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| # Copyright (c) Facebook, Inc. and its affiliates. | |
| import logging | |
| import numpy as np | |
| from typing import Dict, List, Optional, Tuple | |
| from numpy.lib import pad | |
| import torch | |
| from torch import nn | |
| from torch.nn import functional as F | |
| from random import randint | |
| from detectron2.config import configurable | |
| from detectron2.data.detection_utils import convert_image_to_rgb | |
| from detectron2.structures import ImageList, Instances, Boxes | |
| from detectron2.utils.events import get_event_storage | |
| from detectron2.utils.logger import log_first_n | |
| from ..backbone import Backbone, build_backbone, build_text_backbone | |
| from ..postprocessing import detector_postprocess | |
| from ..proposal_generator import build_proposal_generator | |
| from ..roi_heads import build_roi_heads | |
| from .build import META_ARCH_REGISTRY | |
| from PIL import Image | |
| import torchvision | |
| from torchvision.transforms import Resize, CenterCrop | |
| from detectron2.data.datasets.clip_prompt_utils import get_cls_names, pre_tokenize | |
| import copy | |
| from ..backbone.fpn import build_resnet_fpn_backbone | |
| from ..roi_heads.fast_rcnn import fast_rcnn_inference | |
| from detectron2.layers import ShapeSpec | |
| from ..backbone.clip_backbone import build_clip_language_encoder | |
| from detectron2.utils.comm import gather_tensors, MILCrossEntropy, SoftTargetCrossEntropy | |
| __all__ = ["CLIPRCNN", "CLIPFastRCNN", "PretrainFastRCNN"] | |
| class CLIPRCNN(nn.Module): | |
| """ | |
| CLIP in R-CNN format. | |
| It takes the image regions as inputs and classifies each image. | |
| It contains the following two components: | |
| 1. Per-image feature extraction (visual encoder) | |
| 2. Per-image prediction (text-based classifier) | |
| """ | |
| def __init__( | |
| self, | |
| *, | |
| clip: Backbone, | |
| offline_backbone: Backbone, | |
| offline_proposal_generator: nn.Module, | |
| roi_heads: nn.Module, | |
| pixel_mean: Tuple[float], | |
| pixel_std: Tuple[float], | |
| input_format: Optional[str] = None, | |
| vis_period: int = 0, | |
| clip_crop_region_type: str = 'GT', | |
| test_score_thresh: float = 0.0001, | |
| test_nms_thresh: float = 0.5, | |
| test_topk_per_image: float = 300, | |
| ): | |
| """ | |
| Args: | |
| backbone: a backbone module, must follow detectron2's backbone interface | |
| proposal_generator: a module that generates proposals using backbone features | |
| roi_heads: a ROI head that performs per-region computation | |
| pixel_mean, pixel_std: list or tuple with #channels element, representing | |
| the per-channel mean and std to be used to normalize the input image | |
| input_format: describe the meaning of channels of input. Needed by visualization | |
| vis_period: the period to run visualization. Set to 0 to disable. | |
| """ | |
| super().__init__() | |
| self.clip_backbone = clip | |
| self.offline_backbone = offline_backbone | |
| self.offline_proposal_generator = offline_proposal_generator | |
| self.roi_heads = roi_heads | |
| self.input_format = input_format | |
| self.vis_period = vis_period | |
| if vis_period > 0: | |
| assert input_format is not None, "input_format is required for visualization!" | |
| self.register_buffer("pixel_mean", torch.tensor(pixel_mean).view(-1, 1, 1), False) | |
| self.register_buffer("pixel_std", torch.tensor(pixel_std).view(-1, 1, 1), False) | |
| assert ( | |
| self.pixel_mean.shape == self.pixel_std.shape | |
| ), f"{self.pixel_mean} and {self.pixel_std} have different shapes!" | |
| # Detectron2 default pixel mean and std | |
| self.register_buffer("detectron_pixel_mean", torch.tensor([103.530, 116.280, 123.675]).view(-1, 1, 1), False) | |
| self.register_buffer("detectron_pixel_std", torch.tensor([1.0, 1.0, 1.0]).view(-1, 1, 1), False) | |
| # CLIP image loading | |
| if np.sum(pixel_mean) < 3.0: # converrt pixel value to range [0.0, 1.0] by dividing 255.0 | |
| assert input_format == 'RGB' | |
| self.div_pixel = True | |
| else: # default setting | |
| self.div_pixel = False | |
| n_px = 224 | |
| self.clip_resize = Resize(n_px, interpolation=Image.BICUBIC) # shorter side becomes n_px | |
| self.clip_center_crop = CenterCrop(n_px) # crop image into n_px * n_px at the center | |
| self.region_crop_scales = (1.0, 1.5) # (1.0, 2.0) # (1.0, 1.2) # (1.0,) # | |
| # CLIP text prompt loading | |
| print("Working on pre_tokenize...") | |
| cls_names = get_cls_names(filter_novel=False, from_file='/home/v-yiwuzhong/projects/azureblobs/vyiwuzhong_phillytools/trained_models/concept_pool/googlecc_nouns_filtered_100.txt') # filter_novel=True; coco='all', coco='base', coco='target'; from_file: a file path for concept pool | |
| # from_file='/home/v-yiwuzhong/projects/azureblobs/vyiwuzhong_phillytools/trained_models/concept_pool/googlecc_nouns_triplet_parser_filtered_100.txt' | |
| print("Got {} class names: {}\n {} class names in total.".format(len(cls_names), cls_names, len(cls_names))) | |
| input_ids = pre_tokenize(cls_names) | |
| self.num_cls = input_ids.size(0) | |
| self.num_prompt = input_ids.size(1) | |
| self.input_ids_flat = input_ids.view(-1, input_ids.size(2)) # [#cls*#prompts, #context_length] | |
| self.clss_emb_all = None | |
| # CLIP crop image configs | |
| self.clip_crop_region_type = clip_crop_region_type | |
| self.test_score_thresh = test_score_thresh | |
| self.test_nms_thresh = test_nms_thresh | |
| self.test_topk_per_image = test_topk_per_image | |
| def from_config(cls, cfg): | |
| if cfg.MODEL.CLIP.CROP_REGION_TYPE == "RPN": | |
| offline_backbone = build_resnet_fpn_backbone(cfg, ShapeSpec(channels=len(cfg.MODEL.PIXEL_MEAN))) # build_backbone(cfg) | |
| offline_rpn = build_proposal_generator(cfg, offline_backbone.output_shape()) | |
| roi_heads = None # build_roi_heads(cfg, backbone.output_shape()), | |
| elif cfg.MODEL.CLIP.CROP_REGION_TYPE == "GT": | |
| offline_backbone = None | |
| offline_rpn = None | |
| roi_heads = None | |
| clip = build_backbone(cfg) | |
| return { | |
| "clip": clip, | |
| "offline_backbone": offline_backbone, | |
| "offline_proposal_generator": offline_rpn, | |
| "roi_heads": roi_heads, | |
| "input_format": cfg.INPUT.FORMAT, | |
| "vis_period": cfg.VIS_PERIOD, | |
| "pixel_mean": cfg.MODEL.PIXEL_MEAN, | |
| "pixel_std": cfg.MODEL.PIXEL_STD, | |
| "clip_crop_region_type" : cfg.MODEL.CLIP.CROP_REGION_TYPE, | |
| "test_score_thresh" : cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST, | |
| "test_nms_thresh" : cfg.MODEL.ROI_HEADS.NMS_THRESH_TEST, | |
| "test_topk_per_image" : cfg.TEST.DETECTIONS_PER_IMAGE, | |
| } | |
| def device(self): | |
| return self.pixel_mean.device | |
| def forward(self, batched_inputs: List[Dict[str, torch.Tensor]]): | |
| """ | |
| Args: | |
| batched_inputs: a list, batched outputs of :class:`DatasetMapper` . | |
| Each item in the list contains the inputs for one image. | |
| For now, each item in the list is a dict that contains: | |
| * image: Tensor, image in (C, H, W) format. | |
| * instances (optional): groundtruth :class:`Instances` | |
| * proposals (optional): :class:`Instances`, precomputed proposals. | |
| Other information that's included in the original dicts, such as: | |
| * "height", "width" (int): the output resolution of the model, used in inference. | |
| See :meth:`postprocess` for details. | |
| Returns: | |
| list[dict]: | |
| Each dict is the output for one input image. | |
| The dict contains one key "instances" whose value is a :class:`Instances`. | |
| The :class:`Instances` object has the following keys: | |
| "pred_boxes", "pred_classes", "scores", "pred_masks", "pred_keypoints" | |
| """ | |
| if not self.training: | |
| return self.inference(batched_inputs) | |
| # No training mode for this arch | |
| def inference( | |
| self, | |
| batched_inputs: List[Dict[str, torch.Tensor]], | |
| detected_instances: Optional[List[Instances]] = None, | |
| do_postprocess: bool = True, | |
| ): | |
| """ | |
| Run inference on the given inputs. | |
| Args: | |
| batched_inputs (list[dict]): same as in :meth:`forward` | |
| detected_instances (None or list[Instances]): if not None, it | |
| contains an `Instances` object per image. The `Instances` | |
| object contains "pred_boxes" and "pred_classes" which are | |
| known boxes in the image. | |
| The inference will then skip the detection of bounding boxes, | |
| and only predict other per-ROI outputs. | |
| do_postprocess (bool): whether to apply post-processing on the outputs. | |
| Returns: | |
| When do_postprocess=True, same as in :meth:`forward`. | |
| Otherwise, a list[Instances] containing raw network outputs. | |
| """ | |
| assert not self.training | |
| # get the label prompt, and use CLIP.encode_text() to compute text emb only once | |
| if self.clss_emb_all is None: # compute only once | |
| num_instances = self.input_ids_flat.size(0) | |
| per_split = 1000 | |
| num_splits = num_instances // per_split | |
| input_ids_flat = self.input_ids_flat.to(self.device) | |
| #self.clss_emb_all = torch.ones((1203, 512)).to(self.device) | |
| clss_emb_all = [] | |
| for i in range(num_splits+1): | |
| if i < num_splits: | |
| clss_emb_i = self.clip_backbone.encode_text(input_ids_flat[per_split*i:per_split*(i+1)]) # per_split x D | |
| else: | |
| clss_emb_i = self.clip_backbone.encode_text(input_ids_flat[per_split*i:]) # per_split x D | |
| # clss_emb_i = clip_model.encode_label(torch.arange(0, 1000).view(-1, 1).long().to(device)) # per_split x D | |
| clss_emb_all.append(clss_emb_i) | |
| self.clss_emb_all = torch.cat(clss_emb_all, 0).view(self.num_cls, self.num_prompt, -1) # [#cls, #prompts, D] | |
| self.clss_emb_all = self.clss_emb_all.mean(1) # ensemble different prompts for each class | |
