nsfwjs-patched
Version:
Detect NSFW content client-side with fixes of buffer and latest packages
463 lines • 21.8 kB
JavaScript
"use strict";
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ar[i] = from[i];
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}
return to.concat(ar || Array.prototype.slice.call(from));
};
Object.defineProperty(exports, "__esModule", { value: true });
exports.NSFWJS = void 0;
exports.load = load;
var tf = __importStar(require("@tensorflow/tfjs"));
var buffer_1 = require("buffer/");
var nsfw_classes_1 = require("./nsfw_classes");
var availableModels = {
MobileNetV2: { numOfWeightBundles: 1 },
MobileNetV2Mid: {
numOfWeightBundles: 2,
options: { type: "graph" },
},
InceptionV3: {
numOfWeightBundles: 6,
options: { size: 299 },
},
};
var DEFAULT_MODEL_NAME = "MobileNetV2";
var IMAGE_SIZE = 224;
var getGlobal = function () {
if (typeof globalThis !== "undefined")
return globalThis;
if (typeof global !== "undefined")
return global;
if (typeof window !== "undefined")
return window;
if (typeof self !== "undefined")
return self;
throw new Error("Unable to locate global object");
};
function isModelName(name) {
return !!name && name in availableModels;
}
var getModelJson = function (modelName) { return __awaiter(void 0, void 0, void 0, function () {
var globalModel, modelJson;
return __generator(this, function (_a) {
switch (_a.label) {
case 0:
globalModel = getGlobal().model;
if (globalModel) {
return [2, globalModel];
}
if (!(modelName === "MobileNetV2")) return [3, 2];
return [4, Promise.resolve().then(function () { return __importStar(require("./model_imports/mobilenet_v2")); })];
case 1:
(modelJson = (_a.sent()).modelJson);
return [3, 6];
case 2:
if (!(modelName === "MobileNetV2Mid")) return [3, 4];
return [4, Promise.resolve().then(function () { return __importStar(require("./model_imports/mobilenet_v2_mid")); })];
case 3:
(modelJson = (_a.sent()).modelJson);
return [3, 6];
case 4:
if (!(modelName === "InceptionV3")) return [3, 6];
return [4, Promise.resolve().then(function () { return __importStar(require("./model_imports/inception_v3")); })];
case 5:
(modelJson = (_a.sent()).modelJson);
_a.label = 6;
case 6: return [4, modelJson()];
case 7: return [2, (_a.sent()).default];
}
});
}); };
var getWeightData = function (modelName) { return __awaiter(void 0, void 0, void 0, function () {
var numOfWeightBundles, bundles, i, bundleName, identifier, globalWeight, weightBundles, weight;
var _a, _b;
return __generator(this, function (_c) {
switch (_c.label) {
case 0:
numOfWeightBundles = availableModels[modelName].numOfWeightBundles;
bundles = [];
i = 0;
_c.label = 1;
case 1:
if (!(i < numOfWeightBundles)) return [3, 11];
bundleName = "group1-shard".concat(i + 1, "of").concat(numOfWeightBundles);
identifier = bundleName.replace(/-/g, "_");
globalWeight = getGlobal()[identifier];
if (!globalWeight) return [3, 2];
bundles.push((_a = {}, _a[bundleName] = globalWeight, _a));
return [3, 10];
case 2:
weightBundles = void 0;
if (!(modelName === "MobileNetV2")) return [3, 4];
return [4, Promise.resolve().then(function () { return __importStar(require("./model_imports/mobilenet_v2")); })];
case 3:
(weightBundles = (_c.sent()).weightBundles);
return [3, 8];
case 4:
if (!(modelName === "MobileNetV2Mid")) return [3, 6];
return [4, Promise.resolve().then(function () { return __importStar(require("./model_imports/mobilenet_v2_mid")); })];
case 5:
(weightBundles = (_c.sent()).weightBundles);
return [3, 8];
case 6:
if (!(modelName === "InceptionV3")) return [3, 8];
return [4, Promise.resolve().then(function () { return __importStar(require("./model_imports/inception_v3")); })];
case 7:
(weightBundles = (_c.sent()).weightBundles);
_c.label = 8;
case 8: return [4, weightBundles[i]()];
case 9:
weight = (_c.sent()).default;
bundles.push((_b = {}, _b[bundleName] = weight, _b));
_c.label = 10;
case 10:
i++;
return [3, 1];
case 11: return [2, Object.assign.apply(Object, __spreadArray([{}], bundles, false))];
}
});
}); };
function loadWeights(modelName) {
