IndexError: index 44 is out of bounds for axis 1 with size 24

Open bhoomikaushik-siemens opened this issue 1 year ago • 2 comments

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Prerequisite

[X] I have searched Issues and Discussions but cannot get the expected help.
[X] The bug has not been fixed in the latest version(https://github.com/open-mmlab/mmpose).

Environment

Package Version Editable project location

addict 2.4.0 aiofiles 23.2.1 aioice 0.9.0 aiortc 1.9.0 albucore 0.0.13 albumentations 1.4.14 aliyun-python-sdk-core 2.15.1 aliyun-python-sdk-kms 2.16.3 altair 5.3.0 annotated-types 0.7.0 anyio 4.4.0 attrs 23.2.0 av 12.3.0 blinker 1.8.2 cachetools 5.4.0 certifi 2022.12.7 cffi 1.16.0 charset-normalizer 2.1.1 chumpy 0.70 click 8.1.7 colorama 0.4.6 contourpy 1.2.1 coverage 7.5.4 crcmod 1.7 cryptography 42.0.8 cycler 0.12.1 Cython 3.0.10 dnspython 2.6.1 eval_type_backport 0.2.0 exceptiongroup 1.2.1 fastapi 0.112.0 ffmpeg 1.4 ffmpy 0.4.0 filelock 3.14.0 flake8 7.1.0 fonttools 4.53.0 fsspec 2024.6.1 gitdb 4.0.11 GitPython 3.1.43 google-crc32c 1.5.0 gradio 4.40.0 gradio_client 1.2.0 h11 0.14.0 httpcore 1.0.5 httpx 0.27.0 huggingface-hub 0.24.5 idna 3.4 ifaddr 0.2.0 imageio 2.34.2 importlib_metadata 7.2.1 importlib_resources 6.4.0 iniconfig 2.0.0 interrogate 1.7.0 isort 4.3.21 Jinja2 3.1.4 jmespath 0.10.0 joblib 1.4.2 json-tricks 3.17.3 jsonlint 0.1 jsonschema 4.23.0 jsonschema-specifications 2023.12.1 kiwisolver 1.4.5 lazy_loader 0.4 Markdown 3.6 markdown-it-py 3.0.0 MarkupSafe 2.1.5 matplotlib 3.9.0 mccabe 0.7.0 mdurl 0.1.2 mmcv 2.1.0 mmdet 3.2.0 mmengine 0.10.4 mmpose 1.3.2 /home/bhoomi/mmpose model-index 0.1.11 munkres 1.1.4 networkx 3.3 numpy 1.26.3 opencv-python 4.10.0.84 opencv-python-headless 4.10.0.84 opendatalab 0.0.10 openmim 0.3.9 openxlab 0.1.0 ordered-set 4.1.0 orjson 3.10.6 oss2 2.17.0 packaging 24.1 pandas 2.2.2 parameterized 0.9.0 pillow 10.2.0 pip 22.0.2 platformdirs 4.2.2 pluggy 1.5.0 protobuf 5.27.3 py 1.11.0 pyarrow 17.0.0 pyav 12.1.0 pycocotools 2.0.8 pycodestyle 2.12.0 pycparser 2.22 pycryptodome 3.20.0 pydantic 2.7.4 pydantic_core 2.18.4 pydeck 0.9.1 pydub 0.25.1 pyee 11.1.0 pyflakes 3.2.0 Pygments 2.18.0 pylibsrtp 0.10.0 pyOpenSSL 24.2.1 pyparsing 3.1.2 pytest 8.2.2 pytest-runner 6.0.1 python-dateutil 2.9.0.post0 python-multipart 0.0.9 pytz 2023.4 PyYAML 6.0.1 referencing 0.35.1 requests 2.32.3 rich 13.4.2 rpds-py 0.19.1 ruff 0.5.6 scikit-image 0.24.0 scikit-learn 1.5.0 scipy 1.13.1 semantic-version 2.10.0 setuptools 60.2.0 shapely 2.0.4 shellingham 1.5.4 six 1.16.0 smmap 5.0.1 sniffio 1.3.1 starlette 0.37.2 streamlit 1.37.0 streamlit-webrtc 0.47.7 tabulate 0.9.0 tenacity 8.5.0 termcolor 2.4.0 terminaltables 3.1.10 threadpoolctl 3.5.0 tifffile 2024.6.18 toml 0.10.2 tomli 2.0.1 tomlkit 0.12.0 toolz 0.12.1 torch 1.11.0+cu115 torchaudio 0.11.0+cu115 torchvision 0.12.0+cu115 tornado 6.4.1 tqdm 4.65.2 typer 0.12.3 typing_extensions 4.9.0 tzdata 2024.1 urllib3 2.2.2 uvicorn 0.30.5 watchdog 4.0.1 websockets 12.0 xdoctest 1.1.5 xtcocotools 1.14.3 yapf 0.40.2 zipp 3.19.2

