Cache onnx-trt models, they are faster than using two trt-engines. Removed trt-runtime again.
This commit is contained in:
Daniel
@ -6,9 +6,6 @@ from typing import List
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import cv2
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import numpy as np
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import onnxruntime as ort
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import pycuda.autoinit # noqa: F401
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import pycuda.driver as cuda
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import tensorrt as trt
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from tqdm import tqdm
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# ==================================================================================================
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@ -22,10 +19,7 @@ class BaseModel(ABC):
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if not os.path.exists(model_path):
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raise FileNotFoundError("File not found:", model_path)
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if model_path.endswith(".engine"):
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self.init_trt_engine(model_path)
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self.runtime = "trt"
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elif model_path.endswith(".onnx"):
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if model_path.endswith(".onnx"):
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self.init_onnxruntime(model_path)
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self.runtime = "ort"
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else:
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@ -46,7 +40,15 @@ class BaseModel(ABC):
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self.providers = []
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if usetrt and "TensorrtExecutionProvider" in providers:
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self.providers.append("TensorrtExecutionProvider")
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self.providers.append(
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(
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"TensorrtExecutionProvider",
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{
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"trt_engine_cache_enable": True,
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"trt_engine_cache_path": "/RapidPoseTriangulation/data/trt_cache/",
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},
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)
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)
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if usegpu and "CUDAExecutionProvider" in providers:
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self.providers.append("CUDAExecutionProvider")
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self.providers.append("CPUExecutionProvider")
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@ -75,51 +77,6 @@ class BaseModel(ABC):
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raise ValueError("Undefined input type:", input_type)
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self.input_types.append(itype)
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def init_trt_engine(self, engine_path):
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# https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html#python_topics
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# https://stackoverflow.com/a/79076885
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self.trt_logger = trt.Logger(trt.Logger.WARNING)
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with open(engine_path, "rb") as f:
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runtime = trt.Runtime(self.trt_logger)
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self.engine = runtime.deserialize_cuda_engine(f.read())
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self.context = self.engine.create_execution_context()
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self.stream = cuda.Stream()
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self.inputs, self.outputs, self.bindings = [], [], []
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self.input_names = []
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self.input_shapes = []
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self.input_types = []
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for i in range(self.engine.num_io_tensors):
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tensor_name = self.engine.get_tensor_name(i)
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shape = self.engine.get_tensor_shape(tensor_name)
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dtype = trt.nptype(self.engine.get_tensor_dtype(tensor_name))
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if -1 in shape:
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print("WARNING: Replacing dynamic shape with fixed for:", tensor_name)
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shape[list(shape).index(-1)] = 10
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# Allocate host and device buffers
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size = trt.volume(shape)
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host_mem = cuda.pagelocked_empty(size, dtype)
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device_mem = cuda.mem_alloc(host_mem.nbytes)
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self.bindings.append(int(device_mem))
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# Append to the appropriate input/output list
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if self.engine.get_tensor_mode(tensor_name) == trt.TensorIOMode.INPUT:
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self.inputs.append((host_mem, device_mem, shape))
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self.input_names.append(tensor_name)
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self.input_shapes.append(shape)
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self.input_types.append(dtype)
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else:
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self.outputs.append((host_mem, device_mem, shape))
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# Set tensor address
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self.context.set_tensor_address(
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self.engine.get_tensor_name(i), self.bindings[i]
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)
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@abstractmethod
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def preprocess(self, **kwargs):
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pass
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@ -168,7 +125,7 @@ class BaseModel(ABC):
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tensor = tensor.astype(self.input_types[i])
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inputs[iname] = tensor
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self.call_model(list(inputs.values()))
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self.call_model_ort(list(inputs.values()))
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def call_model_ort(self, tensor):
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inputs = {}
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@ -178,45 +135,9 @@ class BaseModel(ABC):
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result = self.session.run(None, inputs)
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return result
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def call_model_trt(self, tensor):
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# Transfer input data to device
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for i, input_data in enumerate(tensor):
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np.copyto(self.inputs[i][0], input_data.ravel())
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cuda.memcpy_htod_async(self.inputs[i][1], self.inputs[i][0], self.stream)
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# Empty the output buffers
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for i in range(len(self.outputs)):
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self.outputs[i][0].fill(0)
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cuda.memcpy_htod_async(self.outputs[i][1], self.outputs[i][0], self.stream)
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# Run inference
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self.context.execute_async_v3(stream_handle=self.stream.handle)
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# Transfer predictions back
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for i in range(len(self.outputs)):
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cuda.memcpy_dtoh_async(self.outputs[i][0], self.outputs[i][1], self.stream)
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# Synchronize the stream
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self.stream.synchronize()
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# Un-flatten the outputs
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outputs = []
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for i in range(len(self.outputs)):
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output = self.outputs[i][0].reshape(self.outputs[i][2])
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outputs.append(output)
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return outputs
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def call_model(self, tensor):
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if self.runtime == "trt":
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result = self.call_model_trt(tensor)
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elif self.runtime == "ort":
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result = self.call_model_ort(tensor)
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return result
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def __call__(self, **kwargs):
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tensor = self.preprocess(**kwargs)
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result = self.call_model(tensor)
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result = self.call_model_ort(tensor)
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output = self.postprocess(result=result, **kwargs)
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return output
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@ -544,10 +465,8 @@ def load_model(min_bbox_score=0.3, min_bbox_area=0.1 * 0.1):
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print("Loading 2D model ...")
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model = TopDown(
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# "/RapidPoseTriangulation/extras/mmdeploy/exports/rtmdet-nano_320x320_fp16_extra-steps.onnx",
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# "/RapidPoseTriangulation/extras/mmdeploy/exports/rtmpose-m_384x288_fp16_extra-steps.onnx",
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"/RapidPoseTriangulation/extras/mmdeploy/exports/rtmdet-nano_1x320x320x3_fp16_extra-steps.engine",
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"/RapidPoseTriangulation/extras/mmdeploy/exports/rtmpose-m_1x384x288x3_fp16_extra-steps.engine",
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"/RapidPoseTriangulation/extras/mmdeploy/exports/rtmdet-nano_320x320_fp16_extra-steps.onnx",
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"/RapidPoseTriangulation/extras/mmdeploy/exports/rtmpose-m_384x288_fp16_extra-steps.onnx",
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box_conf_threshold=min_bbox_score,
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box_min_area=min_bbox_area,
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warmup=30,
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