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MKL-DNN Quantization Examples and README #12808

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Oct 19, 2018
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MKL-DNN Quantization Examples and README #12808

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1d7c88e
add gluoncv support
xinyu-intel Oct 8, 2018
855a4dd
add ssd readme
xinyu-intel Oct 9, 2018
68df9bc
improve ssd readme
xinyu-intel Oct 9, 2018
f9e30fe
add custom readme
xinyu-intel Oct 9, 2018
8d349f5
add ssd model link
xinyu-intel Oct 9, 2018
73dc2bf
add squeezenet
xinyu-intel Oct 9, 2018
82aac56
add ssd quantization script
xinyu-intel Oct 9, 2018
9942129
fix topo of args
xinyu-intel Oct 9, 2018
a93bbff
improve custom readme
xinyu-intel Oct 10, 2018
f7f6bcb
fix topo bug
xinyu-intel Oct 11, 2018
9530732
fix squeezenet
xinyu-intel Oct 12, 2018
b93cb29
add squeezenet accuracy
xinyu-intel Oct 12, 2018
15545fd
Add initializer for min max to support quantization
ZhennanQin Oct 12, 2018
1baaeaf
add dummy data inference
xinyu-intel Oct 12, 2018
a049351
rebase code
xinyu-intel Oct 12, 2018
19283ad
add test case for init_param
xinyu-intel Oct 12, 2018
40195bc
add subgraph docs
xinyu-intel Oct 12, 2018
9c0c2bc
improve docs
xinyu-intel Oct 14, 2018
2b20043
add two models and fix default rgb_std to 1
xinyu-intel Oct 14, 2018
1df46d4
fix doc link
xinyu-intel Oct 14, 2018
a5b309d
improve MKLDNN_README
xinyu-intel Oct 14, 2018
4d1338d
add quantization for mobilenetv1
xinyu-intel Oct 15, 2018
c7a35dc
Merge remote-tracking branch 'upstream/master' into mkldnn_quantizati…
xinyu-intel Oct 15, 2018
f8fbc3f
fix ssd benchmark_score label shapes
xinyu-intel Oct 15, 2018
05b88cd
add resnet101_v1 and inceptionv3 support
xinyu-intel Oct 16, 2018
32b44d3
Refine some descriptions in the MKLDNN_README
juliusshufan Oct 16, 2018
a80a628
improve docs
xinyu-intel Oct 16, 2018
bf35236
improve link in perf.md
xinyu-intel Oct 16, 2018
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improve ssd readme
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@xinyu-intel
xinyu-intel committed Oct 9, 2018
commit 68df9bce67e67a1adb942225a2aaa6103726eb6a
4 changes: 2 additions & 2 deletions example/quantization/README.md
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Expand Up @@ -56,7 +56,7 @@ python imagenet_inference.py --symbol-file=./model/resnet50_v1-quantized-5batche

## SSD-VGG

Following the [instruction](https://github.com/apache/incubator-mxnet/tree/master/example/ssd#train-the-model) in [example/ssd](https://github.com/apache/incubator-mxnet/tree/master/example/ssd) to train a FP32 `SSD-VGG16_reduced_300x300` model based on Pascal VOC dataset. You can also download our pre-trained model and packed binary data from [here](http://data.mxnet.io/data/) and extract to `model/` and `data/`dictionary.
Go to [example/ssd](https://github.com/apache/incubator-mxnet/tree/master/example/ssd) dictionary. Following the [instruction](https://github.com/apache/incubator-mxnet/tree/master/example/ssd#train-the-model) in [example/ssd](https://github.com/apache/incubator-mxnet/tree/master/example/ssd) to train a FP32 `SSD-VGG16_reduced_300x300` model based on Pascal VOC dataset. You can also download our pre-trained model and packed binary data from [here](http://data.mxnet.io/data/) and extract to `model/` and `data/`dictionary.

Then, use the following command for quantization. By default, this script use 5 batches(32 samples per batch) for naive calib:
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not sure what naive calib is... maybe use the full word, calibration?
Also maybe define what this is doing?

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I see in the code that there are three modes. I think these should be discussed in the documentation and not just in the code comments / help output.


Expand All @@ -75,4 +75,4 @@ python evaluate.py --cpu --num-batch 10 --batch-size 224 --deploy --prefix=./mod

# Launch INT8 Inference
python evaluate.py --cpu --num-batch 10 --batch-size 224 --deploy --prefix=./model/cqssd_
```
```