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In the data processing pipeline for bottom-up pose estimation, the conventions used for results['input_size'] are inconsistent in the common_transforms.py flip function (
). This bug doesn't show for most common bottom-up cases because the height and widths usually are the same. But when height and width are different, the flip function keypoint outputs are incorrect.
The fix is to change Line 209 in common_transforms.py to w, h = results.get('input_size', results['img_shape']). Unless it clashes with definitions used in top-down transforms
Reproduces the problem - code sample
Not relevant. (Maybe running dekr on crowdpose with 640x512 image size?)
Reproduces the problem - command or script
Not relevant
Reproduces the problem - error message
Not relevant. The program runs fine but the trained model is not proper.
Additional information
Not applicable
The text was updated successfully, but these errors were encountered:
Prerequisite
Environment
In the data processing pipeline for bottom-up pose estimation, the conventions used for results['input_size'] are inconsistent in the common_transforms.py flip function (
mmpose/mmpose/datasets/transforms/common_transforms.py
Line 209 in 5a3be94
mmpose/mmpose/datasets/transforms/bottomup_transforms.py
Line 337 in 5a3be94
mmpose/mmpose/datasets/transforms/bottomup_transforms.py
Line 408 in 5a3be94
The fix is to change Line 209 in common_transforms.py to
w, h = results.get('input_size', results['img_shape'])
. Unless it clashes with definitions used in top-down transformsReproduces the problem - code sample
Not relevant. (Maybe running dekr on crowdpose with 640x512 image size?)
Reproduces the problem - command or script
Not relevant
Reproduces the problem - error message
Not relevant. The program runs fine but the trained model is not proper.
Additional information
Not applicable
The text was updated successfully, but these errors were encountered: