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coreml.js
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coreml.js
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var coreml = {};
var base = require('./base');
var json = require('./json');
var protobuf = require('./protobuf');
coreml.ModelFactory = class {
match(context) {
const stream = context.stream;
const identifier = context.identifier.toLowerCase();
const extension = identifier.split('.').pop().toLowerCase();
const tags = context.tags('pb');
if (tags.get(1) === 0 && tags.get(2) === 2) {
if (extension === 'pb') {
const tags = context.tags('pb+');
const keys = Object.keys(tags).map((key) => parseInt(key, 10));
const match = (key) =>
(key >= 200 && key < 220) ||
(key >= 300 && key < 320) ||
(key >= 400 && key < 420) ||
(key >= 500 && key < 520) ||
(key >= 550 && key < 560) ||
(key >= 600 && key < 620) ||
(key === 900) ||
(key >= 2000 && key < 2010) ||
(key === 3000);
if (!keys.some((key) => match(key))) {
return null;
}
}
return 'coreml.pb';
}
if (identifier === 'manifest.json') {
const obj = context.open('json');
if (obj && obj.rootModelIdentifier && obj.itemInfoEntries) {
const entries = Object.keys(obj.itemInfoEntries).map((key) => obj.itemInfoEntries[key]);
if (entries.filter((entry) => entry.path.toLowerCase().endsWith('.mlmodel').length === 1)) {
return 'coreml.manifest';
}
}
}
if (identifier === 'metadata.json') {
const obj = context.open('json');
if (obj && obj.rootModelIdentifier && obj.itemInfoEntries) {
return 'coreml.metadata';
}
}
if (identifier === 'featuredescriptions.json') {
const obj = context.open('json');
if (obj && (obj.Inputs || obj.Outputs)) {
return 'coreml.featuredescriptions';
}
}
if (extension === 'bin' && stream.length > 16) {
const buffer = stream.peek(Math.min(256, stream.length));
for (let i = 0; i < buffer.length - 4; i++) {
const signature = (buffer[i] | buffer[i + 1] << 8 | buffer[i + 2] << 16 | buffer [i + 3] << 24) >>> 0;
if (signature === 0xdeadbeef) {
return 'coreml.weights';
}
}
}
return undefined;
}
open(context, match) {
return context.require('./coreml-proto').then(() => {
return context.metadata('coreml-metadata.json').then((metadata) => {
const openModel = (stream, context, path, format) => {
let model = null;
try {
coreml.proto = protobuf.get('coreml').CoreML.Specification;
const reader = protobuf.BinaryReader.open(stream);
model = coreml.proto.Model.decode(reader);
}
catch (error) {
const message = error && error.message ? error.message : error.toString();
throw new coreml.Error('File format is not coreml.Model (' + message.replace(/\.$/, '') + ').');
}
const weightPaths = new Set();
const walkProgram = (program) => {
for (const entry of Object.entries(program.functions)) {
const func = entry[1];
for (const entry of Object.entries(func.block_specializations)) {
const block = entry[1];
for (const operation of block.operations) {
for (const entry of Object.entries(operation.attributes)) {
const value = entry[1];
if (value.blobFileValue && value.blobFileValue.fileName) {
weightPaths.add(value.blobFileValue.fileName);
}
}
}
}
}
};
const walkModel = (model) => {
if (model.mlProgram) {
walkProgram(model.mlProgram);
}
if (model.pipeline && model.pipeline.models) {
for (const node of model.pipeline.models) {
walkModel(node);
}
}
if (model.pipelineClassifier && model.pipelineClassifier.pipeline && model.pipelineClassifier.pipeline.models) {
for (const node of model.pipelineClassifier.pipeline.models) {
walkModel(node);
}
}
if (model.pipelineRegressor && model.pipelineRegressor.pipeline && model.pipelineRegressor.pipeline.models) {
for (const node of model.pipelineRegressor.pipeline.models) {
walkModel(node);
}
}
};
walkModel(model);
if (weightPaths.size > 0) {
const items = path.split('/');
items.pop();
const folder = items.join('/');
const keys = Array.from(weightPaths);
const paths = keys.map((path) => {
const items = path.split('/');
if (items[0] === '@model_path') {
items[0] = folder;
}
return items.join('/');
