143 lines
5.0 KiB
Python
143 lines
5.0 KiB
Python
from model.CBAM import CBAM
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import torch
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import torch.nn as nn
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from model.Tool import GeM as gem
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class Bottleneck(nn.Module):
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expansion = 4
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def __init__(self, inchannel, outchannel, stride=1, dowsample=None):
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# super(Bottleneck, self).__init__()
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super().__init__()
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self.conv1 = nn.Conv2d(in_channels=inchannel, out_channels=outchannel, kernel_size=1, stride=1, bias=False)
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self.bn1 = nn.BatchNorm2d(outchannel)
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self.conv2 = nn.Conv2d(in_channels=outchannel, out_channels=outchannel, kernel_size=3, bias=False,
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stride=stride, padding=1)
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self.bn2 = nn.BatchNorm2d(outchannel)
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self.conv3 = nn.Conv2d(in_channels=outchannel, out_channels=outchannel * self.expansion, stride=1, bias=False,
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kernel_size=1)
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self.bn3 = nn.BatchNorm2d(outchannel * self.expansion)
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self.relu = nn.ReLU(inplace=True)
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self.downsample = dowsample
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def forward(self, x):
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self.identity = x
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# print('>>>>>>>>',type(x))
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if self.downsample is not None:
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# print('>>>>downsample>>>>', type(self.downsample))
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self.identity = self.downsample(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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# print('>>>>out>>>identity',out.size(),self.identity.size())
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out = out + self.identity
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out = self.relu(out)
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return out
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class resnet(nn.Module):
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def __init__(self, block=Bottleneck, block_num=[3, 4, 6, 3], num_class=1000):
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super().__init__()
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self.in_channel = 64
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self.conv1 = nn.Conv2d(in_channels=3,
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out_channels=self.in_channel,
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stride=2,
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kernel_size=7,
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padding=3,
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bias=False)
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self.bn1 = nn.BatchNorm2d(self.in_channel)
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self.relu = nn.ReLU(inplace=True)
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self.cbam = CBAM(self.in_channel)
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self.cbam1 = CBAM(2048)
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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, block_num[0], stride=1)
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self.layer2 = self._make_layer(block, 128, block_num[1], stride=2)
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self.layer3 = self._make_layer(block, 256, block_num[2], stride=2)
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self.layer4 = self._make_layer(block, 512, block_num[3], stride=2)
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self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
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self.gem = gem()
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self.fc = nn.Linear(512 * block.expansion, num_class)
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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nn.init.kaiming_normal(m.weight, mode='fan_out',
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nonlinearity='relu')
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if isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
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nn.init.constant_(m.weight, 1.0)
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nn.init.constant_(m.bias, 1.0)
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def _make_layer(self, block, channel, block_num, stride=1):
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downsample = None
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if stride != 1 or self.in_channel != channel * block.expansion:
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downsample = nn.Sequential(
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nn.Conv2d(self.in_channel, channel * block.expansion, kernel_size=1, stride=stride, bias=False),
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nn.BatchNorm2d(channel * block.expansion))
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layer = []
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layer.append(block(self.in_channel, channel, stride, downsample))
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self.in_channel = channel * block.expansion
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for _ in range(1, block_num):
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layer.append(block(self.in_channel, channel))
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return nn.Sequential(*layer)
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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.cbam(x)
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x = self.layer1(x)
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x = self.layer2(x)
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x = self.layer3(x)
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x = self.layer4(x)
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x = self.cbam1(x)
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# x = self.avgpool(x)
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x = self.gem(x)
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x = torch.flatten(x, 1)
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x = self.fc(x)
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return x
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class TripletNet(nn.Module):
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def __init__(self, num_class, flag=True):
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super(TripletNet, self).__init__()
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self.initnet = rescbam(num_class)
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self.flag = flag
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def forward(self, x1, x2=None, x3=None):
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if self.flag:
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output1 = self.initnet(x1)
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output2 = self.initnet(x2)
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output3 = self.initnet(x3)
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return output1, output2, output3
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else:
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output = self.initnet(x1)
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return output
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def rescbam(num_class):
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return resnet(block=Bottleneck, block_num=[3, 4, 6, 3], num_class=num_class)
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if __name__ == '__main__':
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input1 = torch.randn(4, 3, 640, 640)
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input2 = torch.randn(4, 3, 640, 640)
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input3 = torch.randn(4, 3, 640, 640)
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# rescbam测试
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# Resnet50 = rescbam(512)
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# output = Resnet50.forward(input1)
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# print(Resnet50)
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# trnet测试
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trnet = TripletNet(512)
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output = trnet(input1, input2, input3)
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print(output)
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