model = dict( type='SelfSupDetector', backbone=dict( type='SelfSupDETR', backbone=dict( type='ResNet', depth=50, num_stages=4, out_indices=(3, ), frozen_stages=4, norm_cfg=dict(type='BN', requires_grad=False), norm_eval=True, style='pytorch', init_cfg=dict( type='Pretrained', checkpoint='torchvision://resnet50')), bbox_head=dict( type='SelfSupDETRHead', num_classes=256, in_channels=2048, frozen_roi_proj=False, query_init_path=None, transformer=dict( type='Transformer', encoder=dict( type='DetrTransformerEncoder', num_layers=6, transformerlayers=dict( type='BaseTransformerLayer', attn_cfgs=[ dict( type='MultiheadAttention', embed_dims=256, num_heads=8, dropout=0.1) ], feedforward_channels=2048, ffn_dropout=0.1, operation_order=('self_attn', 'norm', 'ffn', 'norm'))), decoder=dict( type='DetrTransformerDecoder', return_intermediate=True, num_layers=6, transformerlayers=dict( type='DetrTransformerDecoderLayer', attn_cfgs=dict( type='MultiheadAttention', embed_dims=256, num_heads=8, dropout=0.1), feedforward_channels=2048, ffn_dropout=0.1, operation_order=('self_attn', 'norm', 'cross_attn', 'norm', 'ffn', 'norm')))), positional_encoding=dict( type='SinePositionalEncoding', num_feats=128, normalize=True), loss_cls=dict(type='ContrastiveLoss', loss_weight=1.0), loss_bbox=dict(type='L1Loss', loss_weight=5.0), loss_iou=dict(type='GIoULoss', loss_weight=2.0)), train_cfg=dict( assigner=dict( type='HungarianAssigner', cls_cost=dict(type='ClassificationCost', weight=1.0), reg_cost=dict( type='BBoxL1Cost', weight=5.0, box_format='xywh'), iou_cost=dict(type='IoUCost', iou_mode='giou', weight=2.0))), test_cfg=dict(max_per_img=100))) train_dataset_type = 'MultiViewCocoDataset' test_dataset_type = 'CocoDataset' data_root = 'data/coco/' classes = [ 'cluster_1', 'cluster_2', 'cluster_3', 'cluster_4', 'cluster_5', 'cluster_6', 'cluster_7', 'cluster_8', 'cluster_9', 'cluster_10', 'cluster_11', 'cluster_12', 'cluster_13', 'cluster_14', 'cluster_15', 'cluster_16', 'cluster_17', 'cluster_18', 'cluster_19', 'cluster_20', 'cluster_21', 'cluster_22', 'cluster_23', 'cluster_24', 'cluster_25', 'cluster_26', 'cluster_27', 'cluster_28', 'cluster_29', 'cluster_30', 'cluster_31', 'cluster_32', 'cluster_33', 'cluster_34', 'cluster_35', 'cluster_36', 'cluster_37', 'cluster_38', 'cluster_39', 'cluster_40', 'cluster_41', 'cluster_42', 'cluster_43', 'cluster_44', 'cluster_45', 'cluster_46', 'cluster_47', 'cluster_48', 'cluster_49', 'cluster_50', 'cluster_51', 'cluster_52', 'cluster_53', 'cluster_54', 'cluster_55', 'cluster_56', 'cluster_57', 'cluster_58', 'cluster_59', 'cluster_60', 'cluster_61', 'cluster_62', 'cluster_63', 'cluster_64', 'cluster_65', 'cluster_66', 'cluster_67', 'cluster_68', 'cluster_69', 'cluster_70', 'cluster_71', 'cluster_72', 'cluster_73', 'cluster_74', 'cluster_75', 'cluster_76', 'cluster_77', 'cluster_78', 'cluster_79', 'cluster_80', 'cluster_81', 'cluster_82', 'cluster_83', 'cluster_84', 'cluster_85', 'cluster_86', 'cluster_87', 'cluster_88', 'cluster_89', 'cluster_90', 'cluster_91', 'cluster_92', 'cluster_93', 'cluster_94', 'cluster_95', 'cluster_96', 'cluster_97', 'cluster_98', 'cluster_99', 'cluster_100', 'cluster_101', 'cluster_102', 'cluster_103', 'cluster_104', 'cluster_105', 'cluster_106', 'cluster_107', 'cluster_108', 'cluster_109', 'cluster_110', 'cluster_111', 