深度学习(三)——AlexNet+Pytorch实现

简介

在2010年的ImageNet LSVRC-2010上，AlexNet在给包含有1000种类别的共120万张高分辨率图片的分类任务中，在测试集上的top-1和top-5错误率为37.5%和17.0%（top-5 错误率：即对一张图像预测5个类别，只要有一个和人工标注类别相同就算对，否则算错。同理top-1对一张图像只预测1个类别），在ImageNet LSVRC-2012的比赛中，取得了top-5错误率为15.3%的成绩，而第二名的成绩为26.2%，可见AlexNet在当时有多强大。

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网络结构

网络放在两块GPU上，一块GPU放一半。

第一阶段卷积

input 224*224*3
Conv(kernel_size=11*11, kernel_num=96, stride=4, padding=2)
output (224-11+2*2)/4+1=55 -> 55*55*96
Relu
Pool(kernel_size=3*3, stride=2)
output (55-3)/2+1=27 -> 27*27*96
Local Response Normalization(local_size=5)
output 27*27*96

第二阶段卷积

input 27*27*96
Conv(kernel_size=5*5, kernel_num=256, stride=1, padding=2)
output (27-5+2*2)/1+1=27 -> 27*27*256
Relu
Pool(kernel_size=3*3, stride=2)
output (27-3)/2+1=13 -> 13*13*256
Local Response Normalization(local_size=5)
output 13*13*256

第三阶段卷积

input 13*13*256
Conv(kernel_size=3*3, kernel_num=384, stride=1, padding=1)
output (13-3+2*1)/1+1=13 -> 13*13*384
Relu
output 13*13*256

第四阶段卷积

input 13*13*256
Conv(kernel_size=3*3, kernel_num=384, stride=1, padding=1)
output (13-3+2*1)/1+1=13 -> 13*13*384
Relu
output 13*13*384

第五阶段卷积

input 13*13*256
Conv(kernel_size=3*3, kernel_num=256, stride=1, padding=1)
output (13-3+2*1)/1+1=13 -> 13*13*256
Relu
Pool(kernel_size=3*3, stride=2)
output (13-3)/2+1=6 -> 6*6*256

第六阶段全连接

input 6*6*256	
Fc
Relu
Dropout
output 4096

第七阶段全连接

input 4096	
Fc
Relu
Dropout
output 4096

第八阶段全连接

input 4096	
Fc
Relu
Dropout
output 1000

Alexnet网络中各个层发挥的作用如下表所述：

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Pytorch实现

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(  # 打包
            nn.Conv2d(3, 48, kernel_size=11, stride=4, padding=2),  # input[3, 224, 224]  output[48, 55, 55] 自动舍去小数点后
            nn.ReLU(inplace=True),  # inplace 可以载入更大模型
            nn.MaxPool2d(kernel_size=3, stride=2),                  # output[48, 27, 27] kernel_num为原论文一半
            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),
            # 全连接
            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)
        x = torch.flatten(x, start_dim=1)  # 展平   或者view()
        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')  # 何教授方法
                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)  # 正态分布赋值
                nn.init.constant_(m.bias, 0)

从http://download.tensorflow.org/example_images/flower_photos.tgz下载数据集
执行下面代码，将数据集划分为训练集与验证集。
split_data.py

import os
from shutil import copy
import random


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


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!")

训练模型 train.py

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


# device : GPU or CPU
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(device)


# 数据转换
data_transform = {
    "train": transforms.Compose([transforms.RandomResizedCrop(224),
                                 transforms.RandomHorizontalFlip(),
                                 transforms.ToTensor(),
                                 transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]),
    "val": transforms.Compose([transforms.Resize((224, 224)),  # cannot 224, must (224, 224)
                               transforms.ToTensor(),
                               transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])}

# data_root = os.path.abspath(os.path.join(os.getcwd(), "../.."))  # get data root path
data_root = os.getcwd()
image_path = data_root + "/flower_data/"  # flower data set path
train_dataset = datasets.ImageFolder(root=image_path + "/train",
                                     transform=data_transform["train"])
train_num = len(train_dataset)

# {'daisy':0, 'dandelion':1, 'roses':2, 'sunflower':3, 'tulips':4}
flower_list = train_dataset.class_to_idx
cla_dict = dict((val, key) for key, val in flower_list.items())
# write dict into json file

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
train_loader = torch.utils.data.DataLoader(train_dataset,
                                           batch_size=batch_size, shuffle=True,
                                           num_workers=0)

validate_dataset = datasets.ImageFolder(root=image_path + "/val",
                                        transform=data_transform["val"])
val_num = len(validate_dataset)
validate_loader = torch.utils.data.DataLoader(validate_dataset,
                                              batch_size=batch_size, shuffle=True,
                                              num_workers=0)

test_data_iter = iter(validate_loader)
test_image, test_label = test_data_iter.next()
# print(test_image[0].size(),type(test_image[0]))
# print(test_label[0],test_label[0].item(),type(test_label[0]))


# 显示图像，之前需把validate_loader中batch_size改为4
# def imshow(img):
#     img = img / 2 + 0.5  # unnormalize
#     npimg = img.numpy()
#     plt.imshow(np.transpose(npimg, (1, 2, 0)))
#     plt.show()
#
# print(' '.join('%5s' % cla_dict[test_label[j].item()] for j in range(4)))
# imshow(utils.make_grid(test_image))


net = AlexNet(num_classes=5, init_weights=True)

net.to(device)
# 损失函数:这里用交叉熵
loss_function = nn.CrossEntropyLoss()
# 优化器 这里用Adam
optimizer = optim.Adam(net.parameters(), lr=0.0002)
# 训练参数保存路径
save_path = './AlexNet.pth'
# 训练过程中最高准确率
best_acc = 0.0

# 开始进行训练和测试，训练一轮，测试一轮
for epoch in range(10):
    # train
    net.train()    # 训练过程中，使用之前定义网络中的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_function(outputs, labels.to(device))
        loss.backward()
        optimizer.step()

        # print statistics
        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)

    # validate
    net.eval()    # 测试过程中不需要dropout，使用所有的神经元
    acc = 0.0  # accumulate accurate number / epoch
    with torch.no_grad():
        for val_data in validate_loader:
            val_images, val_labels = val_data
            outputs = net(val_images.to(device))
            predict_y = torch.max(outputs, dim=1)[1]
            acc += (predict_y == val_labels.to(device)).sum().item()
        val_accurate = acc / val_num
        if val_accurate > best_acc:
            best_acc = val_accurate
            torch.save(net.state_dict(), save_path)
        print('[epoch %d] train_loss: %.3f  test_accuracy: %.3f' %
              (epoch + 1, running_loss / step, val_accurate))

print('Finished Training')

Output如下

使用向日葵图片进行测试 predict.py

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

os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"

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 = Image.open("./sunflower.jpg")  # 验证太阳花
# img = Image.open("./roses.jpg")     # 验证玫瑰花
plt.imshow(img)
# [N, C, H, W]
img = data_transform(img)
# expand batch dimension
img = torch.unsqueeze(img, dim=0)

# 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)

# create model
model = AlexNet(num_classes=5)
# load model weights
model_weight_path = "./AlexNet.pth"
model.load_state_dict(torch.load(model_weight_path))
model.eval()
with torch.no_grad():
    # predict class
    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()

Output如下