[minibench] Run benchmark on background thread

extension/benchmark/android/benchmark/app/src/main/java/org/pytorch/minibench/BenchmarkActivity.java

@@ -10,6 +10,7 @@
 
 import android.app.Activity;
 import android.content.Intent;
+import android.os.AsyncTask;
 import android.os.Bundle;
 import android.system.ErrnoException;
 import android.system.Os;
@@ -47,43 +48,57 @@ protected void onCreate(Bundle savedInstanceState) {
     // TODO: Format the string with a parsable format
     Stats stats = new Stats();
 
-    // Record the time it takes to load the model and the forward method
-    stats.loadStart = System.nanoTime();
-    Module module = Module.load(model.getPath());
-    stats.errorCode = module.loadMethod("forward");
-    stats.loadEnd = System.nanoTime();
+    new AsyncTask<Void, Void, Void>() {
+      @Override
+      protected Void doInBackground(Void... voids) {
 
-    for (int i = 0; i < numIter; i++) {
-      long start = System.nanoTime();
-      module.forward();
-      double forwardMs = (System.nanoTime() - start) * 1e-6;
-      stats.latency.add(forwardMs);
-    }
+        // Record the time it takes to load the model and the forward method
+        stats.loadStart = System.nanoTime();
+        Module module = Module.load(model.getPath());
+        stats.errorCode = module.loadMethod("forward");
+        stats.loadEnd = System.nanoTime();
 
-    final BenchmarkMetric.BenchmarkModel benchmarkModel =
-        BenchmarkMetric.extractBackendAndQuantization(model.getName().replace(".pte", ""));
-    final List<BenchmarkMetric> results = new ArrayList<>();
-    // The list of metrics we have atm includes:
-    // Avg inference latency after N iterations
-    results.add(
-        new BenchmarkMetric(
-            benchmarkModel,
-            "avg_inference_latency(ms)",
-            stats.latency.stream().mapToDouble(l -> l).average().orElse(0.0f),
-            0.0f));
-    // Model load time
-    results.add(
-        new BenchmarkMetric(
-            benchmarkModel, "model_load_time(ms)", (stats.loadEnd - stats.loadStart) * 1e-6, 0.0f));
-    // Load status
-    results.add(new BenchmarkMetric(benchmarkModel, "load_status", stats.errorCode, 0));
+        for (int i = 0; i < numIter; i++) {
+          long start = System.nanoTime();
+          module.forward();
+          double forwardMs = (System.nanoTime() - start) * 1e-6;
+          stats.latency.add(forwardMs);
+        }
+        return null;
+      }
 
-    try (FileWriter writer = new FileWriter(getFilesDir() + "/benchmark_results.json")) {
-      Gson gson = new Gson();
-      writer.write(gson.toJson(results));
-    } catch (IOException e) {
-      e.printStackTrace();
-    }
+      @Override
+      protected void onPostExecute(Void aVoid) {
+
+        final BenchmarkMetric.BenchmarkModel benchmarkModel =
+            BenchmarkMetric.extractBackendAndQuantization(model.getName().replace(".pte", ""));
+        final List<BenchmarkMetric> results = new ArrayList<>();
+        // The list of metrics we have atm includes:
+        // Avg inference latency after N iterations
+        results.add(
+            new BenchmarkMetric(
+                benchmarkModel,
+                "avg_inference_latency(ms)",
+                stats.latency.stream().mapToDouble(l -> l).average().orElse(0.0f),
+                0.0f));
+        // Model load time
+        results.add(
+            new BenchmarkMetric(
+                benchmarkModel,
+                "model_load_time(ms)",
+                (stats.loadEnd - stats.loadStart) * 1e-6,
+                0.0f));
+        // Load status
+        results.add(new BenchmarkMetric(benchmarkModel, "load_status", stats.errorCode, 0));
+
+        try (FileWriter writer = new FileWriter(getFilesDir() + "/benchmark_results.json")) {
+          Gson gson = new Gson();
+          writer.write(gson.toJson(results));
+        } catch (IOException e) {
+          e.printStackTrace();
+        }
+      }
+    }.execute();
   }
 }