TensorRT Support

Installation

Install TensorRT

Please install TensorRT 8 follow install-guide.

Note:

• pip Wheel File Installation is not supported yet in this repo.

• We strongly suggest you install TensorRT through tar file

• After installation, you’d better add TensorRT environment variables to bashrc by:

  cd ${TENSORRT_DIR} # To TensorRT root directory
  echo '# set env for TensorRT' >> ~/.bashrc
  echo "export TENSORRT_DIR=${TENSORRT_DIR}" >> ~/.bashrc
  echo 'export LD_LIBRARY_PATH=$TENSORRT_DIR/lib:$TENSORRT_DIR' >> ~/.bashrc
  source ~/.bashrc
  

Build custom ops

Some custom ops are created to support models in OpenMMLab, and the custom ops can be built as follow:

cd ${MMDEPLOY_DIR} # To MMDeploy root directory
mkdir -p build && cd build
cmake -DMMDEPLOY_TARGET_BACKENDS=trt ..
make -j$(nproc)

If you haven’t installed TensorRT in the default path, Please add -DTENSORRT_DIR flag in CMake.

 cmake -DMMDEPLOY_TARGET_BACKENDS=trt -DTENSORRT_DIR=${TENSORRT_DIR} ..
 make -j$(nproc) && make install

Convert model

Please follow the tutorial in How to convert model. Note that the device must be cuda device.

Int8 Support

Since TensorRT supports INT8 mode, a custom dataset config can be given to calibrate the model. Following is an example for MMDetection:

# calibration_dataset.py

# dataset settings, same format as the codebase in OpenMMLab
dataset_type = 'CalibrationDataset'
data_root = 'calibration/dataset/root'
img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='MultiScaleFlipAug',
        img_scale=(1333, 800),
        flip=False,
        transforms=[
            dict(type='Resize', keep_ratio=True),
            dict(type='RandomFlip'),
            dict(type='Normalize', **img_norm_cfg),
            dict(type='Pad', size_divisor=32),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='Collect', keys=['img']),
        ])
]
data = dict(
    samples_per_gpu=2,
    workers_per_gpu=2,
    val=dict(
        type=dataset_type,
        ann_file=data_root + 'val_annotations.json',
        pipeline=test_pipeline),
    test=dict(
        type=dataset_type,
        ann_file=data_root + 'test_annotations.json',
        pipeline=test_pipeline))
evaluation = dict(interval=1, metric='bbox')

Convert your model with this calibration dataset:

python tools/deploy.py \
    ...
    --calib-dataset-cfg calibration_dataset.py

If the calibration dataset is not given, the data will be calibrated with the dataset in model config.

FAQs

• Error Cannot found TensorRT headers or Cannot found TensorRT libs

  Try cmake with flag -DTENSORRT_DIR:

  cmake -DBUILD_TENSORRT_OPS=ON -DTENSORRT_DIR=${TENSORRT_DIR} ..
  make -j$(nproc)
  

  Please make sure there are libs and headers in ${TENSORRT_DIR}.

• Error error: parameter check failed at: engine.cpp::setBindingDimensions::1046, condition: profileMinDims.d[i] <= dimensions.d[i]

  There is an input shape limit in deployment config:

  backend_config = dict(
      # other configs
      model_inputs=[
          dict(
              input_shapes=dict(
                  input=dict(
                      min_shape=[1, 3, 320, 320],
                      opt_shape=[1, 3, 800, 1344],
                      max_shape=[1, 3, 1344, 1344])))
      ])
      # other configs
  

  The shape of the tensor input must be limited between input_shapes["input"]["min_shape"] and input_shapes["input"]["max_shape"].

• Error error: [TensorRT] INTERNAL ERROR: Assertion failed: cublasStatus == CUBLAS_STATUS_SUCCESS

  TRT 7.2.1 switches to use cuBLASLt (previously it was cuBLAS). cuBLASLt is the default choice for SM version >= 7.0. However, you may need CUDA-10.2 Patch 1 (Released Aug 26, 2020) to resolve some cuBLASLt issues. Another option is to use the new TacticSource API and disable cuBLASLt tactics if you don’t want to upgrade.

  Read this for detail.

• Install mmdeploy on Jetson

  We provide a tutorial to get start on Jetsons here.