Function loadLayersModelFromIOHandler

tfjs-layers/src/models.ts:282–342

(
    handler: io.IOHandler, customObjects?: serialization.ConfigDict,
    options?: io.LoadOptions)

 *   Default: `true`.
 */
export async function loadLayersModelFromIOHandler(
    handler: io.IOHandler, customObjects?: serialization.ConfigDict,
    options?: io.LoadOptions): Promise<LayersModel> {
  if (options == null) {
    options = {};
  }
  if (handler.load == null) {
    throw new ValueError(
        'Cannot proceed with model loading because the IOHandler provided ' +
        'does not have the `load` method implemented.');
  }
  const artifacts = await handler.load();
  let modelTopology = artifacts.modelTopology as PyJsonDict;
  if (modelTopology['model_config'] != null) {
    modelTopology = modelTopology['model_config'] as PyJsonDict;
  }

  const strict = options.strict == null ? true : options.strict;
  // If weights are provided and the weight-loading mode is strict, use
  // fast weight initialization. This skips costly initializers such as
  // 'orthogonal' and saves unnecessary computation in cases where
  // the initialized weight values will immediately be overwritten by
  // loaded weight values.
  const fastWeightInit =
      artifacts.weightData != null && artifacts.weightSpecs != null && strict;
  const model =
      deserialize(
          convertPythonicToTs(modelTopology) as serialization.ConfigDict,
          customObjects, fastWeightInit) as LayersModel;

  const trainingConfig = artifacts.trainingConfig as TrainingConfig;
  if (trainingConfig != null) {
    model.loadTrainingConfig(trainingConfig);
  }
  if (artifacts.userDefinedMetadata != null) {
    model.setUserDefinedMetadata(artifacts.userDefinedMetadata);
  }

  // If weightData is present, load the weights into the model.
  if (artifacts.weightData != null) {
    // Loading weights requires weightSpecs.
    if (artifacts.weightSpecs == null) {
      throw new ValueError(
          'LayersModel artifacts contains weight data, but not weight specs. ' +
          'Therefore loading of weights cannot proceed.');
    }

    const {modelWeights, optimizerWeights} = decodeModelAndOptimizerWeights(
        artifacts.weightData, artifacts.weightSpecs);
    model.loadWeights(modelWeights, strict);

    if (model.optimizer != null && optimizerWeights.length > 0) {
      await model.optimizer.setWeights(optimizerWeights);
    }

    // Dispose temporary weight values.
    dispose(modelWeights);
    dispose(optimizerWeights.map(w => w.tensor));