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Micaela Verucchi
2020-06-03 11:01:17 +02:00
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@@ -177,24 +177,31 @@ N.b. Using FP16 inference will lead to some errors in the results (first or seco
### INT8 inference
To run the an object detection demo with INT8 inference follow these steps (example with yolov3):
To run the an object detection demo with INT8 inference three environment variables need to be set:
* ```export TKDNN_MODE=INT8```: set the 8-bit integer optimization
* ```export TKDNN_CALIB_IMG_PATH=/path/to/calibration/image_list.txt``` : image_list.txt has in each line the absolute path to a calibration image
* ```export TKDNN_CALIB_LABEL_PATH=/path/to/calibration/label_list.txt```: label_list.txt has in each line the absolute path to a calibration label
You should provide image_list.txt and label_list.txt, using training images. However, if you want to quickly test the INT8 inference you can run (from this repo root folder)
```
export TKDNN_MODE=INT8 # set the 8-bit integer optimization
bash scripts/download_validation.sh COCO
```
to automatically download COCO2017 validation (inside demo folder) and create those needed file. Use BDD insted of COCO to download BDD validation.
# image_list.txt contains the list of the absolute paths to the calibration images
export TKDNN_CALIB_IMG_PATH=/path/to/calibration/image_list.txt
# label_list.txt contains the list of the absolute paths to the calibration labels
export TKDNN_CALIB_LABEL_PATH=/path/to/calibration/label_list.txt
Then a complete example using yolo3 and COCO dataset would be:
```
export TKDNN_MODE=INT8
export TKDNN_CALIB_LABEL_PATH=../demo/COCO_val2017/all_labels.txt
export TKDNN_CALIB_IMG_PATH=../demo/COCO_val2017/all_images.txt
rm yolo3_int8.rt # be sure to delete(or move) old tensorRT files
./test_yolo3 # run the yolo test (is slow)
./demo yolo3_int8.rt ../demo/yolo_test.mp4 y
```
N.b. Using INT8 inference will lead to some errors in the results.
N.b. The test will be slower: this is due to the INT8 calibration, which may take some time to complete.
N.b. INT8 calibration requires TensorRT version greater than or equal to 6.0
N.B.
* Using INT8 inference will lead to some errors in the results.
* The test will be slower: this is due to the INT8 calibration, which may take some time to complete.
* INT8 calibration requires TensorRT version greater than or equal to 6.0
* Only 100 images are used to create the calibration table by default (set in the code).
### BatchSize bigger than 1
```