});
const promises = paths.map((path) => context.request(path, null));
return Promise.all(promises).then((streams) => {
const weights = new Map();
for (let i = 0; i < keys.length; i++) {
weights.set(keys[i], streams[i]);
}
return new coreml.Model(metadata, format, model, weights);
}).catch((/* err */) => {
return new coreml.Model(metadata, format, model, new Map());
});
}
return new coreml.Model(metadata, format, model, new Map());
};
const openManifest = (obj, context, path) => {
const entries = Object.keys(obj.itemInfoEntries).map((key) => obj.itemInfoEntries[key]);
const entry = entries.filter((entry) => entry.path.toLowerCase().endsWith('.mlmodel'))[0];
const file = path + 'Data/' + entry.path;
return context.request(file, null).then((stream) => {
return openModel(stream, context, file, 'Core ML Package');
});
};
const openManifestStream = (context, path) => {
return context.request(path + 'Manifest.json', null).then((stream) => {
const reader = json.TextReader.open(stream);
const obj = reader.read();
return openManifest(obj, context, path);
});
};
switch (match) {
case 'coreml.pb': {
return openModel(context.stream, context, context.identifier);
}
case 'coreml.manifest': {
const obj = context.open('json');
return openManifest(obj, context, '');
}
case 'coreml.featuredescriptions':
case 'coreml.metadata': {
return openManifestStream(context, '../../');
}
case 'coreml.weights': {
return openManifestStream(context, '../../../');
}
default: {
throw new coreml.Error("Unsupported Core ML format '" + match + "'.");
}
}
});
});
}
};
coreml.Model = class {
constructor(metadata, format, model, weights) {
this._format = (format || 'Core ML') + ' v' + model.specificationVersion.toString();
this._metadata = [];
this._graphs = [ new coreml.Graph(metadata, model, weights) ];
if (model.description && model.description.metadata) {
const properties = model.description.metadata;
if (properties.versionString) {
this._version = properties.versionString;
}
if (properties.shortDescription) {
this._description = properties.shortDescription;
}
if (properties.author) {
this._metadata.push({ name: 'author', value: properties.author });
}
if (properties.license) {
this._metadata.push({ name: 'license', value: properties.license });
}
if (metadata.userDefined && Object.keys(properties.userDefined).length > 0) {
/* empty */
}
}
}
get format() {
return this._format;
}
get version() {
return this._version || null;
}
get description() {
return this._description || null;
}
get metadata() {
return this._metadata;
}
get graphs() {
return this._graphs;
}
};
coreml.Graph = class {
constructor(metadata, model, weights) {
this._metadata = metadata;
this._description = model.description;
this._groups = false;
this._inputs = [];
this._outputs = [];
this._nodes = [];
const args = new Map();
args.input = (name) => {
if (!args.has(name)) {
const argument = new coreml.Argument(name);
args.set(name, { counter: 0, argument: argument });
}
return args.get(name).argument;
};
args.output = (name) => {
if (args.has(name)) {
const value = args.get(name);
value.counter++;
const next = name + '\n' + value.counter.toString(); // custom argument id
value.argument = new coreml.Argument(next);
}
else {
const argument = new coreml.Argument(name);
const value = { counter: 0, argument: argument };
args.set(name, value);
}
return args.get(name).argument;
};
const update = (argument, description) => {
if (!argument.type) {
argument.type = coreml.Utility.featureType(description.type);
}
if (!argument.description && description.shortDescription) {
argument.description = description.shortDescription;
}
return argument;
};
if (this._description) {
this._inputs = this._description.input.map((input) => {
const argument = args.output(input.name);
update(argument, input);
return new coreml.Parameter(input.name, true, [ argument ]);
});
}
this._type = this._loadModel(model, args, '', weights);
if (this._description) {
this._outputs = this._description.output.map((output) => {
