OpenMMLab Video Perception Toolbox. It supports Video Object Detection (VID), Multiple Object Tracking (MOT), Single Object Tracking (SOT), Video Instance Segmentation (VIS) with a unified framework.

In "/mmtracking/mmtrack/models/motion/flownet_simple.py," the init parameters "flow_img_norm_std=[255.0, 255.0, 255.0]" and "flow_img_norm_mean=[0.411, 0.432, 0.450]" . What's the meaning of these parameters? I'm using a type of data with 10 channels, how should I set these parameters?

Also, in "prepare_imgs" method, "img_metas[0]['img_norm_cfg']['mean']" and "img_metas[0]['img_norm_cfg']['std']" are both initialized with 0.Is it necessary to reassign the value while training or testing? If necessary, how and what value should I assign to these variables?

hello everyone. thank you for your answers in advance. i am new here, forgive me if i can't explain myself. I am trying to train the mot dataset. but i have a problem with pytorch. torch.distributed.launch is giving me error. i need to change to torhcrun (Transitioning from torch.distributed.launch to torchrun)but i couldn't modify the train.py script. can you please help me with that? thanks again.

我的日志如下：

2021-12-26 16:04:19,060 - mmtrack - INFO - Environment info:

sys.platform: linux Python: 3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0] CUDA available: True GPU 0,1,2: GeForce RTX 2080 Ti CUDA_HOME: /usr/local/cuda NVCC: Cuda compilation tools, release 10.0, V10.0.130 GCC: gcc (Ubuntu 5.4.0-6ubuntu1~16.04.12) 5.4.0 20160609 PyTorch: 1.5.0 PyTorch compiling details: PyTorch built with:

• GCC 7.3
• C++ Version: 201402
• Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications
• Intel(R) MKL-DNN v0.21.1 (Git Hash 7d2fd500bc78936d1d648ca713b901012f470dbc)
• OpenMP 201511 (a.k.a. OpenMP 4.5)
• NNPACK is enabled
• CPU capability usage: AVX2
• CUDA Runtime 10.1
• 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_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_37,code=compute_37
• CuDNN 7.6.3
• Magma 2.5.2
• Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_INTERNAL_THREADPOOL_IMPL -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-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -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, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=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, USE_STATIC_DISPATCH=OFF,

TorchVision: 0.6.0a0+82fd1c8 OpenCV: 4.5.4 MMCV: 1.4.1 MMCV Compiler: GCC 7.3 MMCV CUDA Compiler: 10.1 MMTracking: 0.8.0+

2021-12-26 16:04:19,061 - mmtrack - INFO - Distributed training: True 2021-12-26 16:04:19,761 - mmtrack - INFO - Config: model = dict( 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'), 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='SelsaRoIHead', bbox_roi_extractor=dict( type='TemporalRoIAlign', roi_layer=dict( type='RoIAlign', output_size=7, sampling_ratio=2), out_channels=512, featmap_strides=[16], num_most_similar_points=2, num_temporal_attention_blocks=4), bbox_head=dict( type='SelsaBBoxHead', 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=3, aggregator=dict( type='SelsaAggregator', 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='SELSA') dataset_type = 'ImagenetVIDDataset' data_root = 'data/FALD_VID/' 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=2, train=dict( type='ImagenetVIDDataset', ann_file= 'data/FALD_VID/COCOVIDannotations/imagenet_vid_train_every10frames.json', img_prefix='data/FALD_VID/Data/VID', ref_img_sampler=dict( num_ref_imgs=2, frame_range=9, 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/FALD_VID/annotations/imagenet_vid_val.json', img_prefix='data/FALD_VID/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/FALD_VID/annotations/imagenet_vid_val.json', img_prefix='data/FALD_VID/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)) optimizer = dict(type='SGD', lr=0.005, 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)] lr_config = dict( policy='step', warmup='linear', warmup_iters=500, warmup_ratio=0.3333333333333333, step=[2, 5]) total_epochs = 4 evaluation = dict(metric=['bbox'], interval=4) work_dir = './work_dirs/20211226_001_try3/' gpu_ids = range(0, 1)

2021-12-26 16:04:24,438 - mmtrack - INFO - Set random seed to 2034425034, deterministic: False 2021-12-26 16:04:25,201 - mmtrack - INFO - initialize ResNet with init_cfg [{'type': 'Kaiming', 'layer': 'Conv2d'}, {'type': 'Constant', 'val': 1, 'layer': ['_BatchNorm', 'GroupNorm']}] 2021-12-26 16:04:25,466 - mmtrack - INFO - initialize Bottleneck with init_cfg {'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}} 2021-12-26 16:04:25,467 - mmtrack - INFO - initialize Bottleneck with init_cfg {'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}} 2021-12-26 16:04:25,468 - mmtrack - INFO - initialize Bottleneck with init_cfg {'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}} 2021-12-26 16:04:25,470 - mmtrack - INFO - initialize Bottleneck with init_cfg {'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}} 2021-12-26 16:04:25,471 - mmtrack - INFO - initialize Bottleneck with init_cfg {'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}} 2021-12-26 16:04:25,472 - mmtrack - INFO - initialize Bottleneck with init_cfg {'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}} 2021-12-26 16:04:25,473 - mmtrack - INFO - initialize Bottleneck with init_cfg {'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}} 2021-12-26 16:04:25,475 - mmtrack - INFO - initialize Bottleneck with init_cfg {'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}} 2021-12-26 16:04:25,477 - mmtrack - INFO - initialize Bottleneck with init_cfg {'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}} 2021-12-26 16:04:25,479 - mmtrack - INFO - initialize Bottleneck with init_cfg {'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}} 2021-12-26 16:04:25,481 - mmtrack - INFO - initialize Bottleneck with init_cfg {'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}} 2021-12-26 16:04:25,482 - mmtrack - INFO - initialize Bottleneck with init_cfg {'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}} 2021-12-26 16:04:25,484 - mmtrack - INFO - initialize Bottleneck with init_cfg {'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}} 2021-12-26 16:04:25,490 - mmtrack - INFO - initialize Bottleneck with init_cfg {'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}} 2021-12-26 16:04:25,496 - mmtrack - INFO - initialize Bottleneck with init_cfg {'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}} 2021-12-26 16:04:25,500 - mmtrack - INFO - initialize Bottleneck with init_cfg {'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}} 2021-12-26 16:04:25,523 - mmtrack - INFO - initialize ChannelMapper with init_cfg {'type': 'Xavier', 'layer': 'Conv2d', 'distribution': 'uniform'} 2021-12-26 16:04:25,583 - mmtrack - INFO - initialize RPNHead with init_cfg {'type': 'Normal', 'layer': 'Conv2d', 'std': 0.01} 2021-12-26 16:04:25,637 - mmtrack - INFO - initialize SelsaBBoxHead 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'}]}] Name of parameter - Initialization information

detector.backbone.conv1.weight - torch.Size([64, 3, 7, 7]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0

detector.backbone.bn1.weight - torch.Size([64]): The value is the same before and after calling init_weights of SELSA

detector.backbone.bn1.bias - torch.Size([64]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer1.0.conv1.weight - torch.Size([64, 64, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0

detector.backbone.layer1.0.bn1.weight - torch.Size([64]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer1.0.bn1.bias - torch.Size([64]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer1.0.conv2.weight - torch.Size([64, 64, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0

detector.backbone.layer1.0.bn2.weight - torch.Size([64]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer1.0.bn2.bias - torch.Size([64]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer1.0.conv3.weight - torch.Size([256, 64, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0

detector.backbone.layer1.0.bn3.weight - torch.Size([256]): ConstantInit: val=0, bias=0

detector.backbone.layer1.0.bn3.bias - torch.Size([256]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer1.0.downsample.0.weight - torch.Size([256, 64, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0

detector.backbone.layer1.0.downsample.1.weight - torch.Size([256]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer1.0.downsample.1.bias - torch.Size([256]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer1.1.conv1.weight - torch.Size([64, 256, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0

