mindspore.dataset.EnWik9Dataset

class mindspore.dataset.EnWik9Dataset(dataset_dir, num_samples=None, num_parallel_workers=None, shuffle=True, num_shards=None, shard_id=None, cache=None)[源代码]

A source dataset that reads and parses EnWik9 dataset.

The generated dataset has one column [text] with type string.

Parameters

dataset_dir (str) – Path to the root directory that contains the dataset.
num_samples (int, optional) – The number of samples to be included in the dataset (default=None, will include all samples).
num_parallel_workers (int, optional) – Number of workers to read the data (default=None, number set in the config).
shuffle (Union[bool, Shuffle], optional) –
Perform reshuffling of the data every epoch (default=True). Bool type and Shuffle enum are both supported to pass in. If shuffle is False, no shuffling will be performed. If shuffle is True, performs global shuffle. There are three levels of shuffling, desired shuffle enum defined by mindspore.dataset.Shuffle.
- Shuffle.GLOBAL: Shuffle both the files and samples, same as setting shuffle to True.
- Shuffle.FILES: Shuffle files only.
num_shards (int, optional) – Number of shards that the dataset will be divided into (default=None). When this argument is specified, num_samples reflects the maximum sample number of per shard.
shard_id (int, optional) – The shard ID within num_shards (default=None). This argument can only be specified when num_shards is also specified.
cache (DatasetCache, optional) – Use tensor caching service to speed up dataset processing. More details: Single-Node Data Cache (default=None, which means no cache is used).

Examples

>>> en_wik9_dataset_dir = "/path/to/en_wik9_dataset"
>>> dataset2 = ds.EnWik9Dataset(dataset_dir=en_wik9_dataset_dir, num_samples=2,
...                             shuffle=True)

About EnWik9 dataset:

The data of EnWik9 is UTF-8 encoded XML consisting primarily of English text. It contains 243,426 article titles, of which 85,560 are #REDIRECT to fix broken links, and the rest are regular articles.

The data is UTF-8 clean. All characters are in the range U’0000 to U’10FFFF with valid encodings of 1 to 4 bytes. The byte values 0xC0, 0xC1, and 0xF5-0xFF never occur. Also, in the Wikipedia dumps, there are no control characters in the range 0x00-0x1F except for 0x09 (tab) and 0x0A (linefeed). Linebreaks occur only on paragraph boundaries, so they always have a semantic purpose.

You can unzip the dataset files into the following directory structure and read by MindSpore’s API.

.
└── EnWik9
     ├── enwik9

Citation:

@NetworkResource{Hutter_prize,
author    = {English Wikipedia},
url       = "https://cs.fit.edu/~mmahoney/compression/textdata.html",
month     = {March},
year      = {2006}
}

apply(apply_func)

Apply a function in this dataset.

Parameters: apply_func (function) – A function that must take one Dataset as an argument and return a preprocessed Dataset.
Returns: Dataset, dataset applied by the function.

Examples

>>> # dataset is an instance object of Dataset
>>>
>>> # Declare an apply_func function which returns a Dataset object
>>> def apply_func(data):
...     data = data.batch(2)
...     return data
>>>
>>> # Use apply to call apply_func
>>> dataset = dataset.apply(apply_func)

Raises

TypeError – If apply_func is not a function.
TypeError – If apply_func doesn’t return a Dataset.

batch(batch_size, drop_remainder=False, num_parallel_workers=None, per_batch_map=None, input_columns=None, output_columns=None, column_order=None, pad_info=None, python_multiprocessing=False, max_rowsize=16)

Combine batch_size number of consecutive rows into batches.

For any column, all the elements within that column must have the same shape. If a per_batch_map callable is provided, it will be applied to the batches of tensors.

Note

The order of using repeat and batch reflects the number of batches and per_batch_map. It is recommended that the repeat operation applied after the batch operation finished.

Parameters

batch_size (Union[int, Callable]) – The number of rows each batch is created with. An int or callable object which takes exactly 1 parameter, BatchInfo.
drop_remainder (bool, optional) – Determines whether or not to drop the last block whose data row number is less than batch size (default=False). If True, and if there are less than batch_size rows available to make the last batch, then those rows will be dropped and not propagated to the child node.
num_parallel_workers (int, optional) – Number of workers(threads) to process the dataset in parallel (default=None).
per_batch_map (Callable[[List[numpy.ndarray], ..., List[numpy.ndarray], BatchInfo], (List[numpy.ndarray], ..., List[numpy.ndarray])], optional) – Per batch map callable (default=None). A callable which takes (List[numpy.ndarray], …, List[numpy.ndarray], BatchInfo) as input parameters. Each list[numpy.ndarray] represents a batch of numpy.ndarray on a given column. The number of lists should match with the number of entries in input_columns. The last parameter of the callable should always be a BatchInfo object. Per_batch_map should return (list[numpy.ndarray], list[numpy.ndarray], …). The length of each list in output should be the same as the input. output_columns is required if the number of output lists is different from input.
input_columns (Union[str, list[str]], optional) – List of names of the input columns. The size of the list should match with signature of per_batch_map callable (default=None).
output_columns (Union[str, list[str]], optional) – List of names assigned to the columns outputted by the last operation. This parameter is mandatory if len(input_columns) != len(output_columns). The size of this list must match the number of output columns of the last operation. (default=None, output columns will have the same name as the input columns, i.e., the columns will be replaced).
column_order (Union[str, list[str]], optional) – Specifies the list of all the columns you need in the whole dataset (default=None). The parameter is required when len(input_column) != len(output_column). Caution: the list here is not just the columns specified in parameter input_columns and output_columns.
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 (default=None).
python_multiprocessing (bool, optional) – Parallelize Python function per_batch_map with multi-processing. This option could be beneficial if the function is computational heavy (default=False).
max_rowsize (int, optional) – Maximum size of row in MB that is used for shared memory allocation to copy data between processes. This is only used if python_multiprocessing is set to True (default=16).