Reproduces the problem - code sample

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

runtime

max_epochs = 270 stage2_num_epochs = 30 base_lr = 4e-3

train_cfg = dict(max_epochs=max_epochs, val_interval=10) randomness = dict(seed=21)

optimizer

optim_wrapper = dict( type='OptimWrapper', optimizer=dict(type='AdamW', lr=base_lr, weight_decay=0.05), paramwise_cfg=dict( norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True))

learning rate

param_scheduler = [ dict( type='LinearLR', start_factor=1.0e-5, by_epoch=False, begin=0, end=1000), dict( type='CosineAnnealingLR', eta_min=base_lr * 0.05, begin=max_epochs // 2, end=max_epochs, T_max=max_epochs // 2, by_epoch=True, convert_to_iter_based=True), ]

automatically scaling LR based on the actual training batch size

auto_scale_lr = dict(base_batch_size=512)

codec settings

codec = dict( type='SimCCLabel', input_size=(192, 256), sigma=(4.9, 5.66), simcc_split_ratio=2.0, normalize=False, use_dark=False)

model settings

model = dict( type='TopdownPoseEstimator', 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( scope='mmdet', type='CSPNeXt', arch='P5', expand_ratio=0.5, deepen_factor=0.67, widen_factor=0.75, out_indices=(4, ), channel_attention=True, norm_cfg=dict(type='SyncBN'), act_cfg=dict(type='SiLU'), # Remove init_cfg for training from scratch init_cfg=None # Ensure no pre-trained weights are used ), head=dict( type='RTMCCHead', in_channels=768, out_channels=64, input_size=codec['input_size'], in_featuremap_size=tuple([s // 32 for s in codec['input_size']]), simcc_split_ratio=codec['simcc_split_ratio'], final_layer_kernel_size=7, gau_cfg=dict( hidden_dims=256, s=128, expansion_factor=2, dropout_rate=0., drop_path=0., act_fn='SiLU', use_rel_bias=False, pos_enc=False ), loss=dict( type='KLDiscretLoss', use_target_weight=True, beta=10., label_softmax=True ), decoder=codec ), test_cfg=dict( flip_test=True ) )

base dataset settings

dataset_type = 'CocoDataset' data_mode = 'topdown' data_root = 'data/coco/'

backend_args = dict(backend='local')

backend_args = dict(

backend='petrel',

path_mapping=dict({

f'{data_root}': 's3://openmmlab/datasets/detection/coco/',

f'{data_root}': 's3://openmmlab/datasets/detection/coco/'

}))

pipelines

train_pipeline = [ dict(type='LoadImage', backend_args=backend_args), dict(type='GetBBoxCenterScale'), dict(type='RandomFlip', direction='horizontal'), dict(type='RandomHalfBody'), dict( type='RandomBBoxTransform', scale_factor=[0.6, 1.4], rotate_factor=80), dict(type='TopdownAffine', input_size=codec['input_size']), dict(type='mmdet.YOLOXHSVRandomAug'), dict( type='Albumentation', transforms=[ dict(type='Blur', p=0.1), dict(type='MedianBlur', p=0.1), dict( type='CoarseDropout', max_holes=1, max_height=0.4, max_width=0.4, min_holes=1, min_height=0.2, min_width=0.2, p=1.0), ]), dict(type='GenerateTarget', encoder=codec), dict(type='PackPoseInputs') ] val_pipeline = [ dict(type='LoadImage', backend_args=backend_args), dict(type='GetBBoxCenterScale'), dict(type='TopdownAffine', input_size=codec['input_size']), dict(type='PackPoseInputs') ]

train_pipeline_stage2 = [ dict(type='LoadImage', backend_args=backend_args), dict(type='GetBBoxCenterScale'), dict(type='RandomFlip', direction='horizontal'), dict(type='RandomHalfBody'), dict( type='RandomBBoxTransform', shift_factor=0., scale_factor=[0.75, 1.25], rotate_factor=60), dict(type='TopdownAffine', input_size=codec['input_size']), dict(type='mmdet.YOLOXHSVRandomAug'), dict( type='Albumentation', transforms=[ dict(type='Blur', p=0.1), dict(type='MedianBlur', p=0.1), dict( type='CoarseDropout', max_holes=1, max_height=0.4, max_width=0.4, min_holes=1, min_height=0.2, min_width=0.2, p=0.5), ]), dict(type='GenerateTarget', encoder=codec), dict(type='PackPoseInputs') ]

data loaders

train_dataloader = dict( batch_size=64, 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/coco_wholebody_train_v1.0.json',

