基于Pytorch框架的深度学习图像分类模型

首先声明！！！

---

• 1、脚本为本人总结，如有使用注明出处。
• 2、模型基于Pytorch框架实现及训练。
• 3、脚本内有注释。

---

一、经典图像分类数据集：

注：pytorch官网数据集 Datasets — Torchvision 0.15 documentation (pytorch.org)

1.CIFAR10

transform = transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
    ])

    # 数据集路径
    train_set = torchvision.datasets.CIFAR10(root="../data", train=True, transform=transform)
    test_set = torchvision.datasets.CIFAR10(root="../data", train=False, transform=transform)

    train_loader = torch.utils.data.DataLoader(train_set, batch_size=384, num_workers=4, shuffle=True, pin_memory=True, drop_last=True)
    test_loader = torch.utils.data.DataLoader(test_set, batch_size=384, num_workers=4, shuffle=False, pin_memory=True, drop_last=True)

2.CIFAR100

transform = transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
    ])

    # 数据集路径
    train_set = torchvision.datasets.CIFAR100(root="../data", train=True, transform=transform)
    test_set = torchvision.datasets.CIFAR100(root="../data", train=False, transform=transform)

    train_loader = torch.utils.data.DataLoader(train_set, batch_size=128, num_workers=4, shuffle=True, pin_memory=True, drop_last=True)
    test_loader = torch.utils.data.DataLoader(test_set, batch_size=128, num_workers=4, shuffle=False, pin_memory=True, drop_last=True)

3.ImageNet (ILSVRC2012)

transform = transforms.Compose([
        transforms.Resize(299),
        transforms.CenterCrop(299),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
    ])

    # 数据集路径
    train_set = torchvision.datasets.ImageFolder(root="/训练集的位置", transform=transform)
    test_set = torchvision.datasets.ImageFolder(root="/测试集的位置", transform=transform)

    train_loader = torch.utils.data.DataLoader(train_set, batch_size=32, num_workers=4, shuffle=True, pin_memory=True, drop_last=True)
    test_loader = torch.utils.data.DataLoader(test_set, batch_size=32, num_workers=4, shuffle=False, pin_memory=True, drop_last=True)

二、图像分类经典模型（基于CIFAR10数据集）：

注：硬件配置显存最好有12g，根据显存大小调整batch_size的大小。

1.AlexNet（自己写的模型）

import time
import torch.nn
import torchvision
import torch.optim as optim
import torchvision.transforms as transforms
from torch.utils.tensorboard import SummaryWriter
from torch.utils.data import DataLoader
from AlexNet import *



def weight_init(m):
    if isinstance(m, nn.Linear):
        nn.init.xavier_normal_(m.weight)
        nn.init.constant_(m.bias, 0)
    elif isinstance(m, nn.Conv2d):
        nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
        nn.init.constant_(m.bias, 0)
    elif isinstance(m, nn.BatchNorm2d):
        nn.init.constant_(m.weight, 1)
        nn.init.constant_(m.bias, 0)


# 主训练函数
def train():
    transform = transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
    ])

    # 数据集路径
    train_set = torchvision.datasets.CIFAR10(root="../data", train=True, transform=transform)
    test_set = torchvision.datasets.CIFAR10(root="../data", train=False, transform=transform)

    train_loader = torch.utils.data.DataLoader(train_set, batch_size=384, num_workers=4, shuffle=True, pin_memory=True, drop_last=True)
    test_loader = torch.utils.data.DataLoader(test_set, batch_size=384, num_workers=4, shuffle=False, pin_memory=True, drop_last=True)

    # 写入tensorboard
    writer = SummaryWriter("../train_AlexNet_1")

    train_data_size = len(train_set)
    test_data_size = len(test_set)
    print("训练数据集的长度为：{}".format(train_data_size))
    print("测试数据集的长度为：{} \n".format(test_data_size))

    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model = AlexNet()
    model.to(device)
    model.apply(weight_init)

    # 记录训练的轮数
    total_train_step = 0
    # 记录测试的次数
    total_test_step = 0
    # 控制训练轮数
    epochs = 60

    optimizer = optim.SGD(model.parameters(), lr=1e-2, momentum=0.9, weight_decay=0.0005)
    criteon = nn.CrossEntropyLoss().to(device)
    scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=100, eta_min=0.0001, last_epoch=-1)

    start_time = time.time()
    for epoch in range(epochs):
        print("-----第{}轮训练开始-----".format(epoch + 1))

        # 训练开始
        model.train()
        global train_loss
        train_loss = 0
        for idx, (inputs, label) in enumerate(train_loader):
            optimizer.zero_grad()
            inputs, label = inputs.to(device), label.to(device)
            outputs = model(inputs)
            loss = criteon(outputs, label)
            loss.backward()
            optimizer.step()
            train_loss += loss.item()
            total_train_step += 1

        print("train_batchs:{}".format(total_train_step))
        end_time = time.time()
        print("time: %.2fs" % (end_time - start_time))
        print("train_loss:%.6f" % (train_loss / len(train_set) * 256))

