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Signed-off-by: Davide Sapienza <sapienza.dav@gmail.com>
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# tkDNN
tkDNN is a Deep Neural Network library built with cuDNN and tensorRT primitives, specifically thought to work on NVIDIA Jetson Boards. It has been tested on TK1(branch cudnn2), TX1, TX2, AGX Xavier and several discrete GPU.
tkDNN is a Deep Neural Network library built with cuDNN and tensorRT primitives, specifically thought to work on NVIDIA Jetson Boards. It has been tested on TK1(branch cudnn2), TX1, TX2, AGX Xavier, Nano and several discrete GPUs.
The main goal of this project is to exploit NVIDIA boards as much as possible to obtain the best inference performance. It does not allow training.
If you use tkDNN in your research, please cite the [following paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9212130&casa_token=sQTJXi7tJNoAAAAA:BguH9xCIY48MxbtDS3LXzIXzO-9sWArm7Hd7y7BwaLmqRuM_Gx8bOYizFPNMNtpo5K0kB-P-). For use in commercial solutions, write at gattifrancesco@hotmail.it and micaela.verucchi@unimore.it or refer to https://hipert.unimore.it/ .
```
@inproceedings{verucchi2020systematic,
title={A Systematic Assessment of Embedded Neural Networks for Object Detection},
author={Verucchi, Micaela and Brilli, Gianluca and Sapienza, Davide and Verasani, Mattia and Arena, Marco and Gatti, Francesco and Capotondi, Alessandro and Cavicchioli, Roberto and Bertogna, Marko and Solieri, Marco},
booktitle={2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)},
volume={1},
pages={937--944},
year={2020},
organization={IEEE}
}
```
## FPS Results
Inference FPS of yolov4 with tkDNN, average of 1200 images with the same dimension as the input size, on
* RTX 2080Ti (CUDA 10.2, TensorRT 7.0.0, Cudnn 7.6.5);
* Xavier AGX, Jetpack 4.3 (CUDA 10.0, CUDNN 7.6.3, tensorrt 6.0.1 );
* Xavier NX, Jetpack 4.4 (CUDA 10.2, CUDNN 8.0.0, tensorrt 7.1.0 ).
* Tx2, Jetpack 4.2 (CUDA 10.0, CUDNN 7.3.1, tensorrt 5.0.6 );
* Jetson Nano, Jetpack 4.4 (CUDA 10.2, CUDNN 8.0.0, tensorrt 7.1.0 ).
| Platform | Network | FP32, B=1 | FP32, B=4 | FP16, B=1 | FP16, B=4 | INT8, B=1 | INT8, B=4 |
| :------: | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: |
| RTX 2080Ti | yolo4 320 | 118.59 | 237.31 | 207.81 | 443.32 | 262.37 | 530.93 |
| RTX 2080Ti | yolo4 416 | 104.81 | 162.86 | 169.06 | 293.78 | 206.93 | 353.26 |
| RTX 2080Ti | yolo4 512 | 92.98 | 132.43 | 140.36 | 215.17 | 165.35 | 254.96 |
| RTX 2080Ti | yolo4 608 | 63.77 | 81.53 | 111.39 | 152.89 | 127.79 | 184.72 |
| AGX Xavier | yolo4 320 | 26.78 | 32.05 | 57.14 | 79.05 | 73.15 | 97.56 |
| AGX Xavier | yolo4 416 | 19.96 | 21.52 | 41.01 | 49.00 | 50.81 | 60.61 |
| AGX Xavier | yolo4 512 | 16.58 | 16.98 | 31.12 | 33.84 | 37.82 | 41.28 |
| AGX Xavier | yolo4 608 | 9.45 | 10.13 | 21.92 | 23.36 | 27.05 | 28.93 |
| Xavier NX | yolo4 320 | 14.56 | 16.25 | 30.14 | 41.15 | 42.13 | 53.42 |
| Xavier NX | yolo4 416 | 10.02 | 10.60 | 22.43 | 25.59 | 29.08 | 32.94 |
| Xavier NX | yolo4 512 | 8.10 | 8.32 | 15.78 | 17.13 | 20.51 | 22.46 |
| Xavier NX | yolo4 608 | 5.26 | 5.18 | 11.54 | 12.06 | 15.09 | 15.82 |
| Tx2 | yolo4 320 | 11.18 | 12.07 | 15.32 | 16.31 | - | - |
| Tx2 | yolo4 416 | 7.30 | 7.58 | 9.45 | 9.90 | - | - |
| Tx2 | yolo4 512 | 5.96 | 5.95 | 7.22 | 7.23 | - | - |
