forked from mrq/DL-Art-School
87 lines
2.7 KiB
Python
87 lines
2.7 KiB
Python
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, in_channels, out_channels, kernel_size, stride=1, bias=False,
|
|
bn=True, act=nn.ReLU(True)):
|
|
|
|
m = [nn.Conv2d(
|
|
in_channels, out_channels, kernel_size,
|
|
padding=(kernel_size//2), stride=stride, 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_feats, 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_feats, n_feats, kernel_size, bias=bias))
|
|
if bn: m.append(nn.BatchNorm2d(n_feats))
|
|
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_feats, bn=False, act=False, bias=False):
|
|
|
|
m = []
|
|
if (scale & (scale - 1)) == 0: # Is scale = 2^n?
|
|
for _ in range(int(math.log(scale, 2))):
|
|
m.append(conv(n_feats, 4 * n_feats, 3, bias))
|
|
m.append(nn.PixelShuffle(2))
|
|
if bn: m.append(nn.BatchNorm2d(n_feats))
|
|
|
|
if act == 'relu':
|
|
m.append(nn.ReLU(True))
|
|
elif act == 'prelu':
|
|
m.append(nn.PReLU(n_feats))
|
|
|
|
elif scale == 3:
|
|
m.append(conv(n_feats, 9 * n_feats, 3, bias))
|
|
m.append(nn.PixelShuffle(3))
|
|
if bn: m.append(nn.BatchNorm2d(n_feats))
|
|
|
|
if act == 'relu':
|
|
m.append(nn.ReLU(True))
|
|
elif act == 'prelu':
|
|
m.append(nn.PReLU(n_feats))
|
|
else:
|
|
raise NotImplementedError
|
|
|
|
super(Upsampler, self).__init__(*m)
|