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Commit fb7b4c85 by vincent
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clean up dist folder

parent a6723ed0
Showing with 0 additions and 221 deletions
dist/faceRecognitionNet.d.ts deleted 100644 → 0
import * as tf from '@tensorflow/tfjs-core';
export declare type ConvLayerParams = {
filters: tf.Tensor4D;
biases: tf.Tensor1D;
};
export declare type ScaleLayerParams = {
weights: tf.Tensor1D;
biases: tf.Tensor1D;
};
export declare type ConvBlockParams = {
conv: ConvLayerParams;
scale: ScaleLayerParams;
};
export declare type ResBlockParams = {
conv1: ConvBlockParams;
conv2: ConvBlockParams;
};
export declare type ParamMap = {
conv32_in: ConvBlockParams;
conv32_1: ResBlockParams;
conv32_2: ResBlockParams;
conv32_3: ResBlockParams;
conv64_in: ResBlockParams;
conv64_1: ResBlockParams;
conv64_2: ResBlockParams;
conv64_3: ResBlockParams;
conv128_in: ResBlockParams;
conv128_1: ResBlockParams;
conv128_2: ResBlockParams;
conv256_in: ResBlockParams;
conv256_1: ResBlockParams;
conv256_2: ResBlockParams;
conv256_3: ResBlockParams;
fc: tf.Tensor2D;
};
export declare function faceRecognitionNet(weights: Float32Array): (input: number[]) => number[];
dist/faceRecognitionNet.js deleted 100644 → 0
import * as tf from '@tensorflow/tfjs-core';
function scale(x, params) {
return tf.add(tf.mul(x, params.weights), params.biases);
}
function convLayer(x, params, stride, withRelu, padding) {
if (padding === void 0) { padding = 'same'; }
var _a = params.conv, filters = _a.filters, biases = _a.biases;
var out = tf.conv2d(x, filters, [stride, stride], padding);
out = tf.add(out, biases);
out = scale(out, params.scale);
return withRelu ? tf.relu(out) : out;
}
function conv(x, params) {
return convLayer(x, params, 1, true);
}
function convNoRelu(x, params) {
return convLayer(x, params, 1, false);
}
function convDown(x, params) {
return convLayer(x, params, 2, true, 'valid');
}
function res(x, params) {
var out = conv(x, params.conv1);
out = convNoRelu(out, params.conv2);
out = tf.add(out, x);
out = tf.relu(out);
return out;
}
function resDown(x, params) {
var out = convDown(x, params.conv1);
out = convNoRelu(out, params.conv2);
var pooled = tf.avgPool(x, 2, 2, 'valid');
var zeros = tf.zeros(pooled.shape);
var isPad = pooled.shape[3] !== out.shape[3];
var isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2];
if (isAdjustShape) {
var padShapeX = out.shape.slice();
padShapeX[1] = 1;
var zerosW = tf.zeros(padShapeX);
out = tf.concat([out, zerosW], 1);
var padShapeY = out.shape.slice();
padShapeY[2] = 1;
var zerosH = tf.zeros(padShapeY);
out = tf.concat([out, zerosH], 2);
}
pooled = isPad ? tf.concat([pooled, zeros], 3) : pooled;
out = tf.add(pooled, out);
out = tf.relu(out);
return out;
}
function normalize(arr) {
var avg_r = 122.782;
var avg_g = 117.001;
var avg_b = 104.298;
var avgs = [avg_r, avg_g, avg_b];
return arr.map(function (val, i) {
var avg = avgs[i % 3];
return (val - avg) / 256;
});
}
function computeFaceDescriptor(input, params) {
var x = tf.tensor4d(normalize(input), [1, 150, 150, 3]);
var out = convDown(x, params.conv32_in);
out = tf.maxPool(out, 3, 2, 'valid');
out = res(out, params.conv32_1);
out = res(out, params.conv32_2);
out = res(out, params.conv32_3);
out = resDown(out, params.conv64_in);
out = res(out, params.conv64_1);
out = res(out, params.conv64_2);
out = res(out, params.conv64_3);
out = resDown(out, params.conv128_in);
out = res(out, params.conv128_1);
out = res(out, params.conv128_2);
out = resDown(out, params.conv256_in);
out = res(out, params.conv256_1);
out = res(out, params.conv256_2);
out = resDown(out, params.conv256_3);
// global average pooling of each of the 256 filters -> retrieve 256 entry vector
