From f899353ae5d5250e558adea9fcf0c3f20ed291db Mon Sep 17 00:00:00 2001 From: chuanqiw Date: Mon, 25 Mar 2019 17:26:56 +0800 Subject: [PATCH] Update Readme with new accuracy and performance data measured on 6271 test=release/1.3 --- .../fluid/contrib/int8_inference/README.md | 26 ++++++++++++------- 1 file changed, 17 insertions(+), 9 deletions(-) diff --git a/python/paddle/fluid/contrib/int8_inference/README.md b/python/paddle/fluid/contrib/int8_inference/README.md index b19be63723..840677bc56 100644 --- a/python/paddle/fluid/contrib/int8_inference/README.md +++ b/python/paddle/fluid/contrib/int8_inference/README.md @@ -45,28 +45,36 @@ You can load INT8 model by load_inference_model [API](https://github.com/PaddleP ``` ## 3. Result -We provide the results of accuracy measurd on [Intel® Xeon® Platinum Gold Processor](https://ark.intel.com/products/120489/Intel-Xeon-Gold-6148-Processor-27-5M-Cache-2-40-GHz- "Intel® Xeon® Gold 6148 Processor") (also known as Intel® Xeon® Skylake6148). +We provide the results of accuracy and performance measured on Intel(R) Xeon(R) Gold 6271 single core. | Model | Dataset | FP32 Accuracy | INT8 Accuracy | Accuracy Diff | | ------------ | ------------ | ------------ | ------------ | ------------ | -| ResNet-50 | Small | 72.00% | 72.00% | 0.00% | -| MobileNet-V1 | Small | 62.00% | 62.00% | 0.00% | -| ResNet-50 | Full ImageNet Val | 76.63% | 76.17% | 0.46% | -| MobileNet-V1 | Full ImageNet Val | 70.78% | 70.49% | 0.29% | +| ResNet-50 | Full ImageNet Val | 76.63% | 76.23% | 0.40% | +| MobileNet-V1 | Full ImageNet Val | 70.78% | 70.47% | 0.31% | -Please note that [Small](http://paddle-inference-dist.bj.bcebos.com/int8/calibration_test_data.tar.gz "Small") is a subset of [full ImageNet validation dataset](http://www.image-net.org/challenges/LSVRC/2012/nnoupb/ILSVRC2012_img_val.tar "full ImageNet validation dataset"). +| Model | Dataset | FP32 Throughput(images/second) | INT8 Throughput(images/second) | Ratio(INT8/FP32) | +| ------------ | ------------ | ------------ | ------------ | ------------ | +| ResNet-50 | Full ImageNet Val | 11.54 | 32.2 | 2.79 | +| MobileNet-V1 | Full ImageNet Val | 49.21 | 108.37 | 2.2 | + +Please note that [full ImageNet validation dataset](http://www.image-net.org/challenges/LSVRC/2012/nnoupb/ILSVRC2012_img_val.tar "full ImageNet validation dataset") can be downloaded by script `test_calibration.py` with `DATASET=full`. Notes: * The accuracy measurement requires the model with `label`. -* The INT8 theoretical speedup is ~1.33X on Intel® Xeon® Skylake Server (please refer to `This allows for 4x more input at the cost of 3x more instructions or 33.33% more compute` in [Reference](https://software.intel.com/en-us/articles/lower-numerical-precision-deep-learning-inference-and-training "Reference")). +* The INT8 theoretical speedup is 4X on Intel® Xeon® Cascadelake Server (please refer to `providing a theoretical peak compute gain of 4x int8 OPS over fp32 OPS` in [Reference](https://software.intel.com/en-us/articles/lower-numerical-precision-deep-learning-inference-and-training "Reference")). ## 4. How to reproduce the results -* Small dataset +* Small dataset (Single core) ```bash FLAGS_use_mkldnn=true python python/paddle/fluid/contrib/tests/test_calibration.py ``` -* Full dataset +* Full dataset (Single core) ```bash FLAGS_use_mkldnn=true DATASET=full python python/paddle/fluid/contrib/tests/test_calibration.py ``` + +* Full dataset (Multi-core) +```bash +FLAGS_use_mkldnn=true KMP_BLOCKTIME=1 KMP_AFFINITY=granularity=fine,compact,1,0 OMP_NUM_THREADS=20 taskset -c 0-19 DATASET=full python python/paddle/fluid/contrib/tests/test_calibration.py +``` -- GitLab