'cluster_112', 'cluster_113', 'cluster_114', 'cluster_115', 'cluster_116', 'cluster_117', 'cluster_118', 'cluster_119', 'cluster_120', 'cluster_121', 'cluster_122', 'cluster_123', 'cluster_124', 'cluster_125', 'cluster_126', 'cluster_127', 'cluster_128', 'cluster_129', 'cluster_130', 'cluster_131', 'cluster_132', 'cluster_133', 'cluster_134', 'cluster_135', 'cluster_136', 'cluster_137', 'cluster_138', 'cluster_139', 'cluster_140', 'cluster_141', 'cluster_142', 'cluster_143', 'cluster_144', 'cluster_145', 'cluster_146', 'cluster_147', 'cluster_148', 'cluster_149', 'cluster_150', 'cluster_151', 'cluster_152', 'cluster_153', 'cluster_154', 'cluster_155', 'cluster_156', 'cluster_157', 'cluster_158', 'cluster_159', 'cluster_160', 'cluster_161', 'cluster_162', 'cluster_163', 'cluster_164', 'cluster_165', 'cluster_166', 'cluster_167', 'cluster_168', 'cluster_169', 'cluster_170', 'cluster_171', 'cluster_172', 'cluster_173', 'cluster_174', 'cluster_175', 'cluster_176', 'cluster_177', 'cluster_178', 'cluster_179', 'cluster_180', 'cluster_181', 'cluster_182', 'cluster_183', 'cluster_184', 'cluster_185', 'cluster_186', 'cluster_187', 'cluster_188', 'cluster_189', 'cluster_190', 'cluster_191', 'cluster_192', 'cluster_193', 'cluster_194', 'cluster_195', 'cluster_196', 'cluster_197', 'cluster_198', 'cluster_199', 'cluster_200', 'cluster_201', 'cluster_202', 'cluster_203', 'cluster_204', 'cluster_205', 'cluster_206', 'cluster_207', 'cluster_208', 'cluster_209', 'cluster_210', 'cluster_211', 'cluster_212', 'cluster_213', 'cluster_214', 'cluster_215', 'cluster_216', 'cluster_217', 'cluster_218', 'cluster_219', 'cluster_220', 'cluster_221', 'cluster_222', 'cluster_223', 'cluster_224', 'cluster_225', 'cluster_226', 'cluster_227', 'cluster_228', 'cluster_229', 'cluster_230', 'cluster_231', 'cluster_232', 'cluster_233', 'cluster_234', 'cluster_235', 'cluster_236', 'cluster_237', 'cluster_238', 'cluster_239', 'cluster_240', 'cluster_241', 'cluster_242', 'cluster_243', 'cluster_244', 'cluster_245', 'cluster_246', 'cluster_247', 'cluster_248', 'cluster_249', 'cluster_250', 'cluster_251', 'cluster_252', 'cluster_253', 'cluster_254', 'cluster_255', 'cluster_256' ] img_norm_cfg = dict( mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) load_pipeline = [ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations', with_bbox=True, with_mask=False), dict(type='SelectTopKProposals', topk=50) ] train_pipeline1 = [ dict( type='Resize', img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), (1333, 768), (1333, 800)], multiscale_mode='value', keep_ratio=True), dict(type='FilterAnnotations', min_gt_bbox_wh=(0.01, 0.01)), dict(type='Pad', size_divisor=32), dict(type='RandFlip', flip_ratio=0.5), dict( type='OneOf', transforms=[ dict(type='Identity'), dict(type='AutoContrast'), dict(type='RandEqualize'), dict(type='RandSolarize'), dict(type='RandColor'), dict(type='RandContrast'), dict(type='RandBrightness'), dict(type='RandSharpness'), dict(type='RandPosterize') ]), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='DefaultFormatBundle'), dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) ] train_pipeline2 = [ dict( type='Resize', img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), (1333, 768), (1333, 800)], multiscale_mode='value', keep_ratio=True), dict(type='FilterAnnotations', min_gt_bbox_wh=(0.01, 