55 iter: 3999 total_loss: 1.764 loss_cls: 0.6275 loss_box_reg: 0.6422 loss_rpn_cls: 0.1146 loss_rpn_loc: 0.3194 time: 0.2634 last_time: 0.1845 data_time: 0.0049 last_data_time: 0.0049 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:10:38 d2.utils.events]: \u001b[0m eta: 5:16:52 iter: 4019 total_loss: 1.653 loss_cls: 0.6147 loss_box_reg: 0.6761 loss_rpn_cls: 0.08849 loss_rpn_loc: 0.2941 time: 0.2633 last_time: 0.2400 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:10:43 d2.utils.events]: \u001b[0m eta: 5:16:53 iter: 4039 total_loss: 1.612 loss_cls: 0.6064 loss_box_reg: 0.6539 loss_rpn_cls: 0.08502 loss_rpn_loc: 0.2498 time: 0.2631 last_time: 0.2317 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:10:48 d2.utils.events]: \u001b[0m eta: 5:17:05 iter: 4059 total_loss: 1.599 loss_cls: 0.597 loss_box_reg: 0.6301 loss_rpn_cls: 0.07132 loss_rpn_loc: 0.28 time: 0.2630 last_time: 0.2566 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:10:52 d2.utils.events]: \u001b[0m eta: 5:16:41 iter: 4079 total_loss: 1.631 loss_cls: 0.5981 loss_box_reg: 0.7063 loss_rpn_cls: 0.0849 loss_rpn_loc: 0.2678 time: 0.2629 last_time: 0.2330 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:10:57 d2.utils.events]: \u001b[0m eta: 5:16:36 iter: 4099 total_loss: 1.584 loss_cls: 0.5449 loss_box_reg: 0.6665 loss_rpn_cls: 0.07372 loss_rpn_loc: 0.2714 time: 0.2628 last_time: 0.2377 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:11:02 d2.utils.events]: \u001b[0m eta: 5:16:26 iter: 4119 total_loss: 1.696 loss_cls: 0.6464 loss_box_reg: 0.6714 loss_rpn_cls: 0.09279 loss_rpn_loc: 0.2899 time: 0.2626 last_time: 0.2237 data_time: 0.0048 last_data_time: 0.0053 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:11:06 d2.utils.events]: \u001b[0m eta: 5:16:22 iter: 4139 total_loss: 1.54 loss_cls: 0.5435 loss_box_reg: 0.6397 loss_rpn_cls: 0.09283 loss_rpn_loc: 0.2746 time: 0.2625 last_time: 0.2328 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:11:11 d2.utils.events]: \u001b[0m eta: 5:16:10 iter: 4159 total_loss: 1.704 loss_cls: 0.616 loss_box_reg: 0.6661 loss_rpn_cls: 0.1103 loss_rpn_loc: 0.2952 time: 0.2623 last_time: 0.2195 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:11:16 d2.utils.events]: \u001b[0m eta: 5:16:11 iter: 4179 total_loss: 1.526 loss_cls: 0.5716 loss_box_reg: 0.643 loss_rpn_cls: 0.08071 loss_rpn_loc: 0.294 time: 0.2622 last_time: 0.2499 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:11:20 d2.utils.events]: \u001b[0m eta: 5:16:00 iter: 4199 total_loss: 1.555 loss_cls: 0.5394 loss_box_reg: 0.6235 loss_rpn_cls: 0.07582 loss_rpn_loc: 0.2626 time: 0.2621 last_time: 0.2181 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:11:25 d2.utils.events]: \u001b[0m eta: 5:16:02 iter: 4219 total_loss: 1.556 loss_cls: 0.5692 loss_box_reg: 0.6409 loss_rpn_cls: 0.092 loss_rpn_loc: 0.2805 time: 0.2619 last_time: 0.2655 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:11:30 d2.utils.events]: \u001b[0m eta: 5:15:57 iter: 4239 total_loss: 1.607 loss_cls: 0.5905 loss_box_reg: 0.6693 loss_rpn_cls: 0.07481 loss_rpn_loc: 0.2609 time: 0.2618 last_time: 0.2296 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:11:35 d2.utils.events]: \u001b[0m eta: 5:15:58 iter: 4259 total_loss: 1.599 loss_cls: 0.565 loss_box_reg: 0.6565 loss_rpn_cls: 0.09762 loss_rpn_loc: 0.2736 time: 0.2617 last_time: 0.2310 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:11:39 d2.utils.events]: \u001b[0m eta: 5:15:49 iter: 4279 total_loss: 1.594 loss_cls: 0.5805 loss_box_reg: 0.6599 loss_rpn_cls: 0.08115 loss_rpn_loc: 0.2896 time: 0.2616 last_time: 0.2329 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:11:44 d2.utils.events]: \u001b[0m eta: 5:15:43 iter: 4299 total_loss: 1.693 loss_cls: 0.625 loss_box_reg: 0.6689 loss_rpn_cls: 0.08685 loss_rpn_loc: 0.2984 time: 0.2615 last_time: 0.2411 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:11:49 d2.utils.events]: \u001b[0m eta: 5:15:28 iter: 4319 total_loss: 1.695 loss_cls: 0.6092 loss_box_reg: 0.6631 loss_rpn_cls: 0.09404 loss_rpn_loc: 0.2945 time: 0.2613 last_time: 0.2476 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:11:54 d2.utils.events]: \u001b[0m eta: 5:15:28 iter: 4339 total_loss: 1.669 loss_cls: 0.6224 loss_box_reg: 0.6463 loss_rpn_cls: 0.07231 loss_rpn_loc: 0.2748 time: 0.2612 last_time: 0.2351 data_time: 0.0048 last_data_time: 0.0049 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:11:58 d2.utils.events]: \u001b[0m eta: 5:15:31 iter: 4359 total_loss: 1.557 loss_cls: 0.6019 loss_box_reg: 0.6 loss_rpn_cls: 0.09052 loss_rpn_loc: 0.2971 time: 0.2611 last_time: 0.2301 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:12:03 d2.utils.events]: \u001b[0m eta: 5:15:26 iter: 4379 total_loss: 1.566 loss_cls: 0.5725 loss_box_reg: 0.6296 loss_rpn_cls: 0.0848 loss_rpn_loc: 0.2462 time: 0.2610 last_time: 0.2559 data_time: 0.0046 last_data_time: 0.0042 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:12:08 d2.utils.events]: \u001b[0m eta: 5:15:19 iter: 4399 total_loss: 1.615 loss_cls: 0.571 loss_box_reg: 0.6114 loss_rpn_cls: 0.0839 loss_rpn_loc: 0.278 time: 0.2609 last_time: 0.2247 data_time: 0.0047 last_data_time: 0.0043 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:12:12 d2.utils.events]: \u001b[0m eta: 5:15:09 iter: 4419 total_loss: 1.598 loss_cls: 0.5426 loss_box_reg: 0.6201 loss_rpn_cls: 0.08374 loss_rpn_loc: 0.2972 time: 0.2608 last_time: 0.2308 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:12:17 d2.utils.events]: \u001b[0m eta: 5:15:08 iter: 4439 total_loss: 1.716 loss_cls: 0.6446 loss_box_reg: 0.6741 loss_rpn_cls: 0.09111 loss_rpn_loc: 0.2906 time: 0.2606 last_time: 0.2392 data_time: 0.0048 last_data_time: 0.0044 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:12:22 d2.utils.events]: \u001b[0m eta: 5:15:05 iter: 4459 total_loss: 1.603 loss_cls: 0.5966 loss_box_reg: 0.6509 loss_rpn_cls: 0.07917 loss_rpn_loc: 0.2836 time: 0.2605 last_time: 0.2229 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:12:27 d2.utils.events]: \u001b[0m eta: 5:15:01 iter: 4479 total_loss: 1.549 loss_cls: 0.5804 loss_box_reg: 0.5934 loss_rpn_cls: 0.07232 loss_rpn_loc: 0.2533 time: 0.2604 last_time: 0.2034 data_time: 0.0046 last_data_time: 0.0049 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:12:31 d2.utils.events]: \u001b[0m eta: 5:14:57 iter: 4499 total_loss: 1.649 loss_cls: 0.6035 loss_box_reg: 0.666 loss_rpn_cls: 0.07949 loss_rpn_loc: 0.2697 time: 0.2603 last_time: 0.2153 data_time: 0.0047 last_data_time: 0.0050 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:12:36 d2.utils.events]: \u001b[0m eta: 5:14:49 iter: 4519 total_loss: 1.434 loss_cls: 0.5286 loss_box_reg: 0.6072 loss_rpn_cls: 0.09235 loss_rpn_loc: 0.2618 time: 0.2602 last_time: 0.2603 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:12:41 d2.utils.events]: \u001b[0m eta: 5:14:39 iter: 4539 total_loss: 1.627 loss_cls: 0.5697 loss_box_reg: 0.6408 loss_rpn_cls: 0.104 loss_rpn_loc: 0.3286 time: 0.2601 last_time: 0.2043 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:12:45 d2.utils.events]: \u001b[0m eta: 5:14:39 iter: 4559 total_loss: 1.602 loss_cls: 0.558 loss_box_reg: 0.6098 loss_rpn_cls: 0.08665 loss_rpn_loc: 0.2914 time: 0.2600 last_time: 0.2521 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:12:50 d2.utils.events]: \u001b[0m eta: 5:14:37 iter: 4579 total_loss: 1.515 loss_cls: 0.5383 loss_box_reg: 0.6088 loss_rpn_cls: 0.07239 loss_rpn_loc: 0.2913 time: 0.2599 last_time: 0.2486 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:12:55 d2.utils.events]: \u001b[0m eta: 5:14:36 iter: 4599 total_loss: 1.697 loss_cls: 0.6337 loss_box_reg: 0.6531 loss_rpn_cls: 0.1019 loss_rpn_loc: 0.2947 time: 0.2598 last_time: 0.2267 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:13:00 d2.utils.events]: \u001b[0m eta: 5:14:34 iter: 4619 total_loss: 1.448 loss_cls: 0.5588 loss_box_reg: 0.6007 loss_rpn_cls: 0.07561 loss_rpn_loc: 0.2667 time: 0.2597 last_time: 0.2363 data_time: 0.0046 last_data_time: 0.0051 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:13:04 d2.utils.events]: \u001b[0m eta: 5:14:41 iter: 4639 total_loss: 1.534 loss_cls: 0.54 loss_box_reg: 0.5998 loss_rpn_cls: 0.08072 loss_rpn_loc: 0.2355 time: 0.2596 last_time: 0.2655 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:13:09 d2.utils.events]: \u001b[0m eta: 5:14:50 iter: 4659 total_loss: 1.549 loss_cls: 0.5623 loss_box_reg: 0.6093 loss_rpn_cls: 0.08412 loss_rpn_loc: 0.2549 time: 0.2595 last_time: 0.2642 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:13:14 d2.utils.events]: \u001b[0m eta: 5:14:58 iter: 4679 total_loss: 1.531 loss_cls: 0.5665 loss_box_reg: 0.5809 loss_rpn_cls: 0.07844 loss_rpn_loc: 0.2664 time: 0.2594 last_time: 0.2527 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:13:19 d2.utils.events]: \u001b[0m eta: 5:14:56 iter: 4699 total_loss: 1.483 loss_cls: 0.5716 loss_box_reg: 0.6272 loss_rpn_cls: 0.08394 loss_rpn_loc: 0.2645 time: 0.2593 last_time: 0.2429 data_time: 0.0048 last_data_time: 0.0051 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:13:23 d2.utils.events]: \u001b[0m eta: 5:14:53 iter: 4719 total_loss: 1.478 loss_cls: 0.5241 loss_box_reg: 0.5798 loss_rpn_cls: 0.09459 loss_rpn_loc: 0.2913 time: 0.2592 last_time: 0.2345 data_time: 0.0049 last_data_time: 0.0049 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:13:28 d2.utils.events]: \u001b[0m eta: 5:15:05 iter: 4739 total_loss: 1.586 loss_cls: 0.5965 loss_box_reg: 0.5982 loss_rpn_cls: 0.07459 loss_rpn_loc: 0.2923 time: 0.2591 last_time: 0.2621 data_time: 0.0047 last_data_time: 0.0050 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:13:33 d2.utils.events]: \u001b[0m eta: 5:15:13 iter: 4759 total_loss: 1.475 loss_cls: 0.5165 loss_box_reg: 0.6104 loss_rpn_cls: 0.06521 loss_rpn_loc: 0.2335 time: 0.2590 last_time: 0.2248 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:13:37 d2.utils.events]: \u001b[0m eta: 5:15:02 iter: 4779 total_loss: 1.488 loss_cls: 0.5413 loss_box_reg: 0.5897 loss_rpn_cls: 0.06903 loss_rpn_loc: 0.2659 time: 0.2589 last_time: 0.2226 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:13:42 d2.utils.events]: \u001b[0m eta: 5:15:01 iter: 4799 total_loss: 1.654 loss_cls: 0.6447 loss_box_reg: 0.6529 loss_rpn_cls: 0.08728 loss_rpn_loc: 0.2924 time: 0.2588 last_time: 0.2249 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:13:47 d2.utils.events]: \u001b[0m eta: 5:14:59 iter: 4819 total_loss: 1.553 loss_cls: 0.5611 loss_box_reg: 0.6389 loss_rpn_cls: 0.0767 loss_rpn_loc: 0.2675 time: 0.2587 last_time: 0.2496 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:13:52 d2.utils.events]: \u001b[0m eta: 5:14:51 iter: 4839 total_loss: 1.601 loss_cls: 0.5698 loss_box_reg: 0.6104 loss_rpn_cls: 0.08849 loss_rpn_loc: 0.2972 time: 0.2586 last_time: 0.2558 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:13:57 d2.utils.events]: \u001b[0m eta: 5:14:50 iter: 4859 total_loss: 1.516 loss_cls: 0.5261 loss_box_reg: 0.5858 loss_rpn_cls: 0.08337 loss_rpn_loc: 0.287 time: 0.2585 last_time: 0.2342 data_time: 0.0047 last_data_time: 0.0051 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:14:01 d2.utils.events]: \u001b[0m eta: 5:14:43 iter: 4879 total_loss: 1.58 loss_cls: 0.5522 loss_box_reg: 0.6092 loss_rpn_cls: 0.09357 loss_rpn_loc: 0.3048 time: 0.2584 last_time: 0.2471 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:14:06 d2.utils.events]: \u001b[0m eta: 5:14:43 iter: 4899 total_loss: 1.699 loss_cls: 0.6808 loss_box_reg: 0.6467 loss_rpn_cls: 0.07491 loss_rpn_loc: 0.2826 time: 0.2583 last_time: 0.2274 data_time: 0.0049 last_data_time: 0.0047 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:14:10 d2.utils.events]: \u001b[0m eta: 5:14:35 iter: 4919 total_loss: 1.501 loss_cls: 0.5388 loss_box_reg: 0.6078 loss_rpn_cls: 0.08164 loss_rpn_loc: 0.2876 time: 0.2582 last_time: 0.2587 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:14:15 d2.utils.events]: \u001b[0m eta: 5:14:34 iter: 4939 total_loss: 1.614 loss_cls: 0.5254 loss_box_reg: 0.6057 loss_rpn_cls: 0.09557 loss_rpn_loc: 0.2611 time: 0.2581 last_time: 0.2229 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:14:20 d2.utils.events]: \u001b[0m eta: 5:14:37 iter: 4959 total_loss: 1.358 loss_cls: 0.5022 loss_box_reg: 0.5737 loss_rpn_cls: 0.07938 loss_rpn_loc: 0.2252 time: 0.2580 last_time: 0.2392 data_time: 0.0047 last_data_time: 0.0041 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:14:24 d2.utils.events]: \u001b[0m eta: 5:14:32 iter: 4979 total_loss: 1.365 loss_cls: 0.4963 loss_box_reg: 0.5926 loss_rpn_cls: 0.0665 loss_rpn_loc: 0.234 time: 0.2578 last_time: 0.2116 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:14:29 d2.utils.events]: \u001b[0m eta: 5:14:27 iter: 4999 total_loss: 1.386 loss_cls: 0.4884 loss_box_reg: 0.5905 loss_rpn_cls: 0.06896 loss_rpn_loc: 0.2396 time: 0.2577 last_time: 0.2507 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:14:34 d2.utils.events]: \u001b[0m eta: 5:14:12 iter: 5019 total_loss: 1.698 loss_cls: 0.6118 loss_box_reg: 0.62 loss_rpn_cls: 0.08666 loss_rpn_loc: 0.3069 time: 0.2577 last_time: 0.2324 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:14:39 d2.utils.events]: \u001b[0m eta: 5:14:04 iter: 5039 total_loss: 1.571 loss_cls: 0.5828 loss_box_reg: 0.618 loss_rpn_cls: 0.0906 loss_rpn_loc: 0.2835 time: 0.2576 last_time: 0.2587 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:14:44 d2.utils.events]: \u001b[0m eta: 5:13:49 iter: 5059 total_loss: 1.45 loss_cls: 0.5155 loss_box_reg: 0.5732 loss_rpn_cls: 0.07001 loss_rpn_loc: 0.2421 time: 0.2575 last_time: 0.2097 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:14:48 d2.utils.events]: \u001b[0m eta: 5:13:46 iter: 5079 total_loss: 1.435 loss_cls: 0.512 loss_box_reg: 0.5832 loss_rpn_cls: 0.06049 loss_rpn_loc: 0.2326 time: 0.2574 last_time: 0.2530 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:14:53 d2.utils.events]: \u001b[0m eta: 5:13:33 iter: 5099 total_loss: 1.58 loss_cls: 0.5786 loss_box_reg: 0.576 loss_rpn_cls: 0.1059 loss_rpn_loc: 0.2977 time: 0.2573 last_time: 0.2415 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:14:58 d2.utils.events]: \u001b[0m eta: 5:13:51 iter: 5119 total_loss: 1.409 loss_cls: 0.4897 loss_box_reg: 0.5695 loss_rpn_cls: 0.05869 loss_rpn_loc: 0.2611 time: 0.2572 last_time: 0.2800 data_time: 0.0048 last_data_time: 0.0056 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:15:03 d2.utils.events]: \u001b[0m eta: 5:13:32 iter: 5139 total_loss: 1.474 loss_cls: 0.5268 loss_box_reg: 0.5897 loss_rpn_cls: 0.07327 loss_rpn_loc: 0.2606 time: 0.2571 last_time: 0.2346 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:15:07 d2.utils.events]: \u001b[0m eta: 5:13:32 iter: 5159 total_loss: 1.567 loss_cls: 0.605 loss_box_reg: 0.6313 loss_rpn_cls: 0.08243 loss_rpn_loc: 0.2855 time: 0.2570 last_time: 0.2143 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:15:12 d2.utils.events]: \u001b[0m eta: 5:13:15 iter: 5179 total_loss: 1.566 loss_cls: 0.5665 loss_box_reg: 0.6087 loss_rpn_cls: 0.09403 loss_rpn_loc: 0.2899 time: 0.2569 last_time: 0.1864 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:15:16 d2.utils.events]: \u001b[0m eta: 5:13:16 iter: 5199 total_loss: 1.468 loss_cls: 0.553 loss_box_reg: 0.5729 loss_rpn_cls: 0.07461 loss_rpn_loc: 0.2682 time: 0.2568 last_time: 0.2101 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:15:21 d2.utils.events]: \u001b[0m eta: 5:13:13 iter: 5219 total_loss: 1.507 loss_cls: 0.5738 loss_box_reg: 0.5845 loss_rpn_cls: 0.07823 loss_rpn_loc: 0.2691 time: 0.2567 last_time: 0.1925 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:15:26 d2.utils.events]: \u001b[0m eta: 5:13:04 iter: 5239 total_loss: 1.597 loss_cls: 0.5809 loss_box_reg: 0.5915 loss_rpn_cls: 0.1002 loss_rpn_loc: 0.2864 time: 0.2566 last_time: 0.2455 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:15:30 d2.utils.events]: \u001b[0m eta: 5:12:55 iter: 5259 total_loss: 1.464 loss_cls: 0.5547 loss_box_reg: 0.5985 loss_rpn_cls: 0.08626 loss_rpn_loc: 0.2278 time: 0.2565 last_time: 0.2336 data_time: 0.0046 last_data_time: 0.0042 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:15:35 d2.utils.events]: \u001b[0m eta: 5:12:57 iter: 5279 total_loss: 1.469 loss_cls: 0.581 loss_box_reg: 0.6143 loss_rpn_cls: 0.07363 loss_rpn_loc: 0.2882 time: 0.2564 last_time: 0.2426 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:15:40 d2.utils.events]: \u001b[0m eta: 5:12:53 iter: 5299 total_loss: 1.585 loss_cls: 0.5487 loss_box_reg: 0.5998 loss_rpn_cls: 0.08251 loss_rpn_loc: 0.2902 time: 0.2564 last_time: 0.2278 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:15:44 d2.utils.events]: \u001b[0m eta: 5:12:58 iter: 5319 total_loss: 1.605 loss_cls: 0.5949 loss_box_reg: 0.5923 loss_rpn_cls: 0.09521 loss_rpn_loc: 0.3166 time: 0.2563 last_time: 0.1974 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:15:49 d2.utils.events]: \u001b[0m eta: 5:12:36 iter: 5339 total_loss: 1.618 loss_cls: 0.6178 loss_box_reg: 0.6094 loss_rpn_cls: 0.1121 loss_rpn_loc: 0.2661 time: 0.2562 last_time: 0.2355 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:15:54 d2.utils.events]: \u001b[0m eta: 5:12:25 iter: 5359 total_loss: 1.505 loss_cls: 0.5358 loss_box_reg: 0.5919 loss_rpn_cls: 0.0708 loss_rpn_loc: 0.2744 time: 0.2561 last_time: 0.2116 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:15:58 d2.utils.events]: \u001b[0m eta: 5:12:23 iter: 5379 total_loss: 1.505 loss_cls: 0.5457 loss_box_reg: 0.5917 loss_rpn_cls: 0.07993 loss_rpn_loc: 0.2706 time: 0.2560 last_time: 0.2462 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:16:03 d2.utils.events]: \u001b[0m eta: 5:12:09 iter: 5399 total_loss: 1.568 loss_cls: 0.566 loss_box_reg: 0.603 loss_rpn_cls: 0.07834 loss_rpn_loc: 0.265 time: 0.2559 last_time: 0.2575 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:16:08 d2.utils.events]: \u001b[0m eta: 5:12:17 iter: 5419 total_loss: 1.436 loss_cls: 0.5398 loss_box_reg: 0.5782 loss_rpn_cls: 0.07333 loss_rpn_loc: 0.2493 time: 0.2558 last_time: 0.2382 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:16:12 d2.utils.events]: \u001b[0m eta: 5:12:11 iter: 5439 total_loss: 1.583 loss_cls: 0.5794 loss_box_reg: 0.5986 loss_rpn_cls: 0.07962 loss_rpn_loc: 0.277 time: 0.2558 last_time: 0.2381 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:16:17 d2.utils.events]: \u001b[0m eta: 5:11:54 iter: 5459 total_loss: 1.478 loss_cls: 0.5667 loss_box_reg: 0.5969 loss_rpn_cls: 0.08732 loss_rpn_loc: 0.2655 time: 0.2557 last_time: 0.2222 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:16:22 d2.utils.events]: \u001b[0m eta: 5:11:49 iter: 5479 total_loss: 1.491 loss_cls: 0.5127 loss_box_reg: 0.5404 loss_rpn_cls: 0.07511 loss_rpn_loc: 0.2892 time: 0.2556 last_time: 0.2517 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:16:26 d2.utils.events]: \u001b[0m eta: 5:11:46 iter: 5499 total_loss: 1.435 loss_cls: 0.5382 loss_box_reg: 0.5704 loss_rpn_cls: 0.08639 loss_rpn_loc: 0.2544 time: 0.2555 last_time: 0.2378 data_time: 0.0045 last_data_time: 0.0054 lr: 1e-05 max_mem: 2384M\n","\u001b[32m[08/23 16:16:31 d2.utils.events]: \u001b[0m eta: 5:11:41 iter: 5519 total_loss: 1.469 loss_cls: 0.5029 loss_box_reg: 0.5421 loss_rpn_cls: 0.08131 loss_rpn_loc: 0.2698 time: 0.2554 last_time: 0.2199 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:16:36 d2.utils.events]: \u001b[0m eta: 5:11:49 iter: 5539 total_loss: 1.546 loss_cls: 0.5783 loss_box_reg: 0.61 loss_rpn_cls: 0.08352 loss_rpn_loc: 0.2673 time: 0.2554 last_time: 0.2594 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:16:41 d2.utils.events]: \u001b[0m eta: 5:11:53 iter: 5559 total_loss: 1.539 loss_cls: 0.5416 loss_box_reg: 0.5773 loss_rpn_cls: 0.08875 loss_rpn_loc: 0.2968 time: 0.2553 last_time: 0.2528 data_time: 0.0052 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:16:45 d2.utils.events]: \u001b[0m eta: 5:11:40 iter: 5579 total_loss: 1.484 loss_cls: 0.5425 loss_box_reg: 0.545 loss_rpn_cls: 0.08774 loss_rpn_loc: 0.3011 time: 0.2552 last_time: 0.1823 data_time: 0.0046 last_data_time: 0.0041 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:16:50 d2.utils.events]: \u001b[0m eta: 5:11:35 iter: 5599 total_loss: 1.432 loss_cls: 0.5004 loss_box_reg: 0.5518 loss_rpn_cls: 0.07968 loss_rpn_loc: 0.2864 time: 0.2552 last_time: 0.3245 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:16:55 d2.utils.events]: \u001b[0m eta: 5:11:30 iter: 5619 total_loss: 1.419 loss_cls: 0.4954 loss_box_reg: 0.538 loss_rpn_cls: 0.08173 loss_rpn_loc: 0.2585 time: 0.2551 last_time: 0.2467 data_time: 0.0053 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:17:00 d2.utils.events]: \u001b[0m eta: 5:11:25 iter: 5639 total_loss: 1.499 loss_cls: 0.5593 loss_box_reg: 0.5804 loss_rpn_cls: 0.08628 loss_rpn_loc: 0.2684 time: 0.2551 last_time: 0.2395 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:17:04 d2.utils.events]: \u001b[0m eta: 5:11:20 iter: 5659 total_loss: 1.527 loss_cls: 0.5615 loss_box_reg: 0.5726 loss_rpn_cls: 0.07962 loss_rpn_loc: 0.2858 time: 0.2550 last_time: 0.2328 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:17:09 d2.utils.events]: \u001b[0m eta: 5:11:02 iter: 5679 total_loss: 1.497 loss_cls: 0.5659 loss_box_reg: 0.5769 loss_rpn_cls: 0.06922 loss_rpn_loc: 0.2416 time: 0.2549 last_time: 0.2639 data_time: 0.0045 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:17:14 d2.utils.events]: \u001b[0m eta: 5:10:49 iter: 5699 total_loss: 1.493 loss_cls: 0.5776 loss_box_reg: 0.5505 loss_rpn_cls: 0.06935 loss_rpn_loc: 0.2558 time: 0.2549 last_time: 0.2186 data_time: 0.0047 last_data_time: 0.0041 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:17:19 d2.utils.events]: \u001b[0m eta: 5:10:48 iter: 5719 total_loss: 1.452 loss_cls: 0.5061 loss_box_reg: 0.5581 loss_rpn_cls: 0.07547 loss_rpn_loc: 0.2944 time: 0.2548 last_time: 0.2094 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:17:23 d2.utils.events]: \u001b[0m eta: 5:10:43 iter: 5739 total_loss: 1.521 loss_cls: 0.545 loss_box_reg: 0.584 loss_rpn_cls: 0.0709 loss_rpn_loc: 0.2805 time: 0.2547 last_time: 0.2621 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:17:28 d2.utils.events]: \u001b[0m eta: 5:10:43 iter: 5759 total_loss: 1.54 loss_cls: 0.5284 loss_box_reg: 0.5819 loss_rpn_cls: 0.07647 loss_rpn_loc: 0.2774 time: 0.2547 last_time: 0.2184 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:17:33 d2.utils.events]: \u001b[0m eta: 5:10:49 iter: 5779 total_loss: 1.356 loss_cls: 0.5374 loss_box_reg: 0.5336 loss_rpn_cls: 0.07235 loss_rpn_loc: 0.2542 time: 0.2546 last_time: 0.2438 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:17:38 d2.utils.events]: \u001b[0m eta: 5:10:47 iter: 5799 total_loss: 1.541 loss_cls: 0.5702 loss_box_reg: 0.5828 loss_rpn_cls: 0.08151 loss_rpn_loc: 0.3029 time: 0.2546 last_time: 0.2418 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:17:42 d2.utils.events]: \u001b[0m eta: 5:10:34 iter: 5819 total_loss: 1.511 loss_cls: 0.5841 loss_box_reg: 0.5523 loss_rpn_cls: 0.08646 loss_rpn_loc: 0.2481 time: 0.2545 last_time: 0.2372 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:17:47 d2.utils.events]: \u001b[0m eta: 5:10:37 iter: 5839 total_loss: 1.334 loss_cls: 0.4888 loss_box_reg: 0.5053 loss_rpn_cls: 0.06583 loss_rpn_loc: 0.2401 time: 0.2544 last_time: 0.2331 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:17:52 d2.utils.events]: \u001b[0m eta: 5:10:33 iter: 5859 total_loss: 1.548 loss_cls: 0.5703 loss_box_reg: 0.5963 loss_rpn_cls: 0.08711 loss_rpn_loc: 0.2766 time: 0.2544 last_time: 0.1822 data_time: 0.0048 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:17:57 d2.utils.events]: \u001b[0m eta: 5:10:37 iter: 5879 total_loss: 1.468 loss_cls: 0.5484 loss_box_reg: 0.6184 loss_rpn_cls: 0.09214 loss_rpn_loc: 0.2605 time: 0.2543 last_time: 0.2617 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:18:01 d2.utils.events]: \u001b[0m eta: 5:10:31 iter: 5899 total_loss: 1.41 loss_cls: 0.5424 loss_box_reg: 0.5682 loss_rpn_cls: 0.07671 loss_rpn_loc: 0.3008 time: 0.2543 last_time: 0.2289 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:18:06 d2.utils.events]: \u001b[0m eta: 5:10:26 iter: 5919 total_loss: 1.392 loss_cls: 0.4894 loss_box_reg: 0.5089 loss_rpn_cls: 0.09829 loss_rpn_loc: 0.2953 time: 0.2542 last_time: 0.2380 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:18:11 d2.utils.events]: \u001b[0m eta: 5:10:42 iter: 5939 total_loss: 1.527 loss_cls: 0.5764 loss_box_reg: 0.5607 loss_rpn_cls: 0.09361 loss_rpn_loc: 0.2565 time: 0.2541 last_time: 0.2669 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:18:16 d2.utils.events]: \u001b[0m eta: 5:10:35 iter: 5959 total_loss: 1.513 loss_cls: 0.5585 loss_box_reg: 0.5667 loss_rpn_cls: 0.08827 loss_rpn_loc: 0.2781 time: 0.2541 last_time: 0.2325 data_time: 0.0049 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:18:20 d2.utils.events]: \u001b[0m eta: 5:10:38 iter: 5979 total_loss: 1.386 loss_cls: 0.5214 loss_box_reg: 0.5292 loss_rpn_cls: 0.07777 loss_rpn_loc: 0.2869 time: 0.2540 last_time: 0.2559 data_time: 0.0047 last_data_time: 0.0052 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:18:25 d2.utils.events]: \u001b[0m eta: 5:10:40 iter: 5999 total_loss: 1.372 loss_cls: 0.4908 loss_box_reg: 0.566 loss_rpn_cls: 0.07523 loss_rpn_loc: 0.2359 time: 0.2540 last_time: 0.2687 data_time: 0.0048 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:18:30 d2.utils.events]: \u001b[0m eta: 5:10:49 iter: 6019 total_loss: 1.453 loss_cls: 0.5519 loss_box_reg: 0.5426 loss_rpn_cls: 0.08188 loss_rpn_loc: 0.2739 time: 0.2540 last_time: 0.2612 data_time: 0.0048 last_data_time: 0.0053 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:18:35 d2.utils.events]: \u001b[0m eta: 5:10:52 iter: 6039 total_loss: 1.37 loss_cls: 0.5022 loss_box_reg: 0.5286 loss_rpn_cls: 0.07283 loss_rpn_loc: 0.2539 time: 0.2539 last_time: 0.2453 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:18:40 d2.utils.events]: \u001b[0m eta: 5:10:44 iter: 6059 total_loss: 1.401 loss_cls: 0.532 loss_box_reg: 0.5293 loss_rpn_cls: 0.09591 loss_rpn_loc: 0.2754 time: 0.2539 last_time: 0.2425 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:18:45 d2.utils.events]: \u001b[0m eta: 5:10:50 iter: 6079 total_loss: 1.38 loss_cls: 0.5101 loss_box_reg: 0.5532 loss_rpn_cls: 0.07722 loss_rpn_loc: 0.2586 time: 0.2539 last_time: 0.2324 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:18:50 d2.utils.events]: \u001b[0m eta: 5:11:16 iter: 6099 total_loss: 1.291 loss_cls: 0.4612 loss_box_reg: 0.5064 loss_rpn_cls: 0.07443 loss_rpn_loc: 0.2135 time: 0.2538 last_time: 0.2431 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:18:55 d2.utils.events]: \u001b[0m eta: 5:11:11 iter: 6119 total_loss: 1.482 loss_cls: 0.4739 loss_box_reg: 0.5563 loss_rpn_cls: 0.08682 loss_rpn_loc: 0.2703 time: 0.2538 last_time: 0.2418 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:18:59 d2.utils.events]: \u001b[0m eta: 5:11:22 iter: 6139 total_loss: 1.345 loss_cls: 0.5378 loss_box_reg: 0.5375 loss_rpn_cls: 0.07414 loss_rpn_loc: 0.2219 time: 0.2538 last_time: 0.2656 data_time: 0.0049 last_data_time: 0.0053 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:19:04 d2.utils.events]: \u001b[0m eta: 5:11:25 iter: 6159 total_loss: 1.488 loss_cls: 0.5284 loss_box_reg: 0.5571 loss_rpn_cls: 0.08813 loss_rpn_loc: 0.2764 time: 0.2537 last_time: 0.2184 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:19:09 d2.utils.events]: \u001b[0m eta: 5:12:03 iter: 6179 total_loss: 1.416 loss_cls: 0.5261 loss_box_reg: 0.5318 loss_rpn_cls: 0.06549 loss_rpn_loc: 0.2423 time: 0.2537 last_time: 0.2326 data_time: 0.0047 last_data_time: 0.0052 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:19:14 d2.utils.events]: \u001b[0m eta: 5:12:38 iter: 6199 total_loss: 1.472 loss_cls: 0.5702 loss_box_reg: 0.5823 loss_rpn_cls: 0.0747 loss_rpn_loc: 0.2589 time: 0.2537 last_time: 0.2318 data_time: 0.0049 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:19:19 d2.utils.events]: \u001b[0m eta: 5:12:59 iter: 6219 total_loss: 1.378 loss_cls: 0.5184 loss_box_reg: 0.5092 loss_rpn_cls: 0.0843 loss_rpn_loc: 0.2746 time: 0.2536 last_time: 0.2410 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:19:24 d2.utils.events]: \u001b[0m eta: 5:13:02 iter: 6239 total_loss: 1.452 loss_cls: 0.5728 loss_box_reg: 0.5676 loss_rpn_cls: 0.09407 loss_rpn_loc: 0.2863 time: 0.2536 last_time: 0.2577 data_time: 0.0048 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:19:28 d2.utils.events]: \u001b[0m eta: 5:13:09 iter: 6259 total_loss: 1.424 loss_cls: 0.5169 loss_box_reg: 0.5587 loss_rpn_cls: 0.09136 loss_rpn_loc: 0.2763 time: 0.2535 last_time: 0.2454 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:19:33 d2.utils.events]: \u001b[0m eta: 5:13:18 iter: 6279 total_loss: 1.371 loss_cls: 0.526 loss_box_reg: 0.5409 loss_rpn_cls: 0.06784 loss_rpn_loc: 0.2628 time: 0.2535 last_time: 0.2649 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:19:38 d2.utils.events]: \u001b[0m eta: 5:12:47 iter: 6299 total_loss: 1.409 loss_cls: 0.482 loss_box_reg: 0.5388 loss_rpn_cls: 0.07453 loss_rpn_loc: 0.2672 time: 0.2534 last_time: 0.2453 data_time: 0.0048 last_data_time: 0.0063 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:19:43 d2.utils.events]: \u001b[0m eta: 5:12:49 iter: 6319 total_loss: 1.448 loss_cls: 0.5215 loss_box_reg: 0.5408 loss_rpn_cls: 0.08779 loss_rpn_loc: 0.3021 time: 0.2534 last_time: 0.2367 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:19:48 d2.utils.events]: \u001b[0m eta: 5:13:38 iter: 6339 total_loss: 1.443 loss_cls: 0.5413 loss_box_reg: 0.5132 loss_rpn_cls: 0.1023 loss_rpn_loc: 0.2685 time: 0.2533 last_time: 0.2322 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:19:52 d2.utils.events]: \u001b[0m eta: 5:13:52 iter: 6359 total_loss: 1.467 loss_cls: 0.5493 loss_box_reg: 0.5475 loss_rpn_cls: 0.07987 loss_rpn_loc: 0.2781 time: 0.2533 last_time: 0.2217 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:19:57 d2.utils.events]: \u001b[0m eta: 5:13:47 iter: 6379 total_loss: 1.344 loss_cls: 0.4727 loss_box_reg: 0.5269 loss_rpn_cls: 0.06507 loss_rpn_loc: 0.2699 time: 0.2532 last_time: 0.2579 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:20:02 d2.utils.events]: \u001b[0m eta: 5:14:12 iter: 6399 total_loss: 1.313 loss_cls: 0.4704 loss_box_reg: 0.5262 loss_rpn_cls: 0.06688 loss_rpn_loc: 0.2447 time: 0.2532 last_time: 0.2345 data_time: 0.0046 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:20:07 d2.utils.events]: \u001b[0m eta: 5:13:47 iter: 6419 total_loss: 1.356 loss_cls: 0.5342 loss_box_reg: 0.5212 loss_rpn_cls: 0.0601 loss_rpn_loc: 0.2494 time: 0.2531 last_time: 0.2150 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:20:11 d2.utils.events]: \u001b[0m eta: 5:13:48 iter: 6439 total_loss: 1.404 loss_cls: 0.5188 loss_box_reg: 0.5206 loss_rpn_cls: 0.08779 loss_rpn_loc: 0.2662 time: 0.2531 last_time: 0.2104 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:20:16 d2.utils.events]: \u001b[0m eta: 5:13:58 iter: 6459 total_loss: 1.417 loss_cls: 0.5326 loss_box_reg: 0.5378 loss_rpn_cls: 0.06566 loss_rpn_loc: 0.2502 time: 0.2530 last_time: 0.2537 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:20:21 d2.utils.events]: \u001b[0m eta: 5:13:49 iter: 6479 total_loss: 1.398 loss_cls: 0.5397 loss_box_reg: 0.5241 loss_rpn_cls: 0.07607 loss_rpn_loc: 0.2705 time: 0.2529 last_time: 0.2480 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:20:25 d2.utils.events]: \u001b[0m eta: 5:13:33 iter: 6499 total_loss: 1.479 loss_cls: 0.5424 loss_box_reg: 0.5454 loss_rpn_cls: 0.0839 loss_rpn_loc: 0.2867 time: 0.2529 last_time: 0.2354 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:20:30 d2.utils.events]: \u001b[0m eta: 5:13:14 iter: 6519 total_loss: 1.477 loss_cls: 0.4597 loss_box_reg: 0.5095 loss_rpn_cls: 0.08403 loss_rpn_loc: 0.2754 time: 0.2528 last_time: 0.2333 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:20:34 d2.utils.events]: \u001b[0m eta: 5:12:37 iter: 6539 total_loss: 1.302 loss_cls: 0.4723 loss_box_reg: 0.4961 loss_rpn_cls: 0.06552 loss_rpn_loc: 0.2428 time: 0.2527 last_time: 0.2496 data_time: 0.0048 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:20:39 d2.utils.events]: \u001b[0m eta: 5:12:39 iter: 6559 total_loss: 1.36 loss_cls: 0.4823 loss_box_reg: 0.5336 loss_rpn_cls: 0.06488 loss_rpn_loc: 0.2487 time: 0.2527 last_time: 0.2537 data_time: 0.0048 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:20:44 d2.utils.events]: \u001b[0m eta: 5:12:52 iter: 6579 total_loss: 1.339 loss_cls: 0.5019 loss_box_reg: 0.5529 loss_rpn_cls: 0.07985 loss_rpn_loc: 0.2581 time: 0.2526 last_time: 0.2420 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:20:49 d2.utils.events]: \u001b[0m eta: 5:13:20 iter: 6599 total_loss: 1.352 loss_cls: 0.5048 loss_box_reg: 0.5518 loss_rpn_cls: 0.07735 loss_rpn_loc: 0.2664 time: 0.2526 last_time: 0.2425 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:20:53 d2.utils.events]: \u001b[0m eta: 5:13:05 iter: 6619 total_loss: 1.4 loss_cls: 0.5153 loss_box_reg: 0.4935 loss_rpn_cls: 0.09563 loss_rpn_loc: 0.289 time: 0.2525 last_time: 0.2435 data_time: 0.0046 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:20:58 d2.utils.events]: \u001b[0m eta: 5:12:49 iter: 6639 total_loss: 1.287 loss_cls: 0.4838 loss_box_reg: 0.5086 loss_rpn_cls: 0.06403 loss_rpn_loc: 0.2168 time: 0.2525 last_time: 0.2386 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:21:03 d2.utils.events]: \u001b[0m eta: 5:12:33 iter: 6659 total_loss: 1.406 loss_cls: 0.5279 loss_box_reg: 0.558 loss_rpn_cls: 0.07359 loss_rpn_loc: 0.2096 time: 0.2524 last_time: 0.2234 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:21:07 d2.utils.events]: \u001b[0m eta: 5:12:25 iter: 6679 total_loss: 1.514 loss_cls: 0.5561 loss_box_reg: 0.5706 loss_rpn_cls: 0.08199 loss_rpn_loc: 0.2705 time: 0.2524 last_time: 0.2113 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:21:12 d2.utils.events]: \u001b[0m eta: 5:12:51 iter: 6699 total_loss: 1.309 loss_cls: 0.4709 loss_box_reg: 0.4987 loss_rpn_cls: 0.06122 loss_rpn_loc: 0.2368 time: 0.2523 last_time: 0.2668 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:21:17 d2.utils.events]: \u001b[0m eta: 5:12:30 iter: 6719 total_loss: 1.272 loss_cls: 0.5059 loss_box_reg: 0.5071 loss_rpn_cls: 0.06072 loss_rpn_loc: 0.2338 time: 0.2523 last_time: 0.2280 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:21:22 d2.utils.events]: \u001b[0m eta: 5:12:34 iter: 6739 total_loss: 1.45 loss_cls: 0.5121 loss_box_reg: 0.5279 loss_rpn_cls: 0.0893 loss_rpn_loc: 0.2741 time: 0.2522 last_time: 0.2509 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:21:27 d2.utils.events]: \u001b[0m eta: 5:12:36 iter: 6759 total_loss: 1.285 loss_cls: 0.4733 loss_box_reg: 0.5035 loss_rpn_cls: 0.06352 loss_rpn_loc: 0.2158 time: 0.2522 last_time: 0.2113 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:21:31 d2.utils.events]: \u001b[0m eta: 5:12:31 iter: 6779 total_loss: 1.343 loss_cls: 0.4272 loss_box_reg: 0.535 loss_rpn_cls: 0.07376 loss_rpn_loc: 0.2785 time: 0.2522 last_time: 0.2305 data_time: 0.0047 last_data_time: 0.0052 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:21:36 d2.utils.events]: \u001b[0m eta: 5:12:22 iter: 6799 total_loss: 1.446 loss_cls: 0.5012 loss_box_reg: 0.5277 loss_rpn_cls: 0.08831 loss_rpn_loc: 0.2527 time: 0.2521 last_time: 0.2024 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:21:41 d2.utils.events]: \u001b[0m eta: 5:12:17 iter: 6819 total_loss: 1.393 loss_cls: 0.497 loss_box_reg: 0.5409 loss_rpn_cls: 0.09607 loss_rpn_loc: 0.2796 time: 0.2521 last_time: 0.2190 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:21:46 d2.utils.events]: \u001b[0m eta: 5:12:12 iter: 6839 total_loss: 1.386 loss_cls: 0.5486 loss_box_reg: 0.5489 loss_rpn_cls: 0.06859 loss_rpn_loc: 0.2615 time: 0.2520 last_time: 0.2254 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:21:50 d2.utils.events]: \u001b[0m eta: 5:12:16 iter: 6859 total_loss: 1.386 loss_cls: 0.4896 loss_box_reg: 0.4843 loss_rpn_cls: 0.0916 loss_rpn_loc: 0.2693 time: 0.2520 last_time: 0.1906 data_time: 0.0048 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:21:55 d2.utils.events]: \u001b[0m eta: 5:11:37 iter: 6879 total_loss: 1.38 loss_cls: 0.5191 loss_box_reg: 0.5513 loss_rpn_cls: 0.06508 loss_rpn_loc: 0.2493 time: 0.2519 last_time: 0.2253 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:22:00 d2.utils.events]: \u001b[0m eta: 5:11:34 iter: 6899 total_loss: 1.367 loss_cls: 0.4941 loss_box_reg: 0.5383 loss_rpn_cls: 0.0749 loss_rpn_loc: 0.2744 time: 0.2519 last_time: 0.2389 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:22:04 d2.utils.events]: \u001b[0m eta: 5:11:44 iter: 6919 total_loss: 1.514 loss_cls: 0.5368 loss_box_reg: 0.5712 loss_rpn_cls: 0.08297 loss_rpn_loc: 0.2917 time: 0.2518 last_time: 0.2312 data_time: 0.0048 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:22:09 d2.utils.events]: \u001b[0m eta: 5:11:46 iter: 6939 total_loss: 1.405 loss_cls: 0.4844 loss_box_reg: 0.514 loss_rpn_cls: 0.07206 loss_rpn_loc: 0.3068 time: 0.2518 last_time: 0.2183 data_time: 0.0047 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:22:14 d2.utils.events]: \u001b[0m eta: 5:11:47 iter: 6959 total_loss: 1.382 loss_cls: 0.4595 loss_box_reg: 0.5508 loss_rpn_cls: 0.0794 loss_rpn_loc: 0.2497 time: 0.2518 last_time: 0.2127 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:22:19 d2.utils.events]: \u001b[0m eta: 5:11:52 iter: 6979 total_loss: 1.404 loss_cls: 0.4818 loss_box_reg: 0.5072 loss_rpn_cls: 0.07106 loss_rpn_loc: 0.2653 time: 0.2517 last_time: 0.2514 data_time: 0.0048 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:22:24 d2.utils.events]: \u001b[0m eta: 5:12:09 iter: 6999 total_loss: 1.519 loss_cls: 0.5157 loss_box_reg: 0.5571 loss_rpn_cls: 0.08251 loss_rpn_loc: 0.2716 time: 0.2517 last_time: 0.2468 data_time: 0.0046 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:22:28 d2.utils.events]: \u001b[0m eta: 5:11:27 iter: 7019 total_loss: 1.426 loss_cls: 0.5516 loss_box_reg: 0.5554 loss_rpn_cls: 0.05898 loss_rpn_loc: 0.2305 time: 0.2516 last_time: 0.2378 data_time: 0.0044 last_data_time: 0.0041 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:22:33 d2.utils.events]: \u001b[0m eta: 5:11:01 iter: 7039 total_loss: 1.269 loss_cls: 0.4505 loss_box_reg: 0.5143 loss_rpn_cls: 0.05027 loss_rpn_loc: 0.2187 time: 0.2516 last_time: 0.2529 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:22:38 d2.utils.events]: \u001b[0m eta: 5:11:13 iter: 7059 total_loss: 1.439 loss_cls: 0.5035 loss_box_reg: 0.564 loss_rpn_cls: 0.07851 loss_rpn_loc: 0.2861 time: 0.2516 last_time: 0.1993 data_time: 0.0047 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:22:43 d2.utils.events]: \u001b[0m eta: 5:11:08 iter: 7079 total_loss: 1.254 loss_cls: 0.4637 loss_box_reg: 0.4614 loss_rpn_cls: 0.07169 loss_rpn_loc: 0.2501 time: 0.2515 last_time: 0.2176 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:22:47 d2.utils.events]: \u001b[0m eta: 5:10:46 iter: 7099 total_loss: 1.438 loss_cls: 0.5199 loss_box_reg: 0.5116 loss_rpn_cls: 0.08295 loss_rpn_loc: 0.2715 time: 0.2515 last_time: 0.2496 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:22:52 d2.utils.events]: \u001b[0m eta: 5:10:19 iter: 7119 total_loss: 1.242 loss_cls: 0.446 loss_box_reg: 0.4552 loss_rpn_cls: 0.08 loss_rpn_loc: 0.2599 time: 0.2514 last_time: 0.2492 data_time: 0.0048 last_data_time: 0.0052 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:22:57 d2.utils.events]: \u001b[0m eta: 5:10:10 iter: 7139 total_loss: 1.327 loss_cls: 0.4643 loss_box_reg: 0.5074 loss_rpn_cls: 0.08954 loss_rpn_loc: 0.2485 time: 0.2514 last_time: 0.2583 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:23:02 d2.utils.events]: \u001b[0m eta: 5:10:01 iter: 7159 total_loss: 1.465 loss_cls: 0.5831 loss_box_reg: 0.5296 loss_rpn_cls: 0.08601 loss_rpn_loc: 0.2558 time: 0.2514 last_time: 0.2371 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:23:06 d2.utils.events]: \u001b[0m eta: 5:09:46 iter: 7179 total_loss: 1.43 loss_cls: 0.5462 loss_box_reg: 0.5322 loss_rpn_cls: 0.06636 loss_rpn_loc: 0.2567 time: 0.2513 last_time: 0.2316 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:23:11 d2.utils.events]: \u001b[0m eta: 5:09:22 iter: 7199 total_loss: 1.407 loss_cls: 0.4887 loss_box_reg: 0.5694 loss_rpn_cls: 0.07086 loss_rpn_loc: 0.2757 time: 0.2513 last_time: 0.1952 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:23:16 d2.utils.events]: \u001b[0m eta: 5:08:50 iter: 7219 total_loss: 1.273 loss_cls: 0.4911 loss_box_reg: 0.4831 loss_rpn_cls: 0.06953 loss_rpn_loc: 0.2643 time: 0.2512 last_time: 0.2349 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:23:21 d2.utils.events]: \u001b[0m eta: 5:08:48 iter: 7239 total_loss: 1.337 loss_cls: 0.519 loss_box_reg: 0.5208 loss_rpn_cls: 0.0778 loss_rpn_loc: 0.2595 time: 0.2512 last_time: 0.2380 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:23:25 d2.utils.events]: \u001b[0m eta: 5:09:10 iter: 7259 total_loss: 1.333 loss_cls: 0.4812 loss_box_reg: 0.5082 loss_rpn_cls: 0.06802 loss_rpn_loc: 0.3005 time: 0.2512 last_time: 0.2443 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:23:30 d2.utils.events]: \u001b[0m eta: 5:08:42 iter: 7279 total_loss: 1.264 loss_cls: 0.4481 loss_box_reg: 0.4889 loss_rpn_cls: 0.07474 loss_rpn_loc: 0.2282 time: 0.2511 last_time: 0.2017 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:23:35 d2.utils.events]: \u001b[0m eta: 5:08:33 iter: 7299 total_loss: 1.335 loss_cls: 0.4777 loss_box_reg: 0.4938 loss_rpn_cls: 0.06812 loss_rpn_loc: 0.2906 time: 0.2511 last_time: 0.2290 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:23:40 d2.utils.events]: \u001b[0m eta: 5:08:41 iter: 7319 total_loss: 1.442 loss_cls: 0.5339 loss_box_reg: 0.495 loss_rpn_cls: 0.0792 loss_rpn_loc: 0.2812 time: 0.2511 last_time: 0.2574 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:23:44 d2.utils.events]: \u001b[0m eta: 5:08:28 iter: 7339 total_loss: 1.279 loss_cls: 0.4758 loss_box_reg: 0.4916 loss_rpn_cls: 0.07644 loss_rpn_loc: 0.2342 time: 0.2510 last_time: 0.2750 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:23:49 d2.utils.events]: \u001b[0m eta: 5:08:23 iter: 7359 total_loss: 1.348 loss_cls: 0.496 loss_box_reg: 0.5439 loss_rpn_cls: 0.09018 loss_rpn_loc: 0.2572 time: 0.2510 last_time: 0.2590 data_time: 0.0047 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:23:54 d2.utils.events]: \u001b[0m eta: 5:08:23 iter: 7379 total_loss: 1.347 loss_cls: 0.5083 loss_box_reg: 0.5091 loss_rpn_cls: 0.06453 loss_rpn_loc: 0.2546 time: 0.2509 last_time: 0.2241 data_time: 0.0049 last_data_time: 0.0054 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:23:58 d2.utils.events]: \u001b[0m eta: 5:08:14 iter: 7399 total_loss: 1.325 loss_cls: 0.4362 loss_box_reg: 0.5114 loss_rpn_cls: 0.069 loss_rpn_loc: 0.2311 time: 0.2509 last_time: 0.2579 data_time: 0.0048 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:24:03 d2.utils.events]: \u001b[0m eta: 5:08:03 iter: 7419 total_loss: 1.349 loss_cls: 0.5093 loss_box_reg: 0.5115 loss_rpn_cls: 0.06415 loss_rpn_loc: 0.2723 time: 0.2508 last_time: 0.2568 data_time: 0.0046 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:24:08 d2.utils.events]: \u001b[0m eta: 5:08:04 iter: 7439 total_loss: 1.295 loss_cls: 0.5107 loss_box_reg: 0.5182 loss_rpn_cls: 0.0774 loss_rpn_loc: 0.2391 time: 0.2508 last_time: 0.2540 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:24:13 d2.utils.events]: \u001b[0m eta: 5:07:53 iter: 7459 total_loss: 1.266 loss_cls: 0.4246 loss_box_reg: 0.5278 loss_rpn_cls: 0.07932 loss_rpn_loc: 0.2458 time: 0.2508 last_time: 0.1975 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:24:17 d2.utils.events]: \u001b[0m eta: 5:07:55 iter: 7479 total_loss: 1.311 loss_cls: 0.497 loss_box_reg: 0.5201 loss_rpn_cls: 0.08125 loss_rpn_loc: 0.2489 time: 0.2507 last_time: 0.2165 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:24:22 d2.utils.events]: \u001b[0m eta: 5:08:17 iter: 7499 total_loss: 1.238 loss_cls: 0.4358 loss_box_reg: 0.4708 loss_rpn_cls: 0.0783 loss_rpn_loc: 0.2544 time: 0.2507 last_time: 0.2587 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:24:27 d2.utils.events]: \u001b[0m eta: 5:08:30 iter: 7519 total_loss: 1.331 loss_cls: 0.4714 loss_box_reg: 0.4731 loss_rpn_cls: 0.09221 loss_rpn_loc: 0.2609 time: 0.2506 last_time: 0.2553 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:24:31 d2.utils.events]: \u001b[0m eta: 5:08:19 iter: 7539 total_loss: 1.32 loss_cls: 0.4589 loss_box_reg: 0.5103 loss_rpn_cls: 0.04994 loss_rpn_loc: 0.2379 time: 0.2506 last_time: 0.2365 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:24:36 d2.utils.events]: \u001b[0m eta: 5:08:14 iter: 7559 total_loss: 1.363 loss_cls: 0.4931 loss_box_reg: 0.5036 loss_rpn_cls: 0.07397 loss_rpn_loc: 0.2763 time: 0.2506 last_time: 0.2644 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:24:41 d2.utils.events]: \u001b[0m eta: 5:08:18 iter: 7579 total_loss: 1.29 loss_cls: 0.4685 loss_box_reg: 0.5368 loss_rpn_cls: 0.05386 loss_rpn_loc: 0.2476 time: 0.2506 last_time: 0.2309 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:24:46 d2.utils.events]: \u001b[0m eta: 5:08:11 iter: 7599 total_loss: 1.438 loss_cls: 0.5598 loss_box_reg: 0.5234 loss_rpn_cls: 0.07624 loss_rpn_loc: 0.2751 time: 0.2506 last_time: 0.2602 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:24:51 d2.utils.events]: \u001b[0m eta: 5:08:13 iter: 7619 total_loss: 1.413 loss_cls: 0.5169 loss_box_reg: 0.518 loss_rpn_cls: 0.07355 loss_rpn_loc: 0.2889 time: 0.2505 last_time: 0.2169 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:24:56 d2.utils.events]: \u001b[0m eta: 5:08:38 iter: 7639 total_loss: 1.312 loss_cls: 0.49 loss_box_reg: 0.4787 loss_rpn_cls: 0.06795 loss_rpn_loc: 0.2726 time: 0.2505 last_time: 0.2042 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:25:00 d2.utils.events]: \u001b[0m eta: 5:08:52 iter: 7659 total_loss: 1.292 loss_cls: 0.4849 loss_box_reg: 0.5041 loss_rpn_cls: 0.06642 loss_rpn_loc: 0.205 time: 0.2504 last_time: 0.2565 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:25:05 d2.utils.events]: \u001b[0m eta: 5:08:48 iter: 7679 total_loss: 1.231 loss_cls: 0.427 loss_box_reg: 0.4668 loss_rpn_cls: 0.06073 loss_rpn_loc: 0.2585 time: 0.2504 last_time: 0.2130 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:25:10 d2.utils.events]: \u001b[0m eta: 5:08:26 iter: 7699 total_loss: 1.434 loss_cls: 0.5077 loss_box_reg: 0.5713 loss_rpn_cls: 0.07055 loss_rpn_loc: 0.2261 time: 0.2503 last_time: 0.1837 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:25:14 d2.utils.events]: \u001b[0m eta: 5:08:22 iter: 7719 total_loss: 1.354 loss_cls: 0.5338 loss_box_reg: 0.4985 loss_rpn_cls: 0.06507 loss_rpn_loc: 0.2549 time: 0.2503 last_time: 0.2570 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:25:19 d2.utils.events]: \u001b[0m eta: 5:08:10 iter: 7739 total_loss: 1.362 loss_cls: 0.4799 loss_box_reg: 0.4826 loss_rpn_cls: 0.07448 loss_rpn_loc: 0.2682 time: 0.2502 last_time: 0.2446 data_time: 0.0045 last_data_time: 0.0041 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:25:24 d2.utils.events]: \u001b[0m eta: 5:07:37 iter: 7759 total_loss: 1.442 loss_cls: 0.5362 loss_box_reg: 0.5283 loss_rpn_cls: 0.09293 loss_rpn_loc: 0.273 time: 0.2502 last_time: 0.2147 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:25:28 d2.utils.events]: \u001b[0m eta: 5:07:32 iter: 7779 total_loss: 1.196 loss_cls: 0.4568 loss_box_reg: 0.4929 loss_rpn_cls: 0.06955 loss_rpn_loc: 0.2362 time: 0.2502 last_time: 0.2202 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:25:33 d2.utils.events]: \u001b[0m eta: 5:07:23 iter: 7799 total_loss: 1.337 loss_cls: 0.4841 loss_box_reg: 0.5105 loss_rpn_cls: 0.0878 loss_rpn_loc: 0.2493 time: 0.2501 last_time: 0.1978 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:25:38 d2.utils.events]: \u001b[0m eta: 5:07:12 iter: 7819 total_loss: 1.279 loss_cls: 0.4423 loss_box_reg: 0.4733 loss_rpn_cls: 0.07044 loss_rpn_loc: 0.2592 time: 0.2501 last_time: 0.2263 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:25:43 d2.utils.events]: \u001b[0m eta: 5:07:11 iter: 7839 total_loss: 1.308 loss_cls: 0.4687 loss_box_reg: 0.4528 loss_rpn_cls: 0.07332 loss_rpn_loc: 0.2696 time: 0.2501 last_time: 0.2290 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:25:47 d2.utils.events]: \u001b[0m eta: 5:07:09 iter: 7859 total_loss: 1.307 loss_cls: 0.4982 loss_box_reg: 0.5128 loss_rpn_cls: 0.08 loss_rpn_loc: 0.2511 time: 0.2500 last_time: 0.2141 data_time: 0.0047 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:25:52 d2.utils.events]: \u001b[0m eta: 5:07:08 iter: 7879 total_loss: 1.367 loss_cls: 0.5124 loss_box_reg: 0.4983 loss_rpn_cls: 0.07386 loss_rpn_loc: 0.262 time: 0.2500 last_time: 0.2514 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:25:57 d2.utils.events]: \u001b[0m eta: 5:06:53 iter: 7899 total_loss: 1.318 loss_cls: 0.4926 loss_box_reg: 0.4784 loss_rpn_cls: 0.0661 loss_rpn_loc: 0.2569 time: 0.2500 last_time: 0.2266 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:26:02 d2.utils.events]: \u001b[0m eta: 5:06:51 iter: 7919 total_loss: 1.365 loss_cls: 0.5161 loss_box_reg: 0.5368 loss_rpn_cls: 0.08049 loss_rpn_loc: 0.2515 time: 0.2499 last_time: 0.2488 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:26:06 d2.utils.events]: \u001b[0m eta: 5:06:18 iter: 7939 total_loss: 1.38 loss_cls: 0.4902 loss_box_reg: 0.494 loss_rpn_cls: 0.07692 loss_rpn_loc: 0.3004 time: 0.2499 last_time: 0.2387 data_time: 0.0048 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:26:11 d2.utils.events]: \u001b[0m eta: 5:06:10 iter: 7959 total_loss: 1.4 loss_cls: 0.5063 loss_box_reg: 0.4893 loss_rpn_cls: 0.06689 loss_rpn_loc: 0.2413 time: 0.2498 last_time: 0.2630 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:26:16 d2.utils.events]: \u001b[0m eta: 5:05:30 iter: 7979 total_loss: 1.258 loss_cls: 0.4472 loss_box_reg: 0.5018 loss_rpn_cls: 0.0849 loss_rpn_loc: 0.2719 time: 0.2498 last_time: 0.1799 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:26:20 d2.utils.events]: \u001b[0m eta: 5:05:22 iter: 7999 total_loss: 1.425 loss_cls: 0.4991 loss_box_reg: 0.5188 loss_rpn_cls: 0.08138 loss_rpn_loc: 0.2824 time: 0.2498 last_time: 0.2333 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:26:25 d2.utils.events]: \u001b[0m eta: 5:05:20 iter: 8019 total_loss: 1.378 loss_cls: 0.5132 loss_box_reg: 0.5082 loss_rpn_cls: 0.08297 loss_rpn_loc: 0.2588 time: 0.2497 last_time: 0.2544 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:26:30 d2.utils.events]: \u001b[0m eta: 5:05:16 iter: 8039 total_loss: 1.248 loss_cls: 0.4159 loss_box_reg: 0.4567 loss_rpn_cls: 0.06343 loss_rpn_loc: 0.2589 time: 0.2497 last_time: 0.2658 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:26:34 d2.utils.events]: \u001b[0m eta: 5:05:11 iter: 8059 total_loss: 1.26 loss_cls: 0.4655 loss_box_reg: 0.4569 loss_rpn_cls: 0.07639 loss_rpn_loc: 0.2646 time: 0.2497 last_time: 0.2135 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:26:39 d2.utils.events]: \u001b[0m eta: 5:05:00 iter: 8079 total_loss: 1.377 loss_cls: 0.4783 loss_box_reg: 0.5083 loss_rpn_cls: 0.0678 loss_rpn_loc: 0.2567 time: 0.2496 last_time: 0.1987 data_time: 0.0048 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:26:44 d2.utils.events]: \u001b[0m eta: 5:04:53 iter: 8099 total_loss: 1.322 loss_cls: 0.4512 loss_box_reg: 0.5145 loss_rpn_cls: 0.07519 loss_rpn_loc: 0.2657 time: 0.2496 last_time: 0.2542 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:26:49 d2.utils.events]: \u001b[0m eta: 5:04:26 iter: 8119 total_loss: 1.255 loss_cls: 0.4698 loss_box_reg: 0.4902 loss_rpn_cls: 0.06041 loss_rpn_loc: 0.2112 time: 0.2496 last_time: 0.2487 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:26:54 d2.utils.events]: \u001b[0m eta: 5:04:10 iter: 8139 total_loss: 1.363 loss_cls: 0.5359 loss_box_reg: 0.5405 loss_rpn_cls: 0.06893 loss_rpn_loc: 0.2304 time: 0.2495 last_time: 0.2487 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:26:58 d2.utils.events]: \u001b[0m eta: 5:04:31 iter: 8159 total_loss: 1.412 loss_cls: 0.5372 loss_box_reg: 0.5474 loss_rpn_cls: 0.08127 loss_rpn_loc: 0.2944 time: 0.2495 last_time: 0.2936 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:27:03 d2.utils.events]: \u001b[0m eta: 5:04:26 iter: 8179 total_loss: 1.451 loss_cls: 0.548 loss_box_reg: 0.4958 loss_rpn_cls: 0.0829 loss_rpn_loc: 0.2578 time: 0.2495 last_time: 0.2559 data_time: 0.0050 last_data_time: 0.0052 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:27:08 d2.utils.events]: \u001b[0m eta: 5:04:21 iter: 8199 total_loss: 1.262 loss_cls: 0.4807 loss_box_reg: 0.5157 loss_rpn_cls: 0.07428 loss_rpn_loc: 0.2314 time: 0.2495 last_time: 0.2486 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:27:13 d2.utils.events]: \u001b[0m eta: 5:04:26 iter: 8219 total_loss: 1.272 loss_cls: 0.4676 loss_box_reg: 0.5 loss_rpn_cls: 0.08005 loss_rpn_loc: 0.2683 time: 0.2494 last_time: 0.2545 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:27:18 d2.utils.events]: \u001b[0m eta: 5:04:19 iter: 8239 total_loss: 1.203 loss_cls: 0.4464 loss_box_reg: 0.4648 loss_rpn_cls: 0.0672 loss_rpn_loc: 0.2443 time: 0.2494 last_time: 0.2310 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:27:22 d2.utils.events]: \u001b[0m eta: 5:03:18 iter: 8259 total_loss: 1.375 loss_cls: 0.5209 loss_box_reg: 0.4993 loss_rpn_cls: 0.09208 loss_rpn_loc: 0.2799 time: 0.2494 last_time: 0.2495 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:27:27 d2.utils.events]: \u001b[0m eta: 5:03:04 iter: 8279 total_loss: 1.222 loss_cls: 0.4611 loss_box_reg: 0.4825 loss_rpn_cls: 0.07556 loss_rpn_loc: 0.2316 time: 0.2493 last_time: 0.2348 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:27:32 d2.utils.events]: \u001b[0m eta: 5:03:08 iter: 8299 total_loss: 1.294 loss_cls: 0.492 loss_box_reg: 0.5051 loss_rpn_cls: 0.07249 loss_rpn_loc: 0.2115 time: 0.2493 last_time: 0.2393 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:27:36 d2.utils.events]: \u001b[0m eta: 5:03:01 iter: 8319 total_loss: 1.291 loss_cls: 0.4496 loss_box_reg: 0.5047 loss_rpn_cls: 0.07243 loss_rpn_loc: 0.263 time: 0.2493 last_time: 0.2336 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:27:41 d2.utils.events]: \u001b[0m eta: 5:02:40 iter: 8339 total_loss: 1.373 loss_cls: 0.4902 loss_box_reg: 0.5053 loss_rpn_cls: 0.07274 loss_rpn_loc: 0.2878 time: 0.2492 last_time: 0.2360 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:27:45 d2.utils.events]: \u001b[0m eta: 5:02:10 iter: 8359 total_loss: 1.354 loss_cls: 0.4865 loss_box_reg: 0.4882 loss_rpn_cls: 0.0688 loss_rpn_loc: 0.2364 time: 0.2492 last_time: 0.2345 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:27:50 d2.utils.events]: \u001b[0m eta: 5:01:35 iter: 8379 total_loss: 1.251 loss_cls: 0.4648 loss_box_reg: 0.4992 loss_rpn_cls: 0.0527 loss_rpn_loc: 0.2474 time: 0.2491 last_time: 0.2110 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:27:54 d2.utils.events]: \u001b[0m eta: 5:01:01 iter: 8399 total_loss: 1.333 loss_cls: 0.4877 loss_box_reg: 0.4875 loss_rpn_cls: 0.06782 loss_rpn_loc: 0.2895 time: 0.2491 last_time: 0.2503 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:27:59 d2.utils.events]: \u001b[0m eta: 5:00:54 iter: 8419 total_loss: 1.394 loss_cls: 0.4655 loss_box_reg: 0.4653 loss_rpn_cls: 0.09805 loss_rpn_loc: 0.2862 time: 0.2490 last_time: 0.2349 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:28:04 d2.utils.events]: \u001b[0m eta: 5:00:48 iter: 8439 total_loss: 1.376 loss_cls: 0.4531 loss_box_reg: 0.5136 loss_rpn_cls: 0.09079 loss_rpn_loc: 0.2704 time: 0.2490 last_time: 0.2231 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:28:08 d2.utils.events]: \u001b[0m eta: 5:00:33 iter: 8459 total_loss: 1.25 loss_cls: 0.4797 loss_box_reg: 0.4803 loss_rpn_cls: 0.05931 loss_rpn_loc: 0.2168 time: 0.2489 last_time: 0.2234 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:28:13 d2.utils.events]: \u001b[0m eta: 5:00:23 iter: 8479 total_loss: 1.291 loss_cls: 0.4914 loss_box_reg: 0.4748 loss_rpn_cls: 0.05346 loss_rpn_loc: 0.2538 time: 0.2489 last_time: 0.2346 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:28:18 d2.utils.events]: \u001b[0m eta: 4:59:58 iter: 8499 total_loss: 1.387 loss_cls: 0.4839 loss_box_reg: 0.4892 loss_rpn_cls: 0.08461 loss_rpn_loc: 0.2791 time: 0.2489 last_time: 0.2499 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:28:22 d2.utils.events]: \u001b[0m eta: 4:59:48 iter: 8519 total_loss: 1.22 loss_cls: 0.4057 loss_box_reg: 0.4543 loss_rpn_cls: 0.06012 loss_rpn_loc: 0.2493 time: 0.2488 last_time: 0.2518 data_time: 0.0047 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:28:27 d2.utils.events]: \u001b[0m eta: 4:59:40 iter: 8539 total_loss: 1.278 loss_cls: 0.4682 loss_box_reg: 0.4627 loss_rpn_cls: 0.08324 loss_rpn_loc: 0.2767 time: 0.2488 last_time: 0.2456 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:28:32 d2.utils.events]: \u001b[0m eta: 4:59:24 iter: 8559 total_loss: 1.377 loss_cls: 0.5245 loss_box_reg: 0.5192 loss_rpn_cls: 0.04955 loss_rpn_loc: 0.2525 time: 0.2487 last_time: 0.2330 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:28:36 d2.utils.events]: \u001b[0m eta: 4:59:08 iter: 8579 total_loss: 1.254 loss_cls: 0.4779 loss_box_reg: 0.4644 loss_rpn_cls: 0.06534 loss_rpn_loc: 0.2596 time: 0.2487 last_time: 0.2077 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:28:41 d2.utils.events]: \u001b[0m eta: 4:58:37 iter: 8599 total_loss: 1.249 loss_cls: 0.4186 loss_box_reg: 0.4562 loss_rpn_cls: 0.07746 loss_rpn_loc: 0.2628 time: 0.2486 last_time: 0.2240 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:28:45 d2.utils.events]: \u001b[0m eta: 4:58:24 iter: 8619 total_loss: 1.296 loss_cls: 0.4601 loss_box_reg: 0.4898 loss_rpn_cls: 0.06202 loss_rpn_loc: 0.2526 time: 0.2486 last_time: 0.2336 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:28:50 d2.utils.events]: \u001b[0m eta: 4:58:10 iter: 8639 total_loss: 1.258 loss_cls: 0.4503 loss_box_reg: 0.4745 loss_rpn_cls: 0.07373 loss_rpn_loc: 0.2292 time: 0.2485 last_time: 0.2567 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:28:55 d2.utils.events]: \u001b[0m eta: 4:58:06 iter: 8659 total_loss: 1.241 loss_cls: 0.4361 loss_box_reg: 0.4678 loss_rpn_cls: 0.08997 loss_rpn_loc: 0.2517 time: 0.2485 last_time: 0.2348 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:28:59 d2.utils.events]: \u001b[0m eta: 4:58:14 iter: 8679 total_loss: 1.309 loss_cls: 0.4288 loss_box_reg: 0.4955 loss_rpn_cls: 0.06653 loss_rpn_loc: 0.2845 time: 0.2485 last_time: 0.2488 data_time: 0.0047 last_data_time: 0.0052 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:29:04 d2.utils.events]: \u001b[0m eta: 4:58:09 iter: 8699 total_loss: 1.287 loss_cls: 0.4855 loss_box_reg: 0.504 loss_rpn_cls: 0.07596 loss_rpn_loc: 0.2398 time: 0.2485 last_time: 0.2517 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:29:09 d2.utils.events]: \u001b[0m eta: 4:58:05 iter: 8719 total_loss: 1.303 loss_cls: 0.4447 loss_box_reg: 0.4685 loss_rpn_cls: 0.08932 loss_rpn_loc: 0.28 time: 0.2484 last_time: 0.2234 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:29:13 d2.utils.events]: \u001b[0m eta: 4:57:56 iter: 8739 total_loss: 1.298 loss_cls: 0.4453 loss_box_reg: 0.4865 loss_rpn_cls: 0.06435 loss_rpn_loc: 0.2534 time: 0.2484 last_time: 0.2331 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:29:18 d2.utils.events]: \u001b[0m eta: 4:57:45 iter: 8759 total_loss: 1.263 loss_cls: 0.4591 loss_box_reg: 0.4551 loss_rpn_cls: 0.06198 loss_rpn_loc: 0.2656 time: 0.2483 last_time: 0.1942 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:29:23 d2.utils.events]: \u001b[0m eta: 4:57:34 iter: 8779 total_loss: 1.248 loss_cls: 0.4503 loss_box_reg: 0.4611 loss_rpn_cls: 0.07438 loss_rpn_loc: 0.2523 time: 0.2483 last_time: 0.2345 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:29:27 d2.utils.events]: \u001b[0m eta: 4:57:22 iter: 8799 total_loss: 1.264 loss_cls: 0.4569 loss_box_reg: 0.4605 loss_rpn_cls: 0.07039 loss_rpn_loc: 0.255 time: 0.2482 last_time: 0.2497 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:29:32 d2.utils.events]: \u001b[0m eta: 4:57:17 iter: 8819 total_loss: 1.146 loss_cls: 0.4326 loss_box_reg: 0.4535 loss_rpn_cls: 0.06465 loss_rpn_loc: 0.264 time: 0.2482 last_time: 0.2481 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:29:36 d2.utils.events]: \u001b[0m eta: 4:57:09 iter: 8839 total_loss: 1.266 loss_cls: 0.4905 loss_box_reg: 0.4836 loss_rpn_cls: 0.07538 loss_rpn_loc: 0.2713 time: 0.2481 last_time: 0.1942 data_time: 0.0046 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:29:41 d2.utils.events]: \u001b[0m eta: 4:57:05 iter: 8859 total_loss: 1.305 loss_cls: 0.4308 loss_box_reg: 0.4864 loss_rpn_cls: 0.05813 loss_rpn_loc: 0.2517 time: 0.2481 last_time: 0.2215 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:29:45 d2.utils.events]: \u001b[0m eta: 4:56:48 iter: 8879 total_loss: 1.231 loss_cls: 0.4285 loss_box_reg: 0.4299 loss_rpn_cls: 0.06557 loss_rpn_loc: 0.2278 time: 0.2480 last_time: 0.2344 data_time: 0.0045 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:29:50 d2.utils.events]: \u001b[0m eta: 4:56:49 iter: 8899 total_loss: 1.271 loss_cls: 0.4321 loss_box_reg: 0.4922 loss_rpn_cls: 0.06335 loss_rpn_loc: 0.2401 time: 0.2480 last_time: 0.2503 data_time: 0.0048 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:29:55 d2.utils.events]: \u001b[0m eta: 4:56:41 iter: 8919 total_loss: 1.306 loss_cls: 0.4585 loss_box_reg: 0.4706 loss_rpn_cls: 0.0919 loss_rpn_loc: 0.2773 time: 0.2480 last_time: 0.2334 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:29:59 d2.utils.events]: \u001b[0m eta: 4:56:33 iter: 8939 total_loss: 1.175 loss_cls: 0.4027 loss_box_reg: 0.4414 loss_rpn_cls: 0.06767 loss_rpn_loc: 0.2192 time: 0.2479 last_time: 0.2483 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:30:04 d2.utils.events]: \u001b[0m eta: 4:56:23 iter: 8959 total_loss: 1.297 loss_cls: 0.4777 loss_box_reg: 0.492 loss_rpn_cls: 0.08105 loss_rpn_loc: 0.2494 time: 0.2479 last_time: 0.2493 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:30:09 d2.utils.events]: \u001b[0m eta: 4:56:23 iter: 8979 total_loss: 1.259 loss_cls: 0.4804 loss_box_reg: 0.4228 loss_rpn_cls: 0.08382 loss_rpn_loc: 0.2574 time: 0.2479 last_time: 0.2327 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:30:13 d2.utils.events]: \u001b[0m eta: 4:56:22 iter: 8999 total_loss: 1.25 loss_cls: 0.4177 loss_box_reg: 0.4617 loss_rpn_cls: 0.07828 loss_rpn_loc: 0.2635 time: 0.2479 last_time: 0.2593 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:30:18 d2.utils.events]: \u001b[0m eta: 4:56:22 iter: 9019 total_loss: 1.254 loss_cls: 0.397 loss_box_reg: 0.4824 loss_rpn_cls: 0.0664 loss_rpn_loc: 0.2452 time: 0.2478 last_time: 0.2573 data_time: 0.0048 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:30:23 d2.utils.events]: \u001b[0m eta: 4:56:17 iter: 9039 total_loss: 1.206 loss_cls: 0.449 loss_box_reg: 0.4521 loss_rpn_cls: 0.06911 loss_rpn_loc: 0.2482 time: 0.2478 last_time: 0.2288 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:30:28 d2.utils.events]: \u001b[0m eta: 4:56:14 iter: 9059 total_loss: 1.357 loss_cls: 0.5243 loss_box_reg: 0.499 loss_rpn_cls: 0.08711 loss_rpn_loc: 0.2476 time: 0.2478 last_time: 0.2558 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:30:33 d2.utils.events]: \u001b[0m eta: 4:56:06 iter: 9079 total_loss: 1.102 loss_cls: 0.4057 loss_box_reg: 0.4349 loss_rpn_cls: 0.06145 loss_rpn_loc: 0.2122 time: 0.2478 last_time: 0.2216 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:30:37 d2.utils.events]: \u001b[0m eta: 4:55:55 iter: 9099 total_loss: 1.163 loss_cls: 0.4296 loss_box_reg: 0.4488 loss_rpn_cls: 0.06949 loss_rpn_loc: 0.21 time: 0.2477 last_time: 0.2126 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:30:42 d2.utils.events]: \u001b[0m eta: 4:55:51 iter: 9119 total_loss: 1.286 loss_cls: 0.4486 loss_box_reg: 0.4442 loss_rpn_cls: 0.07544 loss_rpn_loc: 0.2869 time: 0.2477 last_time: 0.2348 data_time: 0.0046 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:30:47 d2.utils.events]: \u001b[0m eta: 4:55:52 iter: 9139 total_loss: 1.266 loss_cls: 0.4958 loss_box_reg: 0.5137 loss_rpn_cls: 0.06207 loss_rpn_loc: 0.233 time: 0.2477 last_time: 0.2341 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:30:53 d2.utils.events]: \u001b[0m eta: 4:55:54 iter: 9159 total_loss: 1.323 loss_cls: 0.4865 loss_box_reg: 0.5103 loss_rpn_cls: 0.0727 loss_rpn_loc: 0.2587 time: 0.2478 last_time: 0.2578 data_time: 0.0049 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:30:58 d2.utils.events]: \u001b[0m eta: 4:55:50 iter: 9179 total_loss: 1.257 loss_cls: 0.4388 loss_box_reg: 0.4368 loss_rpn_cls: 0.07251 loss_rpn_loc: 0.251 time: 0.2478 last_time: 0.2491 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:31:03 d2.utils.events]: \u001b[0m eta: 4:55:51 iter: 9199 total_loss: 1.189 loss_cls: 0.4037 loss_box_reg: 0.4641 loss_rpn_cls: 0.06776 loss_rpn_loc: 0.2505 time: 0.2479 last_time: 0.2501 data_time: 0.0052 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:31:08 d2.utils.events]: \u001b[0m eta: 4:55:45 iter: 9219 total_loss: 1.177 loss_cls: 0.4423 loss_box_reg: 0.447 loss_rpn_cls: 0.06598 loss_rpn_loc: 0.2463 time: 0.2478 last_time: 0.2369 data_time: 0.0047 last_data_time: 0.0052 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:31:13 d2.utils.events]: \u001b[0m eta: 4:55:39 iter: 9239 total_loss: 1.215 loss_cls: 0.4777 loss_box_reg: 0.4735 loss_rpn_cls: 0.05653 loss_rpn_loc: 0.2439 time: 0.2478 last_time: 0.2066 data_time: 0.0048 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:31:17 d2.utils.events]: \u001b[0m eta: 4:55:40 iter: 9259 total_loss: 1.329 loss_cls: 0.4562 loss_box_reg: 0.4689 loss_rpn_cls: 0.0891 loss_rpn_loc: 0.3013 time: 0.2478 last_time: 0.2390 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:31:22 d2.utils.events]: \u001b[0m eta: 4:55:37 iter: 9279 total_loss: 1.231 loss_cls: 0.4278 loss_box_reg: 0.4638 loss_rpn_cls: 0.06593 loss_rpn_loc: 0.2362 time: 0.2478 last_time: 0.2512 data_time: 0.0046 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:31:27 d2.utils.events]: \u001b[0m eta: 4:55:33 iter: 9299 total_loss: 1.212 loss_cls: 0.4395 loss_box_reg: 0.4723 loss_rpn_cls: 0.07215 loss_rpn_loc: 0.2313 time: 0.2477 last_time: 0.2171 data_time: 0.0047 last_data_time: 0.0053 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:31:32 d2.utils.events]: \u001b[0m eta: 4:55:29 iter: 9319 total_loss: 1.223 loss_cls: 0.4613 loss_box_reg: 0.4732 loss_rpn_cls: 0.07715 loss_rpn_loc: 0.2527 time: 0.2477 last_time: 0.2178 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:31:36 d2.utils.events]: \u001b[0m eta: 4:55:25 iter: 9339 total_loss: 1.192 loss_cls: 0.4466 loss_box_reg: 0.4516 loss_rpn_cls: 0.07006 loss_rpn_loc: 0.2437 time: 0.2477 last_time: 0.2588 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:31:41 d2.utils.events]: \u001b[0m eta: 4:55:27 iter: 9359 total_loss: 1.284 loss_cls: 0.4752 loss_box_reg: 0.4633 loss_rpn_cls: 0.07441 loss_rpn_loc: 0.2238 time: 0.2477 last_time: 0.1803 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:31:46 d2.utils.events]: \u001b[0m eta: 4:55:26 iter: 9379 total_loss: 1.367 loss_cls: 0.5189 loss_box_reg: 0.4816 loss_rpn_cls: 0.0818 loss_rpn_loc: 0.2883 time: 0.2476 last_time: 0.1945 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:31:50 d2.utils.events]: \u001b[0m eta: 4:55:21 iter: 9399 total_loss: 1.286 loss_cls: 0.4767 loss_box_reg: 0.4651 loss_rpn_cls: 0.06675 loss_rpn_loc: 0.2233 time: 0.2476 last_time: 0.2126 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:31:55 d2.utils.events]: \u001b[0m eta: 4:55:19 iter: 9419 total_loss: 1.175 loss_cls: 0.3942 loss_box_reg: 0.4501 loss_rpn_cls: 0.07237 loss_rpn_loc: 0.2562 time: 0.2476 last_time: 0.2507 data_time: 0.0046 last_data_time: 0.0041 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:32:00 d2.utils.events]: \u001b[0m eta: 4:55:23 iter: 9439 total_loss: 1.256 loss_cls: 0.4636 loss_box_reg: 0.4637 loss_rpn_cls: 0.0739 loss_rpn_loc: 0.223 time: 0.2475 last_time: 0.2138 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:32:04 d2.utils.events]: \u001b[0m eta: 4:55:20 iter: 9459 total_loss: 1.137 loss_cls: 0.4165 loss_box_reg: 0.4052 loss_rpn_cls: 0.07746 loss_rpn_loc: 0.2397 time: 0.2475 last_time: 0.2303 data_time: 0.0046 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:32:09 d2.utils.events]: \u001b[0m eta: 4:55:23 iter: 9479 total_loss: 1.256 loss_cls: 0.4773 loss_box_reg: 0.4729 loss_rpn_cls: 0.07607 loss_rpn_loc: 0.2426 time: 0.2475 last_time: 0.2602 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:32:13 d2.utils.events]: \u001b[0m eta: 4:55:13 iter: 9499 total_loss: 1.277 loss_cls: 0.461 loss_box_reg: 0.4569 loss_rpn_cls: 0.06647 loss_rpn_loc: 0.2529 time: 0.2474 last_time: 0.2493 data_time: 0.0047 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:32:18 d2.utils.events]: \u001b[0m eta: 4:55:14 iter: 9519 total_loss: 1.277 loss_cls: 0.4411 loss_box_reg: 0.4908 loss_rpn_cls: 0.07025 loss_rpn_loc: 0.2479 time: 0.2474 last_time: 0.2437 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:32:23 d2.utils.events]: \u001b[0m eta: 4:55:11 iter: 9539 total_loss: 1.285 loss_cls: 0.4771 loss_box_reg: 0.4979 loss_rpn_cls: 0.05275 loss_rpn_loc: 0.2252 time: 0.2474 last_time: 0.2316 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:32:28 d2.utils.events]: \u001b[0m eta: 4:55:11 iter: 9559 total_loss: 1.257 loss_cls: 0.4656 loss_box_reg: 0.471 loss_rpn_cls: 0.07676 loss_rpn_loc: 0.2653 time: 0.2474 last_time: 0.2584 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:32:32 d2.utils.events]: \u001b[0m eta: 4:55:10 iter: 9579 total_loss: 1.255 loss_cls: 0.4486 loss_box_reg: 0.513 loss_rpn_cls: 0.07195 loss_rpn_loc: 0.2424 time: 0.2473 last_time: 0.2550 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:32:37 d2.utils.events]: \u001b[0m eta: 4:55:10 iter: 9599 total_loss: 1.279 loss_cls: 0.4451 loss_box_reg: 0.4783 loss_rpn_cls: 0.07133 loss_rpn_loc: 0.2562 time: 0.2473 last_time: 0.2067 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:32:42 d2.utils.events]: \u001b[0m eta: 4:55:26 iter: 9619 total_loss: 1.256 loss_cls: 0.4805 loss_box_reg: 0.465 loss_rpn_cls: 0.0826 loss_rpn_loc: 0.2211 time: 0.2473 last_time: 0.2479 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:32:47 d2.utils.events]: \u001b[0m eta: 4:55:29 iter: 9639 total_loss: 1.188 loss_cls: 0.448 loss_box_reg: 0.4296 loss_rpn_cls: 0.06547 loss_rpn_loc: 0.2254 time: 0.2473 last_time: 0.2428 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:32:51 d2.utils.events]: \u001b[0m eta: 4:55:32 iter: 9659 total_loss: 1.142 loss_cls: 0.4145 loss_box_reg: 0.4518 loss_rpn_cls: 0.06112 loss_rpn_loc: 0.2155 time: 0.2472 last_time: 0.2312 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:32:56 d2.utils.events]: \u001b[0m eta: 4:54:57 iter: 9679 total_loss: 1.145 loss_cls: 0.4265 loss_box_reg: 0.421 loss_rpn_cls: 0.06659 loss_rpn_loc: 0.2229 time: 0.2472 last_time: 0.2177 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:33:01 d2.utils.events]: \u001b[0m eta: 4:55:06 iter: 9699 total_loss: 1.154 loss_cls: 0.3853 loss_box_reg: 0.4551 loss_rpn_cls: 0.06738 loss_rpn_loc: 0.2549 time: 0.2472 last_time: 0.2496 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:33:05 d2.utils.events]: \u001b[0m eta: 4:55:15 iter: 9719 total_loss: 1.18 loss_cls: 0.4096 loss_box_reg: 0.442 loss_rpn_cls: 0.07794 loss_rpn_loc: 0.2413 time: 0.2472 last_time: 0.2628 data_time: 0.0046 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:33:10 d2.utils.events]: \u001b[0m eta: 4:55:32 iter: 9739 total_loss: 1.161 loss_cls: 0.4354 loss_box_reg: 0.4717 loss_rpn_cls: 0.07031 loss_rpn_loc: 0.2123 time: 0.2471 last_time: 0.2498 data_time: 0.0045 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:33:15 d2.utils.events]: \u001b[0m eta: 4:55:29 iter: 9759 total_loss: 1.094 loss_cls: 0.4179 loss_box_reg: 0.4207 loss_rpn_cls: 0.05661 loss_rpn_loc: 0.1948 time: 0.2471 last_time: 0.2533 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:33:20 d2.utils.events]: \u001b[0m eta: 4:55:53 iter: 9779 total_loss: 1.231 loss_cls: 0.4653 loss_box_reg: 0.4585 loss_rpn_cls: 0.05169 loss_rpn_loc: 0.2436 time: 0.2471 last_time: 0.2494 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:33:24 d2.utils.events]: \u001b[0m eta: 4:55:51 iter: 9799 total_loss: 1.188 loss_cls: 0.4456 loss_box_reg: 0.4397 loss_rpn_cls: 0.06588 loss_rpn_loc: 0.2177 time: 0.2471 last_time: 0.2431 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:33:29 d2.utils.events]: \u001b[0m eta: 4:56:09 iter: 9819 total_loss: 1.21 loss_cls: 0.414 loss_box_reg: 0.4893 loss_rpn_cls: 0.06419 loss_rpn_loc: 0.2292 time: 0.2470 last_time: 0.2261 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:33:34 d2.utils.events]: \u001b[0m eta: 4:56:36 iter: 9839 total_loss: 1.223 loss_cls: 0.4242 loss_box_reg: 0.4778 loss_rpn_cls: 0.06286 loss_rpn_loc: 0.2673 time: 0.2470 last_time: 0.2580 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:33:39 d2.utils.events]: \u001b[0m eta: 4:57:16 iter: 9859 total_loss: 1.297 loss_cls: 0.4571 loss_box_reg: 0.4806 loss_rpn_cls: 0.05675 loss_rpn_loc: 0.2522 time: 0.2470 last_time: 0.2126 data_time: 0.0045 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:33:43 d2.utils.events]: \u001b[0m eta: 4:57:36 iter: 9879 total_loss: 1.313 loss_cls: 0.462 loss_box_reg: 0.4791 loss_rpn_cls: 0.07491 loss_rpn_loc: 0.2619 time: 0.2470 last_time: 0.2602 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:33:48 d2.utils.events]: \u001b[0m eta: 4:57:29 iter: 9899 total_loss: 1.198 loss_cls: 0.4379 loss_box_reg: 0.438 loss_rpn_cls: 0.07946 loss_rpn_loc: 0.2412 time: 0.2469 last_time: 0.1853 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:33:52 d2.utils.events]: \u001b[0m eta: 4:57:48 iter: 9919 total_loss: 1.262 loss_cls: 0.4622 loss_box_reg: 0.4744 loss_rpn_cls: 0.06569 loss_rpn_loc: 0.2424 time: 0.2469 last_time: 0.2038 data_time: 0.0046 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:33:57 d2.utils.events]: \u001b[0m eta: 4:57:44 iter: 9939 total_loss: 1.246 loss_cls: 0.4586 loss_box_reg: 0.4629 loss_rpn_cls: 0.07573 loss_rpn_loc: 0.2457 time: 0.2469 last_time: 0.2451 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:34:02 d2.utils.events]: \u001b[0m eta: 4:58:01 iter: 9959 total_loss: 1.131 loss_cls: 0.4157 loss_box_reg: 0.4709 loss_rpn_cls: 0.05595 loss_rpn_loc: 0.2471 time: 0.2468 last_time: 0.2581 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:34:06 d2.utils.events]: \u001b[0m eta: 4:57:54 iter: 9979 total_loss: 1.284 loss_cls: 0.4662 loss_box_reg: 0.5 loss_rpn_cls: 0.06506 loss_rpn_loc: 0.2494 time: 0.2468 last_time: 0.2141 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:34:12 d2.utils.events]: \u001b[0m eta: 4:57:08 iter: 9999 total_loss: 1.18 loss_cls: 0.4277 loss_box_reg: 0.44 loss_rpn_cls: 0.05869 loss_rpn_loc: 0.2396 time: 0.2468 last_time: 0.2347 data_time: 0.0045 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:34:17 d2.utils.events]: \u001b[0m eta: 4:56:44 iter: 10019 total_loss: 1.144 loss_cls: 0.4595 loss_box_reg: 0.4448 loss_rpn_cls: 0.06279 loss_rpn_loc: 0.2457 time: 0.2468 last_time: 0.1998 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:34:22 d2.utils.events]: \u001b[0m eta: 4:56:16 iter: 10039 total_loss: 1.212 loss_cls: 0.4321 loss_box_reg: 0.4397 loss_rpn_cls: 0.08374 loss_rpn_loc: 0.2248 time: 0.2467 last_time: 0.2412 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:34:26 d2.utils.events]: \u001b[0m eta: 4:55:21 iter: 10059 total_loss: 1.224 loss_cls: 0.4478 loss_box_reg: 0.4511 loss_rpn_cls: 0.06958 loss_rpn_loc: 0.2429 time: 0.2467 last_time: 0.2300 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:34:31 d2.utils.events]: \u001b[0m eta: 4:54:57 iter: 10079 total_loss: 1.251 loss_cls: 0.4162 loss_box_reg: 0.4434 loss_rpn_cls: 0.07275 loss_rpn_loc: 0.2572 time: 0.2467 last_time: 0.2141 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:34:36 d2.utils.events]: \u001b[0m eta: 4:55:32 iter: 10099 total_loss: 1.203 loss_cls: 0.4373 loss_box_reg: 0.465 loss_rpn_cls: 0.05522 loss_rpn_loc: 0.2352 time: 0.2466 last_time: 0.2337 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:34:40 d2.utils.events]: \u001b[0m eta: 4:55:43 iter: 10119 total_loss: 1.211 loss_cls: 0.4577 loss_box_reg: 0.4478 loss_rpn_cls: 0.06705 loss_rpn_loc: 0.2342 time: 0.2466 last_time: 0.1812 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:34:45 d2.utils.events]: \u001b[0m eta: 4:55:05 iter: 10139 total_loss: 1.204 loss_cls: 0.4357 loss_box_reg: 0.4682 loss_rpn_cls: 0.05011 loss_rpn_loc: 0.2265 time: 0.2466 last_time: 0.2329 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:34:50 d2.utils.events]: \u001b[0m eta: 4:54:23 iter: 10159 total_loss: 1.136 loss_cls: 0.3834 loss_box_reg: 0.4593 loss_rpn_cls: 0.06315 loss_rpn_loc: 0.2477 time: 0.2466 last_time: 0.2568 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:34:54 d2.utils.events]: \u001b[0m eta: 4:54:09 iter: 10179 total_loss: 1.155 loss_cls: 0.4335 loss_box_reg: 0.4544 loss_rpn_cls: 0.05634 loss_rpn_loc: 0.2446 time: 0.2465 last_time: 0.2421 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:34:59 d2.utils.events]: \u001b[0m eta: 4:54:00 iter: 10199 total_loss: 1.277 loss_cls: 0.4599 loss_box_reg: 0.4863 loss_rpn_cls: 0.0639 loss_rpn_loc: 0.275 time: 0.2465 last_time: 0.2496 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:35:04 d2.utils.events]: \u001b[0m eta: 4:53:59 iter: 10219 total_loss: 1.17 loss_cls: 0.3876 loss_box_reg: 0.4523 loss_rpn_cls: 0.07003 loss_rpn_loc: 0.2144 time: 0.2465 last_time: 0.2246 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:35:09 d2.utils.events]: \u001b[0m eta: 4:54:00 iter: 10239 total_loss: 1.208 loss_cls: 0.4085 loss_box_reg: 0.4615 loss_rpn_cls: 0.06018 loss_rpn_loc: 0.2579 time: 0.2465 last_time: 0.3414 data_time: 0.0048 last_data_time: 0.0054 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:35:14 d2.utils.events]: \u001b[0m eta: 4:54:00 iter: 10259 total_loss: 1.133 loss_cls: 0.4001 loss_box_reg: 0.4241 loss_rpn_cls: 0.06786 loss_rpn_loc: 0.2577 time: 0.2465 last_time: 0.2828 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:35:19 d2.utils.events]: \u001b[0m eta: 4:54:10 iter: 10279 total_loss: 1.142 loss_cls: 0.3953 loss_box_reg: 0.4636 loss_rpn_cls: 0.06032 loss_rpn_loc: 0.2406 time: 0.2465 last_time: 0.2247 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:35:23 d2.utils.events]: \u001b[0m eta: 4:53:50 iter: 10299 total_loss: 1.331 loss_cls: 0.5246 loss_box_reg: 0.4688 loss_rpn_cls: 0.1017 loss_rpn_loc: 0.2231 time: 0.2465 last_time: 0.2392 data_time: 0.0048 last_data_time: 0.0052 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:35:28 d2.utils.events]: \u001b[0m eta: 4:53:31 iter: 10319 total_loss: 1.268 loss_cls: 0.4382 loss_box_reg: 0.4704 loss_rpn_cls: 0.07252 loss_rpn_loc: 0.2247 time: 0.2464 last_time: 0.2320 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:35:33 d2.utils.events]: \u001b[0m eta: 4:53:35 iter: 10339 total_loss: 1.147 loss_cls: 0.4529 loss_box_reg: 0.4799 loss_rpn_cls: 0.06091 loss_rpn_loc: 0.2047 time: 0.2464 last_time: 0.2252 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:35:38 d2.utils.events]: \u001b[0m eta: 4:53:37 iter: 10359 total_loss: 1.147 loss_cls: 0.388 loss_box_reg: 0.4164 loss_rpn_cls: 0.08004 loss_rpn_loc: 0.2586 time: 0.2464 last_time: 0.2468 data_time: 0.0047 last_data_time: 0.0052 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:35:43 d2.utils.events]: \u001b[0m eta: 4:53:32 iter: 10379 total_loss: 1.218 loss_cls: 0.4228 loss_box_reg: 0.4592 loss_rpn_cls: 0.06681 loss_rpn_loc: 0.2332 time: 0.2464 last_time: 0.3166 data_time: 0.0047 last_data_time: 0.0056 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:35:48 d2.utils.events]: \u001b[0m eta: 4:54:51 iter: 10399 total_loss: 1.17 loss_cls: 0.4397 loss_box_reg: 0.4344 loss_rpn_cls: 0.0573 loss_rpn_loc: 0.2543 time: 0.2465 last_time: 0.2961 data_time: 0.0048 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:35:53 d2.utils.events]: \u001b[0m eta: 4:54:47 iter: 10419 total_loss: 1.185 loss_cls: 0.4297 loss_box_reg: 0.4538 loss_rpn_cls: 0.07006 loss_rpn_loc: 0.2473 time: 0.2465 last_time: 0.2108 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:35:58 d2.utils.events]: \u001b[0m eta: 4:54:23 iter: 10439 total_loss: 1.193 loss_cls: 0.4324 loss_box_reg: 0.4492 loss_rpn_cls: 0.08187 loss_rpn_loc: 0.2493 time: 0.2465 last_time: 0.2074 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:36:03 d2.utils.events]: \u001b[0m eta: 4:54:58 iter: 10459 total_loss: 1.184 loss_cls: 0.4635 loss_box_reg: 0.4596 loss_rpn_cls: 0.05794 loss_rpn_loc: 0.2204 time: 0.2464 last_time: 0.2402 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:36:07 d2.utils.events]: \u001b[0m eta: 4:54:34 iter: 10479 total_loss: 1.219 loss_cls: 0.4235 loss_box_reg: 0.4489 loss_rpn_cls: 0.06182 loss_rpn_loc: 0.2617 time: 0.2464 last_time: 0.2110 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:36:12 d2.utils.events]: \u001b[0m eta: 4:54:38 iter: 10499 total_loss: 1.353 loss_cls: 0.472 loss_box_reg: 0.5121 loss_rpn_cls: 0.08007 loss_rpn_loc: 0.2746 time: 0.2464 last_time: 0.2093 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:36:17 d2.utils.events]: \u001b[0m eta: 4:54:16 iter: 10519 total_loss: 1.224 loss_cls: 0.4287 loss_box_reg: 0.4524 loss_rpn_cls: 0.06305 loss_rpn_loc: 0.235 time: 0.2464 last_time: 0.2439 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:36:21 d2.utils.events]: \u001b[0m eta: 4:54:11 iter: 10539 total_loss: 1.202 loss_cls: 0.4682 loss_box_reg: 0.4633 loss_rpn_cls: 0.07758 loss_rpn_loc: 0.2736 time: 0.2463 last_time: 0.2567 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:36:26 d2.utils.events]: \u001b[0m eta: 4:53:23 iter: 10559 total_loss: 1.303 loss_cls: 0.4583 loss_box_reg: 0.4598 loss_rpn_cls: 0.07775 loss_rpn_loc: 0.2625 time: 0.2463 last_time: 0.1834 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:36:31 d2.utils.events]: \u001b[0m eta: 4:53:17 iter: 10579 total_loss: 1.29 loss_cls: 0.4574 loss_box_reg: 0.4735 loss_rpn_cls: 0.08576 loss_rpn_loc: 0.2713 time: 0.2463 last_time: 0.2135 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:36:35 d2.utils.events]: \u001b[0m eta: 4:54:04 iter: 10599 total_loss: 1.195 loss_cls: 0.4275 loss_box_reg: 0.4533 loss_rpn_cls: 0.0687 loss_rpn_loc: 0.2529 time: 0.2463 last_time: 0.2590 data_time: 0.0048 last_data_time: 0.0055 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:36:40 d2.utils.events]: \u001b[0m eta: 4:53:41 iter: 10619 total_loss: 1.281 loss_cls: 0.4499 loss_box_reg: 0.4683 loss_rpn_cls: 0.07897 loss_rpn_loc: 0.2878 time: 0.2463 last_time: 0.2424 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:36:45 d2.utils.events]: \u001b[0m eta: 4:53:07 iter: 10639 total_loss: 1.174 loss_cls: 0.4401 loss_box_reg: 0.4271 loss_rpn_cls: 0.07558 loss_rpn_loc: 0.2408 time: 0.2462 last_time: 0.2313 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:36:49 d2.utils.events]: \u001b[0m eta: 4:52:58 iter: 10659 total_loss: 1.236 loss_cls: 0.4684 loss_box_reg: 0.4593 loss_rpn_cls: 0.07783 loss_rpn_loc: 0.2525 time: 0.2462 last_time: 0.2304 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:36:54 d2.utils.events]: \u001b[0m eta: 4:53:45 iter: 10679 total_loss: 1.213 loss_cls: 0.3837 loss_box_reg: 0.4091 loss_rpn_cls: 0.07147 loss_rpn_loc: 0.2721 time: 0.2462 last_time: 0.2487 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:36:59 d2.utils.events]: \u001b[0m eta: 4:53:57 iter: 10699 total_loss: 1.163 loss_cls: 0.4154 loss_box_reg: 0.4483 loss_rpn_cls: 0.06201 loss_rpn_loc: 0.2371 time: 0.2462 last_time: 0.2314 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:37:04 d2.utils.events]: \u001b[0m eta: 4:53:56 iter: 10719 total_loss: 1.167 loss_cls: 0.4164 loss_box_reg: 0.4399 loss_rpn_cls: 0.05872 loss_rpn_loc: 0.2665 time: 0.2462 last_time: 0.2499 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:37:09 d2.utils.events]: \u001b[0m eta: 4:53:42 iter: 10739 total_loss: 1.28 loss_cls: 0.4604 loss_box_reg: 0.4428 loss_rpn_cls: 0.08146 loss_rpn_loc: 0.2429 time: 0.2461 last_time: 0.2553 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:37:13 d2.utils.events]: \u001b[0m eta: 4:53:52 iter: 10759 total_loss: 1.228 loss_cls: 0.3842 loss_box_reg: 0.4712 loss_rpn_cls: 0.06426 loss_rpn_loc: 0.2685 time: 0.2461 last_time: 0.2322 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:37:18 d2.utils.events]: \u001b[0m eta: 4:53:12 iter: 10779 total_loss: 1.173 loss_cls: 0.4392 loss_box_reg: 0.4545 loss_rpn_cls: 0.06027 loss_rpn_loc: 0.2197 time: 0.2461 last_time: 0.2495 data_time: 0.0047 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:37:22 d2.utils.events]: \u001b[0m eta: 4:52:59 iter: 10799 total_loss: 1.213 loss_cls: 0.4343 loss_box_reg: 0.4406 loss_rpn_cls: 0.08685 loss_rpn_loc: 0.2213 time: 0.2461 last_time: 0.2363 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:37:27 d2.utils.events]: \u001b[0m eta: 4:52:22 iter: 10819 total_loss: 1.373 loss_cls: 0.4735 loss_box_reg: 0.4657 loss_rpn_cls: 0.08675 loss_rpn_loc: 0.2591 time: 0.2460 last_time: 0.1976 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:37:32 d2.utils.events]: \u001b[0m eta: 4:52:00 iter: 10839 total_loss: 1.277 loss_cls: 0.4564 loss_box_reg: 0.4683 loss_rpn_cls: 0.0641 loss_rpn_loc: 0.2708 time: 0.2460 last_time: 0.3083 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:37:37 d2.utils.events]: \u001b[0m eta: 4:52:01 iter: 10859 total_loss: 1.15 loss_cls: 0.4116 loss_box_reg: 0.4423 loss_rpn_cls: 0.07787 loss_rpn_loc: 0.2699 time: 0.2460 last_time: 0.2767 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:37:42 d2.utils.events]: \u001b[0m eta: 4:52:40 iter: 10879 total_loss: 1.189 loss_cls: 0.3845 loss_box_reg: 0.4359 loss_rpn_cls: 0.0618 loss_rpn_loc: 0.2481 time: 0.2461 last_time: 0.2608 data_time: 0.0051 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:37:47 d2.utils.events]: \u001b[0m eta: 4:52:41 iter: 10899 total_loss: 1.187 loss_cls: 0.4075 loss_box_reg: 0.4516 loss_rpn_cls: 0.0739 loss_rpn_loc: 0.2448 time: 0.2461 last_time: 0.2483 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:37:52 d2.utils.events]: \u001b[0m eta: 4:52:36 iter: 10919 total_loss: 1.169 loss_cls: 0.3911 loss_box_reg: 0.4113 loss_rpn_cls: 0.08012 loss_rpn_loc: 0.2672 time: 0.2460 last_time: 0.1812 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:37:57 d2.utils.events]: \u001b[0m eta: 4:52:37 iter: 10939 total_loss: 1.159 loss_cls: 0.3943 loss_box_reg: 0.4504 loss_rpn_cls: 0.05439 loss_rpn_loc: 0.2546 time: 0.2460 last_time: 0.2274 data_time: 0.0046 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:38:01 d2.utils.events]: \u001b[0m eta: 4:52:32 iter: 10959 total_loss: 1.208 loss_cls: 0.4154 loss_box_reg: 0.4236 loss_rpn_cls: 0.05919 loss_rpn_loc: 0.2826 time: 0.2460 last_time: 0.1969 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:38:06 d2.utils.events]: \u001b[0m eta: 4:52:41 iter: 10979 total_loss: 1.129 loss_cls: 0.4189 loss_box_reg: 0.4168 loss_rpn_cls: 0.06241 loss_rpn_loc: 0.2394 time: 0.2460 last_time: 0.2625 data_time: 0.0044 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:38:11 d2.utils.events]: \u001b[0m eta: 4:52:41 iter: 10999 total_loss: 1.19 loss_cls: 0.4236 loss_box_reg: 0.4758 loss_rpn_cls: 0.07074 loss_rpn_loc: 0.2727 time: 0.2460 last_time: 0.2587 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:38:16 d2.utils.events]: \u001b[0m eta: 4:53:08 iter: 11019 total_loss: 1.09 loss_cls: 0.4194 loss_box_reg: 0.3982 loss_rpn_cls: 0.06491 loss_rpn_loc: 0.2567 time: 0.2460 last_time: 0.2599 data_time: 0.0046 last_data_time: 0.0041 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:38:21 d2.utils.events]: \u001b[0m eta: 4:53:48 iter: 11039 total_loss: 1.13 loss_cls: 0.3963 loss_box_reg: 0.4339 loss_rpn_cls: 0.06328 loss_rpn_loc: 0.2175 time: 0.2460 last_time: 0.2655 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:38:26 d2.utils.events]: \u001b[0m eta: 4:53:59 iter: 11059 total_loss: 1.265 loss_cls: 0.4341 loss_box_reg: 0.4668 loss_rpn_cls: 0.08718 loss_rpn_loc: 0.2839 time: 0.2460 last_time: 0.2399 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:38:31 d2.utils.events]: \u001b[0m eta: 4:54:47 iter: 11079 total_loss: 1.12 loss_cls: 0.4088 loss_box_reg: 0.4108 loss_rpn_cls: 0.09069 loss_rpn_loc: 0.2305 time: 0.2460 last_time: 0.2743 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:38:36 d2.utils.events]: \u001b[0m eta: 4:54:38 iter: 11099 total_loss: 1.142 loss_cls: 0.3731 loss_box_reg: 0.4413 loss_rpn_cls: 0.04954 loss_rpn_loc: 0.2277 time: 0.2460 last_time: 0.2273 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:38:40 d2.utils.events]: \u001b[0m eta: 4:54:34 iter: 11119 total_loss: 1.229 loss_cls: 0.4394 loss_box_reg: 0.4742 loss_rpn_cls: 0.07052 loss_rpn_loc: 0.2651 time: 0.2460 last_time: 0.2416 data_time: 0.0047 last_data_time: 0.0053 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:38:45 d2.utils.events]: \u001b[0m eta: 4:54:54 iter: 11139 total_loss: 1.123 loss_cls: 0.4002 loss_box_reg: 0.4209 loss_rpn_cls: 0.05805 loss_rpn_loc: 0.2478 time: 0.2460 last_time: 0.2286 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:38:50 d2.utils.events]: \u001b[0m eta: 4:54:32 iter: 11159 total_loss: 1.15 loss_cls: 0.4479 loss_box_reg: 0.4048 loss_rpn_cls: 0.05864 loss_rpn_loc: 0.2443 time: 0.2460 last_time: 0.1895 data_time: 0.0048 last_data_time: 0.0052 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:38:55 d2.utils.events]: \u001b[0m eta: 4:54:45 iter: 11179 total_loss: 1.176 loss_cls: 0.4425 loss_box_reg: 0.4357 loss_rpn_cls: 0.07684 loss_rpn_loc: 0.2448 time: 0.2459 last_time: 0.2548 data_time: 0.0049 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:39:00 d2.utils.events]: \u001b[0m eta: 4:54:43 iter: 11199 total_loss: 1.109 loss_cls: 0.376 loss_box_reg: 0.4084 loss_rpn_cls: 0.07446 loss_rpn_loc: 0.2357 time: 0.2459 last_time: 0.2429 data_time: 0.0047 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:39:04 d2.utils.events]: \u001b[0m eta: 4:54:47 iter: 11219 total_loss: 1.145 loss_cls: 0.3898 loss_box_reg: 0.4464 loss_rpn_cls: 0.05907 loss_rpn_loc: 0.2294 time: 0.2459 last_time: 0.2540 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:39:09 d2.utils.events]: \u001b[0m eta: 4:54:52 iter: 11239 total_loss: 1.238 loss_cls: 0.4645 loss_box_reg: 0.4579 loss_rpn_cls: 0.07737 loss_rpn_loc: 0.2879 time: 0.2459 last_time: 0.1940 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:39:14 d2.utils.events]: \u001b[0m eta: 4:54:04 iter: 11259 total_loss: 1.084 loss_cls: 0.3626 loss_box_reg: 0.4335 loss_rpn_cls: 0.06777 loss_rpn_loc: 0.2282 time: 0.2459 last_time: 0.2420 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:39:19 d2.utils.events]: \u001b[0m eta: 4:53:49 iter: 11279 total_loss: 1.068 loss_cls: 0.4284 loss_box_reg: 0.4425 loss_rpn_cls: 0.05031 loss_rpn_loc: 0.2063 time: 0.2459 last_time: 0.2463 data_time: 0.0046 last_data_time: 0.0054 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:39:23 d2.utils.events]: \u001b[0m eta: 4:53:58 iter: 11299 total_loss: 1.166 loss_cls: 0.4195 loss_box_reg: 0.4248 loss_rpn_cls: 0.07194 loss_rpn_loc: 0.2501 time: 0.2459 last_time: 0.2423 data_time: 0.0047 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:39:28 d2.utils.events]: \u001b[0m eta: 4:54:33 iter: 11319 total_loss: 1.234 loss_cls: 0.4144 loss_box_reg: 0.4799 loss_rpn_cls: 0.07556 loss_rpn_loc: 0.2488 time: 0.2459 last_time: 0.2592 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:39:33 d2.utils.events]: \u001b[0m eta: 4:54:23 iter: 11339 total_loss: 1.237 loss_cls: 0.4372 loss_box_reg: 0.4572 loss_rpn_cls: 0.07088 loss_rpn_loc: 0.2746 time: 0.2458 last_time: 0.2421 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:39:38 d2.utils.events]: \u001b[0m eta: 4:53:52 iter: 11359 total_loss: 1.185 loss_cls: 0.4169 loss_box_reg: 0.4104 loss_rpn_cls: 0.08943 loss_rpn_loc: 0.2507 time: 0.2458 last_time: 0.2126 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:39:42 d2.utils.events]: \u001b[0m eta: 4:53:57 iter: 11379 total_loss: 1.13 loss_cls: 0.3986 loss_box_reg: 0.4307 loss_rpn_cls: 0.06196 loss_rpn_loc: 0.2256 time: 0.2458 last_time: 0.2027 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:39:47 d2.utils.events]: \u001b[0m eta: 4:52:49 iter: 11399 total_loss: 1.163 loss_cls: 0.4139 loss_box_reg: 0.4697 loss_rpn_cls: 0.04992 loss_rpn_loc: 0.2252 time: 0.2458 last_time: 0.2334 data_time: 0.0047 last_data_time: 0.0053 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:39:52 d2.utils.events]: \u001b[0m eta: 4:52:30 iter: 11419 total_loss: 1.192 loss_cls: 0.3655 loss_box_reg: 0.435 loss_rpn_cls: 0.08146 loss_rpn_loc: 0.2341 time: 0.2458 last_time: 0.1849 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:39:57 d2.utils.events]: \u001b[0m eta: 4:52:52 iter: 11439 total_loss: 1.166 loss_cls: 0.378 loss_box_reg: 0.43 loss_rpn_cls: 0.06551 loss_rpn_loc: 0.2555 time: 0.2457 last_time: 0.2335 data_time: 0.0047 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:40:01 d2.utils.events]: \u001b[0m eta: 4:52:20 iter: 11459 total_loss: 1.105 loss_cls: 0.4403 loss_box_reg: 0.4384 loss_rpn_cls: 0.07675 loss_rpn_loc: 0.2023 time: 0.2457 last_time: 0.2408 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:40:06 d2.utils.events]: \u001b[0m eta: 4:52:15 iter: 11479 total_loss: 1.132 loss_cls: 0.3947 loss_box_reg: 0.4298 loss_rpn_cls: 0.05328 loss_rpn_loc: 0.2119 time: 0.2457 last_time: 0.2018 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:40:11 d2.utils.events]: \u001b[0m eta: 4:52:09 iter: 11499 total_loss: 1.082 loss_cls: 0.3751 loss_box_reg: 0.4134 loss_rpn_cls: 0.0543 loss_rpn_loc: 0.2409 time: 0.2457 last_time: 0.2290 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:40:16 d2.utils.events]: \u001b[0m eta: 4:52:35 iter: 11519 total_loss: 1.228 loss_cls: 0.4575 loss_box_reg: 0.443 loss_rpn_cls: 0.06941 loss_rpn_loc: 0.2605 time: 0.2457 last_time: 0.2597 data_time: 0.0051 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:40:21 d2.utils.events]: \u001b[0m eta: 4:52:51 iter: 11539 total_loss: 1.135 loss_cls: 0.4289 loss_box_reg: 0.4287 loss_rpn_cls: 0.05366 loss_rpn_loc: 0.2383 time: 0.2457 last_time: 0.2274 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:40:25 d2.utils.events]: \u001b[0m eta: 4:52:58 iter: 11559 total_loss: 1.106 loss_cls: 0.3802 loss_box_reg: 0.389 loss_rpn_cls: 0.06713 loss_rpn_loc: 0.2415 time: 0.2457 last_time: 0.2225 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:40:30 d2.utils.events]: \u001b[0m eta: 4:53:26 iter: 11579 total_loss: 1.143 loss_cls: 0.4184 loss_box_reg: 0.4243 loss_rpn_cls: 0.05816 loss_rpn_loc: 0.2187 time: 0.2457 last_time: 0.2514 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:40:35 d2.utils.events]: \u001b[0m eta: 4:52:54 iter: 11599 total_loss: 1.206 loss_cls: 0.412 loss_box_reg: 0.4309 loss_rpn_cls: 0.06855 loss_rpn_loc: 0.266 time: 0.2456 last_time: 0.2374 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:40:39 d2.utils.events]: \u001b[0m eta: 4:52:49 iter: 11619 total_loss: 1.127 loss_cls: 0.4192 loss_box_reg: 0.4386 loss_rpn_cls: 0.06276 loss_rpn_loc: 0.2341 time: 0.2456 last_time: 0.2468 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:40:44 d2.utils.events]: \u001b[0m eta: 4:53:04 iter: 11639 total_loss: 1.166 loss_cls: 0.4276 loss_box_reg: 0.4291 loss_rpn_cls: 0.05845 loss_rpn_loc: 0.2581 time: 0.2456 last_time: 0.2530 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:40:49 d2.utils.events]: \u001b[0m eta: 4:52:40 iter: 11659 total_loss: 1.15 loss_cls: 0.4282 loss_box_reg: 0.4251 loss_rpn_cls: 0.05495 loss_rpn_loc: 0.2394 time: 0.2456 last_time: 0.2385 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:40:53 d2.utils.events]: \u001b[0m eta: 4:52:25 iter: 11679 total_loss: 1.126 loss_cls: 0.4191 loss_box_reg: 0.4237 loss_rpn_cls: 0.06916 loss_rpn_loc: 0.2221 time: 0.2455 last_time: 0.2495 data_time: 0.0045 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:40:58 d2.utils.events]: \u001b[0m eta: 4:51:21 iter: 11699 total_loss: 1.287 loss_cls: 0.4715 loss_box_reg: 0.4528 loss_rpn_cls: 0.09861 loss_rpn_loc: 0.2613 time: 0.2455 last_time: 0.2296 data_time: 0.0046 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:41:03 d2.utils.events]: \u001b[0m eta: 4:51:16 iter: 11719 total_loss: 1.121 loss_cls: 0.4061 loss_box_reg: 0.4625 loss_rpn_cls: 0.05817 loss_rpn_loc: 0.2414 time: 0.2455 last_time: 0.2476 data_time: 0.0046 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:41:07 d2.utils.events]: \u001b[0m eta: 4:51:09 iter: 11739 total_loss: 1.176 loss_cls: 0.3956 loss_box_reg: 0.4123 loss_rpn_cls: 0.06377 loss_rpn_loc: 0.2533 time: 0.2455 last_time: 0.2352 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:41:12 d2.utils.events]: \u001b[0m eta: 4:51:01 iter: 11759 total_loss: 1.107 loss_cls: 0.3771 loss_box_reg: 0.4147 loss_rpn_cls: 0.0597 loss_rpn_loc: 0.2461 time: 0.2455 last_time: 0.2614 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:41:17 d2.utils.events]: \u001b[0m eta: 4:51:02 iter: 11779 total_loss: 1.097 loss_cls: 0.3981 loss_box_reg: 0.4126 loss_rpn_cls: 0.06806 loss_rpn_loc: 0.228 time: 0.2455 last_time: 0.2372 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:41:22 d2.utils.events]: \u001b[0m eta: 4:50:57 iter: 11799 total_loss: 1.11 loss_cls: 0.3953 loss_box_reg: 0.4138 loss_rpn_cls: 0.04732 loss_rpn_loc: 0.2279 time: 0.2454 last_time: 0.2282 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:41:26 d2.utils.events]: \u001b[0m eta: 4:50:56 iter: 11819 total_loss: 1.188 loss_cls: 0.4261 loss_box_reg: 0.4326 loss_rpn_cls: 0.07771 loss_rpn_loc: 0.2368 time: 0.2454 last_time: 0.3142 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:41:31 d2.utils.events]: \u001b[0m eta: 4:51:39 iter: 11839 total_loss: 1.248 loss_cls: 0.4139 loss_box_reg: 0.4602 loss_rpn_cls: 0.06707 loss_rpn_loc: 0.2578 time: 0.2454 last_time: 0.2006 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:41:36 d2.utils.events]: \u001b[0m eta: 4:51:34 iter: 11859 total_loss: 1.202 loss_cls: 0.4059 loss_box_reg: 0.421 loss_rpn_cls: 0.06532 loss_rpn_loc: 0.2674 time: 0.2454 last_time: 0.2239 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:41:40 d2.utils.events]: \u001b[0m eta: 4:50:32 iter: 11879 total_loss: 1.104 loss_cls: 0.3998 loss_box_reg: 0.4327 loss_rpn_cls: 0.06813 loss_rpn_loc: 0.2575 time: 0.2454 last_time: 0.1960 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:41:45 d2.utils.events]: \u001b[0m eta: 4:50:23 iter: 11899 total_loss: 1.206 loss_cls: 0.4336 loss_box_reg: 0.4809 loss_rpn_cls: 0.07048 loss_rpn_loc: 0.2518 time: 0.2453 last_time: 0.2506 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:41:49 d2.utils.events]: \u001b[0m eta: 4:50:11 iter: 11919 total_loss: 1.164 loss_cls: 0.4116 loss_box_reg: 0.447 loss_rpn_cls: 0.06633 loss_rpn_loc: 0.2536 time: 0.2453 last_time: 0.2092 data_time: 0.0046 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:41:54 d2.utils.events]: \u001b[0m eta: 4:49:55 iter: 11939 total_loss: 1.208 loss_cls: 0.4365 loss_box_reg: 0.4247 loss_rpn_cls: 0.0678 loss_rpn_loc: 0.2231 time: 0.2453 last_time: 0.2257 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:41:59 d2.utils.events]: \u001b[0m eta: 4:49:41 iter: 11959 total_loss: 1.166 loss_cls: 0.4266 loss_box_reg: 0.4503 loss_rpn_cls: 0.0608 loss_rpn_loc: 0.2203 time: 0.2453 last_time: 0.2190 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:42:03 d2.utils.events]: \u001b[0m eta: 4:49:29 iter: 11979 total_loss: 1.205 loss_cls: 0.3807 loss_box_reg: 0.4359 loss_rpn_cls: 0.05366 loss_rpn_loc: 0.2318 time: 0.2452 last_time: 0.2578 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:42:08 d2.utils.events]: \u001b[0m eta: 4:49:21 iter: 11999 total_loss: 1.154 loss_cls: 0.387 loss_box_reg: 0.4325 loss_rpn_cls: 0.07944 loss_rpn_loc: 0.2525 time: 0.2452 last_time: 0.2110 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:42:13 d2.utils.events]: \u001b[0m eta: 4:49:11 iter: 12019 total_loss: 1.227 loss_cls: 0.4541 loss_box_reg: 0.4397 loss_rpn_cls: 0.07137 loss_rpn_loc: 0.2658 time: 0.2452 last_time: 0.2624 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:42:18 d2.utils.events]: \u001b[0m eta: 4:49:11 iter: 12039 total_loss: 1.176 loss_cls: 0.4244 loss_box_reg: 0.4621 loss_rpn_cls: 0.06987 loss_rpn_loc: 0.2485 time: 0.2452 last_time: 0.2504 data_time: 0.0048 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:42:22 d2.utils.events]: \u001b[0m eta: 4:48:51 iter: 12059 total_loss: 1.185 loss_cls: 0.4172 loss_box_reg: 0.4088 loss_rpn_cls: 0.07432 loss_rpn_loc: 0.2607 time: 0.2452 last_time: 0.2598 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:42:27 d2.utils.events]: \u001b[0m eta: 4:48:26 iter: 12079 total_loss: 1.149 loss_cls: 0.4009 loss_box_reg: 0.4311 loss_rpn_cls: 0.06434 loss_rpn_loc: 0.2867 time: 0.2452 last_time: 0.2128 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:42:32 d2.utils.events]: \u001b[0m eta: 4:48:05 iter: 12099 total_loss: 1.255 loss_cls: 0.4352 loss_box_reg: 0.4367 loss_rpn_cls: 0.07639 loss_rpn_loc: 0.2779 time: 0.2451 last_time: 0.1856 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:42:36 d2.utils.events]: \u001b[0m eta: 4:47:48 iter: 12119 total_loss: 1.091 loss_cls: 0.3937 loss_box_reg: 0.4056 loss_rpn_cls: 0.06614 loss_rpn_loc: 0.2375 time: 0.2451 last_time: 0.2298 data_time: 0.0046 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:42:41 d2.utils.events]: \u001b[0m eta: 4:47:32 iter: 12139 total_loss: 1.242 loss_cls: 0.4107 loss_box_reg: 0.4314 loss_rpn_cls: 0.05908 loss_rpn_loc: 0.2453 time: 0.2451 last_time: 0.2349 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:42:47 d2.utils.events]: \u001b[0m eta: 4:47:34 iter: 12159 total_loss: 1.11 loss_cls: 0.4135 loss_box_reg: 0.437 loss_rpn_cls: 0.06098 loss_rpn_loc: 0.2182 time: 0.2451 last_time: 0.2997 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:42:52 d2.utils.events]: \u001b[0m eta: 4:47:37 iter: 12179 total_loss: 1.164 loss_cls: 0.4282 loss_box_reg: 0.4633 loss_rpn_cls: 0.07384 loss_rpn_loc: 0.2215 time: 0.2452 last_time: 0.2202 data_time: 0.0048 last_data_time: 0.0053 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:42:57 d2.utils.events]: \u001b[0m eta: 4:47:24 iter: 12199 total_loss: 0.9979 loss_cls: 0.3221 loss_box_reg: 0.3813 loss_rpn_cls: 0.07254 loss_rpn_loc: 0.2269 time: 0.2452 last_time: 0.2145 data_time: 0.0049 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:43:02 d2.utils.events]: \u001b[0m eta: 4:47:56 iter: 12219 total_loss: 1.16 loss_cls: 0.3899 loss_box_reg: 0.4372 loss_rpn_cls: 0.08073 loss_rpn_loc: 0.2632 time: 0.2452 last_time: 0.2585 data_time: 0.0049 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:43:07 d2.utils.events]: \u001b[0m eta: 4:47:39 iter: 12239 total_loss: 1.221 loss_cls: 0.4539 loss_box_reg: 0.455 loss_rpn_cls: 0.07373 loss_rpn_loc: 0.2359 time: 0.2452 last_time: 0.2520 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:43:12 d2.utils.events]: \u001b[0m eta: 4:47:47 iter: 12259 total_loss: 1.17 loss_cls: 0.4069 loss_box_reg: 0.439 loss_rpn_cls: 0.06416 loss_rpn_loc: 0.239 time: 0.2452 last_time: 0.2912 data_time: 0.0049 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:43:16 d2.utils.events]: \u001b[0m eta: 4:47:21 iter: 12279 total_loss: 1.118 loss_cls: 0.376 loss_box_reg: 0.3964 loss_rpn_cls: 0.06266 loss_rpn_loc: 0.2272 time: 0.2452 last_time: 0.2243 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:43:21 d2.utils.events]: \u001b[0m eta: 4:47:03 iter: 12299 total_loss: 1.119 loss_cls: 0.3843 loss_box_reg: 0.3931 loss_rpn_cls: 0.08613 loss_rpn_loc: 0.249 time: 0.2452 last_time: 0.2348 data_time: 0.0049 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:43:26 d2.utils.events]: \u001b[0m eta: 4:47:01 iter: 12319 total_loss: 1.03 loss_cls: 0.3918 loss_box_reg: 0.3637 loss_rpn_cls: 0.06922 loss_rpn_loc: 0.2096 time: 0.2452 last_time: 0.3112 data_time: 0.0051 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:43:31 d2.utils.events]: \u001b[0m eta: 4:46:51 iter: 12339 total_loss: 1.135 loss_cls: 0.4035 loss_box_reg: 0.4137 loss_rpn_cls: 0.06603 loss_rpn_loc: 0.2522 time: 0.2452 last_time: 0.3236 data_time: 0.0050 last_data_time: 0.0052 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:43:36 d2.utils.events]: \u001b[0m eta: 4:46:51 iter: 12359 total_loss: 1.204 loss_cls: 0.4155 loss_box_reg: 0.4209 loss_rpn_cls: 0.06971 loss_rpn_loc: 0.2859 time: 0.2452 last_time: 0.1807 data_time: 0.0049 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:43:41 d2.utils.events]: \u001b[0m eta: 4:46:46 iter: 12379 total_loss: 1.23 loss_cls: 0.4295 loss_box_reg: 0.4058 loss_rpn_cls: 0.06822 loss_rpn_loc: 0.2733 time: 0.2452 last_time: 0.2324 data_time: 0.0050 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:43:46 d2.utils.events]: \u001b[0m eta: 4:47:01 iter: 12399 total_loss: 1.125 loss_cls: 0.4207 loss_box_reg: 0.3996 loss_rpn_cls: 0.0528 loss_rpn_loc: 0.231 time: 0.2452 last_time: 0.2567 data_time: 0.0051 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:43:50 d2.utils.events]: \u001b[0m eta: 4:47:06 iter: 12419 total_loss: 1.18 loss_cls: 0.4185 loss_box_reg: 0.419 loss_rpn_cls: 0.08797 loss_rpn_loc: 0.2679 time: 0.2451 last_time: 0.2240 data_time: 0.0049 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:43:55 d2.utils.events]: \u001b[0m eta: 4:47:01 iter: 12439 total_loss: 1.092 loss_cls: 0.3724 loss_box_reg: 0.4241 loss_rpn_cls: 0.05623 loss_rpn_loc: 0.2312 time: 0.2451 last_time: 0.2583 data_time: 0.0050 last_data_time: 0.0052 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:44:00 d2.utils.events]: \u001b[0m eta: 4:47:01 iter: 12459 total_loss: 1.218 loss_cls: 0.4485 loss_box_reg: 0.4463 loss_rpn_cls: 0.06622 loss_rpn_loc: 0.2584 time: 0.2451 last_time: 0.2375 data_time: 0.0049 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:44:05 d2.utils.events]: \u001b[0m eta: 4:47:11 iter: 12479 total_loss: 1.167 loss_cls: 0.372 loss_box_reg: 0.4386 loss_rpn_cls: 0.06665 loss_rpn_loc: 0.2557 time: 0.2451 last_time: 0.2489 data_time: 0.0049 last_data_time: 0.0052 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:44:09 d2.utils.events]: \u001b[0m eta: 4:47:09 iter: 12499 total_loss: 1.015 loss_cls: 0.3512 loss_box_reg: 0.4063 loss_rpn_cls: 0.05761 loss_rpn_loc: 0.207 time: 0.2451 last_time: 0.2374 data_time: 0.0050 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:44:14 d2.utils.events]: \u001b[0m eta: 4:46:47 iter: 12519 total_loss: 1.132 loss_cls: 0.3695 loss_box_reg: 0.4528 loss_rpn_cls: 0.05443 loss_rpn_loc: 0.2363 time: 0.2451 last_time: 0.2506 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:44:19 d2.utils.events]: \u001b[0m eta: 4:46:42 iter: 12539 total_loss: 1.149 loss_cls: 0.414 loss_box_reg: 0.4195 loss_rpn_cls: 0.05169 loss_rpn_loc: 0.2365 time: 0.2451 last_time: 0.2350 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:44:24 d2.utils.events]: \u001b[0m eta: 4:46:51 iter: 12559 total_loss: 1.182 loss_cls: 0.3773 loss_box_reg: 0.4235 loss_rpn_cls: 0.08145 loss_rpn_loc: 0.2346 time: 0.2451 last_time: 0.2153 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:44:29 d2.utils.events]: \u001b[0m eta: 4:46:43 iter: 12579 total_loss: 1.214 loss_cls: 0.4212 loss_box_reg: 0.4156 loss_rpn_cls: 0.07466 loss_rpn_loc: 0.2574 time: 0.2451 last_time: 0.2519 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:44:33 d2.utils.events]: \u001b[0m eta: 4:46:33 iter: 12599 total_loss: 1.219 loss_cls: 0.3991 loss_box_reg: 0.4359 loss_rpn_cls: 0.07967 loss_rpn_loc: 0.2724 time: 0.2450 last_time: 0.2368 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:44:38 d2.utils.events]: \u001b[0m eta: 4:46:33 iter: 12619 total_loss: 1.14 loss_cls: 0.4041 loss_box_reg: 0.4255 loss_rpn_cls: 0.06286 loss_rpn_loc: 0.2669 time: 0.2450 last_time: 0.2307 data_time: 0.0046 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:44:43 d2.utils.events]: \u001b[0m eta: 4:46:19 iter: 12639 total_loss: 1.172 loss_cls: 0.3808 loss_box_reg: 0.4431 loss_rpn_cls: 0.06842 loss_rpn_loc: 0.2454 time: 0.2450 last_time: 0.2125 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:44:48 d2.utils.events]: \u001b[0m eta: 4:46:08 iter: 12659 total_loss: 1.029 loss_cls: 0.3855 loss_box_reg: 0.397 loss_rpn_cls: 0.05199 loss_rpn_loc: 0.1913 time: 0.2450 last_time: 0.2340 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:44:52 d2.utils.events]: \u001b[0m eta: 4:46:07 iter: 12679 total_loss: 1.163 loss_cls: 0.3928 loss_box_reg: 0.4222 loss_rpn_cls: 0.06052 loss_rpn_loc: 0.271 time: 0.2450 last_time: 0.2179 data_time: 0.0049 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:44:57 d2.utils.events]: \u001b[0m eta: 4:46:37 iter: 12699 total_loss: 1.166 loss_cls: 0.3943 loss_box_reg: 0.4126 loss_rpn_cls: 0.07408 loss_rpn_loc: 0.2453 time: 0.2450 last_time: 0.2448 data_time: 0.0049 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:45:02 d2.utils.events]: \u001b[0m eta: 4:46:29 iter: 12719 total_loss: 1.223 loss_cls: 0.4138 loss_box_reg: 0.4251 loss_rpn_cls: 0.06607 loss_rpn_loc: 0.2659 time: 0.2450 last_time: 0.2041 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:45:08 d2.utils.events]: \u001b[0m eta: 4:47:39 iter: 12739 total_loss: 1.256 loss_cls: 0.4721 loss_box_reg: 0.4383 loss_rpn_cls: 0.05632 loss_rpn_loc: 0.2571 time: 0.2451 last_time: 0.3782 data_time: 0.0050 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:45:17 d2.utils.events]: \u001b[0m eta: 4:48:58 iter: 12759 total_loss: 1.151 loss_cls: 0.3433 loss_box_reg: 0.4367 loss_rpn_cls: 0.06412 loss_rpn_loc: 0.2724 time: 0.2454 last_time: 0.4216 data_time: 0.0049 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:45:25 d2.utils.events]: \u001b[0m eta: 4:49:40 iter: 12779 total_loss: 1.118 loss_cls: 0.4049 loss_box_reg: 0.4139 loss_rpn_cls: 0.07361 loss_rpn_loc: 0.2499 time: 0.2456 last_time: 0.4330 data_time: 0.0046 last_data_time: 0.0041 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:45:34 d2.utils.events]: \u001b[0m eta: 4:50:51 iter: 12799 total_loss: 1.161 loss_cls: 0.3748 loss_box_reg: 0.3912 loss_rpn_cls: 0.06017 loss_rpn_loc: 0.2538 time: 0.2459 last_time: 0.3236 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:45:42 d2.utils.events]: \u001b[0m eta: 4:51:43 iter: 12819 total_loss: 1.104 loss_cls: 0.4249 loss_box_reg: 0.4059 loss_rpn_cls: 0.05739 loss_rpn_loc: 0.233 time: 0.2462 last_time: 0.3734 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:45:50 d2.utils.events]: \u001b[0m eta: 4:52:16 iter: 12839 total_loss: 1.117 loss_cls: 0.3915 loss_box_reg: 0.4048 loss_rpn_cls: 0.06996 loss_rpn_loc: 0.2312 time: 0.2464 last_time: 0.4119 data_time: 0.0047 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:45:58 d2.utils.events]: \u001b[0m eta: 4:53:33 iter: 12859 total_loss: 1.144 loss_cls: 0.3959 loss_box_reg: 0.3851 loss_rpn_cls: 0.07717 loss_rpn_loc: 0.272 time: 0.2467 last_time: 0.3652 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:46:06 d2.utils.events]: \u001b[0m eta: 4:56:18 iter: 12879 total_loss: 1.082 loss_cls: 0.3726 loss_box_reg: 0.404 loss_rpn_cls: 0.06535 loss_rpn_loc: 0.2236 time: 0.2469 last_time: 0.3897 data_time: 0.0047 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:46:14 d2.utils.events]: \u001b[0m eta: 4:58:40 iter: 12899 total_loss: 1.14 loss_cls: 0.3607 loss_box_reg: 0.4188 loss_rpn_cls: 0.05595 loss_rpn_loc: 0.2503 time: 0.2471 last_time: 0.4380 data_time: 0.0047 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:46:22 d2.utils.events]: \u001b[0m eta: 4:59:16 iter: 12919 total_loss: 1.117 loss_cls: 0.359 loss_box_reg: 0.4059 loss_rpn_cls: 0.0615 loss_rpn_loc: 0.2123 time: 0.2474 last_time: 0.4129 data_time: 0.0048 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:46:30 d2.utils.events]: \u001b[0m eta: 5:00:16 iter: 12939 total_loss: 1.13 loss_cls: 0.3946 loss_box_reg: 0.4167 loss_rpn_cls: 0.05849 loss_rpn_loc: 0.2212 time: 0.2476 last_time: 0.4111 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:46:38 d2.utils.events]: \u001b[0m eta: 5:00:45 iter: 12959 total_loss: 1.113 loss_cls: 0.3604 loss_box_reg: 0.4178 loss_rpn_cls: 0.06181 loss_rpn_loc: 0.2458 time: 0.2479 last_time: 0.4193 data_time: 0.0049 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:46:46 d2.utils.events]: \u001b[0m eta: 5:01:26 iter: 12979 total_loss: 1.122 loss_cls: 0.3989 loss_box_reg: 0.4301 loss_rpn_cls: 0.05129 loss_rpn_loc: 0.2305 time: 0.2481 last_time: 0.4471 data_time: 0.0049 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:46:55 d2.utils.events]: \u001b[0m eta: 5:02:19 iter: 12999 total_loss: 1.126 loss_cls: 0.4301 loss_box_reg: 0.445 loss_rpn_cls: 0.06628 loss_rpn_loc: 0.2619 time: 0.2484 last_time: 0.4424 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:47:03 d2.utils.events]: \u001b[0m eta: 5:03:11 iter: 13019 total_loss: 1.12 loss_cls: 0.4228 loss_box_reg: 0.4008 loss_rpn_cls: 0.07962 loss_rpn_loc: 0.2545 time: 0.2486 last_time: 0.4679 data_time: 0.0049 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:47:12 d2.utils.events]: \u001b[0m eta: 5:04:18 iter: 13039 total_loss: 1.102 loss_cls: 0.3721 loss_box_reg: 0.3855 loss_rpn_cls: 0.06901 loss_rpn_loc: 0.2501 time: 0.2489 last_time: 0.3390 data_time: 0.0049 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:47:21 d2.utils.events]: \u001b[0m eta: 5:05:18 iter: 13059 total_loss: 1.266 loss_cls: 0.4234 loss_box_reg: 0.4104 loss_rpn_cls: 0.08226 loss_rpn_loc: 0.2533 time: 0.2492 last_time: 0.4290 data_time: 0.0049 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:47:30 d2.utils.events]: \u001b[0m eta: 5:06:43 iter: 13079 total_loss: 1.247 loss_cls: 0.4373 loss_box_reg: 0.4274 loss_rpn_cls: 0.07104 loss_rpn_loc: 0.2706 time: 0.2495 last_time: 0.4141 data_time: 0.0048 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:47:39 d2.utils.events]: \u001b[0m eta: 5:08:36 iter: 13099 total_loss: 1.119 loss_cls: 0.4009 loss_box_reg: 0.4068 loss_rpn_cls: 0.05862 loss_rpn_loc: 0.2411 time: 0.2498 last_time: 0.4850 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:47:47 d2.utils.events]: \u001b[0m eta: 5:09:50 iter: 13119 total_loss: 1.062 loss_cls: 0.3864 loss_box_reg: 0.3824 loss_rpn_cls: 0.06374 loss_rpn_loc: 0.2519 time: 0.2501 last_time: 0.3728 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:47:55 d2.utils.events]: \u001b[0m eta: 5:12:24 iter: 13139 total_loss: 1.116 loss_cls: 0.3821 loss_box_reg: 0.4007 loss_rpn_cls: 0.04807 loss_rpn_loc: 0.2251 time: 0.2503 last_time: 0.4154 data_time: 0.0049 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:48:03 d2.utils.events]: \u001b[0m eta: 5:13:38 iter: 13159 total_loss: 1.24 loss_cls: 0.4658 loss_box_reg: 0.4267 loss_rpn_cls: 0.07023 loss_rpn_loc: 0.2436 time: 0.2506 last_time: 0.3200 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:48:13 d2.utils.events]: \u001b[0m eta: 5:18:01 iter: 13179 total_loss: 1.134 loss_cls: 0.3876 loss_box_reg: 0.4064 loss_rpn_cls: 0.06587 loss_rpn_loc: 0.2457 time: 0.2509 last_time: 0.3925 data_time: 0.0047 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:48:21 d2.utils.events]: \u001b[0m eta: 5:33:06 iter: 13199 total_loss: 1.143 loss_cls: 0.4078 loss_box_reg: 0.4097 loss_rpn_cls: 0.0633 loss_rpn_loc: 0.2336 time: 0.2511 last_time: 0.4134 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:48:29 d2.utils.events]: \u001b[0m eta: 6:05:37 iter: 13219 total_loss: 1.177 loss_cls: 0.4161 loss_box_reg: 0.4317 loss_rpn_cls: 0.05793 loss_rpn_loc: 0.2318 time: 0.2514 last_time: 0.4129 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:48:37 d2.utils.events]: \u001b[0m eta: 6:21:10 iter: 13239 total_loss: 1.017 loss_cls: 0.3511 loss_box_reg: 0.3652 loss_rpn_cls: 0.05664 loss_rpn_loc: 0.2318 time: 0.2516 last_time: 0.4220 data_time: 0.0045 last_data_time: 0.0040 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:48:45 d2.utils.events]: \u001b[0m eta: 6:46:19 iter: 13259 total_loss: 1.156 loss_cls: 0.4239 loss_box_reg: 0.401 loss_rpn_cls: 0.05259 loss_rpn_loc: 0.2746 time: 0.2518 last_time: 0.4297 data_time: 0.0044 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:48:53 d2.utils.events]: \u001b[0m eta: 6:53:13 iter: 13279 total_loss: 1.055 loss_cls: 0.4135 loss_box_reg: 0.3913 loss_rpn_cls: 0.05838 loss_rpn_loc: 0.2231 time: 0.2520 last_time: 0.3908 data_time: 0.0044 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:49:01 d2.utils.events]: \u001b[0m eta: 7:20:10 iter: 13299 total_loss: 1.055 loss_cls: 0.3383 loss_box_reg: 0.4026 loss_rpn_cls: 0.05539 loss_rpn_loc: 0.2352 time: 0.2523 last_time: 0.3416 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:49:09 d2.utils.events]: \u001b[0m eta: 7:25:13 iter: 13319 total_loss: 1.221 loss_cls: 0.4458 loss_box_reg: 0.3969 loss_rpn_cls: 0.05984 loss_rpn_loc: 0.2622 time: 0.2525 last_time: 0.3467 data_time: 0.0044 last_data_time: 0.0040 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:49:17 d2.utils.events]: \u001b[0m eta: 7:26:47 iter: 13339 total_loss: 1.167 loss_cls: 0.4142 loss_box_reg: 0.407 loss_rpn_cls: 0.06812 loss_rpn_loc: 0.2397 time: 0.2527 last_time: 0.4259 data_time: 0.0044 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:49:26 d2.utils.events]: \u001b[0m eta: 7:29:51 iter: 13359 total_loss: 1.093 loss_cls: 0.4079 loss_box_reg: 0.4044 loss_rpn_cls: 0.05841 loss_rpn_loc: 0.2234 time: 0.2529 last_time: 0.3804 data_time: 0.0044 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:49:33 d2.utils.events]: \u001b[0m eta: 7:38:45 iter: 13379 total_loss: 1.008 loss_cls: 0.3778 loss_box_reg: 0.3953 loss_rpn_cls: 0.05195 loss_rpn_loc: 0.2161 time: 0.2532 last_time: 0.3154 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:49:42 d2.utils.events]: \u001b[0m eta: 7:45:34 iter: 13399 total_loss: 1.099 loss_cls: 0.3929 loss_box_reg: 0.3919 loss_rpn_cls: 0.06539 loss_rpn_loc: 0.2229 time: 0.2534 last_time: 0.4179 data_time: 0.0045 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:49:50 d2.utils.events]: \u001b[0m eta: 7:48:43 iter: 13419 total_loss: 1.096 loss_cls: 0.3756 loss_box_reg: 0.4151 loss_rpn_cls: 0.06961 loss_rpn_loc: 0.2461 time: 0.2536 last_time: 0.4108 data_time: 0.0044 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:49:58 d2.utils.events]: \u001b[0m eta: 7:50:28 iter: 13439 total_loss: 1.155 loss_cls: 0.4161 loss_box_reg: 0.4216 loss_rpn_cls: 0.0743 loss_rpn_loc: 0.2644 time: 0.2539 last_time: 0.3963 data_time: 0.0047 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:50:06 d2.utils.events]: \u001b[0m eta: 7:52:01 iter: 13459 total_loss: 1.163 loss_cls: 0.4368 loss_box_reg: 0.4176 loss_rpn_cls: 0.06635 loss_rpn_loc: 0.2235 time: 0.2541 last_time: 0.3979 data_time: 0.0046 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:50:15 d2.utils.events]: \u001b[0m eta: 7:54:43 iter: 13479 total_loss: 1.087 loss_cls: 0.3652 loss_box_reg: 0.4101 loss_rpn_cls: 0.05519 loss_rpn_loc: 0.2563 time: 0.2544 last_time: 0.4513 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:50:24 d2.utils.events]: \u001b[0m eta: 7:57:48 iter: 13499 total_loss: 1.089 loss_cls: 0.3998 loss_box_reg: 0.4199 loss_rpn_cls: 0.06284 loss_rpn_loc: 0.246 time: 0.2547 last_time: 0.5118 data_time: 0.0049 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:50:33 d2.utils.events]: \u001b[0m eta: 8:01:35 iter: 13519 total_loss: 1.062 loss_cls: 0.3551 loss_box_reg: 0.354 loss_rpn_cls: 0.06665 loss_rpn_loc: 0.2317 time: 0.2550 last_time: 0.3743 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:50:42 d2.utils.events]: \u001b[0m eta: 8:07:40 iter: 13539 total_loss: 1.182 loss_cls: 0.4302 loss_box_reg: 0.4483 loss_rpn_cls: 0.06291 loss_rpn_loc: 0.269 time: 0.2552 last_time: 0.4698 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:50:51 d2.utils.events]: \u001b[0m eta: 8:10:12 iter: 13559 total_loss: 1.167 loss_cls: 0.3983 loss_box_reg: 0.4087 loss_rpn_cls: 0.05472 loss_rpn_loc: 0.2448 time: 0.2555 last_time: 0.4431 data_time: 0.0049 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:50:59 d2.utils.events]: \u001b[0m eta: 8:10:54 iter: 13579 total_loss: 1.131 loss_cls: 0.3842 loss_box_reg: 0.4143 loss_rpn_cls: 0.07159 loss_rpn_loc: 0.2438 time: 0.2557 last_time: 0.4173 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:51:07 d2.utils.events]: \u001b[0m eta: 8:11:54 iter: 13599 total_loss: 1.139 loss_cls: 0.4417 loss_box_reg: 0.4058 loss_rpn_cls: 0.06652 loss_rpn_loc: 0.2446 time: 0.2559 last_time: 0.3437 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:51:15 d2.utils.events]: \u001b[0m eta: 8:12:40 iter: 13619 total_loss: 1.118 loss_cls: 0.3869 loss_box_reg: 0.4231 loss_rpn_cls: 0.06254 loss_rpn_loc: 0.2328 time: 0.2562 last_time: 0.4206 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:51:24 d2.utils.events]: \u001b[0m eta: 8:14:06 iter: 13639 total_loss: 1.132 loss_cls: 0.3804 loss_box_reg: 0.3863 loss_rpn_cls: 0.08003 loss_rpn_loc: 0.2387 time: 0.2564 last_time: 0.5106 data_time: 0.0048 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:51:33 d2.utils.events]: \u001b[0m eta: 8:15:08 iter: 13659 total_loss: 1.206 loss_cls: 0.4484 loss_box_reg: 0.4094 loss_rpn_cls: 0.0669 loss_rpn_loc: 0.2547 time: 0.2567 last_time: 0.4423 data_time: 0.0048 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:51:42 d2.utils.events]: \u001b[0m eta: 8:16:26 iter: 13679 total_loss: 1.152 loss_cls: 0.3892 loss_box_reg: 0.3888 loss_rpn_cls: 0.06171 loss_rpn_loc: 0.2237 time: 0.2570 last_time: 0.4868 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:51:51 d2.utils.events]: \u001b[0m eta: 8:18:05 iter: 13699 total_loss: 1.081 loss_cls: 0.3916 loss_box_reg: 0.4021 loss_rpn_cls: 0.05847 loss_rpn_loc: 0.224 time: 0.2573 last_time: 0.5070 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:52:00 d2.utils.events]: \u001b[0m eta: 8:18:58 iter: 13719 total_loss: 1 loss_cls: 0.3526 loss_box_reg: 0.3566 loss_rpn_cls: 0.07463 loss_rpn_loc: 0.2299 time: 0.2576 last_time: 0.3939 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:52:09 d2.utils.events]: \u001b[0m eta: 8:20:04 iter: 13739 total_loss: 1.111 loss_cls: 0.3807 loss_box_reg: 0.3977 loss_rpn_cls: 0.06654 loss_rpn_loc: 0.2339 time: 0.2579 last_time: 0.5135 data_time: 0.0049 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:52:19 d2.utils.events]: \u001b[0m eta: 8:20:37 iter: 13759 total_loss: 1.094 loss_cls: 0.391 loss_box_reg: 0.4016 loss_rpn_cls: 0.06413 loss_rpn_loc: 0.2595 time: 0.2582 last_time: 0.4397 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:52:27 d2.utils.events]: \u001b[0m eta: 8:20:07 iter: 13779 total_loss: 1.205 loss_cls: 0.4462 loss_box_reg: 0.4289 loss_rpn_cls: 0.07686 loss_rpn_loc: 0.2595 time: 0.2584 last_time: 0.4055 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:52:35 d2.utils.events]: \u001b[0m eta: 8:19:11 iter: 13799 total_loss: 1.098 loss_cls: 0.3893 loss_box_reg: 0.4189 loss_rpn_cls: 0.06793 loss_rpn_loc: 0.2375 time: 0.2586 last_time: 0.3948 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:52:43 d2.utils.events]: \u001b[0m eta: 8:18:36 iter: 13819 total_loss: 1.053 loss_cls: 0.3805 loss_box_reg: 0.4049 loss_rpn_cls: 0.06498 loss_rpn_loc: 0.2155 time: 0.2588 last_time: 0.4154 data_time: 0.0046 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:52:51 d2.utils.events]: \u001b[0m eta: 8:18:30 iter: 13839 total_loss: 1.065 loss_cls: 0.3776 loss_box_reg: 0.4008 loss_rpn_cls: 0.06282 loss_rpn_loc: 0.2424 time: 0.2590 last_time: 0.4434 data_time: 0.0046 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:52:59 d2.utils.events]: \u001b[0m eta: 8:18:00 iter: 13859 total_loss: 1.07 loss_cls: 0.4176 loss_box_reg: 0.39 loss_rpn_cls: 0.06819 loss_rpn_loc: 0.2028 time: 0.2592 last_time: 0.4108 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:53:07 d2.utils.events]: \u001b[0m eta: 8:18:06 iter: 13879 total_loss: 1.131 loss_cls: 0.415 loss_box_reg: 0.4244 loss_rpn_cls: 0.06854 loss_rpn_loc: 0.2354 time: 0.2594 last_time: 0.3443 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:53:15 d2.utils.events]: \u001b[0m eta: 8:17:43 iter: 13899 total_loss: 1.022 loss_cls: 0.373 loss_box_reg: 0.382 loss_rpn_cls: 0.06187 loss_rpn_loc: 0.2271 time: 0.2596 last_time: 0.3720 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:53:24 d2.utils.events]: \u001b[0m eta: 8:17:34 iter: 13919 total_loss: 1.146 loss_cls: 0.3924 loss_box_reg: 0.4067 loss_rpn_cls: 0.0677 loss_rpn_loc: 0.2277 time: 0.2599 last_time: 0.4169 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:53:32 d2.utils.events]: \u001b[0m eta: 8:17:12 iter: 13939 total_loss: 1.081 loss_cls: 0.3476 loss_box_reg: 0.3731 loss_rpn_cls: 0.06173 loss_rpn_loc: 0.2272 time: 0.2601 last_time: 0.3757 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:53:40 d2.utils.events]: \u001b[0m eta: 8:17:18 iter: 13959 total_loss: 1.087 loss_cls: 0.3485 loss_box_reg: 0.3927 loss_rpn_cls: 0.06226 loss_rpn_loc: 0.2372 time: 0.2603 last_time: 0.3988 data_time: 0.0046 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:53:48 d2.utils.events]: \u001b[0m eta: 8:16:49 iter: 13979 total_loss: 1.028 loss_cls: 0.3305 loss_box_reg: 0.3938 loss_rpn_cls: 0.0652 loss_rpn_loc: 0.2173 time: 0.2605 last_time: 0.4114 data_time: 0.0044 last_data_time: 0.0041 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:53:56 d2.utils.events]: \u001b[0m eta: 8:16:47 iter: 13999 total_loss: 1.1 loss_cls: 0.3882 loss_box_reg: 0.3906 loss_rpn_cls: 0.06441 loss_rpn_loc: 0.2405 time: 0.2607 last_time: 0.3920 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:54:04 d2.utils.events]: \u001b[0m eta: 8:16:28 iter: 14019 total_loss: 1.048 loss_cls: 0.3655 loss_box_reg: 0.3548 loss_rpn_cls: 0.05559 loss_rpn_loc: 0.2461 time: 0.2609 last_time: 0.3835 data_time: 0.0048 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:54:12 d2.utils.events]: \u001b[0m eta: 8:16:00 iter: 14039 total_loss: 1.138 loss_cls: 0.4147 loss_box_reg: 0.3835 loss_rpn_cls: 0.06249 loss_rpn_loc: 0.2496 time: 0.2611 last_time: 0.3685 data_time: 0.0045 last_data_time: 0.0041 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:54:20 d2.utils.events]: \u001b[0m eta: 8:15:08 iter: 14059 total_loss: 1.125 loss_cls: 0.4077 loss_box_reg: 0.4103 loss_rpn_cls: 0.06486 loss_rpn_loc: 0.2078 time: 0.2613 last_time: 0.3766 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:54:28 d2.utils.events]: \u001b[0m eta: 8:14:41 iter: 14079 total_loss: 1.202 loss_cls: 0.3906 loss_box_reg: 0.4204 loss_rpn_cls: 0.06712 loss_rpn_loc: 0.2639 time: 0.2615 last_time: 0.4397 data_time: 0.0045 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:54:36 d2.utils.events]: \u001b[0m eta: 8:14:14 iter: 14099 total_loss: 1.16 loss_cls: 0.4173 loss_box_reg: 0.4068 loss_rpn_cls: 0.06093 loss_rpn_loc: 0.2399 time: 0.2617 last_time: 0.3948 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:54:45 d2.utils.events]: \u001b[0m eta: 8:13:53 iter: 14119 total_loss: 1.16 loss_cls: 0.3947 loss_box_reg: 0.4194 loss_rpn_cls: 0.05989 loss_rpn_loc: 0.2356 time: 0.2619 last_time: 0.3936 data_time: 0.0046 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:54:53 d2.utils.events]: \u001b[0m eta: 8:13:27 iter: 14139 total_loss: 1.114 loss_cls: 0.3955 loss_box_reg: 0.4058 loss_rpn_cls: 0.05985 loss_rpn_loc: 0.2053 time: 0.2621 last_time: 0.4338 data_time: 0.0045 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:55:01 d2.utils.events]: \u001b[0m eta: 8:13:10 iter: 14159 total_loss: 1.107 loss_cls: 0.3649 loss_box_reg: 0.3788 loss_rpn_cls: 0.0592 loss_rpn_loc: 0.2215 time: 0.2623 last_time: 0.4422 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:55:09 d2.utils.events]: \u001b[0m eta: 8:12:33 iter: 14179 total_loss: 1.061 loss_cls: 0.3722 loss_box_reg: 0.4101 loss_rpn_cls: 0.0682 loss_rpn_loc: 0.2524 time: 0.2625 last_time: 0.3904 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:55:17 d2.utils.events]: \u001b[0m eta: 8:11:44 iter: 14199 total_loss: 1.096 loss_cls: 0.381 loss_box_reg: 0.4053 loss_rpn_cls: 0.07178 loss_rpn_loc: 0.2529 time: 0.2627 last_time: 0.4387 data_time: 0.0046 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:55:25 d2.utils.events]: \u001b[0m eta: 8:11:18 iter: 14219 total_loss: 1.14 loss_cls: 0.4 loss_box_reg: 0.396 loss_rpn_cls: 0.06367 loss_rpn_loc: 0.2542 time: 0.2629 last_time: 0.3521 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:55:33 d2.utils.events]: \u001b[0m eta: 8:11:21 iter: 14239 total_loss: 1.06 loss_cls: 0.3604 loss_box_reg: 0.3611 loss_rpn_cls: 0.05648 loss_rpn_loc: 0.2171 time: 0.2631 last_time: 0.4183 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:55:41 d2.utils.events]: \u001b[0m eta: 8:11:19 iter: 14259 total_loss: 1.015 loss_cls: 0.3508 loss_box_reg: 0.4011 loss_rpn_cls: 0.04635 loss_rpn_loc: 0.2017 time: 0.2633 last_time: 0.3422 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:55:49 d2.utils.events]: \u001b[0m eta: 8:11:04 iter: 14279 total_loss: 1.287 loss_cls: 0.4892 loss_box_reg: 0.4497 loss_rpn_cls: 0.07599 loss_rpn_loc: 0.2673 time: 0.2635 last_time: 0.4133 data_time: 0.0045 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:55:57 d2.utils.events]: \u001b[0m eta: 8:11:02 iter: 14299 total_loss: 1.115 loss_cls: 0.3947 loss_box_reg: 0.3881 loss_rpn_cls: 0.06145 loss_rpn_loc: 0.2265 time: 0.2637 last_time: 0.4349 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:56:05 d2.utils.events]: \u001b[0m eta: 8:10:58 iter: 14319 total_loss: 1.229 loss_cls: 0.4418 loss_box_reg: 0.4333 loss_rpn_cls: 0.0711 loss_rpn_loc: 0.2401 time: 0.2639 last_time: 0.4407 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:56:13 d2.utils.events]: \u001b[0m eta: 8:10:35 iter: 14339 total_loss: 1.084 loss_cls: 0.3789 loss_box_reg: 0.4084 loss_rpn_cls: 0.05022 loss_rpn_loc: 0.2327 time: 0.2641 last_time: 0.4386 data_time: 0.0045 last_data_time: 0.0040 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:56:21 d2.utils.events]: \u001b[0m eta: 8:10:16 iter: 14359 total_loss: 1.131 loss_cls: 0.4236 loss_box_reg: 0.4239 loss_rpn_cls: 0.06421 loss_rpn_loc: 0.2283 time: 0.2643 last_time: 0.4180 data_time: 0.0045 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:56:29 d2.utils.events]: \u001b[0m eta: 8:10:00 iter: 14379 total_loss: 1.123 loss_cls: 0.4275 loss_box_reg: 0.3943 loss_rpn_cls: 0.07014 loss_rpn_loc: 0.2391 time: 0.2645 last_time: 0.3996 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:56:37 d2.utils.events]: \u001b[0m eta: 8:09:46 iter: 14399 total_loss: 1.106 loss_cls: 0.3862 loss_box_reg: 0.406 loss_rpn_cls: 0.06426 loss_rpn_loc: 0.2415 time: 0.2646 last_time: 0.4392 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:56:45 d2.utils.events]: \u001b[0m eta: 8:09:27 iter: 14419 total_loss: 1.098 loss_cls: 0.3886 loss_box_reg: 0.3683 loss_rpn_cls: 0.07184 loss_rpn_loc: 0.2143 time: 0.2648 last_time: 0.3982 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:56:53 d2.utils.events]: \u001b[0m eta: 8:09:15 iter: 14439 total_loss: 1.047 loss_cls: 0.4031 loss_box_reg: 0.4002 loss_rpn_cls: 0.06163 loss_rpn_loc: 0.2352 time: 0.2650 last_time: 0.4424 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:57:01 d2.utils.events]: \u001b[0m eta: 8:09:07 iter: 14459 total_loss: 0.9904 loss_cls: 0.344 loss_box_reg: 0.3883 loss_rpn_cls: 0.05444 loss_rpn_loc: 0.1988 time: 0.2652 last_time: 0.3380 data_time: 0.0045 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:57:10 d2.utils.events]: \u001b[0m eta: 8:08:52 iter: 14479 total_loss: 1.1 loss_cls: 0.3674 loss_box_reg: 0.4034 loss_rpn_cls: 0.06898 loss_rpn_loc: 0.2503 time: 0.2654 last_time: 0.3965 data_time: 0.0045 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:57:18 d2.utils.events]: \u001b[0m eta: 8:08:18 iter: 14499 total_loss: 1.219 loss_cls: 0.4097 loss_box_reg: 0.423 loss_rpn_cls: 0.08543 loss_rpn_loc: 0.2913 time: 0.2656 last_time: 0.3459 data_time: 0.0045 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:57:26 d2.utils.events]: \u001b[0m eta: 8:08:03 iter: 14519 total_loss: 0.9828 loss_cls: 0.3244 loss_box_reg: 0.3556 loss_rpn_cls: 0.05627 loss_rpn_loc: 0.1962 time: 0.2658 last_time: 0.4419 data_time: 0.0044 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:57:34 d2.utils.events]: \u001b[0m eta: 8:07:25 iter: 14539 total_loss: 1.088 loss_cls: 0.3676 loss_box_reg: 0.4021 loss_rpn_cls: 0.0602 loss_rpn_loc: 0.2415 time: 0.2660 last_time: 0.4109 data_time: 0.0045 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:57:42 d2.utils.events]: \u001b[0m eta: 8:07:00 iter: 14559 total_loss: 1.074 loss_cls: 0.3675 loss_box_reg: 0.3817 loss_rpn_cls: 0.05688 loss_rpn_loc: 0.2388 time: 0.2662 last_time: 0.3959 data_time: 0.0044 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:57:50 d2.utils.events]: \u001b[0m eta: 8:07:01 iter: 14579 total_loss: 1.27 loss_cls: 0.4433 loss_box_reg: 0.447 loss_rpn_cls: 0.07061 loss_rpn_loc: 0.2711 time: 0.2664 last_time: 0.4309 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:57:58 d2.utils.events]: \u001b[0m eta: 8:07:00 iter: 14599 total_loss: 1.143 loss_cls: 0.4048 loss_box_reg: 0.4086 loss_rpn_cls: 0.0624 loss_rpn_loc: 0.2647 time: 0.2666 last_time: 0.4435 data_time: 0.0045 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:58:06 d2.utils.events]: \u001b[0m eta: 8:06:41 iter: 14619 total_loss: 1.009 loss_cls: 0.3388 loss_box_reg: 0.3625 loss_rpn_cls: 0.07102 loss_rpn_loc: 0.2191 time: 0.2667 last_time: 0.4453 data_time: 0.0045 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:58:14 d2.utils.events]: \u001b[0m eta: 8:06:23 iter: 14639 total_loss: 1.06 loss_cls: 0.3796 loss_box_reg: 0.4248 loss_rpn_cls: 0.06477 loss_rpn_loc: 0.2016 time: 0.2669 last_time: 0.4236 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:58:22 d2.utils.events]: \u001b[0m eta: 8:05:55 iter: 14659 total_loss: 1.096 loss_cls: 0.4208 loss_box_reg: 0.4187 loss_rpn_cls: 0.04965 loss_rpn_loc: 0.2322 time: 0.2671 last_time: 0.3765 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:58:30 d2.utils.events]: \u001b[0m eta: 8:05:23 iter: 14679 total_loss: 1.125 loss_cls: 0.3823 loss_box_reg: 0.4004 loss_rpn_cls: 0.07752 loss_rpn_loc: 0.2472 time: 0.2673 last_time: 0.3703 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:58:38 d2.utils.events]: \u001b[0m eta: 8:04:05 iter: 14699 total_loss: 1.17 loss_cls: 0.4359 loss_box_reg: 0.4465 loss_rpn_cls: 0.06364 loss_rpn_loc: 0.2396 time: 0.2675 last_time: 0.3743 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:58:46 d2.utils.events]: \u001b[0m eta: 8:03:24 iter: 14719 total_loss: 1.184 loss_cls: 0.4135 loss_box_reg: 0.411 loss_rpn_cls: 0.05637 loss_rpn_loc: 0.2427 time: 0.2676 last_time: 0.4373 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:58:54 d2.utils.events]: \u001b[0m eta: 8:02:30 iter: 14739 total_loss: 0.9467 loss_cls: 0.309 loss_box_reg: 0.3688 loss_rpn_cls: 0.05691 loss_rpn_loc: 0.2367 time: 0.2678 last_time: 0.3953 data_time: 0.0045 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:59:02 d2.utils.events]: \u001b[0m eta: 8:01:06 iter: 14759 total_loss: 1.108 loss_cls: 0.4369 loss_box_reg: 0.4194 loss_rpn_cls: 0.06168 loss_rpn_loc: 0.2515 time: 0.2680 last_time: 0.3846 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:59:10 d2.utils.events]: \u001b[0m eta: 8:01:14 iter: 14779 total_loss: 1.057 loss_cls: 0.3679 loss_box_reg: 0.3829 loss_rpn_cls: 0.04825 loss_rpn_loc: 0.2148 time: 0.2682 last_time: 0.4005 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:59:18 d2.utils.events]: \u001b[0m eta: 8:00:38 iter: 14799 total_loss: 1.099 loss_cls: 0.3525 loss_box_reg: 0.4136 loss_rpn_cls: 0.06131 loss_rpn_loc: 0.2521 time: 0.2683 last_time: 0.3143 data_time: 0.0044 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:59:26 d2.utils.events]: \u001b[0m eta: 8:01:04 iter: 14819 total_loss: 1.142 loss_cls: 0.3977 loss_box_reg: 0.4012 loss_rpn_cls: 0.07036 loss_rpn_loc: 0.2318 time: 0.2685 last_time: 0.3898 data_time: 0.0044 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:59:34 d2.utils.events]: \u001b[0m eta: 8:00:34 iter: 14839 total_loss: 1.193 loss_cls: 0.395 loss_box_reg: 0.4225 loss_rpn_cls: 0.07721 loss_rpn_loc: 0.2399 time: 0.2687 last_time: 0.4436 data_time: 0.0045 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:59:43 d2.utils.events]: \u001b[0m eta: 8:00:36 iter: 14859 total_loss: 1.255 loss_cls: 0.4529 loss_box_reg: 0.4392 loss_rpn_cls: 0.05971 loss_rpn_loc: 0.2427 time: 0.2689 last_time: 0.4472 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:59:51 d2.utils.events]: \u001b[0m eta: 7:59:46 iter: 14879 total_loss: 1.14 loss_cls: 0.3934 loss_box_reg: 0.4052 loss_rpn_cls: 0.06496 loss_rpn_loc: 0.2562 time: 0.2691 last_time: 0.4439 data_time: 0.0044 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 16:59:58 d2.utils.events]: \u001b[0m eta: 7:58:58 iter: 14899 total_loss: 1.098 loss_cls: 0.378 loss_box_reg: 0.4169 loss_rpn_cls: 0.06156 loss_rpn_loc: 0.2249 time: 0.2692 last_time: 0.3448 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:00:06 d2.utils.events]: \u001b[0m eta: 7:58:13 iter: 14919 total_loss: 1.093 loss_cls: 0.3664 loss_box_reg: 0.3994 loss_rpn_cls: 0.06633 loss_rpn_loc: 0.234 time: 0.2694 last_time: 0.4138 data_time: 0.0044 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:00:14 d2.utils.events]: \u001b[0m eta: 7:57:28 iter: 14939 total_loss: 1.073 loss_cls: 0.3968 loss_box_reg: 0.3915 loss_rpn_cls: 0.05496 loss_rpn_loc: 0.2406 time: 0.2696 last_time: 0.3739 data_time: 0.0043 last_data_time: 0.0040 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:00:22 d2.utils.events]: \u001b[0m eta: 7:56:35 iter: 14959 total_loss: 1.041 loss_cls: 0.3462 loss_box_reg: 0.3872 loss_rpn_cls: 0.06115 loss_rpn_loc: 0.1975 time: 0.2697 last_time: 0.4222 data_time: 0.0044 last_data_time: 0.0041 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:00:30 d2.utils.events]: \u001b[0m eta: 7:56:27 iter: 14979 total_loss: 1.096 loss_cls: 0.4191 loss_box_reg: 0.4324 loss_rpn_cls: 0.05835 loss_rpn_loc: 0.2466 time: 0.2699 last_time: 0.3908 data_time: 0.0045 last_data_time: 0.0062 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:00:41 d2.utils.events]: \u001b[0m eta: 7:55:34 iter: 14999 total_loss: 1.024 loss_cls: 0.3954 loss_box_reg: 0.3834 loss_rpn_cls: 0.04575 loss_rpn_loc: 0.2095 time: 0.2701 last_time: 0.3440 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:00:49 d2.utils.events]: \u001b[0m eta: 7:55:29 iter: 15019 total_loss: 1.071 loss_cls: 0.3957 loss_box_reg: 0.4138 loss_rpn_cls: 0.07047 loss_rpn_loc: 0.2258 time: 0.2703 last_time: 0.4408 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:00:57 d2.utils.events]: \u001b[0m eta: 7:55:35 iter: 15039 total_loss: 1.108 loss_cls: 0.394 loss_box_reg: 0.392 loss_rpn_cls: 0.06669 loss_rpn_loc: 0.2486 time: 0.2705 last_time: 0.3903 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:01:05 d2.utils.events]: \u001b[0m eta: 7:55:54 iter: 15059 total_loss: 1.167 loss_cls: 0.3914 loss_box_reg: 0.3969 loss_rpn_cls: 0.08007 loss_rpn_loc: 0.2908 time: 0.2707 last_time: 0.3575 data_time: 0.0044 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:01:13 d2.utils.events]: \u001b[0m eta: 7:55:46 iter: 15079 total_loss: 1.051 loss_cls: 0.3625 loss_box_reg: 0.4042 loss_rpn_cls: 0.07972 loss_rpn_loc: 0.2211 time: 0.2708 last_time: 0.3721 data_time: 0.0044 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:01:21 d2.utils.events]: \u001b[0m eta: 7:55:38 iter: 15099 total_loss: 0.9969 loss_cls: 0.3608 loss_box_reg: 0.3774 loss_rpn_cls: 0.05604 loss_rpn_loc: 0.2054 time: 0.2710 last_time: 0.3945 data_time: 0.0043 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:01:29 d2.utils.events]: \u001b[0m eta: 7:56:14 iter: 15119 total_loss: 1.135 loss_cls: 0.3737 loss_box_reg: 0.3793 loss_rpn_cls: 0.0508 loss_rpn_loc: 0.2351 time: 0.2712 last_time: 0.4098 data_time: 0.0045 last_data_time: 0.0041 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:01:37 d2.utils.events]: \u001b[0m eta: 7:55:49 iter: 15139 total_loss: 1.091 loss_cls: 0.3785 loss_box_reg: 0.403 loss_rpn_cls: 0.05343 loss_rpn_loc: 0.2455 time: 0.2713 last_time: 0.3415 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:01:45 d2.utils.events]: \u001b[0m eta: 7:55:36 iter: 15159 total_loss: 1.142 loss_cls: 0.404 loss_box_reg: 0.4315 loss_rpn_cls: 0.06609 loss_rpn_loc: 0.2207 time: 0.2715 last_time: 0.3425 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:01:53 d2.utils.events]: \u001b[0m eta: 7:55:36 iter: 15179 total_loss: 1.196 loss_cls: 0.3999 loss_box_reg: 0.4156 loss_rpn_cls: 0.09366 loss_rpn_loc: 0.2362 time: 0.2717 last_time: 0.3474 data_time: 0.0045 last_data_time: 0.0041 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:02:01 d2.utils.events]: \u001b[0m eta: 7:55:34 iter: 15199 total_loss: 1.029 loss_cls: 0.3684 loss_box_reg: 0.3827 loss_rpn_cls: 0.05809 loss_rpn_loc: 0.2192 time: 0.2719 last_time: 0.4326 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:02:09 d2.utils.events]: \u001b[0m eta: 7:55:37 iter: 15219 total_loss: 1.116 loss_cls: 0.4152 loss_box_reg: 0.3874 loss_rpn_cls: 0.07165 loss_rpn_loc: 0.2423 time: 0.2720 last_time: 0.3722 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:02:18 d2.utils.events]: \u001b[0m eta: 7:55:52 iter: 15239 total_loss: 1.133 loss_cls: 0.4278 loss_box_reg: 0.4231 loss_rpn_cls: 0.05378 loss_rpn_loc: 0.233 time: 0.2722 last_time: 0.3404 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:02:26 d2.utils.events]: \u001b[0m eta: 7:55:20 iter: 15259 total_loss: 1.146 loss_cls: 0.4314 loss_box_reg: 0.3867 loss_rpn_cls: 0.06325 loss_rpn_loc: 0.2229 time: 0.2724 last_time: 0.4414 data_time: 0.0044 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:02:34 d2.utils.events]: \u001b[0m eta: 7:55:41 iter: 15279 total_loss: 1.045 loss_cls: 0.3651 loss_box_reg: 0.3944 loss_rpn_cls: 0.07312 loss_rpn_loc: 0.2317 time: 0.2725 last_time: 0.4119 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:02:42 d2.utils.events]: \u001b[0m eta: 7:55:45 iter: 15299 total_loss: 1.059 loss_cls: 0.3566 loss_box_reg: 0.3757 loss_rpn_cls: 0.06047 loss_rpn_loc: 0.2438 time: 0.2727 last_time: 0.4248 data_time: 0.0045 last_data_time: 0.0041 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:02:50 d2.utils.events]: \u001b[0m eta: 7:55:00 iter: 15319 total_loss: 1.082 loss_cls: 0.3779 loss_box_reg: 0.3967 loss_rpn_cls: 0.06154 loss_rpn_loc: 0.2507 time: 0.2729 last_time: 0.3716 data_time: 0.0045 last_data_time: 0.0041 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:02:58 d2.utils.events]: \u001b[0m eta: 7:55:42 iter: 15339 total_loss: 1.04 loss_cls: 0.3797 loss_box_reg: 0.4103 loss_rpn_cls: 0.06571 loss_rpn_loc: 0.2756 time: 0.2730 last_time: 0.4111 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:03:06 d2.utils.events]: \u001b[0m eta: 7:55:16 iter: 15359 total_loss: 1.006 loss_cls: 0.3696 loss_box_reg: 0.3822 loss_rpn_cls: 0.06102 loss_rpn_loc: 0.1985 time: 0.2732 last_time: 0.4074 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:03:14 d2.utils.events]: \u001b[0m eta: 7:55:08 iter: 15379 total_loss: 1.024 loss_cls: 0.3561 loss_box_reg: 0.3683 loss_rpn_cls: 0.07384 loss_rpn_loc: 0.2345 time: 0.2734 last_time: 0.3950 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:03:22 d2.utils.events]: \u001b[0m eta: 7:55:19 iter: 15399 total_loss: 1.038 loss_cls: 0.3609 loss_box_reg: 0.402 loss_rpn_cls: 0.05828 loss_rpn_loc: 0.2412 time: 0.2735 last_time: 0.4339 data_time: 0.0045 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:03:30 d2.utils.events]: \u001b[0m eta: 7:55:11 iter: 15419 total_loss: 1.085 loss_cls: 0.3616 loss_box_reg: 0.4091 loss_rpn_cls: 0.05994 loss_rpn_loc: 0.2147 time: 0.2737 last_time: 0.3694 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:03:38 d2.utils.events]: \u001b[0m eta: 7:55:01 iter: 15439 total_loss: 1.107 loss_cls: 0.3721 loss_box_reg: 0.4082 loss_rpn_cls: 0.0683 loss_rpn_loc: 0.2292 time: 0.2739 last_time: 0.3408 data_time: 0.0046 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:03:46 d2.utils.events]: \u001b[0m eta: 7:54:29 iter: 15459 total_loss: 1.045 loss_cls: 0.3649 loss_box_reg: 0.393 loss_rpn_cls: 0.06609 loss_rpn_loc: 0.2314 time: 0.2740 last_time: 0.4471 data_time: 0.0046 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:03:54 d2.utils.events]: \u001b[0m eta: 7:53:47 iter: 15479 total_loss: 1.037 loss_cls: 0.3435 loss_box_reg: 0.3514 loss_rpn_cls: 0.04581 loss_rpn_loc: 0.2293 time: 0.2742 last_time: 0.3642 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:04:02 d2.utils.events]: \u001b[0m eta: 7:53:29 iter: 15499 total_loss: 0.8942 loss_cls: 0.3274 loss_box_reg: 0.3492 loss_rpn_cls: 0.04966 loss_rpn_loc: 0.1971 time: 0.2743 last_time: 0.4411 data_time: 0.0044 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:04:10 d2.utils.events]: \u001b[0m eta: 7:53:17 iter: 15519 total_loss: 1.079 loss_cls: 0.4032 loss_box_reg: 0.4182 loss_rpn_cls: 0.04634 loss_rpn_loc: 0.2384 time: 0.2745 last_time: 0.3600 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:04:18 d2.utils.events]: \u001b[0m eta: 7:53:12 iter: 15539 total_loss: 1.149 loss_cls: 0.423 loss_box_reg: 0.4184 loss_rpn_cls: 0.05412 loss_rpn_loc: 0.2378 time: 0.2747 last_time: 0.4429 data_time: 0.0047 last_data_time: 0.0054 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:04:27 d2.utils.events]: \u001b[0m eta: 7:53:19 iter: 15559 total_loss: 0.9215 loss_cls: 0.3273 loss_box_reg: 0.3322 loss_rpn_cls: 0.06047 loss_rpn_loc: 0.218 time: 0.2749 last_time: 0.4179 data_time: 0.0050 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:04:36 d2.utils.events]: \u001b[0m eta: 7:54:11 iter: 15579 total_loss: 1.109 loss_cls: 0.397 loss_box_reg: 0.3828 loss_rpn_cls: 0.06748 loss_rpn_loc: 0.2405 time: 0.2751 last_time: 0.4885 data_time: 0.0048 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:04:45 d2.utils.events]: \u001b[0m eta: 7:54:16 iter: 15599 total_loss: 1.035 loss_cls: 0.3506 loss_box_reg: 0.3455 loss_rpn_cls: 0.06062 loss_rpn_loc: 0.2491 time: 0.2754 last_time: 0.5095 data_time: 0.0050 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:04:55 d2.utils.events]: \u001b[0m eta: 7:54:46 iter: 15619 total_loss: 1.071 loss_cls: 0.3752 loss_box_reg: 0.3835 loss_rpn_cls: 0.05823 loss_rpn_loc: 0.2422 time: 0.2756 last_time: 0.5124 data_time: 0.0051 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:05:03 d2.utils.events]: \u001b[0m eta: 7:55:14 iter: 15639 total_loss: 1.016 loss_cls: 0.3469 loss_box_reg: 0.3514 loss_rpn_cls: 0.05993 loss_rpn_loc: 0.2265 time: 0.2758 last_time: 0.4074 data_time: 0.0048 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:05:11 d2.utils.events]: \u001b[0m eta: 7:54:48 iter: 15659 total_loss: 1.001 loss_cls: 0.3227 loss_box_reg: 0.3783 loss_rpn_cls: 0.05541 loss_rpn_loc: 0.2062 time: 0.2760 last_time: 0.3685 data_time: 0.0046 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:05:19 d2.utils.events]: \u001b[0m eta: 7:54:21 iter: 15679 total_loss: 1.046 loss_cls: 0.3712 loss_box_reg: 0.3791 loss_rpn_cls: 0.05911 loss_rpn_loc: 0.2304 time: 0.2761 last_time: 0.4209 data_time: 0.0046 last_data_time: 0.0052 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:05:27 d2.utils.events]: \u001b[0m eta: 7:54:16 iter: 15699 total_loss: 1.01 loss_cls: 0.3373 loss_box_reg: 0.3875 loss_rpn_cls: 0.05401 loss_rpn_loc: 0.2085 time: 0.2763 last_time: 0.4362 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:05:35 d2.utils.events]: \u001b[0m eta: 7:54:23 iter: 15719 total_loss: 1.064 loss_cls: 0.3871 loss_box_reg: 0.3899 loss_rpn_cls: 0.05378 loss_rpn_loc: 0.2248 time: 0.2765 last_time: 0.4300 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:05:43 d2.utils.events]: \u001b[0m eta: 7:54:46 iter: 15739 total_loss: 1.097 loss_cls: 0.345 loss_box_reg: 0.3542 loss_rpn_cls: 0.06447 loss_rpn_loc: 0.2308 time: 0.2766 last_time: 0.4186 data_time: 0.0045 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:05:52 d2.utils.events]: \u001b[0m eta: 7:54:27 iter: 15759 total_loss: 1.056 loss_cls: 0.3443 loss_box_reg: 0.3948 loss_rpn_cls: 0.06348 loss_rpn_loc: 0.2419 time: 0.2768 last_time: 0.3753 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:06:00 d2.utils.events]: \u001b[0m eta: 7:54:19 iter: 15779 total_loss: 1.022 loss_cls: 0.3749 loss_box_reg: 0.3599 loss_rpn_cls: 0.05103 loss_rpn_loc: 0.2183 time: 0.2770 last_time: 0.3659 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:06:08 d2.utils.events]: \u001b[0m eta: 7:54:44 iter: 15799 total_loss: 1.148 loss_cls: 0.4175 loss_box_reg: 0.3935 loss_rpn_cls: 0.06675 loss_rpn_loc: 0.2426 time: 0.2771 last_time: 0.3988 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:06:16 d2.utils.events]: \u001b[0m eta: 7:54:02 iter: 15819 total_loss: 1.07 loss_cls: 0.3848 loss_box_reg: 0.3722 loss_rpn_cls: 0.06052 loss_rpn_loc: 0.2567 time: 0.2773 last_time: 0.3967 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:06:24 d2.utils.events]: \u001b[0m eta: 7:53:29 iter: 15839 total_loss: 1.167 loss_cls: 0.3842 loss_box_reg: 0.4233 loss_rpn_cls: 0.06447 loss_rpn_loc: 0.2119 time: 0.2774 last_time: 0.4239 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:06:32 d2.utils.events]: \u001b[0m eta: 7:53:15 iter: 15859 total_loss: 1.115 loss_cls: 0.3853 loss_box_reg: 0.3942 loss_rpn_cls: 0.06747 loss_rpn_loc: 0.2642 time: 0.2776 last_time: 0.4337 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:06:40 d2.utils.events]: \u001b[0m eta: 7:53:41 iter: 15879 total_loss: 0.9363 loss_cls: 0.3174 loss_box_reg: 0.3635 loss_rpn_cls: 0.04765 loss_rpn_loc: 0.2128 time: 0.2777 last_time: 0.3398 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:06:48 d2.utils.events]: \u001b[0m eta: 7:53:40 iter: 15899 total_loss: 1.157 loss_cls: 0.4119 loss_box_reg: 0.399 loss_rpn_cls: 0.07377 loss_rpn_loc: 0.2724 time: 0.2779 last_time: 0.4237 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:06:56 d2.utils.events]: \u001b[0m eta: 7:53:46 iter: 15919 total_loss: 1.145 loss_cls: 0.3944 loss_box_reg: 0.4498 loss_rpn_cls: 0.0553 loss_rpn_loc: 0.2272 time: 0.2780 last_time: 0.4458 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:07:04 d2.utils.events]: \u001b[0m eta: 7:53:56 iter: 15939 total_loss: 1.104 loss_cls: 0.4192 loss_box_reg: 0.4383 loss_rpn_cls: 0.06704 loss_rpn_loc: 0.2302 time: 0.2782 last_time: 0.3732 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:07:12 d2.utils.events]: \u001b[0m eta: 7:53:59 iter: 15959 total_loss: 1.047 loss_cls: 0.3804 loss_box_reg: 0.3704 loss_rpn_cls: 0.06309 loss_rpn_loc: 0.2125 time: 0.2784 last_time: 0.3173 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:07:20 d2.utils.events]: \u001b[0m eta: 7:53:58 iter: 15979 total_loss: 1.001 loss_cls: 0.3455 loss_box_reg: 0.346 loss_rpn_cls: 0.06767 loss_rpn_loc: 0.2203 time: 0.2785 last_time: 0.3671 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:07:29 d2.utils.events]: \u001b[0m eta: 7:54:09 iter: 15999 total_loss: 1.072 loss_cls: 0.3933 loss_box_reg: 0.39 loss_rpn_cls: 0.07007 loss_rpn_loc: 0.215 time: 0.2787 last_time: 0.4245 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:07:37 d2.utils.events]: \u001b[0m eta: 7:54:11 iter: 16019 total_loss: 1.012 loss_cls: 0.3502 loss_box_reg: 0.3722 loss_rpn_cls: 0.05506 loss_rpn_loc: 0.2018 time: 0.2789 last_time: 0.4291 data_time: 0.0046 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:07:45 d2.utils.events]: \u001b[0m eta: 7:53:46 iter: 16039 total_loss: 1.095 loss_cls: 0.3671 loss_box_reg: 0.4146 loss_rpn_cls: 0.05299 loss_rpn_loc: 0.2285 time: 0.2790 last_time: 0.4329 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:07:53 d2.utils.events]: \u001b[0m eta: 7:53:45 iter: 16059 total_loss: 1.069 loss_cls: 0.3888 loss_box_reg: 0.3941 loss_rpn_cls: 0.06408 loss_rpn_loc: 0.225 time: 0.2792 last_time: 0.4171 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:08:01 d2.utils.events]: \u001b[0m eta: 7:53:29 iter: 16079 total_loss: 0.9722 loss_cls: 0.3474 loss_box_reg: 0.3729 loss_rpn_cls: 0.06626 loss_rpn_loc: 0.2101 time: 0.2793 last_time: 0.4280 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:08:09 d2.utils.events]: \u001b[0m eta: 7:53:38 iter: 16099 total_loss: 1.085 loss_cls: 0.4345 loss_box_reg: 0.3716 loss_rpn_cls: 0.05817 loss_rpn_loc: 0.2172 time: 0.2795 last_time: 0.3971 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:08:17 d2.utils.events]: \u001b[0m eta: 7:53:28 iter: 16119 total_loss: 1.125 loss_cls: 0.422 loss_box_reg: 0.3843 loss_rpn_cls: 0.07217 loss_rpn_loc: 0.2444 time: 0.2796 last_time: 0.3662 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:08:25 d2.utils.events]: \u001b[0m eta: 7:53:34 iter: 16139 total_loss: 1.103 loss_cls: 0.363 loss_box_reg: 0.377 loss_rpn_cls: 0.06628 loss_rpn_loc: 0.1995 time: 0.2798 last_time: 0.4136 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:08:33 d2.utils.events]: \u001b[0m eta: 7:54:09 iter: 16159 total_loss: 1.072 loss_cls: 0.3845 loss_box_reg: 0.4068 loss_rpn_cls: 0.04901 loss_rpn_loc: 0.2252 time: 0.2799 last_time: 0.4009 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:08:42 d2.utils.events]: \u001b[0m eta: 7:54:07 iter: 16179 total_loss: 1.019 loss_cls: 0.3963 loss_box_reg: 0.4079 loss_rpn_cls: 0.05984 loss_rpn_loc: 0.2007 time: 0.2801 last_time: 0.3949 data_time: 0.0046 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:08:50 d2.utils.events]: \u001b[0m eta: 7:53:55 iter: 16199 total_loss: 0.9628 loss_cls: 0.3028 loss_box_reg: 0.3445 loss_rpn_cls: 0.0689 loss_rpn_loc: 0.2466 time: 0.2803 last_time: 0.3902 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:08:58 d2.utils.events]: \u001b[0m eta: 7:53:47 iter: 16219 total_loss: 1.123 loss_cls: 0.4051 loss_box_reg: 0.3994 loss_rpn_cls: 0.05858 loss_rpn_loc: 0.2386 time: 0.2804 last_time: 0.4454 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:09:06 d2.utils.events]: \u001b[0m eta: 7:53:36 iter: 16239 total_loss: 1.066 loss_cls: 0.3934 loss_box_reg: 0.3722 loss_rpn_cls: 0.05966 loss_rpn_loc: 0.2093 time: 0.2806 last_time: 0.4336 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:09:14 d2.utils.events]: \u001b[0m eta: 7:53:21 iter: 16259 total_loss: 1.053 loss_cls: 0.3746 loss_box_reg: 0.3604 loss_rpn_cls: 0.04767 loss_rpn_loc: 0.2232 time: 0.2807 last_time: 0.4429 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:09:22 d2.utils.events]: \u001b[0m eta: 7:53:22 iter: 16279 total_loss: 1.087 loss_cls: 0.3662 loss_box_reg: 0.3482 loss_rpn_cls: 0.06278 loss_rpn_loc: 0.2433 time: 0.2809 last_time: 0.4124 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:09:30 d2.utils.events]: \u001b[0m eta: 7:53:11 iter: 16299 total_loss: 0.9401 loss_cls: 0.3672 loss_box_reg: 0.3585 loss_rpn_cls: 0.05238 loss_rpn_loc: 0.2113 time: 0.2810 last_time: 0.3945 data_time: 0.0046 last_data_time: 0.0040 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:09:38 d2.utils.events]: \u001b[0m eta: 7:53:18 iter: 16319 total_loss: 1.059 loss_cls: 0.4058 loss_box_reg: 0.3795 loss_rpn_cls: 0.0641 loss_rpn_loc: 0.2003 time: 0.2812 last_time: 0.4107 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:09:46 d2.utils.events]: \u001b[0m eta: 7:53:02 iter: 16339 total_loss: 1.105 loss_cls: 0.3724 loss_box_reg: 0.3936 loss_rpn_cls: 0.0554 loss_rpn_loc: 0.254 time: 0.2813 last_time: 0.3962 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:09:55 d2.utils.events]: \u001b[0m eta: 7:52:56 iter: 16359 total_loss: 1.061 loss_cls: 0.3666 loss_box_reg: 0.4093 loss_rpn_cls: 0.05456 loss_rpn_loc: 0.1937 time: 0.2815 last_time: 0.3366 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:10:03 d2.utils.events]: \u001b[0m eta: 7:53:00 iter: 16379 total_loss: 1.018 loss_cls: 0.3671 loss_box_reg: 0.3621 loss_rpn_cls: 0.05008 loss_rpn_loc: 0.2266 time: 0.2816 last_time: 0.4382 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:10:11 d2.utils.events]: \u001b[0m eta: 7:52:45 iter: 16399 total_loss: 1.11 loss_cls: 0.3913 loss_box_reg: 0.3831 loss_rpn_cls: 0.07658 loss_rpn_loc: 0.2525 time: 0.2818 last_time: 0.3960 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:10:19 d2.utils.events]: \u001b[0m eta: 7:52:31 iter: 16419 total_loss: 1.098 loss_cls: 0.3882 loss_box_reg: 0.3941 loss_rpn_cls: 0.07083 loss_rpn_loc: 0.2522 time: 0.2819 last_time: 0.4395 data_time: 0.0045 last_data_time: 0.0041 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:10:26 d2.utils.events]: \u001b[0m eta: 7:52:22 iter: 16439 total_loss: 1.06 loss_cls: 0.3961 loss_box_reg: 0.3947 loss_rpn_cls: 0.05966 loss_rpn_loc: 0.2183 time: 0.2820 last_time: 0.4208 data_time: 0.0045 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:10:35 d2.utils.events]: \u001b[0m eta: 7:52:14 iter: 16459 total_loss: 0.9205 loss_cls: 0.2923 loss_box_reg: 0.3391 loss_rpn_cls: 0.05554 loss_rpn_loc: 0.1817 time: 0.2822 last_time: 0.3748 data_time: 0.0045 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:10:43 d2.utils.events]: \u001b[0m eta: 7:52:12 iter: 16479 total_loss: 1.081 loss_cls: 0.3948 loss_box_reg: 0.3846 loss_rpn_cls: 0.05989 loss_rpn_loc: 0.2313 time: 0.2823 last_time: 0.4218 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:10:50 d2.utils.events]: \u001b[0m eta: 7:52:04 iter: 16499 total_loss: 1.03 loss_cls: 0.3699 loss_box_reg: 0.3533 loss_rpn_cls: 0.05982 loss_rpn_loc: 0.2367 time: 0.2825 last_time: 0.3408 data_time: 0.0045 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:10:58 d2.utils.events]: \u001b[0m eta: 7:52:07 iter: 16519 total_loss: 0.965 loss_cls: 0.3255 loss_box_reg: 0.3578 loss_rpn_cls: 0.05519 loss_rpn_loc: 0.2164 time: 0.2826 last_time: 0.3656 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:11:06 d2.utils.events]: \u001b[0m eta: 7:51:38 iter: 16539 total_loss: 1.14 loss_cls: 0.4203 loss_box_reg: 0.4024 loss_rpn_cls: 0.07109 loss_rpn_loc: 0.2136 time: 0.2827 last_time: 0.4030 data_time: 0.0044 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:11:14 d2.utils.events]: \u001b[0m eta: 7:51:19 iter: 16559 total_loss: 1.129 loss_cls: 0.4308 loss_box_reg: 0.4054 loss_rpn_cls: 0.0512 loss_rpn_loc: 0.2658 time: 0.2829 last_time: 0.3923 data_time: 0.0045 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:11:22 d2.utils.events]: \u001b[0m eta: 7:49:44 iter: 16579 total_loss: 1.041 loss_cls: 0.3384 loss_box_reg: 0.381 loss_rpn_cls: 0.05866 loss_rpn_loc: 0.231 time: 0.2830 last_time: 0.4380 data_time: 0.0045 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:11:30 d2.utils.events]: \u001b[0m eta: 7:48:03 iter: 16599 total_loss: 1.046 loss_cls: 0.3382 loss_box_reg: 0.383 loss_rpn_cls: 0.06846 loss_rpn_loc: 0.2616 time: 0.2832 last_time: 0.4397 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:11:38 d2.utils.events]: \u001b[0m eta: 7:45:55 iter: 16619 total_loss: 1.123 loss_cls: 0.3996 loss_box_reg: 0.4199 loss_rpn_cls: 0.06808 loss_rpn_loc: 0.2447 time: 0.2833 last_time: 0.4409 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:11:46 d2.utils.events]: \u001b[0m eta: 7:45:25 iter: 16639 total_loss: 1.201 loss_cls: 0.3895 loss_box_reg: 0.4198 loss_rpn_cls: 0.07397 loss_rpn_loc: 0.2763 time: 0.2834 last_time: 0.4430 data_time: 0.0045 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:11:55 d2.utils.events]: \u001b[0m eta: 7:46:12 iter: 16659 total_loss: 1.189 loss_cls: 0.4431 loss_box_reg: 0.3804 loss_rpn_cls: 0.0604 loss_rpn_loc: 0.2197 time: 0.2836 last_time: 0.4331 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:12:02 d2.utils.events]: \u001b[0m eta: 7:46:04 iter: 16679 total_loss: 1.024 loss_cls: 0.3513 loss_box_reg: 0.3574 loss_rpn_cls: 0.05663 loss_rpn_loc: 0.2365 time: 0.2837 last_time: 0.4282 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:12:11 d2.utils.events]: \u001b[0m eta: 7:46:16 iter: 16699 total_loss: 1.002 loss_cls: 0.3446 loss_box_reg: 0.3555 loss_rpn_cls: 0.0581 loss_rpn_loc: 0.2501 time: 0.2839 last_time: 0.4298 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:12:19 d2.utils.events]: \u001b[0m eta: 7:46:08 iter: 16719 total_loss: 0.9473 loss_cls: 0.346 loss_box_reg: 0.3839 loss_rpn_cls: 0.0682 loss_rpn_loc: 0.2235 time: 0.2840 last_time: 0.3460 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:12:27 d2.utils.events]: \u001b[0m eta: 7:46:24 iter: 16739 total_loss: 1.087 loss_cls: 0.3595 loss_box_reg: 0.3676 loss_rpn_cls: 0.06218 loss_rpn_loc: 0.223 time: 0.2842 last_time: 0.4203 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:12:35 d2.utils.events]: \u001b[0m eta: 7:47:03 iter: 16759 total_loss: 1.137 loss_cls: 0.3683 loss_box_reg: 0.4098 loss_rpn_cls: 0.0761 loss_rpn_loc: 0.2443 time: 0.2843 last_time: 0.4401 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:12:43 d2.utils.events]: \u001b[0m eta: 7:46:58 iter: 16779 total_loss: 1.055 loss_cls: 0.386 loss_box_reg: 0.3737 loss_rpn_cls: 0.05415 loss_rpn_loc: 0.2279 time: 0.2845 last_time: 0.4419 data_time: 0.0046 last_data_time: 0.0041 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:12:52 d2.utils.events]: \u001b[0m eta: 7:46:49 iter: 16799 total_loss: 0.9776 loss_cls: 0.316 loss_box_reg: 0.3369 loss_rpn_cls: 0.06346 loss_rpn_loc: 0.2376 time: 0.2846 last_time: 0.4198 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:13:00 d2.utils.events]: \u001b[0m eta: 7:47:04 iter: 16819 total_loss: 0.9318 loss_cls: 0.3194 loss_box_reg: 0.3505 loss_rpn_cls: 0.05368 loss_rpn_loc: 0.2062 time: 0.2848 last_time: 0.3761 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:13:08 d2.utils.events]: \u001b[0m eta: 7:46:46 iter: 16839 total_loss: 1.023 loss_cls: 0.3416 loss_box_reg: 0.3823 loss_rpn_cls: 0.05217 loss_rpn_loc: 0.2251 time: 0.2849 last_time: 0.3932 data_time: 0.0045 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:13:16 d2.utils.events]: \u001b[0m eta: 7:46:33 iter: 16859 total_loss: 1.099 loss_cls: 0.3838 loss_box_reg: 0.4072 loss_rpn_cls: 0.04811 loss_rpn_loc: 0.2095 time: 0.2850 last_time: 0.4103 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:13:24 d2.utils.events]: \u001b[0m eta: 7:46:17 iter: 16879 total_loss: 1 loss_cls: 0.3305 loss_box_reg: 0.3664 loss_rpn_cls: 0.05252 loss_rpn_loc: 0.2216 time: 0.2852 last_time: 0.3777 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:13:32 d2.utils.events]: \u001b[0m eta: 7:46:07 iter: 16899 total_loss: 1.118 loss_cls: 0.3852 loss_box_reg: 0.3952 loss_rpn_cls: 0.06294 loss_rpn_loc: 0.2566 time: 0.2853 last_time: 0.3942 data_time: 0.0047 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:13:40 d2.utils.events]: \u001b[0m eta: 7:46:00 iter: 16919 total_loss: 1.047 loss_cls: 0.3806 loss_box_reg: 0.3756 loss_rpn_cls: 0.05381 loss_rpn_loc: 0.2551 time: 0.2855 last_time: 0.4768 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:13:48 d2.utils.events]: \u001b[0m eta: 7:45:52 iter: 16939 total_loss: 1.032 loss_cls: 0.3444 loss_box_reg: 0.3802 loss_rpn_cls: 0.06513 loss_rpn_loc: 0.2071 time: 0.2856 last_time: 0.4603 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:13:56 d2.utils.events]: \u001b[0m eta: 7:45:35 iter: 16959 total_loss: 1.141 loss_cls: 0.3497 loss_box_reg: 0.4093 loss_rpn_cls: 0.06241 loss_rpn_loc: 0.2302 time: 0.2857 last_time: 0.3782 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:14:04 d2.utils.events]: \u001b[0m eta: 7:45:30 iter: 16979 total_loss: 0.9811 loss_cls: 0.3713 loss_box_reg: 0.3717 loss_rpn_cls: 0.05736 loss_rpn_loc: 0.21 time: 0.2859 last_time: 0.4543 data_time: 0.0048 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:14:13 d2.utils.events]: \u001b[0m eta: 7:45:31 iter: 16999 total_loss: 1.108 loss_cls: 0.3701 loss_box_reg: 0.3726 loss_rpn_cls: 0.06709 loss_rpn_loc: 0.2515 time: 0.2860 last_time: 0.4258 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:14:21 d2.utils.events]: \u001b[0m eta: 7:45:19 iter: 17019 total_loss: 1.044 loss_cls: 0.3532 loss_box_reg: 0.3761 loss_rpn_cls: 0.06936 loss_rpn_loc: 0.2706 time: 0.2862 last_time: 0.3445 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:14:29 d2.utils.events]: \u001b[0m eta: 7:45:25 iter: 17039 total_loss: 1.058 loss_cls: 0.3321 loss_box_reg: 0.3834 loss_rpn_cls: 0.05638 loss_rpn_loc: 0.2178 time: 0.2863 last_time: 0.4400 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:14:37 d2.utils.events]: \u001b[0m eta: 7:45:12 iter: 17059 total_loss: 1.069 loss_cls: 0.3911 loss_box_reg: 0.3877 loss_rpn_cls: 0.06134 loss_rpn_loc: 0.2155 time: 0.2865 last_time: 0.3988 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:14:45 d2.utils.events]: \u001b[0m eta: 7:45:07 iter: 17079 total_loss: 1.066 loss_cls: 0.3613 loss_box_reg: 0.3884 loss_rpn_cls: 0.05553 loss_rpn_loc: 0.2339 time: 0.2866 last_time: 0.3698 data_time: 0.0044 last_data_time: 0.0041 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:14:53 d2.utils.events]: \u001b[0m eta: 7:44:43 iter: 17099 total_loss: 1.033 loss_cls: 0.4071 loss_box_reg: 0.3594 loss_rpn_cls: 0.07179 loss_rpn_loc: 0.219 time: 0.2867 last_time: 0.3523 data_time: 0.0044 last_data_time: 0.0041 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:15:01 d2.utils.events]: \u001b[0m eta: 7:44:37 iter: 17119 total_loss: 0.9377 loss_cls: 0.3183 loss_box_reg: 0.3485 loss_rpn_cls: 0.0486 loss_rpn_loc: 0.2193 time: 0.2869 last_time: 0.4101 data_time: 0.0044 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:15:09 d2.utils.events]: \u001b[0m eta: 7:43:58 iter: 17139 total_loss: 1.054 loss_cls: 0.3423 loss_box_reg: 0.3493 loss_rpn_cls: 0.07208 loss_rpn_loc: 0.228 time: 0.2870 last_time: 0.4061 data_time: 0.0044 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:15:17 d2.utils.events]: \u001b[0m eta: 7:43:20 iter: 17159 total_loss: 1.147 loss_cls: 0.3966 loss_box_reg: 0.3732 loss_rpn_cls: 0.06467 loss_rpn_loc: 0.2111 time: 0.2871 last_time: 0.4148 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:15:25 d2.utils.events]: \u001b[0m eta: 7:42:32 iter: 17179 total_loss: 1.07 loss_cls: 0.3747 loss_box_reg: 0.3777 loss_rpn_cls: 0.05752 loss_rpn_loc: 0.2326 time: 0.2873 last_time: 0.4434 data_time: 0.0046 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:15:33 d2.utils.events]: \u001b[0m eta: 7:42:20 iter: 17199 total_loss: 0.8962 loss_cls: 0.2917 loss_box_reg: 0.3408 loss_rpn_cls: 0.05365 loss_rpn_loc: 0.1964 time: 0.2874 last_time: 0.3730 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:15:42 d2.utils.events]: \u001b[0m eta: 7:42:39 iter: 17219 total_loss: 0.9041 loss_cls: 0.2918 loss_box_reg: 0.312 loss_rpn_cls: 0.05946 loss_rpn_loc: 0.2126 time: 0.2875 last_time: 0.3158 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:15:50 d2.utils.events]: \u001b[0m eta: 7:41:37 iter: 17239 total_loss: 0.9831 loss_cls: 0.3519 loss_box_reg: 0.3729 loss_rpn_cls: 0.05464 loss_rpn_loc: 0.2328 time: 0.2877 last_time: 0.3397 data_time: 0.0044 last_data_time: 0.0041 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:15:58 d2.utils.events]: \u001b[0m eta: 7:41:59 iter: 17259 total_loss: 1.036 loss_cls: 0.3991 loss_box_reg: 0.395 loss_rpn_cls: 0.05186 loss_rpn_loc: 0.2209 time: 0.2878 last_time: 0.3624 data_time: 0.0044 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:16:05 d2.utils.events]: \u001b[0m eta: 7:41:21 iter: 17279 total_loss: 1.055 loss_cls: 0.3955 loss_box_reg: 0.3992 loss_rpn_cls: 0.06897 loss_rpn_loc: 0.202 time: 0.2879 last_time: 0.3374 data_time: 0.0044 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:16:13 d2.utils.events]: \u001b[0m eta: 7:41:42 iter: 17299 total_loss: 0.9453 loss_cls: 0.3512 loss_box_reg: 0.3186 loss_rpn_cls: 0.04387 loss_rpn_loc: 0.1906 time: 0.2880 last_time: 0.4184 data_time: 0.0044 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:16:21 d2.utils.events]: \u001b[0m eta: 7:40:52 iter: 17319 total_loss: 1.002 loss_cls: 0.3566 loss_box_reg: 0.3483 loss_rpn_cls: 0.05093 loss_rpn_loc: 0.2133 time: 0.2882 last_time: 0.4193 data_time: 0.0044 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:16:29 d2.utils.events]: \u001b[0m eta: 7:40:34 iter: 17339 total_loss: 1.115 loss_cls: 0.398 loss_box_reg: 0.4018 loss_rpn_cls: 0.06927 loss_rpn_loc: 0.2238 time: 0.2883 last_time: 0.4413 data_time: 0.0044 last_data_time: 0.0041 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:16:37 d2.utils.events]: \u001b[0m eta: 7:40:13 iter: 17359 total_loss: 0.9715 loss_cls: 0.3198 loss_box_reg: 0.3617 loss_rpn_cls: 0.05801 loss_rpn_loc: 0.2192 time: 0.2884 last_time: 0.3917 data_time: 0.0044 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:16:45 d2.utils.events]: \u001b[0m eta: 7:40:15 iter: 17379 total_loss: 1.041 loss_cls: 0.3801 loss_box_reg: 0.3867 loss_rpn_cls: 0.0518 loss_rpn_loc: 0.2074 time: 0.2886 last_time: 0.3949 data_time: 0.0045 last_data_time: 0.0041 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:16:53 d2.utils.events]: \u001b[0m eta: 7:40:32 iter: 17399 total_loss: 1.046 loss_cls: 0.3887 loss_box_reg: 0.3848 loss_rpn_cls: 0.06693 loss_rpn_loc: 0.2647 time: 0.2887 last_time: 0.4407 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:17:02 d2.utils.events]: \u001b[0m eta: 7:40:53 iter: 17419 total_loss: 1.057 loss_cls: 0.3727 loss_box_reg: 0.3723 loss_rpn_cls: 0.0495 loss_rpn_loc: 0.2235 time: 0.2888 last_time: 0.4281 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:17:10 d2.utils.events]: \u001b[0m eta: 7:41:37 iter: 17439 total_loss: 1.133 loss_cls: 0.3906 loss_box_reg: 0.3675 loss_rpn_cls: 0.07242 loss_rpn_loc: 0.2412 time: 0.2890 last_time: 0.3452 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:17:18 d2.utils.events]: \u001b[0m eta: 7:40:39 iter: 17459 total_loss: 1.009 loss_cls: 0.3423 loss_box_reg: 0.3745 loss_rpn_cls: 0.05468 loss_rpn_loc: 0.2177 time: 0.2891 last_time: 0.3313 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:17:26 d2.utils.events]: \u001b[0m eta: 7:39:59 iter: 17479 total_loss: 1.059 loss_cls: 0.3565 loss_box_reg: 0.3715 loss_rpn_cls: 0.06571 loss_rpn_loc: 0.2216 time: 0.2892 last_time: 0.4493 data_time: 0.0045 last_data_time: 0.0041 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:17:34 d2.utils.events]: \u001b[0m eta: 7:40:55 iter: 17499 total_loss: 0.9614 loss_cls: 0.3497 loss_box_reg: 0.3776 loss_rpn_cls: 0.06169 loss_rpn_loc: 0.2121 time: 0.2893 last_time: 0.3997 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:17:42 d2.utils.events]: \u001b[0m eta: 7:40:26 iter: 17519 total_loss: 1.088 loss_cls: 0.355 loss_box_reg: 0.3932 loss_rpn_cls: 0.06709 loss_rpn_loc: 0.2346 time: 0.2895 last_time: 0.4383 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:17:50 d2.utils.events]: \u001b[0m eta: 7:41:21 iter: 17539 total_loss: 1.122 loss_cls: 0.4386 loss_box_reg: 0.3874 loss_rpn_cls: 0.07215 loss_rpn_loc: 0.2473 time: 0.2896 last_time: 0.3506 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:17:58 d2.utils.events]: \u001b[0m eta: 7:40:30 iter: 17559 total_loss: 1.141 loss_cls: 0.3944 loss_box_reg: 0.4 loss_rpn_cls: 0.07448 loss_rpn_loc: 0.2312 time: 0.2897 last_time: 0.3699 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:18:06 d2.utils.events]: \u001b[0m eta: 7:40:01 iter: 17579 total_loss: 1.062 loss_cls: 0.3714 loss_box_reg: 0.3819 loss_rpn_cls: 0.05582 loss_rpn_loc: 0.2402 time: 0.2898 last_time: 0.4262 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:18:14 d2.utils.events]: \u001b[0m eta: 7:41:11 iter: 17599 total_loss: 1.098 loss_cls: 0.3951 loss_box_reg: 0.3787 loss_rpn_cls: 0.06376 loss_rpn_loc: 0.2531 time: 0.2900 last_time: 0.4056 data_time: 0.0045 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:18:22 d2.utils.events]: \u001b[0m eta: 7:41:22 iter: 17619 total_loss: 0.9353 loss_cls: 0.3197 loss_box_reg: 0.3569 loss_rpn_cls: 0.06148 loss_rpn_loc: 0.2197 time: 0.2901 last_time: 0.4524 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:18:30 d2.utils.events]: \u001b[0m eta: 7:40:55 iter: 17639 total_loss: 1.039 loss_cls: 0.319 loss_box_reg: 0.361 loss_rpn_cls: 0.07469 loss_rpn_loc: 0.2478 time: 0.2903 last_time: 0.4509 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:18:38 d2.utils.events]: \u001b[0m eta: 7:40:20 iter: 17659 total_loss: 1.028 loss_cls: 0.345 loss_box_reg: 0.3679 loss_rpn_cls: 0.0691 loss_rpn_loc: 0.2245 time: 0.2904 last_time: 0.3991 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:18:46 d2.utils.events]: \u001b[0m eta: 7:40:38 iter: 17679 total_loss: 0.9558 loss_cls: 0.3679 loss_box_reg: 0.3558 loss_rpn_cls: 0.0499 loss_rpn_loc: 0.1923 time: 0.2905 last_time: 0.3396 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:18:54 d2.utils.events]: \u001b[0m eta: 7:39:33 iter: 17699 total_loss: 1.037 loss_cls: 0.3715 loss_box_reg: 0.3636 loss_rpn_cls: 0.06523 loss_rpn_loc: 0.2346 time: 0.2906 last_time: 0.3919 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:19:03 d2.utils.events]: \u001b[0m eta: 7:39:55 iter: 17719 total_loss: 1.141 loss_cls: 0.389 loss_box_reg: 0.395 loss_rpn_cls: 0.061 loss_rpn_loc: 0.2493 time: 0.2908 last_time: 0.4376 data_time: 0.0045 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:19:11 d2.utils.events]: \u001b[0m eta: 7:40:43 iter: 17739 total_loss: 0.9847 loss_cls: 0.3497 loss_box_reg: 0.3573 loss_rpn_cls: 0.05022 loss_rpn_loc: 0.2202 time: 0.2909 last_time: 0.3795 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:19:20 d2.utils.events]: \u001b[0m eta: 7:41:41 iter: 17759 total_loss: 1.239 loss_cls: 0.4084 loss_box_reg: 0.4202 loss_rpn_cls: 0.07613 loss_rpn_loc: 0.2547 time: 0.2911 last_time: 0.4203 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:19:28 d2.utils.events]: \u001b[0m eta: 7:40:27 iter: 17779 total_loss: 0.9717 loss_cls: 0.3697 loss_box_reg: 0.3827 loss_rpn_cls: 0.0524 loss_rpn_loc: 0.1955 time: 0.2912 last_time: 0.4524 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:19:36 d2.utils.events]: \u001b[0m eta: 7:41:02 iter: 17799 total_loss: 1.031 loss_cls: 0.3833 loss_box_reg: 0.3857 loss_rpn_cls: 0.05672 loss_rpn_loc: 0.2251 time: 0.2913 last_time: 0.4506 data_time: 0.0048 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:19:44 d2.utils.events]: \u001b[0m eta: 7:40:47 iter: 17819 total_loss: 1.03 loss_cls: 0.3942 loss_box_reg: 0.3565 loss_rpn_cls: 0.05719 loss_rpn_loc: 0.2271 time: 0.2914 last_time: 0.4528 data_time: 0.0047 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:19:52 d2.utils.events]: \u001b[0m eta: 7:41:08 iter: 17839 total_loss: 1.057 loss_cls: 0.3715 loss_box_reg: 0.3671 loss_rpn_cls: 0.06386 loss_rpn_loc: 0.2583 time: 0.2916 last_time: 0.4028 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:20:00 d2.utils.events]: \u001b[0m eta: 7:39:54 iter: 17859 total_loss: 1.08 loss_cls: 0.3705 loss_box_reg: 0.3976 loss_rpn_cls: 0.0651 loss_rpn_loc: 0.2144 time: 0.2917 last_time: 0.3992 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:20:08 d2.utils.events]: \u001b[0m eta: 7:39:55 iter: 17879 total_loss: 1.082 loss_cls: 0.3834 loss_box_reg: 0.3448 loss_rpn_cls: 0.06595 loss_rpn_loc: 0.2438 time: 0.2918 last_time: 0.4386 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:20:16 d2.utils.events]: \u001b[0m eta: 7:40:14 iter: 17899 total_loss: 0.9491 loss_cls: 0.3304 loss_box_reg: 0.3327 loss_rpn_cls: 0.05428 loss_rpn_loc: 0.2113 time: 0.2919 last_time: 0.4018 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:20:24 d2.utils.events]: \u001b[0m eta: 7:39:07 iter: 17919 total_loss: 1.117 loss_cls: 0.3465 loss_box_reg: 0.3869 loss_rpn_cls: 0.06142 loss_rpn_loc: 0.2523 time: 0.2921 last_time: 0.4082 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:20:32 d2.utils.events]: \u001b[0m eta: 7:38:59 iter: 17939 total_loss: 1.11 loss_cls: 0.3886 loss_box_reg: 0.3806 loss_rpn_cls: 0.0665 loss_rpn_loc: 0.2315 time: 0.2922 last_time: 0.3778 data_time: 0.0049 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:20:40 d2.utils.events]: \u001b[0m eta: 7:39:50 iter: 17959 total_loss: 1.137 loss_cls: 0.4124 loss_box_reg: 0.4102 loss_rpn_cls: 0.061 loss_rpn_loc: 0.261 time: 0.2923 last_time: 0.3815 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:20:49 d2.utils.events]: \u001b[0m eta: 7:38:57 iter: 17979 total_loss: 1.018 loss_cls: 0.3884 loss_box_reg: 0.3767 loss_rpn_cls: 0.06735 loss_rpn_loc: 0.2265 time: 0.2924 last_time: 0.3969 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:20:57 d2.utils.events]: \u001b[0m eta: 7:37:09 iter: 17999 total_loss: 1.108 loss_cls: 0.4085 loss_box_reg: 0.3949 loss_rpn_cls: 0.05997 loss_rpn_loc: 0.2318 time: 0.2926 last_time: 0.4482 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:21:05 d2.utils.events]: \u001b[0m eta: 7:36:40 iter: 18019 total_loss: 0.9806 loss_cls: 0.3453 loss_box_reg: 0.373 loss_rpn_cls: 0.05777 loss_rpn_loc: 0.2116 time: 0.2927 last_time: 0.3753 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:21:13 d2.utils.events]: \u001b[0m eta: 7:36:02 iter: 18039 total_loss: 0.9606 loss_cls: 0.3491 loss_box_reg: 0.3584 loss_rpn_cls: 0.05098 loss_rpn_loc: 0.225 time: 0.2928 last_time: 0.3402 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:21:21 d2.utils.events]: \u001b[0m eta: 7:37:05 iter: 18059 total_loss: 1.106 loss_cls: 0.3796 loss_box_reg: 0.3993 loss_rpn_cls: 0.06838 loss_rpn_loc: 0.2308 time: 0.2930 last_time: 0.4174 data_time: 0.0049 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:21:30 d2.utils.events]: \u001b[0m eta: 7:37:14 iter: 18079 total_loss: 1.038 loss_cls: 0.3679 loss_box_reg: 0.3697 loss_rpn_cls: 0.06359 loss_rpn_loc: 0.2195 time: 0.2931 last_time: 0.4022 data_time: 0.0048 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:21:38 d2.utils.events]: \u001b[0m eta: 7:39:05 iter: 18099 total_loss: 1.002 loss_cls: 0.3461 loss_box_reg: 0.3749 loss_rpn_cls: 0.0505 loss_rpn_loc: 0.2037 time: 0.2932 last_time: 0.4377 data_time: 0.0048 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:21:46 d2.utils.events]: \u001b[0m eta: 7:40:17 iter: 18119 total_loss: 1.128 loss_cls: 0.4059 loss_box_reg: 0.3929 loss_rpn_cls: 0.06185 loss_rpn_loc: 0.2536 time: 0.2934 last_time: 0.4638 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:21:56 d2.utils.events]: \u001b[0m eta: 7:41:07 iter: 18139 total_loss: 0.9538 loss_cls: 0.3457 loss_box_reg: 0.3474 loss_rpn_cls: 0.06023 loss_rpn_loc: 0.1769 time: 0.2936 last_time: 0.4971 data_time: 0.0049 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:22:05 d2.utils.events]: \u001b[0m eta: 7:41:49 iter: 18159 total_loss: 1.031 loss_cls: 0.3557 loss_box_reg: 0.3677 loss_rpn_cls: 0.06037 loss_rpn_loc: 0.2204 time: 0.2937 last_time: 0.3964 data_time: 0.0050 last_data_time: 0.0052 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:22:13 d2.utils.events]: \u001b[0m eta: 7:41:09 iter: 18179 total_loss: 1.057 loss_cls: 0.348 loss_box_reg: 0.3727 loss_rpn_cls: 0.07043 loss_rpn_loc: 0.243 time: 0.2938 last_time: 0.4979 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:22:22 d2.utils.events]: \u001b[0m eta: 7:41:44 iter: 18199 total_loss: 1.044 loss_cls: 0.3646 loss_box_reg: 0.3773 loss_rpn_cls: 0.05414 loss_rpn_loc: 0.1977 time: 0.2940 last_time: 0.3860 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:22:31 d2.utils.events]: \u001b[0m eta: 7:42:03 iter: 18219 total_loss: 0.9985 loss_cls: 0.331 loss_box_reg: 0.3886 loss_rpn_cls: 0.05817 loss_rpn_loc: 0.2197 time: 0.2942 last_time: 0.4369 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:22:40 d2.utils.events]: \u001b[0m eta: 7:42:21 iter: 18239 total_loss: 0.9793 loss_cls: 0.3768 loss_box_reg: 0.3473 loss_rpn_cls: 0.06731 loss_rpn_loc: 0.1865 time: 0.2944 last_time: 0.4807 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:22:49 d2.utils.events]: \u001b[0m eta: 7:42:49 iter: 18259 total_loss: 1.001 loss_cls: 0.3495 loss_box_reg: 0.355 loss_rpn_cls: 0.05699 loss_rpn_loc: 0.2147 time: 0.2945 last_time: 0.4674 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:22:58 d2.utils.events]: \u001b[0m eta: 7:43:48 iter: 18279 total_loss: 0.9979 loss_cls: 0.3295 loss_box_reg: 0.3399 loss_rpn_cls: 0.06124 loss_rpn_loc: 0.2027 time: 0.2947 last_time: 0.4408 data_time: 0.0049 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:23:07 d2.utils.events]: \u001b[0m eta: 7:44:20 iter: 18299 total_loss: 1.056 loss_cls: 0.3812 loss_box_reg: 0.3935 loss_rpn_cls: 0.05856 loss_rpn_loc: 0.2389 time: 0.2949 last_time: 0.3934 data_time: 0.0049 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:23:16 d2.utils.events]: \u001b[0m eta: 7:44:53 iter: 18319 total_loss: 1.091 loss_cls: 0.4239 loss_box_reg: 0.3725 loss_rpn_cls: 0.06214 loss_rpn_loc: 0.2051 time: 0.2950 last_time: 0.5102 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:23:25 d2.utils.events]: \u001b[0m eta: 7:45:22 iter: 18339 total_loss: 0.9515 loss_cls: 0.3385 loss_box_reg: 0.3742 loss_rpn_cls: 0.05571 loss_rpn_loc: 0.1991 time: 0.2952 last_time: 0.4211 data_time: 0.0050 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:23:34 d2.utils.events]: \u001b[0m eta: 7:45:54 iter: 18359 total_loss: 0.9438 loss_cls: 0.3673 loss_box_reg: 0.3382 loss_rpn_cls: 0.05834 loss_rpn_loc: 0.1862 time: 0.2954 last_time: 0.4907 data_time: 0.0058 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:23:43 d2.utils.events]: \u001b[0m eta: 7:46:14 iter: 18379 total_loss: 1.044 loss_cls: 0.3492 loss_box_reg: 0.418 loss_rpn_cls: 0.06178 loss_rpn_loc: 0.2138 time: 0.2955 last_time: 0.4281 data_time: 0.0050 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:23:52 d2.utils.events]: \u001b[0m eta: 7:46:32 iter: 18399 total_loss: 0.9506 loss_cls: 0.3267 loss_box_reg: 0.3539 loss_rpn_cls: 0.06598 loss_rpn_loc: 0.2127 time: 0.2957 last_time: 0.4307 data_time: 0.0050 last_data_time: 0.0059 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:24:01 d2.utils.events]: \u001b[0m eta: 7:46:41 iter: 18419 total_loss: 1.062 loss_cls: 0.3667 loss_box_reg: 0.397 loss_rpn_cls: 0.05114 loss_rpn_loc: 0.2412 time: 0.2959 last_time: 0.5088 data_time: 0.0049 last_data_time: 0.0054 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:24:09 d2.utils.events]: \u001b[0m eta: 7:46:30 iter: 18439 total_loss: 1.128 loss_cls: 0.3814 loss_box_reg: 0.4031 loss_rpn_cls: 0.05646 loss_rpn_loc: 0.2109 time: 0.2960 last_time: 0.4369 data_time: 0.0048 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:24:18 d2.utils.events]: \u001b[0m eta: 7:46:26 iter: 18459 total_loss: 1.156 loss_cls: 0.4005 loss_box_reg: 0.4112 loss_rpn_cls: 0.06811 loss_rpn_loc: 0.2404 time: 0.2961 last_time: 0.4451 data_time: 0.0049 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:24:26 d2.utils.events]: \u001b[0m eta: 7:46:41 iter: 18479 total_loss: 1.091 loss_cls: 0.3706 loss_box_reg: 0.3847 loss_rpn_cls: 0.06238 loss_rpn_loc: 0.2288 time: 0.2963 last_time: 0.3700 data_time: 0.0050 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:24:35 d2.utils.events]: \u001b[0m eta: 7:46:50 iter: 18499 total_loss: 1.039 loss_cls: 0.375 loss_box_reg: 0.3734 loss_rpn_cls: 0.05219 loss_rpn_loc: 0.2328 time: 0.2964 last_time: 0.4870 data_time: 0.0048 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:24:44 d2.utils.events]: \u001b[0m eta: 7:47:12 iter: 18519 total_loss: 1.001 loss_cls: 0.3331 loss_box_reg: 0.3709 loss_rpn_cls: 0.06875 loss_rpn_loc: 0.2274 time: 0.2966 last_time: 0.4353 data_time: 0.0049 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:24:52 d2.utils.events]: \u001b[0m eta: 7:46:56 iter: 18539 total_loss: 1.078 loss_cls: 0.3746 loss_box_reg: 0.3222 loss_rpn_cls: 0.07148 loss_rpn_loc: 0.2396 time: 0.2967 last_time: 0.3919 data_time: 0.0047 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:25:00 d2.utils.events]: \u001b[0m eta: 7:46:53 iter: 18559 total_loss: 1.084 loss_cls: 0.3887 loss_box_reg: 0.3737 loss_rpn_cls: 0.06405 loss_rpn_loc: 0.2318 time: 0.2968 last_time: 0.3485 data_time: 0.0047 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:25:08 d2.utils.events]: \u001b[0m eta: 7:46:41 iter: 18579 total_loss: 1.087 loss_cls: 0.3572 loss_box_reg: 0.3878 loss_rpn_cls: 0.05817 loss_rpn_loc: 0.241 time: 0.2970 last_time: 0.3907 data_time: 0.0046 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:25:16 d2.utils.events]: \u001b[0m eta: 7:46:36 iter: 18599 total_loss: 1.015 loss_cls: 0.3749 loss_box_reg: 0.3625 loss_rpn_cls: 0.05552 loss_rpn_loc: 0.2397 time: 0.2971 last_time: 0.4395 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:25:25 d2.utils.events]: \u001b[0m eta: 7:46:25 iter: 18619 total_loss: 0.9912 loss_cls: 0.3741 loss_box_reg: 0.3867 loss_rpn_cls: 0.058 loss_rpn_loc: 0.2258 time: 0.2972 last_time: 0.4414 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:25:33 d2.utils.events]: \u001b[0m eta: 7:46:16 iter: 18639 total_loss: 1.074 loss_cls: 0.3727 loss_box_reg: 0.3463 loss_rpn_cls: 0.05536 loss_rpn_loc: 0.2503 time: 0.2973 last_time: 0.3337 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:25:41 d2.utils.events]: \u001b[0m eta: 7:45:58 iter: 18659 total_loss: 1.134 loss_cls: 0.3933 loss_box_reg: 0.3841 loss_rpn_cls: 0.06902 loss_rpn_loc: 0.259 time: 0.2974 last_time: 0.3970 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:25:49 d2.utils.events]: \u001b[0m eta: 7:45:59 iter: 18679 total_loss: 0.997 loss_cls: 0.3457 loss_box_reg: 0.3369 loss_rpn_cls: 0.06504 loss_rpn_loc: 0.2377 time: 0.2975 last_time: 0.3399 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:25:57 d2.utils.events]: \u001b[0m eta: 7:45:54 iter: 18699 total_loss: 1.023 loss_cls: 0.3739 loss_box_reg: 0.3801 loss_rpn_cls: 0.06634 loss_rpn_loc: 0.2444 time: 0.2977 last_time: 0.4382 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:26:05 d2.utils.events]: \u001b[0m eta: 7:45:32 iter: 18719 total_loss: 1.041 loss_cls: 0.3619 loss_box_reg: 0.4029 loss_rpn_cls: 0.06181 loss_rpn_loc: 0.2511 time: 0.2978 last_time: 0.3962 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:26:13 d2.utils.events]: \u001b[0m eta: 7:44:43 iter: 18739 total_loss: 0.9547 loss_cls: 0.3994 loss_box_reg: 0.3498 loss_rpn_cls: 0.05284 loss_rpn_loc: 0.1933 time: 0.2979 last_time: 0.3593 data_time: 0.0045 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:26:21 d2.utils.events]: \u001b[0m eta: 7:44:24 iter: 18759 total_loss: 0.9556 loss_cls: 0.3408 loss_box_reg: 0.3616 loss_rpn_cls: 0.05201 loss_rpn_loc: 0.2321 time: 0.2980 last_time: 0.4178 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:26:29 d2.utils.events]: \u001b[0m eta: 7:44:26 iter: 18779 total_loss: 0.8483 loss_cls: 0.2835 loss_box_reg: 0.3304 loss_rpn_cls: 0.06024 loss_rpn_loc: 0.2066 time: 0.2981 last_time: 0.4002 data_time: 0.0045 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:26:37 d2.utils.events]: \u001b[0m eta: 7:44:17 iter: 18799 total_loss: 0.9855 loss_cls: 0.3369 loss_box_reg: 0.3751 loss_rpn_cls: 0.04867 loss_rpn_loc: 0.2302 time: 0.2982 last_time: 0.3947 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:26:46 d2.utils.events]: \u001b[0m eta: 7:44:25 iter: 18819 total_loss: 1.026 loss_cls: 0.3695 loss_box_reg: 0.3748 loss_rpn_cls: 0.05685 loss_rpn_loc: 0.2344 time: 0.2984 last_time: 0.4175 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:26:54 d2.utils.events]: \u001b[0m eta: 7:44:23 iter: 18839 total_loss: 1.064 loss_cls: 0.3787 loss_box_reg: 0.3781 loss_rpn_cls: 0.05826 loss_rpn_loc: 0.2332 time: 0.2985 last_time: 0.3647 data_time: 0.0046 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:27:02 d2.utils.events]: \u001b[0m eta: 7:44:39 iter: 18859 total_loss: 1.03 loss_cls: 0.3815 loss_box_reg: 0.3606 loss_rpn_cls: 0.05993 loss_rpn_loc: 0.2136 time: 0.2986 last_time: 0.4480 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:27:11 d2.utils.events]: \u001b[0m eta: 7:44:33 iter: 18879 total_loss: 1.045 loss_cls: 0.327 loss_box_reg: 0.3667 loss_rpn_cls: 0.04797 loss_rpn_loc: 0.2224 time: 0.2987 last_time: 0.4190 data_time: 0.0044 last_data_time: 0.0041 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:27:19 d2.utils.events]: \u001b[0m eta: 7:44:28 iter: 18899 total_loss: 1.065 loss_cls: 0.3385 loss_box_reg: 0.3781 loss_rpn_cls: 0.06775 loss_rpn_loc: 0.2475 time: 0.2988 last_time: 0.4467 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:27:27 d2.utils.events]: \u001b[0m eta: 7:44:30 iter: 18919 total_loss: 1.121 loss_cls: 0.3725 loss_box_reg: 0.4006 loss_rpn_cls: 0.07365 loss_rpn_loc: 0.2617 time: 0.2990 last_time: 0.4242 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:27:36 d2.utils.events]: \u001b[0m eta: 7:44:48 iter: 18939 total_loss: 1.001 loss_cls: 0.3855 loss_box_reg: 0.3632 loss_rpn_cls: 0.06204 loss_rpn_loc: 0.2096 time: 0.2991 last_time: 0.4179 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:27:44 d2.utils.events]: \u001b[0m eta: 7:44:42 iter: 18959 total_loss: 0.9113 loss_cls: 0.3018 loss_box_reg: 0.3382 loss_rpn_cls: 0.05162 loss_rpn_loc: 0.2519 time: 0.2992 last_time: 0.3714 data_time: 0.0046 last_data_time: 0.0053 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:27:52 d2.utils.events]: \u001b[0m eta: 7:44:41 iter: 18979 total_loss: 0.9782 loss_cls: 0.3418 loss_box_reg: 0.347 loss_rpn_cls: 0.0544 loss_rpn_loc: 0.2225 time: 0.2993 last_time: 0.4243 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:28:01 d2.utils.events]: \u001b[0m eta: 7:46:17 iter: 18999 total_loss: 1.016 loss_cls: 0.3948 loss_box_reg: 0.352 loss_rpn_cls: 0.05471 loss_rpn_loc: 0.2138 time: 0.2995 last_time: 0.4157 data_time: 0.0051 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:28:11 d2.utils.events]: \u001b[0m eta: 7:48:18 iter: 19019 total_loss: 1.006 loss_cls: 0.3331 loss_box_reg: 0.3822 loss_rpn_cls: 0.05773 loss_rpn_loc: 0.2246 time: 0.2997 last_time: 0.4445 data_time: 0.0050 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:28:20 d2.utils.events]: \u001b[0m eta: 7:50:00 iter: 19039 total_loss: 1.041 loss_cls: 0.3621 loss_box_reg: 0.3705 loss_rpn_cls: 0.06033 loss_rpn_loc: 0.2149 time: 0.2998 last_time: 0.5034 data_time: 0.0050 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:28:29 d2.utils.events]: \u001b[0m eta: 7:52:17 iter: 19059 total_loss: 1.12 loss_cls: 0.3789 loss_box_reg: 0.3992 loss_rpn_cls: 0.05978 loss_rpn_loc: 0.2421 time: 0.3000 last_time: 0.4853 data_time: 0.0050 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:28:39 d2.utils.events]: \u001b[0m eta: 7:53:53 iter: 19079 total_loss: 1.042 loss_cls: 0.3758 loss_box_reg: 0.3856 loss_rpn_cls: 0.06003 loss_rpn_loc: 0.2399 time: 0.3002 last_time: 0.5105 data_time: 0.0050 last_data_time: 0.0054 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:28:47 d2.utils.events]: \u001b[0m eta: 7:53:03 iter: 19099 total_loss: 0.9834 loss_cls: 0.3231 loss_box_reg: 0.3703 loss_rpn_cls: 0.05729 loss_rpn_loc: 0.2489 time: 0.3003 last_time: 0.3890 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:28:55 d2.utils.events]: \u001b[0m eta: 7:52:27 iter: 19119 total_loss: 1.058 loss_cls: 0.4042 loss_box_reg: 0.3805 loss_rpn_cls: 0.07272 loss_rpn_loc: 0.2223 time: 0.3004 last_time: 0.3989 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:29:03 d2.utils.events]: \u001b[0m eta: 7:52:01 iter: 19139 total_loss: 1.076 loss_cls: 0.4014 loss_box_reg: 0.3907 loss_rpn_cls: 0.06836 loss_rpn_loc: 0.2547 time: 0.3005 last_time: 0.4453 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:29:11 d2.utils.events]: \u001b[0m eta: 7:48:18 iter: 19159 total_loss: 1.036 loss_cls: 0.3748 loss_box_reg: 0.3943 loss_rpn_cls: 0.04096 loss_rpn_loc: 0.1883 time: 0.3006 last_time: 0.4002 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:29:19 d2.utils.events]: \u001b[0m eta: 7:48:37 iter: 19179 total_loss: 0.9867 loss_cls: 0.354 loss_box_reg: 0.3276 loss_rpn_cls: 0.05931 loss_rpn_loc: 0.2235 time: 0.3007 last_time: 0.4293 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:29:27 d2.utils.events]: \u001b[0m eta: 7:46:15 iter: 19199 total_loss: 1.032 loss_cls: 0.3609 loss_box_reg: 0.3773 loss_rpn_cls: 0.08152 loss_rpn_loc: 0.2455 time: 0.3008 last_time: 0.4275 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:29:35 d2.utils.events]: \u001b[0m eta: 7:44:01 iter: 19219 total_loss: 1.141 loss_cls: 0.3865 loss_box_reg: 0.4014 loss_rpn_cls: 0.07342 loss_rpn_loc: 0.2688 time: 0.3010 last_time: 0.3820 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:29:43 d2.utils.events]: \u001b[0m eta: 7:43:01 iter: 19239 total_loss: 1.016 loss_cls: 0.3442 loss_box_reg: 0.3662 loss_rpn_cls: 0.05891 loss_rpn_loc: 0.2379 time: 0.3011 last_time: 0.4198 data_time: 0.0046 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:29:52 d2.utils.events]: \u001b[0m eta: 7:42:46 iter: 19259 total_loss: 1.012 loss_cls: 0.3547 loss_box_reg: 0.3753 loss_rpn_cls: 0.05836 loss_rpn_loc: 0.221 time: 0.3012 last_time: 0.4372 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:30:00 d2.utils.events]: \u001b[0m eta: 7:41:25 iter: 19279 total_loss: 0.962 loss_cls: 0.3867 loss_box_reg: 0.3299 loss_rpn_cls: 0.05357 loss_rpn_loc: 0.2237 time: 0.3013 last_time: 0.4041 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:30:08 d2.utils.events]: \u001b[0m eta: 7:40:51 iter: 19299 total_loss: 0.8921 loss_cls: 0.3022 loss_box_reg: 0.3524 loss_rpn_cls: 0.05283 loss_rpn_loc: 0.207 time: 0.3014 last_time: 0.3806 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:30:16 d2.utils.events]: \u001b[0m eta: 7:39:55 iter: 19319 total_loss: 0.9873 loss_cls: 0.3745 loss_box_reg: 0.3567 loss_rpn_cls: 0.05184 loss_rpn_loc: 0.2255 time: 0.3015 last_time: 0.3482 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:30:24 d2.utils.events]: \u001b[0m eta: 7:39:00 iter: 19339 total_loss: 0.9815 loss_cls: 0.3526 loss_box_reg: 0.3372 loss_rpn_cls: 0.06679 loss_rpn_loc: 0.2372 time: 0.3016 last_time: 0.4569 data_time: 0.0045 last_data_time: 0.0041 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:30:32 d2.utils.events]: \u001b[0m eta: 7:38:27 iter: 19359 total_loss: 0.9553 loss_cls: 0.3327 loss_box_reg: 0.3523 loss_rpn_cls: 0.05893 loss_rpn_loc: 0.2145 time: 0.3017 last_time: 0.3963 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:30:40 d2.utils.events]: \u001b[0m eta: 7:37:44 iter: 19379 total_loss: 0.9935 loss_cls: 0.3156 loss_box_reg: 0.3678 loss_rpn_cls: 0.04979 loss_rpn_loc: 0.2371 time: 0.3018 last_time: 0.3446 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:30:48 d2.utils.events]: \u001b[0m eta: 7:37:14 iter: 19399 total_loss: 0.999 loss_cls: 0.3821 loss_box_reg: 0.3696 loss_rpn_cls: 0.05229 loss_rpn_loc: 0.2168 time: 0.3019 last_time: 0.3976 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:30:57 d2.utils.events]: \u001b[0m eta: 7:36:41 iter: 19419 total_loss: 0.9513 loss_cls: 0.2794 loss_box_reg: 0.338 loss_rpn_cls: 0.04731 loss_rpn_loc: 0.2245 time: 0.3020 last_time: 0.4301 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:31:05 d2.utils.events]: \u001b[0m eta: 7:36:33 iter: 19439 total_loss: 1 loss_cls: 0.3381 loss_box_reg: 0.3961 loss_rpn_cls: 0.0448 loss_rpn_loc: 0.2282 time: 0.3021 last_time: 0.4393 data_time: 0.0046 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:31:13 d2.utils.events]: \u001b[0m eta: 7:36:22 iter: 19459 total_loss: 0.9672 loss_cls: 0.3337 loss_box_reg: 0.3767 loss_rpn_cls: 0.06709 loss_rpn_loc: 0.2452 time: 0.3022 last_time: 0.4167 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:31:21 d2.utils.events]: \u001b[0m eta: 7:35:53 iter: 19479 total_loss: 0.9747 loss_cls: 0.3668 loss_box_reg: 0.3292 loss_rpn_cls: 0.05548 loss_rpn_loc: 0.2275 time: 0.3023 last_time: 0.3929 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:31:29 d2.utils.events]: \u001b[0m eta: 7:35:23 iter: 19499 total_loss: 1.003 loss_cls: 0.3529 loss_box_reg: 0.3521 loss_rpn_cls: 0.05938 loss_rpn_loc: 0.2282 time: 0.3024 last_time: 0.4371 data_time: 0.0045 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:31:37 d2.utils.events]: \u001b[0m eta: 7:34:12 iter: 19519 total_loss: 1.165 loss_cls: 0.4175 loss_box_reg: 0.4249 loss_rpn_cls: 0.06488 loss_rpn_loc: 0.2166 time: 0.3025 last_time: 0.4065 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:31:45 d2.utils.events]: \u001b[0m eta: 7:34:24 iter: 19539 total_loss: 0.954 loss_cls: 0.3328 loss_box_reg: 0.3818 loss_rpn_cls: 0.05057 loss_rpn_loc: 0.2137 time: 0.3027 last_time: 0.4165 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:31:53 d2.utils.events]: \u001b[0m eta: 7:34:27 iter: 19559 total_loss: 0.9852 loss_cls: 0.3905 loss_box_reg: 0.3482 loss_rpn_cls: 0.0509 loss_rpn_loc: 0.2127 time: 0.3028 last_time: 0.4194 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:32:01 d2.utils.events]: \u001b[0m eta: 7:34:55 iter: 19579 total_loss: 0.9836 loss_cls: 0.305 loss_box_reg: 0.3535 loss_rpn_cls: 0.0636 loss_rpn_loc: 0.2221 time: 0.3029 last_time: 0.5148 data_time: 0.0045 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:32:10 d2.utils.events]: \u001b[0m eta: 7:34:51 iter: 19599 total_loss: 1.07 loss_cls: 0.3519 loss_box_reg: 0.3684 loss_rpn_cls: 0.06253 loss_rpn_loc: 0.2557 time: 0.3030 last_time: 0.4707 data_time: 0.0048 last_data_time: 0.0054 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:32:19 d2.utils.events]: \u001b[0m eta: 7:34:47 iter: 19619 total_loss: 1.012 loss_cls: 0.3127 loss_box_reg: 0.3633 loss_rpn_cls: 0.06966 loss_rpn_loc: 0.2067 time: 0.3031 last_time: 0.4395 data_time: 0.0049 last_data_time: 0.0052 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:32:27 d2.utils.events]: \u001b[0m eta: 7:34:55 iter: 19639 total_loss: 0.9812 loss_cls: 0.3685 loss_box_reg: 0.3492 loss_rpn_cls: 0.0535 loss_rpn_loc: 0.2345 time: 0.3033 last_time: 0.4388 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:32:35 d2.utils.events]: \u001b[0m eta: 7:34:58 iter: 19659 total_loss: 1.091 loss_cls: 0.347 loss_box_reg: 0.343 loss_rpn_cls: 0.06963 loss_rpn_loc: 0.2518 time: 0.3034 last_time: 0.4354 data_time: 0.0046 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:32:43 d2.utils.events]: \u001b[0m eta: 7:34:18 iter: 19679 total_loss: 1.013 loss_cls: 0.3382 loss_box_reg: 0.3366 loss_rpn_cls: 0.05298 loss_rpn_loc: 0.2484 time: 0.3035 last_time: 0.4373 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:32:51 d2.utils.events]: \u001b[0m eta: 7:34:10 iter: 19699 total_loss: 1.071 loss_cls: 0.3661 loss_box_reg: 0.3788 loss_rpn_cls: 0.07237 loss_rpn_loc: 0.2358 time: 0.3036 last_time: 0.5078 data_time: 0.0047 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:33:00 d2.utils.events]: \u001b[0m eta: 7:34:13 iter: 19719 total_loss: 0.9899 loss_cls: 0.3495 loss_box_reg: 0.3716 loss_rpn_cls: 0.06245 loss_rpn_loc: 0.2212 time: 0.3037 last_time: 0.4431 data_time: 0.0049 last_data_time: 0.0054 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:33:08 d2.utils.events]: \u001b[0m eta: 7:33:53 iter: 19739 total_loss: 0.9716 loss_cls: 0.3122 loss_box_reg: 0.3889 loss_rpn_cls: 0.05789 loss_rpn_loc: 0.2195 time: 0.3038 last_time: 0.3759 data_time: 0.0049 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:33:16 d2.utils.events]: \u001b[0m eta: 7:33:49 iter: 19759 total_loss: 1.063 loss_cls: 0.3812 loss_box_reg: 0.4037 loss_rpn_cls: 0.07367 loss_rpn_loc: 0.2186 time: 0.3039 last_time: 0.4178 data_time: 0.0049 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:33:24 d2.utils.events]: \u001b[0m eta: 7:33:51 iter: 19779 total_loss: 1.096 loss_cls: 0.3895 loss_box_reg: 0.3996 loss_rpn_cls: 0.05836 loss_rpn_loc: 0.2598 time: 0.3040 last_time: 0.4467 data_time: 0.0051 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:33:33 d2.utils.events]: \u001b[0m eta: 7:33:59 iter: 19799 total_loss: 0.9437 loss_cls: 0.3227 loss_box_reg: 0.3535 loss_rpn_cls: 0.06568 loss_rpn_loc: 0.2329 time: 0.3041 last_time: 0.4418 data_time: 0.0049 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:33:42 d2.utils.events]: \u001b[0m eta: 7:34:11 iter: 19819 total_loss: 1.125 loss_cls: 0.4084 loss_box_reg: 0.3797 loss_rpn_cls: 0.0554 loss_rpn_loc: 0.2322 time: 0.3043 last_time: 0.4402 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:33:50 d2.utils.events]: \u001b[0m eta: 7:33:50 iter: 19839 total_loss: 0.9652 loss_cls: 0.3359 loss_box_reg: 0.3598 loss_rpn_cls: 0.06864 loss_rpn_loc: 0.2153 time: 0.3044 last_time: 0.4166 data_time: 0.0050 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:33:58 d2.utils.events]: \u001b[0m eta: 7:33:37 iter: 19859 total_loss: 1.093 loss_cls: 0.3811 loss_box_reg: 0.3857 loss_rpn_cls: 0.05799 loss_rpn_loc: 0.2356 time: 0.3045 last_time: 0.3738 data_time: 0.0050 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:34:07 d2.utils.events]: \u001b[0m eta: 7:33:29 iter: 19879 total_loss: 0.9849 loss_cls: 0.3278 loss_box_reg: 0.3374 loss_rpn_cls: 0.05916 loss_rpn_loc: 0.2359 time: 0.3046 last_time: 0.5000 data_time: 0.0049 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:34:16 d2.utils.events]: \u001b[0m eta: 7:33:38 iter: 19899 total_loss: 1.056 loss_cls: 0.3623 loss_box_reg: 0.3733 loss_rpn_cls: 0.05341 loss_rpn_loc: 0.2336 time: 0.3048 last_time: 0.4997 data_time: 0.0050 last_data_time: 0.0053 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:34:25 d2.utils.events]: \u001b[0m eta: 7:33:49 iter: 19919 total_loss: 1.045 loss_cls: 0.3331 loss_box_reg: 0.3574 loss_rpn_cls: 0.05779 loss_rpn_loc: 0.2352 time: 0.3049 last_time: 0.4451 data_time: 0.0049 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:34:34 d2.utils.events]: \u001b[0m eta: 7:33:21 iter: 19939 total_loss: 1.001 loss_cls: 0.3405 loss_box_reg: 0.3735 loss_rpn_cls: 0.0621 loss_rpn_loc: 0.2202 time: 0.3050 last_time: 0.3394 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:34:42 d2.utils.events]: \u001b[0m eta: 7:33:08 iter: 19959 total_loss: 1.034 loss_cls: 0.34 loss_box_reg: 0.365 loss_rpn_cls: 0.05688 loss_rpn_loc: 0.2337 time: 0.3051 last_time: 0.4418 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:34:50 d2.utils.events]: \u001b[0m eta: 7:33:15 iter: 19979 total_loss: 0.9875 loss_cls: 0.3411 loss_box_reg: 0.3574 loss_rpn_cls: 0.0599 loss_rpn_loc: 0.2358 time: 0.3053 last_time: 0.4982 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:35:00 d2.utils.events]: \u001b[0m eta: 7:32:46 iter: 19999 total_loss: 1.037 loss_cls: 0.3553 loss_box_reg: 0.392 loss_rpn_cls: 0.06584 loss_rpn_loc: 0.2287 time: 0.3054 last_time: 0.3892 data_time: 0.0046 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:35:08 d2.utils.events]: \u001b[0m eta: 7:32:05 iter: 20019 total_loss: 1.018 loss_cls: 0.3444 loss_box_reg: 0.411 loss_rpn_cls: 0.06113 loss_rpn_loc: 0.1981 time: 0.3055 last_time: 0.3777 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:35:16 d2.utils.events]: \u001b[0m eta: 7:31:35 iter: 20039 total_loss: 0.9909 loss_cls: 0.344 loss_box_reg: 0.3722 loss_rpn_cls: 0.0572 loss_rpn_loc: 0.2118 time: 0.3056 last_time: 0.4386 data_time: 0.0045 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:35:24 d2.utils.events]: \u001b[0m eta: 7:31:01 iter: 20059 total_loss: 0.9474 loss_cls: 0.3243 loss_box_reg: 0.3531 loss_rpn_cls: 0.04055 loss_rpn_loc: 0.2184 time: 0.3057 last_time: 0.4285 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:35:32 d2.utils.events]: \u001b[0m eta: 7:30:47 iter: 20079 total_loss: 0.9494 loss_cls: 0.3425 loss_box_reg: 0.327 loss_rpn_cls: 0.0566 loss_rpn_loc: 0.2324 time: 0.3058 last_time: 0.3982 data_time: 0.0045 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:35:41 d2.utils.events]: \u001b[0m eta: 7:30:39 iter: 20099 total_loss: 1.104 loss_cls: 0.3612 loss_box_reg: 0.3707 loss_rpn_cls: 0.06041 loss_rpn_loc: 0.2397 time: 0.3059 last_time: 0.4673 data_time: 0.0046 last_data_time: 0.0056 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:35:50 d2.utils.events]: \u001b[0m eta: 7:30:38 iter: 20119 total_loss: 0.9497 loss_cls: 0.3452 loss_box_reg: 0.3562 loss_rpn_cls: 0.05711 loss_rpn_loc: 0.1951 time: 0.3061 last_time: 0.4859 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:35:59 d2.utils.events]: \u001b[0m eta: 7:30:34 iter: 20139 total_loss: 1.058 loss_cls: 0.3716 loss_box_reg: 0.3575 loss_rpn_cls: 0.06633 loss_rpn_loc: 0.2303 time: 0.3062 last_time: 0.4805 data_time: 0.0049 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:36:08 d2.utils.events]: \u001b[0m eta: 7:30:59 iter: 20159 total_loss: 1.057 loss_cls: 0.3731 loss_box_reg: 0.3463 loss_rpn_cls: 0.05636 loss_rpn_loc: 0.247 time: 0.3063 last_time: 0.4602 data_time: 0.0050 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:36:17 d2.utils.events]: \u001b[0m eta: 7:30:58 iter: 20179 total_loss: 0.9693 loss_cls: 0.3366 loss_box_reg: 0.3557 loss_rpn_cls: 0.05495 loss_rpn_loc: 0.2369 time: 0.3065 last_time: 0.4350 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:36:25 d2.utils.events]: \u001b[0m eta: 7:31:04 iter: 20199 total_loss: 1.147 loss_cls: 0.4068 loss_box_reg: 0.4171 loss_rpn_cls: 0.05528 loss_rpn_loc: 0.255 time: 0.3066 last_time: 0.4195 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:36:35 d2.utils.events]: \u001b[0m eta: 7:31:30 iter: 20219 total_loss: 1.061 loss_cls: 0.3584 loss_box_reg: 0.368 loss_rpn_cls: 0.06333 loss_rpn_loc: 0.2295 time: 0.3067 last_time: 0.4413 data_time: 0.0050 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:36:44 d2.utils.events]: \u001b[0m eta: 7:31:55 iter: 20239 total_loss: 1.019 loss_cls: 0.3733 loss_box_reg: 0.3566 loss_rpn_cls: 0.04242 loss_rpn_loc: 0.2218 time: 0.3069 last_time: 0.4469 data_time: 0.0050 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:36:54 d2.utils.events]: \u001b[0m eta: 7:31:58 iter: 20259 total_loss: 0.9365 loss_cls: 0.3564 loss_box_reg: 0.3536 loss_rpn_cls: 0.05339 loss_rpn_loc: 0.2086 time: 0.3071 last_time: 0.4888 data_time: 0.0051 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:37:03 d2.utils.events]: \u001b[0m eta: 7:32:11 iter: 20279 total_loss: 1.057 loss_cls: 0.3149 loss_box_reg: 0.3754 loss_rpn_cls: 0.05469 loss_rpn_loc: 0.238 time: 0.3072 last_time: 0.4409 data_time: 0.0049 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:37:12 d2.utils.events]: \u001b[0m eta: 7:33:10 iter: 20299 total_loss: 1.002 loss_cls: 0.3437 loss_box_reg: 0.3373 loss_rpn_cls: 0.06068 loss_rpn_loc: 0.2232 time: 0.3074 last_time: 0.5178 data_time: 0.0051 last_data_time: 0.0053 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:37:21 d2.utils.events]: \u001b[0m eta: 7:33:37 iter: 20319 total_loss: 0.9003 loss_cls: 0.303 loss_box_reg: 0.3279 loss_rpn_cls: 0.04145 loss_rpn_loc: 0.2171 time: 0.3075 last_time: 0.4120 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:37:29 d2.utils.events]: \u001b[0m eta: 7:33:59 iter: 20339 total_loss: 0.8994 loss_cls: 0.3172 loss_box_reg: 0.346 loss_rpn_cls: 0.04878 loss_rpn_loc: 0.1972 time: 0.3076 last_time: 0.4240 data_time: 0.0045 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:37:37 d2.utils.events]: \u001b[0m eta: 7:33:29 iter: 20359 total_loss: 0.96 loss_cls: 0.3271 loss_box_reg: 0.3479 loss_rpn_cls: 0.05099 loss_rpn_loc: 0.213 time: 0.3077 last_time: 0.4183 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:37:45 d2.utils.events]: \u001b[0m eta: 7:33:21 iter: 20379 total_loss: 1.041 loss_cls: 0.4095 loss_box_reg: 0.3517 loss_rpn_cls: 0.05602 loss_rpn_loc: 0.2016 time: 0.3078 last_time: 0.4444 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:37:53 d2.utils.events]: \u001b[0m eta: 7:32:54 iter: 20399 total_loss: 0.9497 loss_cls: 0.3397 loss_box_reg: 0.3157 loss_rpn_cls: 0.06851 loss_rpn_loc: 0.2214 time: 0.3079 last_time: 0.4278 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:38:02 d2.utils.events]: \u001b[0m eta: 7:32:40 iter: 20419 total_loss: 1.082 loss_cls: 0.3837 loss_box_reg: 0.3971 loss_rpn_cls: 0.06592 loss_rpn_loc: 0.2354 time: 0.3080 last_time: 0.4601 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:38:10 d2.utils.events]: \u001b[0m eta: 7:32:31 iter: 20439 total_loss: 1.095 loss_cls: 0.3783 loss_box_reg: 0.3957 loss_rpn_cls: 0.05352 loss_rpn_loc: 0.2422 time: 0.3081 last_time: 0.4214 data_time: 0.0047 last_data_time: 0.0056 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:38:18 d2.utils.events]: \u001b[0m eta: 7:33:04 iter: 20459 total_loss: 1.04 loss_cls: 0.3535 loss_box_reg: 0.374 loss_rpn_cls: 0.08333 loss_rpn_loc: 0.2262 time: 0.3082 last_time: 0.3685 data_time: 0.0045 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:38:26 d2.utils.events]: \u001b[0m eta: 7:33:09 iter: 20479 total_loss: 1.017 loss_cls: 0.3201 loss_box_reg: 0.3504 loss_rpn_cls: 0.06275 loss_rpn_loc: 0.2439 time: 0.3083 last_time: 0.4400 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:38:34 d2.utils.events]: \u001b[0m eta: 7:33:11 iter: 20499 total_loss: 1.107 loss_cls: 0.3998 loss_box_reg: 0.3965 loss_rpn_cls: 0.05682 loss_rpn_loc: 0.2202 time: 0.3084 last_time: 0.4425 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:38:42 d2.utils.events]: \u001b[0m eta: 7:33:29 iter: 20519 total_loss: 0.9394 loss_cls: 0.3245 loss_box_reg: 0.374 loss_rpn_cls: 0.05673 loss_rpn_loc: 0.226 time: 0.3085 last_time: 0.4345 data_time: 0.0047 last_data_time: 0.0053 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:38:50 d2.utils.events]: \u001b[0m eta: 7:32:01 iter: 20539 total_loss: 0.942 loss_cls: 0.3217 loss_box_reg: 0.3351 loss_rpn_cls: 0.05903 loss_rpn_loc: 0.2029 time: 0.3085 last_time: 0.4197 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:38:58 d2.utils.events]: \u001b[0m eta: 7:32:22 iter: 20559 total_loss: 1.028 loss_cls: 0.4121 loss_box_reg: 0.3945 loss_rpn_cls: 0.04636 loss_rpn_loc: 0.2474 time: 0.3086 last_time: 0.4384 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:39:06 d2.utils.events]: \u001b[0m eta: 7:32:14 iter: 20579 total_loss: 1.083 loss_cls: 0.3581 loss_box_reg: 0.3903 loss_rpn_cls: 0.05966 loss_rpn_loc: 0.2469 time: 0.3087 last_time: 0.4381 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:39:14 d2.utils.events]: \u001b[0m eta: 7:32:05 iter: 20599 total_loss: 1.095 loss_cls: 0.3819 loss_box_reg: 0.378 loss_rpn_cls: 0.05974 loss_rpn_loc: 0.2295 time: 0.3088 last_time: 0.4550 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:39:23 d2.utils.events]: \u001b[0m eta: 7:31:57 iter: 20619 total_loss: 1.058 loss_cls: 0.385 loss_box_reg: 0.3672 loss_rpn_cls: 0.06176 loss_rpn_loc: 0.2425 time: 0.3089 last_time: 0.3478 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:39:32 d2.utils.events]: \u001b[0m eta: 7:32:11 iter: 20639 total_loss: 0.9455 loss_cls: 0.3397 loss_box_reg: 0.3313 loss_rpn_cls: 0.06386 loss_rpn_loc: 0.2335 time: 0.3091 last_time: 0.4165 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:39:41 d2.utils.events]: \u001b[0m eta: 7:33:00 iter: 20659 total_loss: 1.104 loss_cls: 0.369 loss_box_reg: 0.3844 loss_rpn_cls: 0.07422 loss_rpn_loc: 0.2529 time: 0.3092 last_time: 0.4444 data_time: 0.0048 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:39:49 d2.utils.events]: \u001b[0m eta: 7:36:33 iter: 20679 total_loss: 0.9625 loss_cls: 0.3412 loss_box_reg: 0.3539 loss_rpn_cls: 0.04705 loss_rpn_loc: 0.227 time: 0.3093 last_time: 0.4803 data_time: 0.0049 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:39:58 d2.utils.events]: \u001b[0m eta: 7:37:31 iter: 20699 total_loss: 0.9805 loss_cls: 0.3574 loss_box_reg: 0.3468 loss_rpn_cls: 0.05344 loss_rpn_loc: 0.2209 time: 0.3094 last_time: 0.3378 data_time: 0.0048 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:40:07 d2.utils.events]: \u001b[0m eta: 7:38:24 iter: 20719 total_loss: 1.039 loss_cls: 0.3799 loss_box_reg: 0.3452 loss_rpn_cls: 0.0578 loss_rpn_loc: 0.2273 time: 0.3096 last_time: 0.4832 data_time: 0.0048 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:40:15 d2.utils.events]: \u001b[0m eta: 7:38:50 iter: 20739 total_loss: 0.8941 loss_cls: 0.3167 loss_box_reg: 0.3315 loss_rpn_cls: 0.06226 loss_rpn_loc: 0.2106 time: 0.3097 last_time: 0.3402 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:40:23 d2.utils.events]: \u001b[0m eta: 7:38:41 iter: 20759 total_loss: 0.9306 loss_cls: 0.3423 loss_box_reg: 0.3484 loss_rpn_cls: 0.05144 loss_rpn_loc: 0.1971 time: 0.3098 last_time: 0.3535 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:40:31 d2.utils.events]: \u001b[0m eta: 7:38:25 iter: 20779 total_loss: 1.021 loss_cls: 0.3491 loss_box_reg: 0.3898 loss_rpn_cls: 0.05963 loss_rpn_loc: 0.2314 time: 0.3098 last_time: 0.3479 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:40:39 d2.utils.events]: \u001b[0m eta: 7:36:25 iter: 20799 total_loss: 0.973 loss_cls: 0.3563 loss_box_reg: 0.3384 loss_rpn_cls: 0.05111 loss_rpn_loc: 0.229 time: 0.3099 last_time: 0.4443 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:40:47 d2.utils.events]: \u001b[0m eta: 7:32:07 iter: 20819 total_loss: 1.135 loss_cls: 0.3677 loss_box_reg: 0.39 loss_rpn_cls: 0.04864 loss_rpn_loc: 0.2511 time: 0.3100 last_time: 0.4369 data_time: 0.0047 last_data_time: 0.0053 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:40:55 d2.utils.events]: \u001b[0m eta: 7:32:33 iter: 20839 total_loss: 0.9151 loss_cls: 0.2879 loss_box_reg: 0.3391 loss_rpn_cls: 0.05143 loss_rpn_loc: 0.2103 time: 0.3101 last_time: 0.4404 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:41:04 d2.utils.events]: \u001b[0m eta: 7:31:59 iter: 20859 total_loss: 1.054 loss_cls: 0.3497 loss_box_reg: 0.341 loss_rpn_cls: 0.05836 loss_rpn_loc: 0.2395 time: 0.3102 last_time: 0.4058 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:41:12 d2.utils.events]: \u001b[0m eta: 7:30:57 iter: 20879 total_loss: 1.032 loss_cls: 0.3441 loss_box_reg: 0.3969 loss_rpn_cls: 0.05448 loss_rpn_loc: 0.2372 time: 0.3103 last_time: 0.3738 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:41:20 d2.utils.events]: \u001b[0m eta: 7:29:15 iter: 20899 total_loss: 1.008 loss_cls: 0.3541 loss_box_reg: 0.3754 loss_rpn_cls: 0.06383 loss_rpn_loc: 0.2265 time: 0.3104 last_time: 0.4176 data_time: 0.0045 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:41:28 d2.utils.events]: \u001b[0m eta: 7:28:02 iter: 20919 total_loss: 0.9374 loss_cls: 0.3316 loss_box_reg: 0.302 loss_rpn_cls: 0.05418 loss_rpn_loc: 0.2049 time: 0.3105 last_time: 0.4133 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:41:36 d2.utils.events]: \u001b[0m eta: 7:27:48 iter: 20939 total_loss: 1.02 loss_cls: 0.3846 loss_box_reg: 0.3861 loss_rpn_cls: 0.04849 loss_rpn_loc: 0.208 time: 0.3106 last_time: 0.4225 data_time: 0.0044 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:41:44 d2.utils.events]: \u001b[0m eta: 7:27:44 iter: 20959 total_loss: 1.05 loss_cls: 0.345 loss_box_reg: 0.3554 loss_rpn_cls: 0.05893 loss_rpn_loc: 0.2291 time: 0.3107 last_time: 0.4454 data_time: 0.0044 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:41:52 d2.utils.events]: \u001b[0m eta: 7:27:36 iter: 20979 total_loss: 1.073 loss_cls: 0.3731 loss_box_reg: 0.3418 loss_rpn_cls: 0.06903 loss_rpn_loc: 0.2491 time: 0.3108 last_time: 0.4424 data_time: 0.0044 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:42:01 d2.utils.events]: \u001b[0m eta: 7:27:27 iter: 20999 total_loss: 0.9944 loss_cls: 0.3569 loss_box_reg: 0.3484 loss_rpn_cls: 0.05711 loss_rpn_loc: 0.2653 time: 0.3109 last_time: 0.4200 data_time: 0.0045 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:42:09 d2.utils.events]: \u001b[0m eta: 7:27:18 iter: 21019 total_loss: 0.9989 loss_cls: 0.3609 loss_box_reg: 0.3684 loss_rpn_cls: 0.05839 loss_rpn_loc: 0.202 time: 0.3110 last_time: 0.3929 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:42:17 d2.utils.events]: \u001b[0m eta: 7:26:47 iter: 21039 total_loss: 1.047 loss_cls: 0.3776 loss_box_reg: 0.3193 loss_rpn_cls: 0.06085 loss_rpn_loc: 0.2275 time: 0.3111 last_time: 0.4383 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:42:25 d2.utils.events]: \u001b[0m eta: 7:26:34 iter: 21059 total_loss: 0.9639 loss_cls: 0.3253 loss_box_reg: 0.3479 loss_rpn_cls: 0.05338 loss_rpn_loc: 0.2409 time: 0.3111 last_time: 0.3929 data_time: 0.0044 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:42:33 d2.utils.events]: \u001b[0m eta: 7:26:12 iter: 21079 total_loss: 1.017 loss_cls: 0.3522 loss_box_reg: 0.337 loss_rpn_cls: 0.07118 loss_rpn_loc: 0.2489 time: 0.3112 last_time: 0.4166 data_time: 0.0044 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:42:41 d2.utils.events]: \u001b[0m eta: 7:25:59 iter: 21099 total_loss: 0.9456 loss_cls: 0.3292 loss_box_reg: 0.352 loss_rpn_cls: 0.05399 loss_rpn_loc: 0.213 time: 0.3113 last_time: 0.3898 data_time: 0.0044 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:42:49 d2.utils.events]: \u001b[0m eta: 7:25:11 iter: 21119 total_loss: 0.9901 loss_cls: 0.3368 loss_box_reg: 0.3618 loss_rpn_cls: 0.06078 loss_rpn_loc: 0.2484 time: 0.3114 last_time: 0.4149 data_time: 0.0044 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:42:58 d2.utils.events]: \u001b[0m eta: 7:24:33 iter: 21139 total_loss: 0.8986 loss_cls: 0.2989 loss_box_reg: 0.3349 loss_rpn_cls: 0.05545 loss_rpn_loc: 0.1895 time: 0.3115 last_time: 0.3310 data_time: 0.0044 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:43:06 d2.utils.events]: \u001b[0m eta: 7:24:11 iter: 21159 total_loss: 0.9036 loss_cls: 0.2887 loss_box_reg: 0.3492 loss_rpn_cls: 0.0463 loss_rpn_loc: 0.2129 time: 0.3116 last_time: 0.3757 data_time: 0.0044 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:43:14 d2.utils.events]: \u001b[0m eta: 7:23:52 iter: 21179 total_loss: 0.9602 loss_cls: 0.3491 loss_box_reg: 0.3519 loss_rpn_cls: 0.05111 loss_rpn_loc: 0.246 time: 0.3117 last_time: 0.4439 data_time: 0.0043 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:43:22 d2.utils.events]: \u001b[0m eta: 7:23:19 iter: 21199 total_loss: 1.097 loss_cls: 0.384 loss_box_reg: 0.3923 loss_rpn_cls: 0.05962 loss_rpn_loc: 0.2368 time: 0.3118 last_time: 0.3979 data_time: 0.0044 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:43:30 d2.utils.events]: \u001b[0m eta: 7:22:46 iter: 21219 total_loss: 0.9766 loss_cls: 0.3084 loss_box_reg: 0.3463 loss_rpn_cls: 0.06228 loss_rpn_loc: 0.2158 time: 0.3119 last_time: 0.4357 data_time: 0.0046 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:43:39 d2.utils.events]: \u001b[0m eta: 7:21:49 iter: 21239 total_loss: 0.9927 loss_cls: 0.3298 loss_box_reg: 0.3625 loss_rpn_cls: 0.05837 loss_rpn_loc: 0.2228 time: 0.3120 last_time: 0.4112 data_time: 0.0045 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:43:47 d2.utils.events]: \u001b[0m eta: 7:21:02 iter: 21259 total_loss: 1.13 loss_cls: 0.4276 loss_box_reg: 0.3866 loss_rpn_cls: 0.06195 loss_rpn_loc: 0.2248 time: 0.3120 last_time: 0.4382 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:43:55 d2.utils.events]: \u001b[0m eta: 7:20:14 iter: 21279 total_loss: 0.9451 loss_cls: 0.3158 loss_box_reg: 0.3521 loss_rpn_cls: 0.05931 loss_rpn_loc: 0.1941 time: 0.3121 last_time: 0.4410 data_time: 0.0044 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:44:03 d2.utils.events]: \u001b[0m eta: 7:19:50 iter: 21299 total_loss: 1.004 loss_cls: 0.3654 loss_box_reg: 0.3784 loss_rpn_cls: 0.05316 loss_rpn_loc: 0.2189 time: 0.3122 last_time: 0.3879 data_time: 0.0044 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:44:11 d2.utils.events]: \u001b[0m eta: 7:19:24 iter: 21319 total_loss: 0.9418 loss_cls: 0.3247 loss_box_reg: 0.3433 loss_rpn_cls: 0.05404 loss_rpn_loc: 0.2094 time: 0.3123 last_time: 0.4261 data_time: 0.0044 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:44:19 d2.utils.events]: \u001b[0m eta: 7:18:56 iter: 21339 total_loss: 0.9778 loss_cls: 0.3587 loss_box_reg: 0.3681 loss_rpn_cls: 0.0625 loss_rpn_loc: 0.2262 time: 0.3124 last_time: 0.4344 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:44:27 d2.utils.events]: \u001b[0m eta: 7:19:08 iter: 21359 total_loss: 0.954 loss_cls: 0.358 loss_box_reg: 0.337 loss_rpn_cls: 0.05525 loss_rpn_loc: 0.2233 time: 0.3125 last_time: 0.4345 data_time: 0.0044 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:44:35 d2.utils.events]: \u001b[0m eta: 7:18:59 iter: 21379 total_loss: 0.9987 loss_cls: 0.3344 loss_box_reg: 0.36 loss_rpn_cls: 0.0566 loss_rpn_loc: 0.225 time: 0.3126 last_time: 0.4068 data_time: 0.0044 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:44:43 d2.utils.events]: \u001b[0m eta: 7:18:31 iter: 21399 total_loss: 0.9217 loss_cls: 0.3157 loss_box_reg: 0.3341 loss_rpn_cls: 0.05797 loss_rpn_loc: 0.2456 time: 0.3127 last_time: 0.4447 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:44:51 d2.utils.events]: \u001b[0m eta: 7:18:03 iter: 21419 total_loss: 0.9393 loss_cls: 0.2999 loss_box_reg: 0.3688 loss_rpn_cls: 0.06019 loss_rpn_loc: 0.2217 time: 0.3127 last_time: 0.4106 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:44:59 d2.utils.events]: \u001b[0m eta: 7:17:50 iter: 21439 total_loss: 1.032 loss_cls: 0.3601 loss_box_reg: 0.3699 loss_rpn_cls: 0.04935 loss_rpn_loc: 0.2239 time: 0.3128 last_time: 0.3769 data_time: 0.0044 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:45:07 d2.utils.events]: \u001b[0m eta: 7:17:53 iter: 21459 total_loss: 1.034 loss_cls: 0.3439 loss_box_reg: 0.3641 loss_rpn_cls: 0.0548 loss_rpn_loc: 0.2194 time: 0.3129 last_time: 0.3761 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:45:16 d2.utils.events]: \u001b[0m eta: 7:17:45 iter: 21479 total_loss: 1.127 loss_cls: 0.3745 loss_box_reg: 0.4111 loss_rpn_cls: 0.0714 loss_rpn_loc: 0.2309 time: 0.3130 last_time: 0.4353 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:45:24 d2.utils.events]: \u001b[0m eta: 7:17:42 iter: 21499 total_loss: 1.066 loss_cls: 0.3714 loss_box_reg: 0.3907 loss_rpn_cls: 0.06492 loss_rpn_loc: 0.2482 time: 0.3131 last_time: 0.4145 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:45:32 d2.utils.events]: \u001b[0m eta: 7:17:41 iter: 21519 total_loss: 0.9465 loss_cls: 0.2806 loss_box_reg: 0.3546 loss_rpn_cls: 0.05467 loss_rpn_loc: 0.2509 time: 0.3131 last_time: 0.4251 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:45:40 d2.utils.events]: \u001b[0m eta: 7:18:06 iter: 21539 total_loss: 0.9994 loss_cls: 0.3723 loss_box_reg: 0.3976 loss_rpn_cls: 0.0548 loss_rpn_loc: 0.2037 time: 0.3132 last_time: 0.4431 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:45:48 d2.utils.events]: \u001b[0m eta: 7:17:23 iter: 21559 total_loss: 0.8862 loss_cls: 0.3473 loss_box_reg: 0.3213 loss_rpn_cls: 0.05371 loss_rpn_loc: 0.2004 time: 0.3133 last_time: 0.4008 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:45:56 d2.utils.events]: \u001b[0m eta: 7:17:03 iter: 21579 total_loss: 1.077 loss_cls: 0.3766 loss_box_reg: 0.3693 loss_rpn_cls: 0.06813 loss_rpn_loc: 0.2566 time: 0.3134 last_time: 0.4224 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:46:04 d2.utils.events]: \u001b[0m eta: 7:16:52 iter: 21599 total_loss: 1.059 loss_cls: 0.3681 loss_box_reg: 0.3661 loss_rpn_cls: 0.07278 loss_rpn_loc: 0.2373 time: 0.3135 last_time: 0.4112 data_time: 0.0045 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:46:12 d2.utils.events]: \u001b[0m eta: 7:16:40 iter: 21619 total_loss: 0.9249 loss_cls: 0.3482 loss_box_reg: 0.3016 loss_rpn_cls: 0.05136 loss_rpn_loc: 0.1934 time: 0.3136 last_time: 0.3983 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:46:20 d2.utils.events]: \u001b[0m eta: 7:16:12 iter: 21639 total_loss: 1.134 loss_cls: 0.3678 loss_box_reg: 0.4101 loss_rpn_cls: 0.08897 loss_rpn_loc: 0.2604 time: 0.3137 last_time: 0.3974 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:46:28 d2.utils.events]: \u001b[0m eta: 7:15:28 iter: 21659 total_loss: 0.9846 loss_cls: 0.3186 loss_box_reg: 0.3438 loss_rpn_cls: 0.06657 loss_rpn_loc: 0.2191 time: 0.3137 last_time: 0.4004 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:46:37 d2.utils.events]: \u001b[0m eta: 7:14:59 iter: 21679 total_loss: 0.9658 loss_cls: 0.3455 loss_box_reg: 0.3505 loss_rpn_cls: 0.0622 loss_rpn_loc: 0.2198 time: 0.3138 last_time: 0.4213 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:46:45 d2.utils.events]: \u001b[0m eta: 7:14:16 iter: 21699 total_loss: 1.03 loss_cls: 0.3503 loss_box_reg: 0.3603 loss_rpn_cls: 0.05755 loss_rpn_loc: 0.2173 time: 0.3139 last_time: 0.3925 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:46:53 d2.utils.events]: \u001b[0m eta: 7:13:41 iter: 21719 total_loss: 0.9048 loss_cls: 0.2791 loss_box_reg: 0.3592 loss_rpn_cls: 0.05996 loss_rpn_loc: 0.2311 time: 0.3140 last_time: 0.4501 data_time: 0.0045 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:47:01 d2.utils.events]: \u001b[0m eta: 7:13:31 iter: 21739 total_loss: 0.951 loss_cls: 0.3363 loss_box_reg: 0.3315 loss_rpn_cls: 0.04818 loss_rpn_loc: 0.2112 time: 0.3141 last_time: 0.4472 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:47:09 d2.utils.events]: \u001b[0m eta: 7:13:24 iter: 21759 total_loss: 1.061 loss_cls: 0.3592 loss_box_reg: 0.3693 loss_rpn_cls: 0.06087 loss_rpn_loc: 0.196 time: 0.3142 last_time: 0.3794 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:47:17 d2.utils.events]: \u001b[0m eta: 7:13:43 iter: 21779 total_loss: 0.9271 loss_cls: 0.3049 loss_box_reg: 0.3146 loss_rpn_cls: 0.05463 loss_rpn_loc: 0.2011 time: 0.3142 last_time: 0.4499 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:47:25 d2.utils.events]: \u001b[0m eta: 7:13:53 iter: 21799 total_loss: 1.034 loss_cls: 0.3882 loss_box_reg: 0.3776 loss_rpn_cls: 0.04547 loss_rpn_loc: 0.2429 time: 0.3143 last_time: 0.3164 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:47:33 d2.utils.events]: \u001b[0m eta: 7:13:34 iter: 21819 total_loss: 0.9473 loss_cls: 0.3289 loss_box_reg: 0.3686 loss_rpn_cls: 0.04673 loss_rpn_loc: 0.2142 time: 0.3144 last_time: 0.3625 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:47:41 d2.utils.events]: \u001b[0m eta: 7:12:59 iter: 21839 total_loss: 1.058 loss_cls: 0.364 loss_box_reg: 0.3495 loss_rpn_cls: 0.0658 loss_rpn_loc: 0.2636 time: 0.3145 last_time: 0.3695 data_time: 0.0046 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:47:50 d2.utils.events]: \u001b[0m eta: 7:13:38 iter: 21859 total_loss: 0.9364 loss_cls: 0.3477 loss_box_reg: 0.3268 loss_rpn_cls: 0.06249 loss_rpn_loc: 0.2036 time: 0.3146 last_time: 0.4750 data_time: 0.0047 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:47:59 d2.utils.events]: \u001b[0m eta: 7:14:08 iter: 21879 total_loss: 0.934 loss_cls: 0.319 loss_box_reg: 0.3572 loss_rpn_cls: 0.06106 loss_rpn_loc: 0.2103 time: 0.3147 last_time: 0.4686 data_time: 0.0049 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:48:08 d2.utils.events]: \u001b[0m eta: 7:14:37 iter: 21899 total_loss: 1.026 loss_cls: 0.3764 loss_box_reg: 0.3407 loss_rpn_cls: 0.05714 loss_rpn_loc: 0.2487 time: 0.3149 last_time: 0.3750 data_time: 0.0051 last_data_time: 0.0062 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:48:17 d2.utils.events]: \u001b[0m eta: 7:15:38 iter: 21919 total_loss: 1.124 loss_cls: 0.362 loss_box_reg: 0.365 loss_rpn_cls: 0.07151 loss_rpn_loc: 0.2483 time: 0.3150 last_time: 0.4357 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:48:26 d2.utils.events]: \u001b[0m eta: 7:15:40 iter: 21939 total_loss: 0.9698 loss_cls: 0.3094 loss_box_reg: 0.3411 loss_rpn_cls: 0.05466 loss_rpn_loc: 0.2368 time: 0.3151 last_time: 0.4879 data_time: 0.0047 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:48:35 d2.utils.events]: \u001b[0m eta: 7:15:48 iter: 21959 total_loss: 1.091 loss_cls: 0.3509 loss_box_reg: 0.3768 loss_rpn_cls: 0.05283 loss_rpn_loc: 0.2194 time: 0.3152 last_time: 0.3842 data_time: 0.0049 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:48:44 d2.utils.events]: \u001b[0m eta: 7:16:09 iter: 21979 total_loss: 0.9846 loss_cls: 0.3938 loss_box_reg: 0.333 loss_rpn_cls: 0.05924 loss_rpn_loc: 0.2204 time: 0.3153 last_time: 0.4480 data_time: 0.0049 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:48:53 d2.utils.events]: \u001b[0m eta: 7:16:18 iter: 21999 total_loss: 0.9986 loss_cls: 0.3443 loss_box_reg: 0.3613 loss_rpn_cls: 0.05641 loss_rpn_loc: 0.1979 time: 0.3155 last_time: 0.4391 data_time: 0.0048 last_data_time: 0.0041 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:49:01 d2.utils.events]: \u001b[0m eta: 7:16:19 iter: 22019 total_loss: 0.9836 loss_cls: 0.3419 loss_box_reg: 0.3651 loss_rpn_cls: 0.05906 loss_rpn_loc: 0.2304 time: 0.3155 last_time: 0.3943 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:49:09 d2.utils.events]: \u001b[0m eta: 7:16:18 iter: 22039 total_loss: 1.102 loss_cls: 0.3866 loss_box_reg: 0.4042 loss_rpn_cls: 0.07036 loss_rpn_loc: 0.2103 time: 0.3156 last_time: 0.4145 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:49:17 d2.utils.events]: \u001b[0m eta: 7:16:08 iter: 22059 total_loss: 0.9884 loss_cls: 0.3265 loss_box_reg: 0.361 loss_rpn_cls: 0.05554 loss_rpn_loc: 0.2383 time: 0.3157 last_time: 0.4384 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:49:25 d2.utils.events]: \u001b[0m eta: 7:15:55 iter: 22079 total_loss: 0.8895 loss_cls: 0.2709 loss_box_reg: 0.3215 loss_rpn_cls: 0.05163 loss_rpn_loc: 0.207 time: 0.3158 last_time: 0.3354 data_time: 0.0046 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:49:33 d2.utils.events]: \u001b[0m eta: 7:15:46 iter: 22099 total_loss: 0.9192 loss_cls: 0.2971 loss_box_reg: 0.3382 loss_rpn_cls: 0.05231 loss_rpn_loc: 0.2185 time: 0.3159 last_time: 0.4077 data_time: 0.0046 last_data_time: 0.0052 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:49:41 d2.utils.events]: \u001b[0m eta: 7:15:19 iter: 22119 total_loss: 0.9742 loss_cls: 0.3356 loss_box_reg: 0.381 loss_rpn_cls: 0.05823 loss_rpn_loc: 0.1909 time: 0.3159 last_time: 0.4454 data_time: 0.0047 last_data_time: 0.0054 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:49:49 d2.utils.events]: \u001b[0m eta: 7:15:06 iter: 22139 total_loss: 1 loss_cls: 0.3502 loss_box_reg: 0.3739 loss_rpn_cls: 0.059 loss_rpn_loc: 0.1935 time: 0.3160 last_time: 0.4176 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:49:58 d2.utils.events]: \u001b[0m eta: 7:14:55 iter: 22159 total_loss: 0.9263 loss_cls: 0.3328 loss_box_reg: 0.3415 loss_rpn_cls: 0.04764 loss_rpn_loc: 0.2006 time: 0.3161 last_time: 0.3737 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:50:06 d2.utils.events]: \u001b[0m eta: 7:14:30 iter: 22179 total_loss: 1.05 loss_cls: 0.3787 loss_box_reg: 0.3713 loss_rpn_cls: 0.04973 loss_rpn_loc: 0.2388 time: 0.3162 last_time: 0.4378 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:50:14 d2.utils.events]: \u001b[0m eta: 7:14:06 iter: 22199 total_loss: 1.012 loss_cls: 0.3776 loss_box_reg: 0.3451 loss_rpn_cls: 0.05818 loss_rpn_loc: 0.227 time: 0.3162 last_time: 0.3941 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:50:22 d2.utils.events]: \u001b[0m eta: 7:13:58 iter: 22219 total_loss: 0.9992 loss_cls: 0.3459 loss_box_reg: 0.3516 loss_rpn_cls: 0.04284 loss_rpn_loc: 0.2254 time: 0.3163 last_time: 0.4350 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:50:30 d2.utils.events]: \u001b[0m eta: 7:13:53 iter: 22239 total_loss: 0.9449 loss_cls: 0.3244 loss_box_reg: 0.3407 loss_rpn_cls: 0.0498 loss_rpn_loc: 0.2199 time: 0.3164 last_time: 0.4212 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:50:38 d2.utils.events]: \u001b[0m eta: 7:13:51 iter: 22259 total_loss: 0.9275 loss_cls: 0.3498 loss_box_reg: 0.3253 loss_rpn_cls: 0.05353 loss_rpn_loc: 0.2214 time: 0.3165 last_time: 0.3395 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:50:47 d2.utils.events]: \u001b[0m eta: 7:13:52 iter: 22279 total_loss: 0.963 loss_cls: 0.3262 loss_box_reg: 0.3494 loss_rpn_cls: 0.04363 loss_rpn_loc: 0.1955 time: 0.3166 last_time: 0.4431 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:50:55 d2.utils.events]: \u001b[0m eta: 7:13:55 iter: 22299 total_loss: 1.113 loss_cls: 0.329 loss_box_reg: 0.3933 loss_rpn_cls: 0.06093 loss_rpn_loc: 0.2269 time: 0.3167 last_time: 0.3739 data_time: 0.0045 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:51:03 d2.utils.events]: \u001b[0m eta: 7:13:46 iter: 22319 total_loss: 1.01 loss_cls: 0.3382 loss_box_reg: 0.3455 loss_rpn_cls: 0.06403 loss_rpn_loc: 0.2298 time: 0.3168 last_time: 0.4146 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:51:11 d2.utils.events]: \u001b[0m eta: 7:13:33 iter: 22339 total_loss: 1.035 loss_cls: 0.3455 loss_box_reg: 0.3732 loss_rpn_cls: 0.06063 loss_rpn_loc: 0.2393 time: 0.3168 last_time: 0.3793 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:51:19 d2.utils.events]: \u001b[0m eta: 7:13:19 iter: 22359 total_loss: 0.9372 loss_cls: 0.3258 loss_box_reg: 0.3394 loss_rpn_cls: 0.05705 loss_rpn_loc: 0.2087 time: 0.3169 last_time: 0.4421 data_time: 0.0046 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:51:28 d2.utils.events]: \u001b[0m eta: 7:13:29 iter: 22379 total_loss: 0.9299 loss_cls: 0.3086 loss_box_reg: 0.334 loss_rpn_cls: 0.04876 loss_rpn_loc: 0.2174 time: 0.3170 last_time: 0.4387 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:51:36 d2.utils.events]: \u001b[0m eta: 7:13:33 iter: 22399 total_loss: 0.8485 loss_cls: 0.3248 loss_box_reg: 0.3132 loss_rpn_cls: 0.05105 loss_rpn_loc: 0.1879 time: 0.3171 last_time: 0.4315 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:51:44 d2.utils.events]: \u001b[0m eta: 7:13:07 iter: 22419 total_loss: 0.9736 loss_cls: 0.3523 loss_box_reg: 0.3618 loss_rpn_cls: 0.05074 loss_rpn_loc: 0.1965 time: 0.3171 last_time: 0.4406 data_time: 0.0045 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:51:52 d2.utils.events]: \u001b[0m eta: 7:13:27 iter: 22439 total_loss: 0.8692 loss_cls: 0.3104 loss_box_reg: 0.3308 loss_rpn_cls: 0.05897 loss_rpn_loc: 0.2175 time: 0.3172 last_time: 0.3953 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:52:00 d2.utils.events]: \u001b[0m eta: 7:13:19 iter: 22459 total_loss: 1.08 loss_cls: 0.3336 loss_box_reg: 0.3816 loss_rpn_cls: 0.05668 loss_rpn_loc: 0.223 time: 0.3173 last_time: 0.3439 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:52:08 d2.utils.events]: \u001b[0m eta: 7:12:49 iter: 22479 total_loss: 1.005 loss_cls: 0.3433 loss_box_reg: 0.3693 loss_rpn_cls: 0.04838 loss_rpn_loc: 0.1859 time: 0.3174 last_time: 0.4469 data_time: 0.0046 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:52:16 d2.utils.events]: \u001b[0m eta: 7:12:52 iter: 22499 total_loss: 0.9051 loss_cls: 0.3616 loss_box_reg: 0.3345 loss_rpn_cls: 0.05285 loss_rpn_loc: 0.2092 time: 0.3175 last_time: 0.4416 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:52:24 d2.utils.events]: \u001b[0m eta: 7:12:28 iter: 22519 total_loss: 1.001 loss_cls: 0.3661 loss_box_reg: 0.372 loss_rpn_cls: 0.06201 loss_rpn_loc: 0.2097 time: 0.3175 last_time: 0.3256 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:52:32 d2.utils.events]: \u001b[0m eta: 7:12:20 iter: 22539 total_loss: 1.033 loss_cls: 0.3788 loss_box_reg: 0.3649 loss_rpn_cls: 0.06893 loss_rpn_loc: 0.2045 time: 0.3176 last_time: 0.4187 data_time: 0.0045 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:52:40 d2.utils.events]: \u001b[0m eta: 7:12:12 iter: 22559 total_loss: 0.984 loss_cls: 0.3658 loss_box_reg: 0.3862 loss_rpn_cls: 0.04993 loss_rpn_loc: 0.1955 time: 0.3177 last_time: 0.4388 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:52:48 d2.utils.events]: \u001b[0m eta: 7:12:03 iter: 22579 total_loss: 1.047 loss_cls: 0.3827 loss_box_reg: 0.3589 loss_rpn_cls: 0.06007 loss_rpn_loc: 0.2349 time: 0.3178 last_time: 0.4243 data_time: 0.0044 last_data_time: 0.0041 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:52:57 d2.utils.events]: \u001b[0m eta: 7:12:23 iter: 22599 total_loss: 0.9844 loss_cls: 0.3385 loss_box_reg: 0.3515 loss_rpn_cls: 0.05802 loss_rpn_loc: 0.2297 time: 0.3179 last_time: 0.4341 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:53:05 d2.utils.events]: \u001b[0m eta: 7:12:11 iter: 22619 total_loss: 0.9875 loss_cls: 0.3518 loss_box_reg: 0.3541 loss_rpn_cls: 0.06048 loss_rpn_loc: 0.2292 time: 0.3179 last_time: 0.3437 data_time: 0.0046 last_data_time: 0.0052 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:53:13 d2.utils.events]: \u001b[0m eta: 7:12:06 iter: 22639 total_loss: 0.989 loss_cls: 0.3484 loss_box_reg: 0.3494 loss_rpn_cls: 0.06949 loss_rpn_loc: 0.2153 time: 0.3180 last_time: 0.4162 data_time: 0.0044 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:53:21 d2.utils.events]: \u001b[0m eta: 7:11:56 iter: 22659 total_loss: 0.9711 loss_cls: 0.333 loss_box_reg: 0.3599 loss_rpn_cls: 0.06374 loss_rpn_loc: 0.1989 time: 0.3181 last_time: 0.3967 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:53:29 d2.utils.events]: \u001b[0m eta: 7:11:46 iter: 22679 total_loss: 0.8736 loss_cls: 0.277 loss_box_reg: 0.3125 loss_rpn_cls: 0.06269 loss_rpn_loc: 0.1989 time: 0.3182 last_time: 0.3953 data_time: 0.0045 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:53:37 d2.utils.events]: \u001b[0m eta: 7:11:21 iter: 22699 total_loss: 0.9027 loss_cls: 0.3328 loss_box_reg: 0.3392 loss_rpn_cls: 0.05415 loss_rpn_loc: 0.1846 time: 0.3182 last_time: 0.4161 data_time: 0.0044 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:53:45 d2.utils.events]: \u001b[0m eta: 7:11:29 iter: 22719 total_loss: 1.041 loss_cls: 0.3521 loss_box_reg: 0.3768 loss_rpn_cls: 0.07077 loss_rpn_loc: 0.2284 time: 0.3183 last_time: 0.4467 data_time: 0.0045 last_data_time: 0.0041 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:53:53 d2.utils.events]: \u001b[0m eta: 7:11:20 iter: 22739 total_loss: 1.11 loss_cls: 0.3654 loss_box_reg: 0.3832 loss_rpn_cls: 0.06301 loss_rpn_loc: 0.2226 time: 0.3184 last_time: 0.4393 data_time: 0.0046 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:54:01 d2.utils.events]: \u001b[0m eta: 7:11:09 iter: 22759 total_loss: 1.027 loss_cls: 0.391 loss_box_reg: 0.3612 loss_rpn_cls: 0.05661 loss_rpn_loc: 0.2102 time: 0.3185 last_time: 0.4142 data_time: 0.0046 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:54:10 d2.utils.events]: \u001b[0m eta: 7:10:48 iter: 22779 total_loss: 0.8769 loss_cls: 0.2937 loss_box_reg: 0.327 loss_rpn_cls: 0.03962 loss_rpn_loc: 0.1905 time: 0.3185 last_time: 0.3733 data_time: 0.0044 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:54:18 d2.utils.events]: \u001b[0m eta: 7:10:40 iter: 22799 total_loss: 0.9396 loss_cls: 0.2929 loss_box_reg: 0.2977 loss_rpn_cls: 0.06279 loss_rpn_loc: 0.2068 time: 0.3186 last_time: 0.3661 data_time: 0.0045 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:54:26 d2.utils.events]: \u001b[0m eta: 7:10:31 iter: 22819 total_loss: 0.9908 loss_cls: 0.35 loss_box_reg: 0.3477 loss_rpn_cls: 0.0429 loss_rpn_loc: 0.2307 time: 0.3187 last_time: 0.4527 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:54:34 d2.utils.events]: \u001b[0m eta: 7:10:39 iter: 22839 total_loss: 1.128 loss_cls: 0.4302 loss_box_reg: 0.3873 loss_rpn_cls: 0.07381 loss_rpn_loc: 0.2295 time: 0.3188 last_time: 0.4463 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:54:42 d2.utils.events]: \u001b[0m eta: 7:10:10 iter: 22859 total_loss: 0.996 loss_cls: 0.3272 loss_box_reg: 0.3405 loss_rpn_cls: 0.05546 loss_rpn_loc: 0.2043 time: 0.3188 last_time: 0.4299 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:54:50 d2.utils.events]: \u001b[0m eta: 7:09:37 iter: 22879 total_loss: 0.9825 loss_cls: 0.3349 loss_box_reg: 0.3621 loss_rpn_cls: 0.06141 loss_rpn_loc: 0.2128 time: 0.3189 last_time: 0.4187 data_time: 0.0045 last_data_time: 0.0041 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:54:58 d2.utils.events]: \u001b[0m eta: 7:09:09 iter: 22899 total_loss: 0.9717 loss_cls: 0.3248 loss_box_reg: 0.35 loss_rpn_cls: 0.05573 loss_rpn_loc: 0.2194 time: 0.3190 last_time: 0.4490 data_time: 0.0045 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:55:06 d2.utils.events]: \u001b[0m eta: 7:08:23 iter: 22919 total_loss: 0.9347 loss_cls: 0.316 loss_box_reg: 0.3202 loss_rpn_cls: 0.03973 loss_rpn_loc: 0.2017 time: 0.3191 last_time: 0.3750 data_time: 0.0047 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:55:15 d2.utils.events]: \u001b[0m eta: 7:08:21 iter: 22939 total_loss: 1.03 loss_cls: 0.3371 loss_box_reg: 0.3582 loss_rpn_cls: 0.06977 loss_rpn_loc: 0.2583 time: 0.3192 last_time: 0.4139 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:55:23 d2.utils.events]: \u001b[0m eta: 7:07:38 iter: 22959 total_loss: 0.8934 loss_cls: 0.2987 loss_box_reg: 0.3255 loss_rpn_cls: 0.0412 loss_rpn_loc: 0.1988 time: 0.3192 last_time: 0.3770 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:55:31 d2.utils.events]: \u001b[0m eta: 7:07:13 iter: 22979 total_loss: 0.8707 loss_cls: 0.3153 loss_box_reg: 0.3244 loss_rpn_cls: 0.05034 loss_rpn_loc: 0.202 time: 0.3193 last_time: 0.4017 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:55:39 d2.utils.events]: \u001b[0m eta: 7:06:22 iter: 22999 total_loss: 1 loss_cls: 0.3284 loss_box_reg: 0.3915 loss_rpn_cls: 0.06852 loss_rpn_loc: 0.2367 time: 0.3194 last_time: 0.4426 data_time: 0.0044 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:55:47 d2.utils.events]: \u001b[0m eta: 7:05:49 iter: 23019 total_loss: 1.014 loss_cls: 0.349 loss_box_reg: 0.3423 loss_rpn_cls: 0.06642 loss_rpn_loc: 0.2374 time: 0.3195 last_time: 0.3826 data_time: 0.0045 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:55:56 d2.utils.events]: \u001b[0m eta: 7:05:41 iter: 23039 total_loss: 0.9786 loss_cls: 0.3413 loss_box_reg: 0.3347 loss_rpn_cls: 0.04431 loss_rpn_loc: 0.216 time: 0.3195 last_time: 0.4404 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:56:04 d2.utils.events]: \u001b[0m eta: 7:05:37 iter: 23059 total_loss: 1.035 loss_cls: 0.3551 loss_box_reg: 0.3641 loss_rpn_cls: 0.06209 loss_rpn_loc: 0.2249 time: 0.3196 last_time: 0.3975 data_time: 0.0045 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:56:12 d2.utils.events]: \u001b[0m eta: 7:05:29 iter: 23079 total_loss: 0.9779 loss_cls: 0.3385 loss_box_reg: 0.3487 loss_rpn_cls: 0.05772 loss_rpn_loc: 0.2113 time: 0.3197 last_time: 0.4443 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:56:20 d2.utils.events]: \u001b[0m eta: 7:05:33 iter: 23099 total_loss: 1.084 loss_cls: 0.3667 loss_box_reg: 0.3875 loss_rpn_cls: 0.05553 loss_rpn_loc: 0.241 time: 0.3198 last_time: 0.3426 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:56:28 d2.utils.events]: \u001b[0m eta: 7:05:35 iter: 23119 total_loss: 0.8141 loss_cls: 0.2969 loss_box_reg: 0.2971 loss_rpn_cls: 0.04313 loss_rpn_loc: 0.2106 time: 0.3198 last_time: 0.4369 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:56:36 d2.utils.events]: \u001b[0m eta: 7:05:24 iter: 23139 total_loss: 0.9814 loss_cls: 0.3402 loss_box_reg: 0.3562 loss_rpn_cls: 0.06024 loss_rpn_loc: 0.2198 time: 0.3199 last_time: 0.4317 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:56:44 d2.utils.events]: \u001b[0m eta: 7:05:16 iter: 23159 total_loss: 0.9371 loss_cls: 0.3385 loss_box_reg: 0.3448 loss_rpn_cls: 0.05448 loss_rpn_loc: 0.2107 time: 0.3200 last_time: 0.4420 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:56:52 d2.utils.events]: \u001b[0m eta: 7:05:07 iter: 23179 total_loss: 1.074 loss_cls: 0.3567 loss_box_reg: 0.384 loss_rpn_cls: 0.05238 loss_rpn_loc: 0.2271 time: 0.3200 last_time: 0.3399 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:57:00 d2.utils.events]: \u001b[0m eta: 7:05:02 iter: 23199 total_loss: 0.9493 loss_cls: 0.3156 loss_box_reg: 0.3586 loss_rpn_cls: 0.06309 loss_rpn_loc: 0.1969 time: 0.3201 last_time: 0.3940 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:57:08 d2.utils.events]: \u001b[0m eta: 7:04:51 iter: 23219 total_loss: 0.8923 loss_cls: 0.3074 loss_box_reg: 0.3135 loss_rpn_cls: 0.05047 loss_rpn_loc: 0.2293 time: 0.3202 last_time: 0.4223 data_time: 0.0046 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:57:16 d2.utils.events]: \u001b[0m eta: 7:04:41 iter: 23239 total_loss: 0.9881 loss_cls: 0.3146 loss_box_reg: 0.3699 loss_rpn_cls: 0.04856 loss_rpn_loc: 0.2004 time: 0.3202 last_time: 0.3419 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:57:24 d2.utils.events]: \u001b[0m eta: 7:04:24 iter: 23259 total_loss: 0.9786 loss_cls: 0.3555 loss_box_reg: 0.3433 loss_rpn_cls: 0.05232 loss_rpn_loc: 0.195 time: 0.3203 last_time: 0.3721 data_time: 0.0044 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:57:32 d2.utils.events]: \u001b[0m eta: 7:03:56 iter: 23279 total_loss: 0.8978 loss_cls: 0.2978 loss_box_reg: 0.3419 loss_rpn_cls: 0.05273 loss_rpn_loc: 0.2225 time: 0.3204 last_time: 0.3961 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:57:40 d2.utils.events]: \u001b[0m eta: 7:03:33 iter: 23299 total_loss: 1.002 loss_cls: 0.3363 loss_box_reg: 0.38 loss_rpn_cls: 0.05574 loss_rpn_loc: 0.219 time: 0.3205 last_time: 0.4203 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:57:48 d2.utils.events]: \u001b[0m eta: 7:03:17 iter: 23319 total_loss: 1.157 loss_cls: 0.4071 loss_box_reg: 0.4004 loss_rpn_cls: 0.07252 loss_rpn_loc: 0.2531 time: 0.3205 last_time: 0.3809 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:57:56 d2.utils.events]: \u001b[0m eta: 7:02:46 iter: 23339 total_loss: 1.004 loss_cls: 0.3457 loss_box_reg: 0.3501 loss_rpn_cls: 0.05659 loss_rpn_loc: 0.2112 time: 0.3206 last_time: 0.3758 data_time: 0.0045 last_data_time: 0.0041 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:58:04 d2.utils.events]: \u001b[0m eta: 7:02:00 iter: 23359 total_loss: 0.8369 loss_cls: 0.2925 loss_box_reg: 0.3239 loss_rpn_cls: 0.0375 loss_rpn_loc: 0.2003 time: 0.3206 last_time: 0.3813 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:58:12 d2.utils.events]: \u001b[0m eta: 7:01:46 iter: 23379 total_loss: 0.9492 loss_cls: 0.3403 loss_box_reg: 0.3408 loss_rpn_cls: 0.06237 loss_rpn_loc: 0.2343 time: 0.3207 last_time: 0.4431 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:58:20 d2.utils.events]: \u001b[0m eta: 7:01:26 iter: 23399 total_loss: 0.9083 loss_cls: 0.2961 loss_box_reg: 0.3157 loss_rpn_cls: 0.05148 loss_rpn_loc: 0.2218 time: 0.3208 last_time: 0.3974 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:58:28 d2.utils.events]: \u001b[0m eta: 7:01:10 iter: 23419 total_loss: 0.9404 loss_cls: 0.3135 loss_box_reg: 0.3446 loss_rpn_cls: 0.05581 loss_rpn_loc: 0.1978 time: 0.3209 last_time: 0.3826 data_time: 0.0047 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:58:36 d2.utils.events]: \u001b[0m eta: 7:01:08 iter: 23439 total_loss: 0.9768 loss_cls: 0.3555 loss_box_reg: 0.3644 loss_rpn_cls: 0.05832 loss_rpn_loc: 0.1819 time: 0.3209 last_time: 0.4396 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:58:44 d2.utils.events]: \u001b[0m eta: 7:00:48 iter: 23459 total_loss: 1.061 loss_cls: 0.3349 loss_box_reg: 0.349 loss_rpn_cls: 0.05691 loss_rpn_loc: 0.2494 time: 0.3210 last_time: 0.3979 data_time: 0.0046 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:58:53 d2.utils.events]: \u001b[0m eta: 7:01:04 iter: 23479 total_loss: 0.9874 loss_cls: 0.3278 loss_box_reg: 0.3653 loss_rpn_cls: 0.05135 loss_rpn_loc: 0.2108 time: 0.3211 last_time: 0.4823 data_time: 0.0048 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:59:02 d2.utils.events]: \u001b[0m eta: 7:01:12 iter: 23499 total_loss: 0.9029 loss_cls: 0.2978 loss_box_reg: 0.3178 loss_rpn_cls: 0.06564 loss_rpn_loc: 0.2258 time: 0.3212 last_time: 0.4794 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:59:11 d2.utils.events]: \u001b[0m eta: 7:01:41 iter: 23519 total_loss: 0.9237 loss_cls: 0.2943 loss_box_reg: 0.3517 loss_rpn_cls: 0.05565 loss_rpn_loc: 0.2268 time: 0.3213 last_time: 0.3959 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:59:19 d2.utils.events]: \u001b[0m eta: 7:01:33 iter: 23539 total_loss: 0.9233 loss_cls: 0.3123 loss_box_reg: 0.3032 loss_rpn_cls: 0.06446 loss_rpn_loc: 0.2338 time: 0.3214 last_time: 0.4460 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:59:27 d2.utils.events]: \u001b[0m eta: 7:01:17 iter: 23559 total_loss: 1.035 loss_cls: 0.3602 loss_box_reg: 0.3647 loss_rpn_cls: 0.05988 loss_rpn_loc: 0.2246 time: 0.3215 last_time: 0.4162 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:59:36 d2.utils.events]: \u001b[0m eta: 7:01:54 iter: 23579 total_loss: 1.003 loss_cls: 0.3412 loss_box_reg: 0.3616 loss_rpn_cls: 0.05331 loss_rpn_loc: 0.2252 time: 0.3216 last_time: 0.4170 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:59:44 d2.utils.events]: \u001b[0m eta: 7:01:30 iter: 23599 total_loss: 0.9867 loss_cls: 0.3438 loss_box_reg: 0.3354 loss_rpn_cls: 0.06661 loss_rpn_loc: 0.237 time: 0.3216 last_time: 0.4182 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 17:59:53 d2.utils.events]: \u001b[0m eta: 7:02:28 iter: 23619 total_loss: 0.9723 loss_cls: 0.3125 loss_box_reg: 0.3392 loss_rpn_cls: 0.05144 loss_rpn_loc: 0.2383 time: 0.3217 last_time: 0.4051 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:00:01 d2.utils.events]: \u001b[0m eta: 7:02:50 iter: 23639 total_loss: 0.9379 loss_cls: 0.3233 loss_box_reg: 0.3152 loss_rpn_cls: 0.06591 loss_rpn_loc: 0.2264 time: 0.3218 last_time: 0.4229 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:00:09 d2.utils.events]: \u001b[0m eta: 7:02:59 iter: 23659 total_loss: 1.036 loss_cls: 0.3522 loss_box_reg: 0.379 loss_rpn_cls: 0.06938 loss_rpn_loc: 0.2321 time: 0.3219 last_time: 0.4379 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:00:17 d2.utils.events]: \u001b[0m eta: 7:02:41 iter: 23679 total_loss: 0.9433 loss_cls: 0.3584 loss_box_reg: 0.3348 loss_rpn_cls: 0.04891 loss_rpn_loc: 0.2283 time: 0.3219 last_time: 0.4242 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:00:25 d2.utils.events]: \u001b[0m eta: 7:02:34 iter: 23699 total_loss: 0.9653 loss_cls: 0.3619 loss_box_reg: 0.3851 loss_rpn_cls: 0.05703 loss_rpn_loc: 0.2188 time: 0.3220 last_time: 0.3413 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:00:34 d2.utils.events]: \u001b[0m eta: 7:02:31 iter: 23719 total_loss: 1.066 loss_cls: 0.3718 loss_box_reg: 0.3714 loss_rpn_cls: 0.07468 loss_rpn_loc: 0.253 time: 0.3221 last_time: 0.5141 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:00:43 d2.utils.events]: \u001b[0m eta: 7:02:42 iter: 23739 total_loss: 1.076 loss_cls: 0.3935 loss_box_reg: 0.3483 loss_rpn_cls: 0.06238 loss_rpn_loc: 0.2217 time: 0.3222 last_time: 0.4890 data_time: 0.0048 last_data_time: 0.0052 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:00:52 d2.utils.events]: \u001b[0m eta: 7:03:03 iter: 23759 total_loss: 0.9783 loss_cls: 0.3223 loss_box_reg: 0.3503 loss_rpn_cls: 0.05635 loss_rpn_loc: 0.2178 time: 0.3223 last_time: 0.3834 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:01:00 d2.utils.events]: \u001b[0m eta: 7:02:53 iter: 23779 total_loss: 0.871 loss_cls: 0.2882 loss_box_reg: 0.3214 loss_rpn_cls: 0.04898 loss_rpn_loc: 0.2015 time: 0.3224 last_time: 0.4208 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:01:08 d2.utils.events]: \u001b[0m eta: 7:02:47 iter: 23799 total_loss: 0.8847 loss_cls: 0.3031 loss_box_reg: 0.3114 loss_rpn_cls: 0.06195 loss_rpn_loc: 0.2112 time: 0.3224 last_time: 0.4148 data_time: 0.0045 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:01:16 d2.utils.events]: \u001b[0m eta: 7:02:41 iter: 23819 total_loss: 0.8873 loss_cls: 0.2984 loss_box_reg: 0.3253 loss_rpn_cls: 0.04444 loss_rpn_loc: 0.2066 time: 0.3225 last_time: 0.4362 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:01:24 d2.utils.events]: \u001b[0m eta: 7:02:23 iter: 23839 total_loss: 0.9208 loss_cls: 0.3121 loss_box_reg: 0.3306 loss_rpn_cls: 0.05528 loss_rpn_loc: 0.2152 time: 0.3226 last_time: 0.3995 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:01:32 d2.utils.events]: \u001b[0m eta: 7:02:03 iter: 23859 total_loss: 0.985 loss_cls: 0.3363 loss_box_reg: 0.3664 loss_rpn_cls: 0.04283 loss_rpn_loc: 0.2218 time: 0.3227 last_time: 0.4154 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:01:41 d2.utils.events]: \u001b[0m eta: 7:01:55 iter: 23879 total_loss: 1.026 loss_cls: 0.3506 loss_box_reg: 0.378 loss_rpn_cls: 0.05655 loss_rpn_loc: 0.2348 time: 0.3227 last_time: 0.4093 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:01:49 d2.utils.events]: \u001b[0m eta: 7:01:46 iter: 23899 total_loss: 0.8897 loss_cls: 0.3167 loss_box_reg: 0.3286 loss_rpn_cls: 0.04786 loss_rpn_loc: 0.2074 time: 0.3228 last_time: 0.4369 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:01:57 d2.utils.events]: \u001b[0m eta: 7:01:18 iter: 23919 total_loss: 0.8629 loss_cls: 0.2882 loss_box_reg: 0.3339 loss_rpn_cls: 0.05314 loss_rpn_loc: 0.209 time: 0.3229 last_time: 0.3950 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:02:05 d2.utils.events]: \u001b[0m eta: 7:01:00 iter: 23939 total_loss: 0.8709 loss_cls: 0.3003 loss_box_reg: 0.2998 loss_rpn_cls: 0.05515 loss_rpn_loc: 0.2187 time: 0.3229 last_time: 0.4300 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:02:13 d2.utils.events]: \u001b[0m eta: 7:01:01 iter: 23959 total_loss: 0.9291 loss_cls: 0.3243 loss_box_reg: 0.3473 loss_rpn_cls: 0.05861 loss_rpn_loc: 0.2003 time: 0.3230 last_time: 0.4282 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:02:21 d2.utils.events]: \u001b[0m eta: 7:00:47 iter: 23979 total_loss: 0.994 loss_cls: 0.3483 loss_box_reg: 0.3417 loss_rpn_cls: 0.04602 loss_rpn_loc: 0.2072 time: 0.3231 last_time: 0.3899 data_time: 0.0047 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:02:29 d2.utils.events]: \u001b[0m eta: 7:00:39 iter: 23999 total_loss: 1.031 loss_cls: 0.3224 loss_box_reg: 0.3545 loss_rpn_cls: 0.06437 loss_rpn_loc: 0.2238 time: 0.3231 last_time: 0.3754 data_time: 0.0045 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:02:37 d2.utils.events]: \u001b[0m eta: 7:00:46 iter: 24019 total_loss: 1.036 loss_cls: 0.3767 loss_box_reg: 0.3709 loss_rpn_cls: 0.05867 loss_rpn_loc: 0.2236 time: 0.3232 last_time: 0.3973 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:02:45 d2.utils.events]: \u001b[0m eta: 7:00:48 iter: 24039 total_loss: 0.9232 loss_cls: 0.2922 loss_box_reg: 0.3095 loss_rpn_cls: 0.04458 loss_rpn_loc: 0.2172 time: 0.3233 last_time: 0.4189 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:02:53 d2.utils.events]: \u001b[0m eta: 7:00:27 iter: 24059 total_loss: 0.9455 loss_cls: 0.3018 loss_box_reg: 0.3793 loss_rpn_cls: 0.06067 loss_rpn_loc: 0.2239 time: 0.3233 last_time: 0.4435 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:03:02 d2.utils.events]: \u001b[0m eta: 7:00:24 iter: 24079 total_loss: 0.9662 loss_cls: 0.3294 loss_box_reg: 0.3754 loss_rpn_cls: 0.0477 loss_rpn_loc: 0.2331 time: 0.3234 last_time: 0.4140 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:03:10 d2.utils.events]: \u001b[0m eta: 7:00:30 iter: 24099 total_loss: 1.03 loss_cls: 0.3603 loss_box_reg: 0.3683 loss_rpn_cls: 0.04901 loss_rpn_loc: 0.23 time: 0.3235 last_time: 0.4468 data_time: 0.0047 last_data_time: 0.0053 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:03:18 d2.utils.events]: \u001b[0m eta: 7:00:17 iter: 24119 total_loss: 0.9785 loss_cls: 0.3418 loss_box_reg: 0.3588 loss_rpn_cls: 0.06466 loss_rpn_loc: 0.2041 time: 0.3236 last_time: 0.3958 data_time: 0.0048 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:03:26 d2.utils.events]: \u001b[0m eta: 7:00:14 iter: 24139 total_loss: 1.03 loss_cls: 0.3615 loss_box_reg: 0.3552 loss_rpn_cls: 0.04681 loss_rpn_loc: 0.2361 time: 0.3236 last_time: 0.4473 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:03:34 d2.utils.events]: \u001b[0m eta: 6:59:51 iter: 24159 total_loss: 0.9415 loss_cls: 0.323 loss_box_reg: 0.3702 loss_rpn_cls: 0.04143 loss_rpn_loc: 0.2014 time: 0.3237 last_time: 0.4140 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:03:42 d2.utils.events]: \u001b[0m eta: 7:00:15 iter: 24179 total_loss: 1.039 loss_cls: 0.3467 loss_box_reg: 0.3559 loss_rpn_cls: 0.05066 loss_rpn_loc: 0.2446 time: 0.3238 last_time: 0.4430 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:03:50 d2.utils.events]: \u001b[0m eta: 7:00:06 iter: 24199 total_loss: 1.018 loss_cls: 0.3546 loss_box_reg: 0.3722 loss_rpn_cls: 0.04309 loss_rpn_loc: 0.1999 time: 0.3238 last_time: 0.3865 data_time: 0.0049 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:03:58 d2.utils.events]: \u001b[0m eta: 6:59:47 iter: 24219 total_loss: 0.9935 loss_cls: 0.3399 loss_box_reg: 0.3354 loss_rpn_cls: 0.05739 loss_rpn_loc: 0.2274 time: 0.3239 last_time: 0.3046 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:04:06 d2.utils.events]: \u001b[0m eta: 6:59:39 iter: 24239 total_loss: 0.9761 loss_cls: 0.3446 loss_box_reg: 0.3537 loss_rpn_cls: 0.04485 loss_rpn_loc: 0.2259 time: 0.3239 last_time: 0.4396 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:04:15 d2.utils.events]: \u001b[0m eta: 6:59:42 iter: 24259 total_loss: 0.9243 loss_cls: 0.3637 loss_box_reg: 0.3548 loss_rpn_cls: 0.0528 loss_rpn_loc: 0.203 time: 0.3240 last_time: 0.4422 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:04:23 d2.utils.events]: \u001b[0m eta: 6:59:25 iter: 24279 total_loss: 0.9701 loss_cls: 0.3454 loss_box_reg: 0.3573 loss_rpn_cls: 0.0565 loss_rpn_loc: 0.2225 time: 0.3241 last_time: 0.4121 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:04:31 d2.utils.events]: \u001b[0m eta: 6:58:47 iter: 24299 total_loss: 1.009 loss_cls: 0.3525 loss_box_reg: 0.3798 loss_rpn_cls: 0.06254 loss_rpn_loc: 0.2271 time: 0.3241 last_time: 0.3729 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:04:39 d2.utils.events]: \u001b[0m eta: 6:58:39 iter: 24319 total_loss: 0.9365 loss_cls: 0.2867 loss_box_reg: 0.3525 loss_rpn_cls: 0.048 loss_rpn_loc: 0.1922 time: 0.3242 last_time: 0.4394 data_time: 0.0048 last_data_time: 0.0052 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:04:47 d2.utils.events]: \u001b[0m eta: 6:59:00 iter: 24339 total_loss: 1.008 loss_cls: 0.3382 loss_box_reg: 0.3682 loss_rpn_cls: 0.05325 loss_rpn_loc: 0.2204 time: 0.3243 last_time: 0.4407 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:04:55 d2.utils.events]: \u001b[0m eta: 6:59:06 iter: 24359 total_loss: 0.9199 loss_cls: 0.3051 loss_box_reg: 0.3349 loss_rpn_cls: 0.05231 loss_rpn_loc: 0.2378 time: 0.3243 last_time: 0.3836 data_time: 0.0047 last_data_time: 0.0053 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:05:03 d2.utils.events]: \u001b[0m eta: 6:58:35 iter: 24379 total_loss: 1.042 loss_cls: 0.3903 loss_box_reg: 0.3936 loss_rpn_cls: 0.05372 loss_rpn_loc: 0.2159 time: 0.3244 last_time: 0.3949 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:05:11 d2.utils.events]: \u001b[0m eta: 6:58:57 iter: 24399 total_loss: 1.11 loss_cls: 0.4074 loss_box_reg: 0.3728 loss_rpn_cls: 0.07916 loss_rpn_loc: 0.2174 time: 0.3245 last_time: 0.3733 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:05:19 d2.utils.events]: \u001b[0m eta: 6:59:23 iter: 24419 total_loss: 0.9788 loss_cls: 0.3221 loss_box_reg: 0.3508 loss_rpn_cls: 0.05574 loss_rpn_loc: 0.235 time: 0.3245 last_time: 0.4254 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:05:27 d2.utils.events]: \u001b[0m eta: 6:59:07 iter: 24439 total_loss: 0.9612 loss_cls: 0.2888 loss_box_reg: 0.325 loss_rpn_cls: 0.0601 loss_rpn_loc: 0.2053 time: 0.3246 last_time: 0.4196 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:05:35 d2.utils.events]: \u001b[0m eta: 6:58:59 iter: 24459 total_loss: 0.9297 loss_cls: 0.2993 loss_box_reg: 0.3555 loss_rpn_cls: 0.04772 loss_rpn_loc: 0.2166 time: 0.3247 last_time: 0.3773 data_time: 0.0047 last_data_time: 0.0040 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:05:44 d2.utils.events]: \u001b[0m eta: 6:58:15 iter: 24479 total_loss: 0.9108 loss_cls: 0.3133 loss_box_reg: 0.3298 loss_rpn_cls: 0.05369 loss_rpn_loc: 0.2115 time: 0.3247 last_time: 0.3677 data_time: 0.0045 last_data_time: 0.0041 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:05:52 d2.utils.events]: \u001b[0m eta: 6:57:29 iter: 24499 total_loss: 0.9076 loss_cls: 0.3346 loss_box_reg: 0.359 loss_rpn_cls: 0.05574 loss_rpn_loc: 0.208 time: 0.3248 last_time: 0.3905 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:06:00 d2.utils.events]: \u001b[0m eta: 6:57:14 iter: 24519 total_loss: 0.9862 loss_cls: 0.3386 loss_box_reg: 0.3403 loss_rpn_cls: 0.06612 loss_rpn_loc: 0.2131 time: 0.3249 last_time: 0.3671 data_time: 0.0045 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:06:08 d2.utils.events]: \u001b[0m eta: 6:57:10 iter: 24539 total_loss: 0.8244 loss_cls: 0.2437 loss_box_reg: 0.3203 loss_rpn_cls: 0.04437 loss_rpn_loc: 0.2049 time: 0.3249 last_time: 0.3957 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:06:16 d2.utils.events]: \u001b[0m eta: 6:57:04 