@tensorflow-models/mobilenet
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Pretrained MobileNet in TensorFlow.js
291 lines • 13.6 kB
JavaScript
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/**
* @license
* Copyright 2019 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.
* =============================================================================
*/
var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
function adopt(value) { return value instanceof P ? value : new P(function (resolve) { resolve(value); }); }
return new (P || (P = Promise))(function (resolve, reject) {
function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
function step(result) { result.done ? resolve(result.value) : adopt(result.value).then(fulfilled, rejected); }
step((generator = generator.apply(thisArg, _arguments || [])).next());
});
};
var __generator = (this && this.__generator) || function (thisArg, body) {
var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
function verb(n) { return function (v) { return step([n, v]); }; }
function step(op) {
if (f) throw new TypeError("Generator is already executing.");
while (_) try {
if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
if (y = 0, t) op = [op[0] & 2, t.value];
switch (op[0]) {
case 0: case 1: t = op; break;
case 4: _.label++; return { value: op[1], done: false };
case 5: _.label++; y = op[1]; op = [0]; continue;
case 7: op = _.ops.pop(); _.trys.pop(); continue;
default:
if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
if (t[2]) _.ops.pop();
_.trys.pop(); continue;
}
op = body.call(thisArg, _);
} catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
}
};
Object.defineProperty(exports, "__esModule", { value: true });
exports.load = exports.version = void 0;
var tfconv = require("@tensorflow/tfjs-converter");
var tf = require("@tensorflow/tfjs-core");
var imagenet_classes_1 = require("./imagenet_classes");
var version_1 = require("./version");
Object.defineProperty(exports, "version", { enumerable: true, get: function () { return version_1.version; } });
var IMAGE_SIZE = 224;
var EMBEDDING_NODES = {
'1.00': 'module_apply_default/MobilenetV1/Logits/global_pool',
'2.00': 'module_apply_default/MobilenetV2/Logits/AvgPool'
};
var MODEL_INFO = {
'1.00': {
'0.25': {
url: 'https://tfhub.dev/google/imagenet/mobilenet_v1_025_224/classification/1',
inputRange: [0, 1]
},
'0.50': {
url: 'https://tfhub.dev/google/imagenet/mobilenet_v1_050_224/classification/1',
inputRange: [0, 1]
},
'0.75': {
url: 'https://tfhub.dev/google/imagenet/mobilenet_v1_075_224/classification/1',
inputRange: [0, 1]
},
'1.00': {
url: 'https://tfhub.dev/google/imagenet/mobilenet_v1_100_224/classification/1',
inputRange: [0, 1]
}
},
'2.00': {
'0.50': {
url: 'https://tfhub.dev/google/imagenet/mobilenet_v2_050_224/classification/2',
inputRange: [0, 1]
},
'0.75': {
url: 'https://tfhub.dev/google/imagenet/mobilenet_v2_075_224/classification/2',
inputRange: [0, 1]
},
'1.00': {
url: 'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/2',
inputRange: [0, 1]
}
}
};
// See ModelConfig documentation for expectations of provided fields.
function load(modelConfig) {
if (modelConfig === void 0) { modelConfig = {
version: 1,
alpha: 1.0
}; }
return __awaiter(this, void 0, void 0, function () {
var versionStr, alphaStr, inputMin, inputMax, mobilenet;
var _a, _b;
return __generator(this, function (_c) {
switch (_c.label) {
case 0:
if (tf == null) {
throw new Error("Cannot find TensorFlow.js. If you are using a <script> tag, please " +
"also include @tensorflow/tfjs on the page before using this model.");
}
versionStr = modelConfig.version.toFixed(2);
alphaStr = modelConfig.alpha ? modelConfig.alpha.toFixed(2) : '';
inputMin = -1;
inputMax = 1;
// User provides versionStr / alphaStr.
if (modelConfig.modelUrl == null) {
if (!(versionStr in MODEL_INFO)) {
throw new Error("Invalid version of MobileNet. Valid versions are: " +
("" + Object.keys(MODEL_INFO)));
}
if (!(alphaStr in MODEL_INFO[versionStr])) {
throw new Error("MobileNet constructed with invalid alpha " + modelConfig.alpha + ". Valid " +
"multipliers for this version are: " +
(Object.keys(MODEL_INFO[versionStr]) + "."));
}
_a = MODEL_INFO[versionStr][alphaStr].inputRange, inputMin = _a[0], inputMax = _a[1];
}
// User provides modelUrl & optional<inputRange>.
