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Use skip_tensor variable
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@@ -48,8 +48,8 @@ class UNet(nn.Module):
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temp_tensor = bottleneck_tensor
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for index, up_sample in enumerate(self.up_samples):
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down_index = -(index + 2)
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temp_tensor = up_sample(temp_tensor, down_features[down_index])
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skip_tensor = down_features[-(index + 2)]
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temp_tensor = up_sample(temp_tensor, skip_tensor)
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up_features.append(temp_tensor)
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output_tensor = nn.functional.interpolate(temp_tensor, scale_factor = 2, mode = 'bilinear', align_corners = False)
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