detector.backbone.layer1.1.bn1.weight - torch.Size([64]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer1.1.bn1.bias - torch.Size([64]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer1.1.conv2.weight - torch.Size([64, 64, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0

detector.backbone.layer1.1.bn2.weight - torch.Size([64]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer1.1.bn2.bias - torch.Size([64]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer1.1.conv3.weight - torch.Size([256, 64, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0

detector.backbone.layer1.1.bn3.weight - torch.Size([256]): ConstantInit: val=0, bias=0

detector.backbone.layer1.1.bn3.bias - torch.Size([256]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer1.2.conv1.weight - torch.Size([64, 256, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0

detector.backbone.layer1.2.bn1.weight - torch.Size([64]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer1.2.bn1.bias - torch.Size([64]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer1.2.conv2.weight - torch.Size([64, 64, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0

detector.backbone.layer1.2.bn2.weight - torch.Size([64]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer1.2.bn2.bias - torch.Size([64]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer1.2.conv3.weight - torch.Size([256, 64, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0

detector.backbone.layer1.2.bn3.weight - torch.Size([256]): ConstantInit: val=0, bias=0

detector.backbone.layer1.2.bn3.bias - torch.Size([256]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer2.0.conv1.weight - torch.Size([128, 256, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0

detector.backbone.layer2.0.bn1.weight - torch.Size([128]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer2.0.bn1.bias - torch.Size([128]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer2.0.conv2.weight - torch.Size([128, 128, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0

detector.backbone.layer2.0.bn2.weight - torch.Size([128]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer2.0.bn2.bias - torch.Size([128]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer2.0.conv3.weight - torch.Size([512, 128, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0

detector.backbone.layer2.0.bn3.weight - torch.Size([512]): ConstantInit: val=0, bias=0

detector.backbone.layer2.0.bn3.bias - torch.Size([512]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer2.0.downsample.0.weight - torch.Size([512, 256, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0

detector.backbone.layer2.0.downsample.1.weight - torch.Size([512]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer2.0.downsample.1.bias - torch.Size([512]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer2.1.conv1.weight - torch.Size([128, 512, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0

detector.backbone.layer2.1.bn1.weight - torch.Size([128]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer2.1.bn1.bias - torch.Size([128]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer2.1.conv2.weight - torch.Size([128, 128, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0

detector.backbone.layer2.1.bn2.weight - torch.Size([128]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer2.1.bn2.bias - torch.Size([128]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer2.1.conv3.weight - torch.Size([512, 128, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0

detector.backbone.layer2.1.bn3.weight - torch.Size([512]): ConstantInit: val=0, bias=0

detector.backbone.layer2.1.bn3.bias - torch.Size([512]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer2.2.conv1.weight - torch.Size([128, 512, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0

detector.backbone.layer2.2.bn1.weight - torch.Size([128]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer2.2.bn1.bias - torch.Size([128]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer2.2.conv2.weight - torch.Size([128, 128, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0

detector.backbone.layer2.2.bn2.weight - torch.Size([128]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer2.2.bn2.bias - torch.Size([128]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer2.2.conv3.weight - torch.Size([512, 128, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0

detector.backbone.layer2.2.bn3.weight - torch.Size([512]): ConstantInit: val=0, bias=0

detector.backbone.layer2.2.bn3.bias - torch.Size([512]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer2.3.conv1.weight - torch.Size([128, 512, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0

detector.backbone.layer2.3.bn1.weight - torch.Size([128]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer2.3.bn1.bias - torch.Size([128]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer2.3.conv2.weight - torch.Size([128, 128, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0

detector.backbone.layer2.3.bn2.weight - torch.Size([128]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer2.3.bn2.bias - torch.Size([128]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer2.3.conv3.weight - torch.Size([512, 128, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0

detector.backbone.layer2.3.bn3.weight - torch.Size([512]): ConstantInit: val=0, bias=0

detector.backbone.layer2.3.bn3.bias - torch.Size([512]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer3.0.conv1.weight - torch.Size([256, 512, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0

detector.backbone.layer3.0.bn1.weight - torch.Size([256]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer3.0.bn1.bias - torch.Size([256]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer3.0.conv2.weight - torch.Size([256, 256, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0

detector.backbone.layer3.0.bn2.weight - torch.Size([256]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer3.0.bn2.bias - torch.Size([256]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer3.0.conv3.weight - torch.Size([1024, 256, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0

detector.backbone.layer3.0.bn3.weight - torch.Size([1024]): ConstantInit: val=0, bias=0

detector.backbone.layer3.0.bn3.bias - torch.Size([1024]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer3.0.downsample.0.weight - torch.Size([1024, 512, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0

detector.backbone.layer3.0.downsample.1.weight - torch.Size([1024]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer3.0.downsample.1.bias - torch.Size([1024]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer3.1.conv1.weight - torch.Size([256, 1024, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0

detector.backbone.layer3.1.bn1.weight - torch.Size([256]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer3.1.bn1.bias - torch.Size([256]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer3.1.conv2.weight - torch.Size([256, 256, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0

detector.backbone.layer3.1.bn2.weight - torch.Size([256]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer3.1.bn2.bias - torch.Size([256]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer3.1.conv3.weight - torch.Size([1024, 256, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0

detector.backbone.layer3.1.bn3.weight - torch.Size([1024]): ConstantInit: val=0, bias=0

detector.backbone.layer3.1.bn3.bias - torch.Size([1024]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer3.2.conv1.weight - torch.Size([256, 1024, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0

detector.backbone.layer3.2.bn1.weight - torch.Size([256]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer3.2.bn1.bias - torch.Size([256]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer3.2.conv2.weight - torch.Size([256, 256, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0

detector.backbone.layer3.2.bn2.weight - torch.Size([256]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer3.2.bn2.bias - torch.Size([256]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer3.2.conv3.weight - torch.Size([1024, 256, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0

detector.backbone.layer3.2.bn3.weight - torch.Size([1024]): ConstantInit: val=0, bias=0

detector.backbone.layer3.2.bn3.bias - torch.Size([1024]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer3.3.conv1.weight - torch.Size([256, 1024, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0

detector.backbone.layer3.3.bn1.weight - torch.Size([256]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer3.3.bn1.bias - torch.Size([256]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer3.3.conv2.weight - torch.Size([256, 256, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0

detector.backbone.layer3.3.bn2.weight - torch.Size([256]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer3.3.bn2.bias - torch.Size([256]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer3.3.conv3.weight - torch.Size([1024, 256, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0

detector.backbone.layer3.3.bn3.weight - torch.Size([1024]): ConstantInit: val=0, bias=0

detector.backbone.layer3.3.bn3.bias - torch.Size([1024]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer3.4.conv1.weight - torch.Size([256, 1024, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0

detector.backbone.layer3.4.bn1.weight - torch.Size([256]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer3.4.bn1.bias - torch.Size([256]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer3.4.conv2.weight - torch.Size([256, 256, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0

detector.backbone.layer3.4.bn2.weight - torch.Size([256]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer3.4.bn2.bias - torch.Size([256]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer3.4.conv3.weight - torch.Size([1024, 256, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0

detector.backbone.layer3.4.bn3.weight - torch.Size([1024]): ConstantInit: val=0, bias=0

detector.backbone.layer3.4.bn3.bias - torch.Size([1024]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer3.5.conv1.weight - torch.Size([256, 1024, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0

detector.backbone.layer3.5.bn1.weight - torch.Size([256]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer3.5.bn1.bias - torch.Size([256]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer3.5.conv2.weight - torch.Size([256, 256, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0

detector.backbone.layer3.5.bn2.weight - torch.Size([256]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer3.5.bn2.bias - torch.Size([256]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer3.5.conv3.weight - torch.Size([1024, 256, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0

detector.backbone.layer3.5.bn3.weight - torch.Size([1024]): ConstantInit: val=0, bias=0

detector.backbone.layer3.5.bn3.bias - torch.Size([1024]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer4.0.conv1.weight - torch.Size([512, 1024, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0

