mindspore.dataset.RandomDataset
- class mindspore.dataset.RandomDataset(total_rows=None, schema=None, columns_list=None, num_samples=None, num_parallel_workers=None, cache=None, shuffle=None, num_shards=None, shard_id=None)[source]
A source dataset that generates random data.
- Parameters
total_rows (int, optional) – Number of samples for the dataset to generate. Default:
None
, number of samples is random.schema (Union[str, Schema], optional) – Data format policy, which specifies the data types and shapes of the data column to be read. Both JSON file path and objects constructed by
mindspore.dataset.Schema
are acceptable. Default:None
.columns_list (list[str], optional) – List of column names of the dataset. Default:
None
, the columns will be named like this "c0", "c1", "c2" etc.num_samples (int, optional) – The number of samples to be included in the dataset. Default:
None
, all samples.num_parallel_workers (int, optional) – Number of worker threads to read the data. Default:
None
, will use global default workers(8), it can be set bymindspore.dataset.config.set_num_parallel_workers()
.cache (DatasetCache, optional) – Use tensor caching service to speed up dataset processing. More details: Single-Node Data Cache . Default:
None
, which means no cache is used.shuffle (bool, optional) – Whether or not to perform shuffle on the dataset. Default:
None
, expected order behavior shown in the table below.num_shards (int, optional) – Number of shards that the dataset will be divided into. Default:
None
. When this argument is specified, num_samples reflects the maximum sample number of per shard.shard_id (int, optional) – The shard ID within num_shards . Default:
None
. This argument can only be specified when num_shards is also specified.
- Raises
RuntimeError – If num_shards is specified but shard_id is None.
RuntimeError – If shard_id is specified but num_shards is None.
ValueError – If num_parallel_workers exceeds the max thread numbers.
ValueError – If shard_id is not in range of [0, num_shards ).
TypeError – If total_rows is not of type int.
TypeError – If num_shards is not of type int.
TypeError – If num_parallel_workers is not of type int.
TypeError – If shuffle is not of type bool.
TypeError – If columns_list is not of type list.
- Tutorial Examples:
Examples
>>> from mindspore import dtype as mstype >>> import mindspore.dataset as ds >>> >>> schema = ds.Schema() >>> schema.add_column('image', de_type=mstype.uint8, shape=[2]) >>> schema.add_column('label', de_type=mstype.uint8, shape=[1]) >>> # apply dataset operations >>> ds1 = ds.RandomDataset(schema=schema, total_rows=50, num_parallel_workers=4)
Pre-processing Operation
Apply a function in this dataset. |
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Concatenate the dataset objects in the input list. |
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Filter dataset by prediction. |
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Map func to each row in dataset and flatten the result. |
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Apply each operation in operations to this dataset. |
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The specified columns will be selected from the dataset and passed into the pipeline with the order specified. |
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Rename the columns in input datasets. |
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Repeat this dataset count times. |
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Reset the dataset for next epoch. |
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Save the dynamic data processed by the dataset pipeline in common dataset format. |
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Shuffle the dataset by creating a cache with the size of buffer_size . |
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Skip the first N elements of this dataset. |
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Split the dataset into smaller, non-overlapping datasets. |
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Take the first specified number of samples from the dataset. |
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Zip the datasets in the sense of input tuple of datasets. |
Batch
Combine batch_size number of consecutive rows into batch which apply per_batch_map to the samples first. |
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Bucket elements according to their lengths. |
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Combine batch_size number of consecutive rows into batch which apply pad_info to the samples first. |
Iterator
Create an iterator over the dataset that yields samples of type dict, while the key is the column name and the value is the data. |
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Create an iterator over the dataset that yields samples of type list, whose elements are the data for each column. |
Attribute
Return the size of batch. |
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Get the mapping dictionary from category names to category indexes. |
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Return the names of the columns in dataset. |
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Return the number of batches in an epoch. |
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Get the replication times in RepeatDataset. |
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Get the column index, which represents the corresponding relationship between the data column order and the network when using the sink mode. |
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Get the number of classes in a dataset. |
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Get the shapes of output data. |
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Get the types of output data. |
Apply Sampler
Add a child sampler for the current dataset. |
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Replace the last child sampler of the current dataset, remaining the parent sampler unchanged. |
Others
Release a blocking condition and trigger callback with given data. |
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Add a blocking condition to the input Dataset and a synchronize action will be applied. |
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Serialize a pipeline into JSON string and dump into file if filename is provided. |