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