mindspore.dataset

This module provides APIs to load and process various datasets: MNIST, CIFAR-10, CIFAR-100, VOC, ImageNet, CelebA dataset, etc. It also supports datasets in special format, including mindrecord, tfrecord, manifest. Users can also create samplers with this module to sample data.

class mindspore.dataset.CLUEDataset(dataset_files, task='AFQMC', usage='train', num_samples=None, num_parallel_workers=None, shuffle=Shuffle.GLOBAL, num_shards=None, shard_id=None)[source]

A source dataset that reads and parses CLUE datasets. CLUE, the Chinese Language Understanding Evaluation Benchmark, a collection of datasets, baselines, pre-trained models, corpus and leaderboard. Here we bring in classification task of CLUE, which are AFQMC, TNEWS, IFLYTEK, CMNLI, WSC and CSL.

Citation of CLUE dataset.

@article{CLUEbenchmark,
title   = {CLUE: A Chinese Language Understanding Evaluation Benchmark},
author  = {Liang Xu, Xuanwei Zhang, Lu Li, Hai Hu, Chenjie Cao, Weitang Liu, Junyi Li, Yudong Li,
           Kai Sun, Yechen Xu, Yiming Cui, Cong Yu, Qianqian Dong, Yin Tian, Dian Yu, Bo Shi, Jun Zeng,
           Rongzhao Wang, Weijian Xie, Yanting Li, Yina Patterson, Zuoyu Tian, Yiwen Zhang, He Zhou,
           Shaoweihua Liu, Qipeng Zhao, Cong Yue, Xinrui Zhang, Zhengliang Yang, Zhenzhong Lan},
journal = {arXiv preprint arXiv:2004.05986},
year    = {2020},
howpublished = {https://github.com/CLUEbenchmark/CLUE},
description  = {CLUE, a Chinese Language Understanding Evaluation benchmark. It contains eight different
                tasks, including single-sentence classification, sentence pair classification, and machine
                reading comprehension.}
}

Parameters

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).
usage (str, optional) – Need train, test or eval data (default=”train”).
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 (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:
- Shuffle.GLOBAL: Shuffle both the files and samples.
- Shuffle.FILES: Shuffle files only.
num_shards (int, optional) – Number of shards that the dataset should be divided into (default=None).
shard_id (int, optional) – The shard ID within num_shards (default=None). This argument should be specified only when num_shards is also specified.

Examples

>>> import mindspore.dataset as ds
>>> dataset_files = ["/path/to/1", "/path/to/2"] # contains 1 or multiple text files
>>> dataset = ds.CLUEDataset(dataset_files=dataset_files, task='AFQMC', usage='train')

apply(apply_func)

Apply a function in this dataset.

The specified apply_func is a function that must take one ‘Dataset’ as an argument and return a preprogressing ‘Dataset’.

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

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> # declare an apply_func function which returns a Dataset object
>>> def apply_func(ds):
>>>     ds = ds.batch(2)
>>>     return ds
>>> # use apply to call apply_func
>>> data = data.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, pad_info=None)

Combine batch_size number of consecutive rows into batches.

For any child node, a batch is treated as a single row. 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. Recommend that repeat operation should be used after batch operation.

Parameters

batch_size (int or 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 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 to process the Dataset in parallel (default=None).
per_batch_map (callable, optional) – Per batch map callable. A callable which takes (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[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.

Returns

BatchDataset, dataset batched.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> # creates a dataset where every 100 rows is combined into a batch
>>> # and drops the last incomplete batch if there is one.
>>> data = data.batch(100, True)

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, and pad and batch the buckets 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 batch_info, and then batched. Each batch will be full, except for maybe the last batch for each bucket.

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, and one 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 len(column_names) arguments and returns an int. If no value is provided, then 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) – Represents how to batch each column. The key corresponds to the column name, 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).

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>>
>>> # creates a dataset where every 100 rows is combined into a batch
>>> # and drops the last incomplete batch if there is one.
>>> column_names = ["col1", "col2"]
>>> buket_boundaries = [5, 10]
>>> bucket_batch_sizes = [5, 1, 1]
>>> element_length_function = (lambda col1, col2: max(len(col1), len(col2)))
>>>
>>> # will pad col1 to shape [2, bucket_boundaries[i]] where i is the
>>> # index of the bucket that is currently being batched.
>>> # will pad col2 to a shape where each dimension is the longest in all
>>> # the elements currently being batched.
>>> pad_info = {"col1", ([2, None], -1)}
>>> pad_to_bucket_boundary = True
>>>
>>> data = data.bucket_batch_by_length(column_names, bucket_boundaries,
>>>                                    bucket_batch_sizes,
>>>                                    element_length_function, pad_info,
>>>                                    pad_to_bucket_boundary)

concat(datasets)

Concat the datasets in the input list of datasets, supported using “+” to reload concat operation.

Note

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

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

Examples

>>> import mindspore.dataset as ds
>>> # ds1 and ds2 are instances of Dataset object
>>> # creates a dataset by concating ds1 and ds2 with "+" operation
>>> data1 = ds1 + ds2
>>> # creates a dataset by concating ds1 and ds2 with concat operation
>>> data1 = ds1.concat(ds2)

create_dict_iterator(num_epochs=- 1)

Create an Iterator over the dataset.

The data retrieved will be a dictionary. The order of the columns in the dictionary may not be the same as the original order.

Returns: Iterator, dictionary of column_name-ndarray pair.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> # creates an iterator. The columns in the data obtained by the
>>> # iterator might be changed.
>>> iterator = data.create_dict_iterator()
>>> for item in iterator:
>>>     # print the data in column1
>>>     print(item["column1"])

create_tuple_iterator(columns=None, num_epochs=- 1)

Create an Iterator over the dataset. The data retrieved will be a list of ndarray of data.

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 not be changed.

Parameters: columns (list[str], optional) – List of columns to be used to specify the order of columns (default=None, means all columns).
Returns: Iterator, list of ndarray.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> # creates an iterator. The columns in the data obtained by the
>>> # iterator will not be changed.
>>> iterator = data.create_tuple_iterator()
>>> for item in iterator:
>>>     # convert the returned tuple to a list and print
>>>     print(list(item))

device_que(prefetch_size=None, send_epoch_end=True)

Return a transferredDataset that transfer data through device.

Parameters

prefetch_size (int, optional) – prefetch number of records ahead of the user’s request (default=None).
send_epoch_end (bool, optional) – whether send end of sequence to device or not.(default=True)

Note

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

Returns: TransferDataset, dataset for transferring.

filter(predicate, input_columns=None, num_parallel_workers=1)

Filter dataset by predicate.

Note

If input_columns not provided or empty, all columns will be used.

Parameters

predicate (callable) – python callable which returns a boolean value, if False then filter the element.
input_columns – (list[str], optional): List of names of the input columns, when default=None, the predicate will be applied on all columns in the dataset.
num_parallel_workers (int, optional) – Number of workers to process the Dataset in parallel (default=None).

Returns

FilterDataset, dataset filter.

Examples

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

flat_map(func)

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

The specified func is a function that must take one ‘Ndarray’ as input and return a ‘Dataset’.

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

Examples

>>> import mindspore.dataset as ds
>>> import mindspore.dataset.text as text
>>> # declare a function which returns a Dataset object
>>> def flat_map_func(x):
>>>     data_dir = text.to_str(x[0])
>>>     d = ds.ImageFolderDatasetV2(data_dir)
>>>     return d
>>> # data is a Dataset object
>>> data = ds.TextFileDataset(DATA_FILE)
>>> data = data.flat_map(flat_map_func)

Raises

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

get_batch_size()

Get the size of a batch.

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

get_class_indexing()

Get the class index.

Returns: Dict, A str-to-int mapping from label name to index.

get_dataset_size()[source]

Get the number of batches in an epoch.

Returns: Number, number of batches.

get_repeat_count()

Get the replication times in RepeatDataset else 1.

Returns: Number, the count of repeat.

map(input_columns=None, operations=None, output_columns=None, columns_order=None, num_parallel_workers=None, python_multiprocessing=False, cache=None)

Apply each operation in operations to this dataset.

The order of operations is determined by the position of each operation in operations. operations[0] will be applied first, then operations[1], then operations[2], etc.

Each operation will be passed one or more columns from the dataset as input, and zero 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.

Only the columns specified in columns_order will be propagated to the child node. These columns will be in the same order as specified in columns_order.

Parameters

input_columns (list[str]) – 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 is required, starting from the first column).
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) != 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).
columns_order (list[str], optional) – list of all the desired columns to propagate to the child node. This list must be a subset of all the columns in the dataset after all operations are applied. The order of the columns in each row propagated to the child node follow the order they appear in this list. The parameter is mandatory if the len(input_columns) != len(output_columns). (default=None, all columns will be propagated to the child node, the order of the columns will remain the same).
num_parallel_workers (int, optional) – Number of threads used to process the dataset in parallel (default=None, the value from the config will be used).
python_multiprocessing (bool, optional) – Parallelize python operations with multiple worker process. This option could be beneficial if the python operation is computational heavy (default=False).
cache (DatasetCache, optional) – Tensor cache to use. (default=None which means no cache is used). The cache feature is under development and is not recommended.

Returns

MapDataset, dataset after mapping operation.

Examples

>>> import mindspore.dataset as ds
>>> import mindspore.dataset.transforms.vision.c_transforms as c_transforms
>>>
>>> # data is an instance of Dataset which has 2 columns, "image" and "label".
>>> # ds_pyfunc is an instance of Dataset which has 3 columns, "col0", "col1", and "col2". Each column is
>>> # a 2d array of integers.
>>>
>>> # This config is a global setting, meaning that all future operations which
>>> # uses this config value will use 2 worker threads, unless if specified
>>> # otherwise in their constructor. set_num_parallel_workers can be called
>>> # again later if a different number of worker threads are needed.
>>> ds.config.set_num_parallel_workers(2)
>>>
>>> # Two operations, which takes 1 column for input and outputs 1 column.
>>> decode_op = c_transforms.Decode(rgb_format=True)
>>> random_jitter_op = c_transforms.RandomColorAdjust((0.8, 0.8), (1, 1), (1, 1), (0, 0))
>>>
>>> # 1) Simple map example
>>>
>>> operations = [decode_op]
>>> input_columns = ["image"]
>>>
>>> # Applies decode_op on column "image". This column will be replaced by the outputed
>>> # column of decode_op. Since columns_order is not provided, both columns "image"
>>> # and "label" will be propagated to the child node in their original order.
>>> ds_decoded = data.map(input_columns, operations)
>>>
>>> # Rename column "image" to "decoded_image"
>>> output_columns = ["decoded_image"]
>>> ds_decoded = data.map(input_columns, operations, output_columns)
>>>
>>> # Specify the order of the columns.
>>> columns_order ["label", "image"]
>>> ds_decoded = data.map(input_columns, operations, None, columns_order)
>>>
>>> # Rename column "image" to "decoded_image" and also specify the order of the columns.
>>> columns_order ["label", "decoded_image"]
>>> output_columns = ["decoded_image"]
>>> ds_decoded = data.map(input_columns, operations, output_columns, columns_order)
>>>
>>> # Rename column "image" to "decoded_image" and keep only this column.
>>> columns_order ["decoded_image"]
>>> output_columns = ["decoded_image"]
>>> ds_decoded = data.map(input_columns, operations, output_columns, columns_order)
>>>
>>> # Simple example using pyfunc. Renaming columns and specifying column order
>>> # work in the same way as the previous examples.
>>> input_columns = ["col0"]
>>> operations = [(lambda x: x + 1)]
>>> ds_mapped = ds_pyfunc.map(input_columns, operations)
>>>
>>> # 2) Map example with more than one operation
>>>
>>> # If this list of operations is used with map, decode_op will be applied
>>> # first, then random_jitter_op will be applied.
>>> operations = [decode_op, random_jitter_op]
>>>
>>> input_columns = ["image"]
>>>
>>> # Creates 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 columns_order is not specified, the order of the
>>> # columns will remain the same.
>>> ds_mapped = data.map(input_columns, operations)
>>>
>>> # Creates a dataset that is identical to ds_mapped, except the column "image"
>>> # that is outputted by random_jitter_op is renamed to "image_transformed".
>>> # Specifying column order works in the same way as examples in 1).
>>> output_columns = ["image_transformed"]
>>> ds_mapped_and_renamed = data.map(input_columns, operation, output_columns)
>>>
>>> # Multiple operations using pyfunc. Renaming columns and specifying column order
>>> # work in the same way as examples in 1).
>>> input_columns = ["col0"]
>>> operations = [(lambda x: x + x), (lambda x: x - 1)]
>>> output_columns = ["col0_mapped"]
>>> ds_mapped = ds_pyfunc.map(input_columns, operations, output_columns)
>>>
>>> # 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[1] 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: because the number of input columns is not the same as the number of
>>> # output columns, the output_columns and columns_order parameter must be
>>> # specified. Otherwise, this map call will also result in an error.
>>> input_columns = ["col2", "col0"]
>>> output_columns = ["mod2", "mod3", "mod5", "mod7"]
>>>
>>> # Propagate all columns to the child node in this order:
>>> columns_order = ["col0", "col2", "mod2", "mod3", "mod5", "mod7", "col1"]
>>> ds_mapped = ds_pyfunc.map(input_columns, operations, output_columns, columns_order)
>>>
>>> # Propagate some columns to the child node in this order:
>>> columns_order = ["mod7", "mod3", "col1"]
>>> ds_mapped = ds_pyfunc.map(input_columns, operations, output_columns, columns_order)

num_classes()

Get the number of classes in a dataset.

Returns: Number, number of classes.

output_shapes()

Get the shapes of output data.

Returns: List, list of shape of each column.

output_types()

Get the types of output data.

Returns: List of data type.

project(columns)

Project certain columns in input datasets.

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

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

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> columns_to_project = ["column3", "column1", "column2"]
>>>
>>> # creates a dataset that consist of column3, column1, column2
>>> # in that order, regardless of the original order of columns.
>>> data = data.project(columns=columns_to_project)

rename(input_columns, output_columns)

Rename the columns in input datasets.

Parameters

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

Returns

RenameDataset, dataset renamed.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> input_columns = ["input_col1", "input_col2", "input_col3"]
>>> output_columns = ["output_col1", "output_col2", "output_col3"]
>>>
>>> # creates 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.
>>> data = data.rename(input_columns=input_columns, output_columns=output_columns)

repeat(count=None)

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

Note

The order of using repeat and batch reflects the number of batches. Recommend that repeat operation should be used after batch operation. If dataset_sink_mode is False, here repeat operation is invalid. If dataset_sink_mode is True, repeat count should be equal to the epoch of training. Otherwise, errors could occur since the amount of data is not the amount training requires.

Parameters: count (int) – Number of times the dataset should be repeated (default=None).
Returns: RepeatDataset, dataset repeated.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> # creates a dataset where the dataset is repeated for 50 epochs
>>> repeated = data.repeat(50)
>>>
>>> # creates a dataset where each epoch is shuffled individually
>>> shuffled_and_repeated = data.shuffle(10)
>>> shuffled_and_repeated = shuffled_and_repeated.repeat(50)
>>>
>>> # creates 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.
>>> repeat_and_shuffle = data.repeat(50)
>>> repeat_and_shuffle = repeat_and_shuffle.shuffle(10)

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

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

Save the dynamic data processed by dataset pipeline as common dataset format, support: mindrecord.

Implicit type casting exists when saving data as mindrecord. Table below shows how to do type casting.

Implicit Type Casting of Saving as mindrecord
type in ‘dataset’	type in ‘mindrecord’	detail
bool	None	Not support
int8	int32
uint8	bytes(1D uint8)	Drop dimension
int16	int32
uint16	int32
int32	int32
uint32	int64
int64	int64
uint64	None	Not support
float16	float32
float32	float32
float64	float64
string	string	Not support multi-dimensional string

Note

To save the samples in order, should set dataset’s shuffle false and num_files 1.
Before call the function, do not use batch, repeat operator or data augmentation operators with random attribute in map operator.
Mindrecord does not support DE_UINT64, multi-dimensional DE_UINT8(drop dimension) and multi-dimensional DE_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’)

shuffle(buffer_size)

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

Make a shuffle buffer that contains the first buffer_size rows.
Randomly select an element from the shuffle buffer to be the next row propogated 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 seed can be provided to be used on the first epoch. 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: ShuffleDataset, dataset shuffled.
Raises: RuntimeError – If exist sync operators before shuffle.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> # optionally set the seed for the first epoch
>>> ds.config.set_seed(58)
>>>
>>> # creates a shuffled dataset using a shuffle buffer of size 4
>>> data = data.shuffle(4)

skip(count)

Skip the first N elements of this dataset.

Parameters: count (int) – Number of elements the dataset should be skipped.
Returns: SkipDataset, dataset skipped.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> # creates a dataset which skips first 3 elements from data
>>> data = data.skip(3)

split(sizes, randomize=True)

Split the dataset into smaller, non-overlapping datasets.

This is a general purpose split function which can be called from any operator in the pipeline. There is another, optimized split function, which will be called automatically if ds.split is called where ds is a MappableDataset.

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 sizes does not equal the original dataset size, an an error will occur. 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 occur. 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 will be added to the first split.
- The sum of split sizes > K, the difference will be removed from the first large enough split such that it will have atleast 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.

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.

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

Examples

>>> import mindspore.dataset as ds
>>>
>>> dataset_dir = "/path/to/text_file.txt"
>>>
>>> # TextFileDataset is not a mappable dataset, so this non optimized split will be called.
>>> # many datasets have shuffle on by default, set shuffle to False if split will be called!
>>> data = ds.TextFileDataset(dataset_dir, shuffle=False)
>>> train, test = data.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 (Union[dict, None]) – The data passed to the callback (default=None).

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

Add a blocking condition to the input Dataset.

Parameters

num_batch (int) – the number of batches without blocking at the start of each epoch.
condition_name (str) – The condition name that is used to toggle sending next row.
callback (function) – The callback funciton that will be invoked when sync_update is called.

Raises

RuntimeError – If condition name already exists.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> data = data.sync_wait("callback1")
>>> data = data.batch(batch_size)
>>> for batch_data in data.create_dict_iterator():
>>>     data = data.sync_update("callback1")

take(count=- 1)

Takes at most given numbers of elements from the dataset.

Note

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

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

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> # creates a dataset where the dataset including 50 elements.
>>> data = data.take(50)

to_device(send_epoch_end=True)

Transfer data through CPU, GPU or Ascend devices.

Parameters: send_epoch_end (bool, optional) – whether send end of sequence to device or not.(default=True)

Note

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

Returns

TransferDataset, dataset for transferring.

Raises

TypeError – If device_type is empty.
ValueError – If device_type is not ‘Ascend’, ‘GPU’ or ‘CPU’.
RuntimeError – If dataset is unknown.
RuntimeError – If distribution file path is given but failed to read.

zip(datasets)

Zip the datasets in the input tuple of datasets. Columns in the input datasets must not have the same name.

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

Examples

>>> import mindspore.dataset as ds
>>> # ds1 and ds2 are instances of Dataset object
>>> # creates a dataset which is the combination of ds1 and ds2
>>> data = ds1.zip(ds2)

class mindspore.dataset.CSVDataset(dataset_files, field_delim=',', column_defaults=None, column_names=None, num_samples=None, num_parallel_workers=None, shuffle=Shuffle.GLOBAL, num_shards=None, shard_id=None)[source]

A source dataset that reads and parses CSV datasets.

Parameters

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[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 (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:
- Shuffle.GLOBAL: Shuffle both the files and samples.
- Shuffle.FILES: Shuffle files only.
num_shards (int, optional) – Number of shards that the dataset should be divided into (default=None).
shard_id (int, optional) – The shard ID within num_shards (default=None). This argument should be specified only when num_shards is also specified.

Examples

>>> import mindspore.dataset as ds
>>> dataset_files = ["/path/to/1", "/path/to/2"] # contains 1 or multiple text files
>>> dataset = ds.CSVDataset(dataset_files=dataset_files, column_names=['col1', 'col2', 'col3', 'col4'])

apply(apply_func)

Apply a function in this dataset.

The specified apply_func is a function that must take one ‘Dataset’ as an argument and return a preprogressing ‘Dataset’.

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

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> # declare an apply_func function which returns a Dataset object
>>> def apply_func(ds):
>>>     ds = ds.batch(2)
>>>     return ds
>>> # use apply to call apply_func
>>> data = data.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, pad_info=None)

Combine batch_size number of consecutive rows into batches.

For any child node, a batch is treated as a single row. 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. Recommend that repeat operation should be used after batch operation.

Parameters

batch_size (int or 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 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 to process the Dataset in parallel (default=None).
per_batch_map (callable, optional) – Per batch map callable. A callable which takes (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[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.

Returns

BatchDataset, dataset batched.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> # creates a dataset where every 100 rows is combined into a batch
>>> # and drops the last incomplete batch if there is one.
>>> data = data.batch(100, True)

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, and pad and batch the buckets 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 batch_info, and then batched. Each batch will be full, except for maybe the last batch for each bucket.

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, and one 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 len(column_names) arguments and returns an int. If no value is provided, then 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) – Represents how to batch each column. The key corresponds to the column name, 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).

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>>
>>> # creates a dataset where every 100 rows is combined into a batch
>>> # and drops the last incomplete batch if there is one.
>>> column_names = ["col1", "col2"]
>>> buket_boundaries = [5, 10]
>>> bucket_batch_sizes = [5, 1, 1]
>>> element_length_function = (lambda col1, col2: max(len(col1), len(col2)))
>>>
>>> # will pad col1 to shape [2, bucket_boundaries[i]] where i is the
>>> # index of the bucket that is currently being batched.
>>> # will pad col2 to a shape where each dimension is the longest in all
>>> # the elements currently being batched.
>>> pad_info = {"col1", ([2, None], -1)}
>>> pad_to_bucket_boundary = True
>>>
>>> data = data.bucket_batch_by_length(column_names, bucket_boundaries,
>>>                                    bucket_batch_sizes,
>>>                                    element_length_function, pad_info,
>>>                                    pad_to_bucket_boundary)

concat(datasets)

Concat the datasets in the input list of datasets, supported using “+” to reload concat operation.

Note

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

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

Examples

>>> import mindspore.dataset as ds
>>> # ds1 and ds2 are instances of Dataset object
>>> # creates a dataset by concating ds1 and ds2 with "+" operation
>>> data1 = ds1 + ds2
>>> # creates a dataset by concating ds1 and ds2 with concat operation
>>> data1 = ds1.concat(ds2)

create_dict_iterator(num_epochs=- 1)

Create an Iterator over the dataset.

The data retrieved will be a dictionary. The order of the columns in the dictionary may not be the same as the original order.

Returns: Iterator, dictionary of column_name-ndarray pair.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> # creates an iterator. The columns in the data obtained by the
>>> # iterator might be changed.
>>> iterator = data.create_dict_iterator()
>>> for item in iterator:
>>>     # print the data in column1
>>>     print(item["column1"])

create_tuple_iterator(columns=None, num_epochs=- 1)

Create an Iterator over the dataset. The data retrieved will be a list of ndarray of data.

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 not be changed.

Parameters: columns (list[str], optional) – List of columns to be used to specify the order of columns (default=None, means all columns).
Returns: Iterator, list of ndarray.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> # creates an iterator. The columns in the data obtained by the
>>> # iterator will not be changed.
>>> iterator = data.create_tuple_iterator()
>>> for item in iterator:
>>>     # convert the returned tuple to a list and print
>>>     print(list(item))

device_que(prefetch_size=None, send_epoch_end=True)

Return a transferredDataset that transfer data through device.

Parameters

prefetch_size (int, optional) – prefetch number of records ahead of the user’s request (default=None).
send_epoch_end (bool, optional) – whether send end of sequence to device or not.(default=True)

Note

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

Returns: TransferDataset, dataset for transferring.

filter(predicate, input_columns=None, num_parallel_workers=1)

Filter dataset by predicate.

Note

If input_columns not provided or empty, all columns will be used.

Parameters

predicate (callable) – python callable which returns a boolean value, if False then filter the element.
input_columns – (list[str], optional): List of names of the input columns, when default=None, the predicate will be applied on all columns in the dataset.
num_parallel_workers (int, optional) – Number of workers to process the Dataset in parallel (default=None).

Returns

FilterDataset, dataset filter.

Examples

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

flat_map(func)

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

The specified func is a function that must take one ‘Ndarray’ as input and return a ‘Dataset’.

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

Examples

>>> import mindspore.dataset as ds
>>> import mindspore.dataset.text as text
>>> # declare a function which returns a Dataset object
>>> def flat_map_func(x):
>>>     data_dir = text.to_str(x[0])
>>>     d = ds.ImageFolderDatasetV2(data_dir)
>>>     return d
>>> # data is a Dataset object
>>> data = ds.TextFileDataset(DATA_FILE)
>>> data = data.flat_map(flat_map_func)

Raises

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

get_batch_size()

Get the size of a batch.

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

get_class_indexing()

Get the class index.

Returns: Dict, A str-to-int mapping from label name to index.

get_dataset_size()[source]

Get the number of batches in an epoch.

Returns: Number, number of batches.

get_repeat_count()

Get the replication times in RepeatDataset else 1.

Returns: Number, the count of repeat.

map(input_columns=None, operations=None, output_columns=None, columns_order=None, num_parallel_workers=None, python_multiprocessing=False, cache=None)

Apply each operation in operations to this dataset.

The order of operations is determined by the position of each operation in operations. operations[0] will be applied first, then operations[1], then operations[2], etc.

Each operation will be passed one or more columns from the dataset as input, and zero 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.

Only the columns specified in columns_order will be propagated to the child node. These columns will be in the same order as specified in columns_order.

Parameters

input_columns (list[str]) – 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 is required, starting from the first column).
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) != 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).
columns_order (list[str], optional) – list of all the desired columns to propagate to the child node. This list must be a subset of all the columns in the dataset after all operations are applied. The order of the columns in each row propagated to the child node follow the order they appear in this list. The parameter is mandatory if the len(input_columns) != len(output_columns). (default=None, all columns will be propagated to the child node, the order of the columns will remain the same).
num_parallel_workers (int, optional) – Number of threads used to process the dataset in parallel (default=None, the value from the config will be used).
python_multiprocessing (bool, optional) – Parallelize python operations with multiple worker process. This option could be beneficial if the python operation is computational heavy (default=False).
cache (DatasetCache, optional) – Tensor cache to use. (default=None which means no cache is used). The cache feature is under development and is not recommended.

Returns

MapDataset, dataset after mapping operation.

Examples

>>> import mindspore.dataset as ds
>>> import mindspore.dataset.transforms.vision.c_transforms as c_transforms
>>>
>>> # data is an instance of Dataset which has 2 columns, "image" and "label".
>>> # ds_pyfunc is an instance of Dataset which has 3 columns, "col0", "col1", and "col2". Each column is
>>> # a 2d array of integers.
>>>
>>> # This config is a global setting, meaning that all future operations which
>>> # uses this config value will use 2 worker threads, unless if specified
>>> # otherwise in their constructor. set_num_parallel_workers can be called
>>> # again later if a different number of worker threads are needed.
>>> ds.config.set_num_parallel_workers(2)
>>>
>>> # Two operations, which takes 1 column for input and outputs 1 column.
>>> decode_op = c_transforms.Decode(rgb_format=True)
>>> random_jitter_op = c_transforms.RandomColorAdjust((0.8, 0.8), (1, 1), (1, 1), (0, 0))
>>>
>>> # 1) Simple map example
>>>
>>> operations = [decode_op]
>>> input_columns = ["image"]
>>>
>>> # Applies decode_op on column "image". This column will be replaced by the outputed
>>> # column of decode_op. Since columns_order is not provided, both columns "image"
>>> # and "label" will be propagated to the child node in their original order.
>>> ds_decoded = data.map(input_columns, operations)
>>>
>>> # Rename column "image" to "decoded_image"
>>> output_columns = ["decoded_image"]
>>> ds_decoded = data.map(input_columns, operations, output_columns)
>>>
>>> # Specify the order of the columns.
>>> columns_order ["label", "image"]
>>> ds_decoded = data.map(input_columns, operations, None, columns_order)
>>>
>>> # Rename column "image" to "decoded_image" and also specify the order of the columns.
>>> columns_order ["label", "decoded_image"]
>>> output_columns = ["decoded_image"]
>>> ds_decoded = data.map(input_columns, operations, output_columns, columns_order)
>>>
>>> # Rename column "image" to "decoded_image" and keep only this column.
>>> columns_order ["decoded_image"]
>>> output_columns = ["decoded_image"]
>>> ds_decoded = data.map(input_columns, operations, output_columns, columns_order)
>>>
>>> # Simple example using pyfunc. Renaming columns and specifying column order
>>> # work in the same way as the previous examples.
>>> input_columns = ["col0"]
>>> operations = [(lambda x: x + 1)]
>>> ds_mapped = ds_pyfunc.map(input_columns, operations)
>>>
>>> # 2) Map example with more than one operation
>>>
>>> # If this list of operations is used with map, decode_op will be applied
>>> # first, then random_jitter_op will be applied.
>>> operations = [decode_op, random_jitter_op]
>>>
>>> input_columns = ["image"]
>>>
>>> # Creates 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 columns_order is not specified, the order of the
>>> # columns will remain the same.
>>> ds_mapped = data.map(input_columns, operations)
>>>
>>> # Creates a dataset that is identical to ds_mapped, except the column "image"
>>> # that is outputted by random_jitter_op is renamed to "image_transformed".
>>> # Specifying column order works in the same way as examples in 1).
>>> output_columns = ["image_transformed"]
>>> ds_mapped_and_renamed = data.map(input_columns, operation, output_columns)
>>>
>>> # Multiple operations using pyfunc. Renaming columns and specifying column order
>>> # work in the same way as examples in 1).
>>> input_columns = ["col0"]
>>> operations = [(lambda x: x + x), (lambda x: x - 1)]
>>> output_columns = ["col0_mapped"]
>>> ds_mapped = ds_pyfunc.map(input_columns, operations, output_columns)
>>>
>>> # 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[1] 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: because the number of input columns is not the same as the number of
>>> # output columns, the output_columns and columns_order parameter must be
>>> # specified. Otherwise, this map call will also result in an error.
>>> input_columns = ["col2", "col0"]
>>> output_columns = ["mod2", "mod3", "mod5", "mod7"]
>>>
>>> # Propagate all columns to the child node in this order:
>>> columns_order = ["col0", "col2", "mod2", "mod3", "mod5", "mod7", "col1"]
>>> ds_mapped = ds_pyfunc.map(input_columns, operations, output_columns, columns_order)
>>>
>>> # Propagate some columns to the child node in this order:
>>> columns_order = ["mod7", "mod3", "col1"]
>>> ds_mapped = ds_pyfunc.map(input_columns, operations, output_columns, columns_order)

num_classes()

Get the number of classes in a dataset.

Returns: Number, number of classes.

output_shapes()

Get the shapes of output data.

Returns: List, list of shape of each column.

output_types()

Get the types of output data.

Returns: List of data type.

project(columns)

Project certain columns in input datasets.

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

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

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> columns_to_project = ["column3", "column1", "column2"]
>>>
>>> # creates a dataset that consist of column3, column1, column2
>>> # in that order, regardless of the original order of columns.
>>> data = data.project(columns=columns_to_project)

rename(input_columns, output_columns)

Rename the columns in input datasets.

Parameters

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

Returns

RenameDataset, dataset renamed.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> input_columns = ["input_col1", "input_col2", "input_col3"]
>>> output_columns = ["output_col1", "output_col2", "output_col3"]
>>>
>>> # creates 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.
>>> data = data.rename(input_columns=input_columns, output_columns=output_columns)

repeat(count=None)

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

Note

The order of using repeat and batch reflects the number of batches. Recommend that repeat operation should be used after batch operation. If dataset_sink_mode is False, here repeat operation is invalid. If dataset_sink_mode is True, repeat count should be equal to the epoch of training. Otherwise, errors could occur since the amount of data is not the amount training requires.

Parameters: count (int) – Number of times the dataset should be repeated (default=None).
Returns: RepeatDataset, dataset repeated.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> # creates a dataset where the dataset is repeated for 50 epochs
>>> repeated = data.repeat(50)
>>>
>>> # creates a dataset where each epoch is shuffled individually
>>> shuffled_and_repeated = data.shuffle(10)
>>> shuffled_and_repeated = shuffled_and_repeated.repeat(50)
>>>
>>> # creates 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.
>>> repeat_and_shuffle = data.repeat(50)
>>> repeat_and_shuffle = repeat_and_shuffle.shuffle(10)

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

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

Save the dynamic data processed by dataset pipeline as common dataset format, support: mindrecord.

Implicit type casting exists when saving data as mindrecord. Table below shows how to do type casting.

Implicit Type Casting of Saving as mindrecord
type in ‘dataset’	type in ‘mindrecord’	detail
bool	None	Not support
int8	int32
uint8	bytes(1D uint8)	Drop dimension
int16	int32
uint16	int32
int32	int32
uint32	int64
int64	int64
uint64	None	Not support
float16	float32
float32	float32
float64	float64
string	string	Not support multi-dimensional string

Note

To save the samples in order, should set dataset’s shuffle false and num_files 1.
Before call the function, do not use batch, repeat operator or data augmentation operators with random attribute in map operator.
Mindrecord does not support DE_UINT64, multi-dimensional DE_UINT8(drop dimension) and multi-dimensional DE_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’)

shuffle(buffer_size)

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

Make a shuffle buffer that contains the first buffer_size rows.
Randomly select an element from the shuffle buffer to be the next row propogated 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 seed can be provided to be used on the first epoch. 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: ShuffleDataset, dataset shuffled.
Raises: RuntimeError – If exist sync operators before shuffle.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> # optionally set the seed for the first epoch
>>> ds.config.set_seed(58)
>>>
>>> # creates a shuffled dataset using a shuffle buffer of size 4
>>> data = data.shuffle(4)

skip(count)

Skip the first N elements of this dataset.

Parameters: count (int) – Number of elements the dataset should be skipped.
Returns: SkipDataset, dataset skipped.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> # creates a dataset which skips first 3 elements from data
>>> data = data.skip(3)

split(sizes, randomize=True)

Split the dataset into smaller, non-overlapping datasets.

This is a general purpose split function which can be called from any operator in the pipeline. There is another, optimized split function, which will be called automatically if ds.split is called where ds is a MappableDataset.

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 sizes does not equal the original dataset size, an an error will occur. 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 occur. 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 will be added to the first split.
- The sum of split sizes > K, the difference will be removed from the first large enough split such that it will have atleast 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.

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.

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

Examples

>>> import mindspore.dataset as ds
>>>
>>> dataset_dir = "/path/to/text_file.txt"
>>>
>>> # TextFileDataset is not a mappable dataset, so this non optimized split will be called.
>>> # many datasets have shuffle on by default, set shuffle to False if split will be called!
>>> data = ds.TextFileDataset(dataset_dir, shuffle=False)
>>> train, test = data.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 (Union[dict, None]) – The data passed to the callback (default=None).

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

Add a blocking condition to the input Dataset.

Parameters

num_batch (int) – the number of batches without blocking at the start of each epoch.
condition_name (str) – The condition name that is used to toggle sending next row.
callback (function) – The callback funciton that will be invoked when sync_update is called.

Raises

RuntimeError – If condition name already exists.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> data = data.sync_wait("callback1")
>>> data = data.batch(batch_size)
>>> for batch_data in data.create_dict_iterator():
>>>     data = data.sync_update("callback1")

take(count=- 1)

Takes at most given numbers of elements from the dataset.

Note

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

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

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> # creates a dataset where the dataset including 50 elements.
>>> data = data.take(50)

to_device(send_epoch_end=True)

Transfer data through CPU, GPU or Ascend devices.

Parameters: send_epoch_end (bool, optional) – whether send end of sequence to device or not.(default=True)

Note

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

Returns

TransferDataset, dataset for transferring.

Raises

TypeError – If device_type is empty.
ValueError – If device_type is not ‘Ascend’, ‘GPU’ or ‘CPU’.
RuntimeError – If dataset is unknown.
RuntimeError – If distribution file path is given but failed to read.

zip(datasets)

Zip the datasets in the input tuple of datasets. Columns in the input datasets must not have the same name.

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

Examples

>>> import mindspore.dataset as ds
>>> # ds1 and ds2 are instances of Dataset object
>>> # creates a dataset which is the combination of ds1 and ds2
>>> data = ds1.zip(ds2)

class mindspore.dataset.CelebADataset(dataset_dir, num_parallel_workers=None, shuffle=None, dataset_type='all', sampler=None, decode=False, extensions=None, num_samples=None, num_shards=None, shard_id=None)[source]

A source dataset for reading and parsing CelebA dataset.Only support list_attr_celeba.txt currently.

Note

The generated dataset has two columns [‘image’, ‘attr’]. The type of the image tensor is uint8. The attr tensor is uint32 and one hot type.

Citation of CelebA dataset.

@article{DBLP:journals/corr/LiuLWT14,
author    = {Ziwei Liu and Ping Luo and Xiaogang Wang and Xiaoou Tang},
title     = {Deep Learning Face Attributes in the Wild},
journal   = {CoRR},
volume    = {abs/1411.7766},
year      = {2014},
url       = {http://arxiv.org/abs/1411.7766},
archivePrefix = {arXiv},
eprint    = {1411.7766},
timestamp = {Tue, 10 Dec 2019 15:37:26 +0100},
biburl    = {https://dblp.org/rec/journals/corr/LiuLWT14.bib},
bibsource = {dblp computer science bibliography, https://dblp.org},
howpublished = {http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html},
description  = {CelebFaces Attributes Dataset (CelebA) is a large-scale face attributes dataset
                with more than 200K celebrity images, each with 40 attribute annotations.
                The images in this dataset cover large pose variations and background clutter.
                CelebA has large diversities, large quantities, and rich annotations, including
                * 10,177 number of identities,
                * 202,599 number of face images, and
                * 5 landmark locations, 40 binary attributes annotations per image.
                The dataset can be employed as the training and test sets for the following computer
                vision tasks: face attribute recognition, face detection, landmark (or facial part)
                localization, and face editing & synthesis.}
}

Parameters

dataset_dir (str) – Path to the root directory that contains the dataset.
num_parallel_workers (int, optional) – Number of workers to read the data (default=value set in the config).
shuffle (bool, optional) – Whether to perform shuffle on the dataset (default=None).
dataset_type (str) – one of ‘all’, ‘train’, ‘valid’ or ‘test’.
sampler (Sampler, optional) – Object used to choose samples from the dataset (default=None).
decode (bool, optional) – decode the images after reading (default=False).
extensions (list[str], optional) – List of file extensions to be included in the dataset (default=None).
num_samples (int, optional) – The number of images to be included in the dataset. (default=None, all images).
num_shards (int, optional) – Number of shards that the dataset should be divided into (default=None).
shard_id (int, optional) – The shard ID within num_shards (default=None). This argument should be specified only when num_shards is also specified.

apply(apply_func)

Apply a function in this dataset.

The specified apply_func is a function that must take one ‘Dataset’ as an argument and return a preprogressing ‘Dataset’.

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

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> # declare an apply_func function which returns a Dataset object
>>> def apply_func(ds):
>>>     ds = ds.batch(2)
>>>     return ds
>>> # use apply to call apply_func
>>> data = data.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, pad_info=None)

Combine batch_size number of consecutive rows into batches.

