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!3532 fix python api doc for mindspore .dataset 

Merge pull request !3532 from guansongsong/gss_fix_api
This commit is contained in:
mindspore-ci-bot 2020-07-28 14:34:29 +08:00 committed by Gitee
parent 6a55d7ca8f e377ffcebc
commit eeb8d72ac9
11 changed files with 145 additions and 140 deletions
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4
mindspore/dataset/core/datatypes.py
View File

@ -27,7 +27,7 @@ def mstype_to_detype(type_):
Get de data type corresponding to mindspore dtype.
Args:
type_ (:class:`mindspore.dtype`): MindSpore's dtype.
type_ (mindspore.dtype): MindSpore's dtype.
Returns:
The data type of de.
@ -57,7 +57,7 @@ def mstypelist_to_detypelist(type_list):
Get list[de type] corresponding to list[mindspore.dtype].
Args:
type_list (:list[mindspore.dtype]): a list of MindSpore's dtype.
type_list (list[mindspore.dtype]): a list of MindSpore's dtype.
Returns:
The list of de data type.

12
mindspore/dataset/core/validator_helpers.py
View File

@ -155,8 +155,8 @@ def parse_user_args(method, *args, **kwargs):
Args:
method (method): a callable function.
*args: user passed args.
**kwargs: user passed kwargs.
args: user passed args.
kwargs: user passed kwargs.
Returns:
user_filled_args (list): values of what the user passed in for the arguments.
@ -181,9 +181,9 @@ def type_check_list(args, types, arg_names):
Check the type of each parameter in the list.
Args:
args (list, tuple): a list or tuple of any variable.
args (Union[list, tuple]): a list or tuple of any variable.
types (tuple): tuple of all valid types for arg.
arg_names (list, tuple of str): the names of args.
arg_names (Union[list, tuple of str]): the names of args.
Returns:
Exception: when the type is not correct, otherwise nothing.
@ -202,7 +202,7 @@ def type_check(arg, types, arg_name):
Check the type of the parameter.
Args:
arg : any variable.
arg (Any) : any variable.
types (tuple): tuple of all valid types for arg.
arg_name (str): the name of arg.
@ -346,7 +346,7 @@ def check_gnn_list_or_ndarray(param, param_name):
Check if the input parameter is list or numpy.ndarray.
Args:
param (list, nd.ndarray): param.
param (Union[list, nd.ndarray]): param.
param_name (str): param_name.
Returns:

86
mindspore/dataset/engine/datasets.py
View File

@ -188,13 +188,13 @@ class Dataset:
except for maybe the last batch for each bucket.
Args:
column_names (list of string): Columns passed to element_length_function.
bucket_boundaries (list of int): A list consisting of the upper boundaries
column_names (list[str]): Columns passed to element_length_function.
bucket_boundaries (list[int]): A list consisting of the upper boundaries
of the buckets. Must be strictly increasing. If there are n boundaries,
n+1 buckets are created: One bucket for [0, bucket_boundaries[0]), one
bucket for [bucket_boundaries[i], bucket_boundaries[i+1]) for each
0<i<n, and one bucket for [bucket_boundaries[n-1], inf).
bucket_batch_sizes (list of int): A list consisting of the batch sizes for
bucket_batch_sizes (list[int]): A list consisting of the batch sizes for
each bucket. Must contain len(bucket_boundaries)+1 elements.
element_length_function (Callable, optional): A function that takes in
len(column_names) arguments and returns an int. If no value is
@ -269,7 +269,7 @@ class Dataset:
(list[Tensor], list[Tensor], ..., BatchInfo) as input parameters. Each list[Tensor] represent a batch of
Tensors on a given column. The number of lists should match with number of entries in input_columns. The
last parameter of the callable should always be a BatchInfo object.
input_columns (list of string, optional): List of names of the input columns. The size of the list should
input_columns (list[str], optional): List of names of the input columns. The size of the list should
match with signature of per_batch_map callable.
pad_info (dict, optional): Whether to perform padding on selected columns. pad_info={"col1":([224,224],0)}
would pad column with name "col1" to a tensor of size [224,224] and fill the missing with 0.
@ -417,7 +417,7 @@ class Dataset:
input columns expected by the first operator. (default=None, the first
operation will be passed however many columns that is required, starting from
the first column).
operations (list[TensorOp] or Python list[functions]): List of operations to be
operations (Union[list[TensorOp], list[functions]]): List of operations to be
applied on the dataset. Operations are applied in the order they appear in this list.
output_columns (list[str], optional): List of names assigned to the columns outputted by
the last operation. This parameter is mandatory if len(input_columns) !=
@ -724,7 +724,7 @@ class Dataset:
called where ds is a MappableDataset.
Args:
sizes (list of int or list of float): If a list of integers [s1, s2, …, sn] is
sizes (Union[list[int], list[float]]): If a list of integers [s1, s2, …, sn] is
provided, the dataset will be split into n datasets of size s1, size s2, …, size sn
respectively. If the sum of all sizes does not equal the original dataset size, an
an error will occur.
@ -806,7 +806,7 @@ class Dataset:
Zip the datasets in the input tuple of datasets. Columns in the input datasets must not have the same name.
Args:
datasets (tuple or class Dataset): A tuple of datasets or a single class Dataset
datasets (Union[tuple, class Dataset]): A tuple of datasets or a single class Dataset
to be zipped together with this dataset.
Returns:
@ -835,7 +835,7 @@ class Dataset:
The column name，column data type and rank of column data should be the same in input datasets.
Args:
datasets (list or class Dataset): A list of datasets or a single class Dataset
datasets (Union[list, class Dataset]): A list of datasets or a single class Dataset
to be concatenated together with this dataset.
Returns:
@ -1261,10 +1261,10 @@ class Dataset:
Args:
condition_name (str): The condition name that is used to toggle sending next row.
num_batch (int or None): The number of batches(rows) that are released.
num_batch (Union[int, None]): The number of batches(rows) that are released.
When num_batch is None, it will default to the number specified by the
sync_wait operator (default=None).
data (dict or None): The data passed to the callback (default=None).
data (Union[dict, None]): The data passed to the callback (default=None).
"""
if isinstance(num_batch, int) and num_batch <= 0:
# throwing exception, disable all sync_wait in pipeline
@ -1343,7 +1343,7 @@ class SourceDataset(Dataset):
Utility function to search for files with the given glob patterns.
Args:
patterns (str or list[str]): string or list of patterns to be searched.
patterns (Union[str, list[str]]): string or list of patterns to be searched.
Returns:
List, files.
@ -1445,7 +1445,7 @@ class MappableDataset(SourceDataset):
that calls this function is a MappableDataset.
Args:
sizes (list of int or list of float): If a list of integers [s1, s2, …, sn] is
sizes (Union[list[int], list[float]]): If a list of integers [s1, s2, …, sn] is
provided, the dataset will be split into n datasets of size s1, size s2, …, size sn
respectively. If the sum of all sizes does not equal the original dataset size, an
an error will occur.
@ -1593,7 +1593,7 @@ class BatchDataset(DatasetOp):
Args:
input_dataset (Dataset): Input Dataset to be batched.
batch_size (int or function): The number of rows each batch is created with. An
batch_size (Union[int, function]): The number of rows each batch is created with. An
int or callable which takes exactly 1 parameter, BatchInfo.
drop_remainder (bool, optional): Determines whether or not to drop the last
possibly incomplete batch (default=False). If True, and if there are less
@ -1604,7 +1604,7 @@ class BatchDataset(DatasetOp):
(list[Tensor], list[Tensor], ..., BatchInfo) as input parameters. Each list[Tensor] represent a batch of
Tensors on a given column. The number of lists should match with number of entries in input_columns. The
last parameter of the callable should always be a BatchInfo object.
input_columns (list of string, optional): List of names of the input columns. The size of the list should
input_columns (list[str], optional): List of names of the input columns. The size of the list should
match with signature of per_batch_map callable.
pad_info (dict, optional): Whether to perform padding on selected columns. pad_info={"col1":([224,224],0)}
would pad column with name "col1" to a tensor of size [224,224] and fill the missing with 0.
@ -2447,7 +2447,7 @@ def _select_sampler(num_samples, input_sampler, shuffle, num_shards, shard_id, n
Args:
num_samples (int): Number of samples.
input_sampler (Iterable / Sampler): Sampler from user.
input_sampler (Union[Iterable, Sampler]): Sampler from user.
shuffle (bool): Shuffle.
num_shards (int): Number of shard for sharding.
shard_id (int): Shard ID.
@ -2786,7 +2786,7 @@ class MindDataset(MappableDataset):
A source dataset that reads from shard files and database.
Args:
dataset_file (str, list[str]): One of file names or file list in dataset.
dataset_file (Union[str, list[str]]): One of file names or file list in dataset.
columns_list (list[str], optional): List of columns to be read (default=None).
num_parallel_workers (int, optional): The number of readers (default=None).
shuffle (bool, optional): Whether or not to perform shuffle on the dataset
@ -3158,7 +3158,7 @@ class GeneratorDataset(MappableDataset):
- not allowed
Args:
source (Callable/Iterable/Random Accessible):
source (Union[Callable, Iterable, Random Accessible]):
A generator callable object, an iterable python object or a random accessible python object.
Callable source is required to return a tuple of numpy array as a row of the dataset on source().next().
Iterable source is required to return a tuple of numpy array as a row of the dataset on iter(source).next().
@ -3168,15 +3168,15 @@ class GeneratorDataset(MappableDataset):
provide either column_names or schema.
column_types (list[mindspore.dtype], optional): List of column data types of the dataset (default=None).
If provided, sanity check will be performed on generator output.
schema (Schema/str, optional): Path to the json schema file or schema object (default=None). Users are
schema (Union[Schema, str], optional): Path to the json schema file or schema object (default=None). Users are
required to provide either column_names or schema. If both are provided, schema will be used.
num_samples (int, optional): The number of samples to be included in the dataset
(default=None, all images).
num_parallel_workers (int, optional): Number of subprocesses used to fetch the dataset in parallel (default=1).
shuffle (bool, optional): Whether or not to perform shuffle on the dataset. Random accessible input is required.
(default=None, expected order behavior shown in the table).
sampler (Sampler/Iterable, optional): Object used to choose samples from the dataset. Random accessible input is
required (default=None, expected order behavior shown in the table).
sampler (Union[Sampler, Iterable], optional): Object used to choose samples from the dataset. Random accessible
input is required (default=None, expected order behavior shown in the table).
num_shards (int, optional): Number of shards that the dataset should be divided into (default=None).
When this argument is specified, 'num_samples' will not effect. Random accessible input is required.
shard_id (int, optional): The shard ID within num_shards (default=None). This argument should be specified only
