Update READMEs
Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>
This commit is contained in:
Micaela Verucchi
@@ -19,6 +19,7 @@ If you use tkDNN in your research, please cite the [following paper](https://iee
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### What's new (20 July 2021)
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- [x] Support to sematic segmentation [README](docs/README_seg.md)
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- [x] Support 2D/3D Object Detection and Tracking [README](docs/README_2d3dtracking.md)
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- [ ] Support to TensorRT8 (WIP)
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## FPS Results
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@@ -70,15 +71,13 @@ Results for COCO val 2017 (5k images), on RTX 2080Ti, with conf threshold=0.001
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- [tkDNN](#tkdnn)
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- [Index](#index)
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- [Dependencies](#dependencies)
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- [About OpenCV](#about-opencv)
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- [How to compile this repo](#how-to-compile-this-repo)
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- [Workflow](#workflow)
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- [How to export weights](#how-to-export-weights)
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- [Run the demo](#run-the-demo)
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- [mAP demo](#map-demo)
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- [Exporting weights](#exporting-weights)
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- [Run the demos](#run-the-demos)
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- [tkDNN on Windows 10 (experimental)](#tkdnn-on-windows-10-experimental)
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- [Existing tests and supported networks](#existing-tests-and-supported-networks)
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- [References](#references)
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- [tkDNN on Windows 10 (experimental)](#tkdnn-on-windows-10-experimental)
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## Dependencies
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@@ -97,7 +96,7 @@ sudo apt install libyaml-cpp-dev curl libeigen3-dev
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```
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## About OpenCV
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#### About OpenCV
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To compile and install OpenCV4 with contrib us the script ```install_OpenCV4.sh```. It will download and compile OpenCV in Download folder.
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```
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bash scripts/install_OpenCV4.sh
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@@ -125,15 +124,21 @@ Steps needed to do inference on tkDNN with a custom neural network.
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## Exporting weights
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For specific details on how to export weights see [here](./docs/exporting_weights.md)
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For specific details on how to export weights see [HERE](./docs/exporting_weights.md).
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## Run the demo
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## Run the demos
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For specific details on how to run the demos see [here](./docs/demo.md)
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For specific details on how to run:
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- 2D object detection demos, details on FP16, INT8 and batching see [HERE](./docs/demo.md).
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- segmentation demos see [HERE](./docs/README_seg.md).
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- 2D/3D object detection and tracking demos see [HERE](./docs/README_2d3dtracking.md).
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- mAP demo to evaluate 2D object detectors see [HERE](./docs/mAP_demo.md).
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## mAP demo
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For specific details on how to run the mAP demo see [here](./docs/mAP_demo.md)
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## tkDNN on Windows 10 (experimental)
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For specific details on how to run tkDNN on Windows 10 see [HERE](./docs/windows.md).
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## Existing tests and supported networks
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@@ -160,14 +165,20 @@ For specific details on how to run the mAP demo see [here](./docs/mAP_demo.md)