Returns

BatchDataset, dataset batched.

Examples

>>> # 1) Create a dataset where every 100 rows are combined into a batch
>>> # and drops the last incomplete batch if there is one.
>>> dataset = dataset.batch(100, True)
>>>
>>> # 2）resize image according to its batch number, if it's 5-th batch, resize to (5^2, 5^2) = (25, 25)
>>> def np_resize(col, BatchInfo):
...     output = col.copy()
...     s = (BatchInfo.get_batch_num() + 1) ** 2
...     index = 0
...     for c in col:
...         img = Image.fromarray(c.astype('uint8')).convert('RGB')
...         img = img.resize((s, s))
...         output[index] = np.array(img)
...         index += 1
...     return (output,)
>>> dataset = dataset.batch(batch_size=8, input_columns=["image"], per_batch_map=np_resize)
>>>
>>> # 3）Create a dataset where its batch size is dynamic
>>> # Define a callable batch size function and let batch size increase 1 each time.
>>> def add_one(BatchInfo):
...     return BatchInfo.get_batch_num() + 1
>>> dataset = dataset.batch(batch_size=add_one, drop_remainder=True)
>>>
>>> # 4）Create a dataset with batch, then specify the column order.
>>> # Assume that the original coulmn order is ["image", "label"] and change to ["label", "image"].
>>> dataset = dataset.batch(32, column_order=["label", "image"])

bucket_batch_by_length(column_names, bucket_boundaries, bucket_batch_sizes, element_length_function=None, pad_info=None, pad_to_bucket_boundary=False, drop_remainder=False)

Bucket elements according to their lengths. Each bucket will be padded and batched when they are full.

A length function is called on each row in the dataset. The row is then bucketed based on its length and bucket boundaries. When a bucket reaches its corresponding size specified in bucket_batch_sizes, the entire bucket will be padded according to pad_info, and then form a batch.

Refer to the following figure for the execution process:

../../_images/bucket_batch_by_length_en.png

Parameters

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-1, and the last bucket for [bucket_boundaries[n-1], inf).
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 M arguments where M = len(column_names) and returns an integer. If no value provided, parameter M the len(column_names) must be 1, and the size of the first dimension of that column will be taken as the length (default=None).
pad_info (dict, optional) – The information about how to batch each column. The key corresponds to the column name, and the value must be a tuple of 2 elements. The first element corresponds to the shape to pad to, and the second element corresponds to the value to pad with. If a column is not specified, then that column will be padded to the longest in the current batch, and 0 will be used as the padding value. Any None dimensions will be padded to the longest in the current batch, unless if pad_to_bucket_boundary is True. If no padding is wanted, set pad_info to None (default=None).
pad_to_bucket_boundary (bool, optional) – If True, will pad each None dimension in pad_info to the bucket_boundary minus 1. If there are any elements that fall into the last bucket, an error will occur (default=False).
drop_remainder (bool, optional) – If True, will drop the last batch for each bucket if it is not a full batch (default=False).

Returns

Dataset, dataset bucketed and batched by length.

Examples

>>> # Create a dataset where certain counts rows are combined into a batch
>>> # and drops the last incomplete batch if there is one.
>>> import numpy as np
>>> def generate_2_columns(n):
...     for i in range(n):
...         yield (np.array([i]), np.array([j for j in range(i + 1)]))
>>>
>>> column_names = ["col1", "col2"]
>>> dataset = ds.GeneratorDataset(generate_2_columns(8), column_names)
>>> bucket_boundaries = [5, 10]
>>> bucket_batch_sizes = [2, 1, 1]
>>> element_length_function = (lambda col1, col2: max(len(col1), len(col2)))
>>> # Will pad col2 to shape [bucket_boundaries[i]] where i is the
>>> # index of the bucket that is currently being batched.
>>> pad_info = {"col2": ([None], -1)}
>>> pad_to_bucket_boundary = True
>>> dataset = dataset.bucket_batch_by_length(column_names, bucket_boundaries,
...                                          bucket_batch_sizes,
...                                          element_length_function, pad_info,
...                                          pad_to_bucket_boundary)

build_sentencepiece_vocab(columns, vocab_size, character_coverage, model_type, params)

Function to create a SentencePieceVocab from source dataset. Desired source dataset is a text type dataset.