        # 测试开始
        model.eval()
        global test_loss, correct
        test_loss = 0
        correct = 0
        for idx, (inputs, label) in enumerate(test_loader):
            with torch.no_grad():
                inputs, label = inputs.to(device), label.to(device)
                outputs = model(inputs)
                test_loss += criteon(outputs, label)
                predict = torch.max(outputs, dim=1)[1]
                correct += torch.eq(predict, label).sum().item()
                total_test_step += 1

        print("test_batchs:{}".format(total_test_step))
        end_time = time.time()
        print("time: %.2fs" % (end_time - start_time))
        print("test_acc: %.6f  val_loss:%.6f \n" %
              ((correct / len(test_set)), test_loss * 256 / len(test_set)))

        writer.add_scalar('loss', train_loss / len(train_loader), epoch)
        writer.add_scalar('acc', correct / len(test_set), epoch)
        scheduler.step()

    torch.save(model, '../models/AlexNet_1.pth')
    print('---Finished Training---')

    writer.close()


if __name__ == "__main__":
    train()
    

import torch.nn as nn


# 手搓一个AlexNet模型
class AlexNet(nn.Module):
    def __init__(self):
        super(AlexNet, self).__init__()

        self.conv1 = nn.Sequential(
                                   nn.Conv2d(3, 96, 11, 4, 2),
                                   nn.ReLU(),
                                   nn.MaxPool2d(3, 2),
                                  )

        self.conv2 = nn.Sequential(
                                   nn.Conv2d(96, 256, 5, 1, 2),
                                   nn.ReLU(),
                                   nn.MaxPool2d(3, 2),
                                  )

        self.conv3 = nn.Sequential(
                                   nn.Conv2d(256, 384, 3, 1, 1),
                                   nn.ReLU(),
                                   nn.Conv2d(384, 384, 3, 1, 1),
                                   nn.ReLU(),
                                   nn.Conv2d(384, 256, 3, 1, 1),
                                   nn.ReLU(),
                                   nn.MaxPool2d(3, 2)
                                  )

        self.fc = nn.Sequential(
                                nn.Linear(256 * 6 * 6, 4096),
                                nn.ReLU(),
                                nn.Dropout(0.5),
                                nn.Linear(4096, 4096),
                                nn.ReLU(),
                                nn.Dropout(0.5),
                                nn.Linear(4096, 100),
                               )

    def forward(self, x):
        x = self.conv1(x)
        x = self.conv2(x)
        x = self.conv3(x)
        output = self.fc(x.view(-1, 256 * 6 * 6))
        return output

2.VGG（models.vgg16()）

import time
import torch.nn
import torchvision
import torch.optim as optim
import torchvision.transforms as transforms
from torch import nn
from torch.utils.tensorboard import SummaryWriter
from torch.utils.data import DataLoader



def weight_init(m):
    if isinstance(m, nn.Linear):
        nn.init.xavier_normal_(m.weight)
        nn.init.constant_(m.bias, 0)
    elif isinstance(m, nn.Conv2d):
        nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
        nn.init.constant_(m.bias, 0)
    elif isinstance(m, nn.BatchNorm2d):
        nn.init.constant_(m.weight, 1)
        nn.init.constant_(m.bias, 0)


# 主训练函数
def train():
    transform = transforms.Compose(
        [transforms.ToTensor(),
         transforms.Resize((224, 224)),
         transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])

    # 数据集路径
    train_set = torchvision.datasets.CIFAR10(root="../data", train=True, transform=transform)
    test_set = torchvision.datasets.CIFAR10(root="../data", train=False, transform=transform)

    train_loader = torch.utils.data.DataLoader(train_set, batch_size=64, num_workers=4, shuffle=True, pin_memory=True, drop_last=True)
    test_loader = torch.utils.data.DataLoader(test_set, batch_size=64, num_workers=4, shuffle=False, pin_memory=True, drop_last=True)

    # 写入tensorboard
    writer = SummaryWriter("../train_VGG_1")

    train_data_size = len(train_set)
    test_data_size = len(test_set)
    print("训练数据集的长度为：{}".format(train_data_size))
    print("测试数据集的长度为：{} \n".format(test_data_size))

    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model = torchvision.models.vgg16()
    model.to(device)
    # model.apply(weight_init)

    # 记录训练的轮数
    total_train_step = 0
    # 记录测试的次数
    total_test_step = 0
    # 控制训练轮数
    epochs = 30

    optimizer = optim.SGD(model.parameters(), lr=1e-2, momentum=0.9, weight_decay=0.0005)
    criteon = nn.CrossEntropyLoss().to(device)
    scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=100, eta_min=0.0001, last_epoch=-1)

    start_time = time.time()
    for epoch in range(epochs):
        print("-----第{}轮训练开始-----".format(epoch + 1))

        # 训练开始
        model.train()
        global train_loss
        train_loss = 0
        for idx, (inputs, label) in enumerate(train_loader):
            optimizer.zero_grad()
            inputs, label = inputs.to(device), label.to(device)
            outputs = model(inputs)
            loss = criteon(outputs, label)
            loss.backward()
            optimizer.step()
            train_loss += loss.item()
            total_train_step += 1

        print("train_batchs:{}".format(total_train_step))
        end_time = time.time()
        print("time: %.2fs" % (end_time - start_time))
        print("train_loss:%.6f" % (train_loss / len(train_set) * 256))

        # 测试开始
        model.eval()
        global test_loss, correct
        test_loss = 0
        correct = 0
        for idx, (inputs, label) in enumerate(test_loader):
            with torch.no_grad():
                inputs, label = inputs.to(device), label.to(device)
                outputs = model(inputs)
                test_loss += criteon(outputs, label)
                predict = torch.max(outputs, dim=1)[1]
                correct += torch.eq(predict, label).sum().item()
                total_test_step += 1

        print("test_batchs:{}".format(total_test_step))
        end_time = time.time()
        print("time: %.2fs" % (end_time - start_time))
        print("test_acc: %.6f  val_loss:%.6f \n" %
              ((correct / len(test_set)), test_loss * 256 / len(test_set)))

        writer.add_scalar('loss', train_loss / len(train_loader), epoch)
        writer.add_scalar('acc', correct / len(test_set), epoch)
        scheduler.step()

    torch.save(model, '../models/VGG_1.pth')
    print('---Finished Training---')

    writer.close()


if __name__ == "__main__":
    train()