| # torch.save(self.clss_emb_all.cpu(), "/home/v-yiwuzhong/projects/azureblobs/vyiwuzhong_phillytools/trained_models/lvis_cls_emb/coco_17_target_cls_emb_notnorm_rn50x4.pth") | |
| self.clss_emb_all = F.normalize(self.clss_emb_all, p=2.0, dim=1) # [#cls, emb_dim] | |
| else: | |
| assert self.clss_emb_all.device == self.device | |
| # get the region proposals, from the backbone & RPN of standard Mask-RCNN, trained on base classes | |
| if self.clip_crop_region_type == "GT": | |
| proposals = None | |
| elif self.clip_crop_region_type == "RPN": | |
| images = self.preprocess_image(batched_inputs) | |
| features = self.offline_backbone(images.tensor) | |
| if detected_instances is None: | |
| if self.offline_proposal_generator is not None: | |
| proposals, _ = self.offline_proposal_generator(images, features, None) | |
| # crop image regions, and use CLIP.encode_image() to get the visual feature | |
| images, bbs, num_bbs = self.preprocess_image_crop(batched_inputs, rpn_proposals=proposals) | |
| img_emb = self.clip_backbone.encode_image(images.tensor) | |
| img_emb = img_emb.view(-1, len(self.region_crop_scales), img_emb.size(1)) | |
| img_emb = torch.sum(img_emb, dim=1) # ensemble different scales for each region | |
| img_emb = F.normalize(img_emb, p=2.0, dim=1) | |
| # cosine similarity as logits | |
| all_scores = torch.mm(img_emb, self.clss_emb_all.T) | |
| all_scores = F.softmax(all_scores, dim=-1) | |
| scores, pred_cls = torch.max(all_scores, dim=-1) # Note: [0, #cls-1] representing the categories. The value #cls represents "background". | |
| # convert model outputs into regular output result format | |
| scores_per_img = scores.split(num_bbs) | |
| pred_cls_per_img = pred_cls.split(num_bbs) | |
| all_scores_per_img = all_scores.split(num_bbs) | |
| # per-class NMS | |
| if self.clip_crop_region_type == "GT": | |
| image_shapes = [x['instances']._image_size for x in batched_inputs] | |
| bbs = [bb.to(self.device) for bb in bbs] | |
| pred_instances, _ = fast_rcnn_inference(bbs, all_scores_per_img, image_shapes, \ | |
| self.test_score_thresh, self.test_nms_thresh, self.test_topk_per_image) | |
| results = pred_instances | |
| # results = [] | |
| # for r_i, (b_input, bb, sc, prd) in enumerate(zip(batched_inputs, bbs, scores_per_img, pred_cls_per_img)): | |
| # this_result = copy.deepcopy(b_input["instances"]) # Instance | |
| # if self.clip_crop_region_type == "GT": | |
| # result_boxes = this_result._fields['gt_boxes'].to(self.device) | |
| # elif self.clip_crop_region_type == "RPN": # directly use RPN boxes without per-class NMS | |
| # result_boxes = bb # result_boxes = Boxes(bb) | |
| # this_result._fields = {'pred_boxes': result_boxes, 'scores': sc, 'pred_classes': prd} | |
| # results.append(this_result) | |
| # sanity check: GT boxes + GT classes | |
| # results = [] | |
| # for b_input in batched_inputs: | |
| # this_result = copy.deepcopy(b_input["instances"]) # Instance | |
| # gt_boxes = this_result._fields['gt_boxes'].to(self.device) | |
| # gt_cls = this_result._fields['gt_classes'].to(self.device) | |
| # this_result._fields = {'pred_boxes': gt_boxes, 'scores': torch.ones(gt_cls.size(0)).to(self.device), 'pred_classes': gt_cls} | |
| # #this_result._fields = {'pred_boxes': gt_boxes, 'scores': sc, 'pred_classes': prd} | |
| # results.append(this_result) | |
| elif self.clip_crop_region_type == "RPN": | |
| image_shapes = [x.image_size for x in proposals] | |
| pred_instances, _ = fast_rcnn_inference(bbs, all_scores_per_img, image_shapes, \ | |
| self.test_score_thresh, self.test_nms_thresh, self.test_topk_per_image) | |
| results = pred_instances | |
| if do_postprocess: | |
| assert not torch.jit.is_scripting(), "Scripting is not supported for postprocess." | |
| return CLIPRCNN._postprocess(results, batched_inputs) | |
| else: | |
| return results | |
| def preprocess_image(self, batched_inputs: List[Dict[str, torch.Tensor]]): | |
| """ | |
| Normalize, pad and batch the input images. Use detectron2 default processing (pixel mean & std). | |
| Note: Due to FPN size_divisibility, images are padded by right/bottom border. So FPN is consistent with C4 and GT boxes. | |
| """ | |
| images = [x["image"].to(self.device) for x in batched_inputs] | |
| images = [(x - self.detectron_pixel_mean) / self.detectron_pixel_std for x in images] | |
| images = ImageList.from_tensors(images, self.offline_backbone.size_divisibility) | |
| return images | |
| def preprocess_image_crop(self, batched_inputs: List[Dict[str, torch.Tensor]], rpn_proposals=None, max_num_rpn=1000): | |
| """ | |
| Crop image regions based on GT or RPN boxes with different scales. | |
| Then apply CLIP tranformation: resizing / cropping the regions into square shape (224 * 224). | |
| Followed by the default preprocessing in Detectron2 as follows. | |
| Normalize, pad and batch the input images. | |
| """ | |
| def clip_crop_region(image, box, scales=(1.0, 1.5)): | |
| """Crop image regions based on given boxes. Return different scales of region crops. (3 hrs)""" | |
| img_h, img_w = image.size(1), image.size(2) | |
| x1, y1, x2, y2 = list(box) | |
| assert x1 < x2 and y1 < y2 and x2 < (img_w + 1) and y2 < (img_h + 1) | |
| x_center = (x1 + x2) / 2.0 | |
| y_center = (y1 + y2) / 2.0 | |
| half_w = x_center - x1 | |
| half_h = y_center - y1 | |
| regions = [] | |
| for scale in scales: # get region coordinates | |
| r_y1 = int(max(0, (y_center - half_h * scale).item())) | |
| r_y2 = int(min(img_h, (y_center + half_h * scale).item())) | |
| r_x1 = int(max(0, (x_center - half_w * scale).item())) | |
| r_x2 = int(min(img_w, (x_center + half_w * scale).item())) | |
| # sanity check | |
| if r_y2 - r_y1 <= 1: | |
| r_y2 = int(min(img_h, r_y2 + 2)) | |
| if r_y2 - r_y1 <= 1: | |
| r_y1 = int(max(0, r_y1 - 2)) | |
| if r_x2 - r_x1 <= 1: | |
| r_x2 = int(min(img_w, r_x2 + 2)) | |
| if r_x2 - r_x1 <= 1: | |
| r_x1 = int(max(0, r_x1 - 2)) | |
| regions.append(image[:, r_y1:r_y2, r_x1:r_x2]) | |
| return regions | |
| def clip_square_crop(image, box, scales=(1.0,)): | |
| """Crop image regions based on given boxes. Ensure square region as much as possible. (1.75 hrs)""" | |
| img_h, img_w = image.size(1), image.size(2) | |
| x1, y1, x2, y2 = list(box) | |
| assert x1 < x2 and y1 < y2 and x2 < (img_w + 1) and y2 < (img_h + 1) | |
| x_center = (x1 + x2) / 2.0 | |
| y_center = (y1 + y2) / 2.0 | |
| half_w = x_center - x1 | |
| half_h = y_center - y1 | |
| square_side = max(half_w, half_h) | |
| half_w = square_side | |
| half_h = square_side | |
| regions = [] | |
| for scale in scales: # get region coordinates | |
| if square_side * square_side < 2500: # crop larger context area for tiny objects | |
| scale = 1.5 if scale == 1.0 else 4.0 | |
| # elif square_side * square_side > 90000: # crop exact area for large objects | |
| # scale = 1.0 if scale == 1.0 else 1.1 | |
| r_y1 = int(max(0, (y_center - half_h * scale).item())) | |
| r_y2 = int(min(img_h, (y_center + half_h * scale).item())) | |
| r_x1 = int(max(0, (x_center - half_w * scale).item())) | |
| r_x2 = int(min(img_w, (x_center + half_w * scale).item())) | |
| # sanity check | |
| if r_y2 - r_y1 <= 1: | |
| r_y2 = int(min(img_h, r_y2 + 2)) | |
| if r_y2 - r_y1 <= 1: | |
| r_y1 = int(max(0, r_y1 - 2)) | |
| if r_x2 - r_x1 <= 1: | |
| r_x2 = int(min(img_w, r_x2 + 2)) | |
| if r_x2 - r_x1 <= 1: | |
| r_x1 = int(max(0, r_x1 - 2)) | |
| #regions.append(image[:, r_y1:r_y2, r_x1:r_x2]) | |
| # if the cropped image isn't square (due to image boundaries), pad the cropped region | |
| crop_image = image[:, r_y1:r_y2, r_x1:r_x2] | |
| r_h, r_w = crop_image.size(1), crop_image.size(2) | |
| pad_image = torch.zeros((3, int(2 * half_h.item() * scale) + 4 , int(2 * half_w.item() * scale) + 4)) #.fill_(torch.mean(crop_image.float())) | |
| p_h, p_w = pad_image.size(1), pad_image.size(2) | |
| pad_image[:, int(((p_h - r_h) / 2)):int(((p_h - r_h) / 2 + r_h)), int(((p_w - r_w) / 2)):int(((p_w - r_w) / 2 + r_w))] = crop_image | |
| regions.append(pad_image.type(torch.uint8)) | |
| return regions | |
| def vis_crop(f_n, images): | |
| """visualize the crop regions to diagnose the accuracy.""" | |
| if f_n not in ['datasets/coco/train2017/000000008691.jpg']: | |
| for p_i, pad_image in enumerate(images): | |
| to_save = pad_image.permute(1, 2, 0).numpy() | |
| to_save = Image.fromarray(np.array(to_save, np.uint8)) | |
| to_save.save("output/regions/" + f_n.split("/")[-1].split(".")[0] + "-{}.png".format(p_i)) | |
| pass | |
| # crop image region | |
| images = [] | |
| bbs = [] | |
| num_bbs = [] | |
| for img_i, b_input in enumerate(batched_inputs): | |
| this_img = b_input["image"] | |
| if self.clip_crop_region_type == "GT": | |
| this_boxes = b_input["instances"]._fields['gt_boxes'].tensor # variant #bbox (eg, max 759), might lead to OOM | |
| elif self.clip_crop_region_type == "RPN": | |
| this_boxes = rpn_proposals[img_i]._fields['proposal_boxes'].tensor[:max_num_rpn] | |
| bbs.append(this_boxes) | |
| num_bbs.append(this_boxes.size(0)) | |
| for this_box in this_boxes: | |
| #images.extend(clip_crop_region(this_img, this_box, self.region_crop_scales)) | |
| images.extend(clip_square_crop(this_img, this_box, self.region_crop_scales)) | |
| #vis_crop(batched_inputs[0]['file_name'], images) | |
| images = [self.clip_resize(x) for x in images] | |
| images = [self.clip_center_crop(x) for x in images] | |
| images = [x.to(self.device) for x in images] | |
| if self.div_pixel: | |
| images = [((x / 255.0) - self.pixel_mean) / self.pixel_std for x in images] | |
| else: | |
| images = [(x - self.pixel_mean) / self.pixel_std for x in images] | |
| images = ImageList.from_tensors(images, self.clip_backbone.size_divisibility) # batch images into single tensor by padding to same size | |
| return images, bbs, num_bbs | |