iter: 24559 total_loss: 1.006 loss_cls: 0.3536 loss_box_reg: 0.35 loss_rpn_cls: 0.05699 loss_rpn_loc: 0.2139 time: 0.3250 last_time: 0.3760 data_time: 0.0044 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:06:24 d2.utils.events]: \u001b[0m eta: 6:56:41 iter: 24579 total_loss: 0.8812 loss_cls: 0.3033 loss_box_reg: 0.3252 loss_rpn_cls: 0.05235 loss_rpn_loc: 0.1973 time: 0.3251 last_time: 0.4414 data_time: 0.0044 last_data_time: 0.0041 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:06:32 d2.utils.events]: \u001b[0m eta: 6:56:32 iter: 24599 total_loss: 0.9669 loss_cls: 0.3476 loss_box_reg: 0.3316 loss_rpn_cls: 0.05447 loss_rpn_loc: 0.2093 time: 0.3251 last_time: 0.4200 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:06:41 d2.utils.events]: \u001b[0m eta: 6:56:22 iter: 24619 total_loss: 1.042 loss_cls: 0.3839 loss_box_reg: 0.3443 loss_rpn_cls: 0.05069 loss_rpn_loc: 0.2221 time: 0.3252 last_time: 0.3731 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:06:49 d2.utils.events]: \u001b[0m eta: 6:56:13 iter: 24639 total_loss: 0.9178 loss_cls: 0.3447 loss_box_reg: 0.3402 loss_rpn_cls: 0.05717 loss_rpn_loc: 0.2298 time: 0.3253 last_time: 0.4516 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:06:57 d2.utils.events]: \u001b[0m eta: 6:56:06 iter: 24659 total_loss: 0.9078 loss_cls: 0.3127 loss_box_reg: 0.336 loss_rpn_cls: 0.05807 loss_rpn_loc: 0.2007 time: 0.3253 last_time: 0.4192 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:07:06 d2.utils.events]: \u001b[0m eta: 6:56:14 iter: 24679 total_loss: 0.9986 loss_cls: 0.348 loss_box_reg: 0.36 loss_rpn_cls: 0.05248 loss_rpn_loc: 0.2346 time: 0.3254 last_time: 0.4658 data_time: 0.0049 last_data_time: 0.0052 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:07:15 d2.utils.events]: \u001b[0m eta: 6:56:49 iter: 24699 total_loss: 0.9896 loss_cls: 0.3223 loss_box_reg: 0.3605 loss_rpn_cls: 0.05569 loss_rpn_loc: 0.2209 time: 0.3256 last_time: 0.4847 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:07:23 d2.utils.events]: \u001b[0m eta: 6:56:06 iter: 24719 total_loss: 0.9753 loss_cls: 0.3485 loss_box_reg: 0.3254 loss_rpn_cls: 0.05026 loss_rpn_loc: 0.2301 time: 0.3256 last_time: 0.5032 data_time: 0.0045 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:07:32 d2.utils.events]: \u001b[0m eta: 6:55:49 iter: 24739 total_loss: 0.9339 loss_cls: 0.2871 loss_box_reg: 0.3457 loss_rpn_cls: 0.04726 loss_rpn_loc: 0.1909 time: 0.3257 last_time: 0.4129 data_time: 0.0045 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:07:40 d2.utils.events]: \u001b[0m eta: 6:55:25 iter: 24759 total_loss: 1.146 loss_cls: 0.3572 loss_box_reg: 0.3686 loss_rpn_cls: 0.07224 loss_rpn_loc: 0.2583 time: 0.3258 last_time: 0.4020 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:07:48 d2.utils.events]: \u001b[0m eta: 6:55:21 iter: 24779 total_loss: 0.9688 loss_cls: 0.3437 loss_box_reg: 0.3558 loss_rpn_cls: 0.05399 loss_rpn_loc: 0.2178 time: 0.3258 last_time: 0.3416 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:07:56 d2.utils.events]: \u001b[0m eta: 6:55:13 iter: 24799 total_loss: 0.983 loss_cls: 0.3433 loss_box_reg: 0.3338 loss_rpn_cls: 0.06967 loss_rpn_loc: 0.2133 time: 0.3259 last_time: 0.4179 data_time: 0.0046 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:08:05 d2.utils.events]: \u001b[0m eta: 6:55:09 iter: 24819 total_loss: 0.9728 loss_cls: 0.3189 loss_box_reg: 0.3378 loss_rpn_cls: 0.06388 loss_rpn_loc: 0.2179 time: 0.3260 last_time: 0.3453 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:08:13 d2.utils.events]: \u001b[0m eta: 6:55:05 iter: 24839 total_loss: 0.9174 loss_cls: 0.3435 loss_box_reg: 0.3372 loss_rpn_cls: 0.05259 loss_rpn_loc: 0.2207 time: 0.3260 last_time: 0.4149 data_time: 0.0045 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:08:21 d2.utils.events]: \u001b[0m eta: 6:54:55 iter: 24859 total_loss: 1.068 loss_cls: 0.3065 loss_box_reg: 0.3794 loss_rpn_cls: 0.06025 loss_rpn_loc: 0.2463 time: 0.3261 last_time: 0.4043 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:08:29 d2.utils.events]: \u001b[0m eta: 6:54:45 iter: 24879 total_loss: 0.968 loss_cls: 0.3494 loss_box_reg: 0.3076 loss_rpn_cls: 0.05936 loss_rpn_loc: 0.229 time: 0.3261 last_time: 0.4085 data_time: 0.0044 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:08:37 d2.utils.events]: \u001b[0m eta: 6:54:22 iter: 24899 total_loss: 0.9559 loss_cls: 0.3063 loss_box_reg: 0.3478 loss_rpn_cls: 0.04177 loss_rpn_loc: 0.1945 time: 0.3262 last_time: 0.4158 data_time: 0.0044 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:08:45 d2.utils.events]: \u001b[0m eta: 6:54:34 iter: 24919 total_loss: 0.8911 loss_cls: 0.301 loss_box_reg: 0.3315 loss_rpn_cls: 0.05335 loss_rpn_loc: 0.2008 time: 0.3263 last_time: 0.4355 data_time: 0.0044 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:08:53 d2.utils.events]: \u001b[0m eta: 6:54:22 iter: 24939 total_loss: 1.032 loss_cls: 0.3403 loss_box_reg: 0.3211 loss_rpn_cls: 0.05473 loss_rpn_loc: 0.2312 time: 0.3263 last_time: 0.4102 data_time: 0.0044 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:09:02 d2.utils.events]: \u001b[0m eta: 6:54:14 iter: 24959 total_loss: 1.012 loss_cls: 0.3116 loss_box_reg: 0.3555 loss_rpn_cls: 0.05661 loss_rpn_loc: 0.2329 time: 0.3264 last_time: 0.4118 data_time: 0.0045 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:09:10 d2.utils.events]: \u001b[0m eta: 6:54:06 iter: 24979 total_loss: 0.9204 loss_cls: 0.3231 loss_box_reg: 0.3463 loss_rpn_cls: 0.05583 loss_rpn_loc: 0.2112 time: 0.3265 last_time: 0.3373 data_time: 0.0044 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:09:19 d2.utils.events]: \u001b[0m eta: 6:54:03 iter: 24999 total_loss: 0.8746 loss_cls: 0.3015 loss_box_reg: 0.2991 loss_rpn_cls: 0.04787 loss_rpn_loc: 0.2175 time: 0.3266 last_time: 0.4155 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:09:27 d2.utils.events]: \u001b[0m eta: 6:53:54 iter: 25019 total_loss: 0.8885 loss_cls: 0.2851 loss_box_reg: 0.3389 loss_rpn_cls: 0.04455 loss_rpn_loc: 0.2001 time: 0.3266 last_time: 0.3701 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:09:35 d2.utils.events]: \u001b[0m eta: 6:53:55 iter: 25039 total_loss: 0.9026 loss_cls: 0.3143 loss_box_reg: 0.3374 loss_rpn_cls: 0.0449 loss_rpn_loc: 0.1924 time: 0.3267 last_time: 0.4142 data_time: 0.0045 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:09:43 d2.utils.events]: \u001b[0m eta: 6:54:06 iter: 25059 total_loss: 1.048 loss_cls: 0.3754 loss_box_reg: 0.344 loss_rpn_cls: 0.05643 loss_rpn_loc: 0.223 time: 0.3268 last_time: 0.4313 data_time: 0.0045 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:09:52 d2.utils.events]: \u001b[0m eta: 6:54:08 iter: 25079 total_loss: 0.932 loss_cls: 0.3265 loss_box_reg: 0.3539 loss_rpn_cls: 0.06358 loss_rpn_loc: 0.2295 time: 0.3268 last_time: 0.4131 data_time: 0.0045 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:10:00 d2.utils.events]: \u001b[0m eta: 6:53:44 iter: 25099 total_loss: 0.9856 loss_cls: 0.3126 loss_box_reg: 0.3744 loss_rpn_cls: 0.05843 loss_rpn_loc: 0.2382 time: 0.3269 last_time: 0.4134 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:10:08 d2.utils.events]: \u001b[0m eta: 6:53:51 iter: 25119 total_loss: 0.9323 loss_cls: 0.3201 loss_box_reg: 0.3465 loss_rpn_cls: 0.03929 loss_rpn_loc: 0.2087 time: 0.3269 last_time: 0.4412 data_time: 0.0044 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:10:16 d2.utils.events]: \u001b[0m eta: 6:53:47 iter: 25139 total_loss: 1.026 loss_cls: 0.3691 loss_box_reg: 0.3392 loss_rpn_cls: 0.06364 loss_rpn_loc: 0.2194 time: 0.3270 last_time: 0.4464 data_time: 0.0046 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:10:24 d2.utils.events]: \u001b[0m eta: 6:53:41 iter: 25159 total_loss: 0.9992 loss_cls: 0.3607 loss_box_reg: 0.376 loss_rpn_cls: 0.04541 loss_rpn_loc: 0.2342 time: 0.3271 last_time: 0.4443 data_time: 0.0047 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:10:32 d2.utils.events]: \u001b[0m eta: 6:53:30 iter: 25179 total_loss: 1.023 loss_cls: 0.3465 loss_box_reg: 0.3887 loss_rpn_cls: 0.04394 loss_rpn_loc: 0.2599 time: 0.3271 last_time: 0.4218 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:10:41 d2.utils.events]: \u001b[0m eta: 6:53:29 iter: 25199 total_loss: 0.8849 loss_cls: 0.2992 loss_box_reg: 0.318 loss_rpn_cls: 0.0522 loss_rpn_loc: 0.2186 time: 0.3272 last_time: 0.4002 data_time: 0.0045 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:10:49 d2.utils.events]: \u001b[0m eta: 6:53:24 iter: 25219 total_loss: 1.018 loss_cls: 0.3337 loss_box_reg: 0.3591 loss_rpn_cls: 0.05502 loss_rpn_loc: 0.2319 time: 0.3273 last_time: 0.4387 data_time: 0.0044 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:10:57 d2.utils.events]: \u001b[0m eta: 6:53:12 iter: 25239 total_loss: 0.9745 loss_cls: 0.3205 loss_box_reg: 0.3447 loss_rpn_cls: 0.05346 loss_rpn_loc: 0.2399 time: 0.3273 last_time: 0.3429 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:11:05 d2.utils.events]: \u001b[0m eta: 6:53:00 iter: 25259 total_loss: 0.9649 loss_cls: 0.3431 loss_box_reg: 0.3321 loss_rpn_cls: 0.06124 loss_rpn_loc: 0.2117 time: 0.3274 last_time: 0.4005 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:11:13 d2.utils.events]: \u001b[0m eta: 6:53:06 iter: 25279 total_loss: 1.054 loss_cls: 0.353 loss_box_reg: 0.3506 loss_rpn_cls: 0.06232 loss_rpn_loc: 0.23 time: 0.3275 last_time: 0.4146 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:11:21 d2.utils.events]: \u001b[0m eta: 6:53:07 iter: 25299 total_loss: 1.087 loss_cls: 0.325 loss_box_reg: 0.3601 loss_rpn_cls: 0.05755 loss_rpn_loc: 0.2234 time: 0.3275 last_time: 0.3957 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:11:29 d2.utils.events]: \u001b[0m eta: 6:52:57 iter: 25319 total_loss: 0.9374 loss_cls: 0.3475 loss_box_reg: 0.354 loss_rpn_cls: 0.05809 loss_rpn_loc: 0.2217 time: 0.3276 last_time: 0.4115 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:11:37 d2.utils.events]: \u001b[0m eta: 6:52:43 iter: 25339 total_loss: 1.017 loss_cls: 0.3475 loss_box_reg: 0.369 loss_rpn_cls: 0.04866 loss_rpn_loc: 0.2198 time: 0.3276 last_time: 0.3328 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:11:45 d2.utils.events]: \u001b[0m eta: 6:52:35 iter: 25359 total_loss: 0.9524 loss_cls: 0.3388 loss_box_reg: 0.363 loss_rpn_cls: 0.06165 loss_rpn_loc: 0.2085 time: 0.3277 last_time: 0.3736 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:11:54 d2.utils.events]: \u001b[0m eta: 6:52:29 iter: 25379 total_loss: 0.9016 loss_cls: 0.3015 loss_box_reg: 0.3149 loss_rpn_cls: 0.05256 loss_rpn_loc: 0.1993 time: 0.3278 last_time: 0.4406 data_time: 0.0047 last_data_time: 0.0052 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:12:02 d2.utils.events]: \u001b[0m eta: 6:52:20 iter: 25399 total_loss: 0.9284 loss_cls: 0.2932 loss_box_reg: 0.3007 loss_rpn_cls: 0.04893 loss_rpn_loc: 0.2305 time: 0.3278 last_time: 0.4044 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:12:10 d2.utils.events]: \u001b[0m eta: 6:52:08 iter: 25419 total_loss: 0.91 loss_cls: 0.2584 loss_box_reg: 0.3806 loss_rpn_cls: 0.05275 loss_rpn_loc: 0.1902 time: 0.3279 last_time: 0.3892 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:12:18 d2.utils.events]: \u001b[0m eta: 6:52:05 iter: 25439 total_loss: 1.001 loss_cls: 0.3732 loss_box_reg: 0.3238 loss_rpn_cls: 0.05927 loss_rpn_loc: 0.2107 time: 0.3279 last_time: 0.4430 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:12:26 d2.utils.events]: \u001b[0m eta: 6:52:04 iter: 25459 total_loss: 0.9381 loss_cls: 0.3137 loss_box_reg: 0.3474 loss_rpn_cls: 0.04877 loss_rpn_loc: 0.2291 time: 0.3280 last_time: 0.4367 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:12:34 d2.utils.events]: \u001b[0m eta: 6:51:50 iter: 25479 total_loss: 0.9536 loss_cls: 0.3164 loss_box_reg: 0.3162 loss_rpn_cls: 0.06317 loss_rpn_loc: 0.2185 time: 0.3281 last_time: 0.3863 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:12:42 d2.utils.events]: \u001b[0m eta: 6:51:53 iter: 25499 total_loss: 0.9426 loss_cls: 0.315 loss_box_reg: 0.3236 loss_rpn_cls: 0.05729 loss_rpn_loc: 0.2384 time: 0.3281 last_time: 0.3151 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:12:50 d2.utils.events]: \u001b[0m eta: 6:51:43 iter: 25519 total_loss: 0.9429 loss_cls: 0.3227 loss_box_reg: 0.3587 loss_rpn_cls: 0.04768 loss_rpn_loc: 0.214 time: 0.3282 last_time: 0.4181 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:12:58 d2.utils.events]: \u001b[0m eta: 6:51:25 iter: 25539 total_loss: 0.9993 loss_cls: 0.334 loss_box_reg: 0.3583 loss_rpn_cls: 0.06654 loss_rpn_loc: 0.2205 time: 0.3282 last_time: 0.3939 data_time: 0.0045 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:13:06 d2.utils.events]: \u001b[0m eta: 6:51:17 iter: 25559 total_loss: 0.9566 loss_cls: 0.2834 loss_box_reg: 0.3144 loss_rpn_cls: 0.04842 loss_rpn_loc: 0.2312 time: 0.3283 last_time: 0.3893 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:13:15 d2.utils.events]: \u001b[0m eta: 6:51:18 iter: 25579 total_loss: 0.905 loss_cls: 0.3153 loss_box_reg: 0.305 loss_rpn_cls: 0.04685 loss_rpn_loc: 0.207 time: 0.3284 last_time: 0.3900 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:13:23 d2.utils.events]: \u001b[0m eta: 6:50:57 iter: 25599 total_loss: 0.9999 loss_cls: 0.3387 loss_box_reg: 0.3819 loss_rpn_cls: 0.04955 loss_rpn_loc: 0.1911 time: 0.3284 last_time: 0.3937 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:13:31 d2.utils.events]: \u001b[0m eta: 6:50:40 iter: 25619 total_loss: 0.892 loss_cls: 0.3149 loss_box_reg: 0.335 loss_rpn_cls: 0.04819 loss_rpn_loc: 0.2224 time: 0.3285 last_time: 0.4393 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:13:39 d2.utils.events]: \u001b[0m eta: 6:50:27 iter: 25639 total_loss: 0.9653 loss_cls: 0.3016 loss_box_reg: 0.3449 loss_rpn_cls: 0.07534 loss_rpn_loc: 0.2325 time: 0.3285 last_time: 0.3407 data_time: 0.0047 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:13:47 d2.utils.events]: \u001b[0m eta: 6:50:14 iter: 25659 total_loss: 0.9275 loss_cls: 0.3474 loss_box_reg: 0.3273 loss_rpn_cls: 0.04851 loss_rpn_loc: 0.2025 time: 0.3286 last_time: 0.3862 data_time: 0.0047 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:13:55 d2.utils.events]: \u001b[0m eta: 6:49:35 iter: 25679 total_loss: 0.9023 loss_cls: 0.3174 loss_box_reg: 0.315 loss_rpn_cls: 0.04965 loss_rpn_loc: 0.1987 time: 0.3286 last_time: 0.4097 data_time: 0.0045 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:14:03 d2.utils.events]: \u001b[0m eta: 6:48:24 iter: 25699 total_loss: 0.9413 loss_cls: 0.2796 loss_box_reg: 0.3467 loss_rpn_cls: 0.04365 loss_rpn_loc: 0.2062 time: 0.3287 last_time: 0.4335 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:14:11 d2.utils.events]: \u001b[0m eta: 6:49:16 iter: 25719 total_loss: 0.8443 loss_cls: 0.3201 loss_box_reg: 0.3005 loss_rpn_cls: 0.05313 loss_rpn_loc: 0.1946 time: 0.3288 last_time: 0.4169 data_time: 0.0047 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:14:19 d2.utils.events]: \u001b[0m eta: 6:48:35 iter: 25739 total_loss: 0.9539 loss_cls: 0.3368 loss_box_reg: 0.3765 loss_rpn_cls: 0.04925 loss_rpn_loc: 0.196 time: 0.3288 last_time: 0.3982 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:14:28 d2.utils.events]: \u001b[0m eta: 6:48:53 iter: 25759 total_loss: 1.066 loss_cls: 0.3562 loss_box_reg: 0.3763 loss_rpn_cls: 0.05946 loss_rpn_loc: 0.2231 time: 0.3289 last_time: 0.4857 data_time: 0.0049 last_data_time: 0.0052 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:14:37 d2.utils.events]: \u001b[0m eta: 6:49:14 iter: 25779 total_loss: 1 loss_cls: 0.3476 loss_box_reg: 0.3315 loss_rpn_cls: 0.04939 loss_rpn_loc: 0.2361 time: 0.3290 last_time: 0.4415 data_time: 0.0050 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:14:46 d2.utils.events]: \u001b[0m eta: 6:49:35 iter: 25799 total_loss: 0.9391 loss_cls: 0.3212 loss_box_reg: 0.3353 loss_rpn_cls: 0.05545 loss_rpn_loc: 0.1984 time: 0.3291 last_time: 0.4123 data_time: 0.0048 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:14:55 d2.utils.events]: \u001b[0m eta: 6:49:45 iter: 25819 total_loss: 0.9814 loss_cls: 0.3257 loss_box_reg: 0.3284 loss_rpn_cls: 0.05085 loss_rpn_loc: 0.2274 time: 0.3292 last_time: 0.4255 data_time: 0.0049 last_data_time: 0.0053 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:15:03 d2.utils.events]: \u001b[0m eta: 6:49:33 iter: 25839 total_loss: 0.9928 loss_cls: 0.3334 loss_box_reg: 0.3533 loss_rpn_cls: 0.05536 loss_rpn_loc: 0.2217 time: 0.3292 last_time: 0.4157 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:15:12 d2.utils.events]: \u001b[0m eta: 6:49:52 iter: 25859 total_loss: 0.9803 loss_cls: 0.3231 loss_box_reg: 0.3749 loss_rpn_cls: 0.05111 loss_rpn_loc: 0.2055 time: 0.3293 last_time: 0.5087 data_time: 0.0049 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:15:20 d2.utils.events]: \u001b[0m eta: 6:49:55 iter: 25879 total_loss: 0.9129 loss_cls: 0.3249 loss_box_reg: 0.3172 loss_rpn_cls: 0.04535 loss_rpn_loc: 0.2021 time: 0.3294 last_time: 0.4442 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:15:28 d2.utils.events]: \u001b[0m eta: 6:49:49 iter: 25899 total_loss: 0.9345 loss_cls: 0.2912 loss_box_reg: 0.3377 loss_rpn_cls: 0.05671 loss_rpn_loc: 0.2032 time: 0.3294 last_time: 0.4170 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:15:37 d2.utils.events]: \u001b[0m eta: 6:49:43 iter: 25919 total_loss: 0.9406 loss_cls: 0.3404 loss_box_reg: 0.3426 loss_rpn_cls: 0.05336 loss_rpn_loc: 0.2091 time: 0.3295 last_time: 0.3840 data_time: 0.0050 last_data_time: 0.0054 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:15:46 d2.utils.events]: \u001b[0m eta: 6:50:00 iter: 25939 total_loss: 0.8248 loss_cls: 0.2855 loss_box_reg: 0.3186 loss_rpn_cls: 0.03894 loss_rpn_loc: 0.1821 time: 0.3296 last_time: 0.3769 data_time: 0.0049 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:15:54 d2.utils.events]: \u001b[0m eta: 6:49:59 iter: 25959 total_loss: 0.9796 loss_cls: 0.3103 loss_box_reg: 0.3509 loss_rpn_cls: 0.07155 loss_rpn_loc: 0.2324 time: 0.3297 last_time: 0.3793 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:16:02 d2.utils.events]: \u001b[0m eta: 6:49:29 iter: 25979 total_loss: 0.962 loss_cls: 0.3633 loss_box_reg: 0.3442 loss_rpn_cls: 0.05112 loss_rpn_loc: 0.1777 time: 0.3297 last_time: 0.3723 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:16:10 d2.utils.events]: \u001b[0m eta: 6:49:18 iter: 25999 total_loss: 0.8488 loss_cls: 0.2933 loss_box_reg: 0.3397 loss_rpn_cls: 0.05049 loss_rpn_loc: 0.2113 time: 0.3298 last_time: 0.4225 data_time: 0.0049 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:16:19 d2.utils.events]: \u001b[0m eta: 6:49:22 iter: 26019 total_loss: 0.9372 loss_cls: 0.3253 loss_box_reg: 0.3651 loss_rpn_cls: 0.04992 loss_rpn_loc: 0.2199 time: 0.3299 last_time: 0.4857 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:16:27 d2.utils.events]: \u001b[0m eta: 6:49:13 iter: 26039 total_loss: 0.9016 loss_cls: 0.3231 loss_box_reg: 0.3127 loss_rpn_cls: 0.04905 loss_rpn_loc: 0.2006 time: 0.3299 last_time: 0.4376 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:16:35 d2.utils.events]: \u001b[0m eta: 6:49:05 iter: 26059 total_loss: 1.001 loss_cls: 0.3375 loss_box_reg: 0.3837 loss_rpn_cls: 0.06523 loss_rpn_loc: 0.2254 time: 0.3300 last_time: 0.3437 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:16:44 d2.utils.events]: \u001b[0m eta: 6:49:22 iter: 26079 total_loss: 1.027 loss_cls: 0.3604 loss_box_reg: 0.3428 loss_rpn_cls: 0.05993 loss_rpn_loc: 0.2313 time: 0.3301 last_time: 0.5010 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:16:53 d2.utils.events]: \u001b[0m eta: 6:49:54 iter: 26099 total_loss: 1.047 loss_cls: 0.3595 loss_box_reg: 0.3712 loss_rpn_cls: 0.05854 loss_rpn_loc: 0.2517 time: 0.3302 last_time: 0.3964 data_time: 0.0048 last_data_time: 0.0052 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:17:02 d2.utils.events]: \u001b[0m eta: 6:50:12 iter: 26119 total_loss: 0.9097 loss_cls: 0.3098 loss_box_reg: 0.3582 loss_rpn_cls: 0.04425 loss_rpn_loc: 0.1846 time: 0.3303 last_time: 0.3231 data_time: 0.0049 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:17:11 d2.utils.events]: \u001b[0m eta: 6:50:08 iter: 26139 total_loss: 0.9375 loss_cls: 0.2989 loss_box_reg: 0.3672 loss_rpn_cls: 0.05036 loss_rpn_loc: 0.2344 time: 0.3304 last_time: 0.4246 data_time: 0.0049 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:17:20 d2.utils.events]: \u001b[0m eta: 6:50:06 iter: 26159 total_loss: 0.9776 loss_cls: 0.3573 loss_box_reg: 0.3313 loss_rpn_cls: 0.06514 loss_rpn_loc: 0.2266 time: 0.3304 last_time: 0.4881 data_time: 0.0049 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:17:28 d2.utils.events]: \u001b[0m eta: 6:49:56 iter: 26179 total_loss: 0.8832 loss_cls: 0.3028 loss_box_reg: 0.3307 loss_rpn_cls: 0.05223 loss_rpn_loc: 0.2171 time: 0.3305 last_time: 0.4361 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:17:37 d2.utils.events]: \u001b[0m eta: 6:50:26 iter: 26199 total_loss: 0.9835 loss_cls: 0.3501 loss_box_reg: 0.3442 loss_rpn_cls: 0.05944 loss_rpn_loc: 0.2355 time: 0.3306 last_time: 0.4868 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:17:46 d2.utils.events]: \u001b[0m eta: 6:50:32 iter: 26219 total_loss: 0.9787 loss_cls: 0.3549 loss_box_reg: 0.3245 loss_rpn_cls: 0.04871 loss_rpn_loc: 0.206 time: 0.3307 last_time: 0.4499 data_time: 0.0048 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:17:54 d2.utils.events]: \u001b[0m eta: 6:50:48 iter: 26239 total_loss: 1.047 loss_cls: 0.3517 loss_box_reg: 0.3803 loss_rpn_cls: 0.06348 loss_rpn_loc: 0.2398 time: 0.3307 last_time: 0.4022 data_time: 0.0046 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:18:03 d2.utils.events]: \u001b[0m eta: 6:50:48 iter: 26259 total_loss: 0.9567 loss_cls: 0.3345 loss_box_reg: 0.3568 loss_rpn_cls: 0.05932 loss_rpn_loc: 0.2056 time: 0.3308 last_time: 0.4136 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:18:12 d2.utils.events]: \u001b[0m eta: 6:50:51 iter: 26279 total_loss: 0.9778 loss_cls: 0.3375 loss_box_reg: 0.3196 loss_rpn_cls: 0.06472 loss_rpn_loc: 0.1814 time: 0.3309 last_time: 0.4920 data_time: 0.0049 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:18:20 d2.utils.events]: \u001b[0m eta: 6:50:41 iter: 26299 total_loss: 1.035 loss_cls: 0.3686 loss_box_reg: 0.3445 loss_rpn_cls: 0.05751 loss_rpn_loc: 0.2474 time: 0.3310 last_time: 0.4485 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:18:28 d2.utils.events]: \u001b[0m eta: 6:50:41 iter: 26319 total_loss: 0.992 loss_cls: 0.3441 loss_box_reg: 0.3367 loss_rpn_cls: 0.057 loss_rpn_loc: 0.2318 time: 0.3310 last_time: 0.4413 data_time: 0.0047 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:18:36 d2.utils.events]: \u001b[0m eta: 6:50:42 iter: 26339 total_loss: 0.9545 loss_cls: 0.3157 loss_box_reg: 0.3203 loss_rpn_cls: 0.06572 loss_rpn_loc: 0.2348 time: 0.3311 last_time: 0.4489 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:18:45 d2.utils.events]: \u001b[0m eta: 6:50:51 iter: 26359 total_loss: 0.9217 loss_cls: 0.3155 loss_box_reg: 0.3489 loss_rpn_cls: 0.05214 loss_rpn_loc: 0.2219 time: 0.3312 last_time: 0.4542 data_time: 0.0048 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:18:54 d2.utils.events]: \u001b[0m eta: 6:51:24 iter: 26379 total_loss: 1.006 loss_cls: 0.3226 loss_box_reg: 0.3706 loss_rpn_cls: 0.07572 loss_rpn_loc: 0.2487 time: 0.3313 last_time: 0.4901 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:19:03 d2.utils.events]: \u001b[0m eta: 6:51:30 iter: 26399 total_loss: 1.036 loss_cls: 0.3638 loss_box_reg: 0.3481 loss_rpn_cls: 0.06212 loss_rpn_loc: 0.2568 time: 0.3314 last_time: 0.4753 data_time: 0.0049 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:19:13 d2.utils.events]: \u001b[0m eta: 6:51:37 iter: 26419 total_loss: 0.9283 loss_cls: 0.2903 loss_box_reg: 0.3405 loss_rpn_cls: 0.05676 loss_rpn_loc: 0.205 time: 0.3315 last_time: 0.4421 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:19:21 d2.utils.events]: \u001b[0m eta: 6:51:34 iter: 26439 total_loss: 1.044 loss_cls: 0.3314 loss_box_reg: 0.3637 loss_rpn_cls: 0.05371 loss_rpn_loc: 0.2247 time: 0.3315 last_time: 0.4256 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:19:29 d2.utils.events]: \u001b[0m eta: 6:51:27 iter: 26459 total_loss: 0.9262 loss_cls: 0.3129 loss_box_reg: 0.3581 loss_rpn_cls: 0.06095 loss_rpn_loc: 0.2289 time: 0.3316 last_time: 0.4438 data_time: 0.0047 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:19:38 d2.utils.events]: \u001b[0m eta: 6:51:29 iter: 26479 total_loss: 0.9256 loss_cls: 0.2939 loss_box_reg: 0.3416 loss_rpn_cls: 0.04834 loss_rpn_loc: 0.2435 time: 0.3316 last_time: 0.3999 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:19:46 d2.utils.events]: \u001b[0m eta: 6:51:39 iter: 26499 total_loss: 0.9813 loss_cls: 0.3259 loss_box_reg: 0.3921 loss_rpn_cls: 0.05386 loss_rpn_loc: 0.2336 time: 0.3317 last_time: 0.4486 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:19:54 d2.utils.events]: \u001b[0m eta: 6:51:49 iter: 26519 total_loss: 1.012 loss_cls: 0.3777 loss_box_reg: 0.3454 loss_rpn_cls: 0.05095 loss_rpn_loc: 0.2107 time: 0.3318 last_time: 0.4513 data_time: 0.0047 last_data_time: 0.0052 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:20:02 d2.utils.events]: \u001b[0m eta: 6:51:40 iter: 26539 total_loss: 0.9119 loss_cls: 0.2678 loss_box_reg: 0.3182 loss_rpn_cls: 0.05074 loss_rpn_loc: 0.186 time: 0.3318 last_time: 0.4433 data_time: 0.0048 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:20:11 d2.utils.events]: \u001b[0m eta: 6:51:50 iter: 26559 total_loss: 0.9362 loss_cls: 0.2943 loss_box_reg: 0.3229 loss_rpn_cls: 0.05041 loss_rpn_loc: 0.2376 time: 0.3319 last_time: 0.3986 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:20:19 d2.utils.events]: \u001b[0m eta: 6:52:31 iter: 26579 total_loss: 0.7894 loss_cls: 0.2805 loss_box_reg: 0.2983 loss_rpn_cls: 0.04197 loss_rpn_loc: 0.1865 time: 0.3320 last_time: 0.4448 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:20:28 d2.utils.events]: \u001b[0m eta: 6:53:26 iter: 26599 total_loss: 0.8986 loss_cls: 0.2872 loss_box_reg: 0.3338 loss_rpn_cls: 0.05232 loss_rpn_loc: 0.2176 time: 0.3321 last_time: 0.4805 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:20:37 d2.utils.events]: \u001b[0m eta: 6:53:21 iter: 26619 total_loss: 0.9943 loss_cls: 0.3501 loss_box_reg: 0.3449 loss_rpn_cls: 0.04688 loss_rpn_loc: 0.2351 time: 0.3321 last_time: 0.3802 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:20:45 d2.utils.events]: \u001b[0m eta: 6:53:10 iter: 26639 total_loss: 0.8959 loss_cls: 0.2986 loss_box_reg: 0.3316 loss_rpn_cls: 0.04799 loss_rpn_loc: 0.2282 time: 0.3322 last_time: 0.3197 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:20:53 d2.utils.events]: \u001b[0m eta: 6:52:48 iter: 26659 total_loss: 0.9417 loss_cls: 0.3204 loss_box_reg: 0.3487 loss_rpn_cls: 0.05258 loss_rpn_loc: 0.243 time: 0.3322 last_time: 0.3252 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:21:02 d2.utils.events]: \u001b[0m eta: 6:53:19 iter: 26679 total_loss: 1.022 loss_cls: 0.35 loss_box_reg: 0.3432 loss_rpn_cls: 0.04967 loss_rpn_loc: 0.2451 time: 0.3323 last_time: 0.4180 data_time: 0.0048 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:21:10 d2.utils.events]: \u001b[0m eta: 6:53:19 iter: 26699 total_loss: 0.929 loss_cls: 0.3405 loss_box_reg: 0.3241 loss_rpn_cls: 0.04537 loss_rpn_loc: 0.2115 time: 0.3324 last_time: 0.3465 data_time: 0.0045 last_data_time: 0.0041 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:21:19 d2.utils.events]: \u001b[0m eta: 6:53:15 iter: 26719 total_loss: 0.9327 loss_cls: 0.2844 loss_box_reg: 0.3197 loss_rpn_cls: 0.05292 loss_rpn_loc: 0.2118 time: 0.3324 last_time: 0.4427 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:21:27 d2.utils.events]: \u001b[0m eta: 6:53:26 iter: 26739 total_loss: 0.9263 loss_cls: 0.3077 loss_box_reg: 0.3428 loss_rpn_cls: 0.04319 loss_rpn_loc: 0.1963 time: 0.3325 last_time: 0.4492 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:21:36 d2.utils.events]: \u001b[0m eta: 6:53:18 iter: 26759 total_loss: 0.857 loss_cls: 0.3049 loss_box_reg: 0.3174 loss_rpn_cls: 0.04374 loss_rpn_loc: 0.2074 time: 0.3326 last_time: 0.4630 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:21:45 d2.utils.events]: \u001b[0m eta: 6:53:00 iter: 26779 total_loss: 0.9351 loss_cls: 0.3044 loss_box_reg: 0.3184 loss_rpn_cls: 0.0566 loss_rpn_loc: 0.2274 time: 0.3327 last_time: 0.4131 data_time: 0.0048 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:21:54 d2.utils.events]: \u001b[0m eta: 6:53:12 iter: 26799 total_loss: 0.9192 loss_cls: 0.3232 loss_box_reg: 0.3283 loss_rpn_cls: 0.04712 loss_rpn_loc: 0.2198 time: 0.3328 last_time: 0.5085 data_time: 0.0050 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:22:02 d2.utils.events]: \u001b[0m eta: 6:52:22 iter: 26819 total_loss: 0.9763 loss_cls: 0.3171 loss_box_reg: 0.3544 loss_rpn_cls: 0.05214 loss_rpn_loc: 0.2302 time: 0.3328 last_time: 0.3681 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:22:11 d2.utils.events]: \u001b[0m eta: 6:53:12 iter: 26839 total_loss: 1.019 loss_cls: 0.3317 loss_box_reg: 0.3571 loss_rpn_cls: 0.05119 loss_rpn_loc: 0.2391 time: 0.3329 last_time: 0.4009 data_time: 0.0049 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:22:20 d2.utils.events]: \u001b[0m eta: 6:52:06 iter: 26859 total_loss: 0.9888 loss_cls: 0.3454 loss_box_reg: 0.3418 loss_rpn_cls: 0.05505 loss_rpn_loc: 0.201 time: 0.3330 last_time: 0.4179 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:22:28 d2.utils.events]: \u001b[0m eta: 6:51:56 iter: 26879 total_loss: 0.8868 loss_cls: 0.3125 loss_box_reg: 0.3514 loss_rpn_cls: 0.05414 loss_rpn_loc: 0.1902 time: 0.3331 last_time: 0.4449 data_time: 0.0048 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:22:37 d2.utils.events]: \u001b[0m eta: 6:52:07 iter: 26899 total_loss: 0.9235 loss_cls: 0.314 loss_box_reg: 0.3263 loss_rpn_cls: 0.05196 loss_rpn_loc: 0.2182 time: 0.3331 last_time: 0.4458 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:22:45 d2.utils.events]: \u001b[0m eta: 6:51:40 iter: 26919 total_loss: 0.8932 loss_cls: 0.3267 loss_box_reg: 0.3253 loss_rpn_cls: 0.04824 loss_rpn_loc: 0.1825 time: 0.3332 last_time: 0.4239 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:22:54 d2.utils.events]: \u001b[0m eta: 6:51:29 iter: 26939 total_loss: 1.071 loss_cls: 0.3447 loss_box_reg: 0.3613 loss_rpn_cls: 0.06275 loss_rpn_loc: 0.2389 time: 0.3333 last_time: 0.4140 data_time: 0.0050 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:23:03 d2.utils.events]: \u001b[0m eta: 6:52:21 iter: 26959 total_loss: 0.9633 loss_cls: 0.3068 loss_box_reg: 0.3944 loss_rpn_cls: 0.03919 loss_rpn_loc: 0.2009 time: 0.3334 last_time: 0.5110 data_time: 0.0048 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:23:13 d2.utils.events]: \u001b[0m eta: 6:57:55 iter: 26979 total_loss: 0.9697 loss_cls: 0.3178 loss_box_reg: 0.3507 loss_rpn_cls: 0.05046 loss_rpn_loc: 0.2265 time: 0.3335 last_time: 0.4079 data_time: 0.0049 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:23:22 d2.utils.events]: \u001b[0m eta: 7:01:39 iter: 26999 total_loss: 0.9192 loss_cls: 0.3054 loss_box_reg: 0.3347 loss_rpn_cls: 0.06096 loss_rpn_loc: 0.2027 time: 0.3335 last_time: 0.4822 data_time: 0.0049 last_data_time: 0.0055 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:23:30 d2.utils.events]: \u001b[0m eta: 7:00:09 iter: 27019 total_loss: 0.8003 loss_cls: 0.2566 loss_box_reg: 0.303 loss_rpn_cls: 0.05303 loss_rpn_loc: 0.2022 time: 0.3336 last_time: 0.3944 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:23:39 d2.utils.events]: \u001b[0m eta: 7:01:16 iter: 27039 total_loss: 0.8754 loss_cls: 0.314 loss_box_reg: 0.3178 loss_rpn_cls: 0.04292 loss_rpn_loc: 0.2031 time: 0.3337 last_time: 0.3979 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:23:47 d2.utils.events]: \u001b[0m eta: 7:02:09 iter: 27059 total_loss: 0.8789 loss_cls: 0.3004 loss_box_reg: 0.302 loss_rpn_cls: 0.06316 loss_rpn_loc: 0.2243 time: 0.3337 last_time: 0.4465 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:23:56 d2.utils.events]: \u001b[0m eta: 7:03:06 iter: 27079 total_loss: 0.9285 loss_cls: 0.3288 loss_box_reg: 0.331 loss_rpn_cls: 0.0647 loss_rpn_loc: 0.2266 time: 0.3338 last_time: 0.4126 data_time: 0.0048 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:24:04 d2.utils.events]: \u001b[0m eta: 7:01:23 iter: 27099 total_loss: 0.9255 loss_cls: 0.3136 loss_box_reg: 0.3467 loss_rpn_cls: 0.06171 loss_rpn_loc: 0.2443 time: 0.3339 last_time: 0.3973 data_time: 0.0046 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:24:13 d2.utils.events]: \u001b[0m eta: 7:00:42 iter: 27119 total_loss: 1.034 loss_cls: 0.3722 loss_box_reg: 0.3329 loss_rpn_cls: 0.05773 loss_rpn_loc: 0.2412 time: 0.3340 last_time: 0.4241 data_time: 0.0047 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:24:22 d2.utils.events]: \u001b[0m eta: 7:00:15 iter: 27139 total_loss: 1.07 loss_cls: 0.3492 loss_box_reg: 0.3558 loss_rpn_cls: 0.0556 loss_rpn_loc: 0.2123 time: 0.3340 last_time: 0.4200 data_time: 0.0048 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:24:31 d2.utils.events]: \u001b[0m eta: 7:00:58 iter: 27159 total_loss: 0.8678 loss_cls: 0.2924 loss_box_reg: 0.288 loss_rpn_cls: 0.05361 loss_rpn_loc: 0.2167 time: 0.3341 last_time: 0.5138 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:24:40 d2.utils.events]: \u001b[0m eta: 7:01:28 iter: 27179 total_loss: 1.111 loss_cls: 0.3558 loss_box_reg: 0.4135 loss_rpn_cls: 0.06895 loss_rpn_loc: 0.2351 time: 0.3342 last_time: 0.4482 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:24:49 d2.utils.events]: \u001b[0m eta: 7:01:13 iter: 27199 total_loss: 0.9054 loss_cls: 0.2917 loss_box_reg: 0.3551 loss_rpn_cls: 0.0551 loss_rpn_loc: 0.2151 time: 0.3343 last_time: 0.4826 data_time: 0.0049 last_data_time: 0.0054 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:24:58 d2.utils.events]: \u001b[0m eta: 7:02:05 iter: 27219 total_loss: 0.8804 loss_cls: 0.2858 loss_box_reg: 0.3088 loss_rpn_cls: 0.05532 loss_rpn_loc: 0.2162 time: 0.3344 last_time: 0.4903 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:25:07 d2.utils.events]: \u001b[0m eta: 7:02:50 iter: 27239 total_loss: 1.115 loss_cls: 0.3453 loss_box_reg: 0.3824 loss_rpn_cls: 0.06928 loss_rpn_loc: 0.1973 time: 0.3345 last_time: 0.4873 data_time: 0.0051 last_data_time: 0.0052 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:25:16 d2.utils.events]: \u001b[0m eta: 7:03:20 iter: 27259 total_loss: 0.8946 loss_cls: 0.3076 loss_box_reg: 0.3029 loss_rpn_cls: 0.04918 loss_rpn_loc: 0.191 time: 0.3346 last_time: 0.4637 data_time: 0.0048 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:25:26 d2.utils.events]: \u001b[0m eta: 7:03:38 iter: 27279 total_loss: 0.936 loss_cls: 0.3195 loss_box_reg: 0.3372 loss_rpn_cls: 0.05546 loss_rpn_loc: 0.2307 time: 0.3347 last_time: 0.4885 data_time: 0.0050 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:25:35 d2.utils.events]: \u001b[0m eta: 7:04:09 iter: 27299 total_loss: 0.8086 loss_cls: 0.2713 loss_box_reg: 0.33 loss_rpn_cls: 0.04024 loss_rpn_loc: 0.2073 time: 0.3348 last_time: 0.3595 data_time: 0.0050 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:25:44 d2.utils.events]: \u001b[0m eta: 7:04:19 iter: 27319 total_loss: 0.871 loss_cls: 0.2867 loss_box_reg: 0.3366 loss_rpn_cls: 0.05487 loss_rpn_loc: 0.1949 time: 0.3348 last_time: 0.4602 data_time: 0.0049 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:25:53 d2.utils.events]: \u001b[0m eta: 7:04:51 iter: 27339 total_loss: 0.9564 loss_cls: 0.3118 loss_box_reg: 0.3295 loss_rpn_cls: 0.05305 loss_rpn_loc: 0.2099 time: 0.3349 last_time: 0.4426 data_time: 0.0049 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:26:03 d2.utils.events]: \u001b[0m eta: 7:04:54 iter: 27359 total_loss: 0.9791 loss_cls: 0.3314 loss_box_reg: 0.3139 loss_rpn_cls: 0.07332 loss_rpn_loc: 0.247 time: 0.3350 last_time: 0.4081 data_time: 0.0051 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:26:12 d2.utils.events]: \u001b[0m eta: 7:05:02 iter: 27379 total_loss: 1.014 loss_cls: 0.3073 loss_box_reg: 0.3671 loss_rpn_cls: 0.06427 loss_rpn_loc: 0.2274 time: 0.3351 last_time: 0.4871 data_time: 0.0049 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:26:22 d2.utils.events]: \u001b[0m eta: 7:05:02 iter: 27399 total_loss: 0.9648 loss_cls: 0.3348 loss_box_reg: 0.3333 loss_rpn_cls: 0.05681 loss_rpn_loc: 0.1849 time: 0.3352 last_time: 0.5135 data_time: 0.0052 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:26:31 d2.utils.events]: \u001b[0m eta: 7:04:45 iter: 27419 total_loss: 1.046 loss_cls: 0.3972 loss_box_reg: 0.3454 loss_rpn_cls: 0.06927 loss_rpn_loc: 0.2026 time: 0.3353 last_time: 0.4816 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:26:39 d2.utils.events]: \u001b[0m eta: 7:04:39 iter: 27439 total_loss: 0.9494 loss_cls: 0.3159 loss_box_reg: 0.3673 loss_rpn_cls: 0.04884 loss_rpn_loc: 0.2229 time: 0.3354 last_time: 0.3619 data_time: 0.0049 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:26:49 d2.utils.events]: \u001b[0m eta: 7:05:00 iter: 27459 total_loss: 0.8811 loss_cls: 0.2984 loss_box_reg: 0.3129 loss_rpn_cls: 0.0568 loss_rpn_loc: 0.2019 time: 0.3355 last_time: 0.4855 data_time: 0.0050 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:26:58 d2.utils.events]: \u001b[0m eta: 7:05:48 iter: 27479 total_loss: 0.8964 loss_cls: 0.2856 loss_box_reg: 0.309 loss_rpn_cls: 0.05272 loss_rpn_loc: 0.2005 time: 0.3356 last_time: 0.4841 data_time: 0.0049 last_data_time: 0.0052 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:27:07 d2.utils.events]: \u001b[0m eta: 7:05:49 iter: 27499 total_loss: 1.041 loss_cls: 0.3287 loss_box_reg: 0.3468 loss_rpn_cls: 0.0578 loss_rpn_loc: 0.2234 time: 0.3357 last_time: 0.4885 data_time: 0.0050 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:27:16 d2.utils.events]: \u001b[0m eta: 7:06:10 iter: 27519 total_loss: 0.939 loss_cls: 0.2756 loss_box_reg: 0.3576 loss_rpn_cls: 0.04884 loss_rpn_loc: 0.2231 time: 0.3358 last_time: 0.5107 data_time: 0.0049 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:27:26 d2.utils.events]: \u001b[0m eta: 7:06:51 iter: 27539 total_loss: 0.9513 loss_cls: 0.3284 loss_box_reg: 0.3517 loss_rpn_cls: 0.05324 loss_rpn_loc: 0.2292 time: 0.3359 last_time: 0.5177 data_time: 0.0050 last_data_time: 0.0054 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:27:35 d2.utils.events]: \u001b[0m eta: 7:06:53 iter: 27559 total_loss: 0.9025 loss_cls: 0.2642 loss_box_reg: 0.304 loss_rpn_cls: 0.04855 loss_rpn_loc: 0.1998 time: 0.3359 last_time: 0.4895 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:27:44 d2.utils.events]: \u001b[0m eta: 7:07:36 iter: 27579 total_loss: 0.9956 loss_cls: 0.3579 loss_box_reg: 0.3241 loss_rpn_cls: 0.05667 loss_rpn_loc: 0.2461 time: 0.3360 last_time: 0.3810 data_time: 0.0050 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:27:53 d2.utils.events]: \u001b[0m eta: 7:06:42 iter: 27599 total_loss: 0.8604 loss_cls: 0.3136 loss_box_reg: 0.3259 loss_rpn_cls: 0.05329 loss_rpn_loc: 0.2125 time: 0.3361 last_time: 0.3944 data_time: 0.0047 last_data_time: 0.0041 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:28:01 d2.utils.events]: \u001b[0m eta: 7:06:15 iter: 27619 total_loss: 0.9652 loss_cls: 0.3318 loss_box_reg: 0.357 loss_rpn_cls: 0.05696 loss_rpn_loc: 0.209 time: 0.3362 last_time: 0.3804 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:28:10 d2.utils.events]: \u001b[0m eta: 7:06:09 iter: 27639 total_loss: 0.9524 loss_cls: 0.3294 loss_box_reg: 0.3132 loss_rpn_cls: 0.05986 loss_rpn_loc: 0.2278 time: 0.3363 last_time: 0.4339 data_time: 0.0047 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:28:19 d2.utils.events]: \u001b[0m eta: 7:06:26 iter: 27659 total_loss: 0.9219 loss_cls: 0.3161 loss_box_reg: 0.3431 loss_rpn_cls: 0.04678 loss_rpn_loc: 0.2093 time: 0.3363 last_time: 0.4378 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:28:28 d2.utils.events]: \u001b[0m eta: 7:06:16 iter: 27679 total_loss: 0.9148 loss_cls: 0.2904 loss_box_reg: 0.2884 loss_rpn_cls: 0.05885 loss_rpn_loc: 0.1932 time: 0.3364 last_time: 0.4455 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:28:37 d2.utils.events]: \u001b[0m eta: 7:06:50 iter: 27699 total_loss: 0.9618 loss_cls: 0.3288 loss_box_reg: 0.3331 loss_rpn_cls: 0.05055 loss_rpn_loc: 0.2027 time: 0.3365 last_time: 0.4541 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:28:46 d2.utils.events]: \u001b[0m eta: 7:07:17 iter: 27719 total_loss: 0.929 loss_cls: 0.3024 loss_box_reg: 0.3167 loss_rpn_cls: 0.04646 loss_rpn_loc: 0.2242 time: 0.3366 last_time: 0.3446 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:28:54 d2.utils.events]: \u001b[0m eta: 7:07:07 iter: 27739 total_loss: 1.029 loss_cls: 0.37 loss_box_reg: 0.3479 loss_rpn_cls: 0.05381 loss_rpn_loc: 0.2345 time: 0.3366 last_time: 0.4054 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:29:03 d2.utils.events]: \u001b[0m eta: 7:06:23 iter: 27759 total_loss: 0.9119 loss_cls: 0.3205 loss_box_reg: 0.3155 loss_rpn_cls: 0.0543 loss_rpn_loc: 0.2037 time: 0.3367 last_time: 0.4201 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:29:11 d2.utils.events]: \u001b[0m eta: 7:05:07 iter: 27779 total_loss: 0.8625 loss_cls: 0.3172 loss_box_reg: 0.3318 loss_rpn_cls: 0.04946 loss_rpn_loc: 0.1881 time: 0.3367 last_time: 0.4159 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:29:20 d2.utils.events]: \u001b[0m eta: 7:04:35 iter: 27799 total_loss: 0.8063 loss_cls: 0.3188 loss_box_reg: 0.3003 loss_rpn_cls: 0.04562 loss_rpn_loc: 0.1864 time: 0.3368 last_time: 0.5120 data_time: 0.0048 last_data_time: 0.0052 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:29:29 d2.utils.events]: \u001b[0m eta: 7:04:39 iter: 27819 total_loss: 0.8916 loss_cls: 0.2655 loss_box_reg: 0.3497 loss_rpn_cls: 0.06153 loss_rpn_loc: 0.198 time: 0.3369 last_time: 0.4393 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:29:37 d2.utils.events]: \u001b[0m eta: 7:04:17 iter: 27839 total_loss: 0.9351 loss_cls: 0.3428 loss_box_reg: 0.3302 loss_rpn_cls: 0.06299 loss_rpn_loc: 0.1977 time: 0.3370 last_time: 0.4855 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:29:46 d2.utils.events]: \u001b[0m eta: 7:04:49 iter: 27859 total_loss: 0.8278 loss_cls: 0.2807 loss_box_reg: 0.3275 loss_rpn_cls: 0.04934 loss_rpn_loc: 0.2011 time: 0.3370 last_time: 0.4883 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:29:55 d2.utils.events]: \u001b[0m eta: 7:05:49 iter: 27879 total_loss: 1.038 loss_cls: 0.3711 loss_box_reg: 0.3763 loss_rpn_cls: 0.05829 loss_rpn_loc: 0.2537 time: 0.3371 last_time: 0.4703 data_time: 0.0051 last_data_time: 0.0052 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:30:04 d2.utils.events]: \u001b[0m eta: 7:05:56 iter: 27899 total_loss: 0.8309 loss_cls: 0.2666 loss_box_reg: 0.3178 loss_rpn_cls: 0.04586 loss_rpn_loc: 0.2075 time: 0.3372 last_time: 0.4328 data_time: 0.0047 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:30:13 d2.utils.events]: \u001b[0m eta: 7:06:25 iter: 27919 total_loss: 0.9233 loss_cls: 0.3172 loss_box_reg: 0.324 loss_rpn_cls: 0.04355 loss_rpn_loc: 0.1778 time: 0.3373 last_time: 0.4736 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:30:22 d2.utils.events]: \u001b[0m eta: 7:06:13 iter: 27939 total_loss: 1.012 loss_cls: 0.3388 loss_box_reg: 0.4056 loss_rpn_cls: 0.05374 loss_rpn_loc: 0.2101 time: 0.3374 last_time: 0.4863 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:30:31 d2.utils.events]: \u001b[0m eta: 7:05:40 iter: 27959 total_loss: 0.9753 loss_cls: 0.3198 loss_box_reg: 0.3529 loss_rpn_cls: 0.06793 loss_rpn_loc: 0.2388 time: 0.3374 last_time: 0.4386 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:30:40 d2.utils.events]: \u001b[0m eta: 7:05:16 iter: 27979 total_loss: 0.9785 loss_cls: 0.3503 loss_box_reg: 0.3156 loss_rpn_cls: 0.0515 loss_rpn_loc: 0.2246 time: 0.3375 last_time: 0.5165 data_time: 0.0049 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:30:49 d2.utils.events]: \u001b[0m eta: 7:04:41 iter: 27999 total_loss: 0.8938 loss_cls: 0.317 loss_box_reg: 0.3394 loss_rpn_cls: 0.06232 loss_rpn_loc: 0.1903 time: 0.3376 last_time: 0.4270 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:30:57 d2.utils.events]: \u001b[0m eta: 7:04:33 iter: 28019 total_loss: 0.8161 loss_cls: 0.2651 loss_box_reg: 0.3389 loss_rpn_cls: 0.04339 loss_rpn_loc: 0.1827 time: 0.3376 last_time: 0.3747 data_time: 0.0046 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:31:06 d2.utils.events]: \u001b[0m eta: 7:04:05 iter: 28039 total_loss: 0.9901 loss_cls: 0.2967 loss_box_reg: 0.3319 loss_rpn_cls: 0.06407 loss_rpn_loc: 0.2358 time: 0.3377 last_time: 0.4101 data_time: 0.0048 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:31:14 d2.utils.events]: \u001b[0m eta: 7:04:02 iter: 28059 total_loss: 0.9808 loss_cls: 0.3016 loss_box_reg: 0.3629 loss_rpn_cls: 0.0566 loss_rpn_loc: 0.244 time: 0.3378 last_time: 0.3871 data_time: 0.0049 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:31:22 d2.utils.events]: \u001b[0m eta: 7:02:50 iter: 28079 total_loss: 0.9777 loss_cls: 0.3222 loss_box_reg: 0.3382 loss_rpn_cls: 0.06432 loss_rpn_loc: 0.2388 time: 0.3378 last_time: 0.4339 data_time: 0.0046 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:31:31 d2.utils.events]: \u001b[0m eta: 7:02:50 iter: 28099 total_loss: 0.958 loss_cls: 0.3115 loss_box_reg: 0.3469 loss_rpn_cls: 0.06362 loss_rpn_loc: 0.2236 time: 0.3379 last_time: 0.5176 data_time: 0.0048 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:31:41 d2.utils.events]: \u001b[0m eta: 7:03:05 iter: 28119 total_loss: 1.051 loss_cls: 0.3505 loss_box_reg: 0.3931 loss_rpn_cls: 0.07047 loss_rpn_loc: 0.2397 time: 0.3380 last_time: 0.5157 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:31:49 d2.utils.events]: \u001b[0m eta: 7:03:12 iter: 28139 total_loss: 0.9469 loss_cls: 0.3416 loss_box_reg: 0.3686 loss_rpn_cls: 0.05785 loss_rpn_loc: 0.2131 time: 0.3380 last_time: 0.4529 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:31:58 d2.utils.events]: \u001b[0m eta: 7:02:47 iter: 28159 total_loss: 0.9303 loss_cls: 0.3459 loss_box_reg: 0.3307 loss_rpn_cls: 0.05338 loss_rpn_loc: 0.2158 time: 0.3381 last_time: 0.4690 data_time: 0.0048 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:32:07 d2.utils.events]: \u001b[0m eta: 7:02:42 iter: 28179 total_loss: 1.05 loss_cls: 0.3807 loss_box_reg: 0.3708 loss_rpn_cls: 0.05824 loss_rpn_loc: 0.2115 time: 0.3382 last_time: 0.3952 data_time: 0.0048 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:32:15 d2.utils.events]: \u001b[0m eta: 7:01:48 iter: 28199 total_loss: 0.9209 loss_cls: 0.338 loss_box_reg: 0.3358 loss_rpn_cls: 0.04739 loss_rpn_loc: 0.2253 time: 0.3382 last_time: 0.5086 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:32:24 d2.utils.events]: \u001b[0m eta: 7:01:39 iter: 28219 total_loss: 0.9616 loss_cls: 0.3358 loss_box_reg: 0.3311 loss_rpn_cls: 0.04293 loss_rpn_loc: 0.2162 time: 0.3383 last_time: 0.4465 data_time: 0.0048 last_data_time: 0.0054 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:32:32 d2.utils.events]: \u001b[0m eta: 7:00:56 iter: 28239 total_loss: 1.028 loss_cls: 0.3122 loss_box_reg: 0.3523 loss_rpn_cls: 0.06665 loss_rpn_loc: 0.2186 time: 0.3384 last_time: 0.4468 data_time: 0.0047 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:32:40 d2.utils.events]: \u001b[0m eta: 7:00:16 iter: 28259 total_loss: 0.9287 loss_cls: 0.3197 loss_box_reg: 0.3493 loss_rpn_cls: 0.06472 loss_rpn_loc: 0.2078 time: 0.3384 last_time: 0.3879 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:32:49 d2.utils.events]: \u001b[0m eta: 6:59:32 iter: 28279 total_loss: 0.9002 loss_cls: 0.2958 loss_box_reg: 0.3296 loss_rpn_cls: 0.04081 loss_rpn_loc: 0.192 time: 0.3385 last_time: 0.3748 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:32:57 d2.utils.events]: \u001b[0m eta: 6:59:01 iter: 28299 total_loss: 0.9781 loss_cls: 0.3537 loss_box_reg: 0.348 loss_rpn_cls: 0.05527 loss_rpn_loc: 0.2061 time: 0.3385 last_time: 0.4449 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:33:05 d2.utils.events]: \u001b[0m eta: 6:58:18 iter: 28319 total_loss: 0.8441 loss_cls: 0.2977 loss_box_reg: 0.3071 loss_rpn_cls: 0.0397 loss_rpn_loc: 0.1999 time: 0.3386 last_time: 0.4198 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:33:14 d2.utils.events]: \u001b[0m eta: 6:57:37 iter: 28339 total_loss: 0.9534 loss_cls: 0.3585 loss_box_reg: 0.3082 loss_rpn_cls: 0.05562 loss_rpn_loc: 0.2384 time: 0.3386 last_time: 0.5036 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:33:23 d2.utils.events]: \u001b[0m eta: 6:57:22 iter: 28359 total_loss: 0.9817 loss_cls: 0.3281 loss_box_reg: 0.3611 loss_rpn_cls: 0.07378 loss_rpn_loc: 0.2248 time: 0.3387 last_time: 0.3392 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:33:31 d2.utils.events]: \u001b[0m eta: 6:56:21 iter: 28379 total_loss: 0.8168 loss_cls: 0.2814 loss_box_reg: 0.3386 loss_rpn_cls: 0.04455 loss_rpn_loc: 0.1991 time: 0.3388 last_time: 0.4451 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:33:39 d2.utils.events]: \u001b[0m eta: 6:55:20 iter: 28399 total_loss: 0.9882 loss_cls: 0.3357 loss_box_reg: 0.3296 loss_rpn_cls: 0.04363 loss_rpn_loc: 0.2235 time: 0.3388 last_time: 0.4289 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:33:48 d2.utils.events]: \u001b[0m eta: 6:55:11 iter: 28419 total_loss: 0.9549 loss_cls: 0.3424 loss_box_reg: 0.3638 loss_rpn_cls: 0.05742 loss_rpn_loc: 0.2103 time: 0.3389 last_time: 0.4996 data_time: 0.0048 last_data_time: 0.0052 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:33:56 d2.utils.events]: \u001b[0m eta: 6:54:42 iter: 28439 total_loss: 0.9885 loss_cls: 0.2881 loss_box_reg: 0.3942 loss_rpn_cls: 0.0405 loss_rpn_loc: 0.2126 time: 0.3389 last_time: 0.3766 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:34:04 d2.utils.events]: \u001b[0m eta: 6:53:37 iter: 28459 total_loss: 1.156 loss_cls: 0.3823 loss_box_reg: 0.3662 loss_rpn_cls: 0.0784 loss_rpn_loc: 0.2272 time: 0.3390 last_time: 0.3730 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:34:13 d2.utils.events]: \u001b[0m eta: 6:53:13 iter: 28479 total_loss: 0.9388 loss_cls: 0.2598 loss_box_reg: 0.3539 loss_rpn_cls: 0.04199 loss_rpn_loc: 0.2165 time: 0.3391 last_time: 0.3242 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:34:22 d2.utils.events]: \u001b[0m eta: 6:53:00 iter: 28499 total_loss: 0.9035 loss_cls: 0.2782 loss_box_reg: 0.3449 loss_rpn_cls: 0.05492 loss_rpn_loc: 0.2426 time: 0.3391 last_time: 0.4190 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:34:30 d2.utils.events]: \u001b[0m eta: 6:51:18 iter: 28519 total_loss: 0.9363 loss_cls: 0.3128 loss_box_reg: 0.3297 loss_rpn_cls: 0.05639 loss_rpn_loc: 0.2037 time: 0.3392 last_time: 0.3932 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:34:38 d2.utils.events]: \u001b[0m eta: 6:49:08 iter: 28539 total_loss: 0.9208 loss_cls: 0.3154 loss_box_reg: 0.3333 loss_rpn_cls: 0.04857 loss_rpn_loc: 0.2169 time: 0.3392 last_time: 0.4396 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:34:46 d2.utils.events]: \u001b[0m eta: 6:47:01 iter: 28559 total_loss: 0.8735 loss_cls: 0.2998 loss_box_reg: 0.3176 loss_rpn_cls: 0.05155 loss_rpn_loc: 0.2046 time: 0.3393 last_time: 0.4371 data_time: 0.0045 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:34:54 d2.utils.events]: \u001b[0m eta: 6:44:21 iter: 28579 total_loss: 0.9219 loss_cls: 0.3166 loss_box_reg: 0.3193 loss_rpn_cls: 0.04759 loss_rpn_loc: 0.2011 time: 0.3393 last_time: 0.4271 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:35:02 d2.utils.events]: \u001b[0m eta: 6:43:03 iter: 28599 total_loss: 0.9148 loss_cls: 0.381 loss_box_reg: 0.3179 loss_rpn_cls: 0.0504 loss_rpn_loc: 0.2179 time: 0.3394 last_time: 0.3129 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:35:10 d2.utils.events]: \u001b[0m eta: 6:42:58 iter: 28619 total_loss: 0.9756 loss_cls: 0.3336 loss_box_reg: 0.3394 loss_rpn_cls: 0.06247 loss_rpn_loc: 0.2431 time: 0.3394 last_time: 0.4380 data_time: 0.0046 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:35:19 d2.utils.events]: \u001b[0m eta: 6:40:22 iter: 28639 total_loss: 0.9829 loss_cls: 0.2989 loss_box_reg: 0.3537 loss_rpn_cls: 0.04618 loss_rpn_loc: 0.2162 time: 0.3395 last_time: 0.4109 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:35:27 d2.utils.events]: \u001b[0m eta: 6:39:49 iter: 28659 total_loss: 0.9439 loss_cls: 0.3432 loss_box_reg: 0.321 loss_rpn_cls: 0.05192 loss_rpn_loc: 0.2018 time: 0.3395 last_time: 0.4449 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:35:36 d2.utils.events]: \u001b[0m eta: 6:38:39 iter: 28679 total_loss: 0.906 loss_cls: 0.3024 loss_box_reg: 0.2877 loss_rpn_cls: 0.05458 loss_rpn_loc: 0.1945 time: 0.3396 last_time: 0.3980 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:35:44 d2.utils.events]: \u001b[0m eta: 6:37:35 iter: 28699 total_loss: 0.9075 loss_cls: 0.314 loss_box_reg: 0.3263 loss_rpn_cls: 0.06173 loss_rpn_loc: 0.2113 time: 0.3396 last_time: 0.4423 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:35:52 d2.utils.events]: \u001b[0m eta: 6:36:29 iter: 28719 total_loss: 1.033 loss_cls: 0.3485 loss_box_reg: 0.3472 loss_rpn_cls: 0.07537 loss_rpn_loc: 0.2333 time: 0.3397 last_time: 0.3113 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:36:00 d2.utils.events]: \u001b[0m eta: 6:36:13 iter: 28739 total_loss: 0.9283 loss_cls: 0.3282 loss_box_reg: 0.3474 loss_rpn_cls: 0.0545 loss_rpn_loc: 0.229 time: 0.3397 last_time: 0.4152 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:36:08 d2.utils.events]: \u001b[0m eta: 6:36:02 iter: 28759 total_loss: 1.005 loss_cls: 0.362 loss_box_reg: 0.3499 loss_rpn_cls: 0.06226 loss_rpn_loc: 0.2188 time: 0.3397 last_time: 0.3945 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:36:16 d2.utils.events]: \u001b[0m eta: 6:35:53 iter: 28779 total_loss: 0.9678 loss_cls: 0.2992 loss_box_reg: 0.3168 loss_rpn_cls: 0.04768 loss_rpn_loc: 0.2142 time: 0.3398 last_time: 0.4023 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:36:24 d2.utils.events]: \u001b[0m eta: 6:35:06 iter: 28799 total_loss: 0.9755 loss_cls: 0.3 loss_box_reg: 0.3855 loss_rpn_cls: 0.06047 loss_rpn_loc: 0.2206 time: 0.3398 last_time: 0.3955 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:36:32 d2.utils.events]: \u001b[0m eta: 6:34:17 iter: 28819 total_loss: 0.9483 loss_cls: 0.3082 loss_box_reg: 0.3078 loss_rpn_cls: 0.04492 loss_rpn_loc: 0.1919 time: 0.3399 last_time: 0.3414 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:36:40 d2.utils.events]: \u001b[0m eta: 6:33:23 iter: 28839 total_loss: 0.9582 loss_cls: 0.306 loss_box_reg: 0.349 loss_rpn_cls: 0.05343 loss_rpn_loc: 0.1875 time: 0.3399 last_time: 0.4281 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:36:48 d2.utils.events]: \u001b[0m eta: 6:32:59 iter: 28859 total_loss: 0.8884 loss_cls: 0.3044 loss_box_reg: 0.3243 loss_rpn_cls: 0.04488 loss_rpn_loc: 0.2091 time: 0.3400 last_time: 0.4418 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:36:57 d2.utils.events]: \u001b[0m eta: 6:32:34 iter: 28879 total_loss: 0.9738 loss_cls: 0.3278 loss_box_reg: 0.3359 loss_rpn_cls: 0.04823 loss_rpn_loc: 0.2232 time: 0.3400 last_time: 0.4145 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:37:05 d2.utils.events]: \u001b[0m eta: 6:32:12 iter: 28899 total_loss: 1.004 loss_cls: 0.3053 loss_box_reg: 0.3436 loss_rpn_cls: 0.06691 loss_rpn_loc: 0.2213 time: 0.3401 last_time: 0.3938 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:37:13 d2.utils.events]: \u001b[0m eta: 6:31:33 iter: 28919 total_loss: 0.8344 loss_cls: 0.2576 loss_box_reg: 0.3043 loss_rpn_cls: 0.03974 loss_rpn_loc: 0.1955 time: 0.3401 last_time: 0.4438 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:37:21 d2.utils.events]: \u001b[0m eta: 6:31:07 iter: 28939 total_loss: 0.9633 loss_cls: 0.3619 loss_box_reg: 0.3471 loss_rpn_cls: 0.0659 loss_rpn_loc: 0.2224 time: 0.3402 last_time: 0.3947 data_time: 0.0045 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:37:29 d2.utils.events]: \u001b[0m eta: 6:30:47 iter: 28959 total_loss: 1.03 loss_cls: 0.3512 loss_box_reg: 0.3536 loss_rpn_cls: 0.05646 loss_rpn_loc: 0.2375 time: 0.3402 last_time: 0.3928 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:37:37 d2.utils.events]: \u001b[0m eta: 6:30:18 iter: 28979 total_loss: 0.9133 loss_cls: 0.27 loss_box_reg: 0.3646 loss_rpn_cls: 0.05299 loss_rpn_loc: 0.2017 time: 0.3402 last_time: 0.3885 data_time: 0.0049 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:37:45 d2.utils.events]: \u001b[0m eta: 6:29:51 iter: 28999 total_loss: 0.8982 loss_cls: 0.3304 loss_box_reg: 0.3463 loss_rpn_cls: 0.05197 loss_rpn_loc: 0.2142 time: 0.3403 last_time: 0.3639 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:37:53 d2.utils.events]: \u001b[0m eta: 6:29:45 iter: 29019 total_loss: 0.9388 loss_cls: 0.33 loss_box_reg: 0.3435 loss_rpn_cls: 0.06117 loss_rpn_loc: 0.1898 time: 0.3403 last_time: 0.4067 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:38:01 d2.utils.events]: \u001b[0m eta: 6:29:35 iter: 29039 total_loss: 0.9483 loss_cls: 0.318 loss_box_reg: 0.3065 loss_rpn_cls: 0.06528 loss_rpn_loc: 0.2029 time: 0.3404 last_time: 0.4371 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:38:09 d2.utils.events]: \u001b[0m eta: 6:29:09 iter: 29059 total_loss: 0.9876 loss_cls: 0.3269 loss_box_reg: 0.3281 loss_rpn_cls: 0.05475 loss_rpn_loc: 0.2422 time: 0.3404 last_time: 0.4087 data_time: 0.0047 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:38:18 d2.utils.events]: \u001b[0m eta: 6:28:48 iter: 29079 total_loss: 0.9855 loss_cls: 0.3129 loss_box_reg: 0.3528 loss_rpn_cls: 0.04665 loss_rpn_loc: 0.2043 time: 0.3405 last_time: 0.3753 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:38:26 d2.utils.events]: \u001b[0m eta: 6:28:15 iter: 29099 total_loss: 0.8577 loss_cls: 0.2833 loss_box_reg: 0.3417 loss_rpn_cls: 0.04757 loss_rpn_loc: 0.1914 time: 0.3405 last_time: 0.3692 data_time: 0.0045 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:38:34 d2.utils.events]: \u001b[0m eta: 6:27:41 iter: 29119 total_loss: 0.8389 loss_cls: 0.2938 loss_box_reg: 0.2851 loss_rpn_cls: 0.03797 loss_rpn_loc: 0.2107 time: 0.3406 last_time: 0.4067 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:38:42 d2.utils.events]: \u001b[0m eta: 6:27:37 iter: 29139 total_loss: 0.866 loss_cls: 0.284 loss_box_reg: 0.3131 loss_rpn_cls: 0.04162 loss_rpn_loc: 0.209 time: 0.3406 last_time: 0.4256 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:38:50 d2.utils.events]: \u001b[0m eta: 6:27:05 iter: 29159 total_loss: 0.9573 loss_cls: 0.3385 loss_box_reg: 0.3287 loss_rpn_cls: 0.06391 loss_rpn_loc: 0.2373 time: 0.3406 last_time: 0.4062 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:38:58 d2.utils.events]: \u001b[0m eta: 6:26:58 iter: 29179 total_loss: 0.926 loss_cls: 0.3118 loss_box_reg: 0.3278 loss_rpn_cls: 0.06494 loss_rpn_loc: 0.2325 time: 0.3407 last_time: 0.4193 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:39:06 d2.utils.events]: \u001b[0m eta: 6:26:47 iter: 29199 total_loss: 1.065 loss_cls: 0.36 loss_box_reg: 0.3579 loss_rpn_cls: 0.06134 loss_rpn_loc: 0.2184 time: 0.3407 last_time: 0.3707 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:39:14 d2.utils.events]: \u001b[0m eta: 6:26:14 iter: 29219 total_loss: 0.9266 loss_cls: 0.3295 loss_box_reg: 0.3494 loss_rpn_cls: 0.04773 loss_rpn_loc: 0.2042 time: 0.3408 last_time: 0.3408 data_time: 0.0047 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:39:22 d2.utils.events]: \u001b[0m eta: 6:25:52 iter: 29239 total_loss: 0.8008 loss_cls: 0.2609 loss_box_reg: 0.2931 loss_rpn_cls: 0.04839 loss_rpn_loc: 0.1921 time: 0.3408 last_time: 0.3817 data_time: 0.0046 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:39:30 d2.utils.events]: \u001b[0m eta: 6:25:44 iter: 29259 total_loss: 0.9122 loss_cls: 0.276 loss_box_reg: 0.3137 loss_rpn_cls: 0.04453 loss_rpn_loc: 0.1753 time: 0.3408 last_time: 0.4466 data_time: 0.0045 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:39:38 d2.utils.events]: \u001b[0m eta: 6:25:25 iter: 29279 total_loss: 0.957 loss_cls: 0.3069 loss_box_reg: 0.3545 loss_rpn_cls: 0.05525 loss_rpn_loc: 0.2186 time: 0.3409 last_time: 0.4123 data_time: 0.0045 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:39:46 d2.utils.events]: \u001b[0m eta: 6:25:12 iter: 29299 total_loss: 1.064 loss_cls: 0.3582 loss_box_reg: 0.3759 loss_rpn_cls: 0.05616 loss_rpn_loc: 0.2376 time: 0.3409 last_time: 0.4127 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:39:54 d2.utils.events]: \u001b[0m eta: 6:24:55 iter: 29319 total_loss: 1.003 loss_cls: 0.3602 loss_box_reg: 0.3702 loss_rpn_cls: 0.06666 loss_rpn_loc: 0.2302 time: 0.3410 last_time: 0.3954 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:40:02 d2.utils.events]: \u001b[0m eta: 6:24:40 iter: 29339 total_loss: 0.9221 loss_cls: 0.3563 loss_box_reg: 0.3399 loss_rpn_cls: 0.05712 loss_rpn_loc: 0.205 time: 0.3410 last_time: 0.4086 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:40:10 d2.utils.events]: \u001b[0m eta: 6:24:04 iter: 29359 total_loss: 0.8774 loss_cls: 0.3224 loss_box_reg: 0.345 loss_rpn_cls: 0.03589 loss_rpn_loc: 0.1838 time: 0.3411 last_time: 0.4242 data_time: 0.0046 last_data_time: 0.0052 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:40:19 d2.utils.events]: \u001b[0m eta: 6:23:56 iter: 29379 total_loss: 0.785 loss_cls: 0.2569 loss_box_reg: 0.358 loss_rpn_cls: 0.04056 loss_rpn_loc: 0.1585 time: 0.3411 last_time: 0.4454 data_time: 0.0045 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:40:27 d2.utils.events]: \u001b[0m eta: 6:23:46 iter: 29399 total_loss: 1.104 loss_cls: 0.3812 loss_box_reg: 0.3738 loss_rpn_cls: 0.06234 loss_rpn_loc: 0.2106 time: 0.3411 last_time: 0.3095 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:40:35 d2.utils.events]: \u001b[0m eta: 6:23:17 iter: 29419 total_loss: 0.8516 loss_cls: 0.2728 loss_box_reg: 0.3233 loss_rpn_cls: 0.03857 loss_rpn_loc: 0.2018 time: 0.3412 last_time: 0.4162 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:40:43 d2.utils.events]: \u001b[0m eta: 6:22:58 iter: 29439 total_loss: 0.8875 loss_cls: 0.3037 loss_box_reg: 0.3125 loss_rpn_cls: 0.04629 loss_rpn_loc: 0.2069 time: 0.3412 last_time: 0.3415 data_time: 0.0045 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:40:51 d2.utils.events]: \u001b[0m eta: 6:22:30 iter: 29459 total_loss: 0.8695 loss_cls: 0.2671 loss_box_reg: 0.3324 loss_rpn_cls: 0.04288 loss_rpn_loc: 0.2082 time: 0.3413 last_time: 0.4164 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:40:59 d2.utils.events]: \u001b[0m eta: 6:22:16 iter: 29479 total_loss: 0.9249 loss_cls: 0.2763 loss_box_reg: 0.3576 loss_rpn_cls: 0.06059 loss_rpn_loc: 0.2108 time: 0.3413 last_time: 0.3413 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:41:07 d2.utils.events]: \u001b[0m eta: 6:21:28 iter: 29499 total_loss: 0.9509 loss_cls: 0.302 loss_box_reg: 0.3112 loss_rpn_cls: 0.04835 loss_rpn_loc: 0.2284 time: 0.3413 last_time: 0.3574 data_time: 0.0044 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:41:14 d2.utils.events]: \u001b[0m eta: 6:21:23 iter: 29519 total_loss: 0.9482 loss_cls: 0.2676 loss_box_reg: 0.351 loss_rpn_cls: 0.06094 loss_rpn_loc: 0.2136 time: 0.3414 last_time: 0.4152 data_time: 0.0044 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:41:22 d2.utils.events]: \u001b[0m eta: 6:21:00 iter: 29539 total_loss: 0.9582 loss_cls: 0.345 loss_box_reg: 0.3332 loss_rpn_cls: 0.06492 loss_rpn_loc: 0.1961 time: 0.3414 last_time: 0.3388 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:41:30 d2.utils.events]: \u001b[0m eta: 6:20:57 iter: 29559 total_loss: 0.8973 loss_cls: 0.2676 loss_box_reg: 0.3052 loss_rpn_cls: 0.06421 loss_rpn_loc: 0.1944 time: 0.3415 last_time: 0.4184 data_time: 0.0046 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:41:39 d2.utils.events]: \u001b[0m eta: 6:20:43 iter: 29579 total_loss: 0.9508 loss_cls: 0.3024 loss_box_reg: 0.3213 loss_rpn_cls: 0.05336 loss_rpn_loc: 0.2069 time: 0.3415 last_time: 0.3826 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:41:47 d2.utils.events]: \u001b[0m eta: 6:20:31 iter: 29599 total_loss: 0.8771 loss_cls: 0.3168 loss_box_reg: 0.3077 loss_rpn_cls: 0.04228 loss_rpn_loc: 0.225 time: 0.3415 last_time: 0.4232 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:41:55 d2.utils.events]: \u001b[0m eta: 6:20:32 iter: 29619 total_loss: 0.9204 loss_cls: 0.3106 loss_box_reg: 0.377 loss_rpn_cls: 0.05105 loss_rpn_loc: 0.2061 time: 0.3416 last_time: 0.4411 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:42:03 d2.utils.events]: \u001b[0m eta: 6:20:11 iter: 29639 total_loss: 0.9156 loss_cls: 0.3007 loss_box_reg: 0.3473 loss_rpn_cls: 0.05581 loss_rpn_loc: 0.2055 time: 0.3416 last_time: 0.4497 data_time: 0.0046 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:42:11 d2.utils.events]: \u001b[0m eta: 6:19:48 iter: 29659 total_loss: 0.8543 loss_cls: 0.326 loss_box_reg: 0.3134 loss_rpn_cls: 0.04537 loss_rpn_loc: 0.1893 time: 0.3417 last_time: 0.4184 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:42:19 d2.utils.events]: \u001b[0m eta: 6:19:44 iter: 29679 total_loss: 0.9502 loss_cls: 0.3353 loss_box_reg: 0.3707 loss_rpn_cls: 0.06125 loss_rpn_loc: 0.2209 time: 0.3417 last_time: 0.4031 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:42:27 d2.utils.events]: \u001b[0m eta: 6:19:27 iter: 29699 total_loss: 0.9507 loss_cls: 0.3334 loss_box_reg: 0.3446 loss_rpn_cls: 0.05275 loss_rpn_loc: 0.2364 time: 0.3417 last_time: 0.4358 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:42:35 d2.utils.events]: \u001b[0m eta: 6:19:13 iter: 29719 total_loss: 0.8229 loss_cls: 0.2843 loss_box_reg: 0.3305 loss_rpn_cls: 0.0605 loss_rpn_loc: 0.2023 time: 0.3418 last_time: 0.4447 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:42:43 d2.utils.events]: \u001b[0m eta: 6:18:54 iter: 29739 total_loss: 0.9922 loss_cls: 0.3221 loss_box_reg: 0.3281 loss_rpn_cls: 0.06036 loss_rpn_loc: 0.2264 time: 0.3418 last_time: 0.3816 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:42:51 d2.utils.events]: \u001b[0m eta: 6:18:36 iter: 29759 total_loss: 0.9201 loss_cls: 0.2896 loss_box_reg: 0.3078 loss_rpn_cls: 0.0492 loss_rpn_loc: 0.2358 time: 0.3419 last_time: 0.4222 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:42:59 d2.utils.events]: \u001b[0m eta: 6:18:17 iter: 29779 total_loss: 0.8105 loss_cls: 0.2732 loss_box_reg: 0.2786 loss_rpn_cls: 0.05081 loss_rpn_loc: 0.1974 time: 0.3419 last_time: 0.4080 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:43:07 d2.utils.events]: \u001b[0m eta: 6:18:08 iter: 29799 total_loss: 0.8368 loss_cls: 0.3151 loss_box_reg: 0.3302 loss_rpn_cls: 0.04447 loss_rpn_loc: 0.1766 time: 0.3419 last_time: 0.3840 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:43:15 d2.utils.events]: \u001b[0m eta: 6:18:32 iter: 29819 total_loss: 0.9929 loss_cls: 0.3214 loss_box_reg: 0.3797 loss_rpn_cls: 0.04998 loss_rpn_loc: 0.2087 time: 0.3420 last_time: 0.3436 data_time: 0.0047 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:43:23 d2.utils.events]: \u001b[0m eta: 6:18:39 iter: 29839 total_loss: 0.9249 loss_cls: 0.2943 loss_box_reg: 0.3233 loss_rpn_cls: 0.04262 loss_rpn_loc: 0.2393 time: 0.3420 last_time: 0.3985 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:43:32 d2.utils.events]: \u001b[0m eta: 6:18:23 iter: 29859 total_loss: 0.8994 loss_cls: 0.3144 loss_box_reg: 0.3509 loss_rpn_cls: 0.05473 loss_rpn_loc: 0.2097 time: 0.3421 last_time: 0.4185 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:43:40 d2.utils.events]: \u001b[0m eta: 6:17:16 iter: 29879 total_loss: 0.8805 loss_cls: 0.2945 loss_box_reg: 0.329 loss_rpn_cls: 0.05041 loss_rpn_loc: 0.2129 time: 0.3421 last_time: 0.4020 data_time: 0.0045 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:43:48 d2.utils.events]: \u001b[0m eta: 6:17:48 iter: 29899 total_loss: 0.8758 loss_cls: 0.2941 loss_box_reg: 0.3346 loss_rpn_cls: 0.04909 loss_rpn_loc: 0.223 time: 0.3422 last_time: 0.4224 data_time: 0.0046 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:43:56 d2.utils.events]: \u001b[0m eta: 6:18:18 iter: 29919 total_loss: 0.9585 loss_cls: 0.3165 loss_box_reg: 0.3205 loss_rpn_cls: 0.0578 loss_rpn_loc: 0.2148 time: 0.3422 last_time: 0.4188 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:44:05 d2.utils.events]: \u001b[0m eta: 6:18:07 iter: 29939 total_loss: 0.9303 loss_cls: 0.3153 loss_box_reg: 0.3265 loss_rpn_cls: 0.04858 loss_rpn_loc: 0.2312 time: 0.3423 last_time: 0.4407 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:44:13 d2.utils.events]: \u001b[0m eta: 6:18:01 iter: 29959 total_loss: 0.9016 loss_cls: 0.3353 loss_box_reg: 0.3435 loss_rpn_cls: 0.05223 loss_rpn_loc: 0.1961 time: 0.3423 last_time: 0.4179 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:44:21 d2.utils.events]: \u001b[0m eta: 6:18:19 iter: 29979 total_loss: 0.862 loss_cls: 0.2772 loss_box_reg: 0.3458 loss_rpn_cls: 0.04516 loss_rpn_loc: 0.1933 time: 0.3424 last_time: 0.4416 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:44:30 d2.utils.events]: \u001b[0m eta: 6:18:44 iter: 29999 total_loss: 0.9685 loss_cls: 0.3486 loss_box_reg: 0.3682 loss_rpn_cls: 0.05058 loss_rpn_loc: 0.2228 time: 0.3424 last_time: 0.4041 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:44:39 d2.utils.events]: \u001b[0m eta: 6:18:39 iter: 30019 total_loss: 0.9149 loss_cls: 0.3171 loss_box_reg: 0.3221 loss_rpn_cls: 0.05427 loss_rpn_loc: 0.1945 time: 0.3425 last_time: 0.4071 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:44:47 d2.utils.events]: \u001b[0m eta: 6:18:29 iter: 30039 total_loss: 0.9566 loss_cls: 0.3241 loss_box_reg: 0.3305 loss_rpn_cls: 0.05648 loss_rpn_loc: 0.213 time: 0.3425 last_time: 0.3707 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:44:55 d2.utils.events]: \u001b[0m eta: 6:18:19 iter: 30059 total_loss: 0.9094 loss_cls: 0.3096 loss_box_reg: 0.367 loss_rpn_cls: 0.04799 loss_rpn_loc: 0.2101 time: 0.3425 last_time: 0.4426 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:45:03 d2.utils.events]: \u001b[0m eta: 6:18:17 iter: 30079 total_loss: 0.9064 loss_cls: 0.3193 loss_box_reg: 0.3092 loss_rpn_cls: 0.03875 loss_rpn_loc: 0.1924 time: 0.3426 last_time: 0.4157 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:45:11 d2.utils.events]: \u001b[0m eta: 6:18:09 iter: 30099 total_loss: 0.9275 loss_cls: 0.3206 loss_box_reg: 0.3468 loss_rpn_cls: 0.05908 loss_rpn_loc: 0.2225 time: 0.3426 last_time: 0.4239 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:45:19 d2.utils.events]: \u001b[0m eta: 6:18:00 iter: 30119 total_loss: 0.9681 loss_cls: 0.3462 loss_box_reg: 0.3471 loss_rpn_cls: 0.06057 loss_rpn_loc: 0.2128 time: 0.3427 last_time: 0.3446 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:45:28 d2.utils.events]: \u001b[0m eta: 6:17:49 iter: 30139 total_loss: 0.9246 loss_cls: 0.3082 loss_box_reg: 0.3396 loss_rpn_cls: 0.05009 loss_rpn_loc: 0.2433 time: 0.3427 last_time: 0.4200 data_time: 0.0047 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:45:36 d2.utils.events]: \u001b[0m eta: 6:17:42 iter: 30159 total_loss: 0.8715 loss_cls: 0.2964 loss_box_reg: 0.3603 loss_rpn_cls: 0.06531 loss_rpn_loc: 0.2162 time: 0.3428 last_time: 0.4035 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:45:44 d2.utils.events]: \u001b[0m eta: 6:17:29 iter: 30179 total_loss: 0.8453 loss_cls: 0.2736 loss_box_reg: 0.3355 loss_rpn_cls: 0.05321 loss_rpn_loc: 0.1789 time: 0.3428 last_time: 0.3750 data_time: 0.0047 last_data_time: 0.0052 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:45:52 d2.utils.events]: \u001b[0m eta: 6:17:25 iter: 30199 total_loss: 0.8327 loss_cls: 0.2849 loss_box_reg: 0.2951 loss_rpn_cls: 0.05228 loss_rpn_loc: 0.1829 time: 0.3428 last_time: 0.4453 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:46:00 d2.utils.events]: \u001b[0m eta: 6:17:21 iter: 30219 total_loss: 0.9429 loss_cls: 0.3309 loss_box_reg: 0.3201 loss_rpn_cls: 0.06395 loss_rpn_loc: 0.2324 time: 0.3429 last_time: 0.4016 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:46:09 d2.utils.events]: \u001b[0m eta: 6:17:18 iter: 30239 total_loss: 0.9548 loss_cls: 0.3218 loss_box_reg: 0.3021 loss_rpn_cls: 0.0527 loss_rpn_loc: 0.1995 time: 0.3429 last_time: 0.5091 data_time: 0.0048 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:46:18 d2.utils.events]: \u001b[0m eta: 6:17:41 iter: 30259 total_loss: 0.9313 loss_cls: 0.3198 loss_box_reg: 0.3139 loss_rpn_cls: 0.04684 loss_rpn_loc: 0.1904 time: 0.3430 last_time: 0.5114 data_time: 0.0051 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:46:27 d2.utils.events]: \u001b[0m eta: 6:18:06 iter: 30279 total_loss: 0.8984 loss_cls: 0.2641 loss_box_reg: 0.3143 loss_rpn_cls: 0.05308 loss_rpn_loc: 0.2202 time: 0.3431 last_time: 0.4084 data_time: 0.0051 last_data_time: 0.0052 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:46:37 d2.utils.events]: \u001b[0m eta: 6:18:55 iter: 30299 total_loss: 0.9678 loss_cls: 0.3184 loss_box_reg: 0.346 loss_rpn_cls: 0.06188 loss_rpn_loc: 0.2107 time: 0.3432 last_time: 0.5047 data_time: 0.0049 last_data_time: 0.0054 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:46:47 d2.utils.events]: \u001b[0m eta: 6:19:16 iter: 30319 total_loss: 0.836 loss_cls: 0.2758 loss_box_reg: 0.3148 loss_rpn_cls: 0.04478 loss_rpn_loc: 0.2099 time: 0.3433 last_time: 0.4871 data_time: 0.0050 last_data_time: 0.0052 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:46:56 d2.utils.events]: \u001b[0m eta: 6:19:26 iter: 30339 total_loss: 0.9528 loss_cls: 0.2871 loss_box_reg: 0.339 loss_rpn_cls: 0.05283 loss_rpn_loc: 0.2135 time: 0.3434 last_time: 0.4545 data_time: 0.0050 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:47:06 d2.utils.events]: \u001b[0m eta: 6:19:49 iter: 30359 total_loss: 0.9554 loss_cls: 0.3279 loss_box_reg: 0.3212 loss_rpn_cls: 0.05893 loss_rpn_loc: 0.2141 time: 0.3435 last_time: 0.4885 data_time: 0.0051 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:47:15 d2.utils.events]: \u001b[0m eta: 6:19:56 iter: 30379 total_loss: 0.9191 loss_cls: 0.3465 loss_box_reg: 0.3416 loss_rpn_cls: 0.04999 loss_rpn_loc: 0.1891 time: 0.3435 last_time: 0.4637 data_time: 0.0050 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:47:24 d2.utils.events]: \u001b[0m eta: 6:20:01 iter: 30399 total_loss: 0.9395 loss_cls: 0.3066 loss_box_reg: 0.3296 loss_rpn_cls: 0.06533 loss_rpn_loc: 0.2025 time: 0.3436 last_time: 0.4153 data_time: 0.0049 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:47:33 d2.utils.events]: \u001b[0m eta: 6:20:10 iter: 30419 total_loss: 0.9255 loss_cls: 0.314 loss_box_reg: 0.3321 loss_rpn_cls: 0.05114 loss_rpn_loc: 0.2061 time: 0.3437 last_time: 0.4641 data_time: 0.0049 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:47:42 d2.utils.events]: \u001b[0m eta: 6:20:21 iter: 30439 total_loss: 1.048 loss_cls: 0.3733 loss_box_reg: 0.3846 loss_rpn_cls: 0.07654 loss_rpn_loc: 0.2099 time: 0.3437 last_time: 0.4853 data_time: 0.0050 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:47:51 d2.utils.events]: \u001b[0m eta: 6:20:40 iter: 30459 total_loss: 0.8742 loss_cls: 0.3103 loss_box_reg: 0.3254 loss_rpn_cls: 0.04707 loss_rpn_loc: 0.2047 time: 0.3438 last_time: 0.4602 data_time: 0.0050 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:48:00 d2.utils.events]: \u001b[0m eta: 6:21:14 iter: 30479 total_loss: 0.9328 loss_cls: 0.3257 loss_box_reg: 0.3012 loss_rpn_cls: 0.059 loss_rpn_loc: 0.2444 time: 0.3439 last_time: 0.5195 data_time: 0.0049 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:48:10 d2.utils.events]: \u001b[0m eta: 6:21:30 iter: 30499 total_loss: 0.8191 loss_cls: 0.258 loss_box_reg: 0.3549 loss_rpn_cls: 0.0414 loss_rpn_loc: 0.1788 time: 0.3440 last_time: 0.5095 data_time: 0.0050 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:48:19 d2.utils.events]: \u001b[0m eta: 6:22:36 iter: 30519 total_loss: 0.8931 loss_cls: 0.2844 loss_box_reg: 0.3286 loss_rpn_cls: 0.05247 loss_rpn_loc: 0.2275 time: 0.3441 last_time: 0.4160 data_time: 0.0050 last_data_time: 0.0055 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:48:27 d2.utils.events]: \u001b[0m eta: 6:22:50 iter: 30539 total_loss: 0.9061 loss_cls: 0.3169 loss_box_reg: 0.3159 loss_rpn_cls: 0.06662 loss_rpn_loc: 0.2061 time: 0.3441 last_time: 0.4846 data_time: 0.0049 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:48:37 d2.utils.events]: \u001b[0m eta: 6:23:20 iter: 30559 total_loss: 0.9088 loss_cls: 0.3113 loss_box_reg: 0.3109 loss_rpn_cls: 0.04698 loss_rpn_loc: 0.2219 time: 0.3442 last_time: 0.5153 data_time: 0.0049 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:48:46 d2.utils.events]: \u001b[0m eta: 6:23:48 iter: 30579 total_loss: 0.8988 loss_cls: 0.2859 loss_box_reg: 0.3294 loss_rpn_cls: 0.06379 loss_rpn_loc: 0.2032 time: 0.3443 last_time: 0.4798 data_time: 0.0050 last_data_time: 0.0059 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:48:54 d2.utils.events]: \u001b[0m eta: 6:23:39 iter: 30599 total_loss: 1.002 loss_cls: 0.3061 loss_box_reg: 0.3467 loss_rpn_cls: 0.0603 loss_rpn_loc: 0.2258 time: 0.3443 last_time: 0.4200 data_time: 0.0048 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:49:02 d2.utils.events]: \u001b[0m eta: 6:23:19 iter: 30619 total_loss: 1.039 loss_cls: 0.3546 loss_box_reg: 0.336 loss_rpn_cls: 0.05732 loss_rpn_loc: 0.2086 time: 0.3443 last_time: 0.4138 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:49:10 d2.utils.events]: \u001b[0m eta: 6:23:33 iter: 30639 total_loss: 0.9624 loss_cls: 0.3417 loss_box_reg: 0.3347 loss_rpn_cls: 0.05966 loss_rpn_loc: 0.2324 time: 0.3444 last_time: 0.4376 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:49:19 d2.utils.events]: \u001b[0m eta: 6:23:20 iter: 30659 total_loss: 0.8647 loss_cls: 0.277 loss_box_reg: 0.2987 loss_rpn_cls: 0.04708 loss_rpn_loc: 0.1877 time: 0.3444 last_time: 0.4217 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:49:27 d2.utils.events]: \u001b[0m eta: 6:22:46 iter: 30679 total_loss: 0.963 loss_cls: 0.3266 loss_box_reg: 0.3013 loss_rpn_cls: 0.05231 loss_rpn_loc: 0.2019 time: 0.3445 last_time: 0.3751 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:49:35 d2.utils.events]: \u001b[0m eta: 6:23:07 iter: 30699 total_loss: 0.9021 loss_cls: 0.3296 loss_box_reg: 0.3378 loss_rpn_cls: 0.0612 loss_rpn_loc: 0.2128 time: 0.3445 last_time: 0.4032 data_time: 0.0045 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:49:43 d2.utils.events]: \u001b[0m eta: 6:23:21 iter: 30719 total_loss: 0.9269 loss_cls: 0.3038 loss_box_reg: 0.3387 loss_rpn_cls: 0.04283 loss_rpn_loc: 0.2159 time: 0.3446 last_time: 0.4221 data_time: 0.0045 last_data_time: 0.0040 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:49:51 d2.utils.events]: \u001b[0m eta: 6:22:53 iter: 30739 total_loss: 0.8788 loss_cls: 0.3414 loss_box_reg: 0.332 loss_rpn_cls: 0.04701 loss_rpn_loc: 0.1758 time: 0.3446 last_time: 0.3761 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:49:59 d2.utils.events]: \u001b[0m eta: 6:22:45 iter: 30759 total_loss: 0.9137 loss_cls: 0.2799 loss_box_reg: 0.3183 loss_rpn_cls: 0.05557 loss_rpn_loc: 0.2004 time: 0.3446 last_time: 0.3941 data_time: 0.0045 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:50:08 d2.utils.events]: \u001b[0m eta: 6:22:44 iter: 30779 total_loss: 1.011 loss_cls: 0.3649 loss_box_reg: 0.3452 loss_rpn_cls: 0.05607 loss_rpn_loc: 0.2275 time: 0.3447 last_time: 0.4041 data_time: 0.0045 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:50:16 d2.utils.events]: \u001b[0m eta: 6:22:42 iter: 30799 total_loss: 0.8975 loss_cls: 0.301 loss_box_reg: 0.3058 loss_rpn_cls: 0.05975 loss_rpn_loc: 0.1993 time: 0.3447 last_time: 0.3517 data_time: 0.0044 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:50:24 d2.utils.events]: \u001b[0m eta: 6:22:17 iter: 30819 total_loss: 0.8772 loss_cls: 0.3089 loss_box_reg: 0.3367 loss_rpn_cls: 0.04909 loss_rpn_loc: 0.2296 time: 0.3448 last_time: 0.4143 data_time: 0.0044 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:50:32 d2.utils.events]: \u001b[0m eta: 6:21:50 iter: 30839 total_loss: 1.01 loss_cls: 0.3189 loss_box_reg: 0.3266 loss_rpn_cls: 0.06983 loss_rpn_loc: 0.2436 time: 0.3448 last_time: 0.3432 data_time: 0.0046 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:50:40 d2.utils.events]: \u001b[0m eta: 6:22:01 iter: 30859 total_loss: 0.8794 loss_cls: 0.2926 loss_box_reg: 0.3148 loss_rpn_cls: 0.0504 loss_rpn_loc: 0.2273 time: 0.3448 last_time: 0.3356 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:50:48 d2.utils.events]: \u001b[0m eta: 6:22:18 iter: 30879 total_loss: 0.8984 loss_cls: 0.3199 loss_box_reg: 0.3277 loss_rpn_cls: 0.06817 loss_rpn_loc: 0.1894 time: 0.3449 last_time: 0.4160 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:50:56 d2.utils.events]: \u001b[0m eta: 6:22:04 iter: 30899 total_loss: 0.8319 loss_cls: 0.2818 loss_box_reg: 0.3333 loss_rpn_cls: 0.04794 loss_rpn_loc: 0.1847 time: 0.3449 last_time: 0.4387 data_time: 0.0044 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:51:04 d2.utils.events]: \u001b[0m eta: 6:21:24 iter: 30919 total_loss: 0.8798 loss_cls: 0.2755 loss_box_reg: 0.3307 loss_rpn_cls: 0.04406 loss_rpn_loc: 0.1655 time: 0.3449 last_time: 0.4155 data_time: 0.0044 last_data_time: 0.0041 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:51:12 d2.utils.events]: \u001b[0m eta: 6:20:57 iter: 30939 total_loss: 0.902 loss_cls: 0.2788 loss_box_reg: 0.3409 loss_rpn_cls: 0.05612 loss_rpn_loc: 0.1919 time: 0.3450 last_time: 0.4108 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:51:20 d2.utils.events]: \u001b[0m eta: 6:21:00 iter: 30959 total_loss: 0.9529 loss_cls: 0.2994 loss_box_reg: 0.3498 loss_rpn_cls: 0.054 loss_rpn_loc: 0.2436 time: 0.3450 last_time: 0.4473 data_time: 0.0045 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:51:28 d2.utils.events]: \u001b[0m eta: 6:20:06 iter: 30979 total_loss: 0.8791 loss_cls: 0.3146 loss_box_reg: 0.3396 loss_rpn_cls: 0.05173 loss_rpn_loc: 0.1923 time: 0.3451 last_time: 0.3129 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:51:37 d2.utils.events]: \u001b[0m eta: 6:19:35 iter: 30999 total_loss: 0.938 loss_cls: 0.3267 loss_box_reg: 0.3082 loss_rpn_cls: 0.05558 loss_rpn_loc: 0.2196 time: 0.3451 last_time: 0.4136 data_time: 0.0044 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:51:45 d2.utils.events]: \u001b[0m eta: 6:19:08 iter: 31019 total_loss: 0.8426 loss_cls: 0.2917 loss_box_reg: 0.3051 loss_rpn_cls: 0.04164 loss_rpn_loc: 0.2058 time: 0.3451 last_time: 0.3474 data_time: 0.0045 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:51:53 d2.utils.events]: \u001b[0m eta: 6:18:47 iter: 31039 total_loss: 0.9574 loss_cls: 0.3054 loss_box_reg: 0.3711 loss_rpn_cls: 0.05023 loss_rpn_loc: 0.2477 time: 0.3452 last_time: 0.4287 data_time: 0.0043 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:52:01 d2.utils.events]: \u001b[0m eta: 6:19:08 iter: 31059 total_loss: 0.9398 loss_cls: 0.2852 loss_box_reg: 0.3562 loss_rpn_cls: 0.05789 loss_rpn_loc: 0.2191 time: 0.3452 last_time: 0.4296 data_time: 0.0046 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:52:09 d2.utils.events]: \u001b[0m eta: 6:19:01 iter: 31079 total_loss: 0.9648 loss_cls: 0.3494 loss_box_reg: 0.3478 loss_rpn_cls: 0.06099 loss_rpn_loc: 0.2131 time: 0.3453 last_time: 0.4459 data_time: 0.0046 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:52:17 d2.utils.events]: \u001b[0m eta: 6:18:47 iter: 31099 total_loss: 1.024 loss_cls: 0.3482 loss_box_reg: 0.3356 loss_rpn_cls: 0.05784 loss_rpn_loc: 0.2446 time: 0.3453 last_time: 0.4037 data_time: 0.0045 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:52:25 d2.utils.events]: \u001b[0m eta: 6:18:42 iter: 31119 total_loss: 1.053 loss_cls: 0.3478 loss_box_reg: 0.3698 loss_rpn_cls: 0.05943 loss_rpn_loc: 0.2492 time: 0.3453 last_time: 0.4005 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:52:33 d2.utils.events]: \u001b[0m eta: 6:18:17 iter: 31139 total_loss: 1.034 loss_cls: 0.3388 loss_box_reg: 0.3784 loss_rpn_cls: 0.0669 loss_rpn_loc: 0.2333 time: 0.3454 last_time: 0.3940 data_time: 0.0044 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:52:41 d2.utils.events]: \u001b[0m eta: 6:18:17 iter: 31159 total_loss: 0.9253 loss_cls: 0.3074 loss_box_reg: 0.3581 loss_rpn_cls: 0.05255 loss_rpn_loc: 0.2399 time: 0.3454 last_time: 0.4268 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:52:49 d2.utils.events]: \u001b[0m eta: 6:18:19 iter: 31179 total_loss: 1.015 loss_cls: 0.3453 loss_box_reg: 0.3721 loss_rpn_cls: 0.08339 loss_rpn_loc: 0.211 time: 0.3454 last_time: 0.4402 data_time: 0.0044 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:52:57 d2.utils.events]: \u001b[0m eta: 6:18:00 iter: 31199 total_loss: 0.9172 loss_cls: 0.3046 loss_box_reg: 0.3218 loss_rpn_cls: 0.03874 loss_rpn_loc: 0.204 time: 0.3455 last_time: 0.4052 data_time: 0.0045 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:53:06 d2.utils.events]: \u001b[0m eta: 6:18:00 iter: 31219 total_loss: 0.9138 loss_cls: 0.2936 loss_box_reg: 0.3348 loss_rpn_cls: 0.05106 loss_rpn_loc: 0.2222 time: 0.3455 last_time: 0.4108 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:53:14 d2.utils.events]: \u001b[0m eta: 6:17:53 iter: 31239 total_loss: 0.8688 loss_cls: 0.3051 loss_box_reg: 0.293 loss_rpn_cls: 0.05138 loss_rpn_loc: 0.1961 time: 0.3456 last_time: 0.3990 data_time: 0.0045 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:53:22 d2.utils.events]: \u001b[0m eta: 6:16:24 iter: 31259 total_loss: 0.9483 loss_cls: 0.3234 loss_box_reg: 0.3572 loss_rpn_cls: 0.04928 loss_rpn_loc: 0.203 time: 0.3456 last_time: 0.4033 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:53:30 d2.utils.events]: \u001b[0m eta: 6:15:41 iter: 31279 total_loss: 0.8959 loss_cls: 0.3063 loss_box_reg: 0.313 loss_rpn_cls: 0.04734 loss_rpn_loc: 0.2272 time: 0.3456 last_time: 0.4359 data_time: 0.0045 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:53:38 d2.utils.events]: \u001b[0m eta: 6:14:53 iter: 31299 total_loss: 0.8974 loss_cls: 0.321 loss_box_reg: 0.3092 loss_rpn_cls: 0.03812 loss_rpn_loc: 0.197 time: 0.3457 last_time: 0.4186 data_time: 0.0044 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:53:46 d2.utils.events]: \u001b[0m eta: 6:14:10 iter: 31319 total_loss: 0.8927 loss_cls: 0.2812 loss_box_reg: 0.3174 loss_rpn_cls: 0.04104 loss_rpn_loc: 0.1948 time: 0.3457 last_time: 0.3416 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:53:54 d2.utils.events]: \u001b[0m eta: 6:13:43 iter: 31339 total_loss: 0.9473 loss_cls: 0.307 loss_box_reg: 0.3477 loss_rpn_cls: 0.04245 loss_rpn_loc: 0.2064 time: 0.3457 last_time: 0.3400 data_time: 0.0046 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:54:02 d2.utils.events]: \u001b[0m eta: 6:13:04 iter: 31359 total_loss: 1.032 loss_cls: 0.3871 loss_box_reg: 0.337 loss_rpn_cls: 0.07045 loss_rpn_loc: 0.2269 time: 0.3458 last_time: 0.4109 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:54:10 d2.utils.events]: \u001b[0m eta: 6:12:10 iter: 31379 total_loss: 0.903 loss_cls: 0.2852 loss_box_reg: 0.3431 loss_rpn_cls: 0.05936 loss_rpn_loc: 0.1963 time: 0.3458 last_time: 0.3952 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:54:18 d2.utils.events]: \u001b[0m eta: 6:11:39 iter: 31399 total_loss: 0.8754 loss_cls: 0.2939 loss_box_reg: 0.3248 loss_rpn_cls: 0.05714 loss_rpn_loc: 0.2139 time: 0.3458 last_time: 0.4428 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:54:26 d2.utils.events]: \u001b[0m eta: 6:11:18 iter: 31419 total_loss: 0.802 loss_cls: 0.2498 loss_box_reg: 0.3041 loss_rpn_cls: 0.04568 loss_rpn_loc: 0.2045 time: 0.3459 last_time: 0.3924 data_time: 0.0046 last_data_time: 0.0041 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:54:34 d2.utils.events]: \u001b[0m eta: 6:10:56 iter: 31439 total_loss: 0.8953 loss_cls: 0.286 loss_box_reg: 0.3222 loss_rpn_cls: 0.05323 loss_rpn_loc: 0.2091 time: 0.3459 last_time: 0.4167 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:54:42 d2.utils.events]: \u001b[0m eta: 6:10:30 iter: 31459 total_loss: 0.9699 loss_cls: 0.3579 loss_box_reg: 0.355 loss_rpn_cls: 0.0656 loss_rpn_loc: 0.2121 time: 0.3459 last_time: 0.3956 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:54:51 d2.utils.events]: \u001b[0m eta: 6:09:51 iter: 31479 total_loss: 0.8525 loss_cls: 0.2694 loss_box_reg: 0.2996 loss_rpn_cls: 0.05766 loss_rpn_loc: 0.1714 time: 0.3460 last_time: 0.5135 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:55:00 d2.utils.events]: \u001b[0m eta: 6:09:51 iter: 31499 total_loss: 0.8606 loss_cls: 0.2776 loss_box_reg: 0.3436 loss_rpn_cls: 0.04318 loss_rpn_loc: 0.2205 time: 0.3461 last_time: 0.4663 data_time: 0.0050 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:55:09 d2.utils.events]: \u001b[0m eta: 6:09:35 iter: 31519 total_loss: 0.94 loss_cls: 0.303 loss_box_reg: 0.3402 loss_rpn_cls: 0.06603 loss_rpn_loc: 0.2282 time: 0.3461 last_time: 0.4599 data_time: 0.0049 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:55:19 d2.utils.events]: \u001b[0m eta: 6:09:53 iter: 31539 total_loss: 0.8708 loss_cls: 0.262 loss_box_reg: 0.3402 loss_rpn_cls: 0.04953 loss_rpn_loc: 0.1932 time: 0.3462 last_time: 0.5135 data_time: 0.0051 last_data_time: 0.0054 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:55:28 d2.utils.events]: \u001b[0m eta: 6:09:41 iter: 31559 total_loss: 0.9699 loss_cls: 0.3375 loss_box_reg: 0.3185 loss_rpn_cls: 0.06508 loss_rpn_loc: 0.2548 time: 0.3463 last_time: 0.3907 data_time: 0.0050 last_data_time: 0.0052 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:55:37 d2.utils.events]: \u001b[0m eta: 6:09:32 iter: 31579 total_loss: 0.9035 loss_cls: 0.2959 loss_box_reg: 0.3259 loss_rpn_cls: 0.04523 loss_rpn_loc: 0.2047 time: 0.3464 last_time: 0.4985 data_time: 0.0050 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:55:47 d2.utils.events]: \u001b[0m eta: 6:09:36 iter: 31599 total_loss: 0.9079 loss_cls: 0.2991 loss_box_reg: 0.3253 loss_rpn_cls: 0.04845 loss_rpn_loc: 0.19 time: 0.3465 last_time: 0.4109 data_time: 0.0049 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:55:56 d2.utils.events]: \u001b[0m eta: 6:09:49 iter: 31619 total_loss: 0.8573 loss_cls: 0.3253 loss_box_reg: 0.3021 loss_rpn_cls: 0.0546 loss_rpn_loc: 0.1922 time: 0.3465 last_time: 0.4833 data_time: 0.0049 last_data_time: 0.0052 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:56:05 d2.utils.events]: \u001b[0m eta: 6:10:15 iter: 31639 total_loss: 0.9237 loss_cls: 0.3067 loss_box_reg: 0.3472 loss_rpn_cls: 0.05092 loss_rpn_loc: 0.2079 time: 0.3466 last_time: 0.4331 data_time: 0.0050 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:56:14 d2.utils.events]: \u001b[0m eta: 6:10:30 iter: 31659 total_loss: 0.9929 loss_cls: 0.2915 loss_box_reg: 0.3557 loss_rpn_cls: 0.05438 loss_rpn_loc: 0.2146 time: 0.3467 last_time: 0.4709 data_time: 0.0052 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:56:23 d2.utils.events]: \u001b[0m eta: 6:10:42 iter: 31679 total_loss: 0.9085 loss_cls: 0.309 loss_box_reg: 0.3295 loss_rpn_cls: 0.05388 loss_rpn_loc: 0.2117 time: 0.3467 last_time: 0.4945 data_time: 0.0051 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:56:33 d2.utils.events]: \u001b[0m eta: 6:10:53 iter: 31699 total_loss: 0.9148 loss_cls: 0.292 loss_box_reg: 0.3497 loss_rpn_cls: 0.06223 loss_rpn_loc: 0.2001 time: 0.3468 last_time: 0.5176 data_time: 0.0051 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:56:42 d2.utils.events]: \u001b[0m eta: 6:11:10 iter: 31719 total_loss: 0.9067 loss_cls: 0.2779 loss_box_reg: 0.328 loss_rpn_cls: 0.05856 loss_rpn_loc: 0.2169 time: 0.3469 last_time: 0.4857 data_time: 0.0049 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:56:50 d2.utils.events]: \u001b[0m eta: 6:11:12 iter: 31739 total_loss: 0.9325 loss_cls: 0.3498 loss_box_reg: 0.3576 loss_rpn_cls: 0.05074 loss_rpn_loc: 0.2317 time: 0.3469 last_time: 0.3997 data_time: 0.0049 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:56:58 d2.utils.events]: \u001b[0m eta: 6:11:09 iter: 31759 total_loss: 0.9391 loss_cls: 0.3014 loss_box_reg: 0.3512 loss_rpn_cls: 0.06225 loss_rpn_loc: 0.2373 time: 0.3470 last_time: 0.4217 data_time: 0.0044 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:57:07 d2.utils.events]: \u001b[0m eta: 6:10:58 iter: 31779 total_loss: 0.8288 loss_cls: 0.2954 loss_box_reg: 0.2951 loss_rpn_cls: 0.04908 loss_rpn_loc: 0.2113 time: 0.3470 last_time: 0.3309 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:57:15 d2.utils.events]: \u001b[0m eta: 6:10:50 iter: 31799 total_loss: 0.9025 loss_cls: 0.3112 loss_box_reg: 0.3206 loss_rpn_cls: 0.04854 loss_rpn_loc: 0.2016 time: 0.3470 last_time: 0.3962 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:57:23 d2.utils.events]: \u001b[0m eta: 6:10:33 iter: 31819 total_loss: 0.9618 loss_cls: 0.3165 loss_box_reg: 0.3333 loss_rpn_cls: 0.06361 loss_rpn_loc: 0.2232 time: 0.3471 last_time: 0.4041 data_time: 0.0046 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:57:31 d2.utils.events]: \u001b[0m eta: 6:10:29 iter: 31839 total_loss: 0.938 loss_cls: 0.326 loss_box_reg: 0.3399 loss_rpn_cls: 0.05949 loss_rpn_loc: 0.227 time: 0.3471 last_time: 0.3916 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:57:39 d2.utils.events]: \u001b[0m eta: 6:10:29 iter: 31859 total_loss: 0.9586 loss_cls: 0.298 loss_box_reg: 0.3263 loss_rpn_cls: 0.05534 loss_rpn_loc: 0.235 time: 0.3471 last_time: 0.3750 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:57:47 d2.utils.events]: \u001b[0m eta: 6:10:12 iter: 31879 total_loss: 0.8669 loss_cls: 0.3102 loss_box_reg: 0.2974 loss_rpn_cls: 0.04647 loss_rpn_loc: 0.191 time: 0.3472 last_time: 0.3609 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:57:55 d2.utils.events]: \u001b[0m eta: 6:09:57 iter: 31899 total_loss: 0.8994 loss_cls: 0.3 loss_box_reg: 0.3194 loss_rpn_cls: 0.04247 loss_rpn_loc: 0.2132 time: 0.3472 last_time: 0.4357 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:58:03 d2.utils.events]: \u001b[0m eta: 6:10:06 iter: 31919 total_loss: 0.8672 loss_cls: 0.2889 loss_box_reg: 0.3311 loss_rpn_cls: 0.0538 loss_rpn_loc: 0.2135 time: 0.3473 last_time: 0.3852 data_time: 0.0049 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:58:13 d2.utils.events]: \u001b[0m eta: 6:10:31 iter: 31939 total_loss: 0.8323 loss_cls: 0.2732 loss_box_reg: 0.3046 loss_rpn_cls: 0.05076 loss_rpn_loc: 0.208 time: 0.3473 last_time: 0.5133 data_time: 0.0051 last_data_time: 0.0054 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:58:22 d2.utils.events]: \u001b[0m eta: 6:10:46 iter: 31959 total_loss: 0.93 loss_cls: 0.3085 loss_box_reg: 0.3333 loss_rpn_cls: 0.05372 loss_rpn_loc: 0.1957 time: 0.3474 last_time: 0.4836 data_time: 0.0050 last_data_time: 0.0052 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:58:31 d2.utils.events]: \u001b[0m eta: 6:10:58 iter: 31979 total_loss: 0.9137 loss_cls: 0.3068 loss_box_reg: 0.3325 loss_rpn_cls: 0.04992 loss_rpn_loc: 0.1705 time: 0.3475 last_time: 0.4569 data_time: 0.0049 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:58:40 d2.utils.events]: \u001b[0m eta: 6:12:04 iter: 31999 total_loss: 0.9038 loss_cls: 0.3106 loss_box_reg: 0.3002 loss_rpn_cls: 0.05289 loss_rpn_loc: 0.1973 time: 0.3475 last_time: 0.5086 data_time: 0.0050 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:58:49 d2.utils.events]: \u001b[0m eta: 6:13:01 iter: 32019 total_loss: 0.8236 loss_cls: 0.2826 loss_box_reg: 0.3414 loss_rpn_cls: 0.04499 loss_rpn_loc: 0.1929 time: 0.3476 last_time: 0.4180 data_time: 0.0050 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:58:59 d2.utils.events]: \u001b[0m eta: 6:13:57 iter: 32039 total_loss: 0.7891 loss_cls: 0.2766 loss_box_reg: 0.2975 loss_rpn_cls: 0.0459 loss_rpn_loc: 0.1899 time: 0.3477 last_time: 0.4656 data_time: 0.0050 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:59:06 d2.utils.events]: \u001b[0m eta: 6:13:18 iter: 32059 total_loss: 0.7957 loss_cls: 0.292 loss_box_reg: 0.3183 loss_rpn_cls: 0.04351 loss_rpn_loc: 0.1854 time: 0.3477 last_time: 0.3412 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:59:15 d2.utils.events]: \u001b[0m eta: 6:13:06 iter: 32079 total_loss: 0.749 loss_cls: 0.2409 loss_box_reg: 0.2921 loss_rpn_cls: 0.05273 loss_rpn_loc: 0.1853 time: 0.3477 last_time: 0.4379 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:59:24 d2.utils.events]: \u001b[0m eta: 6:14:27 iter: 32099 total_loss: 0.8489 loss_cls: 0.2815 loss_box_reg: 0.3235 loss_rpn_cls: 0.04742 loss_rpn_loc: 0.2066 time: 0.3478 last_time: 0.4648 data_time: 0.0049 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:59:33 d2.utils.events]: \u001b[0m eta: 6:16:07 iter: 32119 total_loss: 0.9133 loss_cls: 0.322 loss_box_reg: 0.3287 loss_rpn_cls: 0.06518 loss_rpn_loc: 0.2287 time: 0.3479 last_time: 0.5172 data_time: 0.0049 last_data_time: 0.0053 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:59:42 d2.utils.events]: \u001b[0m eta: 6:18:12 iter: 32139 total_loss: 0.8938 loss_cls: 0.3117 loss_box_reg: 0.3203 loss_rpn_cls: 0.05474 loss_rpn_loc: 0.1961 time: 0.3480 last_time: 0.5110 data_time: 0.0050 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 18:59:52 d2.utils.events]: \u001b[0m eta: 6:19:19 iter: 32159 total_loss: 0.9435 loss_cls: 0.3245 loss_box_reg: 0.3135 loss_rpn_cls: 0.06067 loss_rpn_loc: 0.2175 time: 0.3480 last_time: 0.5087 data_time: 0.0050 last_data_time: 0.0053 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:00:01 d2.utils.events]: \u001b[0m eta: 6:20:24 iter: 32179 total_loss: 0.8782 loss_cls: 0.2608 loss_box_reg: 0.2924 loss_rpn_cls: 0.0507 loss_rpn_loc: 0.2345 time: 0.3481 last_time: 0.4408 data_time: 0.0049 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:00:10 d2.utils.events]: \u001b[0m eta: 6:21:39 iter: 32199 total_loss: 0.8828 loss_cls: 0.2719 loss_box_reg: 0.3167 loss_rpn_cls: 0.03626 loss_rpn_loc: 0.2117 time: 0.3482 last_time: 0.4877 data_time: 0.0050 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:00:19 d2.utils.events]: \u001b[0m eta: 6:22:29 iter: 32219 total_loss: 0.9408 loss_cls: 0.3186 loss_box_reg: 0.3467 loss_rpn_cls: 0.05619 loss_rpn_loc: 0.2303 time: 0.3482 last_time: 0.4625 data_time: 0.0049 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:00:28 d2.utils.events]: \u001b[0m eta: 6:23:06 iter: 32239 total_loss: 0.8712 loss_cls: 0.2852 loss_box_reg: 0.3205 loss_rpn_cls: 0.05225 loss_rpn_loc: 0.2172 time: 0.3483 last_time: 0.4028 data_time: 0.0049 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:00:37 d2.utils.events]: \u001b[0m eta: 6:23:46 iter: 32259 total_loss: 0.8556 loss_cls: 0.2699 loss_box_reg: 0.3121 loss_rpn_cls: 0.05267 loss_rpn_loc: 0.1733 time: 0.3483 last_time: 0.4409 data_time: 0.0049 last_data_time: 0.0054 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:00:46 d2.utils.events]: \u001b[0m eta: 6:25:15 iter: 32279 total_loss: 0.9686 loss_cls: 0.3143 loss_box_reg: 0.323 loss_rpn_cls: 0.06105 loss_rpn_loc: 0.2369 time: 0.3484 last_time: 0.4602 data_time: 0.0050 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:00:55 d2.utils.events]: \u001b[0m eta: 6:25:40 iter: 32299 total_loss: 0.8498 loss_cls: 0.3084 loss_box_reg: 0.3194 loss_rpn_cls: 0.04269 loss_rpn_loc: 0.1854 time: 0.3485 last_time: 0.4577 data_time: 0.0054 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:01:04 d2.utils.events]: \u001b[0m eta: 6:26:07 iter: 32319 total_loss: 0.908 loss_cls: 0.3046 loss_box_reg: 0.3429 loss_rpn_cls: 0.05973 loss_rpn_loc: 0.2084 time: 0.3485 last_time: 0.4642 data_time: 0.0050 last_data_time: 0.0054 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:01:13 d2.utils.events]: \u001b[0m eta: 6:26:38 iter: 32339 total_loss: 0.9036 loss_cls: 0.2934 loss_box_reg: 0.3376 loss_rpn_cls: 0.05312 loss_rpn_loc: 0.2074 time: 0.3486 last_time: 0.4794 data_time: 0.0050 last_data_time: 0.0056 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:01:22 d2.utils.events]: \u001b[0m eta: 6:26:50 iter: 32359 total_loss: 0.8798 loss_cls: 0.2758 loss_box_reg: 0.309 loss_rpn_cls: 0.06011 loss_rpn_loc: 0.2107 time: 0.3487 last_time: 0.4194 data_time: 0.0049 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:01:31 d2.utils.events]: \u001b[0m eta: 6:27:02 iter: 32379 total_loss: 0.9642 loss_cls: 0.3268 loss_box_reg: 0.371 loss_rpn_cls: 0.04803 loss_rpn_loc: 0.2392 time: 0.3487 last_time: 0.4414 data_time: 0.0050 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:01:40 d2.utils.events]: \u001b[0m eta: 6:27:46 iter: 32399 total_loss: 1.019 loss_cls: 0.3611 loss_box_reg: 0.3634 loss_rpn_cls: 0.05404 loss_rpn_loc: 0.2262 time: 0.3488 last_time: 0.4434 data_time: 0.0050 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:01:50 d2.utils.events]: \u001b[0m eta: 6:28:40 iter: 32419 total_loss: 1.04 loss_cls: 0.3225 loss_box_reg: 0.3678 loss_rpn_cls: 0.0555 loss_rpn_loc: 0.2356 time: 0.3489 last_time: 0.4666 data_time: 0.0050 last_data_time: 0.0053 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:01:59 d2.utils.events]: \u001b[0m eta: 6:29:55 iter: 32439 total_loss: 0.8837 loss_cls: 0.2818 loss_box_reg: 0.3335 loss_rpn_cls: 0.05081 loss_rpn_loc: 0.1955 time: 0.3489 last_time: 0.4816 data_time: 0.0049 last_data_time: 0.0052 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:02:08 d2.utils.events]: \u001b[0m eta: 6:30:30 iter: 32459 total_loss: 1.025 loss_cls: 0.3472 loss_box_reg: 0.3923 loss_rpn_cls: 0.06707 loss_rpn_loc: 0.2352 time: 0.3490 last_time: 0.3555 data_time: 0.0050 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:02:17 d2.utils.events]: \u001b[0m eta: 6:31:04 iter: 32479 total_loss: 0.8847 loss_cls: 0.2757 loss_box_reg: 0.346 loss_rpn_cls: 0.04704 loss_rpn_loc: 0.1987 time: 0.3491 last_time: 0.4795 data_time: 0.0049 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:02:27 d2.utils.events]: \u001b[0m eta: 6:30:55 iter: 32499 total_loss: 0.9343 loss_cls: 0.3077 loss_box_reg: 0.3298 loss_rpn_cls: 0.04618 loss_rpn_loc: 0.2329 time: 0.3492 last_time: 0.4366 data_time: 0.0050 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:02:36 d2.utils.events]: \u001b[0m eta: 6:30:46 iter: 32519 total_loss: 0.9275 loss_cls: 0.3557 loss_box_reg: 0.3045 loss_rpn_cls: 0.05155 loss_rpn_loc: 0.2059 time: 0.3492 last_time: 0.4865 data_time: 0.0049 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:02:45 d2.utils.events]: \u001b[0m eta: 6:30:34 iter: 32539 total_loss: 0.9328 loss_cls: 0.3396 loss_box_reg: 0.3298 loss_rpn_cls: 0.05147 loss_rpn_loc: 0.2156 time: 0.3493 last_time: 0.4840 data_time: 0.0049 last_data_time: 0.0053 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:02:54 d2.utils.events]: \u001b[0m eta: 6:30:14 iter: 32559 total_loss: 1.001 loss_cls: 0.344 loss_box_reg: 0.3844 loss_rpn_cls: 0.05091 loss_rpn_loc: 0.2022 time: 0.3493 last_time: 0.5147 data_time: 0.0049 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:03:03 d2.utils.events]: \u001b[0m eta: 6:30:16 iter: 32579 total_loss: 0.9147 loss_cls: 0.3096 loss_box_reg: 0.3371 loss_rpn_cls: 0.05093 loss_rpn_loc: 0.2385 time: 0.3494 last_time: 0.4706 data_time: 0.0051 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:03:13 d2.utils.events]: \u001b[0m eta: 6:30:10 iter: 32599 total_loss: 0.9594 loss_cls: 0.3155 loss_box_reg: 0.3475 loss_rpn_cls: 0.0669 loss_rpn_loc: 0.2203 time: 0.3495 last_time: 0.4561 data_time: 0.0051 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:03:22 d2.utils.events]: \u001b[0m eta: 6:30:33 iter: 32619 total_loss: 0.9617 loss_cls: 0.3257 loss_box_reg: 0.3518 loss_rpn_cls: 0.05466 loss_rpn_loc: 0.2282 time: 0.3496 last_time: 0.4432 data_time: 0.0050 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:03:31 d2.utils.events]: \u001b[0m eta: 6:30:47 iter: 32639 total_loss: 0.8671 loss_cls: 0.2551 loss_box_reg: 0.3006 loss_rpn_cls: 0.04598 loss_rpn_loc: 0.212 time: 0.3496 last_time: 0.4450 data_time: 0.0050 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:03:40 d2.utils.events]: \u001b[0m eta: 6:30:15 iter: 32659 total_loss: 0.9152 loss_cls: 0.3179 loss_box_reg: 0.323 loss_rpn_cls: 0.05481 loss_rpn_loc: 0.2121 time: 0.3497 last_time: 0.3759 data_time: 0.0050 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:03:50 d2.utils.events]: \u001b[0m eta: 6:29:31 iter: 32679 total_loss: 0.9772 loss_cls: 0.344 loss_box_reg: 0.3302 loss_rpn_cls: 0.05887 loss_rpn_loc: 0.2132 time: 0.3498 last_time: 0.5123 data_time: 0.0049 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:03:59 d2.utils.events]: \u001b[0m eta: 6:29:20 iter: 32699 total_loss: 0.8538 loss_cls: 0.2913 loss_box_reg: 0.2898 loss_rpn_cls: 0.0617 loss_rpn_loc: 0.2248 time: 0.3498 last_time: 0.4176 data_time: 0.0049 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:04:08 d2.utils.events]: \u001b[0m eta: 6:29:11 iter: 32719 total_loss: 0.9119 loss_cls: 0.314 loss_box_reg: 0.3606 loss_rpn_cls: 0.05511 loss_rpn_loc: 0.2369 time: 0.3499 last_time: 0.4606 data_time: 0.0049 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:04:17 d2.utils.events]: \u001b[0m eta: 6:29:40 iter: 32739 total_loss: 0.8244 loss_cls: 0.2548 loss_box_reg: 0.3116 loss_rpn_cls: 0.04234 loss_rpn_loc: 0.1895 time: 0.3500 last_time: 0.4936 data_time: 0.0049 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:04:27 d2.utils.events]: \u001b[0m eta: 6:33:29 iter: 32759 total_loss: 0.9585 loss_cls: 0.3771 loss_box_reg: 0.328 loss_rpn_cls: 0.04985 loss_rpn_loc: 0.204 time: 0.3500 last_time: 0.4712 data_time: 0.0050 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:04:36 d2.utils.events]: \u001b[0m eta: 6:35:29 iter: 32779 total_loss: 0.9342 loss_cls: 0.315 loss_box_reg: 0.3287 loss_rpn_cls: 0.04612 loss_rpn_loc: 0.2283 time: 0.3501 last_time: 0.4833 data_time: 0.0050 last_data_time: 0.0052 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:04:45 d2.utils.events]: \u001b[0m eta: 6:36:41 iter: 32799 total_loss: 0.8542 loss_cls: 0.282 loss_box_reg: 0.3165 loss_rpn_cls: 0.04482 loss_rpn_loc: 0.1893 time: 0.3502 last_time: 0.4375 data_time: 0.0050 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:04:54 d2.utils.events]: \u001b[0m eta: 6:37:24 iter: 32819 total_loss: 0.9576 loss_cls: 0.3249 loss_box_reg: 0.3446 loss_rpn_cls: 0.04945 loss_rpn_loc: 0.1923 time: 0.3502 last_time: 0.4565 data_time: 0.0049 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:05:03 d2.utils.events]: \u001b[0m eta: 6:38:03 iter: 32839 total_loss: 0.7658 loss_cls: 0.289 loss_box_reg: 0.3105 loss_rpn_cls: 0.04618 loss_rpn_loc: 0.1659 time: 0.3503 last_time: 0.5106 data_time: 0.0049 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:05:12 d2.utils.events]: \u001b[0m eta: 6:38:32 iter: 32859 total_loss: 0.9143 loss_cls: 0.3008 loss_box_reg: 0.3205 loss_rpn_cls: 0.06303 loss_rpn_loc: 0.2173 time: 0.3504 last_time: 0.4808 data_time: 0.0048 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:05:20 d2.utils.events]: \u001b[0m eta: 6:38:51 iter: 32879 total_loss: 1.06 loss_cls: 0.3575 loss_box_reg: 0.36 loss_rpn_cls: 0.06088 loss_rpn_loc: 0.2467 time: 0.3504 last_time: 0.3985 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:05:29 d2.utils.events]: \u001b[0m eta: 6:39:11 iter: 32899 total_loss: 1.008 loss_cls: 0.3663 loss_box_reg: 0.372 loss_rpn_cls: 0.05618 loss_rpn_loc: 0.2393 time: 0.3505 last_time: 0.4265 data_time: 0.0049 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:05:38 d2.utils.events]: \u001b[0m eta: 6:38:59 iter: 32919 total_loss: 0.8314 loss_cls: 0.249 loss_box_reg: 0.3122 loss_rpn_cls: 0.04468 loss_rpn_loc: 0.1856 time: 0.3505 last_time: 0.4636 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:05:47 d2.utils.events]: \u001b[0m eta: 6:38:35 iter: 32939 total_loss: 0.9055 loss_cls: 0.2901 loss_box_reg: 0.3319 loss_rpn_cls: 0.04975 loss_rpn_loc: 0.1842 time: 0.3506 last_time: 0.5160 data_time: 0.0050 last_data_time: 0.0053 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:05:56 d2.utils.events]: \u001b[0m eta: 6:38:27 iter: 32959 total_loss: 1.003 loss_cls: 0.3602 loss_box_reg: 0.3655 loss_rpn_cls: 0.06365 loss_rpn_loc: 0.1996 time: 0.3506 last_time: 0.4391 data_time: 0.0050 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:06:05 d2.utils.events]: \u001b[0m eta: 6:38:30 iter: 32979 total_loss: 0.946 loss_cls: 0.3144 loss_box_reg: 0.3364 loss_rpn_cls: 0.04905 loss_rpn_loc: 0.2228 time: 0.3507 last_time: 0.4519 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:06:13 d2.utils.events]: \u001b[0m eta: 6:37:36 iter: 32999 total_loss: 0.8582 loss_cls: 0.2833 loss_box_reg: 0.3075 loss_rpn_cls: 0.04818 loss_rpn_loc: 0.2236 time: 0.3507 last_time: 0.3996 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:06:22 d2.utils.events]: \u001b[0m eta: 6:36:49 iter: 33019 total_loss: 0.9783 loss_cls: 0.359 loss_box_reg: 0.3276 loss_rpn_cls: 0.05957 loss_rpn_loc: 0.215 time: 0.3508 last_time: 0.4696 data_time: 0.0049 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:06:30 d2.utils.events]: \u001b[0m eta: 6:36:10 iter: 33039 total_loss: 0.8737 loss_cls: 0.3002 loss_box_reg: 0.3091 loss_rpn_cls: 0.04449 loss_rpn_loc: 0.1853 time: 0.3508 last_time: 0.3819 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:06:38 d2.utils.events]: \u001b[0m eta: 6:36:00 iter: 33059 total_loss: 0.8697 loss_cls: 0.2851 loss_box_reg: 0.2899 loss_rpn_cls: 0.04453 loss_rpn_loc: 0.2063 time: 0.3508 last_time: 0.4178 data_time: 0.0050 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:06:46 d2.utils.events]: \u001b[0m eta: 6:35:51 iter: 33079 total_loss: 0.9328 loss_cls: 0.3017 loss_box_reg: 0.339 loss_rpn_cls: 0.04945 loss_rpn_loc: 0.241 time: 0.3509 last_time: 0.4461 data_time: 0.0049 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:06:54 d2.utils.events]: \u001b[0m eta: 6:34:53 iter: 33099 total_loss: 0.8115 loss_cls: 0.2828 loss_box_reg: 0.333 loss_rpn_cls: 0.04271 loss_rpn_loc: 0.1403 time: 0.3509 last_time: 0.4282 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:07:03 d2.utils.events]: \u001b[0m eta: 6:33:26 iter: 33119 total_loss: 0.956 loss_cls: 0.3508 loss_box_reg: 0.3651 loss_rpn_cls: 0.05628 loss_rpn_loc: 0.2407 time: 0.3509 last_time: 0.4282 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:07:11 d2.utils.events]: \u001b[0m eta: 6:29:28 iter: 33139 total_loss: 0.9105 loss_cls: 0.3248 loss_box_reg: 0.372 loss_rpn_cls: 0.05296 loss_rpn_loc: 0.1894 time: 0.3510 last_time: 0.4443 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:07:19 d2.utils.events]: \u001b[0m eta: 6:26:47 iter: 33159 total_loss: 0.8174 loss_cls: 0.2907 loss_box_reg: 0.2927 loss_rpn_cls: 0.03869 loss_rpn_loc: 0.1708 time: 0.3510 last_time: 0.4202 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:07:27 d2.utils.events]: \u001b[0m eta: 6:25:01 iter: 33179 total_loss: 0.8469 loss_cls: 0.2934 loss_box_reg: 0.318 loss_rpn_cls: 0.04613 loss_rpn_loc: 0.2308 time: 0.3510 last_time: 0.3843 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:07:35 d2.utils.events]: \u001b[0m eta: 6:23:46 iter: 33199 total_loss: 0.9693 loss_cls: 0.288 loss_box_reg: 0.3412 loss_rpn_cls: 0.06881 loss_rpn_loc: 0.2321 time: 0.3511 last_time: 0.4275 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:07:43 d2.utils.events]: \u001b[0m eta: 6:22:55 iter: 33219 total_loss: 0.9062 loss_cls: 0.3109 loss_box_reg: 0.3497 loss_rpn_cls: 0.05161 loss_rpn_loc: 0.1629 time: 0.3511 last_time: 0.3680 data_time: 0.0046 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:07:51 d2.utils.events]: \u001b[0m eta: 6:22:30 iter: 33239 total_loss: 0.9434 loss_cls: 0.279 loss_box_reg: 0.3309 loss_rpn_cls: 0.05226 loss_rpn_loc: 0.2441 time: 0.3511 last_time: 0.4435 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:08:00 d2.utils.events]: \u001b[0m eta: 6:22:09 iter: 33259 total_loss: 0.9683 loss_cls: 0.3277 loss_box_reg: 0.3479 loss_rpn_cls: 0.05227 loss_rpn_loc: 0.2191 time: 0.3512 last_time: 0.4056 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:08:08 d2.utils.events]: \u001b[0m eta: 6:21:27 iter: 33279 total_loss: 0.9362 loss_cls: 0.3278 loss_box_reg: 0.3357 loss_rpn_cls: 0.04773 loss_rpn_loc: 0.1985 time: 0.3512 last_time: 0.3943 data_time: 0.0044 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:08:15 d2.utils.events]: \u001b[0m eta: 6:20:19 iter: 33299 total_loss: 0.7931 loss_cls: 0.2569 loss_box_reg: 0.317 loss_rpn_cls: 0.04959 loss_rpn_loc: 0.1939 time: 0.3512 last_time: 0.3946 data_time: 0.0044 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:08:23 d2.utils.events]: \u001b[0m eta: 6:19:17 iter: 33319 total_loss: 0.8813 loss_cls: 0.3075 loss_box_reg: 0.3296 loss_rpn_cls: 0.05053 loss_rpn_loc: 0.2152 time: 0.3513 last_time: 0.3963 data_time: 0.0045 last_data_time: 0.0041 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:08:32 d2.utils.events]: \u001b[0m eta: 6:18:44 iter: 33339 total_loss: 0.9355 loss_cls: 0.2538 loss_box_reg: 0.3522 loss_rpn_cls: 0.06289 loss_rpn_loc: 0.2408 time: 0.3513 last_time: 0.4006 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:08:39 d2.utils.events]: \u001b[0m eta: 6:17:59 iter: 33359 total_loss: 0.8897 loss_cls: 0.2911 loss_box_reg: 0.314 loss_rpn_cls: 0.05306 loss_rpn_loc: 0.2117 time: 0.3513 last_time: 0.3749 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:08:48 d2.utils.events]: \u001b[0m eta: 6:17:26 iter: 33379 total_loss: 0.8411 loss_cls: 0.2377 loss_box_reg: 0.3074 loss_rpn_cls: 0.04213 loss_rpn_loc: 0.187 time: 0.3513 last_time: 0.3715 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:08:56 d2.utils.events]: \u001b[0m eta: 6:16:30 iter: 33399 total_loss: 0.9548 loss_cls: 0.2963 loss_box_reg: 0.3637 loss_rpn_cls: 0.04727 loss_rpn_loc: 0.2501 time: 0.3514 last_time: 0.3447 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:09:04 d2.utils.events]: \u001b[0m eta: 6:15:20 iter: 33419 total_loss: 0.8921 loss_cls: 0.2856 loss_box_reg: 0.3429 loss_rpn_cls: 0.05158 loss_rpn_loc: 0.2209 time: 0.3514 last_time: 0.3961 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:09:12 d2.utils.events]: \u001b[0m eta: 6:14:01 iter: 33439 total_loss: 0.8823 loss_cls: 0.3061 loss_box_reg: 0.3069 loss_rpn_cls: 0.04023 loss_rpn_loc: 0.2022 time: 0.3514 last_time: 0.4405 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:09:20 d2.utils.events]: \u001b[0m eta: 6:13:09 iter: 33459 total_loss: 0.9072 loss_cls: 0.268 loss_box_reg: 0.3416 loss_rpn_cls: 0.04453 loss_rpn_loc: 0.2137 time: 0.3515 last_time: 0.3969 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:09:28 d2.utils.events]: \u001b[0m eta: 6:12:18 iter: 33479 total_loss: 0.816 loss_cls: 0.3154 loss_box_reg: 0.3179 loss_rpn_cls: 0.04803 loss_rpn_loc: 0.1758 time: 0.3515 last_time: 0.3894 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:09:36 d2.utils.events]: \u001b[0m eta: 6:09:53 iter: 33499 total_loss: 0.9112 loss_cls: 0.2836 loss_box_reg: 0.3234 loss_rpn_cls: 0.05049 loss_rpn_loc: 0.2381 time: 0.3515 last_time: 0.4196 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:09:44 d2.utils.events]: \u001b[0m eta: 6:07:16 iter: 33519 total_loss: 0.7715 loss_cls: 0.2767 loss_box_reg: 0.2948 loss_rpn_cls: 0.05507 loss_rpn_loc: 0.186 time: 0.3516 last_time: 0.3670 data_time: 0.0047 last_data_time: 0.0052 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:09:52 d2.utils.events]: \u001b[0m eta: 6:04:09 iter: 33539 total_loss: 0.8194 loss_cls: 0.3012 loss_box_reg: 0.3163 loss_rpn_cls: 0.0462 loss_rpn_loc: 0.185 time: 0.3516 last_time: 0.4076 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:10:00 d2.utils.events]: \u001b[0m eta: 6:02:31 iter: 33559 total_loss: 0.858 loss_cls: 0.2877 loss_box_reg: 0.3219 loss_rpn_cls: 0.05258 loss_rpn_loc: 0.2103 time: 0.3516 last_time: 0.3873 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:10:09 d2.utils.events]: \u001b[0m eta: 6:01:12 iter: 33579 total_loss: 0.8054 loss_cls: 0.2591 loss_box_reg: 0.2981 loss_rpn_cls: 0.04188 loss_rpn_loc: 0.216 time: 0.3517 last_time: 0.3891 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:10:17 d2.utils.events]: \u001b[0m eta: 6:00:13 iter: 33599 total_loss: 0.8686 loss_cls: 0.3369 loss_box_reg: 0.3176 loss_rpn_cls: 0.04102 loss_rpn_loc: 0.2153 time: 0.3517 last_time: 0.4390 data_time: 0.0047 last_data_time: 0.0041 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:10:25 d2.utils.events]: \u001b[0m eta: 5:59:21 iter: 33619 total_loss: 0.9702 loss_cls: 0.3088 loss_box_reg: 0.3461 loss_rpn_cls: 0.05014 loss_rpn_loc: 0.2108 time: 0.3517 last_time: 0.4445 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:10:33 d2.utils.events]: \u001b[0m eta: 5:58:47 iter: 33639 total_loss: 0.8929 loss_cls: 0.3729 loss_box_reg: 0.324 loss_rpn_cls: 0.05542 loss_rpn_loc: 0.1673 time: 0.3518 last_time: 0.4214 data_time: 0.0047 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:10:41 d2.utils.events]: \u001b[0m eta: 5:58:19 iter: 33659 total_loss: 0.9566 loss_cls: 0.312 loss_box_reg: 0.3321 loss_rpn_cls: 0.04463 loss_rpn_loc: 0.2215 time: 0.3518 last_time: 0.3653 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:10:49 d2.utils.events]: \u001b[0m eta: 5:57:46 iter: 33679 total_loss: 0.9288 loss_cls: 0.296 loss_box_reg: 0.3504 loss_rpn_cls: 0.06017 loss_rpn_loc: 0.2148 time: 0.3518 last_time: 0.3957 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:10:57 d2.utils.events]: \u001b[0m eta: 5:57:15 iter: 33699 total_loss: 0.9339 loss_cls: 0.2999 loss_box_reg: 0.3565 loss_rpn_cls: 0.04834 loss_rpn_loc: 0.2281 time: 0.3518 last_time: 0.3700 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:11:05 d2.utils.events]: \u001b[0m eta: 5:57:03 iter: 33719 total_loss: 0.8891 loss_cls: 0.3009 loss_box_reg: 0.2844 loss_rpn_cls: 0.04463 loss_rpn_loc: 0.1997 time: 0.3519 last_time: 0.3342 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:11:13 d2.utils.events]: \u001b[0m eta: 5:56:23 iter: 33739 total_loss: 0.8329 loss_cls: 0.3039 loss_box_reg: 0.3096 loss_rpn_cls: 0.03824 loss_rpn_loc: 0.1841 time: 0.3519 last_time: 0.3973 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:11:21 d2.utils.events]: \u001b[0m eta: 5:56:00 iter: 33759 total_loss: 0.9019 loss_cls: 0.2975 loss_box_reg: 0.2997 loss_rpn_cls: 0.05286 loss_rpn_loc: 0.2297 time: 0.3519 last_time: 0.3906 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:11:29 d2.utils.events]: \u001b[0m eta: 5:55:39 iter: 33779 total_loss: 0.8864 loss_cls: 0.3241 loss_box_reg: 0.3335 loss_rpn_cls: 0.05203 loss_rpn_loc: 0.2071 time: 0.3520 last_time: 0.3463 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:11:37 d2.utils.events]: \u001b[0m eta: 5:55:04 iter: 33799 total_loss: 0.8891 loss_cls: 0.3149 loss_box_reg: 0.3307 loss_rpn_cls: 0.04952 loss_rpn_loc: 0.1966 time: 0.3520 last_time: 0.3855 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:11:45 d2.utils.events]: \u001b[0m eta: 5:54:17 iter: 33819 total_loss: 0.8922 loss_cls: 0.3032 loss_box_reg: 0.3137 loss_rpn_cls: 0.05488 loss_rpn_loc: 0.22 time: 0.3520 last_time: 0.3802 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:11:53 d2.utils.events]: \u001b[0m eta: 5:53:53 iter: 33839 total_loss: 0.9962 loss_cls: 0.3316 loss_box_reg: 0.3487 loss_rpn_cls: 0.05234 loss_rpn_loc: 0.2262 time: 0.3521 last_time: 0.4267 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:12:02 d2.utils.events]: \u001b[0m eta: 5:53:25 iter: 33859 total_loss: 0.9021 loss_cls: 0.2723 loss_box_reg: 0.3414 loss_rpn_cls: 0.04959 loss_rpn_loc: 0.1873 time: 0.3521 last_time: 0.4221 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:12:10 d2.utils.events]: \u001b[0m eta: 5:53:18 iter: 33879 total_loss: 0.8262 loss_cls: 0.26 loss_box_reg: 0.3109 loss_rpn_cls: 0.05037 loss_rpn_loc: 0.2092 time: 0.3521 last_time: 0.4176 data_time: 0.0047 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:12:18 d2.utils.events]: \u001b[0m eta: 5:52:55 iter: 33899 total_loss: 0.7906 loss_cls: 0.2391 loss_box_reg: 0.277 loss_rpn_cls: 0.04482 loss_rpn_loc: 0.1615 time: 0.3522 last_time: 0.4019 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:12:26 d2.utils.events]: \u001b[0m eta: 5:52:57 iter: 33919 total_loss: 0.9494 loss_cls: 0.2812 loss_box_reg: 0.3305 loss_rpn_cls: 0.04087 loss_rpn_loc: 0.2397 time: 0.3522 last_time: 0.3479 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:12:35 d2.utils.events]: \u001b[0m eta: 5:52:18 iter: 33939 total_loss: 0.8153 loss_cls: 0.2759 loss_box_reg: 0.2955 loss_rpn_cls: 0.04872 loss_rpn_loc: 0.2013 time: 0.3522 last_time: 0.4182 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:12:43 d2.utils.events]: \u001b[0m eta: 5:51:51 iter: 33959 total_loss: 1.048 loss_cls: 0.3086 loss_box_reg: 0.3476 loss_rpn_cls: 0.05544 loss_rpn_loc: 0.2283 time: 0.3523 last_time: 0.4228 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:12:51 d2.utils.events]: \u001b[0m eta: 5:51:18 iter: 33979 total_loss: 0.959 loss_cls: 0.3018 loss_box_reg: 0.3476 loss_rpn_cls: 0.05711 loss_rpn_loc: 0.2279 time: 0.3523 last_time: 0.4329 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:12:59 d2.utils.events]: \u001b[0m eta: 5:51:27 iter: 33999 total_loss: 0.9575 loss_cls: 0.3442 loss_box_reg: 0.332 loss_rpn_cls: 0.05906 loss_rpn_loc: 0.2152 time: 0.3523 last_time: 0.4193 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:13:07 d2.utils.events]: \u001b[0m eta: 5:50:53 iter: 34019 total_loss: 0.9997 loss_cls: 0.3046 loss_box_reg: 0.3272 loss_rpn_cls: 0.05573 loss_rpn_loc: 0.1954 time: 0.3524 last_time: 0.3760 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:13:15 d2.utils.events]: \u001b[0m eta: 5:50:38 iter: 34039 total_loss: 0.8936 loss_cls: 0.3066 loss_box_reg: 0.3368 loss_rpn_cls: 0.05873 loss_rpn_loc: 0.2167 time: 0.3524 last_time: 0.3916 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:13:23 d2.utils.events]: \u001b[0m eta: 5:50:16 iter: 34059 total_loss: 0.8717 loss_cls: 0.2544 loss_box_reg: 0.3107 loss_rpn_cls: 0.05855 loss_rpn_loc: 0.2174 time: 0.3524 last_time: 0.4116 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:13:31 d2.utils.events]: \u001b[0m eta: 5:50:04 iter: 34079 total_loss: 1.038 loss_cls: 0.4065 loss_box_reg: 0.3378 loss_rpn_cls: 0.0626 loss_rpn_loc: 0.2478 time: 0.3525 last_time: 0.3908 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:13:40 d2.utils.events]: \u001b[0m eta: 5:49:56 iter: 34099 total_loss: 0.8 loss_cls: 0.3037 loss_box_reg: 0.2843 loss_rpn_cls: 0.05719 loss_rpn_loc: 0.1819 time: 0.3525 last_time: 0.4139 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:13:47 d2.utils.events]: \u001b[0m eta: 5:49:51 iter: 34119 total_loss: 0.8865 loss_cls: 0.2863 loss_box_reg: 0.311 loss_rpn_cls: 0.06282 loss_rpn_loc: 0.2237 time: 0.3525 last_time: 0.4151 data_time: 0.0048 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:13:56 d2.utils.events]: \u001b[0m eta: 5:49:40 iter: 34139 total_loss: 0.9422 loss_cls: 0.312 loss_box_reg: 0.3259 loss_rpn_cls: 0.06156 loss_rpn_loc: 0.207 time: 0.3525 last_time: 0.3814 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:14:04 d2.utils.events]: \u001b[0m eta: 5:49:31 iter: 34159 total_loss: 0.8849 loss_cls: 0.2644 loss_box_reg: 0.3543 loss_rpn_cls: 0.05165 loss_rpn_loc: 0.2128 time: 0.3526 last_time: 0.4133 data_time: 0.0048 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:14:12 d2.utils.events]: \u001b[0m eta: 5:49:40 iter: 34179 total_loss: 0.8434 loss_cls: 0.3031 loss_box_reg: 0.3008 loss_rpn_cls: 0.04569 loss_rpn_loc: 0.2014 time: 0.3526 last_time: 0.4590 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:14:20 d2.utils.events]: \u001b[0m eta: 5:49:39 iter: 34199 total_loss: 0.9044 loss_cls: 0.3066 loss_box_reg: 0.3515 loss_rpn_cls: 0.04983 loss_rpn_loc: 0.2101 time: 0.3526 last_time: 0.4419 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:14:28 d2.utils.events]: \u001b[0m eta: 5:49:07 iter: 34219 total_loss: 0.8822 loss_cls: 0.2784 loss_box_reg: 0.3126 loss_rpn_cls: 0.0592 loss_rpn_loc: 0.2017 time: 0.3527 last_time: 0.3427 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:14:36 d2.utils.events]: \u001b[0m eta: 5:48:58 iter: 34239 total_loss: 0.7856 loss_cls: 0.2668 loss_box_reg: 0.2757 loss_rpn_cls: 0.04214 loss_rpn_loc: 0.1771 time: 0.3527 last_time: 0.4446 data_time: 0.0048 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:14:44 d2.utils.events]: \u001b[0m eta: 5:48:54 iter: 34259 total_loss: 0.8603 loss_cls: 0.2605 loss_box_reg: 0.2794 loss_rpn_cls: 0.03995 loss_rpn_loc: 0.2177 time: 0.3527 last_time: 0.4198 data_time: 0.0049 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:14:53 d2.utils.events]: \u001b[0m eta: 5:48:42 iter: 34279 total_loss: 0.9815 loss_cls: 0.3048 loss_box_reg: 0.3301 loss_rpn_cls: 0.05602 loss_rpn_loc: 0.2317 time: 0.3528 last_time: 0.4463 data_time: 0.0048 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:15:01 d2.utils.events]: \u001b[0m eta: 5:48:33 iter: 34299 total_loss: 0.8429 loss_cls: 0.2915 loss_box_reg: 0.3015 loss_rpn_cls: 0.05068 loss_rpn_loc: 0.1943 time: 0.3528 last_time: 0.4125 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:15:09 d2.utils.events]: \u001b[0m eta: 5:48:21 iter: 34319 total_loss: 0.968 loss_cls: 0.3414 loss_box_reg: 0.3398 loss_rpn_cls: 0.05009 loss_rpn_loc: 0.2036 time: 0.3528 last_time: 0.4193 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:15:17 d2.utils.events]: \u001b[0m eta: 5:48:34 iter: 34339 total_loss: 0.9305 loss_cls: 0.3332 loss_box_reg: 0.3103 loss_rpn_cls: 0.04898 loss_rpn_loc: 0.2198 time: 0.3529 last_time: 0.3972 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:15:25 d2.utils.events]: \u001b[0m eta: 5:48:19 iter: 34359 total_loss: 0.9497 loss_cls: 0.3271 loss_box_reg: 0.3538 loss_rpn_cls: 0.05175 loss_rpn_loc: 0.2014 time: 0.3529 last_time: 0.4001 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:15:33 d2.utils.events]: \u001b[0m eta: 5:48:38 iter: 34379 total_loss: 0.9751 loss_cls: 0.3032 loss_box_reg: 0.3117 loss_rpn_cls: 0.05172 loss_rpn_loc: 0.213 time: 0.3529 last_time: 0.3758 data_time: 0.0048 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:15:41 d2.utils.events]: \u001b[0m eta: 5:48:20 iter: 34399 total_loss: 0.862 loss_cls: 0.2878 loss_box_reg: 0.301 loss_rpn_cls: 0.0452 loss_rpn_loc: 0.1835 time: 0.3529 last_time: 0.3325 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:15:50 d2.utils.events]: \u001b[0m eta: 5:48:30 iter: 34419 total_loss: 0.856 loss_cls: 0.2802 loss_box_reg: 0.3072 loss_rpn_cls: 0.04405 loss_rpn_loc: 0.2064 time: 0.3530 last_time: 0.4427 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:15:58 d2.utils.events]: \u001b[0m eta: 5:48:27 iter: 34439 total_loss: 0.8059 loss_cls: 0.2878 loss_box_reg: 0.3275 loss_rpn_cls: 0.05595 loss_rpn_loc: 0.1746 time: 0.3530 last_time: 0.4033 data_time: 0.0046 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:16:06 d2.utils.events]: \u001b[0m eta: 5:48:18 iter: 34459 total_loss: 0.8449 loss_cls: 0.2752 loss_box_reg: 0.3033 loss_rpn_cls: 0.05985 loss_rpn_loc: 0.2076 time: 0.3530 last_time: 0.4205 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:16:14 d2.utils.events]: \u001b[0m eta: 5:48:09 iter: 34479 total_loss: 0.8969 loss_cls: 0.3057 loss_box_reg: 0.3345 loss_rpn_cls: 0.05048 loss_rpn_loc: 0.1963 time: 0.3531 last_time: 0.4374 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:16:23 d2.utils.events]: \u001b[0m eta: 5:48:02 iter: 34499 total_loss: 0.8967 loss_cls: 0.265 loss_box_reg: 0.3267 loss_rpn_cls: 0.04837 loss_rpn_loc: 0.2117 time: 0.3531 last_time: 0.3581 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:16:31 d2.utils.events]: \u001b[0m eta: 5:48:03 iter: 34519 total_loss: 0.8005 loss_cls: 0.3213 loss_box_reg: 0.2924 loss_rpn_cls: 0.04492 loss_rpn_loc: 0.2109 time: 0.3531 last_time: 0.4004 data_time: 0.0048 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:16:39 d2.utils.events]: \u001b[0m eta: 5:48:04 iter: 34539 total_loss: 0.9139 loss_cls: 0.2863 loss_box_reg: 0.3293 loss_rpn_cls: 0.04457 loss_rpn_loc: 0.226 time: 0.3532 last_time: 0.4237 data_time: 0.0048 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:16:47 d2.utils.events]: \u001b[0m eta: 5:47:53 iter: 34559 total_loss: 0.8945 loss_cls: 0.2875 loss_box_reg: 0.3473 loss_rpn_cls: 0.06026 loss_rpn_loc: 0.1928 time: 0.3532 last_time: 0.3853 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:16:55 d2.utils.events]: \u001b[0m eta: 5:47:45 iter: 34579 total_loss: 0.9013 loss_cls: 0.2863 loss_box_reg: 0.3195 loss_rpn_cls: 0.04446 loss_rpn_loc: 0.2196 time: 0.3532 last_time: 0.3998 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:17:04 d2.utils.events]: \u001b[0m eta: 5:47:50 iter: 34599 total_loss: 0.9214 loss_cls: 0.3214 loss_box_reg: 0.3142 loss_rpn_cls: 0.0557 loss_rpn_loc: 0.2158 time: 0.3533 last_time: 0.3846 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:17:12 d2.utils.events]: \u001b[0m eta: 5:47:42 iter: 34619 total_loss: 0.8026 loss_cls: 0.2571 loss_box_reg: 0.2877 loss_rpn_cls: 0.0494 loss_rpn_loc: 0.1917 time: 0.3533 last_time: 0.3956 data_time: 0.0047 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:17:20 d2.utils.events]: \u001b[0m eta: 5:47:29 iter: 34639 total_loss: 0.8418 loss_cls: 0.265 loss_box_reg: 0.3106 loss_rpn_cls: 0.05074 loss_rpn_loc: 0.2076 time: 0.3533 last_time: 0.4092 data_time: 0.0046 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:17:28 d2.utils.events]: \u001b[0m eta: 5:47:20 iter: 34659 total_loss: 0.8379 loss_cls: 0.2424 loss_box_reg: 0.2899 loss_rpn_cls: 0.04573 loss_rpn_loc: 0.2197 time: 0.3534 last_time: 0.4196 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:17:36 d2.utils.events]: \u001b[0m eta: 5:47:16 iter: 34679 total_loss: 0.8948 loss_cls: 0.2871 loss_box_reg: 0.3372 loss_rpn_cls: 0.04212 loss_rpn_loc: 0.2192 time: 0.3534 last_time: 0.3392 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:17:44 d2.utils.events]: \u001b[0m eta: 5:47:22 iter: 34699 total_loss: 0.8619 loss_cls: 0.3024 loss_box_reg: 0.3096 loss_rpn_cls: 0.05037 loss_rpn_loc: 0.1795 time: 0.3534 last_time: 0.4454 data_time: 0.0045 last_data_time: 0.0041 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:17:52 d2.utils.events]: \u001b[0m eta: 5:47:03 