const argument = args.input(output.name);
update(argument, output);
return new coreml.Parameter(output.name, true, [ argument ]);
});
}
}
get name() {
return '';
}
get type() {
return this._type;
}
get inputs() {
return this._inputs;
}
get outputs() {
return this._outputs;
}
get nodes() {
return this._nodes;
}
get groups() {
return this._groups;
}
_updateOutput(name, newName) {
for (const node of this._nodes) {
for (const output of node.outputs) {
for (const argument of output.arguments) {
if (argument.name === name) {
argument.name = newName;
}
}
}
}
return newName;
}
_updateClassifierOutput(args, group, classifier) {
let labelProbabilityLayerName = classifier.labelProbabilityLayerName;
if (!labelProbabilityLayerName && this._nodes.length > 0) {
const node = this._nodes.slice(-1).pop();
if (node && node.outputs.length == 1 && node.outputs[0].arguments.length == 1) {
labelProbabilityLayerName = node.outputs[0].arguments[0].name;
}
}
let predictedFeatureName = this._description.predictedFeatureName;
let predictedProbabilitiesName = this._description.predictedProbabilitiesName;
if ((predictedFeatureName || predictedProbabilitiesName) && labelProbabilityLayerName && classifier.ClassLabels) {
predictedFeatureName = predictedFeatureName ? predictedFeatureName : '?';
predictedProbabilitiesName = predictedProbabilitiesName ? predictedProbabilitiesName : '?';
const labelProbabilityInput = this._updateOutput(labelProbabilityLayerName, labelProbabilityLayerName + ':labelProbabilityLayerName');
const type = classifier.ClassLabels;
const inputs = [
new coreml.Parameter('input', true, [ new coreml.Argument(labelProbabilityInput) ])
];
const outputs = [
new coreml.Parameter('probabilities', true, [ args.output(predictedProbabilitiesName) ]),
new coreml.Parameter('feature', true, [ args.output(predictedFeatureName) ])
];
const node = new coreml.Node(this._metadata, this._group, type, null, '', classifier[type], inputs, outputs);
this._nodes.push(node);
}
}
_updatePreprocessing(args, group, preprocessing) {
if (preprocessing && preprocessing.length > 0) {
const preprocessingInput = this._description.input[0].name;
const inputNodes = [];
for (const node of this._nodes) {
if (node.inputs.some((input) => input.arguments.some((arg) => arg.name == preprocessingInput))) {
inputNodes.push(node);
}
}
let currentOutput = preprocessingInput;
let preprocessorOutput = null;
let preprocessorIndex = 0;
for (const p of preprocessing) {
const input = p.featureName ? p.featureName : currentOutput;
currentOutput = preprocessingInput + ':' + preprocessorIndex.toString();
const node = this._createNode(args, group, p.preprocessor, null, '', p[p.preprocessor], [ input ], [ currentOutput ]);
preprocessorOutput = node.outputs[0].arguments[0];
preprocessorIndex++;
}
for (const node of inputNodes) {
for (const input of node.inputs) {
for (let i = 0; i < input.arguments.length; i++) {
if (input.arguments[i].name === preprocessingInput) {
input.arguments[i] = preprocessorOutput;
}
}
}
}
}
}
_loadModel(model, args, group, weights) {
this._groups = this._groups | (group.length > 0 ? true : false);
const description = model && model.description && model.description.metadata && model.description.metadata.shortDescription ? model.description.metadata.shortDescription : '';
switch (model.Type) {
case 'neuralNetworkClassifier': {
const neuralNetworkClassifier = model.neuralNetworkClassifier;
for (const layer of neuralNetworkClassifier.layers) {
this._createNode(args, group, layer.layer, layer.name, group === '' ? '' : description, layer[layer.layer], layer.input, layer.output, layer.inputTensor, layer.outputTensor);
}
this._updateClassifierOutput(args, group, neuralNetworkClassifier);
this._updatePreprocessing(args, group, neuralNetworkClassifier.preprocessing);
return 'Neural Network Classifier';
}
case 'neuralNetwork': {
const neuralNetwork = model.neuralNetwork;
for (const layer of neuralNetwork.layers) {