3.GoogLeNet

import time
import torch.nn
import torchvision
import torch.optim as optim
import torchvision.transforms as transforms
from torch import nn
from torch.utils.tensorboard import SummaryWriter
from torch.utils.data import DataLoader



def weight_init(m):
    if isinstance(m, nn.Linear):
        nn.init.xavier_normal_(m.weight)
        nn.init.constant_(m.bias, 0)
    elif isinstance(m, nn.Conv2d):
        nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
        nn.init.constant_(m.bias, 0)
    elif isinstance(m, nn.BatchNorm2d):
        nn.init.constant_(m.weight, 1)
        nn.init.constant_(m.bias, 0)


# 主训练函数
def train():
    transform = transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
    ])

    # 数据集路径
    train_set = torchvision.datasets.CIFAR10(root="../data", train=True, transform=transform)
    test_set = torchvision.datasets.CIFAR10(root="../data", train=False, transform=transform)

    train_loader = torch.utils.data.DataLoader(train_set, batch_size=128, num_workers=4, shuffle=True, pin_memory=True, drop_last=True)
    test_loader = torch.utils.data.DataLoader(test_set, batch_size=128, num_workers=4, shuffle=False, pin_memory=True, drop_last=True)

    # 写入tensorboard
    writer = SummaryWriter("../train_GoogLeNet_1")

    train_data_size = len(train_set)
    test_data_size = len(test_set)
    print("训练数据集的长度为：{}".format(train_data_size))
    print("测试数据集的长度为：{} \n".format(test_data_size))

    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model = torchvision.models.GoogLeNet()
    model.to(device)
    # model.apply(weight_init)

    # 记录训练的轮数
    total_train_step = 0
    # 记录测试的次数
    total_test_step = 0
    # 控制训练轮数
    epochs = 50

    optimizer = optim.SGD(model.parameters(), lr=1e-2, momentum=0.9, weight_decay=0.0005)
    criteon = nn.CrossEntropyLoss().to(device)
    scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=100, eta_min=0.0001, last_epoch=-1)

    start_time = time.time()
    for epoch in range(epochs):
        print("-----第{}轮训练开始-----".format(epoch + 1))

        # 训练开始
        model.train()
        global train_loss
        train_loss = 0
        for idx, (inputs, label) in enumerate(train_loader):
            optimizer.zero_grad()
            inputs, label = inputs.to(device), label.to(device)
            outputs = model(inputs)
            outputs = outputs.logits
            loss = criteon(outputs, label)
            loss.backward()
            optimizer.step()
            train_loss += loss.item()
            total_train_step += 1

        print("train_batchs:{}".format(total_train_step))
        end_time = time.time()
        print("time: %.1fs" % (end_time - start_time))
        print("train_loss:%.5f" % (train_loss / len(train_set) * 256))

        # 测试开始
        model.eval()
        global test_loss, correct
        test_loss = 0
        correct = 0
        for idx, (inputs, label) in enumerate(test_loader):
            with torch.no_grad():
                inputs, label = inputs.to(device), label.to(device)
                outputs = model(inputs)
                test_loss += criteon(outputs, label)
                predict = torch.max(outputs, dim=1)[1]
                correct += torch.eq(predict, label).sum().item()
                total_test_step += 1

        print("test_batchs:{}".format(total_test_step))
        end_time = time.time()
        print("time: %.1fs" % (end_time - start_time))
        print("test_acc: %.5f  val_loss:%.5f \n" %
              ((correct / len(test_set)), test_loss * 256 / len(test_set)))

        writer.add_scalar('loss', train_loss / len(train_loader), epoch)
        writer.add_scalar('acc', correct / len(test_set), epoch)
        scheduler.step()

    torch.save(model, '../models/GooLeNet_1.pth')
    print('---Finished Training---')

    writer.close()


if __name__ == "__main__":
    train()

4.ResNet（models.resnet101()）

import time
import torch.nn
import torchvision
import torch.optim as optim
import torchvision.transforms as transforms
from torch import nn
from torch.utils.tensorboard import SummaryWriter
from torch.utils.data import DataLoader



def weight_init(m):
    if isinstance(m, nn.Linear):
        nn.init.xavier_normal_(m.weight)
        nn.init.constant_(m.bias, 0)
    elif isinstance(m, nn.Conv2d):
        nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
        nn.init.constant_(m.bias, 0)
    elif isinstance(m, nn.BatchNorm2d):
        nn.init.constant_(m.weight, 1)
        nn.init.constant_(m.bias, 0)


# 主训练函数
def train():
    transform = transforms.Compose(
        [transforms.ToTensor(),
         transforms.Resize((224, 224)),
         transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])

    # 数据集路径
    train_set = torchvision.datasets.CIFAR10(root="../data", train=True, transform=transform)
    test_set = torchvision.datasets.CIFAR10(root="../data", train=False, transform=transform)

    train_loader = torch.utils.data.DataLoader(train_set, batch_size=64, num_workers=4, shuffle=True, pin_memory=True, drop_last=True)
    test_loader = torch.utils.data.DataLoader(test_set, batch_size=64, num_workers=4, shuffle=False, pin_memory=True, drop_last=True)

    # 写入tensorboard
    writer = SummaryWriter("../train_ResNet_1")

    train_data_size = len(train_set)
    test_data_size = len(test_set)
    print("训练数据集的长度为：{}".format(train_data_size))
    print("测试数据集的长度为：{} \n".format(test_data_size))

    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model = torchvision.models.resnet101()
    model.to(device)
    # model.apply(weight_init)

    # 记录训练的轮数
    total_train_step = 0
    # 记录测试的次数
    total_test_step = 0
    # 控制训练轮数
    epochs = 30

    optimizer = optim.SGD(model.parameters(), lr=1e-2, momentum=0.9, weight_decay=0.0005)
    criteon = nn.CrossEntropyLoss().to(device)
    scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=100, eta_min=0.0001, last_epoch=-1)

    start_time = time.time()
    for epoch in range(epochs):
        print("-----第{}轮训练开始-----".format(epoch + 1))

