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# Copyright [2021] Xintao Wang | |
# | |
# Licensed under the Apache License, Version 2.0 (the "License"); | |
# you may not use this file except in compliance with the License. | |
# You may obtain a copy of the License at | |
# | |
# http://www.apache.org/licenses/LICENSE-2.0 | |
# | |
# Unless required by applicable law or agreed to in writing, software | |
# distributed under the License is distributed on an "AS IS" BASIS, | |
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
# See the License for the specific language governing permissions and | |
# limitations under the License. | |
import functools | |
import torch | |
import torch.nn as nn | |
import torch.nn.functional as F | |
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 | |
# mutil.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, out_nc, nf, nb, gc=32): | |
super(RRDBNet, self).__init__() | |
RRDB_block_f = functools.partial(RRDB, nf=nf, gc=gc) | |
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) | |
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'))) | |
fea = self.lrelu(self.upconv2(F.interpolate(fea, scale_factor=2, mode='nearest'))) | |
out = self.conv_last(self.lrelu(self.HRconv(fea))) | |
return out | |