mirror of
https://github.com/Nighthawk42/manga-colorizer.git
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127 lines
4.0 KiB
Python
127 lines
4.0 KiB
Python
import torch.nn as nn
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import math
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'''https://github.com/blandocs/Tag2Pix/blob/master/model/pretrained.py'''
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# Pretrained version
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class Selayer(nn.Module):
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def __init__(self, inplanes):
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super(Selayer, self).__init__()
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self.global_avgpool = nn.AdaptiveAvgPool2d(1)
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self.conv1 = nn.Conv2d(inplanes, inplanes // 16, kernel_size=1, stride=1)
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self.conv2 = nn.Conv2d(inplanes // 16, inplanes, kernel_size=1, stride=1)
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self.relu = nn.ReLU(inplace=True)
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self.sigmoid = nn.Sigmoid()
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def forward(self, x):
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out = self.global_avgpool(x)
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out = self.conv1(out)
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out = self.relu(out)
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out = self.conv2(out)
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out = self.sigmoid(out)
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return x * out
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class BottleneckX_Origin(nn.Module):
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expansion = 4
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def __init__(self, inplanes, planes, cardinality, stride=1, downsample=None):
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super(BottleneckX_Origin, self).__init__()
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self.conv1 = nn.Conv2d(inplanes, planes * 2, kernel_size=1, bias=False)
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self.bn1 = nn.BatchNorm2d(planes * 2)
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self.conv2 = nn.Conv2d(planes * 2, planes * 2, kernel_size=3, stride=stride,
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padding=1, groups=cardinality, bias=False)
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self.bn2 = nn.BatchNorm2d(planes * 2)
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self.conv3 = nn.Conv2d(planes * 2, planes * 4, kernel_size=1, bias=False)
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self.bn3 = nn.BatchNorm2d(planes * 4)
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self.selayer = Selayer(planes * 4)
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self.relu = nn.ReLU(inplace=True)
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self.downsample = downsample
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self.stride = stride
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def forward(self, x):
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residual = x
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out = self.conv1(x)
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out = self.bn1(out)
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out = self.relu(out)
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out = self.conv2(out)
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out = self.bn2(out)
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out = self.relu(out)
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out = self.conv3(out)
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out = self.bn3(out)
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out = self.selayer(out)
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if self.downsample is not None:
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residual = self.downsample(x)
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out += residual
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out = self.relu(out)
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return out
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class SEResNeXt_Origin(nn.Module):
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def __init__(self, block, layers, input_channels=3, cardinality=32, num_classes=1000):
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super(SEResNeXt_Origin, self).__init__()
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self.cardinality = cardinality
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self.inplanes = 64
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self.input_channels = input_channels
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self.conv1 = nn.Conv2d(input_channels, 64, kernel_size=7, stride=2, padding=3,
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bias=False)
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self.bn1 = nn.BatchNorm2d(64)
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self.relu = nn.ReLU(inplace=True)
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self.layer1 = self._make_layer(block, 64, layers[0])
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self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
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self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
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m.weight.data.normal_(0, math.sqrt(2. / n))
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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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m.weight.data.fill_(1)
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m.bias.data.zero_()
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def _make_layer(self, block, planes, blocks, stride=1):
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downsample = None
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if stride != 1 or self.inplanes != planes * block.expansion:
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downsample = nn.Sequential(
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nn.Conv2d(self.inplanes, planes * block.expansion,
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kernel_size=1, stride=stride, bias=False),
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nn.BatchNorm2d(planes * block.expansion),
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)
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layers = []
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layers.append(block(self.inplanes, planes, self.cardinality, stride, downsample))
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self.inplanes = planes * block.expansion
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for i in range(1, blocks):
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layers.append(block(self.inplanes, planes, self.cardinality))
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return nn.Sequential(*layers)
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def forward(self, x):
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x = self.conv1(x)
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x = self.bn1(x)
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x1 = self.relu(x)
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x2 = self.layer1(x1)
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x3 = self.layer2(x2)
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x4 = self.layer3(x3)
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return x1, x2, x3, x4
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