VGGNet 的模型、训练和预测代码

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()