For any child node, a batch is treated as a single row. 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. Recommend that repeat operation should be used after batch operation.

Parameters

batch_size (int or 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 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 to process the Dataset in parallel (default=None).
per_batch_map (callable, optional) – Per batch map callable. A callable which takes (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[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.

Returns

BatchDataset, dataset batched.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> # creates a dataset where every 100 rows is combined into a batch
>>> # and drops the last incomplete batch if there is one.
>>> data = data.batch(100, True)

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, and pad and batch the buckets 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 batch_info, and then batched. Each batch will be full, except for maybe the last batch for each bucket.

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, and one 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 len(column_names) arguments and returns an int. If no value is provided, then 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) – Represents how to batch each column. The key corresponds to the column name, 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).

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>>
>>> # creates a dataset where every 100 rows is combined into a batch
>>> # and drops the last incomplete batch if there is one.
>>> column_names = ["col1", "col2"]
>>> buket_boundaries = [5, 10]
>>> bucket_batch_sizes = [5, 1, 1]
>>> element_length_function = (lambda col1, col2: max(len(col1), len(col2)))
>>>
>>> # will pad col1 to shape [2, bucket_boundaries[i]] where i is the
>>> # index of the bucket that is currently being batched.
>>> # will pad col2 to a shape where each dimension is the longest in all
>>> # the elements currently being batched.
>>> pad_info = {"col1", ([2, None], -1)}
>>> pad_to_bucket_boundary = True
>>>
>>> data = data.bucket_batch_by_length(column_names, bucket_boundaries,
>>>                                    bucket_batch_sizes,
>>>                                    element_length_function, pad_info,
>>>                                    pad_to_bucket_boundary)

concat(datasets)

Concat the datasets in the input list of datasets, supported using “+” to reload concat operation.

Note

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

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

Examples

>>> import mindspore.dataset as ds
>>> # ds1 and ds2 are instances of Dataset object
>>> # creates a dataset by concating ds1 and ds2 with "+" operation
>>> data1 = ds1 + ds2
>>> # creates a dataset by concating ds1 and ds2 with concat operation
>>> data1 = ds1.concat(ds2)

create_dict_iterator(num_epochs=- 1)

Create an Iterator over the dataset.

The data retrieved will be a dictionary. The order of the columns in the dictionary may not be the same as the original order.

Returns: Iterator, dictionary of column_name-ndarray pair.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> # creates an iterator. The columns in the data obtained by the
>>> # iterator might be changed.
>>> iterator = data.create_dict_iterator()
>>> for item in iterator:
>>>     # print the data in column1
>>>     print(item["column1"])

create_tuple_iterator(columns=None, num_epochs=- 1)

Create an Iterator over the dataset. The data retrieved will be a list of ndarray of data.

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 not be changed.

Parameters: columns (list[str], optional) – List of columns to be used to specify the order of columns (default=None, means all columns).
Returns: Iterator, list of ndarray.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> # creates an iterator. The columns in the data obtained by the
>>> # iterator will not be changed.
>>> iterator = data.create_tuple_iterator()
>>> for item in iterator:
>>>     # convert the returned tuple to a list and print
>>>     print(list(item))

device_que(prefetch_size=None, send_epoch_end=True)

Return a transferredDataset that transfer data through device.

Parameters

prefetch_size (int, optional) – prefetch number of records ahead of the user’s request (default=None).
send_epoch_end (bool, optional) – whether send end of sequence to device or not.(default=True)

Note

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

Returns: TransferDataset, dataset for transferring.

filter(predicate, input_columns=None, num_parallel_workers=1)

Filter dataset by predicate.

Note

If input_columns not provided or empty, all columns will be used.

Parameters

predicate (callable) – python callable which returns a boolean value, if False then filter the element.
input_columns – (list[str], optional): List of names of the input columns, when default=None, the predicate will be applied on all columns in the dataset.
num_parallel_workers (int, optional) – Number of workers to process the Dataset in parallel (default=None).

Returns

FilterDataset, dataset filter.

Examples

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

flat_map(func)

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

The specified func is a function that must take one ‘Ndarray’ as input and return a ‘Dataset’.

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

Examples

>>> import mindspore.dataset as ds
>>> import mindspore.dataset.text as text
>>> # declare a function which returns a Dataset object
>>> def flat_map_func(x):
>>>     data_dir = text.to_str(x[0])
>>>     d = ds.ImageFolderDatasetV2(data_dir)
>>>     return d
>>> # data is a Dataset object
>>> data = ds.TextFileDataset(DATA_FILE)
>>> data = data.flat_map(flat_map_func)

Raises

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

get_batch_size()

Get the size of a batch.

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

get_class_indexing()

Get the class index.

Returns: Dict, A str-to-int mapping from label name to index.

get_dataset_size()[source]

Get the number of batches in an epoch.

Returns: Number, number of batches.

get_repeat_count()

Get the replication times in RepeatDataset else 1.

Returns: Number, the count of repeat.

map(input_columns=None, operations=None, output_columns=None, columns_order=None, num_parallel_workers=None, python_multiprocessing=False, cache=None)

Apply each operation in operations to this dataset.

The order of operations is determined by the position of each operation in operations. operations[0] will be applied first, then operations[1], then operations[2], etc.

Each operation will be passed one or more columns from the dataset as input, and zero 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.

Only the columns specified in columns_order will be propagated to the child node. These columns will be in the same order as specified in columns_order.

Parameters

input_columns (list[str]) – 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 is required, starting from the first column).
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) != 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).
columns_order (list[str], optional) – list of all the desired columns to propagate to the child node. This list must be a subset of all the columns in the dataset after all operations are applied. The order of the columns in each row propagated to the child node follow the order they appear in this list. The parameter is mandatory if the len(input_columns) != len(output_columns). (default=None, all columns will be propagated to the child node, the order of the columns will remain the same).
num_parallel_workers (int, optional) – Number of threads used to process the dataset in parallel (default=None, the value from the config will be used).
python_multiprocessing (bool, optional) – Parallelize python operations with multiple worker process. This option could be beneficial if the python operation is computational heavy (default=False).
cache (DatasetCache, optional) – Tensor cache to use. (default=None which means no cache is used). The cache feature is under development and is not recommended.

Returns

MapDataset, dataset after mapping operation.

Examples

>>> import mindspore.dataset as ds
>>> import mindspore.dataset.transforms.vision.c_transforms as c_transforms
>>>
>>> # data is an instance of Dataset which has 2 columns, "image" and "label".
>>> # ds_pyfunc is an instance of Dataset which has 3 columns, "col0", "col1", and "col2". Each column is
>>> # a 2d array of integers.
>>>
>>> # This config is a global setting, meaning that all future operations which
>>> # uses this config value will use 2 worker threads, unless if specified
>>> # otherwise in their constructor. set_num_parallel_workers can be called
>>> # again later if a different number of worker threads are needed.
>>> ds.config.set_num_parallel_workers(2)
>>>
>>> # Two operations, which takes 1 column for input and outputs 1 column.
>>> decode_op = c_transforms.Decode(rgb_format=True)
>>> random_jitter_op = c_transforms.RandomColorAdjust((0.8, 0.8), (1, 1), (1, 1), (0, 0))
>>>
>>> # 1) Simple map example
>>>
>>> operations = [decode_op]
>>> input_columns = ["image"]
>>>
>>> # Applies decode_op on column "image". This column will be replaced by the outputed
>>> # column of decode_op. Since columns_order is not provided, both columns "image"
>>> # and "label" will be propagated to the child node in their original order.
>>> ds_decoded = data.map(input_columns, operations)
>>>
>>> # Rename column "image" to "decoded_image"
>>> output_columns = ["decoded_image"]
>>> ds_decoded = data.map(input_columns, operations, output_columns)
>>>
>>> # Specify the order of the columns.
>>> columns_order ["label", "image"]
>>> ds_decoded = data.map(input_columns, operations, None, columns_order)
>>>
>>> # Rename column "image" to "decoded_image" and also specify the order of the columns.
>>> columns_order ["label", "decoded_image"]
>>> output_columns = ["decoded_image"]
>>> ds_decoded = data.map(input_columns, operations, output_columns, columns_order)
>>>
>>> # Rename column "image" to "decoded_image" and keep only this column.
>>> columns_order ["decoded_image"]
>>> output_columns = ["decoded_image"]
>>> ds_decoded = data.map(input_columns, operations, output_columns, columns_order)
>>>
>>> # Simple example using pyfunc. Renaming columns and specifying column order
>>> # work in the same way as the previous examples.
>>> input_columns = ["col0"]
>>> operations = [(lambda x: x + 1)]
>>> ds_mapped = ds_pyfunc.map(input_columns, operations)
>>>
>>> # 2) Map example with more than one operation
>>>
>>> # If this list of operations is used with map, decode_op will be applied
>>> # first, then random_jitter_op will be applied.
>>> operations = [decode_op, random_jitter_op]
>>>
>>> input_columns = ["image"]
>>>
>>> # Creates 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 columns_order is not specified, the order of the
>>> # columns will remain the same.
>>> ds_mapped = data.map(input_columns, operations)
>>>
>>> # Creates a dataset that is identical to ds_mapped, except the column "image"
>>> # that is outputted by random_jitter_op is renamed to "image_transformed".
>>> # Specifying column order works in the same way as examples in 1).
>>> output_columns = ["image_transformed"]
>>> ds_mapped_and_renamed = data.map(input_columns, operation, output_columns)
>>>
>>> # Multiple operations using pyfunc. Renaming columns and specifying column order
>>> # work in the same way as examples in 1).
>>> input_columns = ["col0"]
>>> operations = [(lambda x: x + x), (lambda x: x - 1)]
>>> output_columns = ["col0_mapped"]
>>> ds_mapped = ds_pyfunc.map(input_columns, operations, output_columns)
>>>
>>> # 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[1] 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: because the number of input columns is not the same as the number of
>>> # output columns, the output_columns and columns_order parameter must be
>>> # specified. Otherwise, this map call will also result in an error.
>>> input_columns = ["col2", "col0"]
>>> output_columns = ["mod2", "mod3", "mod5", "mod7"]
>>>
>>> # Propagate all columns to the child node in this order:
>>> columns_order = ["col0", "col2", "mod2", "mod3", "mod5", "mod7", "col1"]
>>> ds_mapped = ds_pyfunc.map(input_columns, operations, output_columns, columns_order)
>>>
>>> # Propagate some columns to the child node in this order:
>>> columns_order = ["mod7", "mod3", "col1"]
>>> ds_mapped = ds_pyfunc.map(input_columns, operations, output_columns, columns_order)

num_classes()

Get the number of classes in a dataset.

Returns: Number, number of classes.

output_shapes()

Get the shapes of output data.

Returns: List, list of shape of each column.

output_types()

Get the types of output data.

Returns: List of data type.

project(columns)

Project certain columns in input datasets.

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

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

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> columns_to_project = ["column3", "column1", "column2"]
>>>
>>> # creates a dataset that consist of column3, column1, column2
>>> # in that order, regardless of the original order of columns.
>>> data = data.project(columns=columns_to_project)

rename(input_columns, output_columns)

Rename the columns in input datasets.

Parameters

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

Returns

RenameDataset, dataset renamed.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> input_columns = ["input_col1", "input_col2", "input_col3"]
>>> output_columns = ["output_col1", "output_col2", "output_col3"]
>>>
>>> # creates 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.
>>> data = data.rename(input_columns=input_columns, output_columns=output_columns)

repeat(count=None)

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

Note

The order of using repeat and batch reflects the number of batches. Recommend that repeat operation should be used after batch operation. If dataset_sink_mode is False, here repeat operation is invalid. If dataset_sink_mode is True, repeat count should be equal to the epoch of training. Otherwise, errors could occur since the amount of data is not the amount training requires.

Parameters: count (int) – Number of times the dataset should be repeated (default=None).
Returns: RepeatDataset, dataset repeated.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> # creates a dataset where the dataset is repeated for 50 epochs
>>> repeated = data.repeat(50)
>>>
>>> # creates a dataset where each epoch is shuffled individually
>>> shuffled_and_repeated = data.shuffle(10)
>>> shuffled_and_repeated = shuffled_and_repeated.repeat(50)
>>>
>>> # creates 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.
>>> repeat_and_shuffle = data.repeat(50)
>>> repeat_and_shuffle = repeat_and_shuffle.shuffle(10)

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

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

Save the dynamic data processed by dataset pipeline as common dataset format, support: mindrecord.

Implicit type casting exists when saving data as mindrecord. Table below shows how to do type casting.

Implicit Type Casting of Saving as mindrecord
type in ‘dataset’	type in ‘mindrecord’	detail
bool	None	Not support
int8	int32
uint8	bytes(1D uint8)	Drop dimension
int16	int32
uint16	int32
int32	int32
uint32	int64
int64	int64
uint64	None	Not support
float16	float32
float32	float32
float64	float64
string	string	Not support multi-dimensional string

Note

To save the samples in order, should set dataset’s shuffle false and num_files 1.
Before call the function, do not use batch, repeat operator or data augmentation operators with random attribute in map operator.
Mindrecord does not support DE_UINT64, multi-dimensional DE_UINT8(drop dimension) and multi-dimensional DE_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’)

shuffle(buffer_size)

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

Make a shuffle buffer that contains the first buffer_size rows.
Randomly select an element from the shuffle buffer to be the next row propogated 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 seed can be provided to be used on the first epoch. 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: ShuffleDataset, dataset shuffled.
Raises: RuntimeError – If exist sync operators before shuffle.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> # optionally set the seed for the first epoch
>>> ds.config.set_seed(58)
>>>
>>> # creates a shuffled dataset using a shuffle buffer of size 4
>>> data = data.shuffle(4)

skip(count)

Skip the first N elements of this dataset.

Parameters: count (int) – Number of elements the dataset should be skipped.
Returns: SkipDataset, dataset skipped.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> # creates a dataset which skips first 3 elements from data
>>> data = data.skip(3)

split(sizes, randomize=True)

Split the dataset into smaller, non-overlapping datasets.

There is the optimized split function, which will be called automatically when the dataset that calls this function is a MappableDataset.

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 sizes does not equal the original dataset size, an an error will occur. 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 occur. 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 will be added to the first split.
- The sum of split sizes > K, the difference will be removed from the first large enough split such that it will have atleast 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 should not be sharded if split is going to be called. Instead, create a DistributedSampler and specify a split to shard after splitting. If dataset is sharded after a split, it is strongly recommended to set the same seed in each instance of execution, otherwise each shard may not be part of the same split (see Examples).
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. Furthermore, if sharding occurs after split, each shard may not be part of the same 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.

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

Examples

>>> import mindspore.dataset as ds
>>>
>>> dataset_dir = "/path/to/imagefolder_directory"
>>>
>>> # many datasets have shuffle on by default, set shuffle to False if split will be called!
>>> data = ds.ImageFolderDatasetV2(dataset_dir, shuffle=False)
>>>
>>> # sets the seed, and tells split to use this seed when randomizing. This
>>> # is needed because we are sharding later
>>> ds.config.set_seed(58)
>>> train, test = data.split([0.9, 0.1])
>>>
>>> # if we want to shard the train dataset, we can use a DistributedSampler
>>> train_sampler = ds.DistributedSampler(10, 2)
>>> train.use_sampler(train_sampler)

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 (Union[dict, None]) – The data passed to the callback (default=None).

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

Add a blocking condition to the input Dataset.

Parameters

num_batch (int) – the number of batches without blocking at the start of each epoch.
condition_name (str) – The condition name that is used to toggle sending next row.
callback (function) – The callback funciton that will be invoked when sync_update is called.

Raises

RuntimeError – If condition name already exists.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> data = data.sync_wait("callback1")
>>> data = data.batch(batch_size)
>>> for batch_data in data.create_dict_iterator():
>>>     data = data.sync_update("callback1")

take(count=- 1)

Takes at most given numbers of elements from the dataset.

Note

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

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

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> # creates a dataset where the dataset including 50 elements.
>>> data = data.take(50)

to_device(send_epoch_end=True)

Transfer data through CPU, GPU or Ascend devices.

Parameters: send_epoch_end (bool, optional) – whether send end of sequence to device or not.(default=True)

Note

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

Returns

TransferDataset, dataset for transferring.

Raises

TypeError – If device_type is empty.
ValueError – If device_type is not ‘Ascend’, ‘GPU’ or ‘CPU’.
RuntimeError – If dataset is unknown.
RuntimeError – If distribution file path is given but failed to read.

use_sampler(new_sampler)

Will make the current dataset use the new_sampler provided.

Parameters: new_sampler (Sampler) – the sampler to use for the current dataset.
Returns: Dataset, that uses new_sampler.

Examples

>>> import mindspore.dataset as ds
>>>
>>> dataset_dir = "/path/to/imagefolder_directory"
>>> # a SequentialSampler is created by default
>>> data = ds.ImageFolderDatasetV2(dataset_dir)
>>>
>>> # use a DistributedSampler instead of the SequentialSampler
>>> new_sampler = ds.DistributedSampler(10, 2)
>>> data.use_sampler(new_sampler)

zip(datasets)

Zip the datasets in the input tuple of datasets. Columns in the input datasets must not have the same name.

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

Examples

>>> import mindspore.dataset as ds
>>> # ds1 and ds2 are instances of Dataset object
>>> # creates a dataset which is the combination of ds1 and ds2
>>> data = ds1.zip(ds2)

class mindspore.dataset.Cifar100Dataset(dataset_dir, num_samples=None, num_parallel_workers=None, shuffle=None, sampler=None, num_shards=None, shard_id=None)[source]

A source dataset that reads cifar100 data.

The generated dataset has three columns [‘image’, ‘coarse_label’, ‘fine_label’]. The type of the image tensor is uint8. The coarse and fine are just a scalar uint32 tensor. This dataset can take in a sampler. sampler and shuffle are mutually exclusive. Table below shows what input args are allowed and their expected behavior.

Expected Order Behavior of Using ‘sampler’ and ‘shuffle’
Parameter ‘sampler’	Parameter ‘shuffle’	Expected Order Behavior
None	None	random order
None	True	random order
None	False	sequential order
Sampler object	None	order defined by sampler
Sampler object	True	not allowed
Sampler object	False	not allowed

Citation of Cifar100 dataset.

@techreport{Krizhevsky09,
author       = {Alex Krizhevsky},
title        = {Learning multiple layers of features from tiny images},
institution  = {},
year         = {2009},
howpublished = {http://www.cs.toronto.edu/~kriz/cifar.html},
description  = {This dataset is just like the CIFAR-10, except it has 100 classes containing 600 images
                each. There are 500 training images and 100 testing images per class. The 100 classes in
                the CIFAR-100 are grouped into 20 superclasses. Each image comes with a "fine" label (the
                class to which it belongs) and a "coarse" label (the superclass to which it belongs).}
}

Parameters

dataset_dir (str) – Path to the root directory that contains the dataset.
num_samples (int, optional) – The number of images to be included in the dataset. (default=None, all images).
num_parallel_workers (int, optional) – Number of workers to read the data (default=None, number set in the config).
shuffle (bool, optional) – Whether to perform shuffle on the dataset (default=None, expected order behavior shown in the table).
sampler (Sampler, optional) – Object used to choose samples from the dataset (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).
shard_id (int, optional) – The shard ID within num_shards (default=None). This argument should be specified only when num_shards is also specified.

Raises

RuntimeError – If sampler and shuffle are specified at the same time.
RuntimeError – If sampler and sharding are specified at the same time.
RuntimeError – If num_shards is specified but shard_id is None.
RuntimeError – If shard_id is specified but num_shards is None.
ValueError – If shard_id is invalid (< 0 or >= num_shards).

Examples

>>> import mindspore.dataset as ds
>>> dataset_dir = "/path/to/cifar100_dataset_directory"
>>> # 1) get all samples from CIFAR100 dataset in sequence:
>>> cifar100_dataset = ds.Cifar100Dataset(dataset_dir=dataset_dir,shuffle=False)
>>> # 2) randomly select 350 samples from CIFAR100 dataset:
>>> cifar100_dataset = ds.Cifar100Dataset(dataset_dir=dataset_dir,num_samples=350, shuffle=True)
>>> # in CIFAR100 dataset, each dictionary has 3 keys: "image", "fine_label" and "coarse_label"

apply(apply_func)

Apply a function in this dataset.

The specified apply_func is a function that must take one ‘Dataset’ as an argument and return a preprogressing ‘Dataset’.

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

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> # declare an apply_func function which returns a Dataset object
>>> def apply_func(ds):
>>>     ds = ds.batch(2)
>>>     return ds
>>> # use apply to call apply_func
>>> data = data.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, pad_info=None)

Combine batch_size number of consecutive rows into batches.

For any child node, a batch is treated as a single row. 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. Recommend that repeat operation should be used after batch operation.

Parameters

batch_size (int or 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 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 to process the Dataset in parallel (default=None).
per_batch_map (callable, optional) – Per batch map callable. A callable which takes (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[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.

Returns

BatchDataset, dataset batched.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> # creates a dataset where every 100 rows is combined into a batch
>>> # and drops the last incomplete batch if there is one.
>>> data = data.batch(100, True)

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, and pad and batch the buckets 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 batch_info, and then batched. Each batch will be full, except for maybe the last batch for each bucket.

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, and one 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 len(column_names) arguments and returns an int. If no value is provided, then 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) – Represents how to batch each column. The key corresponds to the column name, 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).

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>>
>>> # creates a dataset where every 100 rows is combined into a batch
>>> # and drops the last incomplete batch if there is one.
>>> column_names = ["col1", "col2"]
>>> buket_boundaries = [5, 10]
>>> bucket_batch_sizes = [5, 1, 1]
>>> element_length_function = (lambda col1, col2: max(len(col1), len(col2)))
>>>
>>> # will pad col1 to shape [2, bucket_boundaries[i]] where i is the
>>> # index of the bucket that is currently being batched.
>>> # will pad col2 to a shape where each dimension is the longest in all
>>> # the elements currently being batched.
>>> pad_info = {"col1", ([2, None], -1)}
>>> pad_to_bucket_boundary = True
>>>
>>> data = data.bucket_batch_by_length(column_names, bucket_boundaries,
>>>                                    bucket_batch_sizes,
>>>                                    element_length_function, pad_info,
>>>                                    pad_to_bucket_boundary)

concat(datasets)

Concat the datasets in the input list of datasets, supported using “+” to reload concat operation.

Note

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

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

Examples

>>> import mindspore.dataset as ds
>>> # ds1 and ds2 are instances of Dataset object
>>> # creates a dataset by concating ds1 and ds2 with "+" operation
>>> data1 = ds1 + ds2
>>> # creates a dataset by concating ds1 and ds2 with concat operation
>>> data1 = ds1.concat(ds2)

create_dict_iterator(num_epochs=- 1)

Create an Iterator over the dataset.

The data retrieved will be a dictionary. The order of the columns in the dictionary may not be the same as the original order.

Returns: Iterator, dictionary of column_name-ndarray pair.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> # creates an iterator. The columns in the data obtained by the
>>> # iterator might be changed.
>>> iterator = data.create_dict_iterator()
>>> for item in iterator:
>>>     # print the data in column1
>>>     print(item["column1"])

create_tuple_iterator(columns=None, num_epochs=- 1)

Create an Iterator over the dataset. The data retrieved will be a list of ndarray of data.

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 not be changed.

Parameters: columns (list[str], optional) – List of columns to be used to specify the order of columns (default=None, means all columns).
Returns: Iterator, list of ndarray.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> # creates an iterator. The columns in the data obtained by the
>>> # iterator will not be changed.
>>> iterator = data.create_tuple_iterator()
>>> for item in iterator:
>>>     # convert the returned tuple to a list and print
>>>     print(list(item))

device_que(prefetch_size=None, send_epoch_end=True)

Return a transferredDataset that transfer data through device.

Parameters

prefetch_size (int, optional) – prefetch number of records ahead of the user’s request (default=None).
send_epoch_end (bool, optional) – whether send end of sequence to device or not.(default=True)

Note

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

Returns: TransferDataset, dataset for transferring.

filter(predicate, input_columns=None, num_parallel_workers=1)

Filter dataset by predicate.

Note

If input_columns not provided or empty, all columns will be used.

Parameters

predicate (callable) – python callable which returns a boolean value, if False then filter the element.
input_columns – (list[str], optional): List of names of the input columns, when default=None, the predicate will be applied on all columns in the dataset.
num_parallel_workers (int, optional) – Number of workers to process the Dataset in parallel (default=None).

Returns

FilterDataset, dataset filter.

Examples

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

flat_map(func)

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

The specified func is a function that must take one ‘Ndarray’ as input and return a ‘Dataset’.

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

Examples

>>> import mindspore.dataset as ds
>>> import mindspore.dataset.text as text
>>> # declare a function which returns a Dataset object
>>> def flat_map_func(x):
>>>     data_dir = text.to_str(x[0])
>>>     d = ds.ImageFolderDatasetV2(data_dir)
>>>     return d
>>> # data is a Dataset object
>>> data = ds.TextFileDataset(DATA_FILE)
>>> data = data.flat_map(flat_map_func)

Raises

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

get_batch_size()

Get the size of a batch.

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

get_class_indexing()

Get the class index.

Returns: Dict, A str-to-int mapping from label name to index.

get_dataset_size()[source]

Get the number of batches in an epoch.

Returns: Number, number of batches.

get_repeat_count()

Get the replication times in RepeatDataset else 1.

Returns: Number, the count of repeat.

map(input_columns=None, operations=None, output_columns=None, columns_order=None, num_parallel_workers=None, python_multiprocessing=False, cache=None)

Apply each operation in operations to this dataset.

The order of operations is determined by the position of each operation in operations. operations[0] will be applied first, then operations[1], then operations[2], etc.

Each operation will be passed one or more columns from the dataset as input, and zero 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.

Only the columns specified in columns_order will be propagated to the child node. These columns will be in the same order as specified in columns_order.

Parameters

input_columns (list[str]) – 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 is required, starting from the first column).
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) != 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).
columns_order (list[str], optional) – list of all the desired columns to propagate to the child node. This list must be a subset of all the columns in the dataset after all operations are applied. The order of the columns in each row propagated to the child node follow the order they appear in this list. The parameter is mandatory if the len(input_columns) != len(output_columns). (default=None, all columns will be propagated to the child node, the order of the columns will remain the same).
num_parallel_workers (int, optional) – Number of threads used to process the dataset in parallel (default=None, the value from the config will be used).
python_multiprocessing (bool, optional) – Parallelize python operations with multiple worker process. This option could be beneficial if the python operation is computational heavy (default=False).
cache (DatasetCache, optional) – Tensor cache to use. (default=None which means no cache is used). The cache feature is under development and is not recommended.

Returns

MapDataset, dataset after mapping operation.

Examples

>>> import mindspore.dataset as ds
>>> import mindspore.dataset.transforms.vision.c_transforms as c_transforms
>>>
>>> # data is an instance of Dataset which has 2 columns, "image" and "label".
>>> # ds_pyfunc is an instance of Dataset which has 3 columns, "col0", "col1", and "col2". Each column is
>>> # a 2d array of integers.
>>>
>>> # This config is a global setting, meaning that all future operations which
>>> # uses this config value will use 2 worker threads, unless if specified
>>> # otherwise in their constructor. set_num_parallel_workers can be called
>>> # again later if a different number of worker threads are needed.
>>> ds.config.set_num_parallel_workers(2)
>>>
>>> # Two operations, which takes 1 column for input and outputs 1 column.
>>> decode_op = c_transforms.Decode(rgb_format=True)
>>> random_jitter_op = c_transforms.RandomColorAdjust((0.8, 0.8), (1, 1), (1, 1), (0, 0))
>>>
>>> # 1) Simple map example
>>>
>>> operations = [decode_op]
>>> input_columns = ["image"]
>>>
>>> # Applies decode_op on column "image". This column will be replaced by the outputed
>>> # column of decode_op. Since columns_order is not provided, both columns "image"
>>> # and "label" will be propagated to the child node in their original order.
>>> ds_decoded = data.map(input_columns, operations)
>>>
>>> # Rename column "image" to "decoded_image"
>>> output_columns = ["decoded_image"]
>>> ds_decoded = data.map(input_columns, operations, output_columns)
>>>
>>> # Specify the order of the columns.
>>> columns_order ["label", "image"]
>>> ds_decoded = data.map(input_columns, operations, None, columns_order)
>>>
>>> # Rename column "image" to "decoded_image" and also specify the order of the columns.
>>> columns_order ["label", "decoded_image"]
>>> output_columns = ["decoded_image"]
>>> ds_decoded = data.map(input_columns, operations, output_columns, columns_order)
>>>
>>> # Rename column "image" to "decoded_image" and keep only this column.
>>> columns_order ["decoded_image"]
>>> output_columns = ["decoded_image"]
>>> ds_decoded = data.map(input_columns, operations, output_columns, columns_order)
>>>
>>> # Simple example using pyfunc. Renaming columns and specifying column order
>>> # work in the same way as the previous examples.
>>> input_columns = ["col0"]
>>> operations = [(lambda x: x + 1)]
>>> ds_mapped = ds_pyfunc.map(input_columns, operations)
>>>
>>> # 2) Map example with more than one operation
>>>
>>> # If this list of operations is used with map, decode_op will be applied
>>> # first, then random_jitter_op will be applied.
>>> operations = [decode_op, random_jitter_op]
>>>
>>> input_columns = ["image"]
>>>
>>> # Creates 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 columns_order is not specified, the order of the
>>> # columns will remain the same.
>>> ds_mapped = data.map(input_columns, operations)
>>>
>>> # Creates a dataset that is identical to ds_mapped, except the column "image"
>>> # that is outputted by random_jitter_op is renamed to "image_transformed".
>>> # Specifying column order works in the same way as examples in 1).
>>> output_columns = ["image_transformed"]
>>> ds_mapped_and_renamed = data.map(input_columns, operation, output_columns)
>>>
>>> # Multiple operations using pyfunc. Renaming columns and specifying column order
>>> # work in the same way as examples in 1).
>>> input_columns = ["col0"]
>>> operations = [(lambda x: x + x), (lambda x: x - 1)]
>>> output_columns = ["col0_mapped"]
>>> ds_mapped = ds_pyfunc.map(input_columns, operations, output_columns)
>>>
>>> # 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[1] 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: because the number of input columns is not the same as the number of
>>> # output columns, the output_columns and columns_order parameter must be
>>> # specified. Otherwise, this map call will also result in an error.
>>> input_columns = ["col2", "col0"]
>>> output_columns = ["mod2", "mod3", "mod5", "mod7"]
>>>
>>> # Propagate all columns to the child node in this order:
>>> columns_order = ["col0", "col2", "mod2", "mod3", "mod5", "mod7", "col1"]
>>> ds_mapped = ds_pyfunc.map(input_columns, operations, output_columns, columns_order)
>>>
>>> # Propagate some columns to the child node in this order:
>>> columns_order = ["mod7", "mod3", "col1"]
>>> ds_mapped = ds_pyfunc.map(input_columns, operations, output_columns, columns_order)

num_classes()

Get the number of classes in a dataset.

Returns: Number, number of classes.

output_shapes()

Get the shapes of output data.

Returns: List, list of shape of each column.

output_types()

Get the types of output data.

Returns: List of data type.

project(columns)

Project certain columns in input datasets.

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

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

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> columns_to_project = ["column3", "column1", "column2"]
>>>
>>> # creates a dataset that consist of column3, column1, column2
>>> # in that order, regardless of the original order of columns.
>>> data = data.project(columns=columns_to_project)

rename(input_columns, output_columns)

Rename the columns in input datasets.

Parameters

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

Returns

RenameDataset, dataset renamed.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> input_columns = ["input_col1", "input_col2", "input_col3"]
>>> output_columns = ["output_col1", "output_col2", "output_col3"]
>>>
>>> # creates 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.
>>> data = data.rename(input_columns=input_columns, output_columns=output_columns)

repeat(count=None)

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

Note

The order of using repeat and batch reflects the number of batches. Recommend that repeat operation should be used after batch operation. If dataset_sink_mode is False, here repeat operation is invalid. If dataset_sink_mode is True, repeat count should be equal to the epoch of training. Otherwise, errors could occur since the amount of data is not the amount training requires.

Parameters: count (int) – Number of times the dataset should be repeated (default=None).
Returns: RepeatDataset, dataset repeated.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> # creates a dataset where the dataset is repeated for 50 epochs
>>> repeated = data.repeat(50)
>>>
>>> # creates a dataset where each epoch is shuffled individually
>>> shuffled_and_repeated = data.shuffle(10)
>>> shuffled_and_repeated = shuffled_and_repeated.repeat(50)
>>>
>>> # creates 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.
>>> repeat_and_shuffle = data.repeat(50)
>>> repeat_and_shuffle = repeat_and_shuffle.shuffle(10)

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

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

Save the dynamic data processed by dataset pipeline as common dataset format, support: mindrecord.

Implicit type casting exists when saving data as mindrecord. Table below shows how to do type casting.

Implicit Type Casting of Saving as mindrecord
type in ‘dataset’	type in ‘mindrecord’	detail
bool	None	Not support
int8	int32
uint8	bytes(1D uint8)	Drop dimension
int16	int32
uint16	int32
int32	int32
uint32	int64
int64	int64
uint64	None	Not support
float16	float32
float32	float32
float64	float64
string	string	Not support multi-dimensional string

Note

To save the samples in order, should set dataset’s shuffle false and num_files 1.
Before call the function, do not use batch, repeat operator or data augmentation operators with random attribute in map operator.
Mindrecord does not support DE_UINT64, multi-dimensional DE_UINT8(drop dimension) and multi-dimensional DE_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’)

shuffle(buffer_size)

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

Make a shuffle buffer that contains the first buffer_size rows.
Randomly select an element from the shuffle buffer to be the next row propogated 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 seed can be provided to be used on the first epoch. 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: ShuffleDataset, dataset shuffled.
Raises: RuntimeError – If exist sync operators before shuffle.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> # optionally set the seed for the first epoch
>>> ds.config.set_seed(58)
>>>
>>> # creates a shuffled dataset using a shuffle buffer of size 4
>>> data = data.shuffle(4)

skip(count)

Skip the first N elements of this dataset.

Parameters: count (int) – Number of elements the dataset should be skipped.
Returns: SkipDataset, dataset skipped.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> # creates a dataset which skips first 3 elements from data
>>> data = data.skip(3)

split(sizes, randomize=True)

Split the dataset into smaller, non-overlapping datasets.

There is the optimized split function, which will be called automatically when the dataset that calls this function is a MappableDataset.

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 sizes does not equal the original dataset size, an an error will occur. 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 occur. 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 will be added to the first split.
- The sum of split sizes > K, the difference will be removed from the first large enough split such that it will have atleast 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 should not be sharded if split is going to be called. Instead, create a DistributedSampler and specify a split to shard after splitting. If dataset is sharded after a split, it is strongly recommended to set the same seed in each instance of execution, otherwise each shard may not be part of the same split (see Examples).
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. Furthermore, if sharding occurs after split, each shard may not be part of the same 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.

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

Examples

>>> import mindspore.dataset as ds
>>>
>>> dataset_dir = "/path/to/imagefolder_directory"
>>>
>>> # many datasets have shuffle on by default, set shuffle to False if split will be called!
>>> data = ds.ImageFolderDatasetV2(dataset_dir, shuffle=False)
>>>
>>> # sets the seed, and tells split to use this seed when randomizing. This
>>> # is needed because we are sharding later
>>> ds.config.set_seed(58)
>>> train, test = data.split([0.9, 0.1])
>>>
>>> # if we want to shard the train dataset, we can use a DistributedSampler
>>> train_sampler = ds.DistributedSampler(10, 2)
>>> train.use_sampler(train_sampler)

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 (Union[dict, None]) – The data passed to the callback (default=None).

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

Add a blocking condition to the input Dataset.

Parameters

num_batch (int) – the number of batches without blocking at the start of each epoch.
condition_name (str) – The condition name that is used to toggle sending next row.
callback (function) – The callback funciton that will be invoked when sync_update is called.

Raises

RuntimeError – If condition name already exists.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> data = data.sync_wait("callback1")
>>> data = data.batch(batch_size)
>>> for batch_data in data.create_dict_iterator():
>>>     data = data.sync_update("callback1")

take(count=- 1)

Takes at most given numbers of elements from the dataset.

Note

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

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

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> # creates a dataset where the dataset including 50 elements.
>>> data = data.take(50)

to_device(send_epoch_end=True)

Transfer data through CPU, GPU or Ascend devices.

Parameters: send_epoch_end (bool, optional) – whether send end of sequence to device or not.(default=True)

Note

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

Returns

TransferDataset, dataset for transferring.

Raises

TypeError – If device_type is empty.
ValueError – If device_type is not ‘Ascend’, ‘GPU’ or ‘CPU’.
RuntimeError – If dataset is unknown.
RuntimeError – If distribution file path is given but failed to read.

use_sampler(new_sampler)

Will make the current dataset use the new_sampler provided.

Parameters: new_sampler (Sampler) – the sampler to use for the current dataset.
Returns: Dataset, that uses new_sampler.

Examples

>>> import mindspore.dataset as ds
>>>
>>> dataset_dir = "/path/to/imagefolder_directory"
>>> # a SequentialSampler is created by default
>>> data = ds.ImageFolderDatasetV2(dataset_dir)
>>>
>>> # use a DistributedSampler instead of the SequentialSampler
>>> new_sampler = ds.DistributedSampler(10, 2)
>>> data.use_sampler(new_sampler)

zip(datasets)

Zip the datasets in the input tuple of datasets. Columns in the input datasets must not have the same name.

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

Examples

>>> import mindspore.dataset as ds
>>> # ds1 and ds2 are instances of Dataset object
>>> # creates a dataset which is the combination of ds1 and ds2
>>> data = ds1.zip(ds2)

class mindspore.dataset.Cifar10Dataset(dataset_dir, num_samples=None, num_parallel_workers=None, shuffle=None, sampler=None, num_shards=None, shard_id=None)[source]

A source dataset that reads cifar10 data.

The generated dataset has two columns [‘image’, ‘label’]. The type of the image tensor is uint8. The label is just a scalar uint32 tensor. This dataset can take in a sampler. sampler and shuffle are mutually exclusive. Table below shows what input args are allowed and their expected behavior.

Expected Order Behavior of Using ‘sampler’ and ‘shuffle’
Parameter ‘sampler’	Parameter ‘shuffle’	Expected Order Behavior
None	None	random order
None	True	random order
None	False	sequential order
Sampler object	None	order defined by sampler
Sampler object	True	not allowed
Sampler object	False	not allowed

Citation of Cifar10 dataset.

@techreport{Krizhevsky09,
author       = {Alex Krizhevsky},
title        = {Learning multiple layers of features from tiny images},
institution  = {},
year         = {2009},
howpublished = {http://www.cs.toronto.edu/~kriz/cifar.html},
description  = {The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes,
                with 6000 images per class. There are 50000 training images and 10000 test images.}
}

Parameters

dataset_dir (str) – Path to the root directory that contains the dataset.
num_samples (int, optional) – The number of images to be included in the dataset. (default=None, all images).
num_parallel_workers (int, optional) – Number of workers to read the data (default=None, number set in the config).
shuffle (bool, optional) – Whether to perform shuffle on the dataset (default=None, expected order behavior shown in the table).
sampler (Sampler, optional) – Object used to choose samples from the dataset (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).
shard_id (int, optional) – The shard ID within num_shards (default=None). This argument should be specified only when num_shards is also specified.

