Comments (3)
from dis.
Hey I'm a total noob, also getting errors when I follow step 4:
/home/hobo/.local/lib/python3.8/site-packages/torch/nn/_reduction.py:42: UserWarning: size_average and reduce args will be deprecated, please use reduction='mean' instead.
warnings.warn(warning.format(ret))
building model...
batch size: 8
--- create training dataloader ---
------------------------------ train --------------------------------
--->>> train dataset 0 / 1 DIS5K-TR <<<---
-im- DIS5K-TR ../DIS5K/DIS-TR/im : 3000
-gt- DIS5K-TR ../DIS5K/DIS-TR/gt : 3000
1 train dataloaders created
--- create valid dataloader ---
------------------------------ valid --------------------------------
--->>> valid dataset 0 / 1 DIS5K-VD <<<---
-im- DIS5K-VD ../DIS5K/DIS-VD/im : 470
-gt- DIS5K-VD ../DIS5K/DIS-VD/gt : 470
1 valid dataloaders created
--- build model ---
--- define optimizer ---
/home/hobo/.local/lib/python3.8/site-packages/torch/nn/functional.py:3509: UserWarning: nn.functional.upsample is deprecated. Use nn.functional.interpolate instead.
warnings.warn("nn.functional.upsample is deprecated. Use nn.functional.interpolate instead.")
/home/hobo/.local/lib/python3.8/site-packages/torch/nn/functional.py:3631: UserWarning: Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.
warnings.warn(
[1] 7685 killed /bin/python3 /home/hobo/Documents/dev/DIS/IS-Net/train_valid_inference_main.p
Any idea what's wrong please?
Thanks so much
from dis.
from dis.
Related Issues (20)
- Update environment from CUDA10.2 to CUDA 11.8! Adapt to 4090 and A100!
- Dataset image format issues HOT 2
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- What words is “Adp” short for in paper?
- Fine_Tunning with isnet-general-use.pth HOT 3
- Could you provide the codes of other evaluation metrics except HCE? HOT 4
- DIS v2 Dataset
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- DIS in SDXL Comfy UI
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- Require DIS v2 Dataset HOT 5
- Downloading weights issue HOT 7
- Regarding whether pre-trained encoders are necessary HOT 2
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- Training on custom dataset HOT 4
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from dis.