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open-mmlab / mmpose

OpenMMLab Pose Estimation Toolbox and Benchmark.
https://mmpose.readthedocs.io/en/latest/
Apache License 2.0
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[Bug] CID for other dataset? #3067

Open wux024 opened 3 weeks ago

wux024 commented 3 weeks ago

Prerequisite

Environment

OrderedDict([('sys.platform', 'linux'), ('Python', '3.10.14 (main, Mar 21 2024, 16:24:04) [GCC 11.2.0]'), ('CUDA available', True), ('MUSA available', False), ('numpy_random_seed', 2147483648), ('GPU 0,1', 'NVIDIA GeForce RTX 3090'), ('CUDA_HOME', ':/usr/local/cuda'), ('GCC', 'gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0'), ('PyTorch', '2.1.2+cu121'), ('PyTorch compiling details', 'PyTorch built with:\n - GCC 9.3\n - C++ Version: 201703\n - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications\n - Intel(R) MKL-DNN v3.1.1 (Git Hash 64f6bcbcbab628e96f33a62c3e975f8535a7bde4)\n - OpenMP 201511 (a.k.a. OpenMP 4.5)\n - LAPACK is enabled (usually provided by MKL)\n - NNPACK is enabled\n - CPU capability usage: AVX512\n - CUDA Runtime 12.1\n - NVCC architecture flags: -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;-gencode;arch=compute_90,code=sm_90\n - CuDNN 8.9.2\n - Magma 2.6.1\n - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=12.1, CUDNN_VERSION=8.9.2, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-invalid-partial-specialization -Wno-unused-private-field -Wno-aligned-allocation-unavailable -Wno-missing-braces -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_DISABLE_GPU_ASSERTS=ON, TORCH_VERSION=2.1.2, 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=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, \n'), ('TorchVision', '0.16.2+cu121'), ('OpenCV', '4.9.0'), ('MMEngine', '0.10.3'), ('MMPose', '1.3.1+0a8a104')])

mmcv 2.1.0 mmdet 3.3.0 mmengine 0.10.3 mmpose 1.3.1 /mnt/data1/wux024/mmpose mmpretrain 1.2.0

Reproduces the problem - code sample

base = ['../../../base/default_runtime.py']

runtime

train_cfg = dict(max_epochs=140, val_interval=10)

optimizer

optim_wrapper = dict(optimizer=dict( type='Adam', lr=1e-3, ))

learning policy

param_scheduler = [ dict( type='MultiStepLR', begin=0, end=140, milestones=[90, 120], gamma=0.1, by_epoch=True) ]

automatically scaling LR based on the actual training batch size

auto_scale_lr = dict(base_batch_size=160)

hooks

default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater'))

codec settings

codec = dict( type='DecoupledHeatmap', input_size=(512, 512), heatmap_size=(128, 128))

model settings

model = dict( type='BottomupPoseEstimator', data_preprocessor=dict( type='PoseDataPreprocessor', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], bgr_to_rgb=True), backbone=dict( type='HRNet', in_channels=3, extra=dict( stage1=dict( num_modules=1, num_branches=1, block='BOTTLENECK', num_blocks=(4, ), num_channels=(64, )), stage2=dict( num_modules=1, num_branches=2, block='BASIC', num_blocks=(4, 4), num_channels=(32, 64)), stage3=dict( num_modules=4, num_branches=3, block='BASIC', num_blocks=(4, 4, 4), num_channels=(32, 64, 128)), stage4=dict( num_modules=3, num_branches=4, block='BASIC', num_blocks=(4, 4, 4, 4), num_channels=(32, 64, 128, 256), multiscale_output=True)), init_cfg=dict( type='Pretrained', checkpoint='https://download.openmmlab.com/mmpose/' 'pretrain_models/hrnet_w32-36af842e.pth'), ), neck=dict( type='FeatureMapProcessor', concat=True, ), head=dict( type='CIDHead', in_channels=480, num_keypoints=5, gfd_channels=32, coupled_heatmap_loss=dict(type='FocalHeatmapLoss', loss_weight=1.0), decoupled_heatmap_loss=dict(type='FocalHeatmapLoss', loss_weight=4.0), contrastive_loss=dict( type='InfoNCELoss', temperature=0.05, loss_weight=1.0), decoder=codec, ), train_cfg=dict(max_train_instances=200), test_cfg=dict( multiscale_test=False, flip_test=True, shift_heatmap=False, align_corners=False))

base dataset settings

dataset_type = 'FishDataset' data_mode = 'bottomup' data_root = 'data/fish/'

