xinyu-intel commented on a change in pull request #15910: [Quantization]support
exclude operators while quantization
URL: https://github.com/apache/incubator-mxnet/pull/15910#discussion_r315097897
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File path: python/mxnet/contrib/quantization.py
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@@ -821,16 +803,13 @@ def calib_graph(qsym, arg_params, aux_params, collector,
return qsym, qarg_params, aux_params
-def quantize_net(network, quantized_dtype='auto', exclude_layers=None,
exclude_layers_match=None, calib_data=None,
- data_shapes=None, calib_mode='none', num_calib_examples=None,
ctx=cpu(), logger=logging):
+def quantize_net(network, quantized_dtype='auto',
+ exclude_layers=None, exclude_layers_match=None,
exclude_operators=None,
+ calib_data=None, data_shapes=None, calib_mode='none',
+ num_calib_examples=None, ctx=cpu(), logger=logging):
"""User-level API for Gluon users to generate a quantized SymbolBlock from
a FP32 HybridBlock w/ or w/o calibration.
The backend quantized operators are only enabled for Linux systems. Please
do not run
inference using the quantized models on Windows for now.
- The quantization implementation adopts the TensorFlow's approach:
- https://www.tensorflow.org/performance/quantization.
- The calibration implementation borrows the idea of Nvidia's 8-bit
Inference with TensorRT:
-
http://on-demand.gputechconf.com/gtc/2017/presentation/s7310-8-bit-inference-with-tensorrt.pdf
- and adapts the method to MXNet.
Review comment:
file too long (>1000L):(
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