1. Jetson基本信息查询

使用jetson_release -v查看系统环境：

nvidia@nvidia-desktop:$ jetson_release -v - NVIDIA Jetson Xavier NX (Developer Kit Version)   * Jetpack 4.4 [L4T 32.4.3]   * NV Power Mode: MODE_15W_6CORE - Type: 2   * jetson_stats.service: active - Board info:   * Type: Xavier NX (Developer Kit Version)   * SOC Family: tegra194 - ID:25   * Module: P3668 - Board: P3509-000   * Code Name: arch   * CUDA GPU architecture (ARCH_BIN): 7.2   * Serial Number: 1421420051703 - Libraries:   * CUDA: 10.2.89   * cuDNN: 8.0.0.180   * TensorRT: 7.1.3.0   * Visionworks: 1.6.0.501   * OpenCV: 4.1.1 compiled CUDA: YES   * VPI: 0.3.7   * Vulkan: 1.2.70 - jetson-stats:   * Version 3.0.1   * Works on Python 3.6.9

也可以使用github上的一个小工具检查jetson info

git clone https://github.com/jetsonhacks/jetsonUtilitiescd jetsonUtilities./jetsonInfo.py

我这边返回如下信息：

NVIDIA Jetson Xavier NX (Developer Kit Version) L4T 32.4.3 [ JetPack 4.4 ]   Ubuntu 18.04.4 LTS   Kernel Version: 4.9.140-tegra CUDA 10.2.89   CUDA Architecture: 7.2 OpenCV version: 4.1.1   OpenCV Cuda: YES CUDNN: 8.0.0.180 TensorRT: 7.1.3.0 Vision Works: 1.6.0.501 VPI: 0.3.7 ffmpeg  n4.2.2-15-g6878ea5a44

另外，输入jtop可以对设备进行监控：

2. Jetson拉流

这里不能用imutil，改成cv2.VideoCapture与GStreamer后端一起使用：

import cv2pipeline = "rtspsrc location=\"rtsp://...:5554/mystream001\" ! rtph264depay ! h264parse ! omxh264dec ! nvvidconv ! video/x-raw, format=(string)BGRx! videoconvert ! appsink"capture = cv2.VideoCaputure(pipeline, cv2.CAP_GSTREAMER)res, frame = capture.read()capture.release()...

这里解析一下pipeline：

• rtspsrc: 连接rtsp Server（IPC端）并获取数据。
• rtph264depay: 做为rtspsrc的下游模块，可以接收到rtp包，并遵循RFC3984的规范解包，解完的包会按照NAL的格式对齐；NAL包以PUSH的模式交给下游模块。
• h264parse的作用，顾名思义，用来分割输出H264帧数据
• omxh264dec(或者nv_omx_h264dec)，硬件解码
• nvvidconv: 如名，进行转码
• video/x-raw, format=(string)BGRx! videoconvert 色彩空间转换
• appsink：允许应用程序获取处理后的数据。

3. Jetson推理

首先要安装pycuda：sudo pip3 install --global-option=build_ext --global-option="-I/usr/local/cuda/include" --global-option="-L/usr/local/cuda/lib64" pycuda

还有一种方式是手动指定：

export PATH=/usr/local/cuda-10.2/bin:/usr/local/cuda/bin:$PATHpip3 install pycuda

运行前要export OPENBLAS_CORETYPE=ARMV8，这个也可以写进~/.bashrc文件

下面是一个示例代码，输入输出可以根据需要改变：

import tensorrt as trtimport osimport pycuda.driver as cudaimport cv2import numpy as npclass TensorRTInference(object):    def __init__(self, engine_file_path, input_shape): self.engine_file_path = engine_file_path self.shape = input_shape self.engine = self.load_engine()    def load_engine(self): assert os.path.exists(self.engine_file_path) with open(self.engine_file_path, 'rb') as f, trt.Runtime(trt.Logger()) as runtime:     engine_data = f.read()     engine = runtime.deserialize_cuda_engine(engine_data)     return engine    def infer_once(self, img): engine = self.engine if len(img.shape) == 4:     _, c, h, w = img.shape elif len(img.shape) == 3:     c, h, w = img.shape with engine.create_execution_context() as context:     context.set_binding_shape(engine.get_binding_index('input'), (1, 3, self.shape[0], self.shape[1]))     bindings = []     for binding in engine:  binding_idx = engine.get_binding_index(binding)  size = trt.volume(context.get_binding_shape(binding_idx))  dtype = trt.nptype(engine.get_binding_dtype(binding))  if engine.binding_is_input(binding):      input_buffer = np.ascontiguousarray(img, dtype)      input_memory = cuda.mem_alloc(img.nbytes)      bindings.append(int(input_memory))  else:      output_buffer = cuda.pagelocked_empty(size, dtype)      output_memory = cuda.mem_alloc(output_buffer.nbytes)      bindings.append(int(output_memory))     stream = cuda.Stream()     cuda.memcpy_htod_async(input_memory, input_buffer, stream)     context.execute_async(bindings=bindings, stream_handle=stream.handle)     cuda.memcpy_dtoh_async(output_buffer, output_memory, stream)     stream.synchronize()     #res = np.reshape(output_buffer, (2, h, w)) return output_buffer

# 这段是调用代码import pycuda.autoinitINPUT_SHAPE = (17, 17)engine_file_path = '*.trt'img_path = '10.png'img = cv2.imread(img_path) # hwcimg = cv2.resize(img, (17, 17))img = np.transpose(img, (2,0,1)) # chwtrt_infer = TensorRTInference(engine_file_path, INPUT_SHAPE)engine = trt_infer.load_engine()for i in range(1000):    trt_infer.infer_once((img-255)/122)

4. 直接用onnx推理

参考https://www.elinux.org/Jetson_Zoo，安装jetson版本的onnxruntime即可