0.01)), dict(type='Pad', size_divisor=32), dict(type='RandFlip', flip_ratio=0.5), dict( type='OneOf', transforms=[ dict(type='Identity'), dict(type='AutoContrast'), dict(type='RandEqualize'), dict(type='RandSolarize'), dict(type='RandColor'), dict(type='RandContrast'), dict(type='RandBrightness'), dict(type='RandSharpness'), dict(type='RandPosterize') ]), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='DefaultFormatBundle'), dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) ] test_pipeline = [ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(1333, 800), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='Pad', size_divisor=32), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']) ]) ] data = dict( samples_per_gpu=2, workers_per_gpu=2, train=dict( type='MultiViewCocoDataset', dataset=dict( type='CocoDataset', classes=[ 'cluster_1', 'cluster_2', 'cluster_3', 'cluster_4', 'cluster_5', 'cluster_6', 'cluster_7', 'cluster_8', 'cluster_9', 'cluster_10', 'cluster_11', 'cluster_12', 'cluster_13', 'cluster_14', 'cluster_15', 'cluster_16', 'cluster_17', 'cluster_18', 'cluster_19', 'cluster_20', 'cluster_21', 'cluster_22', 'cluster_23', 'cluster_24', 'cluster_25', 'cluster_26', 'cluster_27', 'cluster_28', 'cluster_29', 'cluster_30', 'cluster_31', 'cluster_32', 'cluster_33', 'cluster_34', 'cluster_35', 'cluster_36', 'cluster_37', 'cluster_38', 'cluster_39', 'cluster_40', 'cluster_41', 'cluster_42', 'cluster_43', 'cluster_44', 'cluster_45', 'cluster_46', 'cluster_47', 'cluster_48', 'cluster_49', 'cluster_50', 'cluster_51', 'cluster_52', 'cluster_53', 'cluster_54', 'cluster_55', 'cluster_56', 'cluster_57', 'cluster_58', 'cluster_59', 'cluster_60', 'cluster_61', 'cluster_62', 'cluster_63', 'cluster_64', 'cluster_65', 'cluster_66', 'cluster_67', 'cluster_68', 'cluster_69', 'cluster_70', 'cluster_71', 'cluster_72', 'cluster_73', 'cluster_74', 'cluster_75', 'cluster_76', 'cluster_77', 'cluster_78', 'cluster_79', 'cluster_80', 'cluster_81', 'cluster_82', 'cluster_83', 'cluster_84', 'cluster_85', 'cluster_86', 'cluster_87', 'cluster_88', 'cluster_89', 'cluster_90', 'cluster_91', 'cluster_92', 'cluster_93', 'cluster_94', 'cluster_95', 'cluster_96', 'cluster_97', 'cluster_98', 'cluster_99', 'cluster_100', 'cluster_101', 'cluster_102', 'cluster_103', 'cluster_104', 'cluster_105', 'cluster_106', 'cluster_107', 'cluster_108', 'cluster_109', 'cluster_110', 'cluster_111', 'cluster_112', 'cluster_113', 'cluster_114', 'cluster_115', 'cluster_116', 'cluster_117', 'cluster_118', 'cluster_119', 'cluster_120', 'cluster_121', 'cluster_122', 'cluster_123', 'cluster_124', 'cluster_125', 'cluster_126', 'cluster_127', 'cluster_128', 'cluster_129', 'cluster_130', 'cluster_131', 'cluster_132', 'cluster_133', 'cluster_134', 'cluster_135', 'cluster_136', 'cluster_137', 'cluster_138', 'cluster_139', 'cluster_140', 'cluster_141', 'cluster_142', 'cluster_143', 'cluster_144', 'cluster_145', 'cluster_146', 'cluster_147', 'cluster_148', 'cluster_149', 'cluster_150', 'cluster_151', 'cluster_152', 'cluster_153', 'cluster_154', 'cluster_155', 'cluster_156', 'cluster_157', 'cluster_158', 'cluster_159', 'cluster_160', 'cluster_161', 'cluster_162', 'cluster_163', 'cluster_164', 'cluster_165', 'cluster_166', 'cluster_167', 'cluster_168', 'cluster_169', 'cluster_170', 