深度学习模型部署(六)TensorRT工作流and入门demo

TensorRT工作流程

官方给出的步骤：

总结下来可以分为两大部分：

• 模型生成：将onnx经过一系列优化，生成tensorrt的engine模型
  • 选择batchsize，选择精度precision，模型转换
• 模型推理：使用python或者C++进行推理

入门Demo

生成trt模型：

trtexec --onnx=yolov5s.onnx --saveEngine=yolov5s.trt
# trtexec是TensorRT自带的工具，如果运行显示is no command，把TensorRT安装路径下的bin文件夹加入到path中然后source一下就行了。

然后就坐等输出模型，我们可以根据log信息看一下tensorRT都干了什么：

 === Model Options ===
 === Build Options ===
 Precision: FP32
 === System Options ===
 === Inference Options ===
 === Reporting Options ===
 # 这几部分是一些选项设置，不用看，目前只需要看精度这一项
 === Device Information ===
 # 设备信息
 [TRT] CUDA lazy loading is not enabled.
 # 这里提到了CUDA lazy loading，这个是CUDA11.8新增的延时加载功能。
 # 初始化时不加载kernel，只有用相应的kernel才会加载，是CUDA层面的特性。
 # 这个特性会导致第一次推理比较慢，因为第一次推理要加载用到的kernel函数
 # 我们后面会先更几篇番外初步速成一下cuda，后面用到cuda的地方会很多
 Start parsing network model.
[03/11/2024-22:37:43] [I] [TRT] ----------------------------------------------------------------
[03/11/2024-22:37:43] [I] [TRT] Input filename:   yolov5s.onnx
[03/11/2024-22:37:43] [I] [TRT] ONNX IR version:  0.0.8
[03/11/2024-22:37:43] [I] [TRT] Opset version:    17
[03/11/2024-22:37:43] [I] [TRT] Producer name:    pytorch
[03/11/2024-22:37:43] [I] [TRT] Producer version: 2.2.1
[03/11/2024-22:37:43] [I] [TRT] Domain:
[03/11/2024-22:37:43] [I] [TRT] Model version:    0
[03/11/2024-22:37:43] [I] [TRT] Doc string:
[03/11/2024-22:37:43] [I] [TRT] ----------------------------------------------------------------
# 解析模型
[TRT] onnx2trt_utils.cpp:374: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32.
# 提醒我们的模型时INT64的，会被压缩到INT32
[TRT] Graph optimization time: 0.021841 seconds.
# 进行图优化
[TRT] [GraphReduction] The approximate region cut reduction algorithm is called.
# 进行图简化/图规约
Using random values for input images
[03/11/2024-22:39:14] [I] Input binding for images with dimensions 1x3x640x640 is created.
[03/11/2024-22:39:14] [I] Output binding for output0 with dimensions 1x25200x85 is created.
[03/11/2024-22:39:14] [I] Starting inference
# 会进行一次推理，tracing数据流过的算子以及时间

得到模型后开始进行部署：

import tensorrt as trt
import numpy as np
import pycuda.driver as cuda
import pycuda.autoinit
N_CLASSES = 80 # yolov5 class label number
BATCH_SIZE=1
PRECISION= np.float32


dummy_input_batch = np.zeros((BATCH_SIZE,3,640,640),dtype=PRECISION)

f = open("yolov5s.trt", "rb")
runtime = trt.Runtime(trt.Logger(trt.Logger.WARNING))

engine = runtime.deserialize_cuda_engine(f.read())
context = engine.create_execution_context()

output = np.empty(N_CLASSES, dtype = PRECISION) # Need to set both input and output precisions to FP16 to fully enable FP16

d_input = cuda.mem_alloc(1 * dummy_input_batch.nbytes)
d_output = cuda.mem_alloc(1 * output.nbytes)

bindings = [int(d_input), int(d_output)]
stream = cuda.Stream()

def predict(batch): # result gets copied into output
    # Transfer input data to device
    cuda.memcpy_htod_async(d_input, batch, stream)
    # Execute model
    context.execute_async_v2(bindings, stream.handle, None)
    # Transfer predictions back
    cuda.memcpy_dtoh_async(output, d_output, stream)
    # Syncronize threads
    stream.synchronize()
    return output

pred = predict(dummy_input_batch)
print(pred.shape)

今天blog的主题是跑通tensorRT的整个流程，yolov5的后处理比较麻烦，这不是今天blog的主题，所以没有写，后面有空补上。

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