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Simplify releasing memory of used ndarrays for calibration
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reminisce committed Jan 16, 2018
1 parent c1fe124 commit 70b9e38
Showing 1 changed file with 5 additions and 7 deletions.
12 changes: 5 additions & 7 deletions python/mxnet/quantization.py
Original file line number Diff line number Diff line change
Expand Up @@ -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


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