'cluster_171', 'cluster_172', 'cluster_173', 'cluster_174', 'cluster_175', 'cluster_176', 'cluster_177', 'cluster_178', 'cluster_179', 'cluster_180', 'cluster_181', 'cluster_182', 'cluster_183', 'cluster_184', 'cluster_185', 'cluster_186', 'cluster_187', 'cluster_188', 'cluster_189', 'cluster_190', 'cluster_191', 'cluster_192', 'cluster_193', 'cluster_194', 'cluster_195', 'cluster_196', 'cluster_197', 'cluster_198', 'cluster_199', 'cluster_200', 'cluster_201', 'cluster_202', 'cluster_203', 'cluster_204', 'cluster_205', 'cluster_206', 'cluster_207', 'cluster_208', 'cluster_209', 'cluster_210', 'cluster_211', 'cluster_212', 'cluster_213', 'cluster_214', 'cluster_215', 'cluster_216', 'cluster_217', 'cluster_218', 'cluster_219', 'cluster_220', 'cluster_221', 'cluster_222', 'cluster_223', 'cluster_224', 'cluster_225', 'cluster_226', 'cluster_227', 'cluster_228', 'cluster_229', 'cluster_230', 'cluster_231', 'cluster_232', 'cluster_233', 'cluster_234', 'cluster_235', 'cluster_236', 'cluster_237', 'cluster_238', 'cluster_239', 'cluster_240', 'cluster_241', 'cluster_242', 'cluster_243', 'cluster_244', 'cluster_245', 'cluster_246', 'cluster_247', 'cluster_248', 'cluster_249', 'cluster_250', 'cluster_251', 'cluster_252', 'cluster_253', 'cluster_254', 'cluster_255', 'cluster_256' ], ann_file='train2017_ratio3size0008@0.5_cluster-id-as-class.json', img_prefix='data/coco/train2017/', pipeline=[ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations', with_bbox=True, with_mask=False), dict(type='SelectTopKProposals', topk=50) ]), num_views=2, pipelines=[[{ 'type': 'Resize', 'img_scale': [(1333, 640), (1333, 672), (1333, 704), (1333, 736), (1333, 768), (1333, 800)], 'multiscale_mode': 'value', 'keep_ratio': True }, { 'type': 'FilterAnnotations', 'min_gt_bbox_wh': (0.01, 0.01) }, { 'type': 'Pad', 'size_divisor': 32 }, { 'type': 'RandFlip', 'flip_ratio': 0.5 }, { 'type': 'OneOf', 'transforms': [{ 'type': 'Identity' }, { 'type': 'AutoContrast' }, { 'type': 'RandEqualize' }, { 'type': 'RandSolarize' }, { 'type': 'RandColor' }, { 'type': 'RandContrast' }, { 'type': 'RandBrightness' }, { 'type': 'RandSharpness' }, { 'type': 'RandPosterize' }] }, { 'type': 'Normalize', 'mean': [123.675, 116.28, 103.53], 'std': [58.395, 57.12, 57.375], 'to_rgb': True }, { 'type': 'DefaultFormatBundle' }, { 'type': 'Collect', 'keys': ['img', 'gt_bboxes', 'gt_labels'] }], [{ 'type': 'Resize', 'img_scale': [(1333, 640), (1333, 672), (1333, 704), (1333, 736), (1333, 768), (1333, 800)], 'multiscale_mode': 'value', 'keep_ratio': True }, { 'type': 'FilterAnnotations', 'min_gt_bbox_wh': (0.01, 0.01) }, { 'type': 'Pad', 'size_divisor': 32 }, { 'type': 'RandFlip', 'flip_ratio': 0.5 }, { 'type': 'OneOf', 'transforms': [{ 'type': 'Identity' }, { 'type': 'AutoContrast' }, { 'type': 'RandEqualize' }, { 'type': 'RandSolarize' }, { 'type': 'RandColor' }, { 'type': 'RandContrast' }, { 'type': 'RandBrightness' }, { 'type': 'RandSharpness' }, { 'type': 'RandPosterize' }] }, { 'type': 'Normalize', 'mean': [123.675, 116.28, 103.53], 'std': [58.395, 57.12, 57.375], 'to_rgb': True }, { 'type': 'DefaultFormatBundle' }, { 'type': 'Collect', 'keys': ['img', 'gt_bboxes', 'gt_labels'] }]]), val=dict( type='CocoDataset', classes=[ 'cluster_1', 'cluster_2', 'cluster_3', 'cluster_4', 'cluster_5', 'cluster_6', 'cluster_7', 