| Tx2 | yolo4 608 | 3.63 | 3.65 | 4.67 | 4.70 | - | - |
| Nano | yolo4 320 | 4.23 | 4.55 | 6.14 | 6.53 | - | - |
| Nano | yolo4 416 | 2.88 | 3.00 | 3.90 | 4.04 | - | - |
| Nano | yolo4 512 | 2.32 | 2.34 | 3.02 | 3.04 | - | - |
| Nano | yolo4 608 | 1.40 | 1.41 | 1.92 | 1.93 | - | - |
## MAP Results
Results for COCO val 2017 (5k images), on RTX 2080Ti, with conf threshold=0.001
| | CodaLab | CodaLab | CodaLab | CodaLab | tkDNN map | tkDNN map |
| -------------------- | :-----------: | :-------: | :-----------: | :---------: | :-----------: | :-------: |
| | **tkDNN** | **tkDNN** | **darknet** | **darknet** | **tkDNN** | **tkDNN** |
| | MAP(0.5:0.95) | AP50 | MAP(0.5:0.95) | AP50 | MAP(0.5:0.95) | AP50 |
| Yolov3 (416x416) | 0.381 | 0.675 | 0.380 | 0.675 | 0.372 | 0.663 |
| yolov4 (416x416) | 0.468 | 0.705 | 0.471 | 0.710 | 0.459 | 0.695 |
| yolov3tiny (416x416) | 0.096 | 0.202 | 0.096 | 0.201 | 0.093 | 0.198 |
| yolov4tiny (416x416) | 0.202 | 0.400 | 0.201 | 0.400 | 0.197 | 0.395 |
| Cnet-dla34 (512x512) | 0.366 | 0.543 | \- | \- | 0.361 | 0.535 |
| mv2SSD (512x512) | 0.226 | 0.381 | \- | \- | 0.223 | 0.378 |
## Index
- [tkDNN](#tkdnn)
- [Index](#index)
@@ -69,10 +129,10 @@ Weights are essential for any network to run inference. For each test a folder o
Therefore, once the weights have been exported, the folders layers and debug should be placed in the corresponding test.
### 1)Export weights from darknet
To export weights for NNs that are defined in darknet framework, use [this](https://github.com/ceccocats/darknet) fork of darknet and follow these steps to obtain a correct debug and layers folder, ready for tkDNN.
To export weights for NNs that are defined in darknet framework, use [this](https://git.hipert.unimore.it/fgatti/darknet.git) fork of darknet and follow these steps to obtain a correct debug and layers folder, ready for tkDNN.
```
git clone https://github.com/ceccocats/darknet
git clone https://git.hipert.unimore.it/fgatti/darknet.git
cd darknet
make
mkdir layers debug
@@ -101,7 +161,6 @@ python demo.py --input_res 512 --arch resdcn_101 ctdet --demo /path/to/image/or/
python demo.py --input_res 512 --arch dla_34 ctdet --demo /path/to/image/or/folder/or/video/or/webcam --load_model ../models/ctdet_coco_dla_2x.pth --exp_wo --exp_wo_dim 512
```
### 4)Export weights for MobileNetSSD
To get the weights needed to run Mobilenet tests use [this](https://github.com/mive93/pytorch-ssd) fork of a Pytorch implementation of SSD network.
```
@@ -110,25 +169,68 @@ cd pytorch-ssd
conda env create -f env_mobv2ssd.yml
python run_ssd_live_demo.py mb2-ssd-lite <pth-model-fil> <labels-file>
```
## Run the demo
To run the an object detection demo follow these steps (example with yolov3):
## Darknet Parser
tkDNN implement and easy parser for darknet cfg files, a network can be converted with *tk::dnn::darknetParser*:
```
rm yolo3_FP32.rt # be sure to delete(or move) old tensorRT files
./test_yolo3 # run the yolo test (is slow)
./demo yolo3_FP32.rt ../demo/yolo_test.mp4 y
// example of parsing yolo4
tk::dnn::Network *net = tk::dnn::darknetParser("yolov4.cfg", "yolov4/layers", "coco.names");
net->print();
```
In general the demo program takes 4 parameters:
All models from darknet are now parsed directly from cfg, you still need to export the weights with the described tools in the previous section.
