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!5971 dataset API docstring: Update transforms and vision desc and examples 

Merge pull request !5971 from cathwong/ckw_api_vision_pytransforms
tags/v1.0.0
mindspore-ci-bot Gitee 5 years ago
parent
c621f6f751 c101aca224
commit
014af71f12
6 changed files with 471 additions and 295 deletions
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  1. +77
    -32
      mindspore/dataset/transforms/c_transforms.py
  2. +15
    -7
      mindspore/dataset/transforms/py_transforms.py
  3. +1
    -1
      mindspore/dataset/vision/__init__.py
  4. +132
    -45
      mindspore/dataset/vision/c_transforms.py
  5. +241
    -205
      mindspore/dataset/vision/py_transforms.py
  6. +5
    -5
      mindspore/dataset/vision/py_transforms_util.py

+ 77
- 32
mindspore/dataset/transforms/c_transforms.py View File

@@ -36,6 +36,16 @@ class OneHot(cde.OneHotOp):

Raises:
RuntimeError: feature size is bigger than num_classes.

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"])
>>> mixup_batch_op = c_vision.MixUpBatch(alpha=0.8)
>>> data1 = data1.batch(4)
>>> data1 = data1.map(operations=mixup_batch_op, input_columns=["image", "label"])
"""

@check_num_classes
@@ -46,12 +56,17 @@ class OneHot(cde.OneHotOp):

class Fill(cde.FillOp):
"""
Tensor operation to create a tensor filled with passed scalar value.
Tensor operation to create a tensor filled with input scalar value.
The output tensor will have the same shape and type as the input tensor.

Args:
fill_value (Union[str, bytes, int, float, bool])) : scalar value
to fill created tensor with.

Examples:
>>> import mindspore.dataset.transforms.c_transforms as c_transforms
>>>
>>> fill_op = c_transforms.Fill(3)
"""

@check_fill_value
@@ -64,7 +79,12 @@ class TypeCast(cde.TypeCastOp):
Tensor operation to cast to a given MindSpore data type.

Args:
data_type (mindspore.dtype): mindspore.dtype to be casted to.
data_type (mindspore.dtype): mindspore.dtype to be cast to.

Examples:
>>> import mindspore.dataset.transforms.c_transforms as c_transforms
>>>
>>> type_cast_op = c_transforms.TypeCast(mstype.int32)
"""

@check_de_type
@@ -78,7 +98,7 @@ class Slice(cde.SliceOp):
"""
Slice operation to extract a tensor out using the given n slices.

The functionality of Slice is similar to NumPy indexing feature.
The functionality of Slice is similar to NumPy's indexing feature.
(Currently only rank-1 tensors are supported).

Args:
@@ -93,17 +113,19 @@ class Slice(cde.SliceOp):
5. :py:obj:`Ellipses`: Slice all dimensions between the two slices. Similar to `...` in Python indexing.

Examples:
>>> # Data before
>>> # | col |
>>> # +---------+
>>> # | [1,2,3] |
>>> # +---------|
>>> data = data.map(operations=Slice(slice(1,3))) # slice indices 1 and 2 only
>>> # Data after
>>> # | col |
>>> # +---------+
>>> # | [2,3] |
>>> # +---------|
>>> import mindspore.dataset.transforms.c_transforms as c_transforms
>>>
>>> # Data before
>>> # | col |
>>> # +---------+
>>> # | [1,2,3] |
>>> # +---------|
>>> data1 = data1.map(operations=c_transforms.Slice(slice(1,3))) # slice indices 1 and 2 only
>>> # Data after
>>> # | col |
>>> # +---------+
>>> # | [2,3] |
>>> # +---------|
"""

@check_slice_op
@@ -143,18 +165,20 @@ class Mask(cde.MaskOp):
Any element of the tensor that matches the predicate will be evaluated to True, otherwise False.

Args:
operator (Relational): One of the relational operator EQ, NE LT, GT, LE or GE
constant (Union[str, int, float, bool]): constant to be compared to.
Constant will be casted to the type of the input tensor
dtype (mindspore.dtype, optional): type of the generated mask. Default to bool
operator (Relational): One of the relational operators EQ, NE LT, GT, LE or GE
constant (Union[str, int, float, bool]): Constant to be compared to.
Constant will be cast to the type of the input tensor.
dtype (mindspore.dtype, optional): Type of the generated mask (Default to bool).

Examples:
>>> import mindspore.dataset.transforms.c_transforms as c_transforms
>>>
>>> # Data before
>>> # | col1 |
>>> # +---------+
>>> # | [1,2,3] |
>>> # +---------+
>>> data = data.map(operations=Mask(Relational.EQ, 2))
>>> data1 = data1.map(operations=c_transforms.Mask(Relational.EQ, 2))
>>> # Data after
>>> # | col1 |
>>> # +--------------------+
@@ -174,18 +198,20 @@ class PadEnd(cde.PadEndOp):
Pad input tensor according to `pad_shape`, need to have same rank.

Args:
pad_shape (list(int)): list on integers representing the shape needed. Dimensions that set to `None` will
pad_shape (list(int)): List of integers representing the shape needed. Dimensions that set to `None` will
not be padded (i.e., original dim will be used). Shorter dimensions will truncate the values.
pad_value (Union[str, bytes, int, float, bool]), optional): value used to pad. Default to 0 or empty
string in case of Tensors of strings.
pad_value (Union[str, bytes, int, float, bool]), optional): Value used to pad. Default to 0 or empty
string in case of tensors of strings.

Examples:
>>> import mindspore.dataset.transforms.c_transforms as c_transforms
>>>
>>> # Data before
>>> # | col |
>>> # +---------+
>>> # | [1,2,3] |
>>> # +---------|
>>> data = data.map(operations=PadEnd(pad_shape=[4], pad_value=10))
>>> data1 = data1.map(operations=c_transforms.PadEnd(pad_shape=[4], pad_value=10))
>>> # Data after
>>> # | col |
>>> # +------------+
@@ -205,9 +231,17 @@ class Concatenate(cde.ConcatenateOp):
Tensor operation that concatenates all columns into a single tensor.

Args:
axis (int, optional): concatenate the tensors along given axis (Default=0).
axis (int, optional): Concatenate the tensors along given axis (Default=0).
prepend (numpy.array, optional): NumPy array to be prepended to the already concatenated tensors (Default=None).
append (numpy.array, optional): NumPy array to be appended to the already concatenated tensors (Default=None).

Examples:
>>> import mindspore.dataset.transforms.c_transforms as c_transforms
>>>
>>> # concatenate string
>>> prepend_tensor = np.array(["dw", "df"], dtype='S')
>>> append_tensor = np.array(["dwsdf", "df"], dtype='S')
>>> concatenate_op = c_transforms.Concatenate(0, prepend_tensor, append_tensor)
"""

@check_concat_type
@@ -224,12 +258,14 @@ class Duplicate(cde.DuplicateOp):
Duplicate the input tensor to a new output tensor. The input tensor is carried over to the output list.

Examples:
>>> import mindspore.dataset.transforms.c_transforms as c_transforms
>>>
>>> # Data before
>>> # | x |
>>> # +---------+
>>> # | [1,2,3] |
>>> # +---------+
>>> data = data.map(operations=Duplicate(), input_columns=["x"],
>>> data1 = data1.map(operations=c_transforms.Duplicate(), input_columns=["x"],
>>> output_columns=["x", "y"], column_order=["x", "y"])
>>> # Data after
>>> # | x | y |
@@ -247,8 +283,11 @@ class Compose(cde.ComposeOp):
transforms (list): List of transformations to be applied.