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| resnet101_cnet | Centernet (Resnet101 backend)<sup>4</sup> | [COCO 2017](http://cocodataset.org/) | 80 | 512x512 | [weights](https://cloud.hipert.unimore.it/s/5BTjHMWBcJk8g3i/download) |
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| 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) |
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| yolo4 | Yolov4 <sup>8</sup> | [COCO 2017](http://cocodataset.org/) | 80 | 416x416 | [weights](https://cloud.hipert.unimore.it/s/d97CFzYqCPCp5Hg/download) |
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| yolo4_320 | Yolov4 <sup>8</sup> | [COCO 2017](http://cocodataset.org/) | 80 | 320x320 | [weights](https://cloud.hipert.unimore.it/s/d97CFzYqCPCp5Hg/download) |
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| yolo4_512 | Yolov4 <sup>8</sup> | [COCO 2017](http://cocodataset.org/) | 80 | 512x512 | [weights](https://cloud.hipert.unimore.it/s/d97CFzYqCPCp5Hg/download) |
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| yolo4_608 | Yolov4 <sup>8</sup> | [COCO 2017](http://cocodataset.org/) | 80 | 608x608 | [weights](https://cloud.hipert.unimore.it/s/d97CFzYqCPCp5Hg/download) |
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| 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) |
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| yolo4tiny | Yolov4 tiny <sup>9</sup> | [COCO 2017](http://cocodataset.org/) | 80 | 416x416 | [weights](https://cloud.hipert.unimore.it/s/iRnc4pSqmx78gJs/download) |
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| yolo4x | Yolov4x-mish <sup>9</sup> | [COCO 2017](http://cocodataset.org/) | 80 | 640x640 | [weights](https://cloud.hipert.unimore.it/s/5MFjtNtgbDGdJEo/download) |
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| yolo4x | Yolov4x-mish <sup>9</sup> | [COCO 2017](http://cocodataset.org/) |
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| yolo4tiny_512 | Yolov4 tiny <sup>9</sup> | [COCO 2017](http://cocodataset.org/) | 80 | 512x512 | [weights](https://cloud.hipert.unimore.it/s/iRnc4pSqmx78gJs/download) |
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80 | 640x640 | [weights](https://cloud.hipert.unimore.it/s/5MFjtNtgbDGdJEo/download) |
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| yolo4x-cps | Scaled Yolov4 <sup>10</sup> | [COCO 2017](http://cocodataset.org/) | 80 | 512x512 | [weights](https://cloud.hipert.unimore.it/s/AfzHE4BfTeEm2gH/download) |
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| shelfnet | ShelfNet18_realtime<sup>11</sup> | [Cityscapes](https://www.cityscapes-dataset.com/) | 19 | 1024x1024 | [weights](https://cloud.hipert.unimore.it/s/mEDZMRJaGCFWSJF/download) |
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| shelfnet_berkeley | ShelfNet18_realtime<sup>11</sup> | [DeepDrive](https://bdd-data.berkeley.edu/) | 20 | 1024x1024 | [weights](https://cloud.hipert.unimore.it/s/m92e7QdD9gYMF7f/download) |
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| dla34_cnet3d | Centernet3D (DLA34 backend)<sup>4</sup> | [KITTI 2017](http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d) | 1 | 512x512 | [weights](https://cloud.hipert.unimore.it/s/2MDyWGzQsTKMjmR/download) |
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| dla34_ctrack | CenterTrack (DLA34 backend)<sup>12</sup> | [NuScenes 3D](https://www.nuscenes.org/) | 7 | 512x512 | [weights](https://cloud.hipert.unimore.it/s/rjNfgGL9FtAXLHp/download) |
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### tkDNN on Windows 10 (experimental)
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For specific details on how to run the demos on Windows 10 see [here](./docs/windows.md)
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## References
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@@ -181,3 +192,5 @@ For specific details on how to run the demos on Windows 10 see [here](./docs/win
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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).
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9. Bochkovskiy, Alexey, "Yolo v4, v3 and v2 for Windows and Linux" (https://github.com/AlexeyAB/darknet)
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10. Wang, Chien-Yao, Alexey Bochkovskiy, and Hong-Yuan Mark Liao. "Scaled-YOLOv4: Scaling Cross Stage Partial Network." arXiv preprint arXiv:2011.08036 (2020).
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11. Zhuang, Juntang, et al. "ShelfNet for fast semantic segmentation." Proceedings of the IEEE International Conference on Computer Vision Workshops. 2019.
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12. Zhou, Xingyi, Vladlen Koltun, and Philipp Krähenbühl. "Tracking objects as points." European Conference on Computer Vision. Springer, Cham, 2020.
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@@ -1,6 +1,42 @@
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# 2D/3D Object Detection and Tracking
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Currently tkDNN supports only CenterTrack as OD & Tracker network.
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Currently tkDNN supports only CenterTrack as 3DOD & 2D/3D Tracker network.
