train.py

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

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))])}

train_dataset = datasets.ImageFolder(root="D:\\李轩楠\\dataset\\val",
                                     transform=data_transform["train"])
train_num = len(train_dataset)

flower_list = train_dataset.class_to_idx
cla_dict = dict((val, key) for key, val in flower_list.items())
json_str = json.dumps(cla_dict, indent=4)
with open('class_indices.json', 'w') as json_file:
    json_file.write(json_str)

batch_size = 10
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="D:\\李轩楠\\dataset\\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))

model_name = "Calligrapher"
net = VGG(num_classes=5, init_weights=True)
net.to(device)
loss_function = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), lr=0.0001)

epochs = 1
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()

        running_loss += loss.item()

        train_bar.desc = "train epoch[{}/{}] loss:{:.3f}".format(epoch + 1,
                                                                 epochs,
                                                                 loss)

    net.eval()
    acc = 0.0
    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')