if (modelConfig.inputRange != null) {
_b = modelConfig.inputRange, inputMin = _b[0], inputMax = _b[1];
}
mobilenet = new MobileNetImpl(versionStr, alphaStr, modelConfig.modelUrl, inputMin, inputMax);
return [4 /*yield*/, mobilenet.load()];
case 1:
_c.sent();
return [2 /*return*/, mobilenet];
}
});
});
}
exports.load = load;
var MobileNetImpl = /** @class */ (function () {
function MobileNetImpl(version, alpha, modelUrl, inputMin, inputMax) {
if (inputMin === void 0) { inputMin = -1; }
if (inputMax === void 0) { inputMax = 1; }
this.version = version;
this.alpha = alpha;
this.modelUrl = modelUrl;
this.inputMin = inputMin;
this.inputMax = inputMax;
this.normalizationConstant = (inputMax - inputMin) / 255.0;
}
MobileNetImpl.prototype.load = function () {
return __awaiter(this, void 0, void 0, function () {
var _a, url, _b, result;
var _this = this;
return __generator(this, function (_c) {
switch (_c.label) {
case 0:
if (!this.modelUrl) return [3 /*break*/, 2];
_a = this;
return [4 /*yield*/, tfconv.loadGraphModel(this.modelUrl)];
case 1:
_a.model = _c.sent();
return [3 /*break*/, 4];
case 2:
url = MODEL_INFO[this.version][this.alpha].url;
_b = this;
return [4 /*yield*/, tfconv.loadGraphModel(url, { fromTFHub: true })];
case 3:
_b.model = _c.sent();
_c.label = 4;
case 4:
result = tf.tidy(function () { return _this.model.predict(tf.zeros([1, IMAGE_SIZE, IMAGE_SIZE, 3])); });
return [4 /*yield*/, result.data()];
case 5:
_c.sent();
result.dispose();
return [2 /*return*/];
}
});
});
};
/**
* Computes the logits (or the embedding) for the provided image.
*
* @param img The image to classify. Can be a tensor or a DOM element image,
* video, or canvas.
* @param embedding If true, it returns the embedding. Otherwise it returns
* the 1000-dim logits.
*/
MobileNetImpl.prototype.infer = function (img, embedding) {
var _this = this;
if (embedding === void 0) { embedding = false; }
return tf.tidy(function () {
if (!(img instanceof tf.Tensor)) {
img = tf.browser.fromPixels(img);
}
// Normalize the image from [0, 255] to [inputMin, inputMax].
var normalized = tf.add(tf.mul(tf.cast(img, 'float32'), _this.normalizationConstant), _this.inputMin);
// Resize the image to
var resized = normalized;
if (img.shape[0] !== IMAGE_SIZE || img.shape[1] !== IMAGE_SIZE) {
var alignCorners = true;
resized = tf.image.resizeBilinear(normalized, [IMAGE_SIZE, IMAGE_SIZE], alignCorners);
}
// Reshape so we can pass it to predict.
var batched = tf.reshape(resized, [-1, IMAGE_SIZE, IMAGE_SIZE, 3]);
var result;
if (embedding) {
var embeddingName = EMBEDDING_NODES[_this.version];
var internal = _this.model.execute(batched, embeddingName);
result = tf.squeeze(internal, [1, 2]);
}
else {
var logits1001 = _this.model.predict(batched);
// Remove the very first logit (background noise).
result = tf.slice(logits1001, [0, 1], [-1, 1000]);
}
return result;
});
};
/**
* Classifies an image from the 1000 ImageNet classes returning a map of
* the most likely class names to their probability.
*
* @param img The image to classify. Can be a tensor or a DOM element image,
* video, or canvas.
* @param topk How many top values to use. Defaults to 3.
*/
MobileNetImpl.prototype.classify = function (img, topk) {
if (topk === void 0) { topk = 3; }
return __awaiter(this, void 0, void 0, function () {
var logits, classes;
return __generator(this, function (_a) {
switch (_a.label) {
case 0:
logits = this.infer(img);
return [4 /*yield*/, getTopKClasses(logits, topk)];
case 1:
classes = _a.sent();
logits.dispose();
return [2 /*return*/, classes];
}
});
});
};
return MobileNetImpl;
}());
function getTopKClasses(logits, topK) {
return __awaiter(this, void 0, void 0, function () {
var softmax, values, valuesAndIndices, i, topkValues, topkIndices, i, topClassesAndProbs, i;
return __generator(this, function (_a) {
switch (_a.label) {
case 0:
softmax = tf.softmax(logits);
return [4 /*yield*/, softmax.data()];
case 1:
values = _a.sent();
softmax.dispose();
valuesAndIndices = [];
for (i = 0; i < values.length; i++) {
valuesAndIndices.push({ value: values[i], index: i });
}
valuesAndIndices.sort(function (a, b) {
return b.value - a.value;
});
topkValues = new Float32Array(topK);
topkIndices = new Int32Array(topK);
for (i = 0; i < topK; i++) {
topkValues[i] = valuesAndIndices[i].value;
topkIndices[i] = valuesAndIndices[i].index;
}
topClassesAndProbs = [];
for (i = 0; i < topkIndices.length; i++) {
topClassesAndProbs.push({
className: imagenet_classes_1.IMAGENET_CLASSES[topkIndices[i]],
probability: topkValues[i]
});
}
return [2 /*return*/, topClassesAndProbs];
}
});
});
}
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