Raises

RuntimeError – If sampler and shuffle are specified at the same time.
RuntimeError – If sampler and sharding are specified at the same time.
RuntimeError – If num_shards is specified but shard_id is None.
RuntimeError – If shard_id is specified but num_shards is None.
ValueError – If shard_id is invalid (< 0 or >= num_shards).

Examples

>>> import mindspore.dataset as ds
>>> dataset_dir = "/path/to/cifar10_dataset_directory"
>>> # 1) get all samples from CIFAR10 dataset in sequence:
>>> dataset = ds.Cifar10Dataset(dataset_dir=dataset_dir,shuffle=False)
>>> # 2) randomly select 350 samples from CIFAR10 dataset:
>>> dataset = ds.Cifar10Dataset(dataset_dir=dataset_dir,num_samples=350, shuffle=True)
>>> # 3) get samples from CIFAR10 dataset for shard 0 in a 2 way distributed training:
>>> dataset = ds.Cifar10Dataset(dataset_dir=dataset_dir,num_shards=2,shard_id=0)
>>> # in CIFAR10 dataset, each dictionary has keys "image" and "label"

apply(apply_func)

Apply a function in this dataset.

The specified apply_func is a function that must take one ‘Dataset’ as an argument and return a preprogressing ‘Dataset’.

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

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> # declare an apply_func function which returns a Dataset object
>>> def apply_func(ds):
>>>     ds = ds.batch(2)
>>>     return ds
>>> # use apply to call apply_func
>>> data = data.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, pad_info=None)

Combine batch_size number of consecutive rows into batches.

For any child node, a batch is treated as a single row. 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. Recommend that repeat operation should be used after batch operation.

Parameters

batch_size (int or 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 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 to process the Dataset in parallel (default=None).
per_batch_map (callable, optional) – Per batch map callable. A callable which takes (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[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.

Returns

BatchDataset, dataset batched.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> # creates a dataset where every 100 rows is combined into a batch
>>> # and drops the last incomplete batch if there is one.
>>> data = data.batch(100, True)

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, and pad and batch the buckets 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 batch_info, and then batched. Each batch will be full, except for maybe the last batch for each bucket.

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, and one 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 len(column_names) arguments and returns an int. If no value is provided, then 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) – Represents how to batch each column. The key corresponds to the column name, 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).

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>>
>>> # creates a dataset where every 100 rows is combined into a batch
>>> # and drops the last incomplete batch if there is one.
>>> column_names = ["col1", "col2"]
>>> buket_boundaries = [5, 10]
>>> bucket_batch_sizes = [5, 1, 1]
>>> element_length_function = (lambda col1, col2: max(len(col1), len(col2)))
>>>
>>> # will pad col1 to shape [2, bucket_boundaries[i]] where i is the
>>> # index of the bucket that is currently being batched.
>>> # will pad col2 to a shape where each dimension is the longest in all
>>> # the elements currently being batched.
>>> pad_info = {"col1", ([2, None], -1)}
>>> pad_to_bucket_boundary = True
>>>
>>> data = data.bucket_batch_by_length(column_names, bucket_boundaries,
>>>                                    bucket_batch_sizes,
>>>                                    element_length_function, pad_info,
>>>                                    pad_to_bucket_boundary)

concat(datasets)

Concat the datasets in the input list of datasets, supported using “+” to reload concat operation.

Note

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

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

Examples

>>> import mindspore.dataset as ds
>>> # ds1 and ds2 are instances of Dataset object
>>> # creates a dataset by concating ds1 and ds2 with "+" operation
>>> data1 = ds1 + ds2
>>> # creates a dataset by concating ds1 and ds2 with concat operation
>>> data1 = ds1.concat(ds2)

create_dict_iterator(num_epochs=- 1)

Create an Iterator over the dataset.

The data retrieved will be a dictionary. The order of the columns in the dictionary may not be the same as the original order.

Returns: Iterator, dictionary of column_name-ndarray pair.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> # creates an iterator. The columns in the data obtained by the
>>> # iterator might be changed.
>>> iterator = data.create_dict_iterator()
>>> for item in iterator:
>>>     # print the data in column1
>>>     print(item["column1"])

create_tuple_iterator(columns=None, num_epochs=- 1)

Create an Iterator over the dataset. The data retrieved will be a list of ndarray of data.

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 not be changed.

Parameters: columns (list[str], optional) – List of columns to be used to specify the order of columns (default=None, means all columns).
Returns: Iterator, list of ndarray.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> # creates an iterator. The columns in the data obtained by the
>>> # iterator will not be changed.
>>> iterator = data.create_tuple_iterator()
>>> for item in iterator:
>>>     # convert the returned tuple to a list and print
>>>     print(list(item))

device_que(prefetch_size=None, send_epoch_end=True)

Return a transferredDataset that transfer data through device.

Parameters

prefetch_size (int, optional) – prefetch number of records ahead of the user’s request (default=None).
send_epoch_end (bool, optional) – whether send end of sequence to device or not.(default=True)

Note

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

Returns: TransferDataset, dataset for transferring.

filter(predicate, input_columns=None, num_parallel_workers=1)

Filter dataset by predicate.

Note

If input_columns not provided or empty, all columns will be used.

Parameters

predicate (callable) – python callable which returns a boolean value, if False then filter the element.
input_columns – (list[str], optional): List of names of the input columns, when default=None, the predicate will be applied on all columns in the dataset.
num_parallel_workers (int, optional) – Number of workers to process the Dataset in parallel (default=None).

Returns

FilterDataset, dataset filter.

Examples

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

flat_map(func)

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

The specified func is a function that must take one ‘Ndarray’ as input and return a ‘Dataset’.

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

Examples

>>> import mindspore.dataset as ds
>>> import mindspore.dataset.text as text
>>> # declare a function which returns a Dataset object
>>> def flat_map_func(x):
>>>     data_dir = text.to_str(x[0])
>>>     d = ds.ImageFolderDatasetV2(data_dir)
>>>     return d
>>> # data is a Dataset object
>>> data = ds.TextFileDataset(DATA_FILE)
>>> data = data.flat_map(flat_map_func)

Raises

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

get_batch_size()

Get the size of a batch.

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

get_class_indexing()

Get the class index.

Returns: Dict, A str-to-int mapping from label name to index.

get_dataset_size()[source]

Get the number of batches in an epoch.

Returns: Number, number of batches.

get_repeat_count()

Get the replication times in RepeatDataset else 1.

Returns: Number, the count of repeat.

map(input_columns=None, operations=None, output_columns=None, columns_order=None, num_parallel_workers=None, python_multiprocessing=False, cache=None)

Apply each operation in operations to this dataset.

The order of operations is determined by the position of each operation in operations. operations[0] will be applied first, then operations[1], then operations[2], etc.

Each operation will be passed one or more columns from the dataset as input, and zero 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.

Only the columns specified in columns_order will be propagated to the child node. These columns will be in the same order as specified in columns_order.

Parameters

input_columns (list[str]) – 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 is required, starting from the first column).
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) != 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).
columns_order (list[str], optional) – list of all the desired columns to propagate to the child node. This list must be a subset of all the columns in the dataset after all operations are applied. The order of the columns in each row propagated to the child node follow the order they appear in this list. The parameter is mandatory if the len(input_columns) != len(output_columns). (default=None, all columns will be propagated to the child node, the order of the columns will remain the same).
num_parallel_workers (int, optional) – Number of threads used to process the dataset in parallel (default=None, the value from the config will be used).
python_multiprocessing (bool, optional) – Parallelize python operations with multiple worker process. This option could be beneficial if the python operation is computational heavy (default=False).
cache (DatasetCache, optional) – Tensor cache to use. (default=None which means no cache is used). The cache feature is under development and is not recommended.

Returns

MapDataset, dataset after mapping operation.

Examples

>>> import mindspore.dataset as ds
>>> import mindspore.dataset.transforms.vision.c_transforms as c_transforms
>>>
>>> # data is an instance of Dataset which has 2 columns, "image" and "label".
>>> # ds_pyfunc is an instance of Dataset which has 3 columns, "col0", "col1", and "col2". Each column is
>>> # a 2d array of integers.
>>>
>>> # This config is a global setting, meaning that all future operations which
>>> # uses this config value will use 2 worker threads, unless if specified
>>> # otherwise in their constructor. set_num_parallel_workers can be called
>>> # again later if a different number of worker threads are needed.
>>> ds.config.set_num_parallel_workers(2)
>>>
>>> # Two operations, which takes 1 column for input and outputs 1 column.
>>> decode_op = c_transforms.Decode(rgb_format=True)
>>> random_jitter_op = c_transforms.RandomColorAdjust((0.8, 0.8), (1, 1), (1, 1), (0, 0))
>>>
>>> # 1) Simple map example
>>>
>>> operations = [decode_op]
>>> input_columns = ["image"]
>>>
>>> # Applies decode_op on column "image". This column will be replaced by the outputed
>>> # column of decode_op. Since columns_order is not provided, both columns "image"
>>> # and "label" will be propagated to the child node in their original order.
>>> ds_decoded = data.map(input_columns, operations)
>>>
>>> # Rename column "image" to "decoded_image"
>>> output_columns = ["decoded_image"]
>>> ds_decoded = data.map(input_columns, operations, output_columns)
>>>
>>> # Specify the order of the columns.
>>> columns_order ["label", "image"]
>>> ds_decoded = data.map(input_columns, operations, None, columns_order)
>>>
>>> # Rename column "image" to "decoded_image" and also specify the order of the columns.
>>> columns_order ["label", "decoded_image"]
>>> output_columns = ["decoded_image"]
>>> ds_decoded = data.map(input_columns, operations, output_columns, columns_order)
>>>
>>> # Rename column "image" to "decoded_image" and keep only this column.
>>> columns_order ["decoded_image"]
>>> output_columns = ["decoded_image"]
>>> ds_decoded = data.map(input_columns, operations, output_columns, columns_order)
>>>
>>> # Simple example using pyfunc. Renaming columns and specifying column order
>>> # work in the same way as the previous examples.
>>> input_columns = ["col0"]
>>> operations = [(lambda x: x + 1)]
>>> ds_mapped = ds_pyfunc.map(input_columns, operations)
>>>
>>> # 2) Map example with more than one operation
>>>
>>> # If this list of operations is used with map, decode_op will be applied
>>> # first, then random_jitter_op will be applied.
>>> operations = [decode_op, random_jitter_op]
>>>
>>> input_columns = ["image"]
>>>
>>> # Creates 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 columns_order is not specified, the order of the
>>> # columns will remain the same.
>>> ds_mapped = data.map(input_columns, operations)
>>>
>>> # Creates a dataset that is identical to ds_mapped, except the column "image"
>>> # that is outputted by random_jitter_op is renamed to "image_transformed".
>>> # Specifying column order works in the same way as examples in 1).
>>> output_columns = ["image_transformed"]
>>> ds_mapped_and_renamed = data.map(input_columns, operation, output_columns)
>>>
>>> # Multiple operations using pyfunc. Renaming columns and specifying column order
>>> # work in the same way as examples in 1).
>>> input_columns = ["col0"]
>>> operations = [(lambda x: x + x), (lambda x: x - 1)]
>>> output_columns = ["col0_mapped"]
>>> ds_mapped = ds_pyfunc.map(input_columns, operations, output_columns)
>>>
>>> # 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[1] 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: because the number of input columns is not the same as the number of
>>> # output columns, the output_columns and columns_order parameter must be
>>> # specified. Otherwise, this map call will also result in an error.
>>> input_columns = ["col2", "col0"]
>>> output_columns = ["mod2", "mod3", "mod5", "mod7"]
>>>
>>> # Propagate all columns to the child node in this order:
>>> columns_order = ["col0", "col2", "mod2", "mod3", "mod5", "mod7", "col1"]
>>> ds_mapped = ds_pyfunc.map(input_columns, operations, output_columns, columns_order)
>>>
>>> # Propagate some columns to the child node in this order:
>>> columns_order = ["mod7", "mod3", "col1"]
>>> ds_mapped = ds_pyfunc.map(input_columns, operations, output_columns, columns_order)

num_classes()

Get the number of classes in a dataset.

Returns: Number, number of classes.

output_shapes()

Get the shapes of output data.

Returns: List, list of shape of each column.

output_types()

Get the types of output data.

Returns: List of data type.

project(columns)

Project certain columns in input datasets.

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

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

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> columns_to_project = ["column3", "column1", "column2"]
>>>
>>> # creates a dataset that consist of column3, column1, column2
>>> # in that order, regardless of the original order of columns.
>>> data = data.project(columns=columns_to_project)

rename(input_columns, output_columns)

Rename the columns in input datasets.

Parameters

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

Returns

RenameDataset, dataset renamed.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> input_columns = ["input_col1", "input_col2", "input_col3"]
>>> output_columns = ["output_col1", "output_col2", "output_col3"]
>>>
>>> # creates 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.
>>> data = data.rename(input_columns=input_columns, output_columns=output_columns)

repeat(count=None)

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

Note

The order of using repeat and batch reflects the number of batches. Recommend that repeat operation should be used after batch operation. If dataset_sink_mode is False, here repeat operation is invalid. If dataset_sink_mode is True, repeat count should be equal to the epoch of training. Otherwise, errors could occur since the amount of data is not the amount training requires.

Parameters: count (int) – Number of times the dataset should be repeated (default=None).
Returns: RepeatDataset, dataset repeated.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> # creates a dataset where the dataset is repeated for 50 epochs
>>> repeated = data.repeat(50)
>>>
>>> # creates a dataset where each epoch is shuffled individually
>>> shuffled_and_repeated = data.shuffle(10)
>>> shuffled_and_repeated = shuffled_and_repeated.repeat(50)
>>>
>>> # creates 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.
>>> repeat_and_shuffle = data.repeat(50)
>>> repeat_and_shuffle = repeat_and_shuffle.shuffle(10)

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

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

Save the dynamic data processed by dataset pipeline as common dataset format, support: mindrecord.

Implicit type casting exists when saving data as mindrecord. Table below shows how to do type casting.

Implicit Type Casting of Saving as mindrecord
type in ‘dataset’	type in ‘mindrecord’	detail
bool	None	Not support
int8	int32
uint8	bytes(1D uint8)	Drop dimension
int16	int32
uint16	int32
int32	int32
uint32	int64
int64	int64
uint64	None	Not support
float16	float32
float32	float32
float64	float64
string	string	Not support multi-dimensional string

Note

To save the samples in order, should set dataset’s shuffle false and num_files 1.
Before call the function, do not use batch, repeat operator or data augmentation operators with random attribute in map operator.
Mindrecord does not support DE_UINT64, multi-dimensional DE_UINT8(drop dimension) and multi-dimensional DE_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’)

shuffle(buffer_size)

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

Make a shuffle buffer that contains the first buffer_size rows.
Randomly select an element from the shuffle buffer to be the next row propogated 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 seed can be provided to be used on the first epoch. 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: ShuffleDataset, dataset shuffled.
Raises: RuntimeError – If exist sync operators before shuffle.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> # optionally set the seed for the first epoch
>>> ds.config.set_seed(58)
>>>
>>> # creates a shuffled dataset using a shuffle buffer of size 4
>>> data = data.shuffle(4)

skip(count)

Skip the first N elements of this dataset.

Parameters: count (int) – Number of elements the dataset should be skipped.
Returns: SkipDataset, dataset skipped.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> # creates a dataset which skips first 3 elements from data
>>> data = data.skip(3)

split(sizes, randomize=True)

Split the dataset into smaller, non-overlapping datasets.

There is the optimized split function, which will be called automatically when the dataset that calls this function is a MappableDataset.

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 sizes does not equal the original dataset size, an an error will occur. 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 occur. 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 will be added to the first split.
- The sum of split sizes > K, the difference will be removed from the first large enough split such that it will have atleast 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 should not be sharded if split is going to be called. Instead, create a DistributedSampler and specify a split to shard after splitting. If dataset is sharded after a split, it is strongly recommended to set the same seed in each instance of execution, otherwise each shard may not be part of the same split (see Examples).
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. Furthermore, if sharding occurs after split, each shard may not be part of the same 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.

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

Examples

>>> import mindspore.dataset as ds
>>>
>>> dataset_dir = "/path/to/imagefolder_directory"
>>>
>>> # many datasets have shuffle on by default, set shuffle to False if split will be called!
>>> data = ds.ImageFolderDatasetV2(dataset_dir, shuffle=False)
>>>
>>> # sets the seed, and tells split to use this seed when randomizing. This
>>> # is needed because we are sharding later
>>> ds.config.set_seed(58)
>>> train, test = data.split([0.9, 0.1])
>>>
>>> # if we want to shard the train dataset, we can use a DistributedSampler
>>> train_sampler = ds.DistributedSampler(10, 2)
>>> train.use_sampler(train_sampler)

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 (Union[dict, None]) – The data passed to the callback (default=None).

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

Add a blocking condition to the input Dataset.

Parameters

num_batch (int) – the number of batches without blocking at the start of each epoch.
condition_name (str) – The condition name that is used to toggle sending next row.
callback (function) – The callback funciton that will be invoked when sync_update is called.

Raises

RuntimeError – If condition name already exists.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> data = data.sync_wait("callback1")
>>> data = data.batch(batch_size)
>>> for batch_data in data.create_dict_iterator():
>>>     data = data.sync_update("callback1")

take(count=- 1)

Takes at most given numbers of elements from the dataset.

Note

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

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

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> # creates a dataset where the dataset including 50 elements.
>>> data = data.take(50)

to_device(send_epoch_end=True)

Transfer data through CPU, GPU or Ascend devices.

Parameters: send_epoch_end (bool, optional) – whether send end of sequence to device or not.(default=True)

Note

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

Returns

TransferDataset, dataset for transferring.

Raises

TypeError – If device_type is empty.
ValueError – If device_type is not ‘Ascend’, ‘GPU’ or ‘CPU’.
RuntimeError – If dataset is unknown.
RuntimeError – If distribution file path is given but failed to read.

use_sampler(new_sampler)

Will make the current dataset use the new_sampler provided.

Parameters: new_sampler (Sampler) – the sampler to use for the current dataset.
Returns: Dataset, that uses new_sampler.

Examples

>>> import mindspore.dataset as ds
>>>
>>> dataset_dir = "/path/to/imagefolder_directory"
>>> # a SequentialSampler is created by default
>>> data = ds.ImageFolderDatasetV2(dataset_dir)
>>>
>>> # use a DistributedSampler instead of the SequentialSampler
>>> new_sampler = ds.DistributedSampler(10, 2)
>>> data.use_sampler(new_sampler)

zip(datasets)

Zip the datasets in the input tuple of datasets. Columns in the input datasets must not have the same name.

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

Examples

>>> import mindspore.dataset as ds
>>> # ds1 and ds2 are instances of Dataset object
>>> # creates a dataset which is the combination of ds1 and ds2
>>> data = ds1.zip(ds2)

class mindspore.dataset.CocoDataset(dataset_dir, annotation_file, task='Detection', num_samples=None, num_parallel_workers=None, shuffle=None, decode=False, sampler=None, num_shards=None, shard_id=None)[source]

A source dataset for reading and parsing COCO dataset.

CocoDataset support four kinds of task: 2017 Train/Val/Test Detection, Keypoints, Stuff, Panoptic.

The generated dataset has multi-columns :

task=’Detection’, column: [[‘image’, dtype=uint8], [‘bbox’, dtype=float32], [‘category_id’, dtype=uint32], [‘iscrowd’, dtype=uint32]].

task=’Stuff’, column: [[‘image’, dtype=uint8], [‘segmentation’,dtype=float32], [‘iscrowd’,dtype=uint32]].

task=’Keypoint’, column: [[‘image’, dtype=uint8], [‘keypoints’, dtype=float32], [‘num_keypoints’, dtype=uint32]].

task=’Panoptic’, column: [[‘image’, dtype=uint8], [‘bbox’, dtype=float32], [‘category_id’, dtype=uint32], [‘iscrowd’, dtype=uint32], [‘area’, dtype=uint32]].

This dataset can take in a sampler. sampler and shuffle are mutually exclusive. CocoDataset doesn’t support PKSampler. Table below shows what input args are allowed and their expected behavior.

Expected Order Behavior of Using ‘sampler’ and ‘shuffle’
Parameter ‘sampler’	Parameter ‘shuffle’	Expected Order Behavior
None	None	random order
None	True	random order
None	False	sequential order
Sampler object	None	order defined by sampler
Sampler object	True	not allowed
Sampler object	False	not allowed

Citation of Coco dataset.

@article{DBLP:journals/corr/LinMBHPRDZ14,
author        = {Tsung{-}Yi Lin and Michael Maire and Serge J. Belongie and
                 Lubomir D. Bourdev and  Ross B. Girshick and James Hays and
                 Pietro Perona and Deva Ramanan and Piotr Doll{'{a}}r and C. Lawrence Zitnick},
title         = {Microsoft {COCO:} Common Objects in Context},
journal       = {CoRR},
volume        = {abs/1405.0312},
year          = {2014},
url           = {http://arxiv.org/abs/1405.0312},
archivePrefix = {arXiv},
eprint        = {1405.0312},
timestamp     = {Mon, 13 Aug 2018 16:48:13 +0200},
biburl        = {https://dblp.org/rec/journals/corr/LinMBHPRDZ14.bib},
bibsource     = {dblp computer science bibliography, https://dblp.org},
description   = {COCO is a large-scale object detection, segmentation, and captioning dataset.
                 It contains 91 common object categories with 82 of them having more than 5,000
                 labeled instances. In contrast to the popular ImageNet dataset, COCO has fewer
                 categories but more instances per category.}
}

Parameters

dataset_dir (str) – Path to the root directory that contains the dataset.
annotation_file (str) – Path to the annotation json.
task (str) – Set the task type of reading coco data, now support ‘Detection’/’Stuff’/’Panoptic’/’Keypoint’ (default=’Detection’).
num_samples (int, optional) – The number of images to be included in the dataset (default=None, all images).
num_parallel_workers (int, optional) – Number of workers to read the data (default=None, number set in the config).
shuffle (bool, optional) – Whether to perform shuffle on the dataset (default=None, expected order behavior shown in the table).
decode (bool, optional) – Decode the images after reading (default=False).
sampler (Sampler, optional) – Object used to choose samples from the dataset (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).
shard_id (int, optional) – The shard ID within num_shards (default=None). This argument should be specified only when num_shards is also specified.

Raises

RuntimeError – If sampler and shuffle are specified at the same time.
RuntimeError – If sampler and sharding are specified at the same time.
RuntimeError – If num_shards is specified but shard_id is None.
RuntimeError – If shard_id is specified but num_shards is None.
RuntimeError – If parse json file failed.
ValueError – If task is not in [‘Detection’, ‘Stuff’, ‘Panoptic’, ‘Keypoint’].
ValueError – If annotation_file is not exist.
ValueError – If dataset_dir is not exist.
ValueError – If shard_id is invalid (< 0 or >= num_shards).

Examples

>>> import mindspore.dataset as ds
>>> dataset_dir = "/path/to/coco_dataset_directory/image_folder"
>>> annotation_file = "/path/to/coco_dataset_directory/annotation_folder/annotation.json"
>>> # 1) read COCO data for Detection task
>>> coco_dataset = ds.CocoDataset(dataset_dir, annotation_file=annotation_file, task='Detection')
>>> # 2) read COCO data for Stuff task
>>> coco_dataset = ds.CocoDataset(dataset_dir, annotation_file=annotation_file, task='Stuff')
>>> # 3) read COCO data for Panoptic task
>>> coco_dataset = ds.CocoDataset(dataset_dir, annotation_file=annotation_file, task='Panoptic')
>>> # 4) read COCO data for Keypoint task
>>> coco_dataset = ds.CocoDataset(dataset_dir, annotation_file=annotation_file, task='Keypoint')
>>> # in COCO dataset, each dictionary has keys "image" and "annotation"

apply(apply_func)

Apply a function in this dataset.

The specified apply_func is a function that must take one ‘Dataset’ as an argument and return a preprogressing ‘Dataset’.

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

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> # declare an apply_func function which returns a Dataset object
>>> def apply_func(ds):
>>>     ds = ds.batch(2)
>>>     return ds
>>> # use apply to call apply_func
>>> data = data.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, pad_info=None)

Combine batch_size number of consecutive rows into batches.

For any child node, a batch is treated as a single row. 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. Recommend that repeat operation should be used after batch operation.

Parameters

batch_size (int or 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 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 to process the Dataset in parallel (default=None).
per_batch_map (callable, optional) – Per batch map callable. A callable which takes (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[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.

Returns

BatchDataset, dataset batched.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> # creates a dataset where every 100 rows is combined into a batch
>>> # and drops the last incomplete batch if there is one.
>>> data = data.batch(100, True)

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, and pad and batch the buckets 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 batch_info, and then batched. Each batch will be full, except for maybe the last batch for each bucket.

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, and one 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 len(column_names) arguments and returns an int. If no value is provided, then 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) – Represents how to batch each column. The key corresponds to the column name, 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).

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>>
>>> # creates a dataset where every 100 rows is combined into a batch
>>> # and drops the last incomplete batch if there is one.
>>> column_names = ["col1", "col2"]
>>> buket_boundaries = [5, 10]
>>> bucket_batch_sizes = [5, 1, 1]
>>> element_length_function = (lambda col1, col2: max(len(col1), len(col2)))
>>>
>>> # will pad col1 to shape [2, bucket_boundaries[i]] where i is the
>>> # index of the bucket that is currently being batched.
>>> # will pad col2 to a shape where each dimension is the longest in all
>>> # the elements currently being batched.
>>> pad_info = {"col1", ([2, None], -1)}
>>> pad_to_bucket_boundary = True
>>>
>>> data = data.bucket_batch_by_length(column_names, bucket_boundaries,
>>>                                    bucket_batch_sizes,
>>>                                    element_length_function, pad_info,
>>>                                    pad_to_bucket_boundary)

concat(datasets)

Concat the datasets in the input list of datasets, supported using “+” to reload concat operation.

Note

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

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

Examples

>>> import mindspore.dataset as ds
>>> # ds1 and ds2 are instances of Dataset object
>>> # creates a dataset by concating ds1 and ds2 with "+" operation
>>> data1 = ds1 + ds2
>>> # creates a dataset by concating ds1 and ds2 with concat operation
>>> data1 = ds1.concat(ds2)

create_dict_iterator(num_epochs=- 1)

Create an Iterator over the dataset.

The data retrieved will be a dictionary. The order of the columns in the dictionary may not be the same as the original order.

Returns: Iterator, dictionary of column_name-ndarray pair.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> # creates an iterator. The columns in the data obtained by the
>>> # iterator might be changed.
>>> iterator = data.create_dict_iterator()
>>> for item in iterator:
>>>     # print the data in column1
>>>     print(item["column1"])

create_tuple_iterator(columns=None, num_epochs=- 1)

Create an Iterator over the dataset. The data retrieved will be a list of ndarray of data.

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 not be changed.

Parameters: columns (list[str], optional) – List of columns to be used to specify the order of columns (default=None, means all columns).
Returns: Iterator, list of ndarray.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> # creates an iterator. The columns in the data obtained by the
>>> # iterator will not be changed.
>>> iterator = data.create_tuple_iterator()
>>> for item in iterator:
>>>     # convert the returned tuple to a list and print
>>>     print(list(item))

device_que(prefetch_size=None, send_epoch_end=True)

Return a transferredDataset that transfer data through device.

Parameters

prefetch_size (int, optional) – prefetch number of records ahead of the user’s request (default=None).
send_epoch_end (bool, optional) – whether send end of sequence to device or not.(default=True)

Note

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

Returns: TransferDataset, dataset for transferring.

filter(predicate, input_columns=None, num_parallel_workers=1)

Filter dataset by predicate.

Note

If input_columns not provided or empty, all columns will be used.

Parameters

predicate (callable) – python callable which returns a boolean value, if False then filter the element.
input_columns – (list[str], optional): List of names of the input columns, when default=None, the predicate will be applied on all columns in the dataset.
num_parallel_workers (int, optional) – Number of workers to process the Dataset in parallel (default=None).

Returns

FilterDataset, dataset filter.

Examples

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

flat_map(func)

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

The specified func is a function that must take one ‘Ndarray’ as input and return a ‘Dataset’.

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

Examples

>>> import mindspore.dataset as ds
>>> import mindspore.dataset.text as text
>>> # declare a function which returns a Dataset object
>>> def flat_map_func(x):
>>>     data_dir = text.to_str(x[0])
>>>     d = ds.ImageFolderDatasetV2(data_dir)
>>>     return d
>>> # data is a Dataset object
>>> data = ds.TextFileDataset(DATA_FILE)
>>> data = data.flat_map(flat_map_func)

Raises

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

get_batch_size()

Get the size of a batch.

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

get_class_indexing()[source]

Get the class index.

Returns: Dict, A str-to-int mapping from label name to index.

get_dataset_size()[source]

Get the number of batches in an epoch.

Returns: Number, number of batches.

get_repeat_count()

Get the replication times in RepeatDataset else 1.

Returns: Number, the count of repeat.

map(input_columns=None, operations=None, output_columns=None, columns_order=None, num_parallel_workers=None, python_multiprocessing=False, cache=None)

Apply each operation in operations to this dataset.

The order of operations is determined by the position of each operation in operations. operations[0] will be applied first, then operations[1], then operations[2], etc.

Each operation will be passed one or more columns from the dataset as input, and zero 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.

Only the columns specified in columns_order will be propagated to the child node. These columns will be in the same order as specified in columns_order.

Parameters

input_columns (list[str]) – 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 is required, starting from the first column).
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) != 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).
columns_order (list[str], optional) – list of all the desired columns to propagate to the child node. This list must be a subset of all the columns in the dataset after all operations are applied. The order of the columns in each row propagated to the child node follow the order they appear in this list. The parameter is mandatory if the len(input_columns) != len(output_columns). (default=None, all columns will be propagated to the child node, the order of the columns will remain the same).
num_parallel_workers (int, optional) – Number of threads used to process the dataset in parallel (default=None, the value from the config will be used).
python_multiprocessing (bool, optional) – Parallelize python operations with multiple worker process. This option could be beneficial if the python operation is computational heavy (default=False).
cache (DatasetCache, optional) – Tensor cache to use. (default=None which means no cache is used). The cache feature is under development and is not recommended.

Returns

MapDataset, dataset after mapping operation.

Examples

>>> import mindspore.dataset as ds
>>> import mindspore.dataset.transforms.vision.c_transforms as c_transforms
>>>
>>> # data is an instance of Dataset which has 2 columns, "image" and "label".
>>> # ds_pyfunc is an instance of Dataset which has 3 columns, "col0", "col1", and "col2". Each column is
>>> # a 2d array of integers.
>>>
>>> # This config is a global setting, meaning that all future operations which
>>> # uses this config value will use 2 worker threads, unless if specified
>>> # otherwise in their constructor. set_num_parallel_workers can be called
>>> # again later if a different number of worker threads are needed.
>>> ds.config.set_num_parallel_workers(2)
>>>
>>> # Two operations, which takes 1 column for input and outputs 1 column.
>>> decode_op = c_transforms.Decode(rgb_format=True)
>>> random_jitter_op = c_transforms.RandomColorAdjust((0.8, 0.8), (1, 1), (1, 1), (0, 0))
>>>
>>> # 1) Simple map example
>>>
>>> operations = [decode_op]
>>> input_columns = ["image"]
>>>
>>> # Applies decode_op on column "image". This column will be replaced by the outputed
>>> # column of decode_op. Since columns_order is not provided, both columns "image"
>>> # and "label" will be propagated to the child node in their original order.
>>> ds_decoded = data.map(input_columns, operations)
>>>
>>> # Rename column "image" to "decoded_image"
>>> output_columns = ["decoded_image"]
>>> ds_decoded = data.map(input_columns, operations, output_columns)
>>>
>>> # Specify the order of the columns.
>>> columns_order ["label", "image"]
>>> ds_decoded = data.map(input_columns, operations, None, columns_order)
>>>
>>> # Rename column "image" to "decoded_image" and also specify the order of the columns.
>>> columns_order ["label", "decoded_image"]
>>> output_columns = ["decoded_image"]
>>> ds_decoded = data.map(input_columns, operations, output_columns, columns_order)
>>>
>>> # Rename column "image" to "decoded_image" and keep only this column.
>>> columns_order ["decoded_image"]
>>> output_columns = ["decoded_image"]
>>> ds_decoded = data.map(input_columns, operations, output_columns, columns_order)
>>>
>>> # Simple example using pyfunc. Renaming columns and specifying column order
>>> # work in the same way as the previous examples.
>>> input_columns = ["col0"]
>>> operations = [(lambda x: x + 1)]
>>> ds_mapped = ds_pyfunc.map(input_columns, operations)
>>>
>>> # 2) Map example with more than one operation
>>>
>>> # If this list of operations is used with map, decode_op will be applied
>>> # first, then random_jitter_op will be applied.
>>> operations = [decode_op, random_jitter_op]
>>>
>>> input_columns = ["image"]
>>>
>>> # Creates 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 columns_order is not specified, the order of the
>>> # columns will remain the same.
>>> ds_mapped = data.map(input_columns, operations)
>>>
>>> # Creates a dataset that is identical to ds_mapped, except the column "image"
>>> # that is outputted by random_jitter_op is renamed to "image_transformed".
>>> # Specifying column order works in the same way as examples in 1).
>>> output_columns = ["image_transformed"]
>>> ds_mapped_and_renamed = data.map(input_columns, operation, output_columns)
>>>
>>> # Multiple operations using pyfunc. Renaming columns and specifying column order
>>> # work in the same way as examples in 1).
>>> input_columns = ["col0"]
>>> operations = [(lambda x: x + x), (lambda x: x - 1)]
>>> output_columns = ["col0_mapped"]
>>> ds_mapped = ds_pyfunc.map(input_columns, operations, output_columns)
>>>
>>> # 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[1] 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: because the number of input columns is not the same as the number of
>>> # output columns, the output_columns and columns_order parameter must be
>>> # specified. Otherwise, this map call will also result in an error.
>>> input_columns = ["col2", "col0"]
>>> output_columns = ["mod2", "mod3", "mod5", "mod7"]
>>>
>>> # Propagate all columns to the child node in this order:
>>> columns_order = ["col0", "col2", "mod2", "mod3", "mod5", "mod7", "col1"]
>>> ds_mapped = ds_pyfunc.map(input_columns, operations, output_columns, columns_order)
>>>
>>> # Propagate some columns to the child node in this order:
>>> columns_order = ["mod7", "mod3", "col1"]
>>> ds_mapped = ds_pyfunc.map(input_columns, operations, output_columns, columns_order)

num_classes()

Get the number of classes in a dataset.

Returns: Number, number of classes.

output_shapes()

Get the shapes of output data.

Returns: List, list of shape of each column.

output_types()

Get the types of output data.

Returns: List of data type.

project(columns)

Project certain columns in input datasets.

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

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

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> columns_to_project = ["column3", "column1", "column2"]
>>>
>>> # creates a dataset that consist of column3, column1, column2
>>> # in that order, regardless of the original order of columns.
>>> data = data.project(columns=columns_to_project)

rename(input_columns, output_columns)

Rename the columns in input datasets.

Parameters

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

Returns

RenameDataset, dataset renamed.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> input_columns = ["input_col1", "input_col2", "input_col3"]
>>> output_columns = ["output_col1", "output_col2", "output_col3"]
>>>
>>> # creates 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.
>>> data = data.rename(input_columns=input_columns, output_columns=output_columns)

repeat(count=None)

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

Note

The order of using repeat and batch reflects the number of batches. Recommend that repeat operation should be used after batch operation. If dataset_sink_mode is False, here repeat operation is invalid. If dataset_sink_mode is True, repeat count should be equal to the epoch of training. Otherwise, errors could occur since the amount of data is not the amount training requires.

Parameters: count (int) – Number of times the dataset should be repeated (default=None).
Returns: RepeatDataset, dataset repeated.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> # creates a dataset where the dataset is repeated for 50 epochs
>>> repeated = data.repeat(50)
>>>
>>> # creates a dataset where each epoch is shuffled individually
>>> shuffled_and_repeated = data.shuffle(10)
>>> shuffled_and_repeated = shuffled_and_repeated.repeat(50)
>>>
>>> # creates 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.
>>> repeat_and_shuffle = data.repeat(50)
>>> repeat_and_shuffle = repeat_and_shuffle.shuffle(10)

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

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

Save the dynamic data processed by dataset pipeline as common dataset format, support: mindrecord.

Implicit type casting exists when saving data as mindrecord. Table below shows how to do type casting.

Implicit Type Casting of Saving as mindrecord
type in ‘dataset’	type in ‘mindrecord’	detail
bool	None	Not support
int8	int32
uint8	bytes(1D uint8)	Drop dimension
int16	int32
uint16	int32
int32	int32
uint32	int64
int64	int64
uint64	None	Not support
float16	float32
float32	float32
float64	float64
string	string	Not support multi-dimensional string

Note

To save the samples in order, should set dataset’s shuffle false and num_files 1.
Before call the function, do not use batch, repeat operator or data augmentation operators with random attribute in map operator.
Mindrecord does not support DE_UINT64, multi-dimensional DE_UINT8(drop dimension) and multi-dimensional DE_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’)

shuffle(buffer_size)

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

Make a shuffle buffer that contains the first buffer_size rows.
Randomly select an element from the shuffle buffer to be the next row propogated 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 seed can be provided to be used on the first epoch. 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: ShuffleDataset, dataset shuffled.
Raises: RuntimeError – If exist sync operators before shuffle.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> # optionally set the seed for the first epoch
>>> ds.config.set_seed(58)
>>>
>>> # creates a shuffled dataset using a shuffle buffer of size 4
>>> data = data.shuffle(4)

skip(count)

Skip the first N elements of this dataset.

Parameters: count (int) – Number of elements the dataset should be skipped.
Returns: SkipDataset, dataset skipped.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> # creates a dataset which skips first 3 elements from data
>>> data = data.skip(3)

split(sizes, randomize=True)

Split the dataset into smaller, non-overlapping datasets.

There is the optimized split function, which will be called automatically when the dataset that calls this function is a MappableDataset.

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 sizes does not equal the original dataset size, an an error will occur. 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 occur. 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 will be added to the first split.
- The sum of split sizes > K, the difference will be removed from the first large enough split such that it will have atleast 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 should not be sharded if split is going to be called. Instead, create a DistributedSampler and specify a split to shard after splitting. If dataset is sharded after a split, it is strongly recommended to set the same seed in each instance of execution, otherwise each shard may not be part of the same split (see Examples).
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. Furthermore, if sharding occurs after split, each shard may not be part of the same 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.

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

Examples

>>> import mindspore.dataset as ds
>>>
>>> dataset_dir = "/path/to/imagefolder_directory"
>>>
>>> # many datasets have shuffle on by default, set shuffle to False if split will be called!
>>> data = ds.ImageFolderDatasetV2(dataset_dir, shuffle=False)
>>>
>>> # sets the seed, and tells split to use this seed when randomizing. This
>>> # is needed because we are sharding later
>>> ds.config.set_seed(58)
>>> train, test = data.split([0.9, 0.1])
>>>
>>> # if we want to shard the train dataset, we can use a DistributedSampler
>>> train_sampler = ds.DistributedSampler(10, 2)
>>> train.use_sampler(train_sampler)

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 (Union[dict, None]) – The data passed to the callback (default=None).

