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tfds.core.SplitInfo

Wraps proto.SplitInfo with an additional property.

tfds.core.SplitInfo(
    shard_lengths: List[int],
    num_bytes: int,
    statistics: statistics_pb2.DatasetFeatureStatistics = <factory>,
    _dataset_name: Optional[str] = None
)

name Name of the split (e.g. train, test,...)
shard_lengths List of length containing the number of examples stored in each file.
num_examples Total number of examples (sum(shard_lengths))
num_shards Number of files (len(shard_lengths))
num_bytes Size of the files
statistics Additional statistics of the split.
file_instructions Returns the list of dict(filename, take, skip).

This allows for creating your own tf.data.Dataset using the low-level TFDS values.

file_instructions = info.splits['train[75%:]'].file_instructions
instruction_ds = tf.data.Dataset.from_generator(
lambda: file_instructions,
output_types={
'filename': tf.string,
'take': tf.int64,
'skip': tf.int64,
},
)
ds = instruction_ds.interleave(
lambda f: tf.data.TFRecordDataset(
f['filename']).skip(f['skip']).take(f['take'])
)

When skip=0 and take=-1, the full shard will be read, so the ds.skip and ds.take could be skipped.

filenames Returns the list of filenames.

Methods

from_proto

View source

@classmethod
from_proto(
    proto: proto_lib.SplitInfo
) -> "SplitInfo"

replace

View source

replace(
    **kwargs
) -> "SplitInfo"

Returns a copy of the SplitInfo with updated attributes.

to_proto

View source

to_proto() -> proto_lib.SplitInfo