未验证 提交 5d627488 编写于 作者: J joanna.wozna.intel 提交者: GitHub

Update paths to Quant models (#32870)

* Update paths to Quant models

* Update description
上级 53580bb4
...@@ -207,13 +207,29 @@ Run the following commands to download and extract Quant model: ...@@ -207,13 +207,29 @@ Run the following commands to download and extract Quant model:
```bash ```bash
mkdir -p /PATH/TO/DOWNLOAD/MODEL/ mkdir -p /PATH/TO/DOWNLOAD/MODEL/
cd /PATH/TO/DOWNLOAD/MODEL/ cd /PATH/TO/DOWNLOAD/MODEL/
export QUANT_MODEL_NAME=resnet50 export QUANT_MODEL_NAME=ResNet50
export QUANT_MODEL_ARCHIVE=${QUANT_MODEL_NAME}_quant.tar.gz export QUANT_MODEL_ARCHIVE=${QUANT_MODEL_NAME}_qat_model.tar.gz
wget http://paddle-inference-dist.bj.bcebos.com/int8/QAT2_models/${QUANT_MODEL_ARCHIVE} wget http://paddle-inference-dist.bj.bcebos.com/int8/QAT_models/${QUANT_MODEL_ARCHIVE}
mkdir ${QUANT_MODEL_NAME} && tar -xvf ${QUANT_MODEL_ARCHIVE} -C ${QUANT_MODEL_NAME} mkdir ${QUANT_MODEL_NAME} && tar -xvf ${QUANT_MODEL_ARCHIVE} -C ${QUANT_MODEL_NAME}
``` ```
To download other Quant models, set the `QUANT_MODEL_NAME` variable in the above commands to one of the values: `resnet101`, `mobilenetv1`, `mobilenetv2`, `vgg16`, `vgg19`. To download other Quant models, set the `QUANT_MODEL_NAME` variable in the above commands to one of the values: `ResNet101`, `MobileNetV1`, `MobileNetV2`, `VGG16`, `VGG19`.
Moreover, there are other variations of these Quant models that use different methods to obtain scales during training, run these commands to download and extract Quant model:
```bash
mkdir -p /PATH/TO/DOWNLOAD/MODEL/
cd /PATH/TO/DOWNLOAD/MODEL/
export QUANT_MODEL_NAME=ResNet50_qat_perf
export QUANT_MODEL_ARCHIVE=${QUANT_MODEL_NAME}.tar.gz
wget http://paddle-inference-dist.bj.bcebos.com/int8/QAT_models/${QUANT_MODEL_ARCHIVE}
mkdir ${QUANT_MODEL_NAME} && tar -xvf ${QUANT_MODEL_ARCHIVE} -C ${QUANT_MODEL_NAME}
```
To download other Quant models, set the `QUANT_MODEL_NAME` variable to on of the values: `ResNet50_qat_perf`, `ResNet50_qat_range`, `ResNet50_qat_channelwise`, `MobileNet_qat_perf`, where:
- `ResNet50_qat_perf`, `MobileNet_qat_perf` with input/output scales in `fake_quantize_moving_average_abs_max` operators, with weight scales in `fake_dequantize_max_abs` operators
- `ResNet50_qat_range`, with input/output scales in `fake_quantize_range_abs_max` operators and the `out_threshold` attributes, with weight scales in `fake_dequantize_max_abs` operators
- `ResNet50_qat_channelwise`, with input/output scales in `fake_quantize_range_abs_max` operators and the `out_threshold` attributes, with weight scales in `fake_channel_wise_dequantize_max_abs` operators
Download clean FP32 model for accuracy comparison against the INT8 model: Download clean FP32 model for accuracy comparison against the INT8 model:
......
Markdown is supported
0% .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑!
想要评论请 注册