[UNetmed/TF] Fix registered metric names

mmarcinkiewicz · nv-kkudrynski · commit 03a93a1cb22d · 2021-09-21T05:00:26.000-07:00
diff --git a/TensorFlow/Segmentation/UNet_Medical/utils/hooks/profiling_hook.py b/TensorFlow/Segmentation/UNet_Medical/utils/hooks/profiling_hook.py
@@ -50,5 +50,6 @@ def begin(self):
     def end(self, session):
         if hvd.rank() == 0:
             stats = process_performance_stats(np.array(self._timestamps),
-                                              self._global_batch_size)
-            self.logger.log(step=(), data={metric: value for (metric, value) in stats})
+                                              self._global_batch_size,
+                                              self.mode)
+            self.logger.log(step=(), data=stats)
diff --git a/TensorFlow/Segmentation/UNet_Medical/utils/parse_results.py b/TensorFlow/Segmentation/UNet_Medical/utils/parse_results.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -17,18 +17,21 @@
 import argparse
 
 
-def process_performance_stats(timestamps, batch_size):
+def process_performance_stats(timestamps, batch_size, mode):
+    """ Get confidence intervals
+
+    :param timestamps: Collection of timestamps
+    :param batch_size: Number of samples per batch
+    :param mode: Estimator's execution mode
+    :return: Stats
+    """
     timestamps_ms = 1000 * timestamps
-    latency_ms = timestamps_ms.mean()
-    std = timestamps_ms.std()
-    n = np.sqrt(len(timestamps_ms))
     throughput_imgps = (1000.0 * batch_size / timestamps_ms).mean()
+    stats = {f"throughput_{mode}": throughput_imgps,
+             f"latency_{mode}_mean": timestamps_ms.mean()}
+    for level in [90, 95, 99]:
+        stats.update({f"latency_{mode}_{level}": np.percentile(timestamps_ms, level)})
 
-    stats = [("Throughput Avg", str(throughput_imgps)),
-             ('Latency Avg:', str(latency_ms))]
-    for ci, lvl in zip(["90%:", "95%:", "99%:"],
-                       [1.645, 1.960, 2.576]):
-        stats.append(("Latency_" + ci, str(latency_ms + lvl * std / n)))
     return stats
 
 
@@ -77,4 +80,3 @@ def parse_convergence_results(path, environment):
         parse_convergence_results(path=args.model_dir, environment=args.env)
     elif args.exec_mode == 'benchmark':
         pass
-    print()
