AlexNet

简介

过拟合与Dropout

网络结构

花数据集

代码

split_data.py

将数据集划分为 9 : 1 的训练集（train）和验证集（val）。

import os
from shutil import copy
import random


def mkfile(file):
    if not os.path.exists(file):
        os.makedirs(file)


# 当前文件所在文件夹下'flower_data/flower_photos'
# ————split_data.py
# ————flower_data
# —————————flower_photos
file = 'flower_data/flower_photos'
flower_class = [cla for cla in os.listdir(file) if ".txt" not in cla]
mkfile('flower_data/train')
for cla in flower_class:
    mkfile('flower_data/train/' + cla)

mkfile('flower_data/val')
for cla in flower_class:
    mkfile('flower_data/val/' + cla)

split_rate = 0.1
for cla in flower_class:
    cla_path = file + '/' + cla + '/'
    images = os.listdir(cla_path)
    num = len(images)
    eval_index = random.sample(images, k=int(num * split_rate))
    for index, image in enumerate(images):
        if image in eval_index:
            image_path = cla_path + image
            new_path = 'flower_data/val/' + cla
            copy(image_path, new_path)
        else:
            image_path = cla_path + image
            new_path = 'flower_data/train/' + cla
            copy(image_path, new_path)
        print("\r[{}] processing [{}/{}]".format(cla, index + 1, num),
              end="")  # processing bar
    print()

print("processing done!")

model.py

import torch
import torch.nn as nn


class AlexNet(nn.Module):
    def __init__(self, num_classes):
        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),                  # (48, 27, 27)
            nn.Conv2d(48, 128, kernel_size=5, padding=2),           # (128, 27, 27)
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),                  # (128, 13, 13)
            nn.Conv2d(128, 192, kernel_size=3, padding=1),          # (192, 13, 13)
            nn.ReLU(inplace=True),
            nn.Conv2d(192, 192, kernel_size=3, padding=1),          # (192, 13, 13)
            nn.ReLU(inplace=True),
            nn.Conv2d(192, 128, kernel_size=3, padding=1),          # (128, 13, 13)
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),                  # (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),
        )

    def forward(self, x):
        x = self.features(x)
        x = torch.flatten(x, start_dim=1)
        x = self.classifier(x)
        return x
        

tran.py

from model import AlexNet
import torch
import torchvision as tv
import json
import torchvision.transforms as transforms
import time

data_transform = {
     
    "train":
    transforms.Compose([
        transforms.RandomResizedCrop(224),  # 随机裁剪为 224 * 224 的图像
        transforms.RandomHorizontalFlip(),  # 水平翻转，提高训练难度，使网络精度更高
        transforms.ToTensor(),
        # Convert a ``PIL Image`` or ``numpy.ndarray`` to tensor.
        # Converts a PIL Image or numpy.ndarray (H x W x C) in the range
        # [0, 255] to a torch.FloatTensor of shape (C x H x W) in the range [0.0, 1.0]
        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
        # Given mean: ``(M1,...,Mn)`` and std: ``(S1,..,Sn)`` for ``n`` channels, 
        # ``output[channel] = (input[channel] - mean[channel]) / std[channel]``
    ]),
    "val":
    transforms.Compose([
        transforms.Resize((224, 224)),  # 缩放图像为 224 * 224, 注意要有()
        transforms.ToTensor(),
        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
    ])
}

# 打开自己的数据集，用torchvision.datasets.ImageFolder()
train_set = tv.datasets.ImageFolder(
    root='C:/Users/14251/Desktop/workspace/AlexNet/flower_data/train',
    transform=data_transform["train"])
val_set = tv.datasets.ImageFolder(
    root='C:/Users/14251/Desktop/workspace/AlexNet/flower_data/val',
    transform=data_transform["val"])

train_loader = torch.utils.data.DataLoader(train_set,
                                           batch_size=32,
                                           shuffle=True,
                                           num_workers=0)
val_loader = torch.utils.data.DataLoader(val_set,
                                         batch_size=4,
                                         shuffle=True,
                                         num_workers=0)

# {'daisy':0, 'dandelion':1, 'roses':2, 'sunflower':3, 'tulips':4}
#  convert to {0:'daisy', 1:'dandelion', 2:'roses', 3:'sunflower', 4:'tulips'}
train_list = train_set.class_to_idx
data_dict = dict((val, key) for key, val in train_list.items())
# write dict into json file
json_str = json.dumps(data_dict, indent=4)
with open('class_indices.json', 'w') as json_file:
    json_file.write(json_str)

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

net = AlexNet(num_classes=5)
net.to(device)

loss_fun = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(net.parameters(), lr=0.0002)

best_accurate = 0.0
for epoch in range(20):
    net.train()  # use Dropout()
    running_loss = 0.0
    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_fun(outputs, labels.to(device))
        loss.backward()
        optimizer.step()

        running_loss += loss.item()
        # print train process
        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)

    accurate = 0.0
    net.eval()  # don't use Dropout()
    with torch.no_grad():
        for val_data in val_loader:
            val_images, val_labels = val_data
            outputs = net(val_images.to(device))    # [batch, 5]
            prediction = torch.max(outputs, dim=1)[1]   # 从第1维 5 开始, 求最大值, [1]代表索引
            accurate += (prediction == val_labels.to(device)).sum().item()
        val_accurate = accurate / len(val_set)
        if val_accurate > best_accurate:
            best_accurate = val_accurate
            torch.save(
                net.state_dict(),
                'C:/Users/14251/Desktop/workspace/AlexNet/AlexNet_dict.pth')
        print('[epoch %d] train_loss: %.3f  test_accuracy: %.3f' %
              (epoch + 1, running_loss / step, val_accurate))

print('Finished Training')

predict.py

import torch
from model import AlexNet
import torchvision.transforms as transforms
from PIL import Image
import json
# import matplotlib.pyplot as plt

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, 单张图像用PIL.Image.Open()
img = Image.open('C:/Users/14251/Desktop/workspace/AlexNet/test.jpg')
# plt.imshow(img)
img = transform(img)    # (H, W, C) -> (C, H, W)
img = img.unsqueeze(dim=0)  # (C, H, W) -> (N, C, H, W)

# read class_indict
try:
    json_file = open('./class_indices.json', 'r')
    class_indict = json.load(json_file)
except Exception as e:
    print(e)
    exit(-1)

net = AlexNet(num_classes=5)
net.load_state_dict(
    torch.load('C:/Users/14251/Desktop/workspace/AlexNet/AlexNet_dict.pth'))

net.eval()
# with torch.no_grad():
#     outputs = net(img)
#     prediction = torch.max(outputs, dim=1)[1].item()

# print(class_indict[str(prediction)])

with torch.no_grad():
    # predict class
    output = torch.squeeze(net(img))
    predict = torch.softmax(output, dim=0)
    predict_cla = torch.argmax(predict).numpy()
print(class_indict[str(predict_cla)], predict[predict_cla].item())