detector.backbone.layer4.0.bn1.weight - torch.Size([512]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer4.0.bn1.bias - torch.Size([512]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer4.0.conv2.weight - torch.Size([512, 512, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0

detector.backbone.layer4.0.bn2.weight - torch.Size([512]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer4.0.bn2.bias - torch.Size([512]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer4.0.conv3.weight - torch.Size([2048, 512, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0

detector.backbone.layer4.0.bn3.weight - torch.Size([2048]): ConstantInit: val=0, bias=0

detector.backbone.layer4.0.bn3.bias - torch.Size([2048]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer4.0.downsample.0.weight - torch.Size([2048, 1024, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0

detector.backbone.layer4.0.downsample.1.weight - torch.Size([2048]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer4.0.downsample.1.bias - torch.Size([2048]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer4.1.conv1.weight - torch.Size([512, 2048, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0

detector.backbone.layer4.1.bn1.weight - torch.Size([512]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer4.1.bn1.bias - torch.Size([512]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer4.1.conv2.weight - torch.Size([512, 512, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0

detector.backbone.layer4.1.bn2.weight - torch.Size([512]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer4.1.bn2.bias - torch.Size([512]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer4.1.conv3.weight - torch.Size([2048, 512, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0

detector.backbone.layer4.1.bn3.weight - torch.Size([2048]): ConstantInit: val=0, bias=0

detector.backbone.layer4.1.bn3.bias - torch.Size([2048]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer4.2.conv1.weight - torch.Size([512, 2048, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0

detector.backbone.layer4.2.bn1.weight - torch.Size([512]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer4.2.bn1.bias - torch.Size([512]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer4.2.conv2.weight - torch.Size([512, 512, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0

detector.backbone.layer4.2.bn2.weight - torch.Size([512]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer4.2.bn2.bias - torch.Size([512]): The value is the same before and after calling init_weights of SELSA

detector.backbone.layer4.2.conv3.weight - torch.Size([2048, 512, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0

detector.backbone.layer4.2.bn3.weight - torch.Size([2048]): ConstantInit: val=0, bias=0

detector.backbone.layer4.2.bn3.bias - torch.Size([2048]): The value is the same before and after calling init_weights of SELSA

detector.neck.convs.0.conv.weight - torch.Size([512, 2048, 3, 3]): XavierInit: gain=1, distribution=uniform, bias=0

detector.neck.convs.0.conv.bias - torch.Size([512]): The value is the same before and after calling init_weights of SELSA

detector.rpn_head.rpn_conv.weight - torch.Size([512, 512, 3, 3]): NormalInit: mean=0, std=0.01, bias=0

detector.rpn_head.rpn_conv.bias - torch.Size([512]): NormalInit: mean=0, std=0.01, bias=0

detector.rpn_head.rpn_cls.weight - torch.Size([12, 512, 1, 1]): NormalInit: mean=0, std=0.01, bias=0

detector.rpn_head.rpn_cls.bias - torch.Size([12]): NormalInit: mean=0, std=0.01, bias=0

detector.rpn_head.rpn_reg.weight - torch.Size([48, 512, 1, 1]): NormalInit: mean=0, std=0.01, bias=0

detector.rpn_head.rpn_reg.bias - torch.Size([48]): NormalInit: mean=0, std=0.01, bias=0

detector.roi_head.bbox_roi_extractor.embed_network.conv.weight - torch.Size([512, 512, 3, 3]): Initialized by user-defined init_weights in ConvModule

detector.roi_head.bbox_roi_extractor.embed_network.conv.bias - torch.Size([512]): The value is the same before and after calling init_weights of SELSA

detector.roi_head.bbox_head.fc_cls.weight - torch.Size([31, 1024]): NormalInit: mean=0, std=0.01, bias=0

detector.roi_head.bbox_head.fc_cls.bias - torch.Size([31]): NormalInit: mean=0, std=0.01, bias=0

detector.roi_head.bbox_head.fc_reg.weight - torch.Size([120, 1024]): NormalInit: mean=0, std=0.001, bias=0

detector.roi_head.bbox_head.fc_reg.bias - torch.Size([120]): NormalInit: mean=0, std=0.001, bias=0

detector.roi_head.bbox_head.shared_fcs.0.weight - torch.Size([1024, 25088]): XavierInit: gain=1, distribution=uniform, bias=0

detector.roi_head.bbox_head.shared_fcs.0.bias - torch.Size([1024]): XavierInit: gain=1, distribution=uniform, bias=0

detector.roi_head.bbox_head.shared_fcs.1.weight - torch.Size([1024, 1024]): XavierInit: gain=1, distribution=uniform, bias=0

detector.roi_head.bbox_head.shared_fcs.1.bias - torch.Size([1024]): XavierInit: gain=1, distribution=uniform, bias=0

detector.roi_head.bbox_head.shared_fcs.2.weight - torch.Size([1024, 1024]): XavierInit: gain=1, distribution=uniform, bias=0

detector.roi_head.bbox_head.shared_fcs.2.bias - torch.Size([1024]): XavierInit: gain=1, distribution=uniform, bias=0

detector.roi_head.bbox_head.aggregator.0.fc_embed.weight - torch.Size([1024, 1024]): The value is the same before and after calling init_weights of SELSA

detector.roi_head.bbox_head.aggregator.0.fc_embed.bias - torch.Size([1024]): The value is the same before and after calling init_weights of SELSA

detector.roi_head.bbox_head.aggregator.0.ref_fc_embed.weight - torch.Size([1024, 1024]): The value is the same before and after calling init_weights of SELSA

detector.roi_head.bbox_head.aggregator.0.ref_fc_embed.bias - torch.Size([1024]): The value is the same before and after calling init_weights of SELSA

detector.roi_head.bbox_head.aggregator.0.fc.weight - torch.Size([1024, 1024]): The value is the same before and after calling init_weights of SELSA

detector.roi_head.bbox_head.aggregator.0.fc.bias - torch.Size([1024]): The value is the same before and after calling init_weights of SELSA

detector.roi_head.bbox_head.aggregator.0.ref_fc.weight - torch.Size([1024, 1024]): The value is the same before and after calling init_weights of SELSA

detector.roi_head.bbox_head.aggregator.0.ref_fc.bias - torch.Size([1024]): The value is the same before and after calling init_weights of SELSA

detector.roi_head.bbox_head.aggregator.1.fc_embed.weight - torch.Size([1024, 1024]): The value is the same before and after calling init_weights of SELSA

detector.roi_head.bbox_head.aggregator.1.fc_embed.bias - torch.Size([1024]): The value is the same before and after calling init_weights of SELSA

detector.roi_head.bbox_head.aggregator.1.ref_fc_embed.weight - torch.Size([1024, 1024]): The value is the same before and after calling init_weights of SELSA

detector.roi_head.bbox_head.aggregator.1.ref_fc_embed.bias - torch.Size([1024]): The value is the same before and after calling init_weights of SELSA

detector.roi_head.bbox_head.aggregator.1.fc.weight - torch.Size([1024, 1024]): The value is the same before and after calling init_weights of SELSA

detector.roi_head.bbox_head.aggregator.1.fc.bias - torch.Size([1024]): The value is the same before and after calling init_weights of SELSA

detector.roi_head.bbox_head.aggregator.1.ref_fc.weight - torch.Size([1024, 1024]): The value is the same before and after calling init_weights of SELSA

detector.roi_head.bbox_head.aggregator.1.ref_fc.bias - torch.Size([1024]): The value is the same before and after calling init_weights of SELSA

detector.roi_head.bbox_head.aggregator.2.fc_embed.weight - torch.Size([1024, 1024]): The value is the same before and after calling init_weights of SELSA

detector.roi_head.bbox_head.aggregator.2.fc_embed.bias - torch.Size([1024]): The value is the same before and after calling init_weights of SELSA

detector.roi_head.bbox_head.aggregator.2.ref_fc_embed.weight - torch.Size([1024, 1024]): The value is the same before and after calling init_weights of SELSA

