深度学习系列5：jetson nano入门

Posted 2021-05-10 IE06

1. Jetson基本信息查询

使用github上的一个小工具检查jetson info

git clone https://github.com/jetsonhacks/jetsonUtilities
cd 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 cv2
pipeline = "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
下面是一个示例代码，输入输出可以根据需要改变：

import tensorrt as trt
import os
import pycuda.driver as cuda
import cv2
import numpy as np
class 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.autoinit
INPUT_SHAPE = (17, 17)
engine_file_path = '*.trt'
img_path = '10.png'
img = cv2.imread(img_path) # hwc
img = cv2.resize(img, (17, 17))
img = np.transpose(img, (2,0,1)) # chw
trt_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. Jetson Linux Multimedia API