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Commit 39069eaf by vincent
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provide sync and async computeFaceDescriptor

parent 49cf1e26
Showing with 31 additions and 9 deletions
dist/face-recognition.min.js
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dist/faceRecognitionNet/index.d.ts
export declare function faceRecognitionNet(weights: Float32Array): (input: number[]) => number[];
export declare function faceRecognitionNet(weights: Float32Array): {
computeFaceDescriptor: (input: number[]) => Promise<Int32Array | Uint8Array | Float32Array>;
computeFaceDescriptorSync: (input: number[]) => Int32Array | Uint8Array | Float32Array;
};
dist/faceRecognitionNet/index.js
......@@ -5,9 +5,10 @@ import { extractParams } from './extractParams';
import { residual, residualDown } from './residualLayer';
export function faceRecognitionNet(weights) {
var params = extractParams(weights);
return function (input) {
function forward(input) {
return tf.tidy(function () {
var x = tf.tensor4d(normalize(input), [1, 150, 150, 3]);
var norm = normalize(input);
var x = tf.tensor4d(norm, [1, 150, 150, 3]);
var out = convDown(x, params.conv32_down);
out = tf.maxPool(out, 3, 2, 'valid');
out = residual(out, params.conv32_1);
......@@ -26,8 +27,14 @@ export function faceRecognitionNet(weights) {
out = residualDown(out, params.conv256_down_out);
var globalAvg = out.mean([1, 2]);
var fullyConnected = tf.matMul(globalAvg, params.fc);
return Array.from(fullyConnected.dataSync());
return fullyConnected;
});
}
var computeFaceDescriptor = function (input) { return forward(input).data(); };
var computeFaceDescriptorSync = function (input) { return forward(input).dataSync(); };
return {
computeFaceDescriptor: computeFaceDescriptor,
computeFaceDescriptorSync: computeFaceDescriptorSync
};
}
//# sourceMappingURL=index.js.map
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dist/faceRecognitionNet/index.js.map
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\ No newline at end of file
src/faceRecognitionNet/index.ts
......@@ -8,11 +8,13 @@ import { residual, residualDown } from './residualLayer';
export function faceRecognitionNet(weights: Float32Array) {
const params = extractParams(weights)
return function (input: number[]) {
function forward(input: number[]) {
return tf.tidy(() => {
const x = tf.tensor4d(normalize(input), [1, 150, 150, 3])
const norm = normalize(input)
const x = tf.tensor4d(norm, [1, 150, 150, 3])
let out = convDown(x, params.conv32_down)
out = tf.maxPool(out, 3, 2, 'valid')
......@@ -38,7 +40,15 @@ export function faceRecognitionNet(weights: Float32Array) {
const globalAvg = out.mean([1, 2]) as tf.Tensor2D
const fullyConnected = tf.matMul(globalAvg, params.fc)
return Array.from(fullyConnected.dataSync())
return fullyConnected
})
}
const computeFaceDescriptor = (input: number[]) => forward(input).data()
const computeFaceDescriptorSync = (input: number[]) => forward(input).dataSync()
return {
computeFaceDescriptor,
computeFaceDescriptorSync
}
}
\ No newline at end of file
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