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