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add uint8 bn mkldnn implementation #16003
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xinyu-intel
reviewed
Aug 26, 2019
| @@ -216,7 +216,7 @@ def save_params(fname, arg_params, aux_params, logger=None): | |||
| if exclude_first_conv: | |||
| excluded_sym_names += ['resnetv10_conv0_fwd'] | |||
| elif args.model.find('resnet') != -1 and args.model.find('v2') != -1: | |||
| excluded_sym_names += ['resnetv20_flatten0_flatten0'] | |||
| excluded_sym_names += ['resnetv20_flatten0_flatten0', 'resnetv20_stage1_batchnorm0_fwd'] | |||
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why exclude the first one?
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This is for the sake of accuracy, if do not exclude this layer, top-1 accuracy will drop to 52.3. Reason of this accuracy drop is under investigation.
xinyu-intel
reviewed
Aug 26, 2019
ZhennanQin
approved these changes
Aug 26, 2019
pengzhao-intel
approved these changes
Aug 26, 2019
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Description
add uint8 batchnorm, mkldnn implementation and test
@PatricZhao @ZhennanQin
Details
Usage
Check the doc in https://github.com/apache/incubator-mxnet/tree/master/example/quantization/README.md to quantize models and do inference.
Quantized bn will be used automatically when a bn operator cannot be fused.
Performance
In most cases, bn can be fused, so quantized bn is not introduced. In reset50 v2, some of the bn operators are standalone, quantizing these bn give a performance as follows: