AlexNet（Pytorch实现）

---

1. model.py

import torch.nn as nn
import torch


class AlexNet(nn.Module):
    def __init__(self, num_classes=1000, init_weights=False):
        super(AlexNet, self).__init__()
        self.features = nn.Sequential(
            nn.Conv2d(3, 48, kernel_size=11, stride=4, padding=2),  # input[3, 224, 224] output[48, 55, 55]
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),                  # output[48, 27, 27]
            nn.Conv2d(48, 128, kernel_size=5, padding=2),           # output[128, 27, 27]
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),                  # output[128, 13, 13]
            nn.Conv2d(128, 192, kernel_size=3, padding=1),          # output[192, 13, 13]
            nn.ReLU(inplace=True),
            nn.Conv2d(192, 192, kernel_size=3, padding=1),          # output[192, 13, 13]
            nn.ReLU(inplace=True),
            nn.Conv2d(192, 128, kernel_size=3, padding=1),          # output[128, 13, 13]
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),                  # output[128, 6, 6]
        )
        self.classifier = nn.Sequential(
            nn.Dropout(p=0.5),
            nn.Linear(128 * 6 * 6, 2048),
            nn.ReLU(inplace=True),
            nn.Dropout(p=0.5),
            nn.Linear(2048, 2048),
            nn.ReLU(inplace=True),
            nn.Linear(2048, num_classes),
        )
        if init_weights:
            self._initialize_weights()

    def forward(self, x):
        x = self.features(x)
        # 展平前：torch.Size([32, 128, 6, 6])
        # print(f'展平前：{x.shape}')
        x = torch.flatten(x, start_dim=1)
        # print(f'展平后：{x.shape}')
        # 展平后：torch.Size([32, 4608])
        x = self.classifier(x)
        return x

    def _initialize_weights(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.Linear):
                nn.init.normal_(m.weight, 0, 0.01)
                nn.init.constant_(m.bias, 0)

2. train.py

import json
import os
import time

import torch
import torch.nn as nn
import numpy as np
from matplotlib import pyplot as plt
from torch import optim
from torchvision import datasets,transforms,utils
from model import AlexNet

def main():
    device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
    print('Using {} device'.format(device))
    # 对数据集进行预处理，transforms
    data_transform={
    
        "train":transforms.Compose([
            # 数据增强
            transforms.RandomResizedCrop(224), # 从训练集中随机裁剪尺寸为224x224的区域
            transforms.RandomHorizontalFlip(), # 随机翻转
            transforms.ToTensor(),
            transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))
        ]),
        "val": transforms.Compose([
            # 数据增强
            transforms.Resize((224,224)),  # 验证集的图片必须是224x224
            transforms.ToTensor(),
            transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
        ])
    }
    # 获取数据集
    data_root = os.path.abspath(os.path.join(os.getcwd())) # 获取数据集路径
    print(data_root)
    image_path = data_root+'/data_set/flower_data/' # 设置图片路径
    train_dataset = datasets.ImageFolder(root=image_path+'/train',
                                         transform=data_transform['train'])

    # 返回对应关系
    flower_list = train_dataset.class_to_idx
    # {'daisy': 0, 'dandelion': 1, 'roses': 2, 'sunflowers': 3, 'tulips': 4}
    cla_dict = dict((val,key) for key,val in flower_list.items())
    # {0: 'daisy', 1: 'dandelion', 2: 'roses', 3: 'sunflowers', 4: 'tulips'}

    # 把字典信息写入到json文件中
    json_str = json.dumps(cla_dict, indent=4)
    with open('class_indices.json', 'w') as json_file:
        json_file.write(json_str)

    batch_size = 32
    # 加载训练集
    train_loader = torch.utils.data.DataLoader(train_dataset,
                                               batch_size = batch_size,
                                               shuffle = True,
                                               num_workers=0)