@ -3328,9 +3328,9 @@ class TFRecordDataset(SourceDataset):
A source dataset that reads and parses datasets stored on disk in TFData format.
Args:
dataset_files (str or list[str]): String or list of files to be read or glob strings to search for a pattern of
files. The list will be sorted in a lexicographical order.
schema (str or Schema, optional): Path to the json schema file or schema object (default=None).
dataset_files (Union[str, list[str]]): String or list of files to be read or glob strings to search for a
pattern of files. The list will be sorted in a lexicographical order.
schema (Union[str, Schema], optional): Path to the json schema file or schema object (default=None).
If the schema is not provided, the meta data from the TFData file is considered the schema.
columns_list (list[str], optional): List of columns to be read (default=None, read all columns)
num_samples (int, optional): number of samples(rows) to read (default=None).
@ -3339,7 +3339,8 @@ class TFRecordDataset(SourceDataset):
If both num_samples and numRows(parsed from schema) are greater than 0, read num_samples rows.
num_parallel_workers (int, optional): number of workers to read the data
(default=None, number set in the config).
shuffle (bool, Shuffle level, optional): perform reshuffling of the data every epoch (default=Shuffle.GLOBAL).
shuffle (Union[bool, Shuffle level], optional): perform reshuffling of the data every epoch
(default=Shuffle.GLOBAL).
If shuffle is False, no shuffling will be performed;
If shuffle is True, the behavior is the same as setting shuffle to be Shuffle.GLOBAL
Otherwise, there are two levels of shuffling:
@ -3920,7 +3921,7 @@ class RandomDataset(SourceDataset):
Args:
total_rows (int): number of rows for the dataset to generate (default=None, number of rows is random)
schema (str or Schema, optional): Path to the json schema file or schema object (default=None).
schema (Union[str, Schema], optional): Path to the json schema file or schema object (default=None).
If the schema is not provided, the random dataset generates a random schema.
columns_list (list[str], optional): List of columns to be read (default=None, read all columns)
num_samples (int): number of samples to draw from the total. (default=None, which means all rows)
@ -4097,7 +4098,7 @@ class Schema:
Parse the columns and add it to self.
Args:
columns (dict or list[dict]): dataset attribution information, decoded from schema file.
columns (Union[dict, list[dict]]): dataset attribution information, decoded from schema file.
- list[dict], 'name' and 'type' must be in keys, 'shape' optional.
@ -4710,7 +4711,7 @@ class CLUEDataset(SourceDataset):
}
Args:
dataset_files (str or a list of strings): String or list of files to be read or glob strings to search for
dataset_files (Union[str, list[str]]): String or list of files to be read or glob strings to search for
a pattern of files. The list will be sorted in a lexicographical order.
task (str, optional): The kind of task, one of 'AFQMC', 'TNEWS', 'IFLYTEK', 'CMNLI', 'WSC' and 'CSL'.
(default=AFQMC).
@ -4718,7 +4719,8 @@ class CLUEDataset(SourceDataset):
num_samples (int, optional): number of samples(rows) to read (default=None, reads the full dataset).
num_parallel_workers (int, optional): number of workers to read the data
(default=None, number set in the config).
shuffle (bool, Shuffle level, optional): perform reshuffling of the data every epoch (default=Shuffle.GLOBAL).
shuffle (Union[bool, Shuffle level], optional): perform reshuffling of the data every epoch
(default=Shuffle.GLOBAL).
If shuffle is False, no shuffling will be performed;
If shuffle is True, the behavior is the same as setting shuffle to be Shuffle.GLOBAL
Otherwise, there are two levels of shuffling:
@ -4923,18 +4925,19 @@ class CSVDataset(SourceDataset):
A source dataset that reads and parses CSV datasets.
Args:
dataset_files (str or a list of strings): String or list of files to be read or glob strings to search
dataset_files (Union[str, list[str]]): String or list of files to be read or glob strings to search
for a pattern of files. The list will be sorted in a lexicographical order.
field_delim (str, optional): A string that indicates the char delimiter to separate fields (default=',').
column_defaults (list, optional): List of default values for the CSV field (default=None). Each item
in the list is either a valid type (float, int, or string). If this is not provided, treats all
columns as string type.
column_names (list of string, optional): List of column names of the dataset (default=None). If this
column_names (list[str], optional): List of column names of the dataset (default=None). If this
is not provided, infers the column_names from the first row of CSV file.
num_samples (int, optional): number of samples(rows) to read (default=None, reads the full dataset).
num_parallel_workers (int, optional): number of workers to read the data
(default=None, number set in the config).
shuffle (bool, Shuffle level, optional): perform reshuffling of the data every epoch (default=Shuffle.GLOBAL).
shuffle (Union[bool, Shuffle level], optional): perform reshuffling of the data every epoch
(default=Shuffle.GLOBAL).
If shuffle is False, no shuffling will be performed;
If shuffle is True, the behavior is the same as setting shuffle to be Shuffle.GLOBAL
Otherwise, there are two levels of shuffling:
@ -5026,12 +5029,13 @@ class TextFileDataset(SourceDataset):
The generated dataset has one columns ['text'].
Args:
dataset_files (str or list[str]): String or list of files to be read or glob strings to search for a pattern of
files. The list will be sorted in a lexicographical order.
dataset_files (Union[str, list[str]]): String or list of files to be read or glob strings to search for a
pattern of files. The list will be sorted in a lexicographical order.
num_samples (int, optional): number of samples(rows) to read (default=None, reads the full dataset).
num_parallel_workers (int, optional): number of workers to read the data
(default=None, number set in the config).
shuffle (bool, Shuffle level, optional): perform reshuffling of the data every epoch (default=Shuffle.GLOBAL).
shuffle (Union[bool, Shuffle level], optional): perform reshuffling of the data every epoch
(default=Shuffle.GLOBAL).
If shuffle is False, no shuffling will be performed;
If shuffle is True, the behavior is the same as setting shuffle to be Shuffle.GLOBAL
Otherwise, there are two levels of shuffling:
@ -5212,17 +5216,17 @@ class NumpySlicesDataset(GeneratorDataset):
- not allowed
Args:
data (list, tuple or dict) Input of Given data, supported data type includes list, tuple, dict and other numpy
format. Input data will be sliced in first dimension and generate many rows, large data is not recommend to
load in this way as data is loading into memory.
data (Union[list, tuple, dict]) Input of Given data, supported data type includes list, tuple, dict and other
numpy format. Input data will be sliced in first dimension and generate many rows, large data is not
recommend to load in this way as data is loading into memory.
column_names (list[str], optional): List of column names of the dataset (default=None). If column_names not
provided, when data is dict, column_names will be its key, otherwise it will be like column_1, column_2 ...
num_samples (int, optional): The number of samples to be included in the dataset (default=None, all images).
num_parallel_workers (int, optional): Number of subprocesses used to fetch the dataset in parallel (default=1).
shuffle (bool, optional): Whether or not to perform shuffle on the dataset. Random accessible input is required.
(default=None, expected order behavior shown in the table).
sampler (Sampler/Iterable, optional): Object used to choose samples from the dataset. Random accessible input is
required (default=None, expected order behavior shown in the table).
sampler (Union[Sampler, Iterable], optional): Object used to choose samples from the dataset. Random accessible
input is required (default=None, expected order behavior shown in the table).
num_shards (int, optional): Number of shards that the dataset should be divided into (default=None).
When this argument is specified, 'num_samples' will not effect. Random accessible input is required.
shard_id (int, optional): The shard ID within num_shards (default=None). This argument should be specified only
@ -5263,8 +5267,8 @@ class BuildVocabDataset(DatasetOp):
Args:
vocab(Vocab): text.vocab object.
columns(str or list, optional): column names to get words from. It can be a list of column names (Default is
None, all columns are used, return error if any column isn't string).
columns(Union[str, list], optional): column names to get words from. It can be a list of column names (Default
is None, all columns are used, return error if any column isn't string).
freq_range(tuple, optional): A tuple of integers (min_frequency, max_frequency). Words within the frequency
range would be kept. 0 <= min_frequency <= max_frequency <= total_words. min_frequency/max_frequency
can be None, which corresponds to 0/total_words separately (default=None, all words are included).