'cluster_8', 'cluster_9', 'cluster_10', 'cluster_11', 'cluster_12', 'cluster_13', 'cluster_14', 'cluster_15', 'cluster_16', 'cluster_17', 'cluster_18', 'cluster_19', 'cluster_20', 'cluster_21', 'cluster_22', 'cluster_23', 'cluster_24', 'cluster_25', 'cluster_26', 'cluster_27', 'cluster_28', 'cluster_29', 'cluster_30', 'cluster_31', 'cluster_32', 'cluster_33', 'cluster_34', 'cluster_35', 'cluster_36', 'cluster_37', 'cluster_38', 'cluster_39', 'cluster_40', 'cluster_41', 'cluster_42', 'cluster_43', 'cluster_44', 'cluster_45', 'cluster_46', 'cluster_47', 'cluster_48', 'cluster_49', 'cluster_50', 'cluster_51', 'cluster_52', 'cluster_53', 'cluster_54', 'cluster_55', 'cluster_56', 'cluster_57', 'cluster_58', 'cluster_59', 'cluster_60', 'cluster_61', 'cluster_62', 'cluster_63', 'cluster_64', 'cluster_65', 'cluster_66', 'cluster_67', 'cluster_68', 'cluster_69', 'cluster_70', 'cluster_71', 'cluster_72', 'cluster_73', 'cluster_74', 'cluster_75', 'cluster_76', 'cluster_77', 'cluster_78', 'cluster_79', 'cluster_80', 'cluster_81', 'cluster_82', 'cluster_83', 'cluster_84', 'cluster_85', 'cluster_86', 'cluster_87', 'cluster_88', 'cluster_89', 'cluster_90', 'cluster_91', 'cluster_92', 'cluster_93', 'cluster_94', 'cluster_95', 'cluster_96', 'cluster_97', 'cluster_98', 'cluster_99', 'cluster_100', 'cluster_101', 'cluster_102', 'cluster_103', 'cluster_104', 'cluster_105', 'cluster_106', 'cluster_107', 'cluster_108', 'cluster_109', 'cluster_110', 'cluster_111', 'cluster_112', 'cluster_113', 'cluster_114', 'cluster_115', 'cluster_116', 'cluster_117', 'cluster_118', 'cluster_119', 'cluster_120', 'cluster_121', 'cluster_122', 'cluster_123', 'cluster_124', 'cluster_125', 'cluster_126', 'cluster_127', 'cluster_128', 'cluster_129', 'cluster_130', 'cluster_131', 'cluster_132', 'cluster_133', 'cluster_134', 'cluster_135', 'cluster_136', 'cluster_137', 'cluster_138', 'cluster_139', 'cluster_140', 'cluster_141', 'cluster_142', 'cluster_143', 'cluster_144', 'cluster_145', 'cluster_146', 'cluster_147', 'cluster_148', 'cluster_149', 'cluster_150', 'cluster_151', 'cluster_152', 'cluster_153', 'cluster_154', 'cluster_155', 'cluster_156', 'cluster_157', 'cluster_158', 'cluster_159', 'cluster_160', 'cluster_161', 'cluster_162', 'cluster_163', 'cluster_164', 'cluster_165', 'cluster_166', 'cluster_167', 'cluster_168', 'cluster_169', 'cluster_170', 'cluster_171', 'cluster_172', 'cluster_173', 'cluster_174', 'cluster_175', 'cluster_176', 'cluster_177', 'cluster_178', 'cluster_179', 'cluster_180', 'cluster_181', 'cluster_182', 'cluster_183', 'cluster_184', 'cluster_185', 'cluster_186', 'cluster_187', 'cluster_188', 'cluster_189', 'cluster_190', 'cluster_191', 'cluster_192', 'cluster_193', 'cluster_194', 'cluster_195', 'cluster_196', 'cluster_197', 'cluster_198', 'cluster_199', 'cluster_200', 'cluster_201', 'cluster_202', 'cluster_203', 'cluster_204', 'cluster_205', 'cluster_206', 'cluster_207', 'cluster_208', 'cluster_209', 'cluster_210', 'cluster_211', 'cluster_212', 'cluster_213', 'cluster_214', 'cluster_215', 'cluster_216', 'cluster_217', 'cluster_218', 'cluster_219', 'cluster_220', 'cluster_221', 'cluster_222', 'cluster_223', 'cluster_224', 'cluster_225', 'cluster_226', 'cluster_227', 'cluster_228', 'cluster_229', 'cluster_230', 'cluster_231', 'cluster_232', 'cluster_233', 'cluster_234', 'cluster_235', 