diff --git "a/pretrain/selfsup_detr_cluster-ids-as-pseudo-labels/20221026_193523.log.json" "b/pretrain/selfsup_detr_cluster-ids-as-pseudo-labels/20221026_193523.log.json" new file mode 100644--- /dev/null +++ "b/pretrain/selfsup_detr_cluster-ids-as-pseudo-labels/20221026_193523.log.json" @@ -0,0 +1,1801 @@ +{"env_info": "sys.platform: linux\nPython: 3.7.3 (default, Jan 22 2021, 20:04:44) [GCC 8.3.0]\nCUDA available: True\nGPU 0,1,2,3,4,5,6,7: A100-SXM-80GB\nCUDA_HOME: /usr/local/cuda\nNVCC: Cuda compilation tools, release 11.3, V11.3.109\nGCC: x86_64-linux-gnu-gcc (Debian 8.3.0-6) 8.3.0\nPyTorch: 1.10.0\nPyTorch compiling details: PyTorch built with:\n - GCC 7.3\n - C++ Version: 201402\n - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n - Intel(R) MKL-DNN v2.2.3 (Git Hash 7336ca9f055cf1bfa13efb658fe15dc9b41f0740)\n - OpenMP 201511 (a.k.a. OpenMP 4.5)\n - LAPACK is enabled (usually provided by MKL)\n - NNPACK is enabled\n - CPU capability usage: AVX512\n - CUDA Runtime 11.3\n - NVCC architecture flags: -gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86\n - CuDNN 8.2\n - Magma 2.5.2\n - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.10.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, \n\nTorchVision: 0.11.1+cu113\nOpenCV: 4.6.0\nMMCV: 1.6.1\nMMCV Compiler: GCC 9.3\nMMCV CUDA Compiler: 11.3\nMMDetection: 2.25.2+2d311f3", "config": "model = dict(\n type='DETR',\n backbone=dict(\n type='ResNet',\n depth=50,\n num_stages=4,\n out_indices=(3, ),\n frozen_stages=4,\n norm_cfg=dict(type='BN', requires_grad=False),\n norm_eval=True,\n style='pytorch',\n init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),\n bbox_head=dict(\n type='DETRHead',\n num_classes=256,\n in_channels=2048,\n transformer=dict(\n type='Transformer',\n encoder=dict(\n type='DetrTransformerEncoder',\n num_layers=6,\n transformerlayers=dict(\n type='BaseTransformerLayer',\n attn_cfgs=[\n dict(\n type='MultiheadAttention',\n embed_dims=256,\n num_heads=8,\n dropout=0.1)\n ],\n feedforward_channels=2048,\n ffn_dropout=0.1,\n operation_order=('self_attn', 'norm', 'ffn', 'norm'))),\n decoder=dict(\n type='DetrTransformerDecoder',\n return_intermediate=True,\n num_layers=6,\n transformerlayers=dict(\n type='DetrTransformerDecoderLayer',\n attn_cfgs=dict(\n type='MultiheadAttention',\n embed_dims=256,\n num_heads=8,\n dropout=0.1),\n feedforward_channels=2048,\n ffn_dropout=0.1,\n operation_order=('self_attn', 'norm', 'cross_attn', 'norm',\n 'ffn', 'norm')))),\n positional_encoding=dict(\n type='SinePositionalEncoding', num_feats=128, normalize=True),\n loss_cls=dict(\n type='CrossEntropyLoss',\n bg_cls_weight=0.1,\n use_sigmoid=False,\n loss_weight=1.0,\n class_weight=1.0),\n loss_bbox=dict(type='L1Loss', loss_weight=5.0),\n loss_iou=dict(type='GIoULoss', loss_weight=2.0)),\n train_cfg=dict(\n assigner=dict(\n type='HungarianAssigner',\n cls_cost=dict(type='ClassificationCost', weight=1.0),\n reg_cost=dict(type='BBoxL1Cost', weight=5.0, box_format='xywh'),\n iou_cost=dict(type='IoUCost', iou_mode='giou', weight=2.0))),\n test_cfg=dict(max_per_img=100))\ndataset_type = 'CocoDataset'\ndata_root = 'data/coco/'\nimg_norm_cfg = dict(\n mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)\ntrain_pipeline = [\n dict(type='LoadImageFromFile'),\n dict(type='LoadAnnotations', with_bbox=True),\n dict(type='RandomFlip', flip_ratio=0.5),\n dict(\n type='AutoAugment',\n policies=[[{\n 'type':\n 'Resize',\n 'img_scale': [(480, 1333), (512, 1333), (544, 1333), (576, 1333),\n (608, 1333), (640, 1333), (672, 1333), (704, 1333),\n (736, 1333), (768, 1333), (800, 1333)],\n 'multiscale_mode':\n 'value',\n 'keep_ratio':\n True\n }],\n [{\n 'type': 'Resize',\n 'img_scale': [(400, 1333), (500, 1333), (600, 1333)],\n 'multiscale_mode': 'value',\n 'keep_ratio': True\n }, {\n 'type': 'RandomCrop',\n 'crop_type': 'absolute_range',\n 'crop_size': (384, 600),\n 'allow_negative_crop': True\n }, {\n 'type':\n 'Resize',\n 'img_scale': [(480, 1333), (512, 1333), (544, 1333),\n (576, 1333), (608, 1333), (640, 1333),\n (672, 1333), (704, 1333), (736, 1333),\n (768, 1333), (800, 1333)],\n 'multiscale_mode':\n 'value',\n 'override':\n True,\n 'keep_ratio':\n True\n }]]),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='Pad', size_divisor=1),\n dict(type='DefaultFormatBundle'),\n dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])\n]\ntest_pipeline = [\n dict(type='LoadImageFromFile'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(1333, 800),\n flip=False,\n transforms=[\n dict(type='Resize', keep_ratio=True),\n dict(type='RandomFlip'),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='Pad', size_divisor=32),\n dict(type='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img'])\n ])\n]\ndata = dict(\n samples_per_gpu=2,\n workers_per_gpu=2,\n train=dict(\n type='CocoDataset',\n ann_file='train2017_ratio3size0008@0.5_cluster-id-as-class.json',\n img_prefix='data/coco/train2017/',\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(type='LoadAnnotations', with_bbox=True),\n dict(type='RandomFlip', flip_ratio=0.5),\n dict(\n type='AutoAugment',\n policies=[[{\n 'type':\n 'Resize',\n 'img_scale': [(480, 1333), (512, 1333), (544, 1333),\n (576, 1333), (608, 1333), (640, 1333),\n (672, 1333), (704, 1333), (736, 1333),\n (768, 1333), (800, 1333)],\n 'multiscale_mode':\n 'value',\n 'keep_ratio':\n True\n }],\n [{\n 'type': 'Resize',\n 'img_scale': [(400, 1333), (500, 1333),\n (600, 1333)],\n 'multiscale_mode': 'value',\n 'keep_ratio': True\n }, {\n 'type': 'RandomCrop',\n 'crop_type': 'absolute_range',\n 'crop_size': (384, 600),\n 'allow_negative_crop': True\n }, {\n 'type':\n 'Resize',\n 'img_scale': [(480, 1333), (512, 1333),\n (544, 1333), (576, 1333),\n (608, 1333), (640, 1333),\n (672, 1333), (704, 1333),\n (736, 1333), (768, 1333),\n (800, 1333)],\n 'multiscale_mode':\n 'value',\n 'override':\n True,\n 'keep_ratio':\n True\n }]]),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='Pad', size_divisor=1),\n dict(type='DefaultFormatBundle'),\n dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])\n ],\n classes=[\n 'cluster_1', 'cluster_2', 'cluster_3', 'cluster_4', 'cluster_5',\n 'cluster_6', 'cluster_7', 'cluster_8', 'cluster_9', 'cluster_10',\n 'cluster_11', 'cluster_12', 'cluster_13', 'cluster_14',\n 'cluster_15', 'cluster_16', 'cluster_17', 