20
mindspore/dataset/engine/graphdata.py
View File

@ -91,7 +91,7 @@ class GraphData:
Get nodes from the edges.
Args:
edge_list (list or numpy.ndarray): The given list of edges.
edge_list (Union[list, numpy.ndarray]): The given list of edges.
Returns:
numpy.ndarray: array of nodes.
@ -107,7 +107,7 @@ class GraphData:
Get `neighbor_type` neighbors of the nodes in `node_list`.
Args:
node_list (list or numpy.ndarray): The given list of nodes.
node_list (Union[list, numpy.ndarray]): The given list of nodes.
neighbor_type (int): Specify the type of neighbor.
Returns:
@ -137,9 +137,9 @@ class GraphData:
2-hop samling result ...]
Args:
node_list (list or numpy.ndarray): The given list of nodes.
neighbor_nums (list or numpy.ndarray): Number of neighbors sampled per hop.
neighbor_types (list or numpy.ndarray): Neighbor type sampled per hop.
node_list (Union[list, numpy.ndarray]): The given list of nodes.
neighbor_nums (Union[list, numpy.ndarray]): Number of neighbors sampled per hop.
neighbor_types (Union[list, numpy.ndarray]): Neighbor type sampled per hop.
Returns:
numpy.ndarray: array of nodes.
@ -164,7 +164,7 @@ class GraphData:
Get `neg_neighbor_type` negative sampled neighbors of the nodes in `node_list`.
Args:
node_list (list or numpy.ndarray): The given list of nodes.
node_list (Union[list, numpy.ndarray]): The given list of nodes.
neg_neighbor_num (int): Number of neighbors sampled.
neg_neighbor_type (int): Specify the type of negative neighbor.
@ -191,8 +191,8 @@ class GraphData:
Get `feature_types` feature of the nodes in `node_list`.
Args:
node_list (list or numpy.ndarray): The given list of nodes.
feature_types (list or numpy.ndarray): The given list of feature types.
node_list (Union[list, numpy.ndarray]): The given list of nodes.
feature_types (Union[list, numpy.ndarray]): The given list of feature types.
Returns:
numpy.ndarray: array of features.
@ -220,8 +220,8 @@ class GraphData:
Get `feature_types` feature of the edges in `edge_list`.
Args:
edge_list (list or numpy.ndarray): The given list of edges.
feature_types (list or numpy.ndarray): The given list of feature types.
edge_list (Union[list, numpy.ndarray]): The given list of edges.
feature_types (Union[list, numpy.ndarray]): The given list of feature types.
Returns:
numpy.ndarray: array of features.

4
mindspore/dataset/engine/serializer_deserializer.py
View File

@ -30,7 +30,7 @@ def serialize(dataset, json_filepath=None):
Args:
dataset (Dataset): the starting node.
json_filepath (string): a filepath where a serialized json file will be generated.
json_filepath (str): a filepath where a serialized json file will be generated.
Returns:
dict containing the serialized dataset graph.
@ -63,7 +63,7 @@ def deserialize(input_dict=None, json_filepath=None):
Args:
input_dict (dict): a python dictionary containing a serialized dataset graph
json_filepath (string): a path to the json file.
json_filepath (str): a path to the json file.
Returns:
de.Dataset or None if error occurs.

8
mindspore/dataset/text/transforms.py
View File

@ -108,7 +108,7 @@ class Ngram(cde.NgramOp):
Refer to https://en.wikipedia.org/wiki/N-gram#Examples for an overview of what n-gram is and how it works.
Args:
n (list of int): n in n-gram, n >= 1. n is a list of positive integers, for e.g. n=[4,3], The result
n (list[int]): n in n-gram, n >= 1. n is a list of positive integers, for e.g. n=[4,3], The result
would be a 4-gram followed by a 3-gram in the same tensor. If number of words is not enough to make up for
a n-gram, an empty string would be returned. For e.g. 3 grams on ["mindspore","best"] would result in an
empty string be produced.
@ -199,7 +199,7 @@ class JiebaTokenizer(cde.JiebaTokenizerOp):
Add user defined word to JiebaTokenizer's dictionary.
Args:
user_dict (str or dict): Dictionary to be added, file path or Python dictionary,
user_dict (Union[str, dict]): Dictionary to be added, file path or Python dictionary,
Python Dict format: {word1:freq1, word2:freq2,...}.
Jieba dictionary format : word(required), freq(optional), such as:
@ -339,9 +339,9 @@ class SentencePieceTokenizer(cde.SentencePieceTokenizerOp):
Tokenize scalar token or 1-D tokens to tokens by sentencepiece.
Args:
mode(str or SentencePieceVocab): If the input parameter is a file, then it is of type string,
mode(Union[str, SentencePieceVocab]): If the input parameter is a file, then it is of type string,
if the input parameter is a SentencePieceVocab object, then it is of type SentencePieceVocab.
out_type(str or int): The type of output.
out_type(Union[str, int]): The type of output.
"""
def __init__(self, mode, out_type):

2
mindspore/dataset/text/utils.py
View File

@ -51,7 +51,7 @@ class Vocab(cde.Vocab):
Args:
dataset(Dataset): dataset to build vocab from.
columns(list of str, optional): column names to get words from. It can be a list of column names.
columns(list[str], optional): column names to get words from. It can be a list of column names.
(default=None, where all columns will be used. If any column isn't string type, will return error).
freq_range(tuple, optional): A tuple of integers (min_frequency, max_frequency). Words within the frequency
range would be kept. 0 <= min_frequency <= max_frequency <= total_words. min_frequency=0 is the same as

