pytorch入门，deep-learning-for-image-processing-master的test3-vggnet

首先是train部分

import os
import sys
import json
 
import torch
import torch.nn as nn
from torchvision import transforms, datasets
import torch.optim as optim
from tqdm import tqdm
 
from model import vgg
 
 
def main():
    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
    print("using {} device.".format(device))
 
    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)),
                                   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
    image_path = os.path.join(data_root, "data_set", "flower_data")  # flower data set path
    assert os.path.exists(image_path), "{} path does not exist.".format(image_path)
    train_dataset = datasets.ImageFolder(root=os.path.join(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
    nw = min([os.cpu_count(), batch_size if batch_size > 1 else 0, 8])  # number of workers
    print('Using {} dataloader workers every process'.format(nw))
 
    train_loader = torch.utils.data.DataLoader(train_dataset,
                                               batch_size=batch_size, shuffle=True,
                                               num_workers=nw)
 
    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=batch_size, shuffle=False,
                                                  num_workers=nw)
    print("using {} images for training, {} images for validation.".format(train_num,
                                                                           val_num))
 
    # test_data_iter = iter(validate_loader)
    # test_image, test_label = test_data_iter.next()
 
    model_name = "vgg16"
    net = vgg(model_name=model_name, num_classes=5, init_weights=True)
    net.to(device)
    loss_function = nn.CrossEntropyLoss()
    optimizer = optim.Adam(net.parameters(), lr=0.0001)
 
    epochs = 30
    best_acc = 0.0
    save_path = './{}Net.pth'.format(model_name)
    train_steps = len(train_loader)
    for epoch in range(epochs):
        # train
        net.train()
        running_loss = 0.0
        train_bar = tqdm(train_loader, file=sys.stdout)
        for step, data in enumerate(train_bar):
            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()
 
            train_bar.desc = "train epoch[{}/{}] loss:{:.3f}".format(epoch + 1,
                                                                     epochs,
                                                                     loss)
 
        # validate
        net.eval()
        acc = 0.0  # accumulate accurate number / epoch
        with torch.no_grad():
            val_bar = tqdm(validate_loader, file=sys.stdout)
            for val_data in val_bar:
                val_images, val_labels = val_data
                outputs = net(val_images.to(device))
                predict_y = torch.max(outputs, dim=1)[1]
                acc += torch.eq(predict_y, val_labels.to(device)).sum().item()
 
        val_accurate = acc / val_num
        print('[epoch %d] train_loss: %.3f  val_accuracy: %.3f' %
              (epoch + 1, running_loss / train_steps, val_accurate))
 
        if val_accurate > best_acc:
            best_acc = val_accurate
            torch.save(net.state_dict(), save_path)
 
    print('Finished Training')
 
 
if __name__ == '__main__':
    main()

def main():
    device=torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
    print("using {} device".format(device))

    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),
            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(),"../.."))
    image_path=os.path.join(data_root,"data_set","flower_data")
    assert os.path.exists(image_path), "{} path is not exist. ".format(image_path)
    train_dataset=datasets.ImageFolder(root=os.path.join(image_path,"train"),
                                       transform=data_transform["train"])
    val_dataset=datasets.ImageFolder(root=os.path.join(image_path,"val"),
                                     transform=data_transform["val"])
    train_num=len(train_dataset)

这里和之前一样，需要注意绝对路径，使用dataset.Imagefolder很方便

flower_list=train_dataset.class_to_idx使用这个，很方便的给图片打数字标

cla_dict=dict((val,key) for key,val in flower_list.items())打包成字典

json_str=json.dumps(cla_dict,indent=4)
with open('class_indices.json','w') as json_file:
    json_file.write(json_str)
    写进json里面

接下来是load

model_name='vgg16'
net=vgg(model_name=model_name,num_classes=5,init_weights=True)
net.to(device)
loss_function=nn.CrossEntropyLoss()
optimizer=optim.Adam(net.parameters(),lr=0.00001)
epochs=30
best_acc=0.0
save_path='./{}Net.path'.format(model_name)
train_steps=len(train_loader)k

开始训练

for epoch in range(epochs):
    net.train()
    running_loss=0.0
    train_bar=tqdm(train_loader,file=sys.stdout)
    for step,data in enumerate(train_bar):
        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()
        train_bar.desc = "train epoch[{}/{}] loss:{:.3f}".format(epoch + 1,
                                                                 epochs,
                                                                 loss)
    然后做测试

net.eval()
acc=0.0
with torch.no_grad():
    val_bar=tqdm(val_loader,file=sys.stdout)
    for val_data in val_bar:
        val_images,val_labels=val_data
        outputs=net(val_images.to(device))
        predict_y=torch.max(outputs,dim=1)[1]
        acc=acc+torch.eq(predict_y,val_labels.to(device).sum().item())

val_acc=acc/val_num
print('[epoch%d] train_loss: %.3f val_acc:%.3f'%(epoch+1,running_loss/train_steps,val_acc))b
 
保存模型：

if val_acc>best_acc:
    best_acc=val_acc
    torch.save(net.state_dict(),save_path)

然后是测试集：

import os
import json
 
import torch
from PIL import Image
from torchvision import transforms
import matplotlib.pyplot as plt
 
from model import vgg
 
 
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 = "../tulip.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 = vgg(model_name="vgg16", num_classes=5).to(device)
    # load model weights
    weights_path = "./vgg16Net.pth"
    assert os.path.exists(weights_path), "file: '{}' dose not exist.".format(weights_path)
    model.load_state_dict(torch.load(weights_path, map_location=device))
 
    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()

正常操作
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))
    ])

    img_path="../tulip.jpg"
    assert os.path.exists(img_path) ,"{} is not exist".format(img_path)
    img=Image.open(img_path)
    plt.show(img)
    img=data_transform
    img=torch.unsqueeze(img,dim=0)

开始读json文件

json_path='./class_indices.json'
assert os.path.exists(json_path),"{} is not exist".format(json_path)
with open(json_path,"r") as f:
    class_indict=json.load(f)
    

使用模型开始测试

model=vgg(model_name="vgg_16",num_class=5).to(device)
weights_path="./vgg16Net.pth"
assert  os.path.exists(weights_path),"{} is not exist".format(weights_path)
model.load_state_dict(torch.load(weights_path,map_location=device))
model.eval()
with torch.no_grad():
    output=model(img.to(device))
    output=torch.squeeze(output).cpu()
    predict=torch.softmax(output,dim=0)
    predict_cla=torch.argmax(predict).numpy()

接下来是model

import torch.nn as nn
import torch
 
# official pretrain weights
model_urls = {
    'vgg11': 'https://download.pytorch.org/models/vgg11-bbd30ac9.pth',
    'vgg13': 'https://download.pytorch.org/models/vgg13-c768596a.pth',
    'vgg16': 'https://download.pytorch.org/models/vgg16-397923af.pth',
    'vgg19': 'https://download.pytorch.org/models/vgg19-dcbb9e9d.pth'
}
 
 
class VGG(nn.Module):
    def __init__(self, features, num_classes=1000, init_weights=False):
        super(VGG, self).__init__()
        self.features = features
        self.classifier = nn.Sequential(
            nn.Linear(512*7*7, 4096),
            nn.ReLU(True),
            nn.Dropout(p=0.5),
            nn.Linear(4096, 4096),
            nn.ReLU(True),
            nn.Dropout(p=0.5),
            nn.Linear(4096, num_classes)
        )
        if init_weights:
            self._initialize_weights()
 
    def forward(self, x):
        # N x 3 x 224 x 224
        x = self.features(x)
        # N x 512 x 7 x 7
        x = torch.flatten(x, start_dim=1)
        # N x 512*7*7
        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')
                nn.init.xavier_uniform_(m.weight)
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.Linear):
                nn.init.xavier_uniform_(m.weight)
                # nn.init.normal_(m.weight, 0, 0.01)
                nn.init.constant_(m.bias, 0)
 
 
def make_features(cfg: list):
    layers = []
    in_channels = 3
    for v in cfg:
        if v == "M":
            layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
        else:
            conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)
            layers += [conv2d, nn.ReLU(True)]
            in_channels = v
    return nn.Sequential(*layers)
 
 
cfgs = {
    'vgg11': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
    'vgg13': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
    'vgg16': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],
    'vgg19': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'],
}
 
 
def vgg(model_name="vgg16", **kwargs):
    assert model_name in cfgs, "Warning: model number {} not in cfgs dict!".format(model_name)
    cfg = cfgs[model_name]
 
    model = VGG(make_features(cfg), **kwargs)
    return model

class VGG(nn.Module):
    def __init__(self,features,num_classes=1000,init_weights=False):
        super(VGG,self).__init__()
        self.features=features
        self.classifier=nn.Sequential(
            nn.Linear(512 * 7 * 7, 4096),
            nn.ReLU(True),
            nn.Dropout(p=0.5),
            nn.Linear(4096, 4096),
            nn.ReLU(True),
            nn.Dropout(p=0.5),
            nn.Linear(4096, num_classes)
        )
        if init_weights:
            self._initialize_weights()

调整维度，扁平化成线性的

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

def make_features(cfg:list):
    layers=[]
    in_channels=3
    for v in cfg:
        if v=="M":
            layers+=[nn.MaxPool2d(kernel_size=2,stride=2)]
        else:
            conv2d=nn.Conv2d(in_channels,v,kernel_size=3,padding=1)
            layers+=[conv2d,nn.ReLU(True)]
            in_channels=v
    return nn.Sequential(*layers)