Parameters

columns (list[str]) – Column names to get words from.
vocab_size (int) – Vocabulary size.
character_coverage (float) – Percentage of characters covered by the model, must be between 0.98 and 1.0 Good defaults are: 0.9995 for languages with rich character sets like Japanese or Chinese character sets, and 1.0 for other languages with small character sets like English or Latin.
model_type (SentencePieceModel) – Model type. Choose from unigram (default), bpe, char, or word. The input sentence must be pretokenized when using word type.
params (dict) – Any extra optional parameters of sentencepiece library according to your raw data

Returns

SentencePieceVocab, vocab built from the dataset.

Examples

>>> from mindspore.dataset.text import SentencePieceModel
>>>
>>> # You can construct any text dataset as source, take TextFileDataset as example.
>>> dataset = ds.TextFileDataset("/path/to/sentence/piece/vocab/file", shuffle=False)
>>> dataset = dataset.build_sentencepiece_vocab(["text"], 5000, 0.9995, SentencePieceModel.UNIGRAM, {})

build_vocab(columns, freq_range, top_k, special_tokens, special_first)

Function to create a Vocab from source dataset. Desired source dataset is a text type dataset.

Build a vocab from a dataset. This would collect all the unique words in a dataset and return a vocab which contains top_k most frequent words (if top_k is specified).

Parameters

columns (Union[str, list[str]]) – Column names to get words from.
freq_range (tuple[int]) – A tuple of integers (min_frequency, max_frequency). Words within the frequency range will be stored. Naturally 0 <= min_frequency <= max_frequency <= total_words. min_frequency/max_frequency can be set to default, which corresponds to 0/total_words separately.
top_k (int) – Number of words to be built into vocab. top_k most frequent words are taken. The top_k is taken after freq_range. If not enough top_k, all words will be taken
special_tokens (list[str]) – A list of strings, each one is a special token.
special_first (bool) – Whether special_tokens will be prepended/appended to vocab, If special_tokens is specified and special_first is set to default, special_tokens will be prepended.

Returns

Vocab, vocab built from the dataset.

Examples

>>> import numpy as np
>>>
>>> def gen_corpus():
...     # key: word, value: number of occurrences, reason for using letters is so their order is apparent
...     corpus = {"Z": 4, "Y": 4, "X": 4, "W": 3, "U": 3, "V": 2, "T": 1}
...     for k, v in corpus.items():
...         yield (np.array([k] * v, dtype='S'),)
>>> column_names = ["column1"]
>>> dataset = ds.GeneratorDataset(gen_corpus, column_names)
>>> dataset = dataset.build_vocab(columns=["column1"],
...                               freq_range=(1, 10), top_k=5,
...                               special_tokens=["<pad>", "<unk>"],
...                               special_first=True)

close_pool(): Close multiprocessing pool in dataset. If you are familiar with multiprocessing library, you can regard this as a destructor for a processingPool object.

Note

This interface will be deleted or invisible in the future. Please don’t use it. When you find that there are residual processes that do not exit correctly, you can use kill -9 PROCESS_ID to end it, or through www.gitee.com/mindspore/mindspore send us an issue.

concat(datasets)

Concatenate the dataset objects in the input list. Performing “+” operation on dataset objects can achieve the same effect.

Note

The column name, and rank and type of the column data must be the same in the input datasets.

Parameters: datasets (Union[list, Dataset]) – A list of datasets or a single class Dataset to be concatenated together with this dataset.
Returns: Dataset, dataset concatenated.

Examples

>>> # Create a dataset by concatenating dataset_1 and dataset_2 with "+" operator
>>> dataset = dataset_1 + dataset_2
>>> # Create a dataset by concatenating dataset_1 and dataset_2 with concat operation
>>> dataset = dataset_1.concat(dataset_2)

create_dict_iterator(num_epochs=- 1, output_numpy=False)

Create an iterator over the dataset. The data retrieved will be a dictionary datatype.

Parameters

num_epochs (int, optional) – Maximum number of epochs that iterator can be iterated (default=-1, iterator can be iterated infinite number of epochs).
output_numpy (bool, optional) – Whether or not to output NumPy datatype, if output_numpy=False, iterator will output MSTensor (default=False).

Returns

Iterator, dictionary iterator over the dataset.

Examples

>>> # dataset is an instance object of Dataset
>>> iterator = dataset.create_dict_iterator()
>>> for item in iterator:
...     # item is a dict
...     print(type(item))
...     break
<class 'dict'>

create_tuple_iterator(columns=None, num_epochs=- 1, output_numpy=False, do_copy=True)

Create an iterator over the dataset. The datatype retrieved back will be a list of numpy.ndarray.