'cluster_18',\n 'cluster_19', 'cluster_20', 'cluster_21', 'cluster_22',\n 'cluster_23', 'cluster_24', 'cluster_25', 'cluster_26',\n 'cluster_27', 'cluster_28', 'cluster_29', 'cluster_30',\n 'cluster_31', 'cluster_32', 'cluster_33', 'cluster_34',\n 'cluster_35', 'cluster_36', 'cluster_37', 'cluster_38',\n 'cluster_39', 'cluster_40', 'cluster_41', 'cluster_42',\n 'cluster_43', 'cluster_44', 'cluster_45', 'cluster_46',\n 'cluster_47', 'cluster_48', 'cluster_49', 'cluster_50',\n 'cluster_51', 'cluster_52', 'cluster_53', 'cluster_54',\n 'cluster_55', 'cluster_56', 'cluster_57', 'cluster_58',\n 'cluster_59', 'cluster_60', 'cluster_61', 'cluster_62',\n 'cluster_63', 'cluster_64', 'cluster_65', 'cluster_66',\n 'cluster_67', 'cluster_68', 'cluster_69', 'cluster_70',\n 'cluster_71', 'cluster_72', 'cluster_73', 'cluster_74',\n 'cluster_75', 'cluster_76', 'cluster_77', 'cluster_78',\n 'cluster_79', 'cluster_80', 'cluster_81', 'cluster_82',\n 'cluster_83', 'cluster_84', 'cluster_85', 'cluster_86',\n 'cluster_87', 'cluster_88', 'cluster_89', 'cluster_90',\n 'cluster_91', 'cluster_92', 'cluster_93', 'cluster_94',\n 'cluster_95', 'cluster_96', 'cluster_97', 'cluster_98',\n 'cluster_99', 'cluster_100', 'cluster_101', 'cluster_102',\n 'cluster_103', 'cluster_104', 'cluster_105', 'cluster_106',\n 'cluster_107', 'cluster_108', 'cluster_109', 'cluster_110',\n 'cluster_111', 'cluster_112', 'cluster_113', 'cluster_114',\n 'cluster_115', 'cluster_116', 'cluster_117', 'cluster_118',\n 'cluster_119', 'cluster_120', 'cluster_121', 'cluster_122',\n 'cluster_123', 'cluster_124', 'cluster_125', 'cluster_126',\n 'cluster_127', 'cluster_128', 'cluster_129', 'cluster_130',\n 'cluster_131', 'cluster_132', 'cluster_133', 'cluster_134',\n 'cluster_135', 'cluster_136', 'cluster_137', 'cluster_138',\n 'cluster_139', 'cluster_140', 'cluster_141', 'cluster_142',\n 'cluster_143', 'cluster_144', 'cluster_145', 'cluster_146',\n 'cluster_147', 'cluster_148', 'cluster_149', 'cluster_150',\n 'cluster_151', 'cluster_152', 'cluster_153', 'cluster_154',\n 'cluster_155', 'cluster_156', 'cluster_157', 'cluster_158',\n 'cluster_159', 'cluster_160', 'cluster_161', 'cluster_162',\n 'cluster_163', 'cluster_164', 'cluster_165', 'cluster_166',\n 'cluster_167', 'cluster_168', 'cluster_169', 'cluster_170',\n 'cluster_171', 'cluster_172', 'cluster_173', 'cluster_174',\n 'cluster_175', 'cluster_176', 'cluster_177', 'cluster_178',\n 'cluster_179', 'cluster_180', 'cluster_181', 'cluster_182',\n 'cluster_183', 'cluster_184', 'cluster_185', 'cluster_186',\n 'cluster_187', 'cluster_188', 'cluster_189', 'cluster_190',\n 'cluster_191', 'cluster_192', 'cluster_193', 'cluster_194',\n 'cluster_195', 'cluster_196', 'cluster_197', 'cluster_198',\n 'cluster_199', 'cluster_200', 'cluster_201', 'cluster_202',\n 'cluster_203', 'cluster_204', 'cluster_205', 'cluster_206',\n 'cluster_207', 'cluster_208', 'cluster_209', 'cluster_210',\n 'cluster_211', 'cluster_212', 'cluster_213', 'cluster_214',\n 'cluster_215', 'cluster_216', 'cluster_217', 'cluster_218',\n 'cluster_219', 'cluster_220', 'cluster_221', 'cluster_222',\n 'cluster_223', 'cluster_224', 'cluster_225', 'cluster_226',\n 'cluster_227', 