        # 训练开始
        model.train()
        global train_loss
        train_loss = 0
        for idx, (inputs, label) in enumerate(train_loader):
            optimizer.zero_grad()
            inputs, label = inputs.to(device), label.to(device)
            outputs = model(inputs)
            loss = criteon(outputs, label)
            loss.backward()
            optimizer.step()
            train_loss += loss.item()
            total_train_step += 1

        print("train_batchs:{}".format(total_train_step))
        end_time = time.time()
        print("time: %.2fs" % (end_time - start_time))
        print("train_loss:%.6f" % (train_loss / len(train_set) * 256))

        # 测试开始
        model.eval()
        global test_loss, correct
        test_loss = 0
        correct = 0
        for idx, (inputs, label) in enumerate(test_loader):
            with torch.no_grad():
                inputs, label = inputs.to(device), label.to(device)
                outputs = model(inputs)
                test_loss += criteon(outputs, label)
                predict = torch.max(outputs, dim=1)[1]
                correct += torch.eq(predict, label).sum().item()
                total_test_step += 1

        print("test_batchs:{}".format(total_test_step))
        end_time = time.time()
        print("time: %.2fs" % (end_time - start_time))
        print("test_acc: %.6f  val_loss:%.6f \n" %
              ((correct / len(test_set)), test_loss * 256 / len(test_set)))

        writer.add_scalar('loss', train_loss / len(train_loader), epoch)
        writer.add_scalar('acc', correct / len(test_set), epoch)
        scheduler.step()

    torch.save(model, '../models/ResNet_1.pth')
    print('---Finished Training---')

    writer.close()


if __name__ == "__main__":
    train()

5.DenseNet（models.densenet121()）

import time
import torch.nn
import torchvision
import torch.optim as optim
import torchvision.transforms as transforms
from torch import nn
from torch.utils.tensorboard import SummaryWriter
from torch.utils.data import DataLoader



def weight_init(m):
    if isinstance(m, nn.Linear):
        nn.init.xavier_normal_(m.weight)
        nn.init.constant_(m.bias, 0)
    elif isinstance(m, nn.Conv2d):
        nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
        nn.init.constant_(m.bias, 0)
    elif isinstance(m, nn.BatchNorm2d):
        nn.init.constant_(m.weight, 1)
        nn.init.constant_(m.bias, 0)


# 主训练函数
def train():
    transform = transforms.Compose(
        [transforms.ToTensor(),
         transforms.Resize((224, 224)),
         transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])

    # 数据集路径
    train_set = torchvision.datasets.CIFAR10(root="../data", transform=transform, train=True)
    test_set = torchvision.datasets.CIFAR10(root="../data", transform=transform, train=False)

    train_loader = torch.utils.data.DataLoader(train_set, batch_size=64, num_workers=4, shuffle=True, pin_memory=True, drop_last=True)
    test_loader = torch.utils.data.DataLoader(test_set, batch_size=64, num_workers=4, shuffle=False, pin_memory=True, drop_last=True)

    # 写入tensorboard
    writer = SummaryWriter("../train_DenseNet_1")

    train_data_size = len(train_set)
    test_data_size = len(test_set)
    print("训练数据集的长度为：{}".format(train_data_size))
    print("测试数据集的长度为：{} \n".format(test_data_size))

    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model = torchvision.models.densenet121()
    model.to(device)
    # model.apply(weight_init)

    # 记录训练的轮数
    total_train_step = 0
    # 记录测试的次数
    total_test_step = 0
    # 控制训练轮数
    epochs = 30

    optimizer = optim.SGD(model.parameters(), lr=1e-2, momentum=0.9, weight_decay=0.0005)
    criteon = nn.CrossEntropyLoss().to(device)
    scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=100, eta_min=0.0001, last_epoch=-1)

    start_time = time.time()
    for epoch in range(epochs):
        print("-----第{}轮训练开始-----".format(epoch + 1))

        # 训练开始
        model.train()
        global train_loss
        train_loss = 0
        for idx, (inputs, label) in enumerate(train_loader):
            optimizer.zero_grad()
            inputs, label = inputs.to(device), label.to(device)
            outputs = model(inputs)
            loss = criteon(outputs, label)
            loss.backward()
            optimizer.step()
            train_loss += loss.item()
            total_train_step += 1

        print("train_batchs:{}".format(total_train_step))
        end_time = time.time()
        print("time: %.2fs" % (end_time - start_time))
        print("train_loss:%.6f" % (train_loss / len(train_set) * 256))

        # 测试开始  dgsd
        model.eval()
        global test_loss, correct
        test_loss = 0
        correct = 0
        for idx, (inputs, label) in enumerate(test_loader):
            with torch.no_grad():
                inputs, label = inputs.to(device), label.to(device)
                outputs = model(inputs)
                test_loss += criteon(outputs, label)
                predict = torch.max(outputs, dim=1)[1]
                correct += torch.eq(predict, label).sum().item()
                total_test_step += 1

        print("test_batchs:{}".format(total_test_step))
        end_time = time.time()
        print("time: %.2fs" % (end_time - start_time))
        print("test_acc: %.6f  val_loss:%.6f \n" %
              ((correct / len(test_set)), test_loss * 256 / len(test_set)))

        writer.add_scalar('loss', train_loss / len(train_loader), epoch)
        writer.add_scalar('acc', correct / len(test_set), epoch)
        scheduler.step()

    torch.save(model, '../models/DenseNet_1.pth')
    print('---Finished Training---')

    writer.close()


if __name__ == "__main__":
    train()

