PyTorch Infer Utils

This package proposes simplified exporting pytorch models to ONNX and TensorRT, and also gives some base interface for model inference.

To install

git clone https://github.com/gorodnitskiy/pytorch_infer_utils.git
pip install /path/to/pytorch_infer_utils/

Export PyTorch model to ONNX

• Check model for denormal weights to achieve better performance. Use load_weights_rounded_model func to load model with weights rounding:

  from pytorch_infer_utils import load_weights_rounded_model
  
  model = ModelClass()
  load_weights_rounded_model(
      model,
      "/path/to/model_state_dict",
      map_location=map_location
  )
  

• Use ONNXExporter.torch2onnx method to export pytorch model to ONNX:

  from pytorch_infer_utils import ONNXExporter
  
  model = ModelClass()
  model.load_state_dict(
      torch.load("/path/to/model_state_dict", map_location=map_location)
  )
  model.eval()
  
  exporter = ONNXExporter()
  input_shapes = [-1, 3, 224, 224] # -1 means that is dynamic shape
  exporter.torch2onnx(model, "/path/to/model.onnx", input_shapes)
  

• Use ONNXExporter.optimize_onnx method to optimize ONNX via onnxoptimizer:

  from pytorch_infer_utils import ONNXExporter
  
  exporter = ONNXExporter()
  exporter.optimize_onnx("/path/to/model.onnx", "/path/to/optimized_model.onnx")
  

• Use ONNXExporter.optimize_onnx_sim method to optimize ONNX via onnx-simplifier. Be careful with onnx-simplifier not to lose dynamic shapes.

  from pytorch_infer_utils import ONNXExporter
  
  exporter = ONNXExporter()
  exporter.optimize_onnx_sim("/path/to/model.onnx", "/path/to/optimized_model.onnx")
  

• Also, a method combined the above methods is available ONNXExporter.torch2optimized_onnx:

  from pytorch_infer_utils import ONNXExporter
  
  model = ModelClass()
  model.load_state_dict(
      torch.load("/path/to/model_state_dict", map_location=map_location)
  )
  model.eval()
  
  exporter = ONNXExporter()
  input_shapes = [-1, 3, -1, -1] # -1 means that is dynamic shape
  exporter.torch2optimized_onnx(model, "/path/to/model.onnx", input_shapes)
  

• Other params that can be used in class initialization:
  • default_shapes: default shapes if dimension is dynamic, default = [1, 3, 224, 224]
  • onnx_export_params:
    • export_params: store the trained parameter weights inside the model file, default = True
    • do_constant_folding: whether to execute constant folding for optimization, default = True
    • input_names: the model's input names, default = ["input"]
    • output_names: the model's output names, default = ["output"]
    • opset_version: the ONNX version to export the model to, default = 11
  • onnx_optimize_params:
    • fixed_point: use fixed point, default = False
    • passes: optimization passes, default = [ "eliminate_deadend", "eliminate_duplicate_initializer", "eliminate_identity", "eliminate_if_with_const_cond", "eliminate_nop_cast", "eliminate_nop_dropout", "eliminate_nop_flatten", "eliminate_nop_monotone_argmax", "eliminate_nop_pad", "eliminate_nop_transpose", "eliminate_unused_initializer", "extract_constant_to_initializer", "fuse_add_bias_into_conv", "fuse_bn_into_conv", "fuse_consecutive_concats", "fuse_consecutive_log_softmax", "fuse_consecutive_reduce_unsqueeze", "fuse_consecutive_squeezes", "fuse_consecutive_transposes", "fuse_matmul_add_bias_into_gemm", "fuse_pad_into_conv", "fuse_transpose_into_gemm", "lift_lexical_references", "nop" ]

Export ONNX to TensorRT

• Check TensorRT health via check_tensorrt_health func
• Use TRTEngineBuilder.build_engine method to export ONNX to TensorRT:

  from pytorch_infer_utils import TRTEngineBuilder
  
  exporter = TRTEngineBuilder()
  # get engine by itself
  engine = exporter.build_engine("/path/to/model.onnx")
  # or save engine to /path/to/model.trt
  exporter.build_engine("/path/to/model.onnx", engine_path="/path/to/model.trt")
  

• fp16_mode is available:

  from pytorch_infer_utils import TRTEngineBuilder
  
  exporter = TRTEngineBuilder()
  engine = exporter.build_engine("/path/to/model.onnx", fp16_mode=True)
  

• int8_mode is available. It requires calibration_set of images as List[Any], load_image_func - func to correctly read and process images, max_image_shape - max image size as [C, H, W] to allocate correct size of memory:

  from pytorch_infer_utils import TRTEngineBuilder
  
  exporter = TRTEngineBuilder()
  engine = exporter.build_engine(
      "/path/to/model.onnx",
      int8_mode=True,
      calibration_set=calibration_set,
      max_image_shape=max_image_shape,
      load_image_func=load_image_func,
  )
  

• Also, additional params for builder config builder.create_builder_config can be put to kwargs.
• Other params that can be used in class initialization:
  • opt_shape_dict: optimal shapes, default = {'input': [[1, 3, 224, 224], [1, 3, 224, 224], [1, 3, 224, 224]]}
  • max_workspace_size: max workspace size, default = [1, 30]
  • stream_batch_size: batch size for forward network during transferring to int8, default = 100
  • cache_file: int8_mode cache filename, default = "model.trt.int8calibration"

