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!28668 update python doc 

Merge pull request !28668 from luoyang/code_docs_doc
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i-robot 2022-01-07 02:02:28 +00:00 committed by Gitee
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14
mindspore/python/mindspore/dataset/text/utils.py
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@ -173,7 +173,7 @@ class Vocab:
Args:
file_path (str): Path to the file which contains the vocab list.
delimiter (str, optional): A delimiter to break up each line in file, the first element is taken to be
the word (default="").
the word (default="", the whole line will be treated as a word).
vocab_size (int, optional): Number of words to read from file_path (default=None, all words are taken).
special_tokens (list, optional): A list of strings, each one is a special token. For example
special_tokens=["<pad>","<unk>"] (default=None, no special tokens will be added).
@ -185,7 +185,19 @@ class Vocab:
Vocab, vocab built from the file.
Examples:
>>> # Assume vocab file contains the following content:
>>> # --- begin of file ---
>>> # apple,apple2
>>> # banana, 333
>>> # cat,00
>>> # --- end of file ---
>>>
>>> # Read file through this API and specify "," as delimiter,
>>> # then the delimiter will break up each line in file, the first element is taken to be the word.
>>> vocab = text.Vocab.from_file("/path/to/simple/vocab/file", ",", None, ["<pad>", "<unk>"], True)
>>>
>>> # Finally, there are 5 words in the vocab: "<pad>", "<unk>", "apple", "banana", "cat"
>>> print(vocab.vocab())
"""
if vocab_size is None:
vocab_size = -1

624
mindspore/python/mindspore/dataset/vision/c_transforms.py
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284
mindspore/python/mindspore/dataset/vision/py_transforms.py
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@ -74,6 +74,9 @@ class ToTensor(py_transforms.PyTensorOperation):
TypeError: If the input is not PIL Image or numpy.ndarray.
TypeError: If the dimension of input is not 2 or 3.
Supported Platforms:
``CPU``
Examples:
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>> # create a list of transformations to be applied to the "image" column of each data row
@ -112,6 +115,9 @@ class ToType(py_transforms.PyTensorOperation):
Raises:
TypeError: If the input is not numpy.ndarray.
Supported Platforms:
``CPU``
Examples:
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>> import numpy as np
@ -149,6 +155,9 @@ class HWC2CHW(py_transforms.PyTensorOperation):
TypeError: If the input is not numpy.ndarray.
TypeError: If the dimension of input is not 3.
Supported Platforms:
``CPU``
Examples:
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>> transforms_list = Compose([py_vision.Decode(),
@ -184,6 +193,9 @@ class ToPIL(py_transforms.PyTensorOperation):
Raises:
TypeError: If the input is not numpy.ndarray or PIL Image.
Supported Platforms:
``CPU``
Examples:
>>> # data is already decoded, but not in PIL Image format
>>> from mindspore.dataset.transforms.py_transforms import Compose
@ -219,6 +231,9 @@ class Decode(py_transforms.PyTensorOperation):
ValueError: If the input is not raw data.
ValueError: If the input image is already decoded.
Supported Platforms:
``CPU``
Examples:
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>> transforms_list = Compose([py_vision.Decode(),
@ -271,6 +286,9 @@ class Normalize(py_transforms.PyTensorOperation):
ValueError: If the lengths of the mean and std are not equal.
ValueError: If the length of the mean or std is neither equal to the channel length nor 1.
Supported Platforms:
``CPU``
Examples:
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>> transforms_list = Compose([py_vision.Decode(),
@ -332,6 +350,9 @@ class NormalizePad(py_transforms.PyTensorOperation):
ValueError: If the length of the mean and std are not equal.
ValueError: If the length of the mean or std is neither equal to the channel length nor 1.
Supported Platforms:
``CPU``
Examples:
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>> transforms_list = Compose([py_vision.Decode(),
@ -393,6 +414,19 @@ class RandomCrop(py_transforms.PyTensorOperation):
- Border.SYMMETRIC, means to pad with reflection of image repeating the last value at the edge.
Raises:
TypeError: If `size` is not of type integer or sequence of integer.
TypeError: If `padding` is not of type integer or sequence of integer.
