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sam-hq-tensorrt

python cuda trt

*: This repository is just for onnx2tensorrt of Depth Anything v2.

Requirments

  • python 3.8.8
  • cuda 11.8
  • tensorrt 8.6.1
pip install -r requirements.txt
pip install -e .

*: About tensorrt, you can download it from NVIDIA TensorRT, and then you can install it by the following command.

export LD_LIBRARY_PATH=/path/to/TensorRT-8.6.1.6/lib:$LD_LIBRARY_PATH
export PATH=$PATH:/path/to/TensorRT-8.6.1.6/bin
source ~/.bashrc
source /etc/profile

cd TensorRT-8.6.1.6/python
pip install tensorrt-8.6.1-cp38-none-linux_x86_64.whl

cd TensorRT-8.6.1.6/graphsurgeon
pip install graphsurgeon-0.4.6-py2.py3-none-any.whl

cd TensorRT-8.6.1.6/onnx_graphsurgeon
pip install onnx_graphsurgeon-0.3.12-py2.py3-none-any.whl

Usage

Firstly, you can download the corresponding pth model file into the checkpoints folder from sam-hq.

Next, You can convert pth model to onnx file for using the corresponding command. (Here is an example for sam_hq_vit_b.pth, if you want to use other model, you can change the model name in the command)

*: The workspace is the maximum memory size that TensorRT can allocate for building an engine. The larger the workspace, the more memory TensorRT can use to optimize the engine, and the faster the inference speed will be. However, the larger the workspace, the more memory will be used, so you need to choose a suitable workspace size according to your own hardware configuration.

  • Transform image embedding model from pth to onnx
python scripts/export_onnx_embed.py --img_pt2onnx --sam_checkpoint pretrained_checkpoint/sam_hq_vit_b.pth --model_type vit_b
  • Transform image embedding model from onnx to tensorrt
python scripts/export_onnx_embed.py --img_onnx2trt --img_onnx_model_path embedding_onnx/sam_vit_b_embedding.onnx
  • Transform sam model from pth to onnx
python scripts/export_onnx_embed.py --prompt_masks_pt2onnx --model_type vit_b --sam_checkpoint pretrained_checkpoint/sam_hq_vit_b.pth
  • Transform sam model from onnx to engine
python scripts/export_onnx_embed.py --sam_onnx2trt --sam_onnx_path ./pretrained_checkpoint/sam_hq_vit_b.onnx

Finally, you can infer the image with the engine file.

python trt_infer.py --img_path assets/truck.jpg --sam_engine_file pretrained_checkpoint/sam_hq_vit_b.engine --embedding_engine_file embedding_onnx/sam_vit_b_embedding.engine --batch_size 1

When you run the command, you will see the following output:

  • The output would be a image with the depth map.

👏 Acknowledgement

This project is based on the following projects: