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import torch |
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import torch.nn as nn |
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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 = 1.0 / torch.sqrt(var + eps) * (g - y * mean_gy - mean_g) |
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return ( |
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gx, |
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(grad_output * y).sum(dim=3).sum(dim=2).sum(dim=0), |
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grad_output.sum(dim=3).sum(dim=2).sum(dim=0), |
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None, |
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) |
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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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class GRN(nn.Module): |
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"""GRN (Global Response Normalization) layer""" |
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def __init__(self, dim): |
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super().__init__() |
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self.gamma = nn.Parameter(torch.zeros(1, dim, 1, 1)) |
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self.beta = nn.Parameter(torch.zeros(1, dim, 1, 1)) |
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def forward(self, x): |
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Gx = torch.norm(x, p=2, dim=(2, 3), keepdim=True) |
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Nx = Gx / (Gx.mean(dim=1, keepdim=True) + 1e-6) |
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return self.gamma * (x * Nx) + self.beta + x |
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