checkpoint_config = dict(interval=9) log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')]) custom_hooks = [dict(type='NumClassCheckHook')] dist_params = dict(backend='nccl') log_level = 'INFO' load_from = None resume_from = None workflow = [('train', 1)] optimizer = dict( type='AdamW', lr=2.5e-05, betas=(0.9, 0.999), weight_decay=0.05, paramwise_cfg=dict( custom_keys=dict( absolute_pos_embed=dict(decay_mult=0.0), relative_position_bias_table=dict(decay_mult=0.0), norm=dict(decay_mult=0.0)))) optimizer_config = dict(grad_clip=None) lr_config = dict( policy='step', warmup='linear', warmup_iters=500, warmup_ratio=0.3333333333333333, step=[6]) runner = dict(type='EpochBasedRunner', max_epochs=9) pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.2/mask_rcnn_swin_tiny_patch4_window7.pth' is_video_model = True model = dict( type='STPN', detector=dict( type='FasterRCNN', backbone=dict( type='STPNSwinTransformer', embed_dims=96, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24], window_size=7, mlp_ratio=4, qkv_bias=True, qk_scale=None, drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.2, patch_norm=True, with_cp=False, convert_weights=True, init_cfg=dict( type='Pretrained', checkpoint= 'https://github.com/SwinTransformer/storage/releases/download/v1.0.2/mask_rcnn_swin_tiny_patch4_window7.pth' ), prompt_cfg=dict( num_tokens=5, location='prepend', deep=False, dropout=0.0, initiation='random')), neck=dict( type='FPN', in_channels=[96, 192, 384, 768], out_channels=256, num_outs=5), rpn_head=dict( type='RPNHead', in_channels=256, feat_channels=256, anchor_generator=dict( type='AnchorGenerator', scales=[8], ratios=[0.5, 1.0, 2.0], strides=[4, 8, 16, 32, 64]), bbox_coder=dict( type='DeltaXYWHBBoxCoder', target_means=[0.0, 0.0, 0.0, 0.0], target_stds=[1.0, 1.0, 1.0, 1.0]), loss_cls=dict( type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), loss_bbox=dict( type='SmoothL1Loss', beta=0.1111111111111111, loss_weight=1.0)), roi_head=dict( type='StandardRoIHead', bbox_roi_extractor=dict( type='SingleRoIExtractor', roi_layer=dict( type='RoIAlign', output_size=7, sampling_ratio=0), out_channels=256, featmap_strides=[4, 8, 16, 32]), bbox_head=dict( type='Shared2FCBBoxHead', in_channels=256, fc_out_channels=1024, roi_feat_size=7, num_classes=30, bbox_coder=dict( type='DeltaXYWHBBoxCoder', target_means=[0.0, 0.0, 0.0, 0.0], target_stds=[0.2, 0.2, 0.2, 0.2]), reg_class_agnostic=False, loss_cls=dict( type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), loss_bbox=dict( type='SmoothL1Loss', beta=0.1111111111111111, loss_weight=1.0))), train_cfg=dict( rpn=dict( assigner=dict( type='MaxIoUAssigner', pos_iou_thr=0.7, neg_iou_thr=0.3, min_pos_iou=0.3, match_low_quality=True, ignore_iof_thr=-1), sampler=dict( type='RandomSampler', num=256, pos_fraction=0.5, neg_pos_ub=-1, add_gt_as_proposals=False), allowed_border=-1, pos_weight=-1, debug=False), rpn_proposal=dict( nms_pre=1000, max_per_img=300, nms=dict(type='nms', iou_threshold=0.7), min_bbox_size=0), rcnn=dict( assigner=dict( type='MaxIoUAssigner', pos_iou_thr=0.5, neg_iou_thr=0.5, min_pos_iou=0.5, match_low_quality=True, ignore_iof_thr=-1), sampler=dict( type='RandomSampler', num=256, pos_fraction=0.25, neg_pos_ub=-1, add_gt_as_proposals=True), mask_size=28, pos_weight=-1, debug=False)), test_cfg=dict( rpn=dict( nms_pre=1000, max_per_img=300, nms=dict(type='nms', iou_threshold=0.7), min_bbox_size=0), rcnn=dict( score_thr=0.0001, nms=dict(type='nms', iou_threshold=0.5), max_per_img=100, mask_thr_binary=0.5)))) dataset_type = 'ImagenetVIDDataset' data_root = 'data/ILSVRC/' 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='LoadMultiImagesFromFile'), dict(type='SeqLoadAnnotations', with_bbox=True, with_mask=False), dict(type='SeqRandomFlip', share_params=True, flip_ratio=0.5), dict( type='AutoAugment', policies=[[{ 'type': 'SeqResize', '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': 'SeqResize', 'img_scale': [(400, 1333), (500, 1333), (600, 1333)], 'multiscale_mode': 'value', 'keep_ratio': True }, { 'type': 'SeqRandomCrop', 'crop_type': 'absolute_range', 'crop_size': (384, 600), 'allow_negative_crop': True }, { 'type': 'SeqMaxSizePad' }, { 'type': 'SeqResize2', '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 }]]), dict( type='SeqNormalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='SeqPad', size_divisor=16), dict(type='VideoCollect', keys=['img', 'gt_bboxes', 'gt_labels']), dict(type='ConcatVideoReferences'), dict(type='SeqDefaultFormatBundle', ref_prefix='ref') ] test_pipeline = [ dict(type='LoadMultiImagesFromFile'), dict(type='SeqResize', img_scale=(1000, 600), keep_ratio=True), dict(type='SeqRandomFlip', share_params=True, flip_ratio=0.0), dict( type='SeqNormalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='SeqPad', size_divisor=16), dict( type='VideoCollect', keys=['img'], meta_keys=('num_left_ref_imgs', 'frame_stride')), dict(type='ConcatVideoReferences'), dict(type='MultiImagesToTensor', ref_prefix='ref'), dict(type='ToList') ] data = dict( samples_per_gpu=1, workers_per_gpu=4, train=[ dict( type='ImagenetVIDDataset', ann_file='data/ILSVRC/annotations/imagenet_vid_train.json', img_prefix='data/ILSVRC/Data/VID', ref_img_sampler=dict( num_ref_imgs=2, frame_range=9, filter_key_img=True, method='bilateral_uniform'), pipeline=[ dict(type='LoadMultiImagesFromFile'), dict( type='SeqLoadAnnotations', with_bbox=True, with_mask=False), dict(type='SeqRandomFlip', share_params=True, flip_ratio=0.5), dict( type='AutoAugment', policies=[[{ 'type': 'SeqResize', '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': 'SeqResize', 'img_scale': [(400, 1333), (500, 1333), (600, 1333)], 'multiscale_mode': 'value', 'keep_ratio': True }, { 'type': 'SeqRandomCrop', 'crop_type': 'absolute_range', 'crop_size': (384, 600), 'allow_negative_crop': True }, { 'type': 'SeqMaxSizePad' }, { 'type': 'SeqResize2', '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 }]]), dict( type='SeqNormalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='SeqPad', size_divisor=16), dict( type='VideoCollect', keys=['img', 'gt_bboxes', 'gt_labels']), dict(type='ConcatVideoReferences'), dict(type='SeqDefaultFormatBundle', ref_prefix='ref') ]), dict( type='ImagenetVIDDataset', load_as_video=False, ann_file='data/ILSVRC/annotations/imagenet_det_30plus1cls.json', img_prefix='data/ILSVRC/Data/DET', ref_img_sampler=dict( num_ref_imgs=2, frame_range=0, filter_key_img=False, method='bilateral_uniform'), pipeline=[ dict(type='LoadMultiImagesFromFile'), dict( type='SeqLoadAnnotations', with_bbox=True, with_mask=False), dict(type='SeqRandomFlip', share_params=True, flip_ratio=0.5), dict( type='AutoAugment', policies=[[{ 'type': 'SeqResize', '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': 'SeqResize', 'img_scale': [(400, 1333), (500, 1333), (600, 1333)], 'multiscale_mode': 'value', 'keep_ratio': True }, { 'type': 'SeqRandomCrop', 'crop_type': 'absolute_range', 'crop_size': (384, 600), 'allow_negative_crop': True }, { 'type': 'SeqMaxSizePad' }, { 'type': 'SeqResize2', '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 }]]), dict( type='SeqNormalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='SeqPad', size_divisor=16), dict( type='VideoCollect', keys=['img', 'gt_bboxes', 'gt_labels']), dict(type='ConcatVideoReferences'), dict(type='SeqDefaultFormatBundle', ref_prefix='ref') ]) ], val=dict( type='ImagenetVIDDataset', ann_file='data/ILSVRC/annotations/imagenet_vid_val.json', img_prefix='data/ILSVRC/Data/VID', ref_img_sampler=dict( num_ref_imgs=14, frame_range=[-7, 7], method='test_with_adaptive_stride'), pipeline=[ dict(type='LoadMultiImagesFromFile'), dict(type='SeqResize', img_scale=(1000, 600), keep_ratio=True), dict(type='SeqRandomFlip', share_params=True, flip_ratio=0.0), dict( type='SeqNormalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='SeqPad', size_divisor=16), dict( type='VideoCollect', keys=['img'], meta_keys=('num_left_ref_imgs', 'frame_stride')), dict(type='ConcatVideoReferences'), dict(type='MultiImagesToTensor', ref_prefix='ref'), dict(type='ToList') ], test_mode=True), test=dict( type='ImagenetVIDDataset', ann_file='data/ILSVRC/annotations/imagenet_vid_val.json', img_prefix='data/ILSVRC/Data/VID', ref_img_sampler=dict( num_ref_imgs=14, frame_range=[-7, 7], method='test_with_adaptive_stride'), pipeline=[ dict(type='LoadMultiImagesFromFile'), dict(type='SeqResize', img_scale=(1000, 600), keep_ratio=True), dict(type='SeqRandomFlip', share_params=True, flip_ratio=0.0), dict( type='SeqNormalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='SeqPad', size_divisor=16), dict( type='VideoCollect', keys=['img'], meta_keys=('num_left_ref_imgs', 'frame_stride')), dict(type='ConcatVideoReferences'), dict(type='MultiImagesToTensor', ref_prefix='ref'), dict(type='ToList') ], test_mode=True)) total_epochs = 9 evaluation = dict(metric=['bbox'], vid_style=True, interval=9) work_dir = './work_dirs/stpn_swint_adam_9x' gpu_ids = range(0, 8)