| def _postprocess(instances, batched_inputs: List[Dict[str, torch.Tensor]]): | |
| """ | |
| Rescale the output instances to the target size. | |
| """ | |
| # note: private function; subject to changes | |
| processed_results = [] | |
| for results_per_image, input_per_image in zip( | |
| instances, batched_inputs): | |
| height = input_per_image["height"] # original image size, before resizing | |
| width = input_per_image["width"] # original image size, before resizing | |
| r = detector_postprocess(results_per_image, height, width) | |
| processed_results.append({"instances": r}) | |
| return processed_results | |
| def inference_on_cifar(self, pseudo_input): | |
| """ Evaluate recoginition accuracy on CIFAR-10 for sanity check """ | |
| # get the label prompt, and use CLIP.encode_text() to compute text emb only once | |
| cifar_cls_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck'] | |
| input_ids = pre_tokenize(cifar_cls_names) | |
| num_cls = input_ids.size(0) | |
| input_ids_flat = input_ids.view(-1, input_ids.size(2)) | |
| input_ids_flat = input_ids_flat.to(self.device) | |
| clss_emb_all = self.clip_backbone.encode_text(input_ids_flat) | |
| clss_emb_all = clss_emb_all.view(num_cls, self.num_prompt, -1) | |
| clss_emb_all = clss_emb_all.mean(1) | |
| clss_emb_all = F.normalize(clss_emb_all, p=2.0, dim=1) # [#cls, emb_dim] | |
| # dataset loads images and labels | |
| testset = torchvision.datasets.CIFAR10(root='./datasets', train=False, | |
| download=False, transform=None) | |
| # testloader = torch.utils.data.DataLoader(testset, batch_size=4, | |
| # shuffle=False, num_workers=0) | |
| # inference on each image and calculate accuracy | |
| correct = 0 | |
| wrong = 0 | |
| for idx, inputs in enumerate(testset): | |
| if idx % 1000 == 0: | |
| print(idx) | |
| # preprocess images | |
| raw_image, label = inputs | |
| image = np.array(raw_image) # [h, w, 3] | |
| image = torch.from_numpy(image) | |
| image = image.permute(2, 0, 1) # [3, h, w] | |
| images = [image] | |
| images = [self.clip_resize(x) for x in images] | |
| images = [self.clip_center_crop(x) for x in images] | |
| images = [x.to(self.device) for x in images] | |
| if self.div_pixel: | |
| images = [((x / 255.0) - self.pixel_mean) / self.pixel_std for x in images] | |
| else: | |
| images = [(x - self.pixel_mean) / self.pixel_std for x in images] | |
| # get image embedding | |
| img_emb = self.clip_backbone.encode_image(images[0].unsqueeze(0)) | |
| img_emb = img_emb.view(-1, 1, img_emb.size(1)) | |
| img_emb = torch.sum(img_emb, dim=1) # ensemble different scales for each region | |
| img_emb = F.normalize(img_emb, p=2.0, dim=1) | |
| # cosine similarity as logits | |
| all_scores = torch.mm(img_emb, clss_emb_all.T) | |
| scores, pred_cls = torch.max(all_scores, dim=1) # Note: [0, #cls-1] representing the categories. The value #cls represents "background". | |
| pred_cls = pred_cls.item() | |
| if pred_cls == label: | |
| correct += 1 | |
| else: | |
| wrong += 1 | |
| print("\n\nGot correct {} and wrong {}. Accuracy is {} / {} = {}\n\n".format(correct,wrong,correct,correct+wrong,correct/(correct+wrong))) | |
| return | |
| class CLIPFastRCNN(nn.Module): | |
| """ | |
| CLIP in Fast R-CNN format, where the cropping is conducted on feature maps instead of raw images. | |
| It contains the following two components: | |
| 1. Localization modules: pretrained backbone+RPN or equivalent modules and is able to output object proposals | |
| 2. Recognition branch: initialized by CLIP and is able to recognize zero-shot regions | |
| """ | |
| def __init__( | |
| self, | |
| *, | |
| offline_backbone: Backbone, | |
| backbone: Backbone, | |
| backbone_type: str = "resnet", | |
| text_backbone: Backbone, | |
| offline_proposal_generator: nn.Module, | |
| roi_heads: nn.Module, | |
| pixel_mean: Tuple[float], | |
| pixel_std: Tuple[float], | |
| input_format: Optional[str] = None, | |
| vis_period: int = 0, | |
| clip_crop_region_type: str = 'GT', | |
| use_clip_c4: False, | |
| use_clip_attpool: False, | |
| offline_input_format: Optional[str] = None, | |
| offline_pixel_mean: Tuple[float], | |
| offline_pixel_std: Tuple[float], | |
| ): | |
| """ | |
| Args: | |
| backbone: a backbone module, must follow detectron2's backbone interface | |
| proposal_generator: a module that generates proposals using backbone features | |
| roi_heads: a ROI head that performs per-region computation | |
| pixel_mean, pixel_std: list or tuple with #channels element, representing | |
| the per-channel mean and std to be used to normalize the input image | |
| input_format: describe the meaning of channels of input. Needed by visualization | |
| vis_period: the period to run visualization. Set to 0 to disable. | |
| """ | |
| super().__init__() | |
| self.offline_backbone = offline_backbone | |
| self.backbone = backbone | |
| self.backbone_type = backbone_type | |
| self.offline_proposal_generator = offline_proposal_generator | |
| self.roi_heads = roi_heads | |
| self.lang_encoder = text_backbone | |
| self.input_format = input_format | |
| self.vis_period = vis_period | |
| if vis_period > 0: | |
| assert input_format is not None, "input_format is required for visualization!" | |
| self.register_buffer("pixel_mean", torch.tensor(pixel_mean).view(-1, 1, 1), False) | |
| self.register_buffer("pixel_std", torch.tensor(pixel_std).view(-1, 1, 1), False) | |
| assert ( | |
| self.pixel_mean.shape == self.pixel_std.shape | |
| ), f"{self.pixel_mean} and {self.pixel_std} have different shapes!" | |
| if np.sum(pixel_mean) < 3.0: # converrt pixel value to range [0.0, 1.0] by dividing 255.0 | |
| assert input_format == 'RGB' | |
| self.div_pixel = True | |
| else: # default setting | |
| self.div_pixel = False | |
| # input format, pixel mean and std for offline modules | |
| if offline_input_format and offline_pixel_mean and offline_pixel_std: | |
| self.offline_input_format = offline_input_format | |
| self.register_buffer("offline_pixel_mean", torch.tensor(offline_pixel_mean).view(-1, 1, 1), False) | |
| self.register_buffer("offline_pixel_std", torch.tensor(offline_pixel_std).view(-1, 1, 1), False) | |
| if np.sum(offline_pixel_mean) < 3.0: # converrt pixel value to range [0.0, 1.0] by dividing 255.0 | |
| assert offline_input_format == 'RGB' | |
| self.offline_div_pixel = True | |
| else: # default setting | |
| self.offline_div_pixel = False | |
| self.clip_crop_region_type = clip_crop_region_type | |
| self.use_clip_c4 = use_clip_c4 # if True, use C4 mode where roi_head uses the last resnet layer from backbone | |
| self.use_clip_attpool = use_clip_attpool # if True (C4+text_emb_as_classifier), use att_pool to replace default mean pool | |
| def from_config(cls, cfg): | |
| if cfg.MODEL.CLIP.CROP_REGION_TYPE == "RPN": # create isolated backbone & RPN | |
| # create offline cfg for the pretrained backbone & RPN | |
| from detectron2.config import get_cfg | |
| offline_cfg = get_cfg() | |
| offline_cfg.merge_from_file(cfg.MODEL.CLIP.OFFLINE_RPN_CONFIG) | |
| if cfg.MODEL.CLIP.OFFLINE_RPN_LSJ_PRETRAINED: # large-scale jittering (LSJ) pretrained RPN | |
| offline_cfg.MODEL.BACKBONE.FREEZE_AT = 0 # make all fronzon layers to "SyncBN" | |
| offline_cfg.MODEL.RESNETS.NORM = "SyncBN" # 5 resnet layers | |
| offline_cfg.MODEL.FPN.NORM = "SyncBN" # fpn layers | |
| offline_cfg.MODEL.RPN.CONV_DIMS = [-1, -1] # rpn layers | |
| if cfg.MODEL.CLIP.OFFLINE_RPN_NMS_THRESH: | |
| offline_cfg.MODEL.RPN.NMS_THRESH = cfg.MODEL.CLIP.OFFLINE_RPN_NMS_THRESH # 0.9 | |
| # create offline backbone and RPN | |
| offline_backbone = build_backbone(offline_cfg) # build_resnet_fpn_backbone(cfg, ShapeSpec(channels=len(cfg.MODEL.PIXEL_MEAN))) | |
| offline_rpn = build_proposal_generator(offline_cfg, offline_backbone.output_shape()) | |
| # convert to evaluation mode | |
| for p in offline_backbone.parameters(): p.requires_grad = False | |
| for p in offline_rpn.parameters(): p.requires_grad = False | |
| offline_backbone.eval() | |
| offline_rpn.eval() | |
| elif cfg.MODEL.CLIP.CROP_REGION_TYPE == "GT": | |
| offline_backbone = None | |
| offline_rpn = None | |
| offline_cfg = None | |
| backbone = build_backbone(cfg) | |
| text_backbone = build_clip_language_encoder(cfg) | |
| backbone_type = "swin" if "swin" in cfg.MODEL.BACKBONE.NAME else "resnet" | |
| if backbone_type == "swin": | |
| roi_heads = build_roi_heads(cfg, backbone.image_encoder.output_shape()) | |
| else: | |
| roi_heads = build_roi_heads(cfg, backbone.output_shape()) | |
| return { | |
| "offline_backbone": offline_backbone, | |
| "offline_proposal_generator": offline_rpn, | |
| "backbone": backbone, | |
| "backbone_type": backbone_type, | |
| "text_backbone": text_backbone, | |
| "roi_heads": roi_heads, | |
| "input_format": cfg.INPUT.FORMAT, | |
| "vis_period": cfg.VIS_PERIOD, | |
| "pixel_mean": cfg.MODEL.PIXEL_MEAN, | |
| "pixel_std": cfg.MODEL.PIXEL_STD, | |
| "clip_crop_region_type" : cfg.MODEL.CLIP.CROP_REGION_TYPE, | |
| "use_clip_c4": 'FPN' not in cfg.MODEL.BACKBONE.NAME, | |
| "use_clip_attpool": cfg.MODEL.ROI_HEADS.NAME in ['CLIPRes5ROIHeads', 'CLIPStandardROIHeads'] and cfg.MODEL.CLIP.USE_TEXT_EMB_CLASSIFIER, | |
| "offline_input_format": offline_cfg.INPUT.FORMAT if offline_cfg else None, | |
| "offline_pixel_mean": offline_cfg.MODEL.PIXEL_MEAN if offline_cfg else None, | |
| "offline_pixel_std": offline_cfg.MODEL.PIXEL_STD if offline_cfg else None, | |
| } | |
| def device(self): | |
| return self.pixel_mean.device | |
| def forward(self, queries, batched_inputs: List[Dict[str, torch.Tensor]]): | |
| """ | |
| Args: | |
| batched_inputs: a list, batched outputs of :class:`DatasetMapper` . | |
| Each item in the list contains the inputs for one image. | |
| For now, each item in the list is a dict that contains: | |
| * image: Tensor, image in (C, H, W) format. | |