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

Add a blocking condition to the input Dataset.

Parameters

num_batch (int) – the number of batches without blocking at the start of each epoch.
condition_name (str) – The condition name that is used to toggle sending next row.
callback (function) – The callback funciton that will be invoked when sync_update is called.

Raises

RuntimeError – If condition name already exists.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> data = data.sync_wait("callback1")
>>> data = data.batch(batch_size)
>>> for batch_data in data.create_dict_iterator():
>>>     data = data.sync_update("callback1")

take(count=- 1)

Takes at most given numbers of elements from the dataset.

Note

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

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

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> # creates a dataset where the dataset including 50 elements.
>>> data = data.take(50)

to_device(send_epoch_end=True)

Transfer data through CPU, GPU or Ascend devices.

Parameters: send_epoch_end (bool, optional) – whether send end of sequence to device or not.(default=True)

Note

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

Returns

TransferDataset, dataset for transferring.

Raises

TypeError – If device_type is empty.
ValueError – If device_type is not ‘Ascend’, ‘GPU’ or ‘CPU’.
RuntimeError – If dataset is unknown.
RuntimeError – If distribution file path is given but failed to read.

use_sampler(new_sampler)

Will make the current dataset use the new_sampler provided.

Parameters: new_sampler (Sampler) – the sampler to use for the current dataset.
Returns: Dataset, that uses new_sampler.

Examples

>>> import mindspore.dataset as ds
>>>
>>> dataset_dir = "/path/to/imagefolder_directory"
>>> # a SequentialSampler is created by default
>>> data = ds.ImageFolderDatasetV2(dataset_dir)
>>>
>>> # use a DistributedSampler instead of the SequentialSampler
>>> new_sampler = ds.DistributedSampler(10, 2)
>>> data.use_sampler(new_sampler)

zip(datasets)

Zip the datasets in the input tuple of datasets. Columns in the input datasets must not have the same name.

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

Examples

>>> import mindspore.dataset as ds
>>> # ds1 and ds2 are instances of Dataset object
>>> # creates a dataset which is the combination of ds1 and ds2
>>> data = ds1.zip(ds2)

class mindspore.dataset.DistributedSampler(num_shards, shard_id, shuffle=True, num_samples=None)[source]

Sampler that access a shard of the dataset.

Parameters

num_shards (int) – Number of shards to divide the dataset into.
shard_id (int) – Shard ID of the current shard within num_shards.
shuffle (bool, optional) – If true, the indices are shuffled (default=True).
num_samples (int, optional) – The number of samples to draw (default=None, all elements).

Examples

>>> import mindspore.dataset as ds
>>>
>>> dataset_dir = "path/to/imagefolder_directory"
>>>
>>> # creates a distributed sampler with 10 shards total. This shard is shard 5
>>> sampler = ds.DistributedSampler(10, 5)
>>> data = ds.ImageFolderDatasetV2(dataset_dir, num_parallel_workers=8, sampler=sampler)

Raises

ValueError – If num_shards is not positive.
ValueError – If shard_id is smaller than 0 or equal to num_shards or larger than num_shards.
ValueError – If shuffle is not a boolean value.

get_num_samples()

All samplers can contain a numeric num_samples value (or it could be set to None). Child sampler can exist or be None. if child sampler exists, then the child sampler count can be a numeric value or None. Given these conditions, we need to output what the sampler count is for this sampler. The following table shows the possible results from calling this function.

child sampler	num_samples	child_samples	result
T	x	y	min(x, y)
T	x	None	x
T	None	y	y
T	None	None	None
None	x	n/a	x
None	None	n/a	None

Returns: int, The number of samples, or None

class mindspore.dataset.GeneratorDataset(source, column_names=None, column_types=None, schema=None, num_samples=None, num_parallel_workers=1, shuffle=None, sampler=None, num_shards=None, shard_id=None)[source]

A source dataset that generate data from python by invoking python data source each epoch.

This dataset can take in a sampler. sampler and shuffle are mutually exclusive. Table below shows what input args are allowed and their expected behavior.

Expected Order Behavior of Using ‘sampler’ and ‘shuffle’
Parameter ‘sampler’	Parameter ‘shuffle’	Expected Order Behavior
None	None	random order
None	True	random order
None	False	sequential order
Sampler object	None	order defined by sampler
Sampler object	True	not allowed
Sampler object	False	not allowed

Parameters

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(). Random accessible source is required to return a tuple of numpy array as a row of the dataset on source[idx].
column_names (list[str], optional) – List of column names of the dataset (default=None). Users are required to 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 (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 (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 when num_shards is also specified. Random accessible input is required.

Examples

>>> import mindspore.dataset as ds
>>> # 1) Multidimensional generator function as callable input
>>> def generator_md():
>>>     for i in range(64):
>>>         yield (np.array([[i, i + 1], [i + 2, i + 3]]),)
>>> # create multi_dimension_generator_dataset with GeneratorMD and column name "multi_dimensional_data"
>>> multi_dimension_generator_dataset = ds.GeneratorDataset(generator_md, ["multi_dimensional_data"])
>>> # 2) Multi-column generator function as callable input
>>> def generator_mc(maxid = 64):
>>>     for i in range(maxid):
>>>         yield (np.array([i]), np.array([[i, i + 1], [i + 2, i + 3]]))
>>> # create multi_column_generator_dataset with GeneratorMC and column names "col1" and "col2"
>>> multi_column_generator_dataset = ds.GeneratorDataset(generator_mc, ["col1", "col2"])
>>> # 3) Iterable dataset as iterable input
>>> class MyIterable():
>>>     def __iter__(self):
>>>         return # User implementation
>>> # create iterable_generator_dataset with MyIterable object
>>> iterable_generator_dataset = ds.GeneratorDataset(MyIterable(), ["col1"])
>>> # 4) Random accessible dataset as Random accessible input
>>> class MyRA():
>>>     def __getitem__(self, index):
>>>         return # User implementation
>>> # create ra_generator_dataset with MyRA object
>>> ra_generator_dataset = ds.GeneratorDataset(MyRA(), ["col1"])
>>> # List/Dict/Tuple is also random accessible
>>> list_generator = ds.GeneratorDataset([(np.array(0),), (np.array(1)), (np.array(2))], ["col1"])
>>> # 5) Built-in Sampler
>>> my_generator = ds.GeneratorDataset(my_ds, ["img", "label"], sampler=samplers.RandomSampler())
>>>

apply(apply_func)

Apply a function in this dataset.

The specified apply_func is a function that must take one ‘Dataset’ as an argument and return a preprogressing ‘Dataset’.

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

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> # declare an apply_func function which returns a Dataset object
>>> def apply_func(ds):
>>>     ds = ds.batch(2)
>>>     return ds
>>> # use apply to call apply_func
>>> data = data.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, pad_info=None)

Combine batch_size number of consecutive rows into batches.

For any child node, a batch is treated as a single row. 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. Recommend that repeat operation should be used after batch operation.

Parameters

batch_size (int or 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 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 to process the Dataset in parallel (default=None).
per_batch_map (callable, optional) – Per batch map callable. A callable which takes (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[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.

Returns

BatchDataset, dataset batched.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> # creates a dataset where every 100 rows is combined into a batch
>>> # and drops the last incomplete batch if there is one.
>>> data = data.batch(100, True)

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, and pad and batch the buckets 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 batch_info, and then batched. Each batch will be full, except for maybe the last batch for each bucket.

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, and one 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 len(column_names) arguments and returns an int. If no value is provided, then 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) – Represents how to batch each column. The key corresponds to the column name, 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).

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>>
>>> # creates a dataset where every 100 rows is combined into a batch
>>> # and drops the last incomplete batch if there is one.
>>> column_names = ["col1", "col2"]
>>> buket_boundaries = [5, 10]
>>> bucket_batch_sizes = [5, 1, 1]
>>> element_length_function = (lambda col1, col2: max(len(col1), len(col2)))
>>>
>>> # will pad col1 to shape [2, bucket_boundaries[i]] where i is the
>>> # index of the bucket that is currently being batched.
>>> # will pad col2 to a shape where each dimension is the longest in all
>>> # the elements currently being batched.
>>> pad_info = {"col1", ([2, None], -1)}
>>> pad_to_bucket_boundary = True
>>>
>>> data = data.bucket_batch_by_length(column_names, bucket_boundaries,
>>>                                    bucket_batch_sizes,
>>>                                    element_length_function, pad_info,
>>>                                    pad_to_bucket_boundary)

concat(datasets)

Concat the datasets in the input list of datasets, supported using “+” to reload concat operation.

Note

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

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

Examples

>>> import mindspore.dataset as ds
>>> # ds1 and ds2 are instances of Dataset object
>>> # creates a dataset by concating ds1 and ds2 with "+" operation
>>> data1 = ds1 + ds2
>>> # creates a dataset by concating ds1 and ds2 with concat operation
>>> data1 = ds1.concat(ds2)

create_dict_iterator(num_epochs=- 1)

Create an Iterator over the dataset.

The data retrieved will be a dictionary. The order of the columns in the dictionary may not be the same as the original order.

Returns: Iterator, dictionary of column_name-ndarray pair.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> # creates an iterator. The columns in the data obtained by the
>>> # iterator might be changed.
>>> iterator = data.create_dict_iterator()
>>> for item in iterator:
>>>     # print the data in column1
>>>     print(item["column1"])

create_tuple_iterator(columns=None, num_epochs=- 1)

Create an Iterator over the dataset. The data retrieved will be a list of ndarray of data.

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 not be changed.

Parameters: columns (list[str], optional) – List of columns to be used to specify the order of columns (default=None, means all columns).
Returns: Iterator, list of ndarray.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> # creates an iterator. The columns in the data obtained by the
>>> # iterator will not be changed.
>>> iterator = data.create_tuple_iterator()
>>> for item in iterator:
>>>     # convert the returned tuple to a list and print
>>>     print(list(item))

device_que(prefetch_size=None, send_epoch_end=True)

Return a transferredDataset that transfer data through device.

Parameters

prefetch_size (int, optional) – prefetch number of records ahead of the user’s request (default=None).
send_epoch_end (bool, optional) – whether send end of sequence to device or not.(default=True)

Note

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

Returns: TransferDataset, dataset for transferring.

filter(predicate, input_columns=None, num_parallel_workers=1)

Filter dataset by predicate.

Note

If input_columns not provided or empty, all columns will be used.

Parameters

predicate (callable) – python callable which returns a boolean value, if False then filter the element.
input_columns – (list[str], optional): List of names of the input columns, when default=None, the predicate will be applied on all columns in the dataset.
num_parallel_workers (int, optional) – Number of workers to process the Dataset in parallel (default=None).

Returns

FilterDataset, dataset filter.

Examples

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

flat_map(func)

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

The specified func is a function that must take one ‘Ndarray’ as input and return a ‘Dataset’.

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

Examples

>>> import mindspore.dataset as ds
>>> import mindspore.dataset.text as text
>>> # declare a function which returns a Dataset object
>>> def flat_map_func(x):
>>>     data_dir = text.to_str(x[0])
>>>     d = ds.ImageFolderDatasetV2(data_dir)
>>>     return d
>>> # data is a Dataset object
>>> data = ds.TextFileDataset(DATA_FILE)
>>> data = data.flat_map(flat_map_func)

Raises

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

get_batch_size()

Get the size of a batch.

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

get_class_indexing()

Get the class index.

Returns: Dict, A str-to-int mapping from label name to index.

get_dataset_size()[source]

Get the number of batches in an epoch.

Returns: Number, number of batches.

get_repeat_count()

Get the replication times in RepeatDataset else 1.

Returns: Number, the count of repeat.

map(input_columns=None, operations=None, output_columns=None, columns_order=None, num_parallel_workers=None, python_multiprocessing=False, cache=None)

Apply each operation in operations to this dataset.

The order of operations is determined by the position of each operation in operations. operations[0] will be applied first, then operations[1], then operations[2], etc.

Each operation will be passed one or more columns from the dataset as input, and zero 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.

Only the columns specified in columns_order will be propagated to the child node. These columns will be in the same order as specified in columns_order.

Parameters

input_columns (list[str]) – 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 is required, starting from the first column).
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) != 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).
columns_order (list[str], optional) – list of all the desired columns to propagate to the child node. This list must be a subset of all the columns in the dataset after all operations are applied. The order of the columns in each row propagated to the child node follow the order they appear in this list. The parameter is mandatory if the len(input_columns) != len(output_columns). (default=None, all columns will be propagated to the child node, the order of the columns will remain the same).
num_parallel_workers (int, optional) – Number of threads used to process the dataset in parallel (default=None, the value from the config will be used).
python_multiprocessing (bool, optional) – Parallelize python operations with multiple worker process. This option could be beneficial if the python operation is computational heavy (default=False).
cache (DatasetCache, optional) – Tensor cache to use. (default=None which means no cache is used). The cache feature is under development and is not recommended.

Returns

MapDataset, dataset after mapping operation.

Examples

>>> import mindspore.dataset as ds
>>> import mindspore.dataset.transforms.vision.c_transforms as c_transforms
>>>
>>> # data is an instance of Dataset which has 2 columns, "image" and "label".
>>> # ds_pyfunc is an instance of Dataset which has 3 columns, "col0", "col1", and "col2". Each column is
>>> # a 2d array of integers.
>>>
>>> # This config is a global setting, meaning that all future operations which
>>> # uses this config value will use 2 worker threads, unless if specified
>>> # otherwise in their constructor. set_num_parallel_workers can be called
>>> # again later if a different number of worker threads are needed.
>>> ds.config.set_num_parallel_workers(2)
>>>
>>> # Two operations, which takes 1 column for input and outputs 1 column.
>>> decode_op = c_transforms.Decode(rgb_format=True)
>>> random_jitter_op = c_transforms.RandomColorAdjust((0.8, 0.8), (1, 1), (1, 1), (0, 0))
>>>
>>> # 1) Simple map example
>>>
>>> operations = [decode_op]
>>> input_columns = ["image"]
>>>
>>> # Applies decode_op on column "image". This column will be replaced by the outputed
>>> # column of decode_op. Since columns_order is not provided, both columns "image"
>>> # and "label" will be propagated to the child node in their original order.
>>> ds_decoded = data.map(input_columns, operations)
>>>
>>> # Rename column "image" to "decoded_image"
>>> output_columns = ["decoded_image"]
>>> ds_decoded = data.map(input_columns, operations, output_columns)
>>>
>>> # Specify the order of the columns.
>>> columns_order ["label", "image"]
>>> ds_decoded = data.map(input_columns, operations, None, columns_order)
>>>
>>> # Rename column "image" to "decoded_image" and also specify the order of the columns.
>>> columns_order ["label", "decoded_image"]
>>> output_columns = ["decoded_image"]
>>> ds_decoded = data.map(input_columns, operations, output_columns, columns_order)
>>>
>>> # Rename column "image" to "decoded_image" and keep only this column.
>>> columns_order ["decoded_image"]
>>> output_columns = ["decoded_image"]
>>> ds_decoded = data.map(input_columns, operations, output_columns, columns_order)
>>>
>>> # Simple example using pyfunc. Renaming columns and specifying column order
>>> # work in the same way as the previous examples.
>>> input_columns = ["col0"]
>>> operations = [(lambda x: x + 1)]
>>> ds_mapped = ds_pyfunc.map(input_columns, operations)
>>>
>>> # 2) Map example with more than one operation
>>>
>>> # If this list of operations is used with map, decode_op will be applied
>>> # first, then random_jitter_op will be applied.
>>> operations = [decode_op, random_jitter_op]
>>>
>>> input_columns = ["image"]
>>>
>>> # Creates 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 columns_order is not specified, the order of the
>>> # columns will remain the same.
>>> ds_mapped = data.map(input_columns, operations)
>>>
>>> # Creates a dataset that is identical to ds_mapped, except the column "image"
>>> # that is outputted by random_jitter_op is renamed to "image_transformed".
>>> # Specifying column order works in the same way as examples in 1).
>>> output_columns = ["image_transformed"]
>>> ds_mapped_and_renamed = data.map(input_columns, operation, output_columns)
>>>
>>> # Multiple operations using pyfunc. Renaming columns and specifying column order
>>> # work in the same way as examples in 1).
>>> input_columns = ["col0"]
>>> operations = [(lambda x: x + x), (lambda x: x - 1)]
>>> output_columns = ["col0_mapped"]
>>> ds_mapped = ds_pyfunc.map(input_columns, operations, output_columns)
>>>
>>> # 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[1] 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: because the number of input columns is not the same as the number of
>>> # output columns, the output_columns and columns_order parameter must be
>>> # specified. Otherwise, this map call will also result in an error.
>>> input_columns = ["col2", "col0"]
>>> output_columns = ["mod2", "mod3", "mod5", "mod7"]
>>>
>>> # Propagate all columns to the child node in this order:
>>> columns_order = ["col0", "col2", "mod2", "mod3", "mod5", "mod7", "col1"]
>>> ds_mapped = ds_pyfunc.map(input_columns, operations, output_columns, columns_order)
>>>
>>> # Propagate some columns to the child node in this order:
>>> columns_order = ["mod7", "mod3", "col1"]
>>> ds_mapped = ds_pyfunc.map(input_columns, operations, output_columns, columns_order)

num_classes()

Get the number of classes in a dataset.

Returns: Number, number of classes.

output_shapes()

Get the shapes of output data.

Returns: List, list of shape of each column.

output_types()

Get the types of output data.

Returns: List of data type.

project(columns)

Project certain columns in input datasets.

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

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

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> columns_to_project = ["column3", "column1", "column2"]
>>>
>>> # creates a dataset that consist of column3, column1, column2
>>> # in that order, regardless of the original order of columns.
>>> data = data.project(columns=columns_to_project)

rename(input_columns, output_columns)

Rename the columns in input datasets.

Parameters

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

Returns

RenameDataset, dataset renamed.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> input_columns = ["input_col1", "input_col2", "input_col3"]
>>> output_columns = ["output_col1", "output_col2", "output_col3"]
>>>
>>> # creates 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.
>>> data = data.rename(input_columns=input_columns, output_columns=output_columns)

repeat(count=None)

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

Note

The order of using repeat and batch reflects the number of batches. Recommend that repeat operation should be used after batch operation. If dataset_sink_mode is False, here repeat operation is invalid. If dataset_sink_mode is True, repeat count should be equal to the epoch of training. Otherwise, errors could occur since the amount of data is not the amount training requires.

Parameters: count (int) – Number of times the dataset should be repeated (default=None).
Returns: RepeatDataset, dataset repeated.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> # creates a dataset where the dataset is repeated for 50 epochs
>>> repeated = data.repeat(50)
>>>
>>> # creates a dataset where each epoch is shuffled individually
>>> shuffled_and_repeated = data.shuffle(10)
>>> shuffled_and_repeated = shuffled_and_repeated.repeat(50)
>>>
>>> # creates 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.
>>> repeat_and_shuffle = data.repeat(50)
>>> repeat_and_shuffle = repeat_and_shuffle.shuffle(10)

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

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

Save the dynamic data processed by dataset pipeline as common dataset format, support: mindrecord.

Implicit type casting exists when saving data as mindrecord. Table below shows how to do type casting.

Implicit Type Casting of Saving as mindrecord
type in ‘dataset’	type in ‘mindrecord’	detail
bool	None	Not support
int8	int32
uint8	bytes(1D uint8)	Drop dimension
int16	int32
uint16	int32
int32	int32
uint32	int64
int64	int64
uint64	None	Not support
float16	float32
float32	float32
float64	float64
string	string	Not support multi-dimensional string

Note

To save the samples in order, should set dataset’s shuffle false and num_files 1.
Before call the function, do not use batch, repeat operator or data augmentation operators with random attribute in map operator.
Mindrecord does not support DE_UINT64, multi-dimensional DE_UINT8(drop dimension) and multi-dimensional DE_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’)

shuffle(buffer_size)

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

Make a shuffle buffer that contains the first buffer_size rows.
Randomly select an element from the shuffle buffer to be the next row propogated 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 seed can be provided to be used on the first epoch. 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: ShuffleDataset, dataset shuffled.
Raises: RuntimeError – If exist sync operators before shuffle.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> # optionally set the seed for the first epoch
>>> ds.config.set_seed(58)
>>>
>>> # creates a shuffled dataset using a shuffle buffer of size 4
>>> data = data.shuffle(4)

skip(count)

Skip the first N elements of this dataset.

Parameters: count (int) – Number of elements the dataset should be skipped.
Returns: SkipDataset, dataset skipped.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> # creates a dataset which skips first 3 elements from data
>>> data = data.skip(3)

split(sizes, randomize=True)

Split the dataset into smaller, non-overlapping datasets.

There is the optimized split function, which will be called automatically when the dataset that calls this function is a MappableDataset.

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 sizes does not equal the original dataset size, an an error will occur. 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 occur. 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 will be added to the first split.
- The sum of split sizes > K, the difference will be removed from the first large enough split such that it will have atleast 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 should not be sharded if split is going to be called. Instead, create a DistributedSampler and specify a split to shard after splitting. If dataset is sharded after a split, it is strongly recommended to set the same seed in each instance of execution, otherwise each shard may not be part of the same split (see Examples).
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. Furthermore, if sharding occurs after split, each shard may not be part of the same 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.

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

Examples

>>> import mindspore.dataset as ds
>>>
>>> dataset_dir = "/path/to/imagefolder_directory"
>>>
>>> # many datasets have shuffle on by default, set shuffle to False if split will be called!
>>> data = ds.ImageFolderDatasetV2(dataset_dir, shuffle=False)
>>>
>>> # sets the seed, and tells split to use this seed when randomizing. This
>>> # is needed because we are sharding later
>>> ds.config.set_seed(58)
>>> train, test = data.split([0.9, 0.1])
>>>
>>> # if we want to shard the train dataset, we can use a DistributedSampler
>>> train_sampler = ds.DistributedSampler(10, 2)
>>> train.use_sampler(train_sampler)

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 (Union[dict, None]) – The data passed to the callback (default=None).

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

Add a blocking condition to the input Dataset.

Parameters

num_batch (int) – the number of batches without blocking at the start of each epoch.
condition_name (str) – The condition name that is used to toggle sending next row.
callback (function) – The callback funciton that will be invoked when sync_update is called.

Raises

RuntimeError – If condition name already exists.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> data = data.sync_wait("callback1")
>>> data = data.batch(batch_size)
>>> for batch_data in data.create_dict_iterator():
>>>     data = data.sync_update("callback1")

take(count=- 1)

Takes at most given numbers of elements from the dataset.

Note

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

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

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> # creates a dataset where the dataset including 50 elements.
>>> data = data.take(50)

to_device(send_epoch_end=True)

Transfer data through CPU, GPU or Ascend devices.

Parameters: send_epoch_end (bool, optional) – whether send end of sequence to device or not.(default=True)

Note

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

Returns

TransferDataset, dataset for transferring.

Raises

TypeError – If device_type is empty.
ValueError – If device_type is not ‘Ascend’, ‘GPU’ or ‘CPU’.
RuntimeError – If dataset is unknown.
RuntimeError – If distribution file path is given but failed to read.

use_sampler(new_sampler)

Will make the current dataset use the new_sampler provided.

Parameters: new_sampler (Sampler) – the sampler to use for the current dataset.
Returns: Dataset, that uses new_sampler.

Examples

>>> import mindspore.dataset as ds
>>>
>>> dataset_dir = "/path/to/imagefolder_directory"
>>> # a SequentialSampler is created by default
>>> data = ds.ImageFolderDatasetV2(dataset_dir)
>>>
>>> # use a DistributedSampler instead of the SequentialSampler
>>> new_sampler = ds.DistributedSampler(10, 2)
>>> data.use_sampler(new_sampler)

zip(datasets)

Zip the datasets in the input tuple of datasets. Columns in the input datasets must not have the same name.

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

Examples

>>> import mindspore.dataset as ds
>>> # ds1 and ds2 are instances of Dataset object
>>> # creates a dataset which is the combination of ds1 and ds2
>>> data = ds1.zip(ds2)

class mindspore.dataset.GraphData(dataset_file, num_parallel_workers=None)[source]

Reads the graph dataset used for GNN training from the shared file and database.

Parameters

dataset_file (str) – One of file names in dataset.
num_parallel_workers (int, optional) – Number of workers to process the Dataset in parallel (default=None).

get_all_edges(edge_type)[source]

Get all edges in the graph.

Parameters: edge_type (int) – Specify the type of edge.
Returns: array of edges.
Return type: numpy.ndarray

Examples

>>> import mindspore.dataset as ds
>>> data_graph = ds.GraphData('dataset_file', 2)
>>> nodes = data_graph.get_all_edges(0)

Raises: TypeError – If edge_type is not integer.

get_all_neighbors(node_list, neighbor_type)[source]

Get neighbor_type neighbors of the nodes in node_list.

Parameters

node_list (Union[list, numpy.ndarray]) – The given list of nodes.
neighbor_type (int) – Specify the type of neighbor.

Returns

array of nodes.

Return type

numpy.ndarray

Examples

>>> import mindspore.dataset as ds
>>> data_graph = ds.GraphData('dataset_file', 2)
>>> nodes = data_graph.get_all_nodes(0)
>>> neighbors = data_graph.get_all_neighbors(nodes, 0)

Raises

TypeError – If node_list is not list or ndarray.
TypeError – If neighbor_type is not integer.

get_all_nodes(node_type)[source]

Get all nodes in the graph.

Parameters: node_type (int) – Specify the type of node.
Returns: array of nodes.
Return type: numpy.ndarray

Examples

>>> import mindspore.dataset as ds
>>> data_graph = ds.GraphData('dataset_file', 2)
>>> nodes = data_graph.get_all_nodes(0)

Raises: TypeError – If node_type is not integer.

get_edge_feature(edge_list, feature_types)[source]

Get feature_types feature of the edges in edge_list.

Parameters

edge_list (Union[list, numpy.ndarray]) – The given list of edges.
feature_types (Union[list, numpy.ndarray]) – The given list of feature types.

Returns

array of features.

Return type

numpy.ndarray

Examples

>>> import mindspore.dataset as ds
>>> data_graph = ds.GraphData('dataset_file', 2)
>>> edges = data_graph.get_all_edges(0)
>>> features = data_graph.get_edge_feature(edges, [1])

Raises

TypeError – If edge_list is not list or ndarray.
TypeError – If feature_types is not list or ndarray.

get_neg_sampled_neighbors(node_list, neg_neighbor_num, neg_neighbor_type)[source]

Get neg_neighbor_type negative sampled neighbors of the nodes in node_list.

Parameters

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.

Returns

array of nodes.

Return type

numpy.ndarray

Examples

>>> import mindspore.dataset as ds
>>> data_graph = ds.GraphData('dataset_file', 2)
>>> nodes = data_graph.get_all_nodes(0)
>>> neg_neighbors = data_graph.get_neg_sampled_neighbors(nodes, 5, 0)

Raises

TypeError – If node_list is not list or ndarray.
TypeError – If neg_neighbor_num is not integer.
TypeError – If neg_neighbor_type is not integer.

get_node_feature(node_list, feature_types)[source]

Get feature_types feature of the nodes in node_list.

Parameters

node_list (Union[list, numpy.ndarray]) – The given list of nodes.
feature_types (Union[list, numpy.ndarray]) – The given list of feature types.

Returns

array of features.

Return type

numpy.ndarray

Examples

>>> import mindspore.dataset as ds
>>> data_graph = ds.GraphData('dataset_file', 2)
>>> nodes = data_graph.get_all_nodes(0)
>>> features = data_graph.get_node_feature(nodes, [1])

Raises

TypeError – If node_list is not list or ndarray.
TypeError – If feature_types is not list or ndarray.

get_nodes_from_edges(edge_list)[source]

Get nodes from the edges.

Parameters: edge_list (Union[list, numpy.ndarray]) – The given list of edges.
Returns: array of nodes.
Return type: numpy.ndarray
Raises: TypeError – If edge_list is not list or ndarray.

get_sampled_neighbors(node_list, neighbor_nums, neighbor_types)[source]

Get sampled neighbor information.

The api supports multi-hop neighbor sampling. That is, the previous sampling result is used as the input of next-hop sampling. A maximum of 6-hop are allowed.

The sampling result is tiled into a list in the format of [input node, 1-hop sampling result, 2-hop samling result …]

Parameters

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

array of nodes.

Return type

numpy.ndarray

Examples

>>> import mindspore.dataset as ds
>>> data_graph = ds.GraphData('dataset_file', 2)
>>> nodes = data_graph.get_all_nodes(0)
>>> neighbors = data_graph.get_all_neighbors(nodes, [2, 2], [0, 0])

Raises

TypeError – If node_list is not list or ndarray.
TypeError – If neighbor_nums is not list or ndarray.
TypeError – If neighbor_types is not list or ndarray.

graph_info()[source]

Get the meta information of the graph, including the number of nodes, the type of nodes, the feature information of nodes, the number of edges, the type of edges, and the feature information of edges.

Returns: Meta information of the graph. The key is node_type, edge_type, node_num, edge_num, node_feature_type and edge_feature_type.
Return type: dict

random_walk(target_nodes, meta_path, step_home_param=1.0, step_away_param=1.0, default_node=- 1)[source]

Random walk in nodes.

Parameters

target_nodes (list[int]) – Start node list in random walk
meta_path (list[int]) – node type for each walk step
step_home_param (float, optional) – return hyper parameter in node2vec algorithm (Default = 1.0).
step_away_param (float, optional) – inout hyper parameter in node2vec algorithm (Default = 1.0).
default_node (int, optional) – default node if no more neighbors found (Default = -1). A default value of -1 indicates that no node is given.

Returns

array of nodes.

Return type

numpy.ndarray

Examples

>>> import mindspore.dataset as ds
>>> data_graph = ds.GraphData('dataset_file', 2)
>>> nodes = data_graph.random_walk([1,2], [1,2,1,2,1])

Raises

TypeError – If target_nodes is not list or ndarray.
TypeError – If meta_path is not list or ndarray.

class mindspore.dataset.ImageFolderDatasetV2(dataset_dir, num_samples=None, num_parallel_workers=None, shuffle=None, sampler=None, extensions=None, class_indexing=None, decode=False, num_shards=None, shard_id=None, cache=None)[source]

A source dataset that reads images from a tree of directories.

All images within one folder have the same label. The generated dataset has two columns [‘image’, ‘label’]. The shape of the image column is [image_size] if decode flag is False, or [H,W,C] otherwise. The type of the image tensor is uint8. The label is just a scalar uint64 tensor. This dataset can take in a sampler. sampler and shuffle are mutually exclusive. Table below shows what input args are allowed and their expected behavior.

Expected Order Behavior of Using ‘sampler’ and ‘shuffle’
Parameter ‘sampler’	Parameter ‘shuffle’	Expected Order Behavior
None	None	random order
None	True	random order
None	False	sequential order
Sampler object	None	order defined by sampler
Sampler object	True	not allowed
Sampler object	False	not allowed

Parameters

dataset_dir (str) – Path to the root directory that contains the dataset.
num_samples (int, optional) – The number of images to be included in the dataset (default=None, all images).
num_parallel_workers (int, optional) – Number of workers to read the data (default=None, set in the config).
shuffle (bool, optional) – Whether or not to perform shuffle on the dataset (default=None, expected order behavior shown in the table).
sampler (Sampler, optional) – Object used to choose samples from the dataset (default=None, expected order behavior shown in the table).
extensions (list[str], optional) – List of file extensions to be included in the dataset (default=None).
class_indexing (dict, optional) – A str-to-int mapping from folder name to index (default=None, the folder names will be sorted alphabetically and each class will be given a unique index starting from 0).
decode (bool, optional) – decode the images after reading (default=False).
num_shards (int, optional) – Number of shards that the dataset should be divided into (default=None).
shard_id (int, optional) – The shard ID within num_shards (default=None). This argument should be specified only when num_shards is also specified.
cache (DatasetCache, optional) – Tensor cache to use. (default=None which means no cache is used). The cache feature is under development and is not recommended.

Raises

RuntimeError – If sampler and shuffle are specified at the same time.
RuntimeError – If sampler and sharding are specified at the same time.
RuntimeError – If num_shards is specified but shard_id is None.
RuntimeError – If shard_id is specified but num_shards is None.
RuntimeError – If class_indexing is not a dictionary.
ValueError – If shard_id is invalid (< 0 or >= num_shards).

Examples

>>> import mindspore.dataset as ds
>>> # path to imagefolder directory. This directory needs to contain sub-directories which contain the images
>>> dataset_dir = "/path/to/imagefolder_directory"
>>> # 1) read all samples (image files) in dataset_dir with 8 threads
>>> imagefolder_dataset = ds.ImageFolderDatasetV2(dataset_dir, num_parallel_workers=8)
>>> # 2) read all samples (image files) from folder cat and folder dog with label 0 and 1
>>> imagefolder_dataset = ds.ImageFolderDatasetV2(dataset_dir,class_indexing={"cat":0,"dog":1})
>>> # 3) read all samples (image files) in dataset_dir with extensions .JPEG and .png (case sensitive)
>>> imagefolder_dataset = ds.ImageFolderDatasetV2(dataset_dir, extensions={".JPEG",".png"})

apply(apply_func)

Apply a function in this dataset.

The specified apply_func is a function that must take one ‘Dataset’ as an argument and return a preprogressing ‘Dataset’.

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

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> # declare an apply_func function which returns a Dataset object
>>> def apply_func(ds):
>>>     ds = ds.batch(2)
>>>     return ds
>>> # use apply to call apply_func
>>> data = data.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, pad_info=None)

Combine batch_size number of consecutive rows into batches.

For any child node, a batch is treated as a single row. 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. Recommend that repeat operation should be used after batch operation.

Parameters

batch_size (int or 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 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 to process the Dataset in parallel (default=None).
per_batch_map (callable, optional) – Per batch map callable. A callable which takes (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[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.

Returns

BatchDataset, dataset batched.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> # creates a dataset where every 100 rows is combined into a batch
>>> # and drops the last incomplete batch if there is one.
>>> data = data.batch(100, True)

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, and pad and batch the buckets 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 batch_info, and then batched. Each batch will be full, except for maybe the last batch for each bucket.

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, and one 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 len(column_names) arguments and returns an int. If no value is provided, then 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) – Represents how to batch each column. The key corresponds to the column name, 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).

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>>
>>> # creates a dataset where every 100 rows is combined into a batch
>>> # and drops the last incomplete batch if there is one.
>>> column_names = ["col1", "col2"]
>>> buket_boundaries = [5, 10]
>>> bucket_batch_sizes = [5, 1, 1]
>>> element_length_function = (lambda col1, col2: max(len(col1), len(col2)))
>>>
>>> # will pad col1 to shape [2, bucket_boundaries[i]] where i is the
>>> # index of the bucket that is currently being batched.
>>> # will pad col2 to a shape where each dimension is the longest in all
>>> # the elements currently being batched.
>>> pad_info = {"col1", ([2, None], -1)}
>>> pad_to_bucket_boundary = True
>>>
>>> data = data.bucket_batch_by_length(column_names, bucket_boundaries,
>>>                                    bucket_batch_sizes,
>>>                                    element_length_function, pad_info,
>>>                                    pad_to_bucket_boundary)

concat(datasets)

Concat the datasets in the input list of datasets, supported using “+” to reload concat operation.

Note

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

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

Examples

>>> import mindspore.dataset as ds
>>> # ds1 and ds2 are instances of Dataset object
>>> # creates a dataset by concating ds1 and ds2 with "+" operation
>>> data1 = ds1 + ds2
>>> # creates a dataset by concating ds1 and ds2 with concat operation
>>> data1 = ds1.concat(ds2)

create_dict_iterator(num_epochs=- 1)

Create an Iterator over the dataset.

The data retrieved will be a dictionary. The order of the columns in the dictionary may not be the same as the original order.

Returns: Iterator, dictionary of column_name-ndarray pair.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> # creates an iterator. The columns in the data obtained by the
>>> # iterator might be changed.
>>> iterator = data.create_dict_iterator()
>>> for item in iterator:
>>>     # print the data in column1
>>>     print(item["column1"])

create_tuple_iterator(columns=None, num_epochs=- 1)

Create an Iterator over the dataset. The data retrieved will be a list of ndarray of data.

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 not be changed.

Parameters: columns (list[str], optional) – List of columns to be used to specify the order of columns (default=None, means all columns).
Returns: Iterator, list of ndarray.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> # creates an iterator. The columns in the data obtained by the
>>> # iterator will not be changed.
>>> iterator = data.create_tuple_iterator()
>>> for item in iterator:
>>>     # convert the returned tuple to a list and print
>>>     print(list(item))

device_que(prefetch_size=None, send_epoch_end=True)

Return a transferredDataset that transfer data through device.

Parameters

prefetch_size (int, optional) – prefetch number of records ahead of the user’s request (default=None).
send_epoch_end (bool, optional) – whether send end of sequence to device or not.(default=True)

Note

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

Returns: TransferDataset, dataset for transferring.

filter(predicate, input_columns=None, num_parallel_workers=1)

Filter dataset by predicate.

Note

If input_columns not provided or empty, all columns will be used.

Parameters

predicate (callable) – python callable which returns a boolean value, if False then filter the element.
input_columns – (list[str], optional): List of names of the input columns, when default=None, the predicate will be applied on all columns in the dataset.
num_parallel_workers (int, optional) – Number of workers to process the Dataset in parallel (default=None).

Returns

FilterDataset, dataset filter.

Examples

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

flat_map(func)

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

The specified func is a function that must take one ‘Ndarray’ as input and return a ‘Dataset’.

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

Examples

>>> import mindspore.dataset as ds
>>> import mindspore.dataset.text as text
>>> # declare a function which returns a Dataset object
>>> def flat_map_func(x):
>>>     data_dir = text.to_str(x[0])
>>>     d = ds.ImageFolderDatasetV2(data_dir)
>>>     return d
>>> # data is a Dataset object
>>> data = ds.TextFileDataset(DATA_FILE)
>>> data = data.flat_map(flat_map_func)

Raises

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

get_batch_size()

Get the size of a batch.

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

get_class_indexing()

Get the class index.

Returns: Dict, A str-to-int mapping from label name to index.

get_dataset_size()[source]

Get the number of batches in an epoch.

Returns: Number, number of batches.

get_repeat_count()

Get the replication times in RepeatDataset else 1.

Returns: Number, the count of repeat.

map(input_columns=None, operations=None, output_columns=None, columns_order=None, num_parallel_workers=None, python_multiprocessing=False, cache=None)

Apply each operation in operations to this dataset.

The order of operations is determined by the position of each operation in operations. operations[0] will be applied first, then operations[1], then operations[2], etc.

Each operation will be passed one or more columns from the dataset as input, and zero 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.

Only the columns specified in columns_order will be propagated to the child node. These columns will be in the same order as specified in columns_order.

Parameters

input_columns (list[str]) – 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 is required, starting from the first column).
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) != 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).
columns_order (list[str], optional) – list of all the desired columns to propagate to the child node. This list must be a subset of all the columns in the dataset after all operations are applied. The order of the columns in each row propagated to the child node follow the order they appear in this list. The parameter is mandatory if the len(input_columns) != len(output_columns). (default=None, all columns will be propagated to the child node, the order of the columns will remain the same).
num_parallel_workers (int, optional) – Number of threads used to process the dataset in parallel (default=None, the value from the config will be used).
python_multiprocessing (bool, optional) – Parallelize python operations with multiple worker process. This option could be beneficial if the python operation is computational heavy (default=False).
cache (DatasetCache, optional) – Tensor cache to use. (default=None which means no cache is used). The cache feature is under development and is not recommended.