this._createNode(args, group, layer.layer, layer.name, group === '' ? '' : description, layer[layer.layer], layer.input, layer.output, layer.inputTensor, layer.outputTensor);
}
this._updatePreprocessing(args, group, neuralNetwork.preprocessing);
return 'Neural Network';
}
case 'neuralNetworkRegressor': {
const neuralNetworkRegressor = model.neuralNetworkRegressor;
for (const layer of neuralNetworkRegressor.layers) {
this._createNode(args, group, layer.layer, layer.name, description, layer[layer.layer], layer.input, layer.output);
}
this._updatePreprocessing(args, group, neuralNetworkRegressor);
return 'Neural Network Regressor';
}
case 'pipeline': {
for (let i = 0; i < model.pipeline.models.length; i++) {
this._loadModel(model.pipeline.models[i], args, (group ? (group + '/') : '') + 'pipeline[' + i.toString() + ']');
}
return 'Pipeline';
}
case 'pipelineClassifier': {
for (let i = 0; i < model.pipelineClassifier.pipeline.models.length; i++) {
this._loadModel(model.pipelineClassifier.pipeline.models[i], args, (group ? (group + '/') : '') + 'pipelineClassifier[' + i.toString() + ']');
}
return 'Pipeline Classifier';
}
case 'pipelineRegressor': {
for (let i = 0; i < model.pipelineRegressor.pipeline.models.length; i++) {
this._loadModel(model.pipelineRegressor.pipeline.models[i], args, (group ? (group + '/') : '') + 'pipelineRegressor[' + i.toString() + ']');
}
return 'Pipeline Regressor';
}
case 'glmClassifier': {
this._createNode(args, group, 'glmClassifier', null, description,
{
classEncoding: model.glmClassifier.classEncoding,
offset: model.glmClassifier.offset,
weights: model.glmClassifier.weights
},
[ model.description.input[0].name ],
[ model.description.predictedProbabilitiesName ]);
this._updateClassifierOutput(args, group, model.glmClassifier);
return 'Generalized Linear Classifier';
}
case 'glmRegressor': {
this._createNode(args, group, 'glmRegressor', null, description,
model.glmRegressor,
[ model.description.input[0].name ],
[ model.description.output[0].name ]);
return 'Generalized Linear Regressor';
}
case 'treeEnsembleClassifier': {
this._createNode(args, group, 'treeEnsembleClassifier', null, description,
model.treeEnsembleClassifier.treeEnsemble,
[ model.description.input[0].name ],
[ model.description.output[0].name ]);
this._updateClassifierOutput(args, group, model.treeEnsembleClassifier);
return 'Tree Ensemble Classifier';
}
case 'treeEnsembleRegressor': {
this._createNode(args, group, 'treeEnsembleRegressor', null, description,
model.treeEnsembleRegressor.treeEnsemble,
[ model.description.input[0].name ],
[ model.description.output[0].name ]);
return 'Tree Ensemble Regressor';
}
case 'supportVectorClassifier': {
this._createNode(args, group, 'supportVectorClassifier', null, description,
{
coefficients: model.supportVectorClassifier.coefficients,
denseSupportVectors: model.supportVectorClassifier.denseSupportVectors,
kernel: model.supportVectorClassifier.kernel,
numberOfSupportVectorsPerClass: model.supportVectorClassifier.numberOfSupportVectorsPerClass,
probA: model.supportVectorClassifier.probA,
probB: model.supportVectorClassifier.probB,
rho: model.supportVectorClassifier.rho,
supportVectors: model.supportVectorClassifier.supportVectors
},
[ model.description.input[0].name ],
[ model.description.output[0].name ]);
this._updateClassifierOutput(args, group, model.supportVectorClassifier);
return 'Support Vector Classifier';
}
case 'supportVectorRegressor': {
this._createNode(args, group, 'supportVectorRegressor', null, description,
{
coefficients: model.supportVectorRegressor.coefficients,
kernel: model.supportVectorRegressor.kernel,
rho: model.supportVectorRegressor.rho,
supportVectors: model.supportVectorRegressor.supportVectors
},
[ model.description.input[0].name ],
[ model.description.output[0].name ]);
return 'Support Vector Regressor';
}
case 'oneHotEncoder': {
const categoryType = model.oneHotEncoder.CategoryType;
const oneHotEncoderParams = { outputSparse: model.oneHotEncoder.outputSparse };