iter: 34719 total_loss: 0.8757 loss_cls: 0.2916 loss_box_reg: 0.3278 loss_rpn_cls: 0.05394 loss_rpn_loc: 0.1855 time: 0.3535 last_time: 0.4453 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:18:00 d2.utils.events]: \u001b[0m eta: 5:46:55 iter: 34739 total_loss: 0.896 loss_cls: 0.2994 loss_box_reg: 0.2964 loss_rpn_cls: 0.04493 loss_rpn_loc: 0.2072 time: 0.3535 last_time: 0.3412 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:18:08 d2.utils.events]: \u001b[0m eta: 5:46:45 iter: 34759 total_loss: 0.8575 loss_cls: 0.2683 loss_box_reg: 0.3095 loss_rpn_cls: 0.05421 loss_rpn_loc: 0.223 time: 0.3535 last_time: 0.4436 data_time: 0.0046 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:18:17 d2.utils.events]: \u001b[0m eta: 5:46:31 iter: 34779 total_loss: 0.9584 loss_cls: 0.3149 loss_box_reg: 0.3123 loss_rpn_cls: 0.06392 loss_rpn_loc: 0.2149 time: 0.3535 last_time: 0.4168 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:18:25 d2.utils.events]: \u001b[0m eta: 5:46:29 iter: 34799 total_loss: 0.9292 loss_cls: 0.2963 loss_box_reg: 0.325 loss_rpn_cls: 0.04646 loss_rpn_loc: 0.2115 time: 0.3536 last_time: 0.4028 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:18:33 d2.utils.events]: \u001b[0m eta: 5:46:44 iter: 34819 total_loss: 0.859 loss_cls: 0.2852 loss_box_reg: 0.3375 loss_rpn_cls: 0.05295 loss_rpn_loc: 0.2028 time: 0.3536 last_time: 0.3897 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:18:41 d2.utils.events]: \u001b[0m eta: 5:46:34 iter: 34839 total_loss: 0.8401 loss_cls: 0.2469 loss_box_reg: 0.2967 loss_rpn_cls: 0.06399 loss_rpn_loc: 0.1718 time: 0.3536 last_time: 0.3996 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:18:50 d2.utils.events]: \u001b[0m eta: 5:46:42 iter: 34859 total_loss: 0.8226 loss_cls: 0.2549 loss_box_reg: 0.2903 loss_rpn_cls: 0.03878 loss_rpn_loc: 0.2183 time: 0.3537 last_time: 0.5160 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:18:58 d2.utils.events]: \u001b[0m eta: 5:46:34 iter: 34879 total_loss: 0.933 loss_cls: 0.3018 loss_box_reg: 0.3255 loss_rpn_cls: 0.05142 loss_rpn_loc: 0.213 time: 0.3537 last_time: 0.3472 data_time: 0.0046 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:19:07 d2.utils.events]: \u001b[0m eta: 5:46:29 iter: 34899 total_loss: 0.7842 loss_cls: 0.2598 loss_box_reg: 0.3044 loss_rpn_cls: 0.04551 loss_rpn_loc: 0.2011 time: 0.3538 last_time: 0.4393 data_time: 0.0046 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:19:15 d2.utils.events]: \u001b[0m eta: 5:46:17 iter: 34919 total_loss: 0.9402 loss_cls: 0.3321 loss_box_reg: 0.334 loss_rpn_cls: 0.05653 loss_rpn_loc: 0.1954 time: 0.3538 last_time: 0.4088 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:19:23 d2.utils.events]: \u001b[0m eta: 5:46:12 iter: 34939 total_loss: 0.8539 loss_cls: 0.2987 loss_box_reg: 0.2949 loss_rpn_cls: 0.05033 loss_rpn_loc: 0.195 time: 0.3538 last_time: 0.3954 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:19:31 d2.utils.events]: \u001b[0m eta: 5:45:56 iter: 34959 total_loss: 0.8562 loss_cls: 0.3125 loss_box_reg: 0.2909 loss_rpn_cls: 0.04466 loss_rpn_loc: 0.172 time: 0.3539 last_time: 0.3876 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:19:39 d2.utils.events]: \u001b[0m eta: 5:45:40 iter: 34979 total_loss: 0.8026 loss_cls: 0.2718 loss_box_reg: 0.3079 loss_rpn_cls: 0.04945 loss_rpn_loc: 0.1925 time: 0.3539 last_time: 0.3945 data_time: 0.0045 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:19:48 d2.utils.events]: \u001b[0m eta: 5:45:29 iter: 34999 total_loss: 0.8328 loss_cls: 0.288 loss_box_reg: 0.3076 loss_rpn_cls: 0.04265 loss_rpn_loc: 0.199 time: 0.3539 last_time: 0.4035 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:19:56 d2.utils.events]: \u001b[0m eta: 5:45:22 iter: 35019 total_loss: 0.8384 loss_cls: 0.2916 loss_box_reg: 0.2924 loss_rpn_cls: 0.04467 loss_rpn_loc: 0.2186 time: 0.3539 last_time: 0.4396 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:20:04 d2.utils.events]: \u001b[0m eta: 5:45:21 iter: 35039 total_loss: 0.9747 loss_cls: 0.3457 loss_box_reg: 0.3376 loss_rpn_cls: 0.04462 loss_rpn_loc: 0.2115 time: 0.3540 last_time: 0.4279 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:20:12 d2.utils.events]: \u001b[0m eta: 5:45:22 iter: 35059 total_loss: 0.8264 loss_cls: 0.2611 loss_box_reg: 0.3219 loss_rpn_cls: 0.04436 loss_rpn_loc: 0.1936 time: 0.3540 last_time: 0.3947 data_time: 0.0044 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:20:21 d2.utils.events]: \u001b[0m eta: 5:45:16 iter: 35079 total_loss: 0.8463 loss_cls: 0.3039 loss_box_reg: 0.2988 loss_rpn_cls: 0.04535 loss_rpn_loc: 0.1874 time: 0.3540 last_time: 0.4055 data_time: 0.0045 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:20:29 d2.utils.events]: \u001b[0m eta: 5:45:20 iter: 35099 total_loss: 0.8897 loss_cls: 0.2927 loss_box_reg: 0.3287 loss_rpn_cls: 0.04603 loss_rpn_loc: 0.2135 time: 0.3541 last_time: 0.4370 data_time: 0.0047 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:20:38 d2.utils.events]: \u001b[0m eta: 5:45:35 iter: 35119 total_loss: 0.8263 loss_cls: 0.2796 loss_box_reg: 0.2956 loss_rpn_cls: 0.0468 loss_rpn_loc: 0.2127 time: 0.3541 last_time: 0.4779 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:20:47 d2.utils.events]: \u001b[0m eta: 5:45:27 iter: 35139 total_loss: 0.9366 loss_cls: 0.3142 loss_box_reg: 0.3246 loss_rpn_cls: 0.04161 loss_rpn_loc: 0.1857 time: 0.3542 last_time: 0.4391 data_time: 0.0047 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:20:55 d2.utils.events]: \u001b[0m eta: 5:45:26 iter: 35159 total_loss: 0.8202 loss_cls: 0.2945 loss_box_reg: 0.3006 loss_rpn_cls: 0.05307 loss_rpn_loc: 0.1974 time: 0.3542 last_time: 0.4349 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:21:03 d2.utils.events]: \u001b[0m eta: 5:45:10 iter: 35179 total_loss: 0.8899 loss_cls: 0.2815 loss_box_reg: 0.3136 loss_rpn_cls: 0.05161 loss_rpn_loc: 0.2153 time: 0.3542 last_time: 0.5092 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:21:12 d2.utils.events]: \u001b[0m eta: 5:45:10 iter: 35199 total_loss: 0.8148 loss_cls: 0.249 loss_box_reg: 0.2918 loss_rpn_cls: 0.04744 loss_rpn_loc: 0.1953 time: 0.3543 last_time: 0.4510 data_time: 0.0048 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:21:21 d2.utils.events]: \u001b[0m eta: 5:45:12 iter: 35219 total_loss: 0.8701 loss_cls: 0.3043 loss_box_reg: 0.3045 loss_rpn_cls: 0.05548 loss_rpn_loc: 0.1863 time: 0.3544 last_time: 0.4411 data_time: 0.0048 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:21:30 d2.utils.events]: \u001b[0m eta: 5:45:04 iter: 35239 total_loss: 0.8786 loss_cls: 0.2946 loss_box_reg: 0.3346 loss_rpn_cls: 0.05559 loss_rpn_loc: 0.2152 time: 0.3544 last_time: 0.3743 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:21:38 d2.utils.events]: \u001b[0m eta: 5:44:52 iter: 35259 total_loss: 0.8745 loss_cls: 0.2633 loss_box_reg: 0.321 loss_rpn_cls: 0.05159 loss_rpn_loc: 0.2033 time: 0.3544 last_time: 0.4152 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:21:47 d2.utils.events]: \u001b[0m eta: 5:45:01 iter: 35279 total_loss: 0.8057 loss_cls: 0.2814 loss_box_reg: 0.3014 loss_rpn_cls: 0.051 loss_rpn_loc: 0.1974 time: 0.3545 last_time: 0.4884 data_time: 0.0049 last_data_time: 0.0053 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:21:56 d2.utils.events]: \u001b[0m eta: 5:45:09 iter: 35299 total_loss: 0.9285 loss_cls: 0.3059 loss_box_reg: 0.3339 loss_rpn_cls: 0.05592 loss_rpn_loc: 0.2193 time: 0.3545 last_time: 0.4589 data_time: 0.0048 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:22:05 d2.utils.events]: \u001b[0m eta: 5:45:12 iter: 35319 total_loss: 0.9675 loss_cls: 0.3209 loss_box_reg: 0.37 loss_rpn_cls: 0.05426 loss_rpn_loc: 0.2048 time: 0.3546 last_time: 0.5096 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:22:13 d2.utils.events]: \u001b[0m eta: 5:44:54 iter: 35339 total_loss: 0.9077 loss_cls: 0.3553 loss_box_reg: 0.3053 loss_rpn_cls: 0.04761 loss_rpn_loc: 0.1874 time: 0.3546 last_time: 0.4110 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:22:21 d2.utils.events]: \u001b[0m eta: 5:44:50 iter: 35359 total_loss: 0.7502 loss_cls: 0.236 loss_box_reg: 0.2933 loss_rpn_cls: 0.04341 loss_rpn_loc: 0.1695 time: 0.3546 last_time: 0.4390 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:22:30 d2.utils.events]: \u001b[0m eta: 5:44:42 iter: 35379 total_loss: 1.03 loss_cls: 0.3571 loss_box_reg: 0.3724 loss_rpn_cls: 0.05813 loss_rpn_loc: 0.2344 time: 0.3547 last_time: 0.3914 data_time: 0.0048 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:22:38 d2.utils.events]: \u001b[0m eta: 5:44:59 iter: 35399 total_loss: 0.921 loss_cls: 0.3279 loss_box_reg: 0.3035 loss_rpn_cls: 0.05867 loss_rpn_loc: 0.2243 time: 0.3547 last_time: 0.4199 data_time: 0.0049 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:22:47 d2.utils.events]: \u001b[0m eta: 5:44:40 iter: 35419 total_loss: 0.8666 loss_cls: 0.2706 loss_box_reg: 0.2954 loss_rpn_cls: 0.0563 loss_rpn_loc: 0.206 time: 0.3548 last_time: 0.4195 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:22:56 d2.utils.events]: \u001b[0m eta: 5:45:03 iter: 35439 total_loss: 0.8659 loss_cls: 0.2751 loss_box_reg: 0.3108 loss_rpn_cls: 0.04899 loss_rpn_loc: 0.2155 time: 0.3548 last_time: 0.4901 data_time: 0.0050 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:23:05 d2.utils.events]: \u001b[0m eta: 5:45:12 iter: 35459 total_loss: 0.8271 loss_cls: 0.2642 loss_box_reg: 0.3192 loss_rpn_cls: 0.05241 loss_rpn_loc: 0.2024 time: 0.3549 last_time: 0.3408 data_time: 0.0049 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:23:13 d2.utils.events]: \u001b[0m eta: 5:45:11 iter: 35479 total_loss: 0.8506 loss_cls: 0.2411 loss_box_reg: 0.3253 loss_rpn_cls: 0.04529 loss_rpn_loc: 0.1846 time: 0.3549 last_time: 0.3991 data_time: 0.0048 last_data_time: 0.0052 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:23:23 d2.utils.events]: \u001b[0m eta: 5:45:08 iter: 35499 total_loss: 0.8902 loss_cls: 0.2897 loss_box_reg: 0.3058 loss_rpn_cls: 0.04523 loss_rpn_loc: 0.2124 time: 0.3550 last_time: 0.4993 data_time: 0.0048 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:23:32 d2.utils.events]: \u001b[0m eta: 5:45:06 iter: 35519 total_loss: 0.992 loss_cls: 0.3321 loss_box_reg: 0.3635 loss_rpn_cls: 0.05046 loss_rpn_loc: 0.2179 time: 0.3550 last_time: 0.4178 data_time: 0.0049 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:23:40 d2.utils.events]: \u001b[0m eta: 5:44:56 iter: 35539 total_loss: 0.9119 loss_cls: 0.3164 loss_box_reg: 0.3147 loss_rpn_cls: 0.05122 loss_rpn_loc: 0.2112 time: 0.3551 last_time: 0.4376 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:23:49 d2.utils.events]: \u001b[0m eta: 5:44:55 iter: 35559 total_loss: 0.8704 loss_cls: 0.2934 loss_box_reg: 0.3139 loss_rpn_cls: 0.04719 loss_rpn_loc: 0.2199 time: 0.3551 last_time: 0.4153 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:23:57 d2.utils.events]: \u001b[0m eta: 5:45:00 iter: 35579 total_loss: 0.8941 loss_cls: 0.2905 loss_box_reg: 0.3241 loss_rpn_cls: 0.04981 loss_rpn_loc: 0.2308 time: 0.3551 last_time: 0.5168 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:24:06 d2.utils.events]: \u001b[0m eta: 5:45:03 iter: 35599 total_loss: 0.904 loss_cls: 0.2967 loss_box_reg: 0.3291 loss_rpn_cls: 0.04707 loss_rpn_loc: 0.211 time: 0.3552 last_time: 0.5143 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:24:15 d2.utils.events]: \u001b[0m eta: 5:44:56 iter: 35619 total_loss: 0.8176 loss_cls: 0.2805 loss_box_reg: 0.2772 loss_rpn_cls: 0.04193 loss_rpn_loc: 0.2118 time: 0.3552 last_time: 0.3945 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:24:23 d2.utils.events]: \u001b[0m eta: 5:45:02 iter: 35639 total_loss: 0.881 loss_cls: 0.3065 loss_box_reg: 0.317 loss_rpn_cls: 0.05605 loss_rpn_loc: 0.2181 time: 0.3553 last_time: 0.4447 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:24:31 d2.utils.events]: \u001b[0m eta: 5:45:03 iter: 35659 total_loss: 0.8195 loss_cls: 0.2867 loss_box_reg: 0.2743 loss_rpn_cls: 0.06004 loss_rpn_loc: 0.1994 time: 0.3553 last_time: 0.4379 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:24:41 d2.utils.events]: \u001b[0m eta: 5:45:26 iter: 35679 total_loss: 0.882 loss_cls: 0.2898 loss_box_reg: 0.3142 loss_rpn_cls: 0.05176 loss_rpn_loc: 0.2133 time: 0.3554 last_time: 0.4386 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:24:50 d2.utils.events]: \u001b[0m eta: 5:45:43 iter: 35699 total_loss: 0.8377 loss_cls: 0.2875 loss_box_reg: 0.3029 loss_rpn_cls: 0.03627 loss_rpn_loc: 0.2116 time: 0.3554 last_time: 0.5090 data_time: 0.0049 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:24:58 d2.utils.events]: \u001b[0m eta: 5:46:18 iter: 35719 total_loss: 0.8531 loss_cls: 0.3042 loss_box_reg: 0.2955 loss_rpn_cls: 0.05175 loss_rpn_loc: 0.1946 time: 0.3555 last_time: 0.5119 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:25:07 d2.utils.events]: \u001b[0m eta: 5:47:25 iter: 35739 total_loss: 0.9427 loss_cls: 0.3182 loss_box_reg: 0.3319 loss_rpn_cls: 0.05764 loss_rpn_loc: 0.2077 time: 0.3555 last_time: 0.4236 data_time: 0.0050 last_data_time: 0.0057 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:25:16 d2.utils.events]: \u001b[0m eta: 5:48:10 iter: 35759 total_loss: 0.9364 loss_cls: 0.3193 loss_box_reg: 0.3593 loss_rpn_cls: 0.04614 loss_rpn_loc: 0.2025 time: 0.3556 last_time: 0.4273 data_time: 0.0049 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:25:25 d2.utils.events]: \u001b[0m eta: 5:50:25 iter: 35779 total_loss: 0.8083 loss_cls: 0.287 loss_box_reg: 0.3138 loss_rpn_cls: 0.04003 loss_rpn_loc: 0.1835 time: 0.3556 last_time: 0.4792 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:25:35 d2.utils.events]: \u001b[0m eta: 5:52:35 iter: 35799 total_loss: 0.8907 loss_cls: 0.2702 loss_box_reg: 0.2882 loss_rpn_cls: 0.05049 loss_rpn_loc: 0.1986 time: 0.3557 last_time: 0.5053 data_time: 0.0049 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:25:44 d2.utils.events]: \u001b[0m eta: 5:53:38 iter: 35819 total_loss: 0.8718 loss_cls: 0.3047 loss_box_reg: 0.2849 loss_rpn_cls: 0.04855 loss_rpn_loc: 0.2346 time: 0.3557 last_time: 0.5111 data_time: 0.0049 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:25:53 d2.utils.events]: \u001b[0m eta: 5:55:00 iter: 35839 total_loss: 0.8348 loss_cls: 0.2615 loss_box_reg: 0.3057 loss_rpn_cls: 0.05555 loss_rpn_loc: 0.2077 time: 0.3558 last_time: 0.4588 data_time: 0.0049 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:26:01 d2.utils.events]: \u001b[0m eta: 5:53:36 iter: 35859 total_loss: 0.8181 loss_cls: 0.2907 loss_box_reg: 0.3149 loss_rpn_cls: 0.03788 loss_rpn_loc: 0.1765 time: 0.3558 last_time: 0.3742 data_time: 0.0048 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:26:09 d2.utils.events]: \u001b[0m eta: 5:52:51 iter: 35879 total_loss: 0.9113 loss_cls: 0.2686 loss_box_reg: 0.3351 loss_rpn_cls: 0.04287 loss_rpn_loc: 0.2172 time: 0.3559 last_time: 0.4058 data_time: 0.0048 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:26:18 d2.utils.events]: \u001b[0m eta: 5:52:22 iter: 35899 total_loss: 0.9288 loss_cls: 0.3022 loss_box_reg: 0.3181 loss_rpn_cls: 0.05055 loss_rpn_loc: 0.2414 time: 0.3559 last_time: 0.4842 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:26:26 d2.utils.events]: \u001b[0m eta: 5:52:49 iter: 35919 total_loss: 0.9175 loss_cls: 0.2862 loss_box_reg: 0.3478 loss_rpn_cls: 0.05462 loss_rpn_loc: 0.1864 time: 0.3559 last_time: 0.3696 data_time: 0.0048 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:26:35 d2.utils.events]: \u001b[0m eta: 5:53:22 iter: 35939 total_loss: 1.039 loss_cls: 0.3401 loss_box_reg: 0.3564 loss_rpn_cls: 0.0541 loss_rpn_loc: 0.2501 time: 0.3560 last_time: 0.3505 data_time: 0.0046 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:26:43 d2.utils.events]: \u001b[0m eta: 5:53:13 iter: 35959 total_loss: 0.8464 loss_cls: 0.2686 loss_box_reg: 0.3207 loss_rpn_cls: 0.04655 loss_rpn_loc: 0.2074 time: 0.3560 last_time: 0.3757 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:26:52 d2.utils.events]: \u001b[0m eta: 5:54:39 iter: 35979 total_loss: 0.8655 loss_cls: 0.2915 loss_box_reg: 0.3363 loss_rpn_cls: 0.0452 loss_rpn_loc: 0.2077 time: 0.3560 last_time: 0.5165 data_time: 0.0048 last_data_time: 0.0053 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:27:00 d2.utils.events]: \u001b[0m eta: 5:55:34 iter: 35999 total_loss: 0.8659 loss_cls: 0.3018 loss_box_reg: 0.2968 loss_rpn_cls: 0.04392 loss_rpn_loc: 0.1847 time: 0.3561 last_time: 0.4676 data_time: 0.0050 last_data_time: 0.0074 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:27:09 d2.utils.events]: \u001b[0m eta: 5:56:18 iter: 36019 total_loss: 0.8803 loss_cls: 0.2607 loss_box_reg: 0.3059 loss_rpn_cls: 0.04506 loss_rpn_loc: 0.2126 time: 0.3561 last_time: 0.5118 data_time: 0.0049 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:27:18 d2.utils.events]: \u001b[0m eta: 5:56:16 iter: 36039 total_loss: 0.8732 loss_cls: 0.2953 loss_box_reg: 0.3189 loss_rpn_cls: 0.05799 loss_rpn_loc: 0.1979 time: 0.3562 last_time: 0.3392 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:27:27 d2.utils.events]: \u001b[0m eta: 5:56:39 iter: 36059 total_loss: 0.9128 loss_cls: 0.337 loss_box_reg: 0.3239 loss_rpn_cls: 0.04397 loss_rpn_loc: 0.1959 time: 0.3562 last_time: 0.4442 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:27:36 d2.utils.events]: \u001b[0m eta: 5:57:27 iter: 36079 total_loss: 0.9446 loss_cls: 0.3259 loss_box_reg: 0.3448 loss_rpn_cls: 0.05812 loss_rpn_loc: 0.2119 time: 0.3563 last_time: 0.3753 data_time: 0.0049 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:27:44 d2.utils.events]: \u001b[0m eta: 5:57:04 iter: 36099 total_loss: 1.033 loss_cls: 0.35 loss_box_reg: 0.3588 loss_rpn_cls: 0.05949 loss_rpn_loc: 0.2278 time: 0.3563 last_time: 0.4884 data_time: 0.0047 last_data_time: 0.0052 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:27:53 d2.utils.events]: \u001b[0m eta: 5:57:09 iter: 36119 total_loss: 0.7953 loss_cls: 0.2873 loss_box_reg: 0.2996 loss_rpn_cls: 0.04636 loss_rpn_loc: 0.1715 time: 0.3564 last_time: 0.4908 data_time: 0.0049 last_data_time: 0.0052 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:28:02 d2.utils.events]: \u001b[0m eta: 5:57:22 iter: 36139 total_loss: 0.8022 loss_cls: 0.2478 loss_box_reg: 0.3152 loss_rpn_cls: 0.04379 loss_rpn_loc: 0.205 time: 0.3564 last_time: 0.4148 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:28:11 d2.utils.events]: \u001b[0m eta: 5:57:26 iter: 36159 total_loss: 0.9115 loss_cls: 0.3062 loss_box_reg: 0.3326 loss_rpn_cls: 0.05673 loss_rpn_loc: 0.2285 time: 0.3565 last_time: 0.4423 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:28:19 d2.utils.events]: \u001b[0m eta: 5:57:19 iter: 36179 total_loss: 0.9166 loss_cls: 0.3173 loss_box_reg: 0.3256 loss_rpn_cls: 0.05099 loss_rpn_loc: 0.2013 time: 0.3565 last_time: 0.4754 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:28:27 d2.utils.events]: \u001b[0m eta: 5:57:05 iter: 36199 total_loss: 0.8125 loss_cls: 0.3031 loss_box_reg: 0.2735 loss_rpn_cls: 0.04916 loss_rpn_loc: 0.1596 time: 0.3565 last_time: 0.4658 data_time: 0.0048 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:28:36 d2.utils.events]: \u001b[0m eta: 5:56:32 iter: 36219 total_loss: 0.9329 loss_cls: 0.3059 loss_box_reg: 0.3313 loss_rpn_cls: 0.06246 loss_rpn_loc: 0.2115 time: 0.3565 last_time: 0.4152 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:28:44 d2.utils.events]: \u001b[0m eta: 5:56:11 iter: 36239 total_loss: 0.9502 loss_cls: 0.3453 loss_box_reg: 0.2909 loss_rpn_cls: 0.05652 loss_rpn_loc: 0.2062 time: 0.3566 last_time: 0.4073 data_time: 0.0047 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:28:52 d2.utils.events]: \u001b[0m eta: 5:56:05 iter: 36259 total_loss: 1.028 loss_cls: 0.3461 loss_box_reg: 0.351 loss_rpn_cls: 0.0685 loss_rpn_loc: 0.219 time: 0.3566 last_time: 0.4533 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:29:01 d2.utils.events]: \u001b[0m eta: 5:55:23 iter: 36279 total_loss: 0.8629 loss_cls: 0.3278 loss_box_reg: 0.3042 loss_rpn_cls: 0.04644 loss_rpn_loc: 0.2118 time: 0.3566 last_time: 0.3744 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:29:09 d2.utils.events]: \u001b[0m eta: 5:54:51 iter: 36299 total_loss: 0.9254 loss_cls: 0.3054 loss_box_reg: 0.3059 loss_rpn_cls: 0.05368 loss_rpn_loc: 0.2206 time: 0.3567 last_time: 0.3845 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:29:18 d2.utils.events]: \u001b[0m eta: 5:55:00 iter: 36319 total_loss: 0.8596 loss_cls: 0.284 loss_box_reg: 0.3484 loss_rpn_cls: 0.05084 loss_rpn_loc: 0.2073 time: 0.3567 last_time: 0.4685 data_time: 0.0049 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:29:28 d2.utils.events]: \u001b[0m eta: 5:55:23 iter: 36339 total_loss: 0.9301 loss_cls: 0.342 loss_box_reg: 0.3225 loss_rpn_cls: 0.05937 loss_rpn_loc: 0.2053 time: 0.3568 last_time: 0.4180 data_time: 0.0051 last_data_time: 0.0055 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:29:37 d2.utils.events]: \u001b[0m eta: 5:55:42 iter: 36359 total_loss: 0.7175 loss_cls: 0.2287 loss_box_reg: 0.304 loss_rpn_cls: 0.04183 loss_rpn_loc: 0.1914 time: 0.3569 last_time: 0.5126 data_time: 0.0048 last_data_time: 0.0052 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:29:46 d2.utils.events]: \u001b[0m eta: 5:56:21 iter: 36379 total_loss: 0.881 loss_cls: 0.2722 loss_box_reg: 0.3509 loss_rpn_cls: 0.04455 loss_rpn_loc: 0.2065 time: 0.3569 last_time: 0.4880 data_time: 0.0050 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:29:56 d2.utils.events]: \u001b[0m eta: 5:56:36 iter: 36399 total_loss: 0.926 loss_cls: 0.2973 loss_box_reg: 0.332 loss_rpn_cls: 0.05151 loss_rpn_loc: 0.2173 time: 0.3570 last_time: 0.4306 data_time: 0.0050 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:30:05 d2.utils.events]: \u001b[0m eta: 5:57:05 iter: 36419 total_loss: 0.9824 loss_cls: 0.3285 loss_box_reg: 0.3637 loss_rpn_cls: 0.05257 loss_rpn_loc: 0.2146 time: 0.3570 last_time: 0.4327 data_time: 0.0050 last_data_time: 0.0053 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:30:14 d2.utils.events]: \u001b[0m eta: 5:56:44 iter: 36439 total_loss: 1.022 loss_cls: 0.3461 loss_box_reg: 0.3671 loss_rpn_cls: 0.05911 loss_rpn_loc: 0.2219 time: 0.3571 last_time: 0.4576 data_time: 0.0050 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:30:23 d2.utils.events]: \u001b[0m eta: 5:56:51 iter: 36459 total_loss: 0.8527 loss_cls: 0.2512 loss_box_reg: 0.2942 loss_rpn_cls: 0.04829 loss_rpn_loc: 0.2071 time: 0.3571 last_time: 0.4857 data_time: 0.0049 last_data_time: 0.0052 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:30:32 d2.utils.events]: \u001b[0m eta: 5:56:55 iter: 36479 total_loss: 0.7939 loss_cls: 0.2656 loss_box_reg: 0.2683 loss_rpn_cls: 0.04748 loss_rpn_loc: 0.2163 time: 0.3572 last_time: 0.4130 data_time: 0.0048 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:30:41 d2.utils.events]: \u001b[0m eta: 5:56:50 iter: 36499 total_loss: 0.85 loss_cls: 0.3439 loss_box_reg: 0.3046 loss_rpn_cls: 0.05058 loss_rpn_loc: 0.2011 time: 0.3572 last_time: 0.5108 data_time: 0.0049 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:30:50 d2.utils.events]: \u001b[0m eta: 5:56:47 iter: 36519 total_loss: 0.8766 loss_cls: 0.2822 loss_box_reg: 0.3148 loss_rpn_cls: 0.05541 loss_rpn_loc: 0.2155 time: 0.3573 last_time: 0.4851 data_time: 0.0051 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:31:00 d2.utils.events]: \u001b[0m eta: 5:57:21 iter: 36539 total_loss: 0.839 loss_cls: 0.2801 loss_box_reg: 0.2729 loss_rpn_cls: 0.04817 loss_rpn_loc: 0.2214 time: 0.3574 last_time: 0.4570 data_time: 0.0049 last_data_time: 0.0052 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:31:08 d2.utils.events]: \u001b[0m eta: 5:57:03 iter: 36559 total_loss: 0.8931 loss_cls: 0.3186 loss_box_reg: 0.3077 loss_rpn_cls: 0.05106 loss_rpn_loc: 0.2125 time: 0.3574 last_time: 0.3991 data_time: 0.0046 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:31:16 d2.utils.events]: \u001b[0m eta: 5:56:34 iter: 36579 total_loss: 0.9639 loss_cls: 0.3362 loss_box_reg: 0.3315 loss_rpn_cls: 0.05624 loss_rpn_loc: 0.2396 time: 0.3574 last_time: 0.4054 data_time: 0.0045 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:31:24 d2.utils.events]: \u001b[0m eta: 5:55:46 iter: 36599 total_loss: 0.897 loss_cls: 0.2987 loss_box_reg: 0.35 loss_rpn_cls: 0.04881 loss_rpn_loc: 0.2189 time: 0.3574 last_time: 0.4399 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:31:32 d2.utils.events]: \u001b[0m eta: 5:54:59 iter: 36619 total_loss: 0.9014 loss_cls: 0.2904 loss_box_reg: 0.3021 loss_rpn_cls: 0.05978 loss_rpn_loc: 0.2147 time: 0.3575 last_time: 0.4368 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:31:40 d2.utils.events]: \u001b[0m eta: 5:54:50 iter: 36639 total_loss: 0.9188 loss_cls: 0.3093 loss_box_reg: 0.3497 loss_rpn_cls: 0.04819 loss_rpn_loc: 0.206 time: 0.3575 last_time: 0.3567 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:31:48 d2.utils.events]: \u001b[0m eta: 5:54:26 iter: 36659 total_loss: 0.9021 loss_cls: 0.294 loss_box_reg: 0.3399 loss_rpn_cls: 0.0475 loss_rpn_loc: 0.2005 time: 0.3575 last_time: 0.3905 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:31:56 d2.utils.events]: \u001b[0m eta: 5:53:33 iter: 36679 total_loss: 0.9022 loss_cls: 0.2901 loss_box_reg: 0.3043 loss_rpn_cls: 0.04204 loss_rpn_loc: 0.1959 time: 0.3575 last_time: 0.3678 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:32:04 d2.utils.events]: \u001b[0m eta: 5:52:37 iter: 36699 total_loss: 0.9262 loss_cls: 0.3249 loss_box_reg: 0.3299 loss_rpn_cls: 0.04796 loss_rpn_loc: 0.2211 time: 0.3576 last_time: 0.4378 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:32:12 d2.utils.events]: \u001b[0m eta: 5:52:11 iter: 36719 total_loss: 0.9026 loss_cls: 0.3142 loss_box_reg: 0.3015 loss_rpn_cls: 0.0469 loss_rpn_loc: 0.2005 time: 0.3576 last_time: 0.3931 data_time: 0.0045 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:32:21 d2.utils.events]: \u001b[0m eta: 5:51:22 iter: 36739 total_loss: 0.9042 loss_cls: 0.3092 loss_box_reg: 0.3278 loss_rpn_cls: 0.04667 loss_rpn_loc: 0.2104 time: 0.3576 last_time: 0.3982 data_time: 0.0045 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:32:29 d2.utils.events]: \u001b[0m eta: 5:49:54 iter: 36759 total_loss: 0.9221 loss_cls: 0.3216 loss_box_reg: 0.3613 loss_rpn_cls: 0.05898 loss_rpn_loc: 0.1873 time: 0.3576 last_time: 0.3979 data_time: 0.0045 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:32:37 d2.utils.events]: \u001b[0m eta: 5:47:44 iter: 36779 total_loss: 0.9888 loss_cls: 0.3384 loss_box_reg: 0.3346 loss_rpn_cls: 0.06408 loss_rpn_loc: 0.2256 time: 0.3577 last_time: 0.3534 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:32:45 d2.utils.events]: \u001b[0m eta: 5:46:01 iter: 36799 total_loss: 0.824 loss_cls: 0.2614 loss_box_reg: 0.2905 loss_rpn_cls: 0.04619 loss_rpn_loc: 0.1819 time: 0.3577 last_time: 0.4215 data_time: 0.0045 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:32:54 d2.utils.events]: \u001b[0m eta: 5:44:48 iter: 36819 total_loss: 0.8792 loss_cls: 0.2776 loss_box_reg: 0.3092 loss_rpn_cls: 0.05085 loss_rpn_loc: 0.2062 time: 0.3577 last_time: 0.4433 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:33:02 d2.utils.events]: \u001b[0m eta: 5:43:59 iter: 36839 total_loss: 0.8915 loss_cls: 0.2865 loss_box_reg: 0.3284 loss_rpn_cls: 0.06126 loss_rpn_loc: 0.2258 time: 0.3578 last_time: 0.4494 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:33:10 d2.utils.events]: \u001b[0m eta: 5:43:37 iter: 36859 total_loss: 0.9115 loss_cls: 0.2993 loss_box_reg: 0.3173 loss_rpn_cls: 0.06864 loss_rpn_loc: 0.1869 time: 0.3578 last_time: 0.3438 data_time: 0.0045 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:33:19 d2.utils.events]: \u001b[0m eta: 5:44:06 iter: 36879 total_loss: 0.8603 loss_cls: 0.3208 loss_box_reg: 0.2978 loss_rpn_cls: 0.0561 loss_rpn_loc: 0.2051 time: 0.3578 last_time: 0.4030 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:33:27 d2.utils.events]: \u001b[0m eta: 5:43:08 iter: 36899 total_loss: 0.8444 loss_cls: 0.2818 loss_box_reg: 0.2979 loss_rpn_cls: 0.04402 loss_rpn_loc: 0.1816 time: 0.3579 last_time: 0.4097 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:33:35 d2.utils.events]: \u001b[0m eta: 5:42:48 iter: 36919 total_loss: 0.8586 loss_cls: 0.2877 loss_box_reg: 0.3151 loss_rpn_cls: 0.0541 loss_rpn_loc: 0.1904 time: 0.3579 last_time: 0.4439 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:33:44 d2.utils.events]: \u001b[0m eta: 5:43:40 iter: 36939 total_loss: 0.7848 loss_cls: 0.2748 loss_box_reg: 0.3179 loss_rpn_cls: 0.05342 loss_rpn_loc: 0.2023 time: 0.3579 last_time: 0.5132 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:33:53 d2.utils.events]: \u001b[0m eta: 5:45:16 iter: 36959 total_loss: 0.8746 loss_cls: 0.2932 loss_box_reg: 0.3287 loss_rpn_cls: 0.03937 loss_rpn_loc: 0.2079 time: 0.3580 last_time: 0.5020 data_time: 0.0048 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:34:02 d2.utils.events]: \u001b[0m eta: 5:45:42 iter: 36979 total_loss: 0.888 loss_cls: 0.2985 loss_box_reg: 0.3103 loss_rpn_cls: 0.05528 loss_rpn_loc: 0.2092 time: 0.3580 last_time: 0.4638 data_time: 0.0048 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:34:10 d2.utils.events]: \u001b[0m eta: 5:45:33 iter: 36999 total_loss: 0.9172 loss_cls: 0.3147 loss_box_reg: 0.3194 loss_rpn_cls: 0.04753 loss_rpn_loc: 0.2094 time: 0.3581 last_time: 0.4269 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:34:19 d2.utils.events]: \u001b[0m eta: 5:42:52 iter: 37019 total_loss: 0.8411 loss_cls: 0.2928 loss_box_reg: 0.3176 loss_rpn_cls: 0.05055 loss_rpn_loc: 0.1612 time: 0.3581 last_time: 0.4376 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:34:27 d2.utils.events]: \u001b[0m eta: 5:42:44 iter: 37039 total_loss: 0.8269 loss_cls: 0.2762 loss_box_reg: 0.3331 loss_rpn_cls: 0.04328 loss_rpn_loc: 0.1922 time: 0.3581 last_time: 0.4281 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:34:35 d2.utils.events]: \u001b[0m eta: 5:40:47 iter: 37059 total_loss: 0.988 loss_cls: 0.3596 loss_box_reg: 0.339 loss_rpn_cls: 0.06139 loss_rpn_loc: 0.2198 time: 0.3581 last_time: 0.4198 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:34:43 d2.utils.events]: \u001b[0m eta: 5:38:50 iter: 37079 total_loss: 0.8775 loss_cls: 0.2642 loss_box_reg: 0.3314 loss_rpn_cls: 0.04643 loss_rpn_loc: 0.2037 time: 0.3582 last_time: 0.3418 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:34:51 d2.utils.events]: \u001b[0m eta: 5:37:38 iter: 37099 total_loss: 0.8373 loss_cls: 0.2641 loss_box_reg: 0.3256 loss_rpn_cls: 0.05353 loss_rpn_loc: 0.2011 time: 0.3582 last_time: 0.4199 data_time: 0.0045 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:34:59 d2.utils.events]: \u001b[0m eta: 5:36:36 iter: 37119 total_loss: 0.8203 loss_cls: 0.267 loss_box_reg: 0.3065 loss_rpn_cls: 0.04177 loss_rpn_loc: 0.1768 time: 0.3582 last_time: 0.4511 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:35:07 d2.utils.events]: \u001b[0m eta: 5:36:30 iter: 37139 total_loss: 0.8849 loss_cls: 0.2711 loss_box_reg: 0.3026 loss_rpn_cls: 0.05554 loss_rpn_loc: 0.2016 time: 0.3582 last_time: 0.4000 data_time: 0.0046 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:35:15 d2.utils.events]: \u001b[0m eta: 5:36:19 iter: 37159 total_loss: 0.8904 loss_cls: 0.2514 loss_box_reg: 0.3103 loss_rpn_cls: 0.06583 loss_rpn_loc: 0.1957 time: 0.3583 last_time: 0.3198 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:35:23 d2.utils.events]: \u001b[0m eta: 5:36:18 iter: 37179 total_loss: 0.8811 loss_cls: 0.3049 loss_box_reg: 0.2974 loss_rpn_cls: 0.05409 loss_rpn_loc: 0.2005 time: 0.3583 last_time: 0.4406 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:35:32 d2.utils.events]: \u001b[0m eta: 5:36:45 iter: 37199 total_loss: 0.9208 loss_cls: 0.281 loss_box_reg: 0.3355 loss_rpn_cls: 0.05231 loss_rpn_loc: 0.2164 time: 0.3583 last_time: 0.4491 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:35:40 d2.utils.events]: \u001b[0m eta: 5:36:28 iter: 37219 total_loss: 0.833 loss_cls: 0.2874 loss_box_reg: 0.286 loss_rpn_cls: 0.05466 loss_rpn_loc: 0.2015 time: 0.3584 last_time: 0.3772 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:35:48 d2.utils.events]: \u001b[0m eta: 5:36:39 iter: 37239 total_loss: 0.8252 loss_cls: 0.2643 loss_box_reg: 0.2824 loss_rpn_cls: 0.06176 loss_rpn_loc: 0.1986 time: 0.3584 last_time: 0.3183 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:35:56 d2.utils.events]: \u001b[0m eta: 5:35:58 iter: 37259 total_loss: 0.8973 loss_cls: 0.2688 loss_box_reg: 0.3156 loss_rpn_cls: 0.04355 loss_rpn_loc: 0.2216 time: 0.3584 last_time: 0.4001 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:36:04 d2.utils.events]: \u001b[0m eta: 5:35:30 iter: 37279 total_loss: 0.8804 loss_cls: 0.3072 loss_box_reg: 0.3082 loss_rpn_cls: 0.05064 loss_rpn_loc: 0.2116 time: 0.3584 last_time: 0.4114 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:36:12 d2.utils.events]: \u001b[0m eta: 5:35:05 iter: 37299 total_loss: 0.8891 loss_cls: 0.2891 loss_box_reg: 0.3102 loss_rpn_cls: 0.04975 loss_rpn_loc: 0.2315 time: 0.3585 last_time: 0.3718 data_time: 0.0046 last_data_time: 0.0041 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:36:20 d2.utils.events]: \u001b[0m eta: 5:34:32 iter: 37319 total_loss: 0.8044 loss_cls: 0.2658 loss_box_reg: 0.2861 loss_rpn_cls: 0.03994 loss_rpn_loc: 0.185 time: 0.3585 last_time: 0.4387 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:36:29 d2.utils.events]: \u001b[0m eta: 5:33:32 iter: 37339 total_loss: 0.8096 loss_cls: 0.2602 loss_box_reg: 0.2962 loss_rpn_cls: 0.05464 loss_rpn_loc: 0.1847 time: 0.3585 last_time: 0.3372 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:36:37 d2.utils.events]: \u001b[0m eta: 5:33:08 iter: 37359 total_loss: 0.8642 loss_cls: 0.281 loss_box_reg: 0.3152 loss_rpn_cls: 0.045 loss_rpn_loc: 0.1929 time: 0.3585 last_time: 0.3961 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:36:45 d2.utils.events]: \u001b[0m eta: 5:32:25 iter: 37379 total_loss: 0.8645 loss_cls: 0.2678 loss_box_reg: 0.3255 loss_rpn_cls: 0.05111 loss_rpn_loc: 0.2044 time: 0.3586 last_time: 0.3958 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:36:53 d2.utils.events]: \u001b[0m eta: 5:31:34 iter: 37399 total_loss: 0.8767 loss_cls: 0.293 loss_box_reg: 0.3059 loss_rpn_cls: 0.05686 loss_rpn_loc: 0.2228 time: 0.3586 last_time: 0.3395 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:37:01 d2.utils.events]: \u001b[0m eta: 5:31:01 iter: 37419 total_loss: 1.004 loss_cls: 0.3131 loss_box_reg: 0.3438 loss_rpn_cls: 0.04842 loss_rpn_loc: 0.2295 time: 0.3586 last_time: 0.4243 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:37:09 d2.utils.events]: \u001b[0m eta: 5:30:41 iter: 37439 total_loss: 0.8961 loss_cls: 0.2858 loss_box_reg: 0.3221 loss_rpn_cls: 0.05804 loss_rpn_loc: 0.223 time: 0.3586 last_time: 0.4409 data_time: 0.0045 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:37:17 d2.utils.events]: \u001b[0m eta: 5:29:56 iter: 37459 total_loss: 0.8522 loss_cls: 0.2698 loss_box_reg: 0.3091 loss_rpn_cls: 0.05414 loss_rpn_loc: 0.2101 time: 0.3587 last_time: 0.4301 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:37:25 d2.utils.events]: \u001b[0m eta: 5:29:25 iter: 37479 total_loss: 0.8866 loss_cls: 0.2939 loss_box_reg: 0.3064 loss_rpn_cls: 0.04791 loss_rpn_loc: 0.2039 time: 0.3587 last_time: 0.3942 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:37:33 d2.utils.events]: \u001b[0m eta: 5:28:52 iter: 37499 total_loss: 0.9046 loss_cls: 0.273 loss_box_reg: 0.2885 loss_rpn_cls: 0.04463 loss_rpn_loc: 0.2287 time: 0.3587 last_time: 0.3390 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:37:42 d2.utils.events]: \u001b[0m eta: 5:28:20 iter: 37519 total_loss: 0.8144 loss_cls: 0.2382 loss_box_reg: 0.2963 loss_rpn_cls: 0.05325 loss_rpn_loc: 0.1875 time: 0.3587 last_time: 0.3983 data_time: 0.0046 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:37:49 d2.utils.events]: \u001b[0m eta: 5:27:46 iter: 37539 total_loss: 0.9901 loss_cls: 0.3324 loss_box_reg: 0.325 loss_rpn_cls: 0.05815 loss_rpn_loc: 0.2241 time: 0.3587 last_time: 0.3168 data_time: 0.0046 last_data_time: 0.0040 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:37:57 d2.utils.events]: \u001b[0m eta: 5:27:38 iter: 37559 total_loss: 0.7928 loss_cls: 0.2567 loss_box_reg: 0.3073 loss_rpn_cls: 0.0343 loss_rpn_loc: 0.158 time: 0.3588 last_time: 0.3729 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:38:06 d2.utils.events]: \u001b[0m eta: 5:27:47 iter: 37579 total_loss: 0.9728 loss_cls: 0.3311 loss_box_reg: 0.3392 loss_rpn_cls: 0.05582 loss_rpn_loc: 0.2151 time: 0.3588 last_time: 0.4150 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:38:14 d2.utils.events]: \u001b[0m eta: 5:27:33 iter: 37599 total_loss: 0.8946 loss_cls: 0.3026 loss_box_reg: 0.3464 loss_rpn_cls: 0.05076 loss_rpn_loc: 0.2076 time: 0.3588 last_time: 0.3963 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:38:22 d2.utils.events]: \u001b[0m eta: 5:27:33 iter: 37619 total_loss: 0.8808 loss_cls: 0.3143 loss_box_reg: 0.3156 loss_rpn_cls: 0.05239 loss_rpn_loc: 0.184 time: 0.3588 last_time: 0.3946 data_time: 0.0046 last_data_time: 0.0053 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:38:30 d2.utils.events]: \u001b[0m eta: 5:27:09 iter: 37639 total_loss: 0.7499 loss_cls: 0.2941 loss_box_reg: 0.3033 loss_rpn_cls: 0.05157 loss_rpn_loc: 0.1615 time: 0.3589 last_time: 0.4192 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:38:38 d2.utils.events]: \u001b[0m eta: 5:27:22 iter: 37659 total_loss: 0.9272 loss_cls: 0.3115 loss_box_reg: 0.3375 loss_rpn_cls: 0.04088 loss_rpn_loc: 0.1851 time: 0.3589 last_time: 0.4205 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:38:46 d2.utils.events]: \u001b[0m eta: 5:27:16 iter: 37679 total_loss: 0.7551 loss_cls: 0.2555 loss_box_reg: 0.2711 loss_rpn_cls: 0.04061 loss_rpn_loc: 0.1852 time: 0.3589 last_time: 0.3932 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:38:55 d2.utils.events]: \u001b[0m eta: 5:27:31 iter: 37699 total_loss: 0.8723 loss_cls: 0.2608 loss_box_reg: 0.2844 loss_rpn_cls: 0.04349 loss_rpn_loc: 0.2182 time: 0.3589 last_time: 0.3915 data_time: 0.0045 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:39:02 d2.utils.events]: \u001b[0m eta: 5:27:22 iter: 37719 total_loss: 0.8676 loss_cls: 0.2974 loss_box_reg: 0.3208 loss_rpn_cls: 0.05452 loss_rpn_loc: 0.2103 time: 0.3590 last_time: 0.4233 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:39:11 d2.utils.events]: \u001b[0m eta: 5:27:22 iter: 37739 total_loss: 0.7401 loss_cls: 0.2714 loss_box_reg: 0.2884 loss_rpn_cls: 0.04962 loss_rpn_loc: 0.1778 time: 0.3590 last_time: 0.4169 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:39:19 d2.utils.events]: \u001b[0m eta: 5:27:12 iter: 37759 total_loss: 0.9413 loss_cls: 0.2933 loss_box_reg: 0.3469 loss_rpn_cls: 0.0503 loss_rpn_loc: 0.242 time: 0.3590 last_time: 0.4187 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:39:27 d2.utils.events]: \u001b[0m eta: 5:27:04 iter: 37779 total_loss: 0.9849 loss_cls: 0.3654 loss_box_reg: 0.3451 loss_rpn_cls: 0.05556 loss_rpn_loc: 0.2507 time: 0.3590 last_time: 0.3996 data_time: 0.0045 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:39:35 d2.utils.events]: \u001b[0m eta: 5:26:56 iter: 37799 total_loss: 0.9205 loss_cls: 0.3173 loss_box_reg: 0.3378 loss_rpn_cls: 0.0461 loss_rpn_loc: 0.2152 time: 0.3591 last_time: 0.4408 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:39:43 d2.utils.events]: \u001b[0m eta: 5:26:38 iter: 37819 total_loss: 0.814 loss_cls: 0.2579 loss_box_reg: 0.3018 loss_rpn_cls: 0.04176 loss_rpn_loc: 0.2093 time: 0.3591 last_time: 0.3902 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:39:51 d2.utils.events]: \u001b[0m eta: 5:26:07 iter: 37839 total_loss: 1.005 loss_cls: 0.3402 loss_box_reg: 0.3636 loss_rpn_cls: 0.06369 loss_rpn_loc: 0.2154 time: 0.3591 last_time: 0.3769 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:39:59 d2.utils.events]: \u001b[0m eta: 5:25:45 iter: 37859 total_loss: 0.8254 loss_cls: 0.2926 loss_box_reg: 0.285 loss_rpn_cls: 0.06036 loss_rpn_loc: 0.1934 time: 0.3591 last_time: 0.3962 data_time: 0.0046 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:40:07 d2.utils.events]: \u001b[0m eta: 5:25:31 iter: 37879 total_loss: 0.866 loss_cls: 0.2924 loss_box_reg: 0.3448 loss_rpn_cls: 0.05021 loss_rpn_loc: 0.1829 time: 0.3592 last_time: 0.3960 data_time: 0.0044 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:40:15 d2.utils.events]: \u001b[0m eta: 5:25:29 iter: 37899 total_loss: 0.8301 loss_cls: 0.2606 loss_box_reg: 0.3295 loss_rpn_cls: 0.04439 loss_rpn_loc: 0.197 time: 0.3592 last_time: 0.3967 data_time: 0.0044 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:40:23 d2.utils.events]: \u001b[0m eta: 5:25:15 iter: 37919 total_loss: 0.9743 loss_cls: 0.3262 loss_box_reg: 0.3385 loss_rpn_cls: 0.06066 loss_rpn_loc: 0.2366 time: 0.3592 last_time: 0.3088 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:40:31 d2.utils.events]: \u001b[0m eta: 5:24:59 iter: 37939 total_loss: 0.7597 loss_cls: 0.2262 loss_box_reg: 0.2883 loss_rpn_cls: 0.05226 loss_rpn_loc: 0.1966 time: 0.3592 last_time: 0.3841 data_time: 0.0044 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:40:39 d2.utils.events]: \u001b[0m eta: 5:24:47 iter: 37959 total_loss: 0.9067 loss_cls: 0.3074 loss_box_reg: 0.295 loss_rpn_cls: 0.05648 loss_rpn_loc: 0.2014 time: 0.3592 last_time: 0.4365 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:40:48 d2.utils.events]: \u001b[0m eta: 5:24:37 iter: 37979 total_loss: 0.8657 loss_cls: 0.27 loss_box_reg: 0.3258 loss_rpn_cls: 0.06421 loss_rpn_loc: 0.2188 time: 0.3593 last_time: 0.4404 data_time: 0.0047 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:40:57 d2.utils.events]: \u001b[0m eta: 5:24:25 iter: 37999 total_loss: 0.8099 loss_cls: 0.2562 loss_box_reg: 0.3065 loss_rpn_cls: 0.04915 loss_rpn_loc: 0.2099 time: 0.3593 last_time: 0.4094 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:41:05 d2.utils.events]: \u001b[0m eta: 5:24:20 iter: 38019 total_loss: 0.9324 loss_cls: 0.3161 loss_box_reg: 0.3162 loss_rpn_cls: 0.04766 loss_rpn_loc: 0.2147 time: 0.3594 last_time: 0.4045 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:41:13 d2.utils.events]: \u001b[0m eta: 5:24:04 iter: 38039 total_loss: 0.914 loss_cls: 0.3141 loss_box_reg: 0.3167 loss_rpn_cls: 0.06533 loss_rpn_loc: 0.2075 time: 0.3594 last_time: 0.4406 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:41:21 d2.utils.events]: \u001b[0m eta: 5:23:40 iter: 38059 total_loss: 0.786 loss_cls: 0.2662 loss_box_reg: 0.2777 loss_rpn_cls: 0.05626 loss_rpn_loc: 0.1927 time: 0.3594 last_time: 0.3737 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:41:29 d2.utils.events]: \u001b[0m eta: 5:23:27 iter: 38079 total_loss: 0.8562 loss_cls: 0.2796 loss_box_reg: 0.3196 loss_rpn_cls: 0.05026 loss_rpn_loc: 0.1632 time: 0.3594 last_time: 0.3966 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:41:37 d2.utils.events]: \u001b[0m eta: 5:23:18 iter: 38099 total_loss: 0.9541 loss_cls: 0.3542 loss_box_reg: 0.3263 loss_rpn_cls: 0.05348 loss_rpn_loc: 0.2323 time: 0.3594 last_time: 0.3859 data_time: 0.0045 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:41:46 d2.utils.events]: \u001b[0m eta: 5:23:32 iter: 38119 total_loss: 0.9172 loss_cls: 0.3434 loss_box_reg: 0.2953 loss_rpn_cls: 0.06333 loss_rpn_loc: 0.2031 time: 0.3595 last_time: 0.4271 data_time: 0.0048 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:41:54 d2.utils.events]: \u001b[0m eta: 5:23:32 iter: 38139 total_loss: 0.824 loss_cls: 0.2836 loss_box_reg: 0.3318 loss_rpn_cls: 0.04737 loss_rpn_loc: 0.2068 time: 0.3595 last_time: 0.4439 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:42:03 d2.utils.events]: \u001b[0m eta: 5:23:30 iter: 38159 total_loss: 0.8625 loss_cls: 0.3273 loss_box_reg: 0.3213 loss_rpn_cls: 0.05524 loss_rpn_loc: 0.2191 time: 0.3596 last_time: 0.4219 data_time: 0.0046 last_data_time: 0.0052 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:42:11 d2.utils.events]: \u001b[0m eta: 5:23:20 iter: 38179 total_loss: 0.8682 loss_cls: 0.2768 loss_box_reg: 0.3294 loss_rpn_cls: 0.04871 loss_rpn_loc: 0.1925 time: 0.3596 last_time: 0.4338 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:42:19 d2.utils.events]: \u001b[0m eta: 5:22:59 iter: 38199 total_loss: 0.9085 loss_cls: 0.3022 loss_box_reg: 0.342 loss_rpn_cls: 0.05525 loss_rpn_loc: 0.1975 time: 0.3596 last_time: 0.3683 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:42:28 d2.utils.events]: \u001b[0m eta: 5:23:01 iter: 38219 total_loss: 0.9598 loss_cls: 0.3068 loss_box_reg: 0.3407 loss_rpn_cls: 0.05478 loss_rpn_loc: 0.2154 time: 0.3596 last_time: 0.4830 data_time: 0.0048 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:42:37 d2.utils.events]: \u001b[0m eta: 5:23:02 iter: 38239 total_loss: 0.9681 loss_cls: 0.324 loss_box_reg: 0.3151 loss_rpn_cls: 0.05954 loss_rpn_loc: 0.2228 time: 0.3597 last_time: 0.4412 data_time: 0.0049 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:42:46 d2.utils.events]: \u001b[0m eta: 5:23:00 iter: 38259 total_loss: 0.8944 loss_cls: 0.2516 loss_box_reg: 0.3127 loss_rpn_cls: 0.05428 loss_rpn_loc: 0.2062 time: 0.3597 last_time: 0.4179 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:42:54 d2.utils.events]: \u001b[0m eta: 5:23:04 iter: 38279 total_loss: 0.8736 loss_cls: 0.275 loss_box_reg: 0.3359 loss_rpn_cls: 0.05259 loss_rpn_loc: 0.1954 time: 0.3597 last_time: 0.4158 data_time: 0.0047 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:43:02 d2.utils.events]: \u001b[0m eta: 5:23:07 iter: 38299 total_loss: 0.8717 loss_cls: 0.3081 loss_box_reg: 0.3239 loss_rpn_cls: 0.0386 loss_rpn_loc: 0.2034 time: 0.3598 last_time: 0.3727 data_time: 0.0049 last_data_time: 0.0052 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:43:10 d2.utils.events]: \u001b[0m eta: 5:23:01 iter: 38319 total_loss: 0.8545 loss_cls: 0.2595 loss_box_reg: 0.3091 loss_rpn_cls: 0.04429 loss_rpn_loc: 0.1953 time: 0.3598 last_time: 0.4824 data_time: 0.0048 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:43:19 d2.utils.events]: \u001b[0m eta: 5:22:53 iter: 38339 total_loss: 0.9388 loss_cls: 0.2884 loss_box_reg: 0.3127 loss_rpn_cls: 0.05835 loss_rpn_loc: 0.2093 time: 0.3599 last_time: 0.3933 data_time: 0.0048 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:43:27 d2.utils.events]: \u001b[0m eta: 5:22:44 iter: 38359 total_loss: 0.8687 loss_cls: 0.2735 loss_box_reg: 0.3006 loss_rpn_cls: 0.04722 loss_rpn_loc: 0.2197 time: 0.3599 last_time: 0.4422 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:43:36 d2.utils.events]: \u001b[0m eta: 5:22:34 iter: 38379 total_loss: 0.8885 loss_cls: 0.3096 loss_box_reg: 0.3154 loss_rpn_cls: 0.05286 loss_rpn_loc: 0.1898 time: 0.3599 last_time: 0.4189 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:43:45 d2.utils.events]: \u001b[0m eta: 5:22:46 iter: 38399 total_loss: 0.8414 loss_cls: 0.2889 loss_box_reg: 0.3113 loss_rpn_cls: 0.05091 loss_rpn_loc: 0.2127 time: 0.3600 last_time: 0.4118 data_time: 0.0049 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:43:54 d2.utils.events]: \u001b[0m eta: 5:22:41 iter: 38419 total_loss: 0.8577 loss_cls: 0.2743 loss_box_reg: 0.3277 loss_rpn_cls: 0.05058 loss_rpn_loc: 0.2164 time: 0.3600 last_time: 0.5111 data_time: 0.0049 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:44:03 d2.utils.events]: \u001b[0m eta: 5:22:41 iter: 38439 total_loss: 0.9474 loss_cls: 0.2918 loss_box_reg: 0.3003 loss_rpn_cls: 0.04438 loss_rpn_loc: 0.2184 time: 0.3601 last_time: 0.4890 data_time: 0.0050 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:44:12 d2.utils.events]: \u001b[0m eta: 5:22:51 iter: 38459 total_loss: 0.9002 loss_cls: 0.2793 loss_box_reg: 0.3284 loss_rpn_cls: 0.0564 loss_rpn_loc: 0.2527 time: 0.3601 last_time: 0.4010 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:44:20 d2.utils.events]: \u001b[0m eta: 5:22:47 iter: 38479 total_loss: 0.8501 loss_cls: 0.2819 loss_box_reg: 0.3231 loss_rpn_cls: 0.05023 loss_rpn_loc: 0.2032 time: 0.3601 last_time: 0.4193 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:44:29 d2.utils.events]: \u001b[0m eta: 5:22:52 iter: 38499 total_loss: 0.9224 loss_cls: 0.2803 loss_box_reg: 0.3301 loss_rpn_cls: 0.05388 loss_rpn_loc: 0.2396 time: 0.3602 last_time: 0.4847 data_time: 0.0049 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:44:38 d2.utils.events]: \u001b[0m eta: 5:22:51 iter: 38519 total_loss: 0.849 loss_cls: 0.3044 loss_box_reg: 0.3019 loss_rpn_cls: 0.0423 loss_rpn_loc: 0.1784 time: 0.3602 last_time: 0.4155 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:44:45 d2.utils.events]: \u001b[0m eta: 5:22:40 iter: 38539 total_loss: 0.786 loss_cls: 0.2891 loss_box_reg: 0.3093 loss_rpn_cls: 0.04597 loss_rpn_loc: 0.143 time: 0.3602 last_time: 0.3740 data_time: 0.0048 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:44:53 d2.utils.events]: \u001b[0m eta: 5:22:34 iter: 38559 total_loss: 0.9305 loss_cls: 0.3184 loss_box_reg: 0.3392 loss_rpn_cls: 0.05598 loss_rpn_loc: 0.2053 time: 0.3602 last_time: 0.3490 data_time: 0.0048 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:45:01 d2.utils.events]: \u001b[0m eta: 5:22:17 iter: 38579 total_loss: 0.8673 loss_cls: 0.2951 loss_box_reg: 0.3186 loss_rpn_cls: 0.05473 loss_rpn_loc: 0.2167 time: 0.3603 last_time: 0.4301 data_time: 0.0048 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:45:10 d2.utils.events]: \u001b[0m eta: 5:22:24 iter: 38599 total_loss: 0.7803 loss_cls: 0.2403 loss_box_reg: 0.2925 loss_rpn_cls: 0.03615 loss_rpn_loc: 0.2177 time: 0.3603 last_time: 0.4311 data_time: 0.0049 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:45:19 d2.utils.events]: \u001b[0m eta: 5:22:26 iter: 38619 total_loss: 0.9071 loss_cls: 0.2381 loss_box_reg: 0.2825 loss_rpn_cls: 0.0605 loss_rpn_loc: 0.2202 time: 0.3603 last_time: 0.3943 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:45:27 d2.utils.events]: \u001b[0m eta: 5:22:34 iter: 38639 total_loss: 0.8597 loss_cls: 0.2838 loss_box_reg: 0.3185 loss_rpn_cls: 0.05231 loss_rpn_loc: 0.2036 time: 0.3604 last_time: 0.3464 data_time: 0.0048 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:45:35 d2.utils.events]: \u001b[0m eta: 5:22:19 iter: 38659 total_loss: 0.8521 loss_cls: 0.2865 loss_box_reg: 0.2968 loss_rpn_cls: 0.04571 loss_rpn_loc: 0.1959 time: 0.3604 last_time: 0.4476 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:45:43 d2.utils.events]: \u001b[0m eta: 5:22:06 iter: 38679 total_loss: 0.8366 loss_cls: 0.2817 loss_box_reg: 0.3198 loss_rpn_cls: 0.04774 loss_rpn_loc: 0.1912 time: 0.3604 last_time: 0.4200 data_time: 0.0047 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:45:52 d2.utils.events]: \u001b[0m eta: 5:22:10 iter: 38699 total_loss: 0.8453 loss_cls: 0.2675 loss_box_reg: 0.3222 loss_rpn_cls: 0.04889 loss_rpn_loc: 0.2013 time: 0.3605 last_time: 0.4328 data_time: 0.0049 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:46:01 d2.utils.events]: \u001b[0m eta: 5:22:08 iter: 38719 total_loss: 0.8306 loss_cls: 0.2485 loss_box_reg: 0.2786 loss_rpn_cls: 0.05588 loss_rpn_loc: 0.1987 time: 0.3605 last_time: 0.3549 data_time: 0.0049 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:46:09 d2.utils.events]: \u001b[0m eta: 5:21:58 iter: 38739 total_loss: 0.8785 loss_cls: 0.3012 loss_box_reg: 0.3433 loss_rpn_cls: 0.06249 loss_rpn_loc: 0.2178 time: 0.3605 last_time: 0.4476 data_time: 0.0048 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:46:16 d2.utils.events]: \u001b[0m eta: 5:21:37 iter: 38759 total_loss: 0.9444 loss_cls: 0.2935 loss_box_reg: 0.3523 loss_rpn_cls: 0.04746 loss_rpn_loc: 0.2089 time: 0.3605 last_time: 0.3135 data_time: 0.0048 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:46:24 d2.utils.events]: \u001b[0m eta: 5:21:25 iter: 38779 total_loss: 0.7909 loss_cls: 0.2749 loss_box_reg: 0.2991 loss_rpn_cls: 0.04514 loss_rpn_loc: 0.1851 time: 0.3605 last_time: 0.4011 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:46:33 d2.utils.events]: \u001b[0m eta: 5:21:31 iter: 38799 total_loss: 0.8698 loss_cls: 0.3066 loss_box_reg: 0.308 loss_rpn_cls: 0.04985 loss_rpn_loc: 0.1854 time: 0.3606 last_time: 0.4187 data_time: 0.0048 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:46:41 d2.utils.events]: \u001b[0m eta: 5:21:20 iter: 38819 total_loss: 0.8495 loss_cls: 0.2563 loss_box_reg: 0.3008 loss_rpn_cls: 0.05891 loss_rpn_loc: 0.204 time: 0.3606 last_time: 0.4245 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:46:49 d2.utils.events]: \u001b[0m eta: 5:21:14 iter: 38839 total_loss: 0.8647 loss_cls: 0.2839 loss_box_reg: 0.3022 loss_rpn_cls: 0.0462 loss_rpn_loc: 0.1908 time: 0.3606 last_time: 0.4216 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:46:57 d2.utils.events]: \u001b[0m eta: 5:21:15 iter: 38859 total_loss: 0.9043 loss_cls: 0.2963 loss_box_reg: 0.3025 loss_rpn_cls: 0.05425 loss_rpn_loc: 0.2156 time: 0.3606 last_time: 0.3991 data_time: 0.0047 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:47:05 d2.utils.events]: \u001b[0m eta: 5:21:00 iter: 38879 total_loss: 0.8788 loss_cls: 0.2666 loss_box_reg: 0.3005 loss_rpn_cls: 0.05151 loss_rpn_loc: 0.2058 time: 0.3607 last_time: 0.4417 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:47:13 d2.utils.events]: \u001b[0m eta: 5:20:52 iter: 38899 total_loss: 0.9404 loss_cls: 0.3242 loss_box_reg: 0.3401 loss_rpn_cls: 0.06256 loss_rpn_loc: 0.2182 time: 0.3607 last_time: 0.3399 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:47:21 d2.utils.events]: \u001b[0m eta: 5:20:39 iter: 38919 total_loss: 0.8764 loss_cls: 0.3298 loss_box_reg: 0.2984 loss_rpn_cls: 0.04497 loss_rpn_loc: 0.2146 time: 0.3607 last_time: 0.3690 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:47:29 d2.utils.events]: \u001b[0m eta: 5:20:24 iter: 38939 total_loss: 0.867 loss_cls: 0.2738 loss_box_reg: 0.3053 loss_rpn_cls: 0.04519 loss_rpn_loc: 0.1865 time: 0.3607 last_time: 0.3946 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:47:37 d2.utils.events]: \u001b[0m eta: 5:20:08 iter: 38959 total_loss: 0.8032 loss_cls: 0.2506 loss_box_reg: 0.2887 loss_rpn_cls: 0.0506 loss_rpn_loc: 0.1952 time: 0.3607 last_time: 0.3703 data_time: 0.0046 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:47:45 d2.utils.events]: \u001b[0m eta: 5:19:40 iter: 38979 total_loss: 0.8906 loss_cls: 0.2963 loss_box_reg: 0.3216 loss_rpn_cls: 0.05158 loss_rpn_loc: 0.1929 time: 0.3608 last_time: 0.3780 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:47:53 d2.utils.events]: \u001b[0m eta: 5:19:22 iter: 38999 total_loss: 0.8592 loss_cls: 0.2732 loss_box_reg: 0.3143 loss_rpn_cls: 0.06659 loss_rpn_loc: 0.2132 time: 0.3608 last_time: 0.3407 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:48:01 d2.utils.events]: \u001b[0m eta: 5:19:14 iter: 39019 total_loss: 0.9172 loss_cls: 0.3097 loss_box_reg: 0.3437 loss_rpn_cls: 0.05385 loss_rpn_loc: 0.1954 time: 0.3608 last_time: 0.3434 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:48:09 d2.utils.events]: \u001b[0m eta: 5:19:09 iter: 39039 total_loss: 0.7585 loss_cls: 0.2197 loss_box_reg: 0.2847 loss_rpn_cls: 0.04158 loss_rpn_loc: 0.2018 time: 0.3608 last_time: 0.4340 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:48:17 d2.utils.events]: \u001b[0m eta: 5:18:50 iter: 39059 total_loss: 0.8386 loss_cls: 0.2716 loss_box_reg: 0.3433 loss_rpn_cls: 0.04463 loss_rpn_loc: 0.2141 time: 0.3608 last_time: 0.4085 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:48:26 d2.utils.events]: \u001b[0m eta: 5:18:55 iter: 39079 total_loss: 0.8724 loss_cls: 0.3009 loss_box_reg: 0.3202 loss_rpn_cls: 0.0625 loss_rpn_loc: 0.1972 time: 0.3609 last_time: 0.4164 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:48:33 d2.utils.events]: \u001b[0m eta: 5:18:41 iter: 39099 total_loss: 0.8537 loss_cls: 0.2855 loss_box_reg: 0.3367 loss_rpn_cls: 0.05186 loss_rpn_loc: 0.181 time: 0.3609 last_time: 0.3980 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:48:42 d2.utils.events]: \u001b[0m eta: 5:18:27 iter: 39119 total_loss: 0.9504 loss_cls: 0.3041 loss_box_reg: 0.3258 loss_rpn_cls: 0.04725 loss_rpn_loc: 0.2259 time: 0.3609 last_time: 0.4131 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:48:50 d2.utils.events]: \u001b[0m eta: 5:18:06 iter: 39139 total_loss: 0.9408 loss_cls: 0.3425 loss_box_reg: 0.3621 loss_rpn_cls: 0.05858 loss_rpn_loc: 0.1865 time: 0.3609 last_time: 0.4136 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:48:58 d2.utils.events]: \u001b[0m eta: 5:17:41 iter: 39159 total_loss: 0.8532 loss_cls: 0.2864 loss_box_reg: 0.3104 loss_rpn_cls: 0.04796 loss_rpn_loc: 0.1948 time: 0.3610 last_time: 0.3692 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:49:06 d2.utils.events]: \u001b[0m eta: 5:17:35 iter: 39179 total_loss: 0.8401 loss_cls: 0.2761 loss_box_reg: 0.3196 loss_rpn_cls: 0.03954 loss_rpn_loc: 0.1937 time: 0.3610 last_time: 0.3997 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:49:14 d2.utils.events]: \u001b[0m eta: 5:17:24 iter: 39199 total_loss: 0.8453 loss_cls: 0.2801 loss_box_reg: 0.3212 loss_rpn_cls: 0.05843 loss_rpn_loc: 0.2098 time: 0.3610 last_time: 0.3751 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:49:22 d2.utils.events]: \u001b[0m eta: 5:17:06 iter: 39219 total_loss: 0.8798 loss_cls: 0.3021 loss_box_reg: 0.3558 loss_rpn_cls: 0.05274 loss_rpn_loc: 0.2082 time: 0.3610 last_time: 0.3673 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:49:30 d2.utils.events]: \u001b[0m eta: 5:16:38 iter: 39239 total_loss: 0.9238 loss_cls: 0.3225 loss_box_reg: 0.3244 loss_rpn_cls: 0.05582 loss_rpn_loc: 0.219 time: 0.3610 last_time: 0.3401 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:49:38 d2.utils.events]: \u001b[0m eta: 5:16:14 iter: 39259 total_loss: 0.7672 loss_cls: 0.2324 loss_box_reg: 0.2842 loss_rpn_cls: 0.04486 loss_rpn_loc: 0.1861 time: 0.3610 last_time: 0.3743 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:49:46 d2.utils.events]: \u001b[0m eta: 5:16:05 iter: 39279 total_loss: 0.9162 loss_cls: 0.3041 loss_box_reg: 0.3061 loss_rpn_cls: 0.05653 loss_rpn_loc: 0.2117 time: 0.3611 last_time: 0.4308 data_time: 0.0047 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:49:54 d2.utils.events]: \u001b[0m eta: 5:15:30 iter: 39299 total_loss: 0.9173 loss_cls: 0.2908 loss_box_reg: 0.3357 loss_rpn_cls: 0.04988 loss_rpn_loc: 0.2078 time: 0.3611 last_time: 0.4149 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:50:02 d2.utils.events]: \u001b[0m eta: 5:15:22 iter: 39319 total_loss: 0.7892 loss_cls: 0.263 loss_box_reg: 0.2978 loss_rpn_cls: 0.04558 loss_rpn_loc: 0.1888 time: 0.3611 last_time: 0.3999 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:50:10 d2.utils.events]: \u001b[0m eta: 5:15:16 iter: 39339 total_loss: 0.8326 loss_cls: 0.2764 loss_box_reg: 0.321 loss_rpn_cls: 0.05253 loss_rpn_loc: 0.1948 time: 0.3611 last_time: 0.3664 data_time: 0.0048 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:50:18 d2.utils.events]: \u001b[0m eta: 5:15:00 iter: 39359 total_loss: 0.8998 loss_cls: 0.2837 loss_box_reg: 0.3032 loss_rpn_cls: 0.06236 loss_rpn_loc: 0.2091 time: 0.3611 last_time: 0.3393 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:50:26 d2.utils.events]: \u001b[0m eta: 5:14:57 iter: 39379 total_loss: 0.8761 loss_cls: 0.2923 loss_box_reg: 0.3058 loss_rpn_cls: 0.05719 loss_rpn_loc: 0.1967 time: 0.3612 last_time: 0.4426 data_time: 0.0048 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:50:34 d2.utils.events]: \u001b[0m eta: 5:14:27 iter: 39399 total_loss: 0.8086 loss_cls: 0.2583 loss_box_reg: 0.3041 loss_rpn_cls: 0.03944 loss_rpn_loc: 0.1807 time: 0.3612 last_time: 0.4001 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:50:42 d2.utils.events]: \u001b[0m eta: 5:13:58 iter: 39419 total_loss: 0.8486 loss_cls: 0.2751 loss_box_reg: 0.351 loss_rpn_cls: 0.04732 loss_rpn_loc: 0.1852 time: 0.3612 last_time: 0.3918 data_time: 0.0046 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:50:50 d2.utils.events]: \u001b[0m eta: 5:13:15 iter: 39439 total_loss: 1.055 loss_cls: 0.3639 loss_box_reg: 0.3516 loss_rpn_cls: 0.05726 loss_rpn_loc: 0.2216 time: 0.3612 last_time: 0.4388 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:50:58 d2.utils.events]: \u001b[0m eta: 5:12:33 iter: 39459 total_loss: 0.7851 loss_cls: 0.2272 loss_box_reg: 0.2915 loss_rpn_cls: 0.05277 loss_rpn_loc: 0.1942 time: 0.3613 last_time: 0.4368 data_time: 0.0045 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:51:06 d2.utils.events]: \u001b[0m eta: 5:12:16 iter: 39479 total_loss: 0.7964 loss_cls: 0.2451 loss_box_reg: 0.3104 loss_rpn_cls: 0.04676 loss_rpn_loc: 0.1991 time: 0.3613 last_time: 0.4045 data_time: 0.0048 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:51:14 d2.utils.events]: \u001b[0m eta: 5:11:49 iter: 39499 total_loss: 0.9269 loss_cls: 0.2796 loss_box_reg: 0.3438 loss_rpn_cls: 0.0486 loss_rpn_loc: 0.2334 time: 0.3613 last_time: 0.4064 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:51:22 d2.utils.events]: \u001b[0m eta: 5:11:13 iter: 39519 total_loss: 0.8317 loss_cls: 0.2557 loss_box_reg: 0.3078 loss_rpn_cls: 0.04693 loss_rpn_loc: 0.2231 time: 0.3613 last_time: 0.4033 data_time: 0.0045 last_data_time: 0.0040 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:51:30 d2.utils.events]: \u001b[0m eta: 5:11:22 iter: 39539 total_loss: 0.8921 loss_cls: 0.3168 loss_box_reg: 0.3344 loss_rpn_cls: 0.04795 loss_rpn_loc: 0.1915 time: 0.3613 last_time: 0.4024 data_time: 0.0045 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:51:38 d2.utils.events]: \u001b[0m eta: 5:11:05 iter: 39559 total_loss: 0.8014 loss_cls: 0.2449 loss_box_reg: 0.2592 loss_rpn_cls: 0.05119 loss_rpn_loc: 0.2017 time: 0.3614 last_time: 0.3744 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:51:47 d2.utils.events]: \u001b[0m eta: 5:10:57 iter: 39579 total_loss: 0.9109 loss_cls: 0.3509 loss_box_reg: 0.3489 loss_rpn_cls: 0.04599 loss_rpn_loc: 0.176 time: 0.3614 last_time: 0.4176 data_time: 0.0046 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:51:55 d2.utils.events]: \u001b[0m eta: 5:09:55 iter: 39599 total_loss: 0.8721 loss_cls: 0.2735 loss_box_reg: 0.3439 loss_rpn_cls: 0.0508 loss_rpn_loc: 0.2108 time: 0.3614 last_time: 0.3662 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:52:03 d2.utils.events]: \u001b[0m eta: 5:09:47 iter: 39619 total_loss: 0.8607 loss_cls: 0.3049 loss_box_reg: 0.3151 loss_rpn_cls: 0.04661 loss_rpn_loc: 0.1989 time: 0.3614 last_time: 0.4369 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:52:11 d2.utils.events]: \u001b[0m eta: 5:09:19 iter: 39639 total_loss: 0.8808 loss_cls: 0.2894 loss_box_reg: 0.3371 loss_rpn_cls: 0.04507 loss_rpn_loc: 0.2084 time: 0.3614 last_time: 0.3704 data_time: 0.0046 last_data_time: 0.0053 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:52:19 d2.utils.events]: \u001b[0m eta: 5:09:24 iter: 39659 total_loss: 0.8494 loss_cls: 0.3038 loss_box_reg: 0.3075 loss_rpn_cls: 0.05376 loss_rpn_loc: 0.2294 time: 0.3615 last_time: 0.4143 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:52:27 d2.utils.events]: \u001b[0m eta: 5:09:37 iter: 39679 total_loss: 0.9093 loss_cls: 0.2949 loss_box_reg: 0.3103 loss_rpn_cls: 0.06417 loss_rpn_loc: 0.2385 time: 0.3615 last_time: 0.3973 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:52:35 d2.utils.events]: \u001b[0m eta: 5:09:13 iter: 39699 total_loss: 0.7721 loss_cls: 0.3078 loss_box_reg: 0.285 loss_rpn_cls: 0.03744 loss_rpn_loc: 0.1724 time: 0.3615 last_time: 0.4440 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:52:43 d2.utils.events]: \u001b[0m eta: 5:08:25 iter: 39719 total_loss: 0.9028 loss_cls: 0.2661 loss_box_reg: 0.3221 loss_rpn_cls: 0.05821 loss_rpn_loc: 0.2258 time: 0.3615 last_time: 0.4193 data_time: 0.0045 last_data_time: 0.0041 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:52:51 d2.utils.events]: \u001b[0m eta: 5:08:02 iter: 39739 total_loss: 0.8548 loss_cls: 0.3024 loss_box_reg: 0.3114 loss_rpn_cls: 0.04853 loss_rpn_loc: 0.188 time: 0.3615 last_time: 0.3744 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:52:59 d2.utils.events]: \u001b[0m eta: 5:08:30 iter: 39759 total_loss: 0.8221 loss_cls: 0.273 loss_box_reg: 0.2953 loss_rpn_cls: 0.05281 loss_rpn_loc: 0.2054 time: 0.3616 last_time: 0.4189 data_time: 0.0045 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:53:07 d2.utils.events]: \u001b[0m eta: 5:08:41 iter: 39779 total_loss: 0.8953 loss_cls: 0.3106 loss_box_reg: 0.3345 loss_rpn_cls: 0.05885 loss_rpn_loc: 0.1981 time: 0.3616 last_time: 0.4156 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:53:15 d2.utils.events]: \u001b[0m eta: 5:07:42 iter: 39799 total_loss: 0.7207 loss_cls: 0.2414 loss_box_reg: 0.2746 loss_rpn_cls: 0.04499 loss_rpn_loc: 0.175 time: 0.3616 last_time: 0.4274 data_time: 0.0047 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:53:24 d2.utils.events]: \u001b[0m eta: 5:08:24 iter: 39819 total_loss: 0.9669 loss_cls: 0.3577 loss_box_reg: 0.3738 loss_rpn_cls: 0.04404 loss_rpn_loc: 0.2113 time: 0.3616 last_time: 0.3857 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:53:32 d2.utils.events]: \u001b[0m eta: 5:07:36 iter: 39839 total_loss: 0.7809 loss_cls: 0.2892 loss_box_reg: 0.2849 loss_rpn_cls: 0.02982 loss_rpn_loc: 0.1777 time: 0.3617 last_time: 0.4428 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:53:40 d2.utils.events]: \u001b[0m eta: 5:07:28 iter: 39859 total_loss: 0.8576 loss_cls: 0.3116 loss_box_reg: 0.3512 loss_rpn_cls: 0.06475 loss_rpn_loc: 0.217 time: 0.3617 last_time: 0.4197 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:53:47 d2.utils.events]: \u001b[0m eta: 5:07:20 iter: 39879 total_loss: 0.8247 loss_cls: 0.2722 loss_box_reg: 0.2962 loss_rpn_cls: 0.04496 loss_rpn_loc: 0.1923 time: 0.3617 last_time: 0.4409 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:53:56 d2.utils.events]: \u001b[0m eta: 5:07:45 iter: 39899 total_loss: 1.031 loss_cls: 0.3671 loss_box_reg: 0.3606 loss_rpn_cls: 0.05449 loss_rpn_loc: 0.2065 time: 0.3617 last_time: 0.3672 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:54:04 d2.utils.events]: \u001b[0m eta: 5:07:43 iter: 39919 total_loss: 0.9771 loss_cls: 0.2748 loss_box_reg: 0.3524 loss_rpn_cls: 0.04454 loss_rpn_loc: 0.2141 time: 0.3617 last_time: 0.4458 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:54:12 d2.utils.events]: \u001b[0m eta: 5:08:12 iter: 39939 total_loss: 0.9449 loss_cls: 0.3072 loss_box_reg: 0.3117 loss_rpn_cls: 0.05134 loss_rpn_loc: 0.2099 time: 0.3618 last_time: 0.4191 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:54:20 d2.utils.events]: \u001b[0m eta: 5:08:06 iter: 39959 total_loss: 0.8226 loss_cls: 0.2763 loss_box_reg: 0.3025 loss_rpn_cls: 0.04255 loss_rpn_loc: 0.2026 time: 0.3618 last_time: 0.4002 data_time: 0.0046 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:54:28 d2.utils.events]: \u001b[0m eta: 5:08:38 iter: 39979 total_loss: 0.7762 loss_cls: 0.244 loss_box_reg: 0.2879 loss_rpn_cls: 0.04404 loss_rpn_loc: 0.1998 time: 0.3618 last_time: 0.4196 data_time: 0.0047 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:54:37 d2.utils.events]: \u001b[0m eta: 5:08:03 iter: 39999 total_loss: 0.8181 loss_cls: 0.2537 loss_box_reg: 0.2795 loss_rpn_cls: 0.05008 loss_rpn_loc: 0.2101 time: 0.3618 last_time: 0.3962 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:54:45 d2.utils.events]: \u001b[0m eta: 5:07:55 iter: 40019 total_loss: 0.8617 loss_cls: 0.2443 loss_box_reg: 0.3115 loss_rpn_cls: 0.04754 loss_rpn_loc: 0.1999 time: 0.3618 last_time: 0.4154 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:54:53 d2.utils.events]: \u001b[0m eta: 5:08:05 iter: 40039 total_loss: 0.9104 loss_cls: 0.2815 loss_box_reg: 0.3462 loss_rpn_cls: 0.05374 loss_rpn_loc: 0.1964 time: 0.3619 last_time: 0.4115 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:55:01 d2.utils.events]: \u001b[0m eta: 5:08:05 iter: 40059 total_loss: 0.9065 loss_cls: 0.287 loss_box_reg: 0.3314 loss_rpn_cls: 0.06082 loss_rpn_loc: 0.1956 time: 0.3619 last_time: 0.4365 data_time: 0.0046 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:55:09 d2.utils.events]: \u001b[0m eta: 5:07:22 iter: 40079 total_loss: 0.8769 loss_cls: 0.321 loss_box_reg: 0.3176 loss_rpn_cls: 0.06012 loss_rpn_loc: 0.2142 time: 0.3619 last_time: 0.3889 data_time: 0.0047 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:55:17 d2.utils.events]: \u001b[0m eta: 5:07:01 iter: 40099 total_loss: 0.8319 loss_cls: 0.2384 loss_box_reg: 0.3029 loss_rpn_cls: 0.04536 loss_rpn_loc: 0.1954 time: 0.3619 last_time: 0.4421 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:55:25 d2.utils.events]: \u001b[0m eta: 5:06:43 iter: 40119 total_loss: 0.8516 loss_cls: 0.3011 loss_box_reg: 0.3355 loss_rpn_cls: 0.04773 loss_rpn_loc: 0.2108 time: 0.3619 last_time: 0.3144 data_time: 0.0048 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:55:33 d2.utils.events]: \u001b[0m eta: 5:06:16 iter: 40139 total_loss: 0.8928 loss_cls: 0.263 loss_box_reg: 0.3205 loss_rpn_cls: 0.04143 loss_rpn_loc: 0.198 time: 0.3620 last_time: 0.4383 data_time: 0.0048 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:55:41 d2.utils.events]: \u001b[0m eta: 5:06:19 iter: 40159 total_loss: 0.845 loss_cls: 0.2668 loss_box_reg: 0.2988 loss_rpn_cls: 0.04262 loss_rpn_loc: 0.1935 time: 0.3620 last_time: 0.4092 data_time: 0.0048 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:55:49 d2.utils.events]: \u001b[0m eta: 5:06:18 iter: 40179 total_loss: 0.9298 loss_cls: 0.2674 loss_box_reg: 0.3436 loss_rpn_cls: 0.04452 loss_rpn_loc: 0.2244 time: 0.3620 last_time: 0.4337 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:55:57 d2.utils.events]: \u001b[0m eta: 5:06:25 iter: 40199 total_loss: 0.8607 loss_cls: 0.3101 loss_box_reg: 0.3268 loss_rpn_cls: 0.04918 loss_rpn_loc: 0.1859 time: 0.3620 last_time: 0.4392 data_time: 0.0045 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:56:05 d2.utils.events]: \u001b[0m eta: 5:06:28 iter: 40219 total_loss: 0.8836 loss_cls: 0.27 loss_box_reg: 0.3166 loss_rpn_cls: 0.05671 loss_rpn_loc: 0.2059 time: 0.3620 last_time: 0.4079 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:56:14 d2.utils.events]: \u001b[0m eta: 5:06:33 iter: 40239 total_loss: 0.8465 loss_cls: 0.2929 loss_box_reg: 0.3521 loss_rpn_cls: 0.04547 loss_rpn_loc: 0.1891 time: 0.3621 last_time: 0.3419 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:56:21 d2.utils.events]: \u001b[0m eta: 5:06:25 iter: 40259 total_loss: 0.8326 loss_cls: 0.28 loss_box_reg: 0.2982 loss_rpn_cls: 0.0426 loss_rpn_loc: 0.1825 time: 0.3621 last_time: 0.4201 data_time: 0.0046 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:56:29 d2.utils.events]: \u001b[0m eta: 5:06:00 iter: 40279 total_loss: 0.9438 loss_cls: 0.3273 loss_box_reg: 0.3078 loss_rpn_cls: 0.06434 loss_rpn_loc: 0.2342 time: 0.3621 last_time: 0.3922 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:56:38 d2.utils.events]: \u001b[0m eta: 5:06:02 iter: 40299 total_loss: 0.78 loss_cls: 0.2431 loss_box_reg: 0.2844 loss_rpn_cls: 0.04664 loss_rpn_loc: 0.199 time: 0.3621 last_time: 0.3877 data_time: 0.0045 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:56:46 d2.utils.events]: \u001b[0m eta: 5:06:09 iter: 40319 total_loss: 0.8437 loss_cls: 0.2978 loss_box_reg: 0.3068 loss_rpn_cls: 0.04992 loss_rpn_loc: 0.2072 time: 0.3621 last_time: 0.4408 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:56:54 d2.utils.events]: \u001b[0m eta: 5:05:52 iter: 40339 total_loss: 0.9526 loss_cls: 0.2734 loss_box_reg: 0.3648 loss_rpn_cls: 0.05453 loss_rpn_loc: 0.2101 time: 0.3622 last_time: 0.4838 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:57:02 d2.utils.events]: \u001b[0m eta: 5:06:09 iter: 40359 total_loss: 0.8152 loss_cls: 0.2737 loss_box_reg: 0.3038 loss_rpn_cls: 0.05072 loss_rpn_loc: 0.196 time: 0.3622 last_time: 0.3076 data_time: 0.0049 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:57:10 d2.utils.events]: \u001b[0m eta: 5:05:49 iter: 40379 total_loss: 0.8753 loss_cls: 0.2618 loss_box_reg: 0.3273 loss_rpn_cls: 0.04608 loss_rpn_loc: 0.2215 time: 0.3622 last_time: 0.4409 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:57:18 d2.utils.events]: \u001b[0m eta: 5:05:45 iter: 40399 total_loss: 0.8584 loss_cls: 0.3048 loss_box_reg: 0.3025 loss_rpn_cls: 0.06067 loss_rpn_loc: 0.1872 time: 0.3622 last_time: 0.4339 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:57:26 d2.utils.events]: \u001b[0m eta: 5:05:41 iter: 40419 total_loss: 0.8495 loss_cls: 0.2627 loss_box_reg: 0.3185 loss_rpn_cls: 0.05038 loss_rpn_loc: 0.2317 time: 0.3623 last_time: 0.4412 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:57:34 d2.utils.events]: \u001b[0m eta: 5:05:11 iter: 40439 total_loss: 0.8095 loss_cls: 0.2245 loss_box_reg: 0.3126 loss_rpn_cls: 0.04942 loss_rpn_loc: 0.203 time: 0.3623 last_time: 0.4414 data_time: 0.0045 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:57:42 d2.utils.events]: \u001b[0m eta: 5:05:11 iter: 40459 total_loss: 0.8917 loss_cls: 0.2922 loss_box_reg: 0.3083 loss_rpn_cls: 0.0509 loss_rpn_loc: 0.2 time: 0.3623 last_time: 0.3713 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:57:50 d2.utils.events]: \u001b[0m eta: 5:05:16 iter: 40479 total_loss: 0.939 loss_cls: 0.3287 loss_box_reg: 0.3418 loss_rpn_cls: 0.06346 loss_rpn_loc: 0.2058 time: 0.3623 last_time: 0.3802 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:57:58 d2.utils.events]: \u001b[0m eta: 5:04:46 iter: 40499 total_loss: 0.854 loss_cls: 0.3043 loss_box_reg: 0.3096 loss_rpn_cls: 0.04928 loss_rpn_loc: 0.1963 time: 0.3623 last_time: 0.3718 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:58:06 d2.utils.events]: \u001b[0m eta: 5:04:54 iter: 40519 total_loss: 0.8936 loss_cls: 0.2879 loss_box_reg: 0.3486 loss_rpn_cls: 0.05212 loss_rpn_loc: 0.2089 time: 0.3623 last_time: 0.3938 data_time: 0.0046 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:58:14 d2.utils.events]: \u001b[0m eta: 5:04:46 iter: 40539 total_loss: 0.788 loss_cls: 0.2297 loss_box_reg: 0.2637 loss_rpn_cls: 0.05798 loss_rpn_loc: 0.2019 time: 0.3624 last_time: 0.3391 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:58:22 d2.utils.events]: \u001b[0m eta: 5:04:43 iter: 40559 total_loss: 0.7828 loss_cls: 0.2648 loss_box_reg: 0.2998 loss_rpn_cls: 0.04326 loss_rpn_loc: 0.158 time: 0.3624 last_time: 0.4422 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:58:30 d2.utils.events]: \u001b[0m eta: 5:04:13 iter: 40579 total_loss: 0.9523 loss_cls: 0.3105 loss_box_reg: 0.3137 loss_rpn_cls: 0.05647 loss_rpn_loc: 0.232 time: 0.3624 last_time: 0.3709 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:58:38 d2.utils.events]: \u001b[0m eta: 5:04:13 iter: 40599 total_loss: 0.8894 loss_cls: 0.2968 loss_box_reg: 0.3116 loss_rpn_cls: 0.05506 loss_rpn_loc: 0.2124 time: 0.3624 last_time: 0.3219 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:58:46 d2.utils.events]: \u001b[0m eta: 5:03:45 iter: 40619 total_loss: 0.7563 loss_cls: 0.2379 loss_box_reg: 0.305 loss_rpn_cls: 0.05755 loss_rpn_loc: 0.2108 time: 0.3624 last_time: 0.3732 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:58:54 d2.utils.events]: \u001b[0m eta: 5:04:10 iter: 40639 total_loss: 0.8584 loss_cls: 0.2816 loss_box_reg: 0.3041 loss_rpn_cls: 0.05181 loss_rpn_loc: 0.2021 time: 0.3625 last_time: 0.4394 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:59:02 d2.utils.events]: \u001b[0m eta: 5:03:57 iter: 40659 total_loss: 0.7582 loss_cls: 0.2501 loss_box_reg: 0.3002 loss_rpn_cls: 0.04759 loss_rpn_loc: 0.1882 time: 0.3625 last_time: 0.3780 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:59:10 d2.utils.events]: \u001b[0m eta: 5:03:37 iter: 40679 total_loss: 0.7591 loss_cls: 0.2648 loss_box_reg: 0.2828 loss_rpn_cls: 0.06519 loss_rpn_loc: 0.193 time: 0.3625 last_time: 0.3937 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:59:19 d2.utils.events]: \u001b[0m eta: 5:03:12 iter: 40699 total_loss: 0.8886 loss_cls: 0.2753 loss_box_reg: 0.3337 loss_rpn_cls: 0.04417 loss_rpn_loc: 0.191 time: 0.3625 last_time: 0.4000 data_time: 0.0046 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:59:26 d2.utils.events]: \u001b[0m eta: 5:02:52 iter: 40719 total_loss: 0.9034 loss_cls: 0.272 loss_box_reg: 0.3267 loss_rpn_cls: 0.04579 loss_rpn_loc: 0.2018 time: 0.3625 last_time: 0.3115 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:59:35 d2.utils.events]: \u001b[0m eta: 5:02:56 iter: 40739 total_loss: 0.8371 loss_cls: 0.2375 loss_box_reg: 0.307 loss_rpn_cls: 0.05211 loss_rpn_loc: 0.1985 time: 0.3626 last_time: 0.3686 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:59:42 d2.utils.events]: \u001b[0m eta: 5:02:36 iter: 40759 total_loss: 0.8254 loss_cls: 0.292 loss_box_reg: 0.2958 loss_rpn_cls: 0.052 loss_rpn_loc: 0.2257 time: 0.3626 last_time: 0.3702 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:59:51 d2.utils.events]: \u001b[0m eta: 5:02:36 iter: 40779 total_loss: 0.7596 loss_cls: 0.2567 loss_box_reg: 0.2957 loss_rpn_cls: 0.04749 loss_rpn_loc: 0.1531 time: 0.3626 last_time: 0.4071 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 19:59:59 d2.utils.events]: \u001b[0m eta: 5:02:16 iter: 40799 total_loss: 0.9055 loss_cls: 0.2916 loss_box_reg: 0.3111 loss_rpn_cls: 0.05526 loss_rpn_loc: 0.22 time: 0.3626 last_time: 0.4182 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:00:07 d2.utils.events]: \u001b[0m eta: 5:01:51 iter: 40819 total_loss: 0.8949 loss_cls: 0.3147 loss_box_reg: 0.3324 loss_rpn_cls: 0.05285 loss_rpn_loc: 0.187 time: 0.3626 last_time: 0.3848 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:00:15 d2.utils.events]: \u001b[0m eta: 5:01:57 iter: 40839 total_loss: 0.7699 loss_cls: 0.2458 loss_box_reg: 0.2717 loss_rpn_cls: 0.04777 loss_rpn_loc: 0.2043 time: 0.3626 last_time: 0.4287 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:00:23 d2.utils.events]: \u001b[0m eta: 5:01:55 iter: 40859 total_loss: 0.8401 loss_cls: 0.2811 loss_box_reg: 0.3187 loss_rpn_cls: 0.03993 loss_rpn_loc: 0.1722 time: 0.3627 last_time: 0.3855 data_time: 0.0046 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:00:31 d2.utils.events]: \u001b[0m eta: 5:02:06 iter: 40879 total_loss: 0.9078 loss_cls: 0.3043 loss_box_reg: 0.3128 loss_rpn_cls: 0.05377 loss_rpn_loc: 0.2249 time: 0.3627 last_time: 0.3937 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:00:39 d2.utils.events]: \u001b[0m eta: 5:02:11 iter: 40899 total_loss: 0.9739 loss_cls: 0.3137 loss_box_reg: 0.3477 loss_rpn_cls: 0.04968 loss_rpn_loc: 0.2018 time: 0.3627 last_time: 0.4324 data_time: 0.0049 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:00:47 d2.utils.events]: \u001b[0m eta: 5:01:56 iter: 40919 total_loss: 0.8449 loss_cls: 0.2692 loss_box_reg: 0.3222 loss_rpn_cls: 0.05084 loss_rpn_loc: 0.1983 time: 0.3627 last_time: 0.3767 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:00:55 d2.utils.events]: \u001b[0m eta: 5:01:32 iter: 40939 total_loss: 0.9665 loss_cls: 0.2832 loss_box_reg: 0.3472 loss_rpn_cls: 0.06012 loss_rpn_loc: 0.2278 time: 0.3628 last_time: 0.4068 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:01:04 d2.utils.events]: \u001b[0m eta: 5:01:31 iter: 40959 total_loss: 0.7665 loss_cls: 0.2423 loss_box_reg: 0.2675 loss_rpn_cls: 0.04224 loss_rpn_loc: 0.1952 time: 0.3628 last_time: 0.3396 data_time: 0.0045 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:01:11 d2.utils.events]: \u001b[0m eta: 5:00:59 iter: 40979 total_loss: 0.8216 loss_cls: 0.2848 loss_box_reg: 0.3399 loss_rpn_cls: 0.05128 loss_rpn_loc: 0.1878 time: 0.3628 last_time: 0.3932 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:01:20 d2.utils.events]: \u001b[0m eta: 5:01:12 iter: 40999 total_loss: 0.8491 loss_cls: 0.2677 loss_box_reg: 0.3037 loss_rpn_cls: 0.05094 loss_rpn_loc: 0.1797 time: 0.3628 last_time: 0.4444 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:01:27 d2.utils.events]: \u001b[0m eta: 5:00:55 iter: 41019 total_loss: 0.8405 loss_cls: 0.2848 loss_box_reg: 0.2968 loss_rpn_cls: 0.05259 loss_rpn_loc: 0.1901 time: 0.3628 last_time: 0.3716 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:01:36 d2.utils.events]: \u001b[0m eta: 5:00:55 iter: 41039 total_loss: 0.8155 loss_cls: 0.2681 loss_box_reg: 0.31 loss_rpn_cls: 0.04406 loss_rpn_loc: 0.2045 time: 0.3629 last_time: 0.4398 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:01:44 d2.utils.events]: \u001b[0m eta: 5:00:47 iter: 41059 total_loss: 0.8354 loss_cls: 0.2652 loss_box_reg: 0.2755 loss_rpn_cls: 0.04532 loss_rpn_loc: 0.2135 time: 0.3629 last_time: 0.4257 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:01:52 d2.utils.events]: \u001b[0m eta: 5:00:55 iter: 41079 total_loss: 0.9229 loss_cls: 0.286 loss_box_reg: 0.3388 loss_rpn_cls: 0.05333 loss_rpn_loc: 0.2361 time: 0.3629 last_time: 0.3790 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:02:00 d2.utils.events]: \u001b[0m eta: 5:00:47 iter: 41099 total_loss: 0.8643 loss_cls: 0.2535 loss_box_reg: 0.3229 loss_rpn_cls: 0.04728 loss_rpn_loc: 0.2041 time: 0.3629 last_time: 0.4116 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:02:08 d2.utils.events]: \u001b[0m eta: 5:01:00 iter: 41119 total_loss: 0.9277 loss_cls: 0.285 loss_box_reg: 0.315 loss_rpn_cls: 0.04365 loss_rpn_loc: 0.1971 time: 0.3629 last_time: 0.3901 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:02:16 d2.utils.events]: \u001b[0m eta: 5:00:47 iter: 41139 total_loss: 0.8324 loss_cls: 0.2584 loss_box_reg: 0.3139 loss_rpn_cls: 0.05469 loss_rpn_loc: 0.1947 time: 0.3629 last_time: 0.4151 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:02:24 d2.utils.events]: \u001b[0m eta: 5:00:42 iter: 41159 total_loss: 0.8832 loss_cls: 0.3105 loss_box_reg: 0.2828 loss_rpn_cls: 0.04945 loss_rpn_loc: 0.199 time: 0.3630 last_time: 0.4282 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:02:32 d2.utils.events]: \u001b[0m eta: 5:00:16 iter: 41179 total_loss: 0.7826 loss_cls: 0.2582 loss_box_reg: 0.2856 loss_rpn_cls: 0.0455 loss_rpn_loc: 0.1829 time: 0.3630 last_time: 0.4437 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:02:40 d2.utils.events]: \u001b[0m eta: 5:00:06 iter: 41199 total_loss: 0.8898 loss_cls: 0.3173 loss_box_reg: 0.2923 loss_rpn_cls: 0.0483 loss_rpn_loc: 0.2193 time: 0.3630 last_time: 0.4095 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:02:48 d2.utils.events]: \u001b[0m eta: 4:59:52 iter: 41219 total_loss: 0.8587 loss_cls: 0.2796 loss_box_reg: 0.3336 loss_rpn_cls: 0.04661 loss_rpn_loc: 0.1868 time: 0.3630 last_time: 0.3710 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:02:56 d2.utils.events]: \u001b[0m eta: 4:59:20 iter: 41239 total_loss: 0.8568 loss_cls: 0.2747 loss_box_reg: 0.3348 loss_rpn_cls: 0.03999 loss_rpn_loc: 0.1994 time: 0.3630 last_time: 0.3971 data_time: 0.0044 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:03:04 d2.utils.events]: \u001b[0m eta: 4:59:12 iter: 41259 total_loss: 0.8561 loss_cls: 0.2602 loss_box_reg: 0.2833 loss_rpn_cls: 0.04881 loss_rpn_loc: 0.1897 time: 0.3631 last_time: 0.4414 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:03:12 d2.utils.events]: \u001b[0m eta: 4:59:12 iter: 41279 total_loss: 0.8729 loss_cls: 0.2858 loss_box_reg: 0.3223 loss_rpn_cls: 0.04827 loss_rpn_loc: 0.2174 time: 0.3631 last_time: 0.4205 data_time: 0.0045 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:03:20 d2.utils.events]: \u001b[0m eta: 4:58:54 iter: 41299 total_loss: 0.801 loss_cls: 0.274 loss_box_reg: 0.2924 loss_rpn_cls: 0.04249 loss_rpn_loc: 0.2164 time: 0.3631 last_time: 0.4276 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:03:28 d2.utils.events]: \u001b[0m eta: 4:58:46 iter: 41319 total_loss: 0.8615 loss_cls: 0.2646 loss_box_reg: 0.3002 loss_rpn_cls: 0.0538 loss_rpn_loc: 0.1917 time: 0.3631 last_time: 0.4242 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:03:36 d2.utils.events]: \u001b[0m eta: 4:58:29 iter: 41339 total_loss: 0.842 loss_cls: 0.2537 loss_box_reg: 0.2976 loss_rpn_cls: 0.04213 loss_rpn_loc: 0.2005 time: 0.3631 last_time: 0.3339 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:03:44 d2.utils.events]: \u001b[0m eta: 4:57:53 iter: 41359 total_loss: 0.8955 loss_cls: 0.3215 loss_box_reg: 0.3359 loss_rpn_cls: 0.07217 loss_rpn_loc: 0.2527 time: 0.3631 last_time: 0.4175 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:03:52 d2.utils.events]: \u001b[0m eta: 4:57:33 iter: 41379 total_loss: 0.8328 loss_cls: 0.2742 loss_box_reg: 0.3061 loss_rpn_cls: 0.04686 loss_rpn_loc: 0.1829 time: 0.3632 last_time: 0.3380 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:04:00 d2.utils.events]: \u001b[0m eta: 4:57:31 iter: 41399 total_loss: 0.8342 loss_cls: 0.2944 loss_box_reg: 0.2859 loss_rpn_cls: 0.0491 loss_rpn_loc: 0.1887 time: 0.3632 last_time: 0.3429 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:04:08 d2.utils.events]: \u001b[0m eta: 4:57:06 iter: 41419 total_loss: 0.9381 loss_cls: 0.2785 loss_box_reg: 0.3335 loss_rpn_cls: 0.06085 loss_rpn_loc: 0.2145 time: 0.3632 last_time: 0.4181 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:04:16 d2.utils.events]: \u001b[0m eta: 4:57:27 iter: 41439 total_loss: 0.9241 loss_cls: 0.289 loss_box_reg: 0.3209 loss_rpn_cls: 0.04486 loss_rpn_loc: 0.1938 time: 0.3632 last_time: 0.3629 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:04:24 d2.utils.events]: \u001b[0m eta: 4:57:11 iter: 41459 total_loss: 0.8674 loss_cls: 0.2649 loss_box_reg: 0.2777 loss_rpn_cls: 0.04596 loss_rpn_loc: 0.1871 time: 0.3632 last_time: 0.3825 data_time: 0.0046 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:04:32 d2.utils.events]: \u001b[0m eta: 4:56:58 iter: 41479 total_loss: 0.8584 loss_cls: 0.2695 loss_box_reg: 0.3042 loss_rpn_cls: 0.0545 loss_rpn_loc: 0.1934 time: 0.3633 last_time: 0.4155 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:04:40 d2.utils.events]: \u001b[0m eta: 4:56:54 iter: 41499 total_loss: 0.7273 loss_cls: 0.2333 loss_box_reg: 0.2679 loss_rpn_cls: 0.0417 loss_rpn_loc: 0.1929 time: 0.3633 last_time: 0.3945 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:04:48 d2.utils.events]: \u001b[0m eta: 4:56:26 iter: 41519 total_loss: 0.8019 loss_cls: 0.248 loss_box_reg: 0.2795 loss_rpn_cls: 0.04821 loss_rpn_loc: 0.1959 time: 0.3633 last_time: 0.4022 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:04:56 d2.utils.events]: \u001b[0m eta: 4:56:38 iter: 41539 total_loss: 0.8419 loss_cls: 0.253 loss_box_reg: 0.3262 loss_rpn_cls: 0.05765 loss_rpn_loc: 0.1849 time: 0.3633 last_time: 0.3339 data_time: 0.0044 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:05:04 d2.utils.events]: \u001b[0m eta: 4:56:10 iter: 41559 total_loss: 0.9415 loss_cls: 0.2947 loss_box_reg: 0.3162 loss_rpn_cls: 0.04912 loss_rpn_loc: 0.2189 time: 0.3633 last_time: 0.4172 data_time: 0.0045 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:05:13 d2.utils.events]: \u001b[0m eta: 4:56:28 iter: 41579 total_loss: 0.9279 loss_cls: 0.324 loss_box_reg: 0.3093 loss_rpn_cls: 0.05042 loss_rpn_loc: 0.2058 time: 0.3633 last_time: 0.3120 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:05:21 d2.utils.events]: \u001b[0m eta: 4:56:09 iter: 41599 total_loss: 0.8217 loss_cls: 0.2604 loss_box_reg: 0.2892 loss_rpn_cls: 0.04559 loss_rpn_loc: 0.1977 time: 0.3634 last_time: 0.4203 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:05:29 d2.utils.events]: \u001b[0m eta: 4:56:28 iter: 41619 total_loss: 0.8549 loss_cls: 0.2607 loss_box_reg: 0.3222 loss_rpn_cls: 0.05305 loss_rpn_loc: 0.1798 time: 0.3634 last_time: 0.3656 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:05:37 d2.utils.events]: \u001b[0m eta: 4:56:00 iter: 41639 total_loss: 0.9177 loss_cls: 0.3071 loss_box_reg: 0.3463 loss_rpn_cls: 0.05422 loss_rpn_loc: 0.2412 time: 0.3634 last_time: 0.3925 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:05:45 d2.utils.events]: \u001b[0m eta: 4:55:52 iter: 41659 total_loss: 0.8916 loss_cls: 0.2921 loss_box_reg: 0.3396 loss_rpn_cls: 0.04617 loss_rpn_loc: 0.2117 time: 0.3634 last_time: 0.4376 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:05:53 d2.utils.events]: \u001b[0m eta: 4:55:54 iter: 41679 total_loss: 0.8551 loss_cls: 0.2872 loss_box_reg: 0.322 loss_rpn_cls: 0.04364 loss_rpn_loc: 0.18 time: 0.3635 last_time: 0.3927 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:06:01 d2.utils.events]: \u001b[0m eta: 4:55:48 iter: 41699 total_loss: 0.8123 loss_cls: 0.2564 loss_box_reg: 0.2942 loss_rpn_cls: 0.0522 loss_rpn_loc: 0.199 time: 0.3635 last_time: 0.4103 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:06:09 d2.utils.events]: \u001b[0m eta: 4:55:40 iter: 41719 total_loss: 0.9351 loss_cls: 0.3224 loss_box_reg: 0.3353 loss_rpn_cls: 0.05245 loss_rpn_loc: 0.2104 time: 0.3635 last_time: 0.4134 data_time: 0.0046 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:06:17 d2.utils.events]: \u001b[0m eta: 4:55:20 iter: 41739 total_loss: 0.8202 loss_cls: 0.2379 loss_box_reg: 0.2856 loss_rpn_cls: 0.04223 loss_rpn_loc: 0.1531 time: 0.3635 last_time: 0.4148 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:06:25 d2.utils.events]: \u001b[0m eta: 4:55:21 iter: 41759 total_loss: 0.8766 loss_cls: 0.29 loss_box_reg: 0.2936 loss_rpn_cls: 0.05666 loss_rpn_loc: 0.1946 time: 0.3635 last_time: 0.3635 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:06:33 d2.utils.events]: \u001b[0m eta: 4:54:56 iter: 41779 total_loss: 0.7865 loss_cls: 0.2701 loss_box_reg: 0.2948 loss_rpn_cls: 0.04196 loss_rpn_loc: 0.1797 time: 0.3635 last_time: 0.3961 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:06:41 d2.utils.events]: \u001b[0m eta: 4:53:55 iter: 41799 total_loss: 0.8063 loss_cls: 0.264 loss_box_reg: 0.2737 loss_rpn_cls: 0.05057 loss_rpn_loc: 0.1934 time: 0.3635 last_time: 0.4014 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:06:49 d2.utils.events]: \u001b[0m eta: 4:54:08 iter: 41819 total_loss: 0.8763 loss_cls: 0.291 loss_box_reg: 0.3262 loss_rpn_cls: 0.05391 loss_rpn_loc: 0.2165 time: 0.3636 last_time: 0.4145 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:06:57 d2.utils.events]: \u001b[0m eta: 4:53:49 iter: 41839 total_loss: 0.9458 loss_cls: 0.3397 loss_box_reg: 0.3247 loss_rpn_cls: 0.05442 loss_rpn_loc: 0.2063 time: 0.3636 last_time: 0.4058 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:07:06 d2.utils.events]: \u001b[0m eta: 4:53:32 iter: 41859 total_loss: 0.937 loss_cls: 0.2613 loss_box_reg: 0.3288 loss_rpn_cls: 0.06644 loss_rpn_loc: 0.2284 time: 0.3636 last_time: 0.4020 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:07:13 d2.utils.events]: \u001b[0m eta: 4:52:54 iter: 41879 total_loss: 0.8927 loss_cls: 0.266 loss_box_reg: 0.3285 loss_rpn_cls: 0.04924 loss_rpn_loc: 0.24 time: 0.3636 last_time: 0.4267 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:07:21 d2.utils.events]: \u001b[0m eta: 4:52:36 iter: 41899 total_loss: 0.8329 loss_cls: 0.2628 loss_box_reg: 0.2964 loss_rpn_cls: 0.05833 loss_rpn_loc: 0.1954 time: 0.3636 last_time: 0.3418 data_time: 0.0045 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:07:29 d2.utils.events]: \u001b[0m eta: 4:52:37 iter: 41919 total_loss: 0.9077 loss_cls: 0.2655 loss_box_reg: 0.341 loss_rpn_cls: 0.05439 loss_rpn_loc: 0.198 time: 0.3637 last_time: 0.4158 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:07:38 d2.utils.events]: \u001b[0m eta: 4:52:35 iter: 41939 total_loss: 0.894 loss_cls: 0.3428 loss_box_reg: 0.2993 loss_rpn_cls: 0.03758 loss_rpn_loc: 0.1944 time: 0.3637 last_time: 0.4208 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:07:46 d2.utils.events]: \u001b[0m eta: 4:52:14 iter: 41959 total_loss: 0.8372 loss_cls: 0.3005 loss_box_reg: 0.2615 loss_rpn_cls: 0.03262 loss_rpn_loc: 0.1838 time: 0.3637 last_time: 0.4449 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:07:54 d2.utils.events]: \u001b[0m eta: 4:52:43 iter: 41979 total_loss: 0.9931 loss_cls: 0.3194 loss_box_reg: 0.3311 loss_rpn_cls: 0.0609 loss_rpn_loc: 0.2353 time: 0.3637 last_time: 0.4307 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:08:02 d2.utils.events]: \u001b[0m eta: 4:52:26 iter: 41999 total_loss: 0.8197 loss_cls: 0.2675 loss_box_reg: 0.2999 loss_rpn_cls: 0.03637 loss_rpn_loc: 0.2078 time: 0.3637 last_time: 0.3387 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:08:10 d2.utils.events]: \u001b[0m eta: 4:53:06 iter: 42019 total_loss: 0.8037 loss_cls: 0.2684 loss_box_reg: 0.2929 loss_rpn_cls: 0.04848 loss_rpn_loc: 0.1768 time: 0.3638 last_time: 0.3964 data_time: 0.0048 last_data_time: 0.0052 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:08:19 d2.utils.events]: \u001b[0m eta: 4:52:53 iter: 42039 total_loss: 0.8617 loss_cls: 0.2745 loss_box_reg: 0.3471 loss_rpn_cls: 0.05479 loss_rpn_loc: 0.1813 time: 0.3638 last_time: 0.4387 data_time: 0.0049 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:08:28 d2.utils.events]: \u001b[0m eta: 4:53:46 iter: 42059 total_loss: 0.8555 loss_cls: 0.2921 loss_box_reg: 0.3136 loss_rpn_cls: 0.04572 loss_rpn_loc: 0.2138 time: 0.3638 last_time: 0.4425 data_time: 0.0049 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:08:36 d2.utils.events]: \u001b[0m eta: 4:53:50 iter: 42079 total_loss: 0.8091 loss_cls: 0.2749 loss_box_reg: 0.2974 loss_rpn_cls: 0.04239 loss_rpn_loc: 0.195 time: 0.3639 last_time: 0.5046 data_time: 0.0049 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:08:45 d2.utils.events]: \u001b[0m eta: 4:54:08 iter: 42099 total_loss: 0.8194 loss_cls: 0.2919 loss_box_reg: 0.2977 loss_rpn_cls: 0.04161 loss_rpn_loc: 0.1979 time: 0.3639 last_time: 0.5073 data_time: 0.0048 last_data_time: 0.0049 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:08:53 d2.utils.events]: \u001b[0m eta: 4:53:53 iter: 42119 total_loss: 0.8695 loss_cls: 0.2769 loss_box_reg: 0.2974 loss_rpn_cls: 0.04443 loss_rpn_loc: 0.196 time: 0.3639 last_time: 0.3931 data_time: 0.0049 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:09:02 d2.utils.events]: \u001b[0m eta: 4:53:51 iter: 42139 total_loss: 0.8019 loss_cls: 0.2805 loss_box_reg: 0.3156 loss_rpn_cls: 0.044 loss_rpn_loc: 0.1944 time: 0.3639 last_time: 0.4221 data_time: 0.0049 last_data_time: 0.0052 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:09:10 d2.utils.events]: \u001b[0m eta: 4:53:36 iter: 42159 total_loss: 0.9749 loss_cls: 0.3107 loss_box_reg: 0.3119 loss_rpn_cls: 0.05428 loss_rpn_loc: 0.2001 time: 0.3640 last_time: 0.3953 data_time: 0.0049 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:09:19 d2.utils.events]: \u001b[0m eta: 4:54:05 iter: 42179 total_loss: 0.7549 loss_cls: 0.2337 loss_box_reg: 0.324 loss_rpn_cls: 0.04599 loss_rpn_loc: 0.1813 time: 0.3640 last_time: 0.4342 data_time: 0.0049 last_data_time: 0.0053 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:09:28 d2.utils.events]: \u001b[0m eta: 4:54:34 iter: 42199 total_loss: 0.8975 loss_cls: 0.2997 loss_box_reg: 0.3117 loss_rpn_cls: 0.05233 loss_rpn_loc: 0.2128 time: 0.3640 last_time: 0.4863 data_time: 0.0050 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:09:36 d2.utils.events]: \u001b[0m eta: 4:54:37 iter: 42219 total_loss: 0.8631 loss_cls: 0.2996 loss_box_reg: 0.2918 loss_rpn_cls: 0.0358 loss_rpn_loc: 0.1951 time: 0.3641 last_time: 0.3670 data_time: 0.0049 last_data_time: 0.0053 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:09:44 d2.utils.events]: \u001b[0m eta: 4:54:35 iter: 42239 total_loss: 0.8114 loss_cls: 0.2754 loss_box_reg: 0.2899 loss_rpn_cls: 0.05136 loss_rpn_loc: 0.205 time: 0.3641 last_time: 0.4485 data_time: 0.0049 last_data_time: 0.0052 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:09:53 d2.utils.events]: \u001b[0m eta: 4:54:45 iter: 42259 total_loss: 0.9006 loss_cls: 0.2982 loss_box_reg: 0.3129 loss_rpn_cls: 0.05768 loss_rpn_loc: 0.2212 time: 0.3641 last_time: 0.4400 data_time: 0.0049 last_data_time: 0.0052 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:10:01 d2.utils.events]: \u001b[0m eta: 4:54:37 iter: 42279 total_loss: 0.9702 loss_cls: 0.3256 loss_box_reg: 0.3295 loss_rpn_cls: 0.04335 loss_rpn_loc: 0.193 time: 0.3642 last_time: 0.3940 data_time: 0.0048 last_data_time: 0.0053 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:10:09 d2.utils.events]: \u001b[0m eta: 4:54:35 iter: 42299 total_loss: 0.8334 loss_cls: 0.2976 loss_box_reg: 0.3152 loss_rpn_cls: 0.04263 loss_rpn_loc: 0.1911 time: 0.3642 last_time: 0.4181 data_time: 0.0050 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:10:18 d2.utils.events]: \u001b[0m eta: 4:54:22 iter: 42319 total_loss: 0.9139 loss_cls: 0.2815 loss_box_reg: 0.3075 loss_rpn_cls: 0.04584 loss_rpn_loc: 0.1897 time: 0.3642 last_time: 0.4452 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:10:26 d2.utils.events]: \u001b[0m eta: 4:54:18 iter: 42339 total_loss: 0.8864 loss_cls: 0.2888 loss_box_reg: 0.3398 loss_rpn_cls: 0.05726 loss_rpn_loc: 0.2327 time: 0.3642 last_time: 0.4422 data_time: 0.0047 last_data_time: 0.0050 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:10:34 d2.utils.events]: \u001b[0m eta: 4:54:32 iter: 42359 total_loss: 0.8193 loss_cls: 0.2818 loss_box_reg: 0.278 loss_rpn_cls: 0.0492 loss_rpn_loc: 0.1991 time: 0.3642 last_time: 0.4612 data_time: 0.0048 last_data_time: 0.0053 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:10:43 d2.utils.events]: \u001b[0m eta: 4:54:31 iter: 42379 total_loss: 0.8636 loss_cls: 0.2788 loss_box_reg: 0.3406 loss_rpn_cls: 0.05025 loss_rpn_loc: 0.1935 time: 0.3643 last_time: 0.4073 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:10:51 d2.utils.events]: \u001b[0m eta: 4:54:37 iter: 42399 total_loss: 0.908 loss_cls: 0.2849 loss_box_reg: 0.2951 loss_rpn_cls: 0.04753 loss_rpn_loc: 0.2228 time: 0.3643 last_time: 0.4490 data_time: 0.0048 last_data_time: 0.0053 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:11:00 d2.utils.events]: \u001b[0m eta: 4:54:41 iter: 42419 total_loss: 0.7806 loss_cls: 0.2648 loss_box_reg: 0.2952 loss_rpn_cls: 0.04223 loss_rpn_loc: 0.1958 time: 0.3643 last_time: 0.4670 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:11:09 d2.utils.events]: \u001b[0m eta: 4:54:33 iter: 42439 total_loss: 0.8979 loss_cls: 0.2768 loss_box_reg: 0.3091 loss_rpn_cls: 0.06421 loss_rpn_loc: 0.1979 time: 0.3644 last_time: 0.4178 data_time: 0.0048 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:11:17 d2.utils.events]: \u001b[0m eta: 4:54:34 iter: 42459 total_loss: 0.7595 loss_cls: 0.222 loss_box_reg: 0.2919 loss_rpn_cls: 0.04121 loss_rpn_loc: 0.1964 time: 0.3644 last_time: 0.4838 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:11:26 d2.utils.events]: \u001b[0m eta: 4:54:26 iter: 42479 total_loss: 0.8538 loss_cls: 0.279 loss_box_reg: 0.3261 loss_rpn_cls: 0.04591 loss_rpn_loc: 0.2178 time: 0.3644 last_time: 0.3505 data_time: 0.0048 last_data_time: 0.0044 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:11:34 d2.utils.events]: \u001b[0m eta: 4:54:34 iter: 42499 total_loss: 0.8143 loss_cls: 0.2482 loss_box_reg: 0.302 loss_rpn_cls: 0.05006 loss_rpn_loc: 0.2043 time: 0.3644 last_time: 0.3952 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:11:42 d2.utils.events]: \u001b[0m eta: 4:54:24 iter: 42519 total_loss: 0.9254 loss_cls: 0.2969 loss_box_reg: 0.3111 loss_rpn_cls: 0.05083 loss_rpn_loc: 0.2004 time: 0.3645 last_time: 0.3232 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:11:50 d2.utils.events]: \u001b[0m eta: 4:54:13 iter: 42539 total_loss: 0.8203 loss_cls: 0.2742 loss_box_reg: 0.3141 loss_rpn_cls: 0.04192 loss_rpn_loc: 0.1867 time: 0.3645 last_time: 0.4398 data_time: 0.0046 last_data_time: 0.0042 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:11:58 d2.utils.events]: \u001b[0m eta: 4:54:09 iter: 42559 total_loss: 0.9498 loss_cls: 0.3219 loss_box_reg: 0.322 loss_rpn_cls: 0.04797 loss_rpn_loc: 0.2046 time: 0.3645 last_time: 0.4339 data_time: 0.0047 last_data_time: 0.0051 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:12:06 d2.utils.events]: \u001b[0m eta: 4:54:01 iter: 42579 total_loss: 0.853 loss_cls: 0.2865 loss_box_reg: 0.3125 loss_rpn_cls: 0.04869 loss_rpn_loc: 0.206 time: 0.3645 last_time: 0.3926 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:12:14 d2.utils.events]: \u001b[0m eta: 4:53:50 iter: 42599 total_loss: 0.774 loss_cls: 0.2349 loss_box_reg: 0.2913 loss_rpn_cls: 0.0414 loss_rpn_loc: 0.1882 time: 0.3645 last_time: 0.3757 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:12:23 d2.utils.events]: \u001b[0m eta: 4:53:32 iter: 42619 total_loss: 0.7448 loss_cls: 0.2367 loss_box_reg: 0.2821 loss_rpn_cls: 0.04049 loss_rpn_loc: 0.1556 time: 0.3646 last_time: 0.4384 data_time: 0.0045 last_data_time: 0.0047 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:12:31 d2.utils.events]: \u001b[0m eta: 4:53:23 iter: 42639 total_loss: 0.8426 loss_cls: 0.2534 loss_box_reg: 0.3042 loss_rpn_cls: 0.04924 loss_rpn_loc: 0.2062 time: 0.3646 last_time: 0.4375 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:12:38 d2.utils.events]: \u001b[0m eta: 4:53:13 iter: 42659 total_loss: 0.9044 loss_cls: 0.2598 loss_box_reg: 0.3167 loss_rpn_cls: 0.06587 loss_rpn_loc: 0.2419 time: 0.3646 last_time: 0.3403 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2643M\n","\u001b[32m[08/23 20:12:47 d2.utils.events]: \u001b[0m eta: 4:52:59 iter: 42679 total_loss: 0.8406 loss_cls: 0.2808 loss_box_reg: 0.3025 loss_rpn_cls: 0.05533 loss_rpn_loc: 0.215 time: 0.3646 last_time: 0.3647 data_time: 0.0045 last_data_time: 0.0042 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:12:55 d2.utils.events]: \u001b[0m eta: 4:52:54 iter: 42699 total_loss: 0.8618 loss_cls: 0.2968 loss_box_reg: 0.3127 loss_rpn_cls: 0.05311 loss_rpn_loc: 0.1968 time: 0.3646 last_time: 0.4083 data_time: 0.0046 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:13:03 d2.utils.events]: \u001b[0m eta: 4:52:48 iter: 42719 total_loss: 0.8063 loss_cls: 0.2569 loss_box_reg: 0.3055 loss_rpn_cls: 0.04874 loss_rpn_loc: 0.1887 time: 0.3646 last_time: 0.3743 data_time: 0.0046 last_data_time: 0.0053 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:13:11 d2.utils.events]: \u001b[0m eta: 4:52:50 iter: 42739 total_loss: 0.7748 loss_cls: 0.2449 loss_box_reg: 0.302 loss_rpn_cls: 0.04477 loss_rpn_loc: 0.2122 time: 0.3647 last_time: 0.4138 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:13:18 d2.utils.events]: \u001b[0m eta: 4:52:22 iter: 42759 total_loss: 0.9319 loss_cls: 0.2975 loss_box_reg: 0.3213 loss_rpn_cls: 0.05796 loss_rpn_loc: 0.2073 time: 0.3647 last_time: 0.3435 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:13:26 d2.utils.events]: \u001b[0m eta: 4:52:13 iter: 42779 total_loss: 0.8709 loss_cls: 0.2696 loss_box_reg: 0.3354 loss_rpn_cls: 0.04618 loss_rpn_loc: 0.2082 time: 0.3647 last_time: 0.3682 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:13:34 d2.utils.events]: \u001b[0m eta: 4:52:19 iter: 42799 total_loss: 0.9196 loss_cls: 0.3209 loss_box_reg: 0.32 loss_rpn_cls: 0.05234 loss_rpn_loc: 0.2254 time: 0.3647 last_time: 0.4442 data_time: 0.0047 last_data_time: 0.0042 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:13:43 d2.utils.events]: \u001b[0m eta: 4:52:04 iter: 42819 total_loss: 0.7887 loss_cls: 0.2174 loss_box_reg: 0.2776 loss_rpn_cls: 0.04997 loss_rpn_loc: 0.1985 time: 0.3647 last_time: 0.4122 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:13:51 d2.utils.events]: \u001b[0m eta: 4:51:53 iter: 42839 total_loss: 0.8751 loss_cls: 0.2659 loss_box_reg: 0.3322 loss_rpn_cls: 0.05032 loss_rpn_loc: 0.2074 time: 0.3647 last_time: 0.3370 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:13:59 d2.utils.events]: \u001b[0m eta: 4:51:41 iter: 42859 total_loss: 0.7885 loss_cls: 0.2456 loss_box_reg: 0.2899 loss_rpn_cls: 0.05099 loss_rpn_loc: 0.2057 time: 0.3648 last_time: 0.3150 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:14:07 d2.utils.events]: \u001b[0m eta: 4:51:31 iter: 42879 total_loss: 0.7493 loss_cls: 0.264 loss_box_reg: 0.329 loss_rpn_cls: 0.05051 loss_rpn_loc: 0.1478 time: 0.3648 last_time: 0.4002 data_time: 0.0046 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:14:15 d2.utils.events]: \u001b[0m eta: 4:51:18 iter: 42899 total_loss: 0.81 loss_cls: 0.2659 loss_box_reg: 0.2965 loss_rpn_cls: 0.0383 loss_rpn_loc: 0.2122 time: 0.3648 last_time: 0.3960 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:14:23 d2.utils.events]: \u001b[0m eta: 4:51:16 iter: 42919 total_loss: 0.8582 loss_cls: 0.3006 loss_box_reg: 0.355 loss_rpn_cls: 0.05206 loss_rpn_loc: 0.2113 time: 0.3648 last_time: 0.3917 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:14:31 d2.utils.events]: \u001b[0m eta: 4:50:58 iter: 42939 total_loss: 0.8656 loss_cls: 0.3012 loss_box_reg: 0.3307 loss_rpn_cls: 0.04928 loss_rpn_loc: 0.224 time: 0.3648 last_time: 0.4158 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:14:38 d2.utils.events]: \u001b[0m eta: 4:50:49 iter: 42959 total_loss: 0.8343 loss_cls: 0.2931 loss_box_reg: 0.297 loss_rpn_cls: 0.05082 loss_rpn_loc: 0.1711 time: 0.3648 last_time: 0.4245 data_time: 0.0046 last_data_time: 0.0042 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:14:46 d2.utils.events]: \u001b[0m eta: 4:50:40 iter: 42979 total_loss: 0.8591 loss_cls: 0.3082 loss_box_reg: 0.3084 loss_rpn_cls: 0.04682 loss_rpn_loc: 0.2264 time: 0.3649 last_time: 0.4124 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:14:54 d2.utils.events]: \u001b[0m eta: 4:50:24 iter: 42999 total_loss: 0.8434 loss_cls: 0.279 loss_box_reg: 0.2815 loss_rpn_cls: 0.05472 loss_rpn_loc: 0.2142 time: 0.3649 last_time: 0.3716 data_time: 0.0046 last_data_time: 0.0042 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:15:02 d2.utils.events]: \u001b[0m eta: 4:50:16 iter: 43019 total_loss: 0.8919 loss_cls: 0.3169 loss_box_reg: 0.3093 loss_rpn_cls: 0.05102 loss_rpn_loc: 0.213 time: 0.3649 last_time: 0.4430 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:15:11 d2.utils.events]: \u001b[0m eta: 4:50:05 iter: 43039 total_loss: 0.8332 loss_cls: 0.281 loss_box_reg: 0.299 loss_rpn_cls: 0.05002 loss_rpn_loc: 0.1966 time: 0.3649 last_time: 0.4223 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:15:19 d2.utils.events]: \u001b[0m eta: 4:49:49 iter: 43059 total_loss: 0.8697 loss_cls: 0.2938 loss_box_reg: 0.308 loss_rpn_cls: 0.05241 loss_rpn_loc: 0.2209 time: 0.3649 last_time: 0.3402 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:15:27 d2.utils.events]: \u001b[0m eta: 4:49:38 iter: 43079 total_loss: 0.7837 loss_cls: 0.2508 loss_box_reg: 0.2882 loss_rpn_cls: 0.04044 loss_rpn_loc: 0.1922 time: 0.3649 last_time: 0.3847 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:15:35 d2.utils.events]: \u001b[0m eta: 4:49:24 iter: 43099 total_loss: 0.9423 loss_cls: 0.3315 loss_box_reg: 0.3246 loss_rpn_cls: 0.05914 loss_rpn_loc: 0.2324 time: 0.3650 last_time: 0.3194 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:15:43 d2.utils.events]: \u001b[0m eta: 4:49:16 iter: 43119 total_loss: 0.9097 loss_cls: 0.325 loss_box_reg: 0.3202 loss_rpn_cls: 0.05558 loss_rpn_loc: 0.2225 time: 0.3650 last_time: 0.4444 data_time: 0.0047 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:15:51 d2.utils.events]: \u001b[0m eta: 4:48:48 iter: 43139 total_loss: 0.8469 loss_cls: 0.2743 loss_box_reg: 0.3001 loss_rpn_cls: 0.05013 loss_rpn_loc: 0.2053 time: 0.3650 last_time: 0.3907 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:15:59 d2.utils.events]: \u001b[0m eta: 4:48:34 iter: 43159 total_loss: 0.8497 loss_cls: 0.2797 loss_box_reg: 0.3293 loss_rpn_cls: 0.05881 loss_rpn_loc: 0.1949 time: 0.3650 last_time: 0.3918 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:16:07 d2.utils.events]: \u001b[0m eta: 4:48:16 iter: 43179 total_loss: 0.8559 loss_cls: 0.2781 loss_box_reg: 0.3286 loss_rpn_cls: 0.04766 loss_rpn_loc: 0.1966 time: 0.3650 last_time: 0.3951 data_time: 0.0045 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:16:15 d2.utils.events]: \u001b[0m eta: 4:47:50 iter: 43199 total_loss: 0.9683 loss_cls: 0.259 loss_box_reg: 0.3177 loss_rpn_cls: 0.05511 loss_rpn_loc: 0.2342 time: 0.3650 last_time: 0.3784 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:16:23 d2.utils.events]: \u001b[0m eta: 4:47:49 iter: 43219 total_loss: 0.8191 loss_cls: 0.276 loss_box_reg: 0.2907 loss_rpn_cls: 0.04656 loss_rpn_loc: 0.1803 time: 0.3651 last_time: 0.3730 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:16:31 d2.utils.events]: \u001b[0m eta: 4:47:38 iter: 43239 total_loss: 0.7686 loss_cls: 0.253 loss_box_reg: 0.2892 loss_rpn_cls: 0.05497 loss_rpn_loc: 0.1884 time: 0.3651 last_time: 0.4394 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:16:39 d2.utils.events]: \u001b[0m eta: 4:47:12 iter: 43259 total_loss: 0.9815 loss_cls: 0.3185 loss_box_reg: 0.3313 loss_rpn_cls: 0.0439 loss_rpn_loc: 0.2219 time: 0.3651 last_time: 0.4053 data_time: 0.0046 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:16:47 d2.utils.events]: \u001b[0m eta: 4:46:57 iter: 43279 total_loss: 0.778 loss_cls: 0.2295 loss_box_reg: 0.2906 loss_rpn_cls: 0.05067 loss_rpn_loc: 0.1926 time: 0.3651 last_time: 0.4165 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:16:55 d2.utils.events]: \u001b[0m eta: 4:46:43 iter: 43299 total_loss: 0.8338 loss_cls: 0.2652 loss_box_reg: 0.3049 loss_rpn_cls: 0.04509 loss_rpn_loc: 0.1954 time: 0.3651 last_time: 0.4523 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:17:03 d2.utils.events]: \u001b[0m eta: 4:46:22 iter: 43319 total_loss: 0.7828 loss_cls: 0.2261 loss_box_reg: 0.2987 loss_rpn_cls: 0.04601 loss_rpn_loc: 0.1915 time: 0.3651 last_time: 0.4488 data_time: 0.0047 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:17:11 d2.utils.events]: \u001b[0m eta: 4:46:01 iter: 43339 total_loss: 0.7987 loss_cls: 0.2778 loss_box_reg: 0.3089 loss_rpn_cls: 0.05358 loss_rpn_loc: 0.1749 time: 0.3652 last_time: 0.4300 data_time: 0.0048 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:17:19 d2.utils.events]: \u001b[0m eta: 4:45:46 iter: 43359 total_loss: 0.8424 loss_cls: 0.2949 loss_box_reg: 0.3196 loss_rpn_cls: 0.0475 loss_rpn_loc: 0.2136 time: 0.3652 last_time: 0.4352 data_time: 0.0047 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:17:27 d2.utils.events]: \u001b[0m eta: 4:44:56 iter: 43379 total_loss: 0.878 loss_cls: 0.325 loss_box_reg: 0.3188 loss_rpn_cls: 0.05054 loss_rpn_loc: 0.2168 time: 0.3652 last_time: 0.3671 data_time: 0.0045 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:17:35 d2.utils.events]: \u001b[0m eta: 4:44:12 iter: 43399 total_loss: 0.8673 loss_cls: 0.2915 loss_box_reg: 0.3202 loss_rpn_cls: 0.03892 loss_rpn_loc: 0.2008 time: 0.3652 last_time: 0.4459 data_time: 0.0047 last_data_time: 0.0057 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:17:43 d2.utils.events]: \u001b[0m eta: 4:43:55 iter: 43419 total_loss: 0.9117 loss_cls: 0.2648 loss_box_reg: 0.3019 loss_rpn_cls: 0.04925 loss_rpn_loc: 0.2547 time: 0.3652 last_time: 0.4187 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:17:52 d2.utils.events]: \u001b[0m eta: 4:43:56 iter: 43439 total_loss: 0.8485 loss_cls: 0.282 loss_box_reg: 0.3352 loss_rpn_cls: 0.05165 loss_rpn_loc: 0.2181 time: 0.3652 last_time: 0.3939 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:18:00 d2.utils.events]: \u001b[0m eta: 4:43:44 iter: 43459 total_loss: 0.7916 loss_cls: 0.2748 loss_box_reg: 0.302 loss_rpn_cls: 0.04192 loss_rpn_loc: 0.2094 time: 0.3653 last_time: 0.4400 data_time: 0.0047 last_data_time: 0.0042 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:18:08 d2.utils.events]: \u001b[0m eta: 4:43:07 iter: 43479 total_loss: 0.8779 loss_cls: 0.3143 loss_box_reg: 0.3246 loss_rpn_cls: 0.05991 loss_rpn_loc: 0.1738 time: 0.3653 last_time: 0.4068 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:18:16 d2.utils.events]: \u001b[0m eta: 4:42:17 iter: 43499 total_loss: 0.9191 loss_cls: 0.2873 loss_box_reg: 0.314 loss_rpn_cls: 0.06056 loss_rpn_loc: 0.1961 time: 0.3653 last_time: 0.4411 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:18:24 d2.utils.events]: \u001b[0m eta: 4:42:27 iter: 43519 total_loss: 0.8116 loss_cls: 0.2836 loss_box_reg: 0.2899 loss_rpn_cls: 0.0437 loss_rpn_loc: 0.2084 time: 0.3653 last_time: 0.3071 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:18:32 d2.utils.events]: \u001b[0m eta: 4:42:13 iter: 43539 total_loss: 0.9119 loss_cls: 0.276 loss_box_reg: 0.3248 loss_rpn_cls: 0.05742 loss_rpn_loc: 0.2343 time: 0.3653 last_time: 0.3366 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:18:40 d2.utils.events]: \u001b[0m eta: 4:41:09 iter: 43559 total_loss: 0.8811 loss_cls: 0.2663 loss_box_reg: 0.3239 loss_rpn_cls: 0.05148 loss_rpn_loc: 0.1998 time: 0.3653 last_time: 0.4063 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:18:48 d2.utils.events]: \u001b[0m eta: 4:40:33 iter: 43579 total_loss: 0.8851 loss_cls: 0.2409 loss_box_reg: 0.3032 loss_rpn_cls: 0.05239 loss_rpn_loc: 0.1758 time: 0.3654 last_time: 0.3918 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:18:56 d2.utils.events]: \u001b[0m eta: 4:40:21 iter: 43599 total_loss: 0.8609 loss_cls: 0.2364 loss_box_reg: 0.3454 loss_rpn_cls: 0.0468 loss_rpn_loc: 0.2187 time: 0.3654 last_time: 0.4460 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:19:04 d2.utils.events]: \u001b[0m eta: 4:39:57 iter: 43619 total_loss: 0.7559 loss_cls: 0.2351 loss_box_reg: 0.2584 loss_rpn_cls: 0.04228 loss_rpn_loc: 0.19 time: 0.3654 last_time: 0.3918 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:19:12 d2.utils.events]: \u001b[0m eta: 4:40:26 iter: 43639 total_loss: 0.7359 loss_cls: 0.25 loss_box_reg: 0.3176 loss_rpn_cls: 0.04449 loss_rpn_loc: 0.2051 time: 0.3654 last_time: 0.3914 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:19:20 d2.utils.events]: \u001b[0m eta: 4:40:28 iter: 43659 total_loss: 0.7816 loss_cls: 0.2304 loss_box_reg: 0.2677 loss_rpn_cls: 0.03628 loss_rpn_loc: 0.1573 time: 0.3654 last_time: 0.3411 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:19:28 d2.utils.events]: \u001b[0m eta: 4:40:30 iter: 43679 total_loss: 0.6895 loss_cls: 0.2483 loss_box_reg: 0.3114 loss_rpn_cls: 0.03579 loss_rpn_loc: 0.1861 time: 0.3654 last_time: 0.3718 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:19:36 d2.utils.events]: \u001b[0m eta: 4:40:12 iter: 43699 total_loss: 0.9023 loss_cls: 0.2799 loss_box_reg: 0.3184 loss_rpn_cls: 0.05181 loss_rpn_loc: 0.2051 time: 0.3655 last_time: 0.3401 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:19:44 d2.utils.events]: \u001b[0m eta: 4:40:10 iter: 43719 total_loss: 0.6689 loss_cls: 0.2257 loss_box_reg: 0.266 loss_rpn_cls: 0.04486 loss_rpn_loc: 0.1791 time: 0.3655 last_time: 0.4141 data_time: 0.0047 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:19:52 d2.utils.events]: \u001b[0m eta: 4:39:58 iter: 43739 total_loss: 0.8072 loss_cls: 0.2682 loss_box_reg: 0.3327 loss_rpn_cls: 0.04176 loss_rpn_loc: 0.2003 time: 0.3655 last_time: 0.3433 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:20:00 d2.utils.events]: \u001b[0m eta: 4:40:17 iter: 43759 total_loss: 0.7847 loss_cls: 0.2598 loss_box_reg: 0.2632 loss_rpn_cls: 0.04983 loss_rpn_loc: 0.1934 time: 0.3655 last_time: 0.3785 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:20:08 d2.utils.events]: \u001b[0m eta: 4:39:50 iter: 43779 total_loss: 0.9571 loss_cls: 0.3259 loss_box_reg: 0.3288 loss_rpn_cls: 0.06417 loss_rpn_loc: 0.1982 time: 0.3655 last_time: 0.3865 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:20:17 d2.utils.events]: \u001b[0m eta: 4:39:31 iter: 43799 total_loss: 0.8844 loss_cls: 0.2945 loss_box_reg: 0.3135 loss_rpn_cls: 0.04802 loss_rpn_loc: 0.2022 time: 0.3655 last_time: 0.4014 data_time: 0.0045 last_data_time: 0.0042 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:20:25 d2.utils.events]: \u001b[0m eta: 4:39:30 iter: 43819 total_loss: 0.7971 loss_cls: 0.2558 loss_box_reg: 0.2755 loss_rpn_cls: 0.05812 loss_rpn_loc: 0.2021 time: 0.3656 last_time: 0.4419 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:20:33 d2.utils.events]: \u001b[0m eta: 4:39:05 iter: 43839 total_loss: 0.8749 loss_cls: 0.2483 loss_box_reg: 0.3193 loss_rpn_cls: 0.04327 loss_rpn_loc: 0.2039 time: 0.3656 last_time: 0.4395 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:20:41 d2.utils.events]: \u001b[0m eta: 4:39:00 iter: 43859 total_loss: 0.7876 loss_cls: 0.2362 loss_box_reg: 0.3024 loss_rpn_cls: 0.04532 loss_rpn_loc: 0.1755 time: 0.3656 last_time: 0.4155 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:20:49 d2.utils.events]: \u001b[0m eta: 4:39:05 iter: 43879 total_loss: 0.8689 loss_cls: 0.2525 loss_box_reg: 0.3018 loss_rpn_cls: 0.05244 loss_rpn_loc: 0.222 time: 0.3656 last_time: 0.4198 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:20:57 d2.utils.events]: \u001b[0m eta: 4:39:01 iter: 43899 total_loss: 0.8897 loss_cls: 0.2902 loss_box_reg: 0.3081 loss_rpn_cls: 0.04956 loss_rpn_loc: 0.2375 time: 0.3656 last_time: 0.3460 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:21:05 d2.utils.events]: \u001b[0m eta: 4:38:42 iter: 43919 total_loss: 0.7979 loss_cls: 0.2664 loss_box_reg: 0.2906 loss_rpn_cls: 0.03587 loss_rpn_loc: 0.1771 time: 0.3656 last_time: 0.3941 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:21:13 d2.utils.events]: \u001b[0m eta: 4:38:32 iter: 43939 total_loss: 0.8096 loss_cls: 0.2464 loss_box_reg: 0.307 loss_rpn_cls: 0.05351 loss_rpn_loc: 0.1995 time: 0.3657 last_time: 0.4079 data_time: 0.0046 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:21:21 d2.utils.events]: \u001b[0m eta: 4:38:24 iter: 43959 total_loss: 0.9103 loss_cls: 0.2811 loss_box_reg: 0.3509 loss_rpn_cls: 0.04713 loss_rpn_loc: 0.1971 time: 0.3657 last_time: 0.3409 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:21:29 d2.utils.events]: \u001b[0m eta: 4:38:16 iter: 43979 total_loss: 0.7919 loss_cls: 0.2788 loss_box_reg: 0.2766 loss_rpn_cls: 0.04339 loss_rpn_loc: 0.2043 time: 0.3657 last_time: 0.4456 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:21:37 d2.utils.events]: \u001b[0m eta: 4:38:08 iter: 43999 total_loss: 0.8174 loss_cls: 0.2619 loss_box_reg: 0.3032 loss_rpn_cls: 0.04872 loss_rpn_loc: 0.1929 time: 0.3657 last_time: 0.3741 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:21:45 d2.utils.events]: \u001b[0m eta: 4:38:02 iter: 44019 total_loss: 0.886 loss_cls: 0.2765 loss_box_reg: 0.3096 loss_rpn_cls: 0.04987 loss_rpn_loc: 0.2202 time: 0.3657 last_time: 0.4137 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:21:53 d2.utils.events]: \u001b[0m eta: 4:37:56 iter: 44039 total_loss: 0.7259 loss_cls: 0.2353 loss_box_reg: 0.2599 loss_rpn_cls: 0.04321 loss_rpn_loc: 0.1676 time: 0.3657 last_time: 0.4412 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:22:01 d2.utils.events]: \u001b[0m eta: 4:37:45 iter: 44059 total_loss: 0.8206 loss_cls: 0.2358 loss_box_reg: 0.2732 loss_rpn_cls: 0.04118 loss_rpn_loc: 0.2143 time: 0.3658 last_time: 0.3707 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:22:09 d2.utils.events]: \u001b[0m eta: 4:36:55 iter: 44079 total_loss: 0.7528 loss_cls: 0.2731 loss_box_reg: 0.2599 loss_rpn_cls: 0.06639 loss_rpn_loc: 0.2056 time: 0.3658 last_time: 0.3714 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:22:17 d2.utils.events]: \u001b[0m eta: 4:37:27 iter: 44099 total_loss: 0.8088 loss_cls: 0.269 loss_box_reg: 0.3003 loss_rpn_cls: 0.0466 loss_rpn_loc: 0.1979 time: 0.3658 last_time: 0.4077 data_time: 0.0046 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:22:25 d2.utils.events]: \u001b[0m eta: 4:37:08 iter: 44119 total_loss: 0.8158 loss_cls: 0.273 loss_box_reg: 0.3145 loss_rpn_cls: 0.04685 loss_rpn_loc: 0.1756 time: 0.3658 last_time: 0.3928 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:22:33 d2.utils.events]: \u001b[0m eta: 4:37:18 iter: 44139 total_loss: 0.82 loss_cls: 0.2597 loss_box_reg: 0.3066 loss_rpn_cls: 0.06091 loss_rpn_loc: 0.2054 time: 0.3658 last_time: 0.4431 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:22:41 d2.utils.events]: \u001b[0m eta: 4:37:15 iter: 44159 total_loss: 0.8498 loss_cls: 0.2436 loss_box_reg: 0.3121 loss_rpn_cls: 0.05362 loss_rpn_loc: 0.2042 time: 0.3658 last_time: 0.3668 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:22:50 d2.utils.events]: \u001b[0m eta: 4:37:07 iter: 44179 total_loss: 0.8371 loss_cls: 0.2856 loss_box_reg: 0.281 loss_rpn_cls: 0.04122 loss_rpn_loc: 0.2106 time: 0.3659 last_time: 0.3962 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:22:58 d2.utils.events]: \u001b[0m eta: 4:37:17 iter: 44199 total_loss: 0.7654 loss_cls: 0.2415 loss_box_reg: 0.2969 loss_rpn_cls: 0.0532 loss_rpn_loc: 0.1895 time: 0.3659 last_time: 0.4505 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:23:06 d2.utils.events]: \u001b[0m eta: 4:37:02 iter: 44219 total_loss: 0.9503 loss_cls: 0.3613 loss_box_reg: 0.3155 loss_rpn_cls: 0.05923 loss_rpn_loc: 0.2057 time: 0.3659 last_time: 0.4452 data_time: 0.0046 last_data_time: 0.0041 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:23:14 d2.utils.events]: \u001b[0m eta: 4:36:48 iter: 44239 total_loss: 0.847 loss_cls: 0.2746 loss_box_reg: 0.3009 loss_rpn_cls: 0.05127 loss_rpn_loc: 0.2074 time: 0.3659 last_time: 0.4227 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:23:22 d2.utils.events]: \u001b[0m eta: 4:36:39 iter: 44259 total_loss: 0.7808 loss_cls: 0.2734 loss_box_reg: 0.319 loss_rpn_cls: 0.05855 loss_rpn_loc: 0.1937 time: 0.3659 last_time: 0.3880 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:23:30 d2.utils.events]: \u001b[0m eta: 4:36:44 iter: 44279 total_loss: 0.7507 loss_cls: 0.2528 loss_box_reg: 0.3033 loss_rpn_cls: 0.03939 loss_rpn_loc: 0.1891 time: 0.3660 last_time: 0.4360 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:23:38 d2.utils.events]: \u001b[0m eta: 4:36:30 iter: 44299 total_loss: 0.8802 loss_cls: 0.2856 loss_box_reg: 0.3409 loss_rpn_cls: 0.06516 loss_rpn_loc: 0.1713 time: 0.3660 last_time: 0.4126 data_time: 0.0044 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:23:46 d2.utils.events]: \u001b[0m eta: 4:36:28 iter: 44319 total_loss: 0.8398 loss_cls: 0.2589 loss_box_reg: 0.3305 loss_rpn_cls: 0.05338 loss_rpn_loc: 0.216 time: 0.3660 last_time: 0.3733 data_time: 0.0045 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:23:54 d2.utils.events]: \u001b[0m eta: 4:36:25 iter: 44339 total_loss: 1.038 loss_cls: 0.3339 loss_box_reg: 0.3557 loss_rpn_cls: 0.05091 loss_rpn_loc: 0.1879 time: 0.3660 last_time: 0.3757 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:24:02 d2.utils.events]: \u001b[0m eta: 4:36:06 iter: 44359 total_loss: 0.9229 loss_cls: 0.2892 loss_box_reg: 0.3008 loss_rpn_cls: 0.04939 loss_rpn_loc: 0.2456 time: 0.3660 last_time: 0.3947 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:24:10 d2.utils.events]: \u001b[0m eta: 4:36:11 iter: 44379 total_loss: 0.848 loss_cls: 0.2739 loss_box_reg: 0.2853 loss_rpn_cls: 0.05831 loss_rpn_loc: 0.1897 time: 0.3660 last_time: 0.3696 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:24:18 d2.utils.events]: \u001b[0m eta: 4:36:08 iter: 44399 total_loss: 0.8565 loss_cls: 0.2871 loss_box_reg: 0.3007 loss_rpn_cls: 0.04178 loss_rpn_loc: 0.1669 time: 0.3660 last_time: 0.4357 data_time: 0.0046 last_data_time: 0.0042 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:24:27 d2.utils.events]: \u001b[0m eta: 4:36:00 iter: 44419 total_loss: 0.847 loss_cls: 0.2829 loss_box_reg: 0.3056 loss_rpn_cls: 0.05514 loss_rpn_loc: 0.2242 time: 0.3661 last_time: 0.4424 data_time: 0.0045 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:24:35 d2.utils.events]: \u001b[0m eta: 4:35:33 iter: 44439 total_loss: 0.8362 loss_cls: 0.2661 loss_box_reg: 0.3106 loss_rpn_cls: 0.05178 loss_rpn_loc: 0.221 time: 0.3661 last_time: 0.4190 data_time: 0.0045 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:24:43 d2.utils.events]: \u001b[0m eta: 4:35:25 iter: 44459 total_loss: 0.9637 loss_cls: 0.3073 loss_box_reg: 0.3308 loss_rpn_cls: 0.06716 loss_rpn_loc: 0.2328 time: 0.3661 last_time: 0.4028 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:24:51 d2.utils.events]: \u001b[0m eta: 4:35:30 iter: 44479 total_loss: 0.9661 loss_cls: 0.3157 loss_box_reg: 0.3694 loss_rpn_cls: 0.05401 loss_rpn_loc: 0.2069 time: 0.3661 last_time: 0.4345 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:24:59 d2.utils.events]: \u001b[0m eta: 4:35:22 iter: 44499 total_loss: 0.8264 loss_cls: 0.2567 loss_box_reg: 0.288 loss_rpn_cls: 0.05287 loss_rpn_loc: 0.2123 time: 0.3661 last_time: 0.3924 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:25:07 d2.utils.events]: \u001b[0m eta: 4:35:14 iter: 44519 total_loss: 0.7485 loss_cls: 0.2299 loss_box_reg: 0.271 loss_rpn_cls: 0.04428 loss_rpn_loc: 0.2002 time: 0.3662 last_time: 0.4221 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:25:15 d2.utils.events]: \u001b[0m eta: 4:35:14 iter: 44539 total_loss: 0.9361 loss_cls: 0.3107 loss_box_reg: 0.3394 loss_rpn_cls: 0.0649 loss_rpn_loc: 0.2222 time: 0.3662 last_time: 0.3638 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:25:23 d2.utils.events]: \u001b[0m eta: 4:35:06 iter: 44559 total_loss: 0.877 loss_cls: 0.275 loss_box_reg: 0.3154 loss_rpn_cls: 0.04793 loss_rpn_loc: 0.233 time: 0.3662 last_time: 0.3964 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:25:31 d2.utils.events]: \u001b[0m eta: 4:35:02 iter: 44579 total_loss: 0.9449 loss_cls: 0.329 loss_box_reg: 0.3148 loss_rpn_cls: 0.05734 loss_rpn_loc: 0.2187 time: 0.3662 last_time: 0.4407 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:25:39 d2.utils.events]: \u001b[0m eta: 4:35:10 iter: 44599 total_loss: 0.8051 loss_cls: 0.2638 loss_box_reg: 0.3152 loss_rpn_cls: 0.04981 loss_rpn_loc: 0.2013 time: 0.3662 last_time: 0.3908 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:25:47 d2.utils.events]: \u001b[0m eta: 4:35:00 iter: 44619 total_loss: 0.8591 loss_cls: 0.2657 loss_box_reg: 0.3153 loss_rpn_cls: 0.04138 loss_rpn_loc: 0.1742 time: 0.3662 last_time: 0.3145 data_time: 0.0046 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:25:55 d2.utils.events]: \u001b[0m eta: 4:34:41 iter: 44639 total_loss: 0.8389 loss_cls: 0.252 loss_box_reg: 0.3044 loss_rpn_cls: 0.05632 loss_rpn_loc: 0.2081 time: 0.3662 last_time: 0.4335 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:26:03 d2.utils.events]: \u001b[0m eta: 4:34:33 iter: 44659 total_loss: 0.8625 loss_cls: 0.2961 loss_box_reg: 0.2944 loss_rpn_cls: 0.05527 loss_rpn_loc: 0.2103 time: 0.3663 last_time: 0.4153 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:26:11 d2.utils.events]: \u001b[0m eta: 4:34:21 iter: 44679 total_loss: 0.7504 loss_cls: 0.2694 loss_box_reg: 0.2872 loss_rpn_cls: 0.03806 loss_rpn_loc: 0.1756 time: 0.3663 last_time: 0.3683 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:26:20 d2.utils.events]: \u001b[0m eta: 4:34:13 iter: 44699 total_loss: 0.8595 loss_cls: 0.2639 loss_box_reg: 0.304 loss_rpn_cls: 0.0436 loss_rpn_loc: 0.2107 time: 0.3663 last_time: 0.3699 data_time: 0.0045 last_data_time: 0.0041 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:26:28 d2.utils.events]: \u001b[0m eta: 4:34:09 iter: 44719 total_loss: 0.8375 loss_cls: 0.246 loss_box_reg: 0.3026 loss_rpn_cls: 0.04717 loss_rpn_loc: 0.2026 time: 0.3663 last_time: 0.4045 data_time: 0.0045 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:26:36 d2.utils.events]: \u001b[0m eta: 4:34:07 iter: 44739 total_loss: 0.8394 loss_cls: 0.3091 loss_box_reg: 0.3003 loss_rpn_cls: 0.04715 loss_rpn_loc: 0.2001 time: 0.3663 last_time: 0.4355 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:26:44 d2.utils.events]: \u001b[0m eta: 4:34:17 iter: 44759 total_loss: 0.921 loss_cls: 0.3206 loss_box_reg: 0.3108 loss_rpn_cls: 0.06375 loss_rpn_loc: 0.2126 time: 0.3664 last_time: 0.4203 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:26:52 d2.utils.events]: \u001b[0m eta: 4:34:32 iter: 44779 total_loss: 0.8823 loss_cls: 0.332 loss_box_reg: 0.3143 loss_rpn_cls: 0.05133 loss_rpn_loc: 0.2189 time: 0.3664 last_time: 0.4399 data_time: 0.0046 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:27:00 d2.utils.events]: \u001b[0m eta: 4:34:11 iter: 44799 total_loss: 0.9124 loss_cls: 0.2591 loss_box_reg: 0.3288 loss_rpn_cls: 0.0486 loss_rpn_loc: 0.1953 time: 0.3664 last_time: 0.3883 data_time: 0.0045 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:27:08 d2.utils.events]: \u001b[0m eta: 4:33:42 iter: 44819 total_loss: 0.8922 loss_cls: 0.2654 loss_box_reg: 0.3046 loss_rpn_cls: 0.05403 loss_rpn_loc: 0.2083 time: 0.3664 last_time: 0.3146 data_time: 0.0045 last_data_time: 0.0041 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:27:16 d2.utils.events]: \u001b[0m eta: 4:34:08 iter: 44839 total_loss: 0.8895 loss_cls: 0.3004 loss_box_reg: 0.3061 loss_rpn_cls: 0.05939 loss_rpn_loc: 0.1859 time: 0.3664 last_time: 0.3958 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:27:24 d2.utils.events]: \u001b[0m eta: 4:33:59 iter: 44859 total_loss: 0.7966 loss_cls: 0.2709 loss_box_reg: 0.2925 loss_rpn_cls: 0.04398 loss_rpn_loc: 0.1676 time: 0.3664 last_time: 0.3630 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:27:32 d2.utils.events]: \u001b[0m eta: 4:33:15 iter: 44879 total_loss: 0.8402 loss_cls: 0.2746 loss_box_reg: 0.3132 loss_rpn_cls: 0.04261 loss_rpn_loc: 0.2146 time: 0.3664 last_time: 0.3639 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:27:40 d2.utils.events]: \u001b[0m eta: 4:33:07 iter: 44899 total_loss: 0.9068 loss_cls: 0.2829 loss_box_reg: 0.3512 loss_rpn_cls: 0.0528 loss_rpn_loc: 0.2189 time: 0.3665 last_time: 0.4196 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:27:48 d2.utils.events]: \u001b[0m eta: 4:32:53 iter: 44919 total_loss: 0.9786 loss_cls: 0.3189 loss_box_reg: 0.2999 loss_rpn_cls: 0.05397 loss_rpn_loc: 0.2047 time: 0.3665 last_time: 0.3144 data_time: 0.0044 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:27:56 d2.utils.events]: \u001b[0m eta: 4:33:13 iter: 44939 total_loss: 0.8718 loss_cls: 0.3086 loss_box_reg: 0.285 loss_rpn_cls: 0.04981 loss_rpn_loc: 0.1698 time: 0.3665 last_time: 0.4196 data_time: 0.0044 last_data_time: 0.0042 