mindspore.dataset.transforms.Compose

class mindspore.dataset.transforms.Compose(transforms)

将多个数据增强操作组合使用。

说明

Compose可以将 mindspore.dataset.transforms / mindspore.dataset.vision 等模块中的数据增强操作以及用户自定义的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：

>>> import numpy as np
>>> import mindspore.dataset as ds
>>> import mindspore.dataset.transforms as transforms
>>> import mindspore.dataset.vision as vision
>>> from mindspore.dataset.transforms import Relational
>>>
>>> # Use the transform in dataset pipeline mode
>>> # create a dataset that reads all files in dataset_dir with 8 threads
>>> data = np.random.randint(0, 255, size=(1, 100, 100, 3)).astype(np.uint8)
>>> numpy_slices_dataset = ds.NumpySlicesDataset(data, ["image"])
>>>
>>> # create a list of transformations to be applied to the image data
>>> transform = transforms.Compose([
...     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
>>> numpy_slices_dataset = numpy_slices_dataset.map(operations=transform, input_columns=["image"])
>>> for item in numpy_slices_dataset.create_dict_iterator(num_epochs=1, output_numpy=True):
...     print(item["image"].shape, item["image"].dtype)
...     break
(3, 100, 100) float32
>>>
>>> # Compose is also be invoked implicitly, by just passing in a list of ops
>>> # the above example then becomes:
>>> numpy_slices_dataset = ds.NumpySlicesDataset(data, ["image"])
>>> transforms_list = [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()
>>> numpy_slices_dataset = numpy_slices_dataset.map(operations=transforms_list, input_columns=["image"])
>>> for item in numpy_slices_dataset.create_dict_iterator(num_epochs=1, output_numpy=True):
...     print(item["image"].shape, item["image"].dtype)
...     break
(3, 100, 100) float32
>>>
>>> # Certain C++ and Python ops can be combined, but not all of them
>>> # An example of combined operations
>>> arr = [0, 1]
>>> numpy_slices_dataset = ds.NumpySlicesDataset(arr, column_names=["cols"], shuffle=False)
>>> transformed_list = [transforms.OneHot(2),
...                     transforms.Mask(transforms.Relational.EQ, 1)]
>>> numpy_slices_dataset = numpy_slices_dataset.map(operations=transformed_list, input_columns=["cols"])
>>> for item in numpy_slices_dataset.create_dict_iterator(num_epochs=1, output_numpy=True):
...     print(item["cols"].shape, item["cols"].dtype)
...     break
(2,) bool
>>>
>>> # Here is an example of mixing vision ops
>>> op_list=[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))]
>>> numpy_slices_dataset = ds.NumpySlicesDataset(data, ["image"])
>>> numpy_slices_dataset = numpy_slices_dataset.map(operations=op_list,  input_columns=["image"])
>>> for item in numpy_slices_dataset.create_dict_iterator(num_epochs=1, output_numpy=True):
...     print(item["image"].shape, item["image"].dtype)
...     break
(24, 24, 3) uint8
>>>
>>> # Use the transform in eager mode
>>> data = np.array([1, 2, 3])
>>> output = transforms.Compose([transforms.Fill(10), transforms.Mask(Relational.EQ, 100)])(data)
>>> print(output.shape, output.dtype)
(3,) bool