6.Train_ImageNet（main.py）

import argparse
import os
import random
import shutil
import time
import warnings
from enum import Enum
import torch
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.multiprocessing as mp
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torchvision.datasets as datasets
import torchvision.models as models
import torchvision.transforms as transforms
from torch.optim.lr_scheduler import StepLR
from torch.utils.data import Subset


model_names = sorted(name for name in models.__dict__
                     if name.islower() and not name.startswith("__")
                     and callable(models.__dict__[name]))

parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
parser.add_argument('data', metavar='DIR', nargs='?', default='imagenet',
                    help='path to dataset (default: imagenet)')
parser.add_argument('-a', '--arch', metavar='ARCH', default='resnet34',
                    choices=model_names,
                    help='model architecture: ' +
                         ' | '.join(model_names) +
                         ' (default: resnet34)')
parser.add_argument('-j', '--workers', default=4, type=int, metavar='N',
                    help='number of data loading workers (default: 4)')
parser.add_argument('--epochs', default=50, type=int, metavar='N',
                    help='number of total epochs to run')
parser.add_argument('--start-epoch', default=1, type=int, metavar='N',
                    help='manual epoch number (useful on restarts)')
parser.add_argument('-b', '--batch-size', default=192, type=int,
                    metavar='N',
                    help='mini-batch size (default: 256), this is the total '
                         'batch size of all GPUs on the current node when '
                         'using Data Parallel or Distributed Data Parallel')
parser.add_argument('--lr', '--learning-rate', default=0.1, type=float,
                    metavar='LR', help='initial learning rate', dest='lr')
parser.add_argument('--momentum', default=0.9, type=float, metavar='M',
                    help='momentum')
parser.add_argument('--wd', '--weight-decay', default=1e-4, type=float,
                    metavar='W', help='weight decay (default: 1e-4)',
                    dest='weight_decay')
parser.add_argument('-p', '--print-freq', default=500, type=int,
                    metavar='N', help='print frequency (default: 500)')
parser.add_argument('--resume', default='', type=str, metavar='PATH',
                    help='path to latest checkpoint (default: none)')
parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true',
                    help='evaluate model on validation set')
parser.add_argument('--pretrained', dest='pretrained', action='store_true',
                    help='use pre-trained model')
parser.add_argument('--world-size', default=-1, type=int,
                    help='number of nodes for distributed training')
parser.add_argument('--rank', default=-1, type=int,
                    help='node rank for distributed training')
parser.add_argument('--dist-url', default='tcp://224.66.41.62:23456', type=str,
                    help='url used to set up distributed training')
parser.add_argument('--dist-backend', default='nccl', type=str,
                    help='distributed backend')
parser.add_argument('--seed', default=None, type=int,
                    help='seed for initializing training. ')
parser.add_argument('--gpu', default=None, type=int,
                    help='GPU id to use.')
parser.add_argument('--multiprocessing-distributed', action='store_true',
                    help='Use multi-processing distributed training to launch '
                         'N processes per node, which has N GPUs. This is the '
                         'fastest way to use PyTorch for either single node or '
                         'multi node data parallel training')
parser.add_argument('--dummy', action='store_true', help="use fake data to benchmark")

best_acc1 = 0


def main():
    args = parser.parse_args()

    if args.seed is not None:
        random.seed(args.seed)
        torch.manual_seed(args.seed)
        cudnn.deterministic = True
        cudnn.benchmark = False
        warnings.warn('You have chosen to seed training. '
                      'This will turn on the CUDNN deterministic setting, '
                      'which can slow down your training considerably! '
                      'You may see unexpected behavior when restarting '
                      'from checkpoints.')

    if args.gpu is not None:
        warnings.warn('You have chosen a specific GPU. This will completely '
                      'disable data parallelism.')

    if args.dist_url == "env://" and args.world_size == -1:
        args.world_size = int(os.environ["WORLD_SIZE"])

    args.distributed = args.world_size > 1 or args.multiprocessing_distributed

    if torch.cuda.is_available():
        ngpus_per_node = torch.cuda.device_count()
    else:
        ngpus_per_node = 1
    if args.multiprocessing_distributed:
        # Since we have ngpus_per_node processes per node, the total world_size
        # needs to be adjusted accordingly
        args.world_size = ngpus_per_node * args.world_size
        # Use torch.multiprocessing.spawn to launch distributed processes: the
        # main_worker process function
        mp.spawn(main_worker, nprocs=ngpus_per_node, args=(ngpus_per_node, args))
    else:
        # Simply call main_worker function
        main_worker(args.gpu, ngpus_per_node, args)


def main_worker(gpu, ngpus_per_node, args):
    global best_acc1
    args.gpu = gpu

    if args.gpu is not None:
        print("Use GPU: {} for training".format(args.gpu))

    if args.distributed:
        if args.dist_url == "env://" and args.rank == -1:
            args.rank = int(os.environ["RANK"])
        if args.multiprocessing_distributed:
            # For multiprocessing distributed training, rank needs to be the
            # global rank among all the processes
            args.rank = args.rank * ngpus_per_node + gpu
        dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url,
                                world_size=args.world_size, rank=args.rank)
    # create model
    if args.pretrained:
        print("=> using pre-trained model '{}'".format(args.arch))
        model = models.__dict__[args.arch](pretrained=True)
    else:
        print("=> creating model '{}'".format(args.arch))
        model = models.__dict__[args.arch]()

