diff --git a/mindspore/dataset/core/config.py b/mindspore/dataset/core/config.py index 904ad83aae..7cf76e6805 100644 --- a/mindspore/dataset/core/config.py +++ b/mindspore/dataset/core/config.py @@ -81,8 +81,6 @@ def set_seed(seed): ValueError: If seed is invalid (< 0 or > MAX_UINT_32). Examples: - >>> import mindspore.dataset as ds - >>> >>> # Set a new global configuration value for the seed value. >>> # Operations with randomness will use the seed value to generate random values. >>> ds.config.set_seed(1000) @@ -116,8 +114,6 @@ def set_prefetch_size(size): ValueError: If prefetch_size is invalid (<= 0 or > MAX_INT_32). Examples: - >>> import mindspore.dataset as ds - >>> >>> # Set a new global configuration value for the prefetch size. >>> ds.config.set_prefetch_size(1000) """ @@ -147,8 +143,6 @@ def set_num_parallel_workers(num): ValueError: If num_parallel_workers is invalid (<= 0 or > MAX_INT_32). Examples: - >>> import mindspore.dataset as ds - >>> >>> # Set a new global configuration value for the number of parallel workers. >>> # Now parallel dataset operators will run with 8 workers. >>> ds.config.set_num_parallel_workers(8) @@ -180,8 +174,6 @@ def set_monitor_sampling_interval(interval): ValueError: If interval is invalid (<= 0 or > MAX_INT_32). Examples: - >>> import mindspore.dataset as ds - >>> >>> # Set a new global configuration value for the monitor sampling interval. >>> ds.config.set_monitor_sampling_interval(100) """ @@ -217,8 +209,6 @@ def set_auto_num_workers(enable): TypeError: If enable is not of boolean type. Examples: - >>> import mindspore.dataset as ds - >>> >>> # Enable auto_num_worker feature, this might override the num_parallel_workers passed in by user >>> ds.config.set_auto_num_workers(True) """ @@ -257,7 +247,7 @@ def get_auto_num_workers(): Returns: Bool, whether auto num worker feature is turned on Examples: - >>> ds.config.get_auto_num_workers() + >>> num_workers = ds.config.get_auto_num_workers() """ return _config.get_auto_num_workers() @@ -274,8 +264,6 @@ def set_callback_timeout(timeout): ValueError: If timeout is invalid (<= 0 or > MAX_INT_32). Examples: - >>> import mindspore.dataset as ds - >>> >>> # Set a new global configuration value for the timeout value. >>> ds.config.set_callback_timeout(100) """ @@ -316,10 +304,8 @@ def load(file): RuntimeError: If file is invalid and parsing fails. Examples: - >>> import mindspore.dataset as ds - >>> >>> # Set new default configuration values according to values in the configuration file. - >>> ds.config.load("path/to/config/file") + >>> ds.config.load("/path/to/config_directory/config.cfg") >>> # example config file: >>> # { >>> # "logFilePath": "/tmp", diff --git a/mindspore/dataset/vision/c_transforms.py b/mindspore/dataset/vision/c_transforms.py index ac84b4ff68..19d52bdba0 100644 --- a/mindspore/dataset/vision/c_transforms.py +++ b/mindspore/dataset/vision/c_transforms.py @@ -23,25 +23,24 @@ to improve their training models. class attributes (self.xxx) to support save() and load(). Examples: - >>> import mindspore.dataset as ds - >>> import mindspore.dataset.transforms.c_transforms as c_transforms - >>> import mindspore.dataset.vision.c_transforms as c_vision >>> from mindspore.dataset.vision import Border, Inter - >>> - >>> dataset_dir = "path/to/imagefolder_directory" + >>> image_folder_dataset_dir = "/path/to/image_folder_dataset_directory" >>> # create a dataset that reads all files in dataset_dir with 8 threads - >>> data1 = ds.ImageFolderDataset(dataset_dir, num_parallel_workers=8) + >>> 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 >>> transforms_list = [c_vision.Decode(), - >>> c_vision.Resize((256, 256), interpolation=Inter.LINEAR), - >>> c_vision.RandomCrop(200, padding_mode=Border.EDGE), - >>> c_vision.RandomRotation((0, 15)), - >>> c_vision.Normalize((100, 115.0, 121.0), (71.0, 68.0, 70.0)), - >>> c_vision.HWC2CHW()] + ... c_vision.Resize((256, 256), interpolation=Inter.LINEAR), + ... c_vision.RandomCrop(200, padding_mode=Border.EDGE), + ... c_vision.RandomRotation((0, 15)), + ... c_vision.Normalize((100, 115.0, 121.0), (71.0, 68.0, 70.0)), + ... c_vision.HWC2CHW()] >>> onehot_op = c_transforms.OneHot(num_classes=10) >>> # apply the transformation to the dataset through data1.map() - >>> data1 = data1.map(operations=transforms_list, input_columns="image") - >>> data1 = data1.map(operations=onehot_op, input_columns="label") + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, + ... input_columns="image") + >>> image_folder_dataset = image_folder_dataset.map(operations=onehot_op, + ... input_columns="label") """ import numbers import numpy as np @@ -91,10 +90,9 @@ class AutoContrast(cde.AutoContrastOp): ignore (Union[int, sequence], optional): Pixel values to ignore (default=None). Examples: - >>> import mindspore.dataset.vision.c_transforms as c_vision - >>> >>> transforms_list = [c_vision.Decode(), c_vision.AutoContrast(cutoff=10.0, ignore=[10, 20])] - >>> data1 = data1.map(operations=transforms_list, input_columns=["image"]) + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, + ... input_columns=["image"]) """ @check_auto_contrast @@ -121,10 +119,9 @@ class RandomSharpness(cde.RandomSharpnessOp): ValueError: If degrees is in (max, min) format instead of (min, max). Examples: - >>> import mindspore.dataset.vision.c_transforms as c_vision - >>> >>> transforms_list = [c_vision.Decode(), c_vision.RandomSharpness(degrees=(0.2, 1.9))] - >>> data1 = data1.map(operations=transforms_list, input_columns=["image"]) + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, + ... input_columns=["image"]) """ @check_positive_degrees @@ -138,10 +135,9 @@ class Equalize(cde.EqualizeOp): Apply histogram equalization on input image. Examples: - >>> import mindspore.dataset.vision.c_transforms as c_vision - >>> >>> transforms_list = [c_vision.Decode(), c_vision.Equalize()] - >>> data1 = data1.map(operations=transforms_list, input_columns=["image"]) + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, + ... input_columns=["image"]) """ @@ -150,10 +146,9 @@ class Invert(cde.InvertOp): Apply invert on input image in RGB mode. Examples: - >>> import mindspore.dataset.vision.c_transforms as c_vision - >>> >>> transforms_list = [c_vision.Decode(), c_vision.Invert()] - >>> data1 = data1.map(operations=transforms_list, input_columns=["image"]) + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, + ... input_columns=["image"]) """ @@ -166,10 +161,9 @@ class Decode(cde.DecodeOp): If True means format of decoded image is RGB else BGR(deprecated). Examples: - >>> import mindspore.dataset.vision.c_transforms as c_vision - >>> >>> transforms_list = [c_vision.Decode(), c_vision.RandomHorizontalFlip()] - >>> data1 = data1.map(operations=transforms_list, input_columns=["image"]) + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, + ... input_columns=["image"]) """ def __init__(self, rgb=True): @@ -205,15 +199,14 @@ class CutMixBatch(cde.CutMixBatchOp): prob (float, optional): The probability by which CutMix is applied to each image (default = 1.0). Examples: - >>> import mindspore.dataset.transforms.c_transforms as c_transforms - >>> import mindspore.dataset.vision.c_transforms as c_vision - >>> from mindspore.dataset.transforms.vision import ImageBatchFormat - >>> + >>> from mindspore.dataset.vision import ImageBatchFormat >>> onehot_op = c_transforms.OneHot(num_classes=10) - >>> data1 = data1.map(operations=onehot_op, input_columns=["label"]) + >>> image_folder_dataset= image_folder_dataset.map(operations=onehot_op, + ... input_columns=["label"]) >>> cutmix_batch_op = c_vision.CutMixBatch(ImageBatchFormat.NHWC, 1.0, 0.5) - >>> data1 = data1.batch(5) - >>> data1 = data1.map(operations=cutmix_batch_op, input_columns=["image", "label"]) + >>> image_folder_dataset = image_folder_dataset.batch(5) + >>> image_folder_dataset = image_folder_dataset.map(operations=cutmix_batch_op, + ... input_columns=["image", "label"]) """ @check_cut_mix_batch_c @@ -233,10 +226,9 @@ class CutOut(cde.CutOutOp): num_patches (int, optional): Number of patches to be cut out of an image (default=1). Examples: - >>> import mindspore.dataset.vision.c_transforms as c_vision - >>> >>> transforms_list = [c_vision.Decode(), c_vision.CutOut(80, num_patches=10)] - >>> data1 = data1.map(operations=transforms_list, input_columns=["image"]) + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, + ... input_columns=["image"]) """ @check_cutout @@ -258,14 +250,13 @@ class MixUpBatch(cde.MixUpBatchOp): alpha (float, optional): Hyperparameter of beta distribution (default = 1.0). Examples: - >>> import mindspore.dataset.transforms.c_transforms as c_transforms - >>> import mindspore.dataset.vision.c_transforms as c_vision - >>> >>> onehot_op = c_transforms.OneHot(num_classes=10) - >>> data1 = data1.map(operations=onehot_op, input_columns=["label"]) + >>> image_folder_dataset= image_folder_dataset.map(operations=onehot_op, + ... input_columns=["label"]) >>> mixup_batch_op = c_vision.MixUpBatch(alpha=0.9) - >>> data1 = data1.batch(5) - >>> data1 = data1.map(operations=mixup_batch_op, input_columns=["image", "label"]) + >>> image_folder_dataset = image_folder_dataset.batch(5) + >>> image_folder_dataset = image_folder_dataset.map(operations=mixup_batch_op, + ... input_columns=["image", "label"]) """ @check_mix_up_batch_c @@ -285,12 +276,11 @@ class Normalize(cde.NormalizeOp): The standard deviation values must be in range (0.0, 255.0]. Examples: - >>> import mindspore.dataset.vision.c_transforms as c_vision - >>> >>> decode_op = c_vision.Decode() >>> normalize_op = c_vision.Normalize(mean=[121.0, 115.0, 100.0], std=[70.0, 68.0, 71.0]) >>> transforms_list = [decode_op, normalize_op] - >>> data1 = data1.map(operations=transforms_list, input_columns=["image"]) + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, + ... input_columns=["image"]) """ @check_normalize_c @@ -332,12 +322,13 @@ class NormalizePad(cde.NormalizePadOp): dtype (str): Set the output data type of normalized image (default is "float32"). Examples: - >>> import mindspore.dataset.vision.c_transforms as c_vision - >>> >>> decode_op = c_vision.Decode() - >>> normalize_op = c_vision.NormalizePad(mean=[121.0, 115.0, 100.0], std=[70.0, 68.0, 71.0], dtype="float32") + >>> normalize_pad_op = c_vision.NormalizePad(mean=[121.0, 115.0, 100.0], + ... std=[70.0, 68.0, 71.0], + ... dtype="float32") >>> transforms_list = [decode_op, normalize_pad_op] - >>> data1 = data1.map(operations=transforms_list, input_columns=["image"]) + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, + ... input_columns=["image"]) """ @check_normalizepad_c @@ -417,14 +408,15 @@ class RandomAffine(cde.RandomAffineOp): TypeError: If fill_value is not a single integer or a 3-tuple. Examples: - >>> import mindspore.dataset.vision.c_transforms as c_vision - >>> from mindspore.dataset.transforms.vision import Inter - >>> + >>> from mindspore.dataset.vision import Inter >>> decode_op = c_vision.Decode() - >>> random_affine_op = c_vision.RandomAffine(degrees=15, translate=(-0.1, 0.1, 0, 0), scale=(0.9, 1.1), - >>> resample=Inter.NEAREST) + >>> random_affine_op = c_vision.RandomAffine(degrees=15, + ... translate=(-0.1, 0.1, 0, 0), + ... scale=(0.9, 1.1), + ... resample=Inter.NEAREST) >>> transforms_list = [decode_op, random_affine_op] - >>> data1 = data1.map(operations=transforms_list, input_columns=["image"]) + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, + ... input_columns=["image"]) """ @check_random_affine @@ -502,12 +494,12 @@ class RandomCrop(cde.RandomCropOp): value of edge. Examples: - >>> import mindspore.dataset.vision.c_transforms as c_vision - >>> + >>> from mindspore.dataset.vision import Border >>> decode_op = c_vision.Decode() >>> random_crop_op = c_vision.RandomCrop(512, [200, 200, 200, 200], padding_mode=Border.EDGE) >>> transforms_list = [decode_op, random_crop_op] - >>> data1 = data1.map(operations=transforms_list, input_columns=["image"]) + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, + ... input_columns=["image"]) """ @check_random_crop @@ -564,12 +556,11 @@ class RandomCropWithBBox(cde.RandomCropWithBBoxOp): value of edge. Examples: - >>> import mindspore.dataset.vision.c_transforms as c_vision - >>> >>> decode_op = c_vision.Decode() >>> random_crop_with_bbox_op = c_vision.RandomCrop([512, 512], [200, 200, 200, 200]) >>> transforms_list = [decode_op, random_crop_with_bbox_op] - >>> data3 = data3.map(operations=transforms_list, input_columns=["image"]) + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, + ... input_columns=["image"]) """ @check_random_crop @@ -602,10 +593,9 @@ class RandomHorizontalFlip(cde.RandomHorizontalFlipOp): prob (float, optional): Probability of the image being flipped (default=0.5). Examples: - >>> import mindspore.dataset.vision.c_transforms as c_vision - >>> >>> transforms_list = [c_vision.Decode(), c_vision.RandomHorizontalFlip(0.75)] - >>> data1 = data1.map(operations=transforms_list, input_columns=["image"]) + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, + ... input_columns=["image"]) """ @check_prob @@ -622,10 +612,9 @@ class RandomHorizontalFlipWithBBox(cde.RandomHorizontalFlipWithBBoxOp): prob (float, optional): Probability of the image being flipped (default=0.5). Examples: - >>> import mindspore.dataset.vision.c_transforms as c_vision - >>> >>> transforms_list = [c_vision.Decode(), c_vision.RandomHorizontalFlipWithBBox(0.70)] - >>> data1 = data1.map(operations=transforms_list, input_columns=["image"]) + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, + ... input_columns=["image"]) """ @check_prob @@ -646,10 +635,9 @@ class RandomPosterize(cde.RandomPosterizeOp): magnitude operation (default=(8, 8)). Examples: - >>> import mindspore.dataset.vision.c_transforms as c_vision - >>> >>> transforms_list = [c_vision.Decode(), c_vision.RandomPosterize((6, 8))] - >>> data1 = data1.map(operations=transforms_list, input_columns=["image"]) + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, + ... input_columns=["image"]) """ @check_posterize @@ -668,10 +656,9 @@ class RandomVerticalFlip(cde.RandomVerticalFlipOp): prob (float, optional): Probability of the image being flipped (default=0.5). Examples: - >>> import mindspore.dataset.vision.c_transforms as c_vision - >>> >>> transforms_list = [c_vision.Decode(), c_vision.RandomVerticalFlip(0.25)] - >>> data1 = data1.map(operations=transforms_list, input_columns=["image"]) + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, + ... input_columns=["image"]) """ @check_prob @@ -688,10 +675,9 @@ class RandomVerticalFlipWithBBox(cde.RandomVerticalFlipWithBBoxOp): prob (float, optional): Probability of the image being flipped (default=0.5). Examples: - >>> import mindspore.dataset.vision.c_transforms as c_vision - >>> >>> transforms_list = [c_vision.Decode(), c_vision.RandomVerticalFlipWithBBox(0.20)] - >>> data1 = data1.map(operations=transforms_list, input_columns=["image"]) + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, + ... input_columns=["image"]) """ @check_prob @@ -711,15 +697,13 @@ class BoundingBoxAugment(cde.BoundingBoxAugmentOp): Range: [0, 1] (default=0.3). Examples: - >>> import mindspore.dataset.vision.c_transforms as c_vision - >>> >>> # set bounding box operation with ratio of 1 to apply rotation on all bounding boxes >>> bbox_aug_op = c_vision.BoundingBoxAugment(c_vision.RandomRotation(90), 1) >>> # map to apply ops - >>> data3 = data3.map(operations=[bbox_aug_op], - >>> input_columns=["image", "bbox"], - >>> output_columns=["image", "bbox"], - >>> column_order=["image", "bbox"]) + >>> image_folder_dataset = image_folder_dataset.map(operations=[bbox_aug_op], + ... input_columns=["image", "bbox"], + ... output_columns=["image", "bbox"], + ... column_order=["image", "bbox"]) """ @check_bounding_box_augment_cpp @@ -748,13 +732,12 @@ class Resize(cde.ResizeOp): - Inter.BICUBIC, means interpolation method is bicubic interpolation. Examples: - >>> import mindspore.dataset.vision.c_transforms as c_vision - >>> from mindspore.dataset.transforms.vision import Inter - >>> + >>> from mindspore.dataset.vision import Inter >>> decode_op = c_vision.Decode() >>> resize_op = c_vision.Resize([100, 75], Inter.BICUBIC) >>> transforms_list = [decode_op, resize_op] - >>> data1 = data1.map(operations=transforms_list, input_columns=["image"]) + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, + ... input_columns=["image"]) """ @check_resize_interpolation @@ -802,13 +785,12 @@ class ResizeWithBBox(cde.ResizeWithBBoxOp): - Inter.BICUBIC, means interpolation method is bicubic interpolation. Examples: - >>> import mindspore.dataset.vision.c_transforms as c_vision - >>> from mindspore.dataset.transforms.vision import Inter - >>> + >>> from mindspore.dataset.vision import Inter >>> decode_op = c_vision.Decode() >>> bbox_op = c_vision.ResizeWithBBox(50, Inter.NEAREST) >>> transforms_list = [decode_op, bbox_op] - >>> data3 = data3.map(operations=transforms_list, input_columns=["image"]) + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, + ... input_columns=["image"]) """ @check_resize_interpolation @@ -846,13 +828,12 @@ class RandomResizedCropWithBBox(cde.RandomCropAndResizeWithBBoxOp): crop area (default=10). If exceeded, fall back to use center crop instead. Examples: - >>> import mindspore.dataset.vision.c_transforms as c_vision - >>> from mindspore.dataset.transforms.vision import Inter - >>> + >>> from mindspore.dataset.vision import Inter >>> decode_op = c_vision.Decode() >>> bbox_op = c_vision.RandomResizedCropWithBBox(size=50, interpolation=Inter.NEAREST) >>> transforms_list = [decode_op, bbox_op] - >>> data3 = data3.map(operations=transforms_list, input_columns=["image"]) + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, + ... input_columns=["image"]) """ @check_random_resize_crop @@ -894,14 +875,13 @@ class RandomResizedCrop(cde.RandomCropAndResizeOp): crop_area (default=10). If exceeded, fall back to use center_crop instead. Examples: - >>> import mindspore.dataset.vision.c_transforms as c_vision - >>> from mindspore.dataset.transforms.vision import Inter - >>> + >>> from mindspore.dataset.vision import Inter >>> decode_op = c_vision.Decode() >>> resize_crop_op = c_vision.RandomResizedCrop(size=(50, 75), scale=(0.25, 0.5), - >>> interpolation=Inter.BILINEAR) + ... interpolation=Inter.BILINEAR) >>> transforms_list = [decode_op, resize_crop_op] - >>> data1 = data1.map(operations=transforms_list, input_columns=["image"]) + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, + ... input_columns=["image"]) """ @check_random_resize_crop @@ -928,14 +908,14 @@ class CenterCrop(cde.CenterCropOp): If size is a sequence of length 2, it should be (height, width). Examples: - >>> import mindspore.dataset.vision.c_transforms as c_vision - >>> >>> # crop image to a square >>> transforms_list1 = [c_vision.Decode(), c_vision.CenterCrop(50)] - >>> data1 = data1.map(operations=transforms_list1, input_columns=["image"]) + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list1, + ... input_columns=["image"]) >>> # crop image to portrait style >>> transforms_list2 = [c_vision.Decode(), c_vision.CenterCrop((60, 40))] - >>> data2 = data2.map(operations=transforms_list2, input_columns=["image"]) + >>> image_folder_dataset_1 = image_folder_dataset_1.map(operations=transforms_list2, + ... input_columns=["image"]) """ @check_crop @@ -957,10 +937,9 @@ class RandomColor(cde.RandomColorOp): single fixed magnitude operation (default=(0.1, 1.9)). Examples: - >>> import mindspore.dataset.vision.c_transforms as c_vision - >>> >>> transforms_list = [c_vision.Decode(), c_vision.RandomColor((0.5, 2.0))] - >>> data1 = data1.map(operations=transforms_list, input_columns=["image"]) + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, + ... input_columns=["image"]) """ @check_positive_degrees @@ -987,12 +966,13 @@ class RandomColorAdjust(cde.RandomColorAdjustOp): If it is a sequence, it should be [min, max] where -0.5 <= min <= max <= 0.5. Examples: - >>> import mindspore.dataset.vision.c_transforms as c_vision - >>> >>> decode_op = c_vision.Decode() - >>> transform_op = c_vision.RandomColorAdjust(brightness=(0.5, 1), contrast=(0.4, 1), saturation=(0.3, 1)) + >>> transform_op = c_vision.RandomColorAdjust(brightness=(0.5, 1), + ... contrast=(0.4, 1), + ... saturation=(0.3, 1)) >>> transforms_list = [decode_op, transform_op] - >>> data1 = data1.map(operations=transforms_list, input_columns=["image"]) + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, + ... input_columns=["image"]) """ @check_random_color_adjust @@ -1048,12 +1028,13 @@ class RandomRotation(cde.RandomRotationOp): If it is an integer, it is used for all RGB channels. Examples: - >>> import mindspore.dataset.vision.c_transforms as c_vision - >>> from mindspore.dataset.transforms.vision import Inter - >>> + >>> from mindspore.dataset.vision import Inter >>> transforms_list = [c_vision.Decode(), - >>> c_vision.RandomRotation(degrees=5.0, resample=Inter.NEAREST, expand=True)] - >>> data1 = data1.map(operations=transforms_list, input_columns=["image"]) + ... c_vision.RandomRotation(degrees=5.0, + ... resample=Inter.NEAREST, + ... expand=True)] + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, + ... input_columns=["image"]) """ @check_random_rotation @@ -1082,10 +1063,9 @@ class Rescale(cde.RescaleOp): shift (float): Shift factor. Examples: - >>> import mindspore.dataset.vision.c_transforms as c_vision - >>> >>> transforms_list = [c_vision.Decode(), c_vision.Rescale(1.0 / 255.0, -1.0)] - >>> data1 = data1.map(operations=transforms_list, input_columns=["image"]) + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, + ... input_columns=["image"]) """ @check_rescale @@ -1122,14 +1102,14 @@ class RandomResize(cde.RandomResizeOp): If size is a sequence of length 2, it should be (height, width). Examples: - >>> import mindspore.dataset.vision.c_transforms as c_vision - >>> >>> # randomly resize image, keeping aspect ratio >>> transforms_list1 = [c_vision.Decode(), c_vision.RandomResize(50)] - >>> data1 = data1.map(operations=transforms_list1, input_columns=["image"]) + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list1, + ... input_columns=["image"]) >>> # randomly resize image to landscape style >>> transforms_list2 = [c_vision.Decode(), c_vision.RandomResize((40, 60))] - >>> data2 = data2.map(operations=transforms_list2, input_columns=["image"]) + >>> image_folder_dataset_1 = image_folder_dataset_1.map(operations=transforms_list2, + ... input_columns=["image"]) """ @check_resize @@ -1152,14 +1132,14 @@ class RandomResizeWithBBox(cde.RandomResizeWithBBoxOp): If size is a sequence of length 2, it should be (height, width). Examples: - >>> import mindspore.dataset.vision.c_transforms as c_vision - >>> >>> # randomly resize image with bounding boxes, keeping aspect ratio >>> transforms_list1 = [c_vision.Decode(), c_vision.RandomResizeWithBBox(60)] - >>> data1 = data1.map(operations=transforms_list1, input_columns=["image"]) + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list1, + ... input_columns=["image"]) >>> # randomly resize image with bounding boxes to portrait style >>> transforms_list2 = [c_vision.Decode(), c_vision.RandomResizeWithBBox((80, 60))] - >>> data2 = data2.map(operations=transforms_list2, input_columns=["image"]) + >>> image_folder_dataset_1 = image_folder_dataset_1.map(operations=transforms_list2, + ... input_columns=["image"]) """ @check_resize @@ -1175,11 +1155,12 @@ class HWC2CHW(cde.ChannelSwapOp): Transpose the input image; shape (H, W, C) to shape (C, H, W). Examples: - >>> import mindspore.dataset.vision.c_transforms as c_vision - >>> - >>> transforms_list = [c_vision.Decode(), c_vision.RandomHorizontalFlip(0.75), c_vision.RandomCrop(512), - >>> c_vision.HWC2CHW()] - >>> data1 = data1.map(operations=transforms_list, input_columns=["image"]) + >>> transforms_list = [c_vision.Decode(), + ... c_vision.RandomHorizontalFlip(0.75), + ... c_vision.RandomCrop(512), + ... c_vision.HWC2CHW()] + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, + ... input_columns=["image"]) """ def __call__(self, img): @@ -1224,13 +1205,14 @@ class RandomCropDecodeResize(cde.RandomCropDecodeResizeOp): If exceeded, fall back to use center_crop instead. Examples: - >>> import mindspore.dataset.vision.c_transforms as c_vision - >>> from mindspore.dataset.transforms.vision import Inter - >>> - >>> resize_crop_decode_op = c_vision.RandomCropDecodeResize(size=(50, 75), scale=(0.25, 0.5), - >>> interpolation=Inter.NEAREST, max_attempts=5) + >>> from mindspore.dataset.vision import Inter + >>> resize_crop_decode_op = c_vision.RandomCropDecodeResize(size=(50, 75), + ... scale=(0.25, 0.5), + ... interpolation=Inter.NEAREST, + ... max_attempts=5) >>> transforms_list = [resize_crop_decode_op] - >>> data1 = data1.map(operations=transforms_list, input_columns=["image"]) + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, + ... input_columns=["image"]) """ @check_random_resize_crop @@ -1277,11 +1259,10 @@ class Pad(cde.PadOp): value of edge. Examples: - >>> import mindspore.dataset.vision.c_transforms as c_vision - >>> from mindspore.dataset.transforms.vision import Border - >>> + >>> from mindspore.dataset.vision import Border >>> transforms_list = [c_vision.Decode(), c_vision.Pad([100, 100, 100, 100])] - >>> data1 = data1.map(operations=transforms_list, input_columns=["image"]) + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, + ... input_columns=["image"]) """ @check_pad @@ -1321,18 +1302,17 @@ class UniformAugment(cde.UniformAugOp): num_ops (int, optional): Number of operations to be selected and applied (default=2). Examples: - >>> import mindspore.dataset.vision.c_transforms as c_vision >>> import mindspore.dataset.vision.py_transforms as py_vision - >>> >>> transforms_list = [c_vision.RandomHorizontalFlip(), - >>> c_vision.RandomVerticalFlip(), - >>> c_vision.RandomColorAdjust(), - >>> c_vision.RandomRotation(degrees=45)] + ... c_vision.RandomVerticalFlip(), + ... c_vision.RandomColorAdjust(), + ... c_vision.RandomRotation(degrees=45)] >>> uni_aug_op = c_vision.UniformAugment(transforms=transforms_list, num_ops=2) >>> transforms_all = [c_vision.Decode(), c_vision.Resize(size=[224, 224]), - >>> uni_aug_op, py_vision.ToTensor()] - >>> data_aug = data1.map(operations=transforms_all, input_columns="image", - >>> num_parallel_workers=1) + ... uni_aug_op, py_vision.ToTensor()] + >>> image_folder_dataset_1 = image_folder_dataset.map(operations=transforms_all, + ... input_columns="image", + ... num_parallel_workers=1) """ @check_uniform_augment_cpp @@ -1352,12 +1332,13 @@ class RandomSelectSubpolicy(cde.RandomSelectSubpolicyOp): policy (list(list(tuple(TensorOp,float))): List of sub-policies to choose