'cluster_228', 'cluster_229', 'cluster_230',\n 'cluster_231', 'cluster_232', 'cluster_233', 'cluster_234',\n 'cluster_235', 'cluster_236', 'cluster_237', 'cluster_238',\n 'cluster_239', 'cluster_240', 'cluster_241', 'cluster_242',\n 'cluster_243', 'cluster_244', 'cluster_245', 'cluster_246',\n 'cluster_247', 'cluster_248', 'cluster_249', 'cluster_250',\n 'cluster_251', 'cluster_252', 'cluster_253', 'cluster_254',\n 'cluster_255', 'cluster_256'\n ]),\n val=dict(\n type='CocoDataset',\n ann_file='data/coco/annotations/instances_val2017.json',\n img_prefix='data/coco/val2017/',\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(1333, 800),\n flip=False,\n transforms=[\n dict(type='Resize', keep_ratio=True),\n dict(type='RandomFlip'),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='Pad', size_divisor=32),\n dict(type='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img'])\n ])\n ]),\n test=dict(\n type='CocoDataset',\n ann_file='data/coco/annotations/instances_val2017.json',\n img_prefix='data/coco/val2017/',\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(1333, 800),\n flip=False,\n transforms=[\n dict(type='Resize', keep_ratio=True),\n dict(type='RandomFlip'),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='Pad', size_divisor=32),\n dict(type='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img'])\n ])\n ]))\nevaluation = dict(\n interval=65535, metric='bbox', save_best='auto', gpu_collect=True)\ncheckpoint_config = dict(interval=1)\nlog_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])\ncustom_hooks = [\n dict(type='NumClassCheckHook'),\n dict(\n type='MMDetWandbHook',\n init_kwargs=dict(project='I2B', group='finetune'),\n interval=50,\n num_eval_images=0,\n log_checkpoint=False)\n]\ndist_params = dict(backend='nccl')\nlog_level = 'INFO'\nload_from = None\nresume_from = None\nworkflow = [('train', 1)]\nopencv_num_threads = 0\nmp_start_method = 'fork'\nauto_scale_lr = dict(enable=True, base_batch_size=64)\ncustom_imports = dict(\n imports=[\n 'mmselfsup.datasets.pipelines',\n 'selfsup.core.hook.momentum_update_hook',\n 'selfsup.datasets.pipelines.selfsup_pipelines',\n 'selfsup.datasets.pipelines.rand_aug',\n 'selfsup.datasets.single_view_coco',\n 'selfsup.datasets.multi_view_coco',\n 'selfsup.models.losses.contrastive_loss',\n 'selfsup.models.dense_heads.fcos_head',\n 'selfsup.models.dense_heads.retina_head',\n 'selfsup.models.dense_heads.detr_head',\n 'selfsup.models.dense_heads.deformable_detr_head',\n 'selfsup.models.roi_heads.bbox_heads.convfc_bbox_head',\n 'selfsup.models.roi_heads.standard_roi_head',\n 'selfsup.models.detectors.selfsup_detector',\n 'selfsup.models.detectors.selfsup_fcos',\n 'selfsup.models.detectors.selfsup_detr',\n 'selfsup.models.detectors.selfsup_deformable_detr',\n 'selfsup.models.detectors.selfsup_retinanet',\n 'selfsup.models.detectors.selfsup_mask_rcnn',\n 'selfsup.core.bbox.assigners.hungarian_assigner',\n 'selfsup.core.bbox.assigners.pseudo_hungarian_assigner',\n 'selfsup.core.bbox.match_costs.match_cost'\n ],\n allow_failed_imports=False)\nclasses = [\n 'cluster_1', 'cluster_2', 'cluster_3', 'cluster_4', 'cluster_5',\n 'cluster_6', 'cluster_7', 