cifar-10分类

import torch
from torchvision import datasets, transforms
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
import onnx
import time
import numpy as np
import matplotlib.pyplot as plt

class Block(nn.Module):
    def __init__(self, inchannel, outchannel, res=True, stride=1):
        super(Block, self).__init__()
        self.res = res     # 是否带残差连接
        self.left = nn.Sequential(
            nn.Conv2d(inchannel, outchannel, kernel_size=3, padding=1, stride=stride, bias=False),
            nn.BatchNorm2d(outchannel),
            nn.ReLU(inplace=True),
            nn.Conv2d(outchannel, outchannel, kernel_size=3, padding=1, stride=1, bias=False),
            nn.BatchNorm2d(outchannel),
        )
        if stride != 1 or inchannel != outchannel:
            self.shortcut = nn.Sequential(
                nn.Conv2d(inchannel, outchannel, kernel_size=1, bias=False),
                nn.BatchNorm2d(outchannel),
            )
        else:
            self.shortcut = nn.Sequential()

        self.relu = nn.Sequential(
            nn.ReLU(inplace=True),
        )

    def forward(self, x):
        out = self.left(x)
        if self.res:
            out += self.shortcut(x)
        out = self.relu(out)
        return out

class myModel(nn.Module):
    def __init__(self, cfg=[64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512,'M'], res=True):
        super(myModel, self).__init__()
        self.res = res       # 是否带残差连接
        self.cfg = cfg       # 配置列表
        self.inchannel = 3   # 初始输入通道数
        self.futures = self.make_layer()
        # 构建卷积层之后的全连接层以及分类器：
        self.classifier = nn.Sequential(nn.Dropout(0.4),           # 两层fc效果还差一些
                                        nn.Linear(4 * 512, 10), )   # fc，最终Cifar10输出是10类

    def make_layer(self):
        layers = []
        for v in self.cfg:
            if v == 'M':
                layers.append(nn.MaxPool2d(kernel_size=2, stride=2))
            else:
                layers.append(Block(self.inchannel, v, self.res))
                self.inchannel = v    # 输入通道数改为上一层的输出通道数
        return nn.Sequential(*layers)

    def forward(self, x):
        out = self.futures(x)
        # view(out.size(0), -1): change tensor size from (N ,H , W) to (N, H*W)
        out = out.view(out.size(0), -1)
        out = self.classifier(out)
        return out

all_start = time.time()
# 使用torchvision可以很方便地下载Cifar10数据集，而torchvision下载的数据集为[0,1]的PILImage格式
# 我们需要将张量Tensor归一化到[-1,1]
norm_mean = [0.485, 0.456, 0.406]  # 均值
norm_std = [0.229, 0.224, 0.225]  # 方差
transform_train = transforms.Compose([transforms.ToTensor(),  # 将PILImage转换为张量
                                      # 将[0,1]归一化到[-1,1]
                                      transforms.Normalize(norm_mean, norm_std),
                                      transforms.RandomHorizontalFlip(),  # 随机水平镜像
                                      transforms.RandomErasing(scale=(0.04, 0.2), ratio=(0.5, 2)),  # 随机遮挡
                                      transforms.RandomCrop(32, padding=4)  # 随机中心裁剪
                                      ])

transform_test = transforms.Compose([transforms.ToTensor(),
                                     transforms.Normalize(norm_mean, norm_std)])

# 超参数：
batch_size = 256
num_epochs = 200   # 训练轮数
LR = 0.01          # 初始学习率

# 选择数据集:
trainset = datasets.CIFAR10(root='Datasets', train=True, download=True, transform=transform_train)
testset = datasets.CIFAR10(root='Datasets', train=False, download=True, transform=transform_test)
# 加载数据:
train_data = DataLoader(dataset=trainset, batch_size=batch_size, shuffle=True)
valid_data = DataLoader(dataset=testset, batch_size=batch_size, shuffle=False)
cifar10_classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')

train_data_size = len(trainset)
valid_data_size = len(testset)

print('train_size: {:4d}  valid_size:{:4d}'.format(train_data_size, valid_data_size))

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

model = myModel(res=True)

# 定义损失函数和优化器
loss_func = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=LR, momentum=0.9, weight_decay=5e-3)

# 学习率调整策略 MultiStep：
scheduler = optim.lr_scheduler.MultiStepLR(optimizer=optimizer,
                                           milestones=[int(num_epochs * 0.56), int(num_epochs * 0.78)],
                                           gamma=0.1, last_epoch=-1)