To specify which columns to list and the order needed, use columns_list. If columns_list is not provided, the order of the columns will remain unchanged.

Parameters

columns (list[str], optional) – List of columns to be used to specify the order of columns (default=None, means all columns).
num_epochs (int, optional) – Maximum number of epochs that iterator can be iterated. (default=-1, iterator can be iterated infinite number of epochs)
output_numpy (bool, optional) – Whether or not to output NumPy datatype. If output_numpy=False, iterator will output MSTensor (default=False).
do_copy (bool, optional) – when output data type is mindspore.Tensor, use this param to select the conversion method, only take False for better performance (default=True).

Returns

Iterator, tuple iterator over the dataset.

Examples

>>> # dataset is an instance object of Dataset
>>> iterator = dataset.create_tuple_iterator()
>>> for item in iterator:
...     # item is a list
...     print(type(item))
...     break
<class 'list'>

device_que(send_epoch_end=True, create_data_info_queue=False)

Return a transferred Dataset that transfers data through a device.

Parameters

send_epoch_end (bool, optional) – Whether to send end of sequence to device or not (default=True).
create_data_info_queue (bool, optional) – Whether to create queue which stores types and shapes of data or not(default=False).

Note

If device is Ascend, features of data will be transferred one by one. The limitation of data transmission per time is 256M.

Returns: Dataset, dataset for transferring.

dynamic_min_max_shapes()

Get minimum and maximum data length of dynamic source data, for dynamic graph compilation.

Returns: lists, min_shapes, max_shapes of source data.

Examples

>>> import numpy as np
>>>
>>> def generator1():
...     for i in range(1, 100):
...         yield np.ones((16, i, 83)), np.array(i)
>>>
>>> dataset = ds.GeneratorDataset(generator1, ["data1", "data2"])
>>> dataset.set_dynamic_columns(columns={"data1": [16, None, 83], "data2": []})
>>> min_shapes, max_shapes = dataset.dynamic_min_max_shapes()

filter(predicate, input_columns=None, num_parallel_workers=None)

Filter dataset by prediction.

Parameters

predicate (callable) – Python callable which returns a boolean value. If False then filter the element.
input_columns (Union[str, list[str]], optional) – List of names of the input columns. If not provided or provided with None, the predicate will be applied on all columns in the dataset (default=None).
num_parallel_workers (int, optional) – Number of workers to process the dataset in parallel (default=None).

Returns

Dataset, dataset filtered.

Examples

>>> # generator data(0 ~ 63)
>>> # filter the data that greater than or equal to 11
>>> dataset = dataset.filter(predicate=lambda data: data < 11, input_columns = ["data"])

flat_map(func)

Map func to each row in dataset and flatten the result.

Parameters: func (function) – A function that must take one numpy.ndarray as an argument and return a Dataset.
Returns: Dataset, dataset applied by the function.

Examples

>>> # 1) flat_map on one column dataset
>>> dataset = ds.NumpySlicesDataset([[0, 1], [2, 3]], shuffle=False)
>>>
>>> def repeat(array):
...     # create a NumpySlicesDataset with the array
...     data = ds.NumpySlicesDataset(array, shuffle=False)
...     # repeat the dataset twice
...     data = data.repeat(2)
...     return data
>>>
>>> dataset = dataset.flat_map(repeat)
>>> # [0, 1, 0, 1, 2, 3, 2, 3]
>>>
>>> # 2) flat_map on multi column dataset
>>> dataset = ds.NumpySlicesDataset(([[0, 1], [2, 3]], [[0, -1], [-2, -3]]), shuffle=False)
>>>
>>> def plus_and_minus(col1, col2):
...     # apply different methods on columns
...     data = ds.NumpySlicesDataset((col1 + 1, col2 - 1), shuffle=False)
...     return data
>>>
>>> dataset = dataset.flat_map(plus_and_minus)
>>> # ([1, 2, 3, 4], [-1, -2, -3, -4])

Raises

TypeError – If func is not a function.
TypeError – If func doesn’t return a Dataset.

get_batch_size()

Return the size of batch.

Returns: int, the number of data in a batch.

Examples

>>> # dataset is an instance object of Dataset
>>> batch_size = dataset.get_batch_size()

get_class_indexing()

Return the class index.

Returns: dict, a str-to-int mapping from label name to index. dict, a str-to-list<int> mapping from label name to index for Coco ONLY. The second number in the list is used to indicate the super category.

Examples

>>> # dataset is an instance object of Dataset
>>> class_indexing = dataset.get_class_indexing()

get_col_names()

Return the names of the columns in dataset.

Returns: list, list of column names in the dataset.

Examples

>>> # dataset is an instance object of Dataset
>>> col_names = dataset.get_col_names()

get_dataset_size()

Return the number of batches in an epoch.

Returns: int, number of batches.

Examples

>>> # dataset is an instance object of Dataset
>>> dataset_size = dataset.get_dataset_size()

get_repeat_count()

Get the replication times in RepeatDataset (default is 1).