'cluster_8', 'cluster_9', 'cluster_10',\n 'cluster_11', 'cluster_12', 'cluster_13', 'cluster_14', 'cluster_15',\n 'cluster_16', 'cluster_17', 'cluster_18', 'cluster_19', 'cluster_20',\n 'cluster_21', 'cluster_22', 'cluster_23', 'cluster_24', 'cluster_25',\n 'cluster_26', 'cluster_27', 'cluster_28', 'cluster_29', 'cluster_30',\n 'cluster_31', 'cluster_32', 'cluster_33', 'cluster_34', 'cluster_35',\n 'cluster_36', 'cluster_37', 'cluster_38', 'cluster_39', 'cluster_40',\n 'cluster_41', 'cluster_42', 'cluster_43', 'cluster_44', 'cluster_45',\n 'cluster_46', 'cluster_47', 'cluster_48', 'cluster_49', 'cluster_50',\n 'cluster_51', 'cluster_52', 'cluster_53', 'cluster_54', 'cluster_55',\n 'cluster_56', 'cluster_57', 'cluster_58', 'cluster_59', 'cluster_60',\n 'cluster_61', 'cluster_62', 'cluster_63', 'cluster_64', 'cluster_65',\n 'cluster_66', 'cluster_67', 'cluster_68', 'cluster_69', 'cluster_70',\n 'cluster_71', 'cluster_72', 'cluster_73', 'cluster_74', 'cluster_75',\n 'cluster_76', 'cluster_77', 'cluster_78', 'cluster_79', 'cluster_80',\n 'cluster_81', 'cluster_82', 'cluster_83', 'cluster_84', 'cluster_85',\n 'cluster_86', 'cluster_87', 'cluster_88', 'cluster_89', 'cluster_90',\n 'cluster_91', 'cluster_92', 'cluster_93', 'cluster_94', 'cluster_95',\n 'cluster_96', 'cluster_97', 'cluster_98', 'cluster_99', 'cluster_100',\n 'cluster_101', 'cluster_102', 'cluster_103', 'cluster_104', 'cluster_105',\n 'cluster_106', 'cluster_107', 'cluster_108', 'cluster_109', 'cluster_110',\n 'cluster_111', 'cluster_112', 'cluster_113', 'cluster_114', 'cluster_115',\n 'cluster_116', 'cluster_117', 'cluster_118', 'cluster_119', 'cluster_120',\n 'cluster_121', 'cluster_122', 'cluster_123', 'cluster_124', 'cluster_125',\n 'cluster_126', 'cluster_127', 'cluster_128', 'cluster_129', 'cluster_130',\n 'cluster_131', 'cluster_132', 'cluster_133', 'cluster_134', 'cluster_135',\n 'cluster_136', 'cluster_137', 'cluster_138', 'cluster_139', 'cluster_140',\n 'cluster_141', 'cluster_142', 'cluster_143', 'cluster_144', 'cluster_145',\n 'cluster_146', 'cluster_147', 'cluster_148', 'cluster_149', 'cluster_150',\n 'cluster_151', 'cluster_152', 'cluster_153', 'cluster_154', 'cluster_155',\n 'cluster_156', 'cluster_157', 'cluster_158', 'cluster_159', 'cluster_160',\n 'cluster_161', 'cluster_162', 'cluster_163', 'cluster_164', 'cluster_165',\n 'cluster_166', 'cluster_167', 'cluster_168', 'cluster_169', 'cluster_170',\n 'cluster_171', 'cluster_172', 'cluster_173', 'cluster_174', 'cluster_175',\n 'cluster_176', 'cluster_177', 'cluster_178', 'cluster_179', 'cluster_180',\n 'cluster_181', 'cluster_182', 'cluster_183', 'cluster_184', 'cluster_185',\n 'cluster_186', 'cluster_187', 'cluster_188', 'cluster_189', 'cluster_190',\n 'cluster_191', 'cluster_192', 'cluster_193', 'cluster_194', 'cluster_195',\n 'cluster_196', 'cluster_197', 'cluster_198', 'cluster_199', 'cluster_200',\n 'cluster_201', 'cluster_202', 'cluster_203', 'cluster_204', 'cluster_205',\n 'cluster_206', 'cluster_207', 'cluster_208', 'cluster_209', 'cluster_210',\n 'cluster_211', 'cluster_212', 'cluster_213', 'cluster_214', 'cluster_215',\n 'cluster_216', 'cluster_217', 'cluster_218', 'cluster_219', 