14
mindspore/dataset/transforms/c_transforms.py
View File

@ -46,7 +46,7 @@ class Fill(cde.FillOp):
The output tensor will have the same shape and type as the input tensor.
Args:
fill_value (python types (str, bytes, int, float, or bool)) : scalar value
fill_value (Union[str, bytes, int, float, bool])) : scalar value
to fill created tensor with.
"""
@ -78,9 +78,9 @@ class Slice(cde.SliceOp):
(Currently only rank-1 tensors are supported).
Args:
slices(Variable length argument list, supported types are, int, list[int], slice, None or Ellipses):
Maximum `n` number of arguments to slice a tensor of rank `n`, one object in slices can be one of:
slices(Union[int, list(int), slice, None, Ellipses]):
Maximum `n` number of arguments to slice a tensor of rank `n`.
One object in slices can be one of:
1. :py:obj:`int`: Slice this index only. Negative index is supported.
2. :py:obj:`list(int)`: Slice these indices ion the list only. Negative indices are supported.
3. :py:obj:`slice`: Slice the generated indices from the slice object. Similar to `start:stop:step`.
@ -139,9 +139,9 @@ class Mask(cde.MaskOp):
Args:
operator (Relational): One of the relational operator EQ, NE LT, GT, LE or GE
constant (python types (str, int, float, or bool): constant to be compared to.
constant (Union[str, int, float, bool]): constant to be compared to.
Constant will be casted to the type of the input tensor
dtype (optional, mindspore.dtype): type of the generated mask. Default to bool
dtype (mindspore.dtype, optional): type of the generated mask. Default to bool
Examples:
>>> # Data before
@ -171,7 +171,7 @@ class PadEnd(cde.PadEndOp):
Args:
pad_shape (list(int)): list on 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 (python types (str, bytes, int, float, or bool), optional): value used to pad. Default to 0 or empty
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:

47
mindspore/dataset/transforms/vision/c_transforms.py
View File

@ -77,7 +77,7 @@ class AutoContrast(cde.AutoContrastOp):
Args:
cutoff (float, optional): Percent of pixels to cut off from the histogram (default=0.0).
ignore (int or sequence, optional): Pixel values to ignore (default=None).
ignore (Union[int, sequence], optional): Pixel values to ignore (default=None).
"""
@check_auto_contrast
@ -151,10 +151,10 @@ class RandomCrop(cde.RandomCropOp):
Crop the input image at a random location.
Args:
size (int or sequence): The output size of the cropped image.
size (Union[int, sequence]): The output size of the cropped image.
If size is an int, a square crop of size (size, size) is returned.
If size is a sequence of length 2, it should be (height, width).
padding (int or sequence, optional): The number of pixels to pad the image (default=None).
padding (Union[int, sequence], optional): The number of pixels to pad the image (default=None).
If padding is not None, pad image firstly with padding values.
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)
@ -163,7 +163,7 @@ class RandomCrop(cde.RandomCropOp):
it pads the left, top, right and bottom respectively.
pad_if_needed (bool, optional): Pad the image if either side is smaller than
the given output size (default=False).
fill_value (int or tuple, optional): The pixel intensity of the borders if
fill_value (Union[int, tuple], optional): 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
@ -206,10 +206,10 @@ class RandomCropWithBBox(cde.RandomCropWithBBoxOp):
Crop the input image at a random location and adjust bounding boxes accordingly.
Args:
size (int or sequence): The output size of the cropped image.
size (Union[int, sequence]): The output size of the cropped image.
If size is an int, a square crop of size (size, size) is returned.
If size is a sequence of length 2, it should be (height, width).
padding (int or sequence, optional): The number of pixels to pad the image (default=None).
padding (Union[int, sequence], optional): The number of pixels to pad the image (default=None).
If padding is not None, pad image firstly with padding values.
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)
@ -217,7 +217,7 @@ class RandomCropWithBBox(cde.RandomCropWithBBoxOp):
If 4 values are provided as a list or tuple,it pads the left, top, right and bottom respectively.
pad_if_needed (bool, optional): Pad the image if either side is smaller than
the given output size (default=False).
fill_value (int or tuple, optional): The pixel intensity of the borders if
fill_value (Union[int, tuple], optional): 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
@ -335,7 +335,7 @@ class Resize(cde.ResizeOp):
Resize the input image to the given size.
Args:
size (int or sequence): The output size of the resized image.
size (Union[int, sequence]): The output size of the resized image.
If size is an int, smaller edge of the image will be resized to this value with
the same image aspect ratio.
If size is a sequence of length 2, it should be (height, width).
@ -365,7 +365,7 @@ class ResizeWithBBox(cde.ResizeWithBBoxOp):
Resize the input image to the given size and adjust bounding boxes accordingly.
Args:
size (int or sequence): The output size of the resized image.
size (Union[int, sequence]): The output size of the resized image.
If size is an int, smaller edge of the image will be resized to this value with
the same image aspect ratio.
If size is a sequence of length 2, it should be (height, width).
@ -395,7 +395,7 @@ class RandomResizedCropWithBBox(cde.RandomCropAndResizeWithBBoxOp):
Crop the input image to a random size and aspect ratio and adjust bounding boxes accordingly.
Args:
size (int or sequence): The size of the output image.
size (Union[int, sequence]): The size of the output image.
If size is an int, a square crop of size (size, size) is returned.
If size is a sequence of length 2, it should be (height, width).
scale (tuple, optional): Range (min, max) of respective size of the original
@ -434,7 +434,7 @@ class RandomResizedCrop(cde.RandomCropAndResizeOp):
Crop the input image to a random size and aspect ratio.
Args:
size (int or sequence): The size of the output image.
size (Union[int, sequence]): The size of the output image.
If size is an int, a square crop of size (size, size) is returned.
If size is a sequence of length 2, it should be (height, width).
scale (tuple, optional): Range (min, max) of respective size of the original
@ -473,7 +473,7 @@ class CenterCrop(cde.CenterCropOp):
Crops the input image at the center to the given size.
Args:
size (int or sequence): The output size of the cropped image.
size (Union[int, sequence]): The output size of the cropped image.
If size is an int, a square crop of size (size, size) is returned.
If size is a sequence of length 2, it should be (height, width).
"""
@ -491,16 +491,16 @@ class RandomColorAdjust(cde.RandomColorAdjustOp):
Randomly adjust the brightness, contrast, saturation, and hue of the input image.
Args:
brightness (float or tuple, optional): Brightness adjustment factor (default=(1, 1)). Cannot be negative.
brightness (Union[float, tuple], optional): Brightness adjustment factor (default=(1, 1)). Cannot be negative.
If it is a float, the factor is uniformly chosen from the range [max(0, 1-brightness), 1+brightness].
If it is a sequence, it should be [min, max] for the range.
contrast (float or tuple, optional): Contrast adjustment factor (default=(1, 1)). Cannot be negative.
contrast (Union[float, tuple], optional): Contrast adjustment factor (default=(1, 1)). Cannot be negative.
If it is a float, the factor is uniformly chosen from the range [max(0, 1-contrast), 1+contrast].
If it is a sequence, it should be [min, max] for the range.
saturation (float or tuple, optional): Saturation adjustment factor (default=(1, 1)). Cannot be negative.
saturation (Union[float, tuple], optional): Saturation adjustment factor (default=(1, 1)). Cannot be negative.
If it is a float, the factor is uniformly chosen from the range [max(0, 1-saturation), 1+saturation].
If it is a sequence, it should be [min, max] for the range.
hue (float or tuple, optional): Hue adjustment factor (default=(0, 0)).
hue (Union[float, tuple], optional): Hue adjustment factor (default=(0, 0)).
If it is a float, the range will be [-hue, hue]. Value should be 0 <= hue <= 0.5.
If it is a sequence, it should be [min, max] where -0.5 <= min <= max <= 0.5.
"""
@ -533,7 +533,7 @@ class RandomRotation(cde.RandomRotationOp):
Rotate the input image by a random angle.
Args:
degrees (int or float or sequence): Range of random rotation degrees.
degrees (Union[int, float, sequence): Range of random rotation degrees.
If degrees is a number, the range will be converted to (-degrees, degrees).
If degrees is a sequence, it should be (min, max).
resample (Inter mode, optional): An optional resampling filter (default=Inter.NEAREST).
@ -552,7 +552,8 @@ class RandomRotation(cde.RandomRotationOp):
Note that the expand flag assumes rotation around the center and no translation.
center (tuple, optional): Optional center of rotation (a 2-tuple) (default=None).
Origin is the top left corner. None sets to the center of the image.
fill_value (int or tuple, optional): Optional fill color for the area outside the rotated image (default=0).
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.
"""
@ -595,7 +596,7 @@ class RandomResize(cde.RandomResizeOp):
Tensor operation to resize the input image using a randomly selected interpolation mode.
Args:
size (int or sequence): The output size of the resized image.
size (Union[int, sequence]): The output size of the resized image.
If size is an int, smaller edge of the image will be resized to this value with
the same image aspect ratio.
If size is a sequence of length 2, it should be (height, width).
@ -616,7 +617,7 @@ class RandomResizeWithBBox(cde.RandomResizeWithBBoxOp):
bounding boxes accordingly.
Args:
size (int or sequence): The output size of the resized image.
size (Union[int, sequence]): The output size of the resized image.
If size is an int, smaller edge of the image will be resized to this value with
the same image aspect ratio.
If size is a sequence of length 2, it should be (height, width).
@ -642,7 +643,7 @@ class RandomCropDecodeResize(cde.RandomCropDecodeResizeOp):
Equivalent to RandomResizedCrop, but crops before decodes.
Args:
size (int or sequence, optional): The size of the output image.
size (Union[int, sequence], optional): The size of the output image.
If size is an int, a square crop of size (size, size) is returned.
If size is a sequence of length 2, it should be (height, width).
scale (tuple, optional): Range (min, max) of respective size of the
@ -681,13 +682,13 @@ class Pad(cde.PadOp):
Pads the image according to padding parameters.
Args:
padding (int or sequence): The number of pixels to pad the image.
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 4 values are provided as a list or tuple,
it pads the left, top, right and bottom respectively.
fill_value (int or tuple, optional): The pixel intensity of the borders if
fill_value (Union[int, tuple], optional): 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): The method of padding (default=Border.CONSTANT). Can be any of

