1、argparse的使用 （Python指令碼時傳入引數的三種方式之一：https://blog.csdn.net/u012426298/article/details/80263507）

import argparse#必備
parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')#必備
parser.add_argument('data', metavar='DIR',
                    help='path to dataset')#
parser.add_argument('-a', '--arch', metavar='ARCH', default='resnet18',
                    choices=model_names,
                    help='model architecture: ' +
                        ' | '.join(model_names) +
                        ' (default: resnet18)')
parser.add_argument('-j', '--workers', default=4, type=int, metavar='N',
                    help='number of data loading workers (default: 4)')
parser.add_argument('--epochs', default=90, type=int, metavar='N',
                    help='number of total epochs to run')

parser.add_argument('--lr', '--learning-rate', default=0.1, type=float,
                    metavar='LR', help='initial learning rate')


args = parser.parse_args()#必備

traindir = os.path.join(args.data, 'train')
    valdir = os.path.join(args.data, 'val')###
optimizer = torch.optim.SGD(model.parameters(), args.lr,#
                                momentum=args.momentum,
                                weight_decay=args.weight_decay)
 

執行檔案：

python main.py -a alexnet --lr 0.01 [imagenet-folder with train and val folders]

2、限制使用哪個GPU

os.environ["CUDA_VISIBLE_DEVICES"] = "1"

3、讀出Tensor裡面的值

4、在分類任務中找出單個類別的準確率（每一個類別）

class_correct = list(0. for i in range(10))#10是類別的個數
class_total = list(0. for i in range(10))
with torch.no_grad():
    for data in testloader:
        images, labels = data
        outputs = net(images)
        _, predicted = torch.max(outputs, 1)
        c = (predicted == labels).squeeze()#每一個batch的(predicted==labels)
        for i in range(4):#4是每一個batch的個數
            label = labels[i]
            class_correct[label] += c[i].item()
            class_total[label] += 1


for i in range(10):
    print('Accuracy of %5s : %2d %%' % (
        classes[i], 100 * class_correct[i] / class_total[i]))#每一個類別的準確率
 

5、