117 lines
3.6 KiB
Python
117 lines
3.6 KiB
Python
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import torch
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from torch import nn
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import torch.nn.functional as F
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from uvr5_pack.lib_v5 import spec_utils
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class Conv2DBNActiv(nn.Module):
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def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
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super(Conv2DBNActiv, self).__init__()
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self.conv = nn.Sequential(
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nn.Conv2d(
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nin, nout,
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kernel_size=ksize,
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stride=stride,
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padding=pad,
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dilation=dilation,
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bias=False),
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nn.BatchNorm2d(nout),
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activ()
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)
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def __call__(self, x):
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return self.conv(x)
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class SeperableConv2DBNActiv(nn.Module):
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def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
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super(SeperableConv2DBNActiv, self).__init__()
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self.conv = nn.Sequential(
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nn.Conv2d(
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nin, nin,
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kernel_size=ksize,
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stride=stride,
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padding=pad,
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dilation=dilation,
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groups=nin,
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bias=False),
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nn.Conv2d(
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nin, nout,
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kernel_size=1,
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bias=False),
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nn.BatchNorm2d(nout),
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activ()
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)
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def __call__(self, x):
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return self.conv(x)
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class Encoder(nn.Module):
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def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU):
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super(Encoder, self).__init__()
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self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)
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self.conv2 = Conv2DBNActiv(nout, nout, ksize, stride, pad, activ=activ)
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def __call__(self, x):
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skip = self.conv1(x)
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h = self.conv2(skip)
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return h, skip
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class Decoder(nn.Module):
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def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False):
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super(Decoder, self).__init__()
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self.conv = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)
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self.dropout = nn.Dropout2d(0.1) if dropout else None
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def __call__(self, x, skip=None):
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x = F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=True)
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if skip is not None:
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skip = spec_utils.crop_center(skip, x)
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x = torch.cat([x, skip], dim=1)
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h = self.conv(x)
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if self.dropout is not None:
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h = self.dropout(h)
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return h
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class ASPPModule(nn.Module):
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def __init__(self, nin, nout, dilations=(4, 8, 16), activ=nn.ReLU):
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super(ASPPModule, self).__init__()
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self.conv1 = nn.Sequential(
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nn.AdaptiveAvgPool2d((1, None)),
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Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ)
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)
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self.conv2 = Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ)
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self.conv3 = SeperableConv2DBNActiv(
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nin, nin, 3, 1, dilations[0], dilations[0], activ=activ)
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self.conv4 = SeperableConv2DBNActiv(
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nin, nin, 3, 1, dilations[1], dilations[1], activ=activ)
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self.conv5 = SeperableConv2DBNActiv(
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nin, nin, 3, 1, dilations[2], dilations[2], activ=activ)
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self.bottleneck = nn.Sequential(
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Conv2DBNActiv(nin * 5, nout, 1, 1, 0, activ=activ),
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nn.Dropout2d(0.1)
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)
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def forward(self, x):
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_, _, h, w = x.size()
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feat1 = F.interpolate(self.conv1(x), size=(h, w), mode='bilinear', align_corners=True)
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feat2 = self.conv2(x)
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feat3 = self.conv3(x)
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feat4 = self.conv4(x)
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feat5 = self.conv5(x)
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out = torch.cat((feat1, feat2, feat3, feat4, feat5), dim=1)
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bottle = self.bottleneck(out)
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return bottle
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