Returns: int, the count of repeat.

Examples

>>> # dataset is an instance object of Dataset
>>> repeat_count = dataset.get_repeat_count()

property input_indexs

Get the column index, which represents the corresponding relationship between the data column order and the network when using the sink mode.

Returns: int, tuple of the input index information.

Examples

>>> # dataset is an instance object of Dataset
>>> # set input_indexs
>>> dataset.input_indexs = 10
>>> print(dataset.input_indexs)
10

map(operations, input_columns=None, output_columns=None, column_order=None, num_parallel_workers=None, python_multiprocessing=False, cache=None, callbacks=None, max_rowsize=16, offload=None)

Apply each operation in operations to this dataset.

Each operation will be passed one or more columns from the dataset as input, and one or more columns will be outputted. The first operation will be passed the columns specified in input_columns as input. If there is more than one operator in operations, the outputted columns of the previous operation are used as the input columns for the next operation.

The columns outputted by the very last operation will be assigned names specified by output_columns, and if not specified, the column name of output column is same as that of input_columns.

Parameters

operations (Union[list[TensorOperation], list[functions]]) – List of operations to be applied on the dataset. Operations are applied in the order they appear in this list.
input_columns (Union[str, list[str]], optional) – List of the names of the columns that will be passed to the first operation as input. The size of this list must match the number of input columns expected by the first operator. (default=None, the first operation will be passed however many columns that are required, starting from the first column).
output_columns (Union[str, list[str]], optional) – List of names assigned to the columns outputted by the last operation. This parameter is mandatory if len(input_columns) != len(output_columns). The size of this list must match the number of output columns of the last operation. (default=None, output columns will have the same name as the input columns, i.e., the columns will be replaced).
column_order (Union[str, list[str]], optional) – Specifies the list of all the columns you need in the whole dataset (default=None). The parameter is required when len(input_column) != len(output_column). Caution: the list here is not just the columns specified in parameter input_columns and output_columns.
num_parallel_workers (int, optional) – Number of threads used to process the dataset in parallel (default=None, the value from the configuration will be used).
python_multiprocessing (bool, optional) – Parallelize Python operations with multiple worker processes. This option could be beneficial if the Python operation is computational heavy (default=False).
cache (DatasetCache, optional) – Use tensor caching service to speed up dataset processing. (default=None, which means no cache is used).
callbacks (DSCallback, list[DSCallback], optional) – List of Dataset callbacks to be called (Default=None).
max_rowsize (int, optional) – Maximum size of row in MB that is used for shared memory allocation to copy data between processes. This is only used if python_multiprocessing is set to True (Default=16).
offload (bool, optional) – Flag to indicate whether offload is used (Default=None).

Note

Input operations accepts TensorOperations defined in mindspore.dataset part, plus user-defined Python functions (PyFuncs).
Do not add network computing operators from mindspore.nn and mindspore.ops or others into this operations.

Returns: Dataset, dataset after mapping operation.