TypeError: If `pad_if_needed` is not of type boolean.
TypeError: If `fill_value` is not of type integer or sequence of integer.
TypeError: If `padding_mode` is not of type Border.
ValueError: If `size` is not positive.
ValueError: If `padding` is negative.
ValueError: If `fill_value` is not in range [0, 255].
Supported Platforms:
``CPU``
Examples:
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>> transforms_list = Compose([py_vision.Decode(),
@ -436,6 +470,14 @@ class RandomHorizontalFlip(py_transforms.PyTensorOperation):
Args:
prob (float, optional): Probability of the image to be horizontally flipped (default=0.5).
Raises:
TypeError: If `prob` is not of type float.
ValueError: If `prob` is not in range [0, 1].
RuntimeError: If given tensor shape is not <H, W> or <H, W, C>.
Supported Platforms:
``CPU``
Examples:
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>> transforms_list = Compose([py_vision.Decode(),
@ -470,6 +512,14 @@ class RandomVerticalFlip(py_transforms.PyTensorOperation):
Args:
prob (float, optional): Probability of the image to be vertically flipped (default=0.5).
Raises:
TypeError: If `prob` is not of type float.
ValueError: If `prob` is not in range [0, 1].
RuntimeError: If given tensor shape is not <H, W> or <H, W, C>.
Supported Platforms:
``CPU``
Examples:
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>> transforms_list = Compose([py_vision.Decode(),
@ -517,6 +567,14 @@ class Resize(py_transforms.PyTensorOperation):
- Inter.BICUBIC, bicubic interpolation.
Raises:
TypeError: If `size` is not of type integer or sequence of integer.
TypeError: If `interpolation` is not of type Inter.
ValueError: If `size` is not positive.
Supported Platforms:
``CPU``
Examples:
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>> transforms_list = Compose([py_vision.Decode(),
@ -572,6 +630,20 @@ class RandomResizedCrop(py_transforms.PyTensorOperation):
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.
Raises:
TypeError: If `size` is not of type integer or sequence of integer.
TypeError: If `scale` is not of type tuple.
TypeError: If `ratio` is not of type tuple.
TypeError: If `interpolation` is not of type Inter.
TypeError: If `max_attempts` is not of type integer.
ValueError: If `size` is not positive.
ValueError: If `scale` is negative.
ValueError: If `ratio` is negative.
ValueError: If `max_attempts` is not positive.
Supported Platforms:
``CPU``
Examples:
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>> transforms_list = Compose([py_vision.Decode(),
@ -614,6 +686,13 @@ class CenterCrop(py_transforms.PyTensorOperation):
If size is an integer, a square of size (size, size) is returned.
If size is a sequence of length 2, it should be in shape of (height, width).
Raises:
TypeError: If `size` is not of type integer or sequence.
ValueError: If `size` is less than or equal to 0.
Supported Platforms:
``CPU``
Examples:
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>> transforms_list = Compose([py_vision.Decode(),
@ -663,6 +742,19 @@ class RandomColorAdjust(py_transforms.PyTensorOperation):
If hue is a float, the range will be [-hue, hue], where 0 <= hue <= 0.5.
If hue is a sequence of length 2, it should be in shape of [min, max], where -0.5 <= min <= max <= 0.5.
Raises:
TypeError: If `brightness` is not of type float or sequence of float.
TypeError: If `contrast` is not of type float or sequence of float.
TypeError: If `saturation` is not of type float or sequence of float.
TypeError: If `hue` is not of type float or sequence of float.
ValueError: If `brightness` is negative.
ValueError: If `contrast` is negative.
ValueError: If `saturation` is negative.
ValueError: If `hue` is not in range [-0.5, 0.5].
Supported Platforms:
``CPU``
Examples:
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>> transforms_list = Compose([py_vision.Decode(),
@ -726,6 +818,18 @@ class RandomRotation(py_transforms.PyTensorOperation):
If fill_value is a tuple of length 3, it is used to fill R, G, B channels respectively.
If fill_value is an integer, it is used to fill all RGB channels.
Raises:
TypeError: If `degrees` is not of type integer, float or sequence.