Returns

MapDataset, dataset after mapping operation.

Examples

>>> import mindspore.dataset as ds
>>> import mindspore.dataset.transforms.vision.c_transforms as c_transforms
>>>
>>> # data is an instance of Dataset which has 2 columns, "image" and "label".
>>> # ds_pyfunc is an instance of Dataset which has 3 columns, "col0", "col1", and "col2". Each column is
>>> # a 2d array of integers.
>>>
>>> # This config is a global setting, meaning that all future operations which
>>> # uses this config value will use 2 worker threads, unless if specified
>>> # otherwise in their constructor. set_num_parallel_workers can be called
>>> # again later if a different number of worker threads are needed.
>>> ds.config.set_num_parallel_workers(2)
>>>
>>> # Two operations, which takes 1 column for input and outputs 1 column.
>>> decode_op = c_transforms.Decode(rgb_format=True)
>>> random_jitter_op = c_transforms.RandomColorAdjust((0.8, 0.8), (1, 1), (1, 1), (0, 0))
>>>
>>> # 1) Simple map example
>>>
>>> operations = [decode_op]
>>> input_columns = ["image"]
>>>
>>> # Applies decode_op on column "image". This column will be replaced by the outputed
>>> # column of decode_op. Since columns_order is not provided, both columns "image"
>>> # and "label" will be propagated to the child node in their original order.
>>> ds_decoded = data.map(input_columns, operations)
>>>
>>> # Rename column "image" to "decoded_image"
>>> output_columns = ["decoded_image"]
>>> ds_decoded = data.map(input_columns, operations, output_columns)
>>>
>>> # Specify the order of the columns.
>>> columns_order ["label", "image"]
>>> ds_decoded = data.map(input_columns, operations, None, columns_order)
>>>
>>> # Rename column "image" to "decoded_image" and also specify the order of the columns.
>>> columns_order ["label", "decoded_image"]
>>> output_columns = ["decoded_image"]
>>> ds_decoded = data.map(input_columns, operations, output_columns, columns_order)
>>>
>>> # Rename column "image" to "decoded_image" and keep only this column.
>>> columns_order ["decoded_image"]
>>> output_columns = ["decoded_image"]
>>> ds_decoded = data.map(input_columns, operations, output_columns, columns_order)
>>>
>>> # Simple example using pyfunc. Renaming columns and specifying column order
>>> # work in the same way as the previous examples.
>>> input_columns = ["col0"]
>>> operations = [(lambda x: x + 1)]
>>> ds_mapped = ds_pyfunc.map(input_columns, operations)
>>>
>>> # 2) Map example with more than one operation
>>>
>>> # If this list of operations is used with map, decode_op will be applied
>>> # first, then random_jitter_op will be applied.
>>> operations = [decode_op, random_jitter_op]
>>>
>>> input_columns = ["image"]
>>>
>>> # Creates 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 columns_order is not specified, the order of the
>>> # columns will remain the same.
>>> ds_mapped = data.map(input_columns, operations)
>>>
>>> # Creates a dataset that is identical to ds_mapped, except the column "image"
>>> # that is outputted by random_jitter_op is renamed to "image_transformed".
>>> # Specifying column order works in the same way as examples in 1).
>>> output_columns = ["image_transformed"]
>>> ds_mapped_and_renamed = data.map(input_columns, operation, output_columns)
>>>
>>> # Multiple operations using pyfunc. Renaming columns and specifying column order
>>> # work in the same way as examples in 1).
>>> input_columns = ["col0"]
>>> operations = [(lambda x: x + x), (lambda x: x - 1)]
>>> output_columns = ["col0_mapped"]
>>> ds_mapped = ds_pyfunc.map(input_columns, operations, output_columns)
>>>
>>> # 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[1] 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: because the number of input columns is not the same as the number of
>>> # output columns, the output_columns and columns_order parameter must be
>>> # specified. Otherwise, this map call will also result in an error.
>>> input_columns = ["col2", "col0"]
>>> output_columns = ["mod2", "mod3", "mod5", "mod7"]
>>>
>>> # Propagate all columns to the child node in this order:
>>> columns_order = ["col0", "col2", "mod2", "mod3", "mod5", "mod7", "col1"]
>>> ds_mapped = ds_pyfunc.map(input_columns, operations, output_columns, columns_order)
>>>
>>> # Propagate some columns to the child node in this order:
>>> columns_order = ["mod7", "mod3", "col1"]
>>> ds_mapped = ds_pyfunc.map(input_columns, operations, output_columns, columns_order)

num_classes()[source]

Get the number of classes in dataset.

Returns: Number, number of classes.

output_shapes()

Get the shapes of output data.

Returns: List, list of shape of each column.

output_types()

Get the types of output data.

Returns: List of data type.

project(columns)

Project certain columns in input datasets.

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

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

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> columns_to_project = ["column3", "column1", "column2"]
>>>
>>> # creates a dataset that consist of column3, column1, column2
>>> # in that order, regardless of the original order of columns.
>>> data = data.project(columns=columns_to_project)

rename(input_columns, output_columns)

Rename the columns in input datasets.

Parameters

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

Returns

RenameDataset, dataset renamed.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> input_columns = ["input_col1", "input_col2", "input_col3"]
>>> output_columns = ["output_col1", "output_col2", "output_col3"]
>>>
>>> # creates 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.
>>> data = data.rename(input_columns=input_columns, output_columns=output_columns)

repeat(count=None)

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

Note

The order of using repeat and batch reflects the number of batches. Recommend that repeat operation should be used after batch operation. If dataset_sink_mode is False, here repeat operation is invalid. If dataset_sink_mode is True, repeat count should be equal to the epoch of training. Otherwise, errors could occur since the amount of data is not the amount training requires.

Parameters: count (int) – Number of times the dataset should be repeated (default=None).
Returns: RepeatDataset, dataset repeated.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> # creates a dataset where the dataset is repeated for 50 epochs
>>> repeated = data.repeat(50)
>>>
>>> # creates a dataset where each epoch is shuffled individually
>>> shuffled_and_repeated = data.shuffle(10)
>>> shuffled_and_repeated = shuffled_and_repeated.repeat(50)
>>>
>>> # creates 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.
>>> repeat_and_shuffle = data.repeat(50)
>>> repeat_and_shuffle = repeat_and_shuffle.shuffle(10)

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

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

Save the dynamic data processed by dataset pipeline as common dataset format, support: mindrecord.

Implicit type casting exists when saving data as mindrecord. Table below shows how to do type casting.

Implicit Type Casting of Saving as mindrecord
type in ‘dataset’	type in ‘mindrecord’	detail
bool	None	Not support
int8	int32
uint8	bytes(1D uint8)	Drop dimension
int16	int32
uint16	int32
int32	int32
uint32	int64
int64	int64
uint64	None	Not support
float16	float32
float32	float32
float64	float64
string	string	Not support multi-dimensional string

Note

To save the samples in order, should set dataset’s shuffle false and num_files 1.
Before call the function, do not use batch, repeat operator or data augmentation operators with random attribute in map operator.
Mindrecord does not support DE_UINT64, multi-dimensional DE_UINT8(drop dimension) and multi-dimensional DE_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’)

shuffle(buffer_size)

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

Make a shuffle buffer that contains the first buffer_size rows.
Randomly select an element from the shuffle buffer to be the next row propogated 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 seed can be provided to be used on the first epoch. 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: ShuffleDataset, dataset shuffled.
Raises: RuntimeError – If exist sync operators before shuffle.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> # optionally set the seed for the first epoch
>>> ds.config.set_seed(58)
>>>
>>> # creates a shuffled dataset using a shuffle buffer of size 4
>>> data = data.shuffle(4)

skip(count)

Skip the first N elements of this dataset.

Parameters: count (int) – Number of elements the dataset should be skipped.
Returns: SkipDataset, dataset skipped.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> # creates a dataset which skips first 3 elements from data
>>> data = data.skip(3)

split(sizes, randomize=True)

Split the dataset into smaller, non-overlapping datasets.

There is the optimized split function, which will be called automatically when the dataset that calls this function is a MappableDataset.

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 sizes does not equal the original dataset size, an an error will occur. 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 occur. 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 will be added to the first split.
- The sum of split sizes > K, the difference will be removed from the first large enough split such that it will have atleast 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 should not be sharded if split is going to be called. Instead, create a DistributedSampler and specify a split to shard after splitting. If dataset is sharded after a split, it is strongly recommended to set the same seed in each instance of execution, otherwise each shard may not be part of the same split (see Examples).
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. Furthermore, if sharding occurs after split, each shard may not be part of the same 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.

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

Examples

>>> import mindspore.dataset as ds
>>>
>>> dataset_dir = "/path/to/imagefolder_directory"
>>>
>>> # many datasets have shuffle on by default, set shuffle to False if split will be called!
>>> data = ds.ImageFolderDatasetV2(dataset_dir, shuffle=False)
>>>
>>> # sets the seed, and tells split to use this seed when randomizing. This
>>> # is needed because we are sharding later
>>> ds.config.set_seed(58)
>>> train, test = data.split([0.9, 0.1])
>>>
>>> # if we want to shard the train dataset, we can use a DistributedSampler
>>> train_sampler = ds.DistributedSampler(10, 2)
>>> train.use_sampler(train_sampler)

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 (Union[dict, None]) – The data passed to the callback (default=None).

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

Add a blocking condition to the input Dataset.

Parameters

num_batch (int) – the number of batches without blocking at the start of each epoch.
condition_name (str) – The condition name that is used to toggle sending next row.
callback (function) – The callback funciton that will be invoked when sync_update is called.

Raises

RuntimeError – If condition name already exists.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> data = data.sync_wait("callback1")
>>> data = data.batch(batch_size)
>>> for batch_data in data.create_dict_iterator():
>>>     data = data.sync_update("callback1")

take(count=- 1)

Takes at most given numbers of elements from the dataset.

Note

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

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

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> # creates a dataset where the dataset including 50 elements.
>>> data = data.take(50)

to_device(send_epoch_end=True)

Transfer data through CPU, GPU or Ascend devices.

Parameters: send_epoch_end (bool, optional) – whether send end of sequence to device or not.(default=True)

Note

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

Returns

TransferDataset, dataset for transferring.

Raises

TypeError – If device_type is empty.
ValueError – If device_type is not ‘Ascend’, ‘GPU’ or ‘CPU’.
RuntimeError – If dataset is unknown.
RuntimeError – If distribution file path is given but failed to read.

use_sampler(new_sampler)

Will make the current dataset use the new_sampler provided.

Parameters: new_sampler (Sampler) – the sampler to use for the current dataset.
Returns: Dataset, that uses new_sampler.

Examples

>>> import mindspore.dataset as ds
>>>
>>> dataset_dir = "/path/to/imagefolder_directory"
>>> # a SequentialSampler is created by default
>>> data = ds.ImageFolderDatasetV2(dataset_dir)
>>>
>>> # use a DistributedSampler instead of the SequentialSampler
>>> new_sampler = ds.DistributedSampler(10, 2)
>>> data.use_sampler(new_sampler)

zip(datasets)

Zip the datasets in the input tuple of datasets. Columns in the input datasets must not have the same name.

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

Examples

>>> import mindspore.dataset as ds
>>> # ds1 and ds2 are instances of Dataset object
>>> # creates a dataset which is the combination of ds1 and ds2
>>> data = ds1.zip(ds2)

class mindspore.dataset.ManifestDataset(dataset_file, usage='train', num_samples=None, num_parallel_workers=None, shuffle=None, sampler=None, class_indexing=None, decode=False, num_shards=None, shard_id=None)[source]

A source dataset that reads images from a manifest file.

The generated dataset has two columns [‘image’, ‘label’]. The shape of the image column is [image_size] if decode flag is False, or [H,W,C] otherwise. The type of the image tensor is uint8. The label is just a scalar uint64 tensor. This dataset can take in a sampler. sampler and shuffle are mutually exclusive. Table below shows what input args are allowed and their expected behavior.

Expected Order Behavior of Using ‘sampler’ and ‘shuffle’
Parameter ‘sampler’	Parameter ‘shuffle’	Expected Order Behavior
None	None	random order
None	True	random order
None	False	sequential order
Sampler object	None	order defined by sampler
Sampler object	True	not allowed
Sampler object	False	not allowed

Parameters

dataset_file (str) – File to be read.
usage (str, optional) – Need train, eval or inference data (default=”train”).
num_samples (int, optional) – The number of images to be included in the dataset. (default=None, all images).
num_parallel_workers (int, optional) – Number of workers to read the data (default=None, number set in the config).
shuffle (bool, optional) – Whether to perform shuffle on the dataset (default=None, expected order behavior shown in the table).
sampler (Sampler, optional) – Object used to choose samples from the dataset (default=None, expected order behavior shown in the table).
class_indexing (dict, optional) – A str-to-int mapping from label name to index (default=None, the folder names will be sorted alphabetically and each class will be given a unique index starting from 0).
decode (bool, optional) – decode the images after reading (default=False).
num_shards (int, optional) – Number of shards that the dataset should be divided into (default=None).
shard_id (int, optional) – The shard ID within num_shards (default=None). This argument should be specified only when num_shards is also specified.

Raises

RuntimeError – If sampler and shuffle are specified at the same time.
RuntimeError – If sampler and sharding are specified at the same time.
RuntimeError – If num_shards is specified but shard_id is None.
RuntimeError – If shard_id is specified but num_shards is None.
RuntimeError – If class_indexing is not a dictionary.
ValueError – If shard_id is invalid (< 0 or >= num_shards).

Examples

>>> import mindspore.dataset as ds
>>> dataset_file = "/path/to/manifest_file.manifest"
>>> # 1) read all samples specified in manifest_file dataset with 8 threads for training:
>>> manifest_dataset = ds.ManifestDataset(dataset_file, usage="train", num_parallel_workers=8)
>>> # 2) reads samples (specified in manifest_file.manifest) for shard 0 in a 2-way distributed training setup:
>>> manifest_dataset = ds.ManifestDataset(dataset_file, num_shards=2, shard_id=0)

apply(apply_func)

Apply a function in this dataset.

The specified apply_func is a function that must take one ‘Dataset’ as an argument and return a preprogressing ‘Dataset’.

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

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> # declare an apply_func function which returns a Dataset object
>>> def apply_func(ds):
>>>     ds = ds.batch(2)
>>>     return ds
>>> # use apply to call apply_func
>>> data = data.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, pad_info=None)

Combine batch_size number of consecutive rows into batches.

For any child node, a batch is treated as a single row. 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. Recommend that repeat operation should be used after batch operation.

Parameters

batch_size (int or 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 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 to process the Dataset in parallel (default=None).
per_batch_map (callable, optional) – Per batch map callable. A callable which takes (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[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.

Returns

BatchDataset, dataset batched.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> # creates a dataset where every 100 rows is combined into a batch
>>> # and drops the last incomplete batch if there is one.
>>> data = data.batch(100, True)

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, and pad and batch the buckets 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 batch_info, and then batched. Each batch will be full, except for maybe the last batch for each bucket.

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, and one 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 len(column_names) arguments and returns an int. If no value is provided, then 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) – Represents how to batch each column. The key corresponds to the column name, 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).

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>>
>>> # creates a dataset where every 100 rows is combined into a batch
>>> # and drops the last incomplete batch if there is one.
>>> column_names = ["col1", "col2"]
>>> buket_boundaries = [5, 10]
>>> bucket_batch_sizes = [5, 1, 1]
>>> element_length_function = (lambda col1, col2: max(len(col1), len(col2)))
>>>
>>> # will pad col1 to shape [2, bucket_boundaries[i]] where i is the
>>> # index of the bucket that is currently being batched.
>>> # will pad col2 to a shape where each dimension is the longest in all
>>> # the elements currently being batched.
>>> pad_info = {"col1", ([2, None], -1)}
>>> pad_to_bucket_boundary = True
>>>
>>> data = data.bucket_batch_by_length(column_names, bucket_boundaries,
>>>                                    bucket_batch_sizes,
>>>                                    element_length_function, pad_info,
>>>                                    pad_to_bucket_boundary)

concat(datasets)

Concat the datasets in the input list of datasets, supported using “+” to reload concat operation.

Note

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

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

Examples

>>> import mindspore.dataset as ds
>>> # ds1 and ds2 are instances of Dataset object
>>> # creates a dataset by concating ds1 and ds2 with "+" operation
>>> data1 = ds1 + ds2
>>> # creates a dataset by concating ds1 and ds2 with concat operation
>>> data1 = ds1.concat(ds2)

create_dict_iterator(num_epochs=- 1)

Create an Iterator over the dataset.

The data retrieved will be a dictionary. The order of the columns in the dictionary may not be the same as the original order.

Returns: Iterator, dictionary of column_name-ndarray pair.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> # creates an iterator. The columns in the data obtained by the
>>> # iterator might be changed.
>>> iterator = data.create_dict_iterator()
>>> for item in iterator:
>>>     # print the data in column1
>>>     print(item["column1"])

create_tuple_iterator(columns=None, num_epochs=- 1)

Create an Iterator over the dataset. The data retrieved will be a list of ndarray of data.

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 not be changed.

Parameters: columns (list[str], optional) – List of columns to be used to specify the order of columns (default=None, means all columns).
Returns: Iterator, list of ndarray.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> # creates an iterator. The columns in the data obtained by the
>>> # iterator will not be changed.
>>> iterator = data.create_tuple_iterator()
>>> for item in iterator:
>>>     # convert the returned tuple to a list and print
>>>     print(list(item))

device_que(prefetch_size=None, send_epoch_end=True)

Return a transferredDataset that transfer data through device.

Parameters

prefetch_size (int, optional) – prefetch number of records ahead of the user’s request (default=None).
send_epoch_end (bool, optional) – whether send end of sequence to device or not.(default=True)

Note

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

Returns: TransferDataset, dataset for transferring.

filter(predicate, input_columns=None, num_parallel_workers=1)

Filter dataset by predicate.

Note

If input_columns not provided or empty, all columns will be used.

Parameters

predicate (callable) – python callable which returns a boolean value, if False then filter the element.
input_columns – (list[str], optional): List of names of the input columns, when default=None, the predicate will be applied on all columns in the dataset.
num_parallel_workers (int, optional) – Number of workers to process the Dataset in parallel (default=None).

Returns

FilterDataset, dataset filter.

Examples

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

flat_map(func)

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

The specified func is a function that must take one ‘Ndarray’ as input and return a ‘Dataset’.

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

Examples

>>> import mindspore.dataset as ds
>>> import mindspore.dataset.text as text
>>> # declare a function which returns a Dataset object
>>> def flat_map_func(x):
>>>     data_dir = text.to_str(x[0])
>>>     d = ds.ImageFolderDatasetV2(data_dir)
>>>     return d
>>> # data is a Dataset object
>>> data = ds.TextFileDataset(DATA_FILE)
>>> data = data.flat_map(flat_map_func)

Raises

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

get_batch_size()

Get the size of a batch.

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

get_class_indexing()[source]

Get the class index.

Returns: Dict, A str-to-int mapping from label name to index.

get_dataset_size()[source]

Get the number of batches in an epoch.

Returns: Number, number of batches.

get_repeat_count()

Get the replication times in RepeatDataset else 1.

Returns: Number, the count of repeat.

map(input_columns=None, operations=None, output_columns=None, columns_order=None, num_parallel_workers=None, python_multiprocessing=False, cache=None)

Apply each operation in operations to this dataset.

The order of operations is determined by the position of each operation in operations. operations[0] will be applied first, then operations[1], then operations[2], etc.

Each operation will be passed one or more columns from the dataset as input, and zero 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.

Only the columns specified in columns_order will be propagated to the child node. These columns will be in the same order as specified in columns_order.

Parameters

input_columns (list[str]) – 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 is required, starting from the first column).
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) != 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).
columns_order (list[str], optional) – list of all the desired columns to propagate to the child node. This list must be a subset of all the columns in the dataset after all operations are applied. The order of the columns in each row propagated to the child node follow the order they appear in this list. The parameter is mandatory if the len(input_columns) != len(output_columns). (default=None, all columns will be propagated to the child node, the order of the columns will remain the same).
num_parallel_workers (int, optional) – Number of threads used to process the dataset in parallel (default=None, the value from the config will be used).
python_multiprocessing (bool, optional) – Parallelize python operations with multiple worker process. This option could be beneficial if the python operation is computational heavy (default=False).
cache (DatasetCache, optional) – Tensor cache to use. (default=None which means no cache is used). The cache feature is under development and is not recommended.

Returns

MapDataset, dataset after mapping operation.

Examples

>>> import mindspore.dataset as ds
>>> import mindspore.dataset.transforms.vision.c_transforms as c_transforms
>>>
>>> # data is an instance of Dataset which has 2 columns, "image" and "label".
>>> # ds_pyfunc is an instance of Dataset which has 3 columns, "col0", "col1", and "col2". Each column is
>>> # a 2d array of integers.
>>>
>>> # This config is a global setting, meaning that all future operations which
>>> # uses this config value will use 2 worker threads, unless if specified
>>> # otherwise in their constructor. set_num_parallel_workers can be called
>>> # again later if a different number of worker threads are needed.
>>> ds.config.set_num_parallel_workers(2)
>>>
>>> # Two operations, which takes 1 column for input and outputs 1 column.
>>> decode_op = c_transforms.Decode(rgb_format=True)
>>> random_jitter_op = c_transforms.RandomColorAdjust((0.8, 0.8), (1, 1), (1, 1), (0, 0))
>>>
>>> # 1) Simple map example
>>>
>>> operations = [decode_op]
>>> input_columns = ["image"]
>>>
>>> # Applies decode_op on column "image". This column will be replaced by the outputed
>>> # column of decode_op. Since columns_order is not provided, both columns "image"
>>> # and "label" will be propagated to the child node in their original order.
>>> ds_decoded = data.map(input_columns, operations)
>>>
>>> # Rename column "image" to "decoded_image"
>>> output_columns = ["decoded_image"]
>>> ds_decoded = data.map(input_columns, operations, output_columns)
>>>
>>> # Specify the order of the columns.
>>> columns_order ["label", "image"]
>>> ds_decoded = data.map(input_columns, operations, None, columns_order)
>>>
>>> # Rename column "image" to "decoded_image" and also specify the order of the columns.
>>> columns_order ["label", "decoded_image"]
>>> output_columns = ["decoded_image"]
>>> ds_decoded = data.map(input_columns, operations, output_columns, columns_order)
>>>
>>> # Rename column "image" to "decoded_image" and keep only this column.
>>> columns_order ["decoded_image"]
>>> output_columns = ["decoded_image"]
>>> ds_decoded = data.map(input_columns, operations, output_columns, columns_order)
>>>
>>> # Simple example using pyfunc. Renaming columns and specifying column order
>>> # work in the same way as the previous examples.
>>> input_columns = ["col0"]
>>> operations = [(lambda x: x + 1)]
>>> ds_mapped = ds_pyfunc.map(input_columns, operations)
>>>
>>> # 2) Map example with more than one operation
>>>
>>> # If this list of operations is used with map, decode_op will be applied
>>> # first, then random_jitter_op will be applied.
>>> operations = [decode_op, random_jitter_op]
>>>
>>> input_columns = ["image"]
>>>
>>> # Creates 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 columns_order is not specified, the order of the
>>> # columns will remain the same.
>>> ds_mapped = data.map(input_columns, operations)
>>>
>>> # Creates a dataset that is identical to ds_mapped, except the column "image"
>>> # that is outputted by random_jitter_op is renamed to "image_transformed".
>>> # Specifying column order works in the same way as examples in 1).
>>> output_columns = ["image_transformed"]
>>> ds_mapped_and_renamed = data.map(input_columns, operation, output_columns)
>>>
>>> # Multiple operations using pyfunc. Renaming columns and specifying column order
>>> # work in the same way as examples in 1).
>>> input_columns = ["col0"]
>>> operations = [(lambda x: x + x), (lambda x: x - 1)]
>>> output_columns = ["col0_mapped"]
>>> ds_mapped = ds_pyfunc.map(input_columns, operations, output_columns)
>>>
>>> # 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[1] 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: because the number of input columns is not the same as the number of
>>> # output columns, the output_columns and columns_order parameter must be
>>> # specified. Otherwise, this map call will also result in an error.
>>> input_columns = ["col2", "col0"]
>>> output_columns = ["mod2", "mod3", "mod5", "mod7"]
>>>
>>> # Propagate all columns to the child node in this order:
>>> columns_order = ["col0", "col2", "mod2", "mod3", "mod5", "mod7", "col1"]
>>> ds_mapped = ds_pyfunc.map(input_columns, operations, output_columns, columns_order)
>>>
>>> # Propagate some columns to the child node in this order:
>>> columns_order = ["mod7", "mod3", "col1"]
>>> ds_mapped = ds_pyfunc.map(input_columns, operations, output_columns, columns_order)

num_classes()[source]

Get the number of classes in a dataset.

Returns: Number, number of classes.

output_shapes()

Get the shapes of output data.

Returns: List, list of shape of each column.

output_types()

Get the types of output data.

Returns: List of data type.

project(columns)

Project certain columns in input datasets.

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

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

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> columns_to_project = ["column3", "column1", "column2"]
>>>
>>> # creates a dataset that consist of column3, column1, column2
>>> # in that order, regardless of the original order of columns.
>>> data = data.project(columns=columns_to_project)

rename(input_columns, output_columns)

Rename the columns in input datasets.

Parameters

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

Returns

RenameDataset, dataset renamed.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> input_columns = ["input_col1", "input_col2", "input_col3"]
>>> output_columns = ["output_col1", "output_col2", "output_col3"]
>>>
>>> # creates 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.
>>> data = data.rename(input_columns=input_columns, output_columns=output_columns)

repeat(count=None)

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

Note

The order of using repeat and batch reflects the number of batches. Recommend that repeat operation should be used after batch operation. If dataset_sink_mode is False, here repeat operation is invalid. If dataset_sink_mode is True, repeat count should be equal to the epoch of training. Otherwise, errors could occur since the amount of data is not the amount training requires.

Parameters: count (int) – Number of times the dataset should be repeated (default=None).
Returns: RepeatDataset, dataset repeated.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> # creates a dataset where the dataset is repeated for 50 epochs
>>> repeated = data.repeat(50)
>>>
>>> # creates a dataset where each epoch is shuffled individually
>>> shuffled_and_repeated = data.shuffle(10)
>>> shuffled_and_repeated = shuffled_and_repeated.repeat(50)
>>>
>>> # creates 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.
>>> repeat_and_shuffle = data.repeat(50)
>>> repeat_and_shuffle = repeat_and_shuffle.shuffle(10)

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

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

Save the dynamic data processed by dataset pipeline as common dataset format, support: mindrecord.

Implicit type casting exists when saving data as mindrecord. Table below shows how to do type casting.

Implicit Type Casting of Saving as mindrecord
type in ‘dataset’	type in ‘mindrecord’	detail
bool	None	Not support
int8	int32
uint8	bytes(1D uint8)	Drop dimension
int16	int32
uint16	int32
int32	int32
uint32	int64
int64	int64
uint64	None	Not support
float16	float32
float32	float32
float64	float64
string	string	Not support multi-dimensional string

Note

To save the samples in order, should set dataset’s shuffle false and num_files 1.
Before call the function, do not use batch, repeat operator or data augmentation operators with random attribute in map operator.
Mindrecord does not support DE_UINT64, multi-dimensional DE_UINT8(drop dimension) and multi-dimensional DE_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’)

shuffle(buffer_size)

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

Make a shuffle buffer that contains the first buffer_size rows.
Randomly select an element from the shuffle buffer to be the next row propogated 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 seed can be provided to be used on the first epoch. 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: ShuffleDataset, dataset shuffled.
Raises: RuntimeError – If exist sync operators before shuffle.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> # optionally set the seed for the first epoch
>>> ds.config.set_seed(58)
>>>
>>> # creates a shuffled dataset using a shuffle buffer of size 4
>>> data = data.shuffle(4)

skip(count)

Skip the first N elements of this dataset.

Parameters: count (int) – Number of elements the dataset should be skipped.
Returns: SkipDataset, dataset skipped.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> # creates a dataset which skips first 3 elements from data
>>> data = data.skip(3)

split(sizes, randomize=True)

Split the dataset into smaller, non-overlapping datasets.

There is the optimized split function, which will be called automatically when the dataset that calls this function is a MappableDataset.

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 sizes does not equal the original dataset size, an an error will occur. 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 occur. 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 will be added to the first split.
- The sum of split sizes > K, the difference will be removed from the first large enough split such that it will have atleast 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 should not be sharded if split is going to be called. Instead, create a DistributedSampler and specify a split to shard after splitting. If dataset is sharded after a split, it is strongly recommended to set the same seed in each instance of execution, otherwise each shard may not be part of the same split (see Examples).
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. Furthermore, if sharding occurs after split, each shard may not be part of the same 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.

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

Examples

>>> import mindspore.dataset as ds
>>>
>>> dataset_dir = "/path/to/imagefolder_directory"
>>>
>>> # many datasets have shuffle on by default, set shuffle to False if split will be called!
>>> data = ds.ImageFolderDatasetV2(dataset_dir, shuffle=False)
>>>
>>> # sets the seed, and tells split to use this seed when randomizing. This
>>> # is needed because we are sharding later
>>> ds.config.set_seed(58)
>>> train, test = data.split([0.9, 0.1])
>>>
>>> # if we want to shard the train dataset, we can use a DistributedSampler
>>> train_sampler = ds.DistributedSampler(10, 2)
>>> train.use_sampler(train_sampler)

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 (Union[dict, None]) – The data passed to the callback (default=None).

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

Add a blocking condition to the input Dataset.

Parameters

num_batch (int) – the number of batches without blocking at the start of each epoch.
condition_name (str) – The condition name that is used to toggle sending next row.
callback (function) – The callback funciton that will be invoked when sync_update is called.

Raises

RuntimeError – If condition name already exists.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> data = data.sync_wait("callback1")
>>> data = data.batch(batch_size)
>>> for batch_data in data.create_dict_iterator():
>>>     data = data.sync_update("callback1")

take(count=- 1)

Takes at most given numbers of elements from the dataset.

Note

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

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

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> # creates a dataset where the dataset including 50 elements.
>>> data = data.take(50)

to_device(send_epoch_end=True)

Transfer data through CPU, GPU or Ascend devices.

Parameters: send_epoch_end (bool, optional) – whether send end of sequence to device or not.(default=True)

Note

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

Returns

TransferDataset, dataset for transferring.

Raises

TypeError – If device_type is empty.
ValueError – If device_type is not ‘Ascend’, ‘GPU’ or ‘CPU’.
RuntimeError – If dataset is unknown.
RuntimeError – If distribution file path is given but failed to read.

use_sampler(new_sampler)

Will make the current dataset use the new_sampler provided.

Parameters: new_sampler (Sampler) – the sampler to use for the current dataset.
Returns: Dataset, that uses new_sampler.

Examples

>>> import mindspore.dataset as ds
>>>
>>> dataset_dir = "/path/to/imagefolder_directory"
>>> # a SequentialSampler is created by default
>>> data = ds.ImageFolderDatasetV2(dataset_dir)
>>>
>>> # use a DistributedSampler instead of the SequentialSampler
>>> new_sampler = ds.DistributedSampler(10, 2)
>>> data.use_sampler(new_sampler)

zip(datasets)

Zip the datasets in the input tuple of datasets. Columns in the input datasets must not have the same name.

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

Examples

>>> import mindspore.dataset as ds
>>> # ds1 and ds2 are instances of Dataset object
>>> # creates a dataset which is the combination of ds1 and ds2
>>> data = ds1.zip(ds2)

class mindspore.dataset.MindDataset(dataset_file, columns_list=None, num_parallel_workers=None, shuffle=None, num_shards=None, shard_id=None, block_reader=False, sampler=None, padded_sample=None, num_padded=None, num_samples=None)[source]

A source dataset that reads from shard files and database.

Parameters

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 (default=None, performs shuffle).
num_shards (int, optional) – Number of shards that the dataset should be divided into (default=None).
shard_id (int, optional) – The shard ID within num_shards (default=None). This argument should be specified only when num_shards is also specified.
block_reader (bool, optional) – Whether read data by block mode (default=False).
sampler (Sampler, optional) – Object used to choose samples from the dataset (default=None, sampler is exclusive with shuffle and block_reader). Support list: SubsetRandomSampler, PkSampler, RandomSampler, SequentialSampler, DistributedSampler.
padded_sample (dict, optional) – Samples will be appended to dataset, which keys are the same as column_list.
num_padded (int, optional) – Number of padding samples.Dataset size plus num_padded should be divisible by num_shards.
num_samples (int, optional) – The number of samples to be included in the dataset (default=None, all samples).

Raises

ValueError – If num_shards is specified but shard_id is None.
ValueError – If shard_id is specified but num_shards is None.
ValueError – If block reader is true but partition is specified.

apply(apply_func)

Apply a function in this dataset.

The specified apply_func is a function that must take one ‘Dataset’ as an argument and return a preprogressing ‘Dataset’.

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

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> # declare an apply_func function which returns a Dataset object
>>> def apply_func(ds):
>>>     ds = ds.batch(2)
>>>     return ds
>>> # use apply to call apply_func
>>> data = data.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, pad_info=None)

Combine batch_size number of consecutive rows into batches.

For any child node, a batch is treated as a single row. 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. Recommend that repeat operation should be used after batch operation.

Parameters

batch_size (int or 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 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 to process the Dataset in parallel (default=None).
per_batch_map (callable, optional) – Per batch map callable. A callable which takes (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[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.

Returns

BatchDataset, dataset batched.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> # creates a dataset where every 100 rows is combined into a batch
>>> # and drops the last incomplete batch if there is one.
>>> data = data.batch(100, True)

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, and pad and batch the buckets 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 batch_info, and then batched. Each batch will be full, except for maybe the last batch for each bucket.

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, and one 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 len(column_names) arguments and returns an int. If no value is provided, then 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) – Represents how to batch each column. The key corresponds to the column name, 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).

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>>
>>> # creates a dataset where every 100 rows is combined into a batch
>>> # and drops the last incomplete batch if there is one.
>>> column_names = ["col1", "col2"]
>>> buket_boundaries = [5, 10]
>>> bucket_batch_sizes = [5, 1, 1]
>>> element_length_function = (lambda col1, col2: max(len(col1), len(col2)))
>>>
>>> # will pad col1 to shape [2, bucket_boundaries[i]] where i is the
>>> # index of the bucket that is currently being batched.
>>> # will pad col2 to a shape where each dimension is the longest in all
>>> # the elements currently being batched.
>>> pad_info = {"col1", ([2, None], -1)}
>>> pad_to_bucket_boundary = True
>>>
>>> data = data.bucket_batch_by_length(column_names, bucket_boundaries,
>>>                                    bucket_batch_sizes,
>>>                                    element_length_function, pad_info,
>>>                                    pad_to_bucket_boundary)

concat(datasets)

Concat the datasets in the input list of datasets, supported using “+” to reload concat operation.

Note

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

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

Examples

>>> import mindspore.dataset as ds
>>> # ds1 and ds2 are instances of Dataset object
>>> # creates a dataset by concating ds1 and ds2 with "+" operation
>>> data1 = ds1 + ds2
>>> # creates a dataset by concating ds1 and ds2 with concat operation
>>> data1 = ds1.concat(ds2)

create_dict_iterator(num_epochs=- 1)

Create an Iterator over the dataset.

The data retrieved will be a dictionary. The order of the columns in the dictionary may not be the same as the original order.

Returns: Iterator, dictionary of column_name-ndarray pair.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> # creates an iterator. The columns in the data obtained by the
>>> # iterator might be changed.
>>> iterator = data.create_dict_iterator()
>>> for item in iterator:
>>>     # print the data in column1
>>>     print(item["column1"])

create_tuple_iterator(columns=None, num_epochs=- 1)

Create an Iterator over the dataset. The data retrieved will be a list of ndarray of data.

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 not be changed.

Parameters: columns (list[str], optional) – List of columns to be used to specify the order of columns (default=None, means all columns).
Returns: Iterator, list of ndarray.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> # creates an iterator. The columns in the data obtained by the
>>> # iterator will not be changed.
>>> iterator = data.create_tuple_iterator()
>>> for item in iterator:
>>>     # convert the returned tuple to a list and print
>>>     print(list(item))

device_que(prefetch_size=None, send_epoch_end=True)

Return a transferredDataset that transfer data through device.

Parameters

prefetch_size (int, optional) – prefetch number of records ahead of the user’s request (default=None).
send_epoch_end (bool, optional) – whether send end of sequence to device or not.(default=True)

Note

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

Returns: TransferDataset, dataset for transferring.

filter(predicate, input_columns=None, num_parallel_workers=1)

Filter dataset by predicate.

Note

If input_columns not provided or empty, all columns will be used.

Parameters

predicate (callable) – python callable which returns a boolean value, if False then filter the element.
input_columns – (list[str], optional): List of names of the input columns, when default=None, the predicate will be applied on all columns in the dataset.
num_parallel_workers (int, optional) – Number of workers to process the Dataset in parallel (default=None).

Returns

FilterDataset, dataset filter.

Examples

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

flat_map(func)

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

The specified func is a function that must take one ‘Ndarray’ as input and return a ‘Dataset’.

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

Examples

>>> import mindspore.dataset as ds
>>> import mindspore.dataset.text as text
>>> # declare a function which returns a Dataset object
>>> def flat_map_func(x):
>>>     data_dir = text.to_str(x[0])
>>>     d = ds.ImageFolderDatasetV2(data_dir)
>>>     return d
>>> # data is a Dataset object
>>> data = ds.TextFileDataset(DATA_FILE)
>>> data = data.flat_map(flat_map_func)

Raises

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

get_batch_size()

Get the size of a batch.

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

get_class_indexing()

Get the class index.

Returns: Dict, A str-to-int mapping from label name to index.

get_dataset_size()[source]

Get the number of batches in an epoch.

Returns: Number, number of batches.

get_repeat_count()

Get the replication times in RepeatDataset else 1.

Returns: Number, the count of repeat.

map(input_columns=None, operations=None, output_columns=None, columns_order=None, num_parallel_workers=None, python_multiprocessing=False, cache=None)

Apply each operation in operations to this dataset.

The order of operations is determined by the position of each operation in operations. operations[0] will be applied first, then operations[1], then operations[2], etc.

Each operation will be passed one or more columns from the dataset as input, and zero 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.

Only the columns specified in columns_order will be propagated to the child node. These columns will be in the same order as specified in columns_order.

Parameters

input_columns (list[str]) – 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 is required, starting from the first column).
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) != 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).
columns_order (list[str], optional) – list of all the desired columns to propagate to the child node. This list must be a subset of all the columns in the dataset after all operations are applied. The order of the columns in each row propagated to the child node follow the order they appear in this list. The parameter is mandatory if the len(input_columns) != len(output_columns). (default=None, all columns will be propagated to the child node, the order of the columns will remain the same).
num_parallel_workers (int, optional) – Number of threads used to process the dataset in parallel (default=None, the value from the config will be used).
python_multiprocessing (bool, optional) – Parallelize python operations with multiple worker process. This option could be beneficial if the python operation is computational heavy (default=False).
cache (DatasetCache, optional) – Tensor cache to use. (default=None which means no cache is used). The cache feature is under development and is not recommended.

