ResNet

'''
导入库
'''
import torch
import torch.nn as nn
import torchvision
import torch.utils.data as Data
from torch.utils import  model_zoo
import math
from torch.autograd import Variable
from torchvision.transforms import Compose, ToTensor, Resize
import gc

gc.collect()
model_urls = {
    'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
    'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',  
    'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',  
    'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
    'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
}

# 对输入图像进行处理，转换为（224，224）,因为resnet18要求输入为（224，224），并转化为tensor
def input_transform():
    return Compose([
        Resize(224),  # 改变尺寸
        ToTensor(),  # 变成tensor
    ])


# Mnist 手写数字，数据导入
train_data = torchvision.datasets.MNIST(
    root='mnist/',  # 保存或者提取位置
    train=True,  # this is training data
    transform=input_transform(),  # 转换 PIL.Image or numpy.ndarray 成
    # torch.FloatTensor (C x H x W), 训练的时候 normalize 成 [0.0, 1.0] 区间
    download=True,  # 没下载就下载, 下载了就不用再下了
)

test_data = torchvision.datasets.MNIST(
    root='mnist/',  # 保存或者提取位置
    train=False,  # this is training data
    transform=input_transform(),  # 转换 PIL.Image or numpy.ndarray 成
    # torch.FloatTensor (C x H x W), 训练的时候 normalize 成 [0.0, 1.0] 区间
    download=True,  # 没下载就下载, 下载了就不用再下了
)

BATCH_SIZE = 32

'''
进行批处理
'''
loader = Data.DataLoader(dataset=train_data,
                         batch_size=BATCH_SIZE,
                         shuffle=True,
                         )

'''
定义resnet18
'''


def conv3x3(in_planes, out_planes, stride=1):
    "3x3 convolution with padding"
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=1, bias=False)


class BasicBlock(nn.Module):
    expansion = 1

    # inplanes其实就是channel,叫法不同
    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = nn.BatchNorm2d(planes)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        # 把shortcut那的channel的维度统一
        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


class ResNet(nn.Module):
    def __init__(self, block, layers, num_classes=10):
        self.inplanes = 64
        super(ResNet, self).__init__()
        self.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3,  # 因为mnist为（1，28，28）灰度图，因此输入通道数为1
                               bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
        self.avgpool = nn.AvgPool2d(7, stride=1)
        self.fc = nn.Linear(512 * block.expansion, num_classes)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2. / n))
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

    def _make_layer(self, block, planes, blocks, stride=1):
        # downsample 主要用来处理H(x)=F(x)+x中F(x)和xchannel维度不匹配问题
        downsample = None
        # self.inplanes为上个box_block的输出channel,planes为当前box_block块的输入channel
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes))

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(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 x


def resnet18(pretrained=False, **kwargs):
    """Constructs a ResNet-18 model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    # [2, 2, 2, 2]和结构图[]X2是对应的
    model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
    if pretrained:  # 加载模型权重
        model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))
    return model

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# print(device)
net = resnet18().to(device)

optimizer = torch.optim.Adam(net.parameters(), lr=0.01)
loss_func = torch.nn.CrossEntropyLoss()
for epoch in range(3):
    for step, (batch_x, batch_y) in enumerate(loader):
        b_x = Variable(batch_x).to(device)
        b_y = Variable(batch_y).to(device)

        predict = net(b_x)
        loss = loss_func(predict, b_y)

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        if step % 5 == 0:
            print('epoch:{}, step:{}, loss:{}'.format(epoch, step, loss))