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:28:04 d2.utils.events]: \u001b[0m eta: 4:32:59 iter: 44959 total_loss: 0.717 loss_cls: 0.2313 loss_box_reg: 0.2648 loss_rpn_cls: 0.04076 loss_rpn_loc: 0.173 time: 0.3665 last_time: 0.4090 data_time: 0.0044 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:28:12 d2.utils.events]: \u001b[0m eta: 4:32:41 iter: 44979 total_loss: 0.8123 loss_cls: 0.2479 loss_box_reg: 0.2997 loss_rpn_cls: 0.0461 loss_rpn_loc: 0.1878 time: 0.3665 last_time: 0.4295 data_time: 0.0044 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:28:21 d2.utils.events]: \u001b[0m eta: 4:33:01 iter: 44999 total_loss: 0.9038 loss_cls: 0.3108 loss_box_reg: 0.3507 loss_rpn_cls: 0.04626 loss_rpn_loc: 0.1783 time: 0.3665 last_time: 0.4126 data_time: 0.0044 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:28:29 d2.utils.events]: \u001b[0m eta: 4:32:41 iter: 45019 total_loss: 0.9283 loss_cls: 0.3196 loss_box_reg: 0.3451 loss_rpn_cls: 0.05795 loss_rpn_loc: 0.1918 time: 0.3666 last_time: 0.3719 data_time: 0.0044 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:28:37 d2.utils.events]: \u001b[0m eta: 4:31:58 iter: 45039 total_loss: 0.7864 loss_cls: 0.2857 loss_box_reg: 0.2745 loss_rpn_cls: 0.05016 loss_rpn_loc: 0.2088 time: 0.3666 last_time: 0.4183 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:28:45 d2.utils.events]: \u001b[0m eta: 4:32:06 iter: 45059 total_loss: 0.8266 loss_cls: 0.2729 loss_box_reg: 0.3086 loss_rpn_cls: 0.04842 loss_rpn_loc: 0.2021 time: 0.3666 last_time: 0.4158 data_time: 0.0044 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:28:53 d2.utils.events]: \u001b[0m eta: 4:32:10 iter: 45079 total_loss: 0.9437 loss_cls: 0.2717 loss_box_reg: 0.3309 loss_rpn_cls: 0.05555 loss_rpn_loc: 0.2126 time: 0.3666 last_time: 0.3946 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:29:01 d2.utils.events]: \u001b[0m eta: 4:32:08 iter: 45099 total_loss: 0.9256 loss_cls: 0.3108 loss_box_reg: 0.3039 loss_rpn_cls: 0.0508 loss_rpn_loc: 0.2349 time: 0.3666 last_time: 0.3953 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:29:09 d2.utils.events]: \u001b[0m eta: 4:32:19 iter: 45119 total_loss: 0.8302 loss_cls: 0.3299 loss_box_reg: 0.2979 loss_rpn_cls: 0.03651 loss_rpn_loc: 0.1952 time: 0.3666 last_time: 0.4195 data_time: 0.0045 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:29:18 d2.utils.events]: \u001b[0m eta: 4:32:21 iter: 45139 total_loss: 0.784 loss_cls: 0.2512 loss_box_reg: 0.289 loss_rpn_cls: 0.04147 loss_rpn_loc: 0.1845 time: 0.3667 last_time: 0.3633 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:29:26 d2.utils.events]: \u001b[0m eta: 4:32:13 iter: 45159 total_loss: 0.9187 loss_cls: 0.309 loss_box_reg: 0.3088 loss_rpn_cls: 0.04921 loss_rpn_loc: 0.1944 time: 0.3667 last_time: 0.4103 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:29:34 d2.utils.events]: \u001b[0m eta: 4:31:51 iter: 45179 total_loss: 0.8782 loss_cls: 0.3012 loss_box_reg: 0.3327 loss_rpn_cls: 0.05945 loss_rpn_loc: 0.202 time: 0.3667 last_time: 0.3945 data_time: 0.0046 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:29:41 d2.utils.events]: \u001b[0m eta: 4:31:09 iter: 45199 total_loss: 0.8844 loss_cls: 0.3002 loss_box_reg: 0.2925 loss_rpn_cls: 0.05568 loss_rpn_loc: 0.1994 time: 0.3667 last_time: 0.4366 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:29:49 d2.utils.events]: \u001b[0m eta: 4:31:01 iter: 45219 total_loss: 0.863 loss_cls: 0.3161 loss_box_reg: 0.2985 loss_rpn_cls: 0.04349 loss_rpn_loc: 0.2039 time: 0.3667 last_time: 0.4189 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:29:57 d2.utils.events]: \u001b[0m eta: 4:30:53 iter: 45239 total_loss: 0.8135 loss_cls: 0.2726 loss_box_reg: 0.3102 loss_rpn_cls: 0.0416 loss_rpn_loc: 0.1964 time: 0.3667 last_time: 0.4048 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:30:06 d2.utils.events]: \u001b[0m eta: 4:30:45 iter: 45259 total_loss: 0.7159 loss_cls: 0.2619 loss_box_reg: 0.2902 loss_rpn_cls: 0.04011 loss_rpn_loc: 0.1585 time: 0.3667 last_time: 0.4348 data_time: 0.0047 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:30:13 d2.utils.events]: \u001b[0m eta: 4:30:22 iter: 45279 total_loss: 0.861 loss_cls: 0.2955 loss_box_reg: 0.3221 loss_rpn_cls: 0.04606 loss_rpn_loc: 0.2059 time: 0.3668 last_time: 0.3939 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:30:21 d2.utils.events]: \u001b[0m eta: 4:30:58 iter: 45299 total_loss: 0.7824 loss_cls: 0.2343 loss_box_reg: 0.2832 loss_rpn_cls: 0.05776 loss_rpn_loc: 0.2044 time: 0.3668 last_time: 0.4328 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:30:29 d2.utils.events]: \u001b[0m eta: 4:30:52 iter: 45319 total_loss: 0.8663 loss_cls: 0.2891 loss_box_reg: 0.3278 loss_rpn_cls: 0.04781 loss_rpn_loc: 0.2052 time: 0.3668 last_time: 0.4015 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:30:38 d2.utils.events]: \u001b[0m eta: 4:30:52 iter: 45339 total_loss: 0.9029 loss_cls: 0.295 loss_box_reg: 0.3054 loss_rpn_cls: 0.06452 loss_rpn_loc: 0.1902 time: 0.3668 last_time: 0.4291 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:30:46 d2.utils.events]: \u001b[0m eta: 4:30:48 iter: 45359 total_loss: 0.8136 loss_cls: 0.2671 loss_box_reg: 0.2878 loss_rpn_cls: 0.04111 loss_rpn_loc: 0.2255 time: 0.3668 last_time: 0.3806 data_time: 0.0047 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:30:53 d2.utils.events]: \u001b[0m eta: 4:30:46 iter: 45379 total_loss: 0.7854 loss_cls: 0.2634 loss_box_reg: 0.2947 loss_rpn_cls: 0.05512 loss_rpn_loc: 0.1971 time: 0.3668 last_time: 0.4157 data_time: 0.0046 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:31:02 d2.utils.events]: \u001b[0m eta: 4:30:35 iter: 45399 total_loss: 0.8418 loss_cls: 0.2743 loss_box_reg: 0.3141 loss_rpn_cls: 0.04476 loss_rpn_loc: 0.1986 time: 0.3668 last_time: 0.4066 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:31:10 d2.utils.events]: \u001b[0m eta: 4:30:56 iter: 45419 total_loss: 0.9555 loss_cls: 0.2919 loss_box_reg: 0.3197 loss_rpn_cls: 0.05135 loss_rpn_loc: 0.2275 time: 0.3669 last_time: 0.4637 data_time: 0.0049 last_data_time: 0.0053 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:31:18 d2.utils.events]: \u001b[0m eta: 4:30:40 iter: 45439 total_loss: 0.853 loss_cls: 0.2844 loss_box_reg: 0.3187 loss_rpn_cls: 0.05288 loss_rpn_loc: 0.2157 time: 0.3669 last_time: 0.4131 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:31:28 d2.utils.events]: \u001b[0m eta: 4:31:00 iter: 45459 total_loss: 0.8243 loss_cls: 0.2541 loss_box_reg: 0.2936 loss_rpn_cls: 0.05797 loss_rpn_loc: 0.2001 time: 0.3669 last_time: 0.4845 data_time: 0.0050 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:31:37 d2.utils.events]: \u001b[0m eta: 4:31:14 iter: 45479 total_loss: 0.7769 loss_cls: 0.2589 loss_box_reg: 0.2739 loss_rpn_cls: 0.052 loss_rpn_loc: 0.201 time: 0.3670 last_time: 0.4677 data_time: 0.0050 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:31:45 d2.utils.events]: \u001b[0m eta: 4:31:33 iter: 45499 total_loss: 0.7175 loss_cls: 0.235 loss_box_reg: 0.2867 loss_rpn_cls: 0.04837 loss_rpn_loc: 0.1981 time: 0.3670 last_time: 0.4142 data_time: 0.0046 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:31:53 d2.utils.events]: \u001b[0m eta: 4:31:23 iter: 45519 total_loss: 0.7575 loss_cls: 0.2063 loss_box_reg: 0.2745 loss_rpn_cls: 0.04888 loss_rpn_loc: 0.1766 time: 0.3670 last_time: 0.4373 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:32:02 d2.utils.events]: \u001b[0m eta: 4:31:15 iter: 45539 total_loss: 0.846 loss_cls: 0.2932 loss_box_reg: 0.2973 loss_rpn_cls: 0.04401 loss_rpn_loc: 0.2279 time: 0.3670 last_time: 0.3968 data_time: 0.0046 last_data_time: 0.0042 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:32:10 d2.utils.events]: \u001b[0m eta: 4:31:18 iter: 45559 total_loss: 0.9029 loss_cls: 0.312 loss_box_reg: 0.3141 loss_rpn_cls: 0.06306 loss_rpn_loc: 0.2198 time: 0.3670 last_time: 0.3955 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:32:18 d2.utils.events]: \u001b[0m eta: 4:31:10 iter: 45579 total_loss: 0.8224 loss_cls: 0.292 loss_box_reg: 0.3471 loss_rpn_cls: 0.0423 loss_rpn_loc: 0.1488 time: 0.3671 last_time: 0.4457 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:32:26 d2.utils.events]: \u001b[0m eta: 4:31:03 iter: 45599 total_loss: 0.9485 loss_cls: 0.2931 loss_box_reg: 0.3209 loss_rpn_cls: 0.05109 loss_rpn_loc: 0.1975 time: 0.3671 last_time: 0.3424 data_time: 0.0047 last_data_time: 0.0042 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:32:34 d2.utils.events]: \u001b[0m eta: 4:30:54 iter: 45619 total_loss: 0.8502 loss_cls: 0.2609 loss_box_reg: 0.3044 loss_rpn_cls: 0.05252 loss_rpn_loc: 0.2021 time: 0.3671 last_time: 0.4370 data_time: 0.0047 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:32:42 d2.utils.events]: \u001b[0m eta: 4:30:48 iter: 45639 total_loss: 0.8169 loss_cls: 0.2491 loss_box_reg: 0.319 loss_rpn_cls: 0.04505 loss_rpn_loc: 0.1764 time: 0.3671 last_time: 0.4001 data_time: 0.0045 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:32:50 d2.utils.events]: \u001b[0m eta: 4:30:38 iter: 45659 total_loss: 0.8654 loss_cls: 0.267 loss_box_reg: 0.3541 loss_rpn_cls: 0.05195 loss_rpn_loc: 0.2017 time: 0.3671 last_time: 0.3131 data_time: 0.0046 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:32:58 d2.utils.events]: \u001b[0m eta: 4:30:33 iter: 45679 total_loss: 0.8978 loss_cls: 0.3141 loss_box_reg: 0.3189 loss_rpn_cls: 0.06088 loss_rpn_loc: 0.1908 time: 0.3671 last_time: 0.4165 data_time: 0.0046 last_data_time: 0.0042 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:33:06 d2.utils.events]: \u001b[0m eta: 4:30:29 iter: 45699 total_loss: 0.7777 loss_cls: 0.229 loss_box_reg: 0.3051 loss_rpn_cls: 0.04704 loss_rpn_loc: 0.1892 time: 0.3671 last_time: 0.3874 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:33:14 d2.utils.events]: \u001b[0m eta: 4:30:15 iter: 45719 total_loss: 0.8243 loss_cls: 0.2667 loss_box_reg: 0.2819 loss_rpn_cls: 0.04708 loss_rpn_loc: 0.201 time: 0.3672 last_time: 0.3664 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:33:22 d2.utils.events]: \u001b[0m eta: 4:30:05 iter: 45739 total_loss: 0.8609 loss_cls: 0.2816 loss_box_reg: 0.3108 loss_rpn_cls: 0.05572 loss_rpn_loc: 0.2111 time: 0.3672 last_time: 0.3720 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:33:30 d2.utils.events]: \u001b[0m eta: 4:29:49 iter: 45759 total_loss: 0.745 loss_cls: 0.2453 loss_box_reg: 0.26 loss_rpn_cls: 0.0507 loss_rpn_loc: 0.189 time: 0.3672 last_time: 0.3887 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:33:37 d2.utils.events]: \u001b[0m eta: 4:29:34 iter: 45779 total_loss: 0.7568 loss_cls: 0.2351 loss_box_reg: 0.2869 loss_rpn_cls: 0.05187 loss_rpn_loc: 0.2092 time: 0.3672 last_time: 0.3846 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:33:45 d2.utils.events]: \u001b[0m eta: 4:29:32 iter: 45799 total_loss: 0.8716 loss_cls: 0.3169 loss_box_reg: 0.2904 loss_rpn_cls: 0.04585 loss_rpn_loc: 0.2099 time: 0.3672 last_time: 0.3681 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:33:54 d2.utils.events]: \u001b[0m eta: 4:29:28 iter: 45819 total_loss: 0.7983 loss_cls: 0.2548 loss_box_reg: 0.3077 loss_rpn_cls: 0.0494 loss_rpn_loc: 0.1825 time: 0.3672 last_time: 0.4297 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:34:02 d2.utils.events]: \u001b[0m eta: 4:29:16 iter: 45839 total_loss: 0.914 loss_cls: 0.3235 loss_box_reg: 0.3121 loss_rpn_cls: 0.04806 loss_rpn_loc: 0.2154 time: 0.3672 last_time: 0.3913 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:34:10 d2.utils.events]: \u001b[0m eta: 4:29:01 iter: 45859 total_loss: 0.7874 loss_cls: 0.2795 loss_box_reg: 0.2607 loss_rpn_cls: 0.06299 loss_rpn_loc: 0.1878 time: 0.3673 last_time: 0.4195 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:34:18 d2.utils.events]: \u001b[0m eta: 4:28:53 iter: 45879 total_loss: 0.7496 loss_cls: 0.2487 loss_box_reg: 0.2777 loss_rpn_cls: 0.04029 loss_rpn_loc: 0.1759 time: 0.3673 last_time: 0.3716 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:34:26 d2.utils.events]: \u001b[0m eta: 4:28:45 iter: 45899 total_loss: 0.9422 loss_cls: 0.3246 loss_box_reg: 0.2921 loss_rpn_cls: 0.0471 loss_rpn_loc: 0.2368 time: 0.3673 last_time: 0.3715 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:34:34 d2.utils.events]: \u001b[0m eta: 4:28:38 iter: 45919 total_loss: 0.9023 loss_cls: 0.3104 loss_box_reg: 0.304 loss_rpn_cls: 0.05491 loss_rpn_loc: 0.2408 time: 0.3673 last_time: 0.4380 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:34:41 d2.utils.events]: \u001b[0m eta: 4:28:19 iter: 45939 total_loss: 0.7686 loss_cls: 0.2536 loss_box_reg: 0.2844 loss_rpn_cls: 0.04256 loss_rpn_loc: 0.2021 time: 0.3673 last_time: 0.3965 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:34:50 d2.utils.events]: \u001b[0m eta: 4:28:17 iter: 45959 total_loss: 0.8345 loss_cls: 0.2706 loss_box_reg: 0.3087 loss_rpn_cls: 0.05084 loss_rpn_loc: 0.1977 time: 0.3673 last_time: 0.4302 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:34:58 d2.utils.events]: \u001b[0m eta: 4:28:18 iter: 45979 total_loss: 0.8592 loss_cls: 0.3127 loss_box_reg: 0.3126 loss_rpn_cls: 0.04112 loss_rpn_loc: 0.1879 time: 0.3673 last_time: 0.4175 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:35:06 d2.utils.events]: \u001b[0m eta: 4:28:12 iter: 45999 total_loss: 0.8096 loss_cls: 0.251 loss_box_reg: 0.2868 loss_rpn_cls: 0.05182 loss_rpn_loc: 0.1836 time: 0.3674 last_time: 0.3847 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:35:14 d2.utils.events]: \u001b[0m eta: 4:28:04 iter: 46019 total_loss: 0.8532 loss_cls: 0.282 loss_box_reg: 0.3443 loss_rpn_cls: 0.05194 loss_rpn_loc: 0.1805 time: 0.3674 last_time: 0.4420 data_time: 0.0046 last_data_time: 0.0042 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:35:22 d2.utils.events]: \u001b[0m eta: 4:28:06 iter: 46039 total_loss: 0.7877 loss_cls: 0.255 loss_box_reg: 0.293 loss_rpn_cls: 0.05283 loss_rpn_loc: 0.178 time: 0.3674 last_time: 0.4419 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:35:30 d2.utils.events]: \u001b[0m eta: 4:27:52 iter: 46059 total_loss: 0.823 loss_cls: 0.2622 loss_box_reg: 0.3243 loss_rpn_cls: 0.03936 loss_rpn_loc: 0.1931 time: 0.3674 last_time: 0.3133 data_time: 0.0048 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:35:38 d2.utils.events]: \u001b[0m eta: 4:27:43 iter: 46079 total_loss: 0.821 loss_cls: 0.2604 loss_box_reg: 0.2996 loss_rpn_cls: 0.04955 loss_rpn_loc: 0.2161 time: 0.3674 last_time: 0.4362 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:35:46 d2.utils.events]: \u001b[0m eta: 4:27:22 iter: 46099 total_loss: 0.776 loss_cls: 0.2265 loss_box_reg: 0.2796 loss_rpn_cls: 0.06167 loss_rpn_loc: 0.184 time: 0.3674 last_time: 0.4118 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:35:54 d2.utils.events]: \u001b[0m eta: 4:27:11 iter: 46119 total_loss: 0.8081 loss_cls: 0.2642 loss_box_reg: 0.3102 loss_rpn_cls: 0.05355 loss_rpn_loc: 0.1807 time: 0.3675 last_time: 0.4182 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:36:02 d2.utils.events]: \u001b[0m eta: 4:26:53 iter: 46139 total_loss: 0.754 loss_cls: 0.2326 loss_box_reg: 0.2758 loss_rpn_cls: 0.04522 loss_rpn_loc: 0.186 time: 0.3675 last_time: 0.3953 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:36:10 d2.utils.events]: \u001b[0m eta: 4:26:35 iter: 46159 total_loss: 0.8053 loss_cls: 0.2612 loss_box_reg: 0.2987 loss_rpn_cls: 0.05114 loss_rpn_loc: 0.1916 time: 0.3675 last_time: 0.3743 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:36:18 d2.utils.events]: \u001b[0m eta: 4:26:36 iter: 46179 total_loss: 0.8165 loss_cls: 0.273 loss_box_reg: 0.3114 loss_rpn_cls: 0.05583 loss_rpn_loc: 0.1872 time: 0.3675 last_time: 0.4379 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:36:27 d2.utils.events]: \u001b[0m eta: 4:26:47 iter: 46199 total_loss: 0.7487 loss_cls: 0.2469 loss_box_reg: 0.2667 loss_rpn_cls: 0.04338 loss_rpn_loc: 0.1926 time: 0.3675 last_time: 0.4451 data_time: 0.0046 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:36:34 d2.utils.events]: \u001b[0m eta: 4:26:25 iter: 46219 total_loss: 0.9021 loss_cls: 0.2834 loss_box_reg: 0.3212 loss_rpn_cls: 0.05268 loss_rpn_loc: 0.2174 time: 0.3675 last_time: 0.3195 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:36:43 d2.utils.events]: \u001b[0m eta: 4:26:24 iter: 46239 total_loss: 0.8623 loss_cls: 0.2918 loss_box_reg: 0.3132 loss_rpn_cls: 0.04892 loss_rpn_loc: 0.2186 time: 0.3675 last_time: 0.4279 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:36:51 d2.utils.events]: \u001b[0m eta: 4:26:19 iter: 46259 total_loss: 0.7757 loss_cls: 0.2831 loss_box_reg: 0.2694 loss_rpn_cls: 0.04016 loss_rpn_loc: 0.1944 time: 0.3676 last_time: 0.3358 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:36:59 d2.utils.events]: \u001b[0m eta: 4:26:18 iter: 46279 total_loss: 0.913 loss_cls: 0.3095 loss_box_reg: 0.3224 loss_rpn_cls: 0.0514 loss_rpn_loc: 0.2117 time: 0.3676 last_time: 0.4324 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:37:07 d2.utils.events]: \u001b[0m eta: 4:26:02 iter: 46299 total_loss: 0.804 loss_cls: 0.2896 loss_box_reg: 0.3212 loss_rpn_cls: 0.05068 loss_rpn_loc: 0.1821 time: 0.3676 last_time: 0.3701 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:37:15 d2.utils.events]: \u001b[0m eta: 4:25:54 iter: 46319 total_loss: 0.8297 loss_cls: 0.2364 loss_box_reg: 0.3019 loss_rpn_cls: 0.04614 loss_rpn_loc: 0.2066 time: 0.3676 last_time: 0.4194 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:37:23 d2.utils.events]: \u001b[0m eta: 4:25:45 iter: 46339 total_loss: 0.8765 loss_cls: 0.2557 loss_box_reg: 0.2894 loss_rpn_cls: 0.04777 loss_rpn_loc: 0.2205 time: 0.3676 last_time: 0.4498 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:37:31 d2.utils.events]: \u001b[0m eta: 4:25:45 iter: 46359 total_loss: 0.7797 loss_cls: 0.2888 loss_box_reg: 0.2786 loss_rpn_cls: 0.05487 loss_rpn_loc: 0.1879 time: 0.3676 last_time: 0.3771 data_time: 0.0045 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:37:39 d2.utils.events]: \u001b[0m eta: 4:25:31 iter: 46379 total_loss: 0.8802 loss_cls: 0.2728 loss_box_reg: 0.3082 loss_rpn_cls: 0.06577 loss_rpn_loc: 0.1996 time: 0.3677 last_time: 0.3964 data_time: 0.0044 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:37:47 d2.utils.events]: \u001b[0m eta: 4:25:13 iter: 46399 total_loss: 0.8638 loss_cls: 0.2874 loss_box_reg: 0.3151 loss_rpn_cls: 0.04992 loss_rpn_loc: 0.2292 time: 0.3677 last_time: 0.3973 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:37:55 d2.utils.events]: \u001b[0m eta: 4:24:56 iter: 46419 total_loss: 0.7691 loss_cls: 0.2511 loss_box_reg: 0.273 loss_rpn_cls: 0.04147 loss_rpn_loc: 0.1631 time: 0.3677 last_time: 0.4401 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:38:03 d2.utils.events]: \u001b[0m eta: 4:24:48 iter: 46439 total_loss: 0.8695 loss_cls: 0.2607 loss_box_reg: 0.3024 loss_rpn_cls: 0.04719 loss_rpn_loc: 0.1953 time: 0.3677 last_time: 0.3134 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:38:11 d2.utils.events]: \u001b[0m eta: 4:23:54 iter: 46459 total_loss: 0.7976 loss_cls: 0.2556 loss_box_reg: 0.2924 loss_rpn_cls: 0.04104 loss_rpn_loc: 0.2122 time: 0.3677 last_time: 0.3327 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:38:20 d2.utils.events]: \u001b[0m eta: 4:23:15 iter: 46479 total_loss: 0.803 loss_cls: 0.2239 loss_box_reg: 0.3084 loss_rpn_cls: 0.03931 loss_rpn_loc: 0.2173 time: 0.3677 last_time: 0.4470 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:38:28 d2.utils.events]: \u001b[0m eta: 4:22:07 iter: 46499 total_loss: 0.837 loss_cls: 0.3222 loss_box_reg: 0.3129 loss_rpn_cls: 0.05043 loss_rpn_loc: 0.1895 time: 0.3677 last_time: 0.4391 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:38:36 d2.utils.events]: \u001b[0m eta: 4:22:15 iter: 46519 total_loss: 0.9304 loss_cls: 0.2862 loss_box_reg: 0.3213 loss_rpn_cls: 0.06482 loss_rpn_loc: 0.2188 time: 0.3678 last_time: 0.4176 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:38:44 d2.utils.events]: \u001b[0m eta: 4:22:07 iter: 46539 total_loss: 0.8581 loss_cls: 0.2759 loss_box_reg: 0.2938 loss_rpn_cls: 0.0544 loss_rpn_loc: 0.181 time: 0.3678 last_time: 0.3725 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:38:52 d2.utils.events]: \u001b[0m eta: 4:21:42 iter: 46559 total_loss: 0.8375 loss_cls: 0.2522 loss_box_reg: 0.2764 loss_rpn_cls: 0.04003 loss_rpn_loc: 0.2068 time: 0.3678 last_time: 0.4316 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:39:00 d2.utils.events]: \u001b[0m eta: 4:21:27 iter: 46579 total_loss: 0.7937 loss_cls: 0.2766 loss_box_reg: 0.2949 loss_rpn_cls: 0.04364 loss_rpn_loc: 0.1858 time: 0.3678 last_time: 0.4153 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:39:08 d2.utils.events]: \u001b[0m eta: 4:21:33 iter: 46599 total_loss: 0.7689 loss_cls: 0.2407 loss_box_reg: 0.2953 loss_rpn_cls: 0.04422 loss_rpn_loc: 0.1766 time: 0.3678 last_time: 0.3922 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:39:16 d2.utils.events]: \u001b[0m eta: 4:21:18 iter: 46619 total_loss: 0.7967 loss_cls: 0.2443 loss_box_reg: 0.2924 loss_rpn_cls: 0.05443 loss_rpn_loc: 0.194 time: 0.3678 last_time: 0.4096 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:39:24 d2.utils.events]: \u001b[0m eta: 4:21:35 iter: 46639 total_loss: 0.8582 loss_cls: 0.2629 loss_box_reg: 0.324 loss_rpn_cls: 0.05771 loss_rpn_loc: 0.2048 time: 0.3679 last_time: 0.3946 data_time: 0.0046 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:39:32 d2.utils.events]: \u001b[0m eta: 4:21:18 iter: 46659 total_loss: 0.8785 loss_cls: 0.2522 loss_box_reg: 0.3191 loss_rpn_cls: 0.0459 loss_rpn_loc: 0.1907 time: 0.3679 last_time: 0.3164 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:39:40 d2.utils.events]: \u001b[0m eta: 4:21:10 iter: 46679 total_loss: 0.8425 loss_cls: 0.2593 loss_box_reg: 0.3103 loss_rpn_cls: 0.05238 loss_rpn_loc: 0.2311 time: 0.3679 last_time: 0.3764 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:39:49 d2.utils.events]: \u001b[0m eta: 4:20:57 iter: 46699 total_loss: 0.7876 loss_cls: 0.2268 loss_box_reg: 0.2786 loss_rpn_cls: 0.04912 loss_rpn_loc: 0.2186 time: 0.3679 last_time: 0.3968 data_time: 0.0047 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:39:57 d2.utils.events]: \u001b[0m eta: 4:20:57 iter: 46719 total_loss: 0.7923 loss_cls: 0.268 loss_box_reg: 0.2834 loss_rpn_cls: 0.04307 loss_rpn_loc: 0.1664 time: 0.3679 last_time: 0.3851 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:40:05 d2.utils.events]: \u001b[0m eta: 4:20:51 iter: 46739 total_loss: 0.8093 loss_cls: 0.256 loss_box_reg: 0.2905 loss_rpn_cls: 0.04283 loss_rpn_loc: 0.2216 time: 0.3679 last_time: 0.4418 data_time: 0.0046 last_data_time: 0.0040 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:40:13 d2.utils.events]: \u001b[0m eta: 4:21:06 iter: 46759 total_loss: 0.8181 loss_cls: 0.2707 loss_box_reg: 0.2888 loss_rpn_cls: 0.06236 loss_rpn_loc: 0.2052 time: 0.3679 last_time: 0.4435 data_time: 0.0046 last_data_time: 0.0041 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:40:21 d2.utils.events]: \u001b[0m eta: 4:21:26 iter: 46779 total_loss: 0.8764 loss_cls: 0.2927 loss_box_reg: 0.3175 loss_rpn_cls: 0.04238 loss_rpn_loc: 0.196 time: 0.3680 last_time: 0.4171 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:40:29 d2.utils.events]: \u001b[0m eta: 4:21:20 iter: 46799 total_loss: 0.8381 loss_cls: 0.2514 loss_box_reg: 0.2965 loss_rpn_cls: 0.04421 loss_rpn_loc: 0.1964 time: 0.3680 last_time: 0.3885 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:40:37 d2.utils.events]: \u001b[0m eta: 4:21:11 iter: 46819 total_loss: 0.8729 loss_cls: 0.3021 loss_box_reg: 0.3521 loss_rpn_cls: 0.05234 loss_rpn_loc: 0.2077 time: 0.3680 last_time: 0.4005 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:40:45 d2.utils.events]: \u001b[0m eta: 4:20:52 iter: 46839 total_loss: 0.7803 loss_cls: 0.2665 loss_box_reg: 0.3114 loss_rpn_cls: 0.05267 loss_rpn_loc: 0.1743 time: 0.3680 last_time: 0.3960 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:40:53 d2.utils.events]: \u001b[0m eta: 4:20:39 iter: 46859 total_loss: 0.8179 loss_cls: 0.2478 loss_box_reg: 0.2788 loss_rpn_cls: 0.04329 loss_rpn_loc: 0.1799 time: 0.3680 last_time: 0.3907 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:41:02 d2.utils.events]: \u001b[0m eta: 4:20:50 iter: 46879 total_loss: 0.6802 loss_cls: 0.2525 loss_box_reg: 0.2739 loss_rpn_cls: 0.03899 loss_rpn_loc: 0.1779 time: 0.3681 last_time: 0.4855 data_time: 0.0049 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:41:11 d2.utils.events]: \u001b[0m eta: 4:21:30 iter: 46899 total_loss: 0.7901 loss_cls: 0.2274 loss_box_reg: 0.3474 loss_rpn_cls: 0.03818 loss_rpn_loc: 0.2011 time: 0.3681 last_time: 0.4418 data_time: 0.0050 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:41:20 d2.utils.events]: \u001b[0m eta: 4:21:36 iter: 46919 total_loss: 0.8082 loss_cls: 0.2477 loss_box_reg: 0.2915 loss_rpn_cls: 0.04863 loss_rpn_loc: 0.2016 time: 0.3681 last_time: 0.4830 data_time: 0.0050 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:41:29 d2.utils.events]: \u001b[0m eta: 4:21:42 iter: 46939 total_loss: 0.7734 loss_cls: 0.2299 loss_box_reg: 0.3125 loss_rpn_cls: 0.04495 loss_rpn_loc: 0.2164 time: 0.3682 last_time: 0.4400 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:41:37 d2.utils.events]: \u001b[0m eta: 4:21:36 iter: 46959 total_loss: 0.7711 loss_cls: 0.2373 loss_box_reg: 0.2879 loss_rpn_cls: 0.04552 loss_rpn_loc: 0.1988 time: 0.3682 last_time: 0.3938 data_time: 0.0045 last_data_time: 0.0042 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:41:45 d2.utils.events]: \u001b[0m eta: 4:21:16 iter: 46979 total_loss: 0.8007 loss_cls: 0.273 loss_box_reg: 0.3273 loss_rpn_cls: 0.04999 loss_rpn_loc: 0.187 time: 0.3682 last_time: 0.4386 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:41:53 d2.utils.events]: \u001b[0m eta: 4:21:20 iter: 46999 total_loss: 0.9005 loss_cls: 0.3173 loss_box_reg: 0.338 loss_rpn_cls: 0.04726 loss_rpn_loc: 0.238 time: 0.3682 last_time: 0.4117 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:42:01 d2.utils.events]: \u001b[0m eta: 4:21:12 iter: 47019 total_loss: 0.8229 loss_cls: 0.256 loss_box_reg: 0.3471 loss_rpn_cls: 0.04562 loss_rpn_loc: 0.2096 time: 0.3682 last_time: 0.4252 data_time: 0.0046 last_data_time: 0.0042 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:42:09 d2.utils.events]: \u001b[0m eta: 4:20:47 iter: 47039 total_loss: 0.7634 loss_cls: 0.2881 loss_box_reg: 0.3245 loss_rpn_cls: 0.04274 loss_rpn_loc: 0.1637 time: 0.3682 last_time: 0.3953 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:42:18 d2.utils.events]: \u001b[0m eta: 4:20:43 iter: 47059 total_loss: 0.935 loss_cls: 0.2763 loss_box_reg: 0.3696 loss_rpn_cls: 0.04919 loss_rpn_loc: 0.1961 time: 0.3683 last_time: 0.3695 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:42:26 d2.utils.events]: \u001b[0m eta: 4:20:33 iter: 47079 total_loss: 0.8539 loss_cls: 0.2885 loss_box_reg: 0.3074 loss_rpn_cls: 0.05074 loss_rpn_loc: 0.1894 time: 0.3683 last_time: 0.3731 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:42:34 d2.utils.events]: \u001b[0m eta: 4:20:30 iter: 47099 total_loss: 0.7677 loss_cls: 0.244 loss_box_reg: 0.2885 loss_rpn_cls: 0.04177 loss_rpn_loc: 0.1926 time: 0.3683 last_time: 0.3701 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:42:42 d2.utils.events]: \u001b[0m eta: 4:20:19 iter: 47119 total_loss: 0.9747 loss_cls: 0.3109 loss_box_reg: 0.3757 loss_rpn_cls: 0.06167 loss_rpn_loc: 0.2137 time: 0.3683 last_time: 0.4277 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:42:50 d2.utils.events]: \u001b[0m eta: 4:20:16 iter: 47139 total_loss: 0.9176 loss_cls: 0.3219 loss_box_reg: 0.3626 loss_rpn_cls: 0.05021 loss_rpn_loc: 0.1967 time: 0.3683 last_time: 0.4427 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:42:58 d2.utils.events]: \u001b[0m eta: 4:20:14 iter: 47159 total_loss: 0.7441 loss_cls: 0.2571 loss_box_reg: 0.2801 loss_rpn_cls: 0.0598 loss_rpn_loc: 0.1871 time: 0.3683 last_time: 0.4346 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:43:08 d2.utils.events]: \u001b[0m eta: 4:20:22 iter: 47179 total_loss: 0.8356 loss_cls: 0.2749 loss_box_reg: 0.3139 loss_rpn_cls: 0.0529 loss_rpn_loc: 0.1988 time: 0.3684 last_time: 0.4710 data_time: 0.0048 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:43:17 d2.utils.events]: \u001b[0m eta: 4:20:23 iter: 47199 total_loss: 0.8695 loss_cls: 0.2393 loss_box_reg: 0.3256 loss_rpn_cls: 0.05386 loss_rpn_loc: 0.2077 time: 0.3684 last_time: 0.4143 data_time: 0.0049 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:43:26 d2.utils.events]: \u001b[0m eta: 4:20:36 iter: 47219 total_loss: 0.7717 loss_cls: 0.2557 loss_box_reg: 0.2678 loss_rpn_cls: 0.04655 loss_rpn_loc: 0.218 time: 0.3684 last_time: 0.4716 data_time: 0.0050 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:43:34 d2.utils.events]: \u001b[0m eta: 4:20:22 iter: 47239 total_loss: 0.8837 loss_cls: 0.3137 loss_box_reg: 0.3118 loss_rpn_cls: 0.05982 loss_rpn_loc: 0.23 time: 0.3685 last_time: 0.4179 data_time: 0.0045 last_data_time: 0.0041 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:43:42 d2.utils.events]: \u001b[0m eta: 4:20:15 iter: 47259 total_loss: 0.9262 loss_cls: 0.2973 loss_box_reg: 0.2784 loss_rpn_cls: 0.05498 loss_rpn_loc: 0.2338 time: 0.3685 last_time: 0.4073 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:43:50 d2.utils.events]: \u001b[0m eta: 4:20:07 iter: 47279 total_loss: 0.8067 loss_cls: 0.2181 loss_box_reg: 0.2731 loss_rpn_cls: 0.04221 loss_rpn_loc: 0.1924 time: 0.3685 last_time: 0.4403 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:43:58 d2.utils.events]: \u001b[0m eta: 4:19:58 iter: 47299 total_loss: 0.7823 loss_cls: 0.2852 loss_box_reg: 0.2815 loss_rpn_cls: 0.04671 loss_rpn_loc: 0.1897 time: 0.3685 last_time: 0.3345 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:44:06 d2.utils.events]: \u001b[0m eta: 4:19:50 iter: 47319 total_loss: 0.8184 loss_cls: 0.2495 loss_box_reg: 0.3166 loss_rpn_cls: 0.05966 loss_rpn_loc: 0.1934 time: 0.3685 last_time: 0.3946 data_time: 0.0046 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:44:14 d2.utils.events]: \u001b[0m eta: 4:19:41 iter: 47339 total_loss: 0.9327 loss_cls: 0.334 loss_box_reg: 0.3299 loss_rpn_cls: 0.05457 loss_rpn_loc: 0.2101 time: 0.3685 last_time: 0.4365 data_time: 0.0045 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:44:22 d2.utils.events]: \u001b[0m eta: 4:19:35 iter: 47359 total_loss: 0.673 loss_cls: 0.2068 loss_box_reg: 0.2815 loss_rpn_cls: 0.039 loss_rpn_loc: 0.1846 time: 0.3685 last_time: 0.4435 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:44:30 d2.utils.events]: \u001b[0m eta: 4:19:25 iter: 47379 total_loss: 0.9001 loss_cls: 0.3114 loss_box_reg: 0.3095 loss_rpn_cls: 0.04696 loss_rpn_loc: 0.181 time: 0.3686 last_time: 0.4388 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:44:39 d2.utils.events]: \u001b[0m eta: 4:19:21 iter: 47399 total_loss: 0.7922 loss_cls: 0.2492 loss_box_reg: 0.2714 loss_rpn_cls: 0.03947 loss_rpn_loc: 0.1875 time: 0.3686 last_time: 0.4174 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:44:47 d2.utils.events]: \u001b[0m eta: 4:19:27 iter: 47419 total_loss: 0.7663 loss_cls: 0.2444 loss_box_reg: 0.2733 loss_rpn_cls: 0.03655 loss_rpn_loc: 0.1999 time: 0.3686 last_time: 0.4021 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:44:55 d2.utils.events]: \u001b[0m eta: 4:19:35 iter: 47439 total_loss: 0.8399 loss_cls: 0.2686 loss_box_reg: 0.3162 loss_rpn_cls: 0.04384 loss_rpn_loc: 0.1751 time: 0.3686 last_time: 0.4476 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:45:03 d2.utils.events]: \u001b[0m eta: 4:19:34 iter: 47459 total_loss: 0.911 loss_cls: 0.2861 loss_box_reg: 0.3171 loss_rpn_cls: 0.05876 loss_rpn_loc: 0.2134 time: 0.3686 last_time: 0.4404 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:45:11 d2.utils.events]: \u001b[0m eta: 4:19:24 iter: 47479 total_loss: 0.842 loss_cls: 0.2931 loss_box_reg: 0.2948 loss_rpn_cls: 0.04459 loss_rpn_loc: 0.2225 time: 0.3686 last_time: 0.4304 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:45:19 d2.utils.events]: \u001b[0m eta: 4:19:16 iter: 47499 total_loss: 0.8379 loss_cls: 0.315 loss_box_reg: 0.295 loss_rpn_cls: 0.05275 loss_rpn_loc: 0.1774 time: 0.3687 last_time: 0.4378 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:45:27 d2.utils.events]: \u001b[0m eta: 4:18:47 iter: 47519 total_loss: 0.9592 loss_cls: 0.2835 loss_box_reg: 0.3184 loss_rpn_cls: 0.04992 loss_rpn_loc: 0.2068 time: 0.3687 last_time: 0.4066 data_time: 0.0045 last_data_time: 0.0042 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:45:35 d2.utils.events]: \u001b[0m eta: 4:18:38 iter: 47539 total_loss: 0.9407 loss_cls: 0.2873 loss_box_reg: 0.3318 loss_rpn_cls: 0.06664 loss_rpn_loc: 0.2069 time: 0.3687 last_time: 0.3395 data_time: 0.0047 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:45:43 d2.utils.events]: \u001b[0m eta: 4:18:15 iter: 47559 total_loss: 0.7595 loss_cls: 0.2419 loss_box_reg: 0.2704 loss_rpn_cls: 0.04642 loss_rpn_loc: 0.1721 time: 0.3687 last_time: 0.4429 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:45:51 d2.utils.events]: \u001b[0m eta: 4:18:11 iter: 47579 total_loss: 0.8216 loss_cls: 0.266 loss_box_reg: 0.3148 loss_rpn_cls: 0.04398 loss_rpn_loc: 0.1856 time: 0.3687 last_time: 0.3932 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:45:59 d2.utils.events]: \u001b[0m eta: 4:18:02 iter: 47599 total_loss: 0.8709 loss_cls: 0.3475 loss_box_reg: 0.2971 loss_rpn_cls: 0.04222 loss_rpn_loc: 0.2029 time: 0.3687 last_time: 0.4197 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:46:16 d2.utils.events]: \u001b[0m eta: 4:18:03 iter: 47619 total_loss: 0.7411 loss_cls: 0.2705 loss_box_reg: 0.2642 loss_rpn_cls: 0.03387 loss_rpn_loc: 0.1778 time: 0.3689 last_time: 0.3686 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:46:24 d2.utils.events]: \u001b[0m eta: 4:17:36 iter: 47639 total_loss: 0.8485 loss_cls: 0.2829 loss_box_reg: 0.3022 loss_rpn_cls: 0.04756 loss_rpn_loc: 0.1845 time: 0.3689 last_time: 0.4113 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:46:32 d2.utils.events]: \u001b[0m eta: 4:17:29 iter: 47659 total_loss: 0.8356 loss_cls: 0.2871 loss_box_reg: 0.3121 loss_rpn_cls: 0.05509 loss_rpn_loc: 0.2124 time: 0.3689 last_time: 0.3900 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:46:40 d2.utils.events]: \u001b[0m eta: 4:17:29 iter: 47679 total_loss: 0.7968 loss_cls: 0.2613 loss_box_reg: 0.3054 loss_rpn_cls: 0.04339 loss_rpn_loc: 0.2115 time: 0.3690 last_time: 0.4897 data_time: 0.0047 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:46:50 d2.utils.events]: \u001b[0m eta: 4:17:51 iter: 47699 total_loss: 0.8582 loss_cls: 0.3012 loss_box_reg: 0.3381 loss_rpn_cls: 0.04273 loss_rpn_loc: 0.1796 time: 0.3690 last_time: 0.4896 data_time: 0.0051 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:46:59 d2.utils.events]: \u001b[0m eta: 4:18:09 iter: 47719 total_loss: 0.7527 loss_cls: 0.2469 loss_box_reg: 0.2773 loss_rpn_cls: 0.0381 loss_rpn_loc: 0.1939 time: 0.3691 last_time: 0.4684 data_time: 0.0050 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:47:08 d2.utils.events]: \u001b[0m eta: 4:18:18 iter: 47739 total_loss: 0.795 loss_cls: 0.2385 loss_box_reg: 0.3081 loss_rpn_cls: 0.04044 loss_rpn_loc: 0.2158 time: 0.3691 last_time: 0.4461 data_time: 0.0050 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:47:18 d2.utils.events]: \u001b[0m eta: 4:18:34 iter: 47759 total_loss: 0.8375 loss_cls: 0.2836 loss_box_reg: 0.3132 loss_rpn_cls: 0.04635 loss_rpn_loc: 0.1852 time: 0.3691 last_time: 0.5139 data_time: 0.0050 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:47:27 d2.utils.events]: \u001b[0m eta: 4:18:52 iter: 47779 total_loss: 0.8575 loss_cls: 0.2929 loss_box_reg: 0.2912 loss_rpn_cls: 0.0433 loss_rpn_loc: 0.206 time: 0.3692 last_time: 0.4694 data_time: 0.0051 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:47:37 d2.utils.events]: \u001b[0m eta: 4:19:01 iter: 47799 total_loss: 0.7627 loss_cls: 0.2297 loss_box_reg: 0.2763 loss_rpn_cls: 0.03661 loss_rpn_loc: 0.2042 time: 0.3692 last_time: 0.4862 data_time: 0.0050 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:47:47 d2.utils.events]: \u001b[0m eta: 4:19:15 iter: 47819 total_loss: 0.8213 loss_cls: 0.2528 loss_box_reg: 0.3181 loss_rpn_cls: 0.04909 loss_rpn_loc: 0.1914 time: 0.3693 last_time: 0.4901 data_time: 0.0050 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:47:56 d2.utils.events]: \u001b[0m eta: 4:19:40 iter: 47839 total_loss: 0.8355 loss_cls: 0.2959 loss_box_reg: 0.3033 loss_rpn_cls: 0.04171 loss_rpn_loc: 0.2161 time: 0.3693 last_time: 0.4645 data_time: 0.0051 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:48:05 d2.utils.events]: \u001b[0m eta: 4:19:59 iter: 47859 total_loss: 0.9332 loss_cls: 0.2808 loss_box_reg: 0.3499 loss_rpn_cls: 0.05783 loss_rpn_loc: 0.208 time: 0.3694 last_time: 0.4787 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:48:15 d2.utils.events]: \u001b[0m eta: 4:19:44 iter: 47879 total_loss: 0.8148 loss_cls: 0.2789 loss_box_reg: 0.2878 loss_rpn_cls: 0.04331 loss_rpn_loc: 0.2102 time: 0.3694 last_time: 0.4945 data_time: 0.0048 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:48:24 d2.utils.events]: \u001b[0m eta: 4:19:42 iter: 47899 total_loss: 0.8083 loss_cls: 0.2245 loss_box_reg: 0.2854 loss_rpn_cls: 0.04816 loss_rpn_loc: 0.2057 time: 0.3694 last_time: 0.5106 data_time: 0.0050 last_data_time: 0.0054 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:48:33 d2.utils.events]: \u001b[0m eta: 4:19:45 iter: 47919 total_loss: 0.7546 loss_cls: 0.2253 loss_box_reg: 0.2668 loss_rpn_cls: 0.05577 loss_rpn_loc: 0.1825 time: 0.3695 last_time: 0.4691 data_time: 0.0051 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:48:43 d2.utils.events]: \u001b[0m eta: 4:20:45 iter: 47939 total_loss: 0.8023 loss_cls: 0.3175 loss_box_reg: 0.2876 loss_rpn_cls: 0.04647 loss_rpn_loc: 0.1821 time: 0.3695 last_time: 0.4577 data_time: 0.0049 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:48:52 d2.utils.events]: \u001b[0m eta: 4:21:43 iter: 47959 total_loss: 0.8921 loss_cls: 0.3135 loss_box_reg: 0.2864 loss_rpn_cls: 0.05309 loss_rpn_loc: 0.217 time: 0.3695 last_time: 0.4673 data_time: 0.0049 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:49:01 d2.utils.events]: \u001b[0m eta: 4:22:52 iter: 47979 total_loss: 0.9261 loss_cls: 0.3 loss_box_reg: 0.3163 loss_rpn_cls: 0.05318 loss_rpn_loc: 0.2067 time: 0.3696 last_time: 0.4360 data_time: 0.0049 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:49:11 d2.utils.events]: \u001b[0m eta: 4:24:13 iter: 47999 total_loss: 0.826 loss_cls: 0.2535 loss_box_reg: 0.3256 loss_rpn_cls: 0.04504 loss_rpn_loc: 0.2165 time: 0.3696 last_time: 0.4851 data_time: 0.0051 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:49:20 d2.utils.events]: \u001b[0m eta: 4:25:42 iter: 48019 total_loss: 0.9959 loss_cls: 0.3694 loss_box_reg: 0.3668 loss_rpn_cls: 0.0679 loss_rpn_loc: 0.2259 time: 0.3697 last_time: 0.5160 data_time: 0.0050 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:49:28 d2.utils.events]: \u001b[0m eta: 4:25:24 iter: 48039 total_loss: 0.7539 loss_cls: 0.2528 loss_box_reg: 0.2625 loss_rpn_cls: 0.03439 loss_rpn_loc: 0.1926 time: 0.3697 last_time: 0.3730 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:49:36 d2.utils.events]: \u001b[0m eta: 4:25:15 iter: 48059 total_loss: 0.8714 loss_cls: 0.2706 loss_box_reg: 0.3129 loss_rpn_cls: 0.04419 loss_rpn_loc: 0.221 time: 0.3697 last_time: 0.3130 data_time: 0.0044 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:49:44 d2.utils.events]: \u001b[0m eta: 4:25:26 iter: 48079 total_loss: 0.7734 loss_cls: 0.2576 loss_box_reg: 0.3042 loss_rpn_cls: 0.04218 loss_rpn_loc: 0.1663 time: 0.3697 last_time: 0.4096 data_time: 0.0045 last_data_time: 0.0042 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:49:52 d2.utils.events]: \u001b[0m eta: 4:25:17 iter: 48099 total_loss: 0.9195 loss_cls: 0.3123 loss_box_reg: 0.3071 loss_rpn_cls: 0.05302 loss_rpn_loc: 0.2152 time: 0.3697 last_time: 0.4132 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:50:00 d2.utils.events]: \u001b[0m eta: 4:25:12 iter: 48119 total_loss: 0.8066 loss_cls: 0.2331 loss_box_reg: 0.2972 loss_rpn_cls: 0.05359 loss_rpn_loc: 0.1948 time: 0.3697 last_time: 0.4236 data_time: 0.0044 last_data_time: 0.0041 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:50:08 d2.utils.events]: \u001b[0m eta: 4:25:03 iter: 48139 total_loss: 0.8055 loss_cls: 0.2528 loss_box_reg: 0.3039 loss_rpn_cls: 0.05386 loss_rpn_loc: 0.1856 time: 0.3698 last_time: 0.3706 data_time: 0.0044 last_data_time: 0.0042 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:50:16 d2.utils.events]: \u001b[0m eta: 4:24:52 iter: 48159 total_loss: 0.792 loss_cls: 0.2624 loss_box_reg: 0.2865 loss_rpn_cls: 0.03643 loss_rpn_loc: 0.208 time: 0.3698 last_time: 0.4249 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:50:24 d2.utils.events]: \u001b[0m eta: 4:23:25 iter: 48179 total_loss: 0.8368 loss_cls: 0.2711 loss_box_reg: 0.2943 loss_rpn_cls: 0.04427 loss_rpn_loc: 0.2007 time: 0.3698 last_time: 0.4423 data_time: 0.0045 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:50:32 d2.utils.events]: \u001b[0m eta: 4:22:03 iter: 48199 total_loss: 0.9508 loss_cls: 0.2991 loss_box_reg: 0.3166 loss_rpn_cls: 0.05145 loss_rpn_loc: 0.2451 time: 0.3698 last_time: 0.4239 data_time: 0.0044 last_data_time: 0.0041 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:50:40 d2.utils.events]: \u001b[0m eta: 4:19:54 iter: 48219 total_loss: 0.8167 loss_cls: 0.2651 loss_box_reg: 0.3078 loss_rpn_cls: 0.05162 loss_rpn_loc: 0.1956 time: 0.3698 last_time: 0.4426 data_time: 0.0043 last_data_time: 0.0041 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:50:48 d2.utils.events]: \u001b[0m eta: 4:20:14 iter: 48239 total_loss: 0.8243 loss_cls: 0.2501 loss_box_reg: 0.2619 loss_rpn_cls: 0.0486 loss_rpn_loc: 0.2143 time: 0.3698 last_time: 0.4263 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:50:56 d2.utils.events]: \u001b[0m eta: 4:19:43 iter: 48259 total_loss: 0.7939 loss_cls: 0.2646 loss_box_reg: 0.3312 loss_rpn_cls: 0.04956 loss_rpn_loc: 0.1704 time: 0.3698 last_time: 0.4194 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:51:04 d2.utils.events]: \u001b[0m eta: 4:19:11 iter: 48279 total_loss: 0.8553 loss_cls: 0.2564 loss_box_reg: 0.3113 loss_rpn_cls: 0.04767 loss_rpn_loc: 0.214 time: 0.3698 last_time: 0.3744 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:51:13 d2.utils.events]: \u001b[0m eta: 4:19:20 iter: 48299 total_loss: 0.7011 loss_cls: 0.2022 loss_box_reg: 0.2535 loss_rpn_cls: 0.04558 loss_rpn_loc: 0.1654 time: 0.3699 last_time: 0.4153 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:51:20 d2.utils.events]: \u001b[0m eta: 4:19:12 iter: 48319 total_loss: 0.8519 loss_cls: 0.2793 loss_box_reg: 0.2993 loss_rpn_cls: 0.04958 loss_rpn_loc: 0.1963 time: 0.3699 last_time: 0.3951 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:51:29 d2.utils.events]: \u001b[0m eta: 4:18:49 iter: 48339 total_loss: 0.8803 loss_cls: 0.3036 loss_box_reg: 0.3048 loss_rpn_cls: 0.04223 loss_rpn_loc: 0.2015 time: 0.3699 last_time: 0.4371 data_time: 0.0045 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:51:36 d2.utils.events]: \u001b[0m eta: 4:17:31 iter: 48359 total_loss: 0.8502 loss_cls: 0.2488 loss_box_reg: 0.3135 loss_rpn_cls: 0.05686 loss_rpn_loc: 0.2237 time: 0.3699 last_time: 0.3480 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:51:44 d2.utils.events]: \u001b[0m eta: 4:16:42 iter: 48379 total_loss: 0.8076 loss_cls: 0.3122 loss_box_reg: 0.3258 loss_rpn_cls: 0.04203 loss_rpn_loc: 0.1778 time: 0.3699 last_time: 0.4124 data_time: 0.0044 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:51:52 d2.utils.events]: \u001b[0m eta: 4:16:24 iter: 48399 total_loss: 0.8806 loss_cls: 0.2759 loss_box_reg: 0.3141 loss_rpn_cls: 0.058 loss_rpn_loc: 0.1928 time: 0.3699 last_time: 0.4194 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:52:00 d2.utils.events]: \u001b[0m eta: 4:16:11 iter: 48419 total_loss: 0.8904 loss_cls: 0.3185 loss_box_reg: 0.3189 loss_rpn_cls: 0.04454 loss_rpn_loc: 0.184 time: 0.3699 last_time: 0.3621 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:52:08 d2.utils.events]: \u001b[0m eta: 4:15:53 iter: 48439 total_loss: 0.827 loss_cls: 0.2442 loss_box_reg: 0.2902 loss_rpn_cls: 0.04953 loss_rpn_loc: 0.2029 time: 0.3699 last_time: 0.4149 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:52:16 d2.utils.events]: \u001b[0m eta: 4:15:45 iter: 48459 total_loss: 0.8011 loss_cls: 0.2365 loss_box_reg: 0.3044 loss_rpn_cls: 0.04567 loss_rpn_loc: 0.1858 time: 0.3700 last_time: 0.3670 data_time: 0.0045 last_data_time: 0.0042 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:52:24 d2.utils.events]: \u001b[0m eta: 4:15:34 iter: 48479 total_loss: 0.8601 loss_cls: 0.2885 loss_box_reg: 0.32 loss_rpn_cls: 0.0454 loss_rpn_loc: 0.2062 time: 0.3700 last_time: 0.4175 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:52:33 d2.utils.events]: \u001b[0m eta: 4:15:33 iter: 48499 total_loss: 0.7894 loss_cls: 0.2608 loss_box_reg: 0.2748 loss_rpn_cls: 0.04161 loss_rpn_loc: 0.1866 time: 0.3700 last_time: 0.3631 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:52:41 d2.utils.events]: \u001b[0m eta: 4:15:24 iter: 48519 total_loss: 0.8975 loss_cls: 0.2922 loss_box_reg: 0.3493 loss_rpn_cls: 0.04487 loss_rpn_loc: 0.2032 time: 0.3700 last_time: 0.3937 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:52:49 d2.utils.events]: \u001b[0m eta: 4:15:16 iter: 48539 total_loss: 0.7854 loss_cls: 0.2577 loss_box_reg: 0.2555 loss_rpn_cls: 0.05569 loss_rpn_loc: 0.2029 time: 0.3700 last_time: 0.3758 data_time: 0.0044 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:52:57 d2.utils.events]: \u001b[0m eta: 4:15:07 iter: 48559 total_loss: 0.756 loss_cls: 0.2306 loss_box_reg: 0.2869 loss_rpn_cls: 0.04523 loss_rpn_loc: 0.2098 time: 0.3700 last_time: 0.4079 data_time: 0.0044 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:53:05 d2.utils.events]: \u001b[0m eta: 4:15:04 iter: 48579 total_loss: 0.8884 loss_cls: 0.279 loss_box_reg: 0.3322 loss_rpn_cls: 0.05785 loss_rpn_loc: 0.2191 time: 0.3700 last_time: 0.4152 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:53:13 d2.utils.events]: \u001b[0m eta: 4:14:46 iter: 48599 total_loss: 0.8576 loss_cls: 0.2988 loss_box_reg: 0.3071 loss_rpn_cls: 0.05038 loss_rpn_loc: 0.1811 time: 0.3700 last_time: 0.3732 data_time: 0.0046 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:53:21 d2.utils.events]: \u001b[0m eta: 4:14:30 iter: 48619 total_loss: 0.9188 loss_cls: 0.2946 loss_box_reg: 0.3209 loss_rpn_cls: 0.04901 loss_rpn_loc: 0.2174 time: 0.3701 last_time: 0.4150 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:53:29 d2.utils.events]: \u001b[0m eta: 4:14:19 iter: 48639 total_loss: 0.7227 loss_cls: 0.2232 loss_box_reg: 0.2381 loss_rpn_cls: 0.03808 loss_rpn_loc: 0.1709 time: 0.3701 last_time: 0.3947 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:53:37 d2.utils.events]: \u001b[0m eta: 4:14:02 iter: 48659 total_loss: 0.8365 loss_cls: 0.2462 loss_box_reg: 0.2935 loss_rpn_cls: 0.04988 loss_rpn_loc: 0.1937 time: 0.3701 last_time: 0.3712 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:53:45 d2.utils.events]: \u001b[0m eta: 4:13:44 iter: 48679 total_loss: 0.7879 loss_cls: 0.2656 loss_box_reg: 0.2985 loss_rpn_cls: 0.05567 loss_rpn_loc: 0.1828 time: 0.3701 last_time: 0.4306 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:53:53 d2.utils.events]: \u001b[0m eta: 4:13:14 iter: 48699 total_loss: 0.783 loss_cls: 0.2501 loss_box_reg: 0.3109 loss_rpn_cls: 0.04875 loss_rpn_loc: 0.1685 time: 0.3701 last_time: 0.4404 data_time: 0.0044 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:54:01 d2.utils.events]: \u001b[0m eta: 4:12:39 iter: 48719 total_loss: 0.7828 loss_cls: 0.2149 loss_box_reg: 0.3261 loss_rpn_cls: 0.04158 loss_rpn_loc: 0.1814 time: 0.3701 last_time: 0.4117 data_time: 0.0045 last_data_time: 0.0041 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:54:09 d2.utils.events]: \u001b[0m eta: 4:12:17 iter: 48739 total_loss: 0.835 loss_cls: 0.3033 loss_box_reg: 0.2851 loss_rpn_cls: 0.04555 loss_rpn_loc: 0.1904 time: 0.3701 last_time: 0.3467 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:54:17 d2.utils.events]: \u001b[0m eta: 4:11:36 iter: 48759 total_loss: 0.7837 loss_cls: 0.2715 loss_box_reg: 0.3125 loss_rpn_cls: 0.03831 loss_rpn_loc: 0.1743 time: 0.3701 last_time: 0.4148 data_time: 0.0045 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:54:25 d2.utils.events]: \u001b[0m eta: 4:10:56 iter: 48779 total_loss: 0.874 loss_cls: 0.2765 loss_box_reg: 0.3206 loss_rpn_cls: 0.05551 loss_rpn_loc: 0.2307 time: 0.3702 last_time: 0.3948 data_time: 0.0045 last_data_time: 0.0041 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:54:33 d2.utils.events]: \u001b[0m eta: 4:10:29 iter: 48799 total_loss: 0.9046 loss_cls: 0.3078 loss_box_reg: 0.3373 loss_rpn_cls: 0.04866 loss_rpn_loc: 0.2004 time: 0.3702 last_time: 0.3683 data_time: 0.0045 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:54:41 d2.utils.events]: \u001b[0m eta: 4:10:09 iter: 48819 total_loss: 0.8624 loss_cls: 0.2912 loss_box_reg: 0.3099 loss_rpn_cls: 0.04724 loss_rpn_loc: 0.2306 time: 0.3702 last_time: 0.4464 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:54:49 d2.utils.events]: \u001b[0m eta: 4:09:38 iter: 48839 total_loss: 0.766 loss_cls: 0.2429 loss_box_reg: 0.2862 loss_rpn_cls: 0.04331 loss_rpn_loc: 0.2027 time: 0.3702 last_time: 0.4112 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:54:57 d2.utils.events]: \u001b[0m eta: 4:09:24 iter: 48859 total_loss: 0.9005 loss_cls: 0.2933 loss_box_reg: 0.3148 loss_rpn_cls: 0.04589 loss_rpn_loc: 0.2005 time: 0.3702 last_time: 0.3685 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:55:05 d2.utils.events]: \u001b[0m eta: 4:09:09 iter: 48879 total_loss: 0.8472 loss_cls: 0.2375 loss_box_reg: 0.2786 loss_rpn_cls: 0.04936 loss_rpn_loc: 0.2198 time: 0.3702 last_time: 0.3966 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:55:13 d2.utils.events]: \u001b[0m eta: 4:08:40 iter: 48899 total_loss: 0.8601 loss_cls: 0.2482 loss_box_reg: 0.3002 loss_rpn_cls: 0.05001 loss_rpn_loc: 0.2177 time: 0.3702 last_time: 0.4249 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:55:21 d2.utils.events]: \u001b[0m eta: 4:08:15 iter: 48919 total_loss: 0.8736 loss_cls: 0.2875 loss_box_reg: 0.3133 loss_rpn_cls: 0.05701 loss_rpn_loc: 0.2042 time: 0.3703 last_time: 0.4194 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:55:29 d2.utils.events]: \u001b[0m eta: 4:07:50 iter: 48939 total_loss: 0.8229 loss_cls: 0.2416 loss_box_reg: 0.2678 loss_rpn_cls: 0.03984 loss_rpn_loc: 0.2092 time: 0.3703 last_time: 0.3927 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:55:37 d2.utils.events]: \u001b[0m eta: 4:07:16 iter: 48959 total_loss: 0.7134 loss_cls: 0.2441 loss_box_reg: 0.2737 loss_rpn_cls: 0.04789 loss_rpn_loc: 0.1981 time: 0.3703 last_time: 0.4314 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:55:45 d2.utils.events]: \u001b[0m eta: 4:06:36 iter: 48979 total_loss: 0.8729 loss_cls: 0.2757 loss_box_reg: 0.3 loss_rpn_cls: 0.05157 loss_rpn_loc: 0.2226 time: 0.3703 last_time: 0.3330 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:55:54 d2.utils.events]: \u001b[0m eta: 4:05:38 iter: 48999 total_loss: 0.8703 loss_cls: 0.3045 loss_box_reg: 0.3161 loss_rpn_cls: 0.03962 loss_rpn_loc: 0.2056 time: 0.3703 last_time: 0.4068 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:56:02 d2.utils.events]: \u001b[0m eta: 4:04:39 iter: 49019 total_loss: 0.741 loss_cls: 0.243 loss_box_reg: 0.2517 loss_rpn_cls: 0.05029 loss_rpn_loc: 0.1841 time: 0.3703 last_time: 0.4392 data_time: 0.0044 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:56:10 d2.utils.events]: \u001b[0m eta: 4:05:16 iter: 49039 total_loss: 0.8242 loss_cls: 0.2476 loss_box_reg: 0.3249 loss_rpn_cls: 0.04839 loss_rpn_loc: 0.1836 time: 0.3703 last_time: 0.4162 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:56:17 d2.utils.events]: \u001b[0m eta: 4:04:28 iter: 49059 total_loss: 0.7814 loss_cls: 0.2634 loss_box_reg: 0.3216 loss_rpn_cls: 0.03995 loss_rpn_loc: 0.1885 time: 0.3703 last_time: 0.4141 data_time: 0.0044 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:56:26 d2.utils.events]: \u001b[0m eta: 4:04:31 iter: 49079 total_loss: 0.7066 loss_cls: 0.1994 loss_box_reg: 0.2803 loss_rpn_cls: 0.02986 loss_rpn_loc: 0.1615 time: 0.3704 last_time: 0.4237 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:56:34 d2.utils.events]: \u001b[0m eta: 4:04:17 iter: 49099 total_loss: 0.8872 loss_cls: 0.2895 loss_box_reg: 0.3492 loss_rpn_cls: 0.05109 loss_rpn_loc: 0.2214 time: 0.3704 last_time: 0.4226 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:56:42 d2.utils.events]: \u001b[0m eta: 4:04:15 iter: 49119 total_loss: 0.8429 loss_cls: 0.2685 loss_box_reg: 0.2987 loss_rpn_cls: 0.04422 loss_rpn_loc: 0.201 time: 0.3704 last_time: 0.3912 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:56:50 d2.utils.events]: \u001b[0m eta: 4:04:19 iter: 49139 total_loss: 0.8967 loss_cls: 0.3184 loss_box_reg: 0.2776 loss_rpn_cls: 0.05481 loss_rpn_loc: 0.2031 time: 0.3704 last_time: 0.4206 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:56:58 d2.utils.events]: \u001b[0m eta: 4:03:42 iter: 49159 total_loss: 0.8421 loss_cls: 0.2527 loss_box_reg: 0.3102 loss_rpn_cls: 0.06274 loss_rpn_loc: 0.2162 time: 0.3704 last_time: 0.4386 data_time: 0.0046 last_data_time: 0.0041 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:57:05 d2.utils.events]: \u001b[0m eta: 4:03:29 iter: 49179 total_loss: 0.8001 loss_cls: 0.2261 loss_box_reg: 0.3029 loss_rpn_cls: 0.04222 loss_rpn_loc: 0.1959 time: 0.3704 last_time: 0.4037 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:57:13 d2.utils.events]: \u001b[0m eta: 4:03:21 iter: 49199 total_loss: 0.7861 loss_cls: 0.2414 loss_box_reg: 0.2749 loss_rpn_cls: 0.05157 loss_rpn_loc: 0.2022 time: 0.3704 last_time: 0.3621 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:57:21 d2.utils.events]: \u001b[0m eta: 4:03:11 iter: 49219 total_loss: 0.7835 loss_cls: 0.2792 loss_box_reg: 0.2952 loss_rpn_cls: 0.04005 loss_rpn_loc: 0.2071 time: 0.3704 last_time: 0.3149 data_time: 0.0045 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:57:30 d2.utils.events]: \u001b[0m eta: 4:03:05 iter: 49239 total_loss: 0.717 loss_cls: 0.2553 loss_box_reg: 0.2909 loss_rpn_cls: 0.03998 loss_rpn_loc: 0.1781 time: 0.3704 last_time: 0.4434 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:57:37 d2.utils.events]: \u001b[0m eta: 4:02:56 iter: 49259 total_loss: 0.9556 loss_cls: 0.2872 loss_box_reg: 0.3361 loss_rpn_cls: 0.05471 loss_rpn_loc: 0.2222 time: 0.3705 last_time: 0.4462 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:57:46 d2.utils.events]: \u001b[0m eta: 4:02:51 iter: 49279 total_loss: 0.8264 loss_cls: 0.2617 loss_box_reg: 0.2866 loss_rpn_cls: 0.04481 loss_rpn_loc: 0.1926 time: 0.3705 last_time: 0.3639 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:57:54 d2.utils.events]: \u001b[0m eta: 4:02:33 iter: 49299 total_loss: 0.7414 loss_cls: 0.249 loss_box_reg: 0.3023 loss_rpn_cls: 0.0412 loss_rpn_loc: 0.174 time: 0.3705 last_time: 0.3971 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:58:02 d2.utils.events]: \u001b[0m eta: 4:02:28 iter: 49319 total_loss: 0.749 loss_cls: 0.2544 loss_box_reg: 0.2582 loss_rpn_cls: 0.03736 loss_rpn_loc: 0.1966 time: 0.3705 last_time: 0.3960 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:58:10 d2.utils.events]: \u001b[0m eta: 4:01:56 iter: 49339 total_loss: 0.7927 loss_cls: 0.2431 loss_box_reg: 0.3217 loss_rpn_cls: 0.03767 loss_rpn_loc: 0.179 time: 0.3705 last_time: 0.4139 data_time: 0.0045 last_data_time: 0.0041 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:58:18 d2.utils.events]: \u001b[0m eta: 4:02:00 iter: 49359 total_loss: 0.7704 loss_cls: 0.254 loss_box_reg: 0.2665 loss_rpn_cls: 0.04045 loss_rpn_loc: 0.2055 time: 0.3705 last_time: 0.3696 data_time: 0.0045 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:58:26 d2.utils.events]: \u001b[0m eta: 4:02:02 iter: 49379 total_loss: 0.8551 loss_cls: 0.2876 loss_box_reg: 0.2932 loss_rpn_cls: 0.05976 loss_rpn_loc: 0.2069 time: 0.3705 last_time: 0.4208 data_time: 0.0044 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:58:34 d2.utils.events]: \u001b[0m eta: 4:01:31 iter: 49399 total_loss: 0.6914 loss_cls: 0.228 loss_box_reg: 0.2652 loss_rpn_cls: 0.0359 loss_rpn_loc: 0.1741 time: 0.3705 last_time: 0.3798 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:58:42 d2.utils.events]: \u001b[0m eta: 4:01:17 iter: 49419 total_loss: 1.096 loss_cls: 0.355 loss_box_reg: 0.3254 loss_rpn_cls: 0.06118 loss_rpn_loc: 0.2426 time: 0.3706 last_time: 0.4178 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:58:50 d2.utils.events]: \u001b[0m eta: 4:01:36 iter: 49439 total_loss: 0.729 loss_cls: 0.2293 loss_box_reg: 0.2686 loss_rpn_cls: 0.03295 loss_rpn_loc: 0.1409 time: 0.3706 last_time: 0.4137 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:58:58 d2.utils.events]: \u001b[0m eta: 4:01:29 iter: 49459 total_loss: 0.8496 loss_cls: 0.2723 loss_box_reg: 0.3151 loss_rpn_cls: 0.05281 loss_rpn_loc: 0.212 time: 0.3706 last_time: 0.4437 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:59:06 d2.utils.events]: \u001b[0m eta: 4:01:21 iter: 49479 total_loss: 0.7656 loss_cls: 0.2417 loss_box_reg: 0.2862 loss_rpn_cls: 0.04442 loss_rpn_loc: 0.2222 time: 0.3706 last_time: 0.4155 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:59:14 d2.utils.events]: \u001b[0m eta: 4:01:10 iter: 49499 total_loss: 0.742 loss_cls: 0.2076 loss_box_reg: 0.284 loss_rpn_cls: 0.03918 loss_rpn_loc: 0.1952 time: 0.3706 last_time: 0.4378 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:59:22 d2.utils.events]: \u001b[0m eta: 4:01:08 iter: 49519 total_loss: 0.8007 loss_cls: 0.2853 loss_box_reg: 0.3041 loss_rpn_cls: 0.05521 loss_rpn_loc: 0.2086 time: 0.3706 last_time: 0.4228 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:59:30 d2.utils.events]: \u001b[0m eta: 4:01:02 iter: 49539 total_loss: 0.8841 loss_cls: 0.321 loss_box_reg: 0.3022 loss_rpn_cls: 0.04345 loss_rpn_loc: 0.2042 time: 0.3706 last_time: 0.4415 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:59:39 d2.utils.events]: \u001b[0m eta: 4:01:46 iter: 49559 total_loss: 0.72 loss_cls: 0.2229 loss_box_reg: 0.2849 loss_rpn_cls: 0.04236 loss_rpn_loc: 0.1651 time: 0.3707 last_time: 0.4117 data_time: 0.0048 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:59:47 d2.utils.events]: \u001b[0m eta: 4:01:38 iter: 49579 total_loss: 0.7491 loss_cls: 0.2122 loss_box_reg: 0.2623 loss_rpn_cls: 0.04077 loss_rpn_loc: 0.1763 time: 0.3707 last_time: 0.3719 data_time: 0.0048 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 20:59:55 d2.utils.events]: \u001b[0m eta: 4:01:30 iter: 49599 total_loss: 0.7588 loss_cls: 0.2672 loss_box_reg: 0.2994 loss_rpn_cls: 0.04426 loss_rpn_loc: 0.1947 time: 0.3707 last_time: 0.4160 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:00:03 d2.utils.events]: \u001b[0m eta: 4:01:30 iter: 49619 total_loss: 0.8565 loss_cls: 0.2858 loss_box_reg: 0.2963 loss_rpn_cls: 0.0458 loss_rpn_loc: 0.1782 time: 0.3707 last_time: 0.4286 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:00:11 d2.utils.events]: \u001b[0m eta: 4:01:28 iter: 49639 total_loss: 0.9401 loss_cls: 0.2969 loss_box_reg: 0.3115 loss_rpn_cls: 0.0653 loss_rpn_loc: 0.2175 time: 0.3707 last_time: 0.4188 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:00:20 d2.utils.events]: \u001b[0m eta: 4:01:31 iter: 49659 total_loss: 0.7852 loss_cls: 0.2483 loss_box_reg: 0.2773 loss_rpn_cls: 0.04919 loss_rpn_loc: 0.2074 time: 0.3707 last_time: 0.4841 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:00:28 d2.utils.events]: \u001b[0m eta: 4:01:21 iter: 49679 total_loss: 0.8322 loss_cls: 0.2433 loss_box_reg: 0.2862 loss_rpn_cls: 0.04751 loss_rpn_loc: 0.1906 time: 0.3708 last_time: 0.3714 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:00:37 d2.utils.events]: \u001b[0m eta: 4:01:46 iter: 49699 total_loss: 0.8701 loss_cls: 0.2434 loss_box_reg: 0.2814 loss_rpn_cls: 0.05035 loss_rpn_loc: 0.2005 time: 0.3708 last_time: 0.4202 data_time: 0.0049 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:00:45 d2.utils.events]: \u001b[0m eta: 4:01:54 iter: 49719 total_loss: 0.8234 loss_cls: 0.2749 loss_box_reg: 0.2624 loss_rpn_cls: 0.04158 loss_rpn_loc: 0.1563 time: 0.3708 last_time: 0.4371 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:00:54 d2.utils.events]: \u001b[0m eta: 4:01:46 iter: 49739 total_loss: 0.738 loss_cls: 0.2258 loss_box_reg: 0.306 loss_rpn_cls: 0.04263 loss_rpn_loc: 0.1753 time: 0.3708 last_time: 0.4133 data_time: 0.0045 last_data_time: 0.0042 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:01:02 d2.utils.events]: \u001b[0m eta: 4:01:43 iter: 49759 total_loss: 0.8527 loss_cls: 0.29 loss_box_reg: 0.3228 loss_rpn_cls: 0.04525 loss_rpn_loc: 0.2151 time: 0.3708 last_time: 0.4422 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:01:10 d2.utils.events]: \u001b[0m eta: 4:01:30 iter: 49779 total_loss: 0.8073 loss_cls: 0.2448 loss_box_reg: 0.2976 loss_rpn_cls: 0.04721 loss_rpn_loc: 0.1985 time: 0.3708 last_time: 0.4432 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:01:19 d2.utils.events]: \u001b[0m eta: 4:01:46 iter: 49799 total_loss: 0.8635 loss_cls: 0.2844 loss_box_reg: 0.3131 loss_rpn_cls: 0.04598 loss_rpn_loc: 0.2236 time: 0.3709 last_time: 0.4880 data_time: 0.0049 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:01:28 d2.utils.events]: \u001b[0m eta: 4:01:57 iter: 49819 total_loss: 0.8371 loss_cls: 0.2783 loss_box_reg: 0.3042 loss_rpn_cls: 0.0454 loss_rpn_loc: 0.2002 time: 0.3709 last_time: 0.4854 data_time: 0.0051 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:01:37 d2.utils.events]: \u001b[0m eta: 4:02:11 iter: 49839 total_loss: 0.8229 loss_cls: 0.2873 loss_box_reg: 0.2881 loss_rpn_cls: 0.04753 loss_rpn_loc: 0.2065 time: 0.3710 last_time: 0.4790 data_time: 0.0049 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:01:46 d2.utils.events]: \u001b[0m eta: 4:02:15 iter: 49859 total_loss: 0.8428 loss_cls: 0.291 loss_box_reg: 0.3166 loss_rpn_cls: 0.05284 loss_rpn_loc: 0.2135 time: 0.3710 last_time: 0.5140 data_time: 0.0050 last_data_time: 0.0056 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:01:56 d2.utils.events]: \u001b[0m eta: 4:02:25 iter: 49879 total_loss: 0.9362 loss_cls: 0.2685 loss_box_reg: 0.3415 loss_rpn_cls: 0.0589 loss_rpn_loc: 0.2268 time: 0.3710 last_time: 0.4885 data_time: 0.0050 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:02:04 d2.utils.events]: \u001b[0m eta: 4:02:26 iter: 49899 total_loss: 0.8369 loss_cls: 0.2889 loss_box_reg: 0.348 loss_rpn_cls: 0.04093 loss_rpn_loc: 0.1898 time: 0.3711 last_time: 0.4983 data_time: 0.0050 last_data_time: 0.0053 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:02:14 d2.utils.events]: \u001b[0m eta: 4:02:35 iter: 49919 total_loss: 0.8932 loss_cls: 0.2872 loss_box_reg: 0.3037 loss_rpn_cls: 0.04589 loss_rpn_loc: 0.221 time: 0.3711 last_time: 0.5048 data_time: 0.0051 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:02:23 d2.utils.events]: \u001b[0m eta: 4:02:47 iter: 49939 total_loss: 0.8254 loss_cls: 0.261 loss_box_reg: 0.299 loss_rpn_cls: 0.04305 loss_rpn_loc: 0.2182 time: 0.3711 last_time: 0.5104 data_time: 0.0050 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:02:33 d2.utils.events]: \u001b[0m eta: 4:03:04 iter: 49959 total_loss: 0.8657 loss_cls: 0.2639 loss_box_reg: 0.3036 loss_rpn_cls: 0.04577 loss_rpn_loc: 0.2164 time: 0.3712 last_time: 0.4688 data_time: 0.0051 last_data_time: 0.0053 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:02:43 d2.utils.events]: \u001b[0m eta: 4:03:34 iter: 49979 total_loss: 0.7989 loss_cls: 0.2752 loss_box_reg: 0.287 loss_rpn_cls: 0.04733 loss_rpn_loc: 0.2006 time: 0.3712 last_time: 0.4500 data_time: 0.0053 last_data_time: 0.0055 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:02:53 d2.utils.events]: \u001b[0m eta: 4:03:44 iter: 49999 total_loss: 0.8181 loss_cls: 0.2808 loss_box_reg: 0.3026 loss_rpn_cls: 0.04991 loss_rpn_loc: 0.195 time: 0.3713 last_time: 0.5124 data_time: 0.0051 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:03:02 d2.utils.events]: \u001b[0m eta: 4:04:07 iter: 50019 total_loss: 0.8232 loss_cls: 0.2641 loss_box_reg: 0.3278 loss_rpn_cls: 0.05338 loss_rpn_loc: 0.1855 time: 0.3713 last_time: 0.4627 data_time: 0.0048 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:03:12 d2.utils.events]: \u001b[0m eta: 4:04:19 iter: 50039 total_loss: 0.8105 loss_cls: 0.2804 loss_box_reg: 0.3116 loss_rpn_cls: 0.0492 loss_rpn_loc: 0.192 time: 0.3713 last_time: 0.4665 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:03:19 d2.utils.events]: \u001b[0m eta: 4:04:12 iter: 50059 total_loss: 0.7611 loss_cls: 0.2582 loss_box_reg: 0.3001 loss_rpn_cls: 0.04747 loss_rpn_loc: 0.173 time: 0.3713 last_time: 0.3785 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:03:28 d2.utils.events]: \u001b[0m eta: 4:04:09 iter: 50079 total_loss: 0.8466 loss_cls: 0.2726 loss_box_reg: 0.3133 loss_rpn_cls: 0.04823 loss_rpn_loc: 0.2143 time: 0.3714 last_time: 0.4440 data_time: 0.0044 last_data_time: 0.0041 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:03:36 d2.utils.events]: \u001b[0m eta: 4:04:04 iter: 50099 total_loss: 0.7209 loss_cls: 0.2077 loss_box_reg: 0.2867 loss_rpn_cls: 0.04993 loss_rpn_loc: 0.1704 time: 0.3714 last_time: 0.3134 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:03:44 d2.utils.events]: \u001b[0m eta: 4:03:57 iter: 50119 total_loss: 0.7807 loss_cls: 0.2585 loss_box_reg: 0.2813 loss_rpn_cls: 0.05248 loss_rpn_loc: 0.2039 time: 0.3714 last_time: 0.3731 data_time: 0.0044 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:03:52 d2.utils.events]: \u001b[0m eta: 4:03:48 iter: 50139 total_loss: 0.8001 loss_cls: 0.25 loss_box_reg: 0.3012 loss_rpn_cls: 0.05303 loss_rpn_loc: 0.1991 time: 0.3714 last_time: 0.3981 data_time: 0.0044 last_data_time: 0.0040 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:04:00 d2.utils.events]: \u001b[0m eta: 4:03:44 iter: 50159 total_loss: 0.8674 loss_cls: 0.3051 loss_box_reg: 0.2847 loss_rpn_cls: 0.03908 loss_rpn_loc: 0.1944 time: 0.3714 last_time: 0.3981 data_time: 0.0044 last_data_time: 0.0041 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:04:08 d2.utils.events]: \u001b[0m eta: 4:03:45 iter: 50179 total_loss: 0.8536 loss_cls: 0.3018 loss_box_reg: 0.3046 loss_rpn_cls: 0.0517 loss_rpn_loc: 0.175 time: 0.3714 last_time: 0.4437 data_time: 0.0043 last_data_time: 0.0041 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:04:16 d2.utils.events]: \u001b[0m eta: 4:03:44 iter: 50199 total_loss: 0.7602 loss_cls: 0.2545 loss_box_reg: 0.2918 loss_rpn_cls: 0.05098 loss_rpn_loc: 0.1819 time: 0.3714 last_time: 0.4167 data_time: 0.0044 last_data_time: 0.0041 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:04:24 d2.utils.events]: \u001b[0m eta: 4:03:37 iter: 50219 total_loss: 0.7913 loss_cls: 0.3012 loss_box_reg: 0.2806 loss_rpn_cls: 0.04134 loss_rpn_loc: 0.1861 time: 0.3714 last_time: 0.4401 data_time: 0.0045 last_data_time: 0.0042 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:04:32 d2.utils.events]: \u001b[0m eta: 4:03:20 iter: 50239 total_loss: 0.8782 loss_cls: 0.2631 loss_box_reg: 0.3005 loss_rpn_cls: 0.05782 loss_rpn_loc: 0.2341 time: 0.3715 last_time: 0.4036 data_time: 0.0044 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:04:40 d2.utils.events]: \u001b[0m eta: 4:03:24 iter: 50259 total_loss: 0.8226 loss_cls: 0.2463 loss_box_reg: 0.2932 loss_rpn_cls: 0.04788 loss_rpn_loc: 0.1752 time: 0.3715 last_time: 0.4322 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:04:48 d2.utils.events]: \u001b[0m eta: 4:03:13 iter: 50279 total_loss: 0.8534 loss_cls: 0.2476 loss_box_reg: 0.2886 loss_rpn_cls: 0.0471 loss_rpn_loc: 0.2093 time: 0.3715 last_time: 0.4001 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:04:57 d2.utils.events]: \u001b[0m eta: 4:03:18 iter: 50299 total_loss: 0.739 loss_cls: 0.2187 loss_box_reg: 0.2764 loss_rpn_cls: 0.04742 loss_rpn_loc: 0.1924 time: 0.3715 last_time: 0.4384 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:05:04 d2.utils.events]: \u001b[0m eta: 4:02:59 iter: 50319 total_loss: 0.9074 loss_cls: 0.2866 loss_box_reg: 0.3217 loss_rpn_cls: 0.03822 loss_rpn_loc: 0.221 time: 0.3715 last_time: 0.3170 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:05:13 d2.utils.events]: \u001b[0m eta: 4:02:56 iter: 50339 total_loss: 0.8985 loss_cls: 0.3303 loss_box_reg: 0.3004 loss_rpn_cls: 0.04833 loss_rpn_loc: 0.2023 time: 0.3715 last_time: 0.4165 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:05:21 d2.utils.events]: \u001b[0m eta: 4:02:50 iter: 50359 total_loss: 0.7904 loss_cls: 0.2514 loss_box_reg: 0.3079 loss_rpn_cls: 0.04263 loss_rpn_loc: 0.1795 time: 0.3715 last_time: 0.4155 data_time: 0.0044 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:05:29 d2.utils.events]: \u001b[0m eta: 4:02:51 iter: 50379 total_loss: 0.7184 loss_cls: 0.1971 loss_box_reg: 0.2743 loss_rpn_cls: 0.04288 loss_rpn_loc: 0.1761 time: 0.3716 last_time: 0.4017 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:05:37 d2.utils.events]: \u001b[0m eta: 4:02:44 iter: 50399 total_loss: 0.8717 loss_cls: 0.2481 loss_box_reg: 0.2922 loss_rpn_cls: 0.0599 loss_rpn_loc: 0.2017 time: 0.3716 last_time: 0.4008 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:05:45 d2.utils.events]: \u001b[0m eta: 4:02:34 iter: 50419 total_loss: 0.9738 loss_cls: 0.2981 loss_box_reg: 0.3346 loss_rpn_cls: 0.05331 loss_rpn_loc: 0.2247 time: 0.3716 last_time: 0.4057 data_time: 0.0046 last_data_time: 0.0041 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:05:54 d2.utils.events]: \u001b[0m eta: 4:02:58 iter: 50439 total_loss: 0.7688 loss_cls: 0.2722 loss_box_reg: 0.274 loss_rpn_cls: 0.03779 loss_rpn_loc: 0.1789 time: 0.3716 last_time: 0.4367 data_time: 0.0044 last_data_time: 0.0042 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:06:02 d2.utils.events]: \u001b[0m eta: 4:02:51 iter: 50459 total_loss: 0.8909 loss_cls: 0.2775 loss_box_reg: 0.3088 loss_rpn_cls: 0.05837 loss_rpn_loc: 0.222 time: 0.3716 last_time: 0.3206 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:06:10 d2.utils.events]: \u001b[0m eta: 4:02:42 iter: 50479 total_loss: 0.7595 loss_cls: 0.249 loss_box_reg: 0.2984 loss_rpn_cls: 0.02964 loss_rpn_loc: 0.1708 time: 0.3716 last_time: 0.3836 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:06:18 d2.utils.events]: \u001b[0m eta: 4:02:34 iter: 50499 total_loss: 0.7625 loss_cls: 0.2546 loss_box_reg: 0.3146 loss_rpn_cls: 0.04536 loss_rpn_loc: 0.1801 time: 0.3716 last_time: 0.3967 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:06:26 d2.utils.events]: \u001b[0m eta: 4:02:09 iter: 50519 total_loss: 0.7366 loss_cls: 0.2411 loss_box_reg: 0.265 loss_rpn_cls: 0.04344 loss_rpn_loc: 0.2129 time: 0.3716 last_time: 0.3382 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:06:34 d2.utils.events]: \u001b[0m eta: 4:01:51 iter: 50539 total_loss: 0.8559 loss_cls: 0.279 loss_box_reg: 0.3125 loss_rpn_cls: 0.05292 loss_rpn_loc: 0.2022 time: 0.3717 last_time: 0.3729 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:06:42 d2.utils.events]: \u001b[0m eta: 4:01:18 iter: 50559 total_loss: 0.6721 loss_cls: 0.2063 loss_box_reg: 0.258 loss_rpn_cls: 0.03883 loss_rpn_loc: 0.1499 time: 0.3717 last_time: 0.3955 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:06:50 d2.utils.events]: \u001b[0m eta: 4:01:04 iter: 50579 total_loss: 0.8173 loss_cls: 0.2631 loss_box_reg: 0.3306 loss_rpn_cls: 0.03611 loss_rpn_loc: 0.1824 time: 0.3717 last_time: 0.3917 data_time: 0.0044 last_data_time: 0.0040 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:06:59 d2.utils.events]: \u001b[0m eta: 4:01:13 iter: 50599 total_loss: 0.7533 loss_cls: 0.2197 loss_box_reg: 0.3169 loss_rpn_cls: 0.03723 loss_rpn_loc: 0.1723 time: 0.3717 last_time: 0.4314 data_time: 0.0044 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:07:07 d2.utils.events]: \u001b[0m eta: 4:01:12 iter: 50619 total_loss: 0.8473 loss_cls: 0.2542 loss_box_reg: 0.2891 loss_rpn_cls: 0.05854 loss_rpn_loc: 0.2204 time: 0.3717 last_time: 0.4265 data_time: 0.0044 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:07:14 d2.utils.events]: \u001b[0m eta: 4:01:09 iter: 50639 total_loss: 1.01 loss_cls: 0.3111 loss_box_reg: 0.3484 loss_rpn_cls: 0.06362 loss_rpn_loc: 0.2672 time: 0.3717 last_time: 0.3391 data_time: 0.0044 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:07:23 d2.utils.events]: \u001b[0m eta: 4:00:51 iter: 50659 total_loss: 0.7766 loss_cls: 0.2293 loss_box_reg: 0.2916 loss_rpn_cls: 0.05074 loss_rpn_loc: 0.2062 time: 0.3717 last_time: 0.4441 data_time: 0.0045 last_data_time: 0.0042 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:07:31 d2.utils.events]: \u001b[0m eta: 4:00:24 iter: 50679 total_loss: 0.8172 loss_cls: 0.242 loss_box_reg: 0.2641 loss_rpn_cls: 0.05967 loss_rpn_loc: 0.2303 time: 0.3718 last_time: 0.3938 data_time: 0.0046 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:07:39 d2.utils.events]: \u001b[0m eta: 3:59:53 iter: 50699 total_loss: 0.8402 loss_cls: 0.2523 loss_box_reg: 0.3043 loss_rpn_cls: 0.03817 loss_rpn_loc: 0.207 time: 0.3718 last_time: 0.3733 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:07:47 d2.utils.events]: \u001b[0m eta: 3:59:44 iter: 50719 total_loss: 0.8115 loss_cls: 0.222 loss_box_reg: 0.3151 loss_rpn_cls: 0.04474 loss_rpn_loc: 0.2126 time: 0.3718 last_time: 0.3940 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:07:55 d2.utils.events]: \u001b[0m eta: 3:59:29 iter: 50739 total_loss: 0.833 loss_cls: 0.2716 loss_box_reg: 0.2766 loss_rpn_cls: 0.04822 loss_rpn_loc: 0.1986 time: 0.3718 last_time: 0.3705 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:08:03 d2.utils.events]: \u001b[0m eta: 3:59:20 iter: 50759 total_loss: 0.8675 loss_cls: 0.2802 loss_box_reg: 0.3242 loss_rpn_cls: 0.04428 loss_rpn_loc: 0.2099 time: 0.3718 last_time: 0.4250 data_time: 0.0045 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:08:11 d2.utils.events]: \u001b[0m eta: 3:58:59 iter: 50779 total_loss: 0.8401 loss_cls: 0.2632 loss_box_reg: 0.2791 loss_rpn_cls: 0.03577 loss_rpn_loc: 0.1705 time: 0.3718 last_time: 0.4168 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:08:19 d2.utils.events]: \u001b[0m eta: 3:58:35 iter: 50799 total_loss: 0.7761 loss_cls: 0.2162 loss_box_reg: 0.2855 loss_rpn_cls: 0.04062 loss_rpn_loc: 0.1938 time: 0.3718 last_time: 0.3333 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:08:27 d2.utils.events]: \u001b[0m eta: 3:57:57 iter: 50819 total_loss: 0.945 loss_cls: 0.2647 loss_box_reg: 0.3027 loss_rpn_cls: 0.05338 loss_rpn_loc: 0.2222 time: 0.3718 last_time: 0.3413 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:08:35 d2.utils.events]: \u001b[0m eta: 3:57:15 iter: 50839 total_loss: 0.8288 loss_cls: 0.2663 loss_box_reg: 0.2977 loss_rpn_cls: 0.04504 loss_rpn_loc: 0.2167 time: 0.3718 last_time: 0.3438 data_time: 0.0046 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:08:43 d2.utils.events]: \u001b[0m eta: 3:56:57 iter: 50859 total_loss: 0.8688 loss_cls: 0.3029 loss_box_reg: 0.335 loss_rpn_cls: 0.05277 loss_rpn_loc: 0.2255 time: 0.3719 last_time: 0.3978 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:08:51 d2.utils.events]: \u001b[0m eta: 3:56:33 iter: 50879 total_loss: 0.7805 loss_cls: 0.2298 loss_box_reg: 0.3008 loss_rpn_cls: 0.04455 loss_rpn_loc: 0.2077 time: 0.3719 last_time: 0.4384 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:08:59 d2.utils.events]: \u001b[0m eta: 3:56:20 iter: 50899 total_loss: 0.7917 loss_cls: 0.2305 loss_box_reg: 0.284 loss_rpn_cls: 0.04003 loss_rpn_loc: 0.1888 time: 0.3719 last_time: 0.4350 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:09:07 d2.utils.events]: \u001b[0m eta: 3:56:01 iter: 50919 total_loss: 0.8458 loss_cls: 0.2889 loss_box_reg: 0.2864 loss_rpn_cls: 0.06786 loss_rpn_loc: 0.1852 time: 0.3719 last_time: 0.4411 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:09:15 d2.utils.events]: \u001b[0m eta: 3:55:36 iter: 50939 total_loss: 0.9254 loss_cls: 0.2684 loss_box_reg: 0.3023 loss_rpn_cls: 0.05801 loss_rpn_loc: 0.2201 time: 0.3719 last_time: 0.3945 data_time: 0.0045 last_data_time: 0.0041 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:09:24 d2.utils.events]: \u001b[0m eta: 3:55:15 iter: 50959 total_loss: 0.8259 loss_cls: 0.2637 loss_box_reg: 0.283 loss_rpn_cls: 0.05399 loss_rpn_loc: 0.2122 time: 0.3719 last_time: 0.4349 data_time: 0.0045 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:09:32 d2.utils.events]: \u001b[0m eta: 3:54:45 iter: 50979 total_loss: 0.9357 loss_cls: 0.3054 loss_box_reg: 0.3287 loss_rpn_cls: 0.06709 loss_rpn_loc: 0.1941 time: 0.3719 last_time: 0.3666 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:09:40 d2.utils.events]: \u001b[0m eta: 3:54:15 iter: 50999 total_loss: 0.8445 loss_cls: 0.271 loss_box_reg: 0.2648 loss_rpn_cls: 0.06029 loss_rpn_loc: 0.2338 time: 0.3720 last_time: 0.4437 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:09:48 d2.utils.events]: \u001b[0m eta: 3:53:35 iter: 51019 total_loss: 0.8706 loss_cls: 0.3006 loss_box_reg: 0.2952 loss_rpn_cls: 0.0476 loss_rpn_loc: 0.2118 time: 0.3720 last_time: 0.3395 data_time: 0.0044 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:09:56 d2.utils.events]: \u001b[0m eta: 3:53:09 iter: 51039 total_loss: 0.8988 loss_cls: 0.3285 loss_box_reg: 0.2867 loss_rpn_cls: 0.04304 loss_rpn_loc: 0.2074 time: 0.3720 last_time: 0.3882 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:10:04 d2.utils.events]: \u001b[0m eta: 3:52:58 iter: 51059 total_loss: 0.7491 loss_cls: 0.2233 loss_box_reg: 0.2676 loss_rpn_cls: 0.0377 loss_rpn_loc: 0.1925 time: 0.3720 last_time: 0.3968 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:10:12 d2.utils.events]: \u001b[0m eta: 3:52:50 iter: 51079 total_loss: 0.8147 loss_cls: 0.2667 loss_box_reg: 0.3058 loss_rpn_cls: 0.04972 loss_rpn_loc: 0.1728 time: 0.3720 last_time: 0.4431 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:10:20 d2.utils.events]: \u001b[0m eta: 3:52:43 iter: 51099 total_loss: 0.7705 loss_cls: 0.2442 loss_box_reg: 0.2568 loss_rpn_cls: 0.04476 loss_rpn_loc: 0.191 time: 0.3720 last_time: 0.4129 data_time: 0.0046 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:10:28 d2.utils.events]: \u001b[0m eta: 3:52:30 iter: 51119 total_loss: 0.8525 loss_cls: 0.2444 loss_box_reg: 0.2819 loss_rpn_cls: 0.0519 loss_rpn_loc: 0.1921 time: 0.3720 last_time: 0.4454 data_time: 0.0045 last_data_time: 0.0041 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:10:36 d2.utils.events]: \u001b[0m eta: 3:52:11 iter: 51139 total_loss: 0.9185 loss_cls: 0.2986 loss_box_reg: 0.3258 loss_rpn_cls: 0.05088 loss_rpn_loc: 0.2103 time: 0.3720 last_time: 0.3675 data_time: 0.0045 last_data_time: 0.0042 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:10:44 d2.utils.events]: \u001b[0m eta: 3:51:57 iter: 51159 total_loss: 0.8582 loss_cls: 0.2862 loss_box_reg: 0.3032 loss_rpn_cls: 0.05576 loss_rpn_loc: 0.2013 time: 0.3720 last_time: 0.3914 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:10:52 d2.utils.events]: \u001b[0m eta: 3:51:43 iter: 51179 total_loss: 0.7663 loss_cls: 0.266 loss_box_reg: 0.3062 loss_rpn_cls: 0.05041 loss_rpn_loc: 0.1723 time: 0.3721 last_time: 0.3696 data_time: 0.0046 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:11:00 d2.utils.events]: \u001b[0m eta: 3:51:29 iter: 51199 total_loss: 0.7758 loss_cls: 0.2439 loss_box_reg: 0.2576 loss_rpn_cls: 0.04871 loss_rpn_loc: 0.1904 time: 0.3721 last_time: 0.4007 data_time: 0.0045 last_data_time: 0.0042 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:11:08 d2.utils.events]: \u001b[0m eta: 3:51:07 iter: 51219 total_loss: 0.9352 loss_cls: 0.308 loss_box_reg: 0.3631 loss_rpn_cls: 0.04824 loss_rpn_loc: 0.2472 time: 0.3721 last_time: 0.3938 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:11:16 d2.utils.events]: \u001b[0m eta: 3:51:08 iter: 51239 total_loss: 0.9265 loss_cls: 0.3059 loss_box_reg: 0.3176 loss_rpn_cls: 0.05948 loss_rpn_loc: 0.2668 time: 0.3721 last_time: 0.3300 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:11:24 d2.utils.events]: \u001b[0m eta: 3:50:59 iter: 51259 total_loss: 0.7599 loss_cls: 0.2484 loss_box_reg: 0.2979 loss_rpn_cls: 0.05324 loss_rpn_loc: 0.2049 time: 0.3721 last_time: 0.4093 data_time: 0.0045 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:11:32 d2.utils.events]: \u001b[0m eta: 3:50:44 iter: 51279 total_loss: 0.7864 loss_cls: 0.2584 loss_box_reg: 0.2715 loss_rpn_cls: 0.03075 loss_rpn_loc: 0.1838 time: 0.3721 last_time: 0.3423 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:11:40 d2.utils.events]: \u001b[0m eta: 3:50:34 iter: 51299 total_loss: 0.7625 loss_cls: 0.2493 loss_box_reg: 0.2427 loss_rpn_cls: 0.03959 loss_rpn_loc: 0.1965 time: 0.3721 last_time: 0.3917 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:11:48 d2.utils.events]: \u001b[0m eta: 3:50:17 iter: 51319 total_loss: 0.8728 loss_cls: 0.2858 loss_box_reg: 0.3594 loss_rpn_cls: 0.04765 loss_rpn_loc: 0.1816 time: 0.3721 last_time: 0.4413 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:11:56 d2.utils.events]: \u001b[0m eta: 3:50:07 iter: 51339 total_loss: 0.8656 loss_cls: 0.2743 loss_box_reg: 0.2722 loss_rpn_cls: 0.04647 loss_rpn_loc: 0.208 time: 0.3721 last_time: 0.4340 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:12:04 d2.utils.events]: \u001b[0m eta: 3:49:56 iter: 51359 total_loss: 0.7852 loss_cls: 0.2692 loss_box_reg: 0.2956 loss_rpn_cls: 0.04457 loss_rpn_loc: 0.1708 time: 0.3722 last_time: 0.3762 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:12:12 d2.utils.events]: \u001b[0m eta: 3:49:47 iter: 51379 total_loss: 0.8047 loss_cls: 0.2655 loss_box_reg: 0.2906 loss_rpn_cls: 0.05954 loss_rpn_loc: 0.1858 time: 0.3722 last_time: 0.3933 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:12:21 d2.utils.events]: \u001b[0m eta: 3:49:35 iter: 51399 total_loss: 0.7736 loss_cls: 0.2941 loss_box_reg: 0.2831 loss_rpn_cls: 0.03816 loss_rpn_loc: 0.1722 time: 0.3722 last_time: 0.4121 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:12:29 d2.utils.events]: \u001b[0m eta: 3:49:30 iter: 51419 total_loss: 0.8367 loss_cls: 0.2993 loss_box_reg: 0.2763 loss_rpn_cls: 0.05734 loss_rpn_loc: 0.206 time: 0.3722 last_time: 0.3457 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:12:37 d2.utils.events]: \u001b[0m eta: 3:49:19 iter: 51439 total_loss: 0.71 loss_cls: 0.237 loss_box_reg: 0.2687 loss_rpn_cls: 0.04033 loss_rpn_loc: 0.1755 time: 0.3722 last_time: 0.4419 data_time: 0.0045 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:12:45 d2.utils.events]: \u001b[0m eta: 3:49:17 iter: 51459 total_loss: 0.8799 loss_cls: 0.3016 loss_box_reg: 0.3014 loss_rpn_cls: 0.0488 loss_rpn_loc: 0.2187 time: 0.3722 last_time: 0.4122 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:12:53 d2.utils.events]: \u001b[0m eta: 3:49:06 iter: 51479 total_loss: 0.9622 loss_cls: 0.3046 loss_box_reg: 0.3408 loss_rpn_cls: 0.06471 loss_rpn_loc: 0.2229 time: 0.3722 last_time: 0.3145 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:13:01 d2.utils.events]: \u001b[0m eta: 3:48:57 iter: 51499 total_loss: 0.8597 loss_cls: 0.2545 loss_box_reg: 0.3062 loss_rpn_cls: 0.05347 loss_rpn_loc: 0.1768 time: 0.3722 last_time: 0.4348 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:13:09 d2.utils.events]: \u001b[0m eta: 3:48:51 iter: 51519 total_loss: 0.8073 loss_cls: 0.2491 loss_box_reg: 0.3024 loss_rpn_cls: 0.05019 loss_rpn_loc: 0.194 time: 0.3723 last_time: 0.3951 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:13:17 d2.utils.events]: \u001b[0m eta: 3:48:32 iter: 51539 total_loss: 0.7568 loss_cls: 0.2523 loss_box_reg: 0.2549 loss_rpn_cls: 0.04996 loss_rpn_loc: 0.1888 time: 0.3723 last_time: 0.3985 data_time: 0.0047 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:13:25 d2.utils.events]: \u001b[0m eta: 3:48:30 iter: 51559 total_loss: 0.845 loss_cls: 0.2789 loss_box_reg: 0.3 loss_rpn_cls: 0.0537 loss_rpn_loc: 0.2077 time: 0.3723 last_time: 0.3677 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:13:33 d2.utils.events]: \u001b[0m eta: 3:48:18 iter: 51579 total_loss: 0.9835 loss_cls: 0.3446 loss_box_reg: 0.3513 loss_rpn_cls: 0.05879 loss_rpn_loc: 0.2405 time: 0.3723 last_time: 0.4189 data_time: 0.0046 last_data_time: 0.0041 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:13:41 d2.utils.events]: \u001b[0m eta: 3:47:57 iter: 51599 total_loss: 0.8792 loss_cls: 0.2815 loss_box_reg: 0.3199 loss_rpn_cls: 0.04827 loss_rpn_loc: 0.2055 time: 0.3723 last_time: 0.3845 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:13:49 d2.utils.events]: \u001b[0m eta: 3:47:33 iter: 51619 total_loss: 0.8441 loss_cls: 0.267 loss_box_reg: 0.334 loss_rpn_cls: 0.05676 loss_rpn_loc: 0.1783 time: 0.3723 last_time: 0.4141 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:13:57 d2.utils.events]: \u001b[0m eta: 3:47:33 iter: 51639 total_loss: 0.8216 loss_cls: 0.2833 loss_box_reg: 0.3064 loss_rpn_cls: 0.05083 loss_rpn_loc: 0.1932 time: 0.3723 last_time: 0.3756 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:14:05 d2.utils.events]: \u001b[0m eta: 3:47:16 iter: 51659 total_loss: 0.7907 loss_cls: 0.2497 loss_box_reg: 0.31 loss_rpn_cls: 0.05202 loss_rpn_loc: 0.201 time: 0.3723 last_time: 0.4234 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:14:13 d2.utils.events]: \u001b[0m eta: 3:47:05 iter: 51679 total_loss: 0.8026 loss_cls: 0.2779 loss_box_reg: 0.2832 loss_rpn_cls: 0.05256 loss_rpn_loc: 0.1879 time: 0.3723 last_time: 0.4065 data_time: 0.0044 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:14:21 d2.utils.events]: \u001b[0m eta: 3:46:52 iter: 51699 total_loss: 0.7355 loss_cls: 0.2546 loss_box_reg: 0.2595 loss_rpn_cls: 0.04285 loss_rpn_loc: 0.1603 time: 0.3724 last_time: 0.3989 data_time: 0.0045 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:14:30 d2.utils.events]: \u001b[0m eta: 3:46:32 iter: 51719 total_loss: 0.8274 loss_cls: 0.2262 loss_box_reg: 0.2816 loss_rpn_cls: 0.03962 loss_rpn_loc: 0.1869 time: 0.3724 last_time: 0.3366 data_time: 0.0046 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:14:38 d2.utils.events]: \u001b[0m eta: 3:46:36 iter: 51739 total_loss: 0.8128 loss_cls: 0.27 loss_box_reg: 0.2863 loss_rpn_cls: 0.04183 loss_rpn_loc: 0.1962 time: 0.3724 last_time: 0.3917 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:14:45 d2.utils.events]: \u001b[0m eta: 3:46:26 iter: 51759 total_loss: 0.7942 loss_cls: 0.2662 loss_box_reg: 0.2883 loss_rpn_cls: 0.0555 loss_rpn_loc: 0.2134 time: 0.3724 last_time: 0.4289 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:14:54 d2.utils.events]: \u001b[0m eta: 3:46:07 iter: 51779 total_loss: 0.8611 loss_cls: 0.2745 loss_box_reg: 0.3111 loss_rpn_cls: 0.03639 loss_rpn_loc: 0.193 time: 0.3724 last_time: 0.3221 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:15:02 d2.utils.events]: \u001b[0m eta: 3:45:43 iter: 51799 total_loss: 0.9131 loss_cls: 0.3183 loss_box_reg: 0.3293 loss_rpn_cls: 0.04255 loss_rpn_loc: 0.2276 time: 0.3724 last_time: 0.3649 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:15:10 d2.utils.events]: \u001b[0m eta: 3:45:36 iter: 51819 total_loss: 0.7381 loss_cls: 0.2386 loss_box_reg: 0.2462 loss_rpn_cls: 0.05098 loss_rpn_loc: 0.1954 time: 0.3724 last_time: 0.4068 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:15:18 d2.utils.events]: \u001b[0m eta: 3:45:30 iter: 51839 total_loss: 0.7698 loss_cls: 0.2538 loss_box_reg: 0.2697 loss_rpn_cls: 0.0437 loss_rpn_loc: 0.1777 time: 0.3724 last_time: 0.4420 data_time: 0.0046 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:15:26 d2.utils.events]: \u001b[0m eta: 3:45:40 iter: 51859 total_loss: 0.8376 loss_cls: 0.2684 loss_box_reg: 0.2907 loss_rpn_cls: 0.0546 loss_rpn_loc: 0.2216 time: 0.3725 last_time: 0.3948 data_time: 0.0046 last_data_time: 0.0041 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:15:34 d2.utils.events]: \u001b[0m eta: 3:45:37 iter: 51879 total_loss: 0.9517 loss_cls: 0.316 loss_box_reg: 0.3346 loss_rpn_cls: 0.06502 loss_rpn_loc: 0.1935 time: 0.3725 last_time: 0.4296 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:15:42 d2.utils.events]: \u001b[0m eta: 3:45:30 iter: 51899 total_loss: 0.7346 loss_cls: 0.2247 loss_box_reg: 0.2734 loss_rpn_cls: 0.03931 loss_rpn_loc: 0.191 time: 0.3725 last_time: 0.3365 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:15:50 d2.utils.events]: \u001b[0m eta: 3:45:24 iter: 51919 total_loss: 0.8379 loss_cls: 0.2757 loss_box_reg: 0.3178 loss_rpn_cls: 0.04083 loss_rpn_loc: 0.2369 time: 0.3725 last_time: 0.3977 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:15:58 d2.utils.events]: \u001b[0m eta: 3:45:12 iter: 51939 total_loss: 0.7415 loss_cls: 0.2357 loss_box_reg: 0.2625 loss_rpn_cls: 0.03999 loss_rpn_loc: 0.1862 time: 0.3725 last_time: 0.3437 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:16:07 d2.utils.events]: \u001b[0m eta: 3:45:04 iter: 51959 total_loss: 0.7932 loss_cls: 0.2404 loss_box_reg: 0.2724 loss_rpn_cls: 0.03784 loss_rpn_loc: 0.182 time: 0.3725 last_time: 0.4288 data_time: 0.0047 last_data_time: 0.0042 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:16:15 d2.utils.events]: \u001b[0m eta: 3:44:57 iter: 51979 total_loss: 0.8078 loss_cls: 0.2416 loss_box_reg: 0.2893 loss_rpn_cls: 0.05405 loss_rpn_loc: 0.1906 time: 0.3725 last_time: 0.3686 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:16:23 d2.utils.events]: \u001b[0m eta: 3:45:08 iter: 51999 total_loss: 0.7914 loss_cls: 0.2369 loss_box_reg: 0.2913 loss_rpn_cls: 0.04041 loss_rpn_loc: 0.2221 time: 0.3726 last_time: 0.4161 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:16:32 d2.utils.events]: \u001b[0m eta: 3:45:31 iter: 52019 total_loss: 0.8413 loss_cls: 0.2704 loss_box_reg: 0.2923 loss_rpn_cls: 0.04481 loss_rpn_loc: 0.1971 time: 0.3726 last_time: 0.4459 data_time: 0.0048 last_data_time: 0.0053 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:16:40 d2.utils.events]: \u001b[0m eta: 3:45:06 iter: 52039 total_loss: 0.7973 loss_cls: 0.2559 loss_box_reg: 0.3034 loss_rpn_cls: 0.05664 loss_rpn_loc: 0.1943 time: 0.3726 last_time: 0.3654 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:16:48 d2.utils.events]: \u001b[0m eta: 3:45:11 iter: 52059 total_loss: 0.7699 loss_cls: 0.2235 loss_box_reg: 0.3057 loss_rpn_cls: 0.04789 loss_rpn_loc: 0.1952 time: 0.3726 last_time: 0.3755 data_time: 0.0047 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:16:56 d2.utils.events]: \u001b[0m eta: 3:44:44 iter: 52079 total_loss: 0.8434 loss_cls: 0.225 loss_box_reg: 0.2663 loss_rpn_cls: 0.05731 loss_rpn_loc: 0.1753 time: 0.3726 last_time: 0.4147 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:17:04 d2.utils.events]: \u001b[0m eta: 3:44:21 iter: 52099 total_loss: 0.8304 loss_cls: 0.2774 loss_box_reg: 0.3397 loss_rpn_cls: 0.04959 loss_rpn_loc: 0.1855 time: 0.3726 last_time: 0.4160 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:17:12 d2.utils.events]: \u001b[0m eta: 3:44:43 iter: 52119 total_loss: 0.784 loss_cls: 0.2405 loss_box_reg: 0.263 loss_rpn_cls: 0.04628 loss_rpn_loc: 0.1889 time: 0.3726 last_time: 0.4460 data_time: 0.0047 last_data_time: 0.0041 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:17:20 d2.utils.events]: \u001b[0m eta: 3:44:43 iter: 52139 total_loss: 0.8159 loss_cls: 0.231 loss_box_reg: 0.3047 loss_rpn_cls: 0.05205 loss_rpn_loc: 0.1944 time: 0.3726 last_time: 0.4414 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:17:28 d2.utils.events]: \u001b[0m eta: 3:44:38 iter: 52159 total_loss: 0.8657 loss_cls: 0.2614 loss_box_reg: 0.2904 loss_rpn_cls: 0.05297 loss_rpn_loc: 0.204 time: 0.3727 last_time: 0.3893 data_time: 0.0046 last_data_time: 0.0041 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:17:36 d2.utils.events]: \u001b[0m eta: 3:44:25 iter: 52179 total_loss: 0.7702 loss_cls: 0.2448 loss_box_reg: 0.3002 loss_rpn_cls: 0.06306 loss_rpn_loc: 0.1856 time: 0.3727 last_time: 0.3386 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:17:44 d2.utils.events]: \u001b[0m eta: 3:44:28 iter: 52199 total_loss: 0.6524 loss_cls: 0.1825 loss_box_reg: 0.2511 loss_rpn_cls: 0.03373 loss_rpn_loc: 0.1719 time: 0.3727 last_time: 0.4185 data_time: 0.0047 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:17:53 d2.utils.events]: \u001b[0m eta: 3:44:35 iter: 52219 total_loss: 0.7898 loss_cls: 0.242 loss_box_reg: 0.3198 loss_rpn_cls: 0.04462 loss_rpn_loc: 0.1729 time: 0.3727 last_time: 0.3793 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:18:01 d2.utils.events]: \u001b[0m eta: 3:44:20 iter: 52239 total_loss: 0.9054 loss_cls: 0.3024 loss_box_reg: 0.3663 loss_rpn_cls: 0.04529 loss_rpn_loc: 0.1952 time: 0.3727 last_time: 0.4088 data_time: 0.0046 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:18:09 d2.utils.events]: \u001b[0m eta: 3:44:03 iter: 52259 total_loss: 0.8316 loss_cls: 0.2536 loss_box_reg: 0.3014 loss_rpn_cls: 0.04143 loss_rpn_loc: 0.2009 time: 0.3727 last_time: 0.4390 data_time: 0.0045 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:18:17 d2.utils.events]: \u001b[0m eta: 3:44:06 iter: 52279 total_loss: 0.7828 loss_cls: 0.2464 loss_box_reg: 0.278 loss_rpn_cls: 0.05391 loss_rpn_loc: 0.1892 time: 0.3727 last_time: 0.3972 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:18:25 d2.utils.events]: \u001b[0m eta: 3:44:09 iter: 52299 total_loss: 0.7424 loss_cls: 0.2028 loss_box_reg: 0.2797 loss_rpn_cls: 0.04729 loss_rpn_loc: 0.1876 time: 0.3727 last_time: 0.4191 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:18:33 d2.utils.events]: \u001b[0m eta: 3:44:25 iter: 52319 total_loss: 0.9499 loss_cls: 0.2892 loss_box_reg: 0.3484 loss_rpn_cls: 0.0609 loss_rpn_loc: 0.225 time: 0.3728 last_time: 0.3723 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:18:42 d2.utils.events]: \u001b[0m eta: 3:44:18 iter: 52339 total_loss: 0.9915 loss_cls: 0.3408 loss_box_reg: 0.3189 loss_rpn_cls: 0.03838 loss_rpn_loc: 0.219 time: 0.3728 last_time: 0.4387 data_time: 0.0049 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:18:50 d2.utils.events]: \u001b[0m eta: 3:44:09 iter: 52359 total_loss: 0.854 loss_cls: 0.2739 loss_box_reg: 0.3015 loss_rpn_cls: 0.05118 loss_rpn_loc: 0.1955 time: 0.3728 last_time: 0.4020 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:18:59 d2.utils.events]: \u001b[0m eta: 3:44:04 iter: 52379 total_loss: 0.9759 loss_cls: 0.3134 loss_box_reg: 0.3509 loss_rpn_cls: 0.05057 loss_rpn_loc: 0.2385 time: 0.3728 last_time: 0.4445 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:19:07 d2.utils.events]: \u001b[0m eta: 3:43:58 iter: 52399 total_loss: 0.8727 loss_cls: 0.3109 loss_box_reg: 0.2982 loss_rpn_cls: 0.04862 loss_rpn_loc: 0.1934 time: 0.3728 last_time: 0.4155 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:19:15 d2.utils.events]: \u001b[0m eta: 3:43:46 iter: 52419 total_loss: 0.8013 loss_cls: 0.229 loss_box_reg: 0.3059 loss_rpn_cls: 0.04092 loss_rpn_loc: 0.1805 time: 0.3728 last_time: 0.4091 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:19:23 d2.utils.events]: \u001b[0m eta: 3:43:39 iter: 52439 total_loss: 0.8234 loss_cls: 0.2795 loss_box_reg: 0.3049 loss_rpn_cls: 0.0523 loss_rpn_loc: 0.1787 time: 0.3728 last_time: 0.4400 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:19:31 d2.utils.events]: \u001b[0m eta: 3:43:37 iter: 52459 total_loss: 0.805 loss_cls: 0.2954 loss_box_reg: 0.2707 loss_rpn_cls: 0.05443 loss_rpn_loc: 0.1743 time: 0.3729 last_time: 0.3349 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:19:40 d2.utils.events]: \u001b[0m eta: 3:43:37 iter: 52479 total_loss: 0.7853 loss_cls: 0.2695 loss_box_reg: 0.2997 loss_rpn_cls: 0.03423 loss_rpn_loc: 0.2097 time: 0.3729 last_time: 0.4315 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:19:48 d2.utils.events]: \u001b[0m eta: 3:43:28 iter: 52499 total_loss: 0.8767 loss_cls: 0.2949 loss_box_reg: 0.3379 loss_rpn_cls: 0.04521 loss_rpn_loc: 0.201 time: 0.3729 last_time: 0.3758 data_time: 0.0046 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:19:56 d2.utils.events]: \u001b[0m eta: 3:43:28 iter: 52519 total_loss: 0.831 loss_cls: 0.2566 loss_box_reg: 0.3002 loss_rpn_cls: 0.06112 loss_rpn_loc: 0.1975 time: 0.3729 last_time: 0.3967 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:20:04 d2.utils.events]: \u001b[0m eta: 3:43:16 iter: 52539 total_loss: 0.7544 loss_cls: 0.2294 loss_box_reg: 0.2791 loss_rpn_cls: 0.04501 loss_rpn_loc: 0.209 time: 0.3729 last_time: 0.4348 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:20:12 d2.utils.events]: \u001b[0m eta: 3:43:11 iter: 52559 total_loss: 0.7878 loss_cls: 0.2353 loss_box_reg: 0.2672 loss_rpn_cls: 0.03337 loss_rpn_loc: 0.2108 time: 0.3729 last_time: 0.4159 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:20:20 d2.utils.events]: \u001b[0m eta: 3:43:08 iter: 52579 total_loss: 0.758 loss_cls: 0.2472 loss_box_reg: 0.3267 loss_rpn_cls: 0.03731 loss_rpn_loc: 0.1773 time: 0.3729 last_time: 0.4194 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:20:28 d2.utils.events]: \u001b[0m eta: 3:42:57 iter: 52599 total_loss: 0.8164 loss_cls: 0.2279 loss_box_reg: 0.293 loss_rpn_cls: 0.05872 loss_rpn_loc: 0.201 time: 0.3729 last_time: 0.3412 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:20:36 d2.utils.events]: \u001b[0m eta: 3:43:04 iter: 52619 total_loss: 0.8803 loss_cls: 0.2924 loss_box_reg: 0.2978 loss_rpn_cls: 0.04432 loss_rpn_loc: 0.2236 time: 0.3730 last_time: 0.3713 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:20:44 d2.utils.events]: \u001b[0m eta: 3:43:01 iter: 52639 total_loss: 0.8261 loss_cls: 0.2718 loss_box_reg: 0.2839 loss_rpn_cls: 0.04582 loss_rpn_loc: 0.1948 time: 0.3730 last_time: 0.4341 data_time: 0.0047 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:20:53 d2.utils.events]: \u001b[0m eta: 3:43:01 iter: 52659 total_loss: 0.9714 loss_cls: 0.3001 loss_box_reg: 0.3098 loss_rpn_cls: 0.06022 loss_rpn_loc: 0.2074 time: 0.3730 last_time: 0.4433 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:21:01 d2.utils.events]: \u001b[0m eta: 3:42:56 iter: 52679 total_loss: 0.927 loss_cls: 0.2959 loss_box_reg: 0.3298 loss_rpn_cls: 0.05908 loss_rpn_loc: 0.2068 time: 0.3730 last_time: 0.4098 data_time: 0.0048 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:21:09 d2.utils.events]: \u001b[0m eta: 3:42:51 iter: 52699 total_loss: 0.8062 loss_cls: 0.2629 loss_box_reg: 0.3104 loss_rpn_cls: 0.06162 loss_rpn_loc: 0.2096 time: 0.3730 last_time: 0.3896 data_time: 0.0048 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:21:17 d2.utils.events]: \u001b[0m eta: 3:42:59 iter: 52719 total_loss: 0.8393 loss_cls: 0.2777 loss_box_reg: 0.2967 loss_rpn_cls: 0.04944 loss_rpn_loc: 0.2299 time: 0.3730 last_time: 0.4158 data_time: 0.0047 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:21:25 d2.utils.events]: \u001b[0m eta: 3:42:46 iter: 52739 total_loss: 0.8075 loss_cls: 0.2506 loss_box_reg: 0.3351 loss_rpn_cls: 0.05586 loss_rpn_loc: 0.1987 time: 0.3730 last_time: 0.3868 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:21:33 d2.utils.events]: \u001b[0m eta: 3:42:41 iter: 52759 total_loss: 0.7813 loss_cls: 0.22 loss_box_reg: 0.2927 loss_rpn_cls: 0.04241 loss_rpn_loc: 0.2144 time: 0.3731 last_time: 0.3672 data_time: 0.0047 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:21:42 d2.utils.events]: \u001b[0m eta: 3:42:32 iter: 52779 total_loss: 0.9233 loss_cls: 0.2433 loss_box_reg: 0.2931 loss_rpn_cls: 0.05331 loss_rpn_loc: 0.2455 time: 0.3731 last_time: 0.4252 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:21:50 d2.utils.events]: \u001b[0m eta: 3:42:27 iter: 52799 total_loss: 0.8612 loss_cls: 0.2677 loss_box_reg: 0.3227 loss_rpn_cls: 0.05368 loss_rpn_loc: 0.2109 time: 0.3731 last_time: 0.3957 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:21:58 d2.utils.events]: \u001b[0m eta: 3:42:19 iter: 52819 total_loss: 0.8373 loss_cls: 0.2921 loss_box_reg: 0.2939 loss_rpn_cls: 0.04523 loss_rpn_loc: 0.1854 time: 0.3731 last_time: 0.3935 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:22:06 d2.utils.events]: \u001b[0m eta: 3:42:09 iter: 52839 total_loss: 0.8727 loss_cls: 0.2835 loss_box_reg: 0.2985 loss_rpn_cls: 0.05555 loss_rpn_loc: 0.1913 time: 0.3731 last_time: 0.4174 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:22:14 d2.utils.events]: \u001b[0m eta: 3:41:56 iter: 52859 total_loss: 0.8237 loss_cls: 0.2559 loss_box_reg: 0.3115 loss_rpn_cls: 0.04724 loss_rpn_loc: 0.1876 time: 0.3731 last_time: 0.4355 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:22:22 d2.utils.events]: \u001b[0m eta: 3:41:45 iter: 52879 total_loss: 0.7334 loss_cls: 0.2214 loss_box_reg: 0.2747 loss_rpn_cls: 0.0506 loss_rpn_loc: 0.1831 time: 0.3731 last_time: 0.4117 data_time: 0.0048 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:22:30 d2.utils.events]: \u001b[0m eta: 3:41:30 iter: 52899 total_loss: 0.7987 loss_cls: 0.2779 loss_box_reg: 0.2942 loss_rpn_cls: 0.04142 loss_rpn_loc: 0.1836 time: 0.3731 last_time: 0.4449 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:22:38 d2.utils.events]: \u001b[0m eta: 3:41:24 iter: 52919 total_loss: 0.7144 loss_cls: 0.2386 loss_box_reg: 0.2543 loss_rpn_cls: 0.04709 loss_rpn_loc: 0.186 time: 0.3732 last_time: 0.4428 data_time: 0.0046 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:22:46 d2.utils.events]: \u001b[0m eta: 3:41:20 iter: 52939 total_loss: 0.8475 loss_cls: 0.2752 loss_box_reg: 0.3083 loss_rpn_cls: 0.05908 loss_rpn_loc: 0.1964 time: 0.3732 last_time: 0.4178 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:22:54 d2.utils.events]: \u001b[0m eta: 3:41:20 iter: 52959 total_loss: 0.8157 loss_cls: 0.2513 loss_box_reg: 0.3138 loss_rpn_cls: 0.03676 loss_rpn_loc: 0.1999 time: 0.3732 last_time: 0.4314 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:23:03 d2.utils.events]: \u001b[0m eta: 3:41:07 iter: 52979 total_loss: 0.7828 loss_cls: 0.228 loss_box_reg: 0.3194 loss_rpn_cls: 0.04932 loss_rpn_loc: 0.1908 time: 0.3732 last_time: 0.4424 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:23:10 d2.utils.events]: \u001b[0m eta: 3:40:44 iter: 52999 total_loss: 0.8375 loss_cls: 0.2742 loss_box_reg: 0.299 loss_rpn_cls: 0.04634 loss_rpn_loc: 0.1923 time: 0.3732 last_time: 0.4135 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:23:18 d2.utils.events]: \u001b[0m eta: 3:40:26 iter: 53019 total_loss: 0.8034 loss_cls: 0.2552 loss_box_reg: 0.299 loss_rpn_cls: 0.05255 loss_rpn_loc: 0.1883 time: 0.3732 last_time: 0.4186 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:23:27 d2.utils.events]: \u001b[0m eta: 3:40:23 iter: 53039 total_loss: 0.782 loss_cls: 0.2526 loss_box_reg: 0.2745 loss_rpn_cls: 0.05452 loss_rpn_loc: 0.2068 time: 0.3732 last_time: 0.4193 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:23:35 d2.utils.events]: \u001b[0m eta: 3:40:12 iter: 53059 total_loss: 0.965 loss_cls: 0.2898 loss_box_reg: 0.3404 loss_rpn_cls: 0.05801 loss_rpn_loc: 0.227 time: 0.3732 last_time: 0.3409 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:23:43 d2.utils.events]: \u001b[0m eta: 3:40:13 iter: 53079 total_loss: 0.862 loss_cls: 0.2391 loss_box_reg: 0.311 loss_rpn_cls: 0.04443 loss_rpn_loc: 0.2074 time: 0.3732 last_time: 0.3918 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:23:51 d2.utils.events]: \u001b[0m eta: 3:40:10 iter: 53099 total_loss: 0.7914 loss_cls: 0.2501 loss_box_reg: 0.2835 loss_rpn_cls: 0.04442 loss_rpn_loc: 0.1905 time: 0.3733 last_time: 0.3977 data_time: 0.0049 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:23:59 d2.utils.events]: \u001b[0m eta: 3:40:08 iter: 53119 total_loss: 0.8422 loss_cls: 0.2541 loss_box_reg: 0.3274 loss_rpn_cls: 0.05075 loss_rpn_loc: 0.2127 time: 0.3733 last_time: 0.4395 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:24:08 d2.utils.events]: \u001b[0m eta: 3:40:01 iter: 53139 total_loss: 0.7566 loss_cls: 0.2674 loss_box_reg: 0.2711 loss_rpn_cls: 0.04265 loss_rpn_loc: 0.1641 time: 0.3733 last_time: 0.4118 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:24:16 d2.utils.events]: \u001b[0m eta: 3:39:54 iter: 53159 total_loss: 0.8155 loss_cls: 0.259 loss_box_reg: 0.2955 loss_rpn_cls: 0.05046 loss_rpn_loc: 0.2203 time: 0.3733 last_time: 0.3970 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:24:24 d2.utils.events]: \u001b[0m eta: 3:39:52 iter: 53179 total_loss: 0.789 loss_cls: 0.2853 loss_box_reg: 0.306 loss_rpn_cls: 0.05301 loss_rpn_loc: 0.1903 time: 0.3733 last_time: 0.2493 data_time: 0.0050 last_data_time: 0.0056 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:24:29 d2.utils.events]: \u001b[0m eta: 3:39:24 iter: 53199 total_loss: 0.8347 loss_cls: 0.2763 loss_box_reg: 0.2697 loss_rpn_cls: 0.05661 loss_rpn_loc: 0.21 time: 0.3733 last_time: 0.2509 data_time: 0.0047 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:24:34 d2.utils.events]: \u001b[0m eta: 3:38:44 iter: 53219 total_loss: 0.7148 loss_cls: 0.2057 loss_box_reg: 0.2587 loss_rpn_cls: 0.03533 loss_rpn_loc: 0.2018 time: 0.3732 last_time: 0.2329 data_time: 0.0050 last_data_time: 0.0053 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:24:39 d2.utils.events]: \u001b[0m eta: 3:38:19 iter: 53239 total_loss: 0.8087 loss_cls: 0.2277 loss_box_reg: 0.3022 loss_rpn_cls: 0.05181 loss_rpn_loc: 0.1947 time: 0.3732 last_time: 0.2085 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:24:44 d2.utils.events]: \u001b[0m eta: 3:37:50 iter: 53259 total_loss: 0.8287 loss_cls: 0.2578 loss_box_reg: 0.2961 loss_rpn_cls: 0.03983 loss_rpn_loc: 0.1687 time: 0.3731 last_time: 0.2313 data_time: 0.0050 last_data_time: 0.0042 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:24:48 d2.utils.events]: \u001b[0m eta: 3:37:12 iter: 53279 total_loss: 0.737 loss_cls: 0.2655 loss_box_reg: 0.2759 loss_rpn_cls: 0.03817 loss_rpn_loc: 0.1735 time: 0.3731 last_time: 0.2515 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:24:53 d2.utils.events]: \u001b[0m eta: 3:36:00 iter: 53299 total_loss: 0.8806 loss_cls: 0.2927 loss_box_reg: 0.3106 loss_rpn_cls: 0.04201 loss_rpn_loc: 0.2104 time: 0.3730 last_time: 0.2095 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:24:58 d2.utils.events]: \u001b[0m eta: 3:34:59 iter: 53319 total_loss: 0.8076 loss_cls: 0.245 loss_box_reg: 0.3002 loss_rpn_cls: 0.04366 loss_rpn_loc: 0.1757 time: 0.3730 last_time: 0.2488 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:25:02 d2.utils.events]: \u001b[0m eta: 3:33:13 iter: 53339 total_loss: 0.7827 loss_cls: 0.1954 loss_box_reg: 0.3016 loss_rpn_cls: 0.04074 loss_rpn_loc: 0.2069 time: 0.3729 last_time: 0.2488 data_time: 0.0045 last_data_time: 0.0042 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:25:07 d2.utils.events]: \u001b[0m eta: 3:30:25 iter: 53359 total_loss: 0.8503 loss_cls: 0.2849 loss_box_reg: 0.3464 loss_rpn_cls: 0.04658 loss_rpn_loc: 0.1994 time: 0.3729 last_time: 0.1931 data_time: 0.0046 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:25:11 d2.utils.events]: \u001b[0m eta: 3:29:32 iter: 53379 total_loss: 0.7499 loss_cls: 0.2473 loss_box_reg: 0.287 loss_rpn_cls: 0.03439 loss_rpn_loc: 0.1664 time: 0.3728 last_time: 0.2385 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:25:16 d2.utils.events]: \u001b[0m eta: 3:28:59 iter: 53399 total_loss: 0.8371 loss_cls: 0.2376 loss_box_reg: 0.3126 loss_rpn_cls: 0.04317 loss_rpn_loc: 0.2163 time: 0.3727 last_time: 0.2154 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:25:21 d2.utils.events]: \u001b[0m eta: 3:28:28 iter: 53419 total_loss: 0.7969 loss_cls: 0.2836 loss_box_reg: 0.2888 loss_rpn_cls: 0.04709 loss_rpn_loc: 0.2016 time: 0.3727 last_time: 0.2305 data_time: 0.0048 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:25:25 d2.utils.events]: \u001b[0m eta: 3:27:54 iter: 53439 total_loss: 0.8399 loss_cls: 0.2764 loss_box_reg: 0.3204 loss_rpn_cls: 0.05669 loss_rpn_loc: 0.1996 time: 0.3726 last_time: 0.2318 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:25:30 d2.utils.events]: \u001b[0m eta: 3:27:03 iter: 53459 total_loss: 0.8138 loss_cls: 0.2541 loss_box_reg: 0.2778 loss_rpn_cls: 0.05255 loss_rpn_loc: 0.1885 time: 0.3726 last_time: 0.2330 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:25:35 d2.utils.events]: \u001b[0m eta: 3:25:51 iter: 53479 total_loss: 0.7664 loss_cls: 0.2209 loss_box_reg: 0.2614 loss_rpn_cls: 0.04442 loss_rpn_loc: 0.1944 time: 0.3725 last_time: 0.2312 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:25:40 d2.utils.events]: \u001b[0m eta: 3:24:52 iter: 53499 total_loss: 0.7841 loss_cls: 0.2294 loss_box_reg: 0.2829 loss_rpn_cls: 0.04705 loss_rpn_loc: 0.193 time: 0.3725 last_time: 0.2402 data_time: 0.0049 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:25:45 d2.utils.events]: \u001b[0m eta: 3:22:32 iter: 53519 total_loss: 0.8465 loss_cls: 0.2806 loss_box_reg: 0.3179 loss_rpn_cls: 0.05015 loss_rpn_loc: 0.1792 time: 0.3724 last_time: 0.2302 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:25:49 d2.utils.events]: \u001b[0m eta: 3:18:47 iter: 53539 total_loss: 0.7859 loss_cls: 0.2609 loss_box_reg: 0.2728 loss_rpn_cls: 0.04565 loss_rpn_loc: 0.1914 time: 0.3724 last_time: 0.2000 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:25:54 d2.utils.events]: \u001b[0m eta: 3:16:42 iter: 53559 total_loss: 0.842 loss_cls: 0.2576 loss_box_reg: 0.2963 loss_rpn_cls: 0.04826 loss_rpn_loc: 0.2032 time: 0.3723 last_time: 0.2582 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:25:59 d2.utils.events]: \u001b[0m eta: 3:15:22 iter: 53579 total_loss: 0.9074 loss_cls: 0.3053 loss_box_reg: 0.3039 loss_rpn_cls: 0.05696 loss_rpn_loc: 0.2499 time: 0.3723 last_time: 0.1997 data_time: 0.0045 last_data_time: 0.0042 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:26:04 d2.utils.events]: \u001b[0m eta: 3:13:49 iter: 53599 total_loss: 0.7953 loss_cls: 0.2452 loss_box_reg: 0.2974 loss_rpn_cls: 0.05367 loss_rpn_loc: 0.1919 time: 0.3722 last_time: 0.2030 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:26:08 d2.utils.events]: \u001b[0m eta: 3:11:01 iter: 53619 total_loss: 0.9028 loss_cls: 0.3017 loss_box_reg: 0.2975 loss_rpn_cls: 0.05567 loss_rpn_loc: 0.2422 time: 0.3722 last_time: 0.2129 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:26:13 d2.utils.events]: \u001b[0m eta: 2:59:37 iter: 53639 total_loss: 0.7705 loss_cls: 0.2467 loss_box_reg: 0.2869 loss_rpn_cls: 0.04889 loss_rpn_loc: 0.1896 time: 0.3721 last_time: 0.2673 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:26:18 d2.utils.events]: \u001b[0m eta: 2:56:44 iter: 53659 total_loss: 0.8675 loss_cls: 0.3014 loss_box_reg: 0.2975 loss_rpn_cls: 0.04016 loss_rpn_loc: 0.1898 time: 0.3721 last_time: 0.2288 data_time: 0.0045 last_data_time: 0.0042 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:26:23 d2.utils.events]: \u001b[0m eta: 2:41:01 iter: 53679 total_loss: 0.755 loss_cls: 0.2221 loss_box_reg: 0.293 loss_rpn_cls: 0.04251 loss_rpn_loc: 0.1771 time: 0.3720 last_time: 0.2440 data_time: 0.0045 last_data_time: 0.0040 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:26:27 d2.utils.events]: \u001b[0m eta: 2:18:14 iter: 53699 total_loss: 0.7083 loss_cls: 0.2123 loss_box_reg: 0.2748 loss_rpn_cls: 0.04658 loss_rpn_loc: 0.1715 time: 0.3720 last_time: 0.2317 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:26:32 d2.utils.events]: \u001b[0m eta: 2:15:37 iter: 53719 total_loss: 0.7326 loss_cls: 0.24 loss_box_reg: 0.2479 loss_rpn_cls: 0.04265 loss_rpn_loc: 0.1904 time: 0.3719 last_time: 0.2313 data_time: 0.0045 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:26:37 d2.utils.events]: \u001b[0m eta: 2:14:41 iter: 53739 total_loss: 0.8691 loss_cls: 0.2905 loss_box_reg: 0.3141 loss_rpn_cls: 0.05712 loss_rpn_loc: 0.222 time: 0.3719 last_time: 0.2746 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:26:42 d2.utils.events]: \u001b[0m eta: 2:14:09 iter: 53759 total_loss: 0.7795 loss_cls: 0.2565 loss_box_reg: 0.319 loss_rpn_cls: 0.04015 loss_rpn_loc: 0.1981 time: 0.3718 last_time: 0.2411 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:26:47 d2.utils.events]: \u001b[0m eta: 2:13:40 iter: 53779 total_loss: 0.8063 loss_cls: 0.2448 loss_box_reg: 0.2721 loss_rpn_cls: 0.05108 loss_rpn_loc: 0.1906 time: 0.3718 last_time: 0.2580 data_time: 0.0046 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:26:51 d2.utils.events]: \u001b[0m eta: 2:13:13 iter: 53799 total_loss: 0.8365 loss_cls: 0.2343 loss_box_reg: 0.2896 loss_rpn_cls: 0.05119 loss_rpn_loc: 0.2128 time: 0.3717 last_time: 0.2927 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:26:56 d2.utils.events]: \u001b[0m eta: 2:12:45 iter: 53819 total_loss: 0.8763 loss_cls: 0.2732 loss_box_reg: 0.3137 loss_rpn_cls: 0.05782 loss_rpn_loc: 0.2101 time: 0.3717 last_time: 0.2300 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:27:01 d2.utils.events]: \u001b[0m eta: 2:11:45 iter: 53839 total_loss: 0.7417 loss_cls: 0.2138 loss_box_reg: 0.2862 loss_rpn_cls: 0.0527 loss_rpn_loc: 0.1899 time: 0.3716 last_time: 0.2310 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:27:06 d2.utils.events]: \u001b[0m eta: 2:10:35 iter: 53859 total_loss: 0.82 loss_cls: 0.2689 loss_box_reg: 0.2938 loss_rpn_cls: 0.03914 loss_rpn_loc: 0.1848 time: 0.3716 last_time: 0.2314 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:27:11 d2.utils.events]: \u001b[0m eta: 2:09:34 iter: 53879 total_loss: 0.8405 loss_cls: 0.272 loss_box_reg: 0.2933 loss_rpn_cls: 0.05421 loss_rpn_loc: 0.2056 time: 0.3715 last_time: 0.2316 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:27:16 d2.utils.events]: \u001b[0m eta: 2:08:53 iter: 53899 total_loss: 0.9112 loss_cls: 0.2913 loss_box_reg: 0.2933 loss_rpn_cls: 0.04916 loss_rpn_loc: 0.2001 time: 0.3715 last_time: 0.2993 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:27:21 d2.utils.events]: \u001b[0m eta: 2:08:17 iter: 53919 total_loss: 0.84 loss_cls: 0.2665 loss_box_reg: 0.2858 loss_rpn_cls: 0.04733 loss_rpn_loc: 0.1912 time: 0.3715 last_time: 0.2399 data_time: 0.0048 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:27:25 d2.utils.events]: \u001b[0m eta: 2:07:55 iter: 53939 total_loss: 0.8518 loss_cls: 0.2731 loss_box_reg: 0.3209 loss_rpn_cls: 0.0496 loss_rpn_loc: 0.1934 time: 0.3714 last_time: 0.1995 data_time: 0.0045 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:27:30 d2.utils.events]: \u001b[0m eta: 2:07:17 iter: 53959 total_loss: 0.8275 loss_cls: 0.2592 loss_box_reg: 0.2945 loss_rpn_cls: 0.0428 loss_rpn_loc: 0.2137 time: 0.3714 last_time: 0.2584 data_time: 0.0046 last_data_time: 0.0042 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:27:35 d2.utils.events]: \u001b[0m eta: 2:06:25 iter: 53979 total_loss: 0.9067 loss_cls: 0.3071 loss_box_reg: 0.3211 loss_rpn_cls: 0.0597 loss_rpn_loc: 0.2108 time: 0.3713 last_time: 0.2413 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:27:39 d2.utils.events]: \u001b[0m eta: 2:06:00 iter: 53999 total_loss: 0.9062 loss_cls: 0.2951 loss_box_reg: 0.3315 loss_rpn_cls: 0.07239 loss_rpn_loc: 0.2199 time: 0.3713 last_time: 0.2509 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:27:44 d2.utils.events]: \u001b[0m eta: 2:05:26 iter: 54019 total_loss: 0.7685 loss_cls: 0.2507 loss_box_reg: 0.2888 loss_rpn_cls: 0.04949 loss_rpn_loc: 0.2093 time: 0.3712 last_time: 0.2336 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:27:48 d2.utils.events]: \u001b[0m eta: 2:04:54 iter: 54039 total_loss: 0.8563 loss_cls: 0.275 loss_box_reg: 0.3195 loss_rpn_cls: 0.06422 loss_rpn_loc: 0.1669 time: 0.3711 last_time: 0.2223 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:27:53 d2.utils.events]: \u001b[0m eta: 2:04:30 iter: 54059 total_loss: 0.7266 loss_cls: 0.2623 loss_box_reg: 0.2641 loss_rpn_cls: 0.04925 loss_rpn_loc: 0.1821 time: 0.3711 last_time: 0.2571 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:27:58 d2.utils.events]: \u001b[0m eta: 2:04:20 iter: 54079 total_loss: 0.8265 loss_cls: 0.2555 loss_box_reg: 0.2732 loss_rpn_cls: 0.05392 loss_rpn_loc: 0.1782 time: 0.3710 last_time: 0.2758 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:28:03 d2.utils.events]: \u001b[0m eta: 2:04:11 iter: 54099 total_loss: 0.7416 loss_cls: 0.2292 loss_box_reg: 0.2776 loss_rpn_cls: 0.03653 loss_rpn_loc: 0.1816 time: 0.3710 last_time: 0.2401 data_time: 0.0049 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:28:08 d2.utils.events]: \u001b[0m eta: 2:03:56 iter: 54119 total_loss: 0.917 loss_cls: 0.2653 loss_box_reg: 0.341 loss_rpn_cls: 0.05268 loss_rpn_loc: 0.2298 time: 0.3710 last_time: 0.2179 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:28:12 d2.utils.events]: \u001b[0m eta: 2:03:40 iter: 54139 total_loss: 0.768 loss_cls: 0.2372 loss_box_reg: 0.271 loss_rpn_cls: 0.04481 loss_rpn_loc: 0.1969 time: 0.3709 last_time: 0.2461 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:28:17 d2.utils.events]: \u001b[0m eta: 2:03:23 iter: 54159 total_loss: 0.8778 loss_cls: 0.2776 loss_box_reg: 0.2827 loss_rpn_cls: 0.04255 loss_rpn_loc: 0.2 time: 0.3709 last_time: 0.2638 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:28:22 d2.utils.events]: \u001b[0m eta: 2:03:04 iter: 54179 total_loss: 0.8405 loss_cls: 0.2695 loss_box_reg: 0.2888 loss_rpn_cls: 0.04362 loss_rpn_loc: 0.1805 time: 0.3708 last_time: 0.2426 data_time: 0.0047 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:28:27 d2.utils.events]: \u001b[0m eta: 2:03:11 iter: 54199 total_loss: 0.8445 loss_cls: 0.249 loss_box_reg: 0.2926 loss_rpn_cls: 0.05282 loss_rpn_loc: 0.2005 time: 0.3708 last_time: 0.2600 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:28:32 d2.utils.events]: \u001b[0m eta: 2:03:08 iter: 54219 total_loss: 0.8839 loss_cls: 0.2865 loss_box_reg: 0.3289 loss_rpn_cls: 0.05158 loss_rpn_loc: 0.2092 time: 0.3707 last_time: 0.2454 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:28:37 d2.utils.events]: \u001b[0m eta: 2:03:13 iter: 54239 total_loss: 0.8403 loss_cls: 0.3222 loss_box_reg: 0.2908 loss_rpn_cls: 0.04949 loss_rpn_loc: 0.2154 time: 0.3707 last_time: 0.2650 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:28:42 d2.utils.events]: \u001b[0m eta: 2:03:08 iter: 54259 total_loss: 0.7644 loss_cls: 0.2505 loss_box_reg: 0.2808 loss_rpn_cls: 0.04697 loss_rpn_loc: 0.1994 time: 0.3706 last_time: 0.2617 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:28:47 d2.utils.events]: \u001b[0m eta: 2:03:08 iter: 54279 total_loss: 0.7472 loss_cls: 0.2485 loss_box_reg: 0.2625 loss_rpn_cls: 0.04501 loss_rpn_loc: 0.1709 time: 0.3706 last_time: 0.2026 data_time: 0.0045 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:28:51 d2.utils.events]: \u001b[0m eta: 2:03:15 iter: 54299 total_loss: 0.8366 loss_cls: 0.2496 loss_box_reg: 0.2713 loss_rpn_cls: 0.04529 loss_rpn_loc: 0.2043 time: 0.3705 last_time: 0.2637 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:28:56 d2.utils.events]: \u001b[0m eta: 2:03:17 iter: 54319 total_loss: 0.8643 loss_cls: 0.2299 loss_box_reg: 0.2965 loss_rpn_cls: 0.05406 loss_rpn_loc: 0.2043 time: 0.3705 last_time: 0.2162 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:29:01 d2.utils.events]: \u001b[0m eta: 2:03:15 iter: 54339 total_loss: 0.815 loss_cls: 0.2676 loss_box_reg: 0.2861 loss_rpn_cls: 0.05108 loss_rpn_loc: 0.1969 time: 0.3704 last_time: 0.2274 data_time: 0.0049 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:29:06 d2.utils.events]: \u001b[0m eta: 2:03:13 iter: 54359 total_loss: 0.7796 loss_cls: 0.2656 loss_box_reg: 0.2665 loss_rpn_cls: 0.03788 loss_rpn_loc: 0.1903 time: 0.3704 last_time: 0.3059 data_time: 0.0047 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:29:11 d2.utils.events]: \u001b[0m eta: 2:03:11 iter: 54379 total_loss: 0.9005 loss_cls: 0.3216 loss_box_reg: 0.2879 loss_rpn_cls: 0.04834 loss_rpn_loc: 0.1796 time: 0.3703 last_time: 0.2168 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:29:16 d2.utils.events]: \u001b[0m eta: 2:03:16 iter: 54399 total_loss: 0.8468 loss_cls: 0.245 loss_box_reg: 0.335 loss_rpn_cls: 0.04648 loss_rpn_loc: 0.2069 time: 0.3703 last_time: 0.2190 data_time: 0.0045 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:29:21 d2.utils.events]: \u001b[0m eta: 2:03:10 iter: 54419 total_loss: 0.911 loss_cls: 0.2923 loss_box_reg: 0.3086 loss_rpn_cls: 0.05369 loss_rpn_loc: 0.2144 time: 0.3703 last_time: 0.2057 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:29:26 d2.utils.events]: \u001b[0m eta: 2:03:19 iter: 54439 total_loss: 0.7888 loss_cls: 0.2226 loss_box_reg: 0.2821 loss_rpn_cls: 0.04947 loss_rpn_loc: 0.2236 time: 0.3702 last_time: 0.3065 data_time: 0.0049 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:29:31 d2.utils.events]: \u001b[0m eta: 2:03:16 iter: 54459 total_loss: 0.8764 loss_cls: 0.2693 loss_box_reg: 0.3234 loss_rpn_cls: 0.04308 loss_rpn_loc: 0.1812 time: 0.3702 last_time: 0.2331 data_time: 0.0049 last_data_time: 0.0042 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:29:36 d2.utils.events]: \u001b[0m eta: 2:03:10 iter: 54479 total_loss: 0.7672 loss_cls: 0.2462 loss_box_reg: 0.2748 loss_rpn_cls: 0.041 loss_rpn_loc: 0.1846 time: 0.3701 last_time: 0.2410 data_time: 0.0049 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:29:41 d2.utils.events]: \u001b[0m eta: 2:03:08 iter: 54499 total_loss: 0.9434 loss_cls: 0.3216 loss_box_reg: 0.3159 loss_rpn_cls: 0.0534 loss_rpn_loc: 0.2279 time: 0.3701 last_time: 0.2853 data_time: 0.0047 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:29:46 d2.utils.events]: \u001b[0m eta: 2:03:07 iter: 54519 total_loss: 0.8977 loss_cls: 0.3016 loss_box_reg: 0.3069 loss_rpn_cls: 0.05266 loss_rpn_loc: 0.2123 time: 0.3700 last_time: 0.2314 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:29:51 d2.utils.events]: \u001b[0m eta: 2:03:05 iter: 54539 total_loss: 0.737 loss_cls: 0.233 loss_box_reg: 0.2666 loss_rpn_cls: 0.05761 loss_rpn_loc: 0.2074 time: 0.3700 last_time: 0.1929 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:29:57 d2.utils.events]: \u001b[0m eta: 2:03:04 iter: 54559 total_loss: 0.745 loss_cls: 0.2322 loss_box_reg: 0.2879 loss_rpn_cls: 0.04124 loss_rpn_loc: 0.1751 time: 0.3700 last_time: 0.2800 data_time: 0.0048 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:30:02 d2.utils.events]: \u001b[0m eta: 2:03:17 iter: 54579 total_loss: 0.8974 loss_cls: 0.2945 loss_box_reg: 0.3241 loss_rpn_cls: 0.0511 loss_rpn_loc: 0.2011 time: 0.3699 last_time: 0.2527 data_time: 0.0049 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:30:08 d2.utils.events]: \u001b[0m eta: 2:03:20 iter: 54599 total_loss: 0.7731 loss_cls: 0.2349 loss_box_reg: 0.2696 loss_rpn_cls: 0.05233 loss_rpn_loc: 0.1915 time: 0.3699 last_time: 0.3222 data_time: 0.0051 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:30:13 d2.utils.events]: \u001b[0m eta: 2:03:23 iter: 54619 total_loss: 0.9494 loss_cls: 0.2858 loss_box_reg: 0.3299 loss_rpn_cls: 0.04281 loss_rpn_loc: 0.226 time: 0.3698 last_time: 0.2259 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:30:18 d2.utils.events]: \u001b[0m eta: 2:03:21 iter: 54639 total_loss: 0.7845 loss_cls: 0.2364 loss_box_reg: 0.3218 loss_rpn_cls: 0.03963 loss_rpn_loc: 0.1855 time: 0.3698 last_time: 0.2405 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:30:23 d2.utils.events]: \u001b[0m eta: 2:03:17 iter: 54659 total_loss: 0.7464 loss_cls: 0.2482 loss_box_reg: 0.2882 loss_rpn_cls: 0.04621 loss_rpn_loc: 0.1766 time: 0.3698 last_time: 0.2197 data_time: 0.0047 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:30:28 d2.utils.events]: \u001b[0m eta: 2:03:20 iter: 54679 total_loss: 0.7709 loss_cls: 0.2474 loss_box_reg: 0.2769 loss_rpn_cls: 0.03272 loss_rpn_loc: 0.1849 time: 0.3697 last_time: 0.2445 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:30:33 d2.utils.events]: \u001b[0m eta: 2:03:13 iter: 54699 total_loss: 0.7436 loss_cls: 0.2731 loss_box_reg: 0.2725 loss_rpn_cls: 0.04476 loss_rpn_loc: 0.1831 time: 0.3697 last_time: 0.2935 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:30:37 d2.utils.events]: \u001b[0m eta: 2:03:14 iter: 54719 total_loss: 0.9373 loss_cls: 0.2946 loss_box_reg: 0.3065 loss_rpn_cls: 0.05189 loss_rpn_loc: 0.2254 time: 0.3696 last_time: 0.2347 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:30:42 d2.utils.events]: \u001b[0m eta: 2:03:15 iter: 54739 total_loss: 0.7727 loss_cls: 0.2305 loss_box_reg: 0.284 loss_rpn_cls: 0.04371 loss_rpn_loc: 0.1842 time: 0.3696 last_time: 0.2503 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:30:47 d2.utils.events]: \u001b[0m eta: 2:03:10 iter: 54759 total_loss: 0.6833 loss_cls: 0.2387 loss_box_reg: 0.2609 loss_rpn_cls: 0.04007 loss_rpn_loc: 0.1909 time: 0.3695 last_time: 0.2381 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:30:52 d2.utils.events]: \u001b[0m eta: 2:03:10 iter: 54779 total_loss: 0.7332 loss_cls: 0.2241 loss_box_reg: 0.2774 loss_rpn_cls: 0.03442 loss_rpn_loc: 0.1827 time: 0.3695 last_time: 0.2566 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:30:58 d2.utils.events]: \u001b[0m eta: 2:03:06 iter: 54799 total_loss: 0.8163 loss_cls: 0.2546 loss_box_reg: 0.2931 loss_rpn_cls: 0.0468 loss_rpn_loc: 0.206 time: 0.3694 last_time: 0.3040 data_time: 0.0047 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:31:02 d2.utils.events]: \u001b[0m eta: 2:02:58 iter: 54819 total_loss: 0.8752 loss_cls: 0.3064 loss_box_reg: 0.3113 loss_rpn_cls: 0.06067 loss_rpn_loc: 0.1933 time: 0.3694 last_time: 0.2440 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:31:07 d2.utils.events]: \u001b[0m eta: 2:02:51 iter: 54839 total_loss: 0.7844 loss_cls: 0.2553 loss_box_reg: 0.2843 loss_rpn_cls: 0.04424 loss_rpn_loc: 0.1851 time: 0.3693 last_time: 0.2533 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:31:12 d2.utils.events]: \u001b[0m eta: 2:02:46 iter: 54859 total_loss: 0.855 loss_cls: 0.2867 loss_box_reg: 0.3395 loss_rpn_cls: 0.03313 loss_rpn_loc: 0.1971 time: 0.3693 last_time: 0.2557 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:31:16 d2.utils.events]: \u001b[0m eta: 2:02:43 iter: 54879 total_loss: 0.7328 loss_cls: 0.2401 loss_box_reg: 0.2635 loss_rpn_cls: 0.02989 loss_rpn_loc: 0.1728 time: 0.3692 last_time: 0.2138 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:31:21 d2.utils.events]: \u001b[0m eta: 2:02:35 iter: 54899 total_loss: 0.9168 loss_cls: 0.2836 loss_box_reg: 0.3256 loss_rpn_cls: 0.06028 loss_rpn_loc: 0.2378 time: 0.3692 last_time: 0.2187 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:31:26 d2.utils.events]: \u001b[0m eta: 2:02:32 iter: 54919 total_loss: 0.8323 loss_cls: 0.2737 loss_box_reg: 0.2759 loss_rpn_cls: 0.05051 loss_rpn_loc: 0.2084 time: 0.3692 last_time: 0.2229 data_time: 0.0051 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:31:31 d2.utils.events]: \u001b[0m eta: 2:02:29 iter: 54939 total_loss: 0.8057 loss_cls: 0.2473 loss_box_reg: 0.3051 loss_rpn_cls: 0.06131 loss_rpn_loc: 0.168 time: 0.3691 last_time: 0.2455 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:31:36 d2.utils.events]: \u001b[0m eta: 2:02:29 iter: 54959 total_loss: 0.7747 loss_cls: 0.2607 loss_box_reg: 0.3193 loss_rpn_cls: 0.03471 loss_rpn_loc: 0.1939 time: 0.3691 last_time: 0.2176 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:31:40 d2.utils.events]: \u001b[0m eta: 2:02:28 iter: 54979 total_loss: 0.828 loss_cls: 0.2545 loss_box_reg: 0.2841 loss_rpn_cls: 0.04793 loss_rpn_loc: 0.1867 time: 0.3690 last_time: 0.2322 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:31:46 d2.utils.events]: \u001b[0m eta: 2:02:27 iter: 54999 total_loss: 0.8513 loss_cls: 0.2525 loss_box_reg: 0.3247 loss_rpn_cls: 0.04034 loss_rpn_loc: 0.2 time: 0.3690 last_time: 0.2589 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:31:51 d2.utils.events]: \u001b[0m eta: 2:02:24 iter: 55019 total_loss: 0.8125 loss_cls: 0.2558 loss_box_reg: 0.3092 loss_rpn_cls: 0.04111 loss_rpn_loc: 0.2008 time: 0.3689 last_time: 0.2632 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:31:56 d2.utils.events]: \u001b[0m eta: 2:02:28 iter: 55039 total_loss: 0.8566 loss_cls: 0.2756 loss_box_reg: 0.3272 loss_rpn_cls: 0.04624 loss_rpn_loc: 0.2138 time: 0.3689 last_time: 0.2458 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:32:00 d2.utils.events]: \u001b[0m eta: 2:02:23 iter: 55059 total_loss: 0.7361 loss_cls: 0.218 loss_box_reg: 0.2677 loss_rpn_cls: 0.04749 loss_rpn_loc: 0.1961 time: 0.3688 last_time: 0.1896 data_time: 0.0045 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:32:05 d2.utils.events]: \u001b[0m eta: 2:02:17 iter: 55079 total_loss: 0.9054 loss_cls: 0.2994 loss_box_reg: 0.3033 loss_rpn_cls: 0.06049 loss_rpn_loc: 0.2152 time: 0.3688 last_time: 0.2633 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:32:10 d2.utils.events]: \u001b[0m eta: 2:02:07 iter: 55099 total_loss: 0.8211 loss_cls: 0.2688 loss_box_reg: 0.2863 loss_rpn_cls: 0.05452 loss_rpn_loc: 0.1799 time: 0.3687 last_time: 0.2444 data_time: 0.0047 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:32:16 d2.utils.events]: \u001b[0m eta: 2:02:08 iter: 55119 total_loss: 0.7836 loss_cls: 0.2446 loss_box_reg: 0.2948 loss_rpn_cls: 0.05054 loss_rpn_loc: 0.1919 time: 0.3687 last_time: 0.2604 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:32:20 d2.utils.events]: \u001b[0m eta: 2:02:02 iter: 55139 total_loss: 0.7282 loss_cls: 0.2111 loss_box_reg: 0.2668 loss_rpn_cls: 0.04078 loss_rpn_loc: 0.1376 time: 0.3687 last_time: 0.2199 data_time: 0.0045 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:32:25 d2.utils.events]: \u001b[0m eta: 2:01:52 iter: 55159 total_loss: 0.77 loss_cls: 0.2414 loss_box_reg: 0.3174 loss_rpn_cls: 0.04353 loss_rpn_loc: 0.2067 time: 0.3686 last_time: 0.2543 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:32:30 d2.utils.events]: \u001b[0m eta: 2:01:46 iter: 55179 