# 训练和验证：
def train_and_valid(model, loss_function, optimizer, epochs=10):
    model.to(device)
    history = []
    best_acc = 0.0
    best_epoch = 0

    for epoch in range(epochs):
        epoch_start = time.time()
        print("Epoch: {}/{}".format(epoch + 1, epochs))

        model.train()

        train_loss = 0.0
        train_acc = 0.0
        valid_loss = 0.0
        valid_acc = 0.0

        for i, (inputs, labels) in enumerate(train_data):
            inputs = inputs.to(device)
            labels = labels.to(device)

            # 因为这里梯度是累加的，所以每次记得清零
            optimizer.zero_grad()

            outputs = model(inputs)

            loss = loss_function(outputs, labels)

            loss.backward()

            optimizer.step()

            train_loss += loss.item() * inputs.size(0)

            ret, predictions = torch.max(outputs.data, 1)
            correct_counts = predictions.eq(labels.data.view_as(predictions))

            acc = torch.mean(correct_counts.type(torch.FloatTensor))

            train_acc += acc.item() * inputs.size(0)

        with torch.no_grad():
            model.eval()

            for j, (inputs, labels) in enumerate(valid_data):
                inputs = inputs.to(device)
                labels = labels.to(device)

                outputs = model(inputs)

                loss = loss_function(outputs, labels)

                valid_loss += loss.item() * inputs.size(0)

                ret, predictions = torch.max(outputs.data, 1)
                correct_counts = predictions.eq(labels.data.view_as(predictions))

                acc = torch.mean(correct_counts.type(torch.FloatTensor))

                valid_acc += acc.item() * inputs.size(0)
        # 更新学习率并查看当前学习率
        scheduler.step()
        print('\t last_lr:', scheduler.get_last_lr())

        avg_train_loss = train_loss / train_data_size
        avg_train_acc = train_acc / train_data_size

        avg_valid_loss = valid_loss / valid_data_size
        avg_valid_acc = valid_acc / valid_data_size

        history.append([avg_train_loss, avg_valid_loss, avg_train_acc, avg_valid_acc])

        if best_acc < avg_valid_acc:
            best_acc = avg_valid_acc
            best_epoch = epoch + 1

        epoch_end = time.time()

        print(
            "\t Training: Loss: {:.4f}, Accuracy: {:.4f}%, "
            "\n\t Validation: Loss: {:.4f}, Accuracy: {:.4f}%, Time: {:.3f}s".format(
                avg_train_loss, avg_train_acc * 100, avg_valid_loss, avg_valid_acc * 100,
                                epoch_end - epoch_start
            ))
        print("\t Best Accuracy for validation : {:.4f} at epoch {:03d}".format(best_acc, best_epoch))

        torch.save(model, '%s/' % 'cifar10_my' + '%02d' % (epoch + 1) + '.pt')  # 保存模型

    return model, history

trained_model, history = train_and_valid(model, loss_func, optimizer, num_epochs)

history = np.array(history)
# Loss曲线
plt.figure(figsize=(10, 10))
plt.plot(history[:, 0:2])
plt.legend(['Tr Loss', 'Val Loss'])
plt.xlabel('Epoch Number')
plt.ylabel('Loss')
# 设置坐标轴刻度
plt.xticks(np.arange(0, num_epochs + 1, step=10))
plt.yticks(np.arange(0, 2.05, 0.1))
plt.grid()  # 画出网格
plt.savefig('cifar10_shuffle_' + '_loss_curve1.png')

# 精度曲线
plt.figure(figsize=(10, 10))
plt.plot(history[:, 2:4])
plt.legend(['Tr Accuracy', 'Val Accuracy'])
plt.xlabel('Epoch Number')
plt.ylabel('Accuracy')
# 设置坐标轴刻度
plt.xticks(np.arange(0, num_epochs + 1, step=10))
plt.yticks(np.arange(0, 1.05, 0.05))
plt.grid()  # 画出网格
plt.savefig('cifar10_shuffle_' + '_accuracy_curve1.png')

all_end = time.time()
all_time = round(all_end - all_start)
print('all time: ', all_time, ' 秒')
print("All Time: {:d} 分 {:d} 秒".format(all_time // 60, all_time % 60))