from. Examples: - >>> import mindspore.dataset.vision.c_transforms as c_vision - >>> - >>> policy = [[(c_vision.RandomRotation((45, 45)), 0.5), (c_vision.RandomVerticalFlip(), 1), - >>> (c_vision.RandomColorAdjust(), 0.8)], - >>> [(c_vision.RandomRotation((90, 90)), 1), (c_vision.RandomColorAdjust(), 0.2)]] - >>> data_policy = data1.map(operations=c_vision.RandomSelectSubpolicy(policy), input_columns=["image"]) + >>> policy = [[(c_vision.RandomRotation((45, 45)), 0.5), + ... (c_vision.RandomVerticalFlip(), 1), + ... (c_vision.RandomColorAdjust(), 0.8)], + ... [(c_vision.RandomRotation((90, 90)), 1), + ... (c_vision.RandomColorAdjust(), 0.2)]] + >>> image_folder_dataset_1 = image_folder_dataset.map(operations=c_vision.RandomSelectSubpolicy(policy), + ... input_columns=["image"]) """ @check_random_select_subpolicy_op @@ -1385,14 +1366,14 @@ class SoftDvppDecodeResizeJpeg(cde.SoftDvppDecodeResizeJpegOp): If size is a sequence of length 2, it should be (height, width). Examples: - >>> import mindspore.dataset.vision.c_transforms as c_vision - >>> >>> # decode and resize image, keeping aspect ratio >>> transforms_list1 = [c_vision.Decode(), c_vision.SoftDvppDecodeResizeJpeg(70)] - >>> data1 = data1.map(operations=transforms_list1, input_columns=["image"]) + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list1, + ... input_columns=["image"]) >>> # decode and resize to portrait style >>> transforms_list2 = [c_vision.Decode(), c_vision.SoftDvppDecodeResizeJpeg((80, 60))] - >>> data2 = data2.map(operations=transforms_list2, input_columns=["image"]) + >>> image_folder_dataset_1 = image_folder_dataset_1.map(operations=transforms_list2, + ... input_columns=["image"]) """ @check_resize @@ -1425,14 +1406,14 @@ class SoftDvppDecodeRandomCropResizeJpeg(cde.SoftDvppDecodeRandomCropResizeJpegO If exceeded, fall back to use center_crop instead. Examples: - >>> import mindspore.dataset.vision.c_transforms as c_vision - >>> >>> # decode, randomly crop and resize image, keeping aspect ratio >>> transforms_list1 = [c_vision.Decode(), c_vision.SoftDvppDecodeRandomCropResizeJpeg(90)] - >>> data1 = data1.map(operations=transforms_list1, input_columns=["image"]) + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list1, + ... input_columns=["image"]) >>> # decode, randomly crop and resize to landscape style >>> transforms_list2 = [c_vision.Decode(), c_vision.SoftDvppDecodeRandomCropResizeJpeg((80, 100))] - >>> data2 = data2.map(operations=transforms_list2, input_columns=["image"]) + >>> image_folder_dataset_1 = image_folder_dataset_1.map(operations=transforms_list2, + ... input_columns=["image"]) """ @check_soft_dvpp_decode_random_crop_resize_jpeg @@ -1456,10 +1437,9 @@ class RandomSolarize(cde.RandomSolarizeOp): be in (min, max) format. If min=max, then it is a single fixed magnitude operation (default=(0, 255)). Examples: - >>> import mindspore.dataset.vision.c_transforms as c_vision - >>> >>> transforms_list = [c_vision.Decode(), c_vision.RandomSolarize(threshold=(10,100))] - >>> data1 = data1.map(operations=transforms_list, input_columns=["image"]) + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, + ... input_columns=["image"]) """ @check_random_solarize diff --git a/mindspore/dataset/vision/py_transforms.py b/mindspore/dataset/vision/py_transforms.py index 2683f7062a..cb5d6338ea 100644 --- a/mindspore/dataset/vision/py_transforms.py +++ b/mindspore/dataset/vision/py_transforms.py @@ -58,11 +58,14 @@ class ToTensor: output_type (NumPy datatype, optional): The datatype of the NumPy output (default=np.float32). Examples: - >>> import mindspore.dataset.vision.py_transforms as py_vision >>> from mindspore.dataset.transforms.py_transforms import Compose - >>> - >>> Compose([py_vision.Decode(), py_vision.RandomHorizontalFlip(0.5), - >>> py_vision.ToTensor()]) + >>> # create a list of transformations to be applied to the "image" column of each data row + >>> transforms_list = Compose([py_vision.Decode(), + ... py_vision.RandomHorizontalFlip(0.5), + ... py_vision.ToTensor()]) + >>> # apply the transform to dataset through map function + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, + ... input_columns="image") """ def __init__(self, output_type=np.float32): @@ -89,13 +92,15 @@ class ToType: output_type (NumPy datatype): The datatype of the NumPy output, e.g. numpy.float32. Examples: - >>> import mindspore.dataset.vision.py_transforms as py_vision >>> from mindspore.dataset.transforms.py_transforms import Compose >>> import numpy as np - >>> - >>> Compose([py_vision.Decode(), py_vision.RandomHorizontalFlip(0.5), - >>> py_vision.ToTensor(), - >>> py_vision.ToType(np.float32)]) + >>> transforms_list =Compose([py_vision.Decode(), + ... py_vision.RandomHorizontalFlip(0.5), + ... py_vision.ToTensor(), + ... py_vision.ToType(np.float32)]) + >>> # apply the transform to dataset through map function + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, + ... input_columns="image") """ def __init__(self, output_type): @@ -119,11 +124,12 @@ class HWC2CHW: Transpose a NumPy image array; shape (H, W, C) to shape (C, H, W). Examples: - >>> import mindspore.dataset.vision.py_transforms as py_vision >>> from mindspore.dataset.transforms.py_transforms import Compose - >>> - >>> Compose([py_vision.Decode(), - >>> py_vision.HWC2CHW()]) + >>> transforms_list = Compose([py_vision.Decode(), + ... py_vision.HWC2CHW()]) + >>> # apply the transform to dataset through map function + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, + ... input_columns="image") """ def __call__(self, img): @@ -145,11 +151,13 @@ class ToPIL: Examples: >>> # data is already decoded, but not in PIL image format - >>> import mindspore.dataset.vision.py_transforms as py_vision >>> from mindspore.dataset.transforms.py_transforms import Compose - >>> - >>> Compose([py_vision.ToPIL(), py_vision.RandomHorizontalFlip(0.5), - >>> py_vision.ToTensor()]) + >>> transforms_list = Compose([py_vision.ToPIL(), + ... py_vision.RandomHorizontalFlip(0.5), + ... py_vision.ToTensor()]) + >>> # apply the transform to dataset through map function + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, + ... input_columns="image") """ def __call__(self, img): @@ -170,12 +178,13 @@ class Decode: Decode the input image to PIL image format in RGB mode. Examples: - >>> import mindspore.dataset.vision.py_transforms as py_vision >>> from mindspore.dataset.transforms.py_transforms import Compose - >>> - >>> Compose([py_vision.Decode(), - >>> py_vision.RandomHorizontalFlip(0.5), - >>> py_vision.ToTensor()]) + >>> transforms_list = Compose([py_vision.Decode(), + ... py_vision.RandomHorizontalFlip(0.5), + ... py_vision.ToTensor()]) + >>> # apply the transform