thebears/deepstream_yolo
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Add models benchmarks

Browse Source 
Add models benchmarks
Minor fixes
This commit is contained in:
unknown
2022-02-10 00:52:40 -03:00
parent 5b9b1f77c8
commit ff785458dd
5 changed files with 68 additions and 26 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
nvdsinfer_custom_impl_Yolo/layers/activation_layer.cpp
View File

@@ -81,7 +81,7 @@ nvinfer1::ILayer* activationLayer(
mish->setName(mishLayerName.c_str()); mish->setName(mishLayerName.c_str());
output = mish; output = mish;
} }
else if (activation == "silu") else if (activation == "silu" || activation == "swish")
{ {
nvinfer1::IActivationLayer* sigmoid = network->addActivation( nvinfer1::IActivationLayer* sigmoid = network->addActivation(
*input, nvinfer1::ActivationType::kSIGMOID); *input, nvinfer1::ActivationType::kSIGMOID);

3
nvdsinfer_custom_impl_Yolo/layers/convolutional_layer.cpp
View File

@@ -14,6 +14,7 @@ nvinfer1::ILayer* convolutionalLayer(
int& weightPtr, int& weightPtr,
std::string weightsType, std::string weightsType,
int& inputChannels, int& inputChannels,
float eps,
nvinfer1::ITensor* input, nvinfer1::ITensor* input,
nvinfer1::INetworkDefinition* network) nvinfer1::INetworkDefinition* network)
{ {
@@ -156,7 +157,7 @@ nvinfer1::ILayer* convolutionalLayer(
} }
for (int i = 0; i < filters; ++i) for (int i = 0; i < filters; ++i)
{ {
bnRunningVar.push_back(sqrt(weights[weightPtr] + 1.0e-3)); bnRunningVar.push_back(sqrt(weights[weightPtr] + eps));
weightPtr++; weightPtr++;
} }
trtWeights.push_back(convWt); trtWeights.push_back(convWt);

1
nvdsinfer_custom_impl_Yolo/layers/convolutional_layer.h
View File

@@ -21,6 +21,7 @@ nvinfer1::ILayer* convolutionalLayer(
int& weightPtr, int& weightPtr,
std::string weightsType, std::string weightsType,
int& inputChannels, int& inputChannels,
float eps,
nvinfer1::ITensor* input, nvinfer1::ITensor* input,
nvinfer1::INetworkDefinition* network); nvinfer1::INetworkDefinition* network);

7
nvdsinfer_custom_impl_Yolo/yolo.cpp
View File

@@ -173,8 +173,12 @@ NvDsInferStatus Yolo::buildYoloNetwork(
} }
else if (m_ConfigBlocks.at(i).at("type") == "convolutional") { else if (m_ConfigBlocks.at(i).at("type") == "convolutional") {
float eps = 1.0e-5;
if (m_NetworkType.find("yolov5") != std::string::npos) {
eps = 1.0e-3;
}
std::string inputVol = dimsToString(previous->getDimensions()); std::string inputVol = dimsToString(previous->getDimensions());
nvinfer1::ILayer* out = convolutionalLayer(i, m_ConfigBlocks.at(i), weights, m_TrtWeights, weightPtr, weightsType, channels, previous, &network); nvinfer1::ILayer* out = convolutionalLayer(i, m_ConfigBlocks.at(i), weights, m_TrtWeights, weightPtr, weightsType, channels, eps, previous, &network);
previous = out->getOutput(0); previous = out->getOutput(0);
assert(previous != nullptr); assert(previous != nullptr);
channels = getNumChannels(previous); channels = getNumChannels(previous);
@@ -442,6 +446,7 @@ Yolo::parseConfigFile (const std::string cfgFilePath)
while (getline(file, line)) while (getline(file, line))
{ {
if (line.size() == 0) continue; if (line.size() == 0) continue;
if (line.front() == ' ') continue;
if (line.front() == '#') continue; if (line.front() == '#') continue;
line = trim(line); line = trim(line);
if (line.front() == '[') if (line.front() == '[')

