【YOLOV5 libtorch】C++加载torchscript模型 GPU 推理 -window10
#include
#include
#include
#include
#include
#include
#include
//#include
std::vector non_max_suppression(torch::Tensor preds, float score_thresh = 0.5, float iou_thresh = 0.5)
{
std::vector output;
for (size_t i = 0; i < preds.sizes()[0]; ++i)
{
torch::Tensor pred = preds.select(0, i);
// Filter by scores
torch::Tensor scores = pred.select(1, 4) * std::get<0>(torch::max(pred.slice(1, 5, pred.sizes()[1]), 1));
pred = torch::index_select(pred, 0, torch::nonzero(scores > score_thresh).select(1, 0));
if (pred.sizes()[0] == 0) continue;
// (center_x, center_y, w, h) to (left, top, right, bottom)
pred.select(1, 0) = pred.select(1, 0) - pred.select(1, 2) / 2;
pred.select(1, 1) = pred.select(1, 1) - pred.select(1, 3) / 2;
pred.select(1, 2) = pred.select(1, 0) + pred.select(1, 2);
pred.select(1, 3) = pred.select(1, 1) + pred.select(1, 3);
// Computing scores and classes
std::tuple max_tuple = torch::max(pred.slice(1, 5, pred.sizes()[1]), 1);
pred.select(1, 4) = pred.select(1, 4) * std::get<0>(max_tuple);
pred.select(1, 5) = std::get<1>(max_tuple);
torch::Tensor dets = pred.slice(1, 0, 6);
torch::Tensor keep = torch::empty({ dets.sizes()[0] });
torch::Tensor areas = (dets.select(1, 3) - dets.select(1, 1)) * (dets.select(1, 2) - dets.select(1, 0));
std::tuple indexes_tuple = torch::sort(dets.select(1, 4), 0, 1);
torch::Tensor v = std::get<0>(indexes_tuple);
torch::Tensor indexes = std::get<1>(indexes_tuple);
int count = 0;
while (indexes.sizes()[0] > 0)
{
keep[count] = (indexes[0].item().toInt());
count += 1;
// Computing overlaps
torch::Tensor lefts = torch::empty(indexes.sizes()[0] - 1);
torch::Tensor tops = torch::empty(indexes.sizes()[0] - 1);
torch::Tensor rights = torch::empty(indexes.sizes()[0] - 1);
torch::Tensor bottoms = torch::empty(indexes.sizes()[0] - 1);
torch::Tensor widths = torch::empty(indexes.sizes()[0] - 1);
torch::Tensor heights = torch::empty(indexes.sizes()[0] - 1);
for (size_t i = 0; i < indexes.sizes()[0] - 1; ++i)
{
lefts[i] = std::max(dets[indexes[0]][0].item().toFloat(), dets[indexes[i + 1]][0].item().toFloat());
tops[i] = std::max(dets[indexes[0]][1].item().toFloat(), dets[indexes[i + 1]][1].item().toFloat());
rights[i] = std::min(dets[indexes[0]][2].item().toFloat(), dets[indexes[i + 1]][2].item().toFloat());
bottoms[i] = std::min(dets[indexes[0]][3].item().toFloat(), dets[indexes[i + 1]][3].item().toFloat());
widths[i] = std::max(float(0), rights[i].item().toFloat() - lefts[i].item().toFloat());
heights[i] = std::max(float(0), bottoms[i].item().toFloat() - tops[i].item().toFloat());
}
torch::Tensor overlaps = widths * heights;
// FIlter by IOUs
torch::Tensor ious = overlaps / (areas.select(0, indexes[0].item().toInt()) + torch::index_select(areas, 0, indexes.slice(0, 1, indexes.sizes()[0])) - overlaps);
indexes = torch::index_select(indexes, 0, torch::nonzero(ious <= iou_thresh).select(1, 0) + 1);
}
keep = keep.toType(torch::kInt64);
output.push_back(torch::index_select(dets, 0, keep.slice(0, 0, count)));
}
return output;
}
int main()
{
cout << "cuda是否可用：" << torch::cuda::is_available() << "\t显卡数量：" << torch::cuda::device_count() << endl;
cout << "cudnn是否可用：" << torch::cuda::cudnn_is_available() << endl;
// Loading Module D:\Software\vs2019+pcl+opencv\libtorch\lib
torch::jit::script::Module module;
//auto device = at::kCUDA;
try
{
LoadLibraryA("ATen_cuda.dll");
LoadLibraryA("c10_cuda.dll");
LoadLibraryA("torch_cuda.dll");
LoadLibraryA("torchvision.dll");
module = torch::jit::load("weights/best.torchscript");
module.to(torch::Device(torch::kCUDA));//
}
catch (const std::exception& e)
{
std::cout << e.what();
}
std::vector classnames;
std::ifstream f("weights/block.txt");
std::string name = "";
while (std::getline(f, name))
{
classnames.push_back(name);