detector.roi_head.bbox_head.aggregator.2.ref_fc_embed.bias - torch.Size([1024]): The value is the same before and after calling init_weights of SELSA

detector.roi_head.bbox_head.aggregator.2.fc.weight - torch.Size([1024, 1024]): The value is the same before and after calling init_weights of SELSA

detector.roi_head.bbox_head.aggregator.2.fc.bias - torch.Size([1024]): The value is the same before and after calling init_weights of SELSA

detector.roi_head.bbox_head.aggregator.2.ref_fc.weight - torch.Size([1024, 1024]): The value is the same before and after calling init_weights of SELSA

detector.roi_head.bbox_head.aggregator.2.ref_fc.bias - torch.Size([1024]): The value is the same before and after calling init_weights of SELSA
2021-12-26 16:04:28,460 - mmtrack - INFO - Start running, host: [email protected], work_dir: /data/yangjiahui/VIDProject/mmtracking/work_dirs/20211226_001_try3 2021-12-26 16:04:28,461 - mmtrack - INFO - Hooks will be executed in the following order: before_run: (VERY_HIGH ) StepLrUpdaterHook
(NORMAL ) CheckpointHook
(NORMAL ) DistEvalHook
(VERY_LOW ) TextLoggerHook

before_train_epoch: (VERY_HIGH ) StepLrUpdaterHook
(NORMAL ) DistSamplerSeedHook
(NORMAL ) DistEvalHook
(LOW ) IterTimerHook
(VERY_LOW ) TextLoggerHook

before_train_iter: (VERY_HIGH ) StepLrUpdaterHook
(NORMAL ) DistEvalHook
(LOW ) IterTimerHook

after_train_iter: (ABOVE_NORMAL) OptimizerHook
(NORMAL ) CheckpointHook
(NORMAL ) DistEvalHook
(LOW ) IterTimerHook
(VERY_LOW ) TextLoggerHook

after_train_epoch: (NORMAL ) CheckpointHook
(NORMAL ) DistEvalHook
(VERY_LOW ) TextLoggerHook

before_val_epoch: (NORMAL ) DistSamplerSeedHook
(LOW ) IterTimerHook
(VERY_LOW ) TextLoggerHook

before_val_iter: (LOW ) IterTimerHook

after_val_iter: (LOW ) IterTimerHook

after_val_epoch: (VERY_LOW ) TextLoggerHook

after_run: (VERY_LOW ) TextLoggerHook

2021-12-26 16:04:28,461 - mmtrack - INFO - workflow: [('train', 1)], max: 4 epochs 2021-12-26 16:04:28,461 - mmtrack - INFO - Checkpoints will be saved to /data/yangjiahui/VIDProject/mmtracking/work_dirs/20211226_001_try3 by HardDiskBackend. 2021-12-26 16:05:00,501 - mmtrack - INFO - Saving checkpoint at 1 epochs 2021-12-26 16:05:32,658 - mmtrack - INFO - Saving checkpoint at 2 epochs 2021-12-26 16:06:04,769 - mmtrack - INFO - Saving checkpoint at 3 epochs 2021-12-26 16:06:37,068 - mmtrack - INFO - Saving checkpoint at 4 epochs

I ran successfully the official tractor repo, but I cannot run this repo. Same command (default training command of Reid model), but different errors on different days.

Checklist

1. I have searched related issues but cannot get the expected help.
2. The bug has not been fixed in the latest version.

Describe the bug A clear and concise description of what the bug is.

Reproduction

1. What command or script did you run?

A placeholder for the command.

2. Did you make any modifications on the code or config? Did you understand what you have modified?
3. What dataset did you use and what task did you run?

Environment

1. Please run python mmtrack/utils/collect_env.py to collect necessary environment information and paste it here.
2. You may add addition that may be helpful for locating the problem, such as
   • How you installed PyTorch [e.g., pip, conda, source]
   • Other environment variables that may be related (such as $PATH, $LD_LIBRARY_PATH, $PYTHONPATH, etc.)

Error traceback If applicable, paste the error trackback here.

A placeholder for trackback.

Bug fix If you have already identified the reason, you can provide the information here. If you are willing to create a PR to fix it, please also leave a comment here and that would be much appreciated!

I was testing the traktor model on some videos and I found that when the camera changes,the id assigned to the object being tracked also changes. For example if we look at the last few seconds of the below inference video(obtained by running traktor code),it seems that the runners are now assigned a new ID. What could be the possible reason,is it because the same person is being viewed from a different camera angle.Or could it be that I need to retrain the re-id model dataset used was MOT 20,configuration was tracktor_faster-rcnn_r50_fpn_8e_mot20-public-half.

Input video https://drive.google.com/file/d/1IVxcL3a5jUH3huJuyVzgDepIpBE62H3F/view?usp=sharing inference video https://drive.google.com/file/d/1Rcl3nrdTQznyPO4GQLm7_juYzsSZtLK4/view?usp=sharing

Thanks for your error report and we appreciate it a lot.

Checklist

1. I have searched related issues but cannot get the expected help.
2. The bug has not been fixed in the latest version.

Describe the bug A clear and concise description of what the bug is.

Reproduction

1. What command or script did you run?

python3 ./tools/train.py configs/reid/resnet50_b32x8_MOT17.py --work-dir work_dirs/resnet50_b32x8_MOT17

2. I did not make any modification on the code except dataset path
3. Im running ReID training on MOT dataset

Environment

1. Please run python mmtrack/utils/collect_env.py to collect necessary environment information and paste it here. sys.platform: linux Python: 3.8.11 (default, Jul 3 2021, 17:53:42) [GCC 7.5.0] CUDA available: True GPU 0: TITAN Xp CUDA_HOME: None GCC: gcc (Ubuntu 9.3.0-17ubuntu1~20.04) 9.3.0 PyTorch: 1.7.1+cu101 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 v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)
• OpenMP 201511 (a.k.a. OpenMP 4.5)
• NNPACK is enabled
• CPU capability usage: AVX2
• CUDA Runtime 10.1
• 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
• CuDNN 7.6.3
• Magma 2.5.2
• Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -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, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=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: 0.8.2+cu101 OpenCV: 4.5.3 MMCV: 1.3.11 MMCV Compiler: GCC 7.3 MMCV CUDA Compiler: 10.1 MMTracking: 0.6.0+4d78b77

2. You may add addition that may be helpful for locating the problem, such as
   • How you installed PyTorch [e.g., pip, conda, source]
   • Other environment variables that may be related (such as $PATH, $LD_LIBRARY_PATH, $PYTHONPATH, etc.)

Error traceback If applicable, paste the error trackback here.

sys.platform: linux
Python: 3.8.11 (default, Jul  3 2021, 17:53:42) [GCC 7.5.0]
CUDA available: True
GPU 0: TITAN Xp
CUDA_HOME: None
GCC: gcc (Ubuntu 9.3.0-17ubuntu1~20.04) 9.3.0
PyTorch: 1.7.1+cu101
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 v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - NNPACK is enabled
  - CPU capability usage: AVX2
  - CUDA Runtime 10.1
  - 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
  - CuDNN 7.6.3
  - Magma 2.5.2
  - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -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, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=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: 0.8.2+cu101
OpenCV: 4.5.3
MMCV: 1.3.11
MMCV Compiler: GCC 7.3
MMCV CUDA Compiler: 10.1
MMTracking: 0.6.0+4d78b77
------------------------------------------------------------