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## 3D Object Detection
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To run the 3D object detection demo follow these steps (example with CenterNet based on DLA34):
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```
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rm dla34_cnet3d_fp32.rt # be sure to delete(or move) old tensorRT files
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./test_dla34_cnet3d # run the yolo test (is slow)
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./demo3D dla34_cnet3d_fp32.rt ../demo/yolo_test.mp4 NULL c
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```
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The demo3D program takes the same parameters of the demo program:
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```
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./demo3D <network-rt-file> <path-to-video> <calibration-file> <kind-of-network> <number-of-classes> <n-batches> <show-flag> <conf-thresh>
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```
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where
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* ```<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.
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## Object Detection and Tracking
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To run the 3D object detection & tracking demo follow these steps (example with CenterTrack based on DLA34):
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```
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rm dla34_ctrack_fp32.rt # be sure to delete(or move) old tensorRT files
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./test_dla34_ctrack # run the yolo test (is slow)
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./demoTracker dla34_ctrack_fp32.rt ../demo/yolo_test.mp4 NULL c
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```
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The demoTracker program takes the same parameters of the demo program:
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```
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./demoTracker <network-rt-file> <path-to-video> <calibration-file> <kind-of-network> <number-of-classes> <n-batches> <show-flag> <conf-thresh> <2D/3D-flag>
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```
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where
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* ```<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.
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* ```<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).
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## FPS Results
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+1
-22
@@ -2,19 +2,6 @@
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Currently tkDNN supports only ShelfNet as semantic segmentation network.
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## Export weights from Shelfnet
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To get the weights needed to run Shelfnet tests use [this](https://git.hipert.unimore.it/mverucchi/shelfnet) fork of a Pytorch implementation of Shelfnet network.
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```
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git clone https://git.hipert.unimore.it/mverucchi/shelfnet
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cd shelfnet
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cd ShelfNet18_realtime
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conda env create --file shelfnet_env.yml
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conda activate shelfnet
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mkdir layer debug
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python export.py
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```
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## Run the demo
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@@ -46,15 +33,7 @@ NB) The batching is not used to work on more streams, rather to work on more til
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For other demo videos refer to [this playlist](https://www.youtube.com/playlist?list=PLv0nEQYDD45y5EdSiywwCGPBmJVUzIWwe).
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## Existing tests and supported networks
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| Test Name | Network | Dataset | N Classes | Input size | Weights |
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| :---------------- | :-------------------------------------------- | :-----------------------------------------------------------: | :-------: | :-----------: | :------------------------------------------------------------------------ |
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| shelfnet | ShelfNet18_realtime<sup>1</sup> | [Cityscapes](https://www.cityscapes-dataset.com/) | 19 | 1024x1024 | [weights](https://cloud.hipert.unimore.it/s/mEDZMRJaGCFWSJF/download) |
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| shelfnet_berkeley | ShelfNet18_realtime<sup>1</sup> | [DeepDrive](https://bdd-data.berkeley.edu/) | 20 | 1024x1024 | [weights](https://cloud.hipert.unimore.it/s/m92e7QdD9gYMF7f/download) |
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1. Zhuang, Juntang, et al. "ShelfNet for fast semantic segmentation." Proceedings of the IEEE International Conference on Computer Vision Workshops. 2019.
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NB) The gif and the videos are obtained with Mapillary Vistas weights, that we cannot publicly share due to its license restrictions. However, you can train Shelfnet using Mapillary and [this](https://git.hipert.unimore.it/mverucchi/shelfnet) fork of the original repo.