Examples:
>>> compose = Compose([vision.Decode(), vision.RandomCrop()])
>>> dataset = ds.map(operations=compose)
>>> import mindspore.dataset.transforms.c_transforms as c_transforms
>>> import mindspore.dataset.vision.c_transforms as c_vision
>>>
>>> compose = c_transforms.Compose([c_vision.Decode(), c_vision.RandomCrop()])
>>> data1 = data1.map(operations=compose)
"""

@check_random_transform_ops
@@ -258,15 +297,18 @@ class Compose(cde.ComposeOp):

class RandomApply(cde.RandomApplyOp):
"""
Randomly performs a series of transforms with a given probability.
Randomly perform a series of transforms with a given probability.

Args:
transforms (list): List of transformations to be applied.
prob (float, optional): The probability to apply the transformation list (default=0.5)

Examples:
>>> rand_apply = RandomApply([vision.RandomCrop()])
>>> dataset = ds.map(operations=rand_apply)
>>> import mindspore.dataset.transforms.c_transforms as c_transforms
>>> import mindspore.dataset.vision.c_transforms as c_vision
>>>
>>> rand_apply = c_transforms.RandomApply([c_vision.RandomCrop()])
>>> data1 = data1.map(operations=rand_apply)
"""

@check_random_transform_ops
@@ -282,8 +324,11 @@ class RandomChoice(cde.RandomChoiceOp):
transforms (list): List of transformations to be chosen from to apply.

Examples:
>>> rand_choice = RandomChoice([vision.CenterCrop(), vision.RandomCrop()])
>>> dataset = ds.map(operations=rand_choice)
>>> import mindspore.dataset.transforms.c_transforms as c_transforms
>>> import mindspore.dataset.vision.c_transforms as c_vision
>>>
>>> rand_choice = c_transforms.RandomChoice([c_vision.CenterCrop(), c_vision.RandomCrop()])
>>> data1 = data1.map(operations=rand_choice)
"""

@check_random_transform_ops


+ 15
- 7
mindspore/dataset/transforms/py_transforms.py View File

@@ -29,6 +29,13 @@ class OneHotOp:
num_classes (int): Number of classes of objects in dataset. Value must be larger than 0.
smoothing_rate (float, optional): Adjustable hyperparameter for label smoothing level.
(Default=0.0 means no smoothing is applied.)

Examples:
>>> import mindspore.dataset.transforms as py_transforms
>>>
>>> transforms_list = [py_transforms.OneHotOp(num_classes=10, smoothing_rate=0.1)]
>>> transform = py_transforms.Compose(transforms_list)
>>> data1 = data1.map(input_columns=["label"], operations=transform())
"""

@check_one_hot_op
@@ -65,17 +72,18 @@ class Compose:

Examples:
>>> import mindspore.dataset as ds
>>> import mindspore.dataset.vision.py_transforms as py_transforms
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>> 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
>>> dataset = ds.ImageFolderDataset(dataset_dir, num_parallel_workers=8)
>>> # create a list of transformations to be applied to the image data
>>> transform = Compose([py_transforms.Decode(),
>>> py_transforms.RandomHorizontalFlip(0.5),
>>> py_transforms.ToTensor(),
>>> py_transforms.Normalize((0.491, 0.482, 0.447), (0.247, 0.243, 0.262)),
>>> py_transforms.RandomErasing()])
>>> transform = py_transform.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()
>>> dataset = dataset.map(operations=transform, input_columns="image")
"""


+ 1
- 1
mindspore/dataset/vision/__init__.py View File

@@ -19,4 +19,4 @@ provide more kinds of image augmentations which is developed with Python PIL.
"""
from . import c_transforms
from . import py_transforms
from .utils import Inter, Border
from .utils import Inter, Border, ImageBatchFormat

+ 132
- 45
mindspore/dataset/vision/c_transforms.py View File

@@ -26,18 +26,19 @@ to improve their training models.
>>> 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.utils import Border, ImageBatchFormat, Inter
>>> from mindspore.dataset.vision import Border, Inter
>>>
>>> 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
>>> transforms_list = [c_vision.Decode(),
>>> c_vision.Resize((256, 256)),
>>> 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.Normalize((100, 115.0, 121.0), (71.0, 68.0, 70.0)),
>>> c_vision.HWC2CHW()]
>>> onehot_op = c_transforms.OneHot(num_classes)
>>> 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")
@@ -88,7 +89,9 @@ class AutoContrast(cde.AutoContrastOp):
ignore (Union[int, sequence], optional): Pixel values to ignore (default=None).

Examples:
>>> transforms_list = [vision.Decode(), c_vision.AutoContrast(cutoff=10.0, ignore=[10, 20])]
>>> 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"])
"""

@@ -116,7 +119,9 @@ class RandomSharpness(cde.RandomSharpnessOp):
ValueError: If degrees is in (max, min) format instead of (min, max).

Examples:
>>> transforms_list = [vision.Decode(), c_vision.RandomSharpness(degrees=(0.2, 1.9))]
>>> 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"])
"""

@@ -131,7 +136,9 @@ class Equalize(cde.EqualizeOp):
Apply histogram equalization on input image.

Examples:
>>> transforms_list = [vision.Decode(), c_vision.Equalize()]
>>> 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"])
"""

@@ -141,7 +148,9 @@ class Invert(cde.InvertOp):
Apply invert on input image in RGB mode.

Examples:
>>> transforms_list = [vision.Decode(), c_vision.Invert()]
>>> 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"])
"""

@@ -154,7 +163,9 @@ class Decode(cde.DecodeOp):
rgb (bool, optional): Mode of decoding input image (default=True).

Examples:
>>> transforms_list = [vision.Decode(), c_vision.RandomHorizontalFlip()]
>>> 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"])
"""

@@ -175,6 +186,10 @@ 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
>>>
>>> onehot_op = c_transforms.OneHot(num_classes=10)
>>> data1 = data1.map(operations=onehot_op, input_columns=["label"])
>>> cutmix_batch_op = c_vision.CutMixBatch(ImageBatchFormat.NHWC, 1.0, 0.5)
@@ -199,7 +214,9 @@ class CutOut(cde.CutOutOp):
num_patches (int, optional): Number of patches to be cut out of an image (default=1).

Examples:
>>> transforms_list = [vision.Decode(), c_vision.CutOut(80, num_patches=10)]
>>> 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"])
"""

@@ -222,6 +239,9 @@ 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"])
>>> mixup_batch_op = c_vision.MixUpBatch(alpha=0.9)
@@ -246,6 +266,8 @@ 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]
@@ -271,19 +293,19 @@ class RandomAffine(cde.RandomAffineOp):
x(horizontal) and y(vertical) directions (default=None).
The horizontal and vertical shift is selected randomly from the range:
(tx_min*width, tx_max*width) and (ty_min*height, ty_max*height), respectively.
If a tuple or list of size 2, then a translate parallel to the x axis in the range of
If a tuple or list of size 2, then a translate parallel to the X axis in the range of
(translate[0], translate[1]) is applied.
If a tuple of list of size 4, then a translate parallel to the x axis in the range of
(translate[0], translate[1]) and a translate parallel to the y axis in the range of
If a tuple of list of size 4, then a translate parallel to the X axis in the range of
(translate[0], translate[1]) and a translate parallel to the Y axis in the range of
(translate[2], translate[3]) are applied.
If None, no translation is applied.
scale (sequence, optional): Scaling factor interval (default=None, original scale is used).
shear (int or float or sequence, optional): Range of shear factor (default=None).
If a number 'shear', then a shear parallel to the x axis in the range of (-shear, +shear) is applied.
If a tuple or list of size 2, then a shear parallel to the x axis in the range of (shear[0], shear[1])
If a number, then a shear parallel to the X axis in the range of (-shear, +shear) is applied.
If a tuple or list of size 2, then a shear parallel to the X axis in the range of (shear[0], shear[1])
is applied.
If a tuple of list of size 4, then a shear parallel to x axis in the range of (shear[0], shear[1])
and a shear parallel to y axis in the range of (shear[2], shear[3]) is applied.
If a tuple of list of size 4, then a shear parallel to X axis in the range of (shear[0], shear[1])
and a shear parallel to Y axis in the range of (shear[2], shear[3]) is applied.
If None, no shear is applied.
resample (Inter mode, optional): An optional resampling filter (default=Inter.NEAREST).
If omitted, or if the image has mode "1" or "P", it is set to be Inter.NEAREST.
@@ -312,6 +334,9 @@ 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
>>>
>>> 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)
@@ -394,6 +419,8 @@ class RandomCrop(cde.RandomCropOp):
value of edge.

