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Using tensorrt runtime directly.

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This commit is contained in:
Daniel
2024-12-18 12:10:45 +01:00
parent 6e8f6a22ba
commit b26ec998b3
2 changed files with 145 additions and 19 deletions
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142
scripts/utils_2d_pose_ort.py
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@ -1,19 +1,45 @@
import math
import os
from abc import ABC, abstractmethod
from typing import List
import cv2
import numpy as np
import onnxruntime as ort
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
from tqdm import tqdm
# ==================================================================================================
class BaseModel(ABC):
def __init__(
self, model_path: str, warmup: int, usetrt: bool = True, usegpu: bool = True
):
def __init__(self, model_path: str, warmup: int):
self.model_path = model_path
self.runtime = ""
if not os.path.exists(model_path):
raise FileNotFoundError("File not found:", model_path)
if model_path.endswith(".engine"):
self.init_trt_engine(model_path)
self.runtime = "trt"
elif model_path.endswith(".onnx"):
self.init_onnxruntime(model_path)
self.runtime = "ort"
else:
raise ValueError("Unsupported model format:", model_path)
if warmup > 0:
print("Running warmup for '{}' ...".format(self.__class__.__name__))
self.warmup(warmup // 2)
self.warmup(warmup // 2)
def init_onnxruntime(self, model_path):
usetrt = True
usegpu = True
self.opt = ort.SessionOptions()
providers = ort.get_available_providers()
# ort.set_default_logger_severity(1)
@ -49,8 +75,50 @@ class BaseModel(ABC):
raise ValueError("Undefined input type:", input_type)
self.input_types.append(itype)
if warmup > 0:
self.warmup(warmup)
def init_trt_engine(self, engine_path):
# https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html#python_topics
# https://stackoverflow.com/a/79076885
self.trt_logger = trt.Logger(trt.Logger.WARNING)
with open(engine_path, "rb") as f:
runtime = trt.Runtime(self.trt_logger)
self.engine = runtime.deserialize_cuda_engine(f.read())
self.context = self.engine.create_execution_context()
self.stream = cuda.Stream()
self.inputs, self.outputs, self.bindings = [], [], []
self.input_names = []
self.input_shapes = []
self.input_types = []
for i in range(self.engine.num_io_tensors):
tensor_name = self.engine.get_tensor_name(i)
shape = self.engine.get_tensor_shape(tensor_name)
dtype = trt.nptype(self.engine.get_tensor_dtype(tensor_name))
if -1 in shape:
print("WARNING: Replacing dynamic shape with fixed for:", tensor_name)
shape[list(shape).index(-1)] = 10
# Allocate host and device buffers
size = trt.volume(shape)
host_mem = cuda.pagelocked_empty(size, dtype)
device_mem = cuda.mem_alloc(host_mem.nbytes)
self.bindings.append(int(device_mem))
# Append to the appropriate input/output list
if self.engine.get_tensor_mode(tensor_name) == trt.TensorIOMode.INPUT:
self.inputs.append((host_mem, device_mem, shape))
self.input_names.append(tensor_name)
self.input_shapes.append(shape)
self.input_types.append(dtype)
else:
self.outputs.append((host_mem, device_mem, shape))
# Set tensor address
self.context.set_tensor_address(
self.engine.get_tensor_name(i), self.bindings[i]
)
@abstractmethod
def preprocess(self, **kwargs):
@ -63,14 +131,13 @@ class BaseModel(ABC):
def warmup(self, epoch: int):
np.random.seed(42)
print("Running warmup for '{}' ...".format(self.__class__.__name__))
for _ in tqdm(range(epoch)):
inputs = {}
for i in range(len(self.input_names)):
iname = self.input_names[i]
if "image" in iname:
ishape = self.input_shapes[i]
ishape = list(self.input_shapes[i])
if "batch_size" in ishape:
if "TensorrtExecutionProvider" in self.providers:
# Using different images sizes for TensorRT warmup takes too long
@ -101,15 +168,55 @@ class BaseModel(ABC):
tensor = tensor.astype(self.input_types[i])
inputs[iname] = tensor
self.session.run(None, inputs)
self.call_model(list(inputs.values()))
def __call__(self, **kwargs):
tensor = self.preprocess(**kwargs)
def call_model_ort(self, tensor):
inputs = {}
for i in range(len(self.input_names)):
iname = self.input_names[i]
inputs[iname] = tensor[i]
result = self.session.run(None, inputs)
return result
def call_model_trt(self, tensor):
# Transfer input data to device
for i, input_data in enumerate(tensor):
np.copyto(self.inputs[i][0], input_data.ravel())
cuda.memcpy_htod_async(self.inputs[i][1], self.inputs[i][0], self.stream)
# Empty the output buffers
for i in range(len(self.outputs)):
self.outputs[i][0].fill(0)
cuda.memcpy_htod_async(self.outputs[i][1], self.outputs[i][0], self.stream)
# Run inference
self.context.execute_async_v3(stream_handle=self.stream.handle)
# Transfer predictions back
for i in range(len(self.outputs)):
cuda.memcpy_dtoh_async(self.outputs[i][0], self.outputs[i][1], self.stream)
# Synchronize the stream
self.stream.synchronize()
# Un-flatten the outputs
outputs = []
for i in range(len(self.outputs)):
output = self.outputs[i][0].reshape(self.outputs[i][2])
outputs.append(output)
return outputs
def call_model(self, tensor):
if self.runtime == "trt":
result = self.call_model_trt(tensor)
elif self.runtime == "ort":
result = self.call_model_ort(tensor)
return result
def __call__(self, **kwargs):
tensor = self.preprocess(**kwargs)
result = self.call_model(tensor)
output = self.postprocess(result=result, **kwargs)
return output
@ -416,11 +523,6 @@ class TopDown:
box_min_area: float,
warmup: int = 30,
):
if (not det_model_path.endswith(".onnx")) or (
not pose_model_path.endswith(".onnx")
):
raise ValueError("Only ONNX models are supported.")
self.det_model = RTMDet(
det_model_path, box_conf_threshold, box_min_area, warmup
)
@ -439,17 +541,19 @@ class TopDown:
def load_model(min_bbox_score=0.3, min_bbox_area=0.1 * 0.1):
print("Loading onnx model ...")
print("Loading 2D model ...")
model = TopDown(
"/RapidPoseTriangulation/extras/mmdeploy/exports/rtmdet-nano_320x320_fp16_extra-steps.onnx",
"/RapidPoseTriangulation/extras/mmdeploy/exports/rtmpose-m_384x288_fp16_extra-steps.onnx",
# "/RapidPoseTriangulation/extras/mmdeploy/exports/rtmdet-nano_320x320_fp16_extra-steps.onnx",
# "/RapidPoseTriangulation/extras/mmdeploy/exports/rtmpose-m_384x288_fp16_extra-steps.onnx",
"/RapidPoseTriangulation/extras/mmdeploy/exports/rtmdet-nano_1x320x320x3_fp16_extra-steps.engine",
"/RapidPoseTriangulation/extras/mmdeploy/exports/rtmpose-m_1x384x288x3_fp16_extra-steps.engine",
box_conf_threshold=min_bbox_score,
box_min_area=min_bbox_area,
warmup=30,
)
print("Loaded onnx model")
print("Loaded 2D model")
return model