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## FPS Results
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+8
-106
@@ -1,8 +1,6 @@
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# tkDNN Demo
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# 2D Object Detection with tkDNN
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## Supported Network
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2D Object Detection:
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## Supported Networks
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* Yolo4, Yolo4-csp, Yolo4x, Yolo4_berkeley, Yolo4tiny
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* Yolo3, Yolo3_berkeley, Yolo3_coco4, Yolo3_flir, Yolo3_512, Yolo3tiny, Yolo3tiny_512
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@@ -11,28 +9,12 @@
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* Resnet101_cnet, Dla34_cnet
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* Mobilenetv2ssd, Mobilenetv2ssd512, Bdd-mobilenetv2ssd
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3D Object Detection:
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* Dla34_cnet3d
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2D/3D Object Detection and Tracking:
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* Dla34_cnet3d_track
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## Index
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- [Run the demo](#run-the-demo)
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- [2D Object Detection](#2d-object-detection)
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- [3D Object Detection](#3d-object-detection)
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- [Object Detection and Tracking](#object-detection-and-tracking)
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- [FP16 inference](#fp16-inference)
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- [INT8 inference](#int8-inference)
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- [Batching](#batching)
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- [Run the demo on Windows](#run-the-demo-on-windows)
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## Run the demo
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N.b. By default it is used FP32 inference
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- [2D Object Detection](#2d-object-detection)
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- [FP16 inference](#fp16-inference)
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- [INT8 inference](#int8-inference)
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- [Batching](#batching)
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### 2D Object Detection
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This is an example using yolov4.
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@@ -66,45 +48,11 @@ where
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* ```<show-flag>``` if set to 0 the demo will not show the visualization but save the video into result.mp4 (if n-batches ==1)
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* ```<conf-thresh>``` confidence threshold for the detector. Only bounding boxes with threshold greater than conf-thresh will be displayed.
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N.B. By default it is used FP32 inference
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### 3D Object Detection
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To run the 3D object detection demo follow these steps (example with CenterNet based on DLA34):
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```
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rm dla34_cnet3d_fp32.rt # be sure to delete(or move) old tensorRT files
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./test_dla34_cnet3d # run the yolo test (is slow)
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./demo3D dla34_cnet3d_fp32.rt ../demo/yolo_test.mp4 NULL c
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```
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The demo3D program takes the same parameters of the demo program:
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```
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./demo3D <network-rt-file> <path-to-video> <calibration-file> <kind-of-network> <number-of-classes> <n-batches> <show-flag> <conf-thresh>
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```
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where
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* ```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.
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### Object Detection and Tracking
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To run the 3D object detection & tracking demo follow these steps (example with CenterTrack based on DLA34):
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```
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rm dla34_ctrack_fp32.rt # be sure to delete(or move) old tensorRT files
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./test_dla34_ctrack # run the yolo test (is slow)
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./demoTracker dla34_ctrack_fp32.rt ../demo/yolo_test.mp4 NULL c
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```
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The demoTracker program takes the same parameters of the demo program:
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```
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./demoTracker <network-rt-file> <path-to-video> <calibration-file> <kind-of-network> <number-of-classes> <n-batches> <show-flag> <conf-thresh> <2D/3D-flag>
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```
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where
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* ```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.
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* ```<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).
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### FP16 inference
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@@ -115,7 +63,7 @@ rm yolo3_fp16.rt # be sure to delete(or move) old tensorRT files
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./test_yolo3 # run the yolo test (is slow)
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./demo yolo3_fp16.rt ../demo/yolo_test.mp4 y
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```
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N.b. Using FP16 inference will lead to some errors in the results (first or second decimal).
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N.B. Using FP16 inference will lead to some errors in the results (first or second decimal).
