@vladmandic/face-api
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FaceAPI: AI-powered Face Detection & Rotation Tracking, Face Description & Recognition, Age & Gender & Emotion Prediction for Browser and NodeJS using TensorFlow/JS
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text/typescript
import * as tf from '../../dist/tfjs.esm';
import { ExtractWeightsFunction, ParamMapping, ConvParams, extractWeightsFactory } from '../common/index';
import { MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';
function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {
function extractDepthwiseConvParams(numChannels: number, mappedPrefix: string): MobileNetV1.DepthwiseConvParams {
const filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]);
const batch_norm_scale = tf.tensor1d(extractWeights(numChannels));
const batch_norm_offset = tf.tensor1d(extractWeights(numChannels));
const batch_norm_mean = tf.tensor1d(extractWeights(numChannels));
const batch_norm_variance = tf.tensor1d(extractWeights(numChannels));
paramMappings.push(
{ paramPath: `${mappedPrefix}/filters` },
{ paramPath: `${mappedPrefix}/batch_norm_scale` },
{ paramPath: `${mappedPrefix}/batch_norm_offset` },
{ paramPath: `${mappedPrefix}/batch_norm_mean` },
{ paramPath: `${mappedPrefix}/batch_norm_variance` },
);
return {
filters,
batch_norm_scale,
batch_norm_offset,
batch_norm_mean,
batch_norm_variance,
};
}
function extractConvParams(
channelsIn: number,
channelsOut: number,
filterSize: number,
mappedPrefix: string,
isPointwiseConv?: boolean,
): ConvParams {
const filters = tf.tensor4d(
extractWeights(channelsIn * channelsOut * filterSize * filterSize),
[filterSize, filterSize, channelsIn, channelsOut],
);
const bias = tf.tensor1d(extractWeights(channelsOut));
paramMappings.push(
{ paramPath: `${mappedPrefix}/filters` },
{ paramPath: `${mappedPrefix}/${isPointwiseConv ? 'batch_norm_offset' : 'bias'}` },
);
return { filters, bias };
}
function extractPointwiseConvParams(
channelsIn: number,
channelsOut: number,
filterSize: number,
mappedPrefix: string,
): PointwiseConvParams {
const {
filters,
bias,
} = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true);
return {
filters,
batch_norm_offset: bias,
};
}
function extractConvPairParams(
channelsIn: number,
channelsOut: number,
mappedPrefix: string,
): MobileNetV1.ConvPairParams {
const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`);
const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`);
return { depthwise_conv, pointwise_conv };
}
function extractMobilenetV1Params(): MobileNetV1.Params {
const conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0');
const conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1');
const conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2');
const conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3');
const conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4');
const conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5');
const conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6');
const conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7');
const conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8');
const conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9');
const conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10');
const conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11');
const conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12');
const conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13');
return {
conv_0,
conv_1,
conv_2,
conv_3,
conv_4,
conv_5,
conv_6,
conv_7,
conv_8,
conv_9,
conv_10,
conv_11,
conv_12,
conv_13,
};
}
function extractPredictionLayerParams(): PredictionLayerParams {
const conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0');
const conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1');
const conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2');
const conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3');
const conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4');
const conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5');
const conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6');
const conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7');
const box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor');
const class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor');
const box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor');
const class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor');
const box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor');
const class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor');
const box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor');
const class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor');
const box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor');
const class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor');
const box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor');
const class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor');
const box_predictor_0 = {
box_encoding_predictor: box_encoding_0_predictor,
class_predictor: class_predictor_0,
};
const box_predictor_1 = {
box_encoding_predictor: box_encoding_1_predictor,
class_predictor: class_predictor_1,
};
const box_predictor_2 = {
box_encoding_predictor: box_encoding_2_predictor,
class_predictor: class_predictor_2,
};
const box_predictor_3 = {
box_encoding_predictor: box_encoding_3_predictor,
class_predictor: class_predictor_3,
};
const box_predictor_4 = {
box_encoding_predictor: box_encoding_4_predictor,
class_predictor: class_predictor_4,
};
const box_predictor_5 = {
box_encoding_predictor: box_encoding_5_predictor,
class_predictor: class_predictor_5,
};
return {
conv_0,
conv_1,
conv_2,
conv_3,
conv_4,
conv_5,
conv_6,
conv_7,
box_predictor_0,
box_predictor_1,
box_predictor_2,
box_predictor_3,
box_predictor_4,
box_predictor_5,
};
}
return {
extractMobilenetV1Params,
extractPredictionLayerParams,
};
}
export function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {
const paramMappings: ParamMapping[] = [];
const {
extractWeights,
getRemainingWeights,
} = extractWeightsFactory(weights);
const {
extractMobilenetV1Params,
extractPredictionLayerParams,
} = extractorsFactory(extractWeights, paramMappings);
const mobilenetv1 = extractMobilenetV1Params();
const prediction_layer = extractPredictionLayerParams();
const extra_dim = tf.tensor3d(
extractWeights(5118 * 4),
[1, 5118, 4],
);
const output_layer = {
extra_dim,
};
paramMappings.push({ paramPath: 'output_layer/extra_dim' });
if (getRemainingWeights().length !== 0) {
throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);
}
return {
params: {
mobilenetv1,
prediction_layer,
output_layer,
},
paramMappings,
};
}