Returns

MapDataset, dataset after mapping operation.

Examples

>>> import mindspore.dataset as ds
>>> import mindspore.dataset.transforms.vision.c_transforms as c_transforms
>>>
>>> # data is an instance of Dataset which has 2 columns, "image" and "label".
>>> # ds_pyfunc is an instance of Dataset which has 3 columns, "col0", "col1", and "col2". Each column is
>>> # a 2d array of integers.
>>>
>>> # This config is a global setting, meaning that all future operations which
>>> # uses this config value will use 2 worker threads, unless if specified
>>> # otherwise in their constructor. set_num_parallel_workers can be called
>>> # again later if a different number of worker threads are needed.
>>> ds.config.set_num_parallel_workers(2)
>>>
>>> # Two operations, which takes 1 column for input and outputs 1 column.
>>> decode_op = c_transforms.Decode(rgb_format=True)
>>> random_jitter_op = c_transforms.RandomColorAdjust((0.8, 0.8), (1, 1), (1, 1), (0, 0))
>>>
>>> # 1) Simple map example
>>>
>>> operations = [decode_op]
>>> input_columns = ["image"]
>>>
>>> # Applies decode_op on column "image". This column will be replaced by the outputed
>>> # column of decode_op. Since columns_order is not provided, both columns "image"
>>> # and "label" will be propagated to the child node in their original order.
>>> ds_decoded = data.map(input_columns, operations)
>>>
>>> # Rename column "image" to "decoded_image"
>>> output_columns = ["decoded_image"]
>>> ds_decoded = data.map(input_columns, operations, output_columns)
>>>
>>> # Specify the order of the columns.
>>> columns_order ["label", "image"]
>>> ds_decoded = data.map(input_columns, operations, None, columns_order)
>>>
>>> # Rename column "image" to "decoded_image" and also specify the order of the columns.
>>> columns_order ["label", "decoded_image"]
>>> output_columns = ["decoded_image"]
>>> ds_decoded = data.map(input_columns, operations, output_columns, columns_order)
>>>
>>> # Rename column "image" to "decoded_image" and keep only this column.
>>> columns_order ["decoded_image"]
>>> output_columns = ["decoded_image"]
>>> ds_decoded = data.map(input_columns, operations, output_columns, columns_order)
>>>
>>> # Simple example using pyfunc. Renaming columns and specifying column order
>>> # work in the same way as the previous examples.
>>> input_columns = ["col0"]
>>> operations = [(lambda x: x + 1)]
>>> ds_mapped = ds_pyfunc.map(input_columns, operations)
>>>
>>> # 2) Map example with more than one operation
>>>
>>> # If this list of operations is used with map, decode_op will be applied
>>> # first, then random_jitter_op will be applied.
>>> operations = [decode_op, random_jitter_op]
>>>
>>> input_columns = ["image"]
>>>
>>> # Creates 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 columns_order is not specified, the order of the
>>> # columns will remain the same.
>>> ds_mapped = data.map(input_columns, operations)
>>>
>>> # Creates a dataset that is identical to ds_mapped, except the column "image"
>>> # that is outputted by random_jitter_op is renamed to "image_transformed".
>>> # Specifying column order works in the same way as examples in 1).
>>> output_columns = ["image_transformed"]
>>> ds_mapped_and_renamed = data.map(input_columns, operation, output_columns)
>>>
>>> # Multiple operations using pyfunc. Renaming columns and specifying column order
>>> # work in the same way as examples in 1).
>>> input_columns = ["col0"]
>>> operations = [(lambda x: x + x), (lambda x: x - 1)]
>>> output_columns = ["col0_mapped"]
>>> ds_mapped = ds_pyfunc.map(input_columns, operations, output_columns)
>>>
>>> # 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[1] 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: because the number of input columns is not the same as the number of
>>> # output columns, the output_columns and columns_order parameter must be
>>> # specified. Otherwise, this map call will also result in an error.
>>> input_columns = ["col2", "col0"]
>>> output_columns = ["mod2", "mod3", "mod5", "mod7"]
>>>
>>> # Propagate all columns to the child node in this order:
>>> columns_order = ["col0", "col2", "mod2", "mod3", "mod5", "mod7", "col1"]
>>> ds_mapped = ds_pyfunc.map(input_columns, operations, output_columns, columns_order)
>>>
>>> # Propagate some columns to the child node in this order:
>>> columns_order = ["mod7", "mod3", "col1"]
>>> ds_mapped = ds_pyfunc.map(input_columns, operations, output_columns, columns_order)

num_classes()

Get the number of classes in a dataset.

Returns: Number, number of classes.

output_shapes()

Get the shapes of output data.

Returns: List, list of shape of each column.

output_types()

Get the types of output data.

Returns: List of data type.

project(columns)

Project certain columns in input datasets.

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

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

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> columns_to_project = ["column3", "column1", "column2"]
>>>
>>> # creates a dataset that consist of column3, column1, column2
>>> # in that order, regardless of the original order of columns.
>>> data = data.project(columns=columns_to_project)

rename(input_columns, output_columns)

Rename the columns in input datasets.

Parameters

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

Returns

RenameDataset, dataset renamed.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> input_columns = ["input_col1", "input_col2", "input_col3"]
>>> output_columns = ["output_col1", "output_col2", "output_col3"]
>>>
>>> # creates 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.
>>> data = data.rename(input_columns=input_columns, output_columns=output_columns)

repeat(count=None)

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

Note

The order of using repeat and batch reflects the number of batches. Recommend that repeat operation should be used after batch operation. If dataset_sink_mode is False, here repeat operation is invalid. If dataset_sink_mode is True, repeat count should be equal to the epoch of training. Otherwise, errors could occur since the amount of data is not the amount training requires.

Parameters: count (int) – Number of times the dataset should be repeated (default=None).
Returns: RepeatDataset, dataset repeated.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> # creates a dataset where the dataset is repeated for 50 epochs
>>> repeated = data.repeat(50)
>>>
>>> # creates a dataset where each epoch is shuffled individually
>>> shuffled_and_repeated = data.shuffle(10)
>>> shuffled_and_repeated = shuffled_and_repeated.repeat(50)
>>>
>>> # creates 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.
>>> repeat_and_shuffle = data.repeat(50)
>>> repeat_and_shuffle = repeat_and_shuffle.shuffle(10)

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

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

Save the dynamic data processed by dataset pipeline as common dataset format, support: mindrecord.

Implicit type casting exists when saving data as mindrecord. Table below shows how to do type casting.

Implicit Type Casting of Saving as mindrecord
type in ‘dataset’	type in ‘mindrecord’	detail
bool	None	Not support
int8	int32
uint8	bytes(1D uint8)	Drop dimension
int16	int32
uint16	int32
int32	int32
uint32	int64
int64	int64
uint64	None	Not support
float16	float32
float32	float32
float64	float64
string	string	Not support multi-dimensional string

Note

To save the samples in order, should set dataset’s shuffle false and num_files 1.
Before call the function, do not use batch, repeat operator or data augmentation operators with random attribute in map operator.
Mindrecord does not support DE_UINT64, multi-dimensional DE_UINT8(drop dimension) and multi-dimensional DE_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’)

shuffle(buffer_size)

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

Make a shuffle buffer that contains the first buffer_size rows.
Randomly select an element from the shuffle buffer to be the next row propogated 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 seed can be provided to be used on the first epoch. 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: ShuffleDataset, dataset shuffled.
Raises: RuntimeError – If exist sync operators before shuffle.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> # optionally set the seed for the first epoch
>>> ds.config.set_seed(58)
>>>
>>> # creates a shuffled dataset using a shuffle buffer of size 4
>>> data = data.shuffle(4)

skip(count)

Skip the first N elements of this dataset.

Parameters: count (int) – Number of elements the dataset should be skipped.
Returns: SkipDataset, dataset skipped.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> # creates a dataset which skips first 3 elements from data
>>> data = data.skip(3)

split(sizes, randomize=True)

Split the dataset into smaller, non-overlapping datasets.

There is the optimized split function, which will be called automatically when the dataset that calls this function is a MappableDataset.

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 sizes does not equal the original dataset size, an an error will occur. 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 occur. 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 will be added to the first split.
- The sum of split sizes > K, the difference will be removed from the first large enough split such that it will have atleast 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 should not be sharded if split is going to be called. Instead, create a DistributedSampler and specify a split to shard after splitting. If dataset is sharded after a split, it is strongly recommended to set the same seed in each instance of execution, otherwise each shard may not be part of the same split (see Examples).
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. Furthermore, if sharding occurs after split, each shard may not be part of the same 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.

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

Examples

>>> import mindspore.dataset as ds
>>>
>>> dataset_dir = "/path/to/imagefolder_directory"
>>>
>>> # many datasets have shuffle on by default, set shuffle to False if split will be called!
>>> data = ds.ImageFolderDatasetV2(dataset_dir, shuffle=False)
>>>
>>> # sets the seed, and tells split to use this seed when randomizing. This
>>> # is needed because we are sharding later
>>> ds.config.set_seed(58)
>>> train, test = data.split([0.9, 0.1])
>>>
>>> # if we want to shard the train dataset, we can use a DistributedSampler
>>> train_sampler = ds.DistributedSampler(10, 2)
>>> train.use_sampler(train_sampler)

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 (Union[dict, None]) – The data passed to the callback (default=None).

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

Add a blocking condition to the input Dataset.

Parameters

num_batch (int) – the number of batches without blocking at the start of each epoch.
condition_name (str) – The condition name that is used to toggle sending next row.
callback (function) – The callback funciton that will be invoked when sync_update is called.

Raises

RuntimeError – If condition name already exists.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> data = data.sync_wait("callback1")
>>> data = data.batch(batch_size)
>>> for batch_data in data.create_dict_iterator():
>>>     data = data.sync_update("callback1")

take(count=- 1)

Takes at most given numbers of elements from the dataset.

Note

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

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

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> # creates a dataset where the dataset including 50 elements.
>>> data = data.take(50)

to_device(send_epoch_end=True)

Transfer data through CPU, GPU or Ascend devices.

Parameters: send_epoch_end (bool, optional) – whether send end of sequence to device or not.(default=True)

Note

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

Returns

TransferDataset, dataset for transferring.

Raises

TypeError – If device_type is empty.
ValueError – If device_type is not ‘Ascend’, ‘GPU’ or ‘CPU’.
RuntimeError – If dataset is unknown.
RuntimeError – If distribution file path is given but failed to read.

use_sampler(new_sampler)

Will make the current dataset use the new_sampler provided.

Parameters: new_sampler (Sampler) – the sampler to use for the current dataset.
Returns: Dataset, that uses new_sampler.

Examples

>>> import mindspore.dataset as ds
>>>
>>> dataset_dir = "/path/to/imagefolder_directory"
>>> # a SequentialSampler is created by default
>>> data = ds.ImageFolderDatasetV2(dataset_dir)
>>>
>>> # use a DistributedSampler instead of the SequentialSampler
>>> new_sampler = ds.DistributedSampler(10, 2)
>>> data.use_sampler(new_sampler)

zip(datasets)

Zip the datasets in the input tuple of datasets. Columns in the input datasets must not have the same name.

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

Examples

>>> import mindspore.dataset as ds
>>> # ds1 and ds2 are instances of Dataset object
>>> # creates a dataset which is the combination of ds1 and ds2
>>> data = ds1.zip(ds2)

class mindspore.dataset.MnistDataset(dataset_dir, num_samples=None, num_parallel_workers=None, shuffle=None, sampler=None, num_shards=None, shard_id=None)[source]

A source dataset for reading and parsing the Mnist dataset.

The generated dataset has two columns [‘image’, ‘label’]. The type of the image tensor is uint8. The label is just a scalar uint32 tensor. This dataset can take in a sampler. sampler and shuffle are mutually exclusive. Table below shows what input args are allowed and their expected behavior.

Expected Order Behavior of Using ‘sampler’ and ‘shuffle’
Parameter ‘sampler’	Parameter ‘shuffle’	Expected Order Behavior
None	None	random order
None	True	random order
None	False	sequential order
Sampler object	None	order defined by sampler
Sampler object	True	not allowed
Sampler object	False	not allowed

Citation of Mnist dataset.

@article{lecun2010mnist,
title        = {MNIST handwritten digit database},
author       = {LeCun, Yann and Cortes, Corinna and Burges, CJ},
journal      = {ATT Labs [Online]},
volume       = {2},
year         = {2010},
howpublished = {http://yann.lecun.com/exdb/mnist},
description  = {The MNIST database of handwritten digits has a training set of 60,000 examples,
                and a test set of 10,000 examples. It is a subset of a larger set available from
                NIST. The digits have been size-normalized and centered in a fixed-size image.}
}

Parameters

dataset_dir (str) – Path to the root directory that contains the dataset.
num_samples (int, optional) – The number of images to be included in the dataset (default=None, all images).
num_parallel_workers (int, optional) – Number of workers to read the data (default=value, set in the config).
shuffle (bool, optional) – Whether or not to perform shuffle on the dataset (default=None, expected order behavior shown in the table).
sampler (Sampler, optional) – Object used to choose samples from the dataset (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).
shard_id (int, optional) – The shard ID within num_shards (default=None). This argument should be specified only when num_shards is also specified.

Raises

RuntimeError – If sampler and shuffle are specified at the same time.
RuntimeError – If sampler and sharding are specified at the same time.
RuntimeError – If num_shards is specified but shard_id is None.
RuntimeError – If shard_id is specified but num_shards is None.
ValueError – If shard_id is invalid (< 0 or >= num_shards).

Examples

>>> import mindspore.dataset as ds
>>> dataset_dir = "/path/to/mnist_folder"
>>> # 1) read 3 samples from mnist_dataset
>>> mnist_dataset = ds.MnistDataset(dataset_dir=dataset_dir, num_samples=3)
>>> # in mnist_dataset dataset, each dictionary has keys "image" and "label"

apply(apply_func)

Apply a function in this dataset.

The specified apply_func is a function that must take one ‘Dataset’ as an argument and return a preprogressing ‘Dataset’.

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

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> # declare an apply_func function which returns a Dataset object
>>> def apply_func(ds):
>>>     ds = ds.batch(2)
>>>     return ds
>>> # use apply to call apply_func
>>> data = data.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, pad_info=None)

Combine batch_size number of consecutive rows into batches.

For any child node, a batch is treated as a single row. 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. Recommend that repeat operation should be used after batch operation.

Parameters

batch_size (int or 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 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 to process the Dataset in parallel (default=None).
per_batch_map (callable, optional) – Per batch map callable. A callable which takes (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[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.

Returns

BatchDataset, dataset batched.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> # creates a dataset where every 100 rows is combined into a batch
>>> # and drops the last incomplete batch if there is one.
>>> data = data.batch(100, True)

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, and pad and batch the buckets 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 batch_info, and then batched. Each batch will be full, except for maybe the last batch for each bucket.

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, and one 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 len(column_names) arguments and returns an int. If no value is provided, then 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) – Represents how to batch each column. The key corresponds to the column name, 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).

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>>
>>> # creates a dataset where every 100 rows is combined into a batch
>>> # and drops the last incomplete batch if there is one.
>>> column_names = ["col1", "col2"]
>>> buket_boundaries = [5, 10]
>>> bucket_batch_sizes = [5, 1, 1]
>>> element_length_function = (lambda col1, col2: max(len(col1), len(col2)))
>>>
>>> # will pad col1 to shape [2, bucket_boundaries[i]] where i is the
>>> # index of the bucket that is currently being batched.
>>> # will pad col2 to a shape where each dimension is the longest in all
>>> # the elements currently being batched.
>>> pad_info = {"col1", ([2, None], -1)}
>>> pad_to_bucket_boundary = True
>>>
>>> data = data.bucket_batch_by_length(column_names, bucket_boundaries,
>>>                                    bucket_batch_sizes,
>>>                                    element_length_function, pad_info,
>>>                                    pad_to_bucket_boundary)

concat(datasets)

Concat the datasets in the input list of datasets, supported using “+” to reload concat operation.

Note

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

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

Examples

>>> import mindspore.dataset as ds
>>> # ds1 and ds2 are instances of Dataset object
>>> # creates a dataset by concating ds1 and ds2 with "+" operation
>>> data1 = ds1 + ds2
>>> # creates a dataset by concating ds1 and ds2 with concat operation
>>> data1 = ds1.concat(ds2)

create_dict_iterator(num_epochs=- 1)

Create an Iterator over the dataset.

The data retrieved will be a dictionary. The order of the columns in the dictionary may not be the same as the original order.

Returns: Iterator, dictionary of column_name-ndarray pair.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> # creates an iterator. The columns in the data obtained by the
>>> # iterator might be changed.
>>> iterator = data.create_dict_iterator()
>>> for item in iterator:
>>>     # print the data in column1
>>>     print(item["column1"])

create_tuple_iterator(columns=None, num_epochs=- 1)

Create an Iterator over the dataset. The data retrieved will be a list of ndarray of data.

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 not be changed.

Parameters: columns (list[str], optional) – List of columns to be used to specify the order of columns (default=None, means all columns).
Returns: Iterator, list of ndarray.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> # creates an iterator. The columns in the data obtained by the
>>> # iterator will not be changed.
>>> iterator = data.create_tuple_iterator()
>>> for item in iterator:
>>>     # convert the returned tuple to a list and print
>>>     print(list(item))

device_que(prefetch_size=None, send_epoch_end=True)

Return a transferredDataset that transfer data through device.

Parameters

prefetch_size (int, optional) – prefetch number of records ahead of the user’s request (default=None).
send_epoch_end (bool, optional) – whether send end of sequence to device or not.(default=True)

Note

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

Returns: TransferDataset, dataset for transferring.

filter(predicate, input_columns=None, num_parallel_workers=1)

Filter dataset by predicate.

Note

If input_columns not provided or empty, all columns will be used.

Parameters

predicate (callable) – python callable which returns a boolean value, if False then filter the element.
input_columns – (list[str], optional): List of names of the input columns, when default=None, the predicate will be applied on all columns in the dataset.
num_parallel_workers (int, optional) – Number of workers to process the Dataset in parallel (default=None).

Returns

FilterDataset, dataset filter.

Examples

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

flat_map(func)

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

The specified func is a function that must take one ‘Ndarray’ as input and return a ‘Dataset’.

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

Examples

>>> import mindspore.dataset as ds
>>> import mindspore.dataset.text as text
>>> # declare a function which returns a Dataset object
>>> def flat_map_func(x):
>>>     data_dir = text.to_str(x[0])
>>>     d = ds.ImageFolderDatasetV2(data_dir)
>>>     return d
>>> # data is a Dataset object
>>> data = ds.TextFileDataset(DATA_FILE)
>>> data = data.flat_map(flat_map_func)

Raises

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

get_batch_size()

Get the size of a batch.

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

get_class_indexing()

Get the class index.

Returns: Dict, A str-to-int mapping from label name to index.

get_dataset_size()[source]

Get the number of batches in an epoch.

Returns: Number, number of batches.

get_repeat_count()

Get the replication times in RepeatDataset else 1.

Returns: Number, the count of repeat.

map(input_columns=None, operations=None, output_columns=None, columns_order=None, num_parallel_workers=None, python_multiprocessing=False, cache=None)

Apply each operation in operations to this dataset.

The order of operations is determined by the position of each operation in operations. operations[0] will be applied first, then operations[1], then operations[2], etc.

Each operation will be passed one or more columns from the dataset as input, and zero 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.

Only the columns specified in columns_order will be propagated to the child node. These columns will be in the same order as specified in columns_order.

Parameters

input_columns (list[str]) – 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 is required, starting from the first column).
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) != 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).
columns_order (list[str], optional) – list of all the desired columns to propagate to the child node. This list must be a subset of all the columns in the dataset after all operations are applied. The order of the columns in each row propagated to the child node follow the order they appear in this list. The parameter is mandatory if the len(input_columns) != len(output_columns). (default=None, all columns will be propagated to the child node, the order of the columns will remain the same).
num_parallel_workers (int, optional) – Number of threads used to process the dataset in parallel (default=None, the value from the config will be used).
python_multiprocessing (bool, optional) – Parallelize python operations with multiple worker process. This option could be beneficial if the python operation is computational heavy (default=False).
cache (DatasetCache, optional) – Tensor cache to use. (default=None which means no cache is used). The cache feature is under development and is not recommended.

Returns

MapDataset, dataset after mapping operation.

Examples

>>> import mindspore.dataset as ds
>>> import mindspore.dataset.transforms.vision.c_transforms as c_transforms
>>>
>>> # data is an instance of Dataset which has 2 columns, "image" and "label".
>>> # ds_pyfunc is an instance of Dataset which has 3 columns, "col0", "col1", and "col2". Each column is
>>> # a 2d array of integers.
>>>
>>> # This config is a global setting, meaning that all future operations which
>>> # uses this config value will use 2 worker threads, unless if specified
>>> # otherwise in their constructor. set_num_parallel_workers can be called
>>> # again later if a different number of worker threads are needed.
>>> ds.config.set_num_parallel_workers(2)
>>>
>>> # Two operations, which takes 1 column for input and outputs 1 column.
>>> decode_op = c_transforms.Decode(rgb_format=True)
>>> random_jitter_op = c_transforms.RandomColorAdjust((0.8, 0.8), (1, 1), (1, 1), (0, 0))
>>>
>>> # 1) Simple map example
>>>
>>> operations = [decode_op]
>>> input_columns = ["image"]
>>>
>>> # Applies decode_op on column "image". This column will be replaced by the outputed
>>> # column of decode_op. Since columns_order is not provided, both columns "image"
>>> # and "label" will be propagated to the child node in their original order.
>>> ds_decoded = data.map(input_columns, operations)
>>>
>>> # Rename column "image" to "decoded_image"
>>> output_columns = ["decoded_image"]
>>> ds_decoded = data.map(input_columns, operations, output_columns)
>>>
>>> # Specify the order of the columns.
>>> columns_order ["label", "image"]
>>> ds_decoded = data.map(input_columns, operations, None, columns_order)
>>>
>>> # Rename column "image" to "decoded_image" and also specify the order of the columns.
>>> columns_order ["label", "decoded_image"]
>>> output_columns = ["decoded_image"]
>>> ds_decoded = data.map(input_columns, operations, output_columns, columns_order)
>>>
>>> # Rename column "image" to "decoded_image" and keep only this column.
>>> columns_order ["decoded_image"]
>>> output_columns = ["decoded_image"]
>>> ds_decoded = data.map(input_columns, operations, output_columns, columns_order)
>>>
>>> # Simple example using pyfunc. Renaming columns and specifying column order
>>> # work in the same way as the previous examples.
>>> input_columns = ["col0"]
>>> operations = [(lambda x: x + 1)]
>>> ds_mapped = ds_pyfunc.map(input_columns, operations)
>>>
>>> # 2) Map example with more than one operation
>>>
>>> # If this list of operations is used with map, decode_op will be applied
>>> # first, then random_jitter_op will be applied.
>>> operations = [decode_op, random_jitter_op]
>>>
>>> input_columns = ["image"]
>>>
>>> # Creates 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 columns_order is not specified, the order of the
>>> # columns will remain the same.
>>> ds_mapped = data.map(input_columns, operations)
>>>
>>> # Creates a dataset that is identical to ds_mapped, except the column "image"
>>> # that is outputted by random_jitter_op is renamed to "image_transformed".
>>> # Specifying column order works in the same way as examples in 1).
>>> output_columns = ["image_transformed"]
>>> ds_mapped_and_renamed = data.map(input_columns, operation, output_columns)
>>>
>>> # Multiple operations using pyfunc. Renaming columns and specifying column order
>>> # work in the same way as examples in 1).
>>> input_columns = ["col0"]
>>> operations = [(lambda x: x + x), (lambda x: x - 1)]
>>> output_columns = ["col0_mapped"]
>>> ds_mapped = ds_pyfunc.map(input_columns, operations, output_columns)
>>>
>>> # 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[1] 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: because the number of input columns is not the same as the number of
>>> # output columns, the output_columns and columns_order parameter must be
>>> # specified. Otherwise, this map call will also result in an error.
>>> input_columns = ["col2", "col0"]
>>> output_columns = ["mod2", "mod3", "mod5", "mod7"]
>>>
>>> # Propagate all columns to the child node in this order:
>>> columns_order = ["col0", "col2", "mod2", "mod3", "mod5", "mod7", "col1"]
>>> ds_mapped = ds_pyfunc.map(input_columns, operations, output_columns, columns_order)
>>>
>>> # Propagate some columns to the child node in this order:
>>> columns_order = ["mod7", "mod3", "col1"]
>>> ds_mapped = ds_pyfunc.map(input_columns, operations, output_columns, columns_order)

num_classes()

Get the number of classes in a dataset.

Returns: Number, number of classes.

output_shapes()

Get the shapes of output data.

Returns: List, list of shape of each column.

output_types()

Get the types of output data.

Returns: List of data type.

project(columns)

Project certain columns in input datasets.

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

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

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> columns_to_project = ["column3", "column1", "column2"]
>>>
>>> # creates a dataset that consist of column3, column1, column2
>>> # in that order, regardless of the original order of columns.
>>> data = data.project(columns=columns_to_project)

rename(input_columns, output_columns)

Rename the columns in input datasets.

Parameters

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

Returns

RenameDataset, dataset renamed.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> input_columns = ["input_col1", "input_col2", "input_col3"]
>>> output_columns = ["output_col1", "output_col2", "output_col3"]
>>>
>>> # creates 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.
>>> data = data.rename(input_columns=input_columns, output_columns=output_columns)

repeat(count=None)

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

Note

The order of using repeat and batch reflects the number of batches. Recommend that repeat operation should be used after batch operation. If dataset_sink_mode is False, here repeat operation is invalid. If dataset_sink_mode is True, repeat count should be equal to the epoch of training. Otherwise, errors could occur since the amount of data is not the amount training requires.

Parameters: count (int) – Number of times the dataset should be repeated (default=None).
Returns: RepeatDataset, dataset repeated.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> # creates a dataset where the dataset is repeated for 50 epochs
>>> repeated = data.repeat(50)
>>>
>>> # creates a dataset where each epoch is shuffled individually
>>> shuffled_and_repeated = data.shuffle(10)
>>> shuffled_and_repeated = shuffled_and_repeated.repeat(50)
>>>
>>> # creates 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.
>>> repeat_and_shuffle = data.repeat(50)
>>> repeat_and_shuffle = repeat_and_shuffle.shuffle(10)

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

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

Save the dynamic data processed by dataset pipeline as common dataset format, support: mindrecord.

Implicit type casting exists when saving data as mindrecord. Table below shows how to do type casting.

Implicit Type Casting of Saving as mindrecord
type in ‘dataset’	type in ‘mindrecord’	detail
bool	None	Not support
int8	int32
uint8	bytes(1D uint8)	Drop dimension
int16	int32
uint16	int32
int32	int32
uint32	int64
int64	int64
uint64	None	Not support
float16	float32
float32	float32
float64	float64
string	string	Not support multi-dimensional string

Note

To save the samples in order, should set dataset’s shuffle false and num_files 1.
Before call the function, do not use batch, repeat operator or data augmentation operators with random attribute in map operator.
Mindrecord does not support DE_UINT64, multi-dimensional DE_UINT8(drop dimension) and multi-dimensional DE_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’)

shuffle(buffer_size)

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

Make a shuffle buffer that contains the first buffer_size rows.
Randomly select an element from the shuffle buffer to be the next row propogated 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 seed can be provided to be used on the first epoch. 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: ShuffleDataset, dataset shuffled.
Raises: RuntimeError – If exist sync operators before shuffle.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> # optionally set the seed for the first epoch
>>> ds.config.set_seed(58)
>>>
>>> # creates a shuffled dataset using a shuffle buffer of size 4
>>> data = data.shuffle(4)

skip(count)

Skip the first N elements of this dataset.

Parameters: count (int) – Number of elements the dataset should be skipped.
Returns: SkipDataset, dataset skipped.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> # creates a dataset which skips first 3 elements from data
>>> data = data.skip(3)

split(sizes, randomize=True)

Split the dataset into smaller, non-overlapping datasets.

There is the optimized split function, which will be called automatically when the dataset that calls this function is a MappableDataset.

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 sizes does not equal the original dataset size, an an error will occur. 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 occur. 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 will be added to the first split.
- The sum of split sizes > K, the difference will be removed from the first large enough split such that it will have atleast 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 should not be sharded if split is going to be called. Instead, create a DistributedSampler and specify a split to shard after splitting. If dataset is sharded after a split, it is strongly recommended to set the same seed in each instance of execution, otherwise each shard may not be part of the same split (see Examples).
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. Furthermore, if sharding occurs after split, each shard may not be part of the same 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.

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

Examples

>>> import mindspore.dataset as ds
>>>
>>> dataset_dir = "/path/to/imagefolder_directory"
>>>
>>> # many datasets have shuffle on by default, set shuffle to False if split will be called!
>>> data = ds.ImageFolderDatasetV2(dataset_dir, shuffle=False)
>>>
>>> # sets the seed, and tells split to use this seed when randomizing. This
>>> # is needed because we are sharding later
>>> ds.config.set_seed(58)
>>> train, test = data.split([0.9, 0.1])
>>>
>>> # if we want to shard the train dataset, we can use a DistributedSampler
>>> train_sampler = ds.DistributedSampler(10, 2)
>>> train.use_sampler(train_sampler)

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 (Union[dict, None]) – The data passed to the callback (default=None).

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

Add a blocking condition to the input Dataset.

Parameters

num_batch (int) – the number of batches without blocking at the start of each epoch.
condition_name (str) – The condition name that is used to toggle sending next row.
callback (function) – The callback funciton that will be invoked when sync_update is called.

Raises

RuntimeError – If condition name already exists.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> data = data.sync_wait("callback1")
>>> data = data.batch(batch_size)
>>> for batch_data in data.create_dict_iterator():
>>>     data = data.sync_update("callback1")

take(count=- 1)

Takes at most given numbers of elements from the dataset.

Note

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

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

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> # creates a dataset where the dataset including 50 elements.
>>> data = data.take(50)

to_device(send_epoch_end=True)

Transfer data through CPU, GPU or Ascend devices.

Parameters: send_epoch_end (bool, optional) – whether send end of sequence to device or not.(default=True)

Note

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

Returns

TransferDataset, dataset for transferring.

Raises

TypeError – If device_type is empty.
ValueError – If device_type is not ‘Ascend’, ‘GPU’ or ‘CPU’.
RuntimeError – If dataset is unknown.
RuntimeError – If distribution file path is given but failed to read.

use_sampler(new_sampler)

Will make the current dataset use the new_sampler provided.

Parameters: new_sampler (Sampler) – the sampler to use for the current dataset.
Returns: Dataset, that uses new_sampler.

Examples

>>> import mindspore.dataset as ds
>>>
>>> dataset_dir = "/path/to/imagefolder_directory"
>>> # a SequentialSampler is created by default
>>> data = ds.ImageFolderDatasetV2(dataset_dir)
>>>
>>> # use a DistributedSampler instead of the SequentialSampler
>>> new_sampler = ds.DistributedSampler(10, 2)
>>> data.use_sampler(new_sampler)

zip(datasets)

Zip the datasets in the input tuple of datasets. Columns in the input datasets must not have the same name.

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

Examples

>>> import mindspore.dataset as ds
>>> # ds1 and ds2 are instances of Dataset object
>>> # creates a dataset which is the combination of ds1 and ds2
>>> data = ds1.zip(ds2)

class mindspore.dataset.NumpySlicesDataset(data, column_names=None, num_samples=None, num_parallel_workers=1, shuffle=None, sampler=None, num_shards=None, shard_id=None)[source]

Create a dataset with given data slices, mainly for loading python data into dataset.

This dataset can take in a sampler. sampler and shuffle are mutually exclusive. Table below shows what input args are allowed and their expected behavior.

Expected Order Behavior of Using ‘sampler’ and ‘shuffle’
Parameter ‘sampler’	Parameter ‘shuffle’	Expected Order Behavior
None	None	random order
None	True	random order
None	False	sequential order
Sampler object	None	order defined by sampler
Sampler object	True	not allowed
Sampler object	False	not allowed

Parameters

data (Union[list, tuple, dict]) – 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 (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 when num_shards is also specified. Random accessible input is required.

Examples

>>> import mindspore.dataset as ds
>>> # 1) Input data can be a list
>>> data = [1, 2, 3]
>>> dataset1 = ds.NumpySlicesDataset(data, column_names=["column_1"])
>>> # 2) Input data can be a dict, and column_names will be its key
>>> data = {"a": [1, 2], "b": [3, 4]}
>>> dataset2 = ds.NumpySlicesDataset(data)
>>> # 3) Input data can be a tuple of lists (or numpy arrays), each tuple element refers to data in each column
>>> data = ([1, 2], [3, 4], [5, 6])
>>> dataset3 = ds.NumpySlicesDataset(data, column_names=["column_1", "column_2", "column_3"])
>>> # 4) Load data from csv file
>>> import pandas as pd
>>> df = pd.read_csv("file.csv")
>>> dataset4 = ds.NumpySlicesDataset(dict(df), shuffle=False)

apply(apply_func)

Apply a function in this dataset.

The specified apply_func is a function that must take one ‘Dataset’ as an argument and return a preprogressing ‘Dataset’.

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

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> # declare an apply_func function which returns a Dataset object
>>> def apply_func(ds):
>>>     ds = ds.batch(2)
>>>     return ds
>>> # use apply to call apply_func
>>> data = data.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, pad_info=None)

Combine batch_size number of consecutive rows into batches.

For any child node, a batch is treated as a single row. 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. Recommend that repeat operation should be used after batch operation.

Parameters

batch_size (int or 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 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 to process the Dataset in parallel (default=None).
per_batch_map (callable, optional) – Per batch map callable. A callable which takes (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[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.

Returns

BatchDataset, dataset batched.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> # creates a dataset where every 100 rows is combined into a batch
>>> # and drops the last incomplete batch if there is one.
>>> data = data.batch(100, True)

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, and pad and batch the buckets 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 batch_info, and then batched. Each batch will be full, except for maybe the last batch for each bucket.

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, and one 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 len(column_names) arguments and returns an int. If no value is provided, then 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) – Represents how to batch each column. The key corresponds to the column name, 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).

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>>
>>> # creates a dataset where every 100 rows is combined into a batch
>>> # and drops the last incomplete batch if there is one.
>>> column_names = ["col1", "col2"]
>>> buket_boundaries = [5, 10]
>>> bucket_batch_sizes = [5, 1, 1]
>>> element_length_function = (lambda col1, col2: max(len(col1), len(col2)))
>>>
>>> # will pad col1 to shape [2, bucket_boundaries[i]] where i is the
>>> # index of the bucket that is currently being batched.
>>> # will pad col2 to a shape where each dimension is the longest in all
>>> # the elements currently being batched.
>>> pad_info = {"col1", ([2, None], -1)}
>>> pad_to_bucket_boundary = True
>>>
>>> data = data.bucket_batch_by_length(column_names, bucket_boundaries,
>>>                                    bucket_batch_sizes,
>>>                                    element_length_function, pad_info,
>>>                                    pad_to_bucket_boundary)

concat(datasets)

Concat the datasets in the input list of datasets, supported using “+” to reload concat operation.

Note

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

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

Examples

>>> import mindspore.dataset as ds
>>> # ds1 and ds2 are instances of Dataset object
>>> # creates a dataset by concating ds1 and ds2 with "+" operation
>>> data1 = ds1 + ds2
>>> # creates a dataset by concating ds1 and ds2 with concat operation
>>> data1 = ds1.concat(ds2)

create_dict_iterator(num_epochs=- 1)

Create an Iterator over the dataset.

The data retrieved will be a dictionary. The order of the columns in the dictionary may not be the same as the original order.

Returns: Iterator, dictionary of column_name-ndarray pair.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> # creates an iterator. The columns in the data obtained by the
>>> # iterator might be changed.
>>> iterator = data.create_dict_iterator()
>>> for item in iterator:
>>>     # print the data in column1
>>>     print(item["column1"])

create_tuple_iterator(columns=None, num_epochs=- 1)

Create an Iterator over the dataset. The data retrieved will be a list of ndarray of data.

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 not be changed.

Parameters: columns (list[str], optional) – List of columns to be used to specify the order of columns (default=None, means all columns).
Returns: Iterator, list of ndarray.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> # creates an iterator. The columns in the data obtained by the
>>> # iterator will not be changed.
>>> iterator = data.create_tuple_iterator()
>>> for item in iterator:
>>>     # convert the returned tuple to a list and print
>>>     print(list(item))

device_que(prefetch_size=None, send_epoch_end=True)

Return a transferredDataset that transfer data through device.

Parameters

prefetch_size (int, optional) – prefetch number of records ahead of the user’s request (default=None).
send_epoch_end (bool, optional) – whether send end of sequence to device or not.(default=True)

Note

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

Returns: TransferDataset, dataset for transferring.

filter(predicate, input_columns=None, num_parallel_workers=1)

Filter dataset by predicate.

Note

If input_columns not provided or empty, all columns will be used.

Parameters

predicate (callable) – python callable which returns a boolean value, if False then filter the element.
input_columns – (list[str], optional): List of names of the input columns, when default=None, the predicate will be applied on all columns in the dataset.
num_parallel_workers (int, optional) – Number of workers to process the Dataset in parallel (default=None).

Returns

FilterDataset, dataset filter.

Examples

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

flat_map(func)

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

The specified func is a function that must take one ‘Ndarray’ as input and return a ‘Dataset’.

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

Examples

>>> import mindspore.dataset as ds
>>> import mindspore.dataset.text as text
>>> # declare a function which returns a Dataset object
>>> def flat_map_func(x):
>>>     data_dir = text.to_str(x[0])
>>>     d = ds.ImageFolderDatasetV2(data_dir)
>>>     return d
>>> # data is a Dataset object
>>> data = ds.TextFileDataset(DATA_FILE)
>>> data = data.flat_map(flat_map_func)

Raises

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

get_batch_size()

Get the size of a batch.

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

get_class_indexing()

Get the class index.

Returns: Dict, A str-to-int mapping from label name to index.

get_dataset_size()

Get the number of batches in an epoch.

Returns: Number, number of batches.

get_repeat_count()

Get the replication times in RepeatDataset else 1.

Returns: Number, the count of repeat.

map(input_columns=None, operations=None, output_columns=None, columns_order=None, num_parallel_workers=None, python_multiprocessing=False, cache=None)

Apply each operation in operations to this dataset.

The order of operations is determined by the position of each operation in operations. operations[0] will be applied first, then operations[1], then operations[2], etc.

Each operation will be passed one or more columns from the dataset as input, and zero 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.

Only the columns specified in columns_order will be propagated to the child node. These columns will be in the same order as specified in columns_order.

Parameters

input_columns (list[str]) – 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 is required, starting from the first column).
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) != 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).
columns_order (list[str], optional) – list of all the desired columns to propagate to the child node. This list must be a subset of all the columns in the dataset after all operations are applied. The order of the columns in each row propagated to the child node follow the order they appear in this list. The parameter is mandatory if the len(input_columns) != len(output_columns). (default=None, all columns will be propagated to the child node, the order of the columns will remain the same).
num_parallel_workers (int, optional) – Number of threads used to process the dataset in parallel (default=None, the value from the config will be used).
python_multiprocessing (bool, optional) – Parallelize python operations with multiple worker process. This option could be beneficial if the python operation is computational heavy (default=False).
cache (DatasetCache, optional) – Tensor cache to use. (default=None which means no cache is used). The cache feature is under development and is not recommended.

