| 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 |