    data_prefix=dict(img='train2017/'),
    metainfo=dict(from_file='configs/_base_/datasets/custom.py'),
    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/coco_wholebody_val_v1.0.json', data_prefix=dict(img='val2017/'), metainfo=dict(from_file='configs/base/datasets/custom.py'), test_mode=True, bbox_file='data/coco/person_detection_results/' 'COCO_val2017_detections_AP_H_56_person.json', pipeline=val_pipeline, )) test_dataloader = val_dataloader

hooks

default_hooks = dict( checkpoint=dict( save_best='coco-wholebody/AP', rule='greater', max_keep_ckpts=1))

custom_hooks = [ dict( type='EMAHook', ema_type='ExpMomentumEMA', momentum=0.0002, update_buffers=True, priority=49), dict( type='mmdet.PipelineSwitchHook', switch_epoch=max_epochs - stage2_num_epochs, switch_pipeline=train_pipeline_stage2) ]

evaluators

val_evaluator = dict( type='CocoWholeBodyMetric', ann_file=data_root + 'annotations/coco_wholebody_val_v1.0.json') test_evaluator = val_evaluator

Reproduces the problem - command or script

python3 tools/train.py configs/wholebody_2d_keypoint/rtmpose/coco-wholebody/custom.py

Reproduces the problem - error message

loading annotations into memory... Done (t=0.80s) creating index... index created! 09/04 14:30:45 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io 09/04 14:30:45 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future. 09/04 14:30:45 - mmengine - INFO - Checkpoints will be saved to /home/bhoomi/mmpose/work_dirs/custom. Traceback (most recent call last): File "/home/bhoomi/mmpose/tools/train.py", line 162, in main() File "/home/bhoomi/mmpose/tools/train.py", line 158, in main runner.train() File "/home/bhoomi/pose/lib/python3.10/site-packages/mmengine/runner/runner.py", line 1777, in train model = self.train_loop.run() # type: ignore File "/home/bhoomi/pose/lib/python3.10/site-packages/mmengine/runner/loops.py", line 96, in run self.run_epoch() File "/home/bhoomi/pose/lib/python3.10/site-packages/mmengine/runner/loops.py", line 112, in run_epoch for idx, data_batch in enumerate(self.dataloader): File "/home/bhoomi/pose/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 530, in next data = self._next_data() File "/home/bhoomi/pose/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1224, in _next_data return self._process_data(data) File "/home/bhoomi/pose/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1250, in _process_data data.reraise() File "/home/bhoomi/pose/lib/python3.10/site-packages/torch/_utils.py", line 457, in reraise raise exception IndexError: Caught IndexError in DataLoader worker process 0. Original Traceback (most recent call last): File "/home/bhoomi/pose/lib/python3.10/site-packages/torch/utils/data/_utils/worker.py", line 287, in _worker_loop data = fetcher.fetch(index) File "/home/bhoomi/pose/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py", line 49, in fetch data = [self.dataset[idx] for idx in possibly_batched_index] File "/home/bhoomi/pose/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py", line 49, in data = [self.dataset[idx] for idx in possibly_batched_index] File "/home/bhoomi/pose/lib/python3.10/site-packages/mmengine/dataset/base_dataset.py", line 410, in getitem data = self.prepare_data(idx) File "/home/bhoomi/pose/lib/python3.10/site-packages/mmengine/dataset/base_dataset.py", line 115, in wrapper return old_func(obj, *args, **kwargs) File "/home/bhoomi/mmpose/mmpose/datasets/datasets/base/base_coco_style_dataset.py", line 170, in prepare_data return self.pipeline(data_info) File "/home/bhoomi/pose/lib/python3.10/site-packages/mmengine/dataset/base_dataset.py", line 60, in call data = t(data) File "/home/bhoomi/pose/lib/python3.10/site-packages/mmcv/transforms/base.py", line 12, in call return self.transform(results) File "/home/bhoomi/mmpose/mmpose/datasets/transforms/common_transforms.py", line 239, in transform keypoints, keypoints_visible = flip_keypoints( File "/home/bhoomi/mmpose/mmpose/structures/keypoint/transforms.py", line 52, in flip_keypoints keypoints = keypoints.take(flip_indices, axis=ndim - 2) IndexError: index 44 is out of bounds for axis 1 with size 24

Additional information

No response

Sep 04 '24 09:09 bhoomikaushik-siemens

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IndexError: index 44 is out of bounds for axis 1 with size 24

Prerequisite

Environment

Reproduces the problem - code sample

runtime

optimizer

learning rate

automatically scaling LR based on the actual training batch size

codec settings

model settings

base dataset settings

backend_args = dict(

backend='petrel',

path_mapping=dict({

f'{data_root}': 's3://openmmlab/datasets/detection/coco/',

f'{data_root}': 's3://openmmlab/datasets/detection/coco/'

}))

pipelines

data loaders

hooks

evaluators

Reproduces the problem - command or script

Reproduces the problem - error message

Additional information

mmpose
mmpose copied to clipboard