Simplify releasing memory of used ndarrays for calibration

reminisce · reminisce · commit 70b9e3863b1c · 2018-01-16T15:05:20.000-08:00
diff --git a/python/mxnet/quantization.py b/python/mxnet/quantization.py
@@ -273,16 +273,14 @@ def _get_optimal_thresholds(nd_dict, num_bins=8001, num_quantized_bins=255, logg
         logger.info('Calculating optimal thresholds for quantization using KL divergence'
                     ' with num_bins=%d and num_quantized_bins=%d' % (num_bins, num_quantized_bins))
     th_dict = {}
-    layer_names = nd_dict.keys()
-    for name in layer_names:
-        assert name in nd_dict
-        min_val, max_val, min_divergence, opt_th = _get_optimal_threshold(nd_dict[name], num_bins=num_bins,
+    for k, v in nd_dict:
+        min_val, max_val, min_divergence, opt_th = _get_optimal_threshold(v, num_bins=num_bins,
                                                                           num_quantized_bins=num_quantized_bins)
-        del nd_dict[name]  # release the memory of ndarray
-        th_dict[name] = (-opt_th, opt_th)
+        del v  # release the memory of ndarray
+        th_dict[k] = (-opt_th, opt_th)
         if logger is not None:
             logger.info('layer=%s, min_val=%f, max_val=%f, min_divergence=%f, optimal_threshold=%f'
-                        % (name, min_val, max_val, min_divergence, opt_th))
+                        % (k, min_val, max_val, min_divergence, opt_th))
     return th_dict
 
 
