deeplabv3.benchmark.html.patch

diff --git a/integration_tests/benchmarks/benchmark.html b/integration_tests/benchmarks/benchmark.html
index 31ecd4e..4a1e977 100644
--- a/integration_tests/benchmarks/benchmark.html
+++ b/integration_tests/benchmarks/benchmark.html
@@ -112,32 +112,42 @@
       <tbody></tbody>
     </table>
   </div>
+  <script src="../../../tfjs-core/dist/tf-core.js"> </script>
+  <script src="../../../tfjs-converter/dist/tf-converter.js"> </script>
+  <script src="../../../tfjs-layers/dist/tf-layers.js"> </script>
+<!--
   <script src="https://unpkg.com/@tensorflow/tfjs-core/dist/tf-core.js"></script>
   <script src="https://unpkg.com/@tensorflow/tfjs-layers/dist/tf-layers.js"></script>
   <script src="https://unpkg.com/@tensorflow/tfjs-converter/dist/tf-converter.js"></script>
+-->
   <script>
     'use strict';
+    // tf.ENV.set('WEBGL_PACK_BATCHNORMALIZATION', true);
+    tf.ENV.set('WEBGL_LAZILY_UNPACK', true);
+
     async function load() {
       //////////////////////////////////
       // Place model loading code here.
       //////////////////////////////////
-      return await tf.loadFrozenModel(
-        'https://storage.googleapis.com/tfjs-models/savedmodel/mobilenet_v2_1.0_224/tensorflowjs_model.pb',
-        'https://storage.googleapis.com/tfjs-models/savedmodel/mobilenet_v2_1.0_224/weights_manifest.json');
+      const MODEL_URL = '../../../segmentation/deeplabv3_mnv2_pascal_train_aug_web_model/argmax/tensorflowjs_model.pb';
+      const WEIGHTS_URL = '../../../segmentation/deeplabv3_mnv2_pascal_train_aug_web_model/argmax/weights_manifest.json';
+      // Model's input and output have width and height of 513.
+      return await tf.loadFrozenModel(MODEL_URL, WEIGHTS_URL);
     }
 
-    const zeros = tf.zeros([1, 224, 224, 3]);
+    const zeros = tf.zeros([1, 513, 513, 3]);
     function predict(model) {
       //////////////////////////////////
       // Place model prediction code here.
       //////////////////////////////////
-      return model.predict(zeros);
+      // return model.predict(zeros);
+      return model.execute({'ImageTensor': zeros});
     }
   </script>
   <script>
     'use strict';
     const state = {
-      numRuns: 20,
+      numRuns: 100,
 
     };
     const modalDiv = document.getElementById('modal-msg');