total_loss: 0.8149 loss_cls: 0.2601 loss_box_reg: 0.286 loss_rpn_cls: 0.04516 loss_rpn_loc: 0.1896 time: 0.3686 last_time: 0.2610 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:32:35 d2.utils.events]: \u001b[0m eta: 2:01:40 iter: 55199 total_loss: 0.7965 loss_cls: 0.2325 loss_box_reg: 0.2933 loss_rpn_cls: 0.04242 loss_rpn_loc: 0.167 time: 0.3685 last_time: 0.2628 data_time: 0.0047 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:32:39 d2.utils.events]: \u001b[0m eta: 2:01:34 iter: 55219 total_loss: 0.8511 loss_cls: 0.2744 loss_box_reg: 0.3388 loss_rpn_cls: 0.04907 loss_rpn_loc: 0.1829 time: 0.3685 last_time: 0.2625 data_time: 0.0046 last_data_time: 0.0042 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:32:44 d2.utils.events]: \u001b[0m eta: 2:01:25 iter: 55239 total_loss: 0.819 loss_cls: 0.2688 loss_box_reg: 0.264 loss_rpn_cls: 0.0551 loss_rpn_loc: 0.164 time: 0.3684 last_time: 0.2193 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:32:49 d2.utils.events]: \u001b[0m eta: 2:01:23 iter: 55259 total_loss: 0.8561 loss_cls: 0.2631 loss_box_reg: 0.2924 loss_rpn_cls: 0.0394 loss_rpn_loc: 0.2156 time: 0.3684 last_time: 0.2214 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:32:54 d2.utils.events]: \u001b[0m eta: 2:01:15 iter: 55279 total_loss: 0.8365 loss_cls: 0.2559 loss_box_reg: 0.299 loss_rpn_cls: 0.04626 loss_rpn_loc: 0.2202 time: 0.3683 last_time: 0.2585 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:32:59 d2.utils.events]: \u001b[0m eta: 2:01:09 iter: 55299 total_loss: 0.8525 loss_cls: 0.2786 loss_box_reg: 0.3231 loss_rpn_cls: 0.04707 loss_rpn_loc: 0.1725 time: 0.3683 last_time: 0.2356 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:33:04 d2.utils.events]: \u001b[0m eta: 2:01:02 iter: 55319 total_loss: 0.7546 loss_cls: 0.2221 loss_box_reg: 0.2752 loss_rpn_cls: 0.03276 loss_rpn_loc: 0.1811 time: 0.3682 last_time: 0.2434 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:33:08 d2.utils.events]: \u001b[0m eta: 2:00:57 iter: 55339 total_loss: 0.7246 loss_cls: 0.2306 loss_box_reg: 0.2474 loss_rpn_cls: 0.03377 loss_rpn_loc: 0.1974 time: 0.3682 last_time: 0.2182 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:33:13 d2.utils.events]: \u001b[0m eta: 2:00:49 iter: 55359 total_loss: 0.7525 loss_cls: 0.2254 loss_box_reg: 0.2659 loss_rpn_cls: 0.03976 loss_rpn_loc: 0.1704 time: 0.3681 last_time: 0.2574 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:33:18 d2.utils.events]: \u001b[0m eta: 2:00:49 iter: 55379 total_loss: 0.7269 loss_cls: 0.2311 loss_box_reg: 0.2709 loss_rpn_cls: 0.04043 loss_rpn_loc: 0.1795 time: 0.3681 last_time: 0.2447 data_time: 0.0047 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:33:23 d2.utils.events]: \u001b[0m eta: 2:00:45 iter: 55399 total_loss: 0.8446 loss_cls: 0.2619 loss_box_reg: 0.3163 loss_rpn_cls: 0.04453 loss_rpn_loc: 0.2189 time: 0.3681 last_time: 0.2347 data_time: 0.0046 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:33:28 d2.utils.events]: \u001b[0m eta: 2:00:40 iter: 55419 total_loss: 0.7914 loss_cls: 0.2525 loss_box_reg: 0.2782 loss_rpn_cls: 0.05727 loss_rpn_loc: 0.1941 time: 0.3680 last_time: 0.2607 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:33:33 d2.utils.events]: \u001b[0m eta: 2:00:28 iter: 55439 total_loss: 0.7796 loss_cls: 0.228 loss_box_reg: 0.3046 loss_rpn_cls: 0.04366 loss_rpn_loc: 0.1731 time: 0.3680 last_time: 0.2373 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:33:38 d2.utils.events]: \u001b[0m eta: 2:00:26 iter: 55459 total_loss: 0.6303 loss_cls: 0.2027 loss_box_reg: 0.236 loss_rpn_cls: 0.04023 loss_rpn_loc: 0.1737 time: 0.3679 last_time: 0.2467 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:33:43 d2.utils.events]: \u001b[0m eta: 2:00:25 iter: 55479 total_loss: 0.7138 loss_cls: 0.2236 loss_box_reg: 0.285 loss_rpn_cls: 0.056 loss_rpn_loc: 0.1917 time: 0.3679 last_time: 0.2455 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:33:48 d2.utils.events]: \u001b[0m eta: 2:00:19 iter: 55499 total_loss: 0.8358 loss_cls: 0.2718 loss_box_reg: 0.3076 loss_rpn_cls: 0.05201 loss_rpn_loc: 0.2038 time: 0.3678 last_time: 0.2628 data_time: 0.0045 last_data_time: 0.0039 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:33:53 d2.utils.events]: \u001b[0m eta: 2:00:12 iter: 55519 total_loss: 0.7338 loss_cls: 0.2328 loss_box_reg: 0.2963 loss_rpn_cls: 0.04072 loss_rpn_loc: 0.1897 time: 0.3678 last_time: 0.2403 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:33:58 d2.utils.events]: \u001b[0m eta: 2:00:05 iter: 55539 total_loss: 0.776 loss_cls: 0.2634 loss_box_reg: 0.2658 loss_rpn_cls: 0.03854 loss_rpn_loc: 0.1823 time: 0.3677 last_time: 0.2323 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:34:02 d2.utils.events]: \u001b[0m eta: 1:59:57 iter: 55559 total_loss: 0.7204 loss_cls: 0.2383 loss_box_reg: 0.2425 loss_rpn_cls: 0.04877 loss_rpn_loc: 0.1837 time: 0.3677 last_time: 0.2340 data_time: 0.0047 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:34:07 d2.utils.events]: \u001b[0m eta: 1:59:52 iter: 55579 total_loss: 0.9161 loss_cls: 0.3032 loss_box_reg: 0.3227 loss_rpn_cls: 0.05581 loss_rpn_loc: 0.221 time: 0.3677 last_time: 0.2452 data_time: 0.0047 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:34:12 d2.utils.events]: \u001b[0m eta: 1:59:41 iter: 55599 total_loss: 0.8339 loss_cls: 0.2644 loss_box_reg: 0.2978 loss_rpn_cls: 0.0494 loss_rpn_loc: 0.2247 time: 0.3676 last_time: 0.2593 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:34:17 d2.utils.events]: \u001b[0m eta: 1:59:36 iter: 55619 total_loss: 0.8537 loss_cls: 0.247 loss_box_reg: 0.3403 loss_rpn_cls: 0.03947 loss_rpn_loc: 0.2025 time: 0.3676 last_time: 0.2453 data_time: 0.0047 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:34:22 d2.utils.events]: \u001b[0m eta: 1:59:31 iter: 55639 total_loss: 0.7215 loss_cls: 0.2457 loss_box_reg: 0.2947 loss_rpn_cls: 0.04601 loss_rpn_loc: 0.1782 time: 0.3675 last_time: 0.2432 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:34:26 d2.utils.events]: \u001b[0m eta: 1:59:30 iter: 55659 total_loss: 0.7986 loss_cls: 0.2549 loss_box_reg: 0.2793 loss_rpn_cls: 0.04532 loss_rpn_loc: 0.185 time: 0.3675 last_time: 0.2471 data_time: 0.0047 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:34:31 d2.utils.events]: \u001b[0m eta: 1:59:21 iter: 55679 total_loss: 0.7806 loss_cls: 0.2509 loss_box_reg: 0.3036 loss_rpn_cls: 0.04335 loss_rpn_loc: 0.1722 time: 0.3674 last_time: 0.2638 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:34:36 d2.utils.events]: \u001b[0m eta: 1:59:16 iter: 55699 total_loss: 0.7165 loss_cls: 0.2335 loss_box_reg: 0.2624 loss_rpn_cls: 0.04019 loss_rpn_loc: 0.1712 time: 0.3674 last_time: 0.2642 data_time: 0.0046 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:34:41 d2.utils.events]: \u001b[0m eta: 1:59:15 iter: 55719 total_loss: 0.9287 loss_cls: 0.2989 loss_box_reg: 0.3073 loss_rpn_cls: 0.04808 loss_rpn_loc: 0.2371 time: 0.3673 last_time: 0.2605 data_time: 0.0045 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:34:46 d2.utils.events]: \u001b[0m eta: 1:59:06 iter: 55739 total_loss: 0.8176 loss_cls: 0.271 loss_box_reg: 0.2979 loss_rpn_cls: 0.04572 loss_rpn_loc: 0.201 time: 0.3673 last_time: 0.2635 data_time: 0.0045 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:34:51 d2.utils.events]: \u001b[0m eta: 1:59:02 iter: 55759 total_loss: 0.8582 loss_cls: 0.2555 loss_box_reg: 0.3119 loss_rpn_cls: 0.05572 loss_rpn_loc: 0.2382 time: 0.3672 last_time: 0.2460 data_time: 0.0045 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:34:55 d2.utils.events]: \u001b[0m eta: 1:58:56 iter: 55779 total_loss: 0.8476 loss_cls: 0.2553 loss_box_reg: 0.3264 loss_rpn_cls: 0.05086 loss_rpn_loc: 0.2056 time: 0.3672 last_time: 0.2627 data_time: 0.0047 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:35:00 d2.utils.events]: \u001b[0m eta: 1:58:51 iter: 55799 total_loss: 0.7815 loss_cls: 0.2664 loss_box_reg: 0.2505 loss_rpn_cls: 0.03833 loss_rpn_loc: 0.1878 time: 0.3672 last_time: 0.1893 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:35:05 d2.utils.events]: \u001b[0m eta: 1:58:47 iter: 55819 total_loss: 0.7681 loss_cls: 0.2448 loss_box_reg: 0.293 loss_rpn_cls: 0.05394 loss_rpn_loc: 0.1732 time: 0.3671 last_time: 0.2361 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:35:11 d2.utils.events]: \u001b[0m eta: 1:58:50 iter: 55839 total_loss: 0.7861 loss_cls: 0.2767 loss_box_reg: 0.289 loss_rpn_cls: 0.04732 loss_rpn_loc: 0.1766 time: 0.3671 last_time: 0.2965 data_time: 0.0049 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:35:15 d2.utils.events]: \u001b[0m eta: 1:58:45 iter: 55859 total_loss: 0.9105 loss_cls: 0.2832 loss_box_reg: 0.3347 loss_rpn_cls: 0.06602 loss_rpn_loc: 0.2089 time: 0.3670 last_time: 0.2187 data_time: 0.0049 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:35:20 d2.utils.events]: \u001b[0m eta: 1:58:41 iter: 55879 total_loss: 0.8 loss_cls: 0.2419 loss_box_reg: 0.2791 loss_rpn_cls: 0.0475 loss_rpn_loc: 0.207 time: 0.3670 last_time: 0.2188 data_time: 0.0046 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:35:25 d2.utils.events]: \u001b[0m eta: 1:58:36 iter: 55899 total_loss: 0.8293 loss_cls: 0.2649 loss_box_reg: 0.3314 loss_rpn_cls: 0.0537 loss_rpn_loc: 0.2082 time: 0.3669 last_time: 0.2458 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:35:30 d2.utils.events]: \u001b[0m eta: 1:58:30 iter: 55919 total_loss: 0.7798 loss_cls: 0.2692 loss_box_reg: 0.3204 loss_rpn_cls: 0.0451 loss_rpn_loc: 0.1961 time: 0.3669 last_time: 0.2456 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:35:35 d2.utils.events]: \u001b[0m eta: 1:58:25 iter: 55939 total_loss: 0.896 loss_cls: 0.28 loss_box_reg: 0.2913 loss_rpn_cls: 0.05441 loss_rpn_loc: 0.1924 time: 0.3668 last_time: 0.2622 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:35:39 d2.utils.events]: \u001b[0m eta: 1:58:17 iter: 55959 total_loss: 0.6816 loss_cls: 0.1819 loss_box_reg: 0.2858 loss_rpn_cls: 0.03719 loss_rpn_loc: 0.1693 time: 0.3668 last_time: 0.2477 data_time: 0.0046 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:35:44 d2.utils.events]: \u001b[0m eta: 1:58:12 iter: 55979 total_loss: 0.8498 loss_cls: 0.2779 loss_box_reg: 0.3033 loss_rpn_cls: 0.03777 loss_rpn_loc: 0.1683 time: 0.3668 last_time: 0.2681 data_time: 0.0047 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:35:49 d2.utils.events]: \u001b[0m eta: 1:58:07 iter: 55999 total_loss: 0.8167 loss_cls: 0.2516 loss_box_reg: 0.2772 loss_rpn_cls: 0.05064 loss_rpn_loc: 0.1969 time: 0.3667 last_time: 0.2591 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:35:55 d2.utils.events]: \u001b[0m eta: 1:58:05 iter: 56019 total_loss: 0.8929 loss_cls: 0.2625 loss_box_reg: 0.3329 loss_rpn_cls: 0.05065 loss_rpn_loc: 0.2289 time: 0.3667 last_time: 0.2182 data_time: 0.0048 last_data_time: 0.0055 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:36:00 d2.utils.events]: \u001b[0m eta: 1:57:59 iter: 56039 total_loss: 0.7578 loss_cls: 0.2165 loss_box_reg: 0.3104 loss_rpn_cls: 0.03646 loss_rpn_loc: 0.217 time: 0.3666 last_time: 0.2467 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:36:05 d2.utils.events]: \u001b[0m eta: 1:57:57 iter: 56059 total_loss: 0.7494 loss_cls: 0.2446 loss_box_reg: 0.2728 loss_rpn_cls: 0.0492 loss_rpn_loc: 0.1829 time: 0.3666 last_time: 0.2604 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:36:10 d2.utils.events]: \u001b[0m eta: 1:57:51 iter: 56079 total_loss: 0.6529 loss_cls: 0.1879 loss_box_reg: 0.2377 loss_rpn_cls: 0.03713 loss_rpn_loc: 0.1556 time: 0.3666 last_time: 0.3353 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:36:15 d2.utils.events]: \u001b[0m eta: 1:57:49 iter: 56099 total_loss: 0.858 loss_cls: 0.2596 loss_box_reg: 0.3072 loss_rpn_cls: 0.04489 loss_rpn_loc: 0.2017 time: 0.3665 last_time: 0.3360 data_time: 0.0049 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:36:21 d2.utils.events]: \u001b[0m eta: 1:57:48 iter: 56119 total_loss: 0.8132 loss_cls: 0.2607 loss_box_reg: 0.3161 loss_rpn_cls: 0.03892 loss_rpn_loc: 0.2035 time: 0.3665 last_time: 0.2563 data_time: 0.0049 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:36:26 d2.utils.events]: \u001b[0m eta: 1:57:43 iter: 56139 total_loss: 0.8386 loss_cls: 0.2491 loss_box_reg: 0.2848 loss_rpn_cls: 0.05804 loss_rpn_loc: 0.2192 time: 0.3664 last_time: 0.3101 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:36:31 d2.utils.events]: \u001b[0m eta: 1:57:42 iter: 56159 total_loss: 0.7246 loss_cls: 0.192 loss_box_reg: 0.3028 loss_rpn_cls: 0.05138 loss_rpn_loc: 0.183 time: 0.3664 last_time: 0.2627 data_time: 0.0051 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:36:36 d2.utils.events]: \u001b[0m eta: 1:57:38 iter: 56179 total_loss: 0.8475 loss_cls: 0.2827 loss_box_reg: 0.2947 loss_rpn_cls: 0.04403 loss_rpn_loc: 0.188 time: 0.3664 last_time: 0.3153 data_time: 0.0049 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:36:41 d2.utils.events]: \u001b[0m eta: 1:57:35 iter: 56199 total_loss: 0.7736 loss_cls: 0.2717 loss_box_reg: 0.2909 loss_rpn_cls: 0.03725 loss_rpn_loc: 0.1659 time: 0.3663 last_time: 0.2543 data_time: 0.0048 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:36:46 d2.utils.events]: \u001b[0m eta: 1:57:31 iter: 56219 total_loss: 0.8497 loss_cls: 0.2641 loss_box_reg: 0.2979 loss_rpn_cls: 0.05014 loss_rpn_loc: 0.1903 time: 0.3663 last_time: 0.2908 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:36:52 d2.utils.events]: \u001b[0m eta: 1:57:32 iter: 56239 total_loss: 0.8554 loss_cls: 0.2824 loss_box_reg: 0.3212 loss_rpn_cls: 0.046 loss_rpn_loc: 0.1972 time: 0.3663 last_time: 0.2615 data_time: 0.0049 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:36:57 d2.utils.events]: \u001b[0m eta: 1:57:29 iter: 56259 total_loss: 0.8108 loss_cls: 0.2502 loss_box_reg: 0.3016 loss_rpn_cls: 0.03818 loss_rpn_loc: 0.2049 time: 0.3662 last_time: 0.2332 data_time: 0.0049 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:37:03 d2.utils.events]: \u001b[0m eta: 1:57:30 iter: 56279 total_loss: 0.7003 loss_cls: 0.2363 loss_box_reg: 0.2729 loss_rpn_cls: 0.0409 loss_rpn_loc: 0.1762 time: 0.3662 last_time: 0.2189 data_time: 0.0052 last_data_time: 0.0055 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:37:08 d2.utils.events]: \u001b[0m eta: 1:57:29 iter: 56299 total_loss: 0.8559 loss_cls: 0.2723 loss_box_reg: 0.2989 loss_rpn_cls: 0.05083 loss_rpn_loc: 0.2024 time: 0.3662 last_time: 0.2606 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:37:13 d2.utils.events]: \u001b[0m eta: 1:57:24 iter: 56319 total_loss: 0.7078 loss_cls: 0.2076 loss_box_reg: 0.2659 loss_rpn_cls: 0.04319 loss_rpn_loc: 0.194 time: 0.3661 last_time: 0.2405 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:37:18 d2.utils.events]: \u001b[0m eta: 1:57:23 iter: 56339 total_loss: 0.956 loss_cls: 0.2752 loss_box_reg: 0.3544 loss_rpn_cls: 0.05164 loss_rpn_loc: 0.2255 time: 0.3661 last_time: 0.2451 data_time: 0.0049 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:37:23 d2.utils.events]: \u001b[0m eta: 1:57:20 iter: 56359 total_loss: 0.8976 loss_cls: 0.2754 loss_box_reg: 0.3021 loss_rpn_cls: 0.05521 loss_rpn_loc: 0.1949 time: 0.3660 last_time: 0.2205 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:37:28 d2.utils.events]: \u001b[0m eta: 1:57:18 iter: 56379 total_loss: 0.716 loss_cls: 0.2226 loss_box_reg: 0.2649 loss_rpn_cls: 0.04563 loss_rpn_loc: 0.172 time: 0.3660 last_time: 0.2646 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:37:33 d2.utils.events]: \u001b[0m eta: 1:57:13 iter: 56399 total_loss: 0.7901 loss_cls: 0.2708 loss_box_reg: 0.272 loss_rpn_cls: 0.05238 loss_rpn_loc: 0.1972 time: 0.3660 last_time: 0.2606 data_time: 0.0047 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:37:38 d2.utils.events]: \u001b[0m eta: 1:57:09 iter: 56419 total_loss: 0.9049 loss_cls: 0.2685 loss_box_reg: 0.3095 loss_rpn_cls: 0.04978 loss_rpn_loc: 0.219 time: 0.3659 last_time: 0.2475 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:37:43 d2.utils.events]: \u001b[0m eta: 1:57:09 iter: 56439 total_loss: 0.8921 loss_cls: 0.2567 loss_box_reg: 0.295 loss_rpn_cls: 0.05701 loss_rpn_loc: 0.1842 time: 0.3659 last_time: 0.1903 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:37:48 d2.utils.events]: \u001b[0m eta: 1:57:01 iter: 56459 total_loss: 0.8833 loss_cls: 0.2758 loss_box_reg: 0.3169 loss_rpn_cls: 0.05669 loss_rpn_loc: 0.1889 time: 0.3658 last_time: 0.2636 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:37:52 d2.utils.events]: \u001b[0m eta: 1:56:56 iter: 56479 total_loss: 0.7301 loss_cls: 0.2063 loss_box_reg: 0.2858 loss_rpn_cls: 0.04453 loss_rpn_loc: 0.1866 time: 0.3658 last_time: 0.2333 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:37:57 d2.utils.events]: \u001b[0m eta: 1:56:50 iter: 56499 total_loss: 0.894 loss_cls: 0.2763 loss_box_reg: 0.3434 loss_rpn_cls: 0.04683 loss_rpn_loc: 0.2018 time: 0.3657 last_time: 0.2060 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:38:02 d2.utils.events]: \u001b[0m eta: 1:56:46 iter: 56519 total_loss: 0.7302 loss_cls: 0.2047 loss_box_reg: 0.2934 loss_rpn_cls: 0.0472 loss_rpn_loc: 0.1601 time: 0.3657 last_time: 0.2434 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:38:07 d2.utils.events]: \u001b[0m eta: 1:56:40 iter: 56539 total_loss: 0.8614 loss_cls: 0.2524 loss_box_reg: 0.3064 loss_rpn_cls: 0.04639 loss_rpn_loc: 0.1971 time: 0.3656 last_time: 0.2028 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:38:12 d2.utils.events]: \u001b[0m eta: 1:56:35 iter: 56559 total_loss: 0.7435 loss_cls: 0.2225 loss_box_reg: 0.2679 loss_rpn_cls: 0.04105 loss_rpn_loc: 0.2041 time: 0.3656 last_time: 0.2604 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:38:16 d2.utils.events]: \u001b[0m eta: 1:56:30 iter: 56579 total_loss: 0.933 loss_cls: 0.2586 loss_box_reg: 0.348 loss_rpn_cls: 0.04786 loss_rpn_loc: 0.2158 time: 0.3656 last_time: 0.2124 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:38:21 d2.utils.events]: \u001b[0m eta: 1:56:29 iter: 56599 total_loss: 0.8596 loss_cls: 0.2684 loss_box_reg: 0.3532 loss_rpn_cls: 0.04837 loss_rpn_loc: 0.1811 time: 0.3655 last_time: 0.2347 data_time: 0.0047 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:38:26 d2.utils.events]: \u001b[0m eta: 1:56:22 iter: 56619 total_loss: 0.7402 loss_cls: 0.2678 loss_box_reg: 0.2593 loss_rpn_cls: 0.04779 loss_rpn_loc: 0.162 time: 0.3655 last_time: 0.2210 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:38:31 d2.utils.events]: \u001b[0m eta: 1:56:15 iter: 56639 total_loss: 0.7751 loss_cls: 0.2284 loss_box_reg: 0.2882 loss_rpn_cls: 0.04578 loss_rpn_loc: 0.1791 time: 0.3654 last_time: 0.2031 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:38:36 d2.utils.events]: \u001b[0m eta: 1:56:10 iter: 56659 total_loss: 0.8326 loss_cls: 0.2802 loss_box_reg: 0.2771 loss_rpn_cls: 0.05158 loss_rpn_loc: 0.2176 time: 0.3654 last_time: 0.2346 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:38:40 d2.utils.events]: \u001b[0m eta: 1:56:05 iter: 56679 total_loss: 0.6866 loss_cls: 0.2118 loss_box_reg: 0.2625 loss_rpn_cls: 0.02925 loss_rpn_loc: 0.1796 time: 0.3653 last_time: 0.2220 data_time: 0.0047 last_data_time: 0.0064 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:38:45 d2.utils.events]: \u001b[0m eta: 1:56:05 iter: 56699 total_loss: 0.8929 loss_cls: 0.2747 loss_box_reg: 0.33 loss_rpn_cls: 0.03693 loss_rpn_loc: 0.2039 time: 0.3653 last_time: 0.2204 data_time: 0.0049 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:38:50 d2.utils.events]: \u001b[0m eta: 1:55:58 iter: 56719 total_loss: 0.7823 loss_cls: 0.2575 loss_box_reg: 0.3077 loss_rpn_cls: 0.04393 loss_rpn_loc: 0.1931 time: 0.3652 last_time: 0.2350 data_time: 0.0047 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:38:55 d2.utils.events]: \u001b[0m eta: 1:55:53 iter: 56739 total_loss: 0.8374 loss_cls: 0.2952 loss_box_reg: 0.2874 loss_rpn_cls: 0.04483 loss_rpn_loc: 0.1927 time: 0.3652 last_time: 0.2188 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:39:00 d2.utils.events]: \u001b[0m eta: 1:55:51 iter: 56759 total_loss: 0.7865 loss_cls: 0.2395 loss_box_reg: 0.2627 loss_rpn_cls: 0.05779 loss_rpn_loc: 0.1923 time: 0.3652 last_time: 0.2350 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:39:05 d2.utils.events]: \u001b[0m eta: 1:55:45 iter: 56779 total_loss: 0.8298 loss_cls: 0.2841 loss_box_reg: 0.3275 loss_rpn_cls: 0.03853 loss_rpn_loc: 0.1948 time: 0.3651 last_time: 0.2041 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:39:10 d2.utils.events]: \u001b[0m eta: 1:55:38 iter: 56799 total_loss: 0.7378 loss_cls: 0.2339 loss_box_reg: 0.2578 loss_rpn_cls: 0.038 loss_rpn_loc: 0.1642 time: 0.3651 last_time: 0.2467 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:39:14 d2.utils.events]: \u001b[0m eta: 1:55:35 iter: 56819 total_loss: 0.8821 loss_cls: 0.2892 loss_box_reg: 0.3022 loss_rpn_cls: 0.04771 loss_rpn_loc: 0.1896 time: 0.3650 last_time: 0.2600 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:39:19 d2.utils.events]: \u001b[0m eta: 1:55:28 iter: 56839 total_loss: 0.7296 loss_cls: 0.2229 loss_box_reg: 0.2904 loss_rpn_cls: 0.04558 loss_rpn_loc: 0.1948 time: 0.3650 last_time: 0.2473 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:39:24 d2.utils.events]: \u001b[0m eta: 1:55:24 iter: 56859 total_loss: 0.871 loss_cls: 0.287 loss_box_reg: 0.3359 loss_rpn_cls: 0.05381 loss_rpn_loc: 0.1968 time: 0.3649 last_time: 0.2432 data_time: 0.0046 last_data_time: 0.0053 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:39:29 d2.utils.events]: \u001b[0m eta: 1:55:20 iter: 56879 total_loss: 0.777 loss_cls: 0.2506 loss_box_reg: 0.289 loss_rpn_cls: 0.04861 loss_rpn_loc: 0.1927 time: 0.3649 last_time: 0.2446 data_time: 0.0046 last_data_time: 0.0042 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:39:34 d2.utils.events]: \u001b[0m eta: 1:55:19 iter: 56899 total_loss: 0.7542 loss_cls: 0.2386 loss_box_reg: 0.2882 loss_rpn_cls: 0.04238 loss_rpn_loc: 0.1864 time: 0.3649 last_time: 0.2620 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:39:39 d2.utils.events]: \u001b[0m eta: 1:55:14 iter: 56919 total_loss: 0.8356 loss_cls: 0.2668 loss_box_reg: 0.2951 loss_rpn_cls: 0.0463 loss_rpn_loc: 0.191 time: 0.3648 last_time: 0.2207 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:39:44 d2.utils.events]: \u001b[0m eta: 1:55:11 iter: 56939 total_loss: 0.791 loss_cls: 0.2695 loss_box_reg: 0.2855 loss_rpn_cls: 0.05282 loss_rpn_loc: 0.1802 time: 0.3648 last_time: 0.2851 data_time: 0.0047 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:39:49 d2.utils.events]: \u001b[0m eta: 1:55:07 iter: 56959 total_loss: 0.8243 loss_cls: 0.2762 loss_box_reg: 0.2983 loss_rpn_cls: 0.04483 loss_rpn_loc: 0.173 time: 0.3647 last_time: 0.2227 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:39:54 d2.utils.events]: \u001b[0m eta: 1:55:04 iter: 56979 total_loss: 0.7626 loss_cls: 0.2431 loss_box_reg: 0.2615 loss_rpn_cls: 0.04765 loss_rpn_loc: 0.187 time: 0.3647 last_time: 0.2478 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:39:58 d2.utils.events]: \u001b[0m eta: 1:55:01 iter: 56999 total_loss: 0.8169 loss_cls: 0.2576 loss_box_reg: 0.2957 loss_rpn_cls: 0.05098 loss_rpn_loc: 0.1925 time: 0.3646 last_time: 0.2685 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:40:03 d2.utils.events]: \u001b[0m eta: 1:54:52 iter: 57019 total_loss: 0.8278 loss_cls: 0.2556 loss_box_reg: 0.2973 loss_rpn_cls: 0.03935 loss_rpn_loc: 0.1843 time: 0.3646 last_time: 0.2395 data_time: 0.0049 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:40:08 d2.utils.events]: \u001b[0m eta: 1:54:45 iter: 57039 total_loss: 0.8253 loss_cls: 0.273 loss_box_reg: 0.3027 loss_rpn_cls: 0.05069 loss_rpn_loc: 0.1822 time: 0.3646 last_time: 0.2384 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:40:13 d2.utils.events]: \u001b[0m eta: 1:54:40 iter: 57059 total_loss: 0.8979 loss_cls: 0.3179 loss_box_reg: 0.331 loss_rpn_cls: 0.04594 loss_rpn_loc: 0.2053 time: 0.3645 last_time: 0.2365 data_time: 0.0047 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:40:17 d2.utils.events]: \u001b[0m eta: 1:54:37 iter: 57079 total_loss: 0.7862 loss_cls: 0.2251 loss_box_reg: 0.2912 loss_rpn_cls: 0.03844 loss_rpn_loc: 0.1767 time: 0.3645 last_time: 0.1882 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:40:22 d2.utils.events]: \u001b[0m eta: 1:54:29 iter: 57099 total_loss: 0.7245 loss_cls: 0.2161 loss_box_reg: 0.2537 loss_rpn_cls: 0.04496 loss_rpn_loc: 0.1772 time: 0.3644 last_time: 0.2517 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:40:27 d2.utils.events]: \u001b[0m eta: 1:54:19 iter: 57119 total_loss: 0.7755 loss_cls: 0.2231 loss_box_reg: 0.2992 loss_rpn_cls: 0.04786 loss_rpn_loc: 0.2074 time: 0.3644 last_time: 0.2537 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:40:32 d2.utils.events]: \u001b[0m eta: 1:54:13 iter: 57139 total_loss: 0.7774 loss_cls: 0.2566 loss_box_reg: 0.2846 loss_rpn_cls: 0.05011 loss_rpn_loc: 0.2024 time: 0.3643 last_time: 0.2511 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:40:36 d2.utils.events]: \u001b[0m eta: 1:53:59 iter: 57159 total_loss: 0.8877 loss_cls: 0.3038 loss_box_reg: 0.3234 loss_rpn_cls: 0.0621 loss_rpn_loc: 0.2273 time: 0.3643 last_time: 0.2385 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:40:41 d2.utils.events]: \u001b[0m eta: 1:53:54 iter: 57179 total_loss: 0.7784 loss_cls: 0.2611 loss_box_reg: 0.2926 loss_rpn_cls: 0.03913 loss_rpn_loc: 0.1559 time: 0.3642 last_time: 0.2522 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:40:46 d2.utils.events]: \u001b[0m eta: 1:53:46 iter: 57199 total_loss: 0.7845 loss_cls: 0.2504 loss_box_reg: 0.2478 loss_rpn_cls: 0.04434 loss_rpn_loc: 0.1743 time: 0.3642 last_time: 0.2537 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:40:50 d2.utils.events]: \u001b[0m eta: 1:53:33 iter: 57219 total_loss: 0.864 loss_cls: 0.2787 loss_box_reg: 0.3003 loss_rpn_cls: 0.04166 loss_rpn_loc: 0.2298 time: 0.3642 last_time: 0.2144 data_time: 0.0048 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:40:55 d2.utils.events]: \u001b[0m eta: 1:53:20 iter: 57239 total_loss: 0.8742 loss_cls: 0.2773 loss_box_reg: 0.292 loss_rpn_cls: 0.04821 loss_rpn_loc: 0.1727 time: 0.3641 last_time: 0.2523 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:41:00 d2.utils.events]: \u001b[0m eta: 1:53:05 iter: 57259 total_loss: 0.7533 loss_cls: 0.2788 loss_box_reg: 0.2754 loss_rpn_cls: 0.04555 loss_rpn_loc: 0.1806 time: 0.3641 last_time: 0.2605 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:41:05 d2.utils.events]: \u001b[0m eta: 1:52:52 iter: 57279 total_loss: 0.7892 loss_cls: 0.2593 loss_box_reg: 0.2896 loss_rpn_cls: 0.0335 loss_rpn_loc: 0.2125 time: 0.3640 last_time: 0.2367 data_time: 0.0048 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:41:09 d2.utils.events]: \u001b[0m eta: 1:52:40 iter: 57299 total_loss: 0.9099 loss_cls: 0.3107 loss_box_reg: 0.3186 loss_rpn_cls: 0.07258 loss_rpn_loc: 0.2425 time: 0.3640 last_time: 0.2554 data_time: 0.0049 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:41:14 d2.utils.events]: \u001b[0m eta: 1:52:29 iter: 57319 total_loss: 0.8089 loss_cls: 0.2698 loss_box_reg: 0.3151 loss_rpn_cls: 0.04322 loss_rpn_loc: 0.1756 time: 0.3639 last_time: 0.2387 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:41:18 d2.utils.events]: \u001b[0m eta: 1:52:04 iter: 57339 total_loss: 0.7024 loss_cls: 0.1931 loss_box_reg: 0.2559 loss_rpn_cls: 0.04457 loss_rpn_loc: 0.1729 time: 0.3639 last_time: 0.2147 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:41:23 d2.utils.events]: \u001b[0m eta: 1:51:53 iter: 57359 total_loss: 0.8759 loss_cls: 0.2937 loss_box_reg: 0.2971 loss_rpn_cls: 0.04885 loss_rpn_loc: 0.1664 time: 0.3638 last_time: 0.2403 data_time: 0.0049 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:41:28 d2.utils.events]: \u001b[0m eta: 1:51:40 iter: 57379 total_loss: 0.8309 loss_cls: 0.2609 loss_box_reg: 0.3168 loss_rpn_cls: 0.04118 loss_rpn_loc: 0.1858 time: 0.3638 last_time: 0.2624 data_time: 0.0052 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:41:33 d2.utils.events]: \u001b[0m eta: 1:51:17 iter: 57399 total_loss: 0.8596 loss_cls: 0.2741 loss_box_reg: 0.2981 loss_rpn_cls: 0.07408 loss_rpn_loc: 0.2023 time: 0.3637 last_time: 0.2535 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:41:38 d2.utils.events]: \u001b[0m eta: 1:51:24 iter: 57419 total_loss: 0.8742 loss_cls: 0.2739 loss_box_reg: 0.2897 loss_rpn_cls: 0.05633 loss_rpn_loc: 0.1911 time: 0.3637 last_time: 0.2361 data_time: 0.0049 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:41:42 d2.utils.events]: \u001b[0m eta: 1:51:00 iter: 57439 total_loss: 0.7549 loss_cls: 0.238 loss_box_reg: 0.2751 loss_rpn_cls: 0.04166 loss_rpn_loc: 0.2143 time: 0.3637 last_time: 0.2361 data_time: 0.0047 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:41:47 d2.utils.events]: \u001b[0m eta: 1:50:53 iter: 57459 total_loss: 0.7784 loss_cls: 0.2011 loss_box_reg: 0.2915 loss_rpn_cls: 0.04027 loss_rpn_loc: 0.2045 time: 0.3636 last_time: 0.2056 data_time: 0.0048 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:41:52 d2.utils.events]: \u001b[0m eta: 1:50:54 iter: 57479 total_loss: 0.7063 loss_cls: 0.2181 loss_box_reg: 0.2805 loss_rpn_cls: 0.0359 loss_rpn_loc: 0.1577 time: 0.3636 last_time: 0.2579 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:41:57 d2.utils.events]: \u001b[0m eta: 1:50:48 iter: 57499 total_loss: 0.8755 loss_cls: 0.2789 loss_box_reg: 0.2891 loss_rpn_cls: 0.06259 loss_rpn_loc: 0.198 time: 0.3635 last_time: 0.2203 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:42:02 d2.utils.events]: \u001b[0m eta: 1:50:33 iter: 57519 total_loss: 0.7521 loss_cls: 0.2383 loss_box_reg: 0.3086 loss_rpn_cls: 0.0519 loss_rpn_loc: 0.1765 time: 0.3635 last_time: 0.2345 data_time: 0.0048 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:42:07 d2.utils.events]: \u001b[0m eta: 1:50:48 iter: 57539 total_loss: 0.8688 loss_cls: 0.2632 loss_box_reg: 0.2961 loss_rpn_cls: 0.05856 loss_rpn_loc: 0.2153 time: 0.3634 last_time: 0.2560 data_time: 0.0048 last_data_time: 0.0053 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:42:11 d2.utils.events]: \u001b[0m eta: 1:50:34 iter: 57559 total_loss: 0.736 loss_cls: 0.22 loss_box_reg: 0.2764 loss_rpn_cls: 0.04483 loss_rpn_loc: 0.1827 time: 0.3634 last_time: 0.1868 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:42:16 d2.utils.events]: \u001b[0m eta: 1:50:33 iter: 57579 total_loss: 0.8515 loss_cls: 0.26 loss_box_reg: 0.2755 loss_rpn_cls: 0.0495 loss_rpn_loc: 0.1928 time: 0.3634 last_time: 0.2612 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:42:21 d2.utils.events]: \u001b[0m eta: 1:50:41 iter: 57599 total_loss: 0.7786 loss_cls: 0.2487 loss_box_reg: 0.272 loss_rpn_cls: 0.04647 loss_rpn_loc: 0.2046 time: 0.3633 last_time: 0.2210 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:42:26 d2.utils.events]: \u001b[0m eta: 1:50:33 iter: 57619 total_loss: 0.7797 loss_cls: 0.2542 loss_box_reg: 0.2891 loss_rpn_cls: 0.04066 loss_rpn_loc: 0.2014 time: 0.3633 last_time: 0.2157 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:42:31 d2.utils.events]: \u001b[0m eta: 1:50:28 iter: 57639 total_loss: 0.8101 loss_cls: 0.2571 loss_box_reg: 0.2698 loss_rpn_cls: 0.0474 loss_rpn_loc: 0.2086 time: 0.3632 last_time: 0.2377 data_time: 0.0047 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:42:36 d2.utils.events]: \u001b[0m eta: 1:49:53 iter: 57659 total_loss: 0.785 loss_cls: 0.273 loss_box_reg: 0.2823 loss_rpn_cls: 0.04605 loss_rpn_loc: 0.2172 time: 0.3632 last_time: 0.2255 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:42:40 d2.utils.events]: \u001b[0m eta: 1:49:46 iter: 57679 total_loss: 0.7319 loss_cls: 0.2343 loss_box_reg: 0.2728 loss_rpn_cls: 0.03947 loss_rpn_loc: 0.1842 time: 0.3631 last_time: 0.2494 data_time: 0.0047 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:42:45 d2.utils.events]: \u001b[0m eta: 1:49:34 iter: 57699 total_loss: 0.7321 loss_cls: 0.2362 loss_box_reg: 0.2722 loss_rpn_cls: 0.04181 loss_rpn_loc: 0.1665 time: 0.3631 last_time: 0.2031 data_time: 0.0048 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:42:50 d2.utils.events]: \u001b[0m eta: 1:49:30 iter: 57719 total_loss: 0.8644 loss_cls: 0.2746 loss_box_reg: 0.3432 loss_rpn_cls: 0.06459 loss_rpn_loc: 0.1961 time: 0.3631 last_time: 0.2555 data_time: 0.0046 last_data_time: 0.0042 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:42:55 d2.utils.events]: \u001b[0m eta: 1:49:10 iter: 57739 total_loss: 0.7985 loss_cls: 0.2366 loss_box_reg: 0.2919 loss_rpn_cls: 0.04991 loss_rpn_loc: 0.202 time: 0.3630 last_time: 0.2279 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:42:59 d2.utils.events]: \u001b[0m eta: 1:48:51 iter: 57759 total_loss: 0.9182 loss_cls: 0.3224 loss_box_reg: 0.3384 loss_rpn_cls: 0.04891 loss_rpn_loc: 0.1874 time: 0.3630 last_time: 0.2403 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:43:04 d2.utils.events]: \u001b[0m eta: 1:48:46 iter: 57779 total_loss: 0.9083 loss_cls: 0.2972 loss_box_reg: 0.3414 loss_rpn_cls: 0.04549 loss_rpn_loc: 0.2184 time: 0.3629 last_time: 0.2550 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:43:09 d2.utils.events]: \u001b[0m eta: 1:48:41 iter: 57799 total_loss: 0.7339 loss_cls: 0.2078 loss_box_reg: 0.268 loss_rpn_cls: 0.03809 loss_rpn_loc: 0.195 time: 0.3629 last_time: 0.1940 data_time: 0.0049 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:43:14 d2.utils.events]: \u001b[0m eta: 1:48:39 iter: 57819 total_loss: 0.8551 loss_cls: 0.2904 loss_box_reg: 0.2753 loss_rpn_cls: 0.04055 loss_rpn_loc: 0.2039 time: 0.3628 last_time: 0.3108 data_time: 0.0048 last_data_time: 0.0055 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:43:19 d2.utils.events]: \u001b[0m eta: 1:48:40 iter: 57839 total_loss: 0.6886 loss_cls: 0.2319 loss_box_reg: 0.2779 loss_rpn_cls: 0.04252 loss_rpn_loc: 0.1701 time: 0.3628 last_time: 0.2317 data_time: 0.0050 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:43:24 d2.utils.events]: \u001b[0m eta: 1:48:30 iter: 57859 total_loss: 0.7534 loss_cls: 0.2246 loss_box_reg: 0.2714 loss_rpn_cls: 0.05029 loss_rpn_loc: 0.2105 time: 0.3628 last_time: 0.2017 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:43:29 d2.utils.events]: \u001b[0m eta: 1:48:25 iter: 57879 total_loss: 0.9038 loss_cls: 0.2854 loss_box_reg: 0.2973 loss_rpn_cls: 0.05545 loss_rpn_loc: 0.2242 time: 0.3627 last_time: 0.2038 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:43:34 d2.utils.events]: \u001b[0m eta: 1:48:23 iter: 57899 total_loss: 0.8891 loss_cls: 0.2804 loss_box_reg: 0.3278 loss_rpn_cls: 0.06892 loss_rpn_loc: 0.2205 time: 0.3627 last_time: 0.2118 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:43:38 d2.utils.events]: \u001b[0m eta: 1:48:08 iter: 57919 total_loss: 0.7735 loss_cls: 0.2377 loss_box_reg: 0.277 loss_rpn_cls: 0.05089 loss_rpn_loc: 0.1912 time: 0.3626 last_time: 0.1857 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:43:43 d2.utils.events]: \u001b[0m eta: 1:47:54 iter: 57939 total_loss: 0.7899 loss_cls: 0.2467 loss_box_reg: 0.2836 loss_rpn_cls: 0.04537 loss_rpn_loc: 0.2068 time: 0.3626 last_time: 0.2403 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:43:47 d2.utils.events]: \u001b[0m eta: 1:47:46 iter: 57959 total_loss: 0.8743 loss_cls: 0.2634 loss_box_reg: 0.2834 loss_rpn_cls: 0.04189 loss_rpn_loc: 0.2212 time: 0.3625 last_time: 0.2289 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:43:52 d2.utils.events]: \u001b[0m eta: 1:47:33 iter: 57979 total_loss: 0.7937 loss_cls: 0.2486 loss_box_reg: 0.2857 loss_rpn_cls: 0.05303 loss_rpn_loc: 0.1965 time: 0.3625 last_time: 0.2132 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:43:57 d2.utils.events]: \u001b[0m eta: 1:47:24 iter: 57999 total_loss: 0.7145 loss_cls: 0.2191 loss_box_reg: 0.2631 loss_rpn_cls: 0.0447 loss_rpn_loc: 0.1659 time: 0.3625 last_time: 0.2555 data_time: 0.0047 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:44:02 d2.utils.events]: \u001b[0m eta: 1:47:22 iter: 58019 total_loss: 0.7165 loss_cls: 0.2073 loss_box_reg: 0.263 loss_rpn_cls: 0.03683 loss_rpn_loc: 0.1899 time: 0.3624 last_time: 0.2942 data_time: 0.0050 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:44:07 d2.utils.events]: \u001b[0m eta: 1:47:26 iter: 58039 total_loss: 0.8635 loss_cls: 0.2633 loss_box_reg: 0.3111 loss_rpn_cls: 0.04592 loss_rpn_loc: 0.1949 time: 0.3624 last_time: 0.2469 data_time: 0.0049 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:44:12 d2.utils.events]: \u001b[0m eta: 1:47:21 iter: 58059 total_loss: 0.7037 loss_cls: 0.2096 loss_box_reg: 0.2766 loss_rpn_cls: 0.04089 loss_rpn_loc: 0.1805 time: 0.3623 last_time: 0.2257 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:44:17 d2.utils.events]: \u001b[0m eta: 1:47:17 iter: 58079 total_loss: 0.8022 loss_cls: 0.2844 loss_box_reg: 0.2961 loss_rpn_cls: 0.05061 loss_rpn_loc: 0.1873 time: 0.3623 last_time: 0.2422 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:44:22 d2.utils.events]: \u001b[0m eta: 1:47:10 iter: 58099 total_loss: 0.7242 loss_cls: 0.2907 loss_box_reg: 0.2777 loss_rpn_cls: 0.03526 loss_rpn_loc: 0.1575 time: 0.3623 last_time: 0.2246 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:44:26 d2.utils.events]: \u001b[0m eta: 1:47:08 iter: 58119 total_loss: 0.8596 loss_cls: 0.2882 loss_box_reg: 0.3018 loss_rpn_cls: 0.04708 loss_rpn_loc: 0.2183 time: 0.3622 last_time: 0.2635 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:44:31 d2.utils.events]: \u001b[0m eta: 1:47:04 iter: 58139 total_loss: 0.7945 loss_cls: 0.2508 loss_box_reg: 0.3043 loss_rpn_cls: 0.03808 loss_rpn_loc: 0.1964 time: 0.3622 last_time: 0.2570 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:44:36 d2.utils.events]: \u001b[0m eta: 1:47:00 iter: 58159 total_loss: 0.8266 loss_cls: 0.28 loss_box_reg: 0.2965 loss_rpn_cls: 0.03897 loss_rpn_loc: 0.1942 time: 0.3621 last_time: 0.3032 data_time: 0.0048 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:44:40 d2.utils.events]: \u001b[0m eta: 1:46:54 iter: 58179 total_loss: 0.7211 loss_cls: 0.2172 loss_box_reg: 0.2712 loss_rpn_cls: 0.05022 loss_rpn_loc: 0.1857 time: 0.3621 last_time: 0.2539 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:44:45 d2.utils.events]: \u001b[0m eta: 1:46:55 iter: 58199 total_loss: 0.7787 loss_cls: 0.2475 loss_box_reg: 0.2836 loss_rpn_cls: 0.0408 loss_rpn_loc: 0.1953 time: 0.3620 last_time: 0.2422 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:44:50 d2.utils.events]: \u001b[0m eta: 1:47:02 iter: 58219 total_loss: 0.7137 loss_cls: 0.2088 loss_box_reg: 0.2651 loss_rpn_cls: 0.04051 loss_rpn_loc: 0.158 time: 0.3620 last_time: 0.2618 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:44:55 d2.utils.events]: \u001b[0m eta: 1:47:04 iter: 58239 total_loss: 0.867 loss_cls: 0.3101 loss_box_reg: 0.311 loss_rpn_cls: 0.05026 loss_rpn_loc: 0.1978 time: 0.3620 last_time: 0.2345 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:45:00 d2.utils.events]: \u001b[0m eta: 1:47:04 iter: 58259 total_loss: 0.8164 loss_cls: 0.2527 loss_box_reg: 0.2403 loss_rpn_cls: 0.04467 loss_rpn_loc: 0.1984 time: 0.3619 last_time: 0.2195 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:45:05 d2.utils.events]: \u001b[0m eta: 1:47:01 iter: 58279 total_loss: 0.7917 loss_cls: 0.2233 loss_box_reg: 0.2904 loss_rpn_cls: 0.03331 loss_rpn_loc: 0.1841 time: 0.3619 last_time: 0.2328 data_time: 0.0049 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:45:10 d2.utils.events]: \u001b[0m eta: 1:46:57 iter: 58299 total_loss: 0.8101 loss_cls: 0.266 loss_box_reg: 0.309 loss_rpn_cls: 0.04434 loss_rpn_loc: 0.1944 time: 0.3618 last_time: 0.2191 data_time: 0.0047 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:45:15 d2.utils.events]: \u001b[0m eta: 1:47:05 iter: 58319 total_loss: 0.7683 loss_cls: 0.2798 loss_box_reg: 0.279 loss_rpn_cls: 0.05221 loss_rpn_loc: 0.2013 time: 0.3618 last_time: 0.2983 data_time: 0.0054 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:45:21 d2.utils.events]: \u001b[0m eta: 1:47:32 iter: 58339 total_loss: 0.8625 loss_cls: 0.2671 loss_box_reg: 0.318 loss_rpn_cls: 0.05073 loss_rpn_loc: 0.2178 time: 0.3618 last_time: 0.2439 data_time: 0.0053 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:45:26 d2.utils.events]: \u001b[0m eta: 1:47:33 iter: 58359 total_loss: 0.826 loss_cls: 0.2237 loss_box_reg: 0.2865 loss_rpn_cls: 0.04419 loss_rpn_loc: 0.1986 time: 0.3617 last_time: 0.2523 data_time: 0.0050 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:45:31 d2.utils.events]: \u001b[0m eta: 1:47:32 iter: 58379 total_loss: 0.822 loss_cls: 0.2687 loss_box_reg: 0.2811 loss_rpn_cls: 0.04021 loss_rpn_loc: 0.1598 time: 0.3617 last_time: 0.2702 data_time: 0.0051 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:45:36 d2.utils.events]: \u001b[0m eta: 1:47:53 iter: 58399 total_loss: 0.793 loss_cls: 0.2426 loss_box_reg: 0.2849 loss_rpn_cls: 0.04153 loss_rpn_loc: 0.1981 time: 0.3617 last_time: 0.2736 data_time: 0.0049 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:45:42 d2.utils.events]: \u001b[0m eta: 1:47:58 iter: 58419 total_loss: 0.8249 loss_cls: 0.2559 loss_box_reg: 0.3122 loss_rpn_cls: 0.05306 loss_rpn_loc: 0.2031 time: 0.3617 last_time: 0.3021 data_time: 0.0052 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:45:47 d2.utils.events]: \u001b[0m eta: 1:47:53 iter: 58439 total_loss: 0.834 loss_cls: 0.2676 loss_box_reg: 0.2914 loss_rpn_cls: 0.04127 loss_rpn_loc: 0.2027 time: 0.3616 last_time: 0.2345 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:45:53 d2.utils.events]: \u001b[0m eta: 1:47:54 iter: 58459 total_loss: 0.7464 loss_cls: 0.216 loss_box_reg: 0.2958 loss_rpn_cls: 0.04433 loss_rpn_loc: 0.211 time: 0.3616 last_time: 0.2590 data_time: 0.0050 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:45:58 d2.utils.events]: \u001b[0m eta: 1:47:52 iter: 58479 total_loss: 0.8486 loss_cls: 0.2616 loss_box_reg: 0.2788 loss_rpn_cls: 0.05404 loss_rpn_loc: 0.185 time: 0.3615 last_time: 0.2331 data_time: 0.0048 last_data_time: 0.0056 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:46:03 d2.utils.events]: \u001b[0m eta: 1:47:55 iter: 58499 total_loss: 0.7662 loss_cls: 0.246 loss_box_reg: 0.2981 loss_rpn_cls: 0.04712 loss_rpn_loc: 0.1924 time: 0.3615 last_time: 0.2628 data_time: 0.0047 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:46:08 d2.utils.events]: \u001b[0m eta: 1:47:51 iter: 58519 total_loss: 0.8664 loss_cls: 0.3038 loss_box_reg: 0.282 loss_rpn_cls: 0.04981 loss_rpn_loc: 0.206 time: 0.3615 last_time: 0.2254 data_time: 0.0049 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:46:13 d2.utils.events]: \u001b[0m eta: 1:47:45 iter: 58539 total_loss: 0.7828 loss_cls: 0.2374 loss_box_reg: 0.2891 loss_rpn_cls: 0.0532 loss_rpn_loc: 0.2057 time: 0.3614 last_time: 0.3297 data_time: 0.0050 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:46:18 d2.utils.events]: \u001b[0m eta: 1:47:34 iter: 58559 total_loss: 0.8421 loss_cls: 0.2686 loss_box_reg: 0.3145 loss_rpn_cls: 0.0518 loss_rpn_loc: 0.2231 time: 0.3614 last_time: 0.2479 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:46:23 d2.utils.events]: \u001b[0m eta: 1:47:31 iter: 58579 total_loss: 0.78 loss_cls: 0.2446 loss_box_reg: 0.282 loss_rpn_cls: 0.04819 loss_rpn_loc: 0.1848 time: 0.3614 last_time: 0.2618 data_time: 0.0054 last_data_time: 0.0115 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:46:27 d2.utils.events]: \u001b[0m eta: 1:47:26 iter: 58599 total_loss: 0.7079 loss_cls: 0.2283 loss_box_reg: 0.271 loss_rpn_cls: 0.04204 loss_rpn_loc: 0.1597 time: 0.3613 last_time: 0.2602 data_time: 0.0050 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:46:32 d2.utils.events]: \u001b[0m eta: 1:47:26 iter: 58619 total_loss: 0.8774 loss_cls: 0.2846 loss_box_reg: 0.3062 loss_rpn_cls: 0.04994 loss_rpn_loc: 0.208 time: 0.3613 last_time: 0.2800 data_time: 0.0048 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:46:37 d2.utils.events]: \u001b[0m eta: 1:47:21 iter: 58639 total_loss: 0.7665 loss_cls: 0.2592 loss_box_reg: 0.2752 loss_rpn_cls: 0.04824 loss_rpn_loc: 0.1763 time: 0.3612 last_time: 0.3042 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:46:42 d2.utils.events]: \u001b[0m eta: 1:47:17 iter: 58659 total_loss: 0.7721 loss_cls: 0.258 loss_box_reg: 0.31 loss_rpn_cls: 0.0387 loss_rpn_loc: 0.185 time: 0.3612 last_time: 0.2345 data_time: 0.0047 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:46:47 d2.utils.events]: \u001b[0m eta: 1:47:15 iter: 58679 total_loss: 0.7783 loss_cls: 0.244 loss_box_reg: 0.2875 loss_rpn_cls: 0.04232 loss_rpn_loc: 0.1671 time: 0.3612 last_time: 0.2596 data_time: 0.0049 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:46:52 d2.utils.events]: \u001b[0m eta: 1:47:06 iter: 58699 total_loss: 0.8026 loss_cls: 0.2806 loss_box_reg: 0.3099 loss_rpn_cls: 0.04022 loss_rpn_loc: 0.1866 time: 0.3611 last_time: 0.2168 data_time: 0.0047 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:46:56 d2.utils.events]: \u001b[0m eta: 1:46:57 iter: 58719 total_loss: 0.8155 loss_cls: 0.2319 loss_box_reg: 0.3125 loss_rpn_cls: 0.04995 loss_rpn_loc: 0.2106 time: 0.3611 last_time: 0.2320 data_time: 0.0048 last_data_time: 0.0053 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:47:01 d2.utils.events]: \u001b[0m eta: 1:46:55 iter: 58739 total_loss: 0.7276 loss_cls: 0.2215 loss_box_reg: 0.291 loss_rpn_cls: 0.04897 loss_rpn_loc: 0.1879 time: 0.3610 last_time: 0.2181 data_time: 0.0049 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:47:06 d2.utils.events]: \u001b[0m eta: 1:46:51 iter: 58759 total_loss: 0.7433 loss_cls: 0.2211 loss_box_reg: 0.2859 loss_rpn_cls: 0.04132 loss_rpn_loc: 0.1886 time: 0.3610 last_time: 0.2426 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:47:10 d2.utils.events]: \u001b[0m eta: 1:46:47 iter: 58779 total_loss: 0.727 loss_cls: 0.2282 loss_box_reg: 0.2667 loss_rpn_cls: 0.04553 loss_rpn_loc: 0.1728 time: 0.3609 last_time: 0.2444 data_time: 0.0049 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:47:15 d2.utils.events]: \u001b[0m eta: 1:46:42 iter: 58799 total_loss: 0.8668 loss_cls: 0.252 loss_box_reg: 0.326 loss_rpn_cls: 0.05083 loss_rpn_loc: 0.2066 time: 0.3609 last_time: 0.2446 data_time: 0.0049 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:47:20 d2.utils.events]: \u001b[0m eta: 1:46:37 iter: 58819 total_loss: 0.7189 loss_cls: 0.2461 loss_box_reg: 0.2616 loss_rpn_cls: 0.04653 loss_rpn_loc: 0.1752 time: 0.3609 last_time: 0.2584 data_time: 0.0048 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:47:25 d2.utils.events]: \u001b[0m eta: 1:46:24 iter: 58839 total_loss: 0.8274 loss_cls: 0.2488 loss_box_reg: 0.3029 loss_rpn_cls: 0.04691 loss_rpn_loc: 0.2113 time: 0.3608 last_time: 0.2372 data_time: 0.0048 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:47:30 d2.utils.events]: \u001b[0m eta: 1:46:20 iter: 58859 total_loss: 0.7779 loss_cls: 0.2495 loss_box_reg: 0.2951 loss_rpn_cls: 0.05887 loss_rpn_loc: 0.1925 time: 0.3608 last_time: 0.2530 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:47:34 d2.utils.events]: \u001b[0m eta: 1:46:10 iter: 58879 total_loss: 0.8168 loss_cls: 0.2401 loss_box_reg: 0.3015 loss_rpn_cls: 0.04407 loss_rpn_loc: 0.203 time: 0.3607 last_time: 0.2448 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:47:39 d2.utils.events]: \u001b[0m eta: 1:45:59 iter: 58899 total_loss: 0.8876 loss_cls: 0.2755 loss_box_reg: 0.3125 loss_rpn_cls: 0.05313 loss_rpn_loc: 0.2105 time: 0.3607 last_time: 0.2321 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:47:44 d2.utils.events]: \u001b[0m eta: 1:45:56 iter: 58919 total_loss: 0.8612 loss_cls: 0.2712 loss_box_reg: 0.3102 loss_rpn_cls: 0.05315 loss_rpn_loc: 0.2099 time: 0.3606 last_time: 0.2583 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:47:49 d2.utils.events]: \u001b[0m eta: 1:45:58 iter: 58939 total_loss: 0.7699 loss_cls: 0.2495 loss_box_reg: 0.2739 loss_rpn_cls: 0.0494 loss_rpn_loc: 0.1987 time: 0.3606 last_time: 0.2339 data_time: 0.0048 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:47:54 d2.utils.events]: \u001b[0m eta: 1:45:56 iter: 58959 total_loss: 0.8639 loss_cls: 0.2832 loss_box_reg: 0.3003 loss_rpn_cls: 0.0447 loss_rpn_loc: 0.2002 time: 0.3606 last_time: 0.2483 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:47:58 d2.utils.events]: \u001b[0m eta: 1:45:54 iter: 58979 total_loss: 0.8238 loss_cls: 0.2648 loss_box_reg: 0.3067 loss_rpn_cls: 0.05643 loss_rpn_loc: 0.1907 time: 0.3605 last_time: 0.2594 data_time: 0.0049 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:48:03 d2.utils.events]: \u001b[0m eta: 1:45:53 iter: 58999 total_loss: 0.7934 loss_cls: 0.2388 loss_box_reg: 0.2928 loss_rpn_cls: 0.05615 loss_rpn_loc: 0.1943 time: 0.3605 last_time: 0.2581 data_time: 0.0047 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:48:08 d2.utils.events]: \u001b[0m eta: 1:45:42 iter: 59019 total_loss: 0.7516 loss_cls: 0.2315 loss_box_reg: 0.3027 loss_rpn_cls: 0.03722 loss_rpn_loc: 0.2031 time: 0.3604 last_time: 0.2259 data_time: 0.0047 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:48:13 d2.utils.events]: \u001b[0m eta: 1:45:36 iter: 59039 total_loss: 0.7424 loss_cls: 0.222 loss_box_reg: 0.2766 loss_rpn_cls: 0.03584 loss_rpn_loc: 0.1665 time: 0.3604 last_time: 0.2440 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:48:18 d2.utils.events]: \u001b[0m eta: 1:45:30 iter: 59059 total_loss: 0.7201 loss_cls: 0.2032 loss_box_reg: 0.2781 loss_rpn_cls: 0.04221 loss_rpn_loc: 0.1918 time: 0.3604 last_time: 0.2422 data_time: 0.0047 last_data_time: 0.0042 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:48:22 d2.utils.events]: \u001b[0m eta: 1:45:19 iter: 59079 total_loss: 0.8709 loss_cls: 0.2966 loss_box_reg: 0.3095 loss_rpn_cls: 0.04723 loss_rpn_loc: 0.2099 time: 0.3603 last_time: 0.2072 data_time: 0.0050 last_data_time: 0.0086 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:48:27 d2.utils.events]: \u001b[0m eta: 1:45:17 iter: 59099 total_loss: 0.7933 loss_cls: 0.2605 loss_box_reg: 0.29 loss_rpn_cls: 0.03776 loss_rpn_loc: 0.1924 time: 0.3603 last_time: 0.2596 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:48:32 d2.utils.events]: \u001b[0m eta: 1:45:13 iter: 59119 total_loss: 0.7874 loss_cls: 0.2346 loss_box_reg: 0.2939 loss_rpn_cls: 0.03636 loss_rpn_loc: 0.1934 time: 0.3602 last_time: 0.2143 data_time: 0.0048 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:48:37 d2.utils.events]: \u001b[0m eta: 1:45:08 iter: 59139 total_loss: 0.6984 loss_cls: 0.2414 loss_box_reg: 0.2583 loss_rpn_cls: 0.04312 loss_rpn_loc: 0.1817 time: 0.3602 last_time: 0.2324 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:48:42 d2.utils.events]: \u001b[0m eta: 1:45:02 iter: 59159 total_loss: 0.8107 loss_cls: 0.3069 loss_box_reg: 0.2805 loss_rpn_cls: 0.04889 loss_rpn_loc: 0.1893 time: 0.3602 last_time: 0.2428 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:48:46 d2.utils.events]: \u001b[0m eta: 1:45:00 iter: 59179 total_loss: 0.8531 loss_cls: 0.3175 loss_box_reg: 0.2985 loss_rpn_cls: 0.04903 loss_rpn_loc: 0.2198 time: 0.3601 last_time: 0.2595 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:48:51 d2.utils.events]: \u001b[0m eta: 1:44:52 iter: 59199 total_loss: 0.8812 loss_cls: 0.2991 loss_box_reg: 0.2986 loss_rpn_cls: 0.04998 loss_rpn_loc: 0.21 time: 0.3601 last_time: 0.1858 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:48:56 d2.utils.events]: \u001b[0m eta: 1:44:44 iter: 59219 total_loss: 0.725 loss_cls: 0.2005 loss_box_reg: 0.2711 loss_rpn_cls: 0.04113 loss_rpn_loc: 0.194 time: 0.3600 last_time: 0.2334 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:49:01 d2.utils.events]: \u001b[0m eta: 1:44:37 iter: 59239 total_loss: 0.7256 loss_cls: 0.2189 loss_box_reg: 0.2566 loss_rpn_cls: 0.03976 loss_rpn_loc: 0.1975 time: 0.3600 last_time: 0.2323 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:49:05 d2.utils.events]: \u001b[0m eta: 1:44:30 iter: 59259 total_loss: 0.8181 loss_cls: 0.2923 loss_box_reg: 0.2876 loss_rpn_cls: 0.04836 loss_rpn_loc: 0.1723 time: 0.3599 last_time: 0.2311 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:49:10 d2.utils.events]: \u001b[0m eta: 1:44:26 iter: 59279 total_loss: 0.779 loss_cls: 0.2773 loss_box_reg: 0.2891 loss_rpn_cls: 0.03186 loss_rpn_loc: 0.1736 time: 0.3599 last_time: 0.2184 data_time: 0.0046 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:49:15 d2.utils.events]: \u001b[0m eta: 1:44:22 iter: 59299 total_loss: 0.7117 loss_cls: 0.2255 loss_box_reg: 0.276 loss_rpn_cls: 0.04206 loss_rpn_loc: 0.1802 time: 0.3599 last_time: 0.2201 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:49:20 d2.utils.events]: \u001b[0m eta: 1:44:12 iter: 59319 total_loss: 0.6877 loss_cls: 0.2252 loss_box_reg: 0.2837 loss_rpn_cls: 0.0494 loss_rpn_loc: 0.2039 time: 0.3598 last_time: 0.2444 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:49:24 d2.utils.events]: \u001b[0m eta: 1:44:03 iter: 59339 total_loss: 0.9756 loss_cls: 0.2928 loss_box_reg: 0.3479 loss_rpn_cls: 0.04209 loss_rpn_loc: 0.2177 time: 0.3598 last_time: 0.2308 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:49:29 d2.utils.events]: \u001b[0m eta: 1:43:59 iter: 59359 total_loss: 0.8423 loss_cls: 0.295 loss_box_reg: 0.3079 loss_rpn_cls: 0.05456 loss_rpn_loc: 0.2137 time: 0.3597 last_time: 0.2421 data_time: 0.0047 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:49:34 d2.utils.events]: \u001b[0m eta: 1:43:53 iter: 59379 total_loss: 0.7122 loss_cls: 0.2453 loss_box_reg: 0.2592 loss_rpn_cls: 0.04832 loss_rpn_loc: 0.1751 time: 0.3597 last_time: 0.2608 data_time: 0.0046 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:49:39 d2.utils.events]: \u001b[0m eta: 1:43:47 iter: 59399 total_loss: 0.8448 loss_cls: 0.2569 loss_box_reg: 0.3069 loss_rpn_cls: 0.05164 loss_rpn_loc: 0.2211 time: 0.3597 last_time: 0.2595 data_time: 0.0047 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:49:44 d2.utils.events]: \u001b[0m eta: 1:43:34 iter: 59419 total_loss: 0.7063 loss_cls: 0.2443 loss_box_reg: 0.2625 loss_rpn_cls: 0.03647 loss_rpn_loc: 0.1986 time: 0.3596 last_time: 0.2405 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:49:48 d2.utils.events]: \u001b[0m eta: 1:43:29 iter: 59439 total_loss: 0.8143 loss_cls: 0.227 loss_box_reg: 0.2847 loss_rpn_cls: 0.04433 loss_rpn_loc: 0.2203 time: 0.3596 last_time: 0.2193 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:49:53 d2.utils.events]: \u001b[0m eta: 1:43:22 iter: 59459 total_loss: 0.834 loss_cls: 0.2276 loss_box_reg: 0.2673 loss_rpn_cls: 0.05028 loss_rpn_loc: 0.206 time: 0.3595 last_time: 0.2160 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:49:58 d2.utils.events]: \u001b[0m eta: 1:43:16 iter: 59479 total_loss: 0.7613 loss_cls: 0.2137 loss_box_reg: 0.3224 loss_rpn_cls: 0.04662 loss_rpn_loc: 0.1898 time: 0.3595 last_time: 0.2302 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:50:03 d2.utils.events]: \u001b[0m eta: 1:43:06 iter: 59499 total_loss: 0.7431 loss_cls: 0.2584 loss_box_reg: 0.2417 loss_rpn_cls: 0.05318 loss_rpn_loc: 0.1999 time: 0.3595 last_time: 0.2310 data_time: 0.0047 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:50:08 d2.utils.events]: \u001b[0m eta: 1:43:00 iter: 59519 total_loss: 0.8098 loss_cls: 0.2487 loss_box_reg: 0.2813 loss_rpn_cls: 0.03984 loss_rpn_loc: 0.1987 time: 0.3594 last_time: 0.2605 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:50:12 d2.utils.events]: \u001b[0m eta: 1:42:53 iter: 59539 total_loss: 0.8254 loss_cls: 0.2843 loss_box_reg: 0.2975 loss_rpn_cls: 0.04782 loss_rpn_loc: 0.2053 time: 0.3594 last_time: 0.2602 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:50:17 d2.utils.events]: \u001b[0m eta: 1:42:55 iter: 59559 total_loss: 0.8213 loss_cls: 0.2783 loss_box_reg: 0.295 loss_rpn_cls: 0.03637 loss_rpn_loc: 0.1939 time: 0.3593 last_time: 0.2454 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:50:22 d2.utils.events]: \u001b[0m eta: 1:42:49 iter: 59579 total_loss: 0.8101 loss_cls: 0.2639 loss_box_reg: 0.2882 loss_rpn_cls: 0.05615 loss_rpn_loc: 0.1964 time: 0.3593 last_time: 0.2610 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:50:27 d2.utils.events]: \u001b[0m eta: 1:42:43 iter: 59599 total_loss: 0.9024 loss_cls: 0.283 loss_box_reg: 0.2962 loss_rpn_cls: 0.05126 loss_rpn_loc: 0.2219 time: 0.3593 last_time: 0.2422 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:50:32 d2.utils.events]: \u001b[0m eta: 1:42:39 iter: 59619 total_loss: 0.8237 loss_cls: 0.2422 loss_box_reg: 0.2929 loss_rpn_cls: 0.04115 loss_rpn_loc: 0.1816 time: 0.3592 last_time: 0.2626 data_time: 0.0046 last_data_time: 0.0042 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:50:37 d2.utils.events]: \u001b[0m eta: 1:42:36 iter: 59639 total_loss: 0.65 loss_cls: 0.2148 loss_box_reg: 0.2599 loss_rpn_cls: 0.03823 loss_rpn_loc: 0.1603 time: 0.3592 last_time: 0.2611 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:50:41 d2.utils.events]: \u001b[0m eta: 1:42:29 iter: 59659 total_loss: 0.7801 loss_cls: 0.2158 loss_box_reg: 0.2867 loss_rpn_cls: 0.03743 loss_rpn_loc: 0.1899 time: 0.3591 last_time: 0.2317 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:50:46 d2.utils.events]: \u001b[0m eta: 1:42:24 iter: 59679 total_loss: 0.702 loss_cls: 0.2069 loss_box_reg: 0.2376 loss_rpn_cls: 0.04352 loss_rpn_loc: 0.1719 time: 0.3591 last_time: 0.2200 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:50:51 d2.utils.events]: \u001b[0m eta: 1:42:21 iter: 59699 total_loss: 0.8649 loss_cls: 0.2694 loss_box_reg: 0.3054 loss_rpn_cls: 0.05204 loss_rpn_loc: 0.2095 time: 0.3591 last_time: 0.2176 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:50:56 d2.utils.events]: \u001b[0m eta: 1:42:19 iter: 59719 total_loss: 0.8238 loss_cls: 0.2918 loss_box_reg: 0.29 loss_rpn_cls: 0.05415 loss_rpn_loc: 0.2124 time: 0.3590 last_time: 0.2356 data_time: 0.0047 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:51:00 d2.utils.events]: \u001b[0m eta: 1:42:13 iter: 59739 total_loss: 0.8949 loss_cls: 0.2802 loss_box_reg: 0.3106 loss_rpn_cls: 0.04349 loss_rpn_loc: 0.2095 time: 0.3590 last_time: 0.2586 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:51:05 d2.utils.events]: \u001b[0m eta: 1:42:08 iter: 59759 total_loss: 0.7941 loss_cls: 0.2853 loss_box_reg: 0.2894 loss_rpn_cls: 0.05238 loss_rpn_loc: 0.2123 time: 0.3589 last_time: 0.2335 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:51:10 d2.utils.events]: \u001b[0m eta: 1:42:05 iter: 59779 total_loss: 0.8509 loss_cls: 0.253 loss_box_reg: 0.3142 loss_rpn_cls: 0.0607 loss_rpn_loc: 0.2098 time: 0.3589 last_time: 0.2198 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:51:15 d2.utils.events]: \u001b[0m eta: 1:42:01 iter: 59799 total_loss: 0.6992 loss_cls: 0.2101 loss_box_reg: 0.2376 loss_rpn_cls: 0.04124 loss_rpn_loc: 0.172 time: 0.3589 last_time: 0.2592 data_time: 0.0046 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:51:20 d2.utils.events]: \u001b[0m eta: 1:41:54 iter: 59819 total_loss: 0.7744 loss_cls: 0.2463 loss_box_reg: 0.2846 loss_rpn_cls: 0.04465 loss_rpn_loc: 0.1775 time: 0.3588 last_time: 0.2441 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:51:25 d2.utils.events]: \u001b[0m eta: 1:41:49 iter: 59839 total_loss: 0.8637 loss_cls: 0.2695 loss_box_reg: 0.2975 loss_rpn_cls: 0.05321 loss_rpn_loc: 0.2051 time: 0.3588 last_time: 0.2617 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:51:30 d2.utils.events]: \u001b[0m eta: 1:41:47 iter: 59859 total_loss: 0.7697 loss_cls: 0.2569 loss_box_reg: 0.2955 loss_rpn_cls: 0.0467 loss_rpn_loc: 0.204 time: 0.3587 last_time: 0.2035 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:51:34 d2.utils.events]: \u001b[0m eta: 1:41:43 iter: 59879 total_loss: 0.8155 loss_cls: 0.2451 loss_box_reg: 0.2988 loss_rpn_cls: 0.04642 loss_rpn_loc: 0.1946 time: 0.3587 last_time: 0.2860 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:51:39 d2.utils.events]: \u001b[0m eta: 1:41:37 iter: 59899 total_loss: 0.7499 loss_cls: 0.2172 loss_box_reg: 0.2785 loss_rpn_cls: 0.05327 loss_rpn_loc: 0.1839 time: 0.3587 last_time: 0.2542 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:51:44 d2.utils.events]: \u001b[0m eta: 1:41:31 iter: 59919 total_loss: 0.9131 loss_cls: 0.2739 loss_box_reg: 0.3151 loss_rpn_cls: 0.05532 loss_rpn_loc: 0.2243 time: 0.3586 last_time: 0.2682 data_time: 0.0047 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:51:50 d2.utils.events]: \u001b[0m eta: 1:41:31 iter: 59939 total_loss: 0.817 loss_cls: 0.2786 loss_box_reg: 0.2898 loss_rpn_cls: 0.04562 loss_rpn_loc: 0.1934 time: 0.3586 last_time: 0.2177 data_time: 0.0053 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:51:55 d2.utils.events]: \u001b[0m eta: 1:41:31 iter: 59959 total_loss: 0.8003 loss_cls: 0.2548 loss_box_reg: 0.2625 loss_rpn_cls: 0.03759 loss_rpn_loc: 0.217 time: 0.3586 last_time: 0.2811 data_time: 0.0054 last_data_time: 0.0053 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:52:01 d2.utils.events]: \u001b[0m eta: 1:41:28 iter: 59979 total_loss: 0.7708 loss_cls: 0.2582 loss_box_reg: 0.2758 loss_rpn_cls: 0.04886 loss_rpn_loc: 0.1967 time: 0.3586 last_time: 0.2342 data_time: 0.0053 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:52:08 d2.utils.events]: \u001b[0m eta: 1:41:26 iter: 59999 total_loss: 0.7836 loss_cls: 0.266 loss_box_reg: 0.2621 loss_rpn_cls: 0.04193 loss_rpn_loc: 0.1873 time: 0.3585 last_time: 0.3420 data_time: 0.0053 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:52:13 d2.utils.events]: \u001b[0m eta: 1:41:21 iter: 60019 total_loss: 0.8077 loss_cls: 0.2672 loss_box_reg: 0.3001 loss_rpn_cls: 0.04665 loss_rpn_loc: 0.211 time: 0.3585 last_time: 0.2217 data_time: 0.0049 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:52:17 d2.utils.events]: \u001b[0m eta: 1:41:15 iter: 60039 total_loss: 0.7609 loss_cls: 0.2497 loss_box_reg: 0.2831 loss_rpn_cls: 0.04793 loss_rpn_loc: 0.1883 time: 0.3585 last_time: 0.2591 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:52:22 d2.utils.events]: \u001b[0m eta: 1:41:10 iter: 60059 total_loss: 0.8469 loss_cls: 0.2561 loss_box_reg: 0.2953 loss_rpn_cls: 0.05016 loss_rpn_loc: 0.1747 time: 0.3584 last_time: 0.2011 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:52:27 d2.utils.events]: \u001b[0m eta: 1:41:06 iter: 60079 total_loss: 0.782 loss_cls: 0.2331 loss_box_reg: 0.2665 loss_rpn_cls: 0.04739 loss_rpn_loc: 0.1799 time: 0.3584 last_time: 0.2048 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:52:32 d2.utils.events]: \u001b[0m eta: 1:41:00 iter: 60099 total_loss: 0.8489 loss_cls: 0.2699 loss_box_reg: 0.3085 loss_rpn_cls: 0.05392 loss_rpn_loc: 0.2061 time: 0.3583 last_time: 0.2576 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:52:36 d2.utils.events]: \u001b[0m eta: 1:40:55 iter: 60119 total_loss: 0.696 loss_cls: 0.2142 loss_box_reg: 0.28 loss_rpn_cls: 0.03586 loss_rpn_loc: 0.1816 time: 0.3583 last_time: 0.2029 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:52:41 d2.utils.events]: \u001b[0m eta: 1:40:52 iter: 60139 total_loss: 0.7371 loss_cls: 0.2156 loss_box_reg: 0.2748 loss_rpn_cls: 0.04152 loss_rpn_loc: 0.2277 time: 0.3583 last_time: 0.2335 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:52:46 d2.utils.events]: \u001b[0m eta: 1:40:46 iter: 60159 total_loss: 0.7046 loss_cls: 0.1981 loss_box_reg: 0.2932 loss_rpn_cls: 0.0463 loss_rpn_loc: 0.1682 time: 0.3582 last_time: 0.2021 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:52:51 d2.utils.events]: \u001b[0m eta: 1:40:40 iter: 60179 total_loss: 0.8462 loss_cls: 0.2952 loss_box_reg: 0.3034 loss_rpn_cls: 0.04562 loss_rpn_loc: 0.1896 time: 0.3582 last_time: 0.2331 data_time: 0.0047 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:52:56 d2.utils.events]: \u001b[0m eta: 1:40:36 iter: 60199 total_loss: 0.8157 loss_cls: 0.2352 loss_box_reg: 0.2947 loss_rpn_cls: 0.04526 loss_rpn_loc: 0.2094 time: 0.3581 last_time: 0.2467 data_time: 0.0045 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:53:00 d2.utils.events]: \u001b[0m eta: 1:40:33 iter: 60219 total_loss: 0.7374 loss_cls: 0.2582 loss_box_reg: 0.2768 loss_rpn_cls: 0.04151 loss_rpn_loc: 0.1752 time: 0.3581 last_time: 0.1880 data_time: 0.0047 last_data_time: 0.0053 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:53:05 d2.utils.events]: \u001b[0m eta: 1:40:29 iter: 60239 total_loss: 0.8281 loss_cls: 0.2635 loss_box_reg: 0.2807 loss_rpn_cls: 0.06539 loss_rpn_loc: 0.2274 time: 0.3581 last_time: 0.2603 data_time: 0.0046 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:53:10 d2.utils.events]: \u001b[0m eta: 1:40:26 iter: 60259 total_loss: 0.7768 loss_cls: 0.2565 loss_box_reg: 0.2997 loss_rpn_cls: 0.05481 loss_rpn_loc: 0.2139 time: 0.3580 last_time: 0.2171 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:53:15 d2.utils.events]: \u001b[0m eta: 1:40:18 iter: 60279 total_loss: 0.781 loss_cls: 0.2677 loss_box_reg: 0.2826 loss_rpn_cls: 0.04237 loss_rpn_loc: 0.19 time: 0.3580 last_time: 0.2588 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:53:19 d2.utils.events]: \u001b[0m eta: 1:40:10 iter: 60299 total_loss: 0.7885 loss_cls: 0.2655 loss_box_reg: 0.3022 loss_rpn_cls: 0.02773 loss_rpn_loc: 0.1917 time: 0.3579 last_time: 0.2439 data_time: 0.0045 last_data_time: 0.0042 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:53:24 d2.utils.events]: \u001b[0m eta: 1:40:09 iter: 60319 total_loss: 0.7376 loss_cls: 0.2346 loss_box_reg: 0.2374 loss_rpn_cls: 0.04605 loss_rpn_loc: 0.178 time: 0.3579 last_time: 0.2429 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:53:29 d2.utils.events]: \u001b[0m eta: 1:40:04 iter: 60339 total_loss: 0.8201 loss_cls: 0.243 loss_box_reg: 0.2878 loss_rpn_cls: 0.04477 loss_rpn_loc: 0.2021 time: 0.3579 last_time: 0.2014 data_time: 0.0047 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:53:34 d2.utils.events]: \u001b[0m eta: 1:39:58 iter: 60359 total_loss: 0.733 loss_cls: 0.2267 loss_box_reg: 0.2942 loss_rpn_cls: 0.04429 loss_rpn_loc: 0.1672 time: 0.3578 last_time: 0.2259 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:53:38 d2.utils.events]: \u001b[0m eta: 1:39:50 iter: 60379 total_loss: 0.8371 loss_cls: 0.2334 loss_box_reg: 0.3183 loss_rpn_cls: 0.05134 loss_rpn_loc: 0.2324 time: 0.3578 last_time: 0.2668 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:53:43 d2.utils.events]: \u001b[0m eta: 1:39:45 iter: 60399 total_loss: 0.9123 loss_cls: 0.2898 loss_box_reg: 0.3175 loss_rpn_cls: 0.04335 loss_rpn_loc: 0.2078 time: 0.3577 last_time: 0.2833 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:53:48 d2.utils.events]: \u001b[0m eta: 1:39:42 iter: 60419 total_loss: 0.7134 loss_cls: 0.2239 loss_box_reg: 0.2747 loss_rpn_cls: 0.04453 loss_rpn_loc: 0.1851 time: 0.3577 last_time: 0.2604 data_time: 0.0045 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:53:53 d2.utils.events]: \u001b[0m eta: 1:39:39 iter: 60439 total_loss: 0.8139 loss_cls: 0.2578 loss_box_reg: 0.2764 loss_rpn_cls: 0.04752 loss_rpn_loc: 0.2064 time: 0.3577 last_time: 0.2613 data_time: 0.0047 last_data_time: 0.0042 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:53:58 d2.utils.events]: \u001b[0m eta: 1:39:37 iter: 60459 total_loss: 0.8209 loss_cls: 0.2965 loss_box_reg: 0.2682 loss_rpn_cls: 0.06044 loss_rpn_loc: 0.2025 time: 0.3576 last_time: 0.2443 data_time: 0.0045 last_data_time: 0.0041 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:54:03 d2.utils.events]: \u001b[0m eta: 1:39:29 iter: 60479 total_loss: 0.7481 loss_cls: 0.2372 loss_box_reg: 0.2968 loss_rpn_cls: 0.0485 loss_rpn_loc: 0.1819 time: 0.3576 last_time: 0.2192 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:54:08 d2.utils.events]: \u001b[0m eta: 1:39:28 iter: 60499 total_loss: 0.8469 loss_cls: 0.2756 loss_box_reg: 0.3044 loss_rpn_cls: 0.04984 loss_rpn_loc: 0.2066 time: 0.3575 last_time: 0.2449 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:54:12 d2.utils.events]: \u001b[0m eta: 1:39:21 iter: 60519 total_loss: 0.8079 loss_cls: 0.2574 loss_box_reg: 0.2914 loss_rpn_cls: 0.05096 loss_rpn_loc: 0.198 time: 0.3575 last_time: 0.2057 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:54:17 d2.utils.events]: \u001b[0m eta: 1:39:18 iter: 60539 total_loss: 0.8614 loss_cls: 0.2711 loss_box_reg: 0.3263 loss_rpn_cls: 0.04912 loss_rpn_loc: 0.204 time: 0.3575 last_time: 0.2009 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:54:22 d2.utils.events]: \u001b[0m eta: 1:39:11 iter: 60559 total_loss: 0.8247 loss_cls: 0.2538 loss_box_reg: 0.2753 loss_rpn_cls: 0.05645 loss_rpn_loc: 0.2156 time: 0.3574 last_time: 0.2061 data_time: 0.0047 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:54:27 d2.utils.events]: \u001b[0m eta: 1:39:06 iter: 60579 total_loss: 0.8317 loss_cls: 0.2459 loss_box_reg: 0.327 loss_rpn_cls: 0.04522 loss_rpn_loc: 0.1996 time: 0.3574 last_time: 0.2196 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:54:32 d2.utils.events]: \u001b[0m eta: 1:39:04 iter: 60599 total_loss: 0.7696 loss_cls: 0.2724 loss_box_reg: 0.2773 loss_rpn_cls: 0.06437 loss_rpn_loc: 0.1939 time: 0.3574 last_time: 0.2540 data_time: 0.0049 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:54:37 d2.utils.events]: \u001b[0m eta: 1:38:57 iter: 60619 total_loss: 0.8081 loss_cls: 0.2816 loss_box_reg: 0.2817 loss_rpn_cls: 0.04117 loss_rpn_loc: 0.1888 time: 0.3573 last_time: 0.2094 data_time: 0.0049 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:54:42 d2.utils.events]: \u001b[0m eta: 1:38:53 iter: 60639 total_loss: 0.8186 loss_cls: 0.2537 loss_box_reg: 0.2868 loss_rpn_cls: 0.0419 loss_rpn_loc: 0.1982 time: 0.3573 last_time: 0.2029 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:54:47 d2.utils.events]: \u001b[0m eta: 1:38:50 iter: 60659 total_loss: 0.8577 loss_cls: 0.302 loss_box_reg: 0.3122 loss_rpn_cls: 0.0517 loss_rpn_loc: 0.1901 time: 0.3572 last_time: 0.1994 data_time: 0.0050 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:54:52 d2.utils.events]: \u001b[0m eta: 1:38:44 iter: 60679 total_loss: 0.8515 loss_cls: 0.2536 loss_box_reg: 0.2994 loss_rpn_cls: 0.04587 loss_rpn_loc: 0.2049 time: 0.3572 last_time: 0.2374 data_time: 0.0049 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:54:56 d2.utils.events]: \u001b[0m eta: 1:38:34 iter: 60699 total_loss: 0.7974 loss_cls: 0.2372 loss_box_reg: 0.2817 loss_rpn_cls: 0.05062 loss_rpn_loc: 0.1771 time: 0.3572 last_time: 0.2397 data_time: 0.0050 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:55:01 d2.utils.events]: \u001b[0m eta: 1:38:29 iter: 60719 total_loss: 0.8656 loss_cls: 0.2744 loss_box_reg: 0.3065 loss_rpn_cls: 0.05233 loss_rpn_loc: 0.2002 time: 0.3571 last_time: 0.2406 data_time: 0.0050 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:55:06 d2.utils.events]: \u001b[0m eta: 1:38:26 iter: 60739 total_loss: 0.7931 loss_cls: 0.2527 loss_box_reg: 0.286 loss_rpn_cls: 0.04422 loss_rpn_loc: 0.1897 time: 0.3571 last_time: 0.1964 data_time: 0.0049 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:55:11 d2.utils.events]: \u001b[0m eta: 1:38:25 iter: 60759 total_loss: 0.7898 loss_cls: 0.2374 loss_box_reg: 0.278 loss_rpn_cls: 0.04766 loss_rpn_loc: 0.2102 time: 0.3571 last_time: 0.2712 data_time: 0.0050 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:55:16 d2.utils.events]: \u001b[0m eta: 1:38:19 iter: 60779 total_loss: 0.73 loss_cls: 0.2034 loss_box_reg: 0.2932 loss_rpn_cls: 0.04244 loss_rpn_loc: 0.1862 time: 0.3570 last_time: 0.2448 data_time: 0.0050 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:55:21 d2.utils.events]: \u001b[0m eta: 1:38:15 iter: 60799 total_loss: 0.6886 loss_cls: 0.2538 loss_box_reg: 0.2754 loss_rpn_cls: 0.04508 loss_rpn_loc: 0.1903 time: 0.3570 last_time: 0.2705 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:55:26 d2.utils.events]: \u001b[0m eta: 1:38:11 iter: 60819 total_loss: 0.7259 loss_cls: 0.2199 loss_box_reg: 0.2835 loss_rpn_cls: 0.04201 loss_rpn_loc: 0.1912 time: 0.3570 last_time: 0.2641 data_time: 0.0049 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:55:31 d2.utils.events]: \u001b[0m eta: 1:38:07 iter: 60839 total_loss: 0.8639 loss_cls: 0.2537 loss_box_reg: 0.3202 loss_rpn_cls: 0.04236 loss_rpn_loc: 0.1897 time: 0.3569 last_time: 0.2069 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:55:36 d2.utils.events]: \u001b[0m eta: 1:38:00 iter: 60859 total_loss: 0.8653 loss_cls: 0.3075 loss_box_reg: 0.2899 loss_rpn_cls: 0.05115 loss_rpn_loc: 0.1849 time: 0.3569 last_time: 0.2597 data_time: 0.0048 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:55:41 d2.utils.events]: \u001b[0m eta: 1:37:57 iter: 60879 total_loss: 0.8398 loss_cls: 0.2791 loss_box_reg: 0.3094 loss_rpn_cls: 0.05771 loss_rpn_loc: 0.1788 time: 0.3568 last_time: 0.2253 data_time: 0.0047 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:55:46 d2.utils.events]: \u001b[0m eta: 1:37:52 iter: 60899 total_loss: 0.8539 loss_cls: 0.2862 loss_box_reg: 0.3171 loss_rpn_cls: 0.04379 loss_rpn_loc: 0.1851 time: 0.3568 last_time: 0.2046 data_time: 0.0048 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:55:51 d2.utils.events]: \u001b[0m eta: 1:37:46 iter: 60919 total_loss: 0.692 loss_cls: 0.1971 loss_box_reg: 0.2676 loss_rpn_cls: 0.03871 loss_rpn_loc: 0.1656 time: 0.3568 last_time: 0.2404 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:55:55 d2.utils.events]: \u001b[0m eta: 1:37:35 iter: 60939 total_loss: 0.7641 loss_cls: 0.2178 loss_box_reg: 0.3091 loss_rpn_cls: 0.04288 loss_rpn_loc: 0.1844 time: 0.3567 last_time: 0.2691 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:56:00 d2.utils.events]: \u001b[0m eta: 1:37:27 iter: 60959 total_loss: 0.8786 loss_cls: 0.2736 loss_box_reg: 0.3026 loss_rpn_cls: 0.05632 loss_rpn_loc: 0.2059 time: 0.3567 last_time: 0.2536 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:56:05 d2.utils.events]: \u001b[0m eta: 1:37:17 iter: 60979 total_loss: 0.7858 loss_cls: 0.2618 loss_box_reg: 0.2929 loss_rpn_cls: 0.04037 loss_rpn_loc: 0.1898 time: 0.3567 last_time: 0.2277 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:56:10 d2.utils.events]: \u001b[0m eta: 1:37:05 iter: 60999 total_loss: 0.9428 loss_cls: 0.2914 loss_box_reg: 0.3469 loss_rpn_cls: 0.05005 loss_rpn_loc: 0.2045 time: 0.3566 last_time: 0.2413 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:56:15 d2.utils.events]: \u001b[0m eta: 1:36:59 iter: 61019 total_loss: 0.8075 loss_cls: 0.2625 loss_box_reg: 0.2947 loss_rpn_cls: 0.05178 loss_rpn_loc: 0.2074 time: 0.3566 last_time: 0.2512 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:56:20 d2.utils.events]: \u001b[0m eta: 1:36:59 iter: 61039 total_loss: 0.7558 loss_cls: 0.2357 loss_box_reg: 0.2729 loss_rpn_cls: 0.04751 loss_rpn_loc: 0.1708 time: 0.3565 last_time: 0.2557 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:56:25 d2.utils.events]: \u001b[0m eta: 1:36:56 iter: 61059 total_loss: 0.8217 loss_cls: 0.2331 loss_box_reg: 0.2878 loss_rpn_cls: 0.07585 loss_rpn_loc: 0.1891 time: 0.3565 last_time: 0.2244 data_time: 0.0047 last_data_time: 0.0042 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:56:29 d2.utils.events]: \u001b[0m eta: 1:36:50 iter: 61079 total_loss: 0.9033 loss_cls: 0.2587 loss_box_reg: 0.3096 loss_rpn_cls: 0.05291 loss_rpn_loc: 0.1746 time: 0.3565 last_time: 0.2334 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:56:34 d2.utils.events]: \u001b[0m eta: 1:36:46 iter: 61099 total_loss: 0.7943 loss_cls: 0.2379 loss_box_reg: 0.2831 loss_rpn_cls: 0.04225 loss_rpn_loc: 0.2011 time: 0.3564 last_time: 0.1977 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:56:39 d2.utils.events]: \u001b[0m eta: 1:36:40 iter: 61119 total_loss: 0.8594 loss_cls: 0.2372 loss_box_reg: 0.304 loss_rpn_cls: 0.03969 loss_rpn_loc: 0.2037 time: 0.3564 last_time: 0.2417 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:56:44 d2.utils.events]: \u001b[0m eta: 1:36:36 iter: 61139 total_loss: 0.8034 loss_cls: 0.207 loss_box_reg: 0.2996 loss_rpn_cls: 0.0422 loss_rpn_loc: 0.2163 time: 0.3564 last_time: 0.2464 data_time: 0.0048 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:56:49 d2.utils.events]: \u001b[0m eta: 1:36:34 iter: 61159 total_loss: 0.8603 loss_cls: 0.3061 loss_box_reg: 0.3366 loss_rpn_cls: 0.05024 loss_rpn_loc: 0.182 time: 0.3563 last_time: 0.2589 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:56:53 d2.utils.events]: \u001b[0m eta: 1:36:28 iter: 61179 total_loss: 0.7298 loss_cls: 0.2496 loss_box_reg: 0.301 loss_rpn_cls: 0.0514 loss_rpn_loc: 0.1971 time: 0.3563 last_time: 0.2449 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:56:58 d2.utils.events]: \u001b[0m eta: 1:36:23 iter: 61199 total_loss: 0.8417 loss_cls: 0.2919 loss_box_reg: 0.3029 loss_rpn_cls: 0.05709 loss_rpn_loc: 0.1899 time: 0.3562 last_time: 0.2310 data_time: 0.0047 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:57:03 d2.utils.events]: \u001b[0m eta: 1:36:20 iter: 61219 total_loss: 0.8239 loss_cls: 0.2732 loss_box_reg: 0.3109 loss_rpn_cls: 0.05142 loss_rpn_loc: 0.1997 time: 0.3562 last_time: 0.2063 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:57:08 d2.utils.events]: \u001b[0m eta: 1:36:15 iter: 61239 total_loss: 0.8204 loss_cls: 0.2462 loss_box_reg: 0.3167 loss_rpn_cls: 0.04542 loss_rpn_loc: 0.2013 time: 0.3562 last_time: 0.2339 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:57:13 d2.utils.events]: \u001b[0m eta: 1:36:10 iter: 61259 total_loss: 0.858 loss_cls: 0.2478 loss_box_reg: 0.3284 loss_rpn_cls: 0.04533 loss_rpn_loc: 0.2141 time: 0.3561 last_time: 0.2399 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:57:17 d2.utils.events]: \u001b[0m eta: 1:36:06 iter: 61279 total_loss: 0.9073 loss_cls: 0.2659 loss_box_reg: 0.323 loss_rpn_cls: 0.0582 loss_rpn_loc: 0.1844 time: 0.3561 last_time: 0.2605 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:57:24 d2.utils.events]: \u001b[0m eta: 1:36:10 iter: 61299 total_loss: 0.7054 loss_cls: 0.2357 loss_box_reg: 0.2828 loss_rpn_cls: 0.04062 loss_rpn_loc: 0.1668 time: 0.3561 last_time: 0.3461 data_time: 0.0053 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:57:29 d2.utils.events]: \u001b[0m eta: 1:36:08 iter: 61319 total_loss: 0.8859 loss_cls: 0.253 loss_box_reg: 0.2865 loss_rpn_cls: 0.05027 loss_rpn_loc: 0.1911 time: 0.3560 last_time: 0.2591 data_time: 0.0053 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:57:35 d2.utils.events]: \u001b[0m eta: 1:36:07 iter: 61339 total_loss: 0.7357 loss_cls: 0.239 loss_box_reg: 0.2404 loss_rpn_cls: 0.03821 loss_rpn_loc: 0.1707 time: 0.3560 last_time: 0.3166 data_time: 0.0051 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:57:40 d2.utils.events]: \u001b[0m eta: 1:36:04 iter: 61359 total_loss: 0.787 loss_cls: 0.2673 loss_box_reg: 0.269 loss_rpn_cls: 0.03799 loss_rpn_loc: 0.1737 time: 0.3560 last_time: 0.2595 data_time: 0.0049 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:57:45 d2.utils.events]: \u001b[0m eta: 1:36:02 iter: 61379 total_loss: 0.9294 loss_cls: 0.2748 loss_box_reg: 0.3059 loss_rpn_cls: 0.05034 loss_rpn_loc: 0.2394 time: 0.3560 last_time: 0.2578 data_time: 0.0049 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:57:50 d2.utils.events]: \u001b[0m eta: 1:35:59 iter: 61399 total_loss: 0.7931 loss_cls: 0.251 loss_box_reg: 0.3091 loss_rpn_cls: 0.04753 loss_rpn_loc: 0.202 time: 0.3559 last_time: 0.1983 data_time: 0.0051 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:57:55 d2.utils.events]: \u001b[0m eta: 1:35:53 iter: 61419 total_loss: 0.808 loss_cls: 0.2419 loss_box_reg: 0.3033 loss_rpn_cls: 0.04489 loss_rpn_loc: 0.1782 time: 0.3559 last_time: 0.2433 data_time: 0.0049 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:58:00 d2.utils.events]: \u001b[0m eta: 1:35:50 iter: 61439 total_loss: 0.7886 loss_cls: 0.2778 loss_box_reg: 0.2884 loss_rpn_cls: 0.04322 loss_rpn_loc: 0.1892 time: 0.3559 last_time: 0.2572 data_time: 0.0049 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:58:06 d2.utils.events]: \u001b[0m eta: 1:35:48 iter: 61459 total_loss: 0.7465 loss_cls: 0.2446 loss_box_reg: 0.2951 loss_rpn_cls: 0.05128 loss_rpn_loc: 0.1792 time: 0.3558 last_time: 0.2471 data_time: 0.0054 last_data_time: 0.0060 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:58:12 d2.utils.events]: \u001b[0m eta: 1:35:56 iter: 61479 total_loss: 0.7311 loss_cls: 0.2426 loss_box_reg: 0.2546 loss_rpn_cls: 0.03352 loss_rpn_loc: 0.1671 time: 0.3558 last_time: 0.3432 data_time: 0.0054 last_data_time: 0.0059 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:58:17 d2.utils.events]: \u001b[0m eta: 1:35:51 iter: 61499 total_loss: 0.9003 loss_cls: 0.2653 loss_box_reg: 0.3169 loss_rpn_cls: 0.05517 loss_rpn_loc: 0.2181 time: 0.3558 last_time: 0.2400 data_time: 0.0050 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:58:23 d2.utils.events]: \u001b[0m eta: 1:35:54 iter: 61519 total_loss: 0.8697 loss_cls: 0.2899 loss_box_reg: 0.2844 loss_rpn_cls: 0.06006 loss_rpn_loc: 0.1788 time: 0.3558 last_time: 0.2786 data_time: 0.0052 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:58:29 d2.utils.events]: \u001b[0m eta: 1:36:00 iter: 61539 total_loss: 0.8115 loss_cls: 0.2584 loss_box_reg: 0.2951 loss_rpn_cls: 0.04738 loss_rpn_loc: 0.2001 time: 0.3557 last_time: 0.3190 data_time: 0.0053 last_data_time: 0.0056 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:58:35 d2.utils.events]: \u001b[0m eta: 1:36:05 iter: 61559 total_loss: 0.7608 loss_cls: 0.2498 loss_box_reg: 0.3073 loss_rpn_cls: 0.05004 loss_rpn_loc: 0.2061 time: 0.3557 last_time: 0.2588 data_time: 0.0052 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:58:41 d2.utils.events]: \u001b[0m eta: 1:36:09 iter: 61579 total_loss: 0.6815 loss_cls: 0.2071 loss_box_reg: 0.2643 loss_rpn_cls: 0.04578 loss_rpn_loc: 0.1728 time: 0.3557 last_time: 0.3172 data_time: 0.0051 last_data_time: 0.0057 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:58:47 d2.utils.events]: \u001b[0m eta: 1:36:09 iter: 61599 total_loss: 0.8079 loss_cls: 0.2831 loss_box_reg: 0.2957 loss_rpn_cls: 0.0353 loss_rpn_loc: 0.1781 time: 0.3557 last_time: 0.2351 data_time: 0.0050 last_data_time: 0.0053 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:58:52 d2.utils.events]: \u001b[0m eta: 1:36:04 iter: 61619 total_loss: 0.8564 loss_cls: 0.3171 loss_box_reg: 0.281 loss_rpn_cls: 0.05368 loss_rpn_loc: 0.2037 time: 0.3557 last_time: 0.2326 data_time: 0.0048 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:58:56 d2.utils.events]: \u001b[0m eta: 1:35:56 iter: 61639 total_loss: 0.8299 loss_cls: 0.2502 loss_box_reg: 0.2965 loss_rpn_cls: 0.06637 loss_rpn_loc: 0.2238 time: 0.3556 last_time: 0.2447 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:59:01 d2.utils.events]: \u001b[0m eta: 1:35:38 iter: 61659 total_loss: 0.7522 loss_cls: 0.2303 loss_box_reg: 0.2807 loss_rpn_cls: 0.03766 loss_rpn_loc: 0.1803 time: 0.3556 last_time: 0.2534 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:59:06 d2.utils.events]: \u001b[0m eta: 1:35:33 iter: 61679 total_loss: 0.7658 loss_cls: 0.2555 loss_box_reg: 0.2645 loss_rpn_cls: 0.039 loss_rpn_loc: 0.194 time: 0.3555 last_time: 0.2683 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:59:11 d2.utils.events]: \u001b[0m eta: 1:35:41 iter: 61699 total_loss: 0.6213 loss_cls: 0.1676 loss_box_reg: 0.2121 loss_rpn_cls: 0.03735 loss_rpn_loc: 0.163 time: 0.3555 last_time: 0.2449 data_time: 0.0047 last_data_time: 0.0042 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:59:16 d2.utils.events]: \u001b[0m eta: 1:35:30 iter: 61719 total_loss: 0.8015 loss_cls: 0.2791 loss_box_reg: 0.3038 loss_rpn_cls: 0.03597 loss_rpn_loc: 0.186 time: 0.3555 last_time: 0.2309 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:59:21 d2.utils.events]: \u001b[0m eta: 1:35:30 iter: 61739 total_loss: 0.8456 loss_cls: 0.2634 loss_box_reg: 0.2969 loss_rpn_cls: 0.04919 loss_rpn_loc: 0.2147 time: 0.3554 last_time: 0.2622 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:59:25 d2.utils.events]: \u001b[0m eta: 1:35:11 iter: 61759 total_loss: 0.7197 loss_cls: 0.2246 loss_box_reg: 0.2539 loss_rpn_cls: 0.04325 loss_rpn_loc: 0.2035 time: 0.3554 last_time: 0.2530 data_time: 0.0045 last_data_time: 0.0041 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:59:30 d2.utils.events]: \u001b[0m eta: 1:35:06 iter: 61779 total_loss: 0.8959 loss_cls: 0.2608 loss_box_reg: 0.3173 loss_rpn_cls: 0.05875 loss_rpn_loc: 0.2172 time: 0.3554 last_time: 0.2190 data_time: 0.0045 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:59:35 d2.utils.events]: \u001b[0m eta: 1:35:01 iter: 61799 total_loss: 0.7963 loss_cls: 0.2458 loss_box_reg: 0.2787 loss_rpn_cls: 0.05334 loss_rpn_loc: 0.1817 time: 0.3553 last_time: 0.2417 data_time: 0.0046 last_data_time: 0.0042 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:59:40 d2.utils.events]: \u001b[0m eta: 1:34:54 iter: 61819 total_loss: 0.8886 loss_cls: 0.2735 loss_box_reg: 0.305 loss_rpn_cls: 0.05378 loss_rpn_loc: 0.1953 time: 0.3553 last_time: 0.2341 data_time: 0.0046 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:59:45 d2.utils.events]: \u001b[0m eta: 1:34:50 iter: 61839 total_loss: 0.8364 loss_cls: 0.2753 loss_box_reg: 0.3061 loss_rpn_cls: 0.04743 loss_rpn_loc: 0.1768 time: 0.3552 last_time: 0.2569 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:59:50 d2.utils.events]: \u001b[0m eta: 1:34:43 iter: 61859 total_loss: 0.7395 loss_cls: 0.2634 loss_box_reg: 0.2776 loss_rpn_cls: 0.03577 loss_rpn_loc: 0.1615 time: 0.3552 last_time: 0.2494 data_time: 0.0047 last_data_time: 0.0042 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:59:55 d2.utils.events]: \u001b[0m eta: 1:34:37 iter: 61879 total_loss: 0.7339 loss_cls: 0.2262 loss_box_reg: 0.2626 loss_rpn_cls: 0.03883 loss_rpn_loc: 0.1877 time: 0.3552 last_time: 0.2616 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 21:59:59 d2.utils.events]: \u001b[0m eta: 1:34:27 iter: 61899 total_loss: 0.7833 loss_cls: 0.2599 loss_box_reg: 0.2801 loss_rpn_cls: 0.05037 loss_rpn_loc: 0.1747 time: 0.3551 last_time: 0.2574 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:00:04 d2.utils.events]: \u001b[0m eta: 1:34:22 iter: 61919 total_loss: 0.8155 loss_cls: 0.2836 loss_box_reg: 0.3062 loss_rpn_cls: 0.03555 loss_rpn_loc: 0.1841 time: 0.3551 last_time: 0.2398 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:00:09 d2.utils.events]: \u001b[0m eta: 1:34:18 iter: 61939 total_loss: 0.8844 loss_cls: 0.2459 loss_box_reg: 0.3075 loss_rpn_cls: 0.03862 loss_rpn_loc: 0.204 time: 0.3551 last_time: 0.2424 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:00:14 d2.utils.events]: \u001b[0m eta: 1:34:15 iter: 61959 total_loss: 0.7279 loss_cls: 0.2315 loss_box_reg: 0.2618 loss_rpn_cls: 0.03803 loss_rpn_loc: 0.1804 time: 0.3550 last_time: 0.2621 data_time: 0.0045 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:00:18 d2.utils.events]: \u001b[0m eta: 1:34:08 iter: 61979 total_loss: 0.9119 loss_cls: 0.2653 loss_box_reg: 0.296 loss_rpn_cls: 0.05415 loss_rpn_loc: 0.2396 time: 0.3550 last_time: 0.2335 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:00:23 d2.utils.events]: \u001b[0m eta: 1:34:03 iter: 61999 total_loss: 0.8212 loss_cls: 0.2557 loss_box_reg: 0.2732 loss_rpn_cls: 0.04053 loss_rpn_loc: 0.2263 time: 0.3549 last_time: 0.2029 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:00:28 d2.utils.events]: \u001b[0m eta: 1:33:57 iter: 62019 total_loss: 0.7998 loss_cls: 0.2211 loss_box_reg: 0.2911 loss_rpn_cls: 0.0457 loss_rpn_loc: 0.2088 time: 0.3549 last_time: 0.2438 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:00:33 d2.utils.events]: \u001b[0m eta: 1:33:50 iter: 62039 total_loss: 0.7224 loss_cls: 0.2231 loss_box_reg: 0.2473 loss_rpn_cls: 0.04452 loss_rpn_loc: 0.1709 time: 0.3549 last_time: 0.2583 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:00:38 d2.utils.events]: \u001b[0m eta: 1:33:47 iter: 62059 total_loss: 0.8285 loss_cls: 0.2596 loss_box_reg: 0.3098 loss_rpn_cls: 0.04335 loss_rpn_loc: 0.183 time: 0.3548 last_time: 0.2630 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:00:42 d2.utils.events]: \u001b[0m eta: 1:33:42 iter: 62079 total_loss: 0.7521 loss_cls: 0.229 loss_box_reg: 0.2875 loss_rpn_cls: 0.03666 loss_rpn_loc: 0.1848 time: 0.3548 last_time: 0.2328 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:00:47 d2.utils.events]: \u001b[0m eta: 1:33:37 iter: 62099 total_loss: 0.8453 loss_cls: 0.287 loss_box_reg: 0.2809 loss_rpn_cls: 0.04234 loss_rpn_loc: 0.2203 time: 0.3548 last_time: 0.2019 data_time: 0.0047 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:00:52 d2.utils.events]: \u001b[0m eta: 1:33:32 iter: 62119 total_loss: 0.7351 loss_cls: 0.2352 loss_box_reg: 0.2596 loss_rpn_cls: 0.04508 loss_rpn_loc: 0.185 time: 0.3547 last_time: 0.2029 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:00:57 d2.utils.events]: \u001b[0m eta: 1:33:29 iter: 62139 total_loss: 0.7353 loss_cls: 0.2384 loss_box_reg: 0.2615 loss_rpn_cls: 0.04563 loss_rpn_loc: 0.201 time: 0.3547 last_time: 0.2602 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:01:01 d2.utils.events]: \u001b[0m eta: 1:33:25 iter: 62159 total_loss: 0.7583 loss_cls: 0.266 loss_box_reg: 0.2802 loss_rpn_cls: 0.04935 loss_rpn_loc: 0.2014 time: 0.3546 last_time: 0.2574 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:01:06 d2.utils.events]: \u001b[0m eta: 1:33:23 iter: 62179 total_loss: 0.7937 loss_cls: 0.274 loss_box_reg: 0.3181 loss_rpn_cls: 0.04755 loss_rpn_loc: 0.1622 time: 0.3546 last_time: 0.2542 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:01:11 d2.utils.events]: \u001b[0m eta: 1:33:17 iter: 62199 total_loss: 0.7453 loss_cls: 0.2465 loss_box_reg: 0.2875 loss_rpn_cls: 0.04 loss_rpn_loc: 0.1653 time: 0.3546 last_time: 0.1990 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:01:15 d2.utils.events]: \u001b[0m eta: 1:33:09 iter: 62219 total_loss: 0.651 loss_cls: 0.2131 loss_box_reg: 0.2593 loss_rpn_cls: 0.04587 loss_rpn_loc: 0.1924 time: 0.3545 last_time: 0.2528 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:01:20 d2.utils.events]: \u001b[0m eta: 1:33:03 iter: 62239 total_loss: 0.9233 loss_cls: 0.3073 loss_box_reg: 0.3129 loss_rpn_cls: 0.0473 loss_rpn_loc: 0.2306 time: 0.3545 last_time: 0.2284 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:01:25 d2.utils.events]: \u001b[0m eta: 1:32:59 iter: 62259 total_loss: 0.7207 loss_cls: 0.2426 loss_box_reg: 0.2938 loss_rpn_cls: 0.03766 loss_rpn_loc: 0.1808 time: 0.3544 last_time: 0.2604 data_time: 0.0048 last_data_time: 0.0054 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:01:30 d2.utils.events]: \u001b[0m eta: 1:32:54 iter: 62279 total_loss: 0.8735 loss_cls: 0.2655 loss_box_reg: 0.3174 loss_rpn_cls: 0.06964 loss_rpn_loc: 0.1919 time: 0.3544 last_time: 0.2334 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:01:34 d2.utils.events]: \u001b[0m eta: 1:32:39 iter: 62299 total_loss: 0.7664 loss_cls: 0.234 loss_box_reg: 0.2829 loss_rpn_cls: 0.05184 loss_rpn_loc: 0.1776 time: 0.3544 last_time: 0.2355 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:01:39 d2.utils.events]: \u001b[0m eta: 1:32:27 iter: 62319 total_loss: 0.8308 loss_cls: 0.2827 loss_box_reg: 0.2975 loss_rpn_cls: 0.05426 loss_rpn_loc: 0.2165 time: 0.3543 last_time: 0.2277 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:01:44 d2.utils.events]: \u001b[0m eta: 1:32:15 iter: 62339 total_loss: 0.8067 loss_cls: 0.2637 loss_box_reg: 0.2838 loss_rpn_cls: 0.0573 loss_rpn_loc: 0.1933 time: 0.3543 last_time: 0.2537 data_time: 0.0047 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:01:48 d2.utils.events]: \u001b[0m eta: 1:32:09 iter: 62359 total_loss: 0.7538 loss_cls: 0.247 loss_box_reg: 0.2844 loss_rpn_cls: 0.04362 loss_rpn_loc: 0.1806 time: 0.3543 last_time: 0.2556 data_time: 0.0047 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:01:53 d2.utils.events]: \u001b[0m eta: 1:32:02 iter: 62379 total_loss: 0.8481 loss_cls: 0.2632 loss_box_reg: 0.2879 loss_rpn_cls: 0.03939 loss_rpn_loc: 0.1931 time: 0.3542 last_time: 0.2001 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:01:58 d2.utils.events]: \u001b[0m eta: 1:31:50 iter: 62399 total_loss: 0.7191 loss_cls: 0.221 loss_box_reg: 0.2832 loss_rpn_cls: 0.0418 loss_rpn_loc: 0.1867 time: 0.3542 last_time: 0.2530 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:02:04 d2.utils.events]: \u001b[0m eta: 1:31:52 iter: 62419 total_loss: 0.6893 loss_cls: 0.227 loss_box_reg: 0.2286 loss_rpn_cls: 0.03941 loss_rpn_loc: 0.1923 time: 0.3542 last_time: 0.3386 data_time: 0.0053 last_data_time: 0.0055 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:02:09 d2.utils.events]: \u001b[0m eta: 1:31:47 iter: 62439 total_loss: 0.7967 loss_cls: 0.2371 loss_box_reg: 0.2857 loss_rpn_cls: 0.03985 loss_rpn_loc: 0.2013 time: 0.3541 last_time: 0.2536 data_time: 0.0052 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:02:14 d2.utils.events]: \u001b[0m eta: 1:31:32 iter: 62459 total_loss: 0.6768 loss_cls: 0.2243 loss_box_reg: 0.2446 loss_rpn_cls: 0.0389 loss_rpn_loc: 0.167 time: 0.3541 last_time: 0.2132 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:02:19 d2.utils.events]: \u001b[0m eta: 1:31:22 iter: 62479 total_loss: 0.7894 loss_cls: 0.2297 loss_box_reg: 0.2961 loss_rpn_cls: 0.04528 loss_rpn_loc: 0.2089 time: 0.3541 last_time: 0.2108 data_time: 0.0047 last_data_time: 0.0055 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:02:23 d2.utils.events]: \u001b[0m eta: 1:31:14 iter: 62499 total_loss: 0.9198 loss_cls: 0.2719 loss_box_reg: 0.3376 loss_rpn_cls: 0.05251 loss_rpn_loc: 0.1995 time: 0.3540 last_time: 0.2618 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:02:28 d2.utils.events]: \u001b[0m eta: 1:30:59 iter: 62519 total_loss: 0.7543 loss_cls: 0.2259 loss_box_reg: 0.287 loss_rpn_cls: 0.05239 loss_rpn_loc: 0.208 time: 0.3540 last_time: 0.2389 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:02:33 d2.utils.events]: \u001b[0m eta: 1:30:44 iter: 62539 total_loss: 0.7226 loss_cls: 0.2204 loss_box_reg: 0.2756 loss_rpn_cls: 0.03008 loss_rpn_loc: 0.1934 time: 0.3539 last_time: 0.2464 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:02:37 d2.utils.events]: \u001b[0m eta: 1:30:31 iter: 62559 total_loss: 0.8127 loss_cls: 0.2497 loss_box_reg: 0.2948 loss_rpn_cls: 0.03183 loss_rpn_loc: 0.197 time: 0.3539 last_time: 0.2003 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:02:42 d2.utils.events]: \u001b[0m eta: 1:30:13 iter: 62579 total_loss: 0.8183 loss_cls: 0.2387 loss_box_reg: 0.2933 loss_rpn_cls: 0.04822 loss_rpn_loc: 0.2094 time: 0.3539 last_time: 0.2347 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:02:47 d2.utils.events]: \u001b[0m eta: 1:30:02 iter: 62599 total_loss: 0.9245 loss_cls: 0.3175 loss_box_reg: 0.3176 loss_rpn_cls: 0.04811 loss_rpn_loc: 0.212 time: 0.3538 last_time: 0.2594 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:02:51 d2.utils.events]: \u001b[0m eta: 1:29:49 iter: 62619 total_loss: 0.7613 loss_cls: 0.2191 loss_box_reg: 0.2864 loss_rpn_cls: 0.03995 loss_rpn_loc: 0.1809 time: 0.3538 last_time: 0.2633 data_time: 0.0047 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:02:56 d2.utils.events]: \u001b[0m eta: 1:29:46 iter: 62639 total_loss: 0.7956 loss_cls: 0.2239 loss_box_reg: 0.2972 loss_rpn_cls: 0.04678 loss_rpn_loc: 0.2071 time: 0.3538 last_time: 0.2298 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:03:01 d2.utils.events]: \u001b[0m eta: 1:29:50 iter: 62659 total_loss: 0.7805 loss_cls: 0.2439 loss_box_reg: 0.2776 loss_rpn_cls: 0.05718 loss_rpn_loc: 0.1905 time: 0.3537 last_time: 0.2192 data_time: 0.0050 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:03:06 d2.utils.events]: \u001b[0m eta: 1:29:46 iter: 62679 total_loss: 0.7026 loss_cls: 0.1905 loss_box_reg: 0.2699 loss_rpn_cls: 0.02763 loss_rpn_loc: 0.1759 time: 0.3537 last_time: 0.2613 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:03:11 d2.utils.events]: \u001b[0m eta: 1:29:37 iter: 62699 total_loss: 0.7073 loss_cls: 0.2167 loss_box_reg: 0.2628 loss_rpn_cls: 0.04827 loss_rpn_loc: 0.1915 time: 0.3536 last_time: 0.2665 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:03:15 d2.utils.events]: \u001b[0m eta: 1:29:35 iter: 62719 total_loss: 0.8531 loss_cls: 0.289 loss_box_reg: 0.3086 loss_rpn_cls: 0.04315 loss_rpn_loc: 0.171 time: 0.3536 last_time: 0.2052 data_time: 0.0046 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:03:20 d2.utils.events]: \u001b[0m eta: 1:29:22 iter: 62739 total_loss: 0.7337 loss_cls: 0.2533 loss_box_reg: 0.2854 loss_rpn_cls: 0.04547 loss_rpn_loc: 0.1782 time: 0.3536 last_time: 0.2325 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:03:25 d2.utils.events]: \u001b[0m eta: 1:29:15 iter: 62759 total_loss: 0.788 loss_cls: 0.2678 loss_box_reg: 0.264 loss_rpn_cls: 0.04587 loss_rpn_loc: 0.1869 time: 0.3535 last_time: 0.2589 data_time: 0.0047 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:03:30 d2.utils.events]: \u001b[0m eta: 1:29:14 iter: 62779 total_loss: 0.7008 loss_cls: 0.2101 loss_box_reg: 0.2685 loss_rpn_cls: 0.03434 loss_rpn_loc: 0.1783 time: 0.3535 last_time: 0.2563 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:03:35 d2.utils.events]: \u001b[0m eta: 1:29:03 iter: 62799 total_loss: 0.9092 loss_cls: 0.33 loss_box_reg: 0.3058 loss_rpn_cls: 0.05337 loss_rpn_loc: 0.2085 time: 0.3535 last_time: 0.2606 data_time: 0.0048 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:03:39 d2.utils.events]: \u001b[0m eta: 1:28:58 iter: 62819 total_loss: 0.8284 loss_cls: 0.2527 loss_box_reg: 0.2847 loss_rpn_cls: 0.06136 loss_rpn_loc: 0.1942 time: 0.3534 last_time: 0.2518 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:03:44 d2.utils.events]: \u001b[0m eta: 1:28:46 iter: 62839 total_loss: 0.7569 loss_cls: 0.2294 loss_box_reg: 0.2739 loss_rpn_cls: 0.04769 loss_rpn_loc: 0.1899 time: 0.3534 last_time: 0.2380 data_time: 0.0046 last_data_time: 0.0042 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:03:49 d2.utils.events]: \u001b[0m eta: 1:28:41 iter: 62859 total_loss: 0.8662 loss_cls: 0.2761 loss_box_reg: 0.2781 loss_rpn_cls: 0.04686 loss_rpn_loc: 0.2128 time: 0.3534 last_time: 0.2680 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:03:54 d2.utils.events]: \u001b[0m eta: 1:28:35 iter: 62879 total_loss: 0.8998 loss_cls: 0.3153 loss_box_reg: 0.2787 loss_rpn_cls: 0.05454 loss_rpn_loc: 0.218 time: 0.3533 last_time: 0.2286 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:03:59 d2.utils.events]: \u001b[0m eta: 1:28:38 iter: 62899 total_loss: 0.7467 loss_cls: 0.2218 loss_box_reg: 0.2948 loss_rpn_cls: 0.04103 loss_rpn_loc: 0.1752 time: 0.3533 last_time: 0.2681 data_time: 0.0046 last_data_time: 0.0042 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:04:04 d2.utils.events]: \u001b[0m eta: 1:28:37 iter: 62919 total_loss: 0.7375 loss_cls: 0.2168 loss_box_reg: 0.2496 loss_rpn_cls: 0.04911 loss_rpn_loc: 0.2056 time: 0.3532 last_time: 0.2291 data_time: 0.0046 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:04:08 d2.utils.events]: \u001b[0m eta: 1:28:21 iter: 62939 total_loss: 0.808 loss_cls: 0.2695 loss_box_reg: 0.2807 loss_rpn_cls: 0.04844 loss_rpn_loc: 0.1687 time: 0.3532 last_time: 0.2402 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:04:13 d2.utils.events]: \u001b[0m eta: 1:28:20 iter: 62959 total_loss: 0.7025 loss_cls: 0.2576 loss_box_reg: 0.2585 loss_rpn_cls: 0.03416 loss_rpn_loc: 0.1906 time: 0.3532 last_time: 0.2703 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:04:18 d2.utils.events]: \u001b[0m eta: 1:28:15 iter: 62979 total_loss: 0.7458 loss_cls: 0.2372 loss_box_reg: 0.2696 loss_rpn_cls: 0.03341 loss_rpn_loc: 0.2042 time: 0.3531 last_time: 0.2440 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:04:23 d2.utils.events]: \u001b[0m eta: 1:28:28 iter: 62999 total_loss: 0.8001 loss_cls: 0.2747 loss_box_reg: 0.2866 loss_rpn_cls: 0.05797 loss_rpn_loc: 0.1942 time: 0.3531 last_time: 0.2345 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:04:28 d2.utils.events]: \u001b[0m eta: 1:28:17 iter: 63019 total_loss: 0.7759 loss_cls: 0.2738 loss_box_reg: 0.3232 loss_rpn_cls: 0.04135 loss_rpn_loc: 0.1546 time: 0.3531 last_time: 0.2258 data_time: 0.0047 last_data_time: 0.0042 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:04:32 d2.utils.events]: \u001b[0m eta: 1:27:57 iter: 63039 total_loss: 0.7701 loss_cls: 0.2811 loss_box_reg: 0.2949 loss_rpn_cls: 0.02963 loss_rpn_loc: 0.1848 time: 0.3530 last_time: 0.2290 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:04:37 d2.utils.events]: \u001b[0m eta: 1:27:46 iter: 63059 total_loss: 0.8251 loss_cls: 0.2887 loss_box_reg: 0.2864 loss_rpn_cls: 0.04408 loss_rpn_loc: 0.1986 time: 0.3530 last_time: 0.2368 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:04:42 d2.utils.events]: \u001b[0m eta: 1:27:46 iter: 63079 total_loss: 0.8692 loss_cls: 0.2888 loss_box_reg: 0.3299 loss_rpn_cls: 0.05711 loss_rpn_loc: 0.2229 time: 0.3530 last_time: 0.2603 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:04:47 d2.utils.events]: \u001b[0m eta: 1:27:44 iter: 63099 total_loss: 0.8444 loss_cls: 0.2559 loss_box_reg: 0.3073 loss_rpn_cls: 0.04267 loss_rpn_loc: 0.2016 time: 0.3529 last_time: 0.2542 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:04:51 d2.utils.events]: \u001b[0m eta: 1:27:36 iter: 63119 total_loss: 0.7437 loss_cls: 0.2266 loss_box_reg: 0.2896 loss_rpn_cls: 0.04418 loss_rpn_loc: 0.1847 time: 0.3529 last_time: 0.2539 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:04:56 d2.utils.events]: \u001b[0m eta: 1:27:27 iter: 63139 total_loss: 0.7506 loss_cls: 0.2138 loss_box_reg: 0.2781 loss_rpn_cls: 0.05913 loss_rpn_loc: 0.2027 time: 0.3529 last_time: 0.2392 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:05:01 d2.utils.events]: \u001b[0m eta: 1:27:20 iter: 63159 total_loss: 0.873 loss_cls: 0.2912 loss_box_reg: 0.3116 loss_rpn_cls: 0.04379 loss_rpn_loc: 0.2046 time: 0.3528 last_time: 0.2431 data_time: 0.0047 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:05:06 d2.utils.events]: \u001b[0m eta: 1:27:16 iter: 63179 total_loss: 0.8179 loss_cls: 0.2927 loss_box_reg: 0.2908 loss_rpn_cls: 0.04647 loss_rpn_loc: 0.2013 time: 0.3528 last_time: 0.2195 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:05:11 d2.utils.events]: \u001b[0m eta: 1:27:17 iter: 63199 total_loss: 0.8041 loss_cls: 0.2443 loss_box_reg: 0.2855 loss_rpn_cls: 0.04845 loss_rpn_loc: 0.2129 time: 0.3527 last_time: 0.2596 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:05:16 d2.utils.events]: \u001b[0m eta: 1:27:15 iter: 63219 total_loss: 0.8184 loss_cls: 0.2873 loss_box_reg: 0.2955 loss_rpn_cls: 0.04562 loss_rpn_loc: 0.1854 time: 0.3527 last_time: 0.2619 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:05:20 d2.utils.events]: \u001b[0m eta: 1:27:18 iter: 63239 total_loss: 0.7275 loss_cls: 0.2326 loss_box_reg: 0.2508 loss_rpn_cls: 0.04218 loss_rpn_loc: 0.1328 time: 0.3527 last_time: 0.2296 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:05:25 d2.utils.events]: \u001b[0m eta: 1:27:13 iter: 63259 total_loss: 0.7305 loss_cls: 0.2563 loss_box_reg: 0.2523 loss_rpn_cls: 0.04071 loss_rpn_loc: 0.1719 time: 0.3526 last_time: 0.2491 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:05:30 d2.utils.events]: \u001b[0m eta: 1:27:06 iter: 63279 total_loss: 0.7919 loss_cls: 0.2416 loss_box_reg: 0.2543 loss_rpn_cls: 0.04832 loss_rpn_loc: 0.1864 time: 0.3526 last_time: 0.2402 data_time: 0.0047 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:05:35 d2.utils.events]: \u001b[0m eta: 1:27:09 iter: 63299 total_loss: 0.6692 loss_cls: 0.2354 loss_box_reg: 0.2689 loss_rpn_cls: 0.05116 loss_rpn_loc: 0.1734 time: 0.3526 last_time: 0.2470 data_time: 0.0048 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:05:40 d2.utils.events]: \u001b[0m eta: 1:27:11 iter: 63319 total_loss: 0.8873 loss_cls: 0.3122 loss_box_reg: 0.3113 loss_rpn_cls: 0.04779 loss_rpn_loc: 0.2319 time: 0.3525 last_time: 0.2613 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:05:45 d2.utils.events]: \u001b[0m eta: 1:27:08 iter: 63339 total_loss: 0.6383 loss_cls: 0.2189 loss_box_reg: 0.2574 loss_rpn_cls: 0.0315 loss_rpn_loc: 0.1574 time: 0.3525 last_time: 0.2485 data_time: 0.0047 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:05:49 d2.utils.events]: \u001b[0m eta: 1:27:04 iter: 63359 total_loss: 0.7353 loss_cls: 0.2258 loss_box_reg: 0.2581 loss_rpn_cls: 0.04541 loss_rpn_loc: 0.1916 time: 0.3525 last_time: 0.2343 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:05:54 d2.utils.events]: \u001b[0m eta: 1:27:00 iter: 63379 total_loss: 0.7326 loss_cls: 0.2385 loss_box_reg: 0.2531 loss_rpn_cls: 0.04465 loss_rpn_loc: 0.1761 time: 0.3524 last_time: 0.2595 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:05:59 d2.utils.events]: \u001b[0m eta: 1:26:59 iter: 63399 total_loss: 0.7135 loss_cls: 0.2182 loss_box_reg: 0.29 loss_rpn_cls: 0.03464 loss_rpn_loc: 0.2055 time: 0.3524 last_time: 0.2276 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:06:04 d2.utils.events]: \u001b[0m eta: 1:26:48 iter: 63419 total_loss: 0.8496 loss_cls: 0.2759 loss_box_reg: 0.3178 loss_rpn_cls: 0.05757 loss_rpn_loc: 0.1977 time: 0.3524 last_time: 0.2645 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:06:09 d2.utils.events]: \u001b[0m eta: 1:26:42 iter: 63439 total_loss: 0.7328 loss_cls: 0.2544 loss_box_reg: 0.2778 loss_rpn_cls: 0.05496 loss_rpn_loc: 0.1955 time: 0.3523 last_time: 0.2417 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:06:13 d2.utils.events]: \u001b[0m eta: 1:26:41 iter: 63459 total_loss: 0.7827 loss_cls: 0.2644 loss_box_reg: 0.2979 loss_rpn_cls: 0.05093 loss_rpn_loc: 0.1913 time: 0.3523 last_time: 0.2343 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:06:18 d2.utils.events]: \u001b[0m eta: 1:26:38 iter: 63479 total_loss: 0.8581 loss_cls: 0.2663 loss_box_reg: 0.3171 loss_rpn_cls: 0.0475 loss_rpn_loc: 0.2035 time: 0.3522 last_time: 0.2656 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:06:23 d2.utils.events]: \u001b[0m eta: 1:26:34 iter: 63499 total_loss: 0.8432 loss_cls: 0.2995 loss_box_reg: 0.2601 loss_rpn_cls: 0.04357 loss_rpn_loc: 0.2036 time: 0.3522 last_time: 0.2465 data_time: 0.0046 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:06:28 d2.utils.events]: \u001b[0m eta: 1:26:35 iter: 63519 total_loss: 0.7052 loss_cls: 0.2169 loss_box_reg: 0.2504 loss_rpn_cls: 0.04716 loss_rpn_loc: 0.1849 time: 0.3522 last_time: 0.2043 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:06:32 d2.utils.events]: \u001b[0m eta: 1:26:32 iter: 63539 total_loss: 0.6386 loss_cls: 0.2254 loss_box_reg: 0.2375 loss_rpn_cls: 0.03655 loss_rpn_loc: 0.1539 time: 0.3521 last_time: 0.2020 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:06:37 d2.utils.events]: \u001b[0m eta: 1:26:21 iter: 63559 total_loss: 0.7015 loss_cls: 0.2407 loss_box_reg: 0.2799 loss_rpn_cls: 0.03984 loss_rpn_loc: 0.1725 time: 0.3521 last_time: 0.2340 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:06:42 d2.utils.events]: \u001b[0m eta: 1:26:18 iter: 63579 total_loss: 0.797 loss_cls: 0.2237 loss_box_reg: 0.3057 loss_rpn_cls: 0.04831 loss_rpn_loc: 0.1751 time: 0.3521 last_time: 0.2458 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:06:47 d2.utils.events]: \u001b[0m eta: 1:26:17 iter: 63599 total_loss: 0.7987 loss_cls: 0.2505 loss_box_reg: 0.3135 loss_rpn_cls: 0.03672 loss_rpn_loc: 0.1873 time: 0.3520 last_time: 0.2593 data_time: 0.0046 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:06:51 d2.utils.events]: \u001b[0m eta: 1:26:08 iter: 63619 total_loss: 0.7747 loss_cls: 0.2821 loss_box_reg: 0.2702 loss_rpn_cls: 0.04953 loss_rpn_loc: 0.1762 time: 0.3520 last_time: 0.2361 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:06:56 d2.utils.events]: \u001b[0m eta: 1:26:05 iter: 63639 total_loss: 0.8117 loss_cls: 0.2578 loss_box_reg: 0.328 loss_rpn_cls: 0.04237 loss_rpn_loc: 0.1884 time: 0.3520 last_time: 0.2598 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:07:01 d2.utils.events]: \u001b[0m eta: 1:26:00 iter: 63659 total_loss: 0.8713 loss_cls: 0.2664 loss_box_reg: 0.3328 loss_rpn_cls: 0.04911 loss_rpn_loc: 0.2218 time: 0.3519 last_time: 0.1918 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:07:06 d2.utils.events]: \u001b[0m eta: 1:25:52 iter: 63679 total_loss: 0.8338 loss_cls: 0.2482 loss_box_reg: 0.3095 loss_rpn_cls: 0.04224 loss_rpn_loc: 0.2298 time: 0.3519 last_time: 0.2462 data_time: 0.0048 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:07:11 d2.utils.events]: \u001b[0m eta: 1:25:53 iter: 63699 total_loss: 0.8376 loss_cls: 0.2435 loss_box_reg: 0.2813 loss_rpn_cls: 0.04718 loss_rpn_loc: 0.2062 time: 0.3519 last_time: 0.2286 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:07:15 d2.utils.events]: \u001b[0m eta: 1:25:51 iter: 63719 total_loss: 0.7241 loss_cls: 0.2297 loss_box_reg: 0.3013 loss_rpn_cls: 0.04786 loss_rpn_loc: 0.1694 time: 0.3518 last_time: 0.2464 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:07:20 d2.utils.events]: \u001b[0m eta: 1:25:53 iter: 63739 total_loss: 0.7849 loss_cls: 0.2305 loss_box_reg: 0.2935 loss_rpn_cls: 0.05424 loss_rpn_loc: 0.1966 time: 0.3518 last_time: 0.2605 data_time: 0.0049 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:07:25 d2.utils.events]: \u001b[0m eta: 1:25:51 iter: 63759 total_loss: 0.8109 loss_cls: 0.2302 loss_box_reg: 0.3083 loss_rpn_cls: 0.05266 loss_rpn_loc: 0.2002 time: 0.3518 last_time: 0.2319 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:07:30 d2.utils.events]: \u001b[0m eta: 1:25:44 iter: 63779 total_loss: 0.7744 loss_cls: 0.2196 loss_box_reg: 0.2827 loss_rpn_cls: 0.04221 loss_rpn_loc: 0.1976 time: 0.3517 last_time: 0.2450 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:07:35 d2.utils.events]: \u001b[0m eta: 1:25:42 iter: 63799 total_loss: 0.6579 loss_cls: 0.164 loss_box_reg: 0.2657 loss_rpn_cls: 0.02996 loss_rpn_loc: 0.1585 time: 0.3517 last_time: 0.2575 data_time: 0.0056 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:07:40 d2.utils.events]: \u001b[0m eta: 1:25:34 iter: 63819 total_loss: 0.7741 loss_cls: 0.242 loss_box_reg: 0.2915 loss_rpn_cls: 0.06301 loss_rpn_loc: 0.2008 time: 0.3516 last_time: 0.1974 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:07:44 d2.utils.events]: \u001b[0m eta: 1:25:27 iter: 63839 total_loss: 0.8139 loss_cls: 0.2722 loss_box_reg: 0.3114 loss_rpn_cls: 0.05801 loss_rpn_loc: 0.1944 time: 0.3516 last_time: 0.2024 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:07:49 d2.utils.events]: \u001b[0m eta: 1:25:15 iter: 63859 total_loss: 0.7882 loss_cls: 0.2326 loss_box_reg: 0.2662 loss_rpn_cls: 0.04719 loss_rpn_loc: 0.2192 time: 0.3516 last_time: 0.2339 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:07:53 d2.utils.events]: \u001b[0m eta: 1:24:58 iter: 63879 total_loss: 0.8137 loss_cls: 0.2745 loss_box_reg: 0.3207 loss_rpn_cls: 0.05562 loss_rpn_loc: 0.1932 time: 0.3515 last_time: 0.2157 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:07:58 d2.utils.events]: \u001b[0m eta: 1:24:41 iter: 63899 total_loss: 0.8604 loss_cls: 0.2804 loss_box_reg: 0.3143 loss_rpn_cls: 0.05332 loss_rpn_loc: 0.2028 time: 0.3515 last_time: 0.2280 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:08:03 d2.utils.events]: \u001b[0m eta: 1:24:25 iter: 63919 total_loss: 0.8704 loss_cls: 0.2822 loss_box_reg: 0.2859 loss_rpn_cls: 0.04677 loss_rpn_loc: 0.2131 time: 0.3515 last_time: 0.2555 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:08:07 d2.utils.events]: \u001b[0m eta: 1:24:19 iter: 63939 total_loss: 0.7278 loss_cls: 0.2165 loss_box_reg: 0.2701 loss_rpn_cls: 0.04539 loss_rpn_loc: 0.1731 time: 0.3514 last_time: 0.2013 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:08:12 d2.utils.events]: \u001b[0m eta: 1:24:03 iter: 63959 total_loss: 0.7555 loss_cls: 0.25 loss_box_reg: 0.2648 loss_rpn_cls: 0.04641 loss_rpn_loc: 0.18 time: 0.3514 last_time: 0.2493 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:08:16 d2.utils.events]: \u001b[0m eta: 1:23:49 iter: 63979 total_loss: 0.753 loss_cls: 0.2587 loss_box_reg: 0.2935 loss_rpn_cls: 0.04137 loss_rpn_loc: 0.187 time: 0.3513 last_time: 0.1999 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:08:21 d2.utils.events]: \u001b[0m eta: 1:23:38 iter: 63999 total_loss: 0.7451 loss_cls: 0.2308 loss_box_reg: 0.264 loss_rpn_cls: 0.04724 loss_rpn_loc: 0.1909 time: 0.3513 last_time: 0.2274 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:08:26 d2.utils.events]: \u001b[0m eta: 1:23:33 iter: 64019 total_loss: 0.7233 loss_cls: 0.2504 loss_box_reg: 0.2873 loss_rpn_cls: 0.03934 loss_rpn_loc: 0.1885 time: 0.3513 last_time: 0.2354 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:08:31 d2.utils.events]: \u001b[0m eta: 1:23:23 iter: 64039 total_loss: 0.9039 loss_cls: 0.2889 loss_box_reg: 0.3136 loss_rpn_cls: 0.05061 loss_rpn_loc: 0.1775 time: 0.3512 last_time: 0.1990 data_time: 0.0047 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:08:35 d2.utils.events]: \u001b[0m eta: 1:23:23 iter: 64059 total_loss: 0.6377 loss_cls: 0.1992 loss_box_reg: 0.2551 loss_rpn_cls: 0.04104 loss_rpn_loc: 0.1537 time: 0.3512 last_time: 0.2287 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:08:40 d2.utils.events]: \u001b[0m eta: 1:23:14 iter: 64079 total_loss: 0.7743 loss_cls: 0.2325 loss_box_reg: 0.2753 loss_rpn_cls: 0.03447 loss_rpn_loc: 0.1747 time: 0.3512 last_time: 0.2398 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:08:45 d2.utils.events]: \u001b[0m eta: 1:23:09 iter: 64099 total_loss: 0.8215 loss_cls: 0.2782 loss_box_reg: 0.278 loss_rpn_cls: 0.06114 loss_rpn_loc: 0.2042 time: 0.3511 last_time: 0.2527 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:08:50 d2.utils.events]: \u001b[0m eta: 1:23:06 iter: 64119 total_loss: 0.7857 loss_cls: 0.2263 loss_box_reg: 0.2822 loss_rpn_cls: 0.04074 loss_rpn_loc: 0.2123 time: 0.3511 last_time: 0.2299 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:08:54 d2.utils.events]: \u001b[0m eta: 1:22:56 iter: 64139 total_loss: 0.7909 loss_cls: 0.2544 loss_box_reg: 0.2705 loss_rpn_cls: 0.0594 loss_rpn_loc: 0.2011 time: 0.3510 last_time: 0.1982 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:08:59 d2.utils.events]: \u001b[0m eta: 1:22:49 iter: 64159 total_loss: 0.7468 loss_cls: 0.2732 loss_box_reg: 0.2854 loss_rpn_cls: 0.04429 loss_rpn_loc: 0.1966 time: 0.3510 last_time: 0.2366 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:09:03 d2.utils.events]: \u001b[0m eta: 1:22:44 iter: 64179 total_loss: 0.7855 loss_cls: 0.2419 loss_box_reg: 0.2788 loss_rpn_cls: 0.03873 loss_rpn_loc: 0.158 time: 0.3510 last_time: 0.2540 data_time: 0.0048 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:09:08 d2.utils.events]: \u001b[0m eta: 1:22:38 iter: 64199 total_loss: 0.78 loss_cls: 0.2325 loss_box_reg: 0.2859 loss_rpn_cls: 0.04669 loss_rpn_loc: 0.2084 time: 0.3509 last_time: 0.2378 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:09:13 d2.utils.events]: \u001b[0m eta: 1:22:32 iter: 64219 total_loss: 0.7967 loss_cls: 0.2535 loss_box_reg: 0.2972 loss_rpn_cls: 0.04686 loss_rpn_loc: 0.2047 time: 0.3509 last_time: 0.1993 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:09:18 d2.utils.events]: \u001b[0m eta: 1:22:29 iter: 64239 total_loss: 0.8324 loss_cls: 0.3168 loss_box_reg: 0.3104 loss_rpn_cls: 0.04662 loss_rpn_loc: 0.2111 time: 0.3509 last_time: 0.2551 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:09:22 d2.utils.events]: \u001b[0m eta: 1:22:23 iter: 64259 total_loss: 0.7844 loss_cls: 0.2535 loss_box_reg: 0.2988 loss_rpn_cls: 0.05063 loss_rpn_loc: 0.1834 time: 0.3508 last_time: 0.2401 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:09:27 d2.utils.events]: \u001b[0m eta: 1:22:13 iter: 64279 total_loss: 0.7695 loss_cls: 0.2431 loss_box_reg: 0.2684 loss_rpn_cls: 0.04528 loss_rpn_loc: 0.1987 time: 0.3508 last_time: 0.2269 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:09:31 d2.utils.events]: \u001b[0m eta: 1:22:06 iter: 64299 total_loss: 0.8027 loss_cls: 0.2487 loss_box_reg: 0.3142 loss_rpn_cls: 0.03665 loss_rpn_loc: 0.1859 time: 0.3508 last_time: 0.2404 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:09:36 d2.utils.events]: \u001b[0m eta: 1:21:57 iter: 64319 total_loss: 0.6563 loss_cls: 0.1922 loss_box_reg: 0.2455 loss_rpn_cls: 0.04326 loss_rpn_loc: 0.1848 time: 0.3507 last_time: 0.2549 data_time: 0.0047 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:09:41 d2.utils.events]: \u001b[0m eta: 1:21:51 iter: 64339 total_loss: 0.7805 loss_cls: 0.3058 loss_box_reg: 0.268 loss_rpn_cls: 0.04064 loss_rpn_loc: 0.1642 time: 0.3507 last_time: 0.2477 data_time: 0.0050 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:09:45 d2.utils.events]: \u001b[0m eta: 1:21:47 iter: 64359 total_loss: 0.8147 loss_cls: 0.2344 loss_box_reg: 0.2994 loss_rpn_cls: 0.04208 loss_rpn_loc: 0.1867 time: 0.3506 last_time: 0.2570 data_time: 0.0049 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:09:50 d2.utils.events]: \u001b[0m eta: 1:21:35 iter: 64379 total_loss: 0.7394 loss_cls: 0.2101 loss_box_reg: 0.259 loss_rpn_cls: 0.0421 loss_rpn_loc: 0.1921 time: 0.3506 last_time: 0.2354 data_time: 0.0048 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:09:55 d2.utils.events]: \u001b[0m eta: 1:21:27 iter: 64399 total_loss: 0.7589 loss_cls: 0.2191 loss_box_reg: 0.2987 loss_rpn_cls: 0.04133 loss_rpn_loc: 0.2023 time: 0.3506 last_time: 0.2442 data_time: 0.0049 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:09:59 d2.utils.events]: \u001b[0m eta: 1:21:18 iter: 64419 total_loss: 0.8813 loss_cls: 0.2421 loss_box_reg: 0.2882 loss_rpn_cls: 0.05105 loss_rpn_loc: 0.2082 time: 0.3505 last_time: 0.2516 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:10:04 d2.utils.events]: \u001b[0m eta: 1:21:07 iter: 64439 total_loss: 0.7317 loss_cls: 0.2525 loss_box_reg: 0.2739 loss_rpn_cls: 0.04365 loss_rpn_loc: 0.2019 time: 0.3505 last_time: 0.2105 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:10:08 d2.utils.events]: \u001b[0m eta: 1:20:57 iter: 64459 total_loss: 0.8305 loss_cls: 0.2849 loss_box_reg: 0.3206 loss_rpn_cls: 0.05799 loss_rpn_loc: 0.1894 time: 0.3505 last_time: 0.2112 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:10:13 d2.utils.events]: \u001b[0m eta: 1:20:52 iter: 64479 total_loss: 0.8472 loss_cls: 0.3019 loss_box_reg: 0.2861 loss_rpn_cls: 0.03262 loss_rpn_loc: 0.1883 time: 0.3504 last_time: 0.2505 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:10:17 d2.utils.events]: \u001b[0m eta: 1:20:44 iter: 64499 total_loss: 0.6662 loss_cls: 0.22 loss_box_reg: 0.2605 loss_rpn_cls: 0.03221 loss_rpn_loc: 0.1676 time: 0.3504 last_time: 0.2487 data_time: 0.0047 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:10:22 d2.utils.events]: \u001b[0m eta: 1:20:33 iter: 64519 total_loss: 0.8199 loss_cls: 0.2408 loss_box_reg: 0.3065 loss_rpn_cls: 0.04121 loss_rpn_loc: 0.2067 time: 0.3503 last_time: 0.2097 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:10:27 d2.utils.events]: \u001b[0m eta: 1:20:26 iter: 64539 total_loss: 0.7878 loss_cls: 0.2734 loss_box_reg: 0.2545 loss_rpn_cls: 0.05019 loss_rpn_loc: 0.1749 time: 0.3503 last_time: 0.2365 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:10:31 d2.utils.events]: \u001b[0m eta: 1:20:21 iter: 64559 total_loss: 0.7909 loss_cls: 0.2368 loss_box_reg: 0.2832 loss_rpn_cls: 0.04221 loss_rpn_loc: 0.1531 time: 0.3503 last_time: 0.2345 data_time: 0.0048 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:10:36 d2.utils.events]: \u001b[0m eta: 1:20:14 iter: 64579 total_loss: 0.7858 loss_cls: 0.2451 loss_box_reg: 0.2923 loss_rpn_cls: 0.04997 loss_rpn_loc: 0.1833 time: 0.3502 last_time: 0.2504 data_time: 0.0047 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:10:41 d2.utils.events]: \u001b[0m eta: 1:20:06 iter: 64599 total_loss: 0.7534 loss_cls: 0.2202 loss_box_reg: 0.2561 loss_rpn_cls: 0.04491 loss_rpn_loc: 0.1949 time: 0.3502 last_time: 0.2329 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:10:45 d2.utils.events]: \u001b[0m eta: 1:20:01 iter: 64619 total_loss: 0.8258 loss_cls: 0.237 loss_box_reg: 0.3104 loss_rpn_cls: 0.04957 loss_rpn_loc: 0.1909 time: 0.3502 last_time: 0.1957 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:10:50 d2.utils.events]: \u001b[0m eta: 1:19:54 iter: 64639 total_loss: 0.8232 loss_cls: 0.2547 loss_box_reg: 0.2742 loss_rpn_cls: 0.0383 loss_rpn_loc: 0.2388 time: 0.3501 last_time: 0.1948 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:10:54 d2.utils.events]: \u001b[0m eta: 1:19:48 iter: 64659 total_loss: 0.7948 loss_cls: 0.2375 loss_box_reg: 0.3169 loss_rpn_cls: 0.04114 loss_rpn_loc: 0.1845 time: 0.3501 last_time: 0.1799 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:10:59 d2.utils.events]: \u001b[0m eta: 1:19:42 iter: 64679 total_loss: 0.7923 loss_cls: 0.254 loss_box_reg: 0.2928 loss_rpn_cls: 0.04589 loss_rpn_loc: 0.23 time: 0.3500 last_time: 0.2356 data_time: 0.0049 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:11:03 d2.utils.events]: \u001b[0m eta: 1:19:27 iter: 64699 total_loss: 0.7919 loss_cls: 0.2728 loss_box_reg: 0.3233 loss_rpn_cls: 0.04467 loss_rpn_loc: 0.1843 time: 0.3500 last_time: 0.2096 data_time: 0.0049 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:11:08 d2.utils.events]: \u001b[0m eta: 1:19:18 iter: 64719 total_loss: 0.8217 loss_cls: 0.2322 loss_box_reg: 0.3194 loss_rpn_cls: 0.05715 loss_rpn_loc: 0.2069 time: 0.3500 last_time: 0.2089 data_time: 0.0048 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:11:12 d2.utils.events]: \u001b[0m eta: 1:19:04 iter: 64739 total_loss: 0.8089 loss_cls: 0.2644 loss_box_reg: 0.2854 loss_rpn_cls: 0.04525 loss_rpn_loc: 0.2004 time: 0.3499 last_time: 0.1967 data_time: 0.0049 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:11:17 d2.utils.events]: \u001b[0m eta: 1:18:53 iter: 64759 total_loss: 0.7265 loss_cls: 0.2132 loss_box_reg: 0.2705 loss_rpn_cls: 0.04446 loss_rpn_loc: 0.1827 time: 0.3499 last_time: 0.2228 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:11:22 d2.utils.events]: \u001b[0m eta: 1:18:46 iter: 64779 total_loss: 0.8163 loss_cls: 0.2267 loss_box_reg: 0.2887 loss_rpn_cls: 0.05113 loss_rpn_loc: 0.2175 time: 0.3499 last_time: 0.2109 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:11:26 d2.utils.events]: \u001b[0m eta: 1:18:38 iter: 64799 total_loss: 0.7444 loss_cls: 0.237 loss_box_reg: 0.2765 loss_rpn_cls: 0.05668 loss_rpn_loc: 0.1867 time: 0.3498 last_time: 0.1930 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:11:31 d2.utils.events]: \u001b[0m eta: 1:18:31 iter: 64819 total_loss: 0.8278 loss_cls: 0.265 loss_box_reg: 0.3034 loss_rpn_cls: 0.03956 loss_rpn_loc: 0.1863 time: 0.3498 last_time: 0.2236 data_time: 0.0046 last_data_time: 0.0042 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:11:35 d2.utils.events]: \u001b[0m eta: 1:18:26 iter: 64839 total_loss: 0.8502 loss_cls: 0.2711 loss_box_reg: 0.3001 loss_rpn_cls: 0.05053 loss_rpn_loc: 0.2258 time: 0.3497 last_time: 0.2107 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:11:40 d2.utils.events]: \u001b[0m eta: 1:18:21 iter: 64859 total_loss: 0.6894 loss_cls: 0.2084 loss_box_reg: 0.2481 loss_rpn_cls: 0.04603 loss_rpn_loc: 0.1815 time: 0.3497 last_time: 0.1945 data_time: 0.0047 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:11:44 d2.utils.events]: \u001b[0m eta: 1:18:18 iter: 64879 total_loss: 0.834 loss_cls: 0.298 loss_box_reg: 0.2865 loss_rpn_cls: 0.05536 loss_rpn_loc: 0.201 time: 0.3497 last_time: 0.2383 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:11:49 d2.utils.events]: \u001b[0m eta: 1:18:14 iter: 64899 total_loss: 0.8393 loss_cls: 0.2794 loss_box_reg: 0.3188 loss_rpn_cls: 0.04396 loss_rpn_loc: 0.1763 time: 0.3496 last_time: 0.2349 data_time: 0.0047 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:11:54 d2.utils.events]: \u001b[0m eta: 1:18:11 iter: 64919 total_loss: 0.7525 loss_cls: 0.24 loss_box_reg: 0.2685 loss_rpn_cls: 0.04964 loss_rpn_loc: 0.1766 time: 0.3496 last_time: 0.2242 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:11:58 d2.utils.events]: \u001b[0m eta: 1:18:07 iter: 64939 total_loss: 0.8098 loss_cls: 0.2423 loss_box_reg: 0.2733 loss_rpn_cls: 0.0454 loss_rpn_loc: 0.1977 time: 0.3496 last_time: 0.2350 data_time: 0.0048 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:12:03 d2.utils.events]: \u001b[0m eta: 1:18:02 iter: 64959 total_loss: 0.8293 loss_cls: 0.2727 loss_box_reg: 0.2935 loss_rpn_cls: 0.03853 loss_rpn_loc: 0.1673 time: 0.3495 last_time: 0.2514 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:12:07 d2.utils.events]: \u001b[0m eta: 1:17:58 iter: 64979 total_loss: 0.7556 loss_cls: 0.2184 loss_box_reg: 0.2852 loss_rpn_cls: 0.04136 loss_rpn_loc: 0.1836 time: 0.3495 last_time: 0.1938 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:12:13 d2.utils.events]: \u001b[0m eta: 1:17:51 iter: 64999 total_loss: 0.7653 loss_cls: 0.2201 loss_box_reg: 0.2789 loss_rpn_cls: 0.04835 loss_rpn_loc: 0.1811 time: 0.3494 last_time: 0.2091 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:12:18 d2.utils.events]: \u001b[0m eta: 1:17:47 iter: 65019 total_loss: 0.8516 loss_cls: 0.2802 loss_box_reg: 0.3239 loss_rpn_cls: 0.05322 loss_rpn_loc: 0.1992 time: 0.3494 last_time: 0.2491 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:12:22 d2.utils.events]: \u001b[0m eta: 1:17:41 iter: 65039 total_loss: 0.7812 loss_cls: 0.2728 loss_box_reg: 0.2808 loss_rpn_cls: 0.05015 loss_rpn_loc: 0.1939 time: 0.3494 last_time: 0.2092 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:12:27 d2.utils.events]: \u001b[0m eta: 1:17:37 iter: 65059 total_loss: 0.8076 loss_cls: 0.2721 loss_box_reg: 0.2954 loss_rpn_cls: 0.04832 loss_rpn_loc: 0.2087 time: 0.3493 last_time: 0.3151 data_time: 0.0049 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:12:32 d2.utils.events]: \u001b[0m eta: 1:17:33 iter: 65079 total_loss: 0.8434 loss_cls: 0.2896 loss_box_reg: 0.3054 loss_rpn_cls: 0.04688 loss_rpn_loc: 0.1987 time: 0.3493 last_time: 0.1819 data_time: 0.0049 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:12:38 d2.utils.events]: \u001b[0m eta: 1:17:30 iter: 65099 total_loss: 0.7303 loss_cls: 0.2269 loss_box_reg: 0.2497 loss_rpn_cls: 0.03602 loss_rpn_loc: 0.1914 time: 0.3493 last_time: 0.2495 data_time: 0.0050 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:12:43 d2.utils.events]: \u001b[0m eta: 1:17:25 iter: 65119 total_loss: 0.7287 loss_cls: 0.2321 loss_box_reg: 0.2907 loss_rpn_cls: 0.03522 loss_rpn_loc: 0.1663 time: 0.3493 last_time: 0.2468 data_time: 0.0049 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:12:48 d2.utils.events]: \u001b[0m eta: 1:17:21 iter: 65139 total_loss: 0.6708 loss_cls: 0.2161 loss_box_reg: 0.2494 loss_rpn_cls: 0.03882 loss_rpn_loc: 0.1599 time: 0.3492 last_time: 0.2205 data_time: 0.0049 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:12:52 d2.utils.events]: \u001b[0m eta: 1:17:17 iter: 65159 total_loss: 0.8421 loss_cls: 0.2763 loss_box_reg: 0.309 loss_rpn_cls: 0.04372 loss_rpn_loc: 0.1883 time: 0.3492 last_time: 0.2466 data_time: 0.0049 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:12:57 d2.utils.events]: \u001b[0m eta: 1:17:13 iter: 65179 total_loss: 0.8451 loss_cls: 0.2612 loss_box_reg: 0.3204 loss_rpn_cls: 0.04618 loss_rpn_loc: 0.1944 time: 0.3492 last_time: 0.2474 data_time: 0.0050 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:13:02 d2.utils.events]: \u001b[0m eta: 1:17:08 iter: 65199 total_loss: 0.8219 loss_cls: 0.266 loss_box_reg: 0.2916 loss_rpn_cls: 0.0563 loss_rpn_loc: 0.1943 time: 0.3491 last_time: 0.2351 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:13:06 d2.utils.events]: \u001b[0m eta: 1:17:02 iter: 65219 total_loss: 0.762 loss_cls: 0.2611 loss_box_reg: 0.2672 loss_rpn_cls: 0.04347 loss_rpn_loc: 0.1791 time: 0.3491 last_time: 0.2227 data_time: 0.0046 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:13:11 d2.utils.events]: \u001b[0m eta: 1:16:57 iter: 65239 total_loss: 0.8823 loss_cls: 0.2583 loss_box_reg: 0.2903 loss_rpn_cls: 0.05239 loss_rpn_loc: 0.2134 time: 0.3490 last_time: 0.2177 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:13:16 d2.utils.events]: \u001b[0m eta: 1:16:52 iter: 65259 total_loss: 0.7579 loss_cls: 0.2582 loss_box_reg: 0.2525 loss_rpn_cls: 0.04083 loss_rpn_loc: 0.1637 time: 0.3490 last_time: 0.2182 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:13:20 d2.utils.events]: \u001b[0m eta: 1:16:48 iter: 65279 total_loss: 0.7556 loss_cls: 0.2525 loss_box_reg: 0.2848 loss_rpn_cls: 0.0418 loss_rpn_loc: 0.224 time: 0.3490 last_time: 0.2541 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:13:25 d2.utils.events]: \u001b[0m eta: 1:16:45 iter: 65299 total_loss: 0.744 loss_cls: 0.227 loss_box_reg: 0.262 loss_rpn_cls: 0.04351 loss_rpn_loc: 0.2014 time: 0.3489 last_time: 0.2555 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:13:30 d2.utils.events]: \u001b[0m eta: 1:16:42 iter: 65319 total_loss: 0.8311 loss_cls: 0.2617 loss_box_reg: 0.2838 loss_rpn_cls: 0.04236 loss_rpn_loc: 0.2045 time: 0.3489 last_time: 0.2577 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:13:35 d2.utils.events]: \u001b[0m eta: 1:16:37 iter: 65339 total_loss: 0.7699 loss_cls: 0.2366 loss_box_reg: 0.2491 loss_rpn_cls: 0.04729 loss_rpn_loc: 0.1896 time: 0.3489 last_time: 0.1819 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:13:39 d2.utils.events]: \u001b[0m eta: 1:16:32 iter: 65359 total_loss: 0.8004 loss_cls: 0.2378 loss_box_reg: 0.3002 loss_rpn_cls: 0.0492 loss_rpn_loc: 0.1922 time: 0.3488 last_time: 0.2506 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:13:44 d2.utils.events]: \u001b[0m eta: 1:16:27 iter: 65379 total_loss: 0.8594 loss_cls: 0.2398 loss_box_reg: 0.306 loss_rpn_cls: 0.05241 loss_rpn_loc: 0.1772 time: 0.3488 last_time: 0.2324 data_time: 0.0049 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:13:49 d2.utils.events]: \u001b[0m eta: 1:16:21 iter: 65399 total_loss: 0.699 loss_cls: 0.1969 loss_box_reg: 0.2799 loss_rpn_cls: 0.03667 loss_rpn_loc: 0.203 time: 0.3488 last_time: 0.2507 data_time: 0.0050 