var global_avg = out.mean([1, 2]);
// fully connected
// TODO: kind of slow here
return Array.from(tf.matMul(global_avg, params.fc).dataSync());
}
function isFloat(num) {
return num % 1 !== 0;
}
function extractorsFactory(extractWeights) {
function extractFilterValues(numFilterValues, numFilters, filterSize) {
var weights = extractWeights(numFilterValues);
var depth = weights.length / (numFilters * filterSize * filterSize);
if (isFloat(depth)) {
throw new Error("depth has to be an integer: " + depth + ", weights.length: " + weights.length + ", numFilters: " + numFilters + ", filterSize: " + filterSize);
}
return tf.transpose(tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]), [2, 3, 1, 0]);
}
function extractScaleLayerParams(numWeights) {
var weights = tf.tensor1d(extractWeights(numWeights));
var biases = tf.tensor1d(extractWeights(numWeights));
return {
weights: weights,
biases: biases
};
}
function extractConvBlockParams(numFilterValues, numFilters, filterSize) {
var conv_filters = extractFilterValues(numFilterValues, numFilters, filterSize);
var conv_biases = tf.tensor1d(extractWeights(numFilters));
var scale = extractScaleLayerParams(numFilters);
return {
conv: {
filters: conv_filters,
biases: conv_biases
},
scale: scale
};
}
function extractResBlockParams(numFilterValues, numFilters, filterSize, isInBlock) {
if (isInBlock === void 0) { isInBlock = false; }
var conv1 = extractConvBlockParams((isInBlock ? 0.5 : 1) * numFilterValues, numFilters, filterSize);
var conv2 = extractConvBlockParams(numFilterValues, numFilters, filterSize);
return {
conv1: conv1,
conv2: conv2
};
}
return {
extractConvBlockParams: extractConvBlockParams,
extractResBlockParams: extractResBlockParams
};
}
function extractParams(weights) {
var extractWeights = function (numWeights) {
var ret = weights.slice(0, numWeights);
weights = weights.slice(numWeights);
return ret;
};
var _a = extractorsFactory(extractWeights), extractConvBlockParams = _a.extractConvBlockParams, extractResBlockParams = _a.extractResBlockParams;
var conv32_in = extractConvBlockParams(4704, 32, 7);
var conv32_1 = extractResBlockParams(9216, 32, 3);
var conv32_2 = extractResBlockParams(9216, 32, 3);
var conv32_3 = extractResBlockParams(9216, 32, 3);
var conv64_in = extractResBlockParams(36864, 64, 3, true);
var conv64_1 = extractResBlockParams(36864, 64, 3);
var conv64_2 = extractResBlockParams(36864, 64, 3);
var conv64_3 = extractResBlockParams(36864, 64, 3);
var conv128_in = extractResBlockParams(147456, 128, 3, true);
var conv128_1 = extractResBlockParams(147456, 128, 3);
var conv128_2 = extractResBlockParams(147456, 128, 3);
var conv256_in = extractResBlockParams(589824, 256, 3, true);
var conv256_1 = extractResBlockParams(589824, 256, 3);
var conv256_2 = extractResBlockParams(589824, 256, 3);
var conv256_3 = extractResBlockParams(589824, 256, 3);
var fc = tf.transpose(tf.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0]);
if (weights.length !== 0) {
throw new Error("weights remaing after extract: " + weights.length);
}
return {
conv32_in: conv32_in,
conv32_1: conv32_1,
conv32_2: conv32_2,
conv32_3: conv32_3,
conv64_in: conv64_in,
conv64_1: conv64_1,
conv64_2: conv64_2,
conv64_3: conv64_3,
conv128_in: conv128_in,
conv128_1: conv128_1,
conv128_2: conv128_2,
conv256_in: conv256_in,
conv256_1: conv256_1,
conv256_2: conv256_2,
conv256_3: conv256_3,
fc: fc
};
}
export function faceRecognitionNet(weights) {
var params = extractParams(weights);
return function (input) {
return computeFaceDescriptor(input, params);
};
}
//# sourceMappingURL=faceRecognitionNet.js.map
\ No newline at end of file
dist/faceRecognitionNet.js.map deleted 100644 → 0
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