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