    if not torch.cuda.is_available() and not torch.backends.mps.is_available():
        print('using CPU, this will be slow')
    elif args.distributed:
        # For multiprocessing distributed, DistributedDataParallel constructor
        # should always set the single device scope, otherwise,
        # DistributedDataParallel will use all available devices.
        if torch.cuda.is_available():
            if args.gpu is not None:
                torch.cuda.set_device(args.gpu)
                model.cuda(args.gpu)
                # When using a single GPU per process and per
                # DistributedDataParallel, we need to divide the batch size
                # ourselves based on the total number of GPUs of the current node.
                args.batch_size = int(args.batch_size / ngpus_per_node)
                args.workers = int((args.workers + ngpus_per_node - 1) / ngpus_per_node)
                model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
            else:
                model.cuda()
                # DistributedDataParallel will divide and allocate batch_size to all
                # available GPUs if device_ids are not set
                model = torch.nn.parallel.DistributedDataParallel(model)
    elif args.gpu is not None and torch.cuda.is_available():
        torch.cuda.set_device(args.gpu)
        model = model.cuda(args.gpu)
    elif torch.backends.mps.is_available():
        device = torch.device("mps")
        model = model.to(device)
    else:
        # DataParallel will divide and allocate batch_size to all available GPUs
        if args.arch.startswith('alexnet') or args.arch.startswith('vgg'):
            model.features = torch.nn.DataParallel(model.features)
            model.cuda()
        else:
            model = torch.nn.DataParallel(model).cuda()

    if torch.cuda.is_available():
        if args.gpu:
            device = torch.device('cuda:{}'.format(args.gpu))
        else:
            device = torch.device("cuda")
    elif torch.backends.mps.is_available():
        device = torch.device("mps")
    else:
        device = torch.device("cpu")
    # define loss function (criterion), optimizer, and learning rate scheduler
    criterion = nn.CrossEntropyLoss().to(device)

    optimizer = torch.optim.SGD(model.parameters(), args.lr,
                                momentum=args.momentum,
                                weight_decay=args.weight_decay)

    """Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
    scheduler = StepLR(optimizer, step_size=30, gamma=0.1)

    # optionally resume from a checkpoint
    if args.resume:
        if os.path.isfile(args.resume):
            print("=> loading checkpoint '{}'".format(args.resume))
            if args.gpu is None:
                checkpoint = torch.load(args.resume)
            elif torch.cuda.is_available():
                # Map model to be loaded to specified single gpu.
                loc = 'cuda:{}'.format(args.gpu)
                checkpoint = torch.load(args.resume, map_location=loc)
            args.start_epoch = checkpoint['epoch']
            best_acc1 = checkpoint['best_acc1']
            if args.gpu is not None:
                # best_acc1 may be from a checkpoint from a different GPU
                best_acc1 = best_acc1.to(args.gpu)
            model.load_state_dict(checkpoint['state_dict'])
            optimizer.load_state_dict(checkpoint['optimizer'])
            scheduler.load_state_dict(checkpoint['scheduler'])
            print("=> loaded checkpoint '{}' (epoch {})"
                  .format(args.resume, checkpoint['epoch']))
        else:
            print("=> no checkpoint found at '{}'".format(args.resume))

    # Data loading code
    if args.dummy:
        print("=> Dummy data is used!")
        train_dataset = datasets.FakeData(1281167, (3, 224, 224), 1000, transforms.ToTensor())
        val_dataset = datasets.FakeData(50000, (3, 224, 224), 1000, transforms.ToTensor())
    else:
        normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                         std=[0.229, 0.224, 0.225])

        train_dataset = datasets.ImageFolder(
            "数据集的路径：",
            transforms.Compose([
                transforms.RandomResizedCrop(224),
                transforms.RandomHorizontalFlip(),
                transforms.ToTensor(),
                normalize,
            ]))

        val_dataset = datasets.ImageFolder(
            "测试集的路径：",
            transforms.Compose([
                transforms.Resize(256),
                transforms.CenterCrop(224),
                transforms.ToTensor(),
                normalize,
            ]))

    if args.distributed:
        train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
        val_sampler = torch.utils.data.distributed.DistributedSampler(val_dataset, shuffle=False, drop_last=True)
    else:
        train_sampler = None
        val_sampler = None

    train_loader = torch.utils.data.DataLoader(
        train_dataset, batch_size=args.batch_size, shuffle=(train_sampler is None),
        num_workers=args.workers, pin_memory=True, sampler=train_sampler)

    val_loader = torch.utils.data.DataLoader(
        val_dataset, batch_size=args.batch_size, shuffle=False,
        num_workers=args.workers, pin_memory=True, sampler=val_sampler)

    if args.evaluate:
        validate(val_loader, model, criterion, args)
        return

    end = time.time()

    for epoch in range(args.start_epoch, args.epochs):

        print("\n-----第{}轮训练开始-----".format(epoch))

        if args.distributed:
            train_sampler.set_epoch(epoch)

        # train for one epoch
        train(train_loader, model, criterion, optimizer, epoch, device, args, end)

        # evaluate on validation set
        acc1 = validate(val_loader, model, criterion, args, end)

        scheduler.step()

        # remember best acc@1 and save checkpoint
        is_best = acc1 > best_acc1
        best_acc1 = max(acc1, best_acc1)

        if not args.multiprocessing_distributed or (args.multiprocessing_distributed
                                                    and args.rank % ngpus_per_node == 0):
            save_checkpoint({
                'epoch': epoch + 1,
                'arch': args.arch,
                'state_dict': model.state_dict(),
                'best_acc1': best_acc1,
                'optimizer': optimizer.state_dict(),
                'scheduler': scheduler.state_dict()
            }, is_best)


def train(train_loader, model, criterion, optimizer, epoch, device, args, end):
    losses = AverageMeter('Loss', ':.4e')
    top1 = AverageMeter('Acc@1', ':6.2f')
    top5 = AverageMeter('Acc@5', ':6.2f')
    progress = ProgressMeter(
        len(train_loader),
        [losses, top1, top5],
        prefix="Epoch: [{}]".format(epoch))

    # switch to train mode
    model.train()

    for i, (images, target) in enumerate(train_loader):
        # measure data loading time

        # move data to the same device as model
        images = images.to(device, non_blocking=True)
        target = target.to(device, non_blocking=True)