2021-08-17 11:24:25,348 - mmtrack - INFO - Distributed training: False
2021-08-17 11:24:26,303 - mmtrack - INFO - Config:
dataset_type = 'ReIDDataset'
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', to_float32=True),
    dict(
        type='SeqResize',
        img_scale=(128, 256),
        share_params=False,
        keep_ratio=False,
        bbox_clip_border=False,
        override=False),
    dict(
        type='SeqRandomFlip',
        share_params=False,
        flip_ratio=0.5,
        direction='horizontal'),
    dict(
        type='SeqNormalize',
        mean=[123.675, 116.28, 103.53],
        std=[58.395, 57.12, 57.375],
        to_rgb=True),
    dict(type='VideoCollect', keys=['img', 'gt_label']),
    dict(type='ReIDFormatBundle')
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='Resize', img_scale=(128, 256), keep_ratio=False),
    dict(
        type='Normalize',
        mean=[123.675, 116.28, 103.53],
        std=[58.395, 57.12, 57.375],
        to_rgb=True),
    dict(type='ImageToTensor', keys=['img']),
    dict(type='Collect', keys=['img'], meta_keys=[])
]
data_root = '/projects/datasets/MOT/MOT17/'
data = dict(
    samples_per_gpu=2,
    workers_per_gpu=2,
    train=dict(
        type='ReIDDataset',
        triplet_sampler=dict(num_ids=8, ins_per_id=4),
        data_prefix='/projects/datasets/MOT/MOT17/reid/imgs',
        ann_file='/projects/datasets/MOT/MOT17/reid/meta/train_80.txt',
        pipeline=[
            dict(type='LoadMultiImagesFromFile', to_float32=True),
            dict(
                type='SeqResize',
                img_scale=(128, 256),
                share_params=False,
                keep_ratio=False,
                bbox_clip_border=False,
                override=False),
            dict(
                type='SeqRandomFlip',
                share_params=False,
                flip_ratio=0.5,
                direction='horizontal'),
            dict(
                type='SeqNormalize',
                mean=[123.675, 116.28, 103.53],
                std=[58.395, 57.12, 57.375],
                to_rgb=True),
            dict(type='VideoCollect', keys=['img', 'gt_label']),
            dict(type='ReIDFormatBundle')
        ]),
    val=dict(
        type='ReIDDataset',
        triplet_sampler=None,
        data_prefix='/projects/datasets/MOT/MOT17/reid/imgs',
        ann_file='/projects/datasets/MOT/MOT17/reid/meta/val_20.txt',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(type='Resize', img_scale=(128, 256), keep_ratio=False),
            dict(
                type='Normalize',
                mean=[123.675, 116.28, 103.53],
                std=[58.395, 57.12, 57.375],
                to_rgb=True),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='Collect', keys=['img'], meta_keys=[])
        ]),
    test=dict(
        type='ReIDDataset',
        triplet_sampler=None,
        data_prefix='/projects/datasets/MOT/MOT17/reid/imgs',
        ann_file='/projects/datasets/MOT/MOT17/reid/meta/val_20.txt',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(type='Resize', img_scale=(128, 256), keep_ratio=False),
            dict(
                type='Normalize',
                mean=[123.675, 116.28, 103.53],
                std=[58.395, 57.12, 57.375],
                to_rgb=True),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='Collect', keys=['img'], meta_keys=[])
        ]))
evaluation = dict(interval=1, metric='mAP')
optimizer = dict(type='SGD', lr=0.0025, 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)]
USE_MMCLS = True
model = dict(
    type='BaseReID',
    backbone=dict(
        type='ResNet',
        depth=50,
        num_stages=4,
        out_indices=(3, ),
        style='pytorch'),
    neck=dict(type='GlobalAveragePooling', kernel_size=(8, 4), stride=1),
    head=dict(
        type='LinearReIDHead',
        num_fcs=1,
        in_channels=2048,
        fc_channels=1024,
        out_channels=128,
        num_classes=378,
        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
        loss_pairwise=dict(type='TripletLoss', margin=0.3, loss_weight=1.0),
        norm_cfg=dict(type='BN1d'),
        act_cfg=dict(type='ReLU')),
    init_cfg=dict(
        type='Pretrained',
        checkpoint=
        'https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_batch256_imagenet_20200708-cfb998bf.pth'
    ))
lr_config = dict(
    policy='step',
    warmup='linear',
    warmup_iters=1000,
    warmup_ratio=0.001,
    step=[5])
total_epochs = 6
work_dir = 'work_dirs/resnet50_b32x8_MOT17'
gpu_ids = range(0, 1)

2021-08-17 11:24:26,638 - mmtrack - INFO - initialize BaseReID with init_cfg {'type': 'Pretrained', 'checkpoint': 'https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_batch256_imagenet_20200708-cfb998bf.pth'}
2021-08-17 11:24:26,638 - mmcv - INFO - load model from: https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_batch256_imagenet_20200708-cfb998bf.pth
2021-08-17 11:24:26,638 - mmcv - INFO - Use load_from_http loader
2021-08-17 11:24:26,844 - mmcv - WARNING - The model and loaded state dict do not match exactly

unexpected key in source state_dict: head.fc.weight, head.fc.bias

missing keys in source state_dict: head.fcs.0.fc.weight, head.fcs.0.fc.bias, head.fcs.0.bn.weight, head.fcs.0.bn.bias, head.fcs.0.bn.running_mean, head.fcs.0.bn.running_var, head.fc_out.weight, head.fc_out.bias, head.bn.weight, head.bn.bias, head.bn.running_mean, head.bn.running_var, head.classifier.weight, head.classifier.bias

