Comments (4)
@cyndixxxxx
if you use the combined target, the output channels of network should be 51, i.e. shape=(64,51,64,48), 17 channels for heatmap and 34 channels for offsetmap
otherwise if you use gaussian heatmap for target, the output channels of network is 17, and then, you should use API 'flip_back' instead of 'flip_back_offset'
you can figure out how the algorithm discriminate these two situations by referring line 157-162 in "UDP-Pose/deep-high-resolution-net.pytorch/lib/core/function.py"
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How to generate the output to be shape=(64,51,64,48)? .yml file is filled with ‘offset’
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@cyndixxxxx just use the default TARGET_TYPE('offset') as in default .yml ( UDP-Pose/deep-high-resolution-net.pytorch/experiments/coco/hrnet/w32_256x192_adam_lr1e-3_offset_ofm.yaml )
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Thank you for your reply
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Related Issues (20)
- ERROR: Could not find a version that satisfies the requirement opencv-python==3.4.1.15 HOT 2
- missing keypoint-aware occlusion augmentation HOT 1
- Question about the pre-process HOT 2
- what is difference pred and preds
- why preds need X scale HOT 1
- why need norm = np.ones((pred.shape[0], 2)) * np.array([h, w]) / 10 in accuracy HOT 4
- why your json's bboxes have so many no human's joints HOT 1
- Confuse about merge origin hrnet into mmpose HOT 9
- About the multi-scale testing HOT 1
- About KPD HOT 5
- Good job,but a little flaw? HOT 1
- AID
- Results on COCO val2017
- Download the pre-trained model
- test accuracy too low use offset
- pose_hrnet_w48* means using additional data from AI challenger for training?有 AI challenger 和coco数据集合并完成的数据集提供吗? HOT 1
- Can you give some explanation on apply affine transform, i.e, the use of get_warpmatrix and warpAffine?
- About evaluation index
- can you release the udp code for simple-R50 and pretrained weight?
- 可以在UDP的heatmap的基础上运用simcc的方法吗?
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