}
cv::VideoCapture cap = cv::VideoCapture("1.mp4");
//cap.set(cv::CAP_PROP_FRAME_WIDTH, 1920);
//cap.set(cv::CAP_PROP_FRAME_HEIGHT, 1080);
cv::Mat frame, img;
while (cap.isOpened())
{
clock_t start = clock();
cap.read(frame);
if (frame.empty())
{
std::cout << "Read frame failed!" << std::endl;
break;
}
// Preparing input tensor
cv::resize(frame, img, cv::Size(416, 416));
cv::cvtColor(img, img, cv::COLOR_BGR2RGB);
//img.convertTo(img, CV_32FC3, 1.0f / 255.0f);
//torch::Tensor imgTensor = torch::from_blob(img.data, { img.rows, img.cols,3 }, at::kByte);
imgTensor = imgTensor.permute({ 0, 3, 1, 2 }).contiguous();
//imgTensor = imgTensor.permute({ 2,0,1 });
//imgTensor = imgTensor.toType(torch::kFloat);
//imgTensor = imgTensor.div(255);
//imgTensor = imgTensor.unsqueeze(0);
torch::Tensor ten_img = torch::from_blob(img.data, { 1, img.rows, img.cols, 3 }, torch::kByte).to(torch::Device(torch::kCUDA));
ten_img = ten_img.permute({ 0, 3, 1, 2 });
ten_img = ten_img.toType(torch::kFloat);
ten_img = ten_img.div(255);
//int h = ten_img.sizes()[2], w = ten_img.sizes()[3];
// preds: [?, 15120, 9]
torch::Tensor preds;
//try
{//
preds = module.forward({ ten_img }).toTuple()->elements()[0].toTensor();//libtorch cu113 应该与cuda toolkit 11.1 版本一致。
}
//catch (const std::exception& e)
//{
// std::cout << e.what();
//}
std::vector dets = non_max_suppression(preds.to(at::kCPU), 0.4, 0.5);//OK
if (dets.size() > 0)
{
// Visualize result
for (size_t i = 0; i < dets[0].sizes()[0]; ++i)
{
float left = dets[0][i][0].item().toFloat() * frame.cols / 416;
float top = dets[0][i][1].item().toFloat() * frame.rows / 416;
float right = dets[0][i][2].item().toFloat() * frame.cols / 416;
float bottom = dets[0][i][3].item().toFloat() * frame.rows / 416;
float score = dets[0][i][4].item().toFloat();
int classID = dets[0][i][5].item().toInt();
cv::rectangle(frame, cv::Rect(left, top, (right - left), (bottom - top)), cv::Scalar(0, 255, 0), 2);
cv::putText(frame,
classnames[classID] + ": " + cv::format("%.2f", score),
cv::Point(left, top),
cv::FONT_HERSHEY_SIMPLEX, (right - left) / 200, cv::Scalar(0, 255, 0), 2);
}
}
cv::putText(frame, "FPS: " + std::to_string(int(1e7 / (clock() - start))),
cv::Point(50, 50),
cv::FONT_HERSHEY_SIMPLEX, 1, cv::Scalar(0, 255, 0), 2);
cv::imshow("", frame);
if (cv::waitKey(1) == 27) break;
}
return 0;
} python export.py --weights best.pt --img 416 --batch 1export.py
def parse_opt():
parser = argparse.ArgumentParser()
parser.add_argument('--data', type=str, default=ROOT / 'data/block.yaml', help='dataset.yaml path')
parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'best.pt', help='model.pt path(s)')
parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[416, 416], help='image (h, w)')
parser.add_argument('--batch-size', type=int, default=1, help='batch size')
parser.add_argument('--device', default='0', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
parser.add_argument('--half', action='store_true', help='FP16 half-precision export')
parser.add_argument('--inplace', action='store_true', help='set YOLOv5 Detect() inplace=True')
parser.add_argument('--train', action='store_true', help='model.train() mode')
parser.add_argument('--keras', action='store_true', help='TF: use Keras')
parser.add_argument('--optimize', action='store_true', help='TorchScript: optimize for mobile')
parser.add_argument('--int8', action='store_true', help='CoreML/TF INT8 quantization')
parser.add_argument('--dynamic', action='store_true', help='ONNX/TF: dynamic axes')
parser.add_argument('--simplify', action='store_true', help='ONNX: simplify model')
parser.add_argument('--opset', type=int, default=12, help='ONNX: opset version')
parser.add_argument('--verbose', action='store_true', help='TensorRT: verbose log')
