如何调整图像张量的大小

Question

以下是我的代码，我将每个图像转换为pIL，然后将它们变成pytorch张量：

transform = transforms.Compose([transforms.PILToTensor()])

# choose the training and test datasets
train_data = os.listdir('data/training/')
testing_data = os.listdir('data/testing/')
train_tensors = []
test_tensors = []

for train_image in train_data:
    img = Image.open('data/training/' + train_image)
    train_tensors.append(transform(img))

for test_image in testing_data:
    img = Image.open('data/testing/' + test_image)
    test_tensors.append(transform(img))

# Print out some stats about the training and test data
print('Train data, number of images: ', len(train_data))
print('Test data, number of images: ', len(testing_data))

batch_size = 20

train_loader = DataLoader(train_tensors, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(test_tensors, batch_size=batch_size, shuffle=True)

# specify the image classes
classes = ['checked', 'unchecked', 'other']

# obtain one batch of training images
dataiter = iter(train_loader)
images, labels = dataiter.next()
images = images.numpy()

但是，我遇到了此错误：

RuntimeError: stack expects each tensor to be equal size, but got [4, 66, 268] at entry 0 and [4, 88, 160] at entry 1

这是因为我的图像在pil pil -＆gt之前没有调整大小；张量。调整数据图像的正确方法是什么？

Answer 1

尝试使用 /code>，并且假设图像具有差异大小，则可以使用中心crop 或 RandomresizedCrop 取决于您的任务。检查完整列表 。

这是一个示例：

train_dir = "data/training/"

train_dataset = datasets.ImageFolder(
    train_dir,
    transforms.Compose([
        transforms.RandomResizedCrop(img_size),  # image size int or tuple
        # Add more transforms here
        transforms.ToTensor(),  # convert to tensor at the end
]))

train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)