return __awaiter(this, void 0, void 0, function () {
var weightDataBundles, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
_b.trys.push([0, 2, , 3]);
return [4, getWeightData(modelName)];
case 1:
weightDataBundles = _b.sent();
return [2, weightDataBundles];
case 2:
_a = _b.sent();
throw new Error("Could not load the weight data. Make sure you are importing the correct shard files from the models directory. Ref: https://github.com/infinitered/nsfwjs?tab=readme-ov-file#browserify");
case 3: return [2];
}
});
});
}
function loadModel(modelName) {
return __awaiter(this, void 0, void 0, function () {
var modelJson, weightData, handler, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
if (!isModelName(modelName))
return [2, modelName];
_b.label = 1;
case 1:
_b.trys.push([1, 4, , 5]);
return [4, getModelJson(modelName)];
case 2:
modelJson = _b.sent();
return [4, loadWeights(modelName)];
case 3:
weightData = _b.sent();
handler = new JSONHandler(modelJson, weightData);
return [2, handler];
case 4:
_a = _b.sent();
throw new Error("Could not load the model. Make sure you are importing the model.min.js bundle. Ref: https://github.com/infinitered/nsfwjs?tab=readme-ov-file#browserify");
case 5: return [2];
}
});
});
}
function load(modelOrUrl_1) {
return __awaiter(this, arguments, void 0, function (modelOrUrl, options) {
var modelUrlOrHandler, nsfwnet;
var _a;
if (options === void 0) { options = { size: IMAGE_SIZE }; }
return __generator(this, function (_b) {
switch (_b.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.");
}
if (modelOrUrl === undefined) {
modelOrUrl = DEFAULT_MODEL_NAME;
console.info("%cBy not specifying 'modelOrUrl' parameter, you're using the default model: '".concat(modelOrUrl, "'. See NSFWJS docs for instructions on hosting your own model (https://github.com/infinitered/nsfwjs?tab=readme-ov-file#host-your-own-model)."), "color: lightblue");
}
else if (isModelName(modelOrUrl)) {
console.info("%cYou're using the model: '".concat(modelOrUrl, "'. See NSFWJS docs for instructions on hosting your own model (https://github.com/infinitered/nsfwjs?tab=readme-ov-file#host-your-own-model)."), "color: lightblue");
options = (_a = availableModels[modelOrUrl].options) !== null && _a !== void 0 ? _a : options;
}
options.size = (options === null || options === void 0 ? void 0 : options.size) || IMAGE_SIZE;
return [4, loadModel(modelOrUrl)];
case 1:
modelUrlOrHandler = _b.sent();
nsfwnet = new NSFWJS(modelUrlOrHandler, options);
return [4, nsfwnet.load()];
case 2:
_b.sent();
return [2, nsfwnet];
}
});
});
}
var JSONHandler = (function () {
function JSONHandler(modelJson, weightDataBase64) {
this.modelJson = modelJson;
this.weightDataBase64 = weightDataBase64;
}
JSONHandler.prototype.arrayBufferFromBase64 = function (base64) {
var binaryString = buffer_1.Buffer.from(base64, "base64").toString("binary");
var len = binaryString.length;
var bytes = new Uint8Array(len);
for (var i = 0; i < len; i++) {
bytes[i] = binaryString.charCodeAt(i);
}
return bytes.buffer;
};
JSONHandler.prototype.load = function () {
return __awaiter(this, void 0, void 0, function () {
var modelArtifacts, weightSpecs, weightData, _i, _a, group, _b, _c, path, base64, buffer, weightDataConcat, offset, i;
return __generator(this, function (_d) {
modelArtifacts = {
modelTopology: this.modelJson.modelTopology,
format: this.modelJson.format,
generatedBy: this.modelJson.generatedBy,
convertedBy: this.modelJson.convertedBy,
};
if (this.modelJson.weightsManifest != null) {
weightSpecs = [];
weightData = [];
for (_i = 0, _a = this.modelJson.weightsManifest; _i < _a.length; _i++) {
group = _a[_i];
for (_b = 0, _c = group.paths; _b < _c.length; _b++) {
path = _c[_b];
base64 = this.weightDataBase64[path];
if (!base64) {
throw new Error("Could not find the weight data. Make sure you are importing the correct weight bundle for the model: ".concat(path, ".min.js."));
}
buffer = this.arrayBufferFromBase64(base64);
weightData.push(new Uint8Array(buffer));
}
weightSpecs.push.apply(weightSpecs, group.weights);
