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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)
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