81
readme.md
View File

@@ -7,7 +7,6 @@ NVIDIA DeepStream SDK 6.0 configuration for YOLO models
* New documentation for multiple models * New documentation for multiple models
* DeepStream tutorials * DeepStream tutorials
* Native PP-YOLO support * Native PP-YOLO support
* Models benchmark
* GPU NMS * GPU NMS
* Dynamic batch-size * Dynamic batch-size
@@ -21,9 +20,10 @@ NVIDIA DeepStream SDK 6.0 configuration for YOLO models
* Support for convolutional groups * Support for convolutional groups
* Support for INT8 calibration * Support for INT8 calibration
* Support for non square models * Support for non square models
* **Support for implicit and channel layers (YOLOR)** * Support for implicit and channel layers (YOLOR)
* **YOLOv5 6.0 native support** * YOLOv5 6.0 native support
* **Initial YOLOR native support** * Initial YOLOR native support
* **Models benchmarks**
## ##
@@ -31,6 +31,7 @@ NVIDIA DeepStream SDK 6.0 configuration for YOLO models
* [Requirements](#requirements) * [Requirements](#requirements)
* [Tested models](#tested-models) * [Tested models](#tested-models)
* [Benchmarks](#benchmarks)
* [dGPU installation](#dgpu-installation) * [dGPU installation](#dgpu-installation)
* [Basic usage](#basic-usage) * [Basic usage](#basic-usage)
* [YOLOv5 usage](#yolov5-usage) * [YOLOv5 usage](#yolov5-usage)
@@ -57,25 +58,59 @@ NVIDIA DeepStream SDK 6.0 configuration for YOLO models
## ##
### Tested models ### Tested models
* [YOLOR-CSP](https://github.com/WongKinYiu/yolor) [[cfg]](https://raw.githubusercontent.com/WongKinYiu/yolor/main/cfg/yolor_csp.cfg) [[pt]](https://drive.google.com/file/d/1ZEqGy4kmZyD-Cj3tEFJcLSZenZBDGiyg/view?usp=sharing)
* [YOLOR-CSP*](https://github.com/WongKinYiu/yolor) [[cfg]](https://raw.githubusercontent.com/WongKinYiu/yolor/main/cfg/yolor_csp.cfg) [[pt]](https://drive.google.com/file/d/1OJKgIasELZYxkIjFoiqyn555bcmixUP2/view?usp=sharing) * [Darknet YOLO](https://github.com/AlexeyAB/darknet)
* [YOLOR-CSP-X](https://github.com/WongKinYiu/yolor) [[cfg]](https://raw.githubusercontent.com/WongKinYiu/yolor/main/cfg/yolor_csp_x.cfg) [[pt]](https://drive.google.com/file/d/1L29rfIPNH1n910qQClGftknWpTBgAv6c/view?usp=sharing) * [YOLOv5 6.0](https://github.com/ultralytics/yolov5)
* [YOLOR-CSO-X*](https://github.com/WongKinYiu/yolor) [[cfg]](https://raw.githubusercontent.com/WongKinYiu/yolor/main/cfg/yolor_csp_x.cfg) [[pt]](https://drive.google.com/file/d/1NbMG3ivuBQ4S8kEhFJ0FIqOQXevGje_w/view?usp=sharing) * [YOLOR](https://github.com/WongKinYiu/yolor)
* [YOLOv5 6.0](https://github.com/ultralytics/yolov5) [[pt]](https://github.com/ultralytics/yolov5/releases/tag/v6.0) * [MobileNet-YOLO](https://github.com/dog-qiuqiu/MobileNet-Yolo)
* [YOLOv4x-Mish](https://github.com/AlexeyAB/darknet) [[cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4x-mish.cfg)] [[weights](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4x-mish.weights)] * [YOLO-Fastest](https://github.com/dog-qiuqiu/Yolo-Fastest)