Examples:
>>> import mindspore.dataset.vision.c_transforms as c_vision
>>>
>>> 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]
@@ -454,6 +481,8 @@ 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]
@@ -490,7 +519,9 @@ class RandomHorizontalFlip(cde.RandomHorizontalFlipOp):
prob (float, optional): Probability of the image being flipped (default=0.5).

Examples:
>>> transforms_list = [vision.Decode(), c_vision.RandomHorizontalFlip(0.75)]
>>> 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"])
"""

@@ -508,7 +539,9 @@ class RandomHorizontalFlipWithBBox(cde.RandomHorizontalFlipWithBBoxOp):
prob (float, optional): Probability of the image being flipped (default=0.5).

Examples:
>>> transforms_list = [vision.Decode(), c_vision.RandomHorizontalFlipWithBBox(0.70)]
>>> 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"])
"""

@@ -527,10 +560,12 @@ class RandomPosterize(cde.RandomPosterizeOp):
Bits values must be in range of [1,8], and include at
least one integer value in the given range. It must be in
(min, max) or integer format. If min=max, then it is a single fixed
magnitude operation (default=[4,8]).
magnitude operation (default=[4, 8]).

Examples:
>>> transforms_list = [vision.Decode(), c_vision.RandomPosterize((6,8))]
>>> 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"])
"""

@@ -550,7 +585,9 @@ class RandomVerticalFlip(cde.RandomVerticalFlipOp):
prob (float, optional): Probability of the image being flipped (default=0.5).

Examples:
>>> transforms_list = [vision.Decode(), c_vision.RandomVerticalFlip(0.25)]
>>> 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"])
"""

@@ -568,7 +605,9 @@ class RandomVerticalFlipWithBBox(cde.RandomVerticalFlipWithBBoxOp):
prob (float, optional): Probability of the image being flipped (default=0.5).

Examples:
>>> transforms_list = [vision.Decode(), c_vision.RandomVerticalFlipWithBBox(0.20)]
>>> 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"])
"""

@@ -586,16 +625,18 @@ class BoundingBoxAugment(cde.BoundingBoxAugmentOp):
transform: C++ transformation function to be applied on random selection
of bounding box regions of a given image.
ratio (float, optional): Ratio of bounding boxes to apply augmentation on.
Range: [0,1] (default=0.3).
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"],
>>> columns_order=["image", "bbox"])
>>> column_order=["image", "bbox"])
"""

@check_bounding_box_augment_cpp
@@ -624,6 +665,9 @@ 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
>>>
>>> decode_op = c_vision.Decode()
>>> resize_op = c_vision.Resize([100, 75], Inter.BICUBIC)
>>> transforms_list = [decode_op, resize_op]
@@ -659,6 +703,9 @@ 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
>>>
>>> decode_op = c_vision.Decode()
>>> bbox_op = c_vision.ResizeWithBBox(50, Inter.NEAREST)
>>> transforms_list = [decode_op, bbox_op]
@@ -700,6 +747,9 @@ 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
>>>
>>> decode_op = c_vision.Decode()
>>> bbox_op = c_vision.RandomResizedCropWithBBox(size=50, interpolation=Inter.NEAREST)
>>> transforms_list = [decode_op, bbox_op]
@@ -745,6 +795,9 @@ 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
>>>
>>> decode_op = c_vision.Decode()
>>> resize_crop_op = c_vision.RandomResizedCrop(size=(50, 75), scale=(0.25, 0.5),
>>> interpolation=Inter.BILINEAR)
@@ -776,11 +829,13 @@ 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 = [vision.Decode(), c_vision.CenterCrop(50)]
>>> transforms_list1 = [c_vision.Decode(), c_vision.CenterCrop(50)]
>>> data1 = data1.map(operations=transforms_list1, input_columns=["image"])
>>> # crop image to portrait style
>>> transforms_list2 = [vision.Decode(), c_vision.CenterCrop((60, 40))]
>>> transforms_list2 = [c_vision.Decode(), c_vision.CenterCrop((60, 40))]
>>> data2 = data2.map(operations=transforms_list2, input_columns=["image"])
"""

@@ -800,10 +855,12 @@ class RandomColor(cde.RandomColorOp):
Args:
degrees (sequence, optional): Range of random color adjustment degrees.
It should be in (min, max) format. If min=max, then it is a
single fixed magnitude operation (default=(0.1,1.9)).
single fixed magnitude operation (default=(0.1, 1.9)).

Examples:
>>> transforms_list = [vision.Decode(), c_vision.RandomColor((0.5, 2.0))]
>>> 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"])
"""

@@ -831,6 +888,8 @@ 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))
>>> transforms_list = [decode_op, transform_op]
@@ -887,10 +946,14 @@ class RandomRotation(cde.RandomRotationOp):
fill_value (Union[int, tuple], optional): Optional fill color for the area outside the rotated image
(default=0).
If it is a 3-tuple, it is used for R, G, B channels respectively.
If it is an int, it is used for all RGB channels.
If it is an integer, it is used for all RGB channels.

Examples:
>>> transforms_list = [vision.Decode(), c_vision.RandomRotation(degrees=5.0, expand=True)]
>>> import mindspore.dataset.vision.c_transforms as c_vision
>>> from mindspore.dataset.transforms.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"])
"""

@@ -920,7 +983,9 @@ class Rescale(cde.RescaleOp):
shift (float): Shift factor.

Examples:
>>> transforms_list = [vision.Decode(), c_vision.Rescale(1.0 / 255.0, -1.0)]
>>> 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"])
"""

@@ -942,11 +1007,13 @@ 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 = [vision.Decode(), c_vision.RandomResize(50)]
>>> transforms_list1 = [c_vision.Decode(), c_vision.RandomResize(50)]
>>> data1 = data1.map(operations=transforms_list1, input_columns=["image"])
>>> # randomly resize image to landscape style
>>> transforms_list2 = [vision.Decode(), c_vision.RandomResize((40, 60))]
>>> transforms_list2 = [c_vision.Decode(), c_vision.RandomResize((40, 60))]
>>> data2 = data2.map(operations=transforms_list2, input_columns=["image"])
"""

@@ -970,11 +1037,13 @@ 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 = [vision.Decode(), c_vision.RandomResizeWithBBox(60)]
>>> transforms_list1 = [c_vision.Decode(), c_vision.RandomResizeWithBBox(60)]
>>> data1 = data1.map(operations=transforms_list1, input_columns=["image"])
>>> # randomly resize image with bounding boxes to portrait style
>>> transforms_list2 = [vision.Decode(), c_vision.RandomResizeWithBBox((80, 60))]
>>> transforms_list2 = [c_vision.Decode(), c_vision.RandomResizeWithBBox((80, 60))]
>>> data2 = data2.map(operations=transforms_list2, input_columns=["image"])
"""

@@ -991,7 +1060,9 @@ class HWC2CHW(cde.ChannelSwapOp):
Transpose the input image; shape (H, W, C) to shape (C, H, W).

Examples:
>>> transforms_list = [vision.Decode(), c_vision.RandomHorizontalFlip(0.75), c_vision.RandomCrop(),
>>> import mindspore.dataset.vision.c_transforms as c_vision
>>>
>>> transforms_list = [c_vision.Decode(), c_vision.RandomHorizontalFlip(0.75), c_vision.RandomCrop(),
>>> c_vision.HWC2CHW()]
>>> data1 = data1.map(operations=transforms_list, input_columns=["image"])
"""
@@ -1022,6 +1093,9 @@ 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)
>>> transforms_list = [resize_crop_decode_op]
@@ -1070,7 +1144,10 @@ class Pad(cde.PadOp):
value of edge.

Examples:
>>> transforms_list = [vision.Decode(), c_vision.Pad([100, 100, 100, 100])]
>>> import mindspore.dataset.vision.c_transforms as c_vision
>>> from mindspore.dataset.transforms.vision import Border
>>>
>>> transforms_list = [c_vision.Decode(), c_vision.Pad([100, 100, 100, 100])]
>>> data1 = data1.map(operations=transforms_list, input_columns=["image"])
"""

@@ -1096,7 +1173,9 @@ 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(),
@@ -1125,10 +1204,12 @@ 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=visions.RandomSelectSubpolicy(policy), input_columns=["image"])
>>> data_policy = data1.map(operations=c_vision.RandomSelectSubpolicy(policy), input_columns=["image"])
"""

@check_random_select_subpolicy_op
@@ -1156,11 +1237,13 @@ 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 = [vision.Decode(), c_vision.SoftDvppDecodeResizeJpeg(70)]
>>> transforms_list1 = [c_vision.Decode(), c_vision.SoftDvppDecodeResizeJpeg(70)]
>>> data1 = data1.map(operations=transforms_list1, input_columns=["image"])
>>> # decode and resize to portrait style
>>> transforms_list2 = [vision.Decode(), c_vision.SoftDvppDecodeResizeJpeg((80, 60))]
>>> transforms_list2 = [c_vision.Decode(), c_vision.SoftDvppDecodeResizeJpeg((80, 60))]
>>> data2 = data2.map(operations=transforms_list2, input_columns=["image"])
"""

@@ -1194,11 +1277,13 @@ 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 = [vision.Decode(), c_vision.SoftDvppDecodeRandomCropResizeJpeg(90)]
>>> transforms_list1 = [c_vision.Decode(), c_vision.SoftDvppDecodeRandomCropResizeJpeg(90)]
>>> data1 = data1.map(operations=transforms_list1, input_columns=["image"])
>>> # decode, randomly crop and resize to landscape style
>>> transforms_list2 = [vision.Decode(), c_vision.SoftDvppDecodeRandomCropResizeJpeg((80, 100))]
>>> transforms_list2 = [c_vision.Decode(), c_vision.SoftDvppDecodeRandomCropResizeJpeg((80, 100))]
>>> data2 = data2.map(operations=transforms_list2, input_columns=["image"])
"""

@@ -1223,7 +1308,9 @@ class RandomSolarize(cde.RandomSolarizeOp):
be in (min, max) format. If min=max, then it is a single fixed magnitude operation (default=(0, 255)).

Examples:
>>> transforms_list = [vision.Decode(), c_vision.RandomSolarize(threshold=(10,100))]
>>> 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"])
"""



+ 241
- 205
mindspore/dataset/vision/py_transforms.py View File

@@ -17,9 +17,8 @@
The module vision.py_transforms is implemented based on Python PIL.
This module provides many kinds of image augmentations. It also provides
transferring methods between PIL image and NumPy array. For users who prefer
Python PIL in image learning task, this module is a good tool to process image
augmentations. Users can also self-define their own augmentations with Python
PIL.
Python PIL in image learning tasks, this module is a good tool to process images.
Users can also self-define their own augmentations with Python PIL.
"""
import numbers
import random
@@ -48,22 +47,22 @@ DE_PY_BORDER_TYPE = {Border.CONSTANT: 'constant',

class ToTensor:
"""
Convert the input NumPy image array or PIL image of shape (H,W,C) to a NumPy ndarray of shape (C,H,W).
Convert the input NumPy image array or PIL image of shape (H, W, C) to a NumPy ndarray of shape (C, H, W).

Note:
The ranges of values in height and width dimension changes from [0, 255] to [0.0, 1.0]. Type cast to output_type
(default NumPy float 32).
The ranges of values in height and width dimension are converted from [0, 255] to [0.0, 1.0].
The type is cast to output_type (default NumPy float32).
The range of channel dimension remains the same.

Args:
output_type (Numpy datatype, optional): The datatype of the NumPy output (default=np.float32).
output_type (NumPy datatype, optional): The datatype of the NumPy output (default=np.float32).

Examples:
>>> import mindspore.dataset.vision.py_transforms as py_transforms
>>> import mindspore.dataset.vision.py_transforms as py_vision
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>>
>>> Compose([py_transforms.Decode(), py_transforms.RandomHorizontalFlip(0.5),
>>> py_transforms.ToTensor()])
>>> Compose([py_vision.Decode(), py_vision.RandomHorizontalFlip(0.5),
>>> py_vision.ToTensor()])
"""

def __init__(self, output_type=np.float32):
@@ -87,16 +86,16 @@ class ToType:
Convert the input NumPy image array to desired NumPy dtype.

Args:
output_type (Numpy datatype): The datatype of the NumPy output, e.g. numpy.float32.
output_type (NumPy datatype): The datatype of the NumPy output, e.g. numpy.float32.

Examples:
>>> import mindspore.dataset.vision.py_transforms as py_transforms
>>> import mindspore.dataset.vision.py_transforms as py_vision
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>> import numpy as np
>>>
>>> Compose([py_transforms.Decode(), py_transforms.RandomHorizontalFlip(0.5),
>>> py_transforms.ToTensor(),
>>> py_transforms.ToType(np.float32)])
>>> Compose([py_vision.Decode(), py_vision.RandomHorizontalFlip(0.5),
>>> py_vision.ToTensor(),
>>> py_vision.ToType(np.float32)])
"""

def __init__(self, output_type):
@@ -118,6 +117,13 @@ class ToType:
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()])
"""

def __call__(self, img):
@@ -139,11 +145,11 @@ class ToPIL:

Examples:
>>> # data is already decoded, but not in PIL image format
>>> import mindspore.dataset.vision.py_transforms as py_transforms
>>> import mindspore.dataset.vision.py_transforms as py_vision
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>>
>>> Compose([py_transforms.ToPIL(), py_transforms.RandomHorizontalFlip(0.5),
>>> py_transforms.ToTensor()])
>>> Compose([py_vision.ToPIL(), py_vision.RandomHorizontalFlip(0.5),
>>> py_vision.ToTensor()])
"""

def __call__(self, img):
@@ -164,12 +170,12 @@ class Decode:
Decode the input image to PIL image format in RGB mode.

Examples:
>>> import mindspore.dataset.vision.py_transforms as py_transforms
>>> import mindspore.dataset.vision.py_transforms as py_vision
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>>
>>> Compose([py_transforms.Decode(),
>>> py_transforms.RandomHorizontalFlip(0.5),
>>> py_transforms.ToTensor()])
>>> Compose([py_vision.Decode(),
>>> py_vision.RandomHorizontalFlip(0.5),
>>> py_vision.ToTensor()])
"""

def __call__(self, img):
@@ -189,22 +195,22 @@ class Normalize:
"""
Normalize the input NumPy image array of shape (C, H, W) with the given mean and standard deviation.

The values of the array need to be in range (0.0, 1.0].
The values of the array need to be in the range (0.0, 1.0].

Args:
mean (sequence): List or tuple of mean values for each channel, with respect to channel order.
The mean values must be in range (0.0, 1.0].
The mean values must be in the range (0.0, 1.0].
std (sequence): List or tuple of standard deviations for each channel, w.r.t. channel order.
The standard deviation values must be in range (0.0, 1.0].
The standard deviation values must be in the range (0.0, 1.0].

Examples:
>>> import mindspore.dataset.vision.py_transforms as py_transforms
>>> import mindspore.dataset.vision.py_transforms as py_vision
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>>
>>> Compose([py_transforms.Decode(),
>>> py_transforms.RandomHorizontalFlip(0.5),
>>> py_transforms.ToTensor(),
>>> py_transforms.Normalize((0.491, 0.482, 0.447), (0.247, 0.243, 0.262))])
>>> 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))])
"""

@check_normalize_py
@@ -259,12 +265,12 @@ class RandomCrop:
value of edge.