Inference via onnxruntime on CPU and onnx_tensort on GPU

• Base class ONNXWrapper __init__ has the structure as below:

  def __init__(
      self,
      onnx_path: str,
      gpu_device_id: Optional[int] = None,
      intra_op_num_threads: Optional[int] = 0,
      inter_op_num_threads: Optional[int] = 0,
  ) -> None:
      """
      :param onnx_path: onnx-file path, required
      :param gpu_device_id: gpu device id to use, default = 0
      :param intra_op_num_threads: ort_session_options.intra_op_num_threads,
          to let onnxruntime choose by itself is required 0, default = 0
      :param inter_op_num_threads: ort_session_options.inter_op_num_threads,
          to let onnxruntime choose by itself is required 0, default = 0
      :type onnx_path: str
      :type gpu_device_id: int
      :type intra_op_num_threads: int
      :type inter_op_num_threads: int
      """
      if gpu_device_id is None:
          import onnxruntime
  
          self.is_using_tensorrt = False
          ort_session_options = onnxruntime.SessionOptions()
          ort_session_options.intra_op_num_threads = intra_op_num_threads
          ort_session_options.inter_op_num_threads = inter_op_num_threads
          self.ort_session = onnxruntime.InferenceSession(
              onnx_path, ort_session_options
          )
  
      else:
          import onnx
          import onnx_tensorrt.backend as backend
  
          self.is_using_tensorrt = True
          model_proto = onnx.load(onnx_path)
          for gr_input in model_proto.graph.input:
              gr_input.type.tensor_type.shape.dim[0].dim_value = 1
  
          self.engine = backend.prepare(
              model_proto, device=f"CUDA:{gpu_device_id}"
          )
  

• ONNXWrapper.run method assumes the use of such a structure:

  img = self._process_img_(img)
  if self.is_using_tensorrt:
      preds = self.engine.run(img)
  else:
      ort_inputs = {self.ort_session.get_inputs()[0].name: img}
      preds = self.ort_session.run(None, ort_inputs)
  
  preds = self._process_preds_(preds)
  

Inference via onnxruntime on CPU and TensorRT on GPU

• Base class TRTWrapper __init__ has the structure as below:

  def __init__(
      self,
      onnx_path: Optional[str] = None,
      trt_path: Optional[str] = None,
      gpu_device_id: Optional[int] = None,
      intra_op_num_threads: Optional[int] = 0,
      inter_op_num_threads: Optional[int] = 0,
      fp16_mode: bool = False,
  ) -> None:
      """
      :param onnx_path: onnx-file path, default = None
      :param trt_path: onnx-file path, default = None
      :param gpu_device_id: gpu device id to use, default = 0
      :param intra_op_num_threads: ort_session_options.intra_op_num_threads,
          to let onnxruntime choose by itself is required 0, default = 0
      :param inter_op_num_threads: ort_session_options.inter_op_num_threads,
          to let onnxruntime choose by itself is required 0, default = 0
      :param fp16_mode: use fp16_mode if class initializes only with
          onnx_path on GPU, default = False
      :type onnx_path: str
      :type trt_path: str
      :type gpu_device_id: int
      :type intra_op_num_threads: int
      :type inter_op_num_threads: int
      :type fp16_mode: bool
      """
      if gpu_device_id is None:
          import onnxruntime
  
          self.is_using_tensorrt = False
          ort_session_options = onnxruntime.SessionOptions()
          ort_session_options.intra_op_num_threads = intra_op_num_threads
          ort_session_options.inter_op_num_threads = inter_op_num_threads
          self.ort_session = onnxruntime.InferenceSession(
              onnx_path, ort_session_options
          )
  
      else:
          self.is_using_tensorrt = True
          if trt_path is None:
              builder = TRTEngineBuilder()
              trt_path = builder.build_engine(onnx_path, fp16_mode=fp16_mode)
  
          self.trt_session = TRTRunWrapper(trt_path)
  

• TRTWrapper.run method assumes the use of such a structure:

  img = self._process_img_(img)
  if self.is_using_tensorrt:
      preds = self.trt_session.run(img)
  else:
      ort_inputs = {self.ort_session.get_inputs()[0].name: img}
      preds = self.ort_session.run(None, ort_inputs)
  
  preds = self._process_preds_(preds)
  

Environment

TensorRT

• TensorRT installing guide is here
• Required CUDA-Runtime, CUDA-ToolKit
• Also, required additional python packages not included to setup.cfg (it depends upon CUDA environment version):
  • pycuda
  • nvidia-tensorrt
  • nvidia-pyindex

onnx_tensorrt

• onnx_tensorrt requires cuda-runtime and tensorrt.
• To install:

  git clone --depth 1 --branch 21.02 https://github.com/onnx/onnx-tensorrt.git
  cd onnx-tensorrt
  cp -r onnx_tensorrt /usr/local/lib/python3.8/dist-packages
  cd ..
  rm -rf onnx-tensorrt