| * instances (optional): groundtruth :class:`Instances` | |
| * proposals (optional): :class:`Instances`, precomputed proposals. | |
| Other information that's included in the original dicts, such as: | |
| * "height", "width" (int): the output resolution of the model, used in inference. | |
| See :meth:`postprocess` for details. | |
| Returns: | |
| list[dict]: | |
| Each dict is the output for one input image. | |
| The dict contains one key "instances" whose value is a :class:`Instances`. | |
| The :class:`Instances` object has the following keys: | |
| "pred_boxes", "pred_classes", "scores", "pred_masks", "pred_keypoints" | |
| """ | |
| if not self.training: | |
| return self.inference(queries, batched_inputs) | |
| if "instances" in batched_inputs[0]: | |
| gt_instances = [x["instances"].to(self.device) for x in batched_inputs] | |
| else: | |
| gt_instances = None | |
| # localization branch: offline modules to get the region proposals | |
| with torch.no_grad(): | |
| if self.clip_crop_region_type == "GT": # from ground-truth | |
| proposals = [] | |
| for r_i, b_input in enumerate(batched_inputs): | |
| this_gt = copy.deepcopy(b_input["instances"]) # Instance | |
| gt_boxes = this_gt._fields['gt_boxes'].to(self.device) | |
| this_gt._fields = {'proposal_boxes': gt_boxes, 'objectness_logits': torch.ones(gt_boxes.tensor.size(0)).to(self.device)} | |
| proposals.append(this_gt) | |
| elif self.clip_crop_region_type == "RPN": # from the backbone & RPN of standard Mask-RCNN, trained on base classes | |
| if self.offline_backbone.training or self.offline_proposal_generator.training: # was set to True in training script | |
| self.offline_backbone.eval() | |
| self.offline_proposal_generator.eval() | |
| images = self.offline_preprocess_image(batched_inputs) | |
| features = self.offline_backbone(images.tensor) | |
| if self.offline_proposal_generator is not None: | |
| proposals, _ = self.offline_proposal_generator(images, features, None) | |
| # recognition branch: get 2D feature maps using the backbone of recognition branch | |
| images = self.preprocess_image(batched_inputs) | |
| features = self.backbone(images.tensor) | |
| if self.backbone_type == "resnet": | |
| head = self.backbone.layer4 | |
| elif self.backbone_type == "swin": | |
| head = self.backbone.layers[-1] | |
| # Given the proposals, crop region features from 2D image features and classify the regions | |
| if self.use_clip_c4: # use C4 + resnet weights from CLIP | |
| if self.use_clip_attpool: # use att_pool from CLIP to match dimension | |
| _, detector_losses = self.roi_heads(images, features, proposals, gt_instances, res5=head, attnpool=self.backbone.attnpool) | |
| else: # use default mean pool | |
| _, detector_losses = self.roi_heads(images, features, proposals, gt_instances, res5=head) | |
| else: # default setting | |
| if self.use_clip_attpool: # use att_pool from CLIP to match dimension | |
| _, detector_losses = self.roi_heads(images, features, proposals, gt_instances, attnpool=self.backbone.bottom_up.attnpool) | |
| else: # use default mean pool | |
| _, detector_losses = self.roi_heads(images, features, proposals, gt_instances) | |
| if self.vis_period > 0: | |
| storage = get_event_storage() | |
| if storage.iter % self.vis_period == 0: | |
| self.visualize_training(batched_inputs, proposals) | |
| #visualize_proposals(batched_inputs, proposals, self.input_format) | |
| losses = {} | |
| losses.update(detector_losses) | |
| return losses | |
| def inference( | |
| self, | |
| queries, | |
| batched_inputs: List[Dict[str, torch.Tensor]], | |
| detected_instances: Optional[List[Instances]] = None, | |
| do_postprocess: bool = True, | |
| ): | |
| """ | |
| Run inference on the given inputs. | |
| Args: | |
| batched_inputs (list[dict]): same as in :meth:`forward` | |
| detected_instances (None or list[Instances]): if not None, it | |
| contains an `Instances` object per image. The `Instances` | |
| object contains "pred_boxes" and "pred_classes" which are | |
| known boxes in the image. | |
| The inference will then skip the detection of bounding boxes, | |
| and only predict other per-ROI outputs. | |
| do_postprocess (bool): whether to apply post-processing on the outputs. | |
| Returns: | |
| When do_postprocess=True, same as in :meth:`forward`. | |
| Otherwise, a list[Instances] containing raw network outputs. | |
| """ | |
| assert not self.training | |
| # localization branch: offline modules to get the region proposals | |
| if self.clip_crop_region_type == "GT": # from ground-truth | |
| proposals = [] | |
| for r_i, b_input in enumerate(batched_inputs): | |
| this_gt = copy.deepcopy(b_input["instances"]) # Instance | |
| gt_boxes = this_gt._fields['gt_boxes'].to(self.device) | |
| this_gt._fields = {'proposal_boxes': gt_boxes} #, 'objectness_logits': None} | |
| proposals.append(this_gt) | |
| elif self.clip_crop_region_type == "RPN": # from the backbone & RPN of standard Mask-RCNN, trained on base classes | |
| images = self.offline_preprocess_image(batched_inputs) | |
| features = self.offline_backbone(images.tensor) | |
| if detected_instances is None: | |
| if self.offline_proposal_generator is not None: | |
| proposals, _ = self.offline_proposal_generator(images, features, None) | |
| # recognition branch: get 2D feature maps using the backbone of recognition branch | |
| print(batched_inputs[0]['image'][0][:10, :10]) | |
| print(batched_inputs[0]['image'].shape) | |
| images = self.preprocess_image(batched_inputs) | |
| if self.backbone_type == "swin": | |
| features = self.backbone.encode_image(images.tensor) | |
| text_features = self.backbone.encode_text(queries) | |
| else: | |
| features = self.backbone(images.tensor) | |
| token_embeddings = pre_tokenize([queries])[:, 0].to(images.tensor.device) | |
| text_features = self.lang_encoder.encode_text(token_embeddings) | |
| if self.backbone_type == "resnet": | |
| head = self.backbone.layer4 | |
| downsampler = None | |
| norm = None | |
| vision_projection = None | |
| elif self.backbone_type == "swin": | |
| downsampler = self.backbone.image_encoder.layers[-2].downsample | |
| head = self.backbone.image_encoder.layers[-1] | |
| norm = self.backbone.image_encoder.norm | |
| vision_projection = self.backbone.image_projection | |
| # Given the proposals, crop region features from 2D image features and classify the regions | |
| if self.use_clip_c4: # use C4 + resnet weights from CLIP | |
| if self.use_clip_attpool: # use att_pool from CLIP to match dimension | |
| results, _ = self.roi_heads(images, features, proposals, text_features, None, | |
| res5=head, ds=downsampler, norm=norm, vision_projection=vision_projection, attnpool=self.backbone.attnpool) | |
| else: # use default mean pool | |
| results, _ = self.roi_heads(images, features, proposals, text_features, None, | |
| res5=head, ds=downsampler, norm=norm, vision_projection=vision_projection) | |
| else: # default setting | |
| if self.use_clip_attpool: # use att_pool from CLIP to match dimension | |
| results, _ = self.roi_heads(images, features, proposals, text_features, None, | |
| attnpool=self.backbone.bottom_up.attnpool) | |
| else: | |
| results, _ = self.roi_heads(images, features, proposals, text_features, None) | |
| visualize_proposals(batched_inputs, proposals, self.input_format) | |
| vis = visualize_results(batched_inputs, results, self.input_format) | |
| return vis | |
| def offline_preprocess_image(self, batched_inputs: List[Dict[str, torch.Tensor]]): | |
| """ | |
| Normalize, pad and batch the input images. Use detectron2 default processing (pixel mean & std). | |
| Note: Due to FPN size_divisibility, images are padded by right/bottom border. So FPN is consistent with C4 and GT boxes. | |
| """ | |
| images = [x["image"].to(self.device) for x in batched_inputs] | |
| if (self.input_format == 'RGB' and self.offline_input_format == 'BGR') or \ | |
| (self.input_format == 'BGR' and self.offline_input_format == 'RGB'): # the input image follows the main config format ('RGB' or 'BGR') | |
| images = [x[[2,1,0],:,:] for x in images] | |
| if self.offline_div_pixel: | |
| images = [((x / 255.0) - self.offline_pixel_mean) / self.offline_pixel_std for x in images] | |
| else: | |
| images = [(x - self.offline_pixel_mean) / self.offline_pixel_std for x in images] | |
| images = ImageList.from_tensors(images, self.offline_backbone.size_divisibility) | |
| return images | |
| def preprocess_image(self, batched_inputs: List[Dict[str, torch.Tensor]]): | |
| """ | |
| Normalize, pad and batch the input images. Use CLIP default processing (pixel mean & std). | |
| Note: Due to FPN size_divisibility, images are padded by right/bottom border. So FPN is consistent with C4 and GT boxes. | |
| """ | |
| images = [x["image"].to(self.device) for x in batched_inputs] | |
| if self.div_pixel: | |
| images = [((x / 255.0) - self.pixel_mean) / self.pixel_std for x in images] | |
| else: | |
| images = [(x - self.pixel_mean) / self.pixel_std for x in images] | |
| images = ImageList.from_tensors(images, self.backbone.size_divisibility) | |
| return images | |
| def _postprocess(instances, batched_inputs: List[Dict[str, torch.Tensor]]): | |
| """ | |
| Rescale the output instances to the target size. | |
| """ | |
| # note: private function; subject to changes | |
| processed_results = [] | |
| for results_per_image, input_per_image in zip( | |
| instances, batched_inputs): | |
| height = input_per_image["height"] # original image size, before resizing | |
| width = input_per_image["width"] # original image size, before resizing | |
| r = detector_postprocess(results_per_image, height, width) | |
| processed_results.append({"instances": r}) | |
| return processed_results | |
| class PretrainFastRCNN(nn.Module): | |
| """ | |