pipelines

train_pipeline = [ dict(type='LoadImage'), dict(type='BottomupRandomAffine', input_size=codec['input_size']), dict(type='RandomFlip', direction='horizontal'), dict(type='GenerateTarget', encoder=codec), dict(type='BottomupGetHeatmapMask'), dict(type='PackPoseInputs'), ] val_pipeline = [ dict(type='LoadImage'), dict( type='BottomupResize', input_size=codec['input_size'], size_factor=64, resize_mode='expand'), dict( type='PackPoseInputs', meta_keys=('id', 'img_id', 'img_path', 'crowd_index', 'ori_shape', 'img_shape', 'input_size', 'input_center', 'input_scale', 'flip', 'flip_direction', 'flip_indices', 'raw_ann_info', 'skeleton_links')) ]

data loaders

train_dataloader = dict( batch_size=32, num_workers=8, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=True), dataset=dict( type=dataset_type, data_root=data_root, data_mode=data_mode, ann_file='annotations/train.json', data_prefix=dict(img='images/train/'), pipeline=train_pipeline, )) val_dataloader = dict( batch_size=32, num_workers=8, persistent_workers=True, drop_last=False, sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), dataset=dict( type=dataset_type, data_root=data_root, data_mode=data_mode, ann_file='annotations/val.json', data_prefix=dict(img='images/val/'), test_mode=True, pipeline=val_pipeline, )) test_dataloader = dict( batch_size=32, num_workers=8, persistent_workers=True, drop_last=False, sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), dataset=dict( type=dataset_type, data_root=data_root, data_mode=data_mode, ann_file='annotations/test.json', data_prefix=dict(img='images/test/'), test_mode=True, pipeline=val_pipeline, ))

evaluators

val_evaluator = [dict( type='CocoMetric', ann_file=data_root + 'annotations/val.json', nms_thr=0.8, score_mode='keypoint') ] test_evaluator = [dict( type='CocoMetric', ann_file=data_root + 'annotations/test.json', nms_thr=0.8, score_mode='keypoint') ]

Reproduces the problem - command or script

python tools/train.py configs/...

Reproduces the problem - error message

06/12 11:17:08 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io 06/12 11:17:08 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future. 06/12 11:17:08 - mmengine - INFO - Checkpoints will be saved to /mnt/data1/wux024/mmpose/work_dirs/bottomup/fish/cid_hrnet-w32_8xb32-140e_fish-512x512. /mnt/data1/wux024/mmpose/mmpose/codecs/utils/gaussian_heatmap.py:182: RuntimeWarning: invalid value encountered in divide gaussian = np.exp(-((x - x0)2 + (y - y0)2) / (2 * sigma[n]2)) /mnt/data1/wux024/mmpose/mmpose/codecs/utils/gaussian_heatmap.py:182: RuntimeWarning: invalid value encountered in divide gaussian = np.exp(-((x - x0)2 + (y - y0)2) / (2 sigma[n]2)) Traceback (most recent call last): File "/mnt/data1/wux024/mmpose/tools/train.py", line 162, in main() File "/mnt/data1/wux024/mmpose/tools/train.py", line 158, in main runner.train() File "/home/wux024/.conda/envs/openmmlab/lib/python3.10/site-packages/mmengine/runner/runner.py", line 1777, in train model = self.train_loop.run() # type: ignore File "/home/wux024/.conda/envs/openmmlab/lib/python3.10/site-packages/mmengine/runner/loops.py", line 96, in run self.run_epoch() File "/home/wux024/.conda/envs/openmmlab/lib/python3.10/site-packages/mmengine/runner/loops.py", line 112, in run_epoch self.run_iter(idx, data_batch) File "/home/wux024/.conda/envs/openmmlab/lib/python3.10/site-packages/mmengine/runner/loops.py", line 128, in run_iter outputs = self.runner.model.train_step( File "/home/wux024/.conda/envs/openmmlab/lib/python3.10/site-packages/mmengine/model/base_model/base_model.py", line 114, in train_step losses = self._run_forward(data, mode='loss') # type: ignore File "/home/wux024/.conda/envs/openmmlab/lib/python3.10/site-packages/mmengine/model/base_model/base_model.py", line 361, in _run_forward results = self(data, mode=mode) File "/home/wux024/.conda/envs/openmmlab/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl return self._call_impl(args, kwargs) File "/home/wux024/.conda/envs/openmmlab/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl return forward_call(*args, **kwargs) File "/mnt/data1/wux024/mmpose/mmpose/models/pose_estimators/base.py", line 155, in forward return self.loss(inputs, data_samples) File "/mnt/data1/wux024/mmpose/mmpose/models/pose_estimators/bottomup.py", line 70, in loss self.head.loss(feats, data_samples, train_cfg=self.train_cfg)) File "/mnt/data1/wux024/mmpose/mmpose/models/heads/heatmap_heads/cid_head.py", line 641, in loss gt_heatmaps = torch.stack(gt_heatmaps) RuntimeError: stack expects each tensor to be equal size, but got [6, 128, 128] at entry 0 and [11, 128, 128] at entry 2

Additional information

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