38
mindspore/dataset/transforms/vision/py_transforms.py
View File

@ -100,7 +100,7 @@ class ToTensor:
The range of channel dimension remains the same.
Args:
output_type (numpy datatype, optional): The datatype of the numpy output (default=numpy.float32).
output_type (numpy datatype, optional): The datatype of the numpy output (default=np.float32).
Examples:
>>> py_transforms.ComposeOp([py_transforms.Decode(),
@ -260,10 +260,10 @@ class RandomCrop:
Crop the input PIL Image at a random location.
Args:
size (int or sequence): The output size of the cropped image.
size (Union[int, sequence]): The output size of the cropped image.
If size is an int, a square crop of size (size, size) is returned.
If size is a sequence of length 2, it should be (height, width).
padding (int or sequence, optional): The number of pixels to pad the image (default=None).
padding (Union[int, sequence], optional): The number of pixels to pad the image (default=None).
If padding is not None, pad image firstly with padding values.
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)
@ -385,7 +385,7 @@ class Resize:
Resize the input PIL Image to the given size.
Args:
size (int or sequence): The output size of the resized image.
size (Union[int, sequence]): The output size of the resized image.
If size is an int, smaller edge of the image will be resized to this value with
the same image aspect ratio.
If size is a sequence of length 2, it should be (height, width).
@ -427,7 +427,7 @@ class RandomResizedCrop:
Extract crop from the input image and resize it to a random size and aspect ratio.
Args:
size (int or sequence): The size of the output image.
size (Union[int, sequence]): The size of the output image.
If size is an int, a square crop of size (size, size) is returned.
If size is a sequence of length 2, it should be (height, width).
scale (tuple, optional): Range (min, max) of respective size of the original size
@ -479,7 +479,7 @@ class CenterCrop:
Crop the central reigion of the input PIL Image to the given size.
Args:
size (int or sequence): The output size of the cropped image.
size (Union[int, sequence]): The output size of the cropped image.
If size is an int, a square crop of size (size, size) is returned.
If size is a sequence of length 2, it should be (height, width).
@ -511,16 +511,16 @@ class RandomColorAdjust:
Perform a random brightness, contrast, saturation, and hue adjustment on the input PIL image.
Args:
brightness (float or tuple, optional): Brightness adjustment factor (default=(1, 1)). Cannot be negative.
brightness (Union[float, tuple], optional): Brightness adjustment factor (default=(1, 1)). Cannot be negative.
If it is a float, the factor is uniformly chosen from the range [max(0, 1-brightness), 1+brightness].
If it is a sequence, it should be [min, max] for the range.
contrast (float or tuple, optional): Contrast adjustment factor (default=(1, 1)). Cannot be negative.
contrast (Union[float, tuple], optional): Contrast adjustment factor (default=(1, 1)). Cannot be negative.
If it is a float, the factor is uniformly chosen from the range [max(0, 1-contrast), 1+contrast].
If it is a sequence, it should be [min, max] for the range.
saturation (float or tuple, optional): Saturation adjustment factor (default=(1, 1)). Cannot be negative.
saturation (Union[float, tuple], optional): Saturation adjustment factor (default=(1, 1)). Cannot be negative.
If it is a float, the factor is uniformly chosen from the range [max(0, 1-saturation), 1+saturation].
If it is a sequence, it should be [min, max] for the range.
hue (float or tuple, optional): Hue adjustment factor (default=(0, 0)).
hue (Union[float, tuple], optional): Hue adjustment factor (default=(0, 0)).
If it is a float, the range will be [-hue, hue]. Value should be 0 <= hue <= 0.5.
If it is a sequence, it should be [min, max] where -0.5 <= min <= max <= 0.5.
@ -558,7 +558,7 @@ class RandomRotation:
See https://pillow.readthedocs.io/en/stable/reference/Image.html#PIL.Image.Image.rotate.
Args:
degrees (int or float or sequence): Range of random rotation degrees.
degrees (Union[int, float, sequence]): Range of random rotation degrees.
If degrees is a number, the range will be converted to (-degrees, degrees).
If degrees is a sequence, it should be (min, max).
resample (Inter mode, optional): An optional resampling filter (default=Inter.NEAREST).
@ -743,7 +743,7 @@ class TenCrop:
Generate 10 cropped images (first 5 from FiveCrop, second 5 from their flipped version).
Args:
size (int or sequence): The output size of the crop.
size (Union[int, sequence]): The output size of the crop.
If size is an int, a square crop of size (size, size) is returned.
If size is a sequence of length 2, it should be (height, width).
use_vertical_flip (bool, optional): Flip the image vertically instead of horizontally
@ -853,13 +853,13 @@ class Pad:
Pad the input PIL image according to padding parameters.
Args:
padding (int or sequence): The number of pixels to pad the image.
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 4 values are provided as a list or tuple,
it pads the left, top, right and bottom respectively.
fill_value (int or tuple, optional): Filling value (default=0). The pixel intensity
fill_value (Union[int, tuple], optional): Filling value (default=0). The pixel intensity
of the borders if the padding_mode is Border.CONSTANT.
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).
@ -961,7 +961,7 @@ 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 (int or sequence): Erasing value (default=0).
value (Union[int, sequence]): Erasing value (default=0).
If value is a single int, it is applied to all pixels to be erases.
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.
@ -1088,7 +1088,7 @@ class RandomAffine:
Apply Random affine transformation to the input PIL image.
Args:
degrees (int or float or sequence): Range of the rotation degrees.
degrees (Union[int, float, sequence]): Range of the rotation degrees.
If degrees is a number, the range will be (-degrees, degrees).
If degrees is a sequence, it should be (min, max).
translate (sequence, optional): Sequence (tx, ty) of maximum translation in
@ -1097,7 +1097,7 @@ class RandomAffine:
(-tx*width, tx*width) and (-ty*height, ty*height), respectively.
If None, no translations gets 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).
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.
@ -1114,7 +1114,7 @@ class RandomAffine:
- Inter.BICUBIC, means resample method is bicubic interpolation.
fill_value (tuple or int, optional): Optional fill_value to fill the area outside the transform
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 (default=0, filling is performed).
Raises:
@ -1363,7 +1363,7 @@ class AutoContrast:
Args:
cutoff (float, optional): Percent of pixels to cut off from the histogram (default=0.0).
ignore (int or sequence, optional): Pixel values to ignore (default=None).
ignore (Union[int, sequence], optional): Pixel values to ignore (default=None).
Examples:
>>> py_transforms.ComposeOp([py_transforms.Decode(),