| Open-vocabulary region representation via vision-language pretraining from image-text pairs | |
| 1. image-text level matching: weakly supervised grounding task with contrastive learning based on region-token representation | |
| 2. region-token level matching: use pseudo text to train model, provided by teacher model | |
| """ | |
| def __init__( | |
| self, | |
| *, | |
| offline_backbone: Backbone, | |
| backbone: Backbone, | |
| offline_proposal_generator: nn.Module, | |
| roi_heads: nn.Module, | |
| teacher_backbone: nn.Module, | |
| teacher_roi_heads: nn.Module, | |
| pixel_mean: Tuple[float], | |
| pixel_std: Tuple[float], | |
| input_format: Optional[str] = None, | |
| vis_period: int = 0, | |
| clip_crop_region_type: str = 'GT', | |
| use_clip_c4: False, | |
| use_clip_attpool: False, | |
| offline_input_format: Optional[str] = None, | |
| offline_pixel_mean: Tuple[float], | |
| offline_pixel_std: Tuple[float], | |
| language_encoder: nn.Module, | |
| matching_temp: None, | |
| num_regions_per_img: int = 0, | |
| img_txt_level: None, | |
| gather_gpus: False, | |
| grid_regions: False, | |
| concept_emb: None, | |
| ): | |
| """ | |
| Args: | |
| backbone: a backbone module, must follow detectron2's backbone interface | |
| proposal_generator: a module that generates proposals using backbone features | |
| roi_heads: a ROI head that performs per-region computation | |
| pixel_mean, pixel_std: list or tuple with #channels element, representing | |
| the per-channel mean and std to be used to normalize the input image | |
| input_format: describe the meaning of channels of input. Needed by visualization | |
| vis_period: the period to run visualization. Set to 0 to disable. | |
| """ | |
| super().__init__() | |
| self.offline_backbone = offline_backbone | |
| self.backbone = backbone | |
| self.offline_proposal_generator = offline_proposal_generator | |
| self.roi_heads = roi_heads | |
| self.input_format = input_format | |
| self.vis_period = vis_period | |
| if vis_period > 0: | |
| assert input_format is not None, "input_format is required for visualization!" | |
| self.register_buffer("pixel_mean", torch.tensor(pixel_mean).view(-1, 1, 1), False) | |
| self.register_buffer("pixel_std", torch.tensor(pixel_std).view(-1, 1, 1), False) | |
| assert ( | |
| self.pixel_mean.shape == self.pixel_std.shape | |
| ), f"{self.pixel_mean} and {self.pixel_std} have different shapes!" | |
| if np.sum(pixel_mean) < 3.0: # converrt pixel value to range [0.0, 1.0] by dividing 255.0 | |
| assert input_format == 'RGB' | |
| self.div_pixel = True | |
| else: # default setting | |
| self.div_pixel = False | |
| # input format, pixel mean and std for offline modules | |
| if offline_input_format and offline_pixel_mean and offline_pixel_std: | |
| self.offline_input_format = offline_input_format | |
| self.register_buffer("offline_pixel_mean", torch.tensor(offline_pixel_mean).view(-1, 1, 1), False) | |
| self.register_buffer("offline_pixel_std", torch.tensor(offline_pixel_std).view(-1, 1, 1), False) | |
| if np.sum(offline_pixel_mean) < 3.0: # converrt pixel value to range [0.0, 1.0] by dividing 255.0 | |
| assert offline_input_format == 'RGB' | |
| self.offline_div_pixel = True | |
| else: # default setting | |
| self.offline_div_pixel = False | |
| self.clip_crop_region_type = clip_crop_region_type | |
| self.use_clip_c4 = use_clip_c4 # if True, use C4 mode where roi_head uses the last resnet layer from backbone | |
| self.use_clip_attpool = use_clip_attpool # if True (C4+text_emb_as_classifier), use att_pool to replace default mean pool | |
| # image-text level pretraining | |
| self.img_txt_level = img_txt_level[0] | |
| self.only_eot = img_txt_level[1] | |
| if self.img_txt_level: | |
| self.lang_encoder = language_encoder | |
| for p in self.lang_encoder.parameters(): # freeze language encoder | |
| p.requires_grad = False | |
| if matching_temp > 0.0: # fixed temp | |
| self.matching_temp = matching_temp | |
| else: # leanable temp | |
| self.matching_temp = nn.Parameter(torch.ones([]) * 4.6052) # nn.Parameter(torch.ones([]) * np.log(1 / 0.07)) | |
| self.context_length = 77 # defined in clip_img_txt_pair_tsv class | |
| self.num_regions_per_img = num_regions_per_img | |
| self.gather_gpus = gather_gpus | |
| self.grid_regions = grid_regions | |
| # region-token level pretraining | |
| if concept_emb[0]: | |
| self.register_buffer("concept_emb", torch.load(concept_emb[0]), False) # [#concepts, 1024] | |
| self.concept_thres = concept_emb[1] | |
| self.teacher_backbone = teacher_backbone # None | |
| # when resume, create teacher model in advance to load ckpt | |
| # self.teacher_backbone = copy.deepcopy(self.backbone) | |
| # # # oai_clip = torch.load("/mnt/output_storage/trained_models/oai_clip_weights/RN50_OAI_CLIP.pth") #("/home/v-yiwuzhong/projects/azureblobs/vyiwuzhong_phillytools/trained_models/oai_clip_weights/RN50_OAI_CLIP.pth") | |
| # # # oai_clip_visual = {} | |
| # # # for key in oai_clip['model']: | |
| # # # if 'visual' in key and 'num_batches_tracked' not in key: | |
| # # # oai_clip_visual[key.replace('visual.','')] = oai_clip['model'][key] | |
| # # # self.teacher_backbone.load_state_dict(oai_clip_visual) | |
| for p in self.teacher_backbone.parameters(): # freeze visual encoder of teacher model | |
| p.requires_grad = False | |
| if concept_emb[2] is None: # teacher model uses the same concept embedding as student model | |
| self.register_buffer("teacher_concept_emb", torch.load(concept_emb[0]), False) | |
| else: # teacher model uses a seperate concept embedding | |
| self.register_buffer("teacher_concept_emb", torch.load(concept_emb[2]), False) | |
| self.teacher_roi_heads = teacher_roi_heads | |
| else: | |
| self.concept_emb = None | |
| def from_config(cls, cfg): | |
| if cfg.MODEL.CLIP.CROP_REGION_TYPE == "RPN": # create isolated backbone & RPN | |
| # create offline cfg for the pretrained backbone & RPN | |
| from detectron2.config import get_cfg | |
| offline_cfg = get_cfg() | |
| offline_cfg.merge_from_file(cfg.MODEL.CLIP.OFFLINE_RPN_CONFIG) | |
| if cfg.MODEL.CLIP.OFFLINE_RPN_LSJ_PRETRAINED: # large-scale jittering (LSJ) pretrained RPN | |
| offline_cfg.MODEL.BACKBONE.FREEZE_AT = 0 # make all fronzon layers to "SyncBN" | |
| offline_cfg.MODEL.RESNETS.NORM = "SyncBN" # 5 resnet layers | |
| offline_cfg.MODEL.FPN.NORM = "SyncBN" # fpn layers | |
| offline_cfg.MODEL.RPN.CONV_DIMS = [-1, -1] # rpn layers | |
| if cfg.MODEL.CLIP.PRETRAIN_RPN_REGIONS: | |
| offline_cfg.MODEL.RPN.POST_NMS_TOPK_TEST = cfg.MODEL.CLIP.PRETRAIN_RPN_REGIONS | |
| if cfg.MODEL.CLIP.OFFLINE_RPN_NMS_THRESH: | |
| offline_cfg.MODEL.RPN.NMS_THRESH = cfg.MODEL.CLIP.OFFLINE_RPN_NMS_THRESH # 0.9 | |
| # offline_cfg.MODEL.ROI_HEADS.NMS_THRESH_TEST = 0.6 | |
| # print("\n\n Set offline RPN.NMS_THRESH to {} and ROI_HEADS.NMS_THRESH_TEST to {}.\n\n".format(offline_cfg.MODEL.RPN.NMS_THRESH, offline_cfg.MODEL.ROI_HEADS.NMS_THRESH_TEST)) | |
| # create offline backbone and RPN | |
| offline_backbone = build_backbone(offline_cfg) # build_resnet_fpn_backbone(cfg, ShapeSpec(channels=len(cfg.MODEL.PIXEL_MEAN))) | |
| offline_rpn = build_proposal_generator(offline_cfg, offline_backbone.output_shape()) | |
| # convert to evaluation mode | |
| for p in offline_backbone.parameters(): p.requires_grad = False | |
| for p in offline_rpn.parameters(): p.requires_grad = False | |
| offline_backbone.eval() | |
| offline_rpn.eval() | |
| elif cfg.MODEL.CLIP.CROP_REGION_TYPE in ["GLOBAL", "GRID", "RANDOM"]: | |
| offline_backbone = None | |
| offline_rpn = None | |
| offline_cfg = None | |
| # visual encoder and roi_heads of student model | |
| backbone = build_backbone(cfg) | |
| if "swin" in cfg.MODEL.BACKBONE.NAME: | |
| roi_heads = build_roi_heads(cfg, backbone.image_encoder.output_shape()) | |
| else: | |
| roi_heads = build_roi_heads(cfg, backbone.output_shape()) | |
| # language encoder of student model | |
| language_encoder = build_clip_language_encoder(cfg) | |
| # visual encoder of teacher model | |
| teacher_cfg = copy.deepcopy(cfg) | |
| teacher_cfg.defrost() | |
| teacher_cfg.MODEL.RESNETS.DEPTH = teacher_cfg.MODEL.CLIP.TEACHER_RESNETS_DEPTH | |
| teacher_backbone = build_backbone(teacher_cfg) | |
| teacher_cfg.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION = teacher_cfg.MODEL.CLIP.TEACHER_POOLER_RESOLUTION | |
| teacher_roi_heads = build_roi_heads(teacher_cfg, teacher_backbone.output_shape()) | |
| return { | |
| "offline_backbone": offline_backbone, | |
| "offline_proposal_generator": offline_rpn, | |
| "backbone": backbone, | |
| "roi_heads": roi_heads, | |
| "teacher_backbone": teacher_backbone, | |
| "teacher_roi_heads": teacher_roi_heads, | |
| "input_format": cfg.INPUT.FORMAT, | |
| "vis_period": cfg.VIS_PERIOD, | |
| "pixel_mean": cfg.MODEL.PIXEL_MEAN, | |
| "pixel_std": cfg.MODEL.PIXEL_STD, | |
| "clip_crop_region_type" : cfg.MODEL.CLIP.CROP_REGION_TYPE, | |
| "use_clip_c4": 'FPN' not in cfg.MODEL.BACKBONE.NAME, | |
| "use_clip_attpool": cfg.MODEL.ROI_HEADS.NAME == 'PretrainRes5ROIHeads', | |
| "offline_input_format": offline_cfg.INPUT.FORMAT if offline_cfg else None, | |
| "offline_pixel_mean": offline_cfg.MODEL.PIXEL_MEAN if offline_cfg else None, | |
| "offline_pixel_std": offline_cfg.MODEL.PIXEL_STD if offline_cfg else None, | |
| "language_encoder": language_encoder, | |
| "matching_temp": cfg.MODEL.CLIP.CLSS_TEMP, | |
| "num_regions_per_img": cfg.MODEL.CLIP.PRETRAIN_SAMPLE_REGIONS, | |
| "img_txt_level": (cfg.MODEL.CLIP.PRETRAIN_IMG_TXT_LEVEL, cfg.MODEL.CLIP.PRETRAIN_ONLY_EOT), | |
| "gather_gpus": cfg.MODEL.CLIP.GATHER_GPUS, | |
| "grid_regions": cfg.MODEL.CLIP.GRID_REGIONS, | |
| "concept_emb": (cfg.MODEL.CLIP.CONCEPT_POOL_EMB, cfg.MODEL.CLIP.CONCEPT_THRES, cfg.MODEL.CLIP.TEACHER_CONCEPT_POOL_EMB), | |
| } | |
| def device(self): | |
| return self.pixel_mean.device | |
| def forward(self, batched_inputs: List[Dict[str, torch.Tensor]]): | |
| """ | |
| Args: | |
| batched_inputs: a list, batched outputs of :class:`DatasetMapper` . | |
| Each item in the list contains the inputs for one image. | |
| For now, each item in the list is a dict that contains: | |
| * image: Tensor, image in (C, H, W) format. | |
| * instances (optional): groundtruth :class:`Instances` | |
| * proposals (optional): :class:`Instances`, precomputed proposals. | |
| Other information that's included in the original dicts, such as: | |
| * "height", "width" (int): the output resolution of the model, used in inference. | |
| See :meth:`postprocess` for details. | |
| Returns: | |
| list[dict]: | |
| Each dict is the output for one input image. | |
| The dict contains one key "instances" whose value is a :class:`Instances`. | |
| The :class:`Instances` object has the following keys: | |
| "pred_boxes", "pred_classes", "scores", "pred_masks", "pred_keypoints" | |
| """ | |
| if not self.training: | |
| return self.inference(batched_inputs) | |
| if self.concept_emb is not None and self.teacher_backbone is None: # create a teacher model from an initialized student model; if resume, simply comment out this section | |
| self.teacher_backbone = copy.deepcopy(self.backbone) | |
| for p in self.teacher_backbone.parameters(): # freeze visual encoder of teacher model | |
| p.requires_grad = False | |
| gt_instances = None | |
| losses = {} | |
| # localization branch: offline modules to get the region proposals | |
| proposals = self.get_region_proposals(batched_inputs) | |
| global_proposals = self.create_global_proposals(batched_inputs) | |
| # for prop, g_prop in zip(proposals, global_proposals): # append global proposal into each image | |
| # prop.proposal_boxes.tensor = torch.cat((prop.proposal_boxes.tensor, g_prop.tensor), dim=0) | |
| # recognition branch: get 2D feature maps using the backbone of recognition branch | |
| images = self.preprocess_image(batched_inputs) | |
| features = self.backbone(images.tensor) | |
| region_feats = self.get_region_features(images, features, proposals, gt_instances) | |
| global_feats = self.get_region_features(images, features, global_proposals, gt_instances) | |
| # image-text level matching | |
| if self.img_txt_level: | |
| self.image_text_matching(batched_inputs, proposals, region_feats, losses, global_feats=global_feats, only_global=True) | |
| # region-phrase level matching | |
| if len(batched_inputs[0]) > 6: # controlled by dataset loading | |
| phrase_text_embs = self.encode_phrase_text(batched_inputs) | |
| else: | |
| phrase_text_embs = None | |
| # region-concept level matching | |
| if self.concept_emb is not None: | |
| self.region_concept_matching(images, proposals, gt_instances, region_feats, losses, phrase_embs=phrase_text_embs) | |
| return losses | |
| def encode_phrase_text(self, batched_inputs): | |
| text = [x[6].view(-1,self.context_length).to(self.device) for i, x in enumerate(batched_inputs)] | |
| text = torch.cat(text, dim=0) | |
| text_embs = self.lang_encoder.encode_text(text, only_eot=True) # [#phrases, transformer.width] | |
| return text_embs | |
| def region_concept_matching(self, images, proposals, gt_instances, region_feats, losses, phrase_embs=None): | |
| use_distill = True | |
| use_contrastive = True | |
| # get psuedo concept labels from teacher model | |
| concept_scores, target_inds, keep_regions, target_embs, label_mtx, phrase_label_mtx, phrase_target_regions \ | |
| = self.get_psuedo_concept_labels(images, proposals, gt_instances, phrase_embs=phrase_embs) | |
| # prepare region features for the kept regions | |
| keep_region_feats = region_feats[keep_regions] | |
| keep_region_feats = keep_region_feats / keep_region_feats.norm(dim=-1, keepdim=True) | |
| if use_distill: | |
| # distillation learning: learns from the predictions of teacher model | |
| concept_emb = self.concept_emb / self.concept_emb.norm(dim=-1, keepdim=True) | |
| cls_scores = keep_region_feats @ concept_emb.t() # [#kept_regions, #concepts] | |
| if isinstance(self.matching_temp, float): # Typical good values are 100.0 for euclidean, 10.0 for dot, 0.01 for cosine | |
| cls_scores_temp = cls_scores / self.matching_temp | |
| else: | |
| cls_scores_temp = cls_scores * self.matching_temp.exp() | |
| # loss weights | |
| #rpn_weights = torch.cat([torch.sigmoid(p.objectness_logits) for p in proposals])[keep_regions] | |
| #focal_weights = self.focal_scaling(cls_scores_temp, target_inds) | |
| # calculate loss | |
| cls_loss = F.kl_div(F.softmax(cls_scores_temp, dim=1).log(), concept_scores, reduction='batchmean') # input is log-probabilities, target is probabilities | |
| #cls_loss = SoftTargetCrossEntropy()(cls_scores_temp, concept_scores) | |
| #cls_loss = F.cross_entropy(cls_scores_temp, target_inds) | |
| #cls_loss = (F.cross_entropy(cls_scores_temp, target_inds, reduction="none") * focal_weights).mean() | |
| losses.update({"loss_region_distill": cls_loss}) # * 0.8}) | |
| if use_contrastive: | |
| # contrastive learning: matching student visual features with target teacher concept embs | |
| target_embs = target_embs / target_embs.norm(dim=-1, keepdim=True) | |
| match_scores = keep_region_feats @ target_embs.t() # [#kept_regions, #kept_regions] | |
| if isinstance(self.matching_temp, float): # Typical good values are 100.0 for euclidean, 10.0 for dot, 0.01 for cosine | |
| match_scores_temp = match_scores / self.matching_temp | |
| else: | |
| match_scores_temp = match_scores * self.matching_temp.exp() | |
| # loss weights | |
| #rpn_weights = torch.cat([torch.sigmoid(p.objectness_logits) for p in proposals])[keep_regions] | |
| #focal_weights = (1 - torch.sigmoid(torch.diag(match_scores_temp))) ** 0.8 # 1.0 # 2.0 # | |
| # calculate loss given matching scores and label matrix | |
| contrastive_loss = MILCrossEntropy()(match_scores_temp, label_mtx, weights=None, avg_positives=False) # SoftTargetCrossEntropy()(match_scores_temp, label_mtx) | |
| #contrastive_loss = (MILCrossEntropy()(match_scores, label_mtx) + MILCrossEntropy()(match_scores.t(), label_mtx)) / 2.0 | |
| losses.update({"loss_concept_contrastive": contrastive_loss}) | |
| if phrase_embs is not None: | |
| phrase_embs = phrase_embs / phrase_embs.norm(dim=-1, keepdim=True) | |
| phrase_scores = phrase_embs @ phrase_target_regions.t() | |
| if isinstance(self.matching_temp, float): # Typical good values are 100.0 for euclidean, 10.0 for dot, 0.01 for cosine | |
| phrase_scores_temp = phrase_scores / self.matching_temp | |
| else: | |
| phrase_scores_temp = phrase_scores * self.matching_temp.exp() | |
| contrastive_loss = MILCrossEntropy()(phrase_scores_temp, phrase_label_mtx, weights=None, avg_positives=False) | |
| #contrastive_loss = SoftTargetCrossEntropy()(phrase_scores_temp, phrase_label_mtx) | |
| losses.update({"loss_phrase_contrastive": contrastive_loss}) | |
| def image_text_matching(self, batched_inputs, proposals, region_feats, losses, global_feats=None, only_global=False): | |
| # encode text | |
| num_cap = int(batched_inputs[0][1].size(0) / self.context_length) | |
| if num_cap == 1: # one caption per image | |
| text = [x[1].view(1,-1).to(self.device) for x in batched_inputs] | |
| else: # multiple caption pers image, then randomly pick one | |
| rand_ind = [randint(0, num_cap-1) for _ in range(len(batched_inputs))] | |
| text = [x[1].view(-1,self.context_length)[rand_ind[i]:rand_ind[i]+1].to(self.device) for i, x in enumerate(batched_inputs)] | |
| text = torch.cat(text, dim=0) | |
| text_embs = self.lang_encoder.encode_text(text, only_eot=self.only_eot) # [img_batch, n_ctx, transformer.width] or [img_batch, transformer.width] | |
| eot_pos = text.argmax(dim=-1) | |
| # prepare region features and text embeddings | |
| if isinstance(proposals[0], Boxes): | |
| num_bbs = [len(prop) for prop in proposals] | |
| else: | |
| num_bbs = [len(prop.proposal_boxes) for prop in proposals] | |
| if global_feats is not None and only_global: # only global feature | |
| assert self.only_eot | |
| region_feats = global_feats | |
| region_feats = region_feats / region_feats.norm(dim=-1, keepdim=True) | |
| text_embs = text_embs / text_embs.norm(dim=-1, keepdim=True) | |
| num_bbs = [1 for _ in num_bbs] | |
| elif global_feats is not None and not only_global: # combine both global and region features | |
| assert self.only_eot | |
| keep_num = 20 | |
| region_feats = region_feats.split(num_bbs) | |
| region_feats = [torch.mean(rg_f, dim=0, keepdim=True) for rg_f in region_feats] | |
| region_g_feats = [torch.cat((r_f[:keep_num], global_feats[i:i+1]), dim=0) for i, r_f in enumerate(region_feats)] | |
| region_g_feats = [torch.mean(rg_f, dim=0, keepdim=True) for rg_f in region_g_feats] | |
| region_g_feats = [rg_f / rg_f.norm(dim=-1, keepdim=True) for rg_f in region_g_feats] | |
| region_feats = torch.cat(region_g_feats) | |
| text_embs = text_embs / text_embs.norm(dim=-1, keepdim=True) | |
| num_bbs = [1 for _ in num_bbs] | |
| else: # only region features | |
| num_bbs = torch.tensor(num_bbs).long().to(self.device) | |
| region_feats_full, min_bs = gather_tensors(region_feats) if self.gather_gpus else (region_feats, None) # gather across GPUs | |
| text_embs_full, min_bs = gather_tensors(text_embs) if self.gather_gpus else (text_embs, None) # gather across GPUs | |
| # matching visual features with text embs | |
| match_scores = region_feats_full @ text_embs_full.view(-1, text_embs_full.size(-1)).t() # [#regions, img_batch * n_ctx] | |
| if global_feats is not None: # only global feature or combine both global and region features | |
| img_b = int(region_feats_full.size(0)) | |
| pooled_score = match_scores | |
| else: # only region features | |
