Comments (6)
你试一试
trimap_img = Image.open(trimap_path).convert('L')
alpha_img = Image.open(alpha_path).convert('L')
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这个也不行。。
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你用的什么版本的pytorch。或许这个说法是对的output with shape [1, 28, 28] doesn't match the broadcast shape [3, 28, 28]。
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按照那个说法 input_dataset.py中的76-79行应该改为
self.transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
])
但是依然是 不匹配的错误。我用的是pytorch 1.0版本。
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你试试看最新的这次更新的input_dataset.py
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我记得昨天就是这样改过的,也在这里发布了,怎么没了。不过这个问题可以按照你最新的input_dataset.py来修改,可以的。
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Related Issues (16)
- 另外一个代码 HOT 1
- no such file netD_0.pth HOT 1
- Dataset HOT 1
- Quality of results HOT 1
- Why the discriminator of the train part is not used in your code? HOT 2
- about the pre-trained model HOT 4
- trimap
- Can this be used on Mobile Phone?
- Batch Size=1 improvement HOT 1
- background images data set size HOT 1
- 关于训练的讨论 HOT 2
- Test: Clip vs. Non-clip HOT 1
- Could you offer pretrained model? HOT 2
- would you mind open your pre-trained model? HOT 7
- matting的评价指标? HOT 5
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