提交 38a591d6 编写于 作者: C czhu15 提交者: Tao Luo

Merge PR 18160 and PR 18177 into release 1.5 branch (#18195)

* [Cherry-pick] change dirname mobilenet to mobilenetv1 (#18160)

test=release/1.5

* [Cherry-pick] update mkldnn int8v2 doc (#18177)

test=release/1.5
上级 288bdc7c
...@@ -6,7 +6,7 @@ PaddlePaddle supports offline INT8 calibration to accelerate the inference speed ...@@ -6,7 +6,7 @@ PaddlePaddle supports offline INT8 calibration to accelerate the inference speed
You need to install at least PaddlePaddle-1.3 python package `pip install paddlepaddle==1.3`. You need to install at least PaddlePaddle-1.3 python package `pip install paddlepaddle==1.3`.
## 1. How to generate INT8 model ## 1. How to generate INT8 model
You can refer to the unit test in [test_calibration.py](../tests/test_calibration.py). Basically, there are three steps: You can refer to the unit test in [test_calibration_resnet50.py](../tests/test_calibration_resnet50.py). Basically, there are three steps:
* Construct calibration object. * Construct calibration object.
```python ```python
...@@ -68,18 +68,19 @@ Notes: ...@@ -68,18 +68,19 @@ Notes:
* The INT8 theoretical speedup is 4X on Intel® Xeon® Cascadelake Server (please refer to `The theoretical peak compute gains are 4x int8 OPS over fp32 OPS.` in [Reference](https://software.intel.com/en-us/articles/lower-numerical-precision-deep-learning-inference-and-training "Reference")). Therefore, op-level gain is 4X and topology-level is smaller. * The INT8 theoretical speedup is 4X on Intel® Xeon® Cascadelake Server (please refer to `The theoretical peak compute gains are 4x int8 OPS over fp32 OPS.` in [Reference](https://software.intel.com/en-us/articles/lower-numerical-precision-deep-learning-inference-and-training "Reference")). Therefore, op-level gain is 4X and topology-level is smaller.
## 4. How to reproduce the results ## 4. How to reproduce the results
* Small dataset (Single core) * Small dataset for ResNet-50 (Single core)
```bash ```bash
FLAGS_use_mkldnn=true python python/paddle/fluid/contrib/tests/test_calibration.py FLAGS_use_mkldnn=true python python/paddle/fluid/contrib/tests/test_calibration_resnet50.py
``` ```
>Note: Change `test_calibration_resnet50.py` to `test_calibration_mobilenetv1.py` for MobileNet-V1. Same for the following commands.
* Full dataset (Single core) * Full dataset for ResNet-50 (Single core)
```bash ```bash
FLAGS_use_mkldnn=true DATASET=full python python/paddle/fluid/contrib/tests/test_calibration.py FLAGS_use_mkldnn=true DATASET=full python python/paddle/fluid/contrib/tests/test_calibration_resnet50.py
``` ```
* Full dataset (Multi-core) * Full dataset for ResNet-50 (Multi-core)
```bash ```bash
FLAGS_use_mkldnn=true OMP_NUM_THREADS=20 DATASET=full python python/paddle/fluid/contrib/tests/test_calibration.py FLAGS_use_mkldnn=true OMP_NUM_THREADS=20 DATASET=full python python/paddle/fluid/contrib/tests/test_calibration_resnet50.py
``` ```
> Notes: This is an example command with 20 cores by using set `OMP_NUM_THREADS` value. > Notes: This is an example command with 20 cores by using set `OMP_NUM_THREADS` value.
...@@ -42,7 +42,7 @@ if(LINUX AND WITH_MKLDNN) ...@@ -42,7 +42,7 @@ if(LINUX AND WITH_MKLDNN)
inference_analysis_python_api_int8_test(test_slim_int8_googlenet ${INT8_GOOGLENET_MODEL_DIR} ${INT8_DATA_DIR} ${MKLDNN_INT8_TEST_FILE}) inference_analysis_python_api_int8_test(test_slim_int8_googlenet ${INT8_GOOGLENET_MODEL_DIR} ${INT8_DATA_DIR} ${MKLDNN_INT8_TEST_FILE})
# mobilenet int8 # mobilenet int8
set(INT8_MOBILENET_MODEL_DIR "${INT8_DATA_DIR}/mobilenet") set(INT8_MOBILENET_MODEL_DIR "${INT8_DATA_DIR}/mobilenetv1")
inference_analysis_python_api_int8_test(test_slim_int8_mobilenet ${INT8_MOBILENET_MODEL_DIR} ${INT8_DATA_DIR} ${MKLDNN_INT8_TEST_FILE}) inference_analysis_python_api_int8_test(test_slim_int8_mobilenet ${INT8_MOBILENET_MODEL_DIR} ${INT8_DATA_DIR} ${MKLDNN_INT8_TEST_FILE})
# temporarily adding WITH_SLIM_MKLDNN_FULL_TEST FLAG for QA testing the following UTs locally, # temporarily adding WITH_SLIM_MKLDNN_FULL_TEST FLAG for QA testing the following UTs locally,
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