oneHotEncoderParams[categoryType] = model.oneHotEncoder[categoryType];
this._createNode(args, group, 'oneHotEncoder', null, description,
oneHotEncoderParams,
[ model.description.input[0].name ],
[ model.description.output[0].name ]);
return 'One Hot Encoder';
}
case 'imputer': {
const imputedValue = model.imputer.ImputedValue;
const replaceValue = model.imputer.ReplaceValue;
const imputerParams = {};
imputerParams[imputedValue] = model.imputer[imputedValue];
imputerParams[replaceValue] = model.imputer[replaceValue];
this._createNode(args, group, 'oneHotEncoder', null, description,
imputerParams,
[ model.description.input[0].name ],
[ model.description.output[0].name ]);
return 'Imputer';
}
case 'featureVectorizer': {
this._createNode(args, group, 'featureVectorizer', null, description,
model.featureVectorizer,
coreml.Graph._formatFeatureDescriptionList(model.description.input),
[ model.description.output[0].name ]);
return 'Feature Vectorizer';
}
case 'dictVectorizer': {
this._createNode(args, group, 'dictVectorizer', null, description,
model.dictVectorizer,
[ model.description.input[0].name ],
[ model.description.output[0].name ]);
return 'Dictionary Vectorizer';
}
case 'scaler': {
this._createNode(args, group, 'scaler', null, description,
model.scaler,
[ model.description.input[0].name ],
[ model.description.output[0].name ]);
return 'Scaler';
}
case 'categoricalMapping': {
this._createNode(args, group, 'categoricalMapping', null, description,
model.categoricalMapping,
[ model.description.input[0].name ],
[ model.description.output[0].name ]);
return 'Categorical Mapping';
}
case 'normalizer': {
this._createNode(args, group, 'normalizer', null, description,
model.normalizer,
[ model.description.input[0].name ],
[ model.description.output[0].name ]);
return 'Normalizer';
}
case 'arrayFeatureExtractor': {
this._createNode(args, group, 'arrayFeatureExtractor', null, description,
{ extractIndex: model.arrayFeatureExtractor.extractIndex },
[ model.description.input[0].name ],
[ model.description.output[0].name ]);
return 'Array Feature Extractor';
}
case 'nonMaximumSuppression': {
const nonMaximumSuppressionParams = {
pickTop: model.nonMaximumSuppression.pickTop,
stringClassLabels: model.nonMaximumSuppression.stringClassLabels,
iouThreshold: model.nonMaximumSuppression.iouThreshold,
confidenceThreshold: model.nonMaximumSuppression.confidenceThreshold
};
this._createNode(args, group, 'nonMaximumSuppression', null, description,
nonMaximumSuppressionParams,
[
model.nonMaximumSuppression.confidenceInputFeatureName,
model.nonMaximumSuppression.coordinatesInputFeatureName,
model.nonMaximumSuppression.iouThresholdInputFeatureName,
model.nonMaximumSuppression.confidenceThresholdInputFeatureName,
],
[
model.nonMaximumSuppression.confidenceOutputFeatureName,
model.nonMaximumSuppression.coordinatesOutputFeatureName
]);
return 'Non Maximum Suppression';
}
case 'wordTagger': {
this._createNode(args, group, 'wordTagger', null, description,
model.wordTagger,
[ model.description.input[0].name ],
[
model.wordTagger.tokensOutputFeatureName,
model.wordTagger.tokenTagsOutputFeatureName,
model.wordTagger.tokenLocationsOutputFeatureName,
model.wordTagger.tokenLengthsOutputFeatureName
]);
return 'Word Tagger';
}
case 'textClassifier': {
this._createNode(args, group, 'textClassifier', null, description,
model.textClassifier,
[ model.description.input[0].name ],
[ model.description.output[0].name ]);
return 'Text Classifier';
}
case 'visionFeaturePrint': {
const visionFeaturePrintParams = {
scene: model.visionFeaturePrint.scene
};
this._createNode(args, group, 'visionFeaturePrint', null, description,
visionFeaturePrintParams,
[ model.description.input[0].name ],
[ model.description.output[0].name ]);
return 'Vision Feature Print';
}
case 'soundAnalysisPreprocessing': {
this._createNode(args, group, 'soundAnalysisPreprocessing', null, description,
model.soundAnalysisPreprocessing,
[ model.description.input[0].name ],
[ model.description.output[0].name ]);