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### INT8 inference
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@@ -169,49 +117,3 @@ rm yolo3_fp32.rt # be sure to delete(or move) old tensorRT fil
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./test_yolo3 # build RT file
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./test_rtinference yolo3_fp32.rt 4 # test with a batch size of 4
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```
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### Run the demo on Windows
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This example uses yolo4_tiny.\
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To run the object detection file create .rt file bu running:
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```
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.\test_yolo4tiny.exe
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```
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Once the rt file has been successfully create,run the demo using the following command:
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```
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.\demo.exe yolo4tiny_fp32.rt ..\demo\yolo_test.mp4 y
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```
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For general info on more demo paramters,check Run the demo section on top
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To run the test_all_tests.sh on windows,use git bash or msys2
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### FP16 inference windows
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This is an untested feature on windows.To run the object detection demo with FP16 interference follow the below steps(example with yolo4tiny):
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```
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set TKDNN_MODE=FP16
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del /f yolo4tiny_fp16.rt
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.\test_yolo4tiny.exe
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.\demo.exe yolo4tiny_fp16.rt ..\demo\yolo_test.mp4
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```
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### INT8 inference windows
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To run object detection demo with INT8 (example with yolo4tiny):
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```
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set TKDNN_MODE=INT8
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set TKDNN_CALIB_LABEL_PATH=..\demo\COCO_val2017\all_labels.txt
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set TKDNN_CALIB_IMG_PATH=..\demo\COCO_val2017\all_images.txt
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del /f yolo4tiny_int8.rt # be sure to delete(or move) old tensorRT files
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.\test_yolo4tiny.exe # run the yolo test (is slow)
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.\demo.exe yolo4tiny_int8.rt ..\demo\yolo_test.mp4 y
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```
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### Known issues with tkDNN on Windows
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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
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All Darknet models work properly with demo using MSVC version(16.7-16.9)
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It is recommended to use Nvidia Driver(465+),Cuda unknown errors have been observed when using older drivers on pascal(SM 61) devices.
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@@ -8,6 +8,7 @@
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- [3)Export weights for CenterNet](#3export-weights-for-centernet)
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- [4)Export weights for MobileNetSSD](#4export-weights-for-mobilenetssd)
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- [5)Export weights for CenterTrack](#5export-weights-for-centertrack)
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- [6)Export weights for ShelfNet](#6export-weights-for-shelfnet)
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- [Darknet Parser](#darknet-parser)
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## How to export weights
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@@ -30,7 +31,7 @@ make
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mkdir layers debug
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./darknet export <path-to-cfg-file> <path-to-weights> layers
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||||
```
|
||||
N.b. Use compilation with CPU (leave GPU=0 in Makefile) if you also want debug.
|
||||
N.B. Use compilation with CPU (leave GPU=0 in Makefile) if you also want debug.
|
||||
|
||||
### 2)Export weights for DLA34 and ResNet101
|
||||
To get weights and outputs needed to run the tests dla34 and resnet101 use the Python script and the Anaconda environment included in the repository.
|
||||
@@ -72,6 +73,19 @@ git clone https://github.com/sapienzadavide/CenterTrack.git
|
||||
python demo.py tracking,ddd --load_model ../models/nuScenes_3Dtracking.pth --dataset nuscenes --pre_hm --track_thresh 0.1 --demo /path/to/image/or/folder/or/video/or/webcam --test_focal_length 633 --exp_wo --exp_wo_dim 512 --input_h 512 --input_w 512
|
||||
```
|
||||
|
||||
### 6)Export weights for ShelfNet
|
||||
To get the weights needed to run Shelfnet tests use [this](https://git.hipert.unimore.it/mverucchi/shelfnet) fork of a Pytorch implementation of Shelfnet network.
|
||||
|
||||
```
|
||||
git clone https://git.hipert.unimore.it/mverucchi/shelfnet
|
||||
cd shelfnet
|
||||
cd ShelfNet18_realtime
|
||||
conda env create --file shelfnet_env.yml
|
||||
conda activate shelfnet
|
||||
mkdir layer debug
|
||||
python export.py
|
||||
```
|
||||
|
||||
## Darknet Parser
|
||||
tkDNN implement and easy parser for darknet cfg files, a network can be converted with *tk::dnn::darknetParser*:
|
||||
```
|
||||
|
||||
+2
-4
@@ -1,8 +1,6 @@
|
||||
# mAP demo
|
||||
# Run the mAP demo
|
||||
|
||||
## Run the mAP demo
|
||||
|
||||
To compute mAP, precision, recall and f1score, run the map_demo.
|
||||
To compute mAP, precision, recall and f1score to evaluate 2D object detectors, run the map_demo.
|
||||
|
||||
A validation set is needed.
|
||||
To download COCO_val2017 (80 classes) run (form the root folder):
|
||||
|
||||
Reference in New Issue
Block a user