Fix a stability about multiple measurements

output_template/python/run.py

@@ -38,6 +38,9 @@ def _pre_process(raw_image, pre_process, data_format):
     image = pre_process(image=raw_image)['image']
     if data_format == 'NCHW':
         image = np.transpose(image, [2, 0, 1])
+
+    # add the batch dimension
+    image = np.expand_dims(image, axis=0)
     return image
 
 
@@ -98,7 +101,7 @@ def _run(nn, image_data):
     return nn.run(image_data)
 
 
-def _timerfunc(func, extraArgs, trial):
+def _timerfunc(func, extraArgs, trial=1):
     if sys.version_info.major == 2:
         get_time = time.time
     else:
@@ -112,7 +115,6 @@ def _timerfunc(func, extraArgs, trial):
         runtime += end - start
         msg = "Function {func} took {time} seconds to complete"
         logger.info(msg.format(func=func.__name__, time=end - start))
-    logger.info("Avg(func {}): {} sec.".format(func.__name__, runtime / trial))
     return value, runtime / trial
 
 
@@ -133,34 +135,40 @@ def run_prediction(input_image, model, config_file, trial=1):
     pre_process = build_pre_process(config.PRE_PROCESSOR)
     post_process = build_post_process(config.POST_PROCESSOR)
 
-    # pre process for image
-    image_data, bench_pre = _timerfunc(_pre_process, (image_data, pre_process, config.DATA_FORMAT), trial)
+    results_total = []
+    results_pre = []
+    results_run = []
+    results_post = []
 
-    # add the batch dimension
-    image_data = np.expand_dims(image_data, axis=0)
+    for _ in range(trial):
+        # pre process for image
+        output, bench_pre = _timerfunc(_pre_process, (image_data, pre_process, config.DATA_FORMAT))
 
-    # run the model to inference
-    output, bench_inference = _timerfunc(_run, (nn, image_data), trial)
+        # run the model to inference
+        output, bench_run = _timerfunc(_run, (nn, output))
 
-    logger.info('Output: (before post process)\n{}'.format(output))
+        # pre process for output
+        output, bench_post = _timerfunc(_post_process, (output, post_process))
 
-    # pre process for output
-    output, bench_post = _timerfunc(_post_process, (output, post_process), trial)
+        results_total.append(bench_pre + bench_run + bench_post)
+        results_pre.append(bench_pre)
+        results_run.append(bench_run)
+        results_post.append(bench_post)
 
-    logger.info('Output: (after post process)\n{}'.format(output))
+    time_stat = {
+        "total": {"mean": np.mean(results_total), "std": np.std(results_total)},
+        "pre": {"mean": np.mean(results_pre), "std": np.std(results_pre)},
+        "post": {"mean": np.mean(results_post), "std": np.std(results_post)},
+        "run": {"mean":  np.mean(results_run), "std": np.std(results_run)},
+    }
 
     # json output
     json_output = JsonOutput(
         task=Tasks(config.TASK),
         classes=config.CLASSES,
         image_size=config.IMAGE_SIZE,
         data_format=config.DATA_FORMAT,
-        bench={
-            "total": bench_pre + bench_post + bench_inference,
-            "pre": bench_pre,
-            "post": bench_post,
-            "inference": bench_inference,
-        },
+        bench=time_stat,
     )
 
     image_from_json = ImageFromJson(
@@ -177,8 +185,8 @@ def run_prediction(input_image, model, config_file, trial=1):
     _save_json(output_dir, json_obj)
     filename_images = image_from_json(json_obj, raw_images, image_files)
     _save_images(output_dir, filename_images)
-    logger.info("Benchmark avg result(sec) for {} trials: pre_process: {}  inference: {} post_process: {}  Total: {}"
-                .format(trial, bench_pre, bench_inference, bench_post, bench_pre + bench_post + bench_inference,))
+    logger.info("Benchmark avg result(sec) for {} trials".format(trial))
+    logger.info(time_stat)
 
 
 @click.command(context_settings=dict(help_option_names=['-h', '--help']))