return 'Sound Analysis Preprocessing';
}
case 'kNearestNeighborsClassifier': {
this._createNode(args, group, 'kNearestNeighborsClassifier', null, description,
model.kNearestNeighborsClassifier,
[ model.description.input[0].name ],
[ model.description.output[0].name ]);
this._updateClassifierOutput(args, group, model.kNearestNeighborsClassifier);
return 'Nearest Neighbors Classifier';
}
case 'itemSimilarityRecommender': {
this._createNode(args, group, 'itemSimilarityRecommender', null, description,
{
itemStringIds: model.itemSimilarityRecommender.itemStringIds.vector,
itemItemSimilarities: model.itemSimilarityRecommender.itemItemSimilarities
},
model.description.input.map((feature) => feature.name),
model.description.output.map((feature) => feature.name));
return 'Item Similarity Recommender';
}
case 'audioFeaturePrint': {
this._createNode(args, group, 'audioFeaturePrint', null, description,
model.audioFeaturePrint,
[ model.description.input[0].name ],
[ model.description.output[0].name ]);
return 'Audio Feature Print';
}
case 'linkedModel': {
this._createNode(args, group, 'linkedModel', null, description,
model.linkedModel.linkedModelFile,
[ model.description.input[0].name ],
[ model.description.output[0].name ]);
return 'Linked Model';
}
case 'customModel': {
this._createNode(args, group, 'customModel', null, description,
{ className: model.customModel.className, parameters: model.customModel.parameters },
[ model.description.input[0].name ],
[ model.description.output[0].name ]);
return 'customModel';
}
case 'mlProgram': {
return this._loadProgram(model.mlProgram, args, group, weights);
}
default: {
throw new coreml.Error("Unsupported model type '" + JSON.stringify(Object.keys(model)) + "'.");
}
}
}
_loadProgram(program, _, group, weights) {
// TODO: need to handle functions other than main?
const main = program.functions.main;
// TODO: need to handle more than one block specialization?
const block = main.block_specializations.CoreML5 || main.block_specializations.CoreML6;
const convertValue = (value) => {
switch (value.value) {
case 'immediateValue': {
const tensor = value.immediateValue.tensor;
let values = null;
switch (tensor.value) {
case 'ints':
values = tensor.ints.values;
break;
case 'strings':
values = tensor.strings.values;
break;
case 'bools':
values = tensor.bools.values;
break;
case 'floats':
values = tensor.floats.values;
break;
case 'bytes':
values = tensor.bytes.values;
break;
default:
throw new coreml.Error("Unsupported tensor value '" + tensor.value + "'.");
}
return values;
}
case 'blobFileValue': {
const type = coreml.Utility.valueType(value.type);
const blob = value.blobFileValue;
const offset = blob.offset.toNumber();
const file = blob.fileName;
let data = null;
const stream = weights.get(file);
if (stream) {
stream.seek(offset);
const buffer = stream.read(32);
const reader = new base.BinaryReader(buffer);
const signature = reader.uint32();
if (signature == 0xdeadbeef) {
reader.uint32(); // dataType
const size = reader.uint64();
stream.seek(reader.uint64());
const length = (type.shape.dimensions || []).reduce((a, b) => a * b, 1);
switch (type.dataType) {
case 'float32': {
const buffer = stream.read(size);
data = new Float32Array(buffer.buffer, buffer.byteOffset, length).slice();
break;
}
case 'float16': {
data = stream.read(size);
break;
}
case 'uint8': {
data = stream.read(size);
break;
}
default:
throw new coreml.Error("Unsupported blob data type '" + type.dataType + "'.");
}
}
}
return new coreml.Tensor('Blob', type, data);
}
default: {
throw new coreml.Error("Unsupported value '" + value.value + "'.");
}
}
};
const args = new Map();
const arg = (name) => {
if (!args.has(name)) {
args.set(name, { name: name, to: [], from: [] });
}
return args.get(name);
};
const operations = block.operations.map((op) => {
const operation = {
type: op.type,
attributes: {}
};
for (const entry of Object.entries(op.attributes)) {
const key = entry[0];
const value = entry[1];
operation.attributes[key] = convertValue(value);