Examples

>>> # dataset is an instance of Dataset which has 2 columns, "image" and "label".
>>>
>>> # Define two operations, where each operation accepts 1 input column and outputs 1 column.
>>> decode_op = c_vision.Decode(rgb=True)
>>> random_jitter_op = c_vision.RandomColorAdjust(brightness=(0.8, 0.8), contrast=(1, 1),
...                                               saturation=(1, 1), hue=(0, 0))
>>>
>>> # 1) Simple map example.
>>>
>>> # Apply decode_op on column "image". This column will be replaced by the outputted
>>> # column of decode_op. Since column_order is not provided, both columns "image"
>>> # and "label" will be propagated to the child node in their original order.
>>> dataset = dataset.map(operations=[decode_op], input_columns=["image"])
>>>
>>> # Decode and rename column "image" to "decoded_image".
>>> dataset = dataset.map(operations=[decode_op], input_columns=["image"], output_columns=["decoded_image"])
>>>
>>> # Specify the order of the output columns.
>>> dataset = dataset.map(operations=[decode_op], input_columns=["image"],
...                       output_columns=None, column_order=["label", "image"])
>>>
>>> # Rename column "image" to "decoded_image" and also specify the order of the output columns.
>>> dataset = dataset.map(operations=[decode_op], input_columns=["image"],
...                       output_columns=["decoded_image"], column_order=["label", "decoded_image"])
>>>
>>> # Rename column "image" to "decoded_image" and keep only this column.
>>> dataset = dataset.map(operations=[decode_op], input_columns=["image"],
...                       output_columns=["decoded_image"], column_order=["decoded_image"])
>>>
>>> # A simple example for mapping pyfunc. Renaming columns and specifying column order
>>> # work in the same way as the previous examples.
>>> dataset = ds.NumpySlicesDataset(data=[[0, 1, 2]], column_names=["data"])
>>> dataset = dataset.map(operations=[(lambda x: x + 1)], input_columns=["data"])
>>>
>>> # 2) Map example with more than one operation.
>>>
>>> # Create a dataset where the images are decoded, then randomly color jittered.
>>> # decode_op takes column "image" as input and outputs one column. The column
>>> # outputted by decode_op is passed as input to random_jitter_op.
>>> # random_jitter_op will output one column. Column "image" will be replaced by
>>> # the column outputted by random_jitter_op (the very last operation). All other
>>> # columns are unchanged. Since column_order is not specified, the order of the
>>> # columns will remain the same.
>>> dataset = dataset.map(operations=[decode_op, random_jitter_op], input_columns=["image"])
>>>
>>> # Rename the column outputted by random_jitter_op to "image_mapped".
>>> # Specifying column order works in the same way as examples in 1).
>>> dataset = dataset.map(operations=[decode_op, random_jitter_op], input_columns=["image"],
...                       output_columns=["image_mapped"])
>>>
>>> # Map with multiple operations using pyfunc. Renaming columns and specifying column order
>>> # work in the same way as examples in 1).
>>> dataset = ds.NumpySlicesDataset(data=[[0, 1, 2]], column_names=["data"])
>>> dataset = dataset.map(operations=[(lambda x: x * x), (lambda x: x - 1)], input_columns=["data"],
...                                   output_columns=["data_mapped"])
>>>
>>> # 3) Example where number of input columns is not equal to number of output columns.
>>>
>>> # operations[0] is a lambda that takes 2 columns as input and outputs 3 columns.
>>> # operations[1] is a lambda that takes 3 columns as input and outputs 1 column.
>>> # operations[2] is a lambda that takes 1 column as input and outputs 4 columns.
>>> #
>>> # Note: The number of output columns of operation[i] must equal the number of
>>> # input columns of operation[i+1]. Otherwise, this map call will also result
>>> # in an error.
>>> operations = [(lambda x, y: (x, x + y, x + y + 1)),
...               (lambda x, y, z: x * y * z),
...               (lambda x: (x % 2, x % 3, x % 5, x % 7))]
>>>
>>> # Note: Since the number of input columns is not the same as the number of
>>> # output columns, the output_columns and column_order parameters must be
>>> # specified. Otherwise, this map call will also result in an error.
>>>
>>> dataset = ds.NumpySlicesDataset(data=([[0, 1, 2]], [[3, 4, 5]]), column_names=["x", "y"])
>>>
>>> # Propagate all columns to the child node in this order:
>>> dataset = dataset.map(operations, input_columns=["x", "y"],
...                       output_columns=["mod2", "mod3", "mod5", "mod7"],
...                       column_order=["mod2", "mod3", "mod5", "mod7"])
>>>
>>> # Propagate some columns to the child node in this order:
>>> dataset = dataset.map(operations, input_columns=["x", "y"],
...                       output_columns=["mod2", "mod3", "mod5", "mod7"],
...                       column_order=["mod7", "mod3", "col2"])

num_classes()

Get the number of classes in a dataset.

Returns: int, number of classes.

Examples

>>> # dataset is an instance object of Dataset
>>> num_classes = dataset.num_classes()

output_shapes(estimate=False)

Get the shapes of output data.

Parameters: estimate (bool) – If estimate is False, will return the shapes of first data row. Otherwise, will iterate the whole dataset and return the estimated shapes of data row, where dynamic shape is marked as None (used in dynamic data shapes scenario). Default: False.
Returns: list, list of shapes of each column.

Examples

>>> import numpy as np
>>>
>>> def generator1():
...     for i in range(1, 100):
...         yield np.ones((16, i, 83)), np.array(i)
>>>
>>> dataset = ds.GeneratorDataset(generator1, ["data1", "data2"])
>>> output_shapes = dataset.output_shapes()

output_types()

Get the types of output data.

Returns: list, list of data types.

Examples

>>> # dataset is an instance object of Dataset
>>> output_types = dataset.output_types()

project(columns)

The specified columns will be selected from the dataset and passed into the pipeline with the order specified. The other columns are discarded.

Parameters: columns (Union[str, list[str]]) – List of names of the columns to project.
Returns: Dataset, dataset projected.

Examples

>>> # dataset is an instance object of Dataset
>>> columns_to_project = ["column3", "column1", "column2"]
>>>
>>> # Create a dataset that consists of column3, column1, column2
>>> # in that order, regardless of the original order of columns.
>>> dataset = dataset.project(columns=columns_to_project)

rename(input_columns, output_columns)

Rename the columns in input datasets.

Parameters

input_columns (Union[str, list[str]]) – List of names of the input columns.
output_columns (Union[str, list[str]]) – List of names of the output columns.

Returns

Dataset, dataset renamed.