'cluster_236', 'cluster_237', 'cluster_238', 'cluster_239', 'cluster_240', 'cluster_241', 'cluster_242', 'cluster_243', 'cluster_244', 'cluster_245', 'cluster_246', 'cluster_247', 'cluster_248', 'cluster_249', 'cluster_250', 'cluster_251', 'cluster_252', 'cluster_253', 'cluster_254', 'cluster_255', 'cluster_256' ], ann_file='data/coco/annotations/instances_val2017.json', img_prefix='data/coco/val2017/', pipeline=[ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(1333, 800), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='Pad', size_divisor=32), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']) ]) ]), test=dict( type='CocoDataset', classes=[ 'cluster_1', 'cluster_2', 'cluster_3', 'cluster_4', 'cluster_5', 'cluster_6', 'cluster_7', 'cluster_8', 'cluster_9', 'cluster_10', 'cluster_11', 'cluster_12', 'cluster_13', 'cluster_14', 'cluster_15', 'cluster_16', 'cluster_17', 'cluster_18', 'cluster_19', 'cluster_20', 'cluster_21', 'cluster_22', 'cluster_23', 'cluster_24', 'cluster_25', 'cluster_26', 'cluster_27', 'cluster_28', 'cluster_29', 'cluster_30', 'cluster_31', 'cluster_32', 'cluster_33', 'cluster_34', 'cluster_35', 'cluster_36', 'cluster_37', 'cluster_38', 'cluster_39', 'cluster_40', 'cluster_41', 'cluster_42', 'cluster_43', 'cluster_44', 'cluster_45', 'cluster_46', 'cluster_47', 'cluster_48', 'cluster_49', 'cluster_50', 'cluster_51', 'cluster_52', 'cluster_53', 'cluster_54', 'cluster_55', 'cluster_56', 'cluster_57', 'cluster_58', 'cluster_59', 'cluster_60', 'cluster_61', 'cluster_62', 'cluster_63', 'cluster_64', 'cluster_65', 'cluster_66', 'cluster_67', 'cluster_68', 'cluster_69', 'cluster_70', 'cluster_71', 'cluster_72', 'cluster_73', 'cluster_74', 'cluster_75', 'cluster_76', 'cluster_77', 'cluster_78', 'cluster_79', 'cluster_80', 'cluster_81', 'cluster_82', 'cluster_83', 'cluster_84', 'cluster_85', 'cluster_86', 'cluster_87', 'cluster_88', 'cluster_89', 'cluster_90', 'cluster_91', 'cluster_92', 'cluster_93', 'cluster_94', 'cluster_95', 'cluster_96', 'cluster_97', 'cluster_98', 'cluster_99', 'cluster_100', 'cluster_101', 'cluster_102', 'cluster_103', 'cluster_104', 'cluster_105', 'cluster_106', 'cluster_107', 'cluster_108', 'cluster_109', 'cluster_110', 'cluster_111', 'cluster_112', 'cluster_113', 'cluster_114', 'cluster_115', 'cluster_116', 'cluster_117', 'cluster_118', 'cluster_119', 'cluster_120', 'cluster_121', 'cluster_122', 'cluster_123', 'cluster_124', 'cluster_125', 'cluster_126', 'cluster_127', 'cluster_128', 'cluster_129', 'cluster_130', 'cluster_131', 'cluster_132', 'cluster_133', 'cluster_134', 'cluster_135', 'cluster_136', 'cluster_137', 'cluster_138', 'cluster_139', 'cluster_140', 'cluster_141', 'cluster_142', 'cluster_143', 'cluster_144', 'cluster_145', 'cluster_146', 'cluster_147', 'cluster_148', 'cluster_149', 'cluster_150', 'cluster_151', 'cluster_152', 'cluster_153', 'cluster_154', 'cluster_155', 'cluster_156', 'cluster_157', 'cluster_158', 'cluster_159', 'cluster_160', 'cluster_161', 'cluster_162', 'cluster_163', 'cluster_164', 'cluster_165', 'cluster_166', 'cluster_167', 'cluster_168', 'cluster_169', 'cluster_170', 'cluster_171', 'cluster_172', 'cluster_173', 'cluster_174', 'cluster_175', 'cluster_176', 'cluster_177', 'cluster_178', 'cluster_179', 'cluster_180', 'cluster_181', 'cluster_182', 'cluster_183', 'cluster_184', 