}
operation.inputs = Object.entries(op.inputs).map((entry) => {
const key = entry[0];
const input = entry[1];
const args = input.arguments.map((argument) => {
if (argument.name) {
const value = arg(argument.name);
value.to.push(operation);
return value;
}
return { value: argument.value };
});
return {
name: key,
arguments: args
};
});
operation.outputs = op.outputs.map((output) => {
const value = arg(output.name);
value.type = coreml.Utility.valueType(output.type);
value.from.push(operation);
return {
name: 'output',
arguments: [ value ]
};
});
return operation;
});
for (const op of operations) {
if (op.type === 'const' && op.inputs.length === 0 &&
op.outputs.length === 1 && op.outputs[0].arguments.length === 1) {
const argument = op.outputs[0].arguments[0];
if (op.attributes && op.attributes.val) {
const type = argument.type;
const data = op.attributes.val;
if (data instanceof Uint8Array && data.length === 2 &&
type.dataType === 'float16' && type.shape.dimensions.length === 0) {
const view = new DataView(data.buffer, data.byteOffset, data.byteLength);
argument.value = view.getFloat16(0, true);
}
else {
argument.value = data;
}
argument.const = true;
op.delete = true;
}
}
}
for (const op of operations) {
for (const input of op.inputs) {
if (input.arguments.length > 1 && input.arguments.some((argument) => argument.const)) {
if (input.arguments.every((argument) => argument.value instanceof coreml.Tensor)) {
continue;
}
for (const argument of input.arguments) {
for (const from of argument.from) {
from.delete = false;
}
delete argument.value;
}
}
}
}
for (const op of operations) {
if (op.delete) {
continue;
}
op.inputs = op.inputs.filter((input) => {
if (input.arguments.every((argument) => argument.value === undefined || argument.value instanceof coreml.Tensor)) {
return true;
}
if (input.arguments.length === 1) {
const argument = input.arguments[0];
op.attributes[input.name] = argument.value;
return false;
}
op.attributes[input.name] = input.arguments.map((argument) => argument.value[0]);
return false;
});
}
const tensors = new Map();
const tensor = (arg) => {
if (!tensors.has(arg.name)) {
tensors.set(arg.name, new coreml.Argument(arg.name, arg.type, null, arg.value));
}
return tensors.get(arg.name);
};
for (const op of operations) {
if (op.delete) {
continue;
}
op.inputs = op.inputs.map((input) => new coreml.Parameter(input.name, true, input.arguments.map((argument) => tensor(argument))));
op.outputs = op.outputs.map((output) => new coreml.Parameter(output.name, true, output.arguments.map((argument) => tensor(argument))));
}
for (const op of operations.filter((op) => !op.delete)) {
const type = 'program:' + op.type;
const metadata = this._metadata.type(type);
if (metadata && Array.isArray(metadata.inputs)) {
let index = 1;
const map = new Map(metadata.inputs.map((input) => [ input.name, index++ ]));
op.inputs.sort((a, b) => (map.get(a.name) || map.size) - (map.get(b.name) || map.size));
}
const node = new coreml.Node(this._metadata, group, type, null, null, op.attributes, op.inputs, op.outputs);
this._nodes.push(node);
}
return 'ML Program';
}
_createNode(args, group, type, name, description, data, inputs, outputs, inputTensors, outputTensors) {
inputs = inputs.map((input, index) => {
const argument = args.input(input);
if (!argument.type && inputTensors && index < inputTensors.length) {
const tensor = inputTensors[index];
const shape = tensor && tensor.dimValue ? new coreml.TensorShape(tensor.dimValue) : null;
argument.type = new coreml.TensorType('?', shape);
}
return argument;
});
outputs = outputs.map((output, index) => {
const argument = args.output(output);
if (!argument.type && outputTensors && index < outputTensors.length) {
const tensor = outputTensors[index];
const shape = tensor && tensor.dimValue ? new coreml.TensorShape(tensor.dimValue) : null;
argument.type = new coreml.TensorType('?', shape);
}
return argument;
});
const initializers = [];
const attributes = {};
if (data) {
