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When i change the faster rcnn to Mask r-cnn, it occur the error AttributeError: 'list' object has no attribute 'shape' after the first epoch train.
AttributeError: 'list' object has no attribute 'shape'
Here is my config:
model = dict( detector=dict( type='MaskRCNN', backbone=dict( type='ResNet', depth=50, num_stages=4, out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), norm_eval=True, style='pytorch', init_cfg=dict( type='Pretrained', checkpoint='torchvision://resnet50'), plugins=[ dict( cfg=dict( type='GeneralizedAttention', spatial_range=-1, num_heads=8, attention_type='0010', kv_stride=2), stages=(True, True, False, True), position='after_conv2') ], dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), stage_with_dcn=(True, True, False, True)), neck=dict( type='FPN', in_channels=[256, 512, 1024, 2048], 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], clip_border=False), loss_cls=dict( type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), loss_bbox=dict(type='L1Loss', 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=6, bbox_coder=dict( type='DeltaXYWHBBoxCoder', target_means=[0.0, 0.0, 0.0, 0.0], target_stds=[0.1, 0.1, 0.2, 0.2], clip_border=False), reg_class_agnostic=False, loss_cls=dict( type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), loss_bbox=dict(type='L1Loss', loss_weight=1.0)), mask_roi_extractor=dict( type='SingleRoIExtractor', roi_layer=dict( type='RoIAlign', output_size=14, sampling_ratio=0), out_channels=256, featmap_strides=[4, 8, 16, 32]), mask_head=dict( type='FCNMaskHead', num_convs=4, in_channels=256, conv_out_channels=256, num_classes=6, loss_mask=dict( type='CrossEntropyLoss', use_mask=True, 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=2000, max_per_img=1000, 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=False, ignore_iof_thr=-1), sampler=dict( type='RandomSampler', num=512, 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=1000, nms=dict(type='nms', iou_threshold=0.7), min_bbox_size=0), rcnn=dict( score_thr=0.05, nms=dict(type='nms', iou_threshold=0.5), max_per_img=100, mask_thr_binary=0.5)), init_cfg=dict( type='Pretrained', checkpoint='/home/music/Downloads/mmtracking/epoch_24.pth')), type='QDTrack', track_head=dict( type='QuasiDenseTrackHead', 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]), embed_head=dict( type='QuasiDenseEmbedHead', num_convs=4, num_fcs=1, embed_channels=256, norm_cfg=dict(type='GN', num_groups=32), loss_track=dict(type='MultiPosCrossEntropyLoss', loss_weight=0.25), loss_track_aux=dict( type='L2Loss', neg_pos_ub=3, pos_margin=0, neg_margin=0.1, hard_mining=True, loss_weight=1.0)), loss_bbox=dict(type='L1Loss', loss_weight=1.0), train_cfg=dict( assigner=dict( type='MaxIoUAssigner', pos_iou_thr=0.7, neg_iou_thr=0.5, min_pos_iou=0.5, match_low_quality=False, ignore_iof_thr=-1), sampler=dict( type='CombinedSampler', num=256, pos_fraction=0.5, neg_pos_ub=3, add_gt_as_proposals=True, pos_sampler=dict(type='InstanceBalancedPosSampler'), neg_sampler=dict(type='RandomSampler')))), tracker=dict( type='QuasiDenseTracker', init_score_thr=0.9, obj_score_thr=0.5, match_score_thr=0.5, memo_tracklet_frames=30, memo_backdrop_frames=1, memo_momentum=0.8, nms_conf_thr=0.5, nms_backdrop_iou_thr=0.3, nms_class_iou_thr=0.7, with_cats=True, match_metric='bisoftmax')) optimizer = dict(type='SGD', lr=0.0025, momentum=0.9, weight_decay=0.0001) optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2)) checkpoint_config = dict(interval=1) log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')]) 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' lr_config = dict(policy='step', step=[3]) total_epochs = 24 evaluation = dict(metric=['bbox', 'track'], interval=1) dataset_type = 'CocoVideoDataset' img_norm_cfg = dict( mean=[103.53, 116.28, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) train_pipeline = [ dict(type='LoadMultiImagesFromFile', to_float32=True), dict(type='SeqLoadAnnotations', with_bbox=True, with_mask=True, with_track=True), dict( type='SeqResize', img_scale=(1088, 