2021-08-17 11:24:33,803 - mmtrack - INFO - Start running, host: qljx17@gpu3, work_dir: /home2/qljx17/Open-MMLab/mmtracking/work_dirs/resnet50_b32x8_MOT17
2021-08-17 11:24:33,803 - mmtrack - INFO - Hooks will be executed in the following order:
before_run:
(VERY_HIGH   ) StepLrUpdaterHook                  
(NORMAL      ) CheckpointHook                     
(NORMAL      ) EvalHook                           
(VERY_LOW    ) TextLoggerHook                     
 -------------------- 
before_train_epoch:
(VERY_HIGH   ) StepLrUpdaterHook                  
(NORMAL      ) EvalHook                           
(LOW         ) IterTimerHook                      
(VERY_LOW    ) TextLoggerHook                     
 -------------------- 
before_train_iter:
(VERY_HIGH   ) StepLrUpdaterHook                  
(NORMAL      ) EvalHook                           
(LOW         ) IterTimerHook                      
 -------------------- 
after_train_iter:
(ABOVE_NORMAL) OptimizerHook                      
(NORMAL      ) CheckpointHook                     
(NORMAL      ) EvalHook                           
(LOW         ) IterTimerHook                      
(VERY_LOW    ) TextLoggerHook                     
 -------------------- 
after_train_epoch:
(NORMAL      ) CheckpointHook                     
(NORMAL      ) EvalHook                           
(VERY_LOW    ) TextLoggerHook                     
 -------------------- 
before_val_epoch:
(LOW         ) IterTimerHook                      
(VERY_LOW    ) TextLoggerHook                     
 -------------------- 
before_val_iter:
(LOW         ) IterTimerHook                      
 -------------------- 
after_val_iter:
(LOW         ) IterTimerHook                      
 -------------------- 
after_val_epoch:
(VERY_LOW    ) TextLoggerHook                     
 -------------------- 
2021-08-17 11:24:33,803 - mmtrack - INFO - workflow: [('train', 1)], max: 6 epochs
/pytorch/aten/src/THCUNN/ClassNLLCriterion.cu:59: ClassNLLCriterion_updateOutput_no_reduce_kernel: block: [0,0,0], thread: [44,0,0] Assertion `cur_target >= 0 && cur_target < n_classes` failed.
/pytorch/aten/src/THCUNN/ClassNLLCriterion.cu:59: ClassNLLCriterion_updateOutput_no_reduce_kernel: block: [0,0,0], thread: [45,0,0] Assertion `cur_target >= 0 && cur_target < n_classes` failed.
/pytorch/aten/src/THCUNN/ClassNLLCriterion.cu:59: ClassNLLCriterion_updateOutput_no_reduce_kernel: block: [0,0,0], thread: [46,0,0] Assertion `cur_target >= 0 && cur_target < n_classes` failed.
/pytorch/aten/src/THCUNN/ClassNLLCriterion.cu:59: ClassNLLCriterion_updateOutput_no_reduce_kernel: block: [0,0,0], thread: [47,0,0] Assertion `cur_target >= 0 && cur_target < n_classes` failed.
Traceback (most recent call last):
  File "./tools/train.py", line 174, in <module>
    main()
  File "./tools/train.py", line 163, in main
    train_model(
  File "/home2/qljx17/Open-MMLab/mmtracking/mmtrack/apis/train.py", line 136, in train_model
    runner.run(data_loaders, cfg.workflow, cfg.total_epochs)
  File "/home2/qljx17/Open-MMLab/evenv/lib/python3.8/site-packages/mmcv/runner/epoch_based_runner.py", line 127, in run
    epoch_runner(data_loaders[i], **kwargs)
  File "/home2/qljx17/Open-MMLab/evenv/lib/python3.8/site-packages/mmcv/runner/epoch_based_runner.py", line 50, in train
    self.run_iter(data_batch, train_mode=True, **kwargs)
  File "/home2/qljx17/Open-MMLab/evenv/lib/python3.8/site-packages/mmcv/runner/epoch_based_runner.py", line 29, in run_iter
    outputs = self.model.train_step(data_batch, self.optimizer,
  File "/home2/qljx17/Open-MMLab/evenv/lib/python3.8/site-packages/mmcv/parallel/data_parallel.py", line 67, in train_step
    return self.module.train_step(*inputs[0], **kwargs[0])
  File "/home2/qljx17/Open-MMLab/mmclassification/mmcls/models/classifiers/base.py", line 146, in train_step
    loss, log_vars = self._parse_losses(losses)
  File "/home2/qljx17/Open-MMLab/mmclassification/mmcls/models/classifiers/base.py", line 97, in _parse_losses
    log_vars[loss_name] = loss_value.mean()
RuntimeError: CUDA error: device-side assert triggered
terminate called after throwing an instance of 'c10::Error'
  what():  CUDA error: device-side assert triggered
Exception raised from create_event_internal at /pytorch/c10/cuda/CUDACachingAllocator.cpp:687 (most recent call first):
frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x42 (0x7fc1479138b2 in /home2/qljx17/Open-MMLab/evenv/lib/python3.8/site-packages/torch/lib/libc10.so)
frame #1: c10::cuda::CUDACachingAllocator::raw_delete(void*) + 0xad2 (0x7fc147b65952 in /home2/qljx17/Open-MMLab/evenv/lib/python3.8/site-packages/torch/lib/libc10_cuda.so)
frame #2: c10::TensorImpl::release_resources() + 0x4d (0x7fc1478feb7d in /home2/qljx17/Open-MMLab/evenv/lib/python3.8/site-packages/torch/lib/libc10.so)
frame #3: <unknown function> + 0x5fd7a2 (0x7fc1920fb7a2 in /home2/qljx17/Open-MMLab/evenv/lib/python3.8/site-packages/torch/lib/libtorch_python.so)
frame #4: <unknown function> + 0x5fd856 (0x7fc1920fb856 in /home2/qljx17/Open-MMLab/evenv/lib/python3.8/site-packages/torch/lib/libtorch_python.so)
frame #5: python3() [0x534ce6]
frame #6: python3() [0x51c5d9]
frame #7: python3() [0x52cb15]
frame #8: python3() [0x52cb15]
frame #9: python3() [0x500a2e]
frame #10: python3() [0x57d905]
frame #11: python3() [0x57d8bb]
frame #12: python3() [0x57d8bb]
frame #13: python3() [0x57d8bb]
frame #14: python3() [0x57d8bb]
frame #15: python3() [0x57d8bb]
frame #16: python3() [0x57d8bb]
frame #17: python3() [0x5f25e6]
<omitting python frames>
frame #23: __libc_start_main + 0xf3 (0x7fc1a2ef10b3 in /lib/x86_64-linux-gnu/libc.so.6)

/var/spool/slurmd/job128755/slurm_script: line 21: 3941330 Aborted                 (core dumped) python3 ./tools/train.py configs/reid/resnet50_b32x8_MOT17.py --work-dir work_dirs/resnet50_b32x8_MOT17
^Z

Bug fix From the error above, I can assume that its because of the number of classes. From the default config, num of class is being set as 378, which is taken from train_80.txt, hence the error appear. However, when I set the num of class as 512, which is the number of samples in imgs folder, Im able to run the training without any error. Is there something that I missed, or the number of classes could be the main problem here?

Hello~ Thanks a lot for your awesome job and I appreciate for your effort! However, I have some problems hoping you to help me solve it. When I use the default configure at configs/det/faster-rcnn_r50_fpn_4e_mot17-half.py to train faster-rcnn detector by MMtracking, I got Nan losses. But when I change the downloaded state dict, which is pretrained faster-rcnn on COCO dataset, from 'load_from' entry to 'pretrain' entry of detector, the Nan losses disappears. I wonder how this happen? What's the difference between 'load_from' and 'pretrain', since both of them seem not to strictly load parameters? Thanks a lot again!

I check again, finding that the 'pretrain' entry for detection seems NOT load pretrain dicts as I expected, and directly train from randomly initiated parameters. So how to use the pretrained faster rcnn model dicts anyway?

Hi, I met a problem when I run MOT demo. It said that "IndexError: tensors used as indices must be long, byte or bool tensors /cluster/home/it_stu12/.conda/envs/gjj/lib/python3.7/tempfile.py:798: ResourceWarning: Implicitly cleaning up <TemporaryDirectory '/tmp/tmpd1jtqm1n'> _warnings.warn(warn_message, ResourceWarning)". Here's my error log.

Error Log

Traceback (most recent call last): File "demo/demo_mot.py", line 94, in main() File "demo/demo_mot.py", line 70, in main result = inference_mot(model, img, frame_id=i) File "/cluster/home/it_stu12/main/gjj/mmtracking/mmtrack/apis/inference.py", line 81, in inference_mot data = collate([data], samples_per_gpu=1) File "/cluster/home/it_stu12/main/gjj/mmcv/mmcv/parallel/collate.py", line 81, in collate for key in batch[0] File "/cluster/home/it_stu12/main/gjj/mmcv/mmcv/parallel/collate.py", line 81, in for key in batch[0] File "/cluster/home/it_stu12/main/gjj/mmcv/mmcv/parallel/collate.py", line 77, in collate return [collate(samples, samples_per_gpu) for samples in transposed] File "/cluster/home/it_stu12/main/gjj/mmcv/mmcv/parallel/collate.py", line 77, in return [collate(samples, samples_per_gpu) for samples in transposed] File "/cluster/home/it_stu12/main/gjj/mmcv/mmcv/parallel/collate.py", line 81, in collate for key in batch[0] File "/cluster/home/it_stu12/main/gjj/mmcv/mmcv/parallel/collate.py", line 81, in for key in batch[0] File "/cluster/home/it_stu12/main/gjj/mmcv/mmcv/parallel/collate.py", line 80, in key: collate([d[key] for d in batch], samples_per_gpu) IndexError: tensors used as indices must be long, byte or bool tensors /cluster/home/it_stu12/.conda/envs/gjj/lib/python3.7/tempfile.py:798: ResourceWarning: Implicitly cleaning up <TemporaryDirectory '/tmp/tmpd1jtqm1n'> _warnings.warn(warn_message, ResourceWarning)

Environment

No CUDA runtime is found, using CUDA_HOME='/cluster/apps/cuda/10.1' sys.platform: linux Python: 3.7.10 (default, Jun 4 2021, 14:48:32) [GCC 7.5.0] CUDA available: False GCC: gcc (GCC) 5.4.0 PyTorch: 1.6.0 PyTorch compiling details: PyTorch built with:

• GCC 7.3
• C++ Version: 201402
• Intel(R) Math Kernel Library Version 2019.0.5 Product Build 20190808 for Intel(R) 64 architecture applications
• Intel(R) MKL-DNN v1.5.0 (Git Hash e2ac1fac44c5078ca927cb9b90e1b3066a0b2ed0)
• OpenMP 201511 (a.k.a. OpenMP 4.5)
• NNPACK is enabled
• CPU capability usage: AVX2
• Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -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-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, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=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, USE_STATIC_DISPATCH=OFF,

TorchVision: 0.7.0 OpenCV: 4.1.0 MMCV: 1.4.2 MMCV Compiler: GCC 5.4 MMCV CUDA Compiler: not available MMTracking: 0.8.0+603d6fe

Some Other Problems

• The doc of MMTracking 0.8 says that MMCV version should be mmcv-full>=1.3.8, <1.4.0. But when I install mmcv-full 1.3.9, it told me that my "mmcv-full is too old, please install mmcv-fulll >=1.3.16, <=1.5.0". Which one should I believe?
• The Chinese version doc of MMTracking 0.8 gives a demo script python demo/demo_mot.py configs/mot/deepsort/sort_faster-rcnn_fpn_4e_mot17-private.py --input demo/demo.mp4 --output mot.mp4. But I didn't find demo_mot.py in folder demo but found demo_mot_vis.py. Maybe the Chinese doc should be updated?