}
modelArtifacts.weightSpecs = weightSpecs;
weightDataConcat = new Uint8Array(weightData.reduce(function (a, b) { return a + b.length; }, 0));
offset = 0;
for (i = 0; i < weightData.length; i++) {
weightDataConcat.set(weightData[i], offset);
offset += weightData[i].byteLength;
}
modelArtifacts.weightData = weightDataConcat.buffer;
}
if (this.modelJson.trainingConfig != null) {
modelArtifacts.trainingConfig = this.modelJson.trainingConfig;
}
if (this.modelJson.userDefinedMetadata != null) {
modelArtifacts.userDefinedMetadata = this.modelJson.userDefinedMetadata;
}
return [2, modelArtifacts];
});
});
};
return JSONHandler;
}());
var NSFWJS = (function () {
function NSFWJS(modelUrlOrIOHandler, options) {
this.intermediateModels = {};
this.options = options;
this.normalizationOffset = tf.scalar(255);
this.urlOrIOHandler = modelUrlOrIOHandler;
if (typeof modelUrlOrIOHandler === "string" &&
!modelUrlOrIOHandler.startsWith("indexeddb://") &&
!modelUrlOrIOHandler.startsWith("localstorage://") &&
!modelUrlOrIOHandler.endsWith("model.json")) {
this.urlOrIOHandler = "".concat(modelUrlOrIOHandler, "model.json");
}
else {
this.urlOrIOHandler = modelUrlOrIOHandler;
}
}
NSFWJS.prototype.load = function () {
return __awaiter(this, void 0, void 0, function () {
var _a, size, type, _b, _c, result;
var _this = this;
return __generator(this, function (_d) {
switch (_d.label) {
case 0:
_a = this.options, size = _a.size, type = _a.type;
if (!(type === "graph")) return [3, 2];
_b = this;
return [4, tf.loadGraphModel(this.urlOrIOHandler)];
case 1:
_b.model = _d.sent();
return [3, 4];
case 2:
_c = this;
return [4, tf.loadLayersModel(this.urlOrIOHandler)];
case 3:
_c.model = _d.sent();
this.endpoints = this.model.layers.map(function (l) { return l.name; });
_d.label = 4;
case 4:
result = tf.tidy(function () {
return _this.model.predict(tf.zeros([1, size, size, 3]));
});
return [4, result.data()];
case 5:
_d.sent();
result.dispose();
return [2];
}
});
});
};
NSFWJS.prototype.infer = function (img, endpoint) {
var _this = this;
if (endpoint != null && this.endpoints.indexOf(endpoint) === -1) {
throw new Error("Unknown endpoint ".concat(endpoint, ". Available endpoints: ").concat(this.endpoints, "."));
}
return tf.tidy(function () {
if (!(img instanceof tf.Tensor)) {
img = tf.browser.fromPixels(img);
}
var normalized = img
.toFloat()
.div(_this.normalizationOffset);
var resized = normalized;
var size = _this.options.size;
if (img.shape[0] !== size || img.shape[1] !== size) {
var alignCorners = true;
resized = tf.image.resizeBilinear(normalized, [size, size], alignCorners);
}
var batched = resized.reshape([1, size, size, 3]);
var model;
if (endpoint == null) {
model = _this.model;
}
else {
if (_this.model.hasOwnProperty("layers") &&
_this.intermediateModels[endpoint] == null) {
var layer = _this.model.layers.find(function (l) { return l.name === endpoint; });
_this.intermediateModels[endpoint] = tf.model({
inputs: _this.model.inputs,
outputs: layer.output,
});
}
model = _this.intermediateModels[endpoint];
}
return model.predict(batched);
});
};
NSFWJS.prototype.classify = function (img_1) {
return __awaiter(this, arguments, void 0, function (img, topk) {
var logits, classes;
if (topk === void 0) { topk = 5; }
return __generator(this, function (_a) {
switch (_a.label) {
case 0:
logits = this.infer(img);
return [4, getTopKClasses(logits, topk)];
case 1:
classes = _a.sent();
logits.dispose();
return [2, classes];
}
});
});
};
return NSFWJS;
}());
exports.NSFWJS = NSFWJS;
function getTopKClasses(logits, topK) {
return __awaiter(this, void 0, void 0, function () {
var values, valuesAndIndices, i, topkValues, topkIndices, i, topClassesAndProbs, i;
return __generator(this, function (_a) {
switch (_a.label) {
case 0: return [4, logits.data()];
case 1:
values = _a.sent();
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: nsfw_classes_1.NSFW_CLASSES[topkIndices[i]],
probability: topkValues[i],
});
}
return [2, topClassesAndProbs];
}
});
});
}
//# sourceMappingURL=index.js.map