implemented faster normalize to reduce prediction time

dist/faceRecognitionNet/index.js

@@ -7,8 +7,7 @@ export function faceRecognitionNet(weights) {
     var params = extractParams(weights);
     function forward(input) {
         return tf.tidy(function () {
-            var norm = normalize(input);
-            var x = tf.tensor4d(norm, [1, 150, 150, 3]);
+            var x = normalize(input);
             var out = convDown(x, params.conv32_down);
             out = tf.maxPool(out, 3, 2, 'valid');
             out = residual(out, params.conv32_1);

dist/faceRecognitionNet/index.js.map

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\ No newline at end of file

dist/normalize.d.ts

-export declare function normalize(arr: number[]): number[];
+import * as tf from '@tensorflow/tfjs-core';
+export declare function normalize(input: number[]): tf.Tensor4D;

dist/normalize.js

-export 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;
+import * as tf from '@tensorflow/tfjs-core';
+export function normalize(input) {
+    return tf.tidy(function () {
+        var avg_r = tf.fill([1, 150, 150, 1], 122.782);
+        var avg_g = tf.fill([1, 150, 150, 1], 117.001);
+        var avg_b = tf.fill([1, 150, 150, 1], 104.298);
+        var avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3);
+        var x = tf.tensor4d(input, [1, 150, 150, 3]);
+        return tf.div(tf.sub(x, avg_rgb), tf.fill(x.shape, 256));
     });
 }
 //# sourceMappingURL=normalize.js.map
\ No newline at end of file

dist/normalize.js.map

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\ No newline at end of file

src/faceRecognitionNet/index.ts

@@ -12,9 +12,7 @@ export function faceRecognitionNet(weights: Float32Array) {
 
     return tf.tidy(() => {
 
-      const norm = normalize(input)
-
-      const x = tf.tensor4d(norm, [1, 150, 150, 3])
+      const x = normalize(input)
 
       let out = convDown(x, params.conv32_down)
       out = tf.maxPool(out, 3, 2, 'valid')

src/normalize.ts

-export function normalize(arr: number[]) {
-  const avg_r = 122.782;
-  const avg_g = 117.001;
-  const avg_b = 104.298;
-  const avgs = [avg_r, avg_g, avg_b]
-  return arr.map((val, i) => {
-    const avg = avgs[i % 3]
-    return (val - avg) / 256
+import * as tf from '@tensorflow/tfjs-core';
+
+export function normalize(input: number[]): tf.Tensor4D {
+  return tf.tidy(() => {
+    const avg_r = tf.fill([1, 150, 150, 1], 122.782);
+    const avg_g = tf.fill([1, 150, 150, 1], 117.001);
+    const avg_b = tf.fill([1, 150, 150, 1], 104.298);
+    const avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3)
+
+    const x = tf.tensor4d(input, [1, 150, 150, 3])
+    return tf.div(tf.sub(x, avg_rgb), tf.fill(x.shape, 256))
   })
 }
\ No newline at end of file