| | |
| | 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 |
| | 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__() |
| | |
| | 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) |
| |
|
| | |
| | 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 |
| | print([in_nc, out_nc, nf, nb, gc, 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) |
| | |
| | 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 |
| |
|
