| model = dict( | |
| detector=dict( | |
| type='FasterRCNN', | |
| backbone=dict( | |
| type='ResNet', | |
| depth=101, | |
| num_stages=4, | |
| out_indices=(3, ), | |
| strides=(1, 2, 2, 1), | |
| dilations=(1, 1, 1, 2), | |
| frozen_stages=1, | |
| norm_cfg=dict(type='BN', requires_grad=True), | |
| norm_eval=True, | |
| style='pytorch', | |
| init_cfg=dict( | |
| type='Pretrained', checkpoint='torchvision://resnet101')), | |
| neck=dict( | |
| type='ChannelMapper', | |
| in_channels=[2048], | |
| out_channels=512, | |
| kernel_size=3), | |
| rpn_head=dict( | |
| type='RPNHead', | |
| in_channels=512, | |
| feat_channels=512, | |
| anchor_generator=dict( | |
| type='AnchorGenerator', | |
| scales=[4, 8, 16, 32], | |
| ratios=[0.5, 1.0, 2.0], | |
| strides=[16]), | |
| 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='MambaRoIHead', | |
| bbox_roi_extractor=dict( | |
| type='SingleRoIExtractor', | |
| roi_layer=dict( | |
| type='RoIAlign', output_size=7, sampling_ratio=2), | |
| out_channels=512, | |
| featmap_strides=[16]), | |
| bbox_head=dict( | |
| type='MambaBBoxHead', | |
| in_channels=512, | |
| 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=1.0, loss_weight=1.0), | |
| num_shared_fcs=2, | |
| topk=75, | |
| aggregator=dict( | |
| type='MambaAggregator', | |
| in_channels=1024, | |
| num_attention_blocks=16))), | |
| train_cfg=dict( | |
| rpn=dict( | |
| assigner=dict( | |
| type='MaxIoUAssigner', | |
| pos_iou_thr=0.7, | |
| neg_iou_thr=0.3, | |
| min_pos_iou=0.3, | |
| 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=0, | |
| pos_weight=-1, | |
| debug=False), | |
| rpn_proposal=dict( | |
| nms_pre=6000, | |
| max_per_img=600, | |
| 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, | |
| ignore_iof_thr=-1), | |
| sampler=dict( | |
| type='RandomSampler', | |
| num=256, | |
| pos_fraction=0.25, | |
| neg_pos_ub=-1, | |
| add_gt_as_proposals=True), | |
| pos_weight=-1, | |
| debug=False)), | |
| test_cfg=dict( | |
| rpn=dict( | |
| nms_pre=6000, | |
| 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))), | |
| type='MAMBA') | |
| 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_track=True), | |
| dict(type='SeqResize', img_scale=(1000, 600), keep_ratio=True), | |
| dict(type='SeqRandomFlip', share_params=True, flip_ratio=0.5), | |
| 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', 'gt_instance_ids']), | |
| 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=1000, | |
| filter_key_img=True, | |
| method='bilateral_uniform'), | |
| pipeline=[ | |
| dict(type='LoadMultiImagesFromFile'), | |
| dict( | |
| type='SeqLoadAnnotations', with_bbox=True, | |
| with_track=True), | |
| dict(type='SeqResize', img_scale=(1000, 600), keep_ratio=True), | |
| dict(type='SeqRandomFlip', share_params=True, flip_ratio=0.5), | |
| 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', 'gt_instance_ids']), | |
| 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_track=True), | |
| dict(type='SeqResize', img_scale=(1000, 600), keep_ratio=True), | |
| dict(type='SeqRandomFlip', share_params=True, flip_ratio=0.5), | |
| 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', 'gt_instance_ids']), | |
| 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], | |
| stride=1, | |
| 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, | |
| shuffle_video_frames=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], | |
| stride=1, | |
| 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, | |
| shuffle_video_frames=True)) | |
| checkpoint_config = dict(interval=3) | |
| 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 = 'work_dirs/mamba_r101_dc5_6x/epoch_3.pth' | |
| workflow = [('train', 1)] | |
| optimizer = dict(type='SGD', lr=0.001, momentum=0.9, weight_decay=0.0001) | |
| optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2)) | |
| lr_config = dict( | |
| policy='step', | |
| warmup='linear', | |
| warmup_iters=500, | |
| warmup_ratio=0.3333333333333333, | |
| step=[4]) | |
| runner = dict(type='EpochBasedRunner', max_epochs=6) | |
| is_video_model = True | |
| total_epochs = 6 | |
| evaluation = dict(metric=['bbox'], vid_style=True, interval=1) | |
| work_dir = './work_dirs/mamba_r101_dc5_6x' | |
| gpu_ids = range(0, 8) | |