cifar10主要是由32x32的三通道彩色图, 总共10个类别,这里我们使用残差网络构造网络结构
网络结构:
第一层:首先经过一个卷积,归一化,激活 32x32x16 -> 32x32x16
第二层: 通过一多个残差模型
残差模块的网络构造:
如果stride != 1 or in_channel != out_channel, 就构造downsample网络结构进行降采样操作
利用残差模块进行第一次残差卷积, 将downsample传入
连续进行多次的残差卷积
from torchvision import transforms from torch import nn # 首先对图片进行数据转换 train_transform = transforms.Compose([ transforms.Scale(40), # 相当于是resize操作, transforms.RandomHorizontalFlip(), # 表示进行左右的翻转 transforms.RandomCrop(32), #表示进行随机的裁剪 transforms.ToTensor(), # 将数据转换为tensor格式 transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]) # 进行-均值 / 标准差, 将数据转换为-1, 1 之间 ]) test_transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]) ]) def conv3x3(in_channels, out_channels, stride=1): return nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False) class ResidualBlock(nn.Module): def __init__(self, in_channels, out_channels, stride=1, downsample=None): super(ResidualBlock, self).__init__() self.conv1 = conv3x3(in_channels, out_channels, stride=1) self.bn = nn.BatchNorm2d(out_channels) self.relu = nn.ReLU(True) self.conv2 = conv3x3(out_channels, out_channels, stride=1) self.bn = nn.BatchNorm2d(out_channels) self.downsample = downsample def forward(self, x): residual = x out = self.conv1(x) out = self.bn(x) out = self.relu(x) out = self.conv2(x) out = self.bn(x) if self.downsample: residual = self.downsample(x) out += residual return self.relu(out) class ResNet(nn.Module): def __init__(self, block, layers, num_classes=10): super(ResNet, self).__init__() self.in_channels = 16 self.conv = conv3x3(3, 16) self.bn = nn.BatchNorm2d(self.in_channels) self.relu = nn.ReLU(True) self.layers1 = self.make_block(block, 16, layers[0]) self.layers2 = self.make_block(block, 32, layers[0]) self.layers3 = self.make_block(block, 64, layers[1]) self.avg_pool = nn.AvgPool2d(8) self.fc = nn.Linear(64, num_classes) def make_block(self, block, out_channels, blocks, stride=1): downsample = None if stride != 1 or out_channels != self.in_channels: downsample = nn.Sequential(conv3x3(self.in_channels, out_channels, stride=stride), nn.BatchNorm2d(out_channels)) layers = [] layers.append(block(self.in_channels, out_channels, stride=stride, downsample = downsample)) for i in blocks: layers.append(block(self.out_channels, out_channels, stride=stride, downsample=downsample)) return nn.Sequential(*layers) def forward(self, x): out = self.conv(x) out = self.bn(out) out = self.relu(out) out = self.layers1(out) out= self.layers2(out) out = self.layers3(out) out = self.avg_pool(out) out = self.fc(out) return out