to dataset through map function + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, + ... input_columns="image") """ def __call__(self, img): @@ -204,13 +213,14 @@ class Normalize: The standard deviation values must be in the range (0.0, 1.0]. Examples: - >>> import mindspore.dataset.vision.py_transforms as py_vision >>> from mindspore.dataset.transforms.py_transforms import Compose - >>> - >>> 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))]) + >>> transforms_list = 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))]) + >>> # apply the transform to dataset through map function + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, + ... input_columns="image") """ @check_normalize_py @@ -246,13 +256,14 @@ class NormalizePad: dtype (str): Set the output data type of image (default is "float32"). Examples: - >>> import mindspore.dataset.vision.py_transforms as py_vision >>> from mindspore.dataset.transforms.py_transforms import Compose - >>> - >>> Compose([py_vision.Decode(), - >>> py_vision.RandomHorizontalFlip(0.5), - >>> py_vision.ToTensor(), - >>> py_vision.NormalizePad((0.491, 0.482, 0.447), (0.247, 0.243, 0.262), "float32")]) + >>> transforms_list = Compose([py_vision.Decode(), + ... py_vision.RandomHorizontalFlip(0.5), + ... py_vision.ToTensor(), + ... py_vision.NormalizePad((0.491, 0.482, 0.447), (0.247, 0.243, 0.262), "float32")]) + >>> # apply the transform to dataset through map function + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, + ... input_columns="image") """ @check_normalizepad_py @@ -308,12 +319,13 @@ class RandomCrop: value of edge. Examples: - >>> import mindspore.dataset.vision.py_transforms as py_vision >>> from mindspore.dataset.transforms.py_transforms import Compose - >>> - >>> Compose([py_vision.Decode(), - >>> py_vision.RandomCrop(224), - >>> py_vision.ToTensor()]) + >>> transforms_list = Compose([py_vision.Decode(), + ... py_vision.RandomCrop(224), + ... py_vision.ToTensor()]) + >>> # apply the transform to dataset through map function + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, + ... input_columns="image") """ @check_random_crop @@ -350,12 +362,13 @@ class RandomHorizontalFlip: prob (float, optional): Probability of the image being flipped (default=0.5). Examples: - >>> import mindspore.dataset.vision.py_transforms as py_vision >>> from mindspore.dataset.transforms.py_transforms import Compose - >>> - >>> Compose([py_vision.Decode(), - >>> py_vision.RandomHorizontalFlip(0.5), - >>> py_vision.ToTensor()]) + >>> transforms_list = Compose([py_vision.Decode(), + ... py_vision.RandomHorizontalFlip(0.5), + ... py_vision.ToTensor()]) + >>> # apply the transform to dataset through map function + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, + ... input_columns="image") """ @check_prob @@ -383,12 +396,13 @@ class RandomVerticalFlip: prob (float, optional): Probability of the image being flipped (default=0.5). Examples: - >>> import mindspore.dataset.vision.py_transforms as py_vision >>> from mindspore.dataset.transforms.py_transforms import Compose - >>> - >>> Compose([py_vision.Decode(), - >>> py_vision.RandomVerticalFlip(0.5), - >>> py_vision.ToTensor()]) + >>> transforms_list = Compose([py_vision.Decode(), + ... py_vision.RandomVerticalFlip(0.5), + ... py_vision.ToTensor()]) + >>> # apply the transform to dataset through map function + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, + ... input_columns="image") """ @check_prob @@ -429,12 +443,13 @@ class Resize: - Inter.BICUBIC, means the interpolation method is bicubic interpolation. Examples: - >>> import mindspore.dataset.vision.py_transforms as py_vision >>> from mindspore.dataset.transforms.py_transforms import Compose - >>> - >>> Compose([py_vision.Decode(), - >>> py_vision.Resize(256), - >>> py_vision.ToTensor()]) + >>> transforms_list = Compose([py_vision.Decode(), + ... py_vision.Resize(256), + ... py_vision.ToTensor()]) + >>> # apply the transform to dataset through map function + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, + ... input_columns="image") """ @check_resize_interpolation @@ -481,12 +496,13 @@ class RandomResizedCrop: crop area (default=10). If exceeded, fall back to use center crop instead. Examples: - >>> import mindspore.dataset.vision.py_transforms as py_vision >>> from mindspore.dataset.transforms.py_transforms import Compose - >>> - >>> Compose([py_vision.Decode(), - >>> py_vision.RandomResizedCrop(224), - >>> py_vision.ToTensor()]) + >>> transforms_list = Compose([py_vision.Decode(), + ... py_vision.RandomResizedCrop(224), + ... py_vision.ToTensor()]) + >>> # apply the transform to dataset through map function + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, + ... input_columns="image") """ @check_random_resize_crop @@ -522,12 +538,13 @@ class CenterCrop: If size is a sequence of length 2, it should be (height, width). Examples: - >>> import mindspore.dataset.vision.py_transforms as py_vision >>> from mindspore.dataset.transforms.py_transforms import Compose - >>> - >>> Compose([py_vision.Decode(), - >>> py_vision.CenterCrop(64), - >>> py_vision.ToTensor()]) + >>> transforms_list = Compose([py_vision.Decode(), + ... py_vision.CenterCrop(64), + ... py_vision.ToTensor()]) + >>> # apply the transform to dataset through map function + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, + ... input_columns="image") """ @check_crop @@ -566,12 +583,13 @@ class RandomColorAdjust: If it is a sequence, it should be [min, max] where -0.5 <= min <= max <= 0.5. Examples: - >>> import mindspore.dataset.vision.py_transforms as py_vision >>> from mindspore.dataset.transforms.py_transforms import Compose - >>> - >>> Compose([py_vision.Decode(), - >>> py_vision.RandomColorAdjust(0.4, 0.4, 0.4, 0.1), - >>> py_vision.ToTensor()]) + >>> transforms_list = Compose([py_vision.Decode(), + ... py_vision.RandomColorAdjust(0.4, 0.4, 0.4, 0.1), + ... py_vision.ToTensor()]) + >>> # apply the transform to dataset through map function + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, + ... input_columns="image") """ @check_random_color_adjust @@ -629,12 +647,13 @@ class RandomRotation: If it is an integer, it is used for all RGB channels. Default is 0. Examples: - >>> import mindspore.dataset.vision.py_transforms as py_vision >>> from mindspore.dataset.transforms.py_transforms import Compose - >>> - >>> Compose([py_vision.Decode(), - >>> py_vision.RandomRotation(30), - >>> py_vision.ToTensor()]) + >>> transforms_list = Compose([py_vision.Decode(), + ... py_vision.RandomRotation(30), + ... py_vision.ToTensor()]) + >>> # apply the transform to dataset through map function + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, + ... input_columns="image") """ @check_random_rotation @@ -668,13 +687,14 @@ class FiveCrop: If size is a sequence of length 2, it should be (height, width). Examples: - >>> import mindspore.dataset.vision.py_transforms as py_vision >>> from mindspore.dataset.transforms.py_transforms import Compose - >>> - >>> Compose([py_vision.Decode(), - >>> py_vision.FiveCrop(size=200), - >>> # 4D stack of 5 images - >>> lambda *images: numpy.stack([py_vision.ToTensor()(image) for image in images])]) + >>> transforms_list = Compose([py_vision.Decode(), + ... py_vision.FiveCrop(size=200), + ... # 4D stack of 5 images + ... lambda *images: numpy.stack([py_vision.ToTensor()(image) for image in images])]) + >>> # apply the transform to dataset through map function + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, + ... input_columns="image") """ @check_crop @@ -708,13 +728,14 @@ class TenCrop: if set to True (default=False). Examples: - >>> import mindspore.dataset.vision.py_transforms as py_vision >>> from mindspore.dataset.transforms.py_transforms import Compose - >>> - >>> Compose([py_vision.Decode(), - >>> py_vision.TenCrop(size=200), - >>> # 4D stack of 10 images - >>> lambda *images: numpy.stack([py_vision.ToTensor()(image) for image in images])]) + >>> transforms_list = Compose([py_vision.Decode(), + ... py_vision.TenCrop(size=200), + ... # 4D stack of 10 images + ... lambda *images: numpy.stack([py_vision.ToTensor()(image) for image in images])]) + >>> # apply the transform to dataset through map function + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, + ... input_columns="image") """ @check_ten_crop @@ -748,12 +769,13 @@ class Grayscale: Default is 1. If set to 3, the returned image has 3 identical RGB channels. Examples: - >>> import mindspore.dataset.vision.py_transforms as py_vision >>> from mindspore.dataset.transforms.py_transforms import Compose - >>> - >>> Compose([py_vision.Decode(), - >>> py_vision.Grayscale(3), - >>> py_vision.ToTensor()]) + >>> transforms_list = Compose([py_vision.Decode(), + ... py_vision.Grayscale(3), + ... py_vision.ToTensor()]) + >>> # apply the transform to dataset through map function + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, + ... input_columns="image") """ @check_num_channels @@ -781,12 +803,13 @@ class RandomGrayscale: prob (float, optional): Probability of the image being converted to grayscale (default=0.1). Examples: - >>> import mindspore.dataset.vision.py_transforms as py_vision >>> from mindspore.dataset.transforms.py_transforms import Compose - >>> - >>> Compose([py_vision.Decode(), - >>> py_vision.RandomGrayscale(0.3), - >>> py_vision.ToTensor()]) + >>> transforms_list = Compose([py_vision.Decode(), + ... py_vision.RandomGrayscale(0.3), + ... py_vision.ToTensor()]) + >>> # apply the transform to dataset through map function + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, + ... input_columns="image") """ @check_prob @@ -844,13 +867,14 @@ class Pad: value of edge. Examples: - >>> import mindspore.dataset.vision.py_transforms as py_vision >>> from mindspore.dataset.transforms.py_transforms import Compose - >>> - >>> Compose([py_vision.Decode(), - >>> # adds 10 pixels (default black) to each side of the border of the image - >>> py_vision.Pad(padding=10), - >>> py_vision.ToTensor()]) + >>> transforms_list = Compose([py_vision.Decode(), + ... # adds 10 pixels (default black) to each side of the border of the image + ... py_vision.Pad(padding=10), + ... py_vision.ToTensor()]) + >>> # apply the transform to dataset through map function + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, + ... input_columns="image") """ @check_pad @@ -891,12 +915,13 @@ class RandomPerspective: - Inter.BICUBIC, means the interpolation method is bicubic interpolation. Examples: - >>> import mindspore.dataset.vision.py_transforms as py_vision >>> from mindspore.dataset.transforms.py_transforms import Compose - >>> - >>> Compose([py_vision.Decode(), - >>> py_vision.RandomPerspective(prob=0.1), - >>> py_vision.ToTensor()]) + >>> transforms_list = Compose([py_vision.Decode(), + ... py_vision.RandomPerspective(prob=0.1), + ... py_vision.ToTensor()]) + >>> # apply the transform to dataset through map function + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, + ... input_columns="image") """ @check_random_perspective @@ -944,12 +969,13 @@ class RandomErasing: erase_area (default=10). If exceeded, return the original image. Examples: - >>> import mindspore.dataset.vision.py_transforms as py_vision >>> from mindspore.dataset.transforms.py_transforms import Compose - >>> - >>> Compose([py_vision.Decode(), - >>> py_vision.ToTensor(), - >>> py_vision.RandomErasing(value='random')]) + >>> transforms_list = Compose([py_vision.Decode(), + ... py_vision.ToTensor(), + ... py_vision.RandomErasing(value='random')]) + >>> # apply the transform to dataset through map function + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, + ... input_columns="image") """ @check_random_erasing @@ -991,12 +1017,13 @@ class Cutout: num_patches (int, optional): Number of patches to be cut out of an image (default=1). Examples: - >>> import mindspore.dataset.vision.py_transforms as py_vision >>> from mindspore.dataset.transforms.py_transforms import Compose - >>> - >>> Compose([py_vision.Decode(), - >>> py_vision.ToTensor(), - >>> py_vision.Cutout(80)]) + >>> transforms_list = Compose([py_vision.Decode(), + ... py_vision.ToTensor(), + ... py_vision.Cutout(80)]) + >>> # apply the transform to dataset through map function + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, + ... input_columns="image") """ @check_cutout @@ -1040,13 +1067,19 @@ class LinearTransformation: mean_vector (numpy.ndarray): a NumPy ndarray of shape (D,) where D = C x H x W. Examples: - >>> import mindspore.dataset.vision.py_transforms as py_vision >>> from mindspore.dataset.transforms.py_transforms import Compose - >>> - >>> Compose([py_vision.Decode(), - >>> py_vision.Resize(256), - >>> py_vision.ToTensor(), - >>> py_vision.LinearTransformation(transformation_matrix, mean_vector)]) + >>> import numpy as np + >>> height, width = 32, 32 + >>> dim = 3 * height * width + >>> transformation_matrix = np.ones([dim, dim]) + >>> mean_vector = np.zeros(dim) + >>> transforms_list = Compose([py_vision.Decode(), + ... py_vision.Resize((height,width)), + ... py_vision.ToTensor(), + ... py_vision.LinearTransformation(transformation_matrix, mean_vector)]) + >>> # apply the transform to dataset through map function + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, + ... input_columns="image") """ @check_linear_transform @@ -1116,12 +1149,13 @@ class RandomAffine: TypeError: If fill_value is not a single integer or a 3-tuple. Examples: - >>> import mindspore.dataset.vision.py_transforms as py_vision >>> from mindspore.dataset.transforms.py_transforms import Compose - >>> - >>> Compose([py_vision.Decode(), - >>> py_vision.RandomAffine(degrees=15, translate=(0.1, 0.1), scale=(0.9, 1.1)), - >>> py_vision.ToTensor()]) + >>> transforms_list = Compose([py_vision.Decode(), + ... py_vision.RandomAffine(degrees=15, translate=(0.1, 0.1), scale=(0.9, 1.1)), + ... py_vision.ToTensor()]) + >>> # apply the transform to dataset through map function + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, + ... input_columns="image") """ @check_random_affine @@ -1179,12 +1213,11 @@ class MixUp: Examples: - >>> import mindspore.dataset.vision.py_transforms as py_vision - >>> >>> # Setup multi-batch mixup transformation >>> transform = [py_vision.MixUp(batch_size=16, alpha=0.2, is_single=False)] >>> # Apply the transform to the dataset through dataset.map() - >>> dataset = dataset.map(input_columns="image", operations=transform()) + >>> image_folder_dataset = image_folder_dataset.map(input_columns="image", + ... operations=transform) """ @check_mix_up @@ -1222,13 +1255,14 @@ class RgbToHsv: and (C, H, W) or (N, C, H, W) if False (default=False). Examples: - >>> import mindspore.dataset.vision.py_transforms as py_vision >>> from mindspore.dataset.transforms.py_transforms import Compose - >>> - >>> Compose([py_vision.Decode(), - >>> py_vision.CenterCrop(20), - >>> py_vision.ToTensor(), - >>> py_vision.RgbToHsv()]) + >>> transforms_list = Compose([py_vision.Decode(), + ... py_vision.CenterCrop(20), + ... py_vision.ToTensor(), + ... py_vision.RgbToHsv()]) + >>> # apply the transform to dataset through map function + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, + ... input_columns="image") """ def __init__(self, is_hwc=False): @@ -1257,13 +1291,14 @@ class HsvToRgb: and (C, H, W) or (N, C, H, W) if False (default=False). Examples: - >>> import mindspore.dataset.vision.py_transforms as py_vision >>> from mindspore.dataset.transforms.py_transforms import Compose - >>> - >>> Compose([py_vision.Decode(), - >>> py_vision.CenterCrop(20), - >>> py_vision.ToTensor(), - >>> py_vision.HsvToRgb()]) + >>> transforms_list = Compose([py_vision.Decode(), + ... py_vision.CenterCrop(20), + ... py_vision.ToTensor(), + ... py_vision.HsvToRgb()]) + >>> # apply the transform to dataset through map function + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, + ... input_columns="image") """ def __init__(self, is_hwc=False): @@ -1292,12 +1327,13 @@ class RandomColor: It should be in (min, max) format (default=(0.1,1.9)). Examples: - >>> import mindspore.dataset.vision.py_transforms as py_vision >>> from mindspore.dataset.transforms.py_transforms import Compose - >>> - >>> Compose([py_vision.Decode(), - >>> py_vision.RandomColor((0.5, 2.0)), - >>> py_vision.ToTensor()]) + >>> transforms_list = Compose([py_vision.Decode(), + ... py_vision.RandomColor((0.5, 2.0)), + ... py_vision.ToTensor()]) + >>> # apply the transform to dataset through map function + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, + ... input_columns="image") """ @check_positive_degrees @@ -1327,12 +1363,13 @@ class RandomSharpness: It should be in (min, max) format (default=(0.1,1.9)). Examples: - >>> import mindspore.dataset.vision.py_transforms as py_vision >>> from mindspore.dataset.transforms.py_transforms import Compose - >>> - >>> Compose([py_vision.Decode(), - >>> py_vision.RandomSharpness((0.5, 1.5)), - >>> py_vision.ToTensor()]) + >>> transforms_list = Compose([py_vision.Decode(), + ... py_vision.RandomSharpness((0.5, 1.5)), + ... py_vision.ToTensor()]) + >>> # apply the transform to dataset through map function + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, + ... input_columns="image") """ @check_positive_degrees @@ -1362,13 +1399,13 @@ class AutoContrast: ignore (Union[int, sequence], optional): Pixel values to ignore (default=None). Examples: - >>> import mindspore.dataset.vision.py_transforms as py_vision >>> from mindspore.dataset.transforms.py_transforms import Compose - >>> - >>> Compose([py_vision.Decode(), - >>> py_vision.AutoContrast(), - >>> py_vision.ToTensor()]) - + >>> transforms_list = Compose([py_vision.Decode(), + ... py_vision.AutoContrast(), + ... py_vision.ToTensor()]) + >>> # apply the transform to dataset through map function + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, + ... input_columns="image") """ @check_auto_contrast @@ -1395,13 +1432,13 @@ class Invert: Invert colors of input PIL image. Examples: - >>> import mindspore.dataset.vision.py_transforms as py_vision >>> from mindspore.dataset.transforms.py_transforms import Compose - >>> - >>> Compose([py_vision.Decode(), - >>> py_vision.Invert(), - >>> py_vision.ToTensor()]) - + >>> transforms_list = Compose([py_vision.Decode(), + ... py_vision.Invert(), + ... py_vision.ToTensor()]) + >>> # apply the transform to dataset through map function + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, + ... input_columns="image") """ def __call__(self, img): @@ -1423,12 +1460,13 @@ class Equalize: Equalize the histogram of input PIL image. Examples: - >>> import mindspore.dataset.vision.py_transforms as py_vision >>> from mindspore.dataset.transforms.py_transforms import Compose - >>> - >>> Compose([py_vision.Decode(), - >>> py_vision.Equalize(), - >>> py_vision.ToTensor()]) + >>> transforms_list = Compose([py_vision.Decode(), + ... py_vision.Equalize(), + ... py_vision.ToTensor()]) + >>> # apply the transform to dataset through map function + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, + ... input_columns="image") """ @@ -1459,16 +1497,17 @@ class UniformAugment: num_ops (int, optional): number of transforms to sequentially apply (default=2). Examples: - >>> import mindspore.dataset.vision.py_transforms as py_vision >>> from mindspore.dataset.transforms.py_transforms import Compose - >>> - >>> transforms_list = [py_vision.CenterCrop(64), - >>> py_vision.RandomColor(), - >>> py_vision.RandomSharpness(), - >>> py_vision.RandomRotation(30)] - >>> Compose([py_vision.Decode(), - >>> py_vision.UniformAugment(transforms_list), - >>> py_vision.ToTensor()]) + >>> transforms = [py_vision.CenterCrop(64), + ... py_vision.RandomColor(), + ... py_vision.RandomSharpness(), + ... py_vision.RandomRotation(30)] + >>> transforms_list = Compose([py_vision.Decode(), + ... py_vision.UniformAugment(transforms), + ... py_vision.ToTensor()]) + >>> # apply the transform to dataset through map function + >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, + ... input_columns="image") """ @check_uniform_augment_py