Examples:
>>> import mindspore.dataset.vision.py_transforms as py_transforms
>>> import mindspore.dataset.vision.py_transforms as py_vision
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>>
>>> Compose([py_transforms.Decode(),
>>> py_transforms.RandomCrop(224),
>>> py_transforms.ToTensor()])
>>> Compose([py_vision.Decode(),
>>> py_vision.RandomCrop(224),
>>> py_vision.ToTensor()])
"""

@check_random_crop
@@ -301,12 +307,12 @@ class RandomHorizontalFlip:
prob (float, optional): Probability of the image being flipped (default=0.5).

Examples:
>>> import mindspore.dataset.vision.py_transforms as py_transforms
>>> import mindspore.dataset.vision.py_transforms as py_vision
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>>
>>> Compose([py_transforms.Decode(),
>>> py_transforms.RandomHorizontalFlip(0.5),
>>> py_transforms.ToTensor()])
>>> Compose([py_vision.Decode(),
>>> py_vision.RandomHorizontalFlip(0.5),
>>> py_vision.ToTensor()])
"""

@check_prob
@@ -334,12 +340,12 @@ class RandomVerticalFlip:
prob (float, optional): Probability of the image being flipped (default=0.5).

Examples:
>>> import mindspore.dataset.vision.py_transforms as py_transforms
>>> import mindspore.dataset.vision.py_transforms as py_vision
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>>
>>> Compose([py_transforms.Decode(),
>>> py_transforms.RandomVerticalFlip(0.5),
>>> py_transforms.ToTensor()])
>>> Compose([py_vision.Decode(),
>>> py_vision.RandomVerticalFlip(0.5),
>>> py_vision.ToTensor()])
"""

@check_prob
@@ -371,19 +377,19 @@ class Resize:
interpolation (Inter mode, optional): Image interpolation mode (default=Inter.BILINEAR).
It can be any of [Inter.BILINEAR, Inter.NEAREST, Inter.BICUBIC].

- Inter.BILINEAR, means interpolation method is bilinear interpolation.
- Inter.BILINEAR, means the interpolation method is bilinear interpolation.

- Inter.NEAREST, means interpolation method is nearest-neighbor interpolation.
- Inter.NEAREST, means the interpolation method is nearest-neighbor interpolation.

- Inter.BICUBIC, means interpolation method is bicubic interpolation.
- Inter.BICUBIC, means the interpolation method is bicubic interpolation.

Examples:
>>> import mindspore.dataset.vision.py_transforms as py_transforms
>>> import mindspore.dataset.vision.py_transforms as py_vision
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>>
>>> Compose([py_transforms.Decode(),
>>> py_transforms.Resize(256),
>>> py_transforms.ToTensor()])
>>> Compose([py_vision.Decode(),
>>> py_vision.Resize(256),
>>> py_vision.ToTensor()])
"""

@check_resize_interpolation
@@ -418,22 +424,22 @@ class RandomResizedCrop:
interpolation (Inter mode, optional): Image interpolation mode (default=Inter.BILINEAR).
It can be any of [Inter.BILINEAR, Inter.NEAREST, Inter.BICUBIC].

- Inter.BILINEAR, means interpolation method is bilinear interpolation.
- Inter.BILINEAR, means the interpolation method is bilinear interpolation.

- Inter.NEAREST, means interpolation method is nearest-neighbor interpolation.
- Inter.NEAREST, means the interpolation method is nearest-neighbor interpolation.

- Inter.BICUBIC, means interpolation method is bicubic interpolation.
- Inter.BICUBIC, means the interpolation method is bicubic interpolation.

max_attempts (int, optional): The maximum number of attempts to propose a valid
crop area (default=10). If exceeded, fall back to use center crop instead.

Examples:
>>> import mindspore.dataset.vision.py_transforms as py_transforms
>>> import mindspore.dataset.vision.py_transforms as py_vision
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>>
>>> Compose([py_transforms.Decode(),
>>> py_transforms.RandomResizedCrop(224),
>>> py_transforms.ToTensor()])
>>> Compose([py_vision.Decode(),
>>> py_vision.RandomResizedCrop(224),
>>> py_vision.ToTensor()])
"""

@check_random_resize_crop
@@ -469,12 +475,12 @@ class CenterCrop:
If size is a sequence of length 2, it should be (height, width).

Examples:
>>> import mindspore.dataset.vision.py_transforms as py_transforms
>>> import mindspore.dataset.vision.py_transforms as py_vision
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>>
>>> Compose([py_transforms.Decode(),
>>> py_transforms.CenterCrop(64),
>>> py_transforms.ToTensor()])
>>> Compose([py_vision.Decode(),
>>> py_vision.CenterCrop(64),
>>> py_vision.ToTensor()])
"""

@check_crop
@@ -513,12 +519,12 @@ 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_transforms
>>> import mindspore.dataset.vision.py_transforms as py_vision
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>>
>>> Compose([py_transforms.Decode(),
>>> py_transforms.RandomColorAdjust(0.4, 0.4, 0.4, 0.1),
>>> py_transforms.ToTensor()])
>>> Compose([py_vision.Decode(),
>>> py_vision.RandomColorAdjust(0.4, 0.4, 0.4, 0.1),
>>> py_vision.ToTensor()])
"""

@check_random_color_adjust
@@ -556,11 +562,11 @@ class RandomRotation:
If omitted, or if the image has mode "1" or "P", it is set to be Inter.NEAREST.
It can be any of [Inter.BILINEAR, Inter.NEAREST, Inter.BICUBIC].

- Inter.BILINEAR, means resampling method is bilinear interpolation.
- Inter.BILINEAR, means the resampling method is bilinear interpolation.

- Inter.NEAREST, means resampling method is nearest-neighbor interpolation.
- Inter.NEAREST, means the resampling method is nearest-neighbor interpolation.

- Inter.BICUBIC, means resampling method is bicubic interpolation.
- Inter.BICUBIC, means the resampling method is bicubic interpolation.

expand (bool, optional): Optional expansion flag (default=False). If set to True, expand the output
image to make it large enough to hold the entire rotated image.
@@ -571,15 +577,15 @@ class RandomRotation:
fill_value (int or tuple, optional): Optional fill color for the area outside the rotated
image (default=0).
If it is a 3-tuple, it is used for R, G, B channels respectively.
If it is an int, it is used for all RGB channels. Default is 0.
If it is an integer, it is used for all RGB channels. Default is 0.

Examples:
>>> import mindspore.dataset.vision.py_transforms as py_transforms
>>> import mindspore.dataset.vision.py_transforms as py_vision
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>>
>>> Compose([py_transforms.Decode(),
>>> py_transforms.RandomRotation(30),
>>> py_transforms.ToTensor()])
>>> Compose([py_vision.Decode(),
>>> py_vision.RandomRotation(30),
>>> py_vision.ToTensor()])
"""

@check_random_rotation
@@ -608,15 +614,15 @@ class RandomOrder:
Perform a series of transforms to the input PIL image in a random order.

Args:
transforms (list): List of the transformations to be applied.
transforms (list): List of the transformations to apply.

Examples:
>>> import mindspore.dataset.vision.py_transforms as py_transforms
>>> import mindspore.dataset.vision.py_transforms as py_vision
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>>
>>> Compose([py_transforms.Decode(),
>>> py_transforms.RandomOrder(transforms_list),
>>> py_transforms.ToTensor()])
>>> Compose([py_vision.Decode(),
>>> py_vision.RandomOrder(transforms_list),
>>> py_vision.ToTensor()])
"""

@check_transforms_list
@@ -628,7 +634,7 @@ class RandomOrder:
Call method.

Args:
img (PIL image): Image to be applied transformations in a random order.
img (PIL image): Image to apply transformations in a random order.

