@tensorflow/tfjs-core
Version:
Hardware-accelerated JavaScript library for machine intelligence
104 lines • 3.92 kB
JavaScript
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
class PassthroughLoader {
constructor(modelArtifacts) {
this.modelArtifacts = modelArtifacts;
}
async load() {
return this.modelArtifacts;
}
}
class PassthroughSaver {
constructor(saveHandler) {
this.saveHandler = saveHandler;
}
async save(modelArtifacts) {
return this.saveHandler(modelArtifacts);
}
}
/**
* Creates an IOHandler that loads model artifacts from memory.
*
* When used in conjunction with `tf.loadLayersModel`, an instance of
* `tf.LayersModel` (Keras-style) can be constructed from the loaded artifacts.
*
* ```js
* const model = await tf.loadLayersModel(tf.io.fromMemory(
* modelTopology, weightSpecs, weightData));
* ```
*
* @param modelArtifacts a object containing model topology (i.e., parsed from
* the JSON format).
* @param weightSpecs An array of `WeightsManifestEntry` objects describing the
* names, shapes, types, and quantization of the weight data.
* @param weightData A single `ArrayBuffer` containing the weight data,
* concatenated in the order described by the weightSpecs.
* @param trainingConfig Model training configuration. Optional.
*
* @returns A passthrough `IOHandler` that simply loads the provided data.
*/
export function fromMemory(modelArtifacts, weightSpecs, weightData, trainingConfig) {
if (arguments.length === 1) {
const isModelArtifacts = modelArtifacts.modelTopology != null ||
modelArtifacts.weightSpecs != null;
if (isModelArtifacts) {
return new PassthroughLoader(modelArtifacts);
}
else {
// Legacy support: with only modelTopology.
// TODO(cais): Remove this deprecated API.
console.warn('Please call tf.io.fromMemory() with only one argument. ' +
'The argument should be of type ModelArtifacts. ' +
'The multi-argument signature of tf.io.fromMemory() has been ' +
'deprecated and will be removed in a future release.');
return new PassthroughLoader({ modelTopology: modelArtifacts });
}
}
else {
// Legacy support.
// TODO(cais): Remove this deprecated API.
console.warn('Please call tf.io.fromMemory() with only one argument. ' +
'The argument should be of type ModelArtifacts. ' +
'The multi-argument signature of tf.io.fromMemory() has been ' +
'deprecated and will be removed in a future release.');
return new PassthroughLoader({
modelTopology: modelArtifacts,
weightSpecs,
weightData,
trainingConfig
});
}
}
/**
* Creates an IOHandler that passes saved model artifacts to a callback.
*
* ```js
* function handleSave(artifacts) {
* // ... do something with the artifacts ...
* return {modelArtifactsInfo: {...}, ...};
* }
*
* const saveResult = model.save(tf.io.withSaveHandler(handleSave));
* ```
*
* @param saveHandler A function that accepts a `ModelArtifacts` and returns a
* `SaveResult`.
*/
export function withSaveHandler(saveHandler) {
return new PassthroughSaver(saveHandler);
}
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