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Update README.md

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Francesco Gatti
2020-04-29 18:09:50 +02:00
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parent 3e2d0630b7
commit d6c28c5ba2
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@@ -114,9 +114,9 @@ python run_ssd_live_demo.py mb2-ssd-lite <pth-model-fil> <labels-file>
To run the an object detection demo follow these steps (example with yolov3): To run the an object detection demo follow these steps (example with yolov3):
``` ```
rm yolo3_FP32.rt # be sure to delete(or move) old tensorRT files rm yolo3_fp32.rt # be sure to delete(or move) old tensorRT files
./test_yolo3 # run the yolo test (is slow) ./test_yolo3 # run the yolo test (is slow)
./demo yolo3_FP32.rt ../demo/yolo_test.mp4 y ./demo yolo3_fp32.rt ../demo/yolo_test.mp4 y
``` ```
In general the demo program takes 4 parameters: In general the demo program takes 4 parameters:
``` ```
@@ -136,9 +136,9 @@ N.b. By default it is used FP32 inference
To run the an object detection demo with FP16 inference follow these steps (example with yolov3): To run the an object detection demo with FP16 inference follow these steps (example with yolov3):
``` ```
export TKDNN_MODE=FP16 # set the half floating point optimization export TKDNN_MODE=FP16 # set the half floating point optimization
rm yolo3_FP16.rt # be sure to delete(or move) old tensorRT files rm yolo3_fp16.rt # be sure to delete(or move) old tensorRT files
./test_yolo3 # run the yolo test (is slow) ./test_yolo3 # run the yolo test (is slow)
./demo yolo3_FP16.rt ../demo/yolo_test.mp4 y ./demo yolo3_fp16.rt ../demo/yolo_test.mp4 y
``` ```
N.b. Using FP16 inference will lead to some errors in the results (first or second decimal). N.b. Using FP16 inference will lead to some errors in the results (first or second decimal).
@@ -153,9 +153,9 @@ 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 # 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 export TKDNN_CALIB_LABEL_PATH=/path/to/calibration/label_list.txt
rm yolo3_INT8.rt # be sure to delete(or move) old tensorRT files rm yolo3_int8.rt # be sure to delete(or move) old tensorRT files
./test_yolo3 # run the yolo test (is slow) ./test_yolo3 # run the yolo test (is slow)
./demo yolo3_INT8.rt ../demo/yolo_test.mp4 y ./demo yolo3_int8.rt ../demo/yolo_test.mp4 y
``` ```
N.b. Using INT8 inference will lead to some errors in the results. N.b. Using INT8 inference will lead to some errors in the results.
@@ -166,6 +166,21 @@ N.b. INT8 calibration requires TensorRT version greater than or equal to 6.0
### BatchSize bigger than 1 ### BatchSize bigger than 1
``` ```
export TKDNN_BATCHSIZE=2 export TKDNN_BATCHSIZE=2
# build tensorRT files
```
This will create a TensorRT file with the desidered **max** batch size.
The test will still run with a batch of 1, but the created tensorRT can manage the desidered batch size.
### Test batch Inference
```
./test_rtinference <network-rt-file> <number-of-batches>
# <number-of-batches> should be less or equal to the max batch size of the <network-rt-file>
# example
export TKDNN_BATCHSIZE=4 # set max batch size
rm yolo3_fp32.rt # be sure to delete(or move) old tensorRT files
./test_yolo3 # build RT file
./test_rtinference yolo3_fp32.rt 4 # test with a batch size of 4
``` ```
## mAP demo ## mAP demo