        # compute output
        output = model(images)
        loss = criterion(output, target)

        # measure accuracy and record loss
        acc1, acc5 = accuracy(output, target, topk=(1, 5))
        losses.update(loss.item(), images.size(0))
        top1.update(acc1[0], images.size(0))
        top5.update(acc5[0], images.size(0))

        # compute gradient and do SGD step
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        # measure elapsed time
        if i % args.print_freq == 0:
            progress.display(i + 1)
            print("Time: %.2fs" % (time.time() - end))


def validate(val_loader, model, criterion, args, end):
    def run_validate(loader, base_progress=0):
        with torch.no_grad():
            end = time.time()
            for i, (images, target) in enumerate(loader):
                i = base_progress + i
                if args.gpu is not None and torch.cuda.is_available():
                    images = images.cuda(args.gpu, non_blocking=True)
                if torch.backends.mps.is_available():
                    images = images.to('mps')
                    target = target.to('mps')
                if torch.cuda.is_available():
                    target = target.cuda(args.gpu, non_blocking=True)

                # compute output
                output = model(images)
                loss = criterion(output, target)

                # measure accuracy and record loss
                acc1, acc5 = accuracy(output, target, topk=(1, 5))
                losses.update(loss.item(), images.size(0))
                top1.update(acc1[0], images.size(0))
                top5.update(acc5[0], images.size(0))

                # measure elapsed time

                if i % args.print_freq == 0:
                    progress.display(i + 1)
                    print("time: %.1fs" % (time.time() - end))

    losses = AverageMeter('Loss', ':.4e', Summary.NONE)
    top1 = AverageMeter('Acc@1', ':6.2f', Summary.AVERAGE)
    top5 = AverageMeter('Acc@5', ':6.2f', Summary.AVERAGE)
    progress = ProgressMeter(
        len(val_loader) + (args.distributed and (len(val_loader.sampler) * args.world_size < len(val_loader.dataset))),
        [losses, top1, top5],
        prefix='Test: ')

    # switch to evaluate mode
    model.eval()

    run_validate(val_loader)
    if args.distributed:
        top1.all_reduce()
        top5.all_reduce()

    if args.distributed and (len(val_loader.sampler) * args.world_size < len(val_loader.dataset)):
        aux_val_dataset = Subset(val_loader.dataset,
                                 range(len(val_loader.sampler) * args.world_size, len(val_loader.dataset)))
        aux_val_loader = torch.utils.data.DataLoader(
            aux_val_dataset, batch_size=args.batch_size, shuffle=False,
            num_workers=args.workers, pin_memory=True)
        run_validate(aux_val_loader, len(val_loader))

    progress.display_summary()

    return top1.avg


def save_checkpoint(state, is_best, filename='../models/checkpoint_resnet34.pth.tar'):
    torch.save(state, filename)
    if is_best:
        shutil.copyfile(filename, '../models/model_best_resnet34.pth.tar')


class Summary(Enum):
    NONE = 0
    AVERAGE = 1
    SUM = 2
    COUNT = 3


class AverageMeter(object):
    """Computes and stores the average and current value"""

    def __init__(self, name, fmt=':f', summary_type=Summary.AVERAGE):
        self.name = name
        self.fmt = fmt
        self.summary_type = summary_type
        self.reset()

    def reset(self):
        self.val = 0
        self.avg = 0
        self.sum = 0
        self.count = 0

    def update(self, val, n=1):
        self.val = val
        self.sum += val * n
        self.count += n
        self.avg = self.sum / self.count

    def all_reduce(self):
        if torch.cuda.is_available():
            device = torch.device("cuda")
        elif torch.backends.mps.is_available():
            device = torch.device("mps")
        else:
            device = torch.device("cpu")
        total = torch.tensor([self.sum, self.count], dtype=torch.float32, device=device)
        dist.all_reduce(total, dist.ReduceOp.SUM, async_op=False)
        self.sum, self.count = total.tolist()
        self.avg = self.sum / self.count

    def __str__(self):
        fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})'
        return fmtstr.format(**self.__dict__)

    def summary(self):
        fmtstr = ''
        if self.summary_type is Summary.NONE:
            fmtstr = ''
        elif self.summary_type is Summary.AVERAGE:
            fmtstr = '{name} {avg:.3f}'
        elif self.summary_type is Summary.SUM:
            fmtstr = '{name} {sum:.3f}'
        elif self.summary_type is Summary.COUNT:
            fmtstr = '{name} {count:.3f}'
        else:
            raise ValueError('invalid summary type %r' % self.summary_type)

        return fmtstr.format(**self.__dict__)


class ProgressMeter(object):
    def __init__(self, num_batches, meters, prefix=""):
        self.batch_fmtstr = self._get_batch_fmtstr(num_batches)
        self.meters = meters
        self.prefix = prefix

    def display(self, batch):
        entries = [self.prefix + self.batch_fmtstr.format(batch)]
        entries += [str(meter) for meter in self.meters]
        print('\t'.join(entries))

    def display_summary(self):
        entries = [" *"]
        entries += [meter.summary() for meter in self.meters]
        print(' '.join(entries))

    def _get_batch_fmtstr(self, num_batches):
        num_digits = len(str(num_batches // 1))
        fmt = '{:' + str(num_digits) + 'd}'
        return '[' + fmt + '/' + fmt.format(num_batches) + ']'


def accuracy(output, target, topk=(1,)):
    """Computes the accuracy over the k top predictions for the specified values of k"""
    with torch.no_grad():
        maxk = max(topk)
        batch_size = target.size(0)

        _, pred = output.topk(maxk, 1, True, True)
        pred = pred.t()
        correct = pred.eq(target.view(1, -1).expand_as(pred))

        res = []
        for k in topk:
            correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True)
            res.append(correct_k.mul_(100.0 / batch_size))
        return res


if __name__ == '__main__':
    main()
    