TypeError: If `resample` is not of type Inter.
TypeError: If `expand` is not of type boolean.
TypeError: If `center` is not of type tuple.
TypeError: If `fill_value` is not of type integer or tuple of integer.
ValueError: If `fill_value` is not in range [0, 255].
RuntimeError: If given tensor shape is not <H, W> or <H, W, C>.
Supported Platforms:
``CPU``
Examples:
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>> transforms_list = Compose([py_vision.Decode(),
@ -766,7 +870,15 @@ class FiveCrop(py_transforms.PyTensorOperation):
If size is an integer, a square of size (size, size) is returned.
If size is a sequence of length 2, it should be in shape of (height, width).
Raises:
TypeError: If `size` is not of type integer or sequence of integer.
ValueError: If `size` is not positive.
Supported Platforms:
``CPU``
Examples:
>>> import numpy
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>> transforms_list = Compose([py_vision.Decode(),
... py_vision.FiveCrop(size=200),
@ -806,6 +918,14 @@ class TenCrop(py_transforms.PyTensorOperation):
use_vertical_flip (bool, optional): Whether to flip the image vertically,
otherwise horizontally (default=False).
Raises:
TypeError: If `size` is not of type integer or sequence of integer.
TypeError: If `use_vertical_flip` is not of type boolean.
ValueError: If `size` is not positive.
Supported Platforms:
``CPU``
Examples:
>>> import numpy
>>> from mindspore.dataset.transforms.py_transforms import Compose
@ -848,6 +968,13 @@ class Grayscale(py_transforms.PyTensorOperation):
num_output_channels (int): Number of channels of the output grayscale image, which can be 1 or 3 (default=1).
If num_output_channels is 3, the returned image will have 3 identical RGB channels.
Raises:
TypeError: If `num_output_channels` is not of type integer.
ValueError: If `num_output_channels` is not 1 or 3.
Supported Platforms:
``CPU``
Examples:
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>> transforms_list = Compose([py_vision.Decode(),
@ -883,6 +1010,13 @@ class RandomGrayscale(py_transforms.PyTensorOperation):
Args:
prob (float, optional): Probability of the image being converted to grayscale (default=0.1).
Raises:
TypeError: If `prob` is not of type float.
ValueError: If `prob` is not in range [0, 1].
Supported Platforms:
``CPU``
Examples:
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>> transforms_list = Compose([py_vision.Decode(),
@ -944,6 +1078,17 @@ class Pad(py_transforms.PyTensorOperation):
- Border.SYMMETRIC, pads with reflection of the image repeating the last value on the edge.
Raises:
TypeError: If `padding` is not of type integer or sequence of integer.
TypeError: If `fill_value` is not of type integer or tuple of integer.
TypeError: If `padding_mode` is not of type Border.
ValueError: If `padding` is negative.
ValueError: If `fill_value` is not in range [0, 255].
RuntimeError: If given tensor shape is not <H, W> or <H, W, C>.
Supported Platforms:
``CPU``
Examples:
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>> transforms_list = Compose([py_vision.Decode(),
@ -993,6 +1138,16 @@ class RandomPerspective(py_transforms.PyTensorOperation):
- Inter.BICUBIC, bicubic interpolation.
Raises:
TypeError: If `distortion_scale` is not of type float.
TypeError: If `prob` is not of type float.
TypeError: If `interpolation` is not of type Inter.
ValueError: If `distortion_scale` is not in range [0, 1].
ValueError: If `prob` is not in range [0, 1].
Supported Platforms:
``CPU``
Examples:
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>> transforms_list = Compose([py_vision.Decode(),
@ -1048,6 +1203,22 @@ class RandomErasing(py_transforms.PyTensorOperation):
max_attempts (int, optional): The maximum number of attempts to propose a valid
area to be erased (default=10). If exceeded, return the original image.
Raises:
TypeError: If `prob` is not of type float.
TypeError: If `scale` is not of type sequence.
TypeError: If `ratio` is not of type sequence.
TypeError: If `value` is not of type integer, sequence or string.
TypeError: If `inplace` is not of type boolean.
TypeError: If `max_attempts` is not of type integer.
