tkDNN/docs/demo.md

tkDNN Demo

Supported Network

2D Object Detection:

• Yolo4, Yolo4-csp, Yolo4x, Yolo4_berkeley, Yolo4tiny
• Yolo3, Yolo3_berkeley, Yolo3_coco4, Yolo3_flir, Yolo3_512, Yolo3tiny, Yolo3tiny_512
• Yolo2, Yolo2_voc, Yolo2tiny
• Csresnext50-panet-spp, Csresnext50-panet-spp_berkeley
• Resnet101_cnet, Dla34_cnet
• Mobilenetv2ssd, Mobilenetv2ssd512, Bdd-mobilenetv2ssd

3D Object Detection:

• Dla34_cnet3d

2D/3D Object Detection and Tracking:

• Dla34_cnet3d_track

Index

• Run the demo
  • 2D Object Detection
  • 3D Object Detection
  • Object Detection and Tracking
  • FP16 inference
  • INT8 inference
  • Batching
  • Run the demo on Windows

Run the demo

N.b. By default it is used FP32 inference

2D Object Detection

This is an example using yolov4.

To run the an object detection first create the .rt file by running:

rm yolo4_fp32.rt        # be sure to delete(or move) old tensorRT files
./test_yolo4            # run the yolo test (is slow)

If you get problems in the creation, try to check the error activating the debug of TensorRT in this way:

cmake .. -DDEBUG=True
make

Once you have successfully created your rt file, run the demo:

./demo yolo4_fp32.rt ../demo/yolo_test.mp4 y

In general the demo program takes 7 parameters:

./demo <network-rt-file> <path-to-video> <kind-of-network> <number-of-classes> <n-batches> <show-flag> <conf-thresh>

where

• <network-rt-file> is the rt file generated by a test
• <<path-to-video> is the path to a video file or a camera input
• <kind-of-network> is the type of network. Thee types are currently supported: y (YOLO family), c (CenterNet family) and m (MobileNet-SSD family)
• <number-of-classes>is the number of classes the network is trained on
• <n-batches> number of batches to use in inference (N.B. you should first export TKDNN_BATCHSIZE to the required n_batches and create again the rt file for the network).
• <show-flag> if set to 0 the demo will not show the visualization but save the video into result.mp4 (if n-batches ==1)
• <conf-thresh> confidence threshold for the detector. Only bounding boxes with threshold greater than conf-thresh will be displayed.

3D Object Detection

To run the 3D object detection demo follow these steps (example with CenterNet based on DLA34):

rm dla34_cnet3d_fp32.rt        # be sure to delete(or move) old tensorRT files
./test_dla34_cnet3d            # run the yolo test (is slow)
./demo3D dla34_cnet3d_fp32.rt ../demo/yolo_test.mp4 NULL c

The demo3D program takes the same parameters of the demo program:

./demo3D <network-rt-file> <path-to-video> <calibration-file> <kind-of-network> <number-of-classes> <n-batches> <show-flag> <conf-thresh>

where

• calibration-file is the camera calibration file (opencv format). It is important that the file contains entry "camera_matrix" with sub-entry "rows", "cols", "data". If you do not want to pass the calibration file, pass "NULL" instead.

Object Detection and Tracking

To run the 3D object detection & tracking demo follow these steps (example with CenterTrack based on DLA34):

rm dla34_ctrack_fp32.rt  # be sure to delete(or move) old tensorRT files
./test_dla34_ctrack      # run the yolo test (is slow)
./demoTracker dla34_ctrack_fp32.rt ../demo/yolo_test.mp4 NULL c

The demoTracker program takes the same parameters of the demo program:

./demoTracker <network-rt-file> <path-to-video> <calibration-file> <kind-of-network> <number-of-classes> <n-batches> <show-flag> <conf-thresh> <2D/3D-flag>

where

• calibration-file is the camera calibration file (opencv format). It is important that the file contains entry "camera_matrix" with sub-entry "rows", "cols", "data". If you do not want to pass the calibration file, pass "NULL" instead.
• <2D/3D-flag> if set to 0 the demo will be in the 2D mode, while if set to 1 the demo will be in the 3D mode (Default is 1 - 3D mode).

FP16 inference

To run the demo with FP16 inference follow these steps (example with yolov3):

export TKDNN_MODE=FP16  # set the half floating point optimization
rm yolo3_fp16.rt        # be sure to delete(or move) old tensorRT files
./test_yolo3            # run the yolo test (is slow)
./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).

INT8 inference

To run the 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)

bash scripts/download_validation.sh COCO

to automatically download COCO2017 validation (inside demo folder) and create those needed file. Use BDD instead of COCO to download BDD validation.

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.
• 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).

Batching

BatchSize bigger than 1

export TKDNN_BATCHSIZE=2
# build tensorRT files

This will create a TensorRT file with the desired max batch size. The test will still run with a batch of 1, but the created tensorRT can manage the desired batch size.

Test batch Inference

This will test the network with random input and check if the output of each batch is the same.

./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

Run the demo on Windows

This example uses yolo4_tiny.
To run the object detection file create .rt file bu running:

.\test_yolo4tiny.exe

Once the rt file has been successfully create,run the demo using the following command:

.\demo.exe yolo4tiny_fp32.rt ..\demo\yolo_test.mp4 y 

For general info on more demo paramters,check Run the demo section on top To run the test_all_tests.sh on windows,use git bash or msys2

FP16 inference windows

This is an untested feature on windows.To run the object detection demo with FP16 interference follow the below steps(example with yolo4tiny):

set TKDNN_MODE=FP16
del /f yolo4tiny_fp16.rt
.\test_yolo4tiny.exe
.\demo.exe yolo4tiny_fp16.rt ..\demo\yolo_test.mp4

INT8 inference windows

To run object detection demo with INT8 (example with yolo4tiny):

set TKDNN_MODE=INT8
set TKDNN_CALIB_LABEL_PATH=..\demo\COCO_val2017\all_labels.txt
set TKDNN_CALIB_IMG_PATH=..\demo\COCO_val2017\all_images.txt
del /f  yolo4tiny_int8.rt        # be sure to delete(or move) old tensorRT files
.\test_yolo4tiny.exe           # run the yolo test (is slow)
.\demo.exe yolo4tiny_int8.rt ..\demo\yolo_test.mp4 y

Known issues with tkDNN on Windows

Mobilenet and Centernet demos work properly only when built with msvc 16.7 in Release Mode,when built in debug mode for the mentioned networks one might encounter opencv assert errors

All Darknet models work properly with demo using MSVC version(16.7-16.9)

It is recommended to use Nvidia Driver(465+),Cuda unknown errors have been observed when using older drivers on pascal(SM 61) devices.