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import torch |
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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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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_extractor(nn.Module): |
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def __init__(self, block, layers, input_channels=3, cardinality=32): |
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super(SEResNeXt_extractor, 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.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) |
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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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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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x = self.relu(x) |
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x = self.maxpool(x) |
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x = self.layer1(x) |
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x = self.layer2(x) |
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return x |
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def get_seresnext_extractor(): |
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return SEResNeXt_extractor(BottleneckX_Origin, [3, 4, 6, 3], 1) |
