Comments (5)
We modified the first conv in the network model to take 3 extra channels and initialized them with zeros. You need to change it in a similar way. For speed you can reduce it to a 2-level or even 1-level cascade -- the performance will drop but it will run much faster.
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can you put these codes? I can't modify
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You can find our modifications in models/psp/extractors.py.
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I find:
if v1.shape != tar_v.shape:
# Init the new segmentation channel with zeros
c, _, w, h = v1.shape
tar_v = torch.cat([
tar_v,
torch.zeros((c,3,w,h)),
], 1)
but no use to me.
when I run train.py use "model = PSPNet(sizes=(1, 2, 3, 6), psp_size=2048, deep_features_size=1024, backend='squeezenet1_1')"
or " model = PSPNet(sizes=(1, 2, 3, 6), psp_size=2048, deep_features_size=1024, backend='resnet18') ", I meet error .I can,t know how to change the code to adapt!
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Only ResNet-50 is supported in this repo. You would have to include those extensions by yourself.
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Related Issues (20)
- Error while running demo code!! HOT 4
- How to change epoch? HOT 3
- Segmentation Refinement Runtime Error HOT 11
- Train the Net HOT 4
- How to implement 'Local step' only? HOT 2
- how to solve this problem? HOT 2
- refinement is not a binary image HOT 1
- Can this method be used as post-processing for video object segmentation? HOT 2
- local step only HOT 4
- Help with Colab/local setup HOT 3
- Tree leaves boundary refinement HOT 5
- hashlib error HOT 1
- weight coefficient
- Very slow prediction, high cpu /gpu usage HOT 1
- Car Window Refinement
- Whether this model can be applied to Cityscapes datasets? HOT 1
- How to train PSPNet/DeepLabV3+ just as the result in your paper HOT 12
- Any low hanging fruit for generation speed improvements? HOT 2
- About the problem of dealing with redundant incomplete masks HOT 2
- Query Regarding High-Resolution Image Segmentation with CascadePSP HOT 1
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