三、小实践：可食用蘑菇识别分类模型

训练代码：

import time
import torch.nn
import torchvision
import torch.optim as optim
import torchvision.transforms as transforms
from torch import nn
from torch.utils.tensorboard import SummaryWriter
from torch.utils.data import DataLoader

# 抛出错误数据
from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True


# 设置参量
def weight_init(m):
    if isinstance(m, nn.Linear):
        nn.init.xavier_normal_(m.weight)
        nn.init.constant_(m.bias, 0)
    elif isinstance(m, nn.Conv2d):
        nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
        nn.init.constant_(m.bias, 0)
    elif isinstance(m, nn.BatchNorm2d):
        nn.init.constant_(m.weight, 1)
        nn.init.constant_(m.bias, 0)


# 主训练函数
def train():
    transform = transforms.Compose([
        transforms.Resize(299),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
    ])

    # 数据集路径
    train_set = torchvision.datasets.ImageFolder(root="E:\DeepLearning数据集\\archive (1)\\train", transform=transform)
    test_set = torchvision.datasets.ImageFolder(root="E:\DeepLearning数据集\\archive (1)\\test", transform=transform)

    train_loader = torch.utils.data.DataLoader(train_set, batch_size=64, num_workers=4, shuffle=True, pin_memory=True, drop_last=True)
    test_loader = torch.utils.data.DataLoader(test_set, batch_size=64, num_workers=4, shuffle=False, pin_memory=True, drop_last=True)

    # 写入tensorboard
    writer = SummaryWriter("../train_ResNet_mushrooms")

    train_data_size = len(train_set)
    test_data_size = len(test_set)
    print("训练数据集的长度为：{}".format(train_data_size))
    print("测试数据集的长度为：{} \n".format(test_data_size))

    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model = torchvision.models.resnet18()
    model.to(device)

    # model.apply(weight_init)

    # 记录训练的轮数
    total_train_step = 0
    # 记录测试的次数
    total_test_step = 0
    # 控制训练轮数
    epochs = 50

    optimizer = optim.SGD(model.parameters(), lr=1e-3, momentum=0.9, weight_decay=0.0005)
    criteon = nn.CrossEntropyLoss().to(device)
    scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=100, eta_min=0.0001, last_epoch=-1)

    start_time = time.time()
    for epoch in range(epochs):
        print("-----第{}轮训练开始-----".format(epoch + 1))

        # 训练开始
        model.train()
        global train_loss
        train_loss = 0
        for idx, (inputs, label) in enumerate(train_loader):
            optimizer.zero_grad()
            inputs, label = inputs.to(device), label.to(device)
            outputs = model(inputs)
            loss = criteon(outputs, label)
            loss.backward()
            optimizer.step()
            train_loss += loss.item()
            total_train_step += 1

        print("train_batchs:{}".format(total_train_step))
        end_time = time.time()
        print("time: %.1fs" % (end_time - start_time))
        print("train_loss:%.5f\n" % (train_loss / len(train_set) * 256))

        # 测试开始
        model.eval()
        global test_loss, correct
        test_loss = 0
        correct = 0
        for idx, (inputs, label) in enumerate(test_loader):
            with torch.no_grad():
                inputs, label = inputs.to(device), label.to(device)
                outputs = model(inputs)
                test_loss += criteon(outputs, label)
                predict = torch.max(outputs, dim=1)[1]
                correct += torch.eq(predict, label).sum().item()
                total_test_step += 1

        print("test_batchs:{}".format(total_test_step))
        end_time = time.time()
        print("time: %.1fs" % (end_time - start_time))
        print("test_loss:%.5f" % (test_loss * 256 / len(test_set)))
        print("test_acc:{}%\n".format((correct / len(test_set) * 100.0), '.2f'))

        writer.add_scalar('loss', train_loss / len(train_loader), epoch)
        writer.add_scalar('acc', correct / len(test_set), epoch)
        scheduler.step()

    torch.save(model, '../models/ResNet_mushrooms.pth')
    print('---Finished Training---')

    writer.close()


if __name__ == "__main__":
    train()

测试代码：

import torch
import torch.nn
import torchvision
import torchvision.transforms as transforms
from torch.utils.tensorboard import SummaryWriter
from torch.utils.data import DataLoader

from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True


def test():
    # 转换输入图像
    transform = transforms.Compose([
            transforms.Resize(299),
            transforms.CenterCrop(224),
            transforms.ToTensor(),
            transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
        ])

    # 加载测试集
    test_set = torchvision.datasets.ImageFolder(root="E:\DeepLearning数据集\\archive (1)\\test", transform=transform)

    # 创建数据加载器
    data_loader = torch.utils.data.DataLoader(test_set, batch_size=1, num_workers=4)

    device = torch.device("cpu")

    # 加载模型
    model = torch.load("..\models\\ResNet_mushrooms.pth")
    model.to(device)
    model.eval()

    # 进行预测
    correct = 0
    list = ['Agaricus','Amanita','Boletus','Cortinarius','Entoloma','Hygrocybe','Lactarius','Russula','Suillus']
    for idx, (inputs, label) in enumerate(data_loader):
        with torch.no_grad():
            inputs, label = inputs.to(device), label.to(device)
            outputs = model(inputs)
            predict = torch.max(outputs, dim=1)[1]
            correct += torch.eq(predict, label).sum().item()
            if torch.max(outputs) <= 6:
                print('drug')
            else:
                print(list[predict])
            correct += torch.eq(predict, label).sum().item()
            print(outputs)

    print(correct)
    print("test_acc:{}%\n".format((correct / len(test_set) * 100.0), '.2f'))

    print("Success!")


if __name__ == "__main__":
    test()
    

---