    # 加载验证集
    validate_dataset = datasets.ImageFolder(root=os.path.join(image_path, "val"),
                                            transform=data_transform['val'])
    val_num = len(validate_dataset)
    validate_loader = torch.utils.data.DataLoader(validate_dataset,
                                                  batch_size=4, shuffle=False,
                                                  num_workers=0)
    # 构造模型
    net = AlexNet(num_classes=5,init_weights=True)
    net.to(device)
    loss_function = nn.CrossEntropyLoss()
    optimizer = optim.Adam(net.parameters(),lr=0.0002)

    save_path = './AlexNet.pth'
    best_acc = 0.0
    epochs = 10
    for epoch in range(epochs):
        # 模型训练
        net.train()
        running_loss = 0
        t1 = time.perf_counter()
        for step,data in enumerate(train_loader,start=0):
            images,labels = data
            optimizer.zero_grad()
            outputs = net(images.to(device))
            loss = loss_function(outputs,labels.to(device))
            loss.backward()
            optimizer.step()

            running_loss += loss.item()
            rate = (step+1)/len(train_loader)
            a = '*'*int(rate*50)
            b = '.'*int((1-rate)*50)
            print('\rtrain loss: :{:^3.0f}%[{}->{}]{:.3f}'.format(int(rate*100),a,b,loss),end="")
        print()
        print(time.perf_counter()-t1)
        # 模型验证
        net.eval()
        acc = 0.0
        with torch.no_grad():
            for data_test in validate_loader:
                val_images,val_labels = data_test
                outputs = net(val_images.to(device))
                predict_y = torch.max(outputs,dim=1)[1]
                acc += (predict_y==val_labels.to(device)).sum().item()
            acc_val = acc/val_num
            if acc_val>best_acc:
                best_acc=acc_val
                torch.save(net.state_dict(),save_path)
            print('[epoch %d] train_loss: %.3f val_accuracy: %.3f' %
                  (epoch + 1, running_loss / step, acc_val))


if __name__ == '__main__':
    main()

3. predict.py

import os
import json

import torch
from PIL import Image
from torchvision import transforms
import matplotlib.pyplot as plt

from model import AlexNet


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

    data_transform = transforms.Compose(
        [transforms.Resize((224, 224)),
         transforms.ToTensor(),
         transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

    # load image
    img_path = "rose.jpg"
    assert os.path.exists(img_path), "file: '{}' dose not exist.".format(img_path)
    img = Image.open(img_path)

    plt.imshow(img)
    # [N, C, H, W]
    img = data_transform(img)
    # expand batch dimension
    img = torch.unsqueeze(img, dim=0)

    # read class_indict
    json_path = './class_indices.json'
    assert os.path.exists(json_path), "file: '{}' dose not exist.".format(json_path)

    with open(json_path, "r") as f:
        class_indict = json.load(f)

    # create model
    model = AlexNet(num_classes=5).to(device)

    # load model weights
    weights_path = "./AlexNet.pth"
    assert os.path.exists(weights_path), "file: '{}' dose not exist.".format(weights_path)
    model.load_state_dict(torch.load(weights_path))

    model.eval()
    with torch.no_grad():
        # predict class
        output = torch.squeeze(model(img.to(device))).cpu()
        predict = torch.softmax(output, dim=0)
        predict_cla = torch.argmax(predict).numpy()

    print_res = "class: {} prob: {:.3}".format(class_indict[str(predict_cla)],
                                                 predict[predict_cla].numpy())
    plt.title(print_res)
    for i in range(len(predict)):
        print("class: {:10} prob: {:.3}".format(class_indict[str(i)],
                                                  predict[i].numpy()))
    plt.show()


if __name__ == '__main__':
    main()
# import torch
# from model import AlexNet
# from PIL import Image
# from torchvision import transforms
# import matplotlib.pyplot as plt
# import json
# def main():
# data_transform = transforms.Compose([
# transforms.Resize((224,224)),
# transforms.ToTensor(),
# transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))
# ])
#
# # 加载图片
# img = Image.open('rose.jpg')
# plt.imshow(img)
# plt.show()
# img = data_transform(img)
# # 扩充一个维度，添加batch维度
# img = torch.unsqueeze(img,dim=0)
#
# #解析json
# try:
# json_file = open('./class_indices.json','r')
# class_indict = json.load(json_file)
# except Exception as e:
# print(e)
# exit(-1)
#
# # 创建模型
# model = AlexNet(num_classes=5)
#
# model_weight_path = './AlexNet.pth'
# model.load_state_dict(torch.load(model_weight_path))
# model.eval()
# with torch.no_grad():
# output = torch.squeeze(model(img))
# predict = torch.softmax(output,dim=0)
# predict_cla = torch.argmax(predict).numpy()
#
# print(class_indict[str(predict_cla)],predict[predict_cla].item())
# plt.show()
#
# if __name__=='__main__':
# main()