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Zero
Running
on
Zero
| # From BSRGAN | |
| 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 | |
| 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) | |
| #### 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 | |