| eot_pos_full, min_bs = gather_tensors(eot_pos) if self.gather_gpus else (eot_pos, None) # gather across GPUs | |
| num_bbs_full, min_bs = gather_tensors(num_bbs) if self.gather_gpus else (num_bbs, None) # gather across GPUs | |
| pooled_score = [] | |
| token_b = self.context_length | |
| # region_b = self.num_regions_per_img if global_feats is None else 1 | |
| # img_b = int(region_feats_full.size(0) / region_b) | |
| img_b = num_bbs_full.size(0) | |
| rb_start = 0 # the starting index of regions | |
| for i in range(img_b): # for each image | |
| region_b = num_bbs_full[i].item() | |
| for j in range(img_b): # for each text | |
| if self.only_eot: # sentence level embs | |
| # max pool over regions | |
| this_s = torch.max(match_scores[rb_start:(rb_start+region_b), j:(j+1)], dim=0)[0] | |
| else: # token level embs | |
| # 3. softmax over regions as soft attention, then multiply attention with original logits, finally sum over matrix and divided by #tokens | |
| # this_matrix = match_scores[rb_start:(rb_start+region_b), j*token_b:(j*token_b+eot_pos_full[j]+1)] | |
| # this_att = F.softmax(this_matrix, dim=0) | |
| # this_s = torch.sum(this_matrix * this_att) / (eot_pos_full[j]+1) | |
| # 2. max pool over regions, and then avg over text tokens | |
| # this_s = torch.sum(torch.max(match_scores[rb_start:(rb_start+region_b), j*token_b:(j*token_b+eot_pos_full[j]+1)], dim=0)[0]) / (eot_pos_full[j]+1) | |
| # 1. max pool over regions, and then sum over text tokens | |
| this_s = torch.sum(torch.max(match_scores[rb_start:(rb_start+region_b), j*token_b:(j*token_b+eot_pos_full[j]+1)], dim=0)[0]) | |
| pooled_score.append(this_s.view(1,1)) | |
| rb_start += region_b | |
| assert rb_start == match_scores.size(0) | |
| pooled_score = torch.cat(pooled_score).view(img_b, img_b) # diagnal elements are positive pairs and the others are negative pairs | |
| if isinstance(self.matching_temp,float): # Typical good values are 100.0 for euclidean, 10.0 for dot, 0.01 for cosine | |
| pooled_score = pooled_score / self.matching_temp | |
| else: | |
| pooled_score = pooled_score * self.matching_temp.exp() | |
| contrast_target = torch.arange(img_b).to(self.device) | |
| row_loss = F.cross_entropy(pooled_score, contrast_target) | |
| col_loss = F.cross_entropy(pooled_score.t(), contrast_target) | |
| losses.update({"loss_img_txt_level": (row_loss + col_loss) / 2.0}) # losses.update({"loss_img_txt_level": (row_loss + col_loss) / 4.0}) # | |
| def focal_scaling(self, logits, targets, gamma=1.0): | |
| p = F.softmax(logits, dim=1) | |
| p_t = p[torch.arange(p.size(0)).to(p.device), targets] # get prob of target class | |
| weights = (1 - p_t) ** gamma | |
| return weights | |
| def get_psuedo_concept_labels(self, images, proposals, gt_instances, s_temp=0.01, norm=True, phrase_embs=None): | |
| """ Input images and region proposals, return matching results from teacher model | |
| """ | |
| with torch.no_grad(): | |
| # extract visual features from teacher model | |
| features = self.teacher_backbone(images.tensor) | |
| teacher_region_feats = self.teacher_roi_heads(images, features, proposals, gt_instances, res5=self.teacher_backbone.layer4, attnpool=self.teacher_backbone.attnpool) | |
| # match teacher visual features with teacher concept embs to create pseudo labels | |
| if norm: | |
| teacher_region_feats = teacher_region_feats / teacher_region_feats.norm(dim=-1, keepdim=True) | |
| teacher_concept_emb = self.teacher_concept_emb / self.teacher_concept_emb.norm(dim=-1, keepdim=True) | |
| else: | |
| teacher_concept_emb = self.teacher_concept_emb | |
| concept_scores = teacher_region_feats @ teacher_concept_emb.t() # [#regions, #concepts] | |
| concept_scores = F.softmax(concept_scores / s_temp, dim=1) | |
| max_scores, max_inds = torch.max(concept_scores, dim=1) | |
| keep_regions = max_scores > self.concept_thres # only keep the regions that have high matching score with a concept | |
| if keep_regions.nonzero().size(0) == 0: # if all regions can't match to any concept | |
| print("all regions can't match to any concept!") | |
| keep_regions = max_scores > 0.0 | |
| target_inds = max_inds[keep_regions] | |
| target_embs = self.concept_emb[target_inds] # the target embedding of student model | |
| label_mtx = (target_inds.view(-1, 1) == target_inds.view(1, -1)).type_as(teacher_region_feats) | |
| concept_scores = concept_scores[keep_regions] | |
| # matching kept regions with phrase-text to create labels | |
| if phrase_embs is None: | |
| phrase_label_mtx = None | |
| phrase_target_regions = None | |
| else: | |
| if norm: | |
| phrase_embs = phrase_embs / phrase_embs.norm(dim=-1, keepdim=True) | |
| teacher_kept_feats = teacher_region_feats[keep_regions] | |
| phrase_scores = phrase_embs @ teacher_kept_feats.t() # [#phrases, #keep regions] | |
| phrase_scores = F.softmax(phrase_scores / s_temp, dim=1) | |
| _, max_region_inds = torch.max(phrase_scores, dim=1) | |
| phrase_label_mtx = (max_region_inds.view(-1, 1) == max_region_inds.view(1, -1)).type_as(teacher_region_feats) | |
| phrase_target_regions = teacher_kept_feats[max_region_inds] | |
| return concept_scores, target_inds, keep_regions, target_embs, label_mtx, phrase_label_mtx, phrase_target_regions | |
| def get_region_features(self, images, features, proposals, gt_instances): | |
| """ Input images and region proposals, return region features | |
| """ | |
| # Given the proposals, crop region features from 2D image features | |
| if self.use_clip_c4: # use C4 + resnet weights from CLIP | |
| if self.use_clip_attpool: # use att_pool from CLIP to match dimension | |
| region_feats = self.roi_heads(images, features, proposals, gt_instances, res5=self.backbone.layer4, attnpool=self.backbone.attnpool) | |
| else: # use default mean pool | |
| region_feats = self.roi_heads(images, features, proposals, gt_instances, res5=self.backbone.layer4) | |
| else: # default setting | |
| region_feats = self.roi_heads(images, features, proposals, gt_instances) | |
| return region_feats | |
| def get_region_proposals(self, batched_inputs): | |
| """ Given image, return object proposals | |
| """ | |
| if self.grid_regions: # use grid boxes | |
| proposals = self.create_grid_boxes(batched_inputs) | |
| else: # use object proposals | |
| with torch.no_grad(): | |
| if self.clip_crop_region_type == "GLOBAL": # from a global box per image | |
| proposals = self.create_global_proposals(batched_inputs) | |
| elif self.clip_crop_region_type == "GRID": # from grid proposals | |
| proposals = self.create_grid_boxes(batched_inputs) | |
| elif self.clip_crop_region_type == "RANDOM": # from random proposals | |
| proposals = self.create_rand_boxes(batched_inputs) | |
| elif self.clip_crop_region_type == "RPN": # from the backbone & RPN of standard Mask-RCNN, trained on base classes | |
| if self.offline_backbone.training or self.offline_proposal_generator.training: # was set to True in training script | |
| self.offline_backbone.eval() | |
| self.offline_proposal_generator.eval() | |
| images = self.offline_preprocess_image(batched_inputs) | |
| features = self.offline_backbone(images.tensor) | |
| if self.offline_proposal_generator is not None: | |
| proposals, _ = self.offline_proposal_generator(images, features, None) | |
| #visualize_proposals(batched_inputs, proposals, self.input_format, vis_pretrain=True) | |
| # randomly select proposals to avoid overfitting | |
| if self.training: | |
| #rand_inds = [torch.arange(len(p))[:self.num_regions_per_img].to(self.device) for p in proposals] | |
| rand_inds = [torch.randperm(len(p))[:self.num_regions_per_img].to(self.device) for p in proposals] | |
| proposals = [p[rand_inds[i]] for i, p in enumerate(proposals)] | |
| return proposals | |
| def offline_preprocess_image(self, batched_inputs: List[Dict[str, torch.Tensor]]): | |
| """ | |
| NOTE: the image tsv in pretraining are already normalized pixel values and thus opposite to Detectron2 default input. | |
| Normalize, pad and batch the input images. Use detectron2 default processing (pixel mean & std). | |
| Note: Due to FPN size_divisibility, images are padded by right/bottom border. So FPN is consistent with C4 and GT boxes. | |
| """ | |
| images = [x[0].to(self.device) for x in batched_inputs] | |
| if (self.input_format == 'RGB' and self.offline_input_format == 'BGR') or \ | |
| (self.input_format == 'BGR' and self.offline_input_format == 'RGB'): # the input image follows the main config format ('RGB' or 'BGR') | |
| images = [x[[2,1,0],:,:] for x in images] | |
| if self.offline_div_pixel: | |
| images = [(x - self.offline_pixel_mean) / self.offline_pixel_std for x in images] | |
| else: | |
| images = [((x * 255.0) - self.offline_pixel_mean) / self.offline_pixel_std for x in images] | |
| images = ImageList.from_tensors(images, self.offline_backbone.size_divisibility) | |
| return images | |
| def preprocess_image(self, batched_inputs: List[Dict[str, torch.Tensor]]): | |
| """ | |
| NOTE: the image tsv in pretraining are already normalized pixel values and thus opposite to Detectron2 default input. | |
| Normalize, pad and batch the input images. Use CLIP default processing (pixel mean & std). | |
| Note: Due to FPN size_divisibility, images are padded by right/bottom border. So FPN is consistent with C4 and GT boxes. | |
| """ | |
| images = [x[0].to(self.device) for x in batched_inputs] | |
| if self.div_pixel: | |
| images = [(x - self.pixel_mean) / self.pixel_std for x in images] | |
| else: | |
| images = [((x * 255.0) - self.pixel_mean) / self.pixel_std for x in images] | |
| images = ImageList.from_tensors(images, self.backbone.size_divisibility) | |
| return images | |
| def create_rand_boxes(self, batched_inputs, grid_length=8): | |
| """ create random boxes within an image, output random self.num_regions_per_img boxes | |
| return a list of Boxes | |