<details>
<summary>Supported layers</summary>
convolutional
maxpool
avgpool
shortcut
upsample
route
reorg
region
yolo
</details>
<details>
<summary>Supported activations</summary>
relu
leaky
mish
</details>
## Run the demo
This is an example using yolov4.
To run the an object detection first create the .rt file by running:
```
./demo <network-rt-file> <path-to-video> <kind-of-network> <number-of-classes>
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>
```
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.
N.b. By default it is used FP32 inference
![demo](https://user-images.githubusercontent.com/11562617/72547657-540e7800-388d-11ea-83c6-49dfea2a0607.gif)
### Run the 3D demo
@@ -149,36 +251,59 @@ The demo3D program takes the same parameters of the demo program:
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
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)
./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).
### 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 instead 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
rm yolo3_INT8.rt # be sure to delete(or move) old tensorRT files
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
./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
```
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
```
## mAP demo
@@ -212,6 +337,8 @@ cd build
./map_demo dla34_cnet_FP32.rt c ../demo/COCO_val2017/all_labels.txt ../demo/config.yaml
```
This demo also creates a json file named ```net_name_COCO_res.json``` containing all the detections computed. The detections are in COCO format, the correct format to submit the results to [CodaLab COCO detection challenge](https://competitions.codalab.org/competitions/20794#participate).
## Existing tests and supported networks
| Test Name | Network | Dataset | N Classes | Input size | Weights |
@@ -237,6 +364,9 @@ cd build
| resnet101_cnet | Centernet (Resnet101 backend)<sup>4</sup> | [COCO 2017](http://cocodataset.org/) | 80 | 512x512 | [weights](https://cloud.hipert.unimore.it/s/5BTjHMWBcJk8g3i/download) |
| csresnext50-panet-spp | Cross Stage Partial Network <sup>7</sup> | [COCO 2014](http://cocodataset.org/) | 80 | 416x416 | [weights](https://cloud.hipert.unimore.it/s/Kcs4xBozwY4wFx8/download) |
| yolo4 | Yolov4 <sup>8</sup> | [COCO 2017](http://cocodataset.org/) | 80 | 416x416 | [weights](https://cloud.hipert.unimore.it/s/d97CFzYqCPCp5Hg/download) |
| yolo4_berkeley | Yolov4 <sup>8</sup> | [BDD100K ](https://bair.berkeley.edu/blog/2018/05/30/bdd/) | 10 | 540x320 | [weights](https://cloud.hipert.unimore.it/s/nkWFa5fgb4NTdnB/download) |
| yolo4tiny | Yolov4 tiny <sup>9</sup> | [COCO 2017](http://cocodataset.org/) | 80 | 416x416 | [weights](https://cloud.hipert.unimore.it/s/iRnc4pSqmx78gJs/download) |
| yolo4x | Yolov4x-mish <sup>9</sup> | [COCO 2017](http://cocodataset.org/) | 80 | 672x672 | [weights](https://cloud.hipert.unimore.it/s/BLPpiAigZJLorQD/download) |
## References
@@ -249,3 +379,4 @@ cd build
6. He, Kaiming, et al. "Deep residual learning for image recognition." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
7. Wang, Chien-Yao, et al. "CSPNet: A New Backbone that can Enhance Learning Capability of CNN." arXiv preprint arXiv:1911.11929 (2019).
8. Bochkovskiy, Alexey, Chien-Yao Wang, and Hong-Yuan Mark Liao. "YOLOv4: Optimal Speed and Accuracy of Object Detection." arXiv preprint arXiv:2004.10934 (2020).
9. Bochkovskiy, Alexey, "Yolo v4, v3 and v2 for Windows and Linux" (https://github.com/AlexeyAB/darknet)