Open binliunls opened 1 year ago
Hi in this case I see that the inputs are getting moved to "cuda" device if it finds the target cuda device. Did you try explicitly placing the tensor to ".cuda()"?
Hi @binliunls , I tried running the above steps to test the Runtime warning with dynamo path, but I run into ModuleNotFoundError: Cannot locate class or function path: 'generative.networks.nets.DiffusionModelUNet'.
Is there something missing from requirements.txt?
Hi @apbose, very appreciate for your response. I think you the libraries shown below should be installed as shown in the metadata.json
file.
"monai-generative": "0.2.2"
Thanks, Bin
Bug Description
When run the inference with a converted TensorRT torchscript of MONAI generative model, it reports the warning shown below.
To Reproduce
Steps to reproduce the behavior:
The way to reproduce the error: 1) Build the torch_tensorrt docker from main branch with TENSORRT_VERSION=8.6 2) Start a container with the torch_tensorrt image 3) Clone this branch of MONAI https://github.com/binliunls/MONAI/tree/6838-support-generative-and-hovernet-with-TensorRT 4) Go into the cloned MONAI folder and run
python setup.py develop; pip install -r requirements-dev.txt' 5) Run the
python -m monai.bundle download brats_mri_axial_slices_generative_diffusion --bundle_dir ./to download the model to a local path. 6) Go into the brats_mri_axial_slices_generative_diffusion folder 7) Run the command
python -m monai.bundle trt_export --net_id network_def --filepath models/model_trt.ts --ckpt_file models/model.pt --meta_file configs/metadata.json --config_file configs/inference.json --precision fp32 --use_trace "True" --input_shape "[[1, 1, 64, 64], [1,]]" --converter_kwargs "{'truncate_long_and_double': True}"` to convert the modelRun the inference with code like:
Expected behavior
No need to move tensors from CPU to GPU.
Environment
Additional context