Pytorch数据变换(Transform)
实例化数据库的时候,有一个可选的参数可以对数据进行转换,满足大多神经网络的要求输入固定尺寸的图片,因此要对原图进行Rescale
或者Crop操作,然后返回的数据需要转换成Tensor如:
import FaceLandmarksDataset face_dataset = FaceLandmarksDataset(csv_file='data/faces/face_landmarks.csv', root_dir='data/faces/', transform=transforms.Compose([ Rescale(256), RandomCrop(224), ToTensor()]) )
数据转换(Transfrom)发生在数据库中的__getitem__操作中。以上代码中,transforms.Compose(transform_list),Compose即组合的意思,其参数是一个转换操作的列表。如上是[ Rescale(256), RandomCrop(224), ToTensor()],以下是实现这三个转换类。我们将把它们写成可调用的类,而不是简单的函数,这样在每次调用转换时就不需要传递它的参数。为此,我们只需要实现__call__方法,如果需要,还需要实现__init__方法。然后我们可以使用这样的变换:
#创建一个转换可调用类的实例 tsfm = Transform(params) #使用转换操作实例对样本sample进行转换 transformed_sample = tsfm(sample)
下面观察这些转换是如何应用于图像和标注的。(注:每一个操作对应一个类)
class Rescale(object): """Rescale the image in a sample to a given size. Args: output_size (tuple or int): Desired output size. If tuple, output is matched to output_size. If int, smaller of image edges is matched to output_size keeping aspect ratio the same. """ def __init__(self, output_size): assert isinstance(output_size, (int, tuple)) self.output_size = output_size def __call__(self, sample): image, landmarks = sample['image'], sample['landmarks'] h, w = image.shape[:2] if isinstance(self.output_size, int): if h > w: new_h, new_w = self.output_size * h / w, self.output_size else: new_h, new_w = self.output_size, self.output_size * w / h else: new_h, new_w = self.output_size new_h, new_w = int(new_h), int(new_w) img = transform.resize(image, (new_h, new_w)) # h and w are swapped for landmarks because for images, # x and y axes are axis 1 and 0 respectively landmarks = landmarks * [new_w / w, new_h / h] return {'image': img, 'landmarks': landmarks} class RandomCrop(object): """Crop randomly the image in a sample. Args: output_size (tuple or int): Desired output size. If int, square crop is made. """ def __init__(self, output_size): assert isinstance(output_size, (int, tuple)) if isinstance(output_size, int): self.output_size = (output_size, output_size) else: assert len(output_size) == 2 self.output_size = output_size def __call__(self, sample): image, landmarks = sample['image'], sample['landmarks'] h, w = image.shape[:2] new_h, new_w = self.output_size top = np.random.randint(0, h - new_h) left = np.random.randint(0, w - new_w) image = image[top: top + new_h, left: left + new_w] landmarks = landmarks - [left, top] return {'image': image, 'landmarks': landmarks} class ToTensor(object): """Convert ndarrays in sample to Tensors.""" def __call__(self, sample): image, landmarks = sample['image'], sample['landmarks'] # swap color axis because # numpy image: H x W x C # torch image: C X H X W image = image.transpose((2, 0, 1)) return {'image': torch.from_numpy(image), 'landmarks': torch.from_numpy(landmarks)}
以下来介绍转换的用法。
#获取一条数据 sample = face_dataset[index] #单独进行操作 scale = Rescale(256) crope= RandomCrop(224) scale(sample) crope(sample) #使用Compose组合操作 compose = transforms.Compose([Rescale(256),RandomCrop(224)]) compose(sample)
上述转换后数据仍然是PIL类型,如果要求返回是一个tensor,那么还得在Compose的最后一个元素进行Totensor操作。
手与大脑的距离决定了理想与现实的相似度