ValueError: If `prob` is not in range [0, 1].
ValueError: If `scale` is negative.
ValueError: If `ratio` is negative.
ValueError: If `value` is not in range [0, 255].
ValueError: If `max_attempts` is not positive.
Supported Platforms:
``CPU``
Examples:
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>> transforms_list = Compose([py_vision.Decode(),
@ -1097,6 +1268,16 @@ class Cutout(py_transforms.PyTensorOperation):
length (int): The side length of each square patch.
num_patches (int, optional): Number of patches to be applied to the image (default=1).
Raises:
TypeError: If `length` is not of type integer.
TypeError: If `num_patches` is not of type integer.
ValueError: If `length` is less than or equal 0.
ValueError: If `num_patches` is less than or equal 0.
RuntimeError: If given tensor shape is not <H, W, C>.
Supported Platforms:
``CPU``
Examples:
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>> transforms_list = Compose([py_vision.Decode(),
@ -1152,6 +1333,13 @@ class LinearTransformation(py_transforms.PyTensorOperation):
:math:`D = C \times H \times W`.
mean_vector (numpy.ndarray): A mean vector in shape of (D,), where :math:`D = C \times H \times W`.
Raises:
TypeError: If `transformation_matrix` is not of type numpy.ndarray.
TypeError: If `mean_vector` is not of type numpy.ndarray.
Supported Platforms:
``CPU``
Examples:
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>> import numpy as np
@ -1224,15 +1412,20 @@ class RandomAffine(py_transforms.PyTensorOperation):
Only supported with Pillow version > 5.0.0.
Raises:
TypeError: If `degrees` is not of type integer, float or sequence.
TypeError: If `translate` is not of type sequence.
TypeError: If `scale` is not of type sequence.
TypeError: If `shear` is not of type integer, float or sequence.
TypeError: If `resample` is not of type Inter.
TypeError: If `fill_value` is not of type integer or tuple of integer.
ValueError: If `degrees` is negative.
ValueError: If translation is not between 0 and 1.
ValueError: If scale is not positive.
ValueError: If shear is a non positive number.
TypeError: If `degrees` is not a number or a sequence of length 2.
TypeError: If translate is defined but not a sequence of length 2.
TypeError: If scale is not a sequence of length 2.
TypeError: If shear is not a sequence of length 2 or 4.
TypeError: If fill_value is not an integer or a tuple of length 3.
ValueError: If `translate` is not in range [-1.0, 1.0].
ValueError: If `scale` is negative.
ValueError: If `shear` is not positive.
RuntimeError: If given tensor shape is not <H, W> or <H, W, C>.
Supported Platforms:
``CPU``
Examples:
>>> from mindspore.dataset.transforms.py_transforms import Compose
@ -1299,6 +1492,15 @@ class MixUp(py_transforms.PyTensorOperation):
[img1, ..., img(n), img0] in each batch. Otherwise, it will randomly mix up images with the
output of mixing of previous batch of images (Default=True).
Raises:
TypeError: If `batch_size` is not of type integer.
TypeError: If `alpha` is not of type float.
TypeError: If `is_single` is not of type boolean.
ValueError: If `batch_size` is not positive.
ValueError: If `alpha` is not positive.
Supported Platforms:
``CPU``
Examples:
>>> # Setup multi-batch mixup transformation
@ -1343,6 +1545,9 @@ class RgbToBgr(py_transforms.PyTensorOperation):
is_hwc (bool): Whether the image is in shape of (H, W, C) or (N, H, W, C), otherwise
in shape of (C, H, W) or (N, C, H, W) (default=False).
Supported Platforms:
``CPU``
Examples:
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>> transforms_list = Compose([py_vision.Decode(),
@ -1380,6 +1585,9 @@ class RgbToHsv(py_transforms.PyTensorOperation):
is_hwc (bool): Whether the image is in shape of (H, W, C) or (N, H, W, C), otherwise
in shape of (C, H, W) or (N, C, H, W) (default=False).