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:13:53 d2.utils.events]: \u001b[0m eta: 1:16:17 iter: 65419 total_loss: 0.7444 loss_cls: 0.2217 loss_box_reg: 0.2776 loss_rpn_cls: 0.04492 loss_rpn_loc: 0.1932 time: 0.3487 last_time: 0.2250 data_time: 0.0050 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:13:58 d2.utils.events]: \u001b[0m eta: 1:16:20 iter: 65439 total_loss: 0.6475 loss_cls: 0.187 loss_box_reg: 0.2146 loss_rpn_cls: 0.03553 loss_rpn_loc: 0.1728 time: 0.3487 last_time: 0.2498 data_time: 0.0049 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:14:03 d2.utils.events]: \u001b[0m eta: 1:16:16 iter: 65459 total_loss: 0.7461 loss_cls: 0.2453 loss_box_reg: 0.2927 loss_rpn_cls: 0.03652 loss_rpn_loc: 0.1718 time: 0.3487 last_time: 0.2509 data_time: 0.0048 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:14:08 d2.utils.events]: \u001b[0m eta: 1:16:11 iter: 65479 total_loss: 0.7694 loss_cls: 0.2302 loss_box_reg: 0.3178 loss_rpn_cls: 0.04012 loss_rpn_loc: 0.1595 time: 0.3486 last_time: 0.2114 data_time: 0.0049 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:14:13 d2.utils.events]: \u001b[0m eta: 1:16:07 iter: 65499 total_loss: 0.8284 loss_cls: 0.2437 loss_box_reg: 0.2964 loss_rpn_cls: 0.05755 loss_rpn_loc: 0.1794 time: 0.3486 last_time: 0.2305 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:14:17 d2.utils.events]: \u001b[0m eta: 1:16:02 iter: 65519 total_loss: 0.7587 loss_cls: 0.2445 loss_box_reg: 0.304 loss_rpn_cls: 0.04668 loss_rpn_loc: 0.2075 time: 0.3486 last_time: 0.2352 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:14:22 d2.utils.events]: \u001b[0m eta: 1:15:58 iter: 65539 total_loss: 0.8218 loss_cls: 0.2575 loss_box_reg: 0.2812 loss_rpn_cls: 0.05226 loss_rpn_loc: 0.1999 time: 0.3485 last_time: 0.2120 data_time: 0.0048 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:14:26 d2.utils.events]: \u001b[0m eta: 1:15:53 iter: 65559 total_loss: 0.7817 loss_cls: 0.2511 loss_box_reg: 0.2743 loss_rpn_cls: 0.04641 loss_rpn_loc: 0.1752 time: 0.3485 last_time: 0.1959 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:14:31 d2.utils.events]: \u001b[0m eta: 1:15:48 iter: 65579 total_loss: 0.7343 loss_cls: 0.2215 loss_box_reg: 0.3067 loss_rpn_cls: 0.0363 loss_rpn_loc: 0.1766 time: 0.3485 last_time: 0.2503 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:14:36 d2.utils.events]: \u001b[0m eta: 1:15:45 iter: 65599 total_loss: 0.7713 loss_cls: 0.2494 loss_box_reg: 0.2756 loss_rpn_cls: 0.05263 loss_rpn_loc: 0.1635 time: 0.3484 last_time: 0.2508 data_time: 0.0049 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:14:41 d2.utils.events]: \u001b[0m eta: 1:15:44 iter: 65619 total_loss: 0.8381 loss_cls: 0.2992 loss_box_reg: 0.3003 loss_rpn_cls: 0.05786 loss_rpn_loc: 0.1794 time: 0.3484 last_time: 0.2915 data_time: 0.0051 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:14:46 d2.utils.events]: \u001b[0m eta: 1:15:39 iter: 65639 total_loss: 0.7796 loss_cls: 0.23 loss_box_reg: 0.2561 loss_rpn_cls: 0.04426 loss_rpn_loc: 0.1804 time: 0.3484 last_time: 0.2231 data_time: 0.0049 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:14:51 d2.utils.events]: \u001b[0m eta: 1:15:34 iter: 65659 total_loss: 0.7871 loss_cls: 0.2518 loss_box_reg: 0.2713 loss_rpn_cls: 0.049 loss_rpn_loc: 0.2246 time: 0.3483 last_time: 0.2349 data_time: 0.0048 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:14:56 d2.utils.events]: \u001b[0m eta: 1:15:28 iter: 65679 total_loss: 0.7432 loss_cls: 0.246 loss_box_reg: 0.2821 loss_rpn_cls: 0.03861 loss_rpn_loc: 0.1739 time: 0.3483 last_time: 0.2101 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:15:00 d2.utils.events]: \u001b[0m eta: 1:15:22 iter: 65699 total_loss: 0.6948 loss_cls: 0.2147 loss_box_reg: 0.2583 loss_rpn_cls: 0.03983 loss_rpn_loc: 0.1753 time: 0.3483 last_time: 0.2114 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:15:05 d2.utils.events]: \u001b[0m eta: 1:15:17 iter: 65719 total_loss: 0.6882 loss_cls: 0.2247 loss_box_reg: 0.265 loss_rpn_cls: 0.04075 loss_rpn_loc: 0.1733 time: 0.3482 last_time: 0.2649 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:15:10 d2.utils.events]: \u001b[0m eta: 1:15:16 iter: 65739 total_loss: 0.8165 loss_cls: 0.2478 loss_box_reg: 0.2811 loss_rpn_cls: 0.0402 loss_rpn_loc: 0.2036 time: 0.3482 last_time: 0.2520 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:15:14 d2.utils.events]: \u001b[0m eta: 1:15:12 iter: 65759 total_loss: 0.8004 loss_cls: 0.2655 loss_box_reg: 0.3077 loss_rpn_cls: 0.042 loss_rpn_loc: 0.1766 time: 0.3482 last_time: 0.2107 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:15:19 d2.utils.events]: \u001b[0m eta: 1:15:07 iter: 65779 total_loss: 0.8286 loss_cls: 0.2658 loss_box_reg: 0.3073 loss_rpn_cls: 0.03851 loss_rpn_loc: 0.203 time: 0.3481 last_time: 0.2103 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:15:24 d2.utils.events]: \u001b[0m eta: 1:15:03 iter: 65799 total_loss: 0.7482 loss_cls: 0.2122 loss_box_reg: 0.2543 loss_rpn_cls: 0.03989 loss_rpn_loc: 0.1918 time: 0.3481 last_time: 0.2338 data_time: 0.0045 last_data_time: 0.0042 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:15:28 d2.utils.events]: \u001b[0m eta: 1:15:00 iter: 65819 total_loss: 0.7142 loss_cls: 0.2205 loss_box_reg: 0.2527 loss_rpn_cls: 0.04866 loss_rpn_loc: 0.1846 time: 0.3481 last_time: 0.2228 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:15:33 d2.utils.events]: \u001b[0m eta: 1:14:55 iter: 65839 total_loss: 0.7061 loss_cls: 0.2314 loss_box_reg: 0.269 loss_rpn_cls: 0.04017 loss_rpn_loc: 0.2013 time: 0.3480 last_time: 0.2104 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:15:38 d2.utils.events]: \u001b[0m eta: 1:14:52 iter: 65859 total_loss: 0.7138 loss_cls: 0.228 loss_box_reg: 0.2821 loss_rpn_cls: 0.04336 loss_rpn_loc: 0.1888 time: 0.3480 last_time: 0.2334 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:15:42 d2.utils.events]: \u001b[0m eta: 1:14:46 iter: 65879 total_loss: 0.7627 loss_cls: 0.2726 loss_box_reg: 0.2971 loss_rpn_cls: 0.03746 loss_rpn_loc: 0.191 time: 0.3479 last_time: 0.2535 data_time: 0.0048 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:15:47 d2.utils.events]: \u001b[0m eta: 1:14:42 iter: 65899 total_loss: 0.7484 loss_cls: 0.2346 loss_box_reg: 0.2718 loss_rpn_cls: 0.04576 loss_rpn_loc: 0.1847 time: 0.3479 last_time: 0.2485 data_time: 0.0047 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:15:51 d2.utils.events]: \u001b[0m eta: 1:14:35 iter: 65919 total_loss: 0.8608 loss_cls: 0.289 loss_box_reg: 0.2957 loss_rpn_cls: 0.05334 loss_rpn_loc: 0.1798 time: 0.3479 last_time: 0.2074 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:15:56 d2.utils.events]: \u001b[0m eta: 1:14:30 iter: 65939 total_loss: 0.7763 loss_cls: 0.2614 loss_box_reg: 0.2844 loss_rpn_cls: 0.04481 loss_rpn_loc: 0.1949 time: 0.3478 last_time: 0.2357 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:16:01 d2.utils.events]: \u001b[0m eta: 1:14:28 iter: 65959 total_loss: 0.8017 loss_cls: 0.2333 loss_box_reg: 0.2636 loss_rpn_cls: 0.04238 loss_rpn_loc: 0.1931 time: 0.3478 last_time: 0.2515 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:16:05 d2.utils.events]: \u001b[0m eta: 1:14:22 iter: 65979 total_loss: 0.6745 loss_cls: 0.2558 loss_box_reg: 0.2565 loss_rpn_cls: 0.03618 loss_rpn_loc: 0.1735 time: 0.3478 last_time: 0.2355 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:16:10 d2.utils.events]: \u001b[0m eta: 1:14:18 iter: 65999 total_loss: 0.8038 loss_cls: 0.2399 loss_box_reg: 0.3022 loss_rpn_cls: 0.04992 loss_rpn_loc: 0.1959 time: 0.3477 last_time: 0.1948 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:16:14 d2.utils.events]: \u001b[0m eta: 1:14:13 iter: 66019 total_loss: 0.9158 loss_cls: 0.3009 loss_box_reg: 0.3184 loss_rpn_cls: 0.04316 loss_rpn_loc: 0.2154 time: 0.3477 last_time: 0.2352 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:16:19 d2.utils.events]: \u001b[0m eta: 1:14:10 iter: 66039 total_loss: 0.7298 loss_cls: 0.2205 loss_box_reg: 0.2988 loss_rpn_cls: 0.04492 loss_rpn_loc: 0.1798 time: 0.3477 last_time: 0.2515 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:16:24 d2.utils.events]: \u001b[0m eta: 1:14:04 iter: 66059 total_loss: 0.8342 loss_cls: 0.31 loss_box_reg: 0.2623 loss_rpn_cls: 0.0628 loss_rpn_loc: 0.1911 time: 0.3476 last_time: 0.2350 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:16:28 d2.utils.events]: \u001b[0m eta: 1:13:58 iter: 66079 total_loss: 0.7781 loss_cls: 0.2821 loss_box_reg: 0.2702 loss_rpn_cls: 0.04883 loss_rpn_loc: 0.1843 time: 0.3476 last_time: 0.1930 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:16:33 d2.utils.events]: \u001b[0m eta: 1:13:49 iter: 66099 total_loss: 0.7986 loss_cls: 0.2368 loss_box_reg: 0.2742 loss_rpn_cls: 0.04597 loss_rpn_loc: 0.1945 time: 0.3476 last_time: 0.2499 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:16:37 d2.utils.events]: \u001b[0m eta: 1:13:43 iter: 66119 total_loss: 0.813 loss_cls: 0.2431 loss_box_reg: 0.3169 loss_rpn_cls: 0.0556 loss_rpn_loc: 0.2211 time: 0.3475 last_time: 0.2388 data_time: 0.0046 last_data_time: 0.0053 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:16:42 d2.utils.events]: \u001b[0m eta: 1:13:40 iter: 66139 total_loss: 0.7027 loss_cls: 0.2164 loss_box_reg: 0.2463 loss_rpn_cls: 0.04267 loss_rpn_loc: 0.184 time: 0.3475 last_time: 0.2382 data_time: 0.0048 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:16:47 d2.utils.events]: \u001b[0m eta: 1:13:35 iter: 66159 total_loss: 0.8114 loss_cls: 0.2545 loss_box_reg: 0.3068 loss_rpn_cls: 0.04086 loss_rpn_loc: 0.188 time: 0.3475 last_time: 0.2227 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:16:52 d2.utils.events]: \u001b[0m eta: 1:13:30 iter: 66179 total_loss: 0.7604 loss_cls: 0.2469 loss_box_reg: 0.2915 loss_rpn_cls: 0.04359 loss_rpn_loc: 0.1678 time: 0.3474 last_time: 0.2264 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:16:56 d2.utils.events]: \u001b[0m eta: 1:13:30 iter: 66199 total_loss: 0.9206 loss_cls: 0.2921 loss_box_reg: 0.3397 loss_rpn_cls: 0.04425 loss_rpn_loc: 0.188 time: 0.3474 last_time: 0.2567 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:17:01 d2.utils.events]: \u001b[0m eta: 1:13:23 iter: 66219 total_loss: 0.7545 loss_cls: 0.206 loss_box_reg: 0.3262 loss_rpn_cls: 0.04879 loss_rpn_loc: 0.1817 time: 0.3473 last_time: 0.2282 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:17:05 d2.utils.events]: \u001b[0m eta: 1:13:16 iter: 66239 total_loss: 0.7718 loss_cls: 0.2092 loss_box_reg: 0.2954 loss_rpn_cls: 0.04974 loss_rpn_loc: 0.1836 time: 0.3473 last_time: 0.1871 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:17:10 d2.utils.events]: \u001b[0m eta: 1:13:10 iter: 66259 total_loss: 0.7681 loss_cls: 0.241 loss_box_reg: 0.307 loss_rpn_cls: 0.04034 loss_rpn_loc: 0.1879 time: 0.3473 last_time: 0.2495 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:17:15 d2.utils.events]: \u001b[0m eta: 1:13:04 iter: 66279 total_loss: 0.7304 loss_cls: 0.2076 loss_box_reg: 0.2838 loss_rpn_cls: 0.04149 loss_rpn_loc: 0.2176 time: 0.3472 last_time: 0.2531 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:17:20 d2.utils.events]: \u001b[0m eta: 1:12:58 iter: 66299 total_loss: 0.8021 loss_cls: 0.2276 loss_box_reg: 0.2958 loss_rpn_cls: 0.04462 loss_rpn_loc: 0.2114 time: 0.3472 last_time: 0.2412 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:17:24 d2.utils.events]: \u001b[0m eta: 1:12:52 iter: 66319 total_loss: 0.9046 loss_cls: 0.3087 loss_box_reg: 0.3344 loss_rpn_cls: 0.05788 loss_rpn_loc: 0.1852 time: 0.3472 last_time: 0.2094 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:17:29 d2.utils.events]: \u001b[0m eta: 1:12:49 iter: 66339 total_loss: 0.762 loss_cls: 0.2703 loss_box_reg: 0.2609 loss_rpn_cls: 0.03742 loss_rpn_loc: 0.1684 time: 0.3471 last_time: 0.2477 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:17:33 d2.utils.events]: \u001b[0m eta: 1:12:46 iter: 66359 total_loss: 0.7625 loss_cls: 0.227 loss_box_reg: 0.2565 loss_rpn_cls: 0.03954 loss_rpn_loc: 0.1957 time: 0.3471 last_time: 0.2500 data_time: 0.0047 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:17:38 d2.utils.events]: \u001b[0m eta: 1:12:41 iter: 66379 total_loss: 0.7679 loss_cls: 0.2407 loss_box_reg: 0.3058 loss_rpn_cls: 0.05039 loss_rpn_loc: 0.1783 time: 0.3471 last_time: 0.1990 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:17:43 d2.utils.events]: \u001b[0m eta: 1:12:36 iter: 66399 total_loss: 0.7371 loss_cls: 0.2208 loss_box_reg: 0.2795 loss_rpn_cls: 0.03528 loss_rpn_loc: 0.1908 time: 0.3470 last_time: 0.2512 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:17:47 d2.utils.events]: \u001b[0m eta: 1:12:32 iter: 66419 total_loss: 0.8627 loss_cls: 0.2824 loss_box_reg: 0.3179 loss_rpn_cls: 0.05901 loss_rpn_loc: 0.2024 time: 0.3470 last_time: 0.2599 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:17:52 d2.utils.events]: \u001b[0m eta: 1:12:25 iter: 66439 total_loss: 0.7292 loss_cls: 0.2274 loss_box_reg: 0.2749 loss_rpn_cls: 0.03495 loss_rpn_loc: 0.1881 time: 0.3470 last_time: 0.1894 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:17:57 d2.utils.events]: \u001b[0m eta: 1:12:22 iter: 66459 total_loss: 0.8438 loss_cls: 0.2319 loss_box_reg: 0.2636 loss_rpn_cls: 0.0397 loss_rpn_loc: 0.1785 time: 0.3469 last_time: 0.2013 data_time: 0.0047 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:18:02 d2.utils.events]: \u001b[0m eta: 1:12:18 iter: 66479 total_loss: 0.751 loss_cls: 0.1978 loss_box_reg: 0.2901 loss_rpn_cls: 0.04016 loss_rpn_loc: 0.1757 time: 0.3469 last_time: 0.2570 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:18:06 d2.utils.events]: \u001b[0m eta: 1:12:15 iter: 66499 total_loss: 0.8397 loss_cls: 0.265 loss_box_reg: 0.2913 loss_rpn_cls: 0.05562 loss_rpn_loc: 0.2323 time: 0.3469 last_time: 0.2369 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:18:11 d2.utils.events]: \u001b[0m eta: 1:12:14 iter: 66519 total_loss: 0.7927 loss_cls: 0.2542 loss_box_reg: 0.2998 loss_rpn_cls: 0.04035 loss_rpn_loc: 0.1915 time: 0.3468 last_time: 0.2563 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:18:16 d2.utils.events]: \u001b[0m eta: 1:12:07 iter: 66539 total_loss: 0.7647 loss_cls: 0.2559 loss_box_reg: 0.2904 loss_rpn_cls: 0.05478 loss_rpn_loc: 0.1949 time: 0.3468 last_time: 0.1894 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:18:21 d2.utils.events]: \u001b[0m eta: 1:12:04 iter: 66559 total_loss: 0.7831 loss_cls: 0.2455 loss_box_reg: 0.2998 loss_rpn_cls: 0.05546 loss_rpn_loc: 0.1853 time: 0.3468 last_time: 0.2396 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:18:25 d2.utils.events]: \u001b[0m eta: 1:11:59 iter: 66579 total_loss: 0.7145 loss_cls: 0.2079 loss_box_reg: 0.2776 loss_rpn_cls: 0.03578 loss_rpn_loc: 0.1755 time: 0.3467 last_time: 0.1883 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:18:30 d2.utils.events]: \u001b[0m eta: 1:11:49 iter: 66599 total_loss: 0.8375 loss_cls: 0.2426 loss_box_reg: 0.2994 loss_rpn_cls: 0.04998 loss_rpn_loc: 0.1974 time: 0.3467 last_time: 0.2302 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:18:34 d2.utils.events]: \u001b[0m eta: 1:11:38 iter: 66619 total_loss: 0.7283 loss_cls: 0.2038 loss_box_reg: 0.266 loss_rpn_cls: 0.04349 loss_rpn_loc: 0.1896 time: 0.3467 last_time: 0.2493 data_time: 0.0046 last_data_time: 0.0053 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:18:39 d2.utils.events]: \u001b[0m eta: 1:11:35 iter: 66639 total_loss: 0.7803 loss_cls: 0.2588 loss_box_reg: 0.3194 loss_rpn_cls: 0.04014 loss_rpn_loc: 0.2231 time: 0.3466 last_time: 0.2241 data_time: 0.0046 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:18:44 d2.utils.events]: \u001b[0m eta: 1:11:31 iter: 66659 total_loss: 0.8059 loss_cls: 0.2713 loss_box_reg: 0.2955 loss_rpn_cls: 0.04494 loss_rpn_loc: 0.1842 time: 0.3466 last_time: 0.2572 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:18:48 d2.utils.events]: \u001b[0m eta: 1:11:26 iter: 66679 total_loss: 0.7909 loss_cls: 0.2627 loss_box_reg: 0.2729 loss_rpn_cls: 0.05078 loss_rpn_loc: 0.1966 time: 0.3466 last_time: 0.2436 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:18:53 d2.utils.events]: \u001b[0m eta: 1:11:28 iter: 66699 total_loss: 0.8644 loss_cls: 0.2779 loss_box_reg: 0.3483 loss_rpn_cls: 0.04478 loss_rpn_loc: 0.2016 time: 0.3465 last_time: 0.2408 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:18:58 d2.utils.events]: \u001b[0m eta: 1:11:24 iter: 66719 total_loss: 0.8555 loss_cls: 0.2687 loss_box_reg: 0.2547 loss_rpn_cls: 0.05394 loss_rpn_loc: 0.2344 time: 0.3465 last_time: 0.1939 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:19:02 d2.utils.events]: \u001b[0m eta: 1:11:17 iter: 66739 total_loss: 0.7844 loss_cls: 0.25 loss_box_reg: 0.2718 loss_rpn_cls: 0.03895 loss_rpn_loc: 0.1828 time: 0.3465 last_time: 0.2523 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:19:07 d2.utils.events]: \u001b[0m eta: 1:11:15 iter: 66759 total_loss: 0.805 loss_cls: 0.2554 loss_box_reg: 0.316 loss_rpn_cls: 0.04817 loss_rpn_loc: 0.1957 time: 0.3464 last_time: 0.2288 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:19:12 d2.utils.events]: \u001b[0m eta: 1:11:10 iter: 66779 total_loss: 0.7782 loss_cls: 0.2432 loss_box_reg: 0.2759 loss_rpn_cls: 0.05146 loss_rpn_loc: 0.1685 time: 0.3464 last_time: 0.2399 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:19:17 d2.utils.events]: \u001b[0m eta: 1:11:09 iter: 66799 total_loss: 0.7435 loss_cls: 0.2478 loss_box_reg: 0.2458 loss_rpn_cls: 0.03477 loss_rpn_loc: 0.1847 time: 0.3464 last_time: 0.2312 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:19:21 d2.utils.events]: \u001b[0m eta: 1:11:04 iter: 66819 total_loss: 0.7099 loss_cls: 0.2208 loss_box_reg: 0.2912 loss_rpn_cls: 0.05063 loss_rpn_loc: 0.1672 time: 0.3463 last_time: 0.2311 data_time: 0.0046 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:19:26 d2.utils.events]: \u001b[0m eta: 1:11:01 iter: 66839 total_loss: 0.7052 loss_cls: 0.2314 loss_box_reg: 0.2442 loss_rpn_cls: 0.0364 loss_rpn_loc: 0.1745 time: 0.3463 last_time: 0.2389 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:19:31 d2.utils.events]: \u001b[0m eta: 1:10:55 iter: 66859 total_loss: 0.8386 loss_cls: 0.2857 loss_box_reg: 0.2834 loss_rpn_cls: 0.03474 loss_rpn_loc: 0.1894 time: 0.3463 last_time: 0.2338 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:19:35 d2.utils.events]: \u001b[0m eta: 1:10:50 iter: 66879 total_loss: 0.7808 loss_cls: 0.2397 loss_box_reg: 0.2905 loss_rpn_cls: 0.05066 loss_rpn_loc: 0.1938 time: 0.3462 last_time: 0.2356 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:19:40 d2.utils.events]: \u001b[0m eta: 1:10:46 iter: 66899 total_loss: 0.7786 loss_cls: 0.265 loss_box_reg: 0.2788 loss_rpn_cls: 0.03941 loss_rpn_loc: 0.1888 time: 0.3462 last_time: 0.1999 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:19:45 d2.utils.events]: \u001b[0m eta: 1:10:45 iter: 66919 total_loss: 0.7829 loss_cls: 0.2367 loss_box_reg: 0.2578 loss_rpn_cls: 0.03914 loss_rpn_loc: 0.1647 time: 0.3462 last_time: 0.1953 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:19:49 d2.utils.events]: \u001b[0m eta: 1:10:40 iter: 66939 total_loss: 0.8282 loss_cls: 0.2456 loss_box_reg: 0.3092 loss_rpn_cls: 0.03928 loss_rpn_loc: 0.1837 time: 0.3461 last_time: 0.2358 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:19:54 d2.utils.events]: \u001b[0m eta: 1:10:34 iter: 66959 total_loss: 0.7829 loss_cls: 0.2191 loss_box_reg: 0.3029 loss_rpn_cls: 0.0465 loss_rpn_loc: 0.1844 time: 0.3461 last_time: 0.1801 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:19:58 d2.utils.events]: \u001b[0m eta: 1:10:29 iter: 66979 total_loss: 0.7311 loss_cls: 0.2278 loss_box_reg: 0.2766 loss_rpn_cls: 0.03949 loss_rpn_loc: 0.2135 time: 0.3461 last_time: 0.2257 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:20:04 d2.utils.events]: \u001b[0m eta: 1:10:30 iter: 66999 total_loss: 0.7776 loss_cls: 0.2356 loss_box_reg: 0.2927 loss_rpn_cls: 0.04369 loss_rpn_loc: 0.1961 time: 0.3460 last_time: 0.2981 data_time: 0.0050 last_data_time: 0.0054 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:20:09 d2.utils.events]: \u001b[0m eta: 1:10:25 iter: 67019 total_loss: 0.7944 loss_cls: 0.2348 loss_box_reg: 0.2909 loss_rpn_cls: 0.04144 loss_rpn_loc: 0.1964 time: 0.3460 last_time: 0.1972 data_time: 0.0049 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:20:13 d2.utils.events]: \u001b[0m eta: 1:10:19 iter: 67039 total_loss: 0.7279 loss_cls: 0.2441 loss_box_reg: 0.2801 loss_rpn_cls: 0.04387 loss_rpn_loc: 0.2112 time: 0.3460 last_time: 0.2088 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:20:18 d2.utils.events]: \u001b[0m eta: 1:10:14 iter: 67059 total_loss: 0.8155 loss_cls: 0.2524 loss_box_reg: 0.3003 loss_rpn_cls: 0.05239 loss_rpn_loc: 0.1998 time: 0.3459 last_time: 0.2219 data_time: 0.0047 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:20:23 d2.utils.events]: \u001b[0m eta: 1:10:11 iter: 67079 total_loss: 0.7587 loss_cls: 0.2423 loss_box_reg: 0.2541 loss_rpn_cls: 0.04765 loss_rpn_loc: 0.1645 time: 0.3459 last_time: 0.2224 data_time: 0.0048 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:20:27 d2.utils.events]: \u001b[0m eta: 1:10:06 iter: 67099 total_loss: 0.7506 loss_cls: 0.2031 loss_box_reg: 0.2813 loss_rpn_cls: 0.03533 loss_rpn_loc: 0.1702 time: 0.3459 last_time: 0.1949 data_time: 0.0047 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:20:32 d2.utils.events]: \u001b[0m eta: 1:10:02 iter: 67119 total_loss: 0.8808 loss_cls: 0.2904 loss_box_reg: 0.3135 loss_rpn_cls: 0.04974 loss_rpn_loc: 0.2083 time: 0.3458 last_time: 0.3126 data_time: 0.0048 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:20:37 d2.utils.events]: \u001b[0m eta: 1:09:57 iter: 67139 total_loss: 0.7939 loss_cls: 0.2615 loss_box_reg: 0.2875 loss_rpn_cls: 0.04199 loss_rpn_loc: 0.1848 time: 0.3458 last_time: 0.1938 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:20:41 d2.utils.events]: \u001b[0m eta: 1:09:52 iter: 67159 total_loss: 0.8015 loss_cls: 0.2516 loss_box_reg: 0.3044 loss_rpn_cls: 0.04205 loss_rpn_loc: 0.1667 time: 0.3458 last_time: 0.2499 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:20:46 d2.utils.events]: \u001b[0m eta: 1:09:43 iter: 67179 total_loss: 0.7818 loss_cls: 0.283 loss_box_reg: 0.2411 loss_rpn_cls: 0.05726 loss_rpn_loc: 0.2069 time: 0.3457 last_time: 0.2344 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:20:50 d2.utils.events]: \u001b[0m eta: 1:09:35 iter: 67199 total_loss: 0.8353 loss_cls: 0.2705 loss_box_reg: 0.3247 loss_rpn_cls: 0.04895 loss_rpn_loc: 0.1911 time: 0.3457 last_time: 0.2485 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:20:55 d2.utils.events]: \u001b[0m eta: 1:09:32 iter: 67219 total_loss: 0.8756 loss_cls: 0.2745 loss_box_reg: 0.3344 loss_rpn_cls: 0.04385 loss_rpn_loc: 0.2023 time: 0.3457 last_time: 0.2488 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:20:59 d2.utils.events]: \u001b[0m eta: 1:09:28 iter: 67239 total_loss: 0.7467 loss_cls: 0.2128 loss_box_reg: 0.2675 loss_rpn_cls: 0.04572 loss_rpn_loc: 0.1949 time: 0.3456 last_time: 0.2251 data_time: 0.0049 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:21:04 d2.utils.events]: \u001b[0m eta: 1:09:24 iter: 67259 total_loss: 0.9595 loss_cls: 0.3397 loss_box_reg: 0.3424 loss_rpn_cls: 0.05571 loss_rpn_loc: 0.2283 time: 0.3456 last_time: 0.2499 data_time: 0.0048 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:21:09 d2.utils.events]: \u001b[0m eta: 1:09:20 iter: 67279 total_loss: 0.762 loss_cls: 0.2121 loss_box_reg: 0.3224 loss_rpn_cls: 0.03775 loss_rpn_loc: 0.1699 time: 0.3456 last_time: 0.2103 data_time: 0.0048 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:21:13 d2.utils.events]: \u001b[0m eta: 1:09:11 iter: 67299 total_loss: 0.7119 loss_cls: 0.2087 loss_box_reg: 0.2778 loss_rpn_cls: 0.04435 loss_rpn_loc: 0.1717 time: 0.3455 last_time: 0.1956 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:21:18 d2.utils.events]: \u001b[0m eta: 1:09:06 iter: 67319 total_loss: 0.764 loss_cls: 0.2321 loss_box_reg: 0.2844 loss_rpn_cls: 0.03357 loss_rpn_loc: 0.2019 time: 0.3455 last_time: 0.2513 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:21:22 d2.utils.events]: \u001b[0m eta: 1:09:00 iter: 67339 total_loss: 0.7982 loss_cls: 0.2361 loss_box_reg: 0.2832 loss_rpn_cls: 0.04171 loss_rpn_loc: 0.2095 time: 0.3454 last_time: 0.2249 data_time: 0.0047 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:21:27 d2.utils.events]: \u001b[0m eta: 1:08:55 iter: 67359 total_loss: 0.7857 loss_cls: 0.2499 loss_box_reg: 0.2841 loss_rpn_cls: 0.03432 loss_rpn_loc: 0.1924 time: 0.3454 last_time: 0.2494 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:21:31 d2.utils.events]: \u001b[0m eta: 1:08:50 iter: 67379 total_loss: 0.8747 loss_cls: 0.3131 loss_box_reg: 0.2989 loss_rpn_cls: 0.04354 loss_rpn_loc: 0.207 time: 0.3454 last_time: 0.1818 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:21:36 d2.utils.events]: \u001b[0m eta: 1:08:46 iter: 67399 total_loss: 0.7814 loss_cls: 0.2487 loss_box_reg: 0.3046 loss_rpn_cls: 0.03839 loss_rpn_loc: 0.1915 time: 0.3453 last_time: 0.2479 data_time: 0.0048 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:21:41 d2.utils.events]: \u001b[0m eta: 1:08:40 iter: 67419 total_loss: 0.8107 loss_cls: 0.235 loss_box_reg: 0.302 loss_rpn_cls: 0.04475 loss_rpn_loc: 0.203 time: 0.3453 last_time: 0.2099 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:21:45 d2.utils.events]: \u001b[0m eta: 1:08:35 iter: 67439 total_loss: 0.7384 loss_cls: 0.1967 loss_box_reg: 0.2504 loss_rpn_cls: 0.04124 loss_rpn_loc: 0.1898 time: 0.3453 last_time: 0.2542 data_time: 0.0047 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:21:50 d2.utils.events]: \u001b[0m eta: 1:08:29 iter: 67459 total_loss: 0.7537 loss_cls: 0.2396 loss_box_reg: 0.2933 loss_rpn_cls: 0.04317 loss_rpn_loc: 0.1572 time: 0.3452 last_time: 0.2352 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:21:55 d2.utils.events]: \u001b[0m eta: 1:08:22 iter: 67479 total_loss: 0.6961 loss_cls: 0.2207 loss_box_reg: 0.264 loss_rpn_cls: 0.0381 loss_rpn_loc: 0.1816 time: 0.3452 last_time: 0.1957 data_time: 0.0047 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:22:00 d2.utils.events]: \u001b[0m eta: 1:08:21 iter: 67499 total_loss: 0.6853 loss_cls: 0.2124 loss_box_reg: 0.2634 loss_rpn_cls: 0.03814 loss_rpn_loc: 0.1574 time: 0.3452 last_time: 0.3311 data_time: 0.0053 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:22:06 d2.utils.events]: \u001b[0m eta: 1:08:18 iter: 67519 total_loss: 0.8153 loss_cls: 0.2519 loss_box_reg: 0.2965 loss_rpn_cls: 0.04569 loss_rpn_loc: 0.1904 time: 0.3452 last_time: 0.2858 data_time: 0.0053 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:22:12 d2.utils.events]: \u001b[0m eta: 1:08:16 iter: 67539 total_loss: 0.7052 loss_cls: 0.2301 loss_box_reg: 0.2794 loss_rpn_cls: 0.04017 loss_rpn_loc: 0.1547 time: 0.3452 last_time: 0.2844 data_time: 0.0051 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:22:17 d2.utils.events]: \u001b[0m eta: 1:08:10 iter: 67559 total_loss: 0.8251 loss_cls: 0.2462 loss_box_reg: 0.2878 loss_rpn_cls: 0.05621 loss_rpn_loc: 0.194 time: 0.3451 last_time: 0.2330 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:22:21 d2.utils.events]: \u001b[0m eta: 1:08:05 iter: 67579 total_loss: 0.7709 loss_cls: 0.2168 loss_box_reg: 0.2813 loss_rpn_cls: 0.04491 loss_rpn_loc: 0.1667 time: 0.3451 last_time: 0.1964 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:22:26 d2.utils.events]: \u001b[0m eta: 1:08:01 iter: 67599 total_loss: 0.7389 loss_cls: 0.2161 loss_box_reg: 0.2662 loss_rpn_cls: 0.05161 loss_rpn_loc: 0.1774 time: 0.3451 last_time: 0.2224 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:22:30 d2.utils.events]: \u001b[0m eta: 1:07:56 iter: 67619 total_loss: 0.7359 loss_cls: 0.217 loss_box_reg: 0.2907 loss_rpn_cls: 0.0379 loss_rpn_loc: 0.2026 time: 0.3450 last_time: 0.1967 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:22:35 d2.utils.events]: \u001b[0m eta: 1:07:51 iter: 67639 total_loss: 0.7366 loss_cls: 0.2302 loss_box_reg: 0.2873 loss_rpn_cls: 0.04189 loss_rpn_loc: 0.1855 time: 0.3450 last_time: 0.2248 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:22:40 d2.utils.events]: \u001b[0m eta: 1:07:47 iter: 67659 total_loss: 0.9303 loss_cls: 0.2935 loss_box_reg: 0.3118 loss_rpn_cls: 0.04593 loss_rpn_loc: 0.2247 time: 0.3450 last_time: 0.2526 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:22:44 d2.utils.events]: \u001b[0m eta: 1:07:42 iter: 67679 total_loss: 0.7061 loss_cls: 0.2095 loss_box_reg: 0.2668 loss_rpn_cls: 0.0437 loss_rpn_loc: 0.1579 time: 0.3449 last_time: 0.2228 data_time: 0.0049 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:22:49 d2.utils.events]: \u001b[0m eta: 1:07:37 iter: 67699 total_loss: 0.7075 loss_cls: 0.2493 loss_box_reg: 0.2959 loss_rpn_cls: 0.04551 loss_rpn_loc: 0.1797 time: 0.3449 last_time: 0.2238 data_time: 0.0048 last_data_time: 0.0053 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:22:53 d2.utils.events]: \u001b[0m eta: 1:07:31 iter: 67719 total_loss: 0.7818 loss_cls: 0.2265 loss_box_reg: 0.2866 loss_rpn_cls: 0.05996 loss_rpn_loc: 0.1908 time: 0.3448 last_time: 0.2521 data_time: 0.0049 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:22:58 d2.utils.events]: \u001b[0m eta: 1:07:26 iter: 67739 total_loss: 0.6908 loss_cls: 0.2114 loss_box_reg: 0.2655 loss_rpn_cls: 0.03962 loss_rpn_loc: 0.2031 time: 0.3448 last_time: 0.2090 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:23:02 d2.utils.events]: \u001b[0m eta: 1:07:19 iter: 67759 total_loss: 0.8673 loss_cls: 0.3014 loss_box_reg: 0.2817 loss_rpn_cls: 0.05437 loss_rpn_loc: 0.19 time: 0.3448 last_time: 0.2259 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:23:07 d2.utils.events]: \u001b[0m eta: 1:07:14 iter: 67779 total_loss: 0.7727 loss_cls: 0.2418 loss_box_reg: 0.2879 loss_rpn_cls: 0.05012 loss_rpn_loc: 0.1936 time: 0.3447 last_time: 0.1979 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:23:12 d2.utils.events]: \u001b[0m eta: 1:07:06 iter: 67799 total_loss: 0.7558 loss_cls: 0.2647 loss_box_reg: 0.2763 loss_rpn_cls: 0.04286 loss_rpn_loc: 0.1668 time: 0.3447 last_time: 0.2500 data_time: 0.0048 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:23:16 d2.utils.events]: \u001b[0m eta: 1:07:00 iter: 67819 total_loss: 0.8171 loss_cls: 0.2583 loss_box_reg: 0.3141 loss_rpn_cls: 0.04316 loss_rpn_loc: 0.206 time: 0.3447 last_time: 0.2222 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:23:21 d2.utils.events]: \u001b[0m eta: 1:06:53 iter: 67839 total_loss: 0.7575 loss_cls: 0.2547 loss_box_reg: 0.2972 loss_rpn_cls: 0.03786 loss_rpn_loc: 0.1954 time: 0.3446 last_time: 0.2233 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:23:25 d2.utils.events]: \u001b[0m eta: 1:06:51 iter: 67859 total_loss: 0.7557 loss_cls: 0.2547 loss_box_reg: 0.2745 loss_rpn_cls: 0.03936 loss_rpn_loc: 0.2013 time: 0.3446 last_time: 0.2253 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:23:30 d2.utils.events]: \u001b[0m eta: 1:06:47 iter: 67879 total_loss: 0.7611 loss_cls: 0.2511 loss_box_reg: 0.295 loss_rpn_cls: 0.0402 loss_rpn_loc: 0.1846 time: 0.3446 last_time: 0.2502 data_time: 0.0049 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:23:35 d2.utils.events]: \u001b[0m eta: 1:06:42 iter: 67899 total_loss: 0.8307 loss_cls: 0.2722 loss_box_reg: 0.2923 loss_rpn_cls: 0.04325 loss_rpn_loc: 0.184 time: 0.3445 last_time: 0.2555 data_time: 0.0049 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:23:39 d2.utils.events]: \u001b[0m eta: 1:06:34 iter: 67919 total_loss: 0.7853 loss_cls: 0.2325 loss_box_reg: 0.2832 loss_rpn_cls: 0.04789 loss_rpn_loc: 0.1914 time: 0.3445 last_time: 0.2360 data_time: 0.0049 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:23:44 d2.utils.events]: \u001b[0m eta: 1:06:28 iter: 67939 total_loss: 0.7241 loss_cls: 0.2355 loss_box_reg: 0.2762 loss_rpn_cls: 0.04287 loss_rpn_loc: 0.1932 time: 0.3445 last_time: 0.1964 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:23:48 d2.utils.events]: \u001b[0m eta: 1:06:22 iter: 67959 total_loss: 0.7914 loss_cls: 0.2375 loss_box_reg: 0.3084 loss_rpn_cls: 0.04091 loss_rpn_loc: 0.1973 time: 0.3444 last_time: 0.2240 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:23:53 d2.utils.events]: \u001b[0m eta: 1:06:19 iter: 67979 total_loss: 0.7488 loss_cls: 0.2217 loss_box_reg: 0.2798 loss_rpn_cls: 0.04571 loss_rpn_loc: 0.2187 time: 0.3444 last_time: 0.2620 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:23:57 d2.utils.events]: \u001b[0m eta: 1:06:12 iter: 67999 total_loss: 0.7686 loss_cls: 0.2381 loss_box_reg: 0.2624 loss_rpn_cls: 0.0502 loss_rpn_loc: 0.1863 time: 0.3444 last_time: 0.2523 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:24:02 d2.utils.events]: \u001b[0m eta: 1:06:06 iter: 68019 total_loss: 0.7452 loss_cls: 0.2437 loss_box_reg: 0.2719 loss_rpn_cls: 0.0517 loss_rpn_loc: 0.1927 time: 0.3443 last_time: 0.2358 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:24:07 d2.utils.events]: \u001b[0m eta: 1:06:02 iter: 68039 total_loss: 0.7368 loss_cls: 0.2491 loss_box_reg: 0.2764 loss_rpn_cls: 0.04357 loss_rpn_loc: 0.1809 time: 0.3443 last_time: 0.1939 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:24:11 d2.utils.events]: \u001b[0m eta: 1:05:59 iter: 68059 total_loss: 0.7445 loss_cls: 0.2637 loss_box_reg: 0.2606 loss_rpn_cls: 0.04631 loss_rpn_loc: 0.1761 time: 0.3443 last_time: 0.2343 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:24:16 d2.utils.events]: \u001b[0m eta: 1:05:55 iter: 68079 total_loss: 0.7817 loss_cls: 0.2368 loss_box_reg: 0.3023 loss_rpn_cls: 0.04459 loss_rpn_loc: 0.2104 time: 0.3442 last_time: 0.2508 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:24:20 d2.utils.events]: \u001b[0m eta: 1:05:51 iter: 68099 total_loss: 0.7883 loss_cls: 0.2482 loss_box_reg: 0.2822 loss_rpn_cls: 0.04388 loss_rpn_loc: 0.1812 time: 0.3442 last_time: 0.2231 data_time: 0.0048 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:24:25 d2.utils.events]: \u001b[0m eta: 1:05:44 iter: 68119 total_loss: 0.8733 loss_cls: 0.263 loss_box_reg: 0.2938 loss_rpn_cls: 0.05646 loss_rpn_loc: 0.1964 time: 0.3442 last_time: 0.2076 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:24:30 d2.utils.events]: \u001b[0m eta: 1:05:38 iter: 68139 total_loss: 0.7655 loss_cls: 0.2421 loss_box_reg: 0.2618 loss_rpn_cls: 0.04569 loss_rpn_loc: 0.207 time: 0.3441 last_time: 0.1819 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:24:34 d2.utils.events]: \u001b[0m eta: 1:05:34 iter: 68159 total_loss: 0.709 loss_cls: 0.2171 loss_box_reg: 0.2862 loss_rpn_cls: 0.04397 loss_rpn_loc: 0.1661 time: 0.3441 last_time: 0.2484 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:24:39 d2.utils.events]: \u001b[0m eta: 1:05:29 iter: 68179 total_loss: 0.8091 loss_cls: 0.2302 loss_box_reg: 0.2882 loss_rpn_cls: 0.048 loss_rpn_loc: 0.1953 time: 0.3441 last_time: 0.2116 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:24:44 d2.utils.events]: \u001b[0m eta: 1:05:27 iter: 68199 total_loss: 0.706 loss_cls: 0.2174 loss_box_reg: 0.248 loss_rpn_cls: 0.04334 loss_rpn_loc: 0.183 time: 0.3440 last_time: 0.2515 data_time: 0.0049 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:24:48 d2.utils.events]: \u001b[0m eta: 1:05:22 iter: 68219 total_loss: 0.6913 loss_cls: 0.2207 loss_box_reg: 0.2642 loss_rpn_cls: 0.04729 loss_rpn_loc: 0.1749 time: 0.3440 last_time: 0.2233 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:24:53 d2.utils.events]: \u001b[0m eta: 1:05:18 iter: 68239 total_loss: 0.9621 loss_cls: 0.3066 loss_box_reg: 0.3682 loss_rpn_cls: 0.04797 loss_rpn_loc: 0.2091 time: 0.3440 last_time: 0.2267 data_time: 0.0047 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:24:58 d2.utils.events]: \u001b[0m eta: 1:05:16 iter: 68259 total_loss: 0.772 loss_cls: 0.2618 loss_box_reg: 0.2813 loss_rpn_cls: 0.04328 loss_rpn_loc: 0.187 time: 0.3439 last_time: 0.2504 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:25:02 d2.utils.events]: \u001b[0m eta: 1:05:10 iter: 68279 total_loss: 0.9009 loss_cls: 0.3006 loss_box_reg: 0.2973 loss_rpn_cls: 0.03447 loss_rpn_loc: 0.1926 time: 0.3439 last_time: 0.2096 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:25:07 d2.utils.events]: \u001b[0m eta: 1:05:05 iter: 68299 total_loss: 0.855 loss_cls: 0.2367 loss_box_reg: 0.2895 loss_rpn_cls: 0.051 loss_rpn_loc: 0.2049 time: 0.3439 last_time: 0.2347 data_time: 0.0048 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:25:11 d2.utils.events]: \u001b[0m eta: 1:05:00 iter: 68319 total_loss: 0.6823 loss_cls: 0.2207 loss_box_reg: 0.2445 loss_rpn_cls: 0.04469 loss_rpn_loc: 0.1921 time: 0.3438 last_time: 0.2251 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:25:16 d2.utils.events]: \u001b[0m eta: 1:04:55 iter: 68339 total_loss: 0.7616 loss_cls: 0.2327 loss_box_reg: 0.2741 loss_rpn_cls: 0.05131 loss_rpn_loc: 0.1848 time: 0.3438 last_time: 0.2106 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:25:21 d2.utils.events]: \u001b[0m eta: 1:04:51 iter: 68359 total_loss: 0.8718 loss_cls: 0.2925 loss_box_reg: 0.2793 loss_rpn_cls: 0.05937 loss_rpn_loc: 0.1978 time: 0.3438 last_time: 0.2504 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:25:25 d2.utils.events]: \u001b[0m eta: 1:04:46 iter: 68379 total_loss: 0.8529 loss_cls: 0.2861 loss_box_reg: 0.2694 loss_rpn_cls: 0.05245 loss_rpn_loc: 0.1933 time: 0.3437 last_time: 0.2262 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:25:30 d2.utils.events]: \u001b[0m eta: 1:04:41 iter: 68399 total_loss: 0.8624 loss_cls: 0.2361 loss_box_reg: 0.2859 loss_rpn_cls: 0.04949 loss_rpn_loc: 0.1889 time: 0.3437 last_time: 0.2259 data_time: 0.0049 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:25:34 d2.utils.events]: \u001b[0m eta: 1:04:39 iter: 68419 total_loss: 0.7466 loss_cls: 0.2219 loss_box_reg: 0.2741 loss_rpn_cls: 0.04139 loss_rpn_loc: 0.1499 time: 0.3437 last_time: 0.2659 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:25:39 d2.utils.events]: \u001b[0m eta: 1:04:33 iter: 68439 total_loss: 0.7693 loss_cls: 0.2509 loss_box_reg: 0.2746 loss_rpn_cls: 0.04273 loss_rpn_loc: 0.1873 time: 0.3436 last_time: 0.2250 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:25:44 d2.utils.events]: \u001b[0m eta: 1:04:28 iter: 68459 total_loss: 0.6837 loss_cls: 0.2145 loss_box_reg: 0.2746 loss_rpn_cls: 0.03811 loss_rpn_loc: 0.1855 time: 0.3436 last_time: 0.2320 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:25:48 d2.utils.events]: \u001b[0m eta: 1:04:25 iter: 68479 total_loss: 0.7189 loss_cls: 0.2522 loss_box_reg: 0.263 loss_rpn_cls: 0.03523 loss_rpn_loc: 0.1904 time: 0.3436 last_time: 0.2495 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:25:53 d2.utils.events]: \u001b[0m eta: 1:04:17 iter: 68499 total_loss: 0.8531 loss_cls: 0.2237 loss_box_reg: 0.3161 loss_rpn_cls: 0.04873 loss_rpn_loc: 0.219 time: 0.3435 last_time: 0.2235 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:25:58 d2.utils.events]: \u001b[0m eta: 1:04:09 iter: 68519 total_loss: 0.7431 loss_cls: 0.2366 loss_box_reg: 0.2325 loss_rpn_cls: 0.04985 loss_rpn_loc: 0.178 time: 0.3435 last_time: 0.2230 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:26:03 d2.utils.events]: \u001b[0m eta: 1:04:01 iter: 68539 total_loss: 0.6641 loss_cls: 0.2058 loss_box_reg: 0.2525 loss_rpn_cls: 0.04574 loss_rpn_loc: 0.1883 time: 0.3435 last_time: 0.1933 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:26:07 d2.utils.events]: \u001b[0m eta: 1:03:57 iter: 68559 total_loss: 0.7406 loss_cls: 0.2125 loss_box_reg: 0.2768 loss_rpn_cls: 0.04114 loss_rpn_loc: 0.1866 time: 0.3434 last_time: 0.3049 data_time: 0.0048 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:26:12 d2.utils.events]: \u001b[0m eta: 1:03:52 iter: 68579 total_loss: 0.7036 loss_cls: 0.1885 loss_box_reg: 0.2431 loss_rpn_cls: 0.03337 loss_rpn_loc: 0.1943 time: 0.3434 last_time: 0.2227 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:26:16 d2.utils.events]: \u001b[0m eta: 1:03:48 iter: 68599 total_loss: 0.7159 loss_cls: 0.2269 loss_box_reg: 0.2823 loss_rpn_cls: 0.0477 loss_rpn_loc: 0.1874 time: 0.3434 last_time: 0.2512 data_time: 0.0048 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:26:21 d2.utils.events]: \u001b[0m eta: 1:03:43 iter: 68619 total_loss: 0.8163 loss_cls: 0.2553 loss_box_reg: 0.303 loss_rpn_cls: 0.04566 loss_rpn_loc: 0.1906 time: 0.3433 last_time: 0.2354 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:26:26 d2.utils.events]: \u001b[0m eta: 1:03:38 iter: 68639 total_loss: 0.8074 loss_cls: 0.2834 loss_box_reg: 0.285 loss_rpn_cls: 0.05088 loss_rpn_loc: 0.2001 time: 0.3433 last_time: 0.2242 data_time: 0.0049 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:26:30 d2.utils.events]: \u001b[0m eta: 1:03:32 iter: 68659 total_loss: 0.7393 loss_cls: 0.2411 loss_box_reg: 0.2833 loss_rpn_cls: 0.047 loss_rpn_loc: 0.1663 time: 0.3433 last_time: 0.2202 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:26:35 d2.utils.events]: \u001b[0m eta: 1:03:29 iter: 68679 total_loss: 0.8342 loss_cls: 0.2746 loss_box_reg: 0.3167 loss_rpn_cls: 0.05153 loss_rpn_loc: 0.1958 time: 0.3433 last_time: 0.2320 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:26:40 d2.utils.events]: \u001b[0m eta: 1:03:25 iter: 68699 total_loss: 0.7916 loss_cls: 0.2505 loss_box_reg: 0.2974 loss_rpn_cls: 0.05322 loss_rpn_loc: 0.2034 time: 0.3432 last_time: 0.2566 data_time: 0.0049 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:26:45 d2.utils.events]: \u001b[0m eta: 1:03:23 iter: 68719 total_loss: 0.7377 loss_cls: 0.2406 loss_box_reg: 0.2598 loss_rpn_cls: 0.03857 loss_rpn_loc: 0.181 time: 0.3432 last_time: 0.2488 data_time: 0.0049 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:26:50 d2.utils.events]: \u001b[0m eta: 1:03:23 iter: 68739 total_loss: 0.7098 loss_cls: 0.2189 loss_box_reg: 0.2477 loss_rpn_cls: 0.04461 loss_rpn_loc: 0.1749 time: 0.3432 last_time: 0.2687 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:26:56 d2.utils.events]: \u001b[0m eta: 1:03:22 iter: 68759 total_loss: 0.6852 loss_cls: 0.2422 loss_box_reg: 0.2817 loss_rpn_cls: 0.03321 loss_rpn_loc: 0.1788 time: 0.3432 last_time: 0.2953 data_time: 0.0049 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:27:01 d2.utils.events]: \u001b[0m eta: 1:03:18 iter: 68779 total_loss: 0.7844 loss_cls: 0.2673 loss_box_reg: 0.2605 loss_rpn_cls: 0.05215 loss_rpn_loc: 0.1901 time: 0.3431 last_time: 0.2546 data_time: 0.0049 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:27:07 d2.utils.events]: \u001b[0m eta: 1:03:21 iter: 68799 total_loss: 0.7665 loss_cls: 0.2312 loss_box_reg: 0.2804 loss_rpn_cls: 0.04655 loss_rpn_loc: 0.187 time: 0.3431 last_time: 0.2822 data_time: 0.0050 last_data_time: 0.0053 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:27:12 d2.utils.events]: \u001b[0m eta: 1:03:17 iter: 68819 total_loss: 0.7287 loss_cls: 0.2426 loss_box_reg: 0.2697 loss_rpn_cls: 0.04425 loss_rpn_loc: 0.1998 time: 0.3431 last_time: 0.2581 data_time: 0.0049 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:27:16 d2.utils.events]: \u001b[0m eta: 1:03:12 iter: 68839 total_loss: 0.7808 loss_cls: 0.2708 loss_box_reg: 0.2707 loss_rpn_cls: 0.05835 loss_rpn_loc: 0.1904 time: 0.3430 last_time: 0.2524 data_time: 0.0046 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:27:21 d2.utils.events]: \u001b[0m eta: 1:03:08 iter: 68859 total_loss: 0.722 loss_cls: 0.2213 loss_box_reg: 0.2793 loss_rpn_cls: 0.04005 loss_rpn_loc: 0.1973 time: 0.3430 last_time: 0.2353 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:27:25 d2.utils.events]: \u001b[0m eta: 1:03:00 iter: 68879 total_loss: 0.747 loss_cls: 0.2386 loss_box_reg: 0.2461 loss_rpn_cls: 0.04285 loss_rpn_loc: 0.1757 time: 0.3430 last_time: 0.2260 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:27:30 d2.utils.events]: \u001b[0m eta: 1:02:52 iter: 68899 total_loss: 0.8262 loss_cls: 0.2621 loss_box_reg: 0.3095 loss_rpn_cls: 0.03601 loss_rpn_loc: 0.1947 time: 0.3429 last_time: 0.2476 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:27:35 d2.utils.events]: \u001b[0m eta: 1:02:50 iter: 68919 total_loss: 0.6925 loss_cls: 0.2157 loss_box_reg: 0.2686 loss_rpn_cls: 0.03626 loss_rpn_loc: 0.1762 time: 0.3429 last_time: 0.2674 data_time: 0.0048 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:27:39 d2.utils.events]: \u001b[0m eta: 1:02:49 iter: 68939 total_loss: 0.7326 loss_cls: 0.2187 loss_box_reg: 0.2773 loss_rpn_cls: 0.04265 loss_rpn_loc: 0.1717 time: 0.3429 last_time: 0.2235 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:27:44 d2.utils.events]: \u001b[0m eta: 1:02:45 iter: 68959 total_loss: 0.7073 loss_cls: 0.2242 loss_box_reg: 0.262 loss_rpn_cls: 0.05573 loss_rpn_loc: 0.1871 time: 0.3429 last_time: 0.2486 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:27:49 d2.utils.events]: \u001b[0m eta: 1:02:39 iter: 68979 total_loss: 0.7327 loss_cls: 0.2278 loss_box_reg: 0.2819 loss_rpn_cls: 0.04358 loss_rpn_loc: 0.183 time: 0.3428 last_time: 0.2505 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:27:53 d2.utils.events]: \u001b[0m eta: 1:02:35 iter: 68999 total_loss: 0.8941 loss_cls: 0.2946 loss_box_reg: 0.2994 loss_rpn_cls: 0.05795 loss_rpn_loc: 0.2192 time: 0.3428 last_time: 0.2493 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:27:58 d2.utils.events]: \u001b[0m eta: 1:02:30 iter: 69019 total_loss: 0.6536 loss_cls: 0.1991 loss_box_reg: 0.2614 loss_rpn_cls: 0.03366 loss_rpn_loc: 0.1541 time: 0.3428 last_time: 0.2509 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:28:02 d2.utils.events]: \u001b[0m eta: 1:02:21 iter: 69039 total_loss: 0.7464 loss_cls: 0.2348 loss_box_reg: 0.2874 loss_rpn_cls: 0.04988 loss_rpn_loc: 0.1692 time: 0.3427 last_time: 0.2061 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:28:07 d2.utils.events]: \u001b[0m eta: 1:02:17 iter: 69059 total_loss: 0.787 loss_cls: 0.1958 loss_box_reg: 0.2535 loss_rpn_cls: 0.05052 loss_rpn_loc: 0.2027 time: 0.3427 last_time: 0.2367 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:28:11 d2.utils.events]: \u001b[0m eta: 1:02:11 iter: 69079 total_loss: 0.7782 loss_cls: 0.2257 loss_box_reg: 0.3136 loss_rpn_cls: 0.0493 loss_rpn_loc: 0.1905 time: 0.3427 last_time: 0.2312 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:28:16 d2.utils.events]: \u001b[0m eta: 1:02:07 iter: 69099 total_loss: 0.7085 loss_cls: 0.247 loss_box_reg: 0.2759 loss_rpn_cls: 0.04171 loss_rpn_loc: 0.1949 time: 0.3426 last_time: 0.1817 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:28:20 d2.utils.events]: \u001b[0m eta: 1:02:01 iter: 69119 total_loss: 0.7587 loss_cls: 0.2799 loss_box_reg: 0.2843 loss_rpn_cls: 0.04672 loss_rpn_loc: 0.1862 time: 0.3426 last_time: 0.2249 data_time: 0.0047 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:28:25 d2.utils.events]: \u001b[0m eta: 1:01:56 iter: 69139 total_loss: 0.7539 loss_cls: 0.2341 loss_box_reg: 0.2756 loss_rpn_cls: 0.04687 loss_rpn_loc: 0.1973 time: 0.3426 last_time: 0.2510 data_time: 0.0049 last_data_time: 0.0054 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:28:30 d2.utils.events]: \u001b[0m eta: 1:01:50 iter: 69159 total_loss: 0.7345 loss_cls: 0.2348 loss_box_reg: 0.2824 loss_rpn_cls: 0.04247 loss_rpn_loc: 0.1736 time: 0.3425 last_time: 0.2338 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:28:34 d2.utils.events]: \u001b[0m eta: 1:01:45 iter: 69179 total_loss: 0.7103 loss_cls: 0.2437 loss_box_reg: 0.2657 loss_rpn_cls: 0.04485 loss_rpn_loc: 0.1915 time: 0.3425 last_time: 0.2234 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:28:39 d2.utils.events]: \u001b[0m eta: 1:01:40 iter: 69199 total_loss: 0.808 loss_cls: 0.2434 loss_box_reg: 0.273 loss_rpn_cls: 0.04604 loss_rpn_loc: 0.189 time: 0.3425 last_time: 0.2336 data_time: 0.0049 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:28:43 d2.utils.events]: \u001b[0m eta: 1:01:34 iter: 69219 total_loss: 0.7553 loss_cls: 0.2442 loss_box_reg: 0.2931 loss_rpn_cls: 0.05542 loss_rpn_loc: 0.1651 time: 0.3424 last_time: 0.2326 data_time: 0.0048 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:28:48 d2.utils.events]: \u001b[0m eta: 1:01:28 iter: 69239 total_loss: 0.7902 loss_cls: 0.2277 loss_box_reg: 0.2824 loss_rpn_cls: 0.05213 loss_rpn_loc: 0.1899 time: 0.3424 last_time: 0.2508 data_time: 0.0048 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:28:53 d2.utils.events]: \u001b[0m eta: 1:01:22 iter: 69259 total_loss: 0.7879 loss_cls: 0.2553 loss_box_reg: 0.2891 loss_rpn_cls: 0.05714 loss_rpn_loc: 0.201 time: 0.3424 last_time: 0.2350 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:28:57 d2.utils.events]: \u001b[0m eta: 1:01:17 iter: 69279 total_loss: 0.743 loss_cls: 0.2304 loss_box_reg: 0.269 loss_rpn_cls: 0.0399 loss_rpn_loc: 0.1772 time: 0.3423 last_time: 0.1817 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:29:02 d2.utils.events]: \u001b[0m eta: 1:01:14 iter: 69299 total_loss: 0.7927 loss_cls: 0.2129 loss_box_reg: 0.2742 loss_rpn_cls: 0.04308 loss_rpn_loc: 0.1913 time: 0.3423 last_time: 0.2509 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:29:06 d2.utils.events]: \u001b[0m eta: 1:01:10 iter: 69319 total_loss: 0.6934 loss_cls: 0.2199 loss_box_reg: 0.2512 loss_rpn_cls: 0.04773 loss_rpn_loc: 0.1903 time: 0.3423 last_time: 0.2328 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:29:11 d2.utils.events]: \u001b[0m eta: 1:01:05 iter: 69339 total_loss: 0.6913 loss_cls: 0.2085 loss_box_reg: 0.2398 loss_rpn_cls: 0.03577 loss_rpn_loc: 0.1862 time: 0.3422 last_time: 0.2238 data_time: 0.0046 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:29:16 d2.utils.events]: \u001b[0m eta: 1:01:00 iter: 69359 total_loss: 0.7503 loss_cls: 0.2405 loss_box_reg: 0.3008 loss_rpn_cls: 0.03208 loss_rpn_loc: 0.1772 time: 0.3422 last_time: 0.2500 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:29:20 d2.utils.events]: \u001b[0m eta: 1:00:56 iter: 69379 total_loss: 0.8854 loss_cls: 0.2713 loss_box_reg: 0.2694 loss_rpn_cls: 0.05954 loss_rpn_loc: 0.2203 time: 0.3422 last_time: 0.2361 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:29:25 d2.utils.events]: \u001b[0m eta: 1:00:50 iter: 69399 total_loss: 0.7032 loss_cls: 0.2088 loss_box_reg: 0.2805 loss_rpn_cls: 0.05011 loss_rpn_loc: 0.1914 time: 0.3421 last_time: 0.2314 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:29:29 d2.utils.events]: \u001b[0m eta: 1:00:43 iter: 69419 total_loss: 0.6929 loss_cls: 0.2041 loss_box_reg: 0.2487 loss_rpn_cls: 0.0452 loss_rpn_loc: 0.185 time: 0.3421 last_time: 0.2475 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:29:34 d2.utils.events]: \u001b[0m eta: 1:00:39 iter: 69439 total_loss: 0.6811 loss_cls: 0.2135 loss_box_reg: 0.2505 loss_rpn_cls: 0.04177 loss_rpn_loc: 0.1736 time: 0.3421 last_time: 0.2472 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:29:39 d2.utils.events]: \u001b[0m eta: 1:00:35 iter: 69459 total_loss: 0.7533 loss_cls: 0.2591 loss_box_reg: 0.256 loss_rpn_cls: 0.03492 loss_rpn_loc: 0.194 time: 0.3420 last_time: 0.2242 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:29:43 d2.utils.events]: \u001b[0m eta: 1:00:30 iter: 69479 total_loss: 0.8186 loss_cls: 0.2498 loss_box_reg: 0.3187 loss_rpn_cls: 0.05366 loss_rpn_loc: 0.1872 time: 0.3420 last_time: 0.2521 data_time: 0.0046 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:29:48 d2.utils.events]: \u001b[0m eta: 1:00:27 iter: 69499 total_loss: 0.6528 loss_cls: 0.1883 loss_box_reg: 0.2595 loss_rpn_cls: 0.03534 loss_rpn_loc: 0.1739 time: 0.3420 last_time: 0.2249 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:29:53 d2.utils.events]: \u001b[0m eta: 1:00:22 iter: 69519 total_loss: 0.8026 loss_cls: 0.2472 loss_box_reg: 0.2898 loss_rpn_cls: 0.04041 loss_rpn_loc: 0.1888 time: 0.3419 last_time: 0.2494 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:29:57 d2.utils.events]: \u001b[0m eta: 1:00:18 iter: 69539 total_loss: 0.823 loss_cls: 0.2747 loss_box_reg: 0.3036 loss_rpn_cls: 0.03919 loss_rpn_loc: 0.1859 time: 0.3419 last_time: 0.2247 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:30:02 d2.utils.events]: \u001b[0m eta: 1:00:13 iter: 69559 total_loss: 0.7739 loss_cls: 0.2362 loss_box_reg: 0.2698 loss_rpn_cls: 0.0437 loss_rpn_loc: 0.2266 time: 0.3419 last_time: 0.2519 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:30:06 d2.utils.events]: \u001b[0m eta: 1:00:07 iter: 69579 total_loss: 0.813 loss_cls: 0.2701 loss_box_reg: 0.3117 loss_rpn_cls: 0.03698 loss_rpn_loc: 0.1958 time: 0.3418 last_time: 0.2503 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:30:11 d2.utils.events]: \u001b[0m eta: 1:00:03 iter: 69599 total_loss: 0.7503 loss_cls: 0.2612 loss_box_reg: 0.3022 loss_rpn_cls: 0.02773 loss_rpn_loc: 0.1728 time: 0.3418 last_time: 0.2228 data_time: 0.0047 last_data_time: 0.0055 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:30:16 d2.utils.events]: \u001b[0m eta: 0:59:59 iter: 69619 total_loss: 0.825 loss_cls: 0.2571 loss_box_reg: 0.2788 loss_rpn_cls: 0.05168 loss_rpn_loc: 0.1858 time: 0.3418 last_time: 0.2487 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:30:20 d2.utils.events]: \u001b[0m eta: 0:59:54 iter: 69639 total_loss: 0.7247 loss_cls: 0.2311 loss_box_reg: 0.2791 loss_rpn_cls: 0.04538 loss_rpn_loc: 0.1869 time: 0.3417 last_time: 0.2343 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:30:25 d2.utils.events]: \u001b[0m eta: 0:59:50 iter: 69659 total_loss: 0.8525 loss_cls: 0.2542 loss_box_reg: 0.3305 loss_rpn_cls: 0.0481 loss_rpn_loc: 0.1767 time: 0.3417 last_time: 0.2419 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:30:29 d2.utils.events]: \u001b[0m eta: 0:59:45 iter: 69679 total_loss: 0.7987 loss_cls: 0.2479 loss_box_reg: 0.2781 loss_rpn_cls: 0.04582 loss_rpn_loc: 0.1908 time: 0.3417 last_time: 0.1958 data_time: 0.0048 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:30:34 d2.utils.events]: \u001b[0m eta: 0:59:40 iter: 69699 total_loss: 0.67 loss_cls: 0.2171 loss_box_reg: 0.249 loss_rpn_cls: 0.04481 loss_rpn_loc: 0.1653 time: 0.3416 last_time: 0.2234 data_time: 0.0046 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:30:39 d2.utils.events]: \u001b[0m eta: 0:59:35 iter: 69719 total_loss: 0.9312 loss_cls: 0.2901 loss_box_reg: 0.3044 loss_rpn_cls: 0.05278 loss_rpn_loc: 0.2337 time: 0.3416 last_time: 0.2502 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:30:43 d2.utils.events]: \u001b[0m eta: 0:59:29 iter: 69739 total_loss: 0.6952 loss_cls: 0.2251 loss_box_reg: 0.2698 loss_rpn_cls: 0.04059 loss_rpn_loc: 0.1798 time: 0.3416 last_time: 0.2129 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:30:48 d2.utils.events]: \u001b[0m eta: 0:59:23 iter: 69759 total_loss: 0.8338 loss_cls: 0.2476 loss_box_reg: 0.2844 loss_rpn_cls: 0.04957 loss_rpn_loc: 0.2208 time: 0.3416 last_time: 0.2516 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:30:53 d2.utils.events]: \u001b[0m eta: 0:59:18 iter: 69779 total_loss: 0.7328 loss_cls: 0.273 loss_box_reg: 0.2724 loss_rpn_cls: 0.05237 loss_rpn_loc: 0.1819 time: 0.3415 last_time: 0.2501 data_time: 0.0047 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:30:57 d2.utils.events]: \u001b[0m eta: 0:59:09 iter: 69799 total_loss: 0.7826 loss_cls: 0.2612 loss_box_reg: 0.2838 loss_rpn_cls: 0.04391 loss_rpn_loc: 0.2022 time: 0.3415 last_time: 0.2224 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:31:02 d2.utils.events]: \u001b[0m eta: 0:59:04 iter: 69819 total_loss: 0.8602 loss_cls: 0.2425 loss_box_reg: 0.311 loss_rpn_cls: 0.04159 loss_rpn_loc: 0.2106 time: 0.3415 last_time: 0.2110 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:31:06 d2.utils.events]: \u001b[0m eta: 0:59:00 iter: 69839 total_loss: 0.6501 loss_cls: 0.2054 loss_box_reg: 0.247 loss_rpn_cls: 0.03951 loss_rpn_loc: 0.1703 time: 0.3414 last_time: 0.2228 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:31:11 d2.utils.events]: \u001b[0m eta: 0:58:54 iter: 69859 total_loss: 0.8183 loss_cls: 0.2744 loss_box_reg: 0.333 loss_rpn_cls: 0.05145 loss_rpn_loc: 0.1773 time: 0.3414 last_time: 0.2358 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:31:16 d2.utils.events]: \u001b[0m eta: 0:58:50 iter: 69879 total_loss: 0.805 loss_cls: 0.2663 loss_box_reg: 0.3244 loss_rpn_cls: 0.04295 loss_rpn_loc: 0.2151 time: 0.3414 last_time: 0.2358 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:31:20 d2.utils.events]: \u001b[0m eta: 0:58:46 iter: 69899 total_loss: 0.801 loss_cls: 0.2636 loss_box_reg: 0.3019 loss_rpn_cls: 0.05686 loss_rpn_loc: 0.1935 time: 0.3413 last_time: 0.2487 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:31:25 d2.utils.events]: \u001b[0m eta: 0:58:40 iter: 69919 total_loss: 0.75 loss_cls: 0.2159 loss_box_reg: 0.3038 loss_rpn_cls: 0.04325 loss_rpn_loc: 0.1993 time: 0.3413 last_time: 0.2077 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:31:29 d2.utils.events]: \u001b[0m eta: 0:58:35 iter: 69939 total_loss: 0.6748 loss_cls: 0.2241 loss_box_reg: 0.2505 loss_rpn_cls: 0.04442 loss_rpn_loc: 0.1881 time: 0.3413 last_time: 0.2520 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:31:34 d2.utils.events]: \u001b[0m eta: 0:58:31 iter: 69959 total_loss: 0.7665 loss_cls: 0.2757 loss_box_reg: 0.2682 loss_rpn_cls: 0.05018 loss_rpn_loc: 0.1745 time: 0.3412 last_time: 0.2090 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:31:39 d2.utils.events]: \u001b[0m eta: 0:58:26 iter: 69979 total_loss: 0.7303 loss_cls: 0.2516 loss_box_reg: 0.267 loss_rpn_cls: 0.04061 loss_rpn_loc: 0.1776 time: 0.3412 last_time: 0.2345 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:31:44 d2.utils.events]: \u001b[0m eta: 0:58:21 iter: 69999 total_loss: 0.8093 loss_cls: 0.2613 loss_box_reg: 0.292 loss_rpn_cls: 0.05813 loss_rpn_loc: 0.1885 time: 0.3412 last_time: 0.1967 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:31:48 d2.utils.events]: \u001b[0m eta: 0:58:16 iter: 70019 total_loss: 0.8537 loss_cls: 0.2487 loss_box_reg: 0.2641 loss_rpn_cls: 0.05334 loss_rpn_loc: 0.2242 time: 0.3411 last_time: 0.2482 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:31:53 d2.utils.events]: \u001b[0m eta: 0:58:12 iter: 70039 total_loss: 0.8533 loss_cls: 0.2535 loss_box_reg: 0.2917 loss_rpn_cls: 0.0542 loss_rpn_loc: 0.2271 time: 0.3411 last_time: 0.2229 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:31:58 d2.utils.events]: \u001b[0m eta: 0:58:07 iter: 70059 total_loss: 0.7756 loss_cls: 0.2195 loss_box_reg: 0.2797 loss_rpn_cls: 0.03917 loss_rpn_loc: 0.1943 time: 0.3411 last_time: 0.2525 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:32:02 d2.utils.events]: \u001b[0m eta: 0:58:03 iter: 70079 total_loss: 0.6496 loss_cls: 0.1932 loss_box_reg: 0.2541 loss_rpn_cls: 0.032 loss_rpn_loc: 0.1788 time: 0.3410 last_time: 0.2317 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:32:07 d2.utils.events]: \u001b[0m eta: 0:57:58 iter: 70099 total_loss: 0.6864 loss_cls: 0.2142 loss_box_reg: 0.2405 loss_rpn_cls: 0.0419 loss_rpn_loc: 0.1548 time: 0.3410 last_time: 0.2519 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:32:11 d2.utils.events]: \u001b[0m eta: 0:57:53 iter: 70119 total_loss: 0.7612 loss_cls: 0.2642 loss_box_reg: 0.2853 loss_rpn_cls: 0.04933 loss_rpn_loc: 0.1705 time: 0.3410 last_time: 0.2240 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:32:16 d2.utils.events]: \u001b[0m eta: 0:57:49 iter: 70139 total_loss: 0.7017 loss_cls: 0.2084 loss_box_reg: 0.2809 loss_rpn_cls: 0.03009 loss_rpn_loc: 0.1986 time: 0.3409 last_time: 0.2468 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:32:21 d2.utils.events]: \u001b[0m eta: 0:57:44 iter: 70159 total_loss: 0.7394 loss_cls: 0.2213 loss_box_reg: 0.2601 loss_rpn_cls: 0.03999 loss_rpn_loc: 0.1891 time: 0.3409 last_time: 0.2318 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:32:25 d2.utils.events]: \u001b[0m eta: 0:57:40 iter: 70179 total_loss: 0.8497 loss_cls: 0.2506 loss_box_reg: 0.3059 loss_rpn_cls: 0.04199 loss_rpn_loc: 0.2023 time: 0.3409 last_time: 0.2123 data_time: 0.0045 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:32:30 d2.utils.events]: \u001b[0m eta: 0:57:35 iter: 70199 total_loss: 0.7738 loss_cls: 0.2459 loss_box_reg: 0.2845 loss_rpn_cls: 0.0507 loss_rpn_loc: 0.2199 time: 0.3409 last_time: 0.2336 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:32:35 d2.utils.events]: \u001b[0m eta: 0:57:31 iter: 70219 total_loss: 0.8169 loss_cls: 0.288 loss_box_reg: 0.2884 loss_rpn_cls: 0.05541 loss_rpn_loc: 0.1948 time: 0.3408 last_time: 0.2533 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:32:39 d2.utils.events]: \u001b[0m eta: 0:57:26 iter: 70239 total_loss: 0.7055 loss_cls: 0.2239 loss_box_reg: 0.2553 loss_rpn_cls: 0.04375 loss_rpn_loc: 0.1676 time: 0.3408 last_time: 0.2361 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:32:44 d2.utils.events]: \u001b[0m eta: 0:57:22 iter: 70259 total_loss: 0.8347 loss_cls: 0.2512 loss_box_reg: 0.283 loss_rpn_cls: 0.05015 loss_rpn_loc: 0.2049 time: 0.3408 last_time: 0.1962 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:32:49 d2.utils.events]: \u001b[0m eta: 0:57:20 iter: 70279 total_loss: 0.725 loss_cls: 0.2238 loss_box_reg: 0.2469 loss_rpn_cls: 0.05032 loss_rpn_loc: 0.1844 time: 0.3407 last_time: 0.2497 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:32:53 d2.utils.events]: \u001b[0m eta: 0:57:15 iter: 70299 total_loss: 0.8429 loss_cls: 0.2513 loss_box_reg: 0.2813 loss_rpn_cls: 0.04224 loss_rpn_loc: 0.2142 time: 0.3407 last_time: 0.2103 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:32:58 d2.utils.events]: \u001b[0m eta: 0:57:10 iter: 70319 total_loss: 0.7242 loss_cls: 0.2271 loss_box_reg: 0.2776 loss_rpn_cls: 0.04881 loss_rpn_loc: 0.1957 time: 0.3407 last_time: 0.2279 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:33:03 d2.utils.events]: \u001b[0m eta: 0:57:05 iter: 70339 total_loss: 0.8125 loss_cls: 0.2616 loss_box_reg: 0.3071 loss_rpn_cls: 0.03745 loss_rpn_loc: 0.1943 time: 0.3406 last_time: 0.2223 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:33:07 d2.utils.events]: \u001b[0m eta: 0:56:59 iter: 70359 total_loss: 0.8629 loss_cls: 0.2796 loss_box_reg: 0.3185 loss_rpn_cls: 0.04781 loss_rpn_loc: 0.2157 time: 0.3406 last_time: 0.2543 data_time: 0.0045 last_data_time: 0.0042 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:33:12 d2.utils.events]: \u001b[0m eta: 0:56:55 iter: 70379 total_loss: 0.7722 loss_cls: 0.2203 loss_box_reg: 0.2888 loss_rpn_cls: 0.037 loss_rpn_loc: 0.2124 time: 0.3406 last_time: 0.1839 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:33:16 d2.utils.events]: \u001b[0m eta: 0:56:51 iter: 70399 total_loss: 0.7228 loss_cls: 0.1899 loss_box_reg: 0.2957 loss_rpn_cls: 0.05038 loss_rpn_loc: 0.177 time: 0.3405 last_time: 0.2783 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:33:21 d2.utils.events]: \u001b[0m eta: 0:56:46 iter: 70419 total_loss: 0.7891 loss_cls: 0.2463 loss_box_reg: 0.3171 loss_rpn_cls: 0.04713 loss_rpn_loc: 0.1908 time: 0.3405 last_time: 0.2241 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:33:26 d2.utils.events]: \u001b[0m eta: 0:56:41 iter: 70439 total_loss: 0.7585 loss_cls: 0.2367 loss_box_reg: 0.2852 loss_rpn_cls: 0.04512 loss_rpn_loc: 0.2004 time: 0.3405 last_time: 0.2097 data_time: 0.0044 last_data_time: 0.0042 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:33:30 d2.utils.events]: \u001b[0m eta: 0:56:37 iter: 70459 total_loss: 0.8705 loss_cls: 0.3075 loss_box_reg: 0.2939 loss_rpn_cls: 0.04615 loss_rpn_loc: 0.1829 time: 0.3404 last_time: 0.2520 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:33:35 d2.utils.events]: \u001b[0m eta: 0:56:31 iter: 70479 total_loss: 0.7391 loss_cls: 0.2402 loss_box_reg: 0.264 loss_rpn_cls: 0.04129 loss_rpn_loc: 0.1878 time: 0.3404 last_time: 0.1938 data_time: 0.0044 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:33:39 d2.utils.events]: \u001b[0m eta: 0:56:26 iter: 70499 total_loss: 0.8029 loss_cls: 0.2835 loss_box_reg: 0.3033 loss_rpn_cls: 0.04884 loss_rpn_loc: 0.2011 time: 0.3404 last_time: 0.2266 data_time: 0.0046 last_data_time: 0.0041 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:33:44 d2.utils.events]: \u001b[0m eta: 0:56:20 iter: 70519 total_loss: 0.7962 loss_cls: 0.2447 loss_box_reg: 0.2552 loss_rpn_cls: 0.04638 loss_rpn_loc: 0.1786 time: 0.3403 last_time: 0.1809 data_time: 0.0044 last_data_time: 0.0042 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:33:48 d2.utils.events]: \u001b[0m eta: 0:56:15 iter: 70539 total_loss: 0.8738 loss_cls: 0.2708 loss_box_reg: 0.2773 loss_rpn_cls: 0.05759 loss_rpn_loc: 0.2243 time: 0.3403 last_time: 0.2258 data_time: 0.0044 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:33:53 d2.utils.events]: \u001b[0m eta: 0:56:10 iter: 70559 total_loss: 0.7099 loss_cls: 0.2204 loss_box_reg: 0.2826 loss_rpn_cls: 0.04531 loss_rpn_loc: 0.174 time: 0.3403 last_time: 0.2243 data_time: 0.0044 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:33:57 d2.utils.events]: \u001b[0m eta: 0:56:06 iter: 70579 total_loss: 0.8364 loss_cls: 0.2333 loss_box_reg: 0.3128 loss_rpn_cls: 0.0499 loss_rpn_loc: 0.1952 time: 0.3403 last_time: 0.1805 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:34:02 d2.utils.events]: \u001b[0m eta: 0:56:01 iter: 70599 total_loss: 0.8693 loss_cls: 0.2728 loss_box_reg: 0.2868 loss_rpn_cls: 0.05136 loss_rpn_loc: 0.1879 time: 0.3402 last_time: 0.2102 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:34:07 d2.utils.events]: \u001b[0m eta: 0:55:56 iter: 70619 total_loss: 0.8472 loss_cls: 0.2776 loss_box_reg: 0.3041 loss_rpn_cls: 0.05497 loss_rpn_loc: 0.1971 time: 0.3402 last_time: 0.2527 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:34:11 d2.utils.events]: \u001b[0m eta: 0:55:52 iter: 70639 total_loss: 0.8322 loss_cls: 0.2811 loss_box_reg: 0.2997 loss_rpn_cls: 0.05368 loss_rpn_loc: 0.1919 time: 0.3402 last_time: 0.2529 data_time: 0.0047 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:34:16 d2.utils.events]: \u001b[0m eta: 0:55:48 iter: 70659 total_loss: 0.7125 loss_cls: 0.1919 loss_box_reg: 0.2763 loss_rpn_cls: 0.03584 loss_rpn_loc: 0.1841 time: 0.3401 last_time: 0.2486 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:34:20 d2.utils.events]: \u001b[0m eta: 0:55:42 iter: 70679 total_loss: 0.6775 loss_cls: 0.2004 loss_box_reg: 0.2552 loss_rpn_cls: 0.03725 loss_rpn_loc: 0.1827 time: 0.3401 last_time: 0.2345 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:34:25 d2.utils.events]: \u001b[0m eta: 0:55:36 iter: 70699 total_loss: 0.6872 loss_cls: 0.2105 loss_box_reg: 0.2625 loss_rpn_cls: 0.04189 loss_rpn_loc: 0.1773 time: 0.3401 last_time: 0.2486 data_time: 0.0044 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:34:30 d2.utils.events]: \u001b[0m eta: 0:55:32 iter: 70719 total_loss: 0.8028 loss_cls: 0.231 loss_box_reg: 0.2901 loss_rpn_cls: 0.04953 loss_rpn_loc: 0.2241 time: 0.3400 last_time: 0.2360 data_time: 0.0044 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:34:34 d2.utils.events]: \u001b[0m eta: 0:55:27 iter: 70739 total_loss: 0.834 loss_cls: 0.2636 loss_box_reg: 0.3026 loss_rpn_cls: 0.04345 loss_rpn_loc: 0.2113 time: 0.3400 last_time: 0.2119 data_time: 0.0045 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:34:39 d2.utils.events]: \u001b[0m eta: 0:55:22 iter: 70759 total_loss: 0.7823 loss_cls: 0.2724 loss_box_reg: 0.3224 loss_rpn_cls: 0.03564 loss_rpn_loc: 0.1925 time: 0.3400 last_time: 0.2494 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:34:43 d2.utils.events]: \u001b[0m eta: 0:55:18 iter: 70779 total_loss: 0.7956 loss_cls: 0.228 loss_box_reg: 0.295 loss_rpn_cls: 0.04192 loss_rpn_loc: 0.1855 time: 0.3399 last_time: 0.2507 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:34:48 d2.utils.events]: \u001b[0m eta: 0:55:14 iter: 70799 total_loss: 0.7664 loss_cls: 0.2214 loss_box_reg: 0.2819 loss_rpn_cls: 0.03934 loss_rpn_loc: 0.1891 time: 0.3399 last_time: 0.2488 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:34:53 d2.utils.events]: \u001b[0m eta: 0:55:09 iter: 70819 total_loss: 0.7475 loss_cls: 0.2222 loss_box_reg: 0.3163 loss_rpn_cls: 0.0348 loss_rpn_loc: 0.1907 time: 0.3399 last_time: 0.2349 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:34:57 d2.utils.events]: \u001b[0m eta: 0:55:04 iter: 70839 total_loss: 0.7151 loss_cls: 0.2292 loss_box_reg: 0.2541 loss_rpn_cls: 0.04037 loss_rpn_loc: 0.2027 time: 0.3398 last_time: 0.1963 data_time: 0.0045 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:35:02 d2.utils.events]: \u001b[0m eta: 0:54:59 iter: 70859 total_loss: 0.812 loss_cls: 0.2394 loss_box_reg: 0.3222 loss_rpn_cls: 0.04444 loss_rpn_loc: 0.1971 time: 0.3398 last_time: 0.2230 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:35:06 d2.utils.events]: \u001b[0m eta: 0:54:54 iter: 70879 total_loss: 0.7489 loss_cls: 0.1786 loss_box_reg: 0.2469 loss_rpn_cls: 0.04669 loss_rpn_loc: 0.1897 time: 0.3398 last_time: 0.2419 data_time: 0.0044 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:35:11 d2.utils.events]: \u001b[0m eta: 0:54:50 iter: 70899 total_loss: 0.7153 loss_cls: 0.2116 loss_box_reg: 0.2646 loss_rpn_cls: 0.0423 loss_rpn_loc: 0.1941 time: 0.3398 last_time: 0.2344 data_time: 0.0044 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:35:16 d2.utils.events]: \u001b[0m eta: 0:54:46 iter: 70919 total_loss: 0.7474 loss_cls: 0.2397 loss_box_reg: 0.2594 loss_rpn_cls: 0.03565 loss_rpn_loc: 0.192 time: 0.3397 last_time: 0.2345 data_time: 0.0045 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:35:20 d2.utils.events]: \u001b[0m eta: 0:54:41 iter: 70939 total_loss: 0.7497 loss_cls: 0.2385 loss_box_reg: 0.267 loss_rpn_cls: 0.04571 loss_rpn_loc: 0.198 time: 0.3397 last_time: 0.2496 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:35:25 d2.utils.events]: \u001b[0m eta: 0:54:36 iter: 70959 total_loss: 0.8548 loss_cls: 0.27 loss_box_reg: 0.275 loss_rpn_cls: 0.05557 loss_rpn_loc: 0.1995 time: 0.3397 last_time: 0.2492 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:35:29 d2.utils.events]: \u001b[0m eta: 0:54:32 iter: 70979 total_loss: 0.7958 loss_cls: 0.2715 loss_box_reg: 0.2607 loss_rpn_cls: 0.04714 loss_rpn_loc: 0.1933 time: 0.3396 last_time: 0.2111 data_time: 0.0044 last_data_time: 0.0042 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:35:34 d2.utils.events]: \u001b[0m eta: 0:54:27 iter: 70999 total_loss: 0.8267 loss_cls: 0.2592 loss_box_reg: 0.3196 loss_rpn_cls: 0.03962 loss_rpn_loc: 0.1998 time: 0.3396 last_time: 0.2357 data_time: 0.0044 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:35:39 d2.utils.events]: \u001b[0m eta: 0:54:23 iter: 71019 total_loss: 0.7881 loss_cls: 0.255 loss_box_reg: 0.2837 loss_rpn_cls: 0.05056 loss_rpn_loc: 0.1961 time: 0.3396 last_time: 0.2499 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:35:43 d2.utils.events]: \u001b[0m eta: 0:54:19 iter: 71039 total_loss: 0.6957 loss_cls: 0.2383 loss_box_reg: 0.2375 loss_rpn_cls: 0.05618 loss_rpn_loc: 0.1951 time: 0.3395 last_time: 0.2606 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:35:48 d2.utils.events]: \u001b[0m eta: 0:54:15 iter: 71059 total_loss: 0.689 loss_cls: 0.2261 loss_box_reg: 0.2434 loss_rpn_cls: 0.04839 loss_rpn_loc: 0.1908 time: 0.3395 last_time: 0.2836 data_time: 0.0046 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:35:53 d2.utils.events]: \u001b[0m eta: 0:54:12 iter: 71079 total_loss: 0.8191 loss_cls: 0.2309 loss_box_reg: 0.286 loss_rpn_cls: 0.04407 loss_rpn_loc: 0.1984 time: 0.3395 last_time: 0.2228 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:35:58 d2.utils.events]: \u001b[0m eta: 0:54:07 iter: 71099 total_loss: 0.6848 loss_cls: 0.2027 loss_box_reg: 0.2947 loss_rpn_cls: 0.03592 loss_rpn_loc: 0.1715 time: 0.3395 last_time: 0.1813 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:36:04 d2.utils.events]: \u001b[0m eta: 0:54:05 iter: 71119 total_loss: 0.7349 loss_cls: 0.2265 loss_box_reg: 0.249 loss_rpn_cls: 0.03421 loss_rpn_loc: 0.1845 time: 0.3394 last_time: 0.2389 data_time: 0.0050 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:36:10 d2.utils.events]: \u001b[0m eta: 0:54:01 iter: 71139 total_loss: 0.7591 loss_cls: 0.245 loss_box_reg: 0.2838 loss_rpn_cls: 0.04823 loss_rpn_loc: 0.1888 time: 0.3394 last_time: 0.2650 data_time: 0.0051 last_data_time: 0.0053 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:36:16 d2.utils.events]: \u001b[0m eta: 0:53:58 iter: 71159 total_loss: 0.7158 loss_cls: 0.198 loss_box_reg: 0.2769 loss_rpn_cls: 0.04793 loss_rpn_loc: 0.173 time: 0.3394 last_time: 0.2452 data_time: 0.0052 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:36:22 d2.utils.events]: \u001b[0m eta: 0:53:56 iter: 71179 total_loss: 0.9497 loss_cls: 0.3092 loss_box_reg: 0.3391 loss_rpn_cls: 0.05551 loss_rpn_loc: 0.2056 time: 0.3394 last_time: 0.2780 data_time: 0.0051 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:36:27 d2.utils.events]: \u001b[0m eta: 0:53:55 iter: 71199 total_loss: 0.7942 loss_cls: 0.2545 loss_box_reg: 0.3241 loss_rpn_cls: 0.04615 loss_rpn_loc: 0.1899 time: 0.3394 last_time: 0.2929 data_time: 0.0050 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:36:33 d2.utils.events]: \u001b[0m eta: 0:53:52 iter: 71219 total_loss: 0.764 loss_cls: 0.2429 loss_box_reg: 0.2853 loss_rpn_cls: 0.03343 loss_rpn_loc: 0.172 time: 0.3394 last_time: 0.3140 data_time: 0.0050 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:36:39 d2.utils.events]: \u001b[0m eta: 0:53:49 iter: 71239 total_loss: 0.8222 loss_cls: 0.2551 loss_box_reg: 0.2966 loss_rpn_cls: 0.04959 loss_rpn_loc: 0.2253 time: 0.3394 last_time: 0.2345 data_time: 0.0051 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:36:44 d2.utils.events]: \u001b[0m eta: 0:53:49 iter: 71259 total_loss: 0.7898 loss_cls: 0.2801 loss_box_reg: 0.323 loss_rpn_cls: 0.04452 loss_rpn_loc: 0.1861 time: 0.3393 last_time: 0.2666 data_time: 0.0050 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:36:50 d2.utils.events]: \u001b[0m eta: 0:53:46 iter: 71279 total_loss: 0.8132 loss_cls: 0.2727 loss_box_reg: 0.3114 loss_rpn_cls: 0.05443 loss_rpn_loc: 0.1793 time: 0.3393 last_time: 0.2921 data_time: 0.0051 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:36:55 d2.utils.events]: \u001b[0m eta: 0:53:45 iter: 71299 total_loss: 0.8037 loss_cls: 0.2705 loss_box_reg: 0.3094 loss_rpn_cls: 0.03775 loss_rpn_loc: 0.1923 time: 0.3393 last_time: 0.2675 data_time: 0.0049 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:37:01 d2.utils.events]: \u001b[0m eta: 0:53:43 iter: 71319 total_loss: 0.775 loss_cls: 0.2227 loss_box_reg: 0.2967 loss_rpn_cls: 0.03098 loss_rpn_loc: 0.1838 time: 0.3393 last_time: 0.2919 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:37:06 d2.utils.events]: \u001b[0m eta: 0:53:43 iter: 71339 total_loss: 0.7851 loss_cls: 0.2437 loss_box_reg: 0.2777 loss_rpn_cls: 0.04521 loss_rpn_loc: 0.1999 time: 0.3393 last_time: 0.2388 data_time: 0.0049 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:37:12 d2.utils.events]: \u001b[0m eta: 0:53:56 iter: 71359 total_loss: 0.7935 loss_cls: 0.2293 loss_box_reg: 0.2663 loss_rpn_cls: 0.04226 loss_rpn_loc: 0.1845 time: 0.3393 last_time: 0.2948 data_time: 0.0050 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:37:18 d2.utils.events]: \u001b[0m eta: 0:54:14 iter: 71379 total_loss: 0.6994 loss_cls: 0.1883 loss_box_reg: 0.2729 loss_rpn_cls: 0.0406 loss_rpn_loc: 0.1602 time: 0.3392 last_time: 0.2280 data_time: 0.0051 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:37:24 d2.utils.events]: \u001b[0m eta: 0:54:56 iter: 71399 total_loss: 0.7091 loss_cls: 0.2173 loss_box_reg: 0.2732 loss_rpn_cls: 0.04102 loss_rpn_loc: 0.1916 time: 0.3392 last_time: 0.3165 data_time: 0.0050 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:37:30 d2.utils.events]: \u001b[0m eta: 0:55:51 iter: 71419 total_loss: 0.6341 loss_cls: 0.1937 loss_box_reg: 0.2412 loss_rpn_cls: 0.02765 loss_rpn_loc: 0.1851 time: 0.3392 last_time: 0.3031 data_time: 0.0051 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:37:36 d2.utils.events]: \u001b[0m eta: 0:55:57 iter: 71439 total_loss: 0.7837 loss_cls: 0.2436 loss_box_reg: 0.2615 loss_rpn_cls: 0.05109 loss_rpn_loc: 0.209 time: 0.3392 last_time: 0.3028 data_time: 0.0051 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:37:42 d2.utils.events]: \u001b[0m eta: 0:56:04 iter: 71459 total_loss: 0.7632 loss_cls: 0.2443 loss_box_reg: 0.2679 loss_rpn_cls: 0.05256 loss_rpn_loc: 0.2029 time: 0.3392 last_time: 0.3327 data_time: 0.0050 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:37:48 d2.utils.events]: \u001b[0m eta: 0:56:03 iter: 71479 total_loss: 0.8102 loss_cls: 0.2904 loss_box_reg: 0.2584 loss_rpn_cls: 0.05041 loss_rpn_loc: 0.1889 time: 0.3392 last_time: 0.2961 data_time: 0.0051 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:37:54 d2.utils.events]: \u001b[0m eta: 0:56:05 iter: 71499 total_loss: 0.83 loss_cls: 0.2682 loss_box_reg: 0.2802 loss_rpn_cls: 0.04033 loss_rpn_loc: 0.1847 time: 0.3392 last_time: 0.2754 data_time: 0.0051 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:37:59 d2.utils.events]: \u001b[0m eta: 0:56:04 iter: 71519 total_loss: 0.7798 loss_cls: 0.2087 loss_box_reg: 0.2986 loss_rpn_cls: 0.04595 loss_rpn_loc: 0.1985 time: 0.3392 last_time: 0.2983 data_time: 0.0050 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:38:05 d2.utils.events]: \u001b[0m eta: 0:56:02 iter: 71539 total_loss: 0.8507 loss_cls: 0.2577 loss_box_reg: 0.3075 loss_rpn_cls: 0.04182 loss_rpn_loc: 0.2064 time: 0.3391 last_time: 0.2548 data_time: 0.0049 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:38:11 d2.utils.events]: \u001b[0m eta: 0:56:00 iter: 71559 total_loss: 0.7573 loss_cls: 0.2409 loss_box_reg: 0.2797 loss_rpn_cls: 0.04696 loss_rpn_loc: 0.1792 time: 0.3391 last_time: 0.2318 data_time: 0.0050 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:38:16 d2.utils.events]: \u001b[0m eta: 0:55:56 iter: 71579 total_loss: 0.7015 loss_cls: 0.2143 loss_box_reg: 0.2508 loss_rpn_cls: 0.05012 loss_rpn_loc: 0.1983 time: 0.3391 last_time: 0.2489 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:38:21 d2.utils.events]: \u001b[0m eta: 0:55:52 iter: 71599 total_loss: 0.7845 loss_cls: 0.2179 loss_box_reg: 0.2774 loss_rpn_cls: 0.04371 loss_rpn_loc: 0.2032 time: 0.3391 last_time: 0.2473 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:38:25 d2.utils.events]: \u001b[0m eta: 0:55:45 iter: 71619 total_loss: 0.8152 loss_cls: 0.24 loss_box_reg: 0.2846 loss_rpn_cls: 0.03764 loss_rpn_loc: 0.1835 time: 0.3390 last_time: 0.2501 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:38:30 d2.utils.events]: \u001b[0m eta: 0:55:40 iter: 71639 total_loss: 0.7729 loss_cls: 0.2497 loss_box_reg: 0.2891 loss_rpn_cls: 0.04212 loss_rpn_loc: 0.1757 time: 0.3390 last_time: 0.2235 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:38:35 d2.utils.events]: \u001b[0m eta: 0:55:34 iter: 71659 total_loss: 0.7424 loss_cls: 0.2585 loss_box_reg: 0.2585 loss_rpn_cls: 0.05046 loss_rpn_loc: 0.1718 time: 0.3390 last_time: 0.2101 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:38:40 d2.utils.events]: \u001b[0m eta: 0:55:30 iter: 71679 total_loss: 0.7273 loss_cls: 0.1938 loss_box_reg: 0.3128 loss_rpn_cls: 0.04479 loss_rpn_loc: 0.1988 time: 0.3390 last_time: 0.3081 data_time: 0.0048 last_data_time: 0.0053 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:38:44 d2.utils.events]: \u001b[0m eta: 0:55:25 iter: 71699 total_loss: 0.6612 loss_cls: 0.1948 loss_box_reg: 0.2465 loss_rpn_cls: 0.03772 loss_rpn_loc: 0.1758 time: 0.3389 last_time: 0.1934 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:38:49 d2.utils.events]: \u001b[0m eta: 0:55:21 iter: 71719 total_loss: 0.8093 loss_cls: 0.2711 loss_box_reg: 0.3011 loss_rpn_cls: 0.03715 loss_rpn_loc: 0.2212 time: 0.3389 last_time: 0.2257 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:38:54 d2.utils.events]: \u001b[0m eta: 0:55:15 iter: 71739 total_loss: 0.708 loss_cls: 0.2358 loss_box_reg: 0.2435 loss_rpn_cls: 0.05389 loss_rpn_loc: 0.162 time: 0.3389 last_time: 0.2228 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:38:58 d2.utils.events]: \u001b[0m eta: 0:55:10 iter: 71759 total_loss: 0.6776 loss_cls: 0.2211 loss_box_reg: 0.2486 loss_rpn_cls: 0.03315 loss_rpn_loc: 0.1765 time: 0.3388 last_time: 0.2355 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:39:03 d2.utils.events]: \u001b[0m eta: 0:55:05 iter: 71779 total_loss: 0.7591 loss_cls: 0.2167 loss_box_reg: 0.2845 loss_rpn_cls: 0.04673 loss_rpn_loc: 0.1849 time: 0.3388 last_time: 0.1949 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:39:07 d2.utils.events]: \u001b[0m eta: 0:54:59 iter: 71799 total_loss: 0.6654 loss_cls: 0.2171 loss_box_reg: 0.2907 loss_rpn_cls: 0.03916 loss_rpn_loc: 0.1785 time: 0.3388 last_time: 0.2365 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:39:12 d2.utils.events]: \u001b[0m eta: 0:54:55 iter: 71819 total_loss: 0.5969 loss_cls: 0.202 loss_box_reg: 0.2481 loss_rpn_cls: 0.02686 loss_rpn_loc: 0.1481 time: 0.3388 last_time: 0.2461 data_time: 0.0046 last_data_time: 0.0042 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:39:17 d2.utils.events]: \u001b[0m eta: 0:54:50 iter: 71839 total_loss: 0.8245 loss_cls: 0.2236 loss_box_reg: 0.3149 loss_rpn_cls: 0.05119 loss_rpn_loc: 0.1959 time: 0.3387 last_time: 0.2230 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:39:21 d2.utils.events]: \u001b[0m eta: 0:54:45 iter: 71859 total_loss: 0.7289 loss_cls: 0.2303 loss_box_reg: 0.2453 loss_rpn_cls: 0.03943 loss_rpn_loc: 0.1917 time: 0.3387 last_time: 0.2511 data_time: 0.0048 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:39:26 d2.utils.events]: \u001b[0m eta: 0:54:40 iter: 71879 total_loss: 0.7141 loss_cls: 0.2208 loss_box_reg: 0.2705 loss_rpn_cls: 0.04214 loss_rpn_loc: 0.1827 time: 0.3387 last_time: 0.2500 data_time: 0.0048 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:39:31 d2.utils.events]: \u001b[0m eta: 0:54:35 iter: 71899 total_loss: 0.6683 loss_cls: 0.2093 loss_box_reg: 0.2487 loss_rpn_cls: 0.03925 loss_rpn_loc: 0.1823 time: 0.3386 last_time: 0.2510 data_time: 0.0048 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:39:35 d2.utils.events]: \u001b[0m eta: 0:54:30 iter: 71919 total_loss: 0.8785 loss_cls: 0.2776 loss_box_reg: 0.3041 loss_rpn_cls: 0.05675 loss_rpn_loc: 0.1886 time: 0.3386 last_time: 0.2310 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:39:40 d2.utils.events]: \u001b[0m eta: 0:54:26 iter: 71939 total_loss: 0.7918 loss_cls: 0.2326 loss_box_reg: 0.3137 loss_rpn_cls: 0.0367 loss_rpn_loc: 0.1797 time: 0.3386 last_time: 0.2408 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:39:45 d2.utils.events]: \u001b[0m eta: 0:54:24 iter: 71959 total_loss: 0.7327 loss_cls: 0.2179 loss_box_reg: 0.2925 loss_rpn_cls: 0.03223 loss_rpn_loc: 0.1566 time: 0.3385 last_time: 0.1947 data_time: 0.0046 last_data_time: 0.0042 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:39:49 d2.utils.events]: \u001b[0m eta: 0:54:19 iter: 71979 total_loss: 0.7206 loss_cls: 0.224 loss_box_reg: 0.2687 loss_rpn_cls: 0.04207 loss_rpn_loc: 0.2059 time: 0.3385 last_time: 0.2231 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:39:54 d2.utils.events]: \u001b[0m eta: 0:54:16 iter: 71999 total_loss: 0.6119 loss_cls: 0.192 loss_box_reg: 0.2617 loss_rpn_cls: 0.04085 loss_rpn_loc: 0.1491 time: 0.3385 last_time: 0.2498 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:39:59 d2.utils.events]: \u001b[0m eta: 0:54:14 iter: 72019 total_loss: 0.866 loss_cls: 0.2596 loss_box_reg: 0.2947 loss_rpn_cls: 0.04248 loss_rpn_loc: 0.1675 time: 0.3385 last_time: 0.2625 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:40:04 d2.utils.events]: \u001b[0m eta: 0:54:09 iter: 72039 total_loss: 0.7636 loss_cls: 0.2529 loss_box_reg: 0.2874 loss_rpn_cls: 0.04228 loss_rpn_loc: 0.1783 time: 0.3384 last_time: 0.2233 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:40:09 d2.utils.events]: \u001b[0m eta: 0:54:02 iter: 72059 total_loss: 0.8452 loss_cls: 0.2662 loss_box_reg: 0.2942 loss_rpn_cls: 0.04597 loss_rpn_loc: 0.1947 time: 0.3384 last_time: 0.2491 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:40:13 d2.utils.events]: \u001b[0m eta: 0:53:54 iter: 72079 total_loss: 0.7508 loss_cls: 0.233 loss_box_reg: 0.2835 loss_rpn_cls: 0.05158 loss_rpn_loc: 0.173 time: 0.3384 last_time: 0.2254 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:40:18 d2.utils.events]: \u001b[0m eta: 0:53:49 iter: 72099 total_loss: 0.7581 loss_cls: 0.245 loss_box_reg: 0.2745 loss_rpn_cls: 0.04076 loss_rpn_loc: 0.1812 time: 0.3383 last_time: 0.2235 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:40:22 d2.utils.events]: \u001b[0m eta: 0:53:38 iter: 72119 total_loss: 0.6477 loss_cls: 0.1572 loss_box_reg: 0.2667 loss_rpn_cls: 0.03938 loss_rpn_loc: 0.1864 time: 0.3383 last_time: 0.1934 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:40:27 d2.utils.events]: \u001b[0m eta: 0:53:29 iter: 72139 total_loss: 0.7084 loss_cls: 0.209 loss_box_reg: 0.2673 loss_rpn_cls: 0.04814 loss_rpn_loc: 0.1766 time: 0.3383 last_time: 0.2349 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:40:32 d2.utils.events]: \u001b[0m eta: 0:53:21 iter: 72159 total_loss: 0.772 loss_cls: 0.2502 loss_box_reg: 0.2826 loss_rpn_cls: 0.04896 loss_rpn_loc: 0.1797 time: 0.3383 last_time: 0.2315 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:40:37 d2.utils.events]: \u001b[0m eta: 0:53:15 iter: 72179 total_loss: 0.6562 loss_cls: 0.2086 loss_box_reg: 0.2515 loss_rpn_cls: 0.04201 loss_rpn_loc: 0.1846 time: 0.3382 last_time: 0.2527 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:40:42 d2.utils.events]: \u001b[0m eta: 0:53:07 iter: 72199 total_loss: 0.7864 loss_cls: 0.2454 loss_box_reg: 0.2926 loss_rpn_cls: 0.04304 loss_rpn_loc: 0.1807 time: 0.3382 last_time: 0.2533 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:40:47 d2.utils.events]: \u001b[0m eta: 0:52:59 iter: 72219 total_loss: 0.7018 loss_cls: 0.179 loss_box_reg: 0.2461 loss_rpn_cls: 0.03233 loss_rpn_loc: 0.2147 time: 0.3382 last_time: 0.1942 data_time: 0.0046 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:40:52 d2.utils.events]: \u001b[0m eta: 0:52:49 iter: 72239 total_loss: 0.7219 loss_cls: 0.3097 loss_box_reg: 0.2852 loss_rpn_cls: 0.03525 loss_rpn_loc: 0.156 time: 0.3382 last_time: 0.2521 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:40:56 d2.utils.events]: \u001b[0m eta: 0:52:40 iter: 72259 total_loss: 0.7009 loss_cls: 0.2075 loss_box_reg: 0.2525 loss_rpn_cls: 0.0335 loss_rpn_loc: 0.1588 time: 0.3381 last_time: 0.2362 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:41:01 d2.utils.events]: \u001b[0m eta: 0:52:32 iter: 72279 total_loss: 0.6678 loss_cls: 0.167 loss_box_reg: 0.2748 loss_rpn_cls: 0.04172 loss_rpn_loc: 0.1694 time: 0.3381 last_time: 0.1985 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:41:06 d2.utils.events]: \u001b[0m eta: 0:52:20 iter: 72299 total_loss: 0.7614 loss_cls: 0.2155 loss_box_reg: 0.2994 loss_rpn_cls: 0.04234 loss_rpn_loc: 0.1865 time: 0.3381 last_time: 0.2319 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:41:12 d2.utils.events]: \u001b[0m eta: 0:52:11 iter: 72319 total_loss: 0.673 loss_cls: 0.2503 loss_box_reg: 0.228 loss_rpn_cls: 0.03323 loss_rpn_loc: 0.1594 time: 0.3381 last_time: 0.2350 data_time: 0.0051 last_data_time: 0.0054 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:41:17 d2.utils.events]: \u001b[0m eta: 0:52:02 iter: 72339 total_loss: 0.7844 loss_cls: 0.2512 loss_box_reg: 0.3172 loss_rpn_cls: 0.03806 loss_rpn_loc: 0.1583 time: 0.3380 last_time: 0.2270 data_time: 0.0051 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:41:23 d2.utils.events]: \u001b[0m eta: 0:51:56 iter: 72359 total_loss: 0.8176 loss_cls: 0.2756 loss_box_reg: 0.2887 loss_rpn_cls: 0.04635 loss_rpn_loc: 0.1939 time: 0.3380 last_time: 0.2945 data_time: 0.0049 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:41:28 d2.utils.events]: \u001b[0m eta: 0:51:50 iter: 72379 total_loss: 0.7739 loss_cls: 0.2811 loss_box_reg: 0.3097 loss_rpn_cls: 0.0367 loss_rpn_loc: 0.1779 time: 0.3380 last_time: 0.2862 data_time: 0.0050 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:41:33 d2.utils.events]: \u001b[0m eta: 0:51:25 iter: 72399 total_loss: 0.7399 loss_cls: 0.2453 loss_box_reg: 0.2921 loss_rpn_cls: 0.04761 loss_rpn_loc: 0.1922 time: 0.3380 last_time: 0.2502 data_time: 0.0049 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:41:38 d2.utils.events]: \u001b[0m eta: 0:51:02 iter: 72419 total_loss: 0.6877 loss_cls: 0.1825 loss_box_reg: 0.2628 loss_rpn_cls: 0.04579 loss_rpn_loc: 0.1711 time: 0.3380 last_time: 0.2103 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:41:43 d2.utils.events]: \u001b[0m eta: 0:49:51 iter: 72439 total_loss: 0.6354 loss_cls: 0.2126 loss_box_reg: 0.2763 loss_rpn_cls: 0.03424 loss_rpn_loc: 0.1552 time: 0.3379 last_time: 0.2253 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:41:47 d2.utils.events]: \u001b[0m eta: 0:49:26 iter: 72459 total_loss: 0.7388 loss_cls: 0.2418 loss_box_reg: 0.3156 loss_rpn_cls: 0.0448 loss_rpn_loc: 0.1823 time: 0.3379 last_time: 0.2355 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:41:52 d2.utils.events]: \u001b[0m eta: 0:49:15 iter: 72479 total_loss: 0.7645 loss_cls: 0.2516 loss_box_reg: 0.284 loss_rpn_cls: 0.03918 loss_rpn_loc: 0.1915 time: 0.3379 last_time: 0.2347 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:41:58 d2.utils.events]: \u001b[0m eta: 0:49:11 iter: 72499 total_loss: 0.7446 loss_cls: 0.243 loss_box_reg: 0.3087 loss_rpn_cls: 0.03823 loss_rpn_loc: 0.1899 time: 0.3379 last_time: 0.3057 data_time: 0.0049 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:42:03 d2.utils.events]: \u001b[0m eta: 0:49:06 iter: 72519 total_loss: 0.7333 loss_cls: 0.209 loss_box_reg: 0.263 loss_rpn_cls: 0.03999 loss_rpn_loc: 0.179 time: 0.3378 last_time: 0.2795 data_time: 0.0049 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:42:09 d2.utils.events]: \u001b[0m eta: 0:48:57 iter: 72539 total_loss: 0.7741 loss_cls: 0.22 loss_box_reg: 0.3177 loss_rpn_cls: 0.0435 loss_rpn_loc: 0.1805 time: 0.3378 last_time: 0.3194 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:42:14 d2.utils.events]: \u001b[0m eta: 0:48:49 iter: 72559 total_loss: 0.8013 loss_cls: 0.2207 loss_box_reg: 0.2992 loss_rpn_cls: 0.0547 loss_rpn_loc: 0.212 time: 0.3378 last_time: 0.1805 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:42:19 d2.utils.events]: \u001b[0m eta: 0:48:44 iter: 72579 total_loss: 0.7266 loss_cls: 0.2353 loss_box_reg: 0.2629 loss_rpn_cls: 0.03363 loss_rpn_loc: 0.196 time: 0.3378 last_time: 0.3200 data_time: 0.0048 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:42:24 d2.utils.events]: \u001b[0m eta: 0:48:41 iter: 72599 total_loss: 0.6736 loss_cls: 0.2288 loss_box_reg: 0.2534 loss_rpn_cls: 0.04121 loss_rpn_loc: 0.1625 time: 0.3377 last_time: 0.2703 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:42:29 d2.utils.events]: \u001b[0m eta: 0:48:42 iter: 72619 total_loss: 0.7474 loss_cls: 0.232 loss_box_reg: 0.2641 loss_rpn_cls: 0.03771 loss_rpn_loc: 0.1643 time: 0.3377 last_time: 0.2511 data_time: 0.0049 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:42:34 d2.utils.events]: \u001b[0m eta: 0:48:39 iter: 72639 total_loss: 0.6478 loss_cls: 0.185 loss_box_reg: 0.2281 loss_rpn_cls: 0.03114 loss_rpn_loc: 0.1743 time: 0.3377 last_time: 0.2104 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:42:40 d2.utils.events]: \u001b[0m eta: 0:48:39 iter: 72659 total_loss: 0.7084 loss_cls: 0.2521 loss_box_reg: 0.2726 loss_rpn_cls: 0.04552 loss_rpn_loc: 0.1529 time: 0.3377 last_time: 0.2868 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:42:45 d2.utils.events]: \u001b[0m eta: 0:48:38 iter: 72679 total_loss: 0.804 loss_cls: 0.265 loss_box_reg: 0.2771 loss_rpn_cls: 0.04328 loss_rpn_loc: 0.1796 time: 0.3377 last_time: 0.2380 data_time: 0.0049 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:42:51 d2.utils.events]: \u001b[0m eta: 0:49:01 iter: 72699 total_loss: 0.7906 loss_cls: 0.248 loss_box_reg: 0.2829 loss_rpn_cls: 0.04047 loss_rpn_loc: 0.1923 time: 0.3377 last_time: 0.3191 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:42:57 d2.utils.events]: \u001b[0m eta: 0:49:59 iter: 72719 total_loss: 0.6811 loss_cls: 0.188 loss_box_reg: 0.2653 loss_rpn_cls: 0.03051 loss_rpn_loc: 0.1725 time: 0.3376 last_time: 0.2984 data_time: 0.0048 last_data_time: 0.0054 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:43:03 d2.utils.events]: \u001b[0m eta: 0:50:24 iter: 72739 total_loss: 0.7534 loss_cls: 0.2353 loss_box_reg: 0.2753 loss_rpn_cls: 0.04822 loss_rpn_loc: 0.1812 time: 0.3376 last_time: 0.3039 data_time: 0.0051 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:43:08 d2.utils.events]: \u001b[0m eta: 0:50:25 iter: 72759 total_loss: 0.7188 loss_cls: 0.2765 loss_box_reg: 0.2697 loss_rpn_cls: 0.04656 loss_rpn_loc: 0.1748 time: 0.3376 last_time: 0.2510 data_time: 0.0047 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:43:12 d2.utils.events]: \u001b[0m eta: 0:50:21 iter: 72779 total_loss: 0.7495 loss_cls: 0.2648 loss_box_reg: 0.2669 loss_rpn_cls: 0.03453 loss_rpn_loc: 0.1747 time: 0.3376 last_time: 0.2477 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:43:17 d2.utils.events]: \u001b[0m eta: 0:50:16 iter: 72799 total_loss: 0.7925 loss_cls: 0.2435 loss_box_reg: 0.2638 loss_rpn_cls: 0.05736 loss_rpn_loc: 0.1965 time: 0.3375 last_time: 0.2495 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:43:21 d2.utils.events]: \u001b[0m eta: 0:50:13 iter: 72819 total_loss: 0.6816 loss_cls: 0.2429 loss_box_reg: 0.2521 loss_rpn_cls: 0.04423 loss_rpn_loc: 0.197 time: 0.3375 last_time: 0.2372 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:43:26 d2.utils.events]: \u001b[0m eta: 0:50:07 iter: 72839 total_loss: 0.7433 loss_cls: 0.2327 loss_box_reg: 0.2544 loss_rpn_cls: 0.03822 loss_rpn_loc: 0.1798 time: 0.3375 last_time: 0.2343 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:43:31 d2.utils.events]: \u001b[0m eta: 0:50:01 iter: 72859 total_loss: 0.7518 loss_cls: 0.2457 loss_box_reg: 0.2756 loss_rpn_cls: 0.03719 loss_rpn_loc: 0.1721 time: 0.3375 last_time: 0.2481 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:43:35 d2.utils.events]: \u001b[0m eta: 0:49:50 iter: 72879 total_loss: 0.7198 loss_cls: 0.2119 loss_box_reg: 0.2693 loss_rpn_cls: 0.03677 loss_rpn_loc: 0.1804 time: 0.3374 last_time: 0.2343 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:43:40 d2.utils.events]: \u001b[0m eta: 0:49:45 iter: 72899 total_loss: 0.7195 loss_cls: 0.2326 loss_box_reg: 0.2726 loss_rpn_cls: 0.04877 loss_rpn_loc: 0.174 time: 0.3374 last_time: 0.2480 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:43:44 d2.utils.events]: \u001b[0m eta: 0:49:37 iter: 72919 total_loss: 0.7252 loss_cls: 0.2413 loss_box_reg: 0.2721 loss_rpn_cls: 0.03646 loss_rpn_loc: 0.1874 time: 0.3374 last_time: 0.2103 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:43:49 d2.utils.events]: \u001b[0m eta: 0:49:37 iter: 72939 total_loss: 0.8496 loss_cls: 0.266 loss_box_reg: 0.2797 loss_rpn_cls: 0.05387 loss_rpn_loc: 0.1986 time: 0.3373 last_time: 0.2109 data_time: 0.0047 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:43:54 d2.utils.events]: \u001b[0m eta: 0:49:36 iter: 72959 total_loss: 0.7166 loss_cls: 0.2388 loss_box_reg: 0.2554 loss_rpn_cls: 0.03808 loss_rpn_loc: 0.1855 time: 0.3373 last_time: 0.2521 data_time: 0.0050 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:44:00 d2.utils.events]: \u001b[0m eta: 0:49:37 iter: 72979 total_loss: 0.7706 loss_cls: 0.21 loss_box_reg: 0.2724 loss_rpn_cls: 0.04641 loss_rpn_loc: 0.1733 time: 0.3373 last_time: 0.2621 data_time: 0.0051 last_data_time: 0.0055 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:44:06 d2.utils.events]: \u001b[0m eta: 0:49:35 iter: 72999 total_loss: 0.7794 loss_cls: 0.267 loss_box_reg: 0.2782 loss_rpn_cls: 0.04005 loss_rpn_loc: 0.1788 time: 0.3373 last_time: 0.2959 data_time: 0.0049 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:44:12 d2.utils.events]: \u001b[0m eta: 0:49:35 iter: 73019 total_loss: 0.7002 loss_cls: 0.245 loss_box_reg: 0.2797 loss_rpn_cls: 0.04517 loss_rpn_loc: 0.1608 time: 0.3373 last_time: 0.3320 data_time: 0.0051 last_data_time: 0.0056 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:44:18 d2.utils.events]: \u001b[0m eta: 0:49:35 iter: 73039 total_loss: 0.6664 loss_cls: 0.1923 loss_box_reg: 0.2514 loss_rpn_cls: 0.05174 loss_rpn_loc: 0.1559 time: 0.3373 last_time: 0.2682 data_time: 0.0050 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:44:23 d2.utils.events]: \u001b[0m eta: 0:49:36 iter: 73059 total_loss: 0.8288 loss_cls: 0.2697 loss_box_reg: 0.3246 loss_rpn_cls: 0.04043 loss_rpn_loc: 0.2034 time: 0.3373 last_time: 0.2772 data_time: 0.0051 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:44:29 d2.utils.events]: \u001b[0m eta: 0:49:34 iter: 73079 total_loss: 0.8041 loss_cls: 0.2774 loss_box_reg: 0.2918 loss_rpn_cls: 0.04524 loss_rpn_loc: 0.1583 time: 0.3372 last_time: 0.2533 data_time: 0.0049 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:44:35 d2.utils.events]: \u001b[0m eta: 0:49:33 iter: 73099 total_loss: 0.9299 loss_cls: 0.3176 loss_box_reg: 0.3393 loss_rpn_cls: 0.05239 loss_rpn_loc: 0.2105 time: 0.3372 last_time: 0.3322 data_time: 0.0051 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:44:40 d2.utils.events]: \u001b[0m eta: 0:49:31 iter: 73119 total_loss: 0.7517 loss_cls: 0.2093 loss_box_reg: 0.2758 loss_rpn_cls: 0.04623 loss_rpn_loc: 0.1951 time: 0.3372 last_time: 0.2753 data_time: 0.0052 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:44:46 d2.utils.events]: \u001b[0m eta: 0:49:29 iter: 73139 total_loss: 0.6972 loss_cls: 0.211 loss_box_reg: 0.2564 loss_rpn_cls: 0.03207 loss_rpn_loc: 0.1726 time: 0.3372 last_time: 0.2070 data_time: 0.0051 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:44:50 d2.utils.events]: \u001b[0m eta: 0:49:23 iter: 73159 total_loss: 0.8253 loss_cls: 0.245 loss_box_reg: 0.3033 loss_rpn_cls: 0.04674 loss_rpn_loc: 0.2071 time: 0.3372 last_time: 0.2493 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:44:55 d2.utils.events]: \u001b[0m eta: 0:49:16 iter: 73179 total_loss: 0.825 loss_cls: 0.2475 loss_box_reg: 0.3016 loss_rpn_cls: 0.04835 loss_rpn_loc: 0.22 time: 0.3371 last_time: 0.2463 data_time: 0.0046 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:45:00 d2.utils.events]: \u001b[0m eta: 0:49:10 iter: 73199 total_loss: 0.8043 loss_cls: 0.2415 loss_box_reg: 0.2828 loss_rpn_cls: 0.04407 loss_rpn_loc: 0.2133 time: 0.3371 last_time: 0.2511 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:45:04 d2.utils.events]: \u001b[0m eta: 0:49:05 iter: 73219 total_loss: 0.6509 loss_cls: 0.2188 loss_box_reg: 0.2212 loss_rpn_cls: 0.03421 loss_rpn_loc: 0.195 time: 0.3371 last_time: 0.2337 data_time: 0.0048 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:45:09 d2.utils.events]: \u001b[0m eta: 0:48:59 iter: 73239 total_loss: 0.7452 loss_cls: 0.2402 loss_box_reg: 0.2878 loss_rpn_cls: 0.03917 loss_rpn_loc: 0.1888 time: 0.3370 last_time: 0.2517 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:45:14 d2.utils.events]: \u001b[0m eta: 0:48:54 iter: 73259 total_loss: 0.897 loss_cls: 0.2459 loss_box_reg: 0.3291 loss_rpn_cls: 0.0588 loss_rpn_loc: 0.211 time: 0.3370 last_time: 0.2497 data_time: 0.0046 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:45:18 d2.utils.events]: \u001b[0m eta: 0:48:48 iter: 73279 total_loss: 0.7787 loss_cls: 0.2339 loss_box_reg: 0.2876 loss_rpn_cls: 0.04174 loss_rpn_loc: 0.1862 time: 0.3370 last_time: 0.2346 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:45:23 d2.utils.events]: \u001b[0m eta: 0:48:43 iter: 73299 total_loss: 0.7433 loss_cls: 0.2509 loss_box_reg: 0.2855 loss_rpn_cls: 0.05151 loss_rpn_loc: 0.186 time: 0.3370 last_time: 0.2326 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:45:28 d2.utils.events]: \u001b[0m eta: 0:48:35 iter: 73319 total_loss: 0.7338 loss_cls: 0.2418 loss_box_reg: 0.2712 loss_rpn_cls: 0.04617 loss_rpn_loc: 0.1627 time: 0.3369 last_time: 0.2114 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:45:32 d2.utils.events]: \u001b[0m eta: 0:48:27 iter: 73339 total_loss: 0.7979 loss_cls: 0.2646 loss_box_reg: 0.2668 loss_rpn_cls: 0.05161 loss_rpn_loc: 0.1761 time: 0.3369 last_time: 0.2502 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:45:37 d2.utils.events]: \u001b[0m eta: 0:48:18 iter: 73359 total_loss: 0.7044 loss_cls: 0.2212 loss_box_reg: 0.26 loss_rpn_cls: 0.04388 loss_rpn_loc: 0.1682 time: 0.3369 last_time: 0.2508 data_time: 0.0045 last_data_time: 0.0041 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:45:42 d2.utils.events]: \u001b[0m eta: 0:48:11 iter: 73379 total_loss: 0.6459 loss_cls: 0.1792 loss_box_reg: 0.2322 loss_rpn_cls: 0.04754 loss_rpn_loc: 0.1775 time: 0.3368 last_time: 0.1963 data_time: 0.0049 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:45:46 d2.utils.events]: \u001b[0m eta: 0:48:04 iter: 73399 total_loss: 0.7245 loss_cls: 0.2251 loss_box_reg: 0.2811 loss_rpn_cls: 0.04031 loss_rpn_loc: 0.1788 time: 0.3368 last_time: 0.2129 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:45:51 d2.utils.events]: \u001b[0m eta: 0:47:58 iter: 73419 total_loss: 0.6979 loss_cls: 0.1972 loss_box_reg: 0.2699 loss_rpn_cls: 0.03817 loss_rpn_loc: 0.1763 time: 0.3368 last_time: 0.2489 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:45:55 d2.utils.events]: \u001b[0m eta: 0:47:54 iter: 73439 total_loss: 0.8312 loss_cls: 0.2493 loss_box_reg: 0.2754 loss_rpn_cls: 0.05234 loss_rpn_loc: 0.2113 time: 0.3368 last_time: 0.2499 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:46:00 d2.utils.events]: \u001b[0m eta: 0:47:50 iter: 73459 total_loss: 0.7227 loss_cls: 0.2109 loss_box_reg: 0.2846 loss_rpn_cls: 0.03772 loss_rpn_loc: 0.1715 time: 0.3367 last_time: 0.2249 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:46:05 d2.utils.events]: \u001b[0m eta: 0:47:45 iter: 73479 total_loss: 0.6802 loss_cls: 0.2145 loss_box_reg: 0.2542 loss_rpn_cls: 0.04354 loss_rpn_loc: 0.1929 time: 0.3367 last_time: 0.2505 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:46:09 d2.utils.events]: \u001b[0m eta: 0:47:36 iter: 73499 total_loss: 0.6845 loss_cls: 0.2205 loss_box_reg: 0.274 loss_rpn_cls: 0.03488 loss_rpn_loc: 0.1696 time: 0.3367 last_time: 0.2487 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:46:14 d2.utils.events]: \u001b[0m eta: 0:47:27 iter: 73519 total_loss: 0.7553 loss_cls: 0.2404 loss_box_reg: 0.2611 loss_rpn_cls: 0.04222 loss_rpn_loc: 0.2013 time: 0.3366 last_time: 0.2525 data_time: 0.0044 last_data_time: 0.0042 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:46:18 d2.utils.events]: \u001b[0m eta: 0:47:19 iter: 73539 total_loss: 0.8972 loss_cls: 0.2846 loss_box_reg: 0.3588 loss_rpn_cls: 0.04325 loss_rpn_loc: 0.2251 time: 0.3366 last_time: 0.2347 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:46:23 d2.utils.events]: \u001b[0m eta: 0:47:13 iter: 73559 total_loss: 0.8614 loss_cls: 0.2746 loss_box_reg: 0.3238 loss_rpn_cls: 0.0501 loss_rpn_loc: 0.1925 time: 0.3366 last_time: 0.2367 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:46:28 d2.utils.events]: \u001b[0m eta: 0:47:03 iter: 73579 total_loss: 0.6615 loss_cls: 0.2079 loss_box_reg: 0.256 loss_rpn_cls: 0.03885 loss_rpn_loc: 0.1697 time: 0.3366 last_time: 0.2490 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:46:32 d2.utils.events]: \u001b[0m eta: 0:46:56 iter: 73599 total_loss: 0.7515 loss_cls: 0.2145 loss_box_reg: 0.271 loss_rpn_cls: 0.04979 loss_rpn_loc: 0.1964 time: 0.3365 last_time: 0.2512 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:46:37 d2.utils.events]: \u001b[0m eta: 0:46:25 iter: 73619 total_loss: 0.6606 loss_cls: 0.1822 loss_box_reg: 0.2812 loss_rpn_cls: 0.04012 loss_rpn_loc: 0.1531 time: 0.3365 last_time: 0.2493 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:46:41 d2.utils.events]: \u001b[0m eta: 0:45:39 iter: 73639 total_loss: 0.7789 loss_cls: 0.2798 loss_box_reg: 0.2759 loss_rpn_cls: 0.05608 loss_rpn_loc: 0.1663 time: 0.3365 last_time: 0.1816 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:46:46 d2.utils.events]: \u001b[0m eta: 0:44:43 iter: 73659 total_loss: 0.8517 loss_cls: 0.268 loss_box_reg: 0.3131 loss_rpn_cls: 0.04011 loss_rpn_loc: 0.1823 time: 0.3364 last_time: 0.2078 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:46:51 d2.utils.events]: \u001b[0m eta: 0:44:37 iter: 73679 total_loss: 0.7955 loss_cls: 0.2346 loss_box_reg: 0.2738 loss_rpn_cls: 0.04995 loss_rpn_loc: 0.2136 time: 0.3364 last_time: 0.2488 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:46:55 d2.utils.events]: \u001b[0m eta: 0:44:27 iter: 73699 total_loss: 0.6401 loss_cls: 0.2182 loss_box_reg: 0.2328 loss_rpn_cls: 0.03647 loss_rpn_loc: 0.174 time: 0.3364 last_time: 0.2106 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:47:00 d2.utils.events]: \u001b[0m eta: 0:44:18 iter: 73719 total_loss: 0.7103 loss_cls: 0.2294 loss_box_reg: 0.2716 loss_rpn_cls: 0.04699 loss_rpn_loc: 0.1813 time: 0.3364 last_time: 0.2512 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:47:04 d2.utils.events]: \u001b[0m eta: 0:44:10 iter: 73739 total_loss: 0.7722 loss_cls: 0.2184 loss_box_reg: 0.294 loss_rpn_cls: 0.0492 loss_rpn_loc: 0.1975 time: 0.3363 last_time: 0.1952 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:47:09 d2.utils.events]: \u001b[0m eta: 0:44:05 iter: 73759 total_loss: 0.8332 loss_cls: 0.2586 loss_box_reg: 0.2888 loss_rpn_cls: 0.05762 loss_rpn_loc: 0.2097 time: 0.3363 last_time: 0.2348 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:47:13 d2.utils.events]: \u001b[0m eta: 0:44:00 iter: 73779 total_loss: 0.7778 loss_cls: 0.265 loss_box_reg: 0.2894 loss_rpn_cls: 0.04663 loss_rpn_loc: 0.1589 time: 0.3363 last_time: 0.2344 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:47:18 d2.utils.events]: \u001b[0m eta: 0:43:56 iter: 73799 total_loss: 0.6885 loss_cls: 0.2095 loss_box_reg: 0.2543 loss_rpn_cls: 0.03995 loss_rpn_loc: 0.1668 time: 0.3362 last_time: 0.1940 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:47:22 d2.utils.events]: \u001b[0m eta: 0:43:49 iter: 73819 total_loss: 0.7885 loss_cls: 0.2626 loss_box_reg: 0.2491 loss_rpn_cls: 0.0426 loss_rpn_loc: 0.1845 time: 0.3362 last_time: 0.2235 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:47:27 d2.utils.events]: \u001b[0m eta: 0:43:43 iter: 73839 total_loss: 0.7182 loss_cls: 0.2231 loss_box_reg: 0.245 loss_rpn_cls: 0.04552 loss_rpn_loc: 0.1847 time: 0.3362 last_time: 0.2349 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:47:32 d2.utils.events]: \u001b[0m eta: 0:43:39 iter: 73859 total_loss: 0.7396 loss_cls: 0.2058 loss_box_reg: 0.277 loss_rpn_cls: 0.04241 loss_rpn_loc: 0.1735 time: 0.3361 last_time: 0.1980 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:47:36 d2.utils.events]: \u001b[0m eta: 0:43:35 iter: 73879 total_loss: 0.7651 loss_cls: 0.258 loss_box_reg: 0.2904 loss_rpn_cls: 0.05737 loss_rpn_loc: 0.1873 time: 0.3361 last_time: 0.2350 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:47:41 d2.utils.events]: \u001b[0m eta: 0:43:30 iter: 73899 total_loss: 0.7578 loss_cls: 0.2405 loss_box_reg: 0.2624 loss_rpn_cls: 0.03851 loss_rpn_loc: 0.1861 time: 0.3361 last_time: 0.2093 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:47:45 d2.utils.events]: \u001b[0m eta: 0:43:27 iter: 73919 total_loss: 0.7786 loss_cls: 0.2656 loss_box_reg: 0.2854 loss_rpn_cls: 0.05015 loss_rpn_loc: 0.2073 time: 0.3361 last_time: 0.2488 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:47:50 d2.utils.events]: \u001b[0m eta: 0:43:22 iter: 73939 total_loss: 0.7846 loss_cls: 0.2549 loss_box_reg: 0.2892 loss_rpn_cls: 0.05522 loss_rpn_loc: 0.196 time: 0.3360 last_time: 0.2492 data_time: 0.0045 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:47:55 d2.utils.events]: \u001b[0m eta: 0:43:15 iter: 73959 total_loss: 0.7122 loss_cls: 0.2294 loss_box_reg: 0.2814 loss_rpn_cls: 0.03201 loss_rpn_loc: 0.1732 time: 0.3360 last_time: 0.2363 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:47:59 d2.utils.events]: \u001b[0m eta: 0:43:10 iter: 73979 total_loss: 0.8385 loss_cls: 0.2739 loss_box_reg: 0.2859 loss_rpn_cls: 0.05503 loss_rpn_loc: 0.1883 time: 0.3360 last_time: 0.2362 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:48:04 d2.utils.events]: \u001b[0m eta: 0:43:03 iter: 73999 total_loss: 0.77 loss_cls: 0.2432 loss_box_reg: 0.2644 loss_rpn_cls: 0.05093 loss_rpn_loc: 0.1898 time: 0.3359 last_time: 0.2502 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:48:09 d2.utils.events]: \u001b[0m eta: 0:42:58 iter: 74019 total_loss: 0.7297 loss_cls: 0.2107 loss_box_reg: 0.2657 loss_rpn_cls: 0.05171 loss_rpn_loc: 0.179 time: 0.3359 last_time: 0.2509 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:48:13 d2.utils.events]: \u001b[0m eta: 0:42:52 iter: 74039 total_loss: 0.7385 loss_cls: 0.2153 loss_box_reg: 0.2887 loss_rpn_cls: 0.03864 loss_rpn_loc: 0.1847 time: 0.3359 last_time: 0.2106 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:48:18 d2.utils.events]: \u001b[0m eta: 0:42:47 iter: 74059 total_loss: 0.8368 loss_cls: 0.2864 loss_box_reg: 0.2847 loss_rpn_cls: 0.04105 loss_rpn_loc: 0.2267 time: 0.3359 last_time: 0.2367 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:48:23 d2.utils.events]: \u001b[0m eta: 0:42:40 iter: 74079 total_loss: 0.6792 loss_cls: 0.2046 loss_box_reg: 0.2878 loss_rpn_cls: 0.03761 loss_rpn_loc: 0.1906 time: 0.3358 last_time: 0.2224 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:48:27 d2.utils.events]: \u001b[0m eta: 0:42:34 iter: 74099 total_loss: 0.7445 loss_cls: 0.2346 loss_box_reg: 0.2846 loss_rpn_cls: 0.04829 loss_rpn_loc: 0.19 time: 0.3358 last_time: 0.2231 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:48:32 d2.utils.events]: \u001b[0m eta: 0:42:27 iter: 74119 total_loss: 0.7239 loss_cls: 0.2281 loss_box_reg: 0.2451 loss_rpn_cls: 0.04423 loss_rpn_loc: 0.1623 time: 0.3358 last_time: 0.2353 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:48:36 d2.utils.events]: \u001b[0m eta: 0:42:21 iter: 74139 total_loss: 0.7868 loss_cls: 0.2585 loss_box_reg: 0.3072 loss_rpn_cls: 0.0403 loss_rpn_loc: 0.1669 time: 0.3358 last_time: 0.2467 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:48:41 d2.utils.events]: \u001b[0m eta: 0:42:15 iter: 74159 total_loss: 0.7554 loss_cls: 0.1903 loss_box_reg: 0.2914 loss_rpn_cls: 0.04217 loss_rpn_loc: 0.203 time: 0.3357 last_time: 0.2559 data_time: 0.0049 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:48:46 d2.utils.events]: \u001b[0m eta: 0:42:10 iter: 74179 total_loss: 0.7853 loss_cls: 0.2137 loss_box_reg: 0.2853 loss_rpn_cls: 0.04764 loss_rpn_loc: 0.1943 time: 0.3357 last_time: 0.2144 data_time: 0.0048 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:48:51 d2.utils.events]: \u001b[0m eta: 0:42:07 iter: 74199 total_loss: 0.7411 loss_cls: 0.2234 loss_box_reg: 0.2885 loss_rpn_cls: 0.04136 loss_rpn_loc: 0.1953 time: 0.3357 last_time: 0.2454 data_time: 0.0049 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:48:56 d2.utils.events]: \u001b[0m eta: 0:42:02 iter: 74219 total_loss: 0.7678 loss_cls: 0.247 loss_box_reg: 0.2695 loss_rpn_cls: 0.05017 loss_rpn_loc: 0.1996 time: 0.3356 last_time: 0.2453 data_time: 0.0047 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:49:00 d2.utils.events]: \u001b[0m eta: 0:41:58 iter: 74239 total_loss: 0.6549 loss_cls: 0.1855 loss_box_reg: 0.2398 loss_rpn_cls: 0.04286 loss_rpn_loc: 0.1746 time: 0.3356 last_time: 0.2517 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:49:05 d2.utils.events]: \u001b[0m eta: 0:41:53 iter: 74259 total_loss: 0.7996 loss_cls: 0.2685 loss_box_reg: 0.2737 loss_rpn_cls: 0.04947 loss_rpn_loc: 0.2237 time: 0.3356 last_time: 0.2258 data_time: 0.0048 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:49:10 d2.utils.events]: \u001b[0m eta: 0:41:47 iter: 74279 total_loss: 0.6672 loss_cls: 0.2021 loss_box_reg: 0.2506 loss_rpn_cls: 0.04764 loss_rpn_loc: 0.155 time: 0.3356 last_time: 0.2292 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:49:14 d2.utils.events]: \u001b[0m eta: 0:41:42 iter: 74299 total_loss: 0.7313 loss_cls: 0.2135 loss_box_reg: 0.2541 loss_rpn_cls: 0.04335 loss_rpn_loc: 0.1992 time: 0.3355 last_time: 0.2494 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:49:19 d2.utils.events]: \u001b[0m eta: 0:41:39 iter: 74319 total_loss: 0.7869 loss_cls: 0.2501 loss_box_reg: 0.2732 loss_rpn_cls: 0.05321 loss_rpn_loc: 0.1894 time: 0.3355 last_time: 0.2121 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:49:24 d2.utils.events]: \u001b[0m eta: 0:41:35 iter: 74339 total_loss: 0.8313 loss_cls: 0.2523 loss_box_reg: 0.2775 loss_rpn_cls: 0.0397 loss_rpn_loc: 0.1972 time: 0.3355 last_time: 0.2265 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:49:28 d2.utils.events]: \u001b[0m eta: 0:41:32 iter: 74359 total_loss: 0.7596 loss_cls: 0.24 loss_box_reg: 0.2955 loss_rpn_cls: 0.04381 loss_rpn_loc: 0.1863 time: 0.3355 last_time: 0.2572 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:49:33 d2.utils.events]: \u001b[0m eta: 0:41:27 iter: 74379 total_loss: 0.8202 loss_cls: 0.2363 loss_box_reg: 0.2916 loss_rpn_cls: 0.05559 loss_rpn_loc: 0.1949 time: 0.3354 last_time: 0.2418 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:49:38 d2.utils.events]: \u001b[0m eta: 0:41:23 iter: 74399 total_loss: 0.6754 loss_cls: 0.1842 loss_box_reg: 0.2641 loss_rpn_cls: 0.04578 loss_rpn_loc: 0.1719 time: 0.3354 last_time: 0.2302 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:49:43 d2.utils.events]: \u001b[0m eta: 0:41:17 iter: 74419 total_loss: 0.7877 loss_cls: 0.2315 loss_box_reg: 0.2535 loss_rpn_cls: 0.05383 loss_rpn_loc: 0.1927 time: 0.3354 last_time: 0.2148 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:49:47 d2.utils.events]: \u001b[0m eta: 0:41:13 iter: 74439 total_loss: 0.6854 loss_cls: 0.2326 loss_box_reg: 0.266 loss_rpn_cls: 0.03424 loss_rpn_loc: 0.1691 time: 0.3353 last_time: 0.2570 data_time: 0.0045 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:49:52 d2.utils.events]: \u001b[0m eta: 0:41:09 iter: 74459 total_loss: 0.7092 loss_cls: 0.1886 loss_box_reg: 0.2759 loss_rpn_cls: 0.05416 loss_rpn_loc: 0.1545 time: 0.3353 last_time: 0.2573 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:49:57 d2.utils.events]: \u001b[0m eta: 0:41:05 iter: 74479 total_loss: 0.8624 loss_cls: 0.287 loss_box_reg: 0.3286 loss_rpn_cls: 0.05824 loss_rpn_loc: 0.1949 time: 0.3353 last_time: 0.2583 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:50:02 d2.utils.events]: \u001b[0m eta: 0:41:01 iter: 74499 total_loss: 0.8881 loss_cls: 0.2856 loss_box_reg: 0.2907 loss_rpn_cls: 0.05737 loss_rpn_loc: 0.1988 time: 0.3353 last_time: 0.2547 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:50:06 d2.utils.events]: \u001b[0m eta: 0:40:55 iter: 74519 total_loss: 0.7732 loss_cls: 0.237 loss_box_reg: 0.2595 loss_rpn_cls: 0.03955 loss_rpn_loc: 0.1762 time: 0.3352 last_time: 0.1861 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:50:11 d2.utils.events]: \u001b[0m eta: 0:40:51 iter: 74539 total_loss: 0.7415 loss_cls: 0.2373 loss_box_reg: 0.2649 loss_rpn_cls: 0.04793 loss_rpn_loc: 0.1858 time: 0.3352 last_time: 0.2298 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:50:16 d2.utils.events]: \u001b[0m eta: 0:40:48 iter: 74559 total_loss: 0.6556 loss_cls: 0.1901 loss_box_reg: 0.2682 loss_rpn_cls: 0.03313 loss_rpn_loc: 0.1568 time: 0.3352 last_time: 0.2014 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:50:21 d2.utils.events]: \u001b[0m eta: 0:40:44 iter: 74579 total_loss: 0.6636 loss_cls: 0.2213 loss_box_reg: 0.2478 loss_rpn_cls: 0.02774 loss_rpn_loc: 0.1578 time: 0.3352 last_time: 0.2433 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:50:26 d2.utils.events]: \u001b[0m eta: 0:40:41 iter: 74599 total_loss: 0.695 loss_cls: 0.2332 loss_box_reg: 0.2913 loss_rpn_cls: 0.04829 loss_rpn_loc: 0.1647 time: 0.3351 last_time: 0.2586 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:50:30 d2.utils.events]: \u001b[0m eta: 0:40:36 iter: 74619 total_loss: 0.877 loss_cls: 0.2602 loss_box_reg: 0.2917 loss_rpn_cls: 0.05645 loss_rpn_loc: 0.2353 time: 0.3351 last_time: 0.2589 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:50:35 d2.utils.events]: \u001b[0m eta: 0:40:31 iter: 74639 total_loss: 0.7542 loss_cls: 0.2313 loss_box_reg: 0.2633 loss_rpn_cls: 0.05407 loss_rpn_loc: 0.2029 time: 0.3351 last_time: 0.2280 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:50:40 d2.utils.events]: \u001b[0m eta: 0:40:27 iter: 74659 total_loss: 0.7743 loss_cls: 0.236 loss_box_reg: 0.2905 loss_rpn_cls: 0.04561 loss_rpn_loc: 0.1738 time: 0.3351 last_time: 0.2309 data_time: 0.0047 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:50:45 d2.utils.events]: \u001b[0m eta: 0:40:22 iter: 74679 total_loss: 0.7382 loss_cls: 0.2054 loss_box_reg: 0.2908 loss_rpn_cls: 0.04997 loss_rpn_loc: 0.1987 time: 0.3350 last_time: 0.2559 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:50:49 d2.utils.events]: \u001b[0m eta: 0:40:18 iter: 74699 total_loss: 0.8117 loss_cls: 0.2633 loss_box_reg: 0.2932 loss_rpn_cls: 0.04282 loss_rpn_loc: 0.2182 time: 0.3350 last_time: 0.2270 data_time: 0.0046 last_data_time: 0.0041 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:50:54 d2.utils.events]: \u001b[0m eta: 0:40:15 iter: 74719 total_loss: 0.7152 loss_cls: 0.2244 loss_box_reg: 0.2621 loss_rpn_cls: 0.04054 loss_rpn_loc: 0.147 time: 0.3350 last_time: 0.2551 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:50:59 d2.utils.events]: \u001b[0m eta: 0:40:11 iter: 74739 total_loss: 0.7674 loss_cls: 0.2429 loss_box_reg: 0.2675 loss_rpn_cls: 0.04155 loss_rpn_loc: 0.1807 time: 0.3350 last_time: 0.2558 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:51:04 d2.utils.events]: \u001b[0m eta: 0:40:10 iter: 74759 total_loss: 0.7231 loss_cls: 0.2325 loss_box_reg: 0.2815 loss_rpn_cls: 0.03117 loss_rpn_loc: 0.1846 time: 0.3349 last_time: 0.2402 data_time: 0.0048 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:51:09 d2.utils.events]: \u001b[0m eta: 0:40:07 iter: 74779 total_loss: 0.7858 loss_cls: 0.2572 loss_box_reg: 0.2914 loss_rpn_cls: 0.04033 loss_rpn_loc: 0.1802 time: 0.3349 last_time: 0.2623 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:51:14 d2.utils.events]: \u001b[0m eta: 0:40:04 iter: 74799 total_loss: 0.7566 loss_cls: 0.2449 loss_box_reg: 0.2493 loss_rpn_cls: 0.03504 loss_rpn_loc: 0.1928 time: 0.3349 last_time: 0.2253 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:51:18 d2.utils.events]: \u001b[0m eta: 0:40:00 iter: 74819 total_loss: 0.8056 loss_cls: 0.2478 loss_box_reg: 0.2745 loss_rpn_cls: 0.04741 loss_rpn_loc: 0.1999 time: 0.3349 last_time: 0.2030 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:51:23 d2.utils.events]: \u001b[0m eta: 0:39:55 iter: 74839 total_loss: 0.7801 loss_cls: 0.2452 loss_box_reg: 0.2879 loss_rpn_cls: 0.05474 loss_rpn_loc: 0.1907 time: 0.3348 last_time: 0.2005 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:51:27 d2.utils.events]: \u001b[0m eta: 0:39:52 iter: 74859 total_loss: 0.8089 loss_cls: 0.2642 loss_box_reg: 0.2818 loss_rpn_cls: 0.04737 loss_rpn_loc: 0.17 time: 0.3348 last_time: 0.2308 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:51:32 d2.utils.events]: \u001b[0m eta: 0:39:52 iter: 74879 total_loss: 0.8364 loss_cls: 0.2341 loss_box_reg: 0.3168 loss_rpn_cls: 0.04305 loss_rpn_loc: 0.1942 time: 0.3348 last_time: 0.2568 data_time: 0.0047 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:51:37 d2.utils.events]: \u001b[0m eta: 0:39:50 iter: 74899 total_loss: 0.7242 loss_cls: 0.2267 loss_box_reg: 0.2748 loss_rpn_cls: 0.03917 loss_rpn_loc: 0.172 time: 0.3347 last_time: 0.2175 data_time: 0.0046 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:51:42 d2.utils.events]: \u001b[0m eta: 0:39:51 iter: 74919 total_loss: 0.7922 loss_cls: 0.2449 loss_box_reg: 0.2769 loss_rpn_cls: 0.04495 loss_rpn_loc: 0.1829 time: 0.3347 last_time: 0.2315 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:51:46 d2.utils.events]: \u001b[0m eta: 0:39:49 iter: 74939 total_loss: 0.7038 loss_cls: 0.2059 loss_box_reg: 0.2479 loss_rpn_cls: 0.05034 loss_rpn_loc: 0.179 time: 0.3347 last_time: 0.1891 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:51:51 d2.utils.events]: \u001b[0m eta: 0:39:51 iter: 74959 total_loss: 0.6649 loss_cls: 0.2121 loss_box_reg: 0.2676 loss_rpn_cls: 0.04076 loss_rpn_loc: 0.1629 time: 0.3347 last_time: 0.2581 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:51:56 d2.utils.events]: \u001b[0m eta: 0:39:54 iter: 74979 total_loss: 0.8159 loss_cls: 0.2686 loss_box_reg: 0.287 loss_rpn_cls: 0.04315 loss_rpn_loc: 0.2013 time: 0.3346 last_time: 0.2709 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:52:02 d2.utils.events]: \u001b[0m eta: 0:39:53 iter: 74999 total_loss: 0.7479 loss_cls: 0.2608 loss_box_reg: 0.2447 loss_rpn_cls: 0.04574 loss_rpn_loc: 0.1999 time: 0.3346 last_time: 0.2322 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:52:06 d2.utils.events]: \u001b[0m eta: 0:39:50 iter: 75019 total_loss: 0.6838 loss_cls: 0.2211 loss_box_reg: 0.2629 loss_rpn_cls: 0.0409 loss_rpn_loc: 0.1974 time: 0.3346 last_time: 0.2559 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:52:11 d2.utils.events]: \u001b[0m eta: 0:39:46 iter: 75039 total_loss: 0.7923 loss_cls: 0.2643 loss_box_reg: 0.2869 loss_rpn_cls: 0.0511 loss_rpn_loc: 0.1802 time: 0.3346 last_time: 0.3264 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:52:16 d2.utils.events]: \u001b[0m eta: 0:39:42 iter: 75059 total_loss: 0.7353 loss_cls: 0.2378 loss_box_reg: 0.3128 loss_rpn_cls: 0.0501 loss_rpn_loc: 0.1994 time: 0.3345 last_time: 0.2580 data_time: 0.0047 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:52:21 d2.utils.events]: \u001b[0m eta: 0:39:37 iter: 75079 total_loss: 0.6646 loss_cls: 0.2231 loss_box_reg: 0.2426 loss_rpn_cls: 0.0471 loss_rpn_loc: 0.1446 time: 0.3345 last_time: 0.2585 data_time: 0.0046 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:52:26 d2.utils.events]: \u001b[0m eta: 0:39:32 iter: 75099 total_loss: 0.7827 loss_cls: 0.2501 loss_box_reg: 0.2686 loss_rpn_cls: 0.05087 loss_rpn_loc: 0.2033 time: 0.3345 last_time: 0.2449 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:52:31 d2.utils.events]: \u001b[0m eta: 0:39:32 iter: 75119 total_loss: 0.792 loss_cls: 0.2457 loss_box_reg: 0.3 loss_rpn_cls: 0.03727 loss_rpn_loc: 0.188 time: 0.3345 last_time: 0.2451 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:52:35 d2.utils.events]: \u001b[0m eta: 0:39:31 iter: 75139 total_loss: 0.7726 loss_cls: 0.2395 loss_box_reg: 0.2816 loss_rpn_cls: 0.04826 loss_rpn_loc: 0.1941 time: 0.3344 last_time: 0.2375 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:52:40 d2.utils.events]: \u001b[0m eta: 0:39:27 iter: 75159 total_loss: 0.7792 loss_cls: 0.2457 loss_box_reg: 0.2689 loss_rpn_cls: 0.05163 loss_rpn_loc: 0.1767 time: 0.3344 last_time: 0.2427 data_time: 0.0047 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:52:45 d2.utils.events]: \u001b[0m eta: 0:39:24 iter: 75179 total_loss: 0.8354 loss_cls: 0.2599 loss_box_reg: 0.3186 loss_rpn_cls: 0.06411 loss_rpn_loc: 0.1862 time: 0.3344 last_time: 0.2329 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:52:50 d2.utils.events]: \u001b[0m eta: 0:39:19 iter: 75199 total_loss: 0.7474 loss_cls: 0.2195 loss_box_reg: 0.2633 loss_rpn_cls: 0.04077 loss_rpn_loc: 0.1845 time: 0.3344 last_time: 0.2305 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:52:55 d2.utils.events]: \u001b[0m eta: 0:39:14 iter: 75219 total_loss: 0.7587 loss_cls: 0.2592 loss_box_reg: 0.2751 loss_rpn_cls: 0.03727 loss_rpn_loc: 0.1748 time: 0.3343 last_time: 0.2773 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:53:00 d2.utils.events]: \u001b[0m eta: 0:39:10 iter: 75239 total_loss: 0.8582 loss_cls: 0.26 loss_box_reg: 0.2955 loss_rpn_cls: 0.05467 loss_rpn_loc: 0.2039 time: 0.3343 last_time: 0.2574 data_time: 0.0046 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:53:05 d2.utils.events]: \u001b[0m eta: 0:39:06 iter: 75259 total_loss: 0.7982 loss_cls: 0.2519 loss_box_reg: 0.3056 loss_rpn_cls: 0.04153 loss_rpn_loc: 0.194 time: 0.3343 last_time: 0.2004 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:53:10 d2.utils.events]: \u001b[0m eta: 0:39:02 iter: 75279 total_loss: 0.7381 loss_cls: 0.2408 loss_box_reg: 0.2472 loss_rpn_cls: 0.04392 loss_rpn_loc: 0.1793 time: 0.3343 last_time: 0.2653 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:53:14 d2.utils.events]: \u001b[0m eta: 0:38:57 iter: 75299 total_loss: 0.8404 loss_cls: 0.2612 loss_box_reg: 0.3148 loss_rpn_cls: 0.06741 loss_rpn_loc: 0.1932 time: 0.3343 last_time: 0.2447 data_time: 0.0047 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:53:19 d2.utils.events]: \u001b[0m eta: 0:38:54 iter: 75319 total_loss: 0.7421 loss_cls: 0.2166 loss_box_reg: 0.2797 loss_rpn_cls: 0.04467 loss_rpn_loc: 0.1948 time: 0.3342 last_time: 0.2575 data_time: 0.0049 last_data_time: 0.0060 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:53:24 d2.utils.events]: \u001b[0m eta: 0:38:50 iter: 75339 total_loss: 0.7513 loss_cls: 0.2263 loss_box_reg: 0.2673 loss_rpn_cls: 0.05037 loss_rpn_loc: 0.1975 time: 0.3342 last_time: 0.2596 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:53:29 d2.utils.events]: \u001b[0m eta: 0:38:45 iter: 75359 total_loss: 0.7492 loss_cls: 0.2364 loss_box_reg: 0.2935 loss_rpn_cls: 0.04533 loss_rpn_loc: 0.1889 time: 0.3342 last_time: 0.2731 data_time: 0.0048 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:53:34 d2.utils.events]: \u001b[0m eta: 0:38:42 iter: 75379 total_loss: 0.798 loss_cls: 0.2383 loss_box_reg: 0.2755 loss_rpn_cls: 0.04041 loss_rpn_loc: 0.1813 time: 0.3342 last_time: 0.2821 data_time: 0.0050 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:53:40 d2.utils.events]: \u001b[0m eta: 0:38:39 iter: 75399 total_loss: 0.7968 loss_cls: 0.2104 loss_box_reg: 0.2732 loss_rpn_cls: 0.04611 loss_rpn_loc: 0.1928 time: 0.3341 last_time: 0.2566 data_time: 0.0051 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:53:45 d2.utils.events]: \u001b[0m eta: 0:38:37 iter: 75419 total_loss: 0.7019 loss_cls: 0.2138 loss_box_reg: 0.2815 loss_rpn_cls: 0.04385 loss_rpn_loc: 0.1982 time: 0.3341 last_time: 0.3004 data_time: 0.0051 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:53:52 d2.utils.events]: \u001b[0m eta: 0:38:34 iter: 75439 total_loss: 0.7561 loss_cls: 0.1997 loss_box_reg: 0.3019 loss_rpn_cls: 0.04546 loss_rpn_loc: 0.1747 time: 0.3341 last_time: 0.3384 data_time: 0.0054 last_data_time: 0.0056 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:53:57 d2.utils.events]: \u001b[0m eta: 0:38:31 iter: 75459 total_loss: 0.6242 loss_cls: 0.1855 loss_box_reg: 0.2696 loss_rpn_cls: 0.02957 loss_rpn_loc: 0.1606 time: 0.3341 last_time: 0.2998 data_time: 0.0052 last_data_time: 0.0060 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:54:03 d2.utils.events]: \u001b[0m eta: 0:38:27 iter: 75479 total_loss: 0.8848 loss_cls: 0.2752 loss_box_reg: 0.3145 loss_rpn_cls: 0.03759 loss_rpn_loc: 0.2093 time: 0.3341 last_time: 0.3382 data_time: 0.0054 last_data_time: 0.0053 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:54:10 d2.utils.events]: \u001b[0m eta: 0:38:24 iter: 75499 total_loss: 0.8701 loss_cls: 0.2578 loss_box_reg: 0.3068 loss_rpn_cls: 0.06146 loss_rpn_loc: 0.1848 time: 0.3341 last_time: 0.2915 data_time: 0.0054 last_data_time: 0.0055 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:54:15 d2.utils.events]: \u001b[0m eta: 0:38:25 iter: 75519 total_loss: 0.8128 loss_cls: 0.2303 loss_box_reg: 0.3137 loss_rpn_cls: 0.04904 loss_rpn_loc: 0.2129 time: 0.3341 last_time: 0.2288 data_time: 0.0051 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:54:22 d2.utils.events]: \u001b[0m eta: 0:38:23 iter: 75539 total_loss: 0.7351 loss_cls: 0.2316 loss_box_reg: 0.2659 loss_rpn_cls: 0.03654 loss_rpn_loc: 0.1506 time: 0.3341 last_time: 0.3065 data_time: 0.0051 last_data_time: 0.0053 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:54:27 d2.utils.events]: \u001b[0m eta: 0:38:25 iter: 75559 total_loss: 0.6658 loss_cls: 0.2183 loss_box_reg: 0.2642 loss_rpn_cls: 0.03991 loss_rpn_loc: 0.1912 time: 0.3341 last_time: 0.3268 data_time: 0.0050 last_data_time: 0.0061 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:54:33 d2.utils.events]: \u001b[0m eta: 0:38:27 iter: 75579 total_loss: 0.728 loss_cls: 0.1951 loss_box_reg: 0.2358 loss_rpn_cls: 0.04414 loss_rpn_loc: 0.1796 time: 0.3341 last_time: 0.3145 data_time: 0.0052 last_data_time: 0.0056 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:54:39 d2.utils.events]: \u001b[0m eta: 0:38:23 iter: 75599 total_loss: 0.7118 loss_cls: 0.2144 loss_box_reg: 0.2992 loss_rpn_cls: 0.03214 loss_rpn_loc: 0.1892 time: 0.3340 last_time: 0.1980 data_time: 0.0051 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:54:44 d2.utils.events]: \u001b[0m eta: 0:38:19 iter: 75619 total_loss: 0.7893 loss_cls: 0.241 loss_box_reg: 0.2709 loss_rpn_cls: 0.03701 loss_rpn_loc: 0.2203 time: 0.3340 last_time: 0.2420 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:54:49 d2.utils.events]: \u001b[0m eta: 0:38:23 iter: 75639 total_loss: 0.739 loss_cls: 0.2419 loss_box_reg: 0.2525 loss_rpn_cls: 0.05275 loss_rpn_loc: 0.1673 time: 0.3340 last_time: 0.2244 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:54:53 d2.utils.events]: \u001b[0m eta: 0:38:08 iter: 75659 total_loss: 0.7333 loss_cls: 0.2291 loss_box_reg: 0.2644 loss_rpn_cls: 0.03865 loss_rpn_loc: 0.1648 time: 0.3340 last_time: 0.2413 data_time: 0.0046 last_data_time: 0.0041 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:54:58 d2.utils.events]: \u001b[0m eta: 0:38:03 iter: 75679 total_loss: 0.7464 loss_cls: 0.232 loss_box_reg: 0.2877 loss_rpn_cls: 0.04609 loss_rpn_loc: 0.1881 time: 0.3339 last_time: 0.2016 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:55:03 