We list some common issues faced by many users and their corresponding solutions here. Feel free to enrich the list if you find any frequent issues and have ways to help others to solve them. If the contents here do not cover your issue, please create an issue using the provided templates and make sure you fill in all required information in the template.
Compatibility issue between MMCV and MMPose; "AssertionError: MMCV==xxx is used but incompatible. Please install mmcv>=xxx, <=xxx."
Here are the version correspondences between mmdet
, mmcv
and mmpose
:
Detailed compatible MMPose and MMCV versions are shown as below. Please choose the correct version of MMCV to avoid installation issues.
MMPose version | MMCV/MMEngine version |
---|---|
1.0.0 | mmcv>=2.0.0, mmengine>=0.7.0 |
1.0.0rc1 | mmcv>=2.0.0rc4, mmengine>=0.6.0 |
1.0.0rc0 | mmcv>=2.0.0rc0, mmengine>=0.0.1 |
1.0.0b0 | mmcv>=2.0.0rc0, mmengine>=0.0.1 |
MMPose version | MMCV version |
---|---|
0.x | mmcv-full>=1.3.8, <1.8.0 |
0.29.0 | mmcv-full>=1.3.8, <1.7.0 |
0.28.1 | mmcv-full>=1.3.8, <1.7.0 |
0.28.0 | mmcv-full>=1.3.8, <1.6.0 |
0.27.0 | mmcv-full>=1.3.8, <1.6.0 |
0.26.0 | mmcv-full>=1.3.8, <1.6.0 |
0.25.1 | mmcv-full>=1.3.8, <1.6.0 |
0.25.0 | mmcv-full>=1.3.8, <1.5.0 |
0.24.0 | mmcv-full>=1.3.8, <1.5.0 |
0.23.0 | mmcv-full>=1.3.8, <1.5.0 |
0.22.0 | mmcv-full>=1.3.8, <1.5.0 |
0.21.0 | mmcv-full>=1.3.8, <1.5.0 |
0.20.0 | mmcv-full>=1.3.8, <1.4.0 |
0.19.0 | mmcv-full>=1.3.8, <1.4.0 |
0.18.0 | mmcv-full>=1.3.8, <1.4.0 |
0.17.0 | mmcv-full>=1.3.8, <1.4.0 |
0.16.0 | mmcv-full>=1.3.8, <1.4.0 |
0.14.0 | mmcv-full>=1.1.3, <1.4.0 |
0.13.0 | mmcv-full>=1.1.3, <1.4.0 |
0.12.0 | mmcv-full>=1.1.3, <1.3 |
0.11.0 | mmcv-full>=1.1.3, <1.3 |
0.10.0 | mmcv-full>=1.1.3, <1.3 |
0.9.0 | mmcv-full>=1.1.3, <1.3 |
0.8.0 | mmcv-full>=1.1.1, <1.2 |
0.7.0 | mmcv-full>=1.1.1, <1.2 |
Unable to install xtcocotools
pip install xtcocotools
.If step1 does not work. Try to install it from source.
git clone https://github.com/jin-s13/xtcocoapi
cd xtcocoapi
python setup.py install
No matching distribution found for xtcocotools>=1.6
pip install cython
.Install xtcocotools from source.
git clone https://github.com/jin-s13/xtcocoapi
cd xtcocoapi
python setup.py install
"No module named 'mmcv.ops'"; "No module named 'mmcv._ext'"
pip uninstall mmcv
.We can estimate the bounding box of a person as the minimal box that tightly bounds all the keypoints.
COCO_val2017_detections_AP_H_56_person.json
? Can I train pose models without it?"COCO_val2017_detections_AP_H_56_person.json" contains the "detected" human bounding boxes for COCO validation set, which are generated by FasterRCNN.
One can choose to use gt bounding boxes to evaluate models, by setting bbox_file=None
in val_dataloader.dataset
in config. Or one can use detected boxes to evaluate
the generalizability of models, by setting bbox_file='COCO_val2017_detections_AP_H_56_person.json'
.
Set the environment variables MASTER_PORT=XXX
. For example:
MASTER_PORT=29517 GPUS=16 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh train res50 configs/body_2d_keypoint/topdown_regression/coco/td-reg_res50_8xb64-210e_coco-256x192.py work_dirs/res50_coco_256x192
It's normal that some layers in the pretrained model are not used in the pose model. ImageNet-pretrained classification network and the pose network may have different architectures (e.g. no classification head). So some unexpected keys in source state dict is actually expected.
Refer to Migration - Step3: Model - Backbone.
When training, the unexpected keys will be ignored.
Modify vis_backends
in config file like:
vis_backends = [
dict(type='LocalVisBackend'),
dict(type='TensorboardVisBackend')
]
You can refer to user_guides/visualization.md.
Use smaller log interval. For example, change interval=50
to interval=1
in the config:
# hooks
default_hooks = dict(logger=dict(interval=1))
How to evaluate on MPII test dataset? Since we do not have the ground-truth for test dataset, we cannot evaluate it 'locally'. If you would like to evaluate the performance on test set, you have to upload the pred.mat (which is generated during testing) to the official server via email, according to the MPII guideline.
For top-down 2d pose estimation, why predicted joint coordinates can be out of the bounding box (bbox)? We do not directly use the bbox to crop the image. bbox will be first transformed to center & scale, and the scale will be multiplied by a factor (1.25) to include some context. If the ratio of width/height is different from that of model input (possibly 192/256), we will adjust the bbox.
Run demos with --device=cpu
.
A few approaches may help to improve the inference speed:
flip_test=False
in init_cfg
in the config file.