6.Vgg16--CNN经典网络模型详解（pytorch实现）

1.首先建立一个model.py文件，用来写神经网络，代码如下：

import torch
import torch.nn as nn
class My_VGG16(nn.Module):
    def __init__(self,num_classes=5,init_weight=True):
        super(My_VGG16, self).__init__()
        # 特征提取层
        self.features = nn.Sequential(
            nn.Conv2d(in_channels=3,out_channels=64,kernel_size=3,stride=1,padding=1),
            nn.Conv2d(in_channels=64,out_channels=64,kernel_size=3,stride=1,padding=1),
            nn.MaxPool2d(kernel_size=2,stride=2),
            nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=1, padding=1),
            nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3, stride=1, padding=1),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, stride=1, padding=1),
            nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1),
            nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1),
            nn.MaxPool2d(kernel_size=2,stride=2),
            nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, stride=1, padding=1),
            nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1),
            nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1),
            nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1),
            nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1),
            nn.MaxPool2d(kernel_size=2, stride=2),
        )
        # 分类层
        self.classifier = nn.Sequential(
            nn.Linear(in_features=7*7*512,out_features=4096),
            nn.ReLU(),
            nn.Dropout(0.5),
            nn.Linear(in_features=4096,out_features=4096),
            nn.ReLU(),
            nn.Dropout(0.5),
            nn.Linear(in_features=4096,out_features=num_classes)
        )
 
        # 参数初始化
        if init_weight: # 如果进行参数初始化
            for m in self.modules():  # 对于模型的每一层
                if isinstance(m, nn.Conv2d): # 如果是卷积层
                    # 使用kaiming初始化
                    nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
                    # 如果bias不为空，固定为0
                    if m.bias is not None:
                        nn.init.constant_(m.bias, 0)
                elif isinstance(m, nn.Linear):# 如果是线性层
                    # 正态初始化
                    nn.init.normal_(m.weight, 0, 0.01)
                    # bias则固定为0
                    nn.init.constant_(m.bias, 0)
 
    def forward(self,x):
        x = self.features(x)
        x = torch.flatten(x,1)
        result = self.classifier(x)
        return result

2.下载数据集

DATA_URL = 'http://download.tensorflow.org/example_images/flower_photos.tgz'

3.下载完后写一个spile_data.py文件，将数据集进行分类

#spile_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!")

之后应该是这样：

4.再写一个train.py文件，用来训练模型

import torch
import torch.nn as nn
from torchvision import transforms, datasets
import json
import os
import torch.optim as optim
from model import My_VGG16
 
 
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
data_transform = {
    "train": transforms.Compose([transforms.RandomResizedCrop(224),
                                 transforms.RandomHorizontalFlip(),
                                 transforms.ToTensor(),
                                 transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]),
    "val": transforms.Compose([transforms.Resize(256),
                               transforms.CenterCrop(224),
                               transforms.ToTensor(),
                               transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])}
 
data_root = os.getcwd() # get data root path
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())
 
 
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 = 16
 
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=False,
                                              num_workers=0)
net = My_VGG16(num_classes=5)
 
# load pretrain weights
model_weight_path = "./vgg16.pth"
pre_weights = torch.load(model_weight_path)
 
net.to(device)
 
loss_function = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), lr=0.0001)
 
best_acc = 0.0
save_path = './vgg16_train.pth'
 
for epoch in range(5):
    # train
    net.train()
    running_loss = 0.0
    for step, data in enumerate(train_loader, start=0):
        images, labels = data
        optimizer.zero_grad()
        logits = net(images.to(device))#.to(device)
        print("===>",logits.shape,labels.shape)
        loss = loss_function(logits, 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}%[{}->{}]{:.4f}".format(int(rate*100), a, b, loss), end="")
    print()
 
# validate
    net.eval()
    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))  # eval model only have last output layer
            # loss = loss_function(outputs, test_labels)
            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')