Thank you so much!

I found this library very helpful. Great work. I have to ask, Is it possible to keep export of the log while the video (time + weight of the tracking object) is running either live camera or playing recorded video? Please guide me if how to export that log using this library.

Hello, I have tested on my custom dataset for VID and saved the results to a .pkl file. However, the pickle file seems to have only the det_bboxes and not the det_labels . Is there any way to add det_labels too? Any tips would be helpful!

I have trained the detector model in mmdetection with multiple classes , if i want to fed the "person" class outputs alone from the detector model to the reid model during inference , can i do that using config or any other method ?

And also if i have to fed the mmdetection pretrained model into tracker , what are the config changes have to be done ?

Thank you in advance

Hello! I want to train the masktrackrcnn by the official youtube_vis_dataset but it occur :KeyError: "YouTubeVISDataset: 'image_id'". Here is my datatree

Traceback (most recent call last):
  File "/home/music/miniconda3/envs/mmtrack/lib/python3.8/site-packages/mmcv/utils/registry.py", line 69, in build_from_cfg
    return obj_cls(**args)
  File "/home/music/Downloads/mmtracking-master/mmtrack/datasets/youtube_vis_dataset.py", line 44, in __init__
    super().__init__(*args, **kwargs)
  File "/home/music/Downloads/mmtracking-master/mmtrack/datasets/coco_video_dataset.py", line 46, in __init__
    super().__init__(*args, **kwargs)
  File "/home/music/miniconda3/envs/mmtrack/lib/python3.8/site-packages/mmdet/datasets/custom.py", line 97, in __init__
    self.data_infos = self.load_annotations(local_path)
  File "/home/music/Downloads/mmtracking-master/mmtrack/datasets/coco_video_dataset.py", line 61, in load_annotations
    data_infos = self.load_video_anns(ann_file)
  File "/home/music/Downloads/mmtracking-master/mmtrack/datasets/coco_video_dataset.py", line 73, in load_video_anns
    self.coco = CocoVID(ann_file)
  File "/home/music/Downloads/mmtracking-master/mmtrack/datasets/parsers/coco_video_parser.py", line 22, in __init__
    super(CocoVID, self).__init__(annotation_file=annotation_file)
  File "/home/music/miniconda3/envs/mmtrack/lib/python3.8/site-packages/mmdet/datasets/api_wrappers/coco_api.py", line 23, in __init__
    super().__init__(annotation_file=annotation_file)
  File "/home/music/miniconda3/envs/mmtrack/lib/python3.8/site-packages/pycocotools/coco.py", line 86, in __init__
    self.createIndex()
  File "/home/music/Downloads/mmtracking-master/mmtrack/datasets/parsers/coco_video_parser.py", line 57, in createIndex
    imgToAnns[ann['image_id']].append(ann)
KeyError: 'image_id'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "tools/train.py", line 213, in <module>
    main()
  File "tools/train.py", line 188, in main
    datasets = [build_dataset(cfg.data.train)]
  File "/home/music/miniconda3/envs/mmtrack/lib/python3.8/site-packages/mmdet/datasets/builder.py", line 82, in build_dataset
    dataset = build_from_cfg(cfg, DATASETS, default_args)
  File "/home/music/miniconda3/envs/mmtrack/lib/python3.8/site-packages/mmcv/utils/registry.py", line 72, in build_from_cfg
    raise type(e)(f'{obj_cls.__name__}: {e}')
KeyError: "YouTubeVISDataset: 'image_id'"

Thank you!

Thanks for your error report and we appreciate it a lot.

Checklist

1. I have searched related issues but cannot get the expected help.
2. The bug has not been fixed in the latest version.

Describe the bug I was trying to run qdtrack model for MOT17 in dev-1.x, but it always got this error.

Reproduction

1. What command or script did you run?

srun -p bigdata_s2 --quotatype=auto --gres=gpu:1 python tools/train.py configs/mot/qdtrack/qdtrack_faster-rcnn_r50_fpn_8xb2-4e_mot17halftrain_test-mot17halfval.py

2. Did you make any modifications on the code or config? Did you understand what you have modified? No
3. What dataset did you use and what task did you run? MOT17

Environment

1. Please run python mmtrack/utils/collect_env.py to collect necessary environment information and paste it here. Got error:

Traceback (most recent call last):
  File "mmtrack/utils/collect_env.py", line 2, in <module>
    from mmcv.utils import collect_env as collect_base_env
  File "/mnt/lustre/ouyanglinke/anaconda3/envs/openmmlab/lib/python3.8/site-packages/mmcv/__init__.py", line 3, in <module>
    from .arraymisc import *
  File "/mnt/lustre/ouyanglinke/anaconda3/envs/openmmlab/lib/python3.8/site-packages/mmcv/arraymisc/__init__.py", line 2, in <module>
    from .quantization import dequantize, quantize
  File "/mnt/lustre/ouyanglinke/anaconda3/envs/openmmlab/lib/python3.8/site-packages/mmcv/arraymisc/quantization.py", line 2, in <module>
    from typing import Union
  File "/mnt/petrelfs/ouyanglinke/mmtracking/mmtrack/utils/typing.py", line 3, in <module>
    from typing import Dict, List, Optional, Tuple, Union
ImportError: cannot import name 'Dict' from partially initialized module 'typing' (most likely due to a circular import) (/mnt/petrelfs/ouyanglinke/mmtracking/mmtrack/utils/typing.py)

But I do successfully run SOT model. Python version is 3.8. Pytorch is 1.7.1.

3. You may add addition that may be helpful for locating the problem, such as
   • How you installed PyTorch [e.g., pip, conda, source]
   • Other environment variables that may be related (such as $PATH, $LD_LIBRARY_PATH, $PYTHONPATH, etc.)

Error traceback If applicable, paste the error trackback here.

