mindspore.dataset.transforms.Compose
- class mindspore.dataset.transforms.Compose(transforms)[源代码]
将多个数据增强算子组合使用。
Note
Compose可以将 mindspore.dataset.transforms / mindspore.dataset.vision 等模块中的数据增强算子以及用户自定义的Python可调用对象 合并成单个数据增强。对于用户定义的Python可调用对象，要求其返回值是numpy.ndarray类型。
- 参数：
transforms (list) - 一个数据增强的列表。
- 异常：
TypeError - 参数 transforms 类型不为list。
ValueError - 参数 transforms 是空的list。
TypeError - 参数 transforms 的元素不是Python的可调用对象或audio/text/transforms/vision模块中的数据增强方法。
- 支持平台：
CPU
样例：
>>> compose = transforms.Compose([vision.Decode(), vision.RandomCrop(512)]) >>> image_folder_dataset = image_folder_dataset.map(operations=compose) >>> image_folder_dataset_dir = "/path/to/image_folder_dataset_directory" >>> >>> # create a dataset that reads all files in dataset_dir with 8 threads >>> image_folder_dataset = ds.ImageFolderDataset(image_folder_dataset_dir, num_parallel_workers=8) >>> # create a list of transformations to be applied to the image data >>> transform = transforms.Compose([vision.Decode(to_pil=True), ... vision.RandomHorizontalFlip(0.5), ... vision.ToTensor(), ... vision.Normalize((0.491, 0.482, 0.447), (0.247, 0.243, 0.262), is_hwc=False), ... vision.RandomErasing()]) >>> # apply the transform to the dataset through dataset.map function >>> image_folder_dataset = image_folder_dataset.map(operations=transform, input_columns=["image"]) >>> >>> # Compose is also be invoked implicitly, by just passing in a list of ops >>> # the above example then becomes: >>> transforms_list = [vision.Decode(to_pil=True), ... vision.RandomHorizontalFlip(0.5), ... vision.ToTensor(), ... vision.Normalize((0.491, 0.482, 0.447), (0.247, 0.243, 0.262), is_hwc=False), ... vision.RandomErasing()] >>> >>> # apply the transform to the dataset through dataset.map() >>> image_folder_dataset_1 = image_folder_dataset_1.map(operations=transforms_list, input_columns=["image"]) >>> >>> # Certain C++ and Python ops can be combined, but not all of them >>> # An example of combined operations >>> arr = [0, 1] >>> dataset = ds.NumpySlicesDataset(arr, column_names=["cols"], shuffle=False) >>> transformed_list = [transforms.OneHot(2), ... transforms.Mask(transforms.Relational.EQ, 1)] >>> dataset = dataset.map(operations=transformed_list, input_columns=["cols"]) >>> >>> # Here is an example of mixing vision ops >>> import numpy as np >>> op_list=[vision.Decode(), ... vision.Resize((224, 244)), ... vision.ToPIL(), ... np.array, # need to convert PIL image to a NumPy array to pass it to C++ operation ... vision.Resize((24, 24))] >>> image_folder_dataset = image_folder_dataset.map(operations=op_list, input_columns=["image"])