Delete pretrain/selfsup_detr_cluster-ids-as-pseudo-labels

pretrain/selfsup_detr_cluster-ids-as-pseudo-labels/detr_pseudo_label.py

@@ -1,424 +0,0 @@
-model = dict(
-    type='DETR',
-    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='DETRHead',
-        num_classes=256,
-        in_channels=2048,
-        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='CrossEntropyLoss',
-            bg_cls_weight=0.1,
-            use_sigmoid=False,
-            loss_weight=1.0,
-            class_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))
-dataset_type = 'CocoDataset'
-data_root = 'data/coco/'
-img_norm_cfg = dict(
-    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='LoadAnnotations', with_bbox=True),
-    dict(type='RandomFlip', flip_ratio=0.5),
-    dict(
-        type='AutoAugment',
-        policies=[[{
-            'type':
-            'Resize',
-            'img_scale': [(480, 1333), (512, 1333), (544, 1333), (576, 1333),
-                          (608, 1333), (640, 1333), (672, 1333), (704, 1333),
-                          (736, 1333), (768, 1333), (800, 1333)],
-            'multiscale_mode':
-            'value',
-            'keep_ratio':
-            True
-        }],
-                  [{
-                      'type': 'Resize',
-                      'img_scale': [(400, 1333), (500, 1333), (600, 1333)],
-                      'multiscale_mode': 'value',
-                      'keep_ratio': True
-                  }, {
-                      'type': 'RandomCrop',
-                      'crop_type': 'absolute_range',
-                      'crop_size': (384, 600),
-                      'allow_negative_crop': True
-                  }, {
-                      'type':
-                      'Resize',
-                      'img_scale': [(480, 1333), (512, 1333), (544, 1333),
-                                    (576, 1333), (608, 1333), (640, 1333),
-                                    (672, 1333), (704, 1333), (736, 1333),
-                                    (768, 1333), (800, 1333)],
-                      'multiscale_mode':
-                      'value',
-                      'override':
-                      True,
-                      'keep_ratio':
-                      True
-                  }]]),
-    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=1),
-    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='CocoDataset',
-        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),
-            dict(type='RandomFlip', flip_ratio=0.5),
-            dict(
-                type='AutoAugment',
-                policies=[[{
-                    'type':
-                    'Resize',
-                    'img_scale': [(480, 1333), (512, 1333), (544, 1333),
-                                  (576, 1333), (608, 1333), (640, 1333),
-                                  (672, 1333), (704, 1333), (736, 1333),
-                                  (768, 1333), (800, 1333)],
-                    'multiscale_mode':
-                    'value',
-                    'keep_ratio':
-                    True
-                }],
-                          [{
-                              'type': 'Resize',
-                              'img_scale': [(400, 1333), (500, 1333),
-                                            (600, 1333)],
-                              'multiscale_mode': 'value',
-                              'keep_ratio': True
-                          }, {
-                              'type': 'RandomCrop',
-                              'crop_type': 'absolute_range',
-                              'crop_size': (384, 600),
-                              'allow_negative_crop': True
-                          }, {
-                              'type':
-                              'Resize',
-                              'img_scale': [(480, 1333), (512, 1333),
-                                            (544, 1333), (576, 1333),
-                                            (608, 1333), (640, 1333),
-                                            (672, 1333), (704, 1333),
-                                            (736, 1333), (768, 1333),
-                                            (800, 1333)],
-                              'multiscale_mode':
-                              'value',
-                              'override':
-                              True,
-                              'keep_ratio':
-                              True
-                          }]]),
-            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=1),
-            dict(type='DefaultFormatBundle'),
-            dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
-        ],
-        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'
-        ]),
-    val=dict(
-        type='CocoDataset',
-        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',
-        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, metric='bbox', save_best='auto', gpu_collect=True)
-checkpoint_config = dict(interval=1)
-log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
-custom_hooks = [
-    dict(type='NumClassCheckHook'),
-    dict(
-        type='MMDetWandbHook',
-        init_kwargs=dict(project='I2B', group='finetune'),
-        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)
-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'
-]
-optimizer = dict(
-    type='AdamW',
-    lr=0.0002,
-    weight_decay=0.0001,
-    paramwise_cfg=dict(
-        custom_keys=dict(backbone=dict(lr_mult=0, decay_mult=0))))
-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-ids-as-pseudo-labels'
-auto_resume = False
-gpu_ids = range(0, 32)