wrap computeFaceDescriptors in tf.tidy

dist/faceRecognitionNet/index.js

@@ -6,26 +6,28 @@ import { residual, residualDown } from './residualLayer';
 export function faceRecognitionNet(weights) {
     var params = extractParams(weights);
     return function (input) {
-        var x = tf.tensor4d(normalize(input), [1, 150, 150, 3]);
-        var out = convDown(x, params.conv32_down);
-        out = tf.maxPool(out, 3, 2, 'valid');
-        out = residual(out, params.conv32_1);
-        out = residual(out, params.conv32_2);
-        out = residual(out, params.conv32_3);
-        out = residualDown(out, params.conv64_down);
-        out = residual(out, params.conv64_1);
-        out = residual(out, params.conv64_2);
-        out = residual(out, params.conv64_3);
-        out = residualDown(out, params.conv128_down);
-        out = residual(out, params.conv128_1);
-        out = residual(out, params.conv128_2);
-        out = residualDown(out, params.conv256_down);
-        out = residual(out, params.conv256_1);
-        out = residual(out, params.conv256_2);
-        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 tf.tidy(function () {
+            var x = tf.tensor4d(normalize(input), [1, 150, 150, 3]);
+            var out = convDown(x, params.conv32_down);
+            out = tf.maxPool(out, 3, 2, 'valid');
+            out = residual(out, params.conv32_1);
+            out = residual(out, params.conv32_2);
+            out = residual(out, params.conv32_3);
+            out = residualDown(out, params.conv64_down);
+            out = residual(out, params.conv64_1);
+            out = residual(out, params.conv64_2);
+            out = residual(out, params.conv64_3);
+            out = residualDown(out, params.conv128_down);
+            out = residual(out, params.conv128_1);
+            out = residual(out, params.conv128_2);
+            out = residualDown(out, params.conv256_down);
+            out = residual(out, params.conv256_1);
+            out = residual(out, params.conv256_2);
+            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());
+        });
     };
 }
 //# sourceMappingURL=index.js.map
\ No newline at end of file

dist/faceRecognitionNet/index.js.map

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\ No newline at end of file

src/faceRecognitionNet/index.ts

@@ -10,32 +10,35 @@ export function faceRecognitionNet(weights: Float32Array) {
 
   return function (input: number[]) {
 
-    const x = tf.tensor4d(normalize(input), [1, 150, 150, 3])
+    return tf.tidy(() => {
 
-    let out = convDown(x, params.conv32_down)
-    out = tf.maxPool(out, 3, 2, 'valid')
+      const x = tf.tensor4d(normalize(input), [1, 150, 150, 3])
 
-    out = residual(out, params.conv32_1)
-    out = residual(out, params.conv32_2)
-    out = residual(out, params.conv32_3)
+      let out = convDown(x, params.conv32_down)
+      out = tf.maxPool(out, 3, 2, 'valid')
 
-    out = residualDown(out, params.conv64_down)
-    out = residual(out, params.conv64_1)
-    out = residual(out, params.conv64_2)
-    out = residual(out, params.conv64_3)
+      out = residual(out, params.conv32_1)
+      out = residual(out, params.conv32_2)
+      out = residual(out, params.conv32_3)
 
-    out = residualDown(out, params.conv128_down)
-    out = residual(out, params.conv128_1)
-    out = residual(out, params.conv128_2)
+      out = residualDown(out, params.conv64_down)
+      out = residual(out, params.conv64_1)
+      out = residual(out, params.conv64_2)
+      out = residual(out, params.conv64_3)
 
-    out = residualDown(out, params.conv256_down)
-    out = residual(out, params.conv256_1)
-    out = residual(out, params.conv256_2)
-    out = residualDown(out, params.conv256_down_out)
+      out = residualDown(out, params.conv128_down)
+      out = residual(out, params.conv128_1)
+      out = residual(out, params.conv128_2)
 
-    const globalAvg = out.mean([1, 2]) as tf.Tensor2D
-    const fullyConnected = tf.matMul(globalAvg, params.fc)
+      out = residualDown(out, params.conv256_down)
+      out = residual(out, params.conv256_1)
+      out = residual(out, params.conv256_2)
+      out = residualDown(out, params.conv256_down_out)
 
-    return Array.from(fullyConnected.dataSync())
+      const globalAvg = out.mean([1, 2]) as tf.Tensor2D
+      const fullyConnected = tf.matMul(globalAvg, params.fc)
+
+      return Array.from(fullyConnected.dataSync())
+    })
   }
 }
\ No newline at end of file