Returns

MapDataset, dataset after mapping operation.

Examples

>>> import mindspore.dataset as ds
>>> import mindspore.dataset.transforms.vision.c_transforms as c_transforms
>>>
>>> # data is an instance of Dataset which has 2 columns, "image" and "label".
>>> # ds_pyfunc is an instance of Dataset which has 3 columns, "col0", "col1", and "col2". Each column is
>>> # a 2d array of integers.
>>>
>>> # This config is a global setting, meaning that all future operations which
>>> # uses this config value will use 2 worker threads, unless if specified
>>> # otherwise in their constructor. set_num_parallel_workers can be called
>>> # again later if a different number of worker threads are needed.
>>> ds.config.set_num_parallel_workers(2)
>>>
>>> # Two operations, which takes 1 column for input and outputs 1 column.
>>> decode_op = c_transforms.Decode(rgb_format=True)
>>> random_jitter_op = c_transforms.RandomColorAdjust((0.8, 0.8), (1, 1), (1, 1), (0, 0))
>>>
>>> # 1) Simple map example
>>>
>>> operations = [decode_op]
>>> input_columns = ["image"]
>>>
>>> # Applies decode_op on column "image". This column will be replaced by the outputed
>>> # column of decode_op. Since columns_order is not provided, both columns "image"
>>> # and "label" will be propagated to the child node in their original order.
>>> ds_decoded = data.map(input_columns, operations)
>>>
>>> # Rename column "image" to "decoded_image"
>>> output_columns = ["decoded_image"]
>>> ds_decoded = data.map(input_columns, operations, output_columns)
>>>
>>> # Specify the order of the columns.
>>> columns_order ["label", "image"]
>>> ds_decoded = data.map(input_columns, operations, None, columns_order)
>>>
>>> # Rename column "image" to "decoded_image" and also specify the order of the columns.
>>> columns_order ["label", "decoded_image"]
>>> output_columns = ["decoded_image"]
>>> ds_decoded = data.map(input_columns, operations, output_columns, columns_order)
>>>
>>> # Rename column "image" to "decoded_image" and keep only this column.
>>> columns_order ["decoded_image"]
>>> output_columns = ["decoded_image"]
>>> ds_decoded = data.map(input_columns, operations, output_columns, columns_order)
>>>
>>> # Simple example using pyfunc. Renaming columns and specifying column order
>>> # work in the same way as the previous examples.
>>> input_columns = ["col0"]
>>> operations = [(lambda x: x + 1)]
>>> ds_mapped = ds_pyfunc.map(input_columns, operations)
>>>
>>> # 2) Map example with more than one operation
>>>
>>> # If this list of operations is used with map, decode_op will be applied
>>> # first, then random_jitter_op will be applied.
>>> operations = [decode_op, random_jitter_op]
>>>
>>> input_columns = ["image"]
>>>
>>> # Creates 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 columns_order is not specified, the order of the
>>> # columns will remain the same.
>>> ds_mapped = data.map(input_columns, operations)
>>>
>>> # Creates a dataset that is identical to ds_mapped, except the column "image"
>>> # that is outputted by random_jitter_op is renamed to "image_transformed".
>>> # Specifying column order works in the same way as examples in 1).
>>> output_columns = ["image_transformed"]
>>> ds_mapped_and_renamed = data.map(input_columns, operation, output_columns)
>>>
>>> # Multiple operations using pyfunc. Renaming columns and specifying column order
>>> # work in the same way as examples in 1).
>>> input_columns = ["col0"]
>>> operations = [(lambda x: x + x), (lambda x: x - 1)]
>>> output_columns = ["col0_mapped"]
>>> ds_mapped = ds_pyfunc.map(input_columns, operations, output_columns)
>>>
>>> # 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[1] 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: because the number of input columns is not the same as the number of
>>> # output columns, the output_columns and columns_order parameter must be
>>> # specified. Otherwise, this map call will also result in an error.
>>> input_columns = ["col2", "col0"]
>>> output_columns = ["mod2", "mod3", "mod5", "mod7"]
>>>
>>> # Propagate all columns to the child node in this order:
>>> columns_order = ["col0", "col2", "mod2", "mod3", "mod5", "mod7", "col1"]
>>> ds_mapped = ds_pyfunc.map(input_columns, operations, output_columns, columns_order)
>>>
>>> # Propagate some columns to the child node in this order:
>>> columns_order = ["mod7", "mod3", "col1"]
>>> ds_mapped = ds_pyfunc.map(input_columns, operations, output_columns, columns_order)

num_classes()

Get the number of classes in a dataset.

Returns: Number, number of classes.

output_shapes()

Get the shapes of output data.

Returns: List, list of shape of each column.

output_types()

Get the types of output data.

Returns: List of data type.

project(columns)

Project certain columns in input datasets.

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

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

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> columns_to_project = ["column3", "column1", "column2"]
>>>
>>> # creates a dataset that consist of column3, column1, column2
>>> # in that order, regardless of the original order of columns.
>>> data = data.project(columns=columns_to_project)

rename(input_columns, output_columns)

Rename the columns in input datasets.

Parameters

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

Returns

RenameDataset, dataset renamed.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> input_columns = ["input_col1", "input_col2", "input_col3"]
>>> output_columns = ["output_col1", "output_col2", "output_col3"]
>>>
>>> # creates 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.
>>> data = data.rename(input_columns=input_columns, output_columns=output_columns)

repeat(count=None)

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

Note

The order of using repeat and batch reflects the number of batches. Recommend that repeat operation should be used after batch operation. If dataset_sink_mode is False, here repeat operation is invalid. If dataset_sink_mode is True, repeat count should be equal to the epoch of training. Otherwise, errors could occur since the amount of data is not the amount training requires.

Parameters: count (int) – Number of times the dataset should be repeated (default=None).
Returns: RepeatDataset, dataset repeated.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> # creates a dataset where the dataset is repeated for 50 epochs
>>> repeated = data.repeat(50)
>>>
>>> # creates a dataset where each epoch is shuffled individually
>>> shuffled_and_repeated = data.shuffle(10)
>>> shuffled_and_repeated = shuffled_and_repeated.repeat(50)
>>>
>>> # creates 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.
>>> repeat_and_shuffle = data.repeat(50)
>>> repeat_and_shuffle = repeat_and_shuffle.shuffle(10)

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

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

Save the dynamic data processed by dataset pipeline as common dataset format, support: mindrecord.

Implicit type casting exists when saving data as mindrecord. Table below shows how to do type casting.

Implicit Type Casting of Saving as mindrecord
type in ‘dataset’	type in ‘mindrecord’	detail
bool	None	Not support
int8	int32
uint8	bytes(1D uint8)	Drop dimension
int16	int32
uint16	int32
int32	int32
uint32	int64
int64	int64
uint64	None	Not support
float16	float32
float32	float32
float64	float64
string	string	Not support multi-dimensional string

Note

To save the samples in order, should set dataset’s shuffle false and num_files 1.
Before call the function, do not use batch, repeat operator or data augmentation operators with random attribute in map operator.
Mindrecord does not support DE_UINT64, multi-dimensional DE_UINT8(drop dimension) and multi-dimensional DE_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’)

shuffle(buffer_size)

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

Make a shuffle buffer that contains the first buffer_size rows.
Randomly select an element from the shuffle buffer to be the next row propogated 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 seed can be provided to be used on the first epoch. 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: ShuffleDataset, dataset shuffled.
Raises: RuntimeError – If exist sync operators before shuffle.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> # optionally set the seed for the first epoch
>>> ds.config.set_seed(58)
>>>
>>> # creates a shuffled dataset using a shuffle buffer of size 4
>>> data = data.shuffle(4)

skip(count)

Skip the first N elements of this dataset.

Parameters: count (int) – Number of elements the dataset should be skipped.
Returns: SkipDataset, dataset skipped.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> # creates a dataset which skips first 3 elements from data
>>> data = data.skip(3)

split(sizes, randomize=True)

Split the dataset into smaller, non-overlapping datasets.

There is the optimized split function, which will be called automatically when the dataset that calls this function is a MappableDataset.

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 sizes does not equal the original dataset size, an an error will occur. 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 occur. 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 will be added to the first split.
- The sum of split sizes > K, the difference will be removed from the first large enough split such that it will have atleast 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 should not be sharded if split is going to be called. Instead, create a DistributedSampler and specify a split to shard after splitting. If dataset is sharded after a split, it is strongly recommended to set the same seed in each instance of execution, otherwise each shard may not be part of the same split (see Examples).
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. Furthermore, if sharding occurs after split, each shard may not be part of the same 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.

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

Examples

>>> import mindspore.dataset as ds
>>>
>>> dataset_dir = "/path/to/imagefolder_directory"
>>>
>>> # many datasets have shuffle on by default, set shuffle to False if split will be called!
>>> data = ds.ImageFolderDatasetV2(dataset_dir, shuffle=False)
>>>
>>> # sets the seed, and tells split to use this seed when randomizing. This
>>> # is needed because we are sharding later
>>> ds.config.set_seed(58)
>>> train, test = data.split([0.9, 0.1])
>>>
>>> # if we want to shard the train dataset, we can use a DistributedSampler
>>> train_sampler = ds.DistributedSampler(10, 2)
>>> train.use_sampler(train_sampler)

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 (Union[dict, None]) – The data passed to the callback (default=None).

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

Add a blocking condition to the input Dataset.

Parameters

num_batch (int) – the number of batches without blocking at the start of each epoch.
condition_name (str) – The condition name that is used to toggle sending next row.
callback (function) – The callback funciton that will be invoked when sync_update is called.

Raises

RuntimeError – If condition name already exists.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> data = data.sync_wait("callback1")
>>> data = data.batch(batch_size)
>>> for batch_data in data.create_dict_iterator():
>>>     data = data.sync_update("callback1")

take(count=- 1)

Takes at most given numbers of elements from the dataset.

Note

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

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

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> # creates a dataset where the dataset including 50 elements.
>>> data = data.take(50)

to_device(send_epoch_end=True)

Transfer data through CPU, GPU or Ascend devices.

Parameters: send_epoch_end (bool, optional) – whether send end of sequence to device or not.(default=True)

Note

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

Returns

TransferDataset, dataset for transferring.

Raises

TypeError – If device_type is empty.
ValueError – If device_type is not ‘Ascend’, ‘GPU’ or ‘CPU’.
RuntimeError – If dataset is unknown.
RuntimeError – If distribution file path is given but failed to read.

use_sampler(new_sampler)

Will make the current dataset use the new_sampler provided.

Parameters: new_sampler (Sampler) – the sampler to use for the current dataset.
Returns: Dataset, that uses new_sampler.

Examples

>>> import mindspore.dataset as ds
>>>
>>> dataset_dir = "/path/to/imagefolder_directory"
>>> # a SequentialSampler is created by default
>>> data = ds.ImageFolderDatasetV2(dataset_dir)
>>>
>>> # use a DistributedSampler instead of the SequentialSampler
>>> new_sampler = ds.DistributedSampler(10, 2)
>>> data.use_sampler(new_sampler)

zip(datasets)

Zip the datasets in the input tuple of datasets. Columns in the input datasets must not have the same name.

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

Examples

>>> import mindspore.dataset as ds
>>> # ds1 and ds2 are instances of Dataset object
>>> # creates a dataset which is the combination of ds1 and ds2
>>> data = ds1.zip(ds2)

class mindspore.dataset.PKSampler(num_val, num_class=None, shuffle=False, class_column='label', num_samples=None)[source]

Samples K elements for each P class in the dataset.

Parameters

num_val (int) – Number of elements to sample for each class.
num_class (int, optional) – Number of classes to sample (default=None, all classes).
shuffle (bool, optional) – If true, the class IDs are shuffled (default=False).
class_column (str, optional) – Name of column to classify dataset(default=’label’), for MindDataset.
num_samples (int, optional) – The number of samples to draw (default=None, all elements).

Examples

>>> import mindspore.dataset as ds
>>>
>>> dataset_dir = "path/to/imagefolder_directory"
>>>
>>> # creates a PKSampler that will get 3 samples from every class.
>>> sampler = ds.PKSampler(3)
>>> data = ds.ImageFolderDatasetV2(dataset_dir, num_parallel_workers=8, sampler=sampler)

Raises

ValueError – If num_val is not positive.
NotImplementedError – If num_class is not None.
ValueError – If shuffle is not boolean.

get_num_samples()

All samplers can contain a numeric num_samples value (or it could be set to None). Child sampler can exist or be None. if child sampler exists, then the child sampler count can be a numeric value or None. Given these conditions, we need to output what the sampler count is for this sampler. The following table shows the possible results from calling this function.

child sampler	num_samples	child_samples	result
T	x	y	min(x, y)
T	x	None	x
T	None	y	y
T	None	None	None
None	x	n/a	x
None	None	n/a	None

Returns: int, The number of samples, or None

class mindspore.dataset.RandomSampler(replacement=False, num_samples=None)[source]

Samples the elements randomly.

Parameters

replacement (bool, optional) – If True, put the sample ID back for the next draw (default=False).
num_samples (int, optional) – Number of elements to sample (default=None, all elements).

Examples

>>> import mindspore.dataset as ds
>>>
>>> dataset_dir = "path/to/imagefolder_directory"
>>>
>>> # creates a RandomSampler
>>> sampler = ds.RandomSampler()
>>> data = ds.ImageFolderDatasetV2(dataset_dir, num_parallel_workers=8, sampler=sampler)

Raises

ValueError – If replacement is not boolean.
ValueError – If num_samples is not positive.

get_num_samples()

All samplers can contain a numeric num_samples value (or it could be set to None). Child sampler can exist or be None. if child sampler exists, then the child sampler count can be a numeric value or None. Given these conditions, we need to output what the sampler count is for this sampler. The following table shows the possible results from calling this function.

child sampler	num_samples	child_samples	result
T	x	y	min(x, y)
T	x	None	x
T	None	y	y
T	None	None	None
None	x	n/a	x
None	None	n/a	None

Returns: int, The number of samples, or None

class mindspore.dataset.Schema(schema_file=None)[source]

Class to represent a schema of dataset.

Parameters: schema_file (str) – Path of schema file (default=None).
Returns: Schema object, schema info about dataset.
Raises: RuntimeError – If schema file failed to load.

Example

>>> import mindspore.dataset as ds
>>> import mindspore.common.dtype as mstype
>>> # create schema, specify column name, mindspore.dtype and shape of the column
>>> schema = ds.Schema()
>>> schema.add_column('col1', de_type=mindspore.int64, shape=[2])

add_column(name, de_type, shape=None)[source]

Add new column to the schema.

Parameters

name (str) – name of the column.
de_type (str) – data type of the column.
shape (list[int], optional) – shape of the column (default=None, [-1] which is an unknown shape of rank 1).

Raises

ValueError – If column type is unknown.

from_json(json_obj)[source]

Get schema file from json file.

Parameters

json_obj (dictionary) – object of json parsed.

Raises

RuntimeError – if there is unknown item in the object.
RuntimeError – if dataset type is missing in the object.
RuntimeError – if columns are missing in the object.

parse_columns(columns)[source]

Parse the columns and add it to self.

Parameters

columns (Union[dict, list[dict]]) –

dataset attribution information, decoded from schema file.

list[dict], ‘name’ and ‘type’ must be in keys, ‘shape’ optional.
dict, columns.keys() as name, columns.values() is dict, and ‘type’ inside, ‘shape’ optional.

Raises

RuntimeError – If failed to parse columns.
RuntimeError – If unknown items in columns.
RuntimeError – If column’s name field is missing.
RuntimeError – If column’s type field is missing.

Example

>>> schema = Schema()
>>> columns1 = [{'name': 'image', 'type': 'int8', 'shape': [3, 3]},
>>>             {'name': 'label', 'type': 'int8', 'shape': [1]}]
>>> schema.parse_columns(columns1)
>>> columns2 = {'image': {'shape': [3, 3], 'type': 'int8'}, 'label': {'shape': [1], 'type': 'int8'}}
>>> schema.parse_columns(columns2)

to_json()[source]

Get a JSON string of the schema.

Returns: Str, JSON string of the schema.

class mindspore.dataset.SequentialSampler(start_index=None, num_samples=None)[source]

Samples the dataset elements sequentially, same as not having a sampler.

Parameters

start_index (int, optional) – Index to start sampling at. (dafault=None starts at first id)
num_samples (int, optional) – Number of elements to sample (default=None, all elements).

Examples

>>> import mindspore.dataset as ds
>>>
>>> dataset_dir = "path/to/imagefolder_directory"
>>>
>>> # creates a SequentialSampler
>>> sampler = ds.SequentialSampler()
>>> data = ds.ImageFolderDatasetV2(dataset_dir, num_parallel_workers=8, sampler=sampler)

get_num_samples()

All samplers can contain a numeric num_samples value (or it could be set to None). Child sampler can exist or be None. if child sampler exists, then the child sampler count can be a numeric value or None. Given these conditions, we need to output what the sampler count is for this sampler. The following table shows the possible results from calling this function.

child sampler	num_samples	child_samples	result
T	x	y	min(x, y)
T	x	None	x
T	None	y	y
T	None	None	None
None	x	n/a	x
None	None	n/a	None

Returns: int, The number of samples, or None

class mindspore.dataset.SubsetRandomSampler(indices, num_samples=None)[source]

Samples the elements randomly from a sequence of indices.

Parameters

indices (list[int]) – A sequence of indices.
num_samples (int, optional) – Number of elements to sample (default=None, all elements).

Examples

>>> import mindspore.dataset as ds
>>>
>>> dataset_dir = "path/to/imagefolder_directory"
>>>
>>> indices = [0, 1, 2, 3, 7, 88, 119]
>>>
>>> # creates a SubsetRandomSampler, will sample from the provided indices
>>> sampler = ds.SubsetRandomSampler()
>>> data = ds.ImageFolderDatasetV2(dataset_dir, num_parallel_workers=8, sampler=sampler)

class mindspore.dataset.TFRecordDataset(dataset_files, schema=None, columns_list=None, num_samples=None, num_parallel_workers=None, shuffle=Shuffle.GLOBAL, num_shards=None, shard_id=None, shard_equal_rows=False, cache=None)[source]

A source dataset that reads and parses datasets stored on disk in TFData format.

Parameters

dataset_files (Union[str, list[str]]) – String or list of files to be read or glob strings to search for a
order. (pattern of files. The list will be sorted in a lexicographical) –
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). If num_samples is None and numRows(parsed from schema) is not exist, read the full dataset; If num_samples is None and numRows(parsed from schema) is greater than 0, read numRows rows; 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 (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:
- Shuffle.GLOBAL: Shuffle both the files and samples.
- Shuffle.FILES: Shuffle files only.
num_shards (int, optional) – Number of shards that the dataset should be divided into (default=None).
shard_id (int, optional) – The shard ID within num_shards (default=None). This argument should be specified only when num_shards is also specified.
shard_equal_rows (bool) – Get equal rows for all shards(default=False). If shard_equal_rows is false, number of rows of each shard may be not equal.
cache (DatasetCache, optional) – Tensor cache to use. (default=None which means no cache is used). The cache feature is under development and is not recommended.

Examples

>>> import mindspore.dataset as ds
>>> import mindspore.common.dtype as mstype
>>> dataset_files = ["/path/to/1", "/path/to/2"] # contains 1 or multiple tf data files
>>> # 1) get all rows from dataset_files with no explicit schema:
>>> # The meta-data in the first row will be used as a schema.
>>> tfdataset = ds.TFRecordDataset(dataset_files=dataset_files)
>>> # 2) get all rows from dataset_files with user-defined schema:
>>> schema = ds.Schema()
>>> schema.add_column('col_1d', de_type=mindspore.int64, shape=[2])
>>> tfdataset = ds.TFRecordDataset(dataset_files=dataset_files, schema=schema)
>>> # 3) get all rows from dataset_files with schema file "./schema.json":
>>> tfdataset = ds.TFRecordDataset(dataset_files=dataset_files, schema="./schema.json")

apply(apply_func)

Apply a function in this dataset.

The specified apply_func is a function that must take one ‘Dataset’ as an argument and return a preprogressing ‘Dataset’.

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

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> # declare an apply_func function which returns a Dataset object
>>> def apply_func(ds):
>>>     ds = ds.batch(2)
>>>     return ds
>>> # use apply to call apply_func
>>> data = data.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, pad_info=None)

Combine batch_size number of consecutive rows into batches.

For any child node, a batch is treated as a single row. 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. Recommend that repeat operation should be used after batch operation.

Parameters

batch_size (int or 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 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 to process the Dataset in parallel (default=None).
per_batch_map (callable, optional) – Per batch map callable. A callable which takes (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[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.

Returns

BatchDataset, dataset batched.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> # creates a dataset where every 100 rows is combined into a batch
>>> # and drops the last incomplete batch if there is one.
>>> data = data.batch(100, True)

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, and pad and batch the buckets 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 batch_info, and then batched. Each batch will be full, except for maybe the last batch for each bucket.

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, and one 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 len(column_names) arguments and returns an int. If no value is provided, then 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) – Represents how to batch each column. The key corresponds to the column name, 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).

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>>
>>> # creates a dataset where every 100 rows is combined into a batch
>>> # and drops the last incomplete batch if there is one.
>>> column_names = ["col1", "col2"]
>>> buket_boundaries = [5, 10]
>>> bucket_batch_sizes = [5, 1, 1]
>>> element_length_function = (lambda col1, col2: max(len(col1), len(col2)))
>>>
>>> # will pad col1 to shape [2, bucket_boundaries[i]] where i is the
>>> # index of the bucket that is currently being batched.
>>> # will pad col2 to a shape where each dimension is the longest in all
>>> # the elements currently being batched.
>>> pad_info = {"col1", ([2, None], -1)}
>>> pad_to_bucket_boundary = True
>>>
>>> data = data.bucket_batch_by_length(column_names, bucket_boundaries,
>>>                                    bucket_batch_sizes,
>>>                                    element_length_function, pad_info,
>>>                                    pad_to_bucket_boundary)

concat(datasets)

Concat the datasets in the input list of datasets, supported using “+” to reload concat operation.

Note

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

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

Examples

>>> import mindspore.dataset as ds
>>> # ds1 and ds2 are instances of Dataset object
>>> # creates a dataset by concating ds1 and ds2 with "+" operation
>>> data1 = ds1 + ds2
>>> # creates a dataset by concating ds1 and ds2 with concat operation
>>> data1 = ds1.concat(ds2)

create_dict_iterator(num_epochs=- 1)

Create an Iterator over the dataset.

The data retrieved will be a dictionary. The order of the columns in the dictionary may not be the same as the original order.

Returns: Iterator, dictionary of column_name-ndarray pair.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> # creates an iterator. The columns in the data obtained by the
>>> # iterator might be changed.
>>> iterator = data.create_dict_iterator()
>>> for item in iterator:
>>>     # print the data in column1
>>>     print(item["column1"])

create_tuple_iterator(columns=None, num_epochs=- 1)

Create an Iterator over the dataset. The data retrieved will be a list of ndarray of data.

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 not be changed.

Parameters: columns (list[str], optional) – List of columns to be used to specify the order of columns (default=None, means all columns).
Returns: Iterator, list of ndarray.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> # creates an iterator. The columns in the data obtained by the
>>> # iterator will not be changed.
>>> iterator = data.create_tuple_iterator()
>>> for item in iterator:
>>>     # convert the returned tuple to a list and print
>>>     print(list(item))

device_que(prefetch_size=None, send_epoch_end=True)

Return a transferredDataset that transfer data through device.

Parameters

prefetch_size (int, optional) – prefetch number of records ahead of the user’s request (default=None).
send_epoch_end (bool, optional) – whether send end of sequence to device or not.(default=True)

Note

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

Returns: TransferDataset, dataset for transferring.

filter(predicate, input_columns=None, num_parallel_workers=1)

Filter dataset by predicate.

Note

If input_columns not provided or empty, all columns will be used.

Parameters

predicate (callable) – python callable which returns a boolean value, if False then filter the element.
input_columns – (list[str], optional): List of names of the input columns, when default=None, the predicate will be applied on all columns in the dataset.
num_parallel_workers (int, optional) – Number of workers to process the Dataset in parallel (default=None).

Returns

FilterDataset, dataset filter.

Examples

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

flat_map(func)

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

The specified func is a function that must take one ‘Ndarray’ as input and return a ‘Dataset’.

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

Examples

>>> import mindspore.dataset as ds
>>> import mindspore.dataset.text as text
>>> # declare a function which returns a Dataset object
>>> def flat_map_func(x):
>>>     data_dir = text.to_str(x[0])
>>>     d = ds.ImageFolderDatasetV2(data_dir)
>>>     return d
>>> # data is a Dataset object
>>> data = ds.TextFileDataset(DATA_FILE)
>>> data = data.flat_map(flat_map_func)

Raises

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

get_batch_size()

Get the size of a batch.

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

get_class_indexing()

Get the class index.

Returns: Dict, A str-to-int mapping from label name to index.

get_dataset_size(estimate=False)[source]

Get the number of batches in an epoch.

Parameters: estimate (bool, optional) – Fast estimation of the dataset size instead of a full scan.
Returns: Number, number of batches.

get_repeat_count()

Get the replication times in RepeatDataset else 1.

Returns: Number, the count of repeat.

map(input_columns=None, operations=None, output_columns=None, columns_order=None, num_parallel_workers=None, python_multiprocessing=False, cache=None)

Apply each operation in operations to this dataset.

The order of operations is determined by the position of each operation in operations. operations[0] will be applied first, then operations[1], then operations[2], etc.

Each operation will be passed one or more columns from the dataset as input, and zero 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.

Only the columns specified in columns_order will be propagated to the child node. These columns will be in the same order as specified in columns_order.

Parameters

input_columns (list[str]) – 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 is required, starting from the first column).
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) != 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).
columns_order (list[str], optional) – list of all the desired columns to propagate to the child node. This list must be a subset of all the columns in the dataset after all operations are applied. The order of the columns in each row propagated to the child node follow the order they appear in this list. The parameter is mandatory if the len(input_columns) != len(output_columns). (default=None, all columns will be propagated to the child node, the order of the columns will remain the same).
num_parallel_workers (int, optional) – Number of threads used to process the dataset in parallel (default=None, the value from the config will be used).
python_multiprocessing (bool, optional) – Parallelize python operations with multiple worker process. This option could be beneficial if the python operation is computational heavy (default=False).
cache (DatasetCache, optional) – Tensor cache to use. (default=None which means no cache is used). The cache feature is under development and is not recommended.

Returns

MapDataset, dataset after mapping operation.

Examples

>>> import mindspore.dataset as ds
>>> import mindspore.dataset.transforms.vision.c_transforms as c_transforms
>>>
>>> # data is an instance of Dataset which has 2 columns, "image" and "label".
>>> # ds_pyfunc is an instance of Dataset which has 3 columns, "col0", "col1", and "col2". Each column is
>>> # a 2d array of integers.
>>>
>>> # This config is a global setting, meaning that all future operations which
>>> # uses this config value will use 2 worker threads, unless if specified
>>> # otherwise in their constructor. set_num_parallel_workers can be called
>>> # again later if a different number of worker threads are needed.
>>> ds.config.set_num_parallel_workers(2)
>>>
>>> # Two operations, which takes 1 column for input and outputs 1 column.
>>> decode_op = c_transforms.Decode(rgb_format=True)
>>> random_jitter_op = c_transforms.RandomColorAdjust((0.8, 0.8), (1, 1), (1, 1), (0, 0))
>>>
>>> # 1) Simple map example
>>>
>>> operations = [decode_op]
>>> input_columns = ["image"]
>>>
>>> # Applies decode_op on column "image". This column will be replaced by the outputed
>>> # column of decode_op. Since columns_order is not provided, both columns "image"
>>> # and "label" will be propagated to the child node in their original order.
>>> ds_decoded = data.map(input_columns, operations)
>>>
>>> # Rename column "image" to "decoded_image"
>>> output_columns = ["decoded_image"]
>>> ds_decoded = data.map(input_columns, operations, output_columns)
>>>
>>> # Specify the order of the columns.
>>> columns_order ["label", "image"]
>>> ds_decoded = data.map(input_columns, operations, None, columns_order)
>>>
>>> # Rename column "image" to "decoded_image" and also specify the order of the columns.
>>> columns_order ["label", "decoded_image"]
>>> output_columns = ["decoded_image"]
>>> ds_decoded = data.map(input_columns, operations, output_columns, columns_order)
>>>
>>> # Rename column "image" to "decoded_image" and keep only this column.
>>> columns_order ["decoded_image"]
>>> output_columns = ["decoded_image"]
>>> ds_decoded = data.map(input_columns, operations, output_columns, columns_order)
>>>
>>> # Simple example using pyfunc. Renaming columns and specifying column order
>>> # work in the same way as the previous examples.
>>> input_columns = ["col0"]
>>> operations = [(lambda x: x + 1)]
>>> ds_mapped = ds_pyfunc.map(input_columns, operations)
>>>
>>> # 2) Map example with more than one operation
>>>
>>> # If this list of operations is used with map, decode_op will be applied
>>> # first, then random_jitter_op will be applied.
>>> operations = [decode_op, random_jitter_op]
>>>
>>> input_columns = ["image"]
>>>
>>> # Creates 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 columns_order is not specified, the order of the
>>> # columns will remain the same.
>>> ds_mapped = data.map(input_columns, operations)
>>>
>>> # Creates a dataset that is identical to ds_mapped, except the column "image"
>>> # that is outputted by random_jitter_op is renamed to "image_transformed".
>>> # Specifying column order works in the same way as examples in 1).
>>> output_columns = ["image_transformed"]
>>> ds_mapped_and_renamed = data.map(input_columns, operation, output_columns)
>>>
>>> # Multiple operations using pyfunc. Renaming columns and specifying column order
>>> # work in the same way as examples in 1).
>>> input_columns = ["col0"]
>>> operations = [(lambda x: x + x), (lambda x: x - 1)]
>>> output_columns = ["col0_mapped"]
>>> ds_mapped = ds_pyfunc.map(input_columns, operations, output_columns)
>>>
>>> # 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[1] 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: because the number of input columns is not the same as the number of
>>> # output columns, the output_columns and columns_order parameter must be
>>> # specified. Otherwise, this map call will also result in an error.
>>> input_columns = ["col2", "col0"]
>>> output_columns = ["mod2", "mod3", "mod5", "mod7"]
>>>
>>> # Propagate all columns to the child node in this order:
>>> columns_order = ["col0", "col2", "mod2", "mod3", "mod5", "mod7", "col1"]
>>> ds_mapped = ds_pyfunc.map(input_columns, operations, output_columns, columns_order)
>>>
>>> # Propagate some columns to the child node in this order:
>>> columns_order = ["mod7", "mod3", "col1"]
>>> ds_mapped = ds_pyfunc.map(input_columns, operations, output_columns, columns_order)

num_classes()

Get the number of classes in a dataset.

Returns: Number, number of classes.

output_shapes()

Get the shapes of output data.

Returns: List, list of shape of each column.

output_types()

Get the types of output data.

Returns: List of data type.

project(columns)

Project certain columns in input datasets.

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

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

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> columns_to_project = ["column3", "column1", "column2"]
>>>
>>> # creates a dataset that consist of column3, column1, column2
>>> # in that order, regardless of the original order of columns.
>>> data = data.project(columns=columns_to_project)

rename(input_columns, output_columns)

Rename the columns in input datasets.

Parameters

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

Returns

RenameDataset, dataset renamed.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> input_columns = ["input_col1", "input_col2", "input_col3"]
>>> output_columns = ["output_col1", "output_col2", "output_col3"]
>>>
>>> # creates 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.
>>> data = data.rename(input_columns=input_columns, output_columns=output_columns)

repeat(count=None)

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

Note

The order of using repeat and batch reflects the number of batches. Recommend that repeat operation should be used after batch operation. If dataset_sink_mode is False, here repeat operation is invalid. If dataset_sink_mode is True, repeat count should be equal to the epoch of training. Otherwise, errors could occur since the amount of data is not the amount training requires.

Parameters: count (int) – Number of times the dataset should be repeated (default=None).
Returns: RepeatDataset, dataset repeated.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> # creates a dataset where the dataset is repeated for 50 epochs
>>> repeated = data.repeat(50)
>>>
>>> # creates a dataset where each epoch is shuffled individually
>>> shuffled_and_repeated = data.shuffle(10)
>>> shuffled_and_repeated = shuffled_and_repeated.repeat(50)
>>>
>>> # creates 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.
>>> repeat_and_shuffle = data.repeat(50)
>>> repeat_and_shuffle = repeat_and_shuffle.shuffle(10)

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

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

Save the dynamic data processed by dataset pipeline as common dataset format, support: mindrecord.

Implicit type casting exists when saving data as mindrecord. Table below shows how to do type casting.

Implicit Type Casting of Saving as mindrecord
type in ‘dataset’	type in ‘mindrecord’	detail
bool	None	Not support
int8	int32
uint8	bytes(1D uint8)	Drop dimension
int16	int32
uint16	int32
int32	int32
uint32	int64
int64	int64
uint64	None	Not support
float16	float32
float32	float32
float64	float64
string	string	Not support multi-dimensional string

Note

To save the samples in order, should set dataset’s shuffle false and num_files 1.
Before call the function, do not use batch, repeat operator or data augmentation operators with random attribute in map operator.
Mindrecord does not support DE_UINT64, multi-dimensional DE_UINT8(drop dimension) and multi-dimensional DE_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’)

shuffle(buffer_size)

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

Make a shuffle buffer that contains the first buffer_size rows.
Randomly select an element from the shuffle buffer to be the next row propogated 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 seed can be provided to be used on the first epoch. 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: ShuffleDataset, dataset shuffled.
Raises: RuntimeError – If exist sync operators before shuffle.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> # optionally set the seed for the first epoch
>>> ds.config.set_seed(58)
>>>
>>> # creates a shuffled dataset using a shuffle buffer of size 4
>>> data = data.shuffle(4)

skip(count)

Skip the first N elements of this dataset.

Parameters: count (int) – Number of elements the dataset should be skipped.
Returns: SkipDataset, dataset skipped.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> # creates a dataset which skips first 3 elements from data
>>> data = data.skip(3)

split(sizes, randomize=True)

Split the dataset into smaller, non-overlapping datasets.

This is a general purpose split function which can be called from any operator in the pipeline. There is another, optimized split function, which will be called automatically if ds.split is called where ds is a MappableDataset.

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 sizes does not equal the original dataset size, an an error will occur. 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 occur. 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 will be added to the first split.
- The sum of split sizes > K, the difference will be removed from the first large enough split such that it will have atleast 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.

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.

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

Examples

>>> import mindspore.dataset as ds
>>>
>>> dataset_dir = "/path/to/text_file.txt"
>>>
>>> # TextFileDataset is not a mappable dataset, so this non optimized split will be called.
>>> # many datasets have shuffle on by default, set shuffle to False if split will be called!
>>> data = ds.TextFileDataset(dataset_dir, shuffle=False)
>>> train, test = data.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 (Union[dict, None]) – The data passed to the callback (default=None).

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

Add a blocking condition to the input Dataset.

Parameters

num_batch (int) – the number of batches without blocking at the start of each epoch.
condition_name (str) – The condition name that is used to toggle sending next row.
callback (function) – The callback funciton that will be invoked when sync_update is called.

Raises

RuntimeError – If condition name already exists.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> data = data.sync_wait("callback1")
>>> data = data.batch(batch_size)
>>> for batch_data in data.create_dict_iterator():
>>>     data = data.sync_update("callback1")

take(count=- 1)

Takes at most given numbers of elements from the dataset.

Note

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

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

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> # creates a dataset where the dataset including 50 elements.
>>> data = data.take(50)

to_device(send_epoch_end=True)

Transfer data through CPU, GPU or Ascend devices.

Parameters: send_epoch_end (bool, optional) – whether send end of sequence to device or not.(default=True)

Note

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

Returns

TransferDataset, dataset for transferring.

Raises

TypeError – If device_type is empty.
ValueError – If device_type is not ‘Ascend’, ‘GPU’ or ‘CPU’.
RuntimeError – If dataset is unknown.
RuntimeError – If distribution file path is given but failed to read.

zip(datasets)

Zip the datasets in the input tuple of datasets. Columns in the input datasets must not have the same name.

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

Examples

>>> import mindspore.dataset as ds
>>> # ds1 and ds2 are instances of Dataset object
>>> # creates a dataset which is the combination of ds1 and ds2
>>> data = ds1.zip(ds2)

class mindspore.dataset.TextFileDataset(dataset_files, num_samples=None, num_parallel_workers=None, shuffle=Shuffle.GLOBAL, num_shards=None, shard_id=None)[source]

A source dataset that reads and parses datasets stored on disk in text format. The generated dataset has one columns [‘text’].

Parameters

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 (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:
- Shuffle.GLOBAL: Shuffle both the files and samples.
- Shuffle.FILES: Shuffle files only.
num_shards (int, optional) – Number of shards that the dataset should be divided into (default=None).
shard_id (int, optional) – The shard ID within num_shards (default=None). This argument should be specified only when num_shards is also specified.

Examples

>>> import mindspore.dataset as ds
>>> dataset_files = ["/path/to/1", "/path/to/2"] # contains 1 or multiple text files
>>> dataset = ds.TextFileDataset(dataset_files=dataset_files)

apply(apply_func)

Apply a function in this dataset.

The specified apply_func is a function that must take one ‘Dataset’ as an argument and return a preprogressing ‘Dataset’.

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

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> # declare an apply_func function which returns a Dataset object
>>> def apply_func(ds):
>>>     ds = ds.batch(2)
>>>     return ds
>>> # use apply to call apply_func
>>> data = data.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, pad_info=None)

Combine batch_size number of consecutive rows into batches.

For any child node, a batch is treated as a single row. 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. Recommend that repeat operation should be used after batch operation.

Parameters

batch_size (int or 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 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 to process the Dataset in parallel (default=None).
per_batch_map (callable, optional) – Per batch map callable. A callable which takes (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[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.

Returns

BatchDataset, dataset batched.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> # creates a dataset where every 100 rows is combined into a batch
>>> # and drops the last incomplete batch if there is one.
>>> data = data.batch(100, True)

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, and pad and batch the buckets 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 batch_info, and then batched. Each batch will be full, except for maybe the last batch for each bucket.

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, and one 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 len(column_names) arguments and returns an int. If no value is provided, then 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) – Represents how to batch each column. The key corresponds to the column name, 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).

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>>
>>> # creates a dataset where every 100 rows is combined into a batch
>>> # and drops the last incomplete batch if there is one.
>>> column_names = ["col1", "col2"]
>>> buket_boundaries = [5, 10]
>>> bucket_batch_sizes = [5, 1, 1]
>>> element_length_function = (lambda col1, col2: max(len(col1), len(col2)))
>>>
>>> # will pad col1 to shape [2, bucket_boundaries[i]] where i is the
>>> # index of the bucket that is currently being batched.
>>> # will pad col2 to a shape where each dimension is the longest in all
>>> # the elements currently being batched.
>>> pad_info = {"col1", ([2, None], -1)}
>>> pad_to_bucket_boundary = True
>>>
>>> data = data.bucket_batch_by_length(column_names, bucket_boundaries,
>>>                                    bucket_batch_sizes,
>>>                                    element_length_function, pad_info,
>>>                                    pad_to_bucket_boundary)

concat(datasets)

Concat the datasets in the input list of datasets, supported using “+” to reload concat operation.

