mmtracking
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open-mmlab
TypeError: CocoVideoDataset: list indices must be integers or slices, not str
/home/nhl510wm/xxy/code/mmtracking/mmtrack/core/utils/misc.py:24: UserWarning: Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. warnings.warn( /home/nhl510wm/xxy/code/mmtracking/mmtrack/core/utils/misc.py:34: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. warnings.warn( 2022-10-25 22:30:34,450 - mmtrack - INFO - Environment info:
sys.platform: linux Python: 3.9.13 (main, Oct 13 2022, 21:15:33) [GCC 11.2.0] CUDA available: True GPU 0: NVIDIA GeForce RTX 3090 CUDA_HOME: None GCC: gcc (Ubuntu 11.2.0-19ubuntu1) 11.2.0 PyTorch: 1.10.0+cu111 PyTorch compiling details: PyTorch built with:
- GCC 7.3
- C++ Version: 201402
- Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications
- Intel(R) MKL-DNN v2.2.3 (Git Hash 7336ca9f055cf1bfa13efb658fe15dc9b41f0740)
- OpenMP 201511 (a.k.a. OpenMP 4.5)
- LAPACK is enabled (usually provided by MKL)
- NNPACK is enabled
- CPU capability usage: AVX2
- CUDA Runtime 11.1
- NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86
- CuDNN 8.0.5
- Magma 2.5.2
- Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.1, CUDNN_VERSION=8.0.5, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.10.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON,
TorchVision: 0.11.0+cu111 OpenCV: 4.6.0 MMCV: 1.5.3 MMCV Compiler: GCC 7.3 MMCV CUDA Compiler: 11.1 MMTracking: 0.14.0+2c7a8af
2022-10-25 22:30:34,451 - mmtrack - INFO - Distributed training: False 2022-10-25 22:30:35,631 - mmtrack - INFO - Config: model = dict( type='DFF', detector=dict( type='FasterRCNN', backbone=dict( type='ResNet', depth=50, 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://resnet50')), 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='StandardRoIHead', 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='Shared2FCBBoxHead', 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))), 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_across_levels=False, nms_pre=6000, nms_post=1000, max_num=1000, nms_thr=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=512, pos_fraction=0.25, neg_pos_ub=-1, add_gt_as_proposals=True), pos_weight=-1, debug=False)), test_cfg=dict( rpn=dict( nms_across_levels=False, nms_pre=6000, nms_post=300, max_num=300, nms_thr=0.7, min_bbox_size=0), rcnn=dict( score_thr=0.0001, nms=dict(type='nms', iou_threshold=0.5), max_per_img=100))), motion=dict( type='FlowNetSimple', img_scale_factor=0.5, init_cfg=dict( type='Pretrained', checkpoint= 'https://download.openmmlab.com/mmtracking/pretrained_weights/flownet_simple.pth' )), train_cfg=None, test_cfg=dict(key_frame_interval=10)) dataset_type = 'CocoVideoDataset' classes = ('ps', 'head') data_root = '/home/nhl510wm/xxy/data/detect/' 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='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(1000, 600), 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=16), dict(type='ImageToTensor', keys=['img']), dict(type='VideoCollect', keys=['img']) ]) ] data = dict( samples_per_gpu=1, workers_per_gpu=2, train=[ dict( type='CocoVideoDataset', ann_file= '/home/nhl510wm/xxy/data/detect/mmtracking data/instances_jnu_3.json', img_prefix='/home/nhl510wm/xxy/data/detect/image_all', classes=('ps', 'head'), ref_img_sampler=dict( num_ref_imgs=1, frame_range=9, filter_key_img=False, method='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='CocoVideoDataset', load_as_video=False, ann_file= '/home/nhl510wm/xxy/data/detect/mmtracking data/instances_jnu_3.json', img_prefix='/home/nhl510wm/xxy/data/detect/image_all', classes=('ps', 'head'), ref_img_sampler=dict( num_ref_imgs=1, frame_range=0, filter_key_img=False, method='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='CocoVideoDataset', ann_file= '/home/nhl510wm/xxy/data/detect/mmtracking data/instances_jnu_3.json', img_prefix='/home/nhl510wm/xxy/data/detect/image_all', classes=('ps', 'head'), ref_img_sampler=None, pipeline=[ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(1000, 600), 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=16), dict(type='ImageToTensor', keys=['img']), dict(type='VideoCollect', keys=['img']) ]) ], test_mode=True), test=dict( type='CocoVideoDataset', ann_file= '/home/nhl510wm/xxy/data/detect/mmtracking data/instances_jnu_3.json', img_prefix='/home/nhl510wm/xxy/data/detect/image_all', classes=('ps', 'head'), ref_img_sampler=None, pipeline=[ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(1000, 600), 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=16), dict(type='ImageToTensor', keys=['img']), dict(type='VideoCollect', keys=['img']) ]) ], test_mode=True)) optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001) optimizer_config = dict(grad_clip=None) 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' work_dir = '/home/nhl510wm/xxy/code/work_dirs_tra/MaskTrackRCNN' gpu_ids = [0]
