| 12345678910111213141516171819202122232425 | import torch.nn as nnimport numpy as npimport torchclass 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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