Note

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

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

Examples

>>> import mindspore.dataset as ds
>>> # ds1 and ds2 are instances of Dataset object
>>> # creates a dataset by concating ds1 and ds2 with "+" operation
>>> data1 = ds1 + ds2
>>> # creates a dataset by concating ds1 and ds2 with concat operation
>>> data1 = ds1.concat(ds2)

create_dict_iterator(num_epochs=- 1)

Create an Iterator over the dataset.

The data retrieved will be a dictionary. The order of the columns in the dictionary may not be the same as the original order.

Returns: Iterator, dictionary of column_name-ndarray pair.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> # creates an iterator. The columns in the data obtained by the
>>> # iterator might be changed.
>>> iterator = data.create_dict_iterator()
>>> for item in iterator:
>>>     # print the data in column1
>>>     print(item["column1"])

create_tuple_iterator(columns=None, num_epochs=- 1)

Create an Iterator over the dataset. The data retrieved will be a list of ndarray of data.

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 not be changed.

Parameters: columns (list[str], optional) – List of columns to be used to specify the order of columns (default=None, means all columns).
Returns: Iterator, list of ndarray.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> # creates an iterator. The columns in the data obtained by the
>>> # iterator will not be changed.
>>> iterator = data.create_tuple_iterator()
>>> for item in iterator:
>>>     # convert the returned tuple to a list and print
>>>     print(list(item))

device_que(prefetch_size=None, send_epoch_end=True)

Return a transferredDataset that transfer data through device.

Parameters

prefetch_size (int, optional) – prefetch number of records ahead of the user’s request (default=None).
send_epoch_end (bool, optional) – whether send end of sequence to device or not.(default=True)

Note

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

Returns: TransferDataset, dataset for transferring.

filter(predicate, input_columns=None, num_parallel_workers=1)

Filter dataset by predicate.

Note

If input_columns not provided or empty, all columns will be used.

Parameters

predicate (callable) – python callable which returns a boolean value, if False then filter the element.
input_columns – (list[str], optional): List of names of the input columns, when default=None, the predicate will be applied on all columns in the dataset.
num_parallel_workers (int, optional) – Number of workers to process the Dataset in parallel (default=None).

Returns

FilterDataset, dataset filter.

Examples

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

flat_map(func)

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

The specified func is a function that must take one ‘Ndarray’ as input and return a ‘Dataset’.

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

Examples

>>> import mindspore.dataset as ds
>>> import mindspore.dataset.text as text
>>> # declare a function which returns a Dataset object
>>> def flat_map_func(x):
>>>     data_dir = text.to_str(x[0])
>>>     d = ds.ImageFolderDatasetV2(data_dir)
>>>     return d
>>> # data is a Dataset object
>>> data = ds.TextFileDataset(DATA_FILE)
>>> data = data.flat_map(flat_map_func)

Raises

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

get_batch_size()

Get the size of a batch.

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

get_class_indexing()

Get the class index.

Returns: Dict, A str-to-int mapping from label name to index.

get_dataset_size()[source]

Get the number of batches in an epoch.

Returns: Number, number of batches.

get_repeat_count()

Get the replication times in RepeatDataset else 1.

Returns: Number, the count of repeat.

map(input_columns=None, operations=None, output_columns=None, columns_order=None, num_parallel_workers=None, python_multiprocessing=False, cache=None)

Apply each operation in operations to this dataset.

The order of operations is determined by the position of each operation in operations. operations[0] will be applied first, then operations[1], then operations[2], etc.

Each operation will be passed one or more columns from the dataset as input, and zero 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.

Only the columns specified in columns_order will be propagated to the child node. These columns will be in the same order as specified in columns_order.

Parameters

input_columns (list[str]) – 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 is required, starting from the first column).
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) != 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).
columns_order (list[str], optional) – list of all the desired columns to propagate to the child node. This list must be a subset of all the columns in the dataset after all operations are applied. The order of the columns in each row propagated to the child node follow the order they appear in this list. The parameter is mandatory if the len(input_columns) != len(output_columns). (default=None, all columns will be propagated to the child node, the order of the columns will remain the same).
num_parallel_workers (int, optional) – Number of threads used to process the dataset in parallel (default=None, the value from the config will be used).
python_multiprocessing (bool, optional) – Parallelize python operations with multiple worker process. This option could be beneficial if the python operation is computational heavy (default=False).
cache (DatasetCache, optional) – Tensor cache to use. (default=None which means no cache is used). The cache feature is under development and is not recommended.

Returns

MapDataset, dataset after mapping operation.

Examples

>>> import mindspore.dataset as ds
>>> import mindspore.dataset.transforms.vision.c_transforms as c_transforms
>>>
>>> # data is an instance of Dataset which has 2 columns, "image" and "label".
>>> # ds_pyfunc is an instance of Dataset which has 3 columns, "col0", "col1", and "col2". Each column is
>>> # a 2d array of integers.
>>>
>>> # This config is a global setting, meaning that all future operations which
>>> # uses this config value will use 2 worker threads, unless if specified
>>> # otherwise in their constructor. set_num_parallel_workers can be called
>>> # again later if a different number of worker threads are needed.
>>> ds.config.set_num_parallel_workers(2)
>>>
>>> # Two operations, which takes 1 column for input and outputs 1 column.
>>> decode_op = c_transforms.Decode(rgb_format=True)
>>> random_jitter_op = c_transforms.RandomColorAdjust((0.8, 0.8), (1, 1), (1, 1), (0, 0))
>>>
>>> # 1) Simple map example
>>>
>>> operations = [decode_op]
>>> input_columns = ["image"]
>>>
>>> # Applies decode_op on column "image". This column will be replaced by the outputed
>>> # column of decode_op. Since columns_order is not provided, both columns "image"
>>> # and "label" will be propagated to the child node in their original order.
>>> ds_decoded = data.map(input_columns, operations)
>>>
>>> # Rename column "image" to "decoded_image"
>>> output_columns = ["decoded_image"]
>>> ds_decoded = data.map(input_columns, operations, output_columns)
>>>
>>> # Specify the order of the columns.
>>> columns_order ["label", "image"]
>>> ds_decoded = data.map(input_columns, operations, None, columns_order)
>>>
>>> # Rename column "image" to "decoded_image" and also specify the order of the columns.
>>> columns_order ["label", "decoded_image"]
>>> output_columns = ["decoded_image"]
>>> ds_decoded = data.map(input_columns, operations, output_columns, columns_order)
>>>
>>> # Rename column "image" to "decoded_image" and keep only this column.
>>> columns_order ["decoded_image"]
>>> output_columns = ["decoded_image"]
>>> ds_decoded = data.map(input_columns, operations, output_columns, columns_order)
>>>
>>> # Simple example using pyfunc. Renaming columns and specifying column order
>>> # work in the same way as the previous examples.
>>> input_columns = ["col0"]
>>> operations = [(lambda x: x + 1)]
>>> ds_mapped = ds_pyfunc.map(input_columns, operations)
>>>
>>> # 2) Map example with more than one operation
>>>
>>> # If this list of operations is used with map, decode_op will be applied
>>> # first, then random_jitter_op will be applied.
>>> operations = [decode_op, random_jitter_op]
>>>
>>> input_columns = ["image"]
>>>
>>> # Creates 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 columns_order is not specified, the order of the
>>> # columns will remain the same.
>>> ds_mapped = data.map(input_columns, operations)
>>>
>>> # Creates a dataset that is identical to ds_mapped, except the column "image"
>>> # that is outputted by random_jitter_op is renamed to "image_transformed".
>>> # Specifying column order works in the same way as examples in 1).
>>> output_columns = ["image_transformed"]
>>> ds_mapped_and_renamed = data.map(input_columns, operation, output_columns)
>>>
>>> # Multiple operations using pyfunc. Renaming columns and specifying column order
>>> # work in the same way as examples in 1).
>>> input_columns = ["col0"]
>>> operations = [(lambda x: x + x), (lambda x: x - 1)]
>>> output_columns = ["col0_mapped"]
>>> ds_mapped = ds_pyfunc.map(input_columns, operations, output_columns)
>>>
>>> # 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[1] 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: because the number of input columns is not the same as the number of
>>> # output columns, the output_columns and columns_order parameter must be
>>> # specified. Otherwise, this map call will also result in an error.
>>> input_columns = ["col2", "col0"]
>>> output_columns = ["mod2", "mod3", "mod5", "mod7"]
>>>
>>> # Propagate all columns to the child node in this order:
>>> columns_order = ["col0", "col2", "mod2", "mod3", "mod5", "mod7", "col1"]
>>> ds_mapped = ds_pyfunc.map(input_columns, operations, output_columns, columns_order)
>>>
>>> # Propagate some columns to the child node in this order:
>>> columns_order = ["mod7", "mod3", "col1"]
>>> ds_mapped = ds_pyfunc.map(input_columns, operations, output_columns, columns_order)

num_classes()

Get the number of classes in a dataset.

Returns: Number, number of classes.

output_shapes()

Get the shapes of output data.

Returns: List, list of shape of each column.

output_types()

Get the types of output data.

Returns: List of data type.

project(columns)

Project certain columns in input datasets.

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

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

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> columns_to_project = ["column3", "column1", "column2"]
>>>
>>> # creates a dataset that consist of column3, column1, column2
>>> # in that order, regardless of the original order of columns.
>>> data = data.project(columns=columns_to_project)

rename(input_columns, output_columns)

Rename the columns in input datasets.

Parameters

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

Returns

RenameDataset, dataset renamed.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> input_columns = ["input_col1", "input_col2", "input_col3"]
>>> output_columns = ["output_col1", "output_col2", "output_col3"]
>>>
>>> # creates 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.
>>> data = data.rename(input_columns=input_columns, output_columns=output_columns)

repeat(count=None)

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

Note

The order of using repeat and batch reflects the number of batches. Recommend that repeat operation should be used after batch operation. If dataset_sink_mode is False, here repeat operation is invalid. If dataset_sink_mode is True, repeat count should be equal to the epoch of training. Otherwise, errors could occur since the amount of data is not the amount training requires.

Parameters: count (int) – Number of times the dataset should be repeated (default=None).
Returns: RepeatDataset, dataset repeated.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> # creates a dataset where the dataset is repeated for 50 epochs
>>> repeated = data.repeat(50)
>>>
>>> # creates a dataset where each epoch is shuffled individually
>>> shuffled_and_repeated = data.shuffle(10)
>>> shuffled_and_repeated = shuffled_and_repeated.repeat(50)
>>>
>>> # creates 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.
>>> repeat_and_shuffle = data.repeat(50)
>>> repeat_and_shuffle = repeat_and_shuffle.shuffle(10)

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

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

Save the dynamic data processed by dataset pipeline as common dataset format, support: mindrecord.

Implicit type casting exists when saving data as mindrecord. Table below shows how to do type casting.

Implicit Type Casting of Saving as mindrecord
type in ‘dataset’	type in ‘mindrecord’	detail
bool	None	Not support
int8	int32
uint8	bytes(1D uint8)	Drop dimension
int16	int32
uint16	int32
int32	int32
uint32	int64
int64	int64
uint64	None	Not support
float16	float32
float32	float32
float64	float64
string	string	Not support multi-dimensional string

Note

To save the samples in order, should set dataset’s shuffle false and num_files 1.
Before call the function, do not use batch, repeat operator or data augmentation operators with random attribute in map operator.
Mindrecord does not support DE_UINT64, multi-dimensional DE_UINT8(drop dimension) and multi-dimensional DE_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’)

shuffle(buffer_size)

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

Make a shuffle buffer that contains the first buffer_size rows.
Randomly select an element from the shuffle buffer to be the next row propogated 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 seed can be provided to be used on the first epoch. 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: ShuffleDataset, dataset shuffled.
Raises: RuntimeError – If exist sync operators before shuffle.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> # optionally set the seed for the first epoch
>>> ds.config.set_seed(58)
>>>
>>> # creates a shuffled dataset using a shuffle buffer of size 4
>>> data = data.shuffle(4)

skip(count)

Skip the first N elements of this dataset.

Parameters: count (int) – Number of elements the dataset should be skipped.
Returns: SkipDataset, dataset skipped.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> # creates a dataset which skips first 3 elements from data
>>> data = data.skip(3)

split(sizes, randomize=True)

Split the dataset into smaller, non-overlapping datasets.

This is a general purpose split function which can be called from any operator in the pipeline. There is another, optimized split function, which will be called automatically if ds.split is called where ds is a MappableDataset.

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 sizes does not equal the original dataset size, an an error will occur. 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 occur. 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 will be added to the first split.
- The sum of split sizes > K, the difference will be removed from the first large enough split such that it will have atleast 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.

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.

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

Examples

>>> import mindspore.dataset as ds
>>>
>>> dataset_dir = "/path/to/text_file.txt"
>>>
>>> # TextFileDataset is not a mappable dataset, so this non optimized split will be called.
>>> # many datasets have shuffle on by default, set shuffle to False if split will be called!
>>> data = ds.TextFileDataset(dataset_dir, shuffle=False)
>>> train, test = data.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 (Union[dict, None]) – The data passed to the callback (default=None).

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

Add a blocking condition to the input Dataset.

Parameters

num_batch (int) – the number of batches without blocking at the start of each epoch.
condition_name (str) – The condition name that is used to toggle sending next row.
callback (function) – The callback funciton that will be invoked when sync_update is called.

Raises

RuntimeError – If condition name already exists.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> data = data.sync_wait("callback1")
>>> data = data.batch(batch_size)
>>> for batch_data in data.create_dict_iterator():
>>>     data = data.sync_update("callback1")

take(count=- 1)

Takes at most given numbers of elements from the dataset.

Note

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

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

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> # creates a dataset where the dataset including 50 elements.
>>> data = data.take(50)

to_device(send_epoch_end=True)

Transfer data through CPU, GPU or Ascend devices.

Parameters: send_epoch_end (bool, optional) – whether send end of sequence to device or not.(default=True)

Note

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

Returns

TransferDataset, dataset for transferring.

Raises

TypeError – If device_type is empty.
ValueError – If device_type is not ‘Ascend’, ‘GPU’ or ‘CPU’.
RuntimeError – If dataset is unknown.
RuntimeError – If distribution file path is given but failed to read.

zip(datasets)

Zip the datasets in the input tuple of datasets. Columns in the input datasets must not have the same name.

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

Examples

>>> import mindspore.dataset as ds
>>> # ds1 and ds2 are instances of Dataset object
>>> # creates a dataset which is the combination of ds1 and ds2
>>> data = ds1.zip(ds2)

class mindspore.dataset.VOCDataset(dataset_dir, task='Segmentation', mode='train', class_indexing=None, num_samples=None, num_parallel_workers=None, shuffle=None, decode=False, sampler=None, num_shards=None, shard_id=None)[source]

A source dataset for reading and parsing VOC dataset.

The generated dataset has multi-columns :

task=’Detection’, column: [[‘image’, dtype=uint8], [‘bbox’, dtype=float32], [‘label’, dtype=uint32], [‘difficult’, dtype=uint32], [‘truncate’, dtype=uint32]].

task=’Segmentation’, column: [[‘image’, dtype=uint8], [‘target’,dtype=uint8]].

This dataset can take in a sampler. sampler and shuffle are mutually exclusive. Table below shows what input args are allowed and their expected behavior.

Expected Order Behavior of Using ‘sampler’ and ‘shuffle’
Parameter ‘sampler’	Parameter ‘shuffle’	Expected Order Behavior
None	None	random order
None	True	random order
None	False	sequential order
Sampler object	None	order defined by sampler
Sampler object	True	not allowed
Sampler object	False	not allowed

Citation of VOC dataset.

@article{Everingham10,
author       = {Everingham, M. and Van~Gool, L. and Williams, C. K. I. and Winn, J. and Zisserman, A.},
title        = {The Pascal Visual Object Classes (VOC) Challenge},
journal      = {International Journal of Computer Vision},
volume       = {88},
year         = {2010},
number       = {2},
month        = {jun},
pages        = {303--338},
biburl       = {http://host.robots.ox.ac.uk/pascal/VOC/pubs/everingham10.html#bibtex},
howpublished = {http://host.robots.ox.ac.uk/pascal/VOC/voc{year}/index.html},
description  = {The PASCAL Visual Object Classes (VOC) challenge is a benchmark in visual
                object category recognition and detection, providing the vision and machine
                learning communities with a standard dataset of images and annotation, and
                standard evaluation procedures.}
}

Parameters

dataset_dir (str) – Path to the root directory that contains the dataset.
task (str) – Set the task type of reading voc data, now only support “Segmentation” or “Detection” (default=”Segmentation”).
mode (str) – Set the data list txt file to be readed (default=”train”).
class_indexing (dict, optional) – A str-to-int mapping from label name to index (default=None, the folder names will be sorted alphabetically and each class will be given a unique index starting from 0).
num_samples (int, optional) – The number of images to be included in the dataset (default=None, all images).
num_parallel_workers (int, optional) – Number of workers to read the data (default=None, number set in the config).
shuffle (bool, optional) – Whether to perform shuffle on the dataset (default=None, expected order behavior shown in the table).
decode (bool, optional) – Decode the images after reading (default=False).
sampler (Sampler, optional) – Object used to choose samples from the dataset (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).
shard_id (int, optional) – The shard ID within num_shards (default=None). This argument should be specified only when num_shards is also specified.

Raises

RuntimeError – If xml of Annotations is a invalid format.
RuntimeError – If xml of Annotations loss attribution of “object”.
RuntimeError – If xml of Annotations loss attribution of “bndbox”.
RuntimeError – If sampler and shuffle are specified at the same time.
RuntimeError – If sampler and sharding are specified at the same time.
RuntimeError – If num_shards is specified but shard_id is None.
RuntimeError – If shard_id is specified but num_shards is None.
ValueError – If task is not equal ‘Segmentation’ or ‘Detection’.
ValueError – If task equal ‘Segmentation’ but class_indexing is not None.
ValueError – If txt related to mode is not exist.
ValueError – If shard_id is invalid (< 0 or >= num_shards).

Examples

>>> import mindspore.dataset as ds
>>> dataset_dir = "/path/to/voc_dataset_directory"
>>> # 1) read VOC data for segmenatation train
>>> voc_dataset = ds.VOCDataset(dataset_dir, task="Segmentation", mode="train")
>>> # 2) read VOC data for detection train
>>> voc_dataset = ds.VOCDataset(dataset_dir, task="Detection", mode="train")
>>> # 3) read all VOC dataset samples in dataset_dir with 8 threads in random order:
>>> voc_dataset = ds.VOCDataset(dataset_dir, task="Detection", mode="train", num_parallel_workers=8)
>>> # 4) read then decode all VOC dataset samples in dataset_dir in sequence:
>>> voc_dataset = ds.VOCDataset(dataset_dir, task="Detection", mode="train", decode=True, shuffle=False)
>>> # in VOC dataset, if task='Segmentation', each dictionary has keys "image" and "target"
>>> # in VOC dataset, if task='Detection', each dictionary has keys "image" and "annotation"

apply(apply_func)

Apply a function in this dataset.

The specified apply_func is a function that must take one ‘Dataset’ as an argument and return a preprogressing ‘Dataset’.

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

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> # declare an apply_func function which returns a Dataset object
>>> def apply_func(ds):
>>>     ds = ds.batch(2)
>>>     return ds
>>> # use apply to call apply_func
>>> data = data.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, pad_info=None)

Combine batch_size number of consecutive rows into batches.

For any child node, a batch is treated as a single row. 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. Recommend that repeat operation should be used after batch operation.

Parameters

batch_size (int or 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 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 to process the Dataset in parallel (default=None).
per_batch_map (callable, optional) – Per batch map callable. A callable which takes (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[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.

Returns

BatchDataset, dataset batched.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> # creates a dataset where every 100 rows is combined into a batch
>>> # and drops the last incomplete batch if there is one.
>>> data = data.batch(100, True)

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, and pad and batch the buckets 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 batch_info, and then batched. Each batch will be full, except for maybe the last batch for each bucket.

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, and one 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 len(column_names) arguments and returns an int. If no value is provided, then 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) – Represents how to batch each column. The key corresponds to the column name, 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).

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>>
>>> # creates a dataset where every 100 rows is combined into a batch
>>> # and drops the last incomplete batch if there is one.
>>> column_names = ["col1", "col2"]
>>> buket_boundaries = [5, 10]
>>> bucket_batch_sizes = [5, 1, 1]
>>> element_length_function = (lambda col1, col2: max(len(col1), len(col2)))
>>>
>>> # will pad col1 to shape [2, bucket_boundaries[i]] where i is the
>>> # index of the bucket that is currently being batched.
>>> # will pad col2 to a shape where each dimension is the longest in all
>>> # the elements currently being batched.
>>> pad_info = {"col1", ([2, None], -1)}
>>> pad_to_bucket_boundary = True
>>>
>>> data = data.bucket_batch_by_length(column_names, bucket_boundaries,
>>>                                    bucket_batch_sizes,
>>>                                    element_length_function, pad_info,
>>>                                    pad_to_bucket_boundary)

concat(datasets)

Concat the datasets in the input list of datasets, supported using “+” to reload concat operation.

Note

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

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

Examples

>>> import mindspore.dataset as ds
>>> # ds1 and ds2 are instances of Dataset object
>>> # creates a dataset by concating ds1 and ds2 with "+" operation
>>> data1 = ds1 + ds2
>>> # creates a dataset by concating ds1 and ds2 with concat operation
>>> data1 = ds1.concat(ds2)

create_dict_iterator(num_epochs=- 1)

Create an Iterator over the dataset.

The data retrieved will be a dictionary. The order of the columns in the dictionary may not be the same as the original order.

Returns: Iterator, dictionary of column_name-ndarray pair.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> # creates an iterator. The columns in the data obtained by the
>>> # iterator might be changed.
>>> iterator = data.create_dict_iterator()
>>> for item in iterator:
>>>     # print the data in column1
>>>     print(item["column1"])

create_tuple_iterator(columns=None, num_epochs=- 1)

Create an Iterator over the dataset. The data retrieved will be a list of ndarray of data.

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 not be changed.

Parameters: columns (list[str], optional) – List of columns to be used to specify the order of columns (default=None, means all columns).
Returns: Iterator, list of ndarray.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> # creates an iterator. The columns in the data obtained by the
>>> # iterator will not be changed.
>>> iterator = data.create_tuple_iterator()
>>> for item in iterator:
>>>     # convert the returned tuple to a list and print
>>>     print(list(item))

device_que(prefetch_size=None, send_epoch_end=True)

Return a transferredDataset that transfer data through device.

Parameters

prefetch_size (int, optional) – prefetch number of records ahead of the user’s request (default=None).
send_epoch_end (bool, optional) – whether send end of sequence to device or not.(default=True)

Note

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

Returns: TransferDataset, dataset for transferring.

filter(predicate, input_columns=None, num_parallel_workers=1)

Filter dataset by predicate.

Note

If input_columns not provided or empty, all columns will be used.

Parameters

predicate (callable) – python callable which returns a boolean value, if False then filter the element.
input_columns – (list[str], optional): List of names of the input columns, when default=None, the predicate will be applied on all columns in the dataset.
num_parallel_workers (int, optional) – Number of workers to process the Dataset in parallel (default=None).

Returns

FilterDataset, dataset filter.

Examples

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

flat_map(func)

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

The specified func is a function that must take one ‘Ndarray’ as input and return a ‘Dataset’.

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

Examples

>>> import mindspore.dataset as ds
>>> import mindspore.dataset.text as text
>>> # declare a function which returns a Dataset object
>>> def flat_map_func(x):
>>>     data_dir = text.to_str(x[0])
>>>     d = ds.ImageFolderDatasetV2(data_dir)
>>>     return d
>>> # data is a Dataset object
>>> data = ds.TextFileDataset(DATA_FILE)
>>> data = data.flat_map(flat_map_func)

Raises

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

get_batch_size()

Get the size of a batch.

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

get_class_indexing()[source]

Get the class index.

Returns: Dict, A str-to-int mapping from label name to index.

get_dataset_size()[source]

Get the number of batches in an epoch.

Returns: Number, number of batches.

get_repeat_count()

Get the replication times in RepeatDataset else 1.

Returns: Number, the count of repeat.

map(input_columns=None, operations=None, output_columns=None, columns_order=None, num_parallel_workers=None, python_multiprocessing=False, cache=None)

Apply each operation in operations to this dataset.

The order of operations is determined by the position of each operation in operations. operations[0] will be applied first, then operations[1], then operations[2], etc.

Each operation will be passed one or more columns from the dataset as input, and zero 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.

Only the columns specified in columns_order will be propagated to the child node. These columns will be in the same order as specified in columns_order.

Parameters

input_columns (list[str]) – 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 is required, starting from the first column).
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) != 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).
columns_order (list[str], optional) – list of all the desired columns to propagate to the child node. This list must be a subset of all the columns in the dataset after all operations are applied. The order of the columns in each row propagated to the child node follow the order they appear in this list. The parameter is mandatory if the len(input_columns) != len(output_columns). (default=None, all columns will be propagated to the child node, the order of the columns will remain the same).
num_parallel_workers (int, optional) – Number of threads used to process the dataset in parallel (default=None, the value from the config will be used).
python_multiprocessing (bool, optional) – Parallelize python operations with multiple worker process. This option could be beneficial if the python operation is computational heavy (default=False).
cache (DatasetCache, optional) – Tensor cache to use. (default=None which means no cache is used). The cache feature is under development and is not recommended.

Returns

MapDataset, dataset after mapping operation.

Examples

>>> import mindspore.dataset as ds
>>> import mindspore.dataset.transforms.vision.c_transforms as c_transforms
>>>
>>> # data is an instance of Dataset which has 2 columns, "image" and "label".
>>> # ds_pyfunc is an instance of Dataset which has 3 columns, "col0", "col1", and "col2". Each column is
>>> # a 2d array of integers.
>>>
>>> # This config is a global setting, meaning that all future operations which
>>> # uses this config value will use 2 worker threads, unless if specified
>>> # otherwise in their constructor. set_num_parallel_workers can be called
>>> # again later if a different number of worker threads are needed.
>>> ds.config.set_num_parallel_workers(2)
>>>
>>> # Two operations, which takes 1 column for input and outputs 1 column.
>>> decode_op = c_transforms.Decode(rgb_format=True)
>>> random_jitter_op = c_transforms.RandomColorAdjust((0.8, 0.8), (1, 1), (1, 1), (0, 0))
>>>
>>> # 1) Simple map example
>>>
>>> operations = [decode_op]
>>> input_columns = ["image"]
>>>
>>> # Applies decode_op on column "image". This column will be replaced by the outputed
>>> # column of decode_op. Since columns_order is not provided, both columns "image"
>>> # and "label" will be propagated to the child node in their original order.
>>> ds_decoded = data.map(input_columns, operations)
>>>
>>> # Rename column "image" to "decoded_image"
>>> output_columns = ["decoded_image"]
>>> ds_decoded = data.map(input_columns, operations, output_columns)
>>>
>>> # Specify the order of the columns.
>>> columns_order ["label", "image"]
>>> ds_decoded = data.map(input_columns, operations, None, columns_order)
>>>
>>> # Rename column "image" to "decoded_image" and also specify the order of the columns.
>>> columns_order ["label", "decoded_image"]
>>> output_columns = ["decoded_image"]
>>> ds_decoded = data.map(input_columns, operations, output_columns, columns_order)
>>>
>>> # Rename column "image" to "decoded_image" and keep only this column.
>>> columns_order ["decoded_image"]
>>> output_columns = ["decoded_image"]
>>> ds_decoded = data.map(input_columns, operations, output_columns, columns_order)
>>>
>>> # Simple example using pyfunc. Renaming columns and specifying column order
>>> # work in the same way as the previous examples.
>>> input_columns = ["col0"]
>>> operations = [(lambda x: x + 1)]
>>> ds_mapped = ds_pyfunc.map(input_columns, operations)
>>>
>>> # 2) Map example with more than one operation
>>>
>>> # If this list of operations is used with map, decode_op will be applied
>>> # first, then random_jitter_op will be applied.
>>> operations = [decode_op, random_jitter_op]
>>>
>>> input_columns = ["image"]
>>>
>>> # Creates 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 columns_order is not specified, the order of the
>>> # columns will remain the same.
>>> ds_mapped = data.map(input_columns, operations)
>>>
>>> # Creates a dataset that is identical to ds_mapped, except the column "image"
>>> # that is outputted by random_jitter_op is renamed to "image_transformed".
>>> # Specifying column order works in the same way as examples in 1).
>>> output_columns = ["image_transformed"]
>>> ds_mapped_and_renamed = data.map(input_columns, operation, output_columns)
>>>
>>> # Multiple operations using pyfunc. Renaming columns and specifying column order
>>> # work in the same way as examples in 1).
>>> input_columns = ["col0"]
>>> operations = [(lambda x: x + x), (lambda x: x - 1)]
>>> output_columns = ["col0_mapped"]
>>> ds_mapped = ds_pyfunc.map(input_columns, operations, output_columns)
>>>
>>> # 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[1] 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: because the number of input columns is not the same as the number of
>>> # output columns, the output_columns and columns_order parameter must be
>>> # specified. Otherwise, this map call will also result in an error.
>>> input_columns = ["col2", "col0"]
>>> output_columns = ["mod2", "mod3", "mod5", "mod7"]
>>>
>>> # Propagate all columns to the child node in this order:
>>> columns_order = ["col0", "col2", "mod2", "mod3", "mod5", "mod7", "col1"]
>>> ds_mapped = ds_pyfunc.map(input_columns, operations, output_columns, columns_order)
>>>
>>> # Propagate some columns to the child node in this order:
>>> columns_order = ["mod7", "mod3", "col1"]
>>> ds_mapped = ds_pyfunc.map(input_columns, operations, output_columns, columns_order)

num_classes()

Get the number of classes in a dataset.

Returns: Number, number of classes.

output_shapes()

Get the shapes of output data.

Returns: List, list of shape of each column.

output_types()

Get the types of output data.

Returns: List of data type.

project(columns)

Project certain columns in input datasets.

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

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

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> columns_to_project = ["column3", "column1", "column2"]
>>>
>>> # creates a dataset that consist of column3, column1, column2
>>> # in that order, regardless of the original order of columns.
>>> data = data.project(columns=columns_to_project)

rename(input_columns, output_columns)

Rename the columns in input datasets.

Parameters

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

Returns

RenameDataset, dataset renamed.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> input_columns = ["input_col1", "input_col2", "input_col3"]
>>> output_columns = ["output_col1", "output_col2", "output_col3"]
>>>
>>> # creates 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.
>>> data = data.rename(input_columns=input_columns, output_columns=output_columns)

repeat(count=None)

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

Note

The order of using repeat and batch reflects the number of batches. Recommend that repeat operation should be used after batch operation. If dataset_sink_mode is False, here repeat operation is invalid. If dataset_sink_mode is True, repeat count should be equal to the epoch of training. Otherwise, errors could occur since the amount of data is not the amount training requires.

Parameters: count (int) – Number of times the dataset should be repeated (default=None).
Returns: RepeatDataset, dataset repeated.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> # creates a dataset where the dataset is repeated for 50 epochs
>>> repeated = data.repeat(50)
>>>
>>> # creates a dataset where each epoch is shuffled individually
>>> shuffled_and_repeated = data.shuffle(10)
>>> shuffled_and_repeated = shuffled_and_repeated.repeat(50)
>>>
>>> # creates 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.
>>> repeat_and_shuffle = data.repeat(50)
>>> repeat_and_shuffle = repeat_and_shuffle.shuffle(10)

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

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

Save the dynamic data processed by dataset pipeline as common dataset format, support: mindrecord.

Implicit type casting exists when saving data as mindrecord. Table below shows how to do type casting.

Implicit Type Casting of Saving as mindrecord
type in ‘dataset’	type in ‘mindrecord’	detail
bool	None	Not support
int8	int32
uint8	bytes(1D uint8)	Drop dimension
int16	int32
uint16	int32
int32	int32
uint32	int64
int64	int64
uint64	None	Not support
float16	float32
float32	float32
float64	float64
string	string	Not support multi-dimensional string

Note

To save the samples in order, should set dataset’s shuffle false and num_files 1.
Before call the function, do not use batch, repeat operator or data augmentation operators with random attribute in map operator.
Mindrecord does not support DE_UINT64, multi-dimensional DE_UINT8(drop dimension) and multi-dimensional DE_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’)

shuffle(buffer_size)

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

Make a shuffle buffer that contains the first buffer_size rows.
Randomly select an element from the shuffle buffer to be the next row propogated 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 seed can be provided to be used on the first epoch. 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: ShuffleDataset, dataset shuffled.
Raises: RuntimeError – If exist sync operators before shuffle.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object
>>> # optionally set the seed for the first epoch
>>> ds.config.set_seed(58)
>>>
>>> # creates a shuffled dataset using a shuffle buffer of size 4
>>> data = data.shuffle(4)

skip(count)

Skip the first N elements of this dataset.

Parameters: count (int) – Number of elements the dataset should be skipped.
Returns: SkipDataset, dataset skipped.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> # creates a dataset which skips first 3 elements from data
>>> data = data.skip(3)

split(sizes, randomize=True)

Split the dataset into smaller, non-overlapping datasets.

There is the optimized split function, which will be called automatically when the dataset that calls this function is a MappableDataset.

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 sizes does not equal the original dataset size, an an error will occur. 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 occur. 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 will be added to the first split.
- The sum of split sizes > K, the difference will be removed from the first large enough split such that it will have atleast 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 should not be sharded if split is going to be called. Instead, create a DistributedSampler and specify a split to shard after splitting. If dataset is sharded after a split, it is strongly recommended to set the same seed in each instance of execution, otherwise each shard may not be part of the same split (see Examples).
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. Furthermore, if sharding occurs after split, each shard may not be part of the same 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.

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

Examples

>>> import mindspore.dataset as ds
>>>
>>> dataset_dir = "/path/to/imagefolder_directory"
>>>
>>> # many datasets have shuffle on by default, set shuffle to False if split will be called!
>>> data = ds.ImageFolderDatasetV2(dataset_dir, shuffle=False)
>>>
>>> # sets the seed, and tells split to use this seed when randomizing. This
>>> # is needed because we are sharding later
>>> ds.config.set_seed(58)
>>> train, test = data.split([0.9, 0.1])
>>>
>>> # if we want to shard the train dataset, we can use a DistributedSampler
>>> train_sampler = ds.DistributedSampler(10, 2)
>>> train.use_sampler(train_sampler)

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 (Union[dict, None]) – The data passed to the callback (default=None).

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

Add a blocking condition to the input Dataset.

Parameters

num_batch (int) – the number of batches without blocking at the start of each epoch.
condition_name (str) – The condition name that is used to toggle sending next row.
callback (function) – The callback funciton that will be invoked when sync_update is called.

Raises

RuntimeError – If condition name already exists.

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> data = data.sync_wait("callback1")
>>> data = data.batch(batch_size)
>>> for batch_data in data.create_dict_iterator():
>>>     data = data.sync_update("callback1")

take(count=- 1)

Takes at most given numbers of elements from the dataset.

Note

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

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

Examples

>>> import mindspore.dataset as ds
>>> # data is an instance of Dataset object.
>>> # creates a dataset where the dataset including 50 elements.
>>> data = data.take(50)

to_device(send_epoch_end=True)

Transfer data through CPU, GPU or Ascend devices.

Parameters: send_epoch_end (bool, optional) – whether send end of sequence to device or not.(default=True)

Note

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

Returns

TransferDataset, dataset for transferring.

Raises

TypeError – If device_type is empty.
ValueError – If device_type is not ‘Ascend’, ‘GPU’ or ‘CPU’.
RuntimeError – If dataset is unknown.
RuntimeError – If distribution file path is given but failed to read.

use_sampler(new_sampler)

Will make the current dataset use the new_sampler provided.

Parameters: new_sampler (Sampler) – the sampler to use for the current dataset.
Returns: Dataset, that uses new_sampler.

Examples

>>> import mindspore.dataset as ds
>>>
>>> dataset_dir = "/path/to/imagefolder_directory"
>>> # a SequentialSampler is created by default
>>> data = ds.ImageFolderDatasetV2(dataset_dir)
>>>
>>> # use a DistributedSampler instead of the SequentialSampler
>>> new_sampler = ds.DistributedSampler(10, 2)
>>> data.use_sampler(new_sampler)

zip(datasets)

Zip the datasets in the input tuple of datasets. Columns in the input datasets must not have the same name.

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

Examples

>>> import mindspore.dataset as ds
>>> # ds1 and ds2 are instances of Dataset object
>>> # creates a dataset which is the combination of ds1 and ds2
>>> data = ds1.zip(ds2)

class mindspore.dataset.WeightedRandomSampler(weights, num_samples=None, replacement=True)[source]

Samples the elements from [0, len(weights) - 1] randomly with the given weights (probabilities).

Parameters

weights (list[float]) – A sequence of weights, not necessarily summing up to 1.
num_samples (int, optional) – Number of elements to sample (default=None, all elements).
replacement (bool) – If True, put the sample ID back for the next draw (default=True).

Examples

>>> import mindspore.dataset as ds
>>>
>>> dataset_dir = "path/to/imagefolder_directory"
>>>
>>> weights = [0.9, 0.01, 0.4, 0.8, 0.1, 0.1, 0.3]
>>>
>>> # creates a WeightedRandomSampler that will sample 4 elements without replacement
>>> sampler = ds.WeightedRandomSampler(weights, 4)
>>> data = ds.ImageFolderDatasetV2(dataset_dir, num_parallel_workers=8, sampler=sampler)

Raises

ValueError – If num_samples is not positive.
ValueError – If replacement is not boolean.

get_num_samples()

All samplers can contain a numeric num_samples value (or it could be set to None). Child sampler can exist or be None. if child sampler exists, then the child sampler count can be a numeric value or None. Given these conditions, we need to output what the sampler count is for this sampler. The following table shows the possible results from calling this function.

child sampler	num_samples	child_samples	result
T	x	y	min(x, y)
T	x	None	x
T	None	y	y
T	None	None	None
None	x	n/a	x
None	None	n/a	None

Returns: int, The number of samples, or None

mindspore.dataset.zip(datasets)[source]

Zip the datasets in the input tuple of datasets.

Parameters

datasets (tuple of class Dataset) – A tuple of datasets to be zipped together. The number of datasets should be more than 1.

Returns

DatasetOp, ZipDataset.

Raises

ValueError – If the number of datasets is 1.
TypeError – If datasets is not a tuple.

Examples

>>> import mindspore.dataset as ds
>>>
>>> dataset_dir1 = "path/to/imagefolder_directory1"
>>> dataset_dir2 = "path/to/imagefolder_directory2"
>>> ds1 = ds.ImageFolderDatasetV2(dataset_dir1, num_parallel_workers=8)
>>> ds2 = ds.ImageFolderDatasetV2(dataset_dir2, num_parallel_workers=8)
>>>
>>> # creates a dataset which is the combination of ds1 and ds2
>>> data = ds.zip((ds1, ds2))