Examples

>>> # dataset is an instance object of Dataset
>>> input_columns = ["input_col1", "input_col2", "input_col3"]
>>> output_columns = ["output_col1", "output_col2", "output_col3"]
>>>
>>> # Create a dataset where input_col1 is renamed to output_col1, and
>>> # input_col2 is renamed to output_col2, and input_col3 is renamed
>>> # to output_col3.
>>> dataset = dataset.rename(input_columns=input_columns, output_columns=output_columns)

repeat(count=None)

Repeat this dataset count times. Repeat infinitely if the count is None or -1.

Note

The order of using repeat and batch reflects the number of batches. It is recommended that the repeat operation is used after the batch operation.

Parameters: count (int) – Number of times the dataset is going to be repeated (default=None).
Returns: Dataset, dataset repeated.

Examples

>>> # dataset is an instance object of Dataset
>>>
>>> # Create a dataset where the dataset is repeated for 50 epochs
>>> dataset = dataset.repeat(50)
>>>
>>> # Create a dataset where each epoch is shuffled individually
>>> dataset = dataset.shuffle(10)
>>> dataset = dataset.repeat(50)
>>>
>>> # Create a dataset where the dataset is first repeated for
>>> # 50 epochs before shuffling. The shuffle operator will treat
>>> # the entire 50 epochs as one big dataset.
>>> dataset = dataset.repeat(50)
>>> dataset = dataset.shuffle(10)

reset(): Reset the dataset for next epoch.

save(file_name, num_files=1, file_type='mindrecord')

Save the dynamic data processed by the dataset pipeline in common dataset format. Supported dataset formats: mindrecord only. And you can use MindDataset API to read the saved file(s).

Implicit type casting exists when saving data as mindrecord. The transform table shows how to do type casting.

Implicit Type Casting when Saving as mindrecord
Type in dataset	Type in mindrecord	Details
bool	None	Not supported
int8	int32
uint8	bytes(1D uint8)	Drop dimension
int16	int32
uint16	int32
int32	int32
uint32	int64
int64	int64
uint64	None	Not supported
float16	float32
float32	float32
float64	float64
string	string	Multi-dimensional string not supported

Note

To save the samples in order, set dataset’s shuffle to False and num_files to 1.
Before calling the function, do not use batch operator, repeat operator or data augmentation operators with random attribute in map operator.
When array dimension is variable, one-dimensional arrays or multi-dimensional arrays with variable dimension 0 are supported.
Mindrecord does not support uint64, multi-dimensional uint8(drop dimension) nor multi-dimensional string.

Parameters

file_name (str) – Path to dataset file.
num_files (int, optional) – Number of dataset files (default=1).
file_type (str, optional) – Dataset format (default=’mindrecord’).

set_dynamic_columns(columns=None)

Set dynamic shape information of source data, it should be set after the pipeline is defined.

Parameters: columns (dict) – A dict contains shape information of each column in dataset. The value of shape[i] is None indicates that the data length of shape[i] is dynamic.

Examples

>>> import numpy as np
>>>
>>> def generator1():
...     for i in range(1, 100):
...         yield np.ones((16, i, 83)), np.array(i)
>>>
>>> dataset = ds.GeneratorDataset(generator1, ["data1", "data2"])
>>> dataset.set_dynamic_columns(columns={"data1": [16, None, 83], "data2": []})

shuffle(buffer_size)

Randomly shuffles the rows of this dataset using the following policy:

Make a shuffle buffer that contains the first buffer_size rows.
Randomly select an element from the shuffle buffer to be the next row propagated to the child node.
Get the next row (if any) from the parent node and put it in the shuffle buffer.
Repeat steps 2 and 3 until there are no more rows left in the shuffle buffer.

A random seed can be provided to be used on the first epoch via dataset.config.set_seed. In every subsequent epoch, the seed is changed to a new one, randomly generated value.

Parameters: buffer_size (int) – The size of the buffer (must be larger than 1) for shuffling. Setting buffer_size equal to the number of rows in the entire dataset will result in a global shuffle.
Returns: Dataset, dataset shuffled.
Raises: RuntimeError – If exist sync operators before shuffle.

Examples

>>> # dataset is an instance object of Dataset
>>> # Optionally set the seed for the first epoch
>>> ds.config.set_seed(58)
>>> # Create a shuffled dataset using a shuffle buffer of size 4
>>> dataset = dataset.shuffle(4)

skip(count)

Skip the first N elements of this dataset.

Parameters: count (int) – Number of elements in the dataset to be skipped.
Returns: Dataset, dataset that containing rows like origin rows subtract skipped rows.

Examples

>>> # dataset is an instance object of Dataset
>>> # Create a dataset which skips first 3 elements from data
>>> dataset = dataset.skip(3)

split(sizes, randomize=True)

Split the dataset into smaller, non-overlapping datasets.