| """ | |
| images = self.preprocess_image(batched_inputs) | |
| image_height = images.tensor.size(2) | |
| image_width = images.tensor.size(3) | |
| left_top_x = torch.tensor([i*(grid_length) for i in range(image_width // grid_length)]) | |
| left_top_y = torch.tensor([i*(grid_length) for i in range(image_height // grid_length)]) | |
| right_bot_x = torch.tensor([(i+1)*(grid_length) for i in range(image_width // grid_length)]) | |
| right_bot_y = torch.tensor([(i+1)*(grid_length) for i in range(image_height // grid_length)]) | |
| x_inds = torch.randint(0, left_top_x.size(0), (self.num_regions_per_img,)) | |
| y_inds = torch.randint(0, left_top_y.size(0), (self.num_regions_per_img,)) | |
| proposals = [] | |
| for i in range(self.num_regions_per_img): | |
| rb_x_candidates = right_bot_x[x_inds[i]:] | |
| rb_x = rb_x_candidates[torch.randperm(rb_x_candidates.size(0))[0]] | |
| rb_y_candidates = right_bot_y[y_inds[i]:] | |
| rb_y = rb_y_candidates[torch.randperm(rb_y_candidates.size(0))[0]] | |
| this_box = torch.cat((left_top_x[x_inds[i]].view(1,1), left_top_y[y_inds[i]].view(1,1), rb_x.view(1,1), rb_y.view(1,1)),dim=-1) | |
| proposals.append(this_box) | |
| proposals = torch.cat(proposals).float().to(self.device) | |
| proposals = [Boxes(proposals) for i in range(len(batched_inputs))] # a list of Boxes | |
| return proposals | |
| def create_grid_boxes(self, batched_inputs, grid_length=32): | |
| """ create (image_height/32) * (image_width/32) pseudo grid boxes, and randomly sample self.num_regions_per_img boxes | |
| return a list of Boxes | |
| """ | |
| images = self.preprocess_image(batched_inputs) | |
| image_height = images.tensor.size(2) | |
| image_width = images.tensor.size(3) | |
| left_top_x = torch.tensor([i*(grid_length) for i in range(image_width // grid_length)]) | |
| left_top_y = torch.tensor([i*(grid_length) for i in range(image_height // grid_length)]) | |
| right_bot_x = torch.tensor([(i+1)*(grid_length) for i in range(image_width // grid_length)]) | |
| right_bot_y = torch.tensor([(i+1)*(grid_length) for i in range(image_height // grid_length)]) | |
| left_top_x, left_top_y = torch.meshgrid(left_top_x, left_top_y) | |
| right_bot_x, right_bot_y = torch.meshgrid(right_bot_x, right_bot_y) | |
| grid_boxes = torch.cat((left_top_x.flatten().view(-1,1), left_top_y.flatten().view(-1,1),\ | |
| right_bot_x.flatten().view(-1,1), right_bot_y.flatten().view(-1,1),), dim=1) | |
| sample_ind = torch.randperm(grid_boxes.size(0))[:self.num_regions_per_img] | |
| grid_boxes = grid_boxes[sample_ind] | |
| grid_boxes = grid_boxes.float().to(self.device) | |
| proposals = [Boxes(grid_boxes) for i in range(len(batched_inputs))] # a list of Boxes | |
| return proposals | |
| def create_global_proposals(self, batched_inputs): | |
| """ create a single global box for an image, so as to extract global image features with RoIAlign on high-resolution images. | |
| """ | |
| images = self.preprocess_image(batched_inputs) | |
| image_height = images.tensor.size(2) | |
| image_width = images.tensor.size(3) | |
| global_box = torch.tensor([0, 0, image_width, image_height]).view(1,4).float().to(self.device) | |
| proposals = [Boxes(global_box) for i in range(len(batched_inputs))] # a list of Boxes | |
| return proposals | |
| def inference(self, batched_inputs, detected_instances=None, do_postprocess=True): | |
| """ | |
| Grounding inference: map region features with sentence tokens | |
| return: matching scores between region features and tokenized texts, region boxes in raw image resolution, image id & raw string texts & tokenized texts | |
| """ | |
| assert len(batched_inputs) == 1 # only one instance per image during inference | |
| gt_instances = None | |
| losses = {} | |
| # localization branch: offline modules to get the region proposals | |
| proposals = self.get_region_proposals(batched_inputs) | |
| # recognition branch: get 2D feature maps using the backbone of recognition branch | |
| images = self.preprocess_image(batched_inputs) | |
| features = self.backbone(images.tensor) | |
| region_feats = self.get_region_features(images, features, proposals, gt_instances) | |
| # encode text | |
| num_cap = int(batched_inputs[0][1].size(0) / self.context_length) | |
| text = batched_inputs[0][1].view(num_cap, -1).to(self.device) # [num_cap, context_length] | |
| text_embs = self.lang_encoder.encode_text(text, only_eot=False) # [img_batch, n_ctx, transformer.width] or [img_batch, transformer.width] | |
| # matching visual features with text embs | |
| region_feats = region_feats / region_feats.norm(dim=-1, keepdim=True) | |
| text_embs = text_embs / text_embs.norm(dim=-1, keepdim=True) | |
| match_scores = region_feats @ text_embs.view(-1, text_embs.size(-1)).t() # [#regions, img_batch * n_ctx] | |
| # visualize_proposals(batched_inputs, proposals, self.input_format, vis_pretrain=True) | |
| # multiply RPN logits | |
| rpn_scores = [p.get('objectness_logits') for p in proposals][0] | |
| match_scores = (match_scores * rpn_scores[:, None]) ** 0.5 | |
| # scale the object proposals back to raw image resolution | |
| if do_postprocess: | |
| assert not torch.jit.is_scripting(), "Scripting is not supported for postprocess." | |
| processed_results = PretrainFastRCNN._postprocess(proposals, batched_inputs) | |
| return match_scores, processed_results | |
| def _postprocess(instances, batched_inputs: List[Dict[str, torch.Tensor]]): | |
| """ | |
| Rescale the output instances to the target size. | |
| """ | |
| # note: private function; subject to changes | |
| processed_results = [] | |
| for results_per_image, input_per_image in zip(instances, batched_inputs): | |
| height, width = input_per_image[-1][2] # original image size, before resizing | |
| r = detector_postprocess(results_per_image, height, width) | |
| processed_results.append({"instances": r}) | |
| return processed_results | |
| def visualize_proposals(batched_inputs, proposals, input_format, vis_pretrain=False): | |
| """ | |
| A function used to visualize images and proposals. It shows ground truth | |
| bounding boxes on the original image and up to 20 top-scoring predicted | |
| object proposals on the original image. Users can implement different | |
| visualization functions for different models. | |
| Args: | |
| batched_inputs (list): a list that contains input to the model. | |
| proposals (list): a list that contains predicted proposals. Both | |
| batched_inputs and proposals should have the same length. | |
| """ | |
| from detectron2.utils.visualizer import Visualizer | |
| max_vis_prop = 50 | |
| if vis_pretrain: | |
| for i, (input, prop) in enumerate(zip(batched_inputs, proposals)): | |
| img = input[0] * 255.0 | |
| img = convert_image_to_rgb(img.permute(1, 2, 0), input_format) | |
| box_size = min(len(prop.proposal_boxes), max_vis_prop) | |
| v_pred = Visualizer(img, None) | |
| v_pred = v_pred.overlay_instances( | |
| boxes=prop.proposal_boxes[0:box_size].tensor.cpu().numpy() | |
| ) | |
| prop_img = v_pred.get_image() | |
| vis_img = prop_img | |
| to_save = Image.fromarray(np.array(vis_img, np.uint8)) | |
| to_save.save("output/regions/" + str(i) + ".png") | |
| #break # only visualize one image in a batch | |
| else: | |
| for input, prop in zip(batched_inputs, proposals): | |
| img = input["image"] | |
| img = convert_image_to_rgb(img.permute(1, 2, 0), input_format) | |
| box_size = min(len(prop.proposal_boxes), max_vis_prop) | |
| v_pred = Visualizer(img, None) | |
| v_pred = v_pred.overlay_instances( | |
| boxes=prop.proposal_boxes[0:box_size].tensor.cpu().numpy() | |
| ) | |
| prop_img = v_pred.get_image() | |
| vis_img = prop_img | |
| # f_n = input['file_name'] | |
| to_save = Image.fromarray(np.array(vis_img, np.uint8)) | |
| to_save.save("output/regions/" + "proposals.png") | |
| #break # only visualize one image in a batch | |
| def visualize_results(batched_inputs, results, input_format, vis_pretrain=False): | |
| """ | |
| A function used to visualize images and results. It shows ground truth | |
| bounding boxes on the original image and up to 20 top-scoring predicted | |
| object results on the original image. Users can implement different | |
| visualization functions for different models. | |
| Args: | |
| batched_inputs (list): a list that contains input to the model. | |
| results (list): a list that contains predicted results. Both | |
| batched_inputs and results should have the same length. | |
| """ | |
| from detectron2.utils.visualizer import Visualizer | |
| max_vis_prop = 1 | |
| if vis_pretrain: | |
| for i, (input, prop) in enumerate(zip(batched_inputs, results)): | |
| img = input[0] * 255.0 | |
| img = convert_image_to_rgb(img.permute(1, 2, 0), input_format) | |
| box_size = min(len(prop.proposal_boxes), max_vis_prop) | |
| v_pred = Visualizer(img, None) | |
| v_pred = v_pred.overlay_instances( | |
| boxes=prop.proposal_boxes[0:box_size].tensor.cpu().numpy() | |
| ) | |
| prop_img = v_pred.get_image() | |
| vis_img = prop_img | |
| to_save = Image.fromarray(np.array(vis_img, np.uint8)) | |
| # to_save.save("output/regions/" + str(i) + ".png") | |
| #break # only visualize one image in a batch | |
| else: | |
| for input, prop in zip(batched_inputs, results): | |
| img = input["image"] | |
| img = convert_image_to_rgb(img.permute(1, 2, 0), input_format) | |
| box_size = min(len(prop.pred_boxes), max_vis_prop) | |
| v_pred = Visualizer(img, None) | |
| v_pred = v_pred.overlay_instances( | |
| boxes=prop.pred_boxes[0:box_size].tensor.cpu().numpy() | |
| ) | |
| prop_img = v_pred.get_image() | |
| vis_img = prop_img | |
| # f_n = input['file_name'] | |
| to_save = Image.fromarray(np.array(vis_img, np.uint8)) | |
| to_save.save("output/regions/" + "results.png") | |
| #break # only visualize one image in a batch | |
| return to_save |