50
mindspore/dataset/transforms/vision/py_transforms_util.py
View File

@ -148,7 +148,7 @@ def to_tensor(img, output_type):
Change the input image (PIL Image or Numpy image array) to numpy format.
Args:
img (PIL Image or numpy.ndarray): Image to be converted.
img (Union[PIL Image, numpy.ndarray]): Image to be converted.
output_type: The datatype of the numpy output. e.g. np.float32
Returns:
@ -284,7 +284,7 @@ def resize(img, size, interpolation=Inter.BILINEAR):
Args:
img (PIL Image): Image to be resized.
size (int or sequence): The output size of the resized image.
size (Union[int, sequence]): The output size of the resized image.
If size is an int, smaller edge of the image will be resized to this value with
the same image aspect ratio.
If size is a sequence of (height, width), this will be the desired output size.
@ -321,7 +321,7 @@ def center_crop(img, size):
Args:
img (PIL Image): Image to be cropped.
size (int or tuple): The size of the crop box.
size (Union[int, tuple]): The size of the crop box.
If size is an int, a square crop of size (size, size) is returned.
If size is a sequence of length 2, it should be (height, width).
@ -346,7 +346,7 @@ def random_resize_crop(img, size, scale, ratio, interpolation=Inter.BILINEAR, ma
Args:
img (PIL Image): Image to be randomly cropped and resized.
size (int or sequence): The size of the output image.
size (Union[int, sequence]): The size of the output image.
If size is an int, a square crop of size (size, size) is returned.
If size is a sequence of length 2, it should be (height, width).
scale (tuple): Range (min, max) of respective size of the original size to be cropped.
@ -416,10 +416,10 @@ def random_crop(img, size, padding, pad_if_needed, fill_value, padding_mode):
Args:
img (PIL Image): Image to be randomly cropped.
size (int or sequence): The output size of the cropped image.
size (Union[int, sequence]): The output size of the cropped image.
If size is an int, a square crop of size (size, size) is returned.
If size is a sequence of length 2, it should be (height, width).
padding (int or sequence, optional): The number of pixels to pad the image.
padding (Union[int, sequence], optional): 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.
@ -428,7 +428,7 @@ def random_crop(img, size, padding, pad_if_needed, fill_value, padding_mode):
Default is None.
pad_if_needed (bool): Pad the image if either side is smaller than
the given output size. Default is False.
fill_value (int or tuple): The pixel intensity of the borders if
fill_value (Union[int, tuple]): The pixel intensity of the borders if
the padding_mode is 'constant'. If it is a 3-tuple, it is used to
fill R, G, B channels respectively.
padding_mode (str): The method of padding. Can be any of
@ -602,7 +602,7 @@ def rotate(img, angle, resample, expand, center, fill_value):
Args:
img (PIL Image): Image to be rotated.
angle (int or float): Rotation angle in degrees, counter-clockwise.
resample (Inter.NEAREST, or Inter.BILINEAR, Inter.BICUBIC, optional): An optional resampling filter.
resample (Union[Inter.NEAREST, Inter.BILINEAR, Inter.BICUBIC], optional): An optional resampling filter.
If omitted, or if the image has mode "1" or "P", it is set to be Inter.NEAREST.
expand (bool, optional): Optional expansion flag. If set to True, expand the output
image to make it large enough to hold the entire rotated image.
@ -610,7 +610,7 @@ def rotate(img, angle, resample, expand, center, fill_value):
Note that the expand flag assumes rotation around the center and no translation.
center (tuple, optional): Optional center of rotation (a 2-tuple).
Origin is the top left corner.
fill_value (int or tuple): Optional fill color for the area outside the rotated image.
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.
@ -634,16 +634,16 @@ def random_color_adjust(img, brightness, contrast, saturation, hue):
Args:
img (PIL Image): Image to have its color adjusted randomly.
brightness (float or tuple): Brightness adjustment factor. Cannot be negative.
brightness (Union[float, tuple]): Brightness adjustment factor. Cannot be negative.
If it is a float, the factor is uniformly chosen from the range [max(0, 1-brightness), 1+brightness].
If it is a sequence, it should be [min, max] for the range.
contrast (float or tuple): Contrast adjustment factor. Cannot be negative.
contrast (Union[float, tuple]): Contrast adjustment factor. Cannot be negative.
If it is a float, the factor is uniformly chosen from the range [max(0, 1-contrast), 1+contrast].
If it is a sequence, it should be [min, max] for the range.
saturation (float or tuple): Saturation adjustment factor. Cannot be negative.
saturation (Union[float, tuple]): Saturation adjustment factor. Cannot be negative.
If it is a float, the factor is uniformly chosen from the range [max(0, 1-saturation), 1+saturation].
If it is a sequence, it should be [min, max] for the range.
hue (float or tuple): Hue adjustment factor.
hue (Union[float, tuple]): Hue adjustment factor.
If it is a float, the range will be [-hue, hue]. Value should be 0 <= hue <= 0.5.
If it is a sequence, it should be [min, max] where -0.5 <= min <= max <= 0.5.
@ -696,10 +696,10 @@ def random_rotation(img, degrees, resample, expand, center, fill_value):
Args:
img (PIL Image): Image to be rotated.
degrees (int or float or sequence): Range of random rotation degrees.
degrees (Union[int, float, sequence]): Range of random rotation degrees.
If degrees is a number, the range will be converted to (-degrees, degrees).
If degrees is a sequence, it should be (min, max).
resample (Inter.NEAREST, or Inter.BILINEAR, Inter.BICUBIC, optional): An optional resampling filter.
resample (Union[Inter.NEAREST, Inter.BILINEAR, Inter.BICUBIC], optional): An optional resampling filter.
If omitted, or if the image has mode "1" or "P", it is set to be Inter.NEAREST.
expand (bool, optional): Optional expansion flag. If set to True, expand the output
image to make it large enough to hold the entire rotated image.
@ -707,7 +707,7 @@ def random_rotation(img, degrees, resample, expand, center, fill_value):
Note that the expand flag assumes rotation around the center and no translation.
center (tuple, optional): Optional center of rotation (a 2-tuple).
Origin is the top left corner.
fill_value (int or tuple): Optional fill color for the area outside the rotated image.
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.
@ -789,7 +789,7 @@ def five_crop(img, size):
Args:
img (PIL Image): PIL Image to be cropped.
size (int or sequence): The output size of the crop.
size (Union[int, sequence]): The output size of the crop.
If size is an int, a square crop of size (size, size) is returned.
If size is a sequence of length 2, it should be (height, width).
@ -829,7 +829,7 @@ def ten_crop(img, size, use_vertical_flip=False):
Args:
img (PIL Image): PIL Image to be cropped.
size (int or sequence): The output size of the crop.
size (Union[int, sequence]): The output size of the crop.
If size is an int, a square crop of size (size, size) is returned.
If size is a sequence of length 2, it should be (height, width).
use_vertical_flip (bool): Flip the image vertically instead of horizontally if set to True.
@ -895,14 +895,14 @@ def pad(img, padding, fill_value, padding_mode):
Args:
img (PIL Image): Image to be padded.
padding (int or sequence, optional): The number of pixels to pad the image.
padding (Union[int, sequence], optional): 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 4 values are provided as a list or tuple,
it pads the left, top, right and bottom respectively.
Default is None.
fill_value (int or tuple): The pixel intensity of the borders if
fill_value (Union[int, tuple]): The pixel intensity of the borders if
the padding_mode is "constant". If it is a 3-tuple, it is used to
fill R, G, B channels respectively.
padding_mode (str): The method of padding. Can be any of
@ -1137,12 +1137,12 @@ def random_affine(img, angle, translations, scale, shear, resample, fill_value=0
Args:
img (PIL Image): Image to be applied affine transformation.
angle (int or float): Rotation angle in degrees, clockwise.
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 (float or sequence): Shear amount parallel to x and y axis.
resample (Inter.NEAREST, or Inter.BILINEAR, Inter.BICUBIC, optional): An optional resampling filter.
fill_value (tuple or int, optional): Optional fill_value to fill the area outside the transform
shear (Union[float, sequence]): Shear amount parallel to x 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.
If None, no filling is performed.
@ -1465,7 +1465,7 @@ def auto_contrast(img, cutoff, ignore):
Args:
img (PIL Image): Image to be augmented with AutoContrast.
cutoff (float, optional): Percent of pixels to cut off from the histogram (default=0.0).
ignore (int or sequence, optional): Pixel values to ignore (default=None).
ignore (Union[int, sequence], optional): Pixel values to ignore (default=None).
Returns:
img (PIL Image), Augmented image.