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71 lines
2.2 KiB
71 lines
2.2 KiB
2 years ago
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#!/usr/bin/env python3
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# -*- coding:utf-8 -*-
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#############################################################
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# File: layernorm.py
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# Created Date: Tuesday April 28th 2022
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# Author: Chen Xuanhong
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# Email: chenxuanhongzju@outlook.com
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# Last Modified: Thursday, 20th April 2023 9:28:20 am
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# Modified By: Chen Xuanhong
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# Copyright (c) 2020 Shanghai Jiao Tong University
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#############################################################
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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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