Returns:
img (PIL image), Transformed image.
@@ -641,16 +647,16 @@ class RandomApply:
Randomly perform a series of transforms with a given probability.

Args:
transforms (list): List of transformations to be applied.
transforms (list): List of transformations to apply.
prob (float, optional): The probability to apply the transformation list (default=0.5).

Examples:
>>> import mindspore.dataset.vision.py_transforms as py_transforms
>>> import mindspore.dataset.vision.py_transforms as py_vision
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>>
>>> Compose([py_transforms.Decode(),
>>> py_transforms.RandomApply(transforms_list, prob=0.6),
>>> py_transforms.ToTensor()])
>>> Compose([py_vision.Decode(),
>>> py_vision.RandomApply(transforms_list, prob=0.6),
>>> py_vision.ToTensor()])
"""

@check_random_apply
@@ -673,18 +679,18 @@ class RandomApply:

class RandomChoice:
"""
Randomly select one transform from a series of transforms and applies that on the image.
Randomly select one transform from a series of transforms and apply that transform on the image.

Args:
transforms (list): List of transformations to be chosen from to apply.

Examples:
>>> import mindspore.dataset.vision.py_transforms as py_transforms
>>> import mindspore.dataset.vision.py_transforms as py_vision
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>>
>>> Compose([py_transforms.Decode(),
>>> py_transforms.RandomChoice(transforms_list),
>>> py_transforms.ToTensor()])
>>> Compose([py_vision.Decode(),
>>> py_vision.RandomChoice(transforms_list),
>>> py_vision.ToTensor()])
"""

@check_transforms_list
@@ -696,7 +702,7 @@ class RandomChoice:
Call method.

Args:
img (PIL image): Image to be applied transformation.
img (PIL image): Image to apply transformation.

Returns:
img (PIL image), Transformed image.
@@ -706,7 +712,7 @@ class RandomChoice:

class FiveCrop:
"""
Generate 5 cropped images (one central and four corners).
Generate 5 cropped images (one central image and four corners images).

Args:
size (int or sequence): The output size of the crop.
@@ -714,13 +720,13 @@ class FiveCrop:
If size is a sequence of length 2, it should be (height, width).

Examples:
>>> import mindspore.dataset.vision.py_transforms as py_transforms
>>> import mindspore.dataset.vision.py_transforms as py_vision
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>>
>>> Compose([py_transforms.Decode(),
>>> py_transforms.FiveCrop(size),
>>> Compose([py_vision.Decode(),
>>> py_vision.FiveCrop(size),
>>> # 4D stack of 5 images
>>> lambda images: numpy.stack([py_transforms.ToTensor()(image) for image in images])])
>>> lambda images: numpy.stack([py_vision.ToTensor()(image) for image in images])])
"""

@check_crop
@@ -743,7 +749,8 @@ class FiveCrop:

class TenCrop:
"""
Generate 10 cropped images (first 5 from FiveCrop, second 5 from their flipped version).
Generate 10 cropped images (first 5 images from FiveCrop, second 5 images from their flipped version
as per input flag to flip vertically or horizontally).

Args:
size (Union[int, sequence]): The output size of the crop.
@@ -753,13 +760,13 @@ class TenCrop:
if set to True (default=False).

Examples:
>>> import mindspore.dataset.vision.py_transforms as py_transforms
>>> import mindspore.dataset.vision.py_transforms as py_vision
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>>
>>> Compose([py_transforms.Decode(),
>>> py_transforms.TenCrop(size),
>>> # 4D stack of 10 images
>>> lambda images: numpy.stack([py_transforms.ToTensor()(image) for image in images])])
>>> Compose([py_vision.Decode(),
>>> py_vision.TenCrop(size),
>>> # 4D stack of 10 images
>>> lambda images: numpy.stack([py_vision.ToTensor()(image) for image in images])])
"""

@check_ten_crop
@@ -793,12 +800,12 @@ 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_transforms
>>> import mindspore.dataset.vision.py_transforms as py_vision
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>>
>>> Compose([py_transforms.Decode(),
>>> py_transforms.Grayscale(3),
>>> py_transforms.ToTensor()])
>>> Compose([py_vision.Decode(),
>>> py_vision.Grayscale(3),
>>> py_vision.ToTensor()])
"""

@check_num_channels
@@ -826,12 +833,12 @@ 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_transforms
>>> import mindspore.dataset.vision.py_transforms as py_vision
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>>
>>> Compose([py_transforms.Decode(),
>>> py_transforms.RandomGrayscale(0.3),
>>> py_transforms.ToTensor()])
>>> Compose([py_vision.Decode(),
>>> py_vision.RandomGrayscale(0.3),
>>> py_vision.ToTensor()])
"""

@check_prob
@@ -846,7 +853,7 @@ class RandomGrayscale:
img (PIL image): PIL image to be converted to grayscale randomly.

Returns:
img (PIL image), Randomly grayscale image, same number of channels as input image.
img (PIL image), Randomly apply grayscale to image, same number of channels as the input image.
If input image has 1 channel, the output grayscale image is 1 channel.
If input image has 3 channels, the output image has 3 identical grayscale channels.
"""
@@ -866,16 +873,16 @@ class Pad:

Args:
padding (Union[int, sequence]): The number of pixels to pad the image.
If a single number is provided, it pads all borders with this value.
If a tuple or list of 2 values are provided, it pads the (left and top)
with the first value and (right and bottom) with the second value.
If a single number is provided, pad all borders with this value.
If a tuple or list of 2 values is provided, pad the left and top
with the first value and the right and bottom with the second value.
If 4 values are provided as a list or tuple,
it pads the left, top, right and bottom respectively.
fill_value (Union[int, tuple], optional): Filling value (default=0). The pixel intensity
of the borders if the padding_mode is Border.CONSTANT.
pad the left, top, right and bottom respectively.
fill_value (Union[int, tuple], optional): Filling value for the pixel intensity
of the borders if the padding_mode is Border.CONSTANT (Default=0).
If it is a 3-tuple, it is used to fill R, G, B channels respectively.
padding_mode (Border mode, optional): The method of padding (default=Border.CONSTANT).
Can be any of [Border.CONSTANT, Border.EDGE, Border.REFLECT, Border.SYMMETRIC].
It can be any of [Border.CONSTANT, Border.EDGE, Border.REFLECT, Border.SYMMETRIC].

- Border.CONSTANT, means it fills the border with constant values.

@@ -888,13 +895,13 @@ class Pad:
value of edge.

Examples:
>>> import mindspore.dataset.vision.py_transforms as py_transforms
>>> import mindspore.dataset.vision.py_transforms as py_vision
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>>
>>> Compose([py_transforms.Decode(),
>>> Compose([py_vision.Decode(),
>>> # adds 10 pixels (default black) to each side of the border of the image
>>> py_transforms.Pad(padding=10),
>>> py_transforms.ToTensor()])
>>> py_vision.Pad(padding=10),
>>> py_vision.ToTensor()])
"""

@check_pad
@@ -923,24 +930,24 @@ class RandomPerspective:
Randomly apply perspective transformation to the input PIL image with a given probability.

Args:
distortion_scale (float, optional): The scale of distortion, float between 0 and 1 (default=0.5).
distortion_scale (float, optional): The scale of distortion, a float value between 0 and 1 (default=0.5).
prob (float, optional): Probability of the image being applied perspective transformation (default=0.5).
interpolation (Inter mode, optional): Image interpolation mode (default=Inter.BICUBIC).
It can be any of [Inter.BILINEAR, Inter.NEAREST, Inter.BICUBIC].

- Inter.BILINEAR, means interpolation method is bilinear interpolation.
- Inter.BILINEAR, means the interpolation method is bilinear interpolation.

- Inter.NEAREST, means interpolation method is nearest-neighbor interpolation.
- Inter.NEAREST, means the interpolation method is nearest-neighbor interpolation.

- Inter.BICUBIC, means interpolation method is bicubic interpolation.
- Inter.BICUBIC, means the interpolation method is bicubic interpolation.

