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# ------------------------------------------------------------------------
# Copyright (c) 2022 megvii-model. All Rights Reserved.
# ------------------------------------------------------------------------
# Source: https://github.com/megvii-research/NAFNet
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
class LayerNormFunction(torch.autograd.Function):
@staticmethod
def forward(ctx, x, weight, bias, eps):
ctx.eps = eps
N, C, H, W = x.size()
mu = x.mean(1, keepdim=True)
var = (x - mu).pow(2).mean(1, keepdim=True)
y = (x - mu) / (var + eps).sqrt()
ctx.save_for_backward(y, var, weight)
y = weight.view(1, C, 1, 1) * y + bias.view(1, C, 1, 1)
return y
@staticmethod
def backward(ctx, grad_output):
eps = ctx.eps
N, C, H, W = grad_output.size()
y, var, weight = ctx.saved_variables
g = grad_output * weight.view(1, C, 1, 1)
mean_g = g.mean(dim=1, keepdim=True)
mean_gy = (g * y).mean(dim=1, keepdim=True)
gx = 1. / torch.sqrt(var + eps) * (g - y * mean_gy - mean_g)
return gx, (grad_output * y).sum(dim=3).sum(dim=2).sum(dim=0), grad_output.sum(dim=3).sum(dim=2).sum(
dim=0), None
class LayerNorm2d(nn.Module):
def __init__(self, channels, eps=1e-6):
super(LayerNorm2d, self).__init__()
self.register_parameter('weight', nn.Parameter(torch.ones(channels)))
self.register_parameter('bias', nn.Parameter(torch.zeros(channels)))
self.eps = eps
def forward(self, x):
return LayerNormFunction.apply(x, self.weight, self.bias, self.eps)
class AvgPool2d(nn.Module):
def __init__(self, kernel_size=None, base_size=None, auto_pad=True, fast_imp=False, train_size=None):
super().__init__()
self.kernel_size = kernel_size
self.base_size = base_size
self.auto_pad = auto_pad
# only used for fast implementation
self.fast_imp = fast_imp
self.rs = [5, 4, 3, 2, 1]
self.max_r1 = self.rs[0]
self.max_r2 = self.rs[0]
self.train_size = train_size
def extra_repr(self) -> str:
return 'kernel_size={}, base_size={}, stride={}, fast_imp={}'.format(
self.kernel_size, self.base_size, self.kernel_size, self.fast_imp
)
def forward(self, x):
if self.kernel_size is None and self.base_size:
train_size = self.train_size
if isinstance(self.base_size, int):
self.base_size = (self.base_size, self.base_size)
self.kernel_size = list(self.base_size)
self.kernel_size[0] = x.shape[2] * self.base_size[0] // train_size[-2]
self.kernel_size[1] = x.shape[3] * self.base_size[1] // train_size[-1]
# only used for fast implementation
self.max_r1 = max(1, self.rs[0] * x.shape[2] // train_size[-2])
self.max_r2 = max(1, self.rs[0] * x.shape[3] // train_size[-1])
if self.kernel_size[0] >= x.size(-2) and self.kernel_size[1] >= x.size(-1):
return F.adaptive_avg_pool2d(x, 1)
if self.fast_imp: # Non-equivalent implementation but faster
h, w = x.shape[2:]
if self.kernel_size[0] >= h and self.kernel_size[1] >= w:
out = F.adaptive_avg_pool2d(x, 1)
else:
r1 = [r for r in self.rs if h % r == 0][0]
r2 = [r for r in self.rs if w % r == 0][0]
# reduction_constraint
r1 = min(self.max_r1, r1)
r2 = min(self.max_r2, r2)
s = x[:, :, ::r1, ::r2].cumsum(dim=-1).cumsum(dim=-2)
n, c, h, w = s.shape
k1, k2 = min(h - 1, self.kernel_size[0] // r1), min(w - 1, self.kernel_size[1] // r2)
out = (s[:, :, :-k1, :-k2] - s[:, :, :-k1, k2:] - s[:, :, k1:, :-k2] + s[:, :, k1:, k2:]) / (k1 * k2)
out = torch.nn.functional.interpolate(out, scale_factor=(r1, r2))
else:
n, c, h, w = x.shape
s = x.cumsum(dim=-1).cumsum_(dim=-2)
s = torch.nn.functional.pad(s, (1, 0, 1, 0)) # pad 0 for convenience
k1, k2 = min(h, self.kernel_size[0]), min(w, self.kernel_size[1])
s1, s2, s3, s4 = s[:, :, :-k1, :-k2], s[:, :, :-k1, k2:], s[:, :, k1:, :-k2], s[:, :, k1:, k2:]
out = s4 + s1 - s2 - s3
out = out / (k1 * k2)
if self.auto_pad:
n, c, h, w = x.shape
_h, _w = out.shape[2:]
# print(x.shape, self.kernel_size)
pad2d = ((w - _w) // 2, (w - _w + 1) // 2, (h - _h) // 2, (h - _h + 1) // 2)
out = torch.nn.functional.pad(out, pad2d, mode='replicate')
return out
def replace_layers(model, base_size, train_size, fast_imp, **kwargs):
for n, m in model.named_children():
if len(list(m.children())) > 0:
## compound module, go inside it
replace_layers(m, base_size, train_size, fast_imp, **kwargs)
if isinstance(m, nn.AdaptiveAvgPool2d):
pool = AvgPool2d(base_size=base_size, fast_imp=fast_imp, train_size=train_size)
assert m.output_size == 1
setattr(model, n, pool)
'''
ref.
@article{chu2021tlsc,
title={Revisiting Global Statistics Aggregation for Improving Image Restoration},
author={Chu, Xiaojie and Chen, Liangyu and and Chen, Chengpeng and Lu, Xin},
journal={arXiv preprint arXiv:2112.04491},
year={2021}
}
'''
class Local_Base():
def convert(self, *args, train_size, **kwargs):
replace_layers(self, *args, train_size=train_size, **kwargs)
imgs = torch.rand(train_size)
with torch.no_grad():
self.forward(imgs) |