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update readme, clean up

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wang-xinyu 2020-07-06 17:41:36 +08:00
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10
README.md
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@ -13,6 +13,8 @@ All the models are implemented in pytorch or mxnet first, and export a weights f
- `22 May 2020`. A new branch [trt4](https://github.com/wang-xinyu/tensorrtx/tree/trt4) created, which is using TensorRT 4 API. Now the master branch is using TensorRT 7 API. But only `yolov4` has been migrated to TensorRT 7 API for now. The rest will be migrated soon. And a tutorial for `migarating from TensorRT 4 to 7` provided.
- `28 May 2020`. arcface LResNet50E-IR model from [deepinsight/insightface](https://github.com/deepinsight/insightface) implemented. We got 333fps on GTX1080.
- `2 June 2020`. yolov3 and yolov3-spp migrated to TensorRT 7 API. The new yolov3 is using pytorch implementation [ultralytics/yolov3](https://github.com/ultralytics/yolov3), the yolov3 in branch `trt4` was using pytorch implementation [ayooshkathuria/pytorch-yolo-v3](https://github.com/ayooshkathuria/pytorch-yolo-v3).
- `23 June 2020`. Update yolov5-s model according to [ultralytics/yolov5](https://github.com/ultralytics/yolov5)'s PANet updates on 22 June 2020.
- `6 July 2020`. Add yolov3-tiny, and got 333fps GTX1080.
## Tutorials
@ -46,6 +48,7 @@ Following models are implemented.
|[shufflenet](./shufflenetv2)| ShuffleNetV2 with 0.5x output channels |
|[squeezenet](./squeezenet)| SqueezeNet 1.1 model |
|[vgg](./vgg)| VGG 11-layer model |
|[yolov3-tiny](./yolov3-tiny)| weights and pytorch implementation from [ultralytics/yolov3](https://github.com/ultralytics/yolov3) |
|[yolov3](./yolov3)| darknet-53, weights and pytorch implementation from [ultralytics/yolov3](https://github.com/ultralytics/yolov3) |
|[yolov3-spp](./yolov3-spp)| darknet-53, weights and pytorch implementation from [ultralytics/yolov3](https://github.com/ultralytics/yolov3) |
|[yolov4](./yolov4)| CSPDarknet53, weights from [AlexeyAB/darknet](https://github.com/AlexeyAB/darknet#pre-trained-models), pytorch implementation from [ultralytics/yolov3](https://github.com/ultralytics/yolov3) |
@ -80,15 +83,16 @@ Some tricky operations encountered in these models, already solved, but might ha
| Models | Device | BatchSize | Mode | Input Shape(HxW) | FPS |
|-|-|:-:|:-:|:-:|:-:|
| YOLOv3-tiny | Xeon E5-2620/GTX1080 | 1 | FP16 | 608x608 | 333 |
| YOLOv3(darknet53) | Xeon E5-2620/GTX1080 | 1 | FP16 | 608x608 | 39.2 |
| YOLOv3-spp(darknet53) | Xeon E5-2620/GTX1080 | 1 | FP32 | 256x416 | 94 |
| YOLOv3-spp(darknet53) | Xeon E5-2620/GTX1080 | 1 | FP16 | 608x608 | 38.5 |
| YOLOv4(CSPDarknet53) | Xeon E5-2620/GTX1080 | 1 | FP16 | 608x608 | 35.7 |
| YOLOv4(CSPDarknet53) | Xeon E5-2620/GTX1080 | 4 | FP16 | 608x608 | 40.9 |
| YOLOv4(CSPDarknet53) | Xeon E5-2620/GTX1080 | 8 | FP16 | 608x608 | 41.3 |
| YOLOv5-s | Xeon E5-2620/GTX1080 | 1 | FP16 | 608x608 | 167 |
| YOLOv5-s | Xeon E5-2620/GTX1080 | 4 | FP16 | 608x608 | 182 |
| YOLOv5-s | Xeon E5-2620/GTX1080 | 8 | FP16 | 608x608 | 186 |
| YOLOv5-s | Xeon E5-2620/GTX1080 | 1 | FP16 | 608x608 | 142 |
| YOLOv5-s | Xeon E5-2620/GTX1080 | 4 | FP16 | 608x608 | 173 |
| YOLOv5-s | Xeon E5-2620/GTX1080 | 8 | FP16 | 608x608 | 190 |
| RetinaFace(resnet50) | TX2 | 1 | FP16 | 384x640 | 15 |
| RetinaFace(resnet50) | Xeon E5-2620/GTX1080 | 1 | FP32 | 928x1600 | 15 |
| ArcFace(LResNet50E-IR) | Xeon E5-2620/GTX1080 | 1 | FP32 | 112x112 | 333 |

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yolov3-tiny/yolov3-tiny.cpp
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@ -249,11 +249,6 @@ ICudaEngine* createEngine(unsigned int maxBatchSize, IBuilder* builder, IBuilder
auto pad11 = network->addPaddingNd(*lr10->getOutput(0), DimsHW{0, 0}, DimsHW{1, 1});
auto pool11 = network->addPoolingNd(*pad11->getOutput(0), PoolingType::kMAX, DimsHW{2, 2});
pool11->setStrideNd(DimsHW{1, 1});
Dims dims = pool11->getOutput(0)->getDimensions();
std::cout << "pool11 dims " << dims.d[0] << " " << dims.d[1] << " " << dims.d[2] << std::endl;
auto lr12 = convBnLeaky(network, weightMap, *pool11->getOutput(0), 1024, 3, 1, 1, 12);
auto lr13 = convBnLeaky(network, weightMap, *lr12->getOutput(0), 256, 1, 1, 0, 13);
auto lr14 = convBnLeaky(network, weightMap, *lr13->getOutput(0), 512, 3, 1, 1, 14);

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yolov5/README.md
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@ -2,7 +2,7 @@
The Pytorch implementation is [ultralytics/yolov5](https://github.com/ultralytics/yolov5).
I was using [commit 6fd3939] of [ultralytics/yolov5](https://github.com/ultralytics/yolov5). And I made a copy of [yolov5s.pt(google drive)](https://drive.google.com/file/d/1w38DgmrP3iwiJi_AOdabuuE_zanMhO5_/view?usp=sharing). Just in case the yolov5 model updated.
I was using [ultralytics/yolov5](https://github.com/ultralytics/yolov5)(Commits on Jun 23, 2020). And I made a copy of [yolov5s.pt(google drive)](https://drive.google.com/file/d/1w38DgmrP3iwiJi_AOdabuuE_zanMhO5_/view?usp=sharing). Just in case the yolov5 model updated.
## How to Run