Traceback (most recent call last):
  File "tools/train.py", line 119, in <module>
    main()
  File "tools/train.py", line 115, in main
    runner.train()
  File "/mnt/lustre/ouyanglinke/anaconda3/envs/openmmlab/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1684, in train
    model = self.train_loop.run()  # type: ignore
  File "/mnt/lustre/ouyanglinke/anaconda3/envs/openmmlab/lib/python3.8/site-packages/mmengine/runner/loops.py", line 90, in run
    self.run_epoch()
  File "/mnt/lustre/ouyanglinke/anaconda3/envs/openmmlab/lib/python3.8/site-packages/mmengine/runner/loops.py", line 105, in run_epoch
    for idx, data_batch in enumerate(self.dataloader):
  File "/mnt/lustre/ouyanglinke/anaconda3/envs/openmmlab/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 435, in __next__
    data = self._next_data()
  File "/mnt/lustre/ouyanglinke/anaconda3/envs/openmmlab/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1085, in _next_data
    return self._process_data(data)
  File "/mnt/lustre/ouyanglinke/anaconda3/envs/openmmlab/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1111, in _process_data
    data.reraise()
  File "/mnt/lustre/ouyanglinke/anaconda3/envs/openmmlab/lib/python3.8/site-packages/torch/_utils.py", line 428, in reraise
    raise self.exc_type(msg)
IndexError: Caught IndexError in DataLoader worker process 0.
Original Traceback (most recent call last):
  File "/mnt/lustre/ouyanglinke/anaconda3/envs/openmmlab/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py", line 198, in _worker_loop
    data = fetcher.fetch(index)
  File "/mnt/lustre/ouyanglinke/anaconda3/envs/openmmlab/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/mnt/lustre/ouyanglinke/anaconda3/envs/openmmlab/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 44, in <listcomp>
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/mnt/lustre/ouyanglinke/anaconda3/envs/openmmlab/lib/python3.8/site-packages/mmengine/dataset/base_dataset.py", line 378, in __getitem__
    data = self.prepare_data(idx)
  File "/mnt/petrelfs/ouyanglinke/mmtracking/mmtrack/datasets/base_video_dataset.py", line 387, in prepare_data
    return self.pipeline(final_data_info)
  File "/mnt/lustre/ouyanglinke/anaconda3/envs/openmmlab/lib/python3.8/site-packages/mmengine/dataset/base_dataset.py", line 55, in __call__
    data = t(data)
  File "/mnt/lustre/ouyanglinke/anaconda3/envs/openmmlab/lib/python3.8/site-packages/mmcv/transforms/base.py", line 12, in __call__
    return self.transform(results)
  File "/mnt/petrelfs/ouyanglinke/mmtracking/mmtrack/datasets/transforms/formatting.py", line 237, in transform
    key_anns[key_valid_idx])
IndexError: boolean index did not match indexed array along dimension 0; dimension is 10 but corresponding boolean dimension is 9

Bug fix If you have already identified the reason, you can provide the information here. If you are willing to create a PR to fix it, please also leave a comment here and that would be much appreciated! This message might help: In mmtracking/configs/base/datasets/mot_challenge.py, just block "TransformBroadcaster" would work.

# data pipeline
train_pipeline = [
    dict(
        type='TransformBroadcaster',
        share_random_params=True,
        transforms=[
            dict(type='LoadImageFromFile'),
            dict(type='LoadTrackAnnotations', with_instance_id=True),
            dict(
                type='mmdet.RandomResize',
                scale=(1088, 1088),
                ratio_range=(0.8, 1.2),
                keep_ratio=True,
                clip_object_border=False),
            dict(type='mmdet.PhotoMetricDistortion')
        ]),
    # dict(
    #     type='TransformBroadcaster',
    #     share_random_params=False,
    #     transforms=[
    #         dict(
    #             type='mmdet.RandomCrop',
    #             crop_size=(1088, 1088),
    #             bbox_clip_border=False)
    #     ]),
    dict(
        type='TransformBroadcaster',
        share_random_params=True,
        transforms=[
            dict(type='mmdet.RandomFlip', prob=0.5),
        ]),
    dict(type='PackTrackInputs', ref_prefix='ref', num_key_frames=1)
]

Hello, I want to train the masktrack_rcnn with the coco dataset. So i had reset the dataset of masktrack_rcnn_r50_fpn_12e_youtubevis2019.py------'../../_base_/datasets/coco_instance.py' and the num_classes=6.

By the way, i had reset the /home/music/Downloads/mmtracking-master/mmtrack/datasets/coco_video_dataset.py-------CLASSES = ('aircraft', 'buildings', 'electrical', 'person', 'tree', 'wire') and theload_as_video=False

And my env:

------------------------------------------------------------
sys.platform: linux
Python: 3.8.15 (default, Nov 24 2022, 15:19:38) [GCC 11.2.0]
CUDA available: True
GPU 0: NVIDIA GeForce RTX 3090 Ti
CUDA_HOME: None
GCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0
PyTorch: 1.12.1
PyTorch compiling details: PyTorch built with:
  - GCC 9.3
  - C++ Version: 201402
  - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications
  - Intel(R) MKL-DNN v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815)
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - LAPACK is enabled (usually provided by MKL)
  - NNPACK is enabled
  - CPU capability usage: AVX2
  - CUDA Runtime 11.3
  - 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_61,code=sm_61;-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;-gencode;arch=compute_37,code=compute_37
  - CuDNN 8.3.2  (built against CUDA 11.5)
  - Magma 2.5.2
  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.3.2, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -fabi-version=11 -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-unused-parameter -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 -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.12.1, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, 

TorchVision: 0.13.1
OpenCV: 4.6.0
MMCV: 1.7.0
MMCV Compiler: GCC 9.3
MMCV CUDA Compiler: 11.3
MMTracking: 0.14.0+

This my config:

2022-12-16 10:09:31,585 - mmtrack - INFO - Distributed training: False
2022-12-16 10:09:32,054 - mmtrack - INFO - 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')),
        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]),
            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]),
                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=64,
                    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=200,
                max_per_img=200,
                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=128,
                    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=200,
                max_per_img=200,
                nms=dict(type='nms', iou_threshold=0.7),
                min_bbox_size=0),
            rcnn=dict(
                score_thr=0.01,
                nms=dict(type='nms', iou_threshold=0.5),
                max_per_img=100,
                mask_thr_binary=0.5)),
        init_cfg=dict(
            type='Pretrained',
            checkpoint=
            'https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth'
        )),
    type='MaskTrackRCNN',
    track_head=dict(
        type='RoITrackHead',
        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='RoIEmbedHead',
            num_fcs=2,
            roi_feat_size=7,
            in_channels=256,
            fc_out_channels=1024),
        train_cfg=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=128,
                pos_fraction=0.25,
                neg_pos_ub=-1,
                add_gt_as_proposals=True),
            pos_weight=-1,
            debug=False)),
    tracker=dict(
        type='MaskTrackRCNNTracker',
        match_weights=dict(det_score=1.0, iou=2.0, det_label=10.0),
        num_frames_retain=20))
dataset_type = 'CocoDataset'
data_root = '/home/music/Downloads/mmtracking-master/data/coco/'
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='LoadImageFromFile'),
    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
    dict(type='RandomFlip', flip_ratio=0.5),
    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=32),
    dict(type='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks'])
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='MultiScaleFlipAug',
        img_scale=(1333, 800),
        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=32),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='Collect', keys=['img'])
        ])
]
data = dict(
    samples_per_gpu=6,
    workers_per_gpu=2,
    train=dict(
        type='CocoDataset',
        ann_file=
        '/home/music/Downloads/mmtracking-master/data/coco/annotations/train.json',
        img_prefix=
        '/home/music/Downloads/mmtracking-master/data/coco/train2023/',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
            dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
            dict(type='RandomFlip', flip_ratio=0.5),
            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=32),
            dict(type='DefaultFormatBundle'),
            dict(
                type='Collect',
                keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks'])
        ]),
    val=dict(
        type='CocoDataset',
        ann_file=
        '/home/music/Downloads/mmtracking-master/data/coco/annotations/val.json',
        img_prefix='/home/music/Downloads/mmtracking-master/data/coco/val2023/',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='MultiScaleFlipAug',
                img_scale=(1333, 800),
                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=32),
                    dict(type='ImageToTensor', keys=['img']),
                    dict(type='Collect', keys=['img'])
                ])
        ]),
    test=dict(
        type='CocoDataset',
        ann_file=
        '/home/music/Downloads/mmtracking-master/data/coco/annotations/val.json',
        img_prefix='/home/music/Downloads/mmtracking-master/data/coco/val2023/',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='MultiScaleFlipAug',
                img_scale=(1333, 800),
                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=32),
                    dict(type='ImageToTensor', keys=['img']),
                    dict(type='Collect', keys=['img'])
                ])
        ]))
evaluation = dict(metric=['bbox', 'segm'], classwise=True)
optimizer = dict(type='SGD', lr=0.00125, 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',
    warmup='linear',
    warmup_iters=500,
    warmup_ratio=0.3333333333333333,
    step=[8, 11])
total_epochs = 12
work_dir = 'work_dir/masktrack_coco'
gpu_ids = [0]

Best wish! Thank you!