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import torch |
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import numpy as np |
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from torch import nn as nn |
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from torch.nn import init as init |
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class LayerNormFunction(torch.autograd.Function): |
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@staticmethod |
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def forward(ctx, x, weight, bias, eps): |
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ctx.eps = eps |
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N, C, H, W = x.size() |
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mu = x.mean(1, keepdim=True) |
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var = (x - mu).pow(2).mean(1, keepdim=True) |
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y = (x - mu) / (var + eps).sqrt() |
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ctx.save_for_backward(y, var, weight) |
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y = weight.view(1, C, 1, 1) * y + bias.view(1, C, 1, 1) |
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return y |
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@staticmethod |
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def backward(ctx, grad_output): |
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eps = ctx.eps |
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N, C, H, W = grad_output.size() |
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y, var, weight = ctx.saved_variables |
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g = grad_output * weight.view(1, C, 1, 1) |
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mean_g = g.mean(dim=1, keepdim=True) |
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mean_gy = (g * y).mean(dim=1, keepdim=True) |
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gx = 1. / torch.sqrt(var + eps) * (g - y * mean_gy - mean_g) |
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return gx, (grad_output * y).sum(dim=3).sum(dim=2).sum(dim=0), grad_output.sum(dim=3).sum(dim=2).sum( |
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dim=0), None |
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class LayerNorm2d(nn.Module): |
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def __init__(self, channels, eps=1e-6): |
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super(LayerNorm2d, self).__init__() |
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self.register_parameter('weight', nn.Parameter(torch.ones(channels))) |
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self.register_parameter('bias', nn.Parameter(torch.zeros(channels))) |
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self.eps = eps |
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def forward(self, x): |
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return LayerNormFunction.apply(x, self.weight, self.bias, self.eps) |
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def calc_mean_std(feat, eps=1e-5): |
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""" |
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Calculate mean and std for the given feature map. |
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feat: Tensor of shape [B, C, H, W] |
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eps: small value to avoid division by zero |
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""" |
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B, C, _, _ = feat.size() |
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feat_mean = feat.mean(dim=1, keepdim=True) |
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feat_std = feat.var(dim=1, keepdim=True) + eps |
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feat_std = feat_std.sqrt() |
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return feat_mean, feat_std |
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class CustomSequential(nn.Module): |
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''' |
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Similar to nn.Sequential, but it lets us introduce a second argument in the forward method |
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so adaptors can be considered in the inference. |
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''' |
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def __init__(self, *args): |
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super(CustomSequential, self).__init__() |
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self.modules_list = nn.ModuleList(args) |
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def forward(self, x): |
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for module in self.modules_list: |
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x = module(x) |
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return x |
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if __name__ == '__main__': |
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pass |
