mindspore.dataset.TFRecordDataset

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, compression_type=None)

A source dataset that reads and parses datasets stored on disk in TFData format.

The columns of generated dataset depend on the source TFRecord files.

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 lexicographical order.

• 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 columns to be read. Default: None , read all columns.

• num_samples (int, optional) –

  The number of samples (rows) to be included in the dataset. Default: None . When num_shards and shard_id are specified, it will be interpreted as number of rows per shard. Processing priority for num_samples is as the following:

  • If specify num_samples with value > 0, read num_samples samples.

  • If no num_samples and specify numRows(parsed from schema) with value > 0, read numRows samples.

  • If no num_samples and no schema, read the full dataset.

• 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 by mindspore.dataset.config.set_num_parallel_workers() .

• shuffle (Union[bool, Shuffle], optional) –

  Perform reshuffling of the data every epoch. Default: Shuffle.GLOBAL . Bool type and Shuffle enum are both supported to pass in. If shuffle is False, no shuffling will be performed. If shuffle is True, perform global shuffle. There are three levels of shuffling, desired shuffle enum defined by mindspore.dataset.Shuffle .

  • Shuffle.GLOBAL : Shuffle both the files and samples, same as setting shuffle to True.

  • Shuffle.FILES : Shuffle files only.

• num_shards (int, optional) – Number of shards that the dataset will be divided into. Default: None . When this argument is specified, num_samples reflects the maximum sample number 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.

• shard_equal_rows (bool, optional) – Get equal rows for all shards. Default: False. If shard_equal_rows is False, the number of rows of each shard may not be equal, and may lead to a failure in distributed training. When the number of samples per TFRecord file are not equal, it is suggested to set it to True. This argument should only be specified when num_shards is also specified. When compression_type is not None, and num_samples or numRows (parsed from schema ) is provided, shard_equal_rows will be implied as True.

• 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.

• compression_type (str, optional) – The type of compression used for all files, must be either '', 'GZIP', or 'ZLIB'. Default: None , as in empty string. It is highly recommended to provide num_samples or numRows (parsed from schema) when compression_type is "GZIP" or "ZLIB" to avoid performance degradation caused by multiple decompressions of the same file to obtain the file size.

Raises

• ValueError – If dataset_files are not valid or do not exist.

• ValueError – If num_parallel_workers exceeds the max thread numbers.

• 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 not in range of [0, num_shards ).

• ValueError – If compression_type is not '', 'GZIP' or 'ZLIB' .

• ValueError – If compression_type is provided, but the number of dataset files < num_shards .

• ValueError – If num_samples < 0.

Examples

>>> import mindspore.dataset as ds
>>> from mindspore import dtype as mstype
>>>
>>> tfrecord_dataset_dir = ["/path/to/tfrecord_dataset_file"] # contains 1 or multiple TFRecord files
>>> tfrecord_schema_file = "/path/to/tfrecord_schema_file"
>>>
>>> # 1) Get all rows from tfrecord_dataset_dir with no explicit schema.
>>> # The meta-data in the first row will be used as a schema.
>>> dataset = ds.TFRecordDataset(dataset_files=tfrecord_dataset_dir)
>>>
>>> # 2) Get all rows from tfrecord_dataset_dir with user-defined schema.
>>> schema = ds.Schema()
>>> schema.add_column(name='col_1d', de_type=mstype.int64, shape=[2])
>>> dataset = ds.TFRecordDataset(dataset_files=tfrecord_dataset_dir, schema=schema)
>>>
>>> # 3) Get all rows from tfrecord_dataset_dir with the schema file.
>>> dataset = ds.TFRecordDataset(dataset_files=tfrecord_dataset_dir, schema=tfrecord_schema_file)

Pre-processing Operation

mindspore.dataset.Dataset.apply	Apply a function in this dataset.
mindspore.dataset.Dataset.concat	Concatenate the dataset objects in the input list.
mindspore.dataset.Dataset.filter	Filter dataset by prediction.
mindspore.dataset.Dataset.flat_map	Map func to each row in dataset and flatten the result.
mindspore.dataset.Dataset.map	Apply each operation in operations to this dataset.
mindspore.dataset.Dataset.project	The specified columns will be selected from the dataset and passed into the pipeline with the order specified.
mindspore.dataset.Dataset.rename	Rename the columns in input datasets.
mindspore.dataset.Dataset.repeat	Repeat this dataset count times.
mindspore.dataset.Dataset.reset	Reset the dataset for next epoch.
mindspore.dataset.Dataset.save	Save the dynamic data processed by the dataset pipeline in common dataset format.
mindspore.dataset.Dataset.shuffle	Shuffle the dataset by creating a cache with the size of buffer_size .
mindspore.dataset.Dataset.skip	Skip the first N elements of this dataset.
mindspore.dataset.Dataset.split	Split the dataset into smaller, non-overlapping datasets.
mindspore.dataset.Dataset.take	Takes at most given numbers of elements from the dataset.
mindspore.dataset.Dataset.zip	Zip the datasets in the sense of input tuple of datasets.

Batch

mindspore.dataset.Dataset.batch	Combine batch_size number of consecutive rows into batch which apply per_batch_map to the samples first.
mindspore.dataset.Dataset.bucket_batch_by_length	Bucket elements according to their lengths.
mindspore.dataset.Dataset.padded_batch	Combine batch_size number of consecutive rows into batch which apply pad_info to the samples first.

Iterator

mindspore.dataset.Dataset.create_dict_iterator	Create an iterator over the dataset.
mindspore.dataset.Dataset.create_tuple_iterator	Create an iterator over the dataset.

Attribute

mindspore.dataset.Dataset.get_batch_size	Return the size of batch.
mindspore.dataset.Dataset.get_class_indexing	Return the class index.
mindspore.dataset.Dataset.get_col_names	Return the names of the columns in dataset.
mindspore.dataset.Dataset.get_dataset_size	Return the number of batches in an epoch.
mindspore.dataset.Dataset.get_repeat_count	Get the replication times in RepeatDataset.
mindspore.dataset.Dataset.input_indexs	Get the column index, which represents the corresponding relationship between the data column order and the network when using the sink mode.
mindspore.dataset.Dataset.num_classes	Get the number of classes in a dataset.
mindspore.dataset.Dataset.output_shapes	Get the shapes of output data.
mindspore.dataset.Dataset.output_types	Get the types of output data.

Apply Sampler

mindspore.dataset.MappableDataset.add_sampler	Add a child sampler for the current dataset.
mindspore.dataset.MappableDataset.use_sampler	Replace the last child sampler of the current dataset, remaining the parent sampler unchanged.

Others

mindspore.dataset.Dataset.device_que	Return a transferred Dataset that transfers data through a device.
mindspore.dataset.Dataset.sync_update	Release a blocking condition and trigger callback with given data.
mindspore.dataset.Dataset.sync_wait	Add a blocking condition to the input Dataset and a synchronize action will be applied.
mindspore.dataset.Dataset.to_json	Serialize a pipeline into JSON string and dump into file if filename is provided.