Examples:
>>> import mindspore.dataset.vision.py_transforms as py_transforms
>>> import mindspore.dataset.vision.py_transforms as py_vision
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>>
>>> Compose([py_transforms.Decode(),
>>> py_transforms.RandomPerspective(prob=0.1),
>>> py_transforms.ToTensor()])
>>> Compose([py_vision.Decode(),
>>> py_vision.RandomPerspective(prob=0.1),
>>> py_vision.ToTensor()])
"""

@check_random_perspective
@@ -954,7 +961,7 @@ class RandomPerspective:
Call method.

Args:
img (PIL image): PIL image to be applied perspective transformation randomly.
img (PIL image): PIL image to apply perspective transformation randomly.

Returns:
img (PIL image), Image after being perspectively transformed randomly.
@@ -979,21 +986,21 @@ class RandomErasing:
original image (default=(0.02, 0.33)).
ratio (sequence of floats, optional): Range of the aspect ratio of the erase
area (default=(0.3, 3.3)).
value (Union[int, sequence]): Erasing value (default=0).
If value is a single int, it is applied to all pixels to be erases.
value (Union[int, sequence, string]): Erasing value (default=0).
If value is a single intieger, it is applied to all pixels to be erased.
If value is a sequence of length 3, it is applied to R, G, B channels respectively.
If value is a str 'random', the erase value will be obtained from a standard normal distribution.
inplace (bool, optional): Apply this transform inplace (default=False).
If value is a string 'random', the erase value will be obtained from a standard normal distribution.
inplace (bool, optional): Apply this transform in-place (default=False).
max_attempts (int, optional): The maximum number of attempts to propose a valid
erase_area (default=10). If exceeded, return the original image.

Examples:
>>> import mindspore.dataset.vision.py_transforms as py_transforms
>>> import mindspore.dataset.vision.py_transforms as py_vision
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>>
>>> Compose([py_transforms.Decode(),
>>> py_transforms.ToTensor(),
>>> py_transforms.RandomErasing(value='random')])
>>> Compose([py_vision.Decode(),
>>> py_vision.ToTensor(),
>>> py_vision.RandomErasing(value='random')])
"""

@check_random_erasing
@@ -1035,12 +1042,12 @@ 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_transforms
>>> import mindspore.dataset.vision.py_transforms as py_vision
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>>
>>> Compose([py_transforms.Decode(),
>>> py_transforms.ToTensor(),
>>> py_transforms.Cutout(80)])
>>> Compose([py_vision.Decode(),
>>> py_vision.ToTensor(),
>>> py_vision.Cutout(80)])
"""

@check_cutout
@@ -1074,23 +1081,23 @@ class Cutout:
class LinearTransformation:
"""
Apply linear transformation to the input NumPy image array, given a square transformation matrix and
a mean_vector.
a mean vector.

The transformation first flattens the input array and subtract mean_vector from it, then computes the
dot product with the transformation matrix, and reshapes it back to its original shape.
The transformation first flattens the input array and subtracts the mean vector from it. It then computes
the dot product with the transformation matrix, and reshapes it back to its original shape.

Args:
transformation_matrix (numpy.ndarray): a square transformation matrix of shape (D, D), D = C x H x W.
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_transforms
>>> import mindspore.dataset.vision.py_transforms as py_vision
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>>
>>> Compose([py_transforms.Decode(),
>>> py_transforms.Resize(256),
>>> py_transforms.ToTensor(),
>>> py_transforms.LinearTransformation(transformation_matrix, mean_vector)])
>>> Compose([py_vision.Decode(),
>>> py_vision.Resize(256),
>>> py_vision.ToTensor(),
>>> py_vision.LinearTransformation(transformation_matrix, mean_vector)])
"""

@check_linear_transform
@@ -1121,17 +1128,17 @@ class RandomAffine:
If degrees is a sequence, it should be (min, max).
translate (sequence, optional): Sequence (tx, ty) of maximum translation in
x(horizontal) and y(vertical) directions (default=None).
The horizontal and vertical shift is selected randomly from the range:
The horizontal shift and vertical shift are selected randomly from the range:
(-tx*width, tx*width) and (-ty*height, ty*height), respectively.
If None, no translations gets applied.
If None, no translations are applied.
scale (sequence, optional): Scaling factor interval (default=None, original scale is used).
shear (Union[int, float, sequence], optional): Range of shear factor (default=None).
If a number 'shear', then a shear parallel to the x axis in the range of (-shear, +shear) is applied.
If a tuple or list of size 2, then a shear parallel to the x axis in the range of (shear[0], shear[1])
is applied.
If a tuple of list of size 4, then a shear parallel to x axis in the range of (shear[0], shear[1])
and a shear parallel to y axis in the range of (shear[2], shear[3]) is applied.
If None, no shear is applied.
If shear is an integer, then a shear parallel to the X axis in the range of (-shear, +shear) is applied.
If shear is a tuple or list of size 2, then a shear parallel to the X axis in the range of
(shear[0], shear[1]) is applied.
If shear is a tuple of list of size 4, then a shear parallel to X axis in the range of
(shear[0], shear[1]) and a shear parallel to Y axis in the range of (shear[2], shear[3]) is applied.
If shear is None, no shear is applied.
resample (Inter mode, optional): An optional resampling filter (default=Inter.NEAREST).
If omitted, or if the image has mode "1" or "P", it is set to be Inter.NEAREST.
It can be any of [Inter.BILINEAR, Inter.NEAREST, Inter.BICUBIC].
@@ -1142,8 +1149,9 @@ class RandomAffine:

- Inter.BICUBIC, means resample method is bicubic interpolation.

fill_value (Union[tuple, int], optional): Optional fill_value to fill the area outside the transform
in the output image. There must be three elements in tuple and the value of single element is [0, 255].
fill_value (Union[tuple, int], optional): Optional filling value to fill the area outside the transform
in the output image. There must be three elements in the tuple and the value of a single element is
within the range [0, 255].
Used only in Pillow versions > 5.0.0 (default=0, filling is performed).

Raises:
@@ -1159,12 +1167,12 @@ class RandomAffine:
TypeError: If fill_value is not a single integer or a 3-tuple.

Examples:
>>> import mindspore.dataset.vision.py_transforms as py_transforms
>>> import mindspore.dataset.vision.py_transforms as py_vision
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>>
>>> Compose([py_transforms.Decode(),
>>> py_transforms.RandomAffine(degrees=15, translate=(0.1, 0.1), scale=(0.9, 1.1)),
>>> py_transforms.ToTensor()])
>>> Compose([py_vision.Decode(),
>>> py_vision.RandomAffine(degrees=15, translate=(0.1, 0.1), scale=(0.9, 1.1)),
>>> py_vision.ToTensor()])
"""

@check_random_affine
@@ -1195,7 +1203,7 @@ class RandomAffine:
Call method.

Args:
img (PIL image): Image to be applied affine transformation.
img (PIL image): Image to apply affine transformation.

Returns:
img (PIL image), Randomly affine transformed image.
@@ -1212,12 +1220,22 @@ class RandomAffine:

class MixUp:
"""
Apply mix up transformation to the input image and label, make one input data combined with others.
Apply mix up transformation to the input image and label. Make one input data combined with others.

Args:
batch_size (int): the batch size of dataset.
alpha (float): the mix up rate.
is_single (bool): for deciding using single batch or muti batch mix up transformation.
batch_size (int): Batch size of dataset.
alpha (float): Mix up rate.
is_single (bool): Identify if single batch or multi-batch mix up transformation is to be used
(Default=True, which is single batch).


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())
"""

@check_mix_up
@@ -1234,12 +1252,12 @@ class MixUp:
Call method.

Args:
image (numpy.ndarray): NumPy image to be applied mix up transformation.
label(numpy.ndarray): NumPy label to be applied mix up transformation.
image (numpy.ndarray): NumPy image to apply mix up transformation.
label(numpy.ndarray): NumPy label to apply mix up transformation.

