View source on GitHub
  
 | 
Representative dataset saver in TFRecord format.
tf.quantization.experimental.TfRecordRepresentativeDatasetSaver(
    path_map: Mapping[str, os.PathLike[str]]
)
Saves representative datasets for quantization calibration in TFRecord format.
The samples are serialized as RepresentativeDataSample.
The save method return a signature key to RepresentativeDatasetFile map,
which can be used for QuantizationOptions.
Example usage:
# Creating the representative dataset.
representative_dataset = [{"input": tf.random.uniform(shape=(3, 3))}
                      for _ in range(256)]
# Saving to a TFRecord file.
dataset_file_map = (
  tf.quantization.experimental.TfRecordRepresentativeDatasetSaver(
        path_map={'serving_default': '/tmp/representative_dataset_path'}
    ).save({'serving_default': representative_dataset})
)
# Using in QuantizationOptions.
quantization_options = tf.quantization.experimental.QuantizationOptions(
    signature_keys=['serving_default'],
    representative_datasets=dataset_file_map,
)
tf.quantization.experimental.quantize_saved_model(
    '/tmp/input_model',
    '/tmp/output_model',
    quantization_options=quantization_options,
)
Methods
save
save(
    representative_dataset: RepresentativeDatasetMapping
) -> Mapping[str, _RepresentativeDatasetFile]
Saves the representative dataset.
| Args | |
|---|---|
representative_dataset
 | 
Signature def key -> representative dataset
mapping. Each dataset is saved in a separate TFRecord file whose path
matches the signature def key of path_map.
 | 
| Raises | |
|---|---|
ValueError
 | 
When the signature def key in representative_dataset is not
present in the path_map.
 | 
| Returns | |
|---|---|
| A map from signature key to the RepresentativeDatasetFile instance contains the path to the saved file. | 
    View source on GitHub