1088), share_params=True, ratio_range=(0.8, 1.2), keep_ratio=True, bbox_clip_border=False), dict(type='SeqPhotoMetricDistortion', share_params=True), dict( type='SeqRandomCrop', share_params=False, crop_size=(1088, 1088), bbox_clip_border=False), dict(type='SeqRandomFlip', share_params=True, flip_ratio=0.5), dict( type='SeqNormalize', mean=[103.53, 116.28, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False), dict(type='SeqPad', size_divisor=32), dict(type='MatchInstances', skip_nomatch=True), dict( type='VideoCollect', keys=[ 'img', 'gt_bboxes', 'gt_labels', 'gt_masks','gt_match_indices', 'gt_instance_ids' ]), dict(type='SeqDefaultFormatBundle', ref_prefix='ref') ] test_pipeline = [ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(1088, 1088), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict( type='Normalize', mean=[103.53, 116.28, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False), dict(type='Pad', size_divisor=32), dict(type='ImageToTensor', keys=['img']), dict(type='VideoCollect', keys=['img']) ]) ] data_root = '/home/music/Downloads/mmtracking/video_pic/' data = dict( samples_per_gpu=4, workers_per_gpu=2, train=dict( type='CocoVideoDataset', classes=('aircraft', 'buildings', 'electrical', 'person', 'tree', 'wire'), ann_file= '/home/music/Downloads/mmtracking/video_pic/annotations/train.json', img_prefix='/home/music/Downloads/mmtracking/video_pic/train/', ref_img_sampler=dict( num_ref_imgs=1, frame_range=10, filter_key_img=True, method='uniform'), pipeline=[ dict(type='LoadMultiImagesFromFile', to_float32=True), dict(type='SeqLoadAnnotations', with_bbox=True, with_mask=True, with_track=True), dict( type='SeqResize', img_scale=(1088, 1088), share_params=True, ratio_range=(0.8, 1.2), keep_ratio=True, bbox_clip_border=False), dict(type='SeqPhotoMetricDistortion', share_params=True), dict( type='SeqRandomCrop', share_params=False, crop_size=(1088, 1088), bbox_clip_border=False), dict(type='SeqRandomFlip', share_params=True, flip_ratio=0.5), dict( type='SeqNormalize', mean=[103.53, 116.28, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False), dict(type='SeqPad', size_divisor=32), dict(type='MatchInstances', skip_nomatch=True), dict( type='VideoCollect', keys=[ 'img', 'gt_bboxes', 'gt_labels', 'gt_masks', 'gt_match_indices', 'gt_instance_ids' ]), dict(type='SeqDefaultFormatBundle', ref_prefix='ref') ]), val=dict( type='CocoVideoDataset', classes=('aircraft', 'buildings', 'electrical', 'person', 'tree', 'wire'), ann_file= '/home/music/Downloads/mmtracking/video_pic/annotations/val.json', img_prefix='/home/music/Downloads/mmtracking/video_pic/val/', ref_img_sampler=None, pipeline=[ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(1088, 1088), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict( type='Normalize', mean=[103.53, 116.28, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False), dict(type='Pad', size_divisor=32), dict(type='ImageToTensor', keys=['img']), dict(type='VideoCollect', keys=['img']) ]) ]), test=dict( type='CocoVideoDataset', classes=('aircraft', 'buildings', 'electrical', 'person', 'tree', 'wire'), ann_file= '/home/music/Downloads/mmtracking/video_pic/annotations/val.json', img_prefix='/home/music/Downloads/mmtracking/video_pic/val/', ref_img_sampler=None, pipeline=[ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(1088, 1088), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict( type='Normalize', mean=[103.53, 116.28, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False), dict(type='Pad', size_divisor=32), dict(type='ImageToTensor', keys=['img']), dict(type='VideoCollect', keys=['img']) ]) ])) work_dir = 'work_dirs/qdtrack_6class' gpu_ids = [0]
Could you please give me some advice? Thank you!
The text was updated successfully, but these errors were encountered:
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When i change the faster rcnn to Mask r-cnn, it occur the error
AttributeError: 'list' object has no attribute 'shape'after the first epoch train.Here is my config:
Could you please give me some advice? Thank you!
The text was updated successfully, but these errors were encountered: