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
>>> import mindspore.dataset as ds >>> import mindspore.dataset.vision.py_transforms as py_vision >>> import mindspore.dataset.transforms.py_transforms as py_transforms >>> >>> dataset_dir = "path/to/imagefolder_directory" >>> # create a dataset that reads all files in dataset_dir with 8 threads >>> data1 = ds.ImageFolderDataset(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() >>> data1 = data1.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: >>> transform_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() >>> data2 = data2.map(operations=transform_list, input_columns="image") >>> >>> # Certain C++ and Python ops can be combined, but not all of them >>> # An example of combined operations >>> import mindspore.dataset as ds >>> import mindspore.dataset.transforms.c_transforms as c_transforms >>> import mindspore.dataset.vision.c_transforms as c_vision >>> >>> data3 = ds.NumpySlicesDataset(arr, column_names=["cols"], shuffle=False) >>> transformed_list = [py_transforms.OneHotOp(2), c_transforms.Mask(c_transforms.Relational.EQ, 1)] >>> data3 = data3.map(operations=transformed_list, input_columns=["cols"]) >>> >>> # Here is an example of mixing vision ops >>> data_dir = "/path/to/imagefolder_directory" >>> data4 = ds.ImageFolderDataset(dataset_dir=data_dir, shuffle=False) >>> input_columns = ["column_names"] >>> 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))] >>> data4 = data4.map(operations=op_list, input_columns=input_columns)