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import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
def default_conv(in_channels, out_channels, kernel_size, bias=True):
return nn.Conv2d(
in_channels, out_channels, kernel_size,
padding=(kernel_size//2), bias=bias)
class MeanShift(nn.Conv2d):
def __init__(self, rgb_range, rgb_mean, rgb_std, sign=-1):
super(MeanShift, self).__init__(3, 3, kernel_size=1)
std = torch.Tensor(rgb_std)
self.weight.data = torch.eye(3).view(3, 3, 1, 1)
self.weight.data.div_(std.view(3, 1, 1, 1))
self.bias.data = sign * rgb_range * torch.Tensor(rgb_mean)
self.bias.data.div_(std)
self.requires_grad = False
class BasicBlock(nn.Sequential):
def __init__(
self, conv, in_channels, out_channels, kernel_size, stride=1, bias=True,
bn=False, act=nn.ReLU(True)):
m = [conv(in_channels, out_channels, kernel_size, bias=bias)]
if bn:
m.append(nn.BatchNorm2d(out_channels))
if act is not None:
m.append(act)
super(BasicBlock, self).__init__(*m)
class ResBlock(nn.Module):
def __init__(
self, conv, n_feat, kernel_size,
bias=True, bn=False, act=nn.ReLU(True), res_scale=1):
super(ResBlock, self).__init__()
m = []
for i in range(2):
m.append(conv(n_feat, n_feat, kernel_size, bias=bias))
if bn: m.append(nn.BatchNorm2d(n_feat))
if i == 0: m.append(act)
self.body = nn.Sequential(*m)
self.res_scale = res_scale
def forward(self, x):
res = self.body(x).mul(self.res_scale)
res += x
return res
class Upsampler(nn.Sequential):
def __init__(self, conv, scale, n_feat, bn=False, act=False, bias=True):
m = []
if (scale & (scale - 1)) == 0: # Is scale = 2^n?
for _ in range(int(math.log(scale, 2))):
m.append(conv(n_feat, 4 * n_feat, 3, bias))
m.append(nn.PixelShuffle(2))
if bn: m.append(nn.BatchNorm2d(n_feat))
if act: m.append(act())
elif scale == 3:
m.append(conv(n_feat, 9 * n_feat, 3, bias))
m.append(nn.PixelShuffle(3))
if bn: m.append(nn.BatchNorm2d(n_feat))
if act: m.append(act())
else:
raise NotImplementedError
super(Upsampler, self).__init__(*m)
class DownBlock(nn.Module):
def __init__(self, scale):
super().__init__()
self.scale = scale
def forward(self, x):
n, c, h, w = x.size()
x = x.view(n, c, h//self.scale, self.scale, w//self.scale, self.scale)
x = x.permute(0, 3, 5, 1, 2, 4).contiguous()
x = x.view(n, c * (self.scale**2), h//self.scale, w//self.scale)
return x
# NONLocalBlock2D
# ref: https://github.com/AlexHex7/Non-local_pytorch/blob/master/Non-Local_pytorch_0.4.1_to_1.1.0/lib/non_local_dot_product.py
# ref: https://github.com/yulunzhang/RNAN/blob/master/SR/code/model/common.py
class NonLocalBlock2D(nn.Module):
def __init__(self, in_channels, inter_channels):
super(NonLocalBlock2D, self).__init__()
self.in_channels = in_channels
self.inter_channels = inter_channels
self.g = nn.Conv2d(in_channels=in_channels, out_channels=inter_channels,
kernel_size=1, stride=1, padding=0)
self.W = nn.Conv2d(in_channels=inter_channels, out_channels=in_channels,
kernel_size=1, stride=1, padding=0)
nn.init.constant_(self.W.weight, 0)
nn.init.constant_(self.W.bias, 0)
self.theta = nn.Conv2d(in_channels=self.in_channels, out_channels=self.inter_channels,
kernel_size=1, stride=1, padding=0)
self.phi = nn.Conv2d(in_channels=self.in_channels, out_channels=self.inter_channels,
kernel_size=1, stride=1, padding=0)
def forward(self, x):
batch_size = x.size(0)
g_x = self.g(x).view(batch_size, self.inter_channels, -1)
g_x = g_x.permute(0, 2, 1)
theta_x = self.theta(x).view(batch_size, self.inter_channels, -1)
theta_x = theta_x.permute(0, 2, 1)
phi_x = self.phi(x).view(batch_size, self.inter_channels, -1)
f = torch.matmul(theta_x, phi_x)
# use dot production
# N = f.size(-1)
# f_div_C = f / N
# use embedding gaussian
f_div_C = F.softmax(f, dim=-1)
y = torch.matmul(f_div_C, g_x)
y = y.permute(0, 2, 1).contiguous()
y = y.view(batch_size, self.inter_channels, *x.size()[2:])
W_y = self.W(y)
z = W_y + x
return z |