2022-10-25 22:30:35,631 - mmtrack - INFO - Set random seed to 1006476471, deterministic: False 2022-10-25 22:30:36,689 - mmtrack - INFO - initialize ResNet with init_cfg {'type': 'Pretrained', 'checkpoint': 'torchvision://resnet50'} 2022-10-25 22:30:36,689 - mmcv - INFO - load model from: torchvision://resnet50 2022-10-25 22:30:36,689 - mmcv - INFO - load checkpoint from torchvision path: torchvision://resnet50 2022-10-25 22:30:36,783 - mmcv - WARNING - The model and loaded state dict do not match exactly
unexpected key in source state_dict: fc.weight, fc.bias
2022-10-25 22:30:36,807 - mmtrack - INFO - initialize ChannelMapper with init_cfg {'type': 'Xavier', 'layer': 'Conv2d', 'distribution': 'uniform'} 2022-10-25 22:30:36,867 - mmtrack - INFO - initialize RPNHead with init_cfg {'type': 'Normal', 'layer': 'Conv2d', 'std': 0.01} 2022-10-25 22:30:36,883 - mmtrack - INFO - initialize Shared2FCBBoxHead with init_cfg [{'type': 'Normal', 'std': 0.01, 'override': {'name': 'fc_cls'}}, {'type': 'Normal', 'std': 0.001, 'override': {'name': 'fc_reg'}}, {'type': 'Xavier', 'distribution': 'uniform', 'override': [{'name': 'shared_fcs'}, {'name': 'cls_fcs'}, {'name': 'reg_fcs'}]}] 2022-10-25 22:30:37,136 - mmtrack - INFO - initialize FlowNetSimple with init_cfg {'type': 'Pretrained', 'checkpoint': 'https://download.openmmlab.com/mmtracking/pretrained_weights/flownet_simple.pth'} 2022-10-25 22:30:37,136 - mmcv - INFO - load model from: https://download.openmmlab.com/mmtracking/pretrained_weights/flownet_simple.pth 2022-10-25 22:30:37,136 - mmcv - INFO - load checkpoint from http path: https://download.openmmlab.com/mmtracking/pretrained_weights/flownet_simple.pth loading annotations into memory... Done (t=0.06s) creating index... Traceback (most recent call last): File "/home/nhl510wm/anaconda3/envs/xxy_mmd/lib/python3.9/site-packages/mmcv/utils/registry.py", line 69, in build_from_cfg return obj_cls(**args) File "/home/nhl510wm/xxy/code/mmtracking/mmtrack/datasets/coco_video_dataset.py", line 46, in init super().init(*args, **kwargs) File "/home/nhl510wm/xxy/code/mmdetection/mmdet/datasets/custom.py", line 95, in init self.data_infos = self.load_annotations(local_path) File "/home/nhl510wm/xxy/code/mmtracking/mmtrack/datasets/coco_video_dataset.py", line 61, in load_annotations data_infos = self.load_video_anns(ann_file) File "/home/nhl510wm/xxy/code/mmtracking/mmtrack/datasets/coco_video_dataset.py", line 81, in load_video_anns img_ids = self.coco.get_img_ids_from_vid(vid_id) File "/home/nhl510wm/xxy/code/mmtracking/mmtrack/datasets/parsers/coco_video_parser.py", line 124, in get_img_ids_from_vid ids[img_info['frame_id']] = img_info['id'] TypeError: list indices must be integers or slices, not str
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/nhl510wm/xxy/code/mmtracking/tools/train.py", line 217, in
Process finished with exit code 1
{ "images": [ { "height": 1026, "width": 1295, "id": 1, "file_name": "20190830T115515_109.png", "frame_id": "109", "video_id": 0 }, "type": "instances", "annotations": [ { "segmentation": [ [ 0, 0, 0, 0, 0, 0, 0, 0 ] ], "area": 1, "iscrowd": "false", "ignore": "false", "image_id": 1, "bbox": [ 0, 0, 0, 0 ], "category_id": 1, "id": 1, "video_id": 0, "instance_id": "false", "occluded": "false", "truncated": "false", "is_vid_train_frame": "true", "visibility": 1.0 }, { "segmentation": [ [ 530, 357, 530, 864, 1074, 864, 1074, 357 ] ], "area": 276860, "iscrowd": "false", "ignore": "false", "image_id": 1, "bbox": [ 530, 357, 544, 507 ], "category_id": 2, "id": 2, "video_id": 0, "instance_id": "false", "occluded": "false", "truncated": "false", "is_vid_train_frame": "true", "visibility": 1.0 },
Hi, I have same problem. Did you solve it?
Not yet. But I remember that there were similar problems in the past issues. I refer to it and haven't solved it yet. You can try it.
Can you tell me issue number of "similar problems in the past issues" are?
Hello,have you solve the problem? By the way, Could you please tell me how to make my own CocoVideoDataset? I can pay for you. @Youngforever0911
Hello,have you solve the problem? By the way, Could you please tell me how to make my own CocoVideoDataset? I can pay for you. @Youngforever0911
Have you get it？I have the same difficulty too