* [YOLOv4-CSP](https://github.com/WongKinYiu/ScaledYOLOv4/tree/yolov4-csp) [[cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4-csp.cfg)] [[weights](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-csp.weights)]
* [YOLOv4](https://github.com/AlexeyAB/darknet) [[cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4.cfg)] [[weights](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.weights)] ##
* [YOLOv4-Tiny](https://github.com/AlexeyAB/darknet) [[cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4-tiny.cfg)] [[weights](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.weights)]
* [YOLOv3-SPP](https://github.com/pjreddie/darknet) [[cfg](https://raw.githubusercontent.com/pjreddie/darknet/master/cfg/yolov3-spp.cfg)] [[weights](https://pjreddie.com/media/files/yolov3-spp.weights)] ### Benchmarks
* [YOLOv3](https://github.com/pjreddie/darknet) [[cfg](https://raw.githubusercontent.com/pjreddie/darknet/master/cfg/yolov3.cfg)] [[weights](https://pjreddie.com/media/files/yolov3.weights)]
* [YOLOv3-Tiny-PRN](https://github.com/WongKinYiu/PartialResidualNetworks) [[cfg](https://raw.githubusercontent.com/WongKinYiu/PartialResidualNetworks/master/cfg/yolov3-tiny-prn.cfg)] [[weights](https://github.com/WongKinYiu/PartialResidualNetworks/raw/master/model/yolov3-tiny-prn.weights)] ```
* [YOLOv3-Tiny](https://github.com/pjreddie/darknet) [[cfg](https://raw.githubusercontent.com/pjreddie/darknet/master/cfg/yolov3-tiny.cfg)] [[weights](https://pjreddie.com/media/files/yolov3-tiny.weights)] nms = 0.45 (changed to beta_nms when used in Darknet cfg file) / 0.6 (YOLOv5 and YOLOR models)
* [YOLOv3-Lite](https://github.com/dog-qiuqiu/MobileNet-Yolo) [[cfg](https://raw.githubusercontent.com/dog-qiuqiu/MobileNet-Yolo/master/MobileNetV2-YOLOv3-Lite/COCO/MobileNetV2-YOLOv3-Lite-coco.cfg)] [[weights](https://github.com/dog-qiuqiu/MobileNet-Yolo/raw/master/MobileNetV2-YOLOv3-Lite/COCO/MobileNetV2-YOLOv3-Lite-coco.weights)] pre-cluster-threshold = 0.001 (mAP eval) / 0.25 (FPS measurement)
* [YOLOv3-Nano](https://github.com/dog-qiuqiu/MobileNet-Yolo) [[cfg](https://raw.githubusercontent.com/dog-qiuqiu/MobileNet-Yolo/master/MobileNetV2-YOLOv3-Nano/COCO/MobileNetV2-YOLOv3-Nano-coco.cfg)] [[weights](https://github.com/dog-qiuqiu/MobileNet-Yolo/raw/master/MobileNetV2-YOLOv3-Nano/COCO/MobileNetV2-YOLOv3-Nano-coco.weights)] batch-size = 1
* [YOLO-Fastest 1.1](https://github.com/dog-qiuqiu/Yolo-Fastest) [[cfg](https://raw.githubusercontent.com/dog-qiuqiu/Yolo-Fastest/master/ModelZoo/yolo-fastest-1.1_coco/yolo-fastest-1.1-xl.cfg)] [[weights](https://github.com/dog-qiuqiu/Yolo-Fastest/raw/master/ModelZoo/yolo-fastest-1.1_coco/yolo-fastest-1.1-xl.weights)] valid = val2017 (COCO) - 1000 random images for INT8 calibration
