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| import torch | |
| from torch import nn | |
| import torch.nn.functional as F | |
| from uvr5_pack.lib_v5 import spec_utils | |
| class Conv2DBNActiv(nn.Module): | |
| def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU): | |
| super(Conv2DBNActiv, self).__init__() | |
| self.conv = nn.Sequential( | |
| nn.Conv2d( | |
| nin, | |
| nout, | |
| kernel_size=ksize, | |
| stride=stride, | |
| padding=pad, | |
| dilation=dilation, | |
| bias=False, | |
| ), | |
| nn.BatchNorm2d(nout), | |
| activ(), | |
| ) | |
| def __call__(self, x): | |
| return self.conv(x) | |
| class SeperableConv2DBNActiv(nn.Module): | |
| def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU): | |
| super(SeperableConv2DBNActiv, self).__init__() | |
| self.conv = nn.Sequential( | |
| nn.Conv2d( | |
| nin, | |
| nin, | |
| kernel_size=ksize, | |
| stride=stride, | |
| padding=pad, | |
| dilation=dilation, | |
| groups=nin, | |
| bias=False, | |
| ), | |
| nn.Conv2d(nin, nout, kernel_size=1, bias=False), | |
| nn.BatchNorm2d(nout), | |
| activ(), | |
| ) | |
| def __call__(self, x): | |
| return self.conv(x) | |
| class Encoder(nn.Module): | |
| def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU): | |
| super(Encoder, self).__init__() | |
| self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ) | |
| self.conv2 = Conv2DBNActiv(nout, nout, ksize, stride, pad, activ=activ) | |
| def __call__(self, x): | |
| skip = self.conv1(x) | |
| h = self.conv2(skip) | |
| return h, skip | |
| class Decoder(nn.Module): | |
| def __init__( | |
| self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False | |
| ): | |
| super(Decoder, self).__init__() | |
| self.conv = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ) | |
| self.dropout = nn.Dropout2d(0.1) if dropout else None | |
| def __call__(self, x, skip=None): | |
| x = F.interpolate(x, scale_factor=2, mode="bilinear", align_corners=True) | |
| if skip is not None: | |
| skip = spec_utils.crop_center(skip, x) | |
| x = torch.cat([x, skip], dim=1) | |
| h = self.conv(x) | |
| if self.dropout is not None: | |
| h = self.dropout(h) | |
| return h | |
| class ASPPModule(nn.Module): | |
| def __init__(self, nin, nout, dilations=(4, 8, 16, 32, 64), activ=nn.ReLU): | |
| super(ASPPModule, self).__init__() | |
| self.conv1 = nn.Sequential( | |
| nn.AdaptiveAvgPool2d((1, None)), | |
| Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ), | |
| ) | |
| self.conv2 = Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ) | |
| self.conv3 = SeperableConv2DBNActiv( | |
| nin, nin, 3, 1, dilations[0], dilations[0], activ=activ | |
| ) | |
| self.conv4 = SeperableConv2DBNActiv( | |
| nin, nin, 3, 1, dilations[1], dilations[1], activ=activ | |
| ) | |
| self.conv5 = SeperableConv2DBNActiv( | |
| nin, nin, 3, 1, dilations[2], dilations[2], activ=activ | |
| ) | |
| self.conv6 = SeperableConv2DBNActiv( | |
| nin, nin, 3, 1, dilations[2], dilations[2], activ=activ | |
| ) | |
| self.conv7 = SeperableConv2DBNActiv( | |
| nin, nin, 3, 1, dilations[2], dilations[2], activ=activ | |
| ) | |
| self.bottleneck = nn.Sequential( | |
| Conv2DBNActiv(nin * 7, nout, 1, 1, 0, activ=activ), nn.Dropout2d(0.1) | |
| ) | |
| def forward(self, x): | |
| _, _, h, w = x.size() | |
| feat1 = F.interpolate( | |
| self.conv1(x), size=(h, w), mode="bilinear", align_corners=True | |
| ) | |
| feat2 = self.conv2(x) | |
| feat3 = self.conv3(x) | |
| feat4 = self.conv4(x) | |
| feat5 = self.conv5(x) | |
| feat6 = self.conv6(x) | |
| feat7 = self.conv7(x) | |
| out = torch.cat((feat1, feat2, feat3, feat4, feat5, feat6, feat7), dim=1) | |
| bottle = self.bottleneck(out) | |
| return bottle | |