const map = this._initialize(type, data, initializers);
for (const key of Object.keys(data)) {
if (map[key]) {
continue;
}
attributes[key] = data[key];
}
}
const metadata = this._metadata.type(type);
const inputParams = [];
for (let i = 0; i < inputs.length;) {
const input = metadata && metadata.inputs && i < metadata.inputs.length ? metadata.inputs[i] : { name: i === 0 ? 'input' : i.toString() };
const count = input.type === 'Tensor[]' ? inputs.length - i : 1;
const args = inputs.slice(i, i + count);
inputParams.push(new coreml.Parameter(input.name, true, args));
i += count;
}
inputParams.push(...initializers);
const outputParams = [];
for (let i = 0; i < outputs.length;) {
const output = metadata && metadata.outputs && i < metadata.outputs.length ? metadata.outputs[i] : { name: i === 0 ? 'output' : i.toString() };
const count = output.type === 'Tensor[]' ? outputs.length - i : 1;
const args = outputs.slice(i, i + count);
outputParams.push(new coreml.Parameter(output.name, true, args));
i += count;
}
const node = new coreml.Node(this._metadata, group, type, name, description, attributes, inputParams, outputParams);
this._nodes.push(node);
return node;
}
_initializer(type, initializers, kind, name, shape, data) {
let dataType = '?';
let quantization = null;
let values = null;
if (data) {
if (data.floatValue && data.floatValue.length > 0) {
values = data.floatValue;
dataType = 'float32';
}
else if (data.float16Value && data.float16Value.length > 0) {
values = data.float16Value; // byte[]
dataType = 'float16';
}
else if (data.rawValue && data.rawValue.length > 0) {
if (data.quantization) {
values = data.rawValue;
dataType = 'uint' + data.quantization.numberOfBits.toString();
}
else {
shape = [];
}
}
quantization = data.quantization || null;
}
const tensorType = new coreml.TensorType(dataType, new coreml.TensorShape(shape));
const tensor = new coreml.Tensor(kind, tensorType, values, quantization);
const argument = new coreml.Argument('', null, null, tensor);
const input = this._metadata.input(type, name);
const visible = input && input.visible === false ? false : true;
initializers.push(new coreml.Parameter(name, visible, [ argument ]));
}
_initialize(type, data, initializers) {
switch (type) {
case 'convolution': {
const weightsShape = [ data.outputChannels, data.kernelChannels, data.kernelSize[0], data.kernelSize[1] ];
if (data.isDeconvolution) {
weightsShape[0] = data.kernelChannels;
weightsShape[1] = Math.floor(data.outputChannels / (data.nGroups != 0 ? data.nGroups : 1));
}
this._initializer(type, initializers, 'Weights', 'weights', weightsShape, data.weights);
if (data.hasBias) {
this._initializer(type, initializers, 'Weights', 'bias', [ data.outputChannels ], data.bias);
}
return { 'weights': true, 'bias': data.hasBias };
}
case 'innerProduct':
this._initializer(type, initializers, 'Weights', 'weights', [ data.outputChannels, data.inputChannels ], data.weights);
if (data.hasBias) {
this._initializer(type, initializers, 'Weights', 'bias', [ data.outputChannels ], data.bias);
}
return { 'weights': true, 'bias': data.hasBias };
case 'batchnorm':
this._initializer(type, initializers, 'Weights', 'gamma', [ data.channels ], data.gamma);
this._initializer(type, initializers, 'Weights', 'beta', [ data.channels ], data.beta);
if (data.mean) {
this._initializer(type, initializers, 'Weights', 'mean', [ data.channels ], data.mean);
}
if (data.variance) {
this._initializer(type, initializers, 'Weights', 'variance', [ data.channels ], data.variance);
}
return { 'gamma': true, 'beta': true, 'mean': true, 'variance': true };
case 'embedding':
this._initializer(type, initializers, 'Weights', 'weights', [ data.inputDim, data.outputChannels ], data.weights);
return { 'weights': true };
case 'loadConstant':
case 'loadConstantND':
this._initializer(type, initializers, 'Weights', 'data', data.shape, data.data);
return { 'data': true };
case 'scale':
this._initializer(type, initializers, 'Weights', 'scale', data.shapeScale, data.scale);