RAFT_CPP

本项目是光流算法RAFT的C++实现，基于Libtorch + TensorRT，演示视频。
This project is the C++ implementation of optical flow algorithm RAFT, which is based on Libtorch + Tensorrt. Here is the test video.
raft

Requirements

Ubuntu
Cuda10.2
cudnn8
GCC >=9
TensorRT8
Opencv3.4
Libtorch1.8.2
CMake3.20

Quick Start

0.Run RAFT

首先下载RAFT的源码，并运行成功。
Firstly download RAFT,then run it sucessfully.

1.Export ONNX Model

RAFT内部有3个带参数的子网络，这里分别导出。为此，这里通过对每个子网络编写forward()函数。
RAFT have 3 subnetwork, here export them independently. To do this, write the forward () function for each subnet

1.1 export_onnx.py加载权重
首先加载训练完成的模型权重：
First, load the model weight

parser = argparse.ArgumentParser()
parser.add_argument('--model',default="models/raft-kitti.pth", help="restore checkpoint")
parser.add_argument('--small', action='store_true', help='use small model')
parser.add_argument('--mixed_precision', action='store_true', help='use mixed precision')
parser.add_argument('--alternate_corr', action='store_true', help='use efficent correlation implementation')
args = parser.parse_args()

model = torch.nn.DataParallel(RAFT(args))
model.load_state_dict(torch.load(args.model))

model = model.module
model.to("cuda")
model.eval()

1.1 导出特征提取模块
首先注释原来的forward()函数，并在RAFT类里添加一个forward函数
Annotate the original forward() function and add a new forward() function to the raft class

def forward(self, image1:torch.Tensor, image2:torch.Tensor):
    fmap1, fmap2 = self.fnet([image1, image2],True)
    fmap1 = fmap1.float()
    fmap2 = fmap2.float()
    return fmap1, fmap2

在export_onnx.py中编写trace代码
write trace code:

dummy_input1 = torch.randn(1, 3, 376, 1232,device='cuda')
dummy_input2 = torch.randn(1, 3, 376, 1232,device='cuda')
torch.onnx.export(model,(dummy_input1,dummy_input2),"kitti_fnet.onnx",opset_version=13)

1.2 Trace Context模块
首先注释原来的forward()函数，并在RAFT类里添加一个forward函数 Annotate the original forward() function and add a new forward() function to the raft class

def forward(self, image1:torch.Tensor):
    return self.cnet([image1],False)[0]

在export_onnx.py中编写trace代码

dummy_input3 = torch.randn(1, 3, 376, 1232,device='cuda')
torch.onnx.export(model,dummy_input3,"kitti_cnet.onnx",opset_version=13)

1.3 Trace更新模块
在RAFT类里添加forward() Annotate the original forward() function and add a new forward() function to the raft class

def forward(self,net, inp, corr, flow):
    return self.update_block(net, inp, corr, flow)

在export_onnx.py中编写trace代码

net = torch.randn(1, 128, 47, 154,device='cuda')
inp = torch.randn(1, 128, 47, 154,device='cuda')
corr = torch.randn(1, 324, 47, 154,device='cuda')
flow = torch.randn(1, 2, 47, 154,device='cuda')
torch.onnx.export(model,(net, inp, corr, flow),"kitti_update.onnx")

2.Compile and Run

git clone https://github.com/chenjianqu/RAFT_CPP.git

cd RAFT_CPP
mkdir build && cd build

修改CMakeLists.txt文件，以确保正确的编译。
编译：

cmake ..
make -j10

运行

首先修改配置文件，config.yaml：

%YAML:1.0

fnet_onnx_path: "/home/chen/CLionProjects/RAFT_CPP/weights/kitti_fnet.onnx"
fnet_tensorrt_path: "/home/chen/CLionProjects/RAFT_CPP/weights/kitti_fnet.bin"

cnet_onnx_path: "/home/chen/CLionProjects/RAFT_CPP/weights/kitti_cnet.onnx"
cnet_tensorrt_path: "/home/chen/CLionProjects/RAFT_CPP/weights/kitti_cnet.bin"

update_onnx_path: "/home/chen/CLionProjects/RAFT_CPP/weights/kitti_update.onnx"
update_tensorrt_path: "/home/chen/CLionProjects/RAFT_CPP/weights/kitti_update.bin"

segmentor_log_path: "segmentor_log.txt"
segmentor_log_level: "debug"
segmentor_log_flush: "debug"

DATASET_DIR: "/home/chen/CLionProjects/RAFT_CPP/demo/kitti07/"
WARN_UP_IMAGE_PATH: "/home/chen/CLionProjects/InstanceSegment/config/kitti.png"

然后编译onnx模型：

./build ../config/config.yaml

运行：

./RAFT_CPP ../config/config.yaml

chenjianqu / RAFT_CPP

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