parser.add_argument('--workspace', type=int, default=4, help='TensorRT: workspace size (GB)')
parser.add_argument('--nms', action='store_true', help='TF: add NMS to model')
parser.add_argument('--agnostic-nms', action='store_true', help='TF: add agnostic NMS to model')
parser.add_argument('--topk-per-class', type=int, default=100, help='TF.js NMS: topk per class to keep')
parser.add_argument('--topk-all', type=int, default=100, help='TF.js NMS: topk for all classes to keep')
parser.add_argument('--iou-thres', type=float, default=0.45, help='TF.js NMS: IoU threshold')
parser.add_argument('--conf-thres', type=float, default=0.25, help='TF.js NMS: confidence threshold')
parser.add_argument('--include',
nargs='+',
default=['torchscript', 'onnx'],
help='torchscript, onnx, openvino, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs')
opt = parser.parse_args()
print_args(vars(opt))
return opt
pt模型导出 onnx
D:\test\Train\yolov5 [master ↓30 +10 ~4 -0 !]> python export.py --weights best.pt --img 416 --batch 16
export: data=D:\test\Train\yolov5\data\coco128.yaml, weights=['best.pt'], imgsz=[416], batch_size=16, device=cpu, half=False, inplace=False,
train=False, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=12, verbose=False, workspace=4, nms=False, agnos
tic_nms=False, topk_per_class=100, topk_all=100, iou_thres=0.45, conf_thres=0.25, include=['torchscript', 'onnx']
YOLOv5 v6.1-223-g1dcb774 Python-3.9.13 torch-1.9.0+cu111 CPU
Fusing layers...
YOLOv5s summary: 213 layers, 7012822 parameters, 0 gradients, 15.8 GFLOPs
PyTorch: starting from best.pt with output shape (16, 10647, 6) (13.7 MB)
TorchScript: starting export with torch 1.9.0+cu111...
TorchScript: export success, saved as best.torchscript (27.1 MB)
ONNX: starting export with onnx 1.10.2...
ONNX: export success, saved as best.onnx (26.9 MB)
Export complete (15.79s)
Results saved to D:\test\Train\yolov5
Detect: python detect.py --weights best.onnx
PyTorch Hub: model = torch.hub.load('ultralytics/yolov5', 'custom', 'best.onnx')
Validate: python val.py --weights best.onnx
Visualize: https://netron.app附录 libtorch下载
libtorch 1.10.0
release
# cpu
https://download.pytorch.org/libtorch/cpu/libtorch-win-shared-with-deps-1.10.0%2Bcpu.zip
# cuda
https://download.pytorch.org/libtorch/cu102/libtorch-win-shared-with-deps-1.10.0%2Bcu102.zip
https://download.pytorch.org/libtorch/cu113/libtorch-win-shared-with-deps-1.10.0%2Bcu113.zip
debug
# cpu
https://download.pytorch.org/libtorch/cpu/libtorch-win-shared-with-deps-debug-1.10.0%2Bcpu.zip
# cuda
https://download.pytorch.org/libtorch/cu102/libtorch-win-shared-with-deps-debug-1.10.0%2Bcu102.zip
https://download.pytorch.org/libtorch/cu113/libtorch-win-shared-with-deps-debug-1.10.0%2Bcu113.zip
libtorch 1.9.1
release
# cpu
https://download.pytorch.org/libtorch/cpu/libtorch-win-shared-with-deps-1.9.1%2Bcpu.zip
# cuda
https://download.pytorch.org/libtorch/cu102/libtorch-win-shared-with-deps-1.9.1%2Bcu102.zip
https://download.pytorch.org/libtorch/cu111/libtorch-win-shared-with-deps-1.9.1%2Bcu111.zip
debug
# cpu
https://download.pytorch.org/libtorch/cpu/libtorch-win-shared-with-deps-debug-1.9.1%2Bcpu.zip
# cuda
https://download.pytorch.org/libtorch/cu102/libtorch-win-shared-with-deps-debug-1.9.1%2Bcu102.zip
https://download.pytorch.org/libtorch/cu111/libtorch-win-shared-with-deps-debug-1.9.1%2Bcu111.zip
libtorch 1.8.2 (LTS)
release
# cpu
https://download.pytorch.org/libtorch/lts/1.8/cpu/libtorch-win-shared-with-deps-1.8.2%2Bcpu.zip
# cuda
https://download.pytorch.org/libtorch/lts/1.8/cu102/libtorch-win-shared-with-deps-1.8.2%2Bcu102.zip
https://download.pytorch.org/libtorch/lts/1.8/cu111/libtorch-win-shared-with-deps-1.8.2%2Bcu111.zip
debug
# cpu
https://download.pytorch.org/libtorch/lts/1.8/cpu/libtorch-win-shared-with-deps-debug-1.8.2%2Bcpu.zip