d2.utils.events]: \u001b[0m eta: 0:37:59 iter: 75699 total_loss: 0.7726 loss_cls: 0.2297 loss_box_reg: 0.2613 loss_rpn_cls: 0.03698 loss_rpn_loc: 0.1986 time: 0.3339 last_time: 0.2391 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:55:08 d2.utils.events]: \u001b[0m eta: 0:37:53 iter: 75719 total_loss: 0.7939 loss_cls: 0.2533 loss_box_reg: 0.308 loss_rpn_cls: 0.03426 loss_rpn_loc: 0.1819 time: 0.3339 last_time: 0.2405 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:55:13 d2.utils.events]: \u001b[0m eta: 0:38:10 iter: 75739 total_loss: 0.6229 loss_cls: 0.1884 loss_box_reg: 0.2314 loss_rpn_cls: 0.03069 loss_rpn_loc: 0.177 time: 0.3339 last_time: 0.3012 data_time: 0.0052 last_data_time: 0.0053 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:55:19 d2.utils.events]: \u001b[0m eta: 0:38:29 iter: 75759 total_loss: 0.7708 loss_cls: 0.2455 loss_box_reg: 0.2996 loss_rpn_cls: 0.03042 loss_rpn_loc: 0.1902 time: 0.3339 last_time: 0.2021 data_time: 0.0051 last_data_time: 0.0053 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:55:24 d2.utils.events]: \u001b[0m eta: 0:38:42 iter: 75779 total_loss: 0.7418 loss_cls: 0.1955 loss_box_reg: 0.2737 loss_rpn_cls: 0.04634 loss_rpn_loc: 0.1743 time: 0.3338 last_time: 0.2160 data_time: 0.0050 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:55:29 d2.utils.events]: \u001b[0m eta: 0:38:34 iter: 75799 total_loss: 0.719 loss_cls: 0.2146 loss_box_reg: 0.2741 loss_rpn_cls: 0.04723 loss_rpn_loc: 0.1973 time: 0.3338 last_time: 0.2437 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:55:34 d2.utils.events]: \u001b[0m eta: 0:38:32 iter: 75819 total_loss: 0.7557 loss_cls: 0.2495 loss_box_reg: 0.2906 loss_rpn_cls: 0.04554 loss_rpn_loc: 0.1819 time: 0.3338 last_time: 0.1857 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:55:39 d2.utils.events]: \u001b[0m eta: 0:38:49 iter: 75839 total_loss: 0.7954 loss_cls: 0.23 loss_box_reg: 0.2817 loss_rpn_cls: 0.04776 loss_rpn_loc: 0.1899 time: 0.3338 last_time: 0.2863 data_time: 0.0050 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:55:45 d2.utils.events]: \u001b[0m eta: 0:38:52 iter: 75859 total_loss: 0.794 loss_cls: 0.2289 loss_box_reg: 0.2961 loss_rpn_cls: 0.0437 loss_rpn_loc: 0.1968 time: 0.3338 last_time: 0.2627 data_time: 0.0051 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:55:50 d2.utils.events]: \u001b[0m eta: 0:38:48 iter: 75879 total_loss: 0.7046 loss_cls: 0.2163 loss_box_reg: 0.2669 loss_rpn_cls: 0.03543 loss_rpn_loc: 0.1919 time: 0.3337 last_time: 0.2412 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:55:55 d2.utils.events]: \u001b[0m eta: 0:38:45 iter: 75899 total_loss: 0.7083 loss_cls: 0.2101 loss_box_reg: 0.2651 loss_rpn_cls: 0.04858 loss_rpn_loc: 0.163 time: 0.3337 last_time: 0.2403 data_time: 0.0047 last_data_time: 0.0042 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:55:59 d2.utils.events]: \u001b[0m eta: 0:38:38 iter: 75919 total_loss: 0.7933 loss_cls: 0.2346 loss_box_reg: 0.2759 loss_rpn_cls: 0.04584 loss_rpn_loc: 0.2082 time: 0.3337 last_time: 0.2164 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:56:04 d2.utils.events]: \u001b[0m eta: 0:38:32 iter: 75939 total_loss: 0.786 loss_cls: 0.2281 loss_box_reg: 0.2517 loss_rpn_cls: 0.04405 loss_rpn_loc: 0.2204 time: 0.3337 last_time: 0.2419 data_time: 0.0047 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:56:09 d2.utils.events]: \u001b[0m eta: 0:38:29 iter: 75959 total_loss: 0.8086 loss_cls: 0.2551 loss_box_reg: 0.2713 loss_rpn_cls: 0.05578 loss_rpn_loc: 0.2106 time: 0.3336 last_time: 0.2600 data_time: 0.0049 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:56:14 d2.utils.events]: \u001b[0m eta: 0:38:25 iter: 75979 total_loss: 0.7809 loss_cls: 0.25 loss_box_reg: 0.2991 loss_rpn_cls: 0.04129 loss_rpn_loc: 0.2049 time: 0.3336 last_time: 0.2416 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:56:19 d2.utils.events]: \u001b[0m eta: 0:38:19 iter: 75999 total_loss: 0.6912 loss_cls: 0.1884 loss_box_reg: 0.2476 loss_rpn_cls: 0.03708 loss_rpn_loc: 0.1759 time: 0.3336 last_time: 0.2550 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:56:24 d2.utils.events]: \u001b[0m eta: 0:38:16 iter: 76019 total_loss: 0.7712 loss_cls: 0.2569 loss_box_reg: 0.2752 loss_rpn_cls: 0.04543 loss_rpn_loc: 0.1586 time: 0.3336 last_time: 0.2818 data_time: 0.0050 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:56:30 d2.utils.events]: \u001b[0m eta: 0:38:16 iter: 76039 total_loss: 0.7115 loss_cls: 0.206 loss_box_reg: 0.295 loss_rpn_cls: 0.04726 loss_rpn_loc: 0.1856 time: 0.3336 last_time: 0.3302 data_time: 0.0053 last_data_time: 0.0060 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:56:35 d2.utils.events]: \u001b[0m eta: 0:38:11 iter: 76059 total_loss: 0.7353 loss_cls: 0.201 loss_box_reg: 0.2711 loss_rpn_cls: 0.04017 loss_rpn_loc: 0.1456 time: 0.3335 last_time: 0.2290 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:56:39 d2.utils.events]: \u001b[0m eta: 0:38:06 iter: 76079 total_loss: 0.6547 loss_cls: 0.1886 loss_box_reg: 0.2267 loss_rpn_cls: 0.03749 loss_rpn_loc: 0.172 time: 0.3335 last_time: 0.1871 data_time: 0.0047 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:56:45 d2.utils.events]: \u001b[0m eta: 0:38:03 iter: 76099 total_loss: 0.7158 loss_cls: 0.2359 loss_box_reg: 0.2847 loss_rpn_cls: 0.03612 loss_rpn_loc: 0.1619 time: 0.3335 last_time: 0.2300 data_time: 0.0052 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:56:50 d2.utils.events]: \u001b[0m eta: 0:37:56 iter: 76119 total_loss: 0.7238 loss_cls: 0.2293 loss_box_reg: 0.2828 loss_rpn_cls: 0.03738 loss_rpn_loc: 0.1722 time: 0.3335 last_time: 0.2307 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:56:54 d2.utils.events]: \u001b[0m eta: 0:37:51 iter: 76139 total_loss: 0.7914 loss_cls: 0.2632 loss_box_reg: 0.2942 loss_rpn_cls: 0.04089 loss_rpn_loc: 0.2121 time: 0.3334 last_time: 0.2566 data_time: 0.0045 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:56:59 d2.utils.events]: \u001b[0m eta: 0:37:45 iter: 76159 total_loss: 0.6151 loss_cls: 0.1889 loss_box_reg: 0.2317 loss_rpn_cls: 0.03173 loss_rpn_loc: 0.1631 time: 0.3334 last_time: 0.2416 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:57:04 d2.utils.events]: \u001b[0m eta: 0:37:42 iter: 76179 total_loss: 0.764 loss_cls: 0.2088 loss_box_reg: 0.2535 loss_rpn_cls: 0.03691 loss_rpn_loc: 0.196 time: 0.3334 last_time: 0.2618 data_time: 0.0050 last_data_time: 0.0055 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:57:10 d2.utils.events]: \u001b[0m eta: 0:37:40 iter: 76199 total_loss: 0.832 loss_cls: 0.2672 loss_box_reg: 0.2873 loss_rpn_cls: 0.04894 loss_rpn_loc: 0.1799 time: 0.3334 last_time: 0.2847 data_time: 0.0050 last_data_time: 0.0053 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:57:16 d2.utils.events]: \u001b[0m eta: 0:37:37 iter: 76219 total_loss: 0.7193 loss_cls: 0.2109 loss_box_reg: 0.29 loss_rpn_cls: 0.03679 loss_rpn_loc: 0.2076 time: 0.3334 last_time: 0.3319 data_time: 0.0053 last_data_time: 0.0058 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:57:22 d2.utils.events]: \u001b[0m eta: 0:37:35 iter: 76239 total_loss: 0.7434 loss_cls: 0.2474 loss_box_reg: 0.2718 loss_rpn_cls: 0.04097 loss_rpn_loc: 0.175 time: 0.3334 last_time: 0.2333 data_time: 0.0054 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:57:29 d2.utils.events]: \u001b[0m eta: 0:37:33 iter: 76259 total_loss: 0.8358 loss_cls: 0.2486 loss_box_reg: 0.2897 loss_rpn_cls: 0.05602 loss_rpn_loc: 0.1885 time: 0.3334 last_time: 0.3135 data_time: 0.0055 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:57:35 d2.utils.events]: \u001b[0m eta: 0:37:32 iter: 76279 total_loss: 0.7063 loss_cls: 0.191 loss_box_reg: 0.2973 loss_rpn_cls: 0.03506 loss_rpn_loc: 0.1699 time: 0.3334 last_time: 0.3276 data_time: 0.0053 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:57:41 d2.utils.events]: \u001b[0m eta: 0:37:32 iter: 76299 total_loss: 0.8748 loss_cls: 0.3005 loss_box_reg: 0.2904 loss_rpn_cls: 0.04243 loss_rpn_loc: 0.1651 time: 0.3334 last_time: 0.3289 data_time: 0.0053 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:57:47 d2.utils.events]: \u001b[0m eta: 0:37:31 iter: 76319 total_loss: 0.7187 loss_cls: 0.2476 loss_box_reg: 0.2639 loss_rpn_cls: 0.03914 loss_rpn_loc: 0.1832 time: 0.3334 last_time: 0.3216 data_time: 0.0054 last_data_time: 0.0055 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:57:54 d2.utils.events]: \u001b[0m eta: 0:37:35 iter: 76339 total_loss: 0.7448 loss_cls: 0.2592 loss_box_reg: 0.2681 loss_rpn_cls: 0.04366 loss_rpn_loc: 0.1915 time: 0.3333 last_time: 0.3293 data_time: 0.0054 last_data_time: 0.0054 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:58:00 d2.utils.events]: \u001b[0m eta: 0:37:41 iter: 76359 total_loss: 0.7047 loss_cls: 0.2256 loss_box_reg: 0.3008 loss_rpn_cls: 0.04743 loss_rpn_loc: 0.1791 time: 0.3333 last_time: 0.3283 data_time: 0.0054 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:58:06 d2.utils.events]: \u001b[0m eta: 0:37:40 iter: 76379 total_loss: 0.8033 loss_cls: 0.26 loss_box_reg: 0.3092 loss_rpn_cls: 0.04341 loss_rpn_loc: 0.1934 time: 0.3333 last_time: 0.3271 data_time: 0.0055 last_data_time: 0.0058 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:58:12 d2.utils.events]: \u001b[0m eta: 0:37:34 iter: 76399 total_loss: 0.783 loss_cls: 0.2562 loss_box_reg: 0.2512 loss_rpn_cls: 0.04188 loss_rpn_loc: 0.1978 time: 0.3333 last_time: 0.3226 data_time: 0.0053 last_data_time: 0.0053 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:58:18 d2.utils.events]: \u001b[0m eta: 0:37:25 iter: 76419 total_loss: 0.6426 loss_cls: 0.1775 loss_box_reg: 0.2209 loss_rpn_cls: 0.03286 loss_rpn_loc: 0.1761 time: 0.3333 last_time: 0.3019 data_time: 0.0053 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:58:24 d2.utils.events]: \u001b[0m eta: 0:37:20 iter: 76439 total_loss: 0.8066 loss_cls: 0.2448 loss_box_reg: 0.261 loss_rpn_cls: 0.03813 loss_rpn_loc: 0.1975 time: 0.3333 last_time: 0.2743 data_time: 0.0054 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:58:30 d2.utils.events]: \u001b[0m eta: 0:37:16 iter: 76459 total_loss: 0.7374 loss_cls: 0.244 loss_box_reg: 0.2637 loss_rpn_cls: 0.03784 loss_rpn_loc: 0.1744 time: 0.3333 last_time: 0.2637 data_time: 0.0053 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:58:35 d2.utils.events]: \u001b[0m eta: 0:37:10 iter: 76479 total_loss: 0.7498 loss_cls: 0.2573 loss_box_reg: 0.2766 loss_rpn_cls: 0.04643 loss_rpn_loc: 0.1833 time: 0.3333 last_time: 0.3246 data_time: 0.0050 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:58:41 d2.utils.events]: \u001b[0m eta: 0:36:53 iter: 76499 total_loss: 0.7569 loss_cls: 0.2738 loss_box_reg: 0.2938 loss_rpn_cls: 0.05338 loss_rpn_loc: 0.164 time: 0.3333 last_time: 0.2608 data_time: 0.0050 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:58:45 d2.utils.events]: \u001b[0m eta: 0:36:39 iter: 76519 total_loss: 0.6234 loss_cls: 0.2139 loss_box_reg: 0.2633 loss_rpn_cls: 0.0295 loss_rpn_loc: 0.1725 time: 0.3332 last_time: 0.2566 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:58:50 d2.utils.events]: \u001b[0m eta: 0:36:29 iter: 76539 total_loss: 0.7422 loss_cls: 0.2501 loss_box_reg: 0.2598 loss_rpn_cls: 0.04267 loss_rpn_loc: 0.2064 time: 0.3332 last_time: 0.2610 data_time: 0.0046 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:58:55 d2.utils.events]: \u001b[0m eta: 0:36:21 iter: 76559 total_loss: 0.7396 loss_cls: 0.2305 loss_box_reg: 0.2911 loss_rpn_cls: 0.04665 loss_rpn_loc: 0.194 time: 0.3332 last_time: 0.2394 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:59:00 d2.utils.events]: \u001b[0m eta: 0:36:11 iter: 76579 total_loss: 0.6739 loss_cls: 0.2096 loss_box_reg: 0.2626 loss_rpn_cls: 0.03486 loss_rpn_loc: 0.1469 time: 0.3332 last_time: 0.1870 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:59:04 d2.utils.events]: \u001b[0m eta: 0:36:04 iter: 76599 total_loss: 0.8039 loss_cls: 0.2494 loss_box_reg: 0.2963 loss_rpn_cls: 0.05539 loss_rpn_loc: 0.1846 time: 0.3331 last_time: 0.1871 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:59:09 d2.utils.events]: \u001b[0m eta: 0:35:59 iter: 76619 total_loss: 0.7833 loss_cls: 0.246 loss_box_reg: 0.2606 loss_rpn_cls: 0.04527 loss_rpn_loc: 0.1794 time: 0.3331 last_time: 0.2436 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:59:14 d2.utils.events]: \u001b[0m eta: 0:35:54 iter: 76639 total_loss: 0.7762 loss_cls: 0.2351 loss_box_reg: 0.2962 loss_rpn_cls: 0.0436 loss_rpn_loc: 0.1857 time: 0.3331 last_time: 0.2609 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:59:19 d2.utils.events]: \u001b[0m eta: 0:35:49 iter: 76659 total_loss: 0.9044 loss_cls: 0.264 loss_box_reg: 0.2991 loss_rpn_cls: 0.06674 loss_rpn_loc: 0.2148 time: 0.3331 last_time: 0.2397 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:59:23 d2.utils.events]: \u001b[0m eta: 0:35:43 iter: 76679 total_loss: 0.7403 loss_cls: 0.2236 loss_box_reg: 0.2591 loss_rpn_cls: 0.0423 loss_rpn_loc: 0.1941 time: 0.3330 last_time: 0.2284 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:59:28 d2.utils.events]: \u001b[0m eta: 0:35:37 iter: 76699 total_loss: 0.6697 loss_cls: 0.2075 loss_box_reg: 0.2382 loss_rpn_cls: 0.03698 loss_rpn_loc: 0.1788 time: 0.3330 last_time: 0.2565 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:59:33 d2.utils.events]: \u001b[0m eta: 0:35:32 iter: 76719 total_loss: 0.9475 loss_cls: 0.2574 loss_box_reg: 0.3081 loss_rpn_cls: 0.06317 loss_rpn_loc: 0.217 time: 0.3330 last_time: 0.2557 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:59:38 d2.utils.events]: \u001b[0m eta: 0:35:25 iter: 76739 total_loss: 0.6553 loss_cls: 0.2044 loss_box_reg: 0.2701 loss_rpn_cls: 0.04122 loss_rpn_loc: 0.1742 time: 0.3330 last_time: 0.2185 data_time: 0.0049 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:59:42 d2.utils.events]: \u001b[0m eta: 0:35:15 iter: 76759 total_loss: 0.8149 loss_cls: 0.2949 loss_box_reg: 0.304 loss_rpn_cls: 0.04437 loss_rpn_loc: 0.2213 time: 0.3329 last_time: 0.2411 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:59:47 d2.utils.events]: \u001b[0m eta: 0:35:08 iter: 76779 total_loss: 0.797 loss_cls: 0.2239 loss_box_reg: 0.2924 loss_rpn_cls: 0.04847 loss_rpn_loc: 0.1964 time: 0.3329 last_time: 0.2287 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:59:52 d2.utils.events]: \u001b[0m eta: 0:35:03 iter: 76799 total_loss: 0.6808 loss_cls: 0.209 loss_box_reg: 0.2649 loss_rpn_cls: 0.03515 loss_rpn_loc: 0.1654 time: 0.3329 last_time: 0.2327 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 22:59:57 d2.utils.events]: \u001b[0m eta: 0:34:59 iter: 76819 total_loss: 0.8001 loss_cls: 0.2245 loss_box_reg: 0.2992 loss_rpn_cls: 0.04413 loss_rpn_loc: 0.1896 time: 0.3329 last_time: 0.2326 data_time: 0.0047 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:00:01 d2.utils.events]: \u001b[0m eta: 0:34:52 iter: 76839 total_loss: 0.7889 loss_cls: 0.2533 loss_box_reg: 0.2623 loss_rpn_cls: 0.04468 loss_rpn_loc: 0.1764 time: 0.3328 last_time: 0.2432 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:00:06 d2.utils.events]: \u001b[0m eta: 0:34:40 iter: 76859 total_loss: 0.7584 loss_cls: 0.2431 loss_box_reg: 0.2916 loss_rpn_cls: 0.04403 loss_rpn_loc: 0.2216 time: 0.3328 last_time: 0.2329 data_time: 0.0047 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:00:11 d2.utils.events]: \u001b[0m eta: 0:34:34 iter: 76879 total_loss: 0.7173 loss_cls: 0.1914 loss_box_reg: 0.2501 loss_rpn_cls: 0.03733 loss_rpn_loc: 0.1855 time: 0.3328 last_time: 0.2562 data_time: 0.0047 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:00:15 d2.utils.events]: \u001b[0m eta: 0:34:28 iter: 76899 total_loss: 0.7418 loss_cls: 0.2397 loss_box_reg: 0.2768 loss_rpn_cls: 0.04379 loss_rpn_loc: 0.1984 time: 0.3328 last_time: 0.2322 data_time: 0.0046 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:00:20 d2.utils.events]: \u001b[0m eta: 0:34:23 iter: 76919 total_loss: 0.7232 loss_cls: 0.2349 loss_box_reg: 0.2796 loss_rpn_cls: 0.03965 loss_rpn_loc: 0.1816 time: 0.3327 last_time: 0.2287 data_time: 0.0047 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:00:25 d2.utils.events]: \u001b[0m eta: 0:34:18 iter: 76939 total_loss: 0.7916 loss_cls: 0.2525 loss_box_reg: 0.2857 loss_rpn_cls: 0.04107 loss_rpn_loc: 0.2045 time: 0.3327 last_time: 0.2421 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:00:30 d2.utils.events]: \u001b[0m eta: 0:34:10 iter: 76959 total_loss: 0.7713 loss_cls: 0.2342 loss_box_reg: 0.3154 loss_rpn_cls: 0.05432 loss_rpn_loc: 0.1886 time: 0.3327 last_time: 0.2305 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:00:34 d2.utils.events]: \u001b[0m eta: 0:33:59 iter: 76979 total_loss: 0.7257 loss_cls: 0.2067 loss_box_reg: 0.2891 loss_rpn_cls: 0.03763 loss_rpn_loc: 0.1816 time: 0.3327 last_time: 0.2285 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:00:39 d2.utils.events]: \u001b[0m eta: 0:34:02 iter: 76999 total_loss: 0.7614 loss_cls: 0.2091 loss_box_reg: 0.2746 loss_rpn_cls: 0.04535 loss_rpn_loc: 0.1937 time: 0.3326 last_time: 0.2900 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:00:44 d2.utils.events]: \u001b[0m eta: 0:33:55 iter: 77019 total_loss: 0.7778 loss_cls: 0.233 loss_box_reg: 0.2875 loss_rpn_cls: 0.05683 loss_rpn_loc: 0.1946 time: 0.3326 last_time: 0.2432 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:00:49 d2.utils.events]: \u001b[0m eta: 0:33:33 iter: 77039 total_loss: 0.693 loss_cls: 0.1985 loss_box_reg: 0.2602 loss_rpn_cls: 0.04124 loss_rpn_loc: 0.1544 time: 0.3326 last_time: 0.2426 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:00:54 d2.utils.events]: \u001b[0m eta: 0:33:28 iter: 77059 total_loss: 0.7655 loss_cls: 0.2727 loss_box_reg: 0.2914 loss_rpn_cls: 0.03914 loss_rpn_loc: 0.1805 time: 0.3326 last_time: 0.2397 data_time: 0.0049 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:00:58 d2.utils.events]: \u001b[0m eta: 0:33:19 iter: 77079 total_loss: 0.7656 loss_cls: 0.1811 loss_box_reg: 0.306 loss_rpn_cls: 0.03779 loss_rpn_loc: 0.1894 time: 0.3325 last_time: 0.2023 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:01:03 d2.utils.events]: \u001b[0m eta: 0:33:08 iter: 77099 total_loss: 0.7767 loss_cls: 0.2247 loss_box_reg: 0.2689 loss_rpn_cls: 0.04137 loss_rpn_loc: 0.1887 time: 0.3325 last_time: 0.2157 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:01:08 d2.utils.events]: \u001b[0m eta: 0:33:05 iter: 77119 total_loss: 0.7668 loss_cls: 0.2183 loss_box_reg: 0.2954 loss_rpn_cls: 0.05031 loss_rpn_loc: 0.2149 time: 0.3325 last_time: 0.2461 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:01:13 d2.utils.events]: \u001b[0m eta: 0:32:39 iter: 77139 total_loss: 0.6788 loss_cls: 0.1872 loss_box_reg: 0.2449 loss_rpn_cls: 0.0381 loss_rpn_loc: 0.1886 time: 0.3325 last_time: 0.2395 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:01:18 d2.utils.events]: \u001b[0m eta: 0:33:02 iter: 77159 total_loss: 0.7935 loss_cls: 0.2974 loss_box_reg: 0.2696 loss_rpn_cls: 0.04509 loss_rpn_loc: 0.1962 time: 0.3324 last_time: 0.2589 data_time: 0.0049 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:01:22 d2.utils.events]: \u001b[0m eta: 0:32:06 iter: 77179 total_loss: 0.8291 loss_cls: 0.2749 loss_box_reg: 0.2939 loss_rpn_cls: 0.04968 loss_rpn_loc: 0.1839 time: 0.3324 last_time: 0.1890 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:01:27 d2.utils.events]: \u001b[0m eta: 0:31:50 iter: 77199 total_loss: 0.7549 loss_cls: 0.2209 loss_box_reg: 0.2718 loss_rpn_cls: 0.02947 loss_rpn_loc: 0.1724 time: 0.3324 last_time: 0.2950 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:01:32 d2.utils.events]: \u001b[0m eta: 0:31:39 iter: 77219 total_loss: 0.759 loss_cls: 0.2308 loss_box_reg: 0.2832 loss_rpn_cls: 0.04369 loss_rpn_loc: 0.1935 time: 0.3324 last_time: 0.2761 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:01:37 d2.utils.events]: \u001b[0m eta: 0:31:31 iter: 77239 total_loss: 0.6564 loss_cls: 0.1889 loss_box_reg: 0.2481 loss_rpn_cls: 0.03934 loss_rpn_loc: 0.1671 time: 0.3323 last_time: 0.2435 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:01:41 d2.utils.events]: \u001b[0m eta: 0:31:22 iter: 77259 total_loss: 0.817 loss_cls: 0.2769 loss_box_reg: 0.2904 loss_rpn_cls: 0.04897 loss_rpn_loc: 0.198 time: 0.3323 last_time: 0.2175 data_time: 0.0048 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:01:46 d2.utils.events]: \u001b[0m eta: 0:31:15 iter: 77279 total_loss: 0.745 loss_cls: 0.2205 loss_box_reg: 0.2793 loss_rpn_cls: 0.04366 loss_rpn_loc: 0.1849 time: 0.3323 last_time: 0.2437 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:01:52 d2.utils.events]: \u001b[0m eta: 0:31:08 iter: 77299 total_loss: 0.6455 loss_cls: 0.1798 loss_box_reg: 0.2663 loss_rpn_cls: 0.03354 loss_rpn_loc: 0.1768 time: 0.3323 last_time: 0.2570 data_time: 0.0050 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:01:57 d2.utils.events]: \u001b[0m eta: 0:31:02 iter: 77319 total_loss: 0.7205 loss_cls: 0.2394 loss_box_reg: 0.2824 loss_rpn_cls: 0.03122 loss_rpn_loc: 0.1841 time: 0.3323 last_time: 0.2279 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:02:01 d2.utils.events]: \u001b[0m eta: 0:30:54 iter: 77339 total_loss: 0.701 loss_cls: 0.2035 loss_box_reg: 0.265 loss_rpn_cls: 0.0423 loss_rpn_loc: 0.1911 time: 0.3322 last_time: 0.2144 data_time: 0.0049 last_data_time: 0.0053 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:02:06 d2.utils.events]: \u001b[0m eta: 0:30:48 iter: 77359 total_loss: 0.7595 loss_cls: 0.2367 loss_box_reg: 0.2791 loss_rpn_cls: 0.03322 loss_rpn_loc: 0.1443 time: 0.3322 last_time: 0.2547 data_time: 0.0048 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:02:11 d2.utils.events]: \u001b[0m eta: 0:30:41 iter: 77379 total_loss: 0.7378 loss_cls: 0.2237 loss_box_reg: 0.3082 loss_rpn_cls: 0.04523 loss_rpn_loc: 0.1716 time: 0.3322 last_time: 0.2427 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:02:16 d2.utils.events]: \u001b[0m eta: 0:30:35 iter: 77399 total_loss: 0.6626 loss_cls: 0.1959 loss_box_reg: 0.2352 loss_rpn_cls: 0.03632 loss_rpn_loc: 0.1792 time: 0.3322 last_time: 0.2544 data_time: 0.0047 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:02:21 d2.utils.events]: \u001b[0m eta: 0:30:30 iter: 77419 total_loss: 0.7902 loss_cls: 0.2773 loss_box_reg: 0.3105 loss_rpn_cls: 0.04107 loss_rpn_loc: 0.2017 time: 0.3321 last_time: 0.2759 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:02:25 d2.utils.events]: \u001b[0m eta: 0:30:24 iter: 77439 total_loss: 0.7213 loss_cls: 0.2169 loss_box_reg: 0.2689 loss_rpn_cls: 0.04809 loss_rpn_loc: 0.1765 time: 0.3321 last_time: 0.2625 data_time: 0.0048 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:02:30 d2.utils.events]: \u001b[0m eta: 0:30:18 iter: 77459 total_loss: 0.6754 loss_cls: 0.2128 loss_box_reg: 0.2513 loss_rpn_cls: 0.0452 loss_rpn_loc: 0.1663 time: 0.3321 last_time: 0.2410 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:02:35 d2.utils.events]: \u001b[0m eta: 0:30:12 iter: 77479 total_loss: 0.7437 loss_cls: 0.2388 loss_box_reg: 0.2917 loss_rpn_cls: 0.04069 loss_rpn_loc: 0.1421 time: 0.3321 last_time: 0.2289 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:02:40 d2.utils.events]: \u001b[0m eta: 0:30:08 iter: 77499 total_loss: 0.6587 loss_cls: 0.2102 loss_box_reg: 0.2803 loss_rpn_cls: 0.03345 loss_rpn_loc: 0.1828 time: 0.3320 last_time: 0.2548 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:02:45 d2.utils.events]: \u001b[0m eta: 0:30:03 iter: 77519 total_loss: 0.7482 loss_cls: 0.243 loss_box_reg: 0.2651 loss_rpn_cls: 0.04336 loss_rpn_loc: 0.1824 time: 0.3320 last_time: 0.2164 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:02:49 d2.utils.events]: \u001b[0m eta: 0:29:57 iter: 77539 total_loss: 0.7399 loss_cls: 0.2341 loss_box_reg: 0.2876 loss_rpn_cls: 0.03266 loss_rpn_loc: 0.1805 time: 0.3320 last_time: 0.2294 data_time: 0.0048 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:02:54 d2.utils.events]: \u001b[0m eta: 0:29:52 iter: 77559 total_loss: 0.8012 loss_cls: 0.285 loss_box_reg: 0.2509 loss_rpn_cls: 0.03752 loss_rpn_loc: 0.1797 time: 0.3320 last_time: 0.1929 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:02:59 d2.utils.events]: \u001b[0m eta: 0:29:47 iter: 77579 total_loss: 0.7668 loss_cls: 0.1868 loss_box_reg: 0.3013 loss_rpn_cls: 0.04449 loss_rpn_loc: 0.1879 time: 0.3319 last_time: 0.2407 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:03:03 d2.utils.events]: \u001b[0m eta: 0:29:42 iter: 77599 total_loss: 0.8489 loss_cls: 0.2644 loss_box_reg: 0.3257 loss_rpn_cls: 0.04307 loss_rpn_loc: 0.1999 time: 0.3319 last_time: 0.2159 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:03:08 d2.utils.events]: \u001b[0m eta: 0:29:37 iter: 77619 total_loss: 0.7833 loss_cls: 0.2461 loss_box_reg: 0.2799 loss_rpn_cls: 0.05467 loss_rpn_loc: 0.1776 time: 0.3319 last_time: 0.2001 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:03:13 d2.utils.events]: \u001b[0m eta: 0:29:33 iter: 77639 total_loss: 0.7984 loss_cls: 0.2499 loss_box_reg: 0.2943 loss_rpn_cls: 0.04851 loss_rpn_loc: 0.2052 time: 0.3319 last_time: 0.1871 data_time: 0.0047 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:03:18 d2.utils.events]: \u001b[0m eta: 0:29:28 iter: 77659 total_loss: 0.8323 loss_cls: 0.2163 loss_box_reg: 0.2797 loss_rpn_cls: 0.06041 loss_rpn_loc: 0.2002 time: 0.3318 last_time: 0.2306 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:03:22 d2.utils.events]: \u001b[0m eta: 0:29:24 iter: 77679 total_loss: 0.7194 loss_cls: 0.2111 loss_box_reg: 0.2616 loss_rpn_cls: 0.04729 loss_rpn_loc: 0.1784 time: 0.3318 last_time: 0.2580 data_time: 0.0048 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:03:27 d2.utils.events]: \u001b[0m eta: 0:29:19 iter: 77699 total_loss: 0.7732 loss_cls: 0.2081 loss_box_reg: 0.2864 loss_rpn_cls: 0.04273 loss_rpn_loc: 0.1977 time: 0.3318 last_time: 0.2308 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:03:32 d2.utils.events]: \u001b[0m eta: 0:29:14 iter: 77719 total_loss: 0.6527 loss_cls: 0.213 loss_box_reg: 0.2724 loss_rpn_cls: 0.03655 loss_rpn_loc: 0.1627 time: 0.3318 last_time: 0.2407 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:03:37 d2.utils.events]: \u001b[0m eta: 0:29:09 iter: 77739 total_loss: 0.7611 loss_cls: 0.2371 loss_box_reg: 0.2666 loss_rpn_cls: 0.05697 loss_rpn_loc: 0.1915 time: 0.3317 last_time: 0.2007 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:03:41 d2.utils.events]: \u001b[0m eta: 0:29:04 iter: 77759 total_loss: 0.7228 loss_cls: 0.1802 loss_box_reg: 0.2786 loss_rpn_cls: 0.03495 loss_rpn_loc: 0.174 time: 0.3317 last_time: 0.2563 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:03:46 d2.utils.events]: \u001b[0m eta: 0:28:59 iter: 77779 total_loss: 0.7958 loss_cls: 0.2452 loss_box_reg: 0.2811 loss_rpn_cls: 0.04872 loss_rpn_loc: 0.2127 time: 0.3317 last_time: 0.2392 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:03:50 d2.utils.events]: \u001b[0m eta: 0:28:53 iter: 77799 total_loss: 0.7524 loss_cls: 0.244 loss_box_reg: 0.304 loss_rpn_cls: 0.04672 loss_rpn_loc: 0.1981 time: 0.3317 last_time: 0.2560 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:03:55 d2.utils.events]: \u001b[0m eta: 0:28:49 iter: 77819 total_loss: 0.7311 loss_cls: 0.2208 loss_box_reg: 0.2588 loss_rpn_cls: 0.04445 loss_rpn_loc: 0.1846 time: 0.3316 last_time: 0.2537 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:04:00 d2.utils.events]: \u001b[0m eta: 0:28:43 iter: 77839 total_loss: 0.7393 loss_cls: 0.2379 loss_box_reg: 0.256 loss_rpn_cls: 0.04287 loss_rpn_loc: 0.1635 time: 0.3316 last_time: 0.2551 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:04:05 d2.utils.events]: \u001b[0m eta: 0:28:39 iter: 77859 total_loss: 0.7332 loss_cls: 0.2165 loss_box_reg: 0.2614 loss_rpn_cls: 0.03531 loss_rpn_loc: 0.1765 time: 0.3316 last_time: 0.2408 data_time: 0.0046 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:04:09 d2.utils.events]: \u001b[0m eta: 0:28:34 iter: 77879 total_loss: 0.6976 loss_cls: 0.2227 loss_box_reg: 0.2526 loss_rpn_cls: 0.04028 loss_rpn_loc: 0.1759 time: 0.3316 last_time: 0.2406 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:04:14 d2.utils.events]: \u001b[0m eta: 0:28:30 iter: 77899 total_loss: 0.7202 loss_cls: 0.1973 loss_box_reg: 0.2832 loss_rpn_cls: 0.04688 loss_rpn_loc: 0.1863 time: 0.3315 last_time: 0.2580 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:04:19 d2.utils.events]: \u001b[0m eta: 0:28:25 iter: 77919 total_loss: 0.7516 loss_cls: 0.2189 loss_box_reg: 0.2507 loss_rpn_cls: 0.03989 loss_rpn_loc: 0.1754 time: 0.3315 last_time: 0.2367 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:04:23 d2.utils.events]: \u001b[0m eta: 0:28:20 iter: 77939 total_loss: 0.6858 loss_cls: 0.204 loss_box_reg: 0.2427 loss_rpn_cls: 0.04088 loss_rpn_loc: 0.1677 time: 0.3315 last_time: 0.2181 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:04:28 d2.utils.events]: \u001b[0m eta: 0:28:15 iter: 77959 total_loss: 0.7304 loss_cls: 0.2196 loss_box_reg: 0.2954 loss_rpn_cls: 0.04121 loss_rpn_loc: 0.1907 time: 0.3315 last_time: 0.2542 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:04:33 d2.utils.events]: \u001b[0m eta: 0:28:10 iter: 77979 total_loss: 0.7362 loss_cls: 0.2246 loss_box_reg: 0.2657 loss_rpn_cls: 0.05305 loss_rpn_loc: 0.1783 time: 0.3315 last_time: 0.2588 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:04:38 d2.utils.events]: \u001b[0m eta: 0:28:05 iter: 77999 total_loss: 0.7532 loss_cls: 0.2125 loss_box_reg: 0.2562 loss_rpn_cls: 0.03551 loss_rpn_loc: 0.2113 time: 0.3314 last_time: 0.2421 data_time: 0.0047 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:04:43 d2.utils.events]: \u001b[0m eta: 0:28:00 iter: 78019 total_loss: 0.7357 loss_cls: 0.2319 loss_box_reg: 0.2838 loss_rpn_cls: 0.04555 loss_rpn_loc: 0.1857 time: 0.3314 last_time: 0.2603 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:04:47 d2.utils.events]: \u001b[0m eta: 0:27:54 iter: 78039 total_loss: 0.7641 loss_cls: 0.2312 loss_box_reg: 0.2723 loss_rpn_cls: 0.04365 loss_rpn_loc: 0.1704 time: 0.3314 last_time: 0.2330 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:04:52 d2.utils.events]: \u001b[0m eta: 0:27:50 iter: 78059 total_loss: 0.8499 loss_cls: 0.3055 loss_box_reg: 0.3057 loss_rpn_cls: 0.0537 loss_rpn_loc: 0.1928 time: 0.3314 last_time: 0.2318 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:04:57 d2.utils.events]: \u001b[0m eta: 0:27:45 iter: 78079 total_loss: 0.6695 loss_cls: 0.2006 loss_box_reg: 0.2816 loss_rpn_cls: 0.04661 loss_rpn_loc: 0.1947 time: 0.3313 last_time: 0.2398 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:05:02 d2.utils.events]: \u001b[0m eta: 0:27:40 iter: 78099 total_loss: 0.6275 loss_cls: 0.2033 loss_box_reg: 0.2235 loss_rpn_cls: 0.03062 loss_rpn_loc: 0.1398 time: 0.3313 last_time: 0.2288 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:05:06 d2.utils.events]: \u001b[0m eta: 0:27:35 iter: 78119 total_loss: 0.7451 loss_cls: 0.224 loss_box_reg: 0.2929 loss_rpn_cls: 0.04095 loss_rpn_loc: 0.1857 time: 0.3313 last_time: 0.2021 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:05:11 d2.utils.events]: \u001b[0m eta: 0:27:30 iter: 78139 total_loss: 0.6886 loss_cls: 0.1975 loss_box_reg: 0.2683 loss_rpn_cls: 0.03161 loss_rpn_loc: 0.1796 time: 0.3313 last_time: 0.2399 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:05:16 d2.utils.events]: \u001b[0m eta: 0:27:25 iter: 78159 total_loss: 0.8252 loss_cls: 0.2631 loss_box_reg: 0.3137 loss_rpn_cls: 0.04173 loss_rpn_loc: 0.189 time: 0.3312 last_time: 0.2411 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:05:21 d2.utils.events]: \u001b[0m eta: 0:27:20 iter: 78179 total_loss: 0.7266 loss_cls: 0.2335 loss_box_reg: 0.2663 loss_rpn_cls: 0.04492 loss_rpn_loc: 0.1966 time: 0.3312 last_time: 0.2180 data_time: 0.0047 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:05:25 d2.utils.events]: \u001b[0m eta: 0:27:15 iter: 78199 total_loss: 0.7712 loss_cls: 0.2354 loss_box_reg: 0.2942 loss_rpn_cls: 0.03751 loss_rpn_loc: 0.1849 time: 0.3312 last_time: 0.2002 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:05:30 d2.utils.events]: \u001b[0m eta: 0:27:09 iter: 78219 total_loss: 0.8798 loss_cls: 0.2726 loss_box_reg: 0.3066 loss_rpn_cls: 0.05337 loss_rpn_loc: 0.2166 time: 0.3312 last_time: 0.2421 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:05:35 d2.utils.events]: \u001b[0m eta: 0:27:04 iter: 78239 total_loss: 0.7648 loss_cls: 0.2141 loss_box_reg: 0.2772 loss_rpn_cls: 0.05033 loss_rpn_loc: 0.1795 time: 0.3311 last_time: 0.2417 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:05:39 d2.utils.events]: \u001b[0m eta: 0:26:59 iter: 78259 total_loss: 0.6443 loss_cls: 0.1984 loss_box_reg: 0.2648 loss_rpn_cls: 0.03599 loss_rpn_loc: 0.1664 time: 0.3311 last_time: 0.2305 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:05:44 d2.utils.events]: \u001b[0m eta: 0:26:55 iter: 78279 total_loss: 0.748 loss_cls: 0.2405 loss_box_reg: 0.3063 loss_rpn_cls: 0.03829 loss_rpn_loc: 0.1818 time: 0.3311 last_time: 0.2304 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:05:49 d2.utils.events]: \u001b[0m eta: 0:26:48 iter: 78299 total_loss: 0.7387 loss_cls: 0.2267 loss_box_reg: 0.2805 loss_rpn_cls: 0.04576 loss_rpn_loc: 0.1736 time: 0.3311 last_time: 0.2291 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:05:54 d2.utils.events]: \u001b[0m eta: 0:26:43 iter: 78319 total_loss: 0.7193 loss_cls: 0.2223 loss_box_reg: 0.2783 loss_rpn_cls: 0.05158 loss_rpn_loc: 0.1551 time: 0.3310 last_time: 0.2341 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:05:58 d2.utils.events]: \u001b[0m eta: 0:26:39 iter: 78339 total_loss: 0.7038 loss_cls: 0.2407 loss_box_reg: 0.2811 loss_rpn_cls: 0.03914 loss_rpn_loc: 0.1714 time: 0.3310 last_time: 0.2621 data_time: 0.0047 last_data_time: 0.0042 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:06:03 d2.utils.events]: \u001b[0m eta: 0:26:34 iter: 78359 total_loss: 0.7523 loss_cls: 0.2146 loss_box_reg: 0.2566 loss_rpn_cls: 0.04393 loss_rpn_loc: 0.1925 time: 0.3310 last_time: 0.2588 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:06:08 d2.utils.events]: \u001b[0m eta: 0:26:29 iter: 78379 total_loss: 0.707 loss_cls: 0.2141 loss_box_reg: 0.2392 loss_rpn_cls: 0.03117 loss_rpn_loc: 0.1765 time: 0.3310 last_time: 0.2160 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:06:13 d2.utils.events]: \u001b[0m eta: 0:26:24 iter: 78399 total_loss: 0.7731 loss_cls: 0.257 loss_box_reg: 0.2759 loss_rpn_cls: 0.0507 loss_rpn_loc: 0.1959 time: 0.3309 last_time: 0.2297 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:06:17 d2.utils.events]: \u001b[0m eta: 0:26:19 iter: 78419 total_loss: 0.6954 loss_cls: 0.1913 loss_box_reg: 0.255 loss_rpn_cls: 0.04191 loss_rpn_loc: 0.1628 time: 0.3309 last_time: 0.2004 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:06:22 d2.utils.events]: \u001b[0m eta: 0:26:14 iter: 78439 total_loss: 0.8392 loss_cls: 0.2735 loss_box_reg: 0.297 loss_rpn_cls: 0.05227 loss_rpn_loc: 0.2226 time: 0.3309 last_time: 0.2576 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:06:27 d2.utils.events]: \u001b[0m eta: 0:26:09 iter: 78459 total_loss: 0.7143 loss_cls: 0.2076 loss_box_reg: 0.2527 loss_rpn_cls: 0.04044 loss_rpn_loc: 0.1809 time: 0.3309 last_time: 0.2414 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:06:32 d2.utils.events]: \u001b[0m eta: 0:26:04 iter: 78479 total_loss: 0.8309 loss_cls: 0.2835 loss_box_reg: 0.2915 loss_rpn_cls: 0.04715 loss_rpn_loc: 0.1854 time: 0.3308 last_time: 0.2133 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:06:36 d2.utils.events]: \u001b[0m eta: 0:25:59 iter: 78499 total_loss: 0.7349 loss_cls: 0.2504 loss_box_reg: 0.2489 loss_rpn_cls: 0.03817 loss_rpn_loc: 0.1805 time: 0.3308 last_time: 0.2321 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:06:41 d2.utils.events]: \u001b[0m eta: 0:25:54 iter: 78519 total_loss: 0.6454 loss_cls: 0.1813 loss_box_reg: 0.2485 loss_rpn_cls: 0.04407 loss_rpn_loc: 0.1773 time: 0.3308 last_time: 0.2435 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:06:46 d2.utils.events]: \u001b[0m eta: 0:25:50 iter: 78539 total_loss: 0.7537 loss_cls: 0.2334 loss_box_reg: 0.2628 loss_rpn_cls: 0.0379 loss_rpn_loc: 0.1903 time: 0.3308 last_time: 0.2307 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:06:50 d2.utils.events]: \u001b[0m eta: 0:25:45 iter: 78559 total_loss: 0.6904 loss_cls: 0.2379 loss_box_reg: 0.2498 loss_rpn_cls: 0.03515 loss_rpn_loc: 0.1821 time: 0.3308 last_time: 0.2396 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:06:55 d2.utils.events]: \u001b[0m eta: 0:25:40 iter: 78579 total_loss: 0.7518 loss_cls: 0.2071 loss_box_reg: 0.2767 loss_rpn_cls: 0.04682 loss_rpn_loc: 0.2072 time: 0.3307 last_time: 0.2013 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:07:00 d2.utils.events]: \u001b[0m eta: 0:25:36 iter: 78599 total_loss: 0.8322 loss_cls: 0.2696 loss_box_reg: 0.2757 loss_rpn_cls: 0.05441 loss_rpn_loc: 0.1832 time: 0.3307 last_time: 0.2551 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:07:05 d2.utils.events]: \u001b[0m eta: 0:25:31 iter: 78619 total_loss: 0.8436 loss_cls: 0.2459 loss_box_reg: 0.2978 loss_rpn_cls: 0.03792 loss_rpn_loc: 0.2039 time: 0.3307 last_time: 0.2592 data_time: 0.0047 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:07:09 d2.utils.events]: \u001b[0m eta: 0:25:25 iter: 78639 total_loss: 0.7376 loss_cls: 0.2332 loss_box_reg: 0.2693 loss_rpn_cls: 0.03934 loss_rpn_loc: 0.1877 time: 0.3307 last_time: 0.2144 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:07:14 d2.utils.events]: \u001b[0m eta: 0:25:20 iter: 78659 total_loss: 0.7481 loss_cls: 0.2268 loss_box_reg: 0.3011 loss_rpn_cls: 0.0412 loss_rpn_loc: 0.1979 time: 0.3306 last_time: 0.2031 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:07:19 d2.utils.events]: \u001b[0m eta: 0:25:16 iter: 78679 total_loss: 0.7008 loss_cls: 0.1915 loss_box_reg: 0.287 loss_rpn_cls: 0.04186 loss_rpn_loc: 0.197 time: 0.3306 last_time: 0.2438 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:07:24 d2.utils.events]: \u001b[0m eta: 0:25:11 iter: 78699 total_loss: 0.8172 loss_cls: 0.2749 loss_box_reg: 0.2884 loss_rpn_cls: 0.04465 loss_rpn_loc: 0.2038 time: 0.3306 last_time: 0.2567 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:07:28 d2.utils.events]: \u001b[0m eta: 0:25:06 iter: 78719 total_loss: 0.6842 loss_cls: 0.2 loss_box_reg: 0.2259 loss_rpn_cls: 0.03743 loss_rpn_loc: 0.1701 time: 0.3306 last_time: 0.2292 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:07:33 d2.utils.events]: \u001b[0m eta: 0:25:01 iter: 78739 total_loss: 0.7992 loss_cls: 0.2422 loss_box_reg: 0.2987 loss_rpn_cls: 0.0415 loss_rpn_loc: 0.1741 time: 0.3305 last_time: 0.2176 data_time: 0.0046 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:07:38 d2.utils.events]: \u001b[0m eta: 0:24:56 iter: 78759 total_loss: 0.7081 loss_cls: 0.1952 loss_box_reg: 0.2544 loss_rpn_cls: 0.03199 loss_rpn_loc: 0.1654 time: 0.3305 last_time: 0.2556 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:07:42 d2.utils.events]: \u001b[0m eta: 0:24:52 iter: 78779 total_loss: 0.6873 loss_cls: 0.2561 loss_box_reg: 0.2521 loss_rpn_cls: 0.03098 loss_rpn_loc: 0.1706 time: 0.3305 last_time: 0.2455 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:07:47 d2.utils.events]: \u001b[0m eta: 0:24:47 iter: 78799 total_loss: 0.768 loss_cls: 0.2653 loss_box_reg: 0.2712 loss_rpn_cls: 0.04518 loss_rpn_loc: 0.1757 time: 0.3305 last_time: 0.2587 data_time: 0.0049 last_data_time: 0.0054 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:07:52 d2.utils.events]: \u001b[0m eta: 0:24:42 iter: 78819 total_loss: 0.7085 loss_cls: 0.237 loss_box_reg: 0.2489 loss_rpn_cls: 0.04923 loss_rpn_loc: 0.1875 time: 0.3304 last_time: 0.2594 data_time: 0.0049 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:07:57 d2.utils.events]: \u001b[0m eta: 0:24:37 iter: 78839 total_loss: 0.7692 loss_cls: 0.211 loss_box_reg: 0.2907 loss_rpn_cls: 0.04267 loss_rpn_loc: 0.2093 time: 0.3304 last_time: 0.2169 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:08:01 d2.utils.events]: \u001b[0m eta: 0:24:32 iter: 78859 total_loss: 0.7701 loss_cls: 0.237 loss_box_reg: 0.2828 loss_rpn_cls: 0.05016 loss_rpn_loc: 0.1697 time: 0.3304 last_time: 0.1990 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:08:06 d2.utils.events]: \u001b[0m eta: 0:24:28 iter: 78879 total_loss: 0.8277 loss_cls: 0.2912 loss_box_reg: 0.2887 loss_rpn_cls: 0.04657 loss_rpn_loc: 0.1943 time: 0.3304 last_time: 0.2181 data_time: 0.0048 last_data_time: 0.0053 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:08:11 d2.utils.events]: \u001b[0m eta: 0:24:23 iter: 78899 total_loss: 0.7325 loss_cls: 0.2059 loss_box_reg: 0.2599 loss_rpn_cls: 0.04691 loss_rpn_loc: 0.1954 time: 0.3303 last_time: 0.2272 data_time: 0.0048 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:08:16 d2.utils.events]: \u001b[0m eta: 0:24:18 iter: 78919 total_loss: 0.7305 loss_cls: 0.2104 loss_box_reg: 0.2803 loss_rpn_cls: 0.03893 loss_rpn_loc: 0.1924 time: 0.3303 last_time: 0.2134 data_time: 0.0048 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:08:20 d2.utils.events]: \u001b[0m eta: 0:24:13 iter: 78939 total_loss: 0.9124 loss_cls: 0.2616 loss_box_reg: 0.3538 loss_rpn_cls: 0.0548 loss_rpn_loc: 0.2214 time: 0.3303 last_time: 0.2172 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:08:25 d2.utils.events]: \u001b[0m eta: 0:24:09 iter: 78959 total_loss: 0.7458 loss_cls: 0.1979 loss_box_reg: 0.2747 loss_rpn_cls: 0.03542 loss_rpn_loc: 0.1885 time: 0.3303 last_time: 0.2422 data_time: 0.0046 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:08:30 d2.utils.events]: \u001b[0m eta: 0:24:04 iter: 78979 total_loss: 0.6852 loss_cls: 0.2083 loss_box_reg: 0.2515 loss_rpn_cls: 0.03981 loss_rpn_loc: 0.1676 time: 0.3302 last_time: 0.2397 data_time: 0.0047 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:08:35 d2.utils.events]: \u001b[0m eta: 0:23:59 iter: 78999 total_loss: 0.8278 loss_cls: 0.2296 loss_box_reg: 0.293 loss_rpn_cls: 0.04501 loss_rpn_loc: 0.181 time: 0.3302 last_time: 0.2568 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:08:40 d2.utils.events]: \u001b[0m eta: 0:23:55 iter: 79019 total_loss: 0.7752 loss_cls: 0.27 loss_box_reg: 0.2805 loss_rpn_cls: 0.03942 loss_rpn_loc: 0.1788 time: 0.3302 last_time: 0.2749 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:08:44 d2.utils.events]: \u001b[0m eta: 0:23:50 iter: 79039 total_loss: 0.6449 loss_cls: 0.1765 loss_box_reg: 0.2362 loss_rpn_cls: 0.03607 loss_rpn_loc: 0.1945 time: 0.3302 last_time: 0.2427 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:08:49 d2.utils.events]: \u001b[0m eta: 0:23:46 iter: 79059 total_loss: 0.7763 loss_cls: 0.2136 loss_box_reg: 0.2603 loss_rpn_cls: 0.03743 loss_rpn_loc: 0.2028 time: 0.3302 last_time: 0.2308 data_time: 0.0047 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:08:54 d2.utils.events]: \u001b[0m eta: 0:23:42 iter: 79079 total_loss: 0.7393 loss_cls: 0.2652 loss_box_reg: 0.2346 loss_rpn_cls: 0.049 loss_rpn_loc: 0.1503 time: 0.3301 last_time: 0.2426 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:08:59 d2.utils.events]: \u001b[0m eta: 0:23:37 iter: 79099 total_loss: 0.639 loss_cls: 0.1872 loss_box_reg: 0.2909 loss_rpn_cls: 0.0301 loss_rpn_loc: 0.1629 time: 0.3301 last_time: 0.2399 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:09:03 d2.utils.events]: \u001b[0m eta: 0:23:32 iter: 79119 total_loss: 0.7011 loss_cls: 0.2072 loss_box_reg: 0.2567 loss_rpn_cls: 0.03466 loss_rpn_loc: 0.1725 time: 0.3301 last_time: 0.1875 data_time: 0.0047 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:09:08 d2.utils.events]: \u001b[0m eta: 0:23:27 iter: 79139 total_loss: 0.7357 loss_cls: 0.2356 loss_box_reg: 0.2786 loss_rpn_cls: 0.05501 loss_rpn_loc: 0.1873 time: 0.3301 last_time: 0.2576 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:09:13 d2.utils.events]: \u001b[0m eta: 0:23:23 iter: 79159 total_loss: 0.7545 loss_cls: 0.2622 loss_box_reg: 0.3035 loss_rpn_cls: 0.0567 loss_rpn_loc: 0.2028 time: 0.3300 last_time: 0.2016 data_time: 0.0048 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:09:18 d2.utils.events]: \u001b[0m eta: 0:23:17 iter: 79179 total_loss: 0.8752 loss_cls: 0.2544 loss_box_reg: 0.3003 loss_rpn_cls: 0.04558 loss_rpn_loc: 0.1903 time: 0.3300 last_time: 0.2424 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:09:23 d2.utils.events]: \u001b[0m eta: 0:23:14 iter: 79199 total_loss: 0.7713 loss_cls: 0.229 loss_box_reg: 0.2896 loss_rpn_cls: 0.04635 loss_rpn_loc: 0.2214 time: 0.3300 last_time: 0.3474 data_time: 0.0053 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:09:29 d2.utils.events]: \u001b[0m eta: 0:23:11 iter: 79219 total_loss: 0.8012 loss_cls: 0.2383 loss_box_reg: 0.2765 loss_rpn_cls: 0.04421 loss_rpn_loc: 0.2101 time: 0.3300 last_time: 0.3262 data_time: 0.0051 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:09:35 d2.utils.events]: \u001b[0m eta: 0:23:08 iter: 79239 total_loss: 0.5583 loss_cls: 0.1436 loss_box_reg: 0.2244 loss_rpn_cls: 0.0285 loss_rpn_loc: 0.1558 time: 0.3300 last_time: 0.2556 data_time: 0.0053 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:09:41 d2.utils.events]: \u001b[0m eta: 0:23:03 iter: 79259 total_loss: 0.8127 loss_cls: 0.2871 loss_box_reg: 0.2805 loss_rpn_cls: 0.05219 loss_rpn_loc: 0.1768 time: 0.3300 last_time: 0.2387 data_time: 0.0053 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:09:46 d2.utils.events]: \u001b[0m eta: 0:22:58 iter: 79279 total_loss: 0.749 loss_cls: 0.21 loss_box_reg: 0.2338 loss_rpn_cls: 0.03996 loss_rpn_loc: 0.1931 time: 0.3300 last_time: 0.2159 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:09:50 d2.utils.events]: \u001b[0m eta: 0:22:53 iter: 79299 total_loss: 0.7563 loss_cls: 0.2352 loss_box_reg: 0.2295 loss_rpn_cls: 0.05144 loss_rpn_loc: 0.1996 time: 0.3299 last_time: 0.2289 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:09:55 d2.utils.events]: \u001b[0m eta: 0:22:49 iter: 79319 total_loss: 0.7272 loss_cls: 0.2235 loss_box_reg: 0.2585 loss_rpn_cls: 0.04471 loss_rpn_loc: 0.2241 time: 0.3299 last_time: 0.2413 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:10:00 d2.utils.events]: \u001b[0m eta: 0:22:44 iter: 79339 total_loss: 0.7056 loss_cls: 0.2395 loss_box_reg: 0.2682 loss_rpn_cls: 0.06078 loss_rpn_loc: 0.18 time: 0.3299 last_time: 0.2164 data_time: 0.0045 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:10:04 d2.utils.events]: \u001b[0m eta: 0:22:40 iter: 79359 total_loss: 0.7777 loss_cls: 0.2437 loss_box_reg: 0.3072 loss_rpn_cls: 0.04038 loss_rpn_loc: 0.1898 time: 0.3299 last_time: 0.2424 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:10:09 d2.utils.events]: \u001b[0m eta: 0:22:35 iter: 79379 total_loss: 0.7059 loss_cls: 0.2147 loss_box_reg: 0.2677 loss_rpn_cls: 0.03903 loss_rpn_loc: 0.1658 time: 0.3298 last_time: 0.2560 data_time: 0.0045 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:10:14 d2.utils.events]: \u001b[0m eta: 0:22:30 iter: 79399 total_loss: 0.9044 loss_cls: 0.2577 loss_box_reg: 0.3343 loss_rpn_cls: 0.04566 loss_rpn_loc: 0.2239 time: 0.3298 last_time: 0.2326 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:10:19 d2.utils.events]: \u001b[0m eta: 0:22:25 iter: 79419 total_loss: 0.6959 loss_cls: 0.1995 loss_box_reg: 0.2712 loss_rpn_cls: 0.05158 loss_rpn_loc: 0.1762 time: 0.3298 last_time: 0.2292 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:10:24 d2.utils.events]: \u001b[0m eta: 0:22:20 iter: 79439 total_loss: 0.749 loss_cls: 0.2346 loss_box_reg: 0.2937 loss_rpn_cls: 0.03762 loss_rpn_loc: 0.1865 time: 0.3298 last_time: 0.2601 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:10:28 d2.utils.events]: \u001b[0m eta: 0:22:15 iter: 79459 total_loss: 0.6615 loss_cls: 0.21 loss_box_reg: 0.2756 loss_rpn_cls: 0.0472 loss_rpn_loc: 0.163 time: 0.3297 last_time: 0.2172 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:10:33 d2.utils.events]: \u001b[0m eta: 0:22:11 iter: 79479 total_loss: 0.8633 loss_cls: 0.249 loss_box_reg: 0.3188 loss_rpn_cls: 0.0506 loss_rpn_loc: 0.2432 time: 0.3297 last_time: 0.2402 data_time: 0.0046 last_data_time: 0.0042 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:10:38 d2.utils.events]: \u001b[0m eta: 0:22:05 iter: 79499 total_loss: 0.7906 loss_cls: 0.2555 loss_box_reg: 0.2883 loss_rpn_cls: 0.0493 loss_rpn_loc: 0.173 time: 0.3297 last_time: 0.2621 data_time: 0.0048 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:10:42 d2.utils.events]: \u001b[0m eta: 0:22:00 iter: 79519 total_loss: 0.8221 loss_cls: 0.2363 loss_box_reg: 0.2918 loss_rpn_cls: 0.04781 loss_rpn_loc: 0.21 time: 0.3297 last_time: 0.2159 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:10:47 d2.utils.events]: \u001b[0m eta: 0:21:56 iter: 79539 total_loss: 0.8511 loss_cls: 0.2662 loss_box_reg: 0.3258 loss_rpn_cls: 0.04895 loss_rpn_loc: 0.1874 time: 0.3296 last_time: 0.2305 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:10:52 d2.utils.events]: \u001b[0m eta: 0:21:51 iter: 79559 total_loss: 0.742 loss_cls: 0.228 loss_box_reg: 0.2363 loss_rpn_cls: 0.04786 loss_rpn_loc: 0.2016 time: 0.3296 last_time: 0.2418 data_time: 0.0048 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:10:57 d2.utils.events]: \u001b[0m eta: 0:21:46 iter: 79579 total_loss: 0.7587 loss_cls: 0.2176 loss_box_reg: 0.2656 loss_rpn_cls: 0.04354 loss_rpn_loc: 0.2028 time: 0.3296 last_time: 0.2598 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:11:02 d2.utils.events]: \u001b[0m eta: 0:21:42 iter: 79599 total_loss: 0.7529 loss_cls: 0.217 loss_box_reg: 0.2816 loss_rpn_cls: 0.0412 loss_rpn_loc: 0.1841 time: 0.3296 last_time: 0.2326 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:11:06 d2.utils.events]: \u001b[0m eta: 0:21:37 iter: 79619 total_loss: 0.6769 loss_cls: 0.2124 loss_box_reg: 0.2508 loss_rpn_cls: 0.0443 loss_rpn_loc: 0.1529 time: 0.3296 last_time: 0.2311 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:11:11 d2.utils.events]: \u001b[0m eta: 0:21:32 iter: 79639 total_loss: 0.7268 loss_cls: 0.2244 loss_box_reg: 0.2658 loss_rpn_cls: 0.04877 loss_rpn_loc: 0.1965 time: 0.3295 last_time: 0.2305 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:11:16 d2.utils.events]: \u001b[0m eta: 0:21:27 iter: 79659 total_loss: 0.8559 loss_cls: 0.2893 loss_box_reg: 0.2978 loss_rpn_cls: 0.05586 loss_rpn_loc: 0.2016 time: 0.3295 last_time: 0.2293 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:11:21 d2.utils.events]: \u001b[0m eta: 0:21:22 iter: 79679 total_loss: 0.6947 loss_cls: 0.229 loss_box_reg: 0.2573 loss_rpn_cls: 0.0425 loss_rpn_loc: 0.186 time: 0.3295 last_time: 0.3321 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:11:27 d2.utils.events]: \u001b[0m eta: 0:21:18 iter: 79699 total_loss: 0.8341 loss_cls: 0.2697 loss_box_reg: 0.2937 loss_rpn_cls: 0.05437 loss_rpn_loc: 0.2103 time: 0.3295 last_time: 0.3203 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:11:32 d2.utils.events]: \u001b[0m eta: 0:21:14 iter: 79719 total_loss: 0.7277 loss_cls: 0.2373 loss_box_reg: 0.2622 loss_rpn_cls: 0.04163 loss_rpn_loc: 0.1736 time: 0.3295 last_time: 0.2441 data_time: 0.0049 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:11:37 d2.utils.events]: \u001b[0m eta: 0:21:10 iter: 79739 total_loss: 0.7949 loss_cls: 0.2454 loss_box_reg: 0.2949 loss_rpn_cls: 0.03447 loss_rpn_loc: 0.1988 time: 0.3294 last_time: 0.2335 data_time: 0.0050 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:11:41 d2.utils.events]: \u001b[0m eta: 0:21:05 iter: 79759 total_loss: 0.7468 loss_cls: 0.2516 loss_box_reg: 0.274 loss_rpn_cls: 0.04573 loss_rpn_loc: 0.1929 time: 0.3294 last_time: 0.2431 data_time: 0.0049 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:11:47 d2.utils.events]: \u001b[0m eta: 0:21:01 iter: 79779 total_loss: 0.7654 loss_cls: 0.2403 loss_box_reg: 0.3002 loss_rpn_cls: 0.03768 loss_rpn_loc: 0.2067 time: 0.3294 last_time: 0.3314 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:11:53 d2.utils.events]: \u001b[0m eta: 0:20:58 iter: 79799 total_loss: 0.6884 loss_cls: 0.1628 loss_box_reg: 0.267 loss_rpn_cls: 0.03913 loss_rpn_loc: 0.1667 time: 0.3294 last_time: 0.2478 data_time: 0.0050 last_data_time: 0.0055 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:11:58 d2.utils.events]: \u001b[0m eta: 0:20:54 iter: 79819 total_loss: 0.7455 loss_cls: 0.2133 loss_box_reg: 0.2902 loss_rpn_cls: 0.03679 loss_rpn_loc: 0.1919 time: 0.3294 last_time: 0.2165 data_time: 0.0049 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:12:03 d2.utils.events]: \u001b[0m eta: 0:20:49 iter: 79839 total_loss: 0.7822 loss_cls: 0.2483 loss_box_reg: 0.3014 loss_rpn_cls: 0.04085 loss_rpn_loc: 0.1946 time: 0.3294 last_time: 0.2909 data_time: 0.0049 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:12:09 d2.utils.events]: \u001b[0m eta: 0:20:46 iter: 79859 total_loss: 0.8466 loss_cls: 0.2741 loss_box_reg: 0.2978 loss_rpn_cls: 0.06062 loss_rpn_loc: 0.2107 time: 0.3293 last_time: 0.2517 data_time: 0.0050 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:12:15 d2.utils.events]: \u001b[0m eta: 0:20:42 iter: 79879 total_loss: 0.6766 loss_cls: 0.2287 loss_box_reg: 0.2701 loss_rpn_cls: 0.04475 loss_rpn_loc: 0.2076 time: 0.3293 last_time: 0.2432 data_time: 0.0049 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:12:20 d2.utils.events]: \u001b[0m eta: 0:20:37 iter: 79899 total_loss: 0.7689 loss_cls: 0.2327 loss_box_reg: 0.3115 loss_rpn_cls: 0.04716 loss_rpn_loc: 0.1925 time: 0.3293 last_time: 0.3162 data_time: 0.0049 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:12:25 d2.utils.events]: \u001b[0m eta: 0:20:33 iter: 79919 total_loss: 0.745 loss_cls: 0.225 loss_box_reg: 0.2951 loss_rpn_cls: 0.04289 loss_rpn_loc: 0.1961 time: 0.3293 last_time: 0.2169 data_time: 0.0050 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:12:30 d2.utils.events]: \u001b[0m eta: 0:20:30 iter: 79939 total_loss: 0.7072 loss_cls: 0.1988 loss_box_reg: 0.2624 loss_rpn_cls: 0.0394 loss_rpn_loc: 0.1912 time: 0.3293 last_time: 0.2700 data_time: 0.0049 last_data_time: 0.0055 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:12:36 d2.utils.events]: \u001b[0m eta: 0:20:26 iter: 79959 total_loss: 0.7432 loss_cls: 0.2378 loss_box_reg: 0.2679 loss_rpn_cls: 0.0439 loss_rpn_loc: 0.1614 time: 0.3293 last_time: 0.2571 data_time: 0.0049 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:12:40 d2.utils.events]: \u001b[0m eta: 0:20:20 iter: 79979 total_loss: 0.7817 loss_cls: 0.2139 loss_box_reg: 0.2938 loss_rpn_cls: 0.05771 loss_rpn_loc: 0.1951 time: 0.3292 last_time: 0.2575 data_time: 0.0049 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:12:47 d2.utils.events]: \u001b[0m eta: 0:20:17 iter: 79999 total_loss: 0.7383 loss_cls: 0.2254 loss_box_reg: 0.2709 loss_rpn_cls: 0.04138 loss_rpn_loc: 0.197 time: 0.3292 last_time: 0.2891 data_time: 0.0050 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:12:51 d2.utils.events]: \u001b[0m eta: 0:20:12 iter: 80019 total_loss: 0.9122 loss_cls: 0.2675 loss_box_reg: 0.3174 loss_rpn_cls: 0.05012 loss_rpn_loc: 0.2021 time: 0.3292 last_time: 0.2148 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:12:56 d2.utils.events]: \u001b[0m eta: 0:20:09 iter: 80039 total_loss: 0.7436 loss_cls: 0.229 loss_box_reg: 0.2855 loss_rpn_cls: 0.04954 loss_rpn_loc: 0.1762 time: 0.3292 last_time: 0.2166 data_time: 0.0050 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:13:02 d2.utils.events]: \u001b[0m eta: 0:20:05 iter: 80059 total_loss: 0.7205 loss_cls: 0.2404 loss_box_reg: 0.2511 loss_rpn_cls: 0.05314 loss_rpn_loc: 0.188 time: 0.3292 last_time: 0.3115 data_time: 0.0048 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:13:07 d2.utils.events]: \u001b[0m eta: 0:20:00 iter: 80079 total_loss: 0.7566 loss_cls: 0.2702 loss_box_reg: 0.2682 loss_rpn_cls: 0.06537 loss_rpn_loc: 0.1892 time: 0.3292 last_time: 0.2306 data_time: 0.0049 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:13:12 d2.utils.events]: \u001b[0m eta: 0:19:56 iter: 80099 total_loss: 0.7866 loss_cls: 0.2254 loss_box_reg: 0.2858 loss_rpn_cls: 0.05876 loss_rpn_loc: 0.2048 time: 0.3291 last_time: 0.2829 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:13:17 d2.utils.events]: \u001b[0m eta: 0:19:52 iter: 80119 total_loss: 0.748 loss_cls: 0.2325 loss_box_reg: 0.2572 loss_rpn_cls: 0.05301 loss_rpn_loc: 0.1967 time: 0.3291 last_time: 0.2424 data_time: 0.0049 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:13:22 d2.utils.events]: \u001b[0m eta: 0:19:47 iter: 80139 total_loss: 0.7521 loss_cls: 0.2107 loss_box_reg: 0.2877 loss_rpn_cls: 0.04041 loss_rpn_loc: 0.207 time: 0.3291 last_time: 0.3274 data_time: 0.0049 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:13:28 d2.utils.events]: \u001b[0m eta: 0:19:44 iter: 80159 total_loss: 0.8014 loss_cls: 0.2172 loss_box_reg: 0.2989 loss_rpn_cls: 0.03885 loss_rpn_loc: 0.1965 time: 0.3291 last_time: 0.2594 data_time: 0.0049 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:13:33 d2.utils.events]: \u001b[0m eta: 0:19:44 iter: 80179 total_loss: 0.7253 loss_cls: 0.2365 loss_box_reg: 0.2504 loss_rpn_cls: 0.04775 loss_rpn_loc: 0.1702 time: 0.3291 last_time: 0.2887 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:13:38 d2.utils.events]: \u001b[0m eta: 0:19:40 iter: 80199 total_loss: 0.7654 loss_cls: 0.2632 loss_box_reg: 0.2792 loss_rpn_cls: 0.05047 loss_rpn_loc: 0.171 time: 0.3291 last_time: 0.2898 data_time: 0.0050 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:13:44 d2.utils.events]: \u001b[0m eta: 0:19:34 iter: 80219 total_loss: 0.6761 loss_cls: 0.2076 loss_box_reg: 0.2164 loss_rpn_cls: 0.03627 loss_rpn_loc: 0.1722 time: 0.3290 last_time: 0.3144 data_time: 0.0049 last_data_time: 0.0057 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:13:50 d2.utils.events]: \u001b[0m eta: 0:19:29 iter: 80239 total_loss: 0.7857 loss_cls: 0.2421 loss_box_reg: 0.2863 loss_rpn_cls: 0.04406 loss_rpn_loc: 0.1878 time: 0.3290 last_time: 0.3087 data_time: 0.0051 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:13:54 d2.utils.events]: \u001b[0m eta: 0:19:19 iter: 80259 total_loss: 0.7679 loss_cls: 0.2409 loss_box_reg: 0.2645 loss_rpn_cls: 0.03237 loss_rpn_loc: 0.1748 time: 0.3290 last_time: 0.2569 data_time: 0.0049 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:13:59 d2.utils.events]: \u001b[0m eta: 0:19:19 iter: 80279 total_loss: 0.7351 loss_cls: 0.2085 loss_box_reg: 0.2907 loss_rpn_cls: 0.03959 loss_rpn_loc: 0.1663 time: 0.3290 last_time: 0.2308 data_time: 0.0049 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:14:06 d2.utils.events]: \u001b[0m eta: 0:19:33 iter: 80299 total_loss: 0.7148 loss_cls: 0.2242 loss_box_reg: 0.2641 loss_rpn_cls: 0.03222 loss_rpn_loc: 0.1892 time: 0.3290 last_time: 0.3246 data_time: 0.0053 last_data_time: 0.0055 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:14:12 d2.utils.events]: \u001b[0m eta: 0:19:50 iter: 80319 total_loss: 0.6173 loss_cls: 0.1824 loss_box_reg: 0.2145 loss_rpn_cls: 0.04197 loss_rpn_loc: 0.1744 time: 0.3290 last_time: 0.3049 data_time: 0.0053 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:14:18 d2.utils.events]: \u001b[0m eta: 0:19:51 iter: 80339 total_loss: 0.7092 loss_cls: 0.2016 loss_box_reg: 0.2782 loss_rpn_cls: 0.03771 loss_rpn_loc: 0.1838 time: 0.3290 last_time: 0.3118 data_time: 0.0053 last_data_time: 0.0057 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:14:24 d2.utils.events]: \u001b[0m eta: 0:19:48 iter: 80359 total_loss: 0.7686 loss_cls: 0.224 loss_box_reg: 0.297 loss_rpn_cls: 0.04971 loss_rpn_loc: 0.2129 time: 0.3290 last_time: 0.2995 data_time: 0.0054 last_data_time: 0.0055 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:14:30 d2.utils.events]: \u001b[0m eta: 0:19:45 iter: 80379 total_loss: 0.7085 loss_cls: 0.2051 loss_box_reg: 0.2495 loss_rpn_cls: 0.05079 loss_rpn_loc: 0.1686 time: 0.3290 last_time: 0.2111 data_time: 0.0052 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:14:36 d2.utils.events]: \u001b[0m eta: 0:19:41 iter: 80399 total_loss: 0.7338 loss_cls: 0.2218 loss_box_reg: 0.2609 loss_rpn_cls: 0.03016 loss_rpn_loc: 0.1675 time: 0.3289 last_time: 0.2768 data_time: 0.0052 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:14:42 d2.utils.events]: \u001b[0m eta: 0:19:38 iter: 80419 total_loss: 0.7412 loss_cls: 0.2127 loss_box_reg: 0.2842 loss_rpn_cls: 0.04023 loss_rpn_loc: 0.1753 time: 0.3289 last_time: 0.3228 data_time: 0.0052 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:14:47 d2.utils.events]: \u001b[0m eta: 0:19:34 iter: 80439 total_loss: 0.5944 loss_cls: 0.1825 loss_box_reg: 0.2251 loss_rpn_cls: 0.036 loss_rpn_loc: 0.1459 time: 0.3289 last_time: 0.2743 data_time: 0.0054 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:14:53 d2.utils.events]: \u001b[0m eta: 0:19:30 iter: 80459 total_loss: 0.6818 loss_cls: 0.2026 loss_box_reg: 0.2847 loss_rpn_cls: 0.02521 loss_rpn_loc: 0.1914 time: 0.3289 last_time: 0.2627 data_time: 0.0052 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:14:59 d2.utils.events]: \u001b[0m eta: 0:19:26 iter: 80479 total_loss: 0.7308 loss_cls: 0.2155 loss_box_reg: 0.2705 loss_rpn_cls: 0.04496 loss_rpn_loc: 0.1714 time: 0.3289 last_time: 0.2149 data_time: 0.0050 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:15:05 d2.utils.events]: \u001b[0m eta: 0:19:23 iter: 80499 total_loss: 0.6591 loss_cls: 0.2114 loss_box_reg: 0.2198 loss_rpn_cls: 0.03221 loss_rpn_loc: 0.1779 time: 0.3289 last_time: 0.2430 data_time: 0.0054 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:15:11 d2.utils.events]: \u001b[0m eta: 0:19:21 iter: 80519 total_loss: 0.7837 loss_cls: 0.2522 loss_box_reg: 0.3089 loss_rpn_cls: 0.05159 loss_rpn_loc: 0.1688 time: 0.3289 last_time: 0.2393 data_time: 0.0053 last_data_time: 0.0055 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:15:17 d2.utils.events]: \u001b[0m eta: 0:19:17 iter: 80539 total_loss: 0.8044 loss_cls: 0.2514 loss_box_reg: 0.2706 loss_rpn_cls: 0.04468 loss_rpn_loc: 0.1824 time: 0.3289 last_time: 0.2870 data_time: 0.0050 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:15:23 d2.utils.events]: \u001b[0m eta: 0:19:15 iter: 80559 total_loss: 0.7561 loss_cls: 0.2381 loss_box_reg: 0.2829 loss_rpn_cls: 0.04487 loss_rpn_loc: 0.1507 time: 0.3289 last_time: 0.3038 data_time: 0.0053 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:15:29 d2.utils.events]: \u001b[0m eta: 0:19:21 iter: 80579 total_loss: 0.7876 loss_cls: 0.2213 loss_box_reg: 0.28 loss_rpn_cls: 0.04367 loss_rpn_loc: 0.222 time: 0.3289 last_time: 0.2519 data_time: 0.0054 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:15:34 d2.utils.events]: \u001b[0m eta: 0:19:24 iter: 80599 total_loss: 0.6978 loss_cls: 0.2151 loss_box_reg: 0.2477 loss_rpn_cls: 0.03148 loss_rpn_loc: 0.1927 time: 0.3289 last_time: 0.2973 data_time: 0.0052 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:15:40 d2.utils.events]: \u001b[0m eta: 0:19:36 iter: 80619 total_loss: 0.748 loss_cls: 0.2091 loss_box_reg: 0.2783 loss_rpn_cls: 0.05093 loss_rpn_loc: 0.1859 time: 0.3288 last_time: 0.2998 data_time: 0.0052 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:15:46 d2.utils.events]: \u001b[0m eta: 0:19:42 iter: 80639 total_loss: 0.7873 loss_cls: 0.2124 loss_box_reg: 0.2896 loss_rpn_cls: 0.0415 loss_rpn_loc: 0.2153 time: 0.3288 last_time: 0.3366 data_time: 0.0051 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:15:52 d2.utils.events]: \u001b[0m eta: 0:19:50 iter: 80659 total_loss: 0.7754 loss_cls: 0.2302 loss_box_reg: 0.2774 loss_rpn_cls: 0.04192 loss_rpn_loc: 0.1899 time: 0.3288 last_time: 0.3381 data_time: 0.0053 last_data_time: 0.0054 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:15:58 d2.utils.events]: \u001b[0m eta: 0:19:51 iter: 80679 total_loss: 0.7675 loss_cls: 0.2369 loss_box_reg: 0.2863 loss_rpn_cls: 0.03427 loss_rpn_loc: 0.1703 time: 0.3288 last_time: 0.3416 data_time: 0.0053 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:16:04 d2.utils.events]: \u001b[0m eta: 0:19:45 iter: 80699 total_loss: 0.7769 loss_cls: 0.2393 loss_box_reg: 0.2944 loss_rpn_cls: 0.03669 loss_rpn_loc: 0.1832 time: 0.3288 last_time: 0.2992 data_time: 0.0053 last_data_time: 0.0061 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:16:10 d2.utils.events]: \u001b[0m eta: 0:19:47 iter: 80719 total_loss: 0.7949 loss_cls: 0.2597 loss_box_reg: 0.2984 loss_rpn_cls: 0.03811 loss_rpn_loc: 0.1974 time: 0.3288 last_time: 0.2990 data_time: 0.0051 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:16:16 d2.utils.events]: \u001b[0m eta: 0:19:53 iter: 80739 total_loss: 0.7 loss_cls: 0.238 loss_box_reg: 0.2507 loss_rpn_cls: 0.05049 loss_rpn_loc: 0.1851 time: 0.3288 last_time: 0.2901 data_time: 0.0052 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:16:22 d2.utils.events]: \u001b[0m eta: 0:19:56 iter: 80759 total_loss: 0.7611 loss_cls: 0.2223 loss_box_reg: 0.272 loss_rpn_cls: 0.03348 loss_rpn_loc: 0.1606 time: 0.3288 last_time: 0.2750 data_time: 0.0050 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:16:28 d2.utils.events]: \u001b[0m eta: 0:19:52 iter: 80779 total_loss: 0.7406 loss_cls: 0.1814 loss_box_reg: 0.2528 loss_rpn_cls: 0.03675 loss_rpn_loc: 0.1915 time: 0.3288 last_time: 0.3183 data_time: 0.0051 last_data_time: 0.0058 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:16:34 d2.utils.events]: \u001b[0m eta: 0:19:51 iter: 80799 total_loss: 0.6743 loss_cls: 0.2123 loss_box_reg: 0.2594 loss_rpn_cls: 0.04345 loss_rpn_loc: 0.1668 time: 0.3288 last_time: 0.2466 data_time: 0.0054 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:16:39 d2.utils.events]: \u001b[0m eta: 0:19:46 iter: 80819 total_loss: 0.8265 loss_cls: 0.2534 loss_box_reg: 0.2761 loss_rpn_cls: 0.04032 loss_rpn_loc: 0.1981 time: 0.3288 last_time: 0.3157 data_time: 0.0051 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:16:45 d2.utils.events]: \u001b[0m eta: 0:19:44 iter: 80839 total_loss: 0.7646 loss_cls: 0.2332 loss_box_reg: 0.2895 loss_rpn_cls: 0.03826 loss_rpn_loc: 0.1797 time: 0.3288 last_time: 0.2910 data_time: 0.0053 last_data_time: 0.0057 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:16:51 d2.utils.events]: \u001b[0m eta: 0:19:37 iter: 80859 total_loss: 0.7784 loss_cls: 0.2338 loss_box_reg: 0.2801 loss_rpn_cls: 0.05312 loss_rpn_loc: 0.1994 time: 0.3287 last_time: 0.2748 data_time: 0.0052 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:16:57 d2.utils.events]: \u001b[0m eta: 0:19:31 iter: 80879 total_loss: 0.6159 loss_cls: 0.1968 loss_box_reg: 0.2591 loss_rpn_cls: 0.02756 loss_rpn_loc: 0.1831 time: 0.3287 last_time: 0.2809 data_time: 0.0052 last_data_time: 0.0054 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:17:03 d2.utils.events]: \u001b[0m eta: 0:19:28 iter: 80899 total_loss: 0.7578 loss_cls: 0.2355 loss_box_reg: 0.2672 loss_rpn_cls: 0.04862 loss_rpn_loc: 0.2186 time: 0.3287 last_time: 0.2186 data_time: 0.0053 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:17:07 d2.utils.events]: \u001b[0m eta: 0:19:21 iter: 80919 total_loss: 0.8296 loss_cls: 0.2378 loss_box_reg: 0.2959 loss_rpn_cls: 0.0533 loss_rpn_loc: 0.1818 time: 0.3287 last_time: 0.2408 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:17:12 d2.utils.events]: \u001b[0m eta: 0:19:13 iter: 80939 total_loss: 0.8442 loss_cls: 0.245 loss_box_reg: 0.3077 loss_rpn_cls: 0.04999 loss_rpn_loc: 0.2178 time: 0.3287 last_time: 0.2447 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:17:17 d2.utils.events]: \u001b[0m eta: 0:19:04 iter: 80959 total_loss: 0.7267 loss_cls: 0.2115 loss_box_reg: 0.2648 loss_rpn_cls: 0.05198 loss_rpn_loc: 0.2075 time: 0.3287 last_time: 0.2385 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:17:21 d2.utils.events]: \u001b[0m eta: 0:18:58 iter: 80979 total_loss: 0.7494 loss_cls: 0.2218 loss_box_reg: 0.293 loss_rpn_cls: 0.03361 loss_rpn_loc: 0.1845 time: 0.3286 last_time: 0.2149 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:17:26 d2.utils.events]: \u001b[0m eta: 0:18:46 iter: 80999 total_loss: 0.7692 loss_cls: 0.2164 loss_box_reg: 0.2831 loss_rpn_cls: 0.04314 loss_rpn_loc: 0.2057 time: 0.3286 last_time: 0.2562 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:17:31 d2.utils.events]: \u001b[0m eta: 0:18:39 iter: 81019 total_loss: 0.7837 loss_cls: 0.2619 loss_box_reg: 0.3037 loss_rpn_cls: 0.03335 loss_rpn_loc: 0.193 time: 0.3286 last_time: 0.2125 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:17:35 d2.utils.events]: \u001b[0m eta: 0:18:29 iter: 81039 total_loss: 0.7901 loss_cls: 0.2554 loss_box_reg: 0.2548 loss_rpn_cls: 0.04571 loss_rpn_loc: 0.1969 time: 0.3286 last_time: 0.2414 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:17:40 d2.utils.events]: \u001b[0m eta: 0:18:18 iter: 81059 total_loss: 0.7398 loss_cls: 0.2198 loss_box_reg: 0.2819 loss_rpn_cls: 0.03473 loss_rpn_loc: 0.1802 time: 0.3285 last_time: 0.2571 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:17:45 d2.utils.events]: \u001b[0m eta: 0:18:12 iter: 81079 total_loss: 0.6875 loss_cls: 0.2114 loss_box_reg: 0.2587 loss_rpn_cls: 0.03383 loss_rpn_loc: 0.1668 time: 0.3285 last_time: 0.2608 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:17:50 d2.utils.events]: \u001b[0m eta: 0:18:00 iter: 81099 total_loss: 0.7781 loss_cls: 0.2249 loss_box_reg: 0.2709 loss_rpn_cls: 0.04675 loss_rpn_loc: 0.1945 time: 0.3285 last_time: 0.2292 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:17:54 d2.utils.events]: \u001b[0m eta: 0:17:52 iter: 81119 total_loss: 0.7751 loss_cls: 0.2398 loss_box_reg: 0.2573 loss_rpn_cls: 0.05262 loss_rpn_loc: 0.1886 time: 0.3285 last_time: 0.2602 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:17:59 d2.utils.events]: \u001b[0m eta: 0:17:44 iter: 81139 total_loss: 0.7228 loss_cls: 0.213 loss_box_reg: 0.2739 loss_rpn_cls: 0.0393 loss_rpn_loc: 0.1985 time: 0.3285 last_time: 0.2133 data_time: 0.0046 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:18:04 d2.utils.events]: \u001b[0m eta: 0:17:35 iter: 81159 total_loss: 0.7779 loss_cls: 0.2168 loss_box_reg: 0.2963 loss_rpn_cls: 0.04805 loss_rpn_loc: 0.1923 time: 0.3284 last_time: 0.2418 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:18:09 d2.utils.events]: \u001b[0m eta: 0:17:29 iter: 81179 total_loss: 0.8072 loss_cls: 0.2529 loss_box_reg: 0.3021 loss_rpn_cls: 0.04589 loss_rpn_loc: 0.1954 time: 0.3284 last_time: 0.2255 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:18:13 d2.utils.events]: \u001b[0m eta: 0:17:15 iter: 81199 total_loss: 0.7255 loss_cls: 0.176 loss_box_reg: 0.2928 loss_rpn_cls: 0.03959 loss_rpn_loc: 0.1818 time: 0.3284 last_time: 0.2579 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:18:18 d2.utils.events]: \u001b[0m eta: 0:16:58 iter: 81219 total_loss: 0.7093 loss_cls: 0.2305 loss_box_reg: 0.2574 loss_rpn_cls: 0.04833 loss_rpn_loc: 0.1813 time: 0.3284 last_time: 0.2303 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:18:24 d2.utils.events]: \u001b[0m eta: 0:16:50 iter: 81239 total_loss: 0.716 loss_cls: 0.1975 loss_box_reg: 0.26 loss_rpn_cls: 0.03651 loss_rpn_loc: 0.1636 time: 0.3283 last_time: 0.3332 data_time: 0.0050 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:18:30 d2.utils.events]: \u001b[0m eta: 0:16:59 iter: 81259 total_loss: 0.6519 loss_cls: 0.2057 loss_box_reg: 0.2285 loss_rpn_cls: 0.03275 loss_rpn_loc: 0.1621 time: 0.3283 last_time: 0.2865 data_time: 0.0053 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:18:35 d2.utils.events]: \u001b[0m eta: 0:16:59 iter: 81279 total_loss: 0.7397 loss_cls: 0.2147 loss_box_reg: 0.2796 loss_rpn_cls: 0.05903 loss_rpn_loc: 0.2083 time: 0.3283 last_time: 0.2737 data_time: 0.0051 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:18:41 d2.utils.events]: \u001b[0m eta: 0:16:51 iter: 81299 total_loss: 0.6741 loss_cls: 0.1957 loss_box_reg: 0.2503 loss_rpn_cls: 0.03948 loss_rpn_loc: 0.1844 time: 0.3283 last_time: 0.3341 data_time: 0.0051 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:18:47 d2.utils.events]: \u001b[0m eta: 0:16:45 iter: 81319 total_loss: 0.6709 loss_cls: 0.2286 loss_box_reg: 0.2329 loss_rpn_cls: 0.04062 loss_rpn_loc: 0.1923 time: 0.3283 last_time: 0.3409 data_time: 0.0053 last_data_time: 0.0053 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:18:53 d2.utils.events]: \u001b[0m eta: 0:16:39 iter: 81339 total_loss: 0.646 loss_cls: 0.1871 loss_box_reg: 0.2316 loss_rpn_cls: 0.04687 loss_rpn_loc: 0.1793 time: 0.3283 last_time: 0.2911 data_time: 0.0054 last_data_time: 0.0061 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:18:59 d2.utils.events]: \u001b[0m eta: 0:16:33 iter: 81359 total_loss: 0.8088 loss_cls: 0.2935 loss_box_reg: 0.2988 loss_rpn_cls: 0.06377 loss_rpn_loc: 0.1932 time: 0.3283 last_time: 0.3019 data_time: 0.0053 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:19:05 d2.utils.events]: \u001b[0m eta: 0:16:29 iter: 81379 total_loss: 0.7102 loss_cls: 0.2229 loss_box_reg: 0.2553 loss_rpn_cls: 0.05025 loss_rpn_loc: 0.1767 time: 0.3283 last_time: 0.3059 data_time: 0.0054 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:19:11 d2.utils.events]: \u001b[0m eta: 0:16:22 iter: 81399 total_loss: 0.6504 loss_cls: 0.1932 loss_box_reg: 0.2545 loss_rpn_cls: 0.03618 loss_rpn_loc: 0.1705 time: 0.3283 last_time: 0.3445 data_time: 0.0054 last_data_time: 0.0056 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:19:17 d2.utils.events]: \u001b[0m eta: 0:16:17 iter: 81419 total_loss: 0.7197 loss_cls: 0.2073 loss_box_reg: 0.2692 loss_rpn_cls: 0.04194 loss_rpn_loc: 0.2003 time: 0.3283 last_time: 0.3119 data_time: 0.0055 last_data_time: 0.0054 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:19:23 d2.utils.events]: \u001b[0m eta: 0:16:16 iter: 81439 total_loss: 0.7364 loss_cls: 0.2226 loss_box_reg: 0.2955 loss_rpn_cls: 0.04933 loss_rpn_loc: 0.1823 time: 0.3283 last_time: 0.3458 data_time: 0.0053 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:19:29 d2.utils.events]: \u001b[0m eta: 0:16:11 iter: 81459 total_loss: 0.6786 loss_cls: 0.2272 loss_box_reg: 0.2261 loss_rpn_cls: 0.03681 loss_rpn_loc: 0.1856 time: 0.3283 last_time: 0.2748 data_time: 0.0051 last_data_time: 0.0055 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:19:35 d2.utils.events]: \u001b[0m eta: 0:16:07 iter: 81479 total_loss: 0.7569 loss_cls: 0.2502 loss_box_reg: 0.2675 loss_rpn_cls: 0.03953 loss_rpn_loc: 0.1817 time: 0.3283 last_time: 0.2970 data_time: 0.0053 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:19:41 d2.utils.events]: \u001b[0m eta: 0:16:01 iter: 81499 total_loss: 0.8084 loss_cls: 0.2571 loss_box_reg: 0.3029 loss_rpn_cls: 0.05036 loss_rpn_loc: 0.2166 time: 0.3283 last_time: 0.3025 data_time: 0.0053 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:19:47 d2.utils.events]: \u001b[0m eta: 0:15:53 iter: 81519 total_loss: 0.7033 loss_cls: 0.221 loss_box_reg: 0.2451 loss_rpn_cls: 0.03725 loss_rpn_loc: 0.1689 time: 0.3282 last_time: 0.3007 data_time: 0.0052 last_data_time: 0.0053 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:19:52 d2.utils.events]: \u001b[0m eta: 0:15:45 iter: 81539 total_loss: 0.7397 loss_cls: 0.2346 loss_box_reg: 0.2683 loss_rpn_cls: 0.02648 loss_rpn_loc: 0.1791 time: 0.3282 last_time: 0.2238 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:19:57 d2.utils.events]: \u001b[0m eta: 0:15:31 iter: 81559 total_loss: 0.784 loss_cls: 0.2555 loss_box_reg: 0.2571 loss_rpn_cls: 0.04421 loss_rpn_loc: 0.1871 time: 0.3282 last_time: 0.2323 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:20:02 d2.utils.events]: \u001b[0m eta: 0:15:14 iter: 81579 total_loss: 0.8207 loss_cls: 0.2648 loss_box_reg: 0.2948 loss_rpn_cls: 0.04968 loss_rpn_loc: 0.2266 time: 0.3282 last_time: 0.2762 data_time: 0.0047 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:20:07 d2.utils.events]: \u001b[0m eta: 0:14:59 iter: 81599 total_loss: 0.7823 loss_cls: 0.2653 loss_box_reg: 0.2784 loss_rpn_cls: 0.05436 loss_rpn_loc: 0.1884 time: 0.3282 last_time: 0.2558 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:20:12 d2.utils.events]: \u001b[0m eta: 0:14:44 iter: 81619 total_loss: 0.8282 loss_cls: 0.2417 loss_box_reg: 0.3334 loss_rpn_cls: 0.04993 loss_rpn_loc: 0.1805 time: 0.3281 last_time: 0.2583 data_time: 0.0045 last_data_time: 0.0041 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:20:16 d2.utils.events]: \u001b[0m eta: 0:14:34 iter: 81639 total_loss: 0.7878 loss_cls: 0.2027 loss_box_reg: 0.2677 loss_rpn_cls: 0.04352 loss_rpn_loc: 0.1956 time: 0.3281 last_time: 0.2575 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:20:21 d2.utils.events]: \u001b[0m eta: 0:14:26 iter: 81659 total_loss: 0.7413 loss_cls: 0.2132 loss_box_reg: 0.2703 loss_rpn_cls: 0.04127 loss_rpn_loc: 0.1619 time: 0.3281 last_time: 0.2307 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:20:26 d2.utils.events]: \u001b[0m eta: 0:14:19 iter: 81679 total_loss: 0.859 loss_cls: 0.2618 loss_box_reg: 0.3343 loss_rpn_cls: 0.04595 loss_rpn_loc: 0.2111 time: 0.3281 last_time: 0.1885 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:20:31 d2.utils.events]: \u001b[0m eta: 0:14:12 iter: 81699 total_loss: 0.7435 loss_cls: 0.2057 loss_box_reg: 0.2642 loss_rpn_cls: 0.04292 loss_rpn_loc: 0.1835 time: 0.3281 last_time: 0.2034 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:20:35 d2.utils.events]: \u001b[0m eta: 0:14:05 iter: 81719 total_loss: 0.7661 loss_cls: 0.2359 loss_box_reg: 0.2898 loss_rpn_cls: 0.03852 loss_rpn_loc: 0.186 time: 0.3280 last_time: 0.2771 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:20:40 d2.utils.events]: \u001b[0m eta: 0:13:59 iter: 81739 total_loss: 0.777 loss_cls: 0.2455 loss_box_reg: 0.2981 loss_rpn_cls: 0.0447 loss_rpn_loc: 0.198 time: 0.3280 last_time: 0.2609 data_time: 0.0047 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:20:45 d2.utils.events]: \u001b[0m eta: 0:13:53 iter: 81759 total_loss: 0.7676 loss_cls: 0.235 loss_box_reg: 0.2849 loss_rpn_cls: 0.04164 loss_rpn_loc: 0.1815 time: 0.3280 last_time: 0.2581 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:20:50 d2.utils.events]: \u001b[0m eta: 0:13:46 iter: 81779 total_loss: 0.8029 loss_cls: 0.2155 loss_box_reg: 0.2793 loss_rpn_cls: 0.05578 loss_rpn_loc: 0.185 time: 0.3280 last_time: 0.2179 data_time: 0.0044 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:20:54 d2.utils.events]: \u001b[0m eta: 0:13:40 iter: 81799 total_loss: 0.6757 loss_cls: 0.2083 loss_box_reg: 0.2838 loss_rpn_cls: 0.04044 loss_rpn_loc: 0.1852 time: 0.3279 last_time: 0.2317 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:20:59 d2.utils.events]: \u001b[0m eta: 0:13:33 iter: 81819 total_loss: 0.6485 loss_cls: 0.1896 loss_box_reg: 0.2444 loss_rpn_cls: 0.03726 loss_rpn_loc: 0.1836 time: 0.3279 last_time: 0.2591 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:21:04 d2.utils.events]: \u001b[0m eta: 0:13:26 iter: 81839 total_loss: 0.7398 loss_cls: 0.2835 loss_box_reg: 0.3031 loss_rpn_cls: 0.03959 loss_rpn_loc: 0.1658 time: 0.3279 last_time: 0.2188 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:21:09 d2.utils.events]: \u001b[0m eta: 0:13:21 iter: 81859 total_loss: 0.8005 loss_cls: 0.2576 loss_box_reg: 0.2715 loss_rpn_cls: 0.04111 loss_rpn_loc: 0.1896 time: 0.3279 last_time: 0.2008 data_time: 0.0047 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:21:14 d2.utils.events]: \u001b[0m eta: 0:13:13 iter: 81879 total_loss: 0.6598 loss_cls: 0.2137 loss_box_reg: 0.2489 loss_rpn_cls: 0.04619 loss_rpn_loc: 0.1589 time: 0.3279 last_time: 0.2299 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:21:18 d2.utils.events]: \u001b[0m eta: 0:13:05 iter: 81899 total_loss: 0.7893 loss_cls: 0.2417 loss_box_reg: 0.2671 loss_rpn_cls: 0.03756 loss_rpn_loc: 0.1999 time: 0.3278 last_time: 0.2548 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:21:23 d2.utils.events]: \u001b[0m eta: 0:13:02 iter: 81919 total_loss: 0.7575 loss_cls: 0.2179 loss_box_reg: 0.298 loss_rpn_cls: 0.04301 loss_rpn_loc: 0.1927 time: 0.3278 last_time: 0.2426 data_time: 0.0047 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:21:28 d2.utils.events]: \u001b[0m eta: 0:12:54 iter: 81939 total_loss: 0.7961 loss_cls: 0.2756 loss_box_reg: 0.3016 loss_rpn_cls: 0.05238 loss_rpn_loc: 0.1756 time: 0.3278 last_time: 0.2596 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:21:33 d2.utils.events]: \u001b[0m eta: 0:12:53 iter: 81959 total_loss: 0.7817 loss_cls: 0.2265 loss_box_reg: 0.2824 loss_rpn_cls: 0.03689 loss_rpn_loc: 0.1664 time: 0.3278 last_time: 0.2426 data_time: 0.0047 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:21:37 d2.utils.events]: \u001b[0m eta: 0:12:48 iter: 81979 total_loss: 0.6928 loss_cls: 0.2064 loss_box_reg: 0.2547 loss_rpn_cls: 0.05051 loss_rpn_loc: 0.1826 time: 0.3277 last_time: 0.1879 data_time: 0.0047 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:21:42 d2.utils.events]: \u001b[0m eta: 0:12:38 iter: 81999 total_loss: 0.6647 loss_cls: 0.2029 loss_box_reg: 0.2224 loss_rpn_cls: 0.03087 loss_rpn_loc: 0.1886 time: 0.3277 last_time: 0.2442 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:21:47 d2.utils.events]: \u001b[0m eta: 0:12:38 iter: 82019 total_loss: 0.7005 loss_cls: 0.1868 loss_box_reg: 0.2518 loss_rpn_cls: 0.04781 loss_rpn_loc: 0.1904 time: 0.3277 last_time: 0.2299 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:21:52 d2.utils.events]: \u001b[0m eta: 0:12:31 iter: 82039 total_loss: 0.7864 loss_cls: 0.2766 loss_box_reg: 0.2413 loss_rpn_cls: 0.05119 loss_rpn_loc: 0.2027 time: 0.3277 last_time: 0.2555 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:21:56 d2.utils.events]: \u001b[0m eta: 0:12:26 iter: 82059 total_loss: 0.8277 loss_cls: 0.2819 loss_box_reg: 0.2902 loss_rpn_cls: 0.04514 loss_rpn_loc: 0.1803 time: 0.3277 last_time: 0.2171 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:22:01 d2.utils.events]: \u001b[0m eta: 0:12:23 iter: 82079 total_loss: 0.7872 loss_cls: 0.196 loss_box_reg: 0.2848 loss_rpn_cls: 0.04539 loss_rpn_loc: 0.2107 time: 0.3276 last_time: 0.2641 data_time: 0.0046 last_data_time: 0.0054 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:22:06 d2.utils.events]: \u001b[0m eta: 0:12:18 iter: 82099 total_loss: 0.7461 loss_cls: 0.2277 loss_box_reg: 0.2834 loss_rpn_cls: 0.04007 loss_rpn_loc: 0.2029 time: 0.3276 last_time: 0.2411 data_time: 0.0046 last_data_time: 0.0042 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:22:11 d2.utils.events]: \u001b[0m eta: 0:12:13 iter: 82119 total_loss: 0.7878 loss_cls: 0.2501 loss_box_reg: 0.2861 loss_rpn_cls: 0.05329 loss_rpn_loc: 0.182 time: 0.3276 last_time: 0.2268 data_time: 0.0046 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:22:16 d2.utils.events]: \u001b[0m eta: 0:12:08 iter: 82139 total_loss: 0.8366 loss_cls: 0.2309 loss_box_reg: 0.284 loss_rpn_cls: 0.05732 loss_rpn_loc: 0.2194 time: 0.3276 last_time: 0.2419 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:22:20 d2.utils.events]: \u001b[0m eta: 0:12:02 iter: 82159 total_loss: 0.7 loss_cls: 0.2033 loss_box_reg: 0.2506 loss_rpn_cls: 0.04206 loss_rpn_loc: 0.1844 time: 0.3275 last_time: 0.2402 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:22:25 d2.utils.events]: \u001b[0m eta: 0:11:57 iter: 82179 total_loss: 0.7369 loss_cls: 0.2168 loss_box_reg: 0.2501 loss_rpn_cls: 0.04742 loss_rpn_loc: 0.1953 time: 0.3275 last_time: 0.2562 data_time: 0.0046 last_data_time: 0.0042 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:22:30 d2.utils.events]: \u001b[0m eta: 0:11:49 iter: 82199 total_loss: 0.784 loss_cls: 0.2389 loss_box_reg: 0.2878 loss_rpn_cls: 0.04179 loss_rpn_loc: 0.179 time: 0.3275 last_time: 0.2588 data_time: 0.0046 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:22:34 d2.utils.events]: \u001b[0m eta: 0:11:45 iter: 82219 total_loss: 0.8396 loss_cls: 0.2503 loss_box_reg: 0.2886 loss_rpn_cls: 0.04588 loss_rpn_loc: 0.1803 time: 0.3275 last_time: 0.2558 data_time: 0.0045 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:22:39 d2.utils.events]: \u001b[0m eta: 0:11:32 iter: 82239 total_loss: 0.7072 loss_cls: 0.1995 loss_box_reg: 0.2687 loss_rpn_cls: 0.03698 loss_rpn_loc: 0.1873 time: 0.3275 last_time: 0.2396 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:22:44 d2.utils.events]: \u001b[0m eta: 0:11:22 iter: 82259 total_loss: 0.8304 loss_cls: 0.2319 loss_box_reg: 0.3097 loss_rpn_cls: 0.04347 loss_rpn_loc: 0.2135 time: 0.3274 last_time: 0.2330 data_time: 0.0047 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:22:49 d2.utils.events]: \u001b[0m eta: 0:11:09 iter: 82279 total_loss: 0.7561 loss_cls: 0.243 loss_box_reg: 0.2848 loss_rpn_cls: 0.06307 loss_rpn_loc: 0.208 time: 0.3274 last_time: 0.2303 data_time: 0.0047 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:22:54 d2.utils.events]: \u001b[0m eta: 0:11:00 iter: 82299 total_loss: 0.6623 loss_cls: 0.2026 loss_box_reg: 0.2566 loss_rpn_cls: 0.03539 loss_rpn_loc: 0.155 time: 0.3274 last_time: 0.2425 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:22:58 d2.utils.events]: \u001b[0m eta: 0:10:54 iter: 82319 total_loss: 0.6829 loss_cls: 0.1923 loss_box_reg: 0.2373 loss_rpn_cls: 0.04167 loss_rpn_loc: 0.1715 time: 0.3274 last_time: 0.2575 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:23:03 d2.utils.events]: \u001b[0m eta: 0:10:48 iter: 82339 total_loss: 0.6713 loss_cls: 0.2199 loss_box_reg: 0.2405 loss_rpn_cls: 0.02836 loss_rpn_loc: 0.1718 time: 0.3274 last_time: 0.2435 data_time: 0.0046 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:23:08 d2.utils.events]: \u001b[0m eta: 0:10:42 iter: 82359 total_loss: 0.7577 loss_cls: 0.2613 loss_box_reg: 0.2739 loss_rpn_cls: 0.04134 loss_rpn_loc: 0.1826 time: 0.3273 last_time: 0.2587 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:23:13 d2.utils.events]: \u001b[0m eta: 0:10:36 iter: 82379 total_loss: 0.7301 loss_cls: 0.2178 loss_box_reg: 0.2713 loss_rpn_cls: 0.03758 loss_rpn_loc: 0.1936 time: 0.3273 last_time: 0.2404 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:23:18 d2.utils.events]: \u001b[0m eta: 0:10:31 iter: 82399 total_loss: 0.6584 loss_cls: 0.2039 loss_box_reg: 0.2389 loss_rpn_cls: 0.04008 loss_rpn_loc: 0.162 time: 0.3273 last_time: 0.2569 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:23:22 d2.utils.events]: \u001b[0m eta: 0:10:26 iter: 82419 total_loss: 0.6686 loss_cls: 0.1822 loss_box_reg: 0.2543 loss_rpn_cls: 0.03734 loss_rpn_loc: 0.1621 time: 0.3273 last_time: 0.2171 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:23:27 d2.utils.events]: \u001b[0m eta: 0:10:20 iter: 82439 total_loss: 0.6745 loss_cls: 0.1988 loss_box_reg: 0.2461 loss_rpn_cls: 0.04355 loss_rpn_loc: 0.1833 time: 0.3272 last_time: 0.2429 data_time: 0.0045 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:23:32 d2.utils.events]: \u001b[0m eta: 0:10:15 iter: 82459 total_loss: 0.8199 loss_cls: 0.2536 loss_box_reg: 0.3285 loss_rpn_cls: 0.05163 loss_rpn_loc: 0.1893 time: 0.3272 last_time: 0.2026 data_time: 0.0047 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:23:36 d2.utils.events]: \u001b[0m eta: 0:10:09 iter: 82479 total_loss: 0.8501 loss_cls: 0.2608 loss_box_reg: 0.3001 loss_rpn_cls: 0.04849 loss_rpn_loc: 0.1891 time: 0.3272 last_time: 0.2418 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:23:41 d2.utils.events]: \u001b[0m eta: 0:10:04 iter: 82499 total_loss: 0.7302 loss_cls: 0.199 loss_box_reg: 0.2834 loss_rpn_cls: 0.03746 loss_rpn_loc: 0.1962 time: 0.3272 last_time: 0.2431 data_time: 0.0047 last_data_time: 0.0042 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:23:46 d2.utils.events]: \u001b[0m eta: 0:09:59 iter: 82519 total_loss: 0.6706 loss_cls: 0.2268 loss_box_reg: 0.2635 loss_rpn_cls: 0.03799 loss_rpn_loc: 0.1741 time: 0.3272 last_time: 0.2384 data_time: 0.0047 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:23:51 d2.utils.events]: \u001b[0m eta: 0:09:54 iter: 82539 total_loss: 0.8444 loss_cls: 0.2719 loss_box_reg: 0.3303 loss_rpn_cls: 0.0599 loss_rpn_loc: 0.2211 time: 0.3271 last_time: 0.1872 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:23:55 d2.utils.events]: \u001b[0m eta: 0:09:49 iter: 82559 total_loss: 0.6398 loss_cls: 0.1911 loss_box_reg: 0.2615 loss_rpn_cls: 0.04169 loss_rpn_loc: 0.1532 time: 0.3271 last_time: 0.2574 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:24:00 d2.utils.events]: \u001b[0m eta: 0:09:44 iter: 82579 total_loss: 0.8848 loss_cls: 0.2575 loss_box_reg: 0.3365 loss_rpn_cls: 0.05149 loss_rpn_loc: 0.2257 time: 0.3271 last_time: 0.2588 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:24:05 d2.utils.events]: \u001b[0m eta: 0:09:39 iter: 82599 total_loss: 0.8063 loss_cls: 0.2267 loss_box_reg: 0.2767 loss_rpn_cls: 0.05141 loss_rpn_loc: 0.1989 time: 0.3271 last_time: 0.2573 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:24:10 d2.utils.events]: \u001b[0m eta: 0:09:34 iter: 82619 total_loss: 0.836 loss_cls: 0.2748 loss_box_reg: 0.287 loss_rpn_cls: 0.04509 loss_rpn_loc: 0.2032 time: 0.3270 last_time: 0.2638 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:24:14 d2.utils.events]: \u001b[0m eta: 0:09:29 iter: 82639 total_loss: 0.6963 loss_cls: 0.2022 loss_box_reg: 0.2555 loss_rpn_cls: 0.03729 loss_rpn_loc: 0.1728 time: 0.3270 last_time: 0.2573 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:24:19 d2.utils.events]: \u001b[0m eta: 0:09:24 iter: 82659 total_loss: 0.7266 loss_cls: 0.2052 loss_box_reg: 0.2719 loss_rpn_cls: 0.05721 loss_rpn_loc: 0.1976 time: 0.3270 last_time: 0.2169 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:24:24 d2.utils.events]: \u001b[0m eta: 0:09:19 iter: 82679 total_loss: 0.8209 loss_cls: 0.2334 loss_box_reg: 0.289 loss_rpn_cls: 0.03511 loss_rpn_loc: 0.1799 time: 0.3270 last_time: 0.2406 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:24:29 d2.utils.events]: \u001b[0m eta: 0:09:15 iter: 82699 total_loss: 0.6664 loss_cls: 0.212 loss_box_reg: 0.274 loss_rpn_cls: 0.04054 loss_rpn_loc: 0.1366 time: 0.3270 last_time: 0.2296 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:24:33 d2.utils.events]: \u001b[0m eta: 0:09:09 iter: 82719 total_loss: 0.6793 loss_cls: 0.2007 loss_box_reg: 0.268 loss_rpn_cls: 0.03926 loss_rpn_loc: 0.1828 time: 0.3269 last_time: 0.2399 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:24:38 d2.utils.events]: \u001b[0m eta: 0:09:04 iter: 82739 total_loss: 0.7256 loss_cls: 0.2274 loss_box_reg: 0.2487 loss_rpn_cls: 0.05056 loss_rpn_loc: 0.1698 time: 0.3269 last_time: 0.2290 data_time: 0.0046 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:24:43 d2.utils.events]: \u001b[0m eta: 0:09:00 iter: 82759 total_loss: 0.707 loss_cls: 0.2049 loss_box_reg: 0.2678 loss_rpn_cls: 0.04667 loss_rpn_loc: 0.1756 time: 0.3269 last_time: 0.2298 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:24:48 d2.utils.events]: \u001b[0m eta: 0:08:55 iter: 82779 total_loss: 0.7915 loss_cls: 0.2056 loss_box_reg: 0.2881 loss_rpn_cls: 0.05 loss_rpn_loc: 0.184 time: 0.3269 last_time: 0.2593 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:24:52 d2.utils.events]: \u001b[0m eta: 0:08:50 iter: 82799 total_loss: 0.8529 loss_cls: 0.2874 loss_box_reg: 0.312 loss_rpn_cls: 0.05194 loss_rpn_loc: 0.2004 time: 0.3268 last_time: 0.2588 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:24:57 d2.utils.events]: \u001b[0m eta: 0:08:45 iter: 82819 total_loss: 0.7537 loss_cls: 0.2398 loss_box_reg: 0.2831 loss_rpn_cls: 0.04386 loss_rpn_loc: 0.1927 time: 0.3268 last_time: 0.2587 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:25:02 d2.utils.events]: \u001b[0m eta: 0:08:40 iter: 82839 total_loss: 0.8435 loss_cls: 0.2831 loss_box_reg: 0.2693 loss_rpn_cls: 0.04908 loss_rpn_loc: 0.1899 time: 0.3268 last_time: 0.2024 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:25:06 d2.utils.events]: \u001b[0m eta: 0:08:35 iter: 82859 total_loss: 0.7264 loss_cls: 0.2124 loss_box_reg: 0.2736 loss_rpn_cls: 0.03466 loss_rpn_loc: 0.1952 time: 0.3268 last_time: 0.2177 data_time: 0.0045 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:25:11 d2.utils.events]: \u001b[0m eta: 0:08:30 iter: 82879 total_loss: 0.7256 loss_cls: 0.2405 loss_box_reg: 0.2611 loss_rpn_cls: 0.03538 loss_rpn_loc: 0.1628 time: 0.3268 last_time: 0.2591 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:25:16 d2.utils.events]: \u001b[0m eta: 0:08:26 iter: 82899 total_loss: 0.6738 loss_cls: 0.2132 loss_box_reg: 0.2435 loss_rpn_cls: 0.04154 loss_rpn_loc: 0.2042 time: 0.3267 last_time: 0.1876 data_time: 0.0049 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:25:21 d2.utils.events]: \u001b[0m eta: 0:08:21 iter: 82919 total_loss: 0.7313 loss_cls: 0.2277 loss_box_reg: 0.2869 loss_rpn_cls: 0.04508 loss_rpn_loc: 0.1762 time: 0.3267 last_time: 0.2433 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:25:25 d2.utils.events]: \u001b[0m eta: 0:08:16 iter: 82939 total_loss: 0.8405 loss_cls: 0.2758 loss_box_reg: 0.3105 loss_rpn_cls: 0.04861 loss_rpn_loc: 0.199 time: 0.3267 last_time: 0.2449 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:25:30 d2.utils.events]: \u001b[0m eta: 0:08:11 iter: 82959 total_loss: 0.7531 loss_cls: 0.2241 loss_box_reg: 0.2856 loss_rpn_cls: 0.04451 loss_rpn_loc: 0.1732 time: 0.3267 last_time: 0.2175 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:25:35 d2.utils.events]: \u001b[0m eta: 0:08:06 iter: 82979 total_loss: 0.7161 loss_cls: 0.1854 loss_box_reg: 0.2631 loss_rpn_cls: 0.03301 loss_rpn_loc: 0.1726 time: 0.3267 last_time: 0.2525 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:25:40 d2.utils.events]: \u001b[0m eta: 0:08:02 iter: 82999 total_loss: 0.6535 loss_cls: 0.2274 loss_box_reg: 0.2699 loss_rpn_cls: 0.03898 loss_rpn_loc: 0.1417 time: 0.3266 last_time: 0.2565 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:25:45 d2.utils.events]: \u001b[0m eta: 0:07:57 iter: 83019 total_loss: 0.7511 loss_cls: 0.2308 loss_box_reg: 0.2804 loss_rpn_cls: 0.03943 loss_rpn_loc: 0.1678 time: 0.3266 last_time: 0.2295 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:25:49 d2.utils.events]: \u001b[0m eta: 0:07:52 iter: 83039 total_loss: 0.8016 loss_cls: 0.2235 loss_box_reg: 0.2905 loss_rpn_cls: 0.05422 loss_rpn_loc: 0.2417 time: 0.3266 last_time: 0.2533 data_time: 0.0046 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:25:54 d2.utils.events]: \u001b[0m eta: 0:07:47 iter: 83059 total_loss: 0.6148 loss_cls: 0.1963 loss_box_reg: 0.2225 loss_rpn_cls: 0.04235 loss_rpn_loc: 0.1523 time: 0.3266 last_time: 0.2423 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:25:59 d2.utils.events]: \u001b[0m eta: 0:07:42 iter: 83079 total_loss: 0.7466 loss_cls: 0.2563 loss_box_reg: 0.257 loss_rpn_cls: 0.03997 loss_rpn_loc: 0.1752 time: 0.3265 last_time: 0.2569 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:26:03 d2.utils.events]: \u001b[0m eta: 0:07:37 iter: 83099 total_loss: 0.6611 loss_cls: 0.2025 loss_box_reg: 0.2712 loss_rpn_cls: 0.02892 loss_rpn_loc: 0.1514 time: 0.3265 last_time: 0.2591 data_time: 0.0047 last_data_time: 0.0050 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:26:08 d2.utils.events]: \u001b[0m eta: 0:07:32 iter: 83119 total_loss: 0.7677 loss_cls: 0.2201 loss_box_reg: 0.268 loss_rpn_cls: 0.04591 loss_rpn_loc: 0.2026 time: 0.3265 last_time: 0.2597 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:26:13 d2.utils.events]: \u001b[0m eta: 0:07:27 iter: 83139 total_loss: 0.7645 loss_cls: 0.1919 loss_box_reg: 0.2594 loss_rpn_cls: 0.04411 loss_rpn_loc: 0.2013 time: 0.3265 last_time: 0.2606 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:26:18 d2.utils.events]: \u001b[0m eta: 0:07:23 iter: 83159 total_loss: 0.7258 loss_cls: 0.2177 loss_box_reg: 0.2487 loss_rpn_cls: 0.03682 loss_rpn_loc: 0.1615 time: 0.3265 last_time: 0.2022 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:26:23 d2.utils.events]: \u001b[0m eta: 0:07:18 iter: 83179 total_loss: 0.7391 loss_cls: 0.2103 loss_box_reg: 0.2736 loss_rpn_cls: 0.03853 loss_rpn_loc: 0.2004 time: 0.3264 last_time: 0.2023 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:26:27 d2.utils.events]: \u001b[0m eta: 0:07:13 iter: 83199 total_loss: 0.7356 loss_cls: 0.2247 loss_box_reg: 0.2656 loss_rpn_cls: 0.04719 loss_rpn_loc: 0.1764 time: 0.3264 last_time: 0.2129 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:26:32 d2.utils.events]: \u001b[0m eta: 0:07:09 iter: 83219 total_loss: 0.7485 loss_cls: 0.218 loss_box_reg: 0.2868 loss_rpn_cls: 0.03927 loss_rpn_loc: 0.179 time: 0.3264 last_time: 0.2166 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:26:37 d2.utils.events]: \u001b[0m eta: 0:07:04 iter: 83239 total_loss: 0.7857 loss_cls: 0.2427 loss_box_reg: 0.2865 loss_rpn_cls: 0.043 loss_rpn_loc: 0.2116 time: 0.3264 last_time: 0.2571 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:26:42 d2.utils.events]: \u001b[0m eta: 0:06:59 iter: 83259 total_loss: 0.67 loss_cls: 0.2062 loss_box_reg: 0.2382 loss_rpn_cls: 0.04056 loss_rpn_loc: 0.1695 time: 0.3264 last_time: 0.2569 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:26:46 d2.utils.events]: \u001b[0m eta: 0:06:54 iter: 83279 total_loss: 0.7918 loss_cls: 0.2388 loss_box_reg: 0.2882 loss_rpn_cls: 0.05266 loss_rpn_loc: 0.2128 time: 0.3263 last_time: 0.2592 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:26:51 d2.utils.events]: \u001b[0m eta: 0:06:49 iter: 83299 total_loss: 0.6856 loss_cls: 0.2029 loss_box_reg: 0.2551 loss_rpn_cls: 0.04148 loss_rpn_loc: 0.1949 time: 0.3263 last_time: 0.1983 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:26:56 d2.utils.events]: \u001b[0m eta: 0:06:45 iter: 83319 total_loss: 0.7739 loss_cls: 0.2349 loss_box_reg: 0.2832 loss_rpn_cls: 0.04831 loss_rpn_loc: 0.1834 time: 0.3263 last_time: 0.2406 data_time: 0.0046 last_data_time: 0.0049 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:27:01 d2.utils.events]: \u001b[0m eta: 0:06:40 iter: 83339 total_loss: 0.8088 loss_cls: 0.2436 loss_box_reg: 0.287 loss_rpn_cls: 0.04601 loss_rpn_loc: 0.1986 time: 0.3263 last_time: 0.2581 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:27:05 d2.utils.events]: \u001b[0m eta: 0:06:35 iter: 83359 total_loss: 0.7233 loss_cls: 0.2142 loss_box_reg: 0.2576 loss_rpn_cls: 0.04738 loss_rpn_loc: 0.1746 time: 0.3262 last_time: 0.2017 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:27:10 d2.utils.events]: \u001b[0m eta: 0:06:29 iter: 83379 total_loss: 0.7139 loss_cls: 0.2398 loss_box_reg: 0.2602 loss_rpn_cls: 0.03777 loss_rpn_loc: 0.1644 time: 0.3262 last_time: 0.2289 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:27:15 d2.utils.events]: \u001b[0m eta: 0:06:24 iter: 83399 total_loss: 0.6653 loss_cls: 0.1959 loss_box_reg: 0.2338 loss_rpn_cls: 0.03439 loss_rpn_loc: 0.1675 time: 0.3262 last_time: 0.2154 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:27:20 d2.utils.events]: \u001b[0m eta: 0:06:20 iter: 83419 total_loss: 0.7828 loss_cls: 0.2529 loss_box_reg: 0.3021 loss_rpn_cls: 0.03582 loss_rpn_loc: 0.1847 time: 0.3262 last_time: 0.2406 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:27:24 d2.utils.events]: \u001b[0m eta: 0:06:15 iter: 83439 total_loss: 0.6938 loss_cls: 0.2145 loss_box_reg: 0.2427 loss_rpn_cls: 0.04271 loss_rpn_loc: 0.1968 time: 0.3262 last_time: 0.2426 data_time: 0.0045 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:27:29 d2.utils.events]: \u001b[0m eta: 0:06:10 iter: 83459 total_loss: 0.7578 loss_cls: 0.2647 loss_box_reg: 0.304 loss_rpn_cls: 0.03637 loss_rpn_loc: 0.1984 time: 0.3261 last_time: 0.2600 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:27:34 d2.utils.events]: \u001b[0m eta: 0:06:05 iter: 83479 total_loss: 0.811 loss_cls: 0.2367 loss_box_reg: 0.3126 loss_rpn_cls: 0.03722 loss_rpn_loc: 0.1974 time: 0.3261 last_time: 0.1950 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:27:39 d2.utils.events]: \u001b[0m eta: 0:06:01 iter: 83499 total_loss: 0.7849 loss_cls: 0.2715 loss_box_reg: 0.3156 loss_rpn_cls: 0.04955 loss_rpn_loc: 0.1683 time: 0.3261 last_time: 0.2406 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:27:44 d2.utils.events]: \u001b[0m eta: 0:05:56 iter: 83519 total_loss: 0.7048 loss_cls: 0.2049 loss_box_reg: 0.2323 loss_rpn_cls: 0.05499 loss_rpn_loc: 0.1934 time: 0.3261 last_time: 0.2423 data_time: 0.0048 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:27:48 d2.utils.events]: \u001b[0m eta: 0:05:51 iter: 83539 total_loss: 0.7542 loss_cls: 0.2349 loss_box_reg: 0.2659 loss_rpn_cls: 0.03654 loss_rpn_loc: 0.1903 time: 0.3261 last_time: 0.2592 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:27:53 d2.utils.events]: \u001b[0m eta: 0:05:46 iter: 83559 total_loss: 0.7421 loss_cls: 0.2239 loss_box_reg: 0.2605 loss_rpn_cls: 0.04643 loss_rpn_loc: 0.1768 time: 0.3260 last_time: 0.2413 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:27:58 d2.utils.events]: \u001b[0m eta: 0:05:41 iter: 83579 total_loss: 0.7747 loss_cls: 0.2259 loss_box_reg: 0.2696 loss_rpn_cls: 0.03774 loss_rpn_loc: 0.1771 time: 0.3260 last_time: 0.2287 data_time: 0.0048 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:28:03 d2.utils.events]: \u001b[0m eta: 0:05:37 iter: 83599 total_loss: 0.6962 loss_cls: 0.1835 loss_box_reg: 0.2421 loss_rpn_cls: 0.04242 loss_rpn_loc: 0.1947 time: 0.3260 last_time: 0.2407 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:28:07 d2.utils.events]: \u001b[0m eta: 0:05:32 iter: 83619 total_loss: 0.8868 loss_cls: 0.2685 loss_box_reg: 0.2939 loss_rpn_cls: 0.05294 loss_rpn_loc: 0.2241 time: 0.3260 last_time: 0.2032 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:28:12 d2.utils.events]: \u001b[0m eta: 0:05:27 iter: 83639 total_loss: 0.7347 loss_cls: 0.2394 loss_box_reg: 0.2741 loss_rpn_cls: 0.03194 loss_rpn_loc: 0.1839 time: 0.3259 last_time: 0.2019 data_time: 0.0045 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:28:17 d2.utils.events]: \u001b[0m eta: 0:05:22 iter: 83659 total_loss: 0.772 loss_cls: 0.2396 loss_box_reg: 0.2937 loss_rpn_cls: 0.04841 loss_rpn_loc: 0.1893 time: 0.3259 last_time: 0.2562 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:28:21 d2.utils.events]: \u001b[0m eta: 0:05:17 iter: 83679 total_loss: 0.747 loss_cls: 0.2302 loss_box_reg: 0.2713 loss_rpn_cls: 0.0389 loss_rpn_loc: 0.1697 time: 0.3259 last_time: 0.2007 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:28:26 d2.utils.events]: \u001b[0m eta: 0:05:12 iter: 83699 total_loss: 0.6942 loss_cls: 0.2121 loss_box_reg: 0.2475 loss_rpn_cls: 0.03668 loss_rpn_loc: 0.166 time: 0.3259 last_time: 0.2013 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:28:31 d2.utils.events]: \u001b[0m eta: 0:05:07 iter: 83719 total_loss: 0.6291 loss_cls: 0.188 loss_box_reg: 0.2341 loss_rpn_cls: 0.03348 loss_rpn_loc: 0.1508 time: 0.3259 last_time: 0.2568 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:28:35 d2.utils.events]: \u001b[0m eta: 0:05:03 iter: 83739 total_loss: 0.7632 loss_cls: 0.2074 loss_box_reg: 0.2606 loss_rpn_cls: 0.04052 loss_rpn_loc: 0.1955 time: 0.3258 last_time: 0.2590 data_time: 0.0044 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:28:40 d2.utils.events]: \u001b[0m eta: 0:04:58 iter: 83759 total_loss: 0.7651 loss_cls: 0.2586 loss_box_reg: 0.2861 loss_rpn_cls: 0.03353 loss_rpn_loc: 0.1751 time: 0.3258 last_time: 0.2160 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:28:45 d2.utils.events]: \u001b[0m eta: 0:04:53 iter: 83779 total_loss: 0.6649 loss_cls: 0.18 loss_box_reg: 0.2184 loss_rpn_cls: 0.03632 loss_rpn_loc: 0.1557 time: 0.3258 last_time: 0.2417 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:28:50 d2.utils.events]: \u001b[0m eta: 0:04:48 iter: 83799 total_loss: 0.7654 loss_cls: 0.2383 loss_box_reg: 0.294 loss_rpn_cls: 0.0435 loss_rpn_loc: 0.1808 time: 0.3258 last_time: 0.2567 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:28:55 d2.utils.events]: \u001b[0m eta: 0:04:44 iter: 83819 total_loss: 0.7053 loss_cls: 0.2051 loss_box_reg: 0.2648 loss_rpn_cls: 0.03079 loss_rpn_loc: 0.1554 time: 0.3258 last_time: 0.3016 data_time: 0.0050 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:29:01 d2.utils.events]: \u001b[0m eta: 0:04:39 iter: 83839 total_loss: 0.6629 loss_cls: 0.2157 loss_box_reg: 0.2499 loss_rpn_cls: 0.03106 loss_rpn_loc: 0.16 time: 0.3258 last_time: 0.3418 data_time: 0.0052 last_data_time: 0.0056 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:29:07 d2.utils.events]: \u001b[0m eta: 0:04:35 iter: 83859 total_loss: 0.7589 loss_cls: 0.2152 loss_box_reg: 0.2765 loss_rpn_cls: 0.0311 loss_rpn_loc: 0.2198 time: 0.3257 last_time: 0.3115 data_time: 0.0051 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:29:13 d2.utils.events]: \u001b[0m eta: 0:04:30 iter: 83879 total_loss: 0.6795 loss_cls: 0.2296 loss_box_reg: 0.257 loss_rpn_cls: 0.04307 loss_rpn_loc: 0.1886 time: 0.3257 last_time: 0.2781 data_time: 0.0051 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:29:18 d2.utils.events]: \u001b[0m eta: 0:04:25 iter: 83899 total_loss: 0.7871 loss_cls: 0.2324 loss_box_reg: 0.2796 loss_rpn_cls: 0.04576 loss_rpn_loc: 0.1986 time: 0.3257 last_time: 0.2559 data_time: 0.0051 last_data_time: 0.0055 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:29:23 d2.utils.events]: \u001b[0m eta: 0:04:20 iter: 83919 total_loss: 0.8539 loss_cls: 0.2779 loss_box_reg: 0.3233 loss_rpn_cls: 0.04522 loss_rpn_loc: 0.1812 time: 0.3257 last_time: 0.2562 data_time: 0.0047 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:29:28 d2.utils.events]: \u001b[0m eta: 0:04:16 iter: 83939 total_loss: 0.7733 loss_cls: 0.2357 loss_box_reg: 0.2761 loss_rpn_cls: 0.05299 loss_rpn_loc: 0.207 time: 0.3257 last_time: 0.2145 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:29:32 d2.utils.events]: \u001b[0m eta: 0:04:11 iter: 83959 total_loss: 0.707 loss_cls: 0.1933 loss_box_reg: 0.23 loss_rpn_cls: 0.04426 loss_rpn_loc: 0.1725 time: 0.3257 last_time: 0.2578 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:29:37 d2.utils.events]: \u001b[0m eta: 0:04:06 iter: 83979 total_loss: 0.7094 loss_cls: 0.2178 loss_box_reg: 0.2524 loss_rpn_cls: 0.03591 loss_rpn_loc: 0.1713 time: 0.3256 last_time: 0.2415 data_time: 0.0047 last_data_time: 0.0041 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:29:42 d2.utils.events]: \u001b[0m eta: 0:04:01 iter: 83999 total_loss: 0.8334 loss_cls: 0.2556 loss_box_reg: 0.2884 loss_rpn_cls: 0.05774 loss_rpn_loc: 0.2194 time: 0.3256 last_time: 0.2177 data_time: 0.0046 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:29:46 d2.utils.events]: \u001b[0m eta: 0:03:56 iter: 84019 total_loss: 0.6686 loss_cls: 0.2303 loss_box_reg: 0.2503 loss_rpn_cls: 0.03891 loss_rpn_loc: 0.1699 time: 0.3256 last_time: 0.2147 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:29:51 d2.utils.events]: \u001b[0m eta: 0:03:51 iter: 84039 total_loss: 0.7496 loss_cls: 0.2303 loss_box_reg: 0.2763 loss_rpn_cls: 0.03834 loss_rpn_loc: 0.1621 time: 0.3256 last_time: 0.2314 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:29:56 d2.utils.events]: \u001b[0m eta: 0:03:46 iter: 84059 total_loss: 0.817 loss_cls: 0.2386 loss_box_reg: 0.2944 loss_rpn_cls: 0.04973 loss_rpn_loc: 0.1911 time: 0.3256 last_time: 0.2019 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:30:01 d2.utils.events]: \u001b[0m eta: 0:03:42 iter: 84079 total_loss: 0.7127 loss_cls: 0.2303 loss_box_reg: 0.2865 loss_rpn_cls: 0.06124 loss_rpn_loc: 0.168 time: 0.3255 last_time: 0.2410 data_time: 0.0045 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:30:06 d2.utils.events]: \u001b[0m eta: 0:03:37 iter: 84099 total_loss: 0.6867 loss_cls: 0.2088 loss_box_reg: 0.2358 loss_rpn_cls: 0.0405 loss_rpn_loc: 0.1782 time: 0.3255 last_time: 0.2333 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:30:10 d2.utils.events]: \u001b[0m eta: 0:03:32 iter: 84119 total_loss: 0.6723 loss_cls: 0.1994 loss_box_reg: 0.2713 loss_rpn_cls: 0.03688 loss_rpn_loc: 0.155 time: 0.3255 last_time: 0.2588 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:30:15 d2.utils.events]: \u001b[0m eta: 0:03:27 iter: 84139 total_loss: 0.7728 loss_cls: 0.2231 loss_box_reg: 0.2619 loss_rpn_cls: 0.04685 loss_rpn_loc: 0.2067 time: 0.3255 last_time: 0.2578 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:30:20 d2.utils.events]: \u001b[0m eta: 0:03:22 iter: 84159 total_loss: 0.725 loss_cls: 0.2267 loss_box_reg: 0.2939 loss_rpn_cls: 0.04958 loss_rpn_loc: 0.1776 time: 0.3255 last_time: 0.2162 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:30:25 d2.utils.events]: \u001b[0m eta: 0:03:17 iter: 84179 total_loss: 0.6789 loss_cls: 0.2341 loss_box_reg: 0.2516 loss_rpn_cls: 0.04659 loss_rpn_loc: 0.1626 time: 0.3254 last_time: 0.2164 data_time: 0.0046 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:30:30 d2.utils.events]: \u001b[0m eta: 0:03:13 iter: 84199 total_loss: 0.7601 loss_cls: 0.2185 loss_box_reg: 0.2767 loss_rpn_cls: 0.03869 loss_rpn_loc: 0.1749 time: 0.3254 last_time: 0.2561 data_time: 0.0045 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:30:34 d2.utils.events]: \u001b[0m eta: 0:03:08 iter: 84219 total_loss: 0.6675 loss_cls: 0.1932 loss_box_reg: 0.2291 loss_rpn_cls: 0.04338 loss_rpn_loc: 0.1675 time: 0.3254 last_time: 0.2309 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:30:39 d2.utils.events]: \u001b[0m eta: 0:03:03 iter: 84239 total_loss: 0.6791 loss_cls: 0.2144 loss_box_reg: 0.2507 loss_rpn_cls: 0.02784 loss_rpn_loc: 0.1649 time: 0.3254 last_time: 0.2269 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:30:44 d2.utils.events]: \u001b[0m eta: 0:02:58 iter: 84259 total_loss: 0.8008 loss_cls: 0.2383 loss_box_reg: 0.2991 loss_rpn_cls: 0.03958 loss_rpn_loc: 0.1875 time: 0.3253 last_time: 0.2172 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:30:48 d2.utils.events]: \u001b[0m eta: 0:02:53 iter: 84279 total_loss: 0.7257 loss_cls: 0.2319 loss_box_reg: 0.2575 loss_rpn_cls: 0.03957 loss_rpn_loc: 0.1536 time: 0.3253 last_time: 0.2175 data_time: 0.0046 last_data_time: 0.0042 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:30:53 d2.utils.events]: \u001b[0m eta: 0:02:48 iter: 84299 total_loss: 0.7088 loss_cls: 0.2374 loss_box_reg: 0.2873 loss_rpn_cls: 0.05104 loss_rpn_loc: 0.1884 time: 0.3253 last_time: 0.2578 data_time: 0.0044 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:30:58 d2.utils.events]: \u001b[0m eta: 0:02:44 iter: 84319 total_loss: 0.801 loss_cls: 0.2431 loss_box_reg: 0.278 loss_rpn_cls: 0.03793 loss_rpn_loc: 0.2006 time: 0.3253 last_time: 0.2319 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:31:03 d2.utils.events]: \u001b[0m eta: 0:02:39 iter: 84339 total_loss: 0.8081 loss_cls: 0.2281 loss_box_reg: 0.2594 loss_rpn_cls: 0.04222 loss_rpn_loc: 0.1873 time: 0.3253 last_time: 0.2584 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:31:07 d2.utils.events]: \u001b[0m eta: 0:02:34 iter: 84359 total_loss: 0.7163 loss_cls: 0.2 loss_box_reg: 0.2776 loss_rpn_cls: 0.04009 loss_rpn_loc: 0.1899 time: 0.3252 last_time: 0.2290 data_time: 0.0047 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:31:12 d2.utils.events]: \u001b[0m eta: 0:02:29 iter: 84379 total_loss: 0.738 loss_cls: 0.2657 loss_box_reg: 0.2601 loss_rpn_cls: 0.03758 loss_rpn_loc: 0.1711 time: 0.3252 last_time: 0.2416 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:31:17 d2.utils.events]: \u001b[0m eta: 0:02:24 iter: 84399 total_loss: 0.6872 loss_cls: 0.1918 loss_box_reg: 0.263 loss_rpn_cls: 0.04365 loss_rpn_loc: 0.1767 time: 0.3252 last_time: 0.2332 data_time: 0.0046 last_data_time: 0.0051 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:31:22 d2.utils.events]: \u001b[0m eta: 0:02:19 iter: 84419 total_loss: 0.7133 loss_cls: 0.2372 loss_box_reg: 0.2741 loss_rpn_cls: 0.03786 loss_rpn_loc: 0.1672 time: 0.3252 last_time: 0.2018 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:31:26 d2.utils.events]: \u001b[0m eta: 0:02:15 iter: 84439 total_loss: 0.758 loss_cls: 0.2545 loss_box_reg: 0.2608 loss_rpn_cls: 0.04161 loss_rpn_loc: 0.1906 time: 0.3252 last_time: 0.2437 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:31:31 d2.utils.events]: \u001b[0m eta: 0:02:10 iter: 84459 total_loss: 0.6795 loss_cls: 0.2114 loss_box_reg: 0.2514 loss_rpn_cls: 0.03896 loss_rpn_loc: 0.1723 time: 0.3251 last_time: 0.2575 data_time: 0.0045 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:31:36 d2.utils.events]: \u001b[0m eta: 0:02:05 iter: 84479 total_loss: 0.7199 loss_cls: 0.2208 loss_box_reg: 0.2696 loss_rpn_cls: 0.03874 loss_rpn_loc: 0.1759 time: 0.3251 last_time: 0.2585 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:31:41 d2.utils.events]: \u001b[0m eta: 0:02:00 iter: 84499 total_loss: 0.7243 loss_cls: 0.2384 loss_box_reg: 0.2703 loss_rpn_cls: 0.05723 loss_rpn_loc: 0.1853 time: 0.3251 last_time: 0.2293 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:31:45 d2.utils.events]: \u001b[0m eta: 0:01:55 iter: 84519 total_loss: 0.7697 loss_cls: 0.2506 loss_box_reg: 0.2589 loss_rpn_cls: 0.04687 loss_rpn_loc: 0.1958 time: 0.3251 last_time: 0.2487 data_time: 0.0046 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:31:50 d2.utils.events]: \u001b[0m eta: 0:01:50 iter: 84539 total_loss: 0.7549 loss_cls: 0.2103 loss_box_reg: 0.2761 loss_rpn_cls: 0.04918 loss_rpn_loc: 0.169 time: 0.3251 last_time: 0.2430 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:31:55 d2.utils.events]: \u001b[0m eta: 0:01:46 iter: 84559 total_loss: 0.6693 loss_cls: 0.1759 loss_box_reg: 0.2519 loss_rpn_cls: 0.04004 loss_rpn_loc: 0.1756 time: 0.3250 last_time: 0.2310 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:32:00 d2.utils.events]: \u001b[0m eta: 0:01:41 iter: 84579 total_loss: 0.7645 loss_cls: 0.2392 loss_box_reg: 0.2909 loss_rpn_cls: 0.04665 loss_rpn_loc: 0.1671 time: 0.3250 last_time: 0.2471 data_time: 0.0046 last_data_time: 0.0044 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:32:04 d2.utils.events]: \u001b[0m eta: 0:01:36 iter: 84599 total_loss: 0.7283 loss_cls: 0.228 loss_box_reg: 0.2678 loss_rpn_cls: 0.03871 loss_rpn_loc: 0.1898 time: 0.3250 last_time: 0.2109 data_time: 0.0046 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:32:09 d2.utils.events]: \u001b[0m eta: 0:01:31 iter: 84619 total_loss: 0.785 loss_cls: 0.2398 loss_box_reg: 0.2786 loss_rpn_cls: 0.0547 loss_rpn_loc: 0.1905 time: 0.3250 last_time: 0.2171 data_time: 0.0045 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:32:14 d2.utils.events]: \u001b[0m eta: 0:01:26 iter: 84639 total_loss: 0.8353 loss_cls: 0.2595 loss_box_reg: 0.2852 loss_rpn_cls: 0.0436 loss_rpn_loc: 0.2093 time: 0.3249 last_time: 0.1999 data_time: 0.0046 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:32:19 d2.utils.events]: \u001b[0m eta: 0:01:22 iter: 84659 total_loss: 0.7675 loss_cls: 0.2268 loss_box_reg: 0.2947 loss_rpn_cls: 0.0358 loss_rpn_loc: 0.1774 time: 0.3249 last_time: 0.2007 data_time: 0.0047 last_data_time: 0.0046 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:32:23 d2.utils.events]: \u001b[0m eta: 0:01:17 iter: 84679 total_loss: 0.6983 loss_cls: 0.2109 loss_box_reg: 0.2508 loss_rpn_cls: 0.04038 loss_rpn_loc: 0.1587 time: 0.3249 last_time: 0.2593 data_time: 0.0045 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:32:28 d2.utils.events]: \u001b[0m eta: 0:01:12 iter: 84699 total_loss: 0.7423 loss_cls: 0.2213 loss_box_reg: 0.3027 loss_rpn_cls: 0.03627 loss_rpn_loc: 0.1924 time: 0.3249 last_time: 0.2573 data_time: 0.0047 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:32:33 d2.utils.events]: \u001b[0m eta: 0:01:07 iter: 84719 total_loss: 0.6647 loss_cls: 0.1857 loss_box_reg: 0.2697 loss_rpn_cls: 0.03574 loss_rpn_loc: 0.1761 time: 0.3249 last_time: 0.2314 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:32:38 d2.utils.events]: \u001b[0m eta: 0:01:02 iter: 84739 total_loss: 0.8818 loss_cls: 0.2471 loss_box_reg: 0.301 loss_rpn_cls: 0.05529 loss_rpn_loc: 0.2063 time: 0.3248 last_time: 0.2568 data_time: 0.0048 last_data_time: 0.0052 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:32:42 d2.utils.events]: \u001b[0m eta: 0:00:57 iter: 84759 total_loss: 0.7207 loss_cls: 0.226 loss_box_reg: 0.2634 loss_rpn_cls: 0.03927 loss_rpn_loc: 0.1681 time: 0.3248 last_time: 0.2388 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:32:47 d2.utils.events]: \u001b[0m eta: 0:00:53 iter: 84779 total_loss: 0.7375 loss_cls: 0.2035 loss_box_reg: 0.2401 loss_rpn_cls: 0.03592 loss_rpn_loc: 0.2072 time: 0.3248 last_time: 0.2298 data_time: 0.0048 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:32:52 d2.utils.events]: \u001b[0m eta: 0:00:48 iter: 84799 total_loss: 0.7006 loss_cls: 0.219 loss_box_reg: 0.2711 loss_rpn_cls: 0.04214 loss_rpn_loc: 0.1534 time: 0.3248 last_time: 0.2417 data_time: 0.0050 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:32:57 d2.utils.events]: \u001b[0m eta: 0:00:43 iter: 84819 total_loss: 0.7354 loss_cls: 0.2388 loss_box_reg: 0.2574 loss_rpn_cls: 0.04769 loss_rpn_loc: 0.2252 time: 0.3248 last_time: 0.2531 data_time: 0.0046 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:33:01 d2.utils.events]: \u001b[0m eta: 0:00:38 iter: 84839 total_loss: 0.8351 loss_cls: 0.2179 loss_box_reg: 0.3048 loss_rpn_cls: 0.03615 loss_rpn_loc: 0.1937 time: 0.3247 last_time: 0.2555 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:33:06 d2.utils.events]: \u001b[0m eta: 0:00:33 iter: 84859 total_loss: 0.8141 loss_cls: 0.2494 loss_box_reg: 0.2858 loss_rpn_cls: 0.05581 loss_rpn_loc: 0.2154 time: 0.3247 last_time: 0.2554 data_time: 0.0046 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:33:11 d2.utils.events]: \u001b[0m eta: 0:00:28 iter: 84879 total_loss: 0.7389 loss_cls: 0.2169 loss_box_reg: 0.252 loss_rpn_cls: 0.05277 loss_rpn_loc: 0.193 time: 0.3247 last_time: 0.2171 data_time: 0.0046 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:33:16 d2.utils.events]: \u001b[0m eta: 0:00:24 iter: 84899 total_loss: 0.7956 loss_cls: 0.2694 loss_box_reg: 0.3041 loss_rpn_cls: 0.043 loss_rpn_loc: 0.1912 time: 0.3247 last_time: 0.2416 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:33:20 d2.utils.events]: \u001b[0m eta: 0:00:19 iter: 84919 total_loss: 0.7485 loss_cls: 0.2342 loss_box_reg: 0.286 loss_rpn_cls: 0.04626 loss_rpn_loc: 0.209 time: 0.3247 last_time: 0.2162 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:33:25 d2.utils.events]: \u001b[0m eta: 0:00:14 iter: 84939 total_loss: 0.7825 loss_cls: 0.2182 loss_box_reg: 0.2766 loss_rpn_cls: 0.04915 loss_rpn_loc: 0.2105 time: 0.3246 last_time: 0.2165 data_time: 0.0047 last_data_time: 0.0047 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:33:30 d2.utils.events]: \u001b[0m eta: 0:00:09 iter: 84959 total_loss: 0.8001 loss_cls: 0.2394 loss_box_reg: 0.3 loss_rpn_cls: 0.04218 loss_rpn_loc: 0.1981 time: 0.3246 last_time: 0.2382 data_time: 0.0045 last_data_time: 0.0043 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:33:35 d2.utils.events]: \u001b[0m eta: 0:00:04 iter: 84979 total_loss: 0.7843 loss_cls: 0.2543 loss_box_reg: 0.2729 loss_rpn_cls: 0.05414 loss_rpn_loc: 0.1902 time: 0.3246 last_time: 0.2523 data_time: 0.0047 last_data_time: 0.0045 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:33:41 d2.utils.events]: \u001b[0m eta: 0:00:00 iter: 84999 total_loss: 0.7338 loss_cls: 0.2099 loss_box_reg: 0.2785 loss_rpn_cls: 0.03472 loss_rpn_loc: 0.2014 time: 0.3246 last_time: 0.2022 data_time: 0.0048 last_data_time: 0.0048 lr: 1e-05 max_mem: 2746M\n","\u001b[32m[08/23 23:33:41 d2.engine.hooks]: \u001b[0mOverall training speed: 84998 iterations in 7:39:49 (0.3246 s / it)\n","\u001b[32m[08/23 23:33:41 d2.engine.hooks]: \u001b[0mTotal training time: 7:40:43 (0:00:53 on hooks)\n","\u001b[32m[08/23 23:33:42 d2.data.build]: \u001b[0mDistribution of instances among all 11 categories:\n","\u001b[36m| category | #instances | category | #instances | category | #instances |\n","|:----------:|:-------------|:-------------:|:-------------|:-----------:|:-------------|\n","| Caption | 1543 | Footnote | 387 | Formula | 1966 |\n","| List-item | 10522 | Page-footer | 3994 | Page-header | 3366 |\n","| Picture | 3534 | Section-hea.. | 8550 | Table | 2394 |\n","| Text | 29940 | Title | 335 | | |\n","| total | 66531 | | | | |\u001b[0m\n","\u001b[32m[08/23 23:33:42 d2.data.dataset_mapper]: \u001b[0m[DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]\n","\u001b[32m[08/23 23:33:42 d2.data.common]: \u001b[0mSerializing the dataset using: \n","\u001b[32m[08/23 23:33:42 d2.data.common]: \u001b[0mSerializing 4999 elements to byte tensors and concatenating them all ...\n","\u001b[32m[08/23 23:33:42 d2.data.common]: \u001b[0mSerialized dataset takes 4.74 MiB\n","\u001b[5m\u001b[31mWARNING\u001b[0m \u001b[32m[08/23 23:33:42 d2.engine.defaults]: \u001b[0mNo evaluator found. Use `DefaultTrainer.test(evaluators=)`, or implement its `build_evaluator` method.\n"]}],"source":["trainer = DefaultTrainer(cfg)\n","trainer.resume_or_load(resume=False)\n","trainer.train()"]},{"cell_type":"code","execution_count":8,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["\u001b[32m[08/23 23:33:43 d2.checkpoint.detection_checkpoint]: \u001b[0m[DetectionCheckpointer] Loading from ./output/model_final.pth ...\n"]},{"name":"stderr","output_type":"stream","text":["/home/alex/Projects/Detectron2_DocLayNet/.venv/lib/python3.10/site-packages/fvcore/common/checkpoint.py:252: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n"," return torch.load(f, map_location=torch.device(\"cpu\"))\n"]}],"source":["cfg.MODEL.WEIGHTS = os.path.join(cfg.OUTPUT_DIR, \"model_final.pth\")\n","cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.7 # set the testing threshold for this model\n","predictor = DefaultPredictor(cfg)"]},{"cell_type":"code","execution_count":19,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"},{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"},{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["import random\n","import cv2\n","\n","dataset_dicts = test_dataset\n","for d in random.sample(dataset_dicts, 3):\n"," im = cv2.imread(d[\"file_name\"])\n"," outputs = predictor(im)\n"," v = Visualizer(im[:, :, ::-1], metadata=MetadataCatalog.get(\"train\"), scale=0.8)\n"," v = v.draw_instance_predictions(outputs[\"instances\"].to(\"cpu\"))\n"," plt.figure(figsize=(14, 10))\n"," plt.imshow(cv2.cvtColor(v.get_image()[:, :, ::-1], cv2.COLOR_BGR2RGB))\n"," plt.axis(\"off\")\n"," plt.show()"]},{"cell_type":"code","execution_count":20,"metadata":{},"outputs":[],"source":["# im = cv2.imread(\"test.png\")\n","# outputs = predictor(im)\n","# v = Visualizer(im[:, :, ::-1], metadata=MetadataCatalog.get(\"train\"), scale=0.8)\n","# v = v.draw_instance_predictions(outputs[\"instances\"].to(\"cpu\"))\n","# plt.figure(figsize=(14, 10))\n","# plt.imshow(cv2.cvtColor(v.get_image()[:, :, ::-1], cv2.COLOR_BGR2RGB))\n","# plt.axis(\"off\")\n","# plt.show()"]},{"cell_type":"code","execution_count":11,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["CUDNN_BENCHMARK: false\n","DATALOADER:\n"," ASPECT_RATIO_GROUPING: true\n"," FILTER_EMPTY_ANNOTATIONS: true\n"," NUM_WORKERS: 2\n"," REPEAT_SQRT: true\n"," REPEAT_THRESHOLD: 0.0\n"," SAMPLER_TRAIN: TrainingSampler\n","DATASETS:\n"," PRECOMPUTED_PROPOSAL_TOPK_TEST: 1000\n"," PRECOMPUTED_PROPOSAL_TOPK_TRAIN: 2000\n"," PROPOSAL_FILES_TEST: []\n"," PROPOSAL_FILES_TRAIN: []\n"," TEST:\n"," - test\n"," TRAIN:\n"," - train\n","GLOBAL:\n"," HACK: 1.0\n","INPUT:\n"," CROP:\n"," ENABLED: false\n"," SIZE:\n"," - 0.9\n"," - 0.9\n"," TYPE: relative_range\n"," FORMAT: BGR\n"," MASK_FORMAT: polygon\n"," MAX_SIZE_TEST: 1333\n"," MAX_SIZE_TRAIN: 1333\n"," MIN_SIZE_TEST: 800\n"," MIN_SIZE_TRAIN:\n"," - 640\n"," - 672\n"," - 704\n"," - 736\n"," - 768\n"," - 800\n"," MIN_SIZE_TRAIN_SAMPLING: choice\n"," RANDOM_FLIP: horizontal\n","MODEL:\n"," ANCHOR_GENERATOR:\n"," ANGLES:\n"," - - -90\n"," - 0\n"," - 90\n"," ASPECT_RATIOS:\n"," - - 0.5\n"," - 1.0\n"," - 2.0\n"," NAME: DefaultAnchorGenerator\n"," OFFSET: 0.0\n"," SIZES:\n"," - - 32\n"," - - 64\n"," - - 128\n"," - - 256\n"," - - 512\n"," BACKBONE:\n"," FREEZE_AT: 2\n"," NAME: build_resnet_fpn_backbone\n"," DEVICE: cuda\n"," FPN:\n"," FUSE_TYPE: sum\n"," IN_FEATURES:\n"," - res2\n"," - res3\n"," - res4\n"," - res5\n"," NORM: ''\n"," OUT_CHANNELS: 256\n"," KEYPOINT_ON: false\n"," LOAD_PROPOSALS: false\n"," MASK_ON: false\n"," META_ARCHITECTURE: GeneralizedRCNN\n"," PANOPTIC_FPN:\n"," COMBINE:\n"," ENABLED: true\n"," INSTANCES_CONFIDENCE_THRESH: 0.5\n"," OVERLAP_THRESH: 0.5\n"," STUFF_AREA_LIMIT: 4096\n"," INSTANCE_LOSS_WEIGHT: 1.0\n"," PIXEL_MEAN:\n"," - 103.53\n"," - 116.28\n"," - 123.675\n"," PIXEL_STD:\n"," - 1.0\n"," - 1.0\n"," - 1.0\n"," PROPOSAL_GENERATOR:\n"," MIN_SIZE: 0\n"," NAME: RPN\n"," RESNETS:\n"," DEFORM_MODULATED: false\n"," DEFORM_NUM_GROUPS: 1\n"," DEFORM_ON_PER_STAGE:\n"," - false\n"," - false\n"," - false\n"," - false\n"," DEPTH: 101\n"," NORM: FrozenBN\n"," NUM_GROUPS: 1\n"," OUT_FEATURES:\n"," - res2\n"," - res3\n"," - res4\n"," - res5\n"," RES2_OUT_CHANNELS: 256\n"," RES5_DILATION: 1\n"," STEM_OUT_CHANNELS: 64\n"," STRIDE_IN_1X1: true\n"," WIDTH_PER_GROUP: 64\n"," RETINANET:\n"," BBOX_REG_LOSS_TYPE: smooth_l1\n"," BBOX_REG_WEIGHTS: &id002\n"," - 1.0\n"," - 1.0\n"," - 1.0\n"," - 1.0\n"," FOCAL_LOSS_ALPHA: 0.25\n"," FOCAL_LOSS_GAMMA: 2.0\n"," IN_FEATURES:\n"," - p3\n"," - p4\n"," - p5\n"," - p6\n"," - p7\n"," IOU_LABELS:\n"," - 0\n"," - -1\n"," - 1\n"," IOU_THRESHOLDS:\n"," - 0.4\n"," - 0.5\n"," NMS_THRESH_TEST: 0.5\n"," NORM: ''\n"," NUM_CLASSES: 80\n"," NUM_CONVS: 4\n"," PRIOR_PROB: 0.01\n"," SCORE_THRESH_TEST: 0.05\n"," SMOOTH_L1_LOSS_BETA: 0.1\n"," TOPK_CANDIDATES_TEST: 1000\n"," ROI_BOX_CASCADE_HEAD:\n"," BBOX_REG_WEIGHTS:\n"," - &id001\n"," - 10.0\n"," - 10.0\n"," - 5.0\n"," - 5.0\n"," - - 20.0\n"," - 20.0\n"," - 10.0\n"," - 10.0\n"," - - 30.0\n"," - 30.0\n"," - 15.0\n"," - 15.0\n"," IOUS:\n"," - 0.5\n"," - 0.6\n"," - 0.7\n"," ROI_BOX_HEAD:\n"," BBOX_REG_LOSS_TYPE: smooth_l1\n"," BBOX_REG_LOSS_WEIGHT: 1.0\n"," BBOX_REG_WEIGHTS: *id001\n"," CLS_AGNOSTIC_BBOX_REG: false\n"," CONV_DIM: 256\n"," FC_DIM: 1024\n"," FED_LOSS_FREQ_WEIGHT_POWER: 0.5\n"," FED_LOSS_NUM_CLASSES: 50\n"," NAME: FastRCNNConvFCHead\n"," NORM: ''\n"," NUM_CONV: 0\n"," NUM_FC: 2\n"," POOLER_RESOLUTION: 7\n"," POOLER_SAMPLING_RATIO: 0\n"," POOLER_TYPE: ROIAlignV2\n"," SMOOTH_L1_BETA: 0.0\n"," TRAIN_ON_PRED_BOXES: false\n"," USE_FED_LOSS: false\n"," USE_SIGMOID_CE: false\n"," ROI_HEADS:\n"," BATCH_SIZE_PER_IMAGE: 128\n"," IN_FEATURES:\n"," - p2\n"," - p3\n"," - p4\n"," - p5\n"," IOU_LABELS:\n"," - 0\n"," - 1\n"," IOU_THRESHOLDS:\n"," - 0.5\n"," NAME: StandardROIHeads\n"," NMS_THRESH_TEST: 0.5\n"," NUM_CLASSES: 11\n"," POSITIVE_FRACTION: 0.25\n"," PROPOSAL_APPEND_GT: true\n"," SCORE_THRESH_TEST: 0.7\n"," ROI_KEYPOINT_HEAD:\n"," CONV_DIMS:\n"," - 512\n"," - 512\n"," - 512\n"," - 512\n"," - 512\n"," - 512\n"," - 512\n"," - 512\n"," LOSS_WEIGHT: 1.0\n"," MIN_KEYPOINTS_PER_IMAGE: 1\n"," NAME: KRCNNConvDeconvUpsampleHead\n"," NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS: true\n"," NUM_KEYPOINTS: 17\n"," POOLER_RESOLUTION: 14\n"," POOLER_SAMPLING_RATIO: 0\n"," POOLER_TYPE: ROIAlignV2\n"," ROI_MASK_HEAD:\n"," CLS_AGNOSTIC_MASK: false\n"," CONV_DIM: 256\n"," NAME: MaskRCNNConvUpsampleHead\n"," NORM: ''\n"," NUM_CONV: 4\n"," POOLER_RESOLUTION: 14\n"," POOLER_SAMPLING_RATIO: 0\n"," POOLER_TYPE: ROIAlignV2\n"," RPN:\n"," BATCH_SIZE_PER_IMAGE: 256\n"," BBOX_REG_LOSS_TYPE: smooth_l1\n"," BBOX_REG_LOSS_WEIGHT: 1.0\n"," BBOX_REG_WEIGHTS: *id002\n"," BOUNDARY_THRESH: -1\n"," CONV_DIMS:\n"," - -1\n"," HEAD_NAME: StandardRPNHead\n"," IN_FEATURES:\n"," - p2\n"," - p3\n"," - p4\n"," - p5\n"," - p6\n"," IOU_LABELS:\n"," - 0\n"," - -1\n"," - 1\n"," IOU_THRESHOLDS:\n"," - 0.3\n"," - 0.7\n"," LOSS_WEIGHT: 1.0\n"," NMS_THRESH: 0.7\n"," POSITIVE_FRACTION: 0.5\n"," POST_NMS_TOPK_TEST: 1000\n"," POST_NMS_TOPK_TRAIN: 1000\n"," PRE_NMS_TOPK_TEST: 1000\n"," PRE_NMS_TOPK_TRAIN: 2000\n"," SMOOTH_L1_BETA: 0.0\n"," SEM_SEG_HEAD:\n"," COMMON_STRIDE: 4\n"," CONVS_DIM: 128\n"," IGNORE_VALUE: 255\n"," IN_FEATURES:\n"," - p2\n"," - p3\n"," - p4\n"," - p5\n"," LOSS_WEIGHT: 1.0\n"," NAME: SemSegFPNHead\n"," NORM: GN\n"," NUM_CLASSES: 54\n"," WEIGHTS: ./output/model_final.pth\n","OUTPUT_DIR: ./output\n","SEED: -1\n","SOLVER:\n"," AMP:\n"," ENABLED: false\n"," BASE_LR: 1.0e-05\n"," BASE_LR_END: 0.0\n"," BIAS_LR_FACTOR: 1.0\n"," CHECKPOINT_PERIOD: 5000\n"," CLIP_GRADIENTS:\n"," CLIP_TYPE: value\n"," CLIP_VALUE: 1.0\n"," ENABLED: false\n"," NORM_TYPE: 2.0\n"," GAMMA: 0.1\n"," IMS_PER_BATCH: 2\n"," LR_SCHEDULER_NAME: WarmupMultiStepLR\n"," MAX_ITER: 85000\n"," MOMENTUM: 0.9\n"," NESTEROV: false\n"," NUM_DECAYS: 3\n"," REFERENCE_WORLD_SIZE: 0\n"," RESCALE_INTERVAL: false\n"," STEPS:\n"," - 210000\n"," - 250000\n"," WARMUP_FACTOR: 0.001\n"," WARMUP_ITERS: 1000\n"," WARMUP_METHOD: linear\n"," WEIGHT_DECAY: 0.0001\n"," WEIGHT_DECAY_BIAS: null\n"," WEIGHT_DECAY_NORM: 0.0\n","TEST:\n"," AUG:\n"," ENABLED: false\n"," FLIP: true\n"," MAX_SIZE: 4000\n"," MIN_SIZES:\n"," - 400\n"," - 500\n"," - 600\n"," - 700\n"," - 800\n"," - 900\n"," - 1000\n"," - 1100\n"," - 1200\n"," DETECTIONS_PER_IMAGE: 100\n"," EVAL_PERIOD: 0\n"," EXPECTED_RESULTS: []\n"," KEYPOINT_OKS_SIGMAS: []\n"," PRECISE_BN:\n"," ENABLED: false\n"," NUM_ITER: 200\n","VERSION: 2\n","VIS_PERIOD: 0\n","\n"]}],"source":["print(cfg.dump())"]},{"cell_type":"code","execution_count":12,"metadata":{},"outputs":[],"source":["with open(\"config.yml\", \"w\") as f:\n"," f.write(cfg.dump())"]},{"cell_type":"code","execution_count":16,"metadata":{},"outputs":[{"data":{"text/plain":[""]},"execution_count":16,"metadata":{},"output_type":"execute_result"}],"source":["metadata = MetadataCatalog.get(\"train\")\n","metadata.as_dict"]},{"cell_type":"code","execution_count":17,"metadata":{},"outputs":[],"source":["import json\n","\n","with open(\"metadata.json\", \"w\") as f:\n"," json.dump(metadata.as_dict(), f)"]}],"metadata":{"accelerator":"GPU","colab":{"authorship_tag":"ABX9TyPT2aVE0RP0kC1vKyY7Dyty","gpuType":"T4","provenance":[]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.12"}},"nbformat":4,"nbformat_minor":0}