102 lines
3.6 KiB
Python
102 lines
3.6 KiB
Python
import functools
|
|
import torch
|
|
import torch.nn as nn
|
|
import torch.nn.functional as F
|
|
import torch.nn.init as init
|
|
|
|
|
|
def initialize_weights(net_l, scale=1):
|
|
if not isinstance(net_l, list):
|
|
net_l = [net_l]
|
|
for net in net_l:
|
|
for m in net.modules():
|
|
if isinstance(m, nn.Conv2d):
|
|
init.kaiming_normal_(m.weight, a=0, mode='fan_in')
|
|
m.weight.data *= scale # for residual block
|
|
if m.bias is not None:
|
|
m.bias.data.zero_()
|
|
elif isinstance(m, nn.Linear):
|
|
init.kaiming_normal_(m.weight, a=0, mode='fan_in')
|
|
m.weight.data *= scale
|
|
if m.bias is not None:
|
|
m.bias.data.zero_()
|
|
elif isinstance(m, nn.BatchNorm2d):
|
|
init.constant_(m.weight, 1)
|
|
init.constant_(m.bias.data, 0.0)
|
|
|
|
|
|
def make_layer(block, n_layers):
|
|
layers = []
|
|
for _ in range(n_layers):
|
|
layers.append(block())
|
|
return nn.Sequential(*layers)
|
|
|
|
|
|
class ResidualDenseBlock_5C(nn.Module):
|
|
def __init__(self, nf=64, gc=32, bias=True):
|
|
super(ResidualDenseBlock_5C, self).__init__()
|
|
# gc: growth channel, i.e. intermediate channels
|
|
self.conv1 = nn.Conv2d(nf, gc, 3, 1, 1, bias=bias)
|
|
self.conv2 = nn.Conv2d(nf + gc, gc, 3, 1, 1, bias=bias)
|
|
self.conv3 = nn.Conv2d(nf + 2 * gc, gc, 3, 1, 1, bias=bias)
|
|
self.conv4 = nn.Conv2d(nf + 3 * gc, gc, 3, 1, 1, bias=bias)
|
|
self.conv5 = nn.Conv2d(nf + 4 * gc, nf, 3, 1, 1, bias=bias)
|
|
self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
|
|
|
|
# initialization
|
|
initialize_weights([self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1)
|
|
|
|
def forward(self, x):
|
|
x1 = self.lrelu(self.conv1(x))
|
|
x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1)))
|
|
x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1)))
|
|
x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1)))
|
|
x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
|
|
return x5 * 0.2 + x
|
|
|
|
|
|
class RRDB(nn.Module):
|
|
'''Residual in Residual Dense Block'''
|
|
|
|
def __init__(self, nf, gc=32):
|
|
super(RRDB, self).__init__()
|
|
self.RDB1 = ResidualDenseBlock_5C(nf, gc)
|
|
self.RDB2 = ResidualDenseBlock_5C(nf, gc)
|
|
self.RDB3 = ResidualDenseBlock_5C(nf, gc)
|
|
|
|
def forward(self, x):
|
|
out = self.RDB1(x)
|
|
out = self.RDB2(out)
|
|
out = self.RDB3(out)
|
|
return out * 0.2 + x
|
|
|
|
|
|
class RRDBNet(nn.Module):
|
|
def __init__(self, in_nc=3, out_nc=3, nf=64, nb=23, gc=32, sf=4):
|
|
super(RRDBNet, self).__init__()
|
|
RRDB_block_f = functools.partial(RRDB, nf=nf, gc=gc)
|
|
self.sf = sf
|
|
|
|
self.conv_first = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True)
|
|
self.RRDB_trunk = make_layer(RRDB_block_f, nb)
|
|
self.trunk_conv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
|
|
#### upsampling
|
|
self.upconv1 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
|
|
if self.sf==4:
|
|
self.upconv2 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
|
|
self.HRconv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
|
|
self.conv_last = nn.Conv2d(nf, out_nc, 3, 1, 1, bias=True)
|
|
|
|
self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
|
|
|
|
def forward(self, x):
|
|
fea = self.conv_first(x)
|
|
trunk = self.trunk_conv(self.RRDB_trunk(fea))
|
|
fea = fea + trunk
|
|
|
|
fea = self.lrelu(self.upconv1(F.interpolate(fea, scale_factor=2, mode='nearest')))
|
|
if self.sf==4:
|
|
fea = self.lrelu(self.upconv2(F.interpolate(fea, scale_factor=2, mode='nearest')))
|
|
out = self.conv_last(self.lrelu(self.HRconv(fea)))
|
|
|
|
return out |