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							- import torch.nn as nn
 
- import numpy as np
 
- import torch
 
- class SpecificNorm(nn.Module):
 
-     def __init__(self, epsilon=1e-8):
 
-         """
 
-             @notice: avoid in-place ops.
 
-             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
 
-         """
 
-         super(SpecificNorm, self).__init__()
 
-         self.mean = np.array([0.485, 0.456, 0.406])
 
-         self.mean = torch.from_numpy(self.mean).float().cuda()
 
-         self.mean = self.mean.view([1, 3, 1, 1])
 
-         self.std = np.array([0.229, 0.224, 0.225])
 
-         self.std = torch.from_numpy(self.std).float().cuda()
 
-         self.std = self.std.view([1, 3, 1, 1])
 
-     def forward(self, x):
 
-         mean = self.mean.expand([1, 3, x.shape[2], x.shape[3]])
 
-         std = self.std.expand([1, 3, x.shape[2], x.shape[3]])
 
-         x = (x - mean) / std
 
-         return x
 
 
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