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@@ -0,0 +1,25 @@
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+import torch.nn as nn
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+import numpy as np
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+import torch
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+class SpecificNorm(nn.Module):
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+ def __init__(self, epsilon=1e-8):
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+ """
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+ @notice: avoid in-place ops.
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+ https://discuss.pytorch.org/t/encounter-the-runtimeerror-one-of-the-variables-needed-for-gradient-computation-has-been-modified-by-an-inplace-operation/836/3
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+ """
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+ super(SpecificNorm, self).__init__()
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+ self.mean = np.array([0.485, 0.456, 0.406])
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+ self.mean = torch.from_numpy(self.mean).float().cuda()
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+ self.mean = self.mean.view([1, 3, 1, 1])
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+
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+ self.std = np.array([0.229, 0.224, 0.225])
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+ self.std = torch.from_numpy(self.std).float().cuda()
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+ self.std = self.std.view([1, 3, 1, 1])
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+
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+ def forward(self, x):
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+ mean = self.mean.expand([1, 3, x.shape[2], x.shape[3]])
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+ std = self.std.expand([1, 3, x.shape[2], x.shape[3]])
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+
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+ x = (x - mean) / std
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+
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+ return x
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