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)