* [YOLO-Fastest-XL 1.1](https://github.com/dog-qiuqiu/Yolo-Fastest) [[cfg](https://raw.githubusercontent.com/dog-qiuqiu/Yolo-Fastest/master/ModelZoo/yolo-fastest-1.1_coco/yolo-fastest-1.1.cfg)] [[weights](https://github.com/dog-qiuqiu/Yolo-Fastest/raw/master/ModelZoo/yolo-fastest-1.1_coco/yolo-fastest-1.1.weights)] NOTE: Used maintain-aspect-ratio=1 in config_infer file for YOLOv4 (with letter_box=1), YOLOv5 and YOLOR models.
* [YOLOv2](https://github.com/pjreddie/darknet) [[cfg](https://raw.githubusercontent.com/pjreddie/darknet/master/cfg/yolov2.cfg)] [[weights](https://pjreddie.com/media/files/yolov2.weights)] ```
* [YOLOv2-Tiny](https://github.com/pjreddie/darknet) [[cfg](https://raw.githubusercontent.com/pjreddie/darknet/master/cfg/yolov2-tiny.cfg)] [[weights](https://pjreddie.com/media/files/yolov2-tiny.weights)]
<details><summary>NVIDIA GTX 1050 (4GB Mobile)</summary>
| DeepStream | Precision | Resolution | IoU=0.5:0.95 | IoU=0.5 | IoU=0.75 | FPS<br />(without display) |
|:------------------:|:---------:|:----------:|:------------:|:-------:|:--------:|:--------------------------:|
| YOLOR-CSP-X* | FP32 | 640 | 0.473 | 0.664 | 0.513 | 7.59 |
| YOLOR-CSP-X | FP32 | 640 | 0.470 | 0.661 | 0.507 | 7.52 |
| YOLOR-CSP* | FP32 | 640 | 0.459 | 0.652 | 0.496 | 13.28 |
| YOLOR-CSP | FP32 | 640 | 0.449 | 0.639 | 0.483 | 13.32 |
| YOLOv5x6 6.0 | FP32 | 1280 | 0.504 | 0.681 | 0.547 | 2.22 |
| YOLOv5l6 6.0 | FP32 | 1280 | 0.492 | 0.670 | 0.535 | 4.05 |
| YOLOv5m6 6.0 | FP32 | 1280 | 0.463 | 0.642 | 0.504 | 7.54 |
| YOLOv5s6 6.0 | FP32 | 1280 | 0.394 | 0.572 | 0.424 | 18.64 |
| YOLOv5n6 6.0 | FP32 | 1280 | 0.294 | 0.452 | 0.314 | 26.94 |
| YOLOv5x 6.0 | FP32 | 640 | 0.469 | 0.654 | 0.509 | 8.24 |
| YOLOv5l 6.0 | FP32 | 640 | 0.450 | 0.634 | 0.487 | 14.96 |
| YOLOv5m 6.0 | FP32 | 640 | 0.415 | 0.601 | 0.448 | 28.30 |
| YOLOv5s 6.0 | FP32 | 640 | 0.334 | 0.516 | 0.355 | 63.55 |
| YOLOv5n 6.0 | FP32 | 640 | 0.250 | 0.417 | 0.260 | 110.25 |
| YOLOv4-P6 | FP32 | 1280 | 0.499 | 0.685 | 0.542 | 2.57 |
| YOLOv4-P5 | FP32 | 896 | 0.472 | 0.659 | 0.513 | 5.48 |
| YOLOv4-CSP-X-SWISH | FP32 | 640 | 0.473 | 0.664 | 0.513 | 7.51 |
| YOLOv4-CSP-SWISH | FP32 | 640 | 0.459 | 0.652 | 0.496 | 13.13 |
| YOLOv4x-MISH | FP32 | 640 | 0.459 | 0.650 | 0.495 | 7.53 |
| YOLOv4-CSP | FP32 | 640 | 0.440 | 0.632 | 0.474 | 13.19 |
| YOLOv4 | FP32 | 608 | 0.498 | 0.740 | 0.549 | 12.18 |
| YOLOv4-Tiny | FP32 | 416 | 0.215 | 0.403 | 0.206 | 201.20 |
| YOLOv3-SPP | FP32 | 608 | 0.411 | 0.686 | 0.433 | 12.22 |
| YOLOv3-Tiny-PRN | FP32 | 416 | 0.167 | 0.382 | 0.125 | 277.14 |
| YOLOv3 | FP32 | 608 | 0.377 | 0.672 | 0.385 | 12.51 |
| YOLOv3-Tiny | FP32 | 416 | 0.095 | 0.203 | 0.079 | 218.42 |
| YOLOv2 | FP32 | 608 | 0.286 | 0.541 | 0.273 | 25.28 |
| YOLOv2-Tiny | FP32 | 416 | 0.102 | 0.258 | 0.061 | 231.36 |
</details>
## ##