PyTorch实现ResNet50、ResNet101和ResNet152示例

Python  /  管理员 发布于 5年前   409

PyTorch: https://github.com/shanglianlm0525/PyTorch-Networks

import torchimport torch.nn as nnimport torchvisionimport numpy as npprint("PyTorch Version: ",torch.__version__)print("Torchvision Version: ",torchvision.__version__)__all__ = ['ResNet50', 'ResNet101','ResNet152']def Conv1(in_planes, places, stride=2):  return nn.Sequential(    nn.Conv2d(in_channels=in_planes,out_channels=places,kernel_size=7,stride=stride,padding=3, bias=False),    nn.BatchNorm2d(places),    nn.ReLU(inplace=True),    nn.MaxPool2d(kernel_size=3, stride=2, padding=1)  )class Bottleneck(nn.Module):  def __init__(self,in_places,places, stride=1,downsampling=False, expansion = 4):    super(Bottleneck,self).__init__()    self.expansion = expansion    self.downsampling = downsampling    self.bottleneck = nn.Sequential(      nn.Conv2d(in_channels=in_places,out_channels=places,kernel_size=1,stride=1, bias=False),      nn.BatchNorm2d(places),      nn.ReLU(inplace=True),      nn.Conv2d(in_channels=places, out_channels=places, kernel_size=3, stride=stride, padding=1, bias=False),      nn.BatchNorm2d(places),      nn.ReLU(inplace=True),      nn.Conv2d(in_channels=places, out_channels=places*self.expansion, kernel_size=1, stride=1, bias=False),      nn.BatchNorm2d(places*self.expansion),    )    if self.downsampling:      self.downsample = nn.Sequential(        nn.Conv2d(in_channels=in_places, out_channels=places*self.expansion, kernel_size=1, stride=stride, bias=False),        nn.BatchNorm2d(places*self.expansion)      )    self.relu = nn.ReLU(inplace=True)  def forward(self, x):    residual = x    out = self.bottleneck(x)    if self.downsampling:      residual = self.downsample(x)    out += residual    out = self.relu(out)    return outclass ResNet(nn.Module):  def __init__(self,blocks, num_classes=1000, expansion = 4):    super(ResNet,self).__init__()    self.expansion = expansion    self.conv1 = Conv1(in_planes = 3, places= 64)    self.layer1 = self.make_layer(in_places = 64, places= 64, block=blocks[0], stride=1)    self.layer2 = self.make_layer(in_places = 256,places=128, block=blocks[1], stride=2)    self.layer3 = self.make_layer(in_places=512,places=256, block=blocks[2], stride=2)    self.layer4 = self.make_layer(in_places=1024,places=512, block=blocks[3], stride=2)    self.avgpool = nn.AvgPool2d(7, stride=1)    self.fc = nn.Linear(2048,num_classes)    for m in self.modules():      if isinstance(m, nn.Conv2d):        nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')      elif isinstance(m, nn.BatchNorm2d):        nn.init.constant_(m.weight, 1)        nn.init.constant_(m.bias, 0)  def make_layer(self, in_places, places, block, stride):    layers = []    layers.append(Bottleneck(in_places, places,stride, downsampling =True))    for i in range(1, block):      layers.append(Bottleneck(places*self.expansion, places))    return nn.Sequential(*layers)  def forward(self, x):    x = self.conv1(x)    x = self.layer1(x)    x = self.layer2(x)    x = self.layer3(x)    x = self.layer4(x)    x = self.avgpool(x)    x = x.view(x.size(0), -1)    x = self.fc(x)    return xdef ResNet50():  return ResNet([3, 4, 6, 3])def ResNet101():  return ResNet([3, 4, 23, 3])def ResNet152():  return ResNet([3, 8, 36, 3])if __name__=='__main__':  #model = torchvision.models.resnet50()  model = ResNet50()  print(model)  input = torch.randn(1, 3, 224, 224)  out = model(input)  print(out.shape)

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