'cluster_185', 'cluster_186', 'cluster_187', 'cluster_188', 'cluster_189', 'cluster_190', 'cluster_191', 'cluster_192', 'cluster_193', 'cluster_194', 'cluster_195', 'cluster_196', 'cluster_197', 'cluster_198', 'cluster_199', 'cluster_200', 'cluster_201', 'cluster_202', 'cluster_203', 'cluster_204', 'cluster_205', 'cluster_206', 'cluster_207', 'cluster_208', 'cluster_209', 'cluster_210', 'cluster_211', 'cluster_212', 'cluster_213', 'cluster_214', 'cluster_215', 'cluster_216', 'cluster_217', 'cluster_218', 'cluster_219', 'cluster_220', 'cluster_221', 'cluster_222', 'cluster_223', 'cluster_224', 'cluster_225', 'cluster_226', 'cluster_227', 'cluster_228', 'cluster_229', 'cluster_230', 'cluster_231', 'cluster_232', 'cluster_233', 'cluster_234', 'cluster_235', 'cluster_236', 'cluster_237', 'cluster_238', 'cluster_239', 'cluster_240', 'cluster_241', 'cluster_242', 'cluster_243', 'cluster_244', 'cluster_245', 'cluster_246', 'cluster_247', 'cluster_248', 'cluster_249', 'cluster_250', 'cluster_251', 'cluster_252', 'cluster_253', 'cluster_254', 'cluster_255', 'cluster_256' ], ann_file='data/coco/annotations/instances_val2017.json', img_prefix='data/coco/val2017/', pipeline=[ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(1333, 800), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='Pad', size_divisor=32), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']) ]) ])) evaluation = dict(interval=65535, gpu_collect=True) checkpoint_config = dict(interval=1) log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')]) custom_hooks = [ dict(type='MomentumUpdateHook'), dict( type='MMDetWandbHook', init_kwargs=dict(project='I2B', group='pretrain'), interval=50, num_eval_images=0, log_checkpoint=False) ] dist_params = dict(backend='nccl') log_level = 'INFO' load_from = None resume_from = None workflow = [('train', 1)] opencv_num_threads = 0 mp_start_method = 'fork' auto_scale_lr = dict(enable=True, base_batch_size=64) custom_imports = dict( imports=[ 'mmselfsup.datasets.pipelines', 'selfsup.core.hook.momentum_update_hook', 'selfsup.datasets.pipelines.selfsup_pipelines', 'selfsup.datasets.pipelines.rand_aug', 'selfsup.datasets.single_view_coco', 'selfsup.datasets.multi_view_coco', 'selfsup.models.losses.contrastive_loss', 'selfsup.models.dense_heads.fcos_head', 'selfsup.models.dense_heads.retina_head', 'selfsup.models.dense_heads.detr_head', 'selfsup.models.dense_heads.deformable_detr_head', 'selfsup.models.roi_heads.bbox_heads.convfc_bbox_head', 'selfsup.models.roi_heads.standard_roi_head', 'selfsup.models.detectors.selfsup_detector', 'selfsup.models.detectors.selfsup_fcos', 'selfsup.models.detectors.selfsup_detr', 'selfsup.models.detectors.selfsup_deformable_detr', 'selfsup.models.detectors.selfsup_retinanet', 'selfsup.models.detectors.selfsup_mask_rcnn', 'selfsup.core.bbox.assigners.hungarian_assigner', 'selfsup.core.bbox.assigners.pseudo_hungarian_assigner', 'selfsup.core.bbox.match_costs.match_cost' ], allow_failed_imports=False) optimizer = dict(type='AdamW', lr=0.0002, weight_decay=0.0001) optimizer_config = dict(grad_clip=dict(max_norm=0.1, norm_type=2)) lr_config = dict(policy='step', step=[40]) runner = dict(type='EpochBasedRunner', max_epochs=50) work_dir = 'work_dirs/selfsup_detr_cluster-id-as-class_contrastive' auto_resume = False gpu_ids = range(0, 32)