mindspore.dataset.transforms.py_transforms.Compose
- class mindspore.dataset.transforms.py_transforms.Compose(transforms)[source]
Compose a list of transforms.
Note
Compose takes a list of transformations either provided in py_transforms or from user-defined implementation; each can be an initialized transformation class or a lambda function, as long as the output from the last transformation is a single tensor of type numpy.ndarray. See below for an example of how to use Compose with py_transforms classes and check out FiveCrop or TenCrop for the use of them in conjunction with lambda functions.
- Parameters
transforms (list) – List of transformations to be applied.
Examples
>>> 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 = py_transforms.Compose([py_vision.Decode(), ... py_vision.RandomHorizontalFlip(0.5), ... py_vision.ToTensor(), ... py_vision.Normalize((0.491, 0.482, 0.447), (0.247, 0.243, 0.262)), ... py_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 = [py_vision.Decode(), ... py_vision.RandomHorizontalFlip(0.5), ... py_vision.ToTensor(), ... py_vision.Normalize((0.491, 0.482, 0.447), (0.247, 0.243, 0.262)), ... py_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 = [py_transforms.OneHotOp(2), c_transforms.Mask(c_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=[c_vision.Decode(), ... c_vision.Resize((224, 244)), ... py_vision.ToPIL(), ... np.array, # need to convert PIL image to a NumPy array to pass it to C++ operation ... c_vision.Resize((24, 24))] >>> image_folder_dataset = image_folder_dataset.map(operations=op_list, input_columns=["image"])