'cluster_220',\n 'cluster_221', 'cluster_222', 'cluster_223', 'cluster_224', 'cluster_225',\n 'cluster_226', 'cluster_227', 'cluster_228', 'cluster_229', 'cluster_230',\n 'cluster_231', 'cluster_232', 'cluster_233', 'cluster_234', 'cluster_235',\n 'cluster_236', 'cluster_237', 'cluster_238', 'cluster_239', 'cluster_240',\n 'cluster_241', 'cluster_242', 'cluster_243', 'cluster_244', 'cluster_245',\n 'cluster_246', 'cluster_247', 'cluster_248', 'cluster_249', 'cluster_250',\n 'cluster_251', 'cluster_252', 'cluster_253', 'cluster_254', 'cluster_255',\n 'cluster_256'\n]\noptimizer = dict(\n type='AdamW',\n lr=0.0002,\n weight_decay=0.0001,\n paramwise_cfg=dict(\n custom_keys=dict(backbone=dict(lr_mult=0, decay_mult=0))))\noptimizer_config = dict(grad_clip=dict(max_norm=0.1, norm_type=2))\nlr_config = dict(policy='step', step=[40])\nrunner = dict(type='EpochBasedRunner', max_epochs=50)\nwork_dir = 'work_dirs/selfsup_detr_cluster-ids-as-pseudo-labels'\nauto_resume = False\ngpu_ids = range(0, 32)\n", "seed": 42, "exp_name": "detr_pseudo_label.py", "hook_msgs": {}} +{"mode": "train", "epoch": 1, "iter": 50, "lr": 0.0002, "memory": 2811, "data_time": 0.0623, "loss_cls": 5.31698, "loss_bbox": 2.83475, "loss_iou": 2.43296, "d0.loss_cls": 5.32024, "d0.loss_bbox": 2.92208, "d0.loss_iou": 2.53088, "d1.loss_cls": 5.32123, "d1.loss_bbox": 2.85021, "d1.loss_iou": 2.43984, "d2.loss_cls": 5.30548, "d2.loss_bbox": 2.86614, "d2.loss_iou": 2.46044, "d3.loss_cls": 5.31586, "d3.loss_bbox": 2.84728, "d3.loss_iou": 2.4528, "d4.loss_cls": 5.31638, "d4.loss_bbox": 2.85334, "d4.loss_iou": 2.45995, "loss": 63.84684, "grad_norm": 193.23898, "time": 0.28251} +{"mode": "train", "epoch": 1, "iter": 100, "lr": 0.0002, "memory": 2811, "data_time": 0.00437, "loss_cls": 5.23036, "loss_bbox": 1.55615, "loss_iou": 1.68331, "d0.loss_cls": 5.26613, "d0.loss_bbox": 1.64499, "d0.loss_iou": 1.77564, "d1.loss_cls": 5.2456, "d1.loss_bbox": 1.54113, "d1.loss_iou": 1.6928, "d2.loss_cls": 5.22374, "d2.loss_bbox": 1.54959, "d2.loss_iou": 1.67808, "d3.loss_cls": 5.22831, "d3.loss_bbox": 1.54851, "d3.loss_iou": 1.6805, "d4.loss_cls": 5.22617, "d4.loss_bbox": 1.55808, "d4.loss_iou": 1.68775, "loss": 51.01682, "grad_norm": 299.16537, "time": 0.16456} +{"mode": "train", "epoch": 1, "iter": 150, "lr": 0.0002, "memory": 2836, "data_time": 0.00451, "loss_cls": 5.01157, "loss_bbox": 1.45304, "loss_iou": 1.66319, "d0.loss_cls": 5.1534, "d0.loss_bbox": 1.52685, "d0.loss_iou": 1.67998, "d1.loss_cls": 5.07149, "d1.loss_bbox": 1.39387, "d1.loss_iou": 1.66417, "d2.loss_cls": 5.03095, "d2.loss_bbox": 1.38341, "d2.loss_iou": 1.66251, "d3.loss_cls": 5.02868, "d3.loss_bbox": 1.39381, "d3.loss_iou": 1.65226, "d4.loss_cls": 5.0218, "d4.loss_bbox": 1.41342, "d4.loss_iou": 1.6548, "loss": 48.85919, "grad_norm": 208.16036, "time": 0.16751} +{"mode": "train", "epoch": 1, "iter": 200, "lr": 0.0002, "memory": 3053, "data_time": 0.00441, "loss_cls": 4.7955, "loss_bbox": 1.29464, "loss_iou": 1.6497, "d0.loss_cls": 4.98689, "d0.loss_bbox": 1.42484, "d0.loss_iou": 1.64496, "d1.loss_cls": 4.87597, "d1.loss_bbox": 1.29868, "d1.loss_iou": 1.6424, 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