Create norm.py

util/norm.py

@@ -0,0 +1,25 @@
+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