Supported Platforms:
``CPU``
Examples:
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>> transforms_list = Compose([py_vision.Decode(),
@ -1417,6 +1625,9 @@ class HsvToRgb(py_transforms.PyTensorOperation):
is_hwc (bool): Whether the image is in shape of (H, W, C) or (N, H, W, C), otherwise
in shape of (C, H, W) or (N, C, H, W) (default=False).
Supported Platforms:
``CPU``
Examples:
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>> transforms_list = Compose([py_vision.Decode(),
@ -1454,6 +1665,14 @@ class RandomColor(py_transforms.PyTensorOperation):
degrees (sequence): Range of color adjustment degree to be randomly chosen from,
which should be in shape of (min, max) (default=(0.1,1.9)).
Raises:
TypeError: If `degrees` is not of type sequence of float.
ValueError: If `degrees` is negative.
RuntimeError: If given tensor shape is not <H, W, C>.
Supported Platforms:
``CPU``
Examples:
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>> transforms_list = Compose([py_vision.Decode(),
@ -1489,6 +1708,14 @@ class RandomLighting:
Args:
alpha (float, optional): Intensity of the image (default=0.05).
Raises:
TypeError: If `alpha` is not of type float.
ValueError: If `alpha` is negative.
RuntimeError: If given tensor shape is not <H, W, C>.
Supported Platforms:
``CPU``
Examples:
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>>
@ -1528,6 +1755,14 @@ class RandomSharpness(py_transforms.PyTensorOperation):
Degree of 0.0 gives a blurred image, degree of 1.0 gives the original image,
and degree of 2.0 increases the sharpness by a factor of 2.
Raises:
TypeError : If `degrees` is not a list or a tuple.
ValueError: If `degrees` is negative.
ValueError: If `degrees` is in (max, min) format instead of (min, max).
Supported Platforms:
``CPU``
Examples:
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>> transforms_list = Compose([py_vision.Decode(),
@ -1564,6 +1799,15 @@ class AdjustGamma(py_transforms.PyTensorOperation):
gamma (float): Gamma parameter in the correction equation, which must be non negative.
gain (float, optional): The constant multiplier (default=1.0).
Raises:
TypeError: If `gain` is not of type float.
TypeError: If `gamma` is not of type float.
ValueError: If `gamma` is less than 0.
RuntimeError: If given tensor shape is not <H, W> or <H, W, C>.
Supported Platforms:
``CPU``
Examples:
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>> transforms_list = Compose([py_vision.Decode(),
@ -1603,6 +1847,16 @@ class AutoContrast(py_transforms.PyTensorOperation):
which must be in range of [0.0, 50.0) (default=0.0).
ignore (Union[int, sequence], optional): Pixel values to be ignored (default=None).
Raises:
TypeError: If `cutoff` is not of type float.
TypeError: If `ignore` is not of type int or sequence.
ValueError: If `cutoff` is not in range [0, 50.0).
ValueError: If `ignore` is not in range [0, 255].
RuntimeError: If given tensor shape is not <H, W> or <H, W, C>.
Supported Platforms:
``CPU``
Examples:
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>> transforms_list = Compose([py_vision.Decode(),
@ -1637,6 +1891,9 @@ class Invert(py_transforms.PyTensorOperation):
"""
Invert the colors of the input PIL Image.
Supported Platforms:
``CPU``
Examples:
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>> transforms_list = Compose([py_vision.Decode(),
@ -1668,6 +1925,9 @@ class Equalize(py_transforms.PyTensorOperation):
"""
Apply histogram equalization on the input PIL Image.
Supported Platforms:
``CPU``
Examples:
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>> transforms_list = Compose([py_vision.Decode(),
@ -1709,6 +1969,14 @@ class UniformAugment(py_transforms.PyTensorOperation):
transforms (sequence): Sequence of transformations to be chosen from.
num_ops (int, optional): Number of transformations to be sequentially and randomly applied (default=2).
Raises:
TypeError: If `transforms` is not of type ImageTensorOperation.
TypeError: If `num_ops` is not of type integer.
ValueError: If `num_ops` is not positive.
Supported Platforms:
``CPU``
Examples:
>>> from mindspore.dataset.transforms.py_transforms import Compose
>>> transforms = [py_vision.CenterCrop(64),