# cuda
https://download.pytorch.org/libtorch/lts/1.8/cu102/libtorch-win-shared-with-deps-debug-1.8.2%2Bcu102.zip
https://download.pytorch.org/libtorch/lts/1.8/cu111/libtorch-win-shared-with-deps-debug-1.8.2%2Bcu111.ziptrian.py opt
def parse_opt(known=False):
parser = argparse.ArgumentParser()
parser.add_argument('--weights', type=str, default=ROOT / 'yolov5s.pt', help='initial weights path')
parser.add_argument('--cfg', type=str, default='models/yolov5s.yaml', help='model.yaml path')
parser.add_argument('--data', type=str, default=ROOT / './data/block.yaml', help='dataset.yaml path')
parser.add_argument('--hyp', type=str, default=ROOT / 'data/hyps/hyp.scratch-low.yaml', help='hyperparameters path')
parser.add_argument('--epochs', type=int, default=300)
parser.add_argument('--batch-size', type=int, default=16, help='total batch size for all GPUs, -1 for autobatch')#16
parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=416, help='train, val image size (pixels)')#640
parser.add_argument('--rect', action='store_true', help='rectangular training')
parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training')
parser.add_argument('--nosave', action='store_true', help='only save final checkpoint')
parser.add_argument('--noval', action='store_true', help='only validate final epoch')
parser.add_argument('--noautoanchor', action='store_true', help='disable AutoAnchor')
parser.add_argument('--noplots', action='store_true', help='save no plot files')
parser.add_argument('--evolve', type=int, nargs='?', const=300, help='evolve hyperparameters for x generations')
parser.add_argument('--bucket', type=str, default='', help='gsutil bucket')
parser.add_argument('--cache', type=str, nargs='?', const='ram', help='--cache images in "ram" (default) or "disk"')
parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training')
parser.add_argument('--device', default='0', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%')
parser.add_argument('--single-cls', action='store_true', help='train multi-class data as single-class')
parser.add_argument('--optimizer', type=str, choices=['SGD', 'Adam', 'AdamW'], default='SGD', help='optimizer')
parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode')
parser.add_argument('--workers', type=int, default=5, help='max dataloader workers (per RANK in DDP mode)')#8 error __init__() missing 1 required positional argument: 'dtype'
parser.add_argument('--project', default=ROOT / 'runs/train', help='save to project/name')
parser.add_argument('--name', default='exp', help='save to project/name')
parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
parser.add_argument('--quad', action='store_true', help='quad dataloader')
parser.add_argument('--cos-lr', action='store_true', help='cosine LR scheduler')
parser.add_argument('--label-smoothing', type=float, default=0.0, help='Label smoothing epsilon')
parser.add_argument('--patience', type=int, default=100, help='EarlyStopping patience (epochs without improvement)')
parser.add_argument('--freeze', nargs='+', type=int, default=[0], help='Freeze layers: backbone=10, first3=0 1 2')
parser.add_argument('--save-period', type=int, default=-1, help='Save checkpoint every x epochs (disabled if < 1)')
parser.add_argument('--local_rank', type=int, default=-1, help='DDP parameter, do not modify')
# Weights & Biases arguments
parser.add_argument('--entity', default=None, help='W&B: Entity')
parser.add_argument('--upload_dataset', nargs='?', const=True, default=False, help='W&B: Upload data, "val" option')
parser.add_argument('--bbox_interval', type=int, default=-1, help='W&B: Set bounding-box image logging interval')
parser.add_argument('--artifact_alias', type=str, default='latest', help='W&B: Version of dataset artifact to use')
opt = parser.parse_known_args()[0] if known else parser.parse_args()
return opt