Parameters

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 input sizes does not equal the original dataset size, an error will throw. If a list of floats [f1, f2, …, fn] is provided, all floats must be between 0 and 1 and must sum to 1, otherwise an error will throw. The dataset will be split into n Datasets of size round(f1*K), round(f2*K), …, round(fn*K) where K is the size of the original dataset. If after rounding:
- Any size equals 0, an error will occur.
- The sum of split sizes < K, the difference of K - sigma(round(fi * k)) will be added to the first split.
- The sum of split sizes > K, the difference of sigma(round(fi * K)) - K will be removed from the first large enough split such that it will have at least 1 row after removing the difference.
randomize (bool, optional) – Determines whether or not to split the data randomly (default=True). If True, the data will be randomly split. Otherwise, each split will be created with consecutive rows from the dataset.

Note

Dataset cannot be sharded if split is going to be called.
It is strongly recommended to not shuffle the dataset, but use randomize=True instead. Shuffling the dataset may not be deterministic, which means the data in each split will be different in each epoch.

Returns

tuple(Dataset), a tuple of datasets that have been split.

Raises

RuntimeError – If get_dataset_size returns None or is not supported for this dataset.
RuntimeError – If sizes is list of integers and sum of all elements in sizes does not equal the dataset size.
RuntimeError – If sizes is list of float and there is a split with size 0 after calculations.
RuntimeError – If the dataset is sharded prior to calling split.
ValueError – If sizes is list of float and not all floats are between 0 and 1, or if the floats don’t sum to 1.

Examples

>>> # TextFileDataset is not a mappable dataset, so this non-optimized split will be called.
>>> # Since many datasets have shuffle on by default, set shuffle to False if split will be called!
>>> dataset = ds.TextFileDataset(text_file_dataset_dir, shuffle=False)
>>> train_dataset, test_dataset = dataset.split([0.9, 0.1])

sync_update(condition_name, num_batch=None, data=None)

Release a blocking condition and trigger callback with given data.

Parameters

condition_name (str) – The condition name that is used to toggle sending next row.
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 (Any) – The data passed to the callback, user defined (default=None).

sync_wait(condition_name, num_batch=1, callback=None)

Add a blocking condition to the input Dataset and a synchronize action will be applied.

Parameters

condition_name (str) – The condition name that is used to toggle sending next row.
num_batch (int) – the number of batches without blocking at the start of each epoch (default=1).
callback (function) – The callback function that will be invoked when sync_update is called (default=None).

Returns

SyncWaitDataset, dataset added a blocking condition.

Raises

RuntimeError – If condition name already exists.

Examples

>>> import numpy as np
>>> def gen():
...     for i in range(100):
...         yield (np.array(i),)
>>>
>>> class Augment:
...     def __init__(self, loss):
...         self.loss = loss
...
...     def preprocess(self, input_):
...         return input_
...
...     def update(self, data):
...         self.loss = data["loss"]
>>>
>>> batch_size = 4
>>> dataset = ds.GeneratorDataset(gen, column_names=["input"])
>>>
>>> aug = Augment(0)
>>> dataset = dataset.sync_wait(condition_name="policy", callback=aug.update)
>>> dataset = dataset.map(operations=[aug.preprocess], input_columns=["input"])
>>> dataset = dataset.batch(batch_size)
>>> count = 0
>>> for data in dataset.create_dict_iterator(num_epochs=1, output_numpy=True):
...     assert data["input"][0] == count
...     count += batch_size
...     data = {"loss": count}
...     dataset.sync_update(condition_name="policy", data=data)

take(count=- 1)

Takes at most given numbers of elements from the dataset.

Note

If count is greater than the number of elements in the dataset or equal to -1, all the elements in dataset will be taken.
The order of using take and batch matters. If take is before batch operation, then take the given number of rows; otherwise take the given number of batches.

Parameters: count (int, optional) – Number of elements to be taken from the dataset (default=-1).
Returns: Dataset, dataset taken.

Examples

>>> # dataset is an instance object of Dataset
>>> # Create a dataset where the dataset includes 50 elements.
>>> dataset = dataset.take(50)

to_device(send_epoch_end=True, create_data_info_queue=False)

Transfer data from CPU to GPU or Ascend or other devices.

Parameters

send_epoch_end (bool, optional) – Whether to send the end of sequence to device or not (default=True).
create_data_info_queue (bool, optional) – Whether to create queue which stores types and shapes of data or not(default=False).

Note

This interface will be deleted or invisible in the future. Please use device_que to enable dataset sink mode. If device is Ascend, features of data will be transferred one by one. The limitation of data transmission per second is 256M.

Returns: TransferDataset, dataset for transferring.
Raises: RuntimeError – If distribution file path is given but failed to read.

to_json(filename='')

Serialize a pipeline into JSON string and dump into file if filename is provided.

Parameters: filename (str) – filename of JSON file to be saved as (default=””).
Returns: str, JSON string of the pipeline.

zip(datasets)

Zip the datasets in the sense of input tuple of datasets. Columns in the input datasets must have different name.

Parameters: datasets (Union[tuple, class Dataset]) – A tuple of datasets or a single class Dataset to be zipped together with this dataset.
Returns: Dataset, dataset zipped.

Examples

>>> # Create a dataset which is the combination of dataset and dataset_1
>>> dataset = dataset.zip(dataset_1)