Returns:
image (numpy.ndarray): NumPy image after being applied mix up transformation.
label(numpy.ndarray): NumPy label after being applied mix up transformation.
image (numpy.ndarray): NumPy image after applying mix up transformation.
label(numpy.ndarray): NumPy label after applying mix up transformation.
"""
if self.is_single:
return util.mix_up_single(self.batch_size, image, label, self.alpha)
@@ -1248,11 +1266,20 @@ class MixUp:

class RgbToHsv:
"""
Convert a NumPy RGB image or one batch NumPy RGB images to HSV images.
Convert a NumPy RGB image or a batch of NumPy RGB images to HSV images.

Args:
is_hwc (bool): The flag of image shape, (H, W, C) or (N, H, W, C) if True
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()])
"""

def __init__(self, is_hwc=False):
@@ -1279,6 +1306,15 @@ class HsvToRgb:
Args:
is_hwc (bool): The flag of image shape, (H, W, C) or (N, H, W, C) if True
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()])
"""

def __init__(self, is_hwc=False):
@@ -1307,12 +1343,12 @@ class RandomColor:
It should be in (min, max) format (default=(0.1,1.9)).

Examples:
>>> import mindspore.dataset.vision.py_transforms as py_transforms
>>> import mindspore.dataset.vision.py_transforms as py_vision
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>>
>>> Compose([py_transforms.Decode(),
>>> py_transforms.RandomColor((0.5,1.5)),
>>> py_transforms.ToTensor()])
>>> Compose([py_vision.Decode(),
>>> py_vision.RandomColor((0.5, 2.0)),
>>> py_vision.ToTensor()])
"""

@check_positive_degrees
@@ -1342,13 +1378,12 @@ class RandomSharpness:
It should be in (min, max) format (default=(0.1,1.9)).

Examples:
>>> import mindspore.dataset.vision.py_transforms as py_transforms
>>> import mindspore.dataset.vision.py_transforms as py_vision
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>>
>>> Compose([py_transforms.Decode(),
>>> py_transforms.RandomSharpness((0.5,1.5)),
>>> py_transforms.ToTensor()])

>>> Compose([py_vision.Decode(),
>>> py_vision.RandomSharpness((0.5, 1.5)),
>>> py_vision.ToTensor()])
"""

@check_positive_degrees
@@ -1378,12 +1413,12 @@ class AutoContrast:
ignore (Union[int, sequence], optional): Pixel values to ignore (default=None).

Examples:
>>> import mindspore.dataset.vision.py_transforms as py_transforms
>>> import mindspore.dataset.vision.py_transforms as py_vision
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>>
>>> Compose([py_transforms.Decode(),
>>> py_transforms.AutoContrast(),
>>> py_transforms.ToTensor()])
>>> Compose([py_vision.Decode(),
>>> py_vision.AutoContrast(),
>>> py_vision.ToTensor()])

"""

@@ -1411,12 +1446,12 @@ class Invert:
Invert colors of input PIL image.

Examples:
>>> import mindspore.dataset.vision.py_transforms as py_transforms
>>> import mindspore.dataset.vision.py_transforms as py_vision
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>>
>>> Compose([py_transforms.Decode(),
>>> py_transforms.Invert(),
>>> py_transforms.ToTensor()])
>>> Compose([py_vision.Decode(),
>>> py_vision.Invert(),
>>> py_vision.ToTensor()])

"""

@@ -1439,12 +1474,12 @@ class Equalize:
Equalize the histogram of input PIL image.

Examples:
>>> import mindspore.dataset.vision.py_transforms as py_transforms
>>> import mindspore.dataset.vision.py_transforms as py_vision
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>>
>>> Compose([py_transforms.Decode(),
>>> py_transforms.Equalize(),
>>> py_transforms.ToTensor()])
>>> Compose([py_vision.Decode(),
>>> py_vision.Equalize(),
>>> py_vision.ToTensor()])

"""

@@ -1465,8 +1500,9 @@ class Equalize:
class UniformAugment:
"""
Uniformly select and apply a number of transforms sequentially from
a list of transforms. Randomly assigns a probability to each transform for
each image to decide whether apply it or not.
a list of transforms. Randomly assign a probability to each transform for
each image to decide whether to apply the transform or not.

All the transforms in transform list must have the same input/output data type.

Args:
@@ -1474,16 +1510,16 @@ class UniformAugment:
num_ops (int, optional): number of transforms to sequentially apply (default=2).

Examples:
>>> import mindspore.dataset.vision.py_transforms as py_transforms
>>> import mindspore.dataset.vision.py_transforms as py_vision
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>>
>>> transforms_list = [py_transforms.CenterCrop(64),
>>> py_transforms.RandomColor(),
>>> py_transforms.RandomSharpness(),
>>> py_transforms.RandomRotation(30)]
>>> Compose([py_transforms.Decode(),
>>> py_transforms.UniformAugment(transforms_list),
>>> py_transforms.ToTensor()])
>>> 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()])
"""

@check_uniform_augment_py
@@ -1496,7 +1532,7 @@ class UniformAugment:
Call method.

Args:
img (PIL image): Image to be applied transformation.
img (PIL image): Image to apply transformation.

Returns:
img (PIL image), Transformed image.


+ 5
- 5
mindspore/dataset/vision/py_transforms_util.py View File

@@ -580,7 +580,7 @@ def rotate(img, angle, resample, expand, center, fill_value):
Origin is the top left corner.
fill_value (Union[int, tuple]): Optional fill color for the area outside the rotated image.
If it is a 3-tuple, it is used for R, G, B channels respectively.
If it is an int, it is used for all RGB channels.
If it is an integer, it is used for all RGB channels.

Returns:
img (PIL image), Rotated image.
@@ -677,7 +677,7 @@ def random_rotation(img, degrees, resample, expand, center, fill_value):
Origin is the top left corner.
fill_value (Union[int, tuple]): Optional fill color for the area outside the rotated image.
If it is a 3-tuple, it is used for R, G, B channels respectively.
If it is an int, it is used for all RGB channels.
If it is an integer, it is used for all RGB channels.

Returns:
img (PIL image), Rotated image.
@@ -1108,7 +1108,7 @@ def random_affine(img, angle, translations, scale, shear, resample, fill_value=0
angle (Union[int, float]): Rotation angle in degrees, clockwise.
translations (sequence): Translations in horizontal and vertical axis.
scale (float): Scale parameter, a single number.
shear (Union[float, sequence]): Shear amount parallel to x and y axis.
shear (Union[float, sequence]): Shear amount parallel to X axis and Y axis.
resample (Union[Inter.NEAREST, Inter.BILINEAR, Inter.BICUBIC], optional): An optional resampling filter.
fill_value (Union[tuple int], optional): Optional fill_value to fill the area outside the transform
in the output image. Used only in Pillow versions > 5.0.0.
@@ -1294,7 +1294,7 @@ def rgb_to_hsvs(np_rgb_imgs, is_hwc):
shape_size = len(np_rgb_imgs.shape)

if not shape_size in (3, 4):
raise TypeError('img shape should be (H, W, C)/(N, H, W, C)/(C,H,W)/(N,C,H,W). \
raise TypeError('img shape should be (H, W, C)/(N, H, W, C)/(C ,H, W)/(N, C, H, W). \
Got {}'.format(np_rgb_imgs.shape))

if shape_size == 3:
@@ -1362,7 +1362,7 @@ def hsv_to_rgbs(np_hsv_imgs, is_hwc):
shape_size = len(np_hsv_imgs.shape)

if not shape_size in (3, 4):
raise TypeError('img shape should be (H, W, C)/(N, H, W, C)/(C,H,W)/(N,C,H,W). \
raise TypeError('img shape should be (H, W, C)/(N, H, W, C)/(C, H, W)/(N, C, H, W). \
Got {}'.format(np_hsv_imgs.shape))

if shape_size == 3:


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