try modify swin
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12
models.py
12
models.py
@ -565,12 +565,12 @@ class FouriER(torch.nn.Module):
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if downsamples[i] or embed_dims[i] != embed_dims[i+1]:
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if downsamples[i] or embed_dims[i] != embed_dims[i+1]:
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# downsampling between two stages
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# downsampling between two stages
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network.append(
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network.append(
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# PatchEmbed(
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PatchEmbed(
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# patch_size=down_patch_size, stride=down_stride,
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patch_size=down_patch_size, stride=down_stride,
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# padding=down_pad,
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padding=down_pad,
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# in_chans=embed_dims[i], embed_dim=embed_dims[i+1]
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in_chans=embed_dims[i], embed_dim=embed_dims[i+1]
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# )
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)
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PatchMerging(dim=embed_dims[i+1])
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# PatchMerging(dim=embed_dims[i+1])
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)
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)
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self.network = nn.ModuleList(network)
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self.network = nn.ModuleList(network)
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