Jetson Nano配置YOLO v3（CUDA+cudnn+OpenCV+TensorRT）
- 一、运行YOLO v3
-
- 1、YOLO介绍
- 2、检查CUDA
- 3、检查OpenCV
- 4、检查cuDNN
- 5、安装YOLO v3
- 6、YOLO的常用命令
-
- (1) 单张测试命令
- (2) 多张测试命令
- (3) 改变阈值
- (4) 实时摄像头
- (5) 本地视频检测
- (6)在预训练的模型上继续训练
- 二、运行TensorRT
-
- 1、TensorRT介绍
- 三、运行TensorFlow
- 1、TensorFlow简介
- 2、安装TensorFlow
一、运行YOLO v3
1、YOLO介绍
这里贴个学习YOLO的参考资料：
Series: YOLO object detector in PyTorch A collection of 5 posts.
在安装YOLO v3之前要先检查已经安装的系统组件，Jetson Nano的OS镜像已经自带了JetPack，cuda，cudnn，opencv等都已经安装好，我们要分别检查一下环境。
2、检查CUDA
Jetson-nano中已经安装了CUDA10.0版本，但必须先将路径加入到环境变量中。
sudo vim ~/.bashrc
在最后添加：
export CUDA_HOME=/usr/local/cuda-10.0
export LD_LIBRARY_PATH=/usr/local/cuda-10.0/lib64:$LD_LIBRARY_PATH
export PATH=/usr/local/cuda-10.0/bin:$PATH
然后再运行一下：
source ~/.bashrc
此时如果执行以下代码应该会出现CUDA的版本：
nvidia@nvidia-desktop:~$ nvcc -V
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2019 NVIDIA Corporation
Built on Wed_Oct_23_21:14:42_PDT_2019
Cuda compilation tools, release 10.2, V10.2.89
但是我在第一次输入后出现了：
bash: nvcc: 未找到命令
先切换到 目录下： cd ~
然后打开 .bashrc 文件：vim .bashr
编辑在文件的末尾添加下面的代码：
export PATH=/usr/local/cuda/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH
export CUDA_ROOT=/usr/local/cud
此时依次按下Esc ： q ！ 将文件强制写入并退出，然后在运行一下：
source ~/.bashrc
再输入以下nvcc -V应该就会出现。
3、检查OpenCV
输入以下代码可以查看当前OpenCV的版本：
pkg-config opencv --modversion
如果OpenCv安装就绪，会显示版本号，但是我的当时一直不能显示OpenCV ，提示需要将OpenCV加入系统环境中，此时可以输入代码：
pkg-config opencv4 --modversion
由于版本的原因，这次就能显示出来：
nvidia@nvidia-desktop:~$ pkg-config opencv --modversion
4.1.1
4、检查cuDNN
Jetson-nano中已经安装好了cuDNN，我们运行一下看是否正常：
cd /usr/src/cudnn_samples_v8/mnistCUDNN #进入例子目录
sudo make #编译一下例子
sudo chmod a+x mnistCUDNN # 为可执行文件添加执行权限
./mnistCUDNN # 执行
显示如下，表示成功：
nvidia@nvidia-desktop:/usr/src/cudnn_samples_v8/mnistCUDNN$ ./mnistCUDNN
Executing: mnistCUDNN
cudnnGetVersion() : 8000 , CUDNN_VERSION from cudnn.h : 8000 (8.0.0)
Host compiler version : GCC 7.5.0
There are 1 CUDA capable devices on your machine :
device 0 : sms 1 Capabilities 5.3, SmClock 921.6 Mhz, MemSize (Mb) 3962, MemClock 12.8 Mhz, Ecc=0, boardGroupID=0
Using device 0
Testing single precision
Loading binary file data/conv1.bin
Loading binary file data/conv1.bias.bin
Loading binary file data/conv2.bin
Loading binary file data/conv2.bias.bin
Loading binary file data/ip1.bin
Loading binary file data/ip1.bias.bin
Loading binary file data/ip2.bin
Loading binary file data/ip2.bias.bin
Loading image data/one_28x28.pgm
Performing forward propagation ...
Testing cudnnGetConvolutionForwardAlgorithm_v7 ...
^^^^ CUDNN_STATUS_SUCCESS for Algo 1: -1.000000 time requiring 0 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 0: -1.000000 time requiring 0 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 2: -1.000000 time requiring 0 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 7: -1.000000 time requiring 2057744 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 4: -1.000000 time requiring 184784 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 5: -1.000000 time requiring 178432 memory
^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 6: -1.000000 time requiring 0 memory
^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 3: -1.000000 time requiring 0 memory
Testing cudnnFindConvolutionForwardAlgorithm ...
^^^^ CUDNN_STATUS_SUCCESS for Algo 0: 0.379532 time requiring 0 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 1: 0.407083 time requiring 0 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 2: 1.107760 time requiring 0 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 5: 15.830000 time requiring 178432 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 7: 28.929480 time requiring 2057744 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 4: 160.134064 time requiring 184784 memory
^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 6: -1.000000 time requiring 0 memory
^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 3: -1.000000 time requiring 0 memory
Testing cudnnGetConvolutionForwardAlgorithm_v7 ...
^^^^ CUDNN_STATUS_SUCCESS for Algo 1: -1.000000 time requiring 0 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 0: -1.000000 time requiring 0 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 2: -1.000000 time requiring 0 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 7: -1.000000 time requiring 1433120 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 4: -1.000000 time requiring 2450080 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 5: -1.000000 time requiring 4656640 memory
^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 6: -1.000000 time requiring 0 memory
^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 3: -1.000000 time requiring 0 memory
Testing cudnnFindConvolutionForwardAlgorithm ...
^^^^ CUDNN_STATUS_SUCCESS for Algo 1: 2.277968 time requiring 0 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 0: 2.286458 time requiring 0 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 2: 2.821927 time requiring 0 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 4: 3.238802 time requiring 2450080 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 7: 8.895677 time requiring 1433120 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 5: 9.272969 time requiring 4656640 memory
^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 6: -1.000000 time requiring 0 memory
^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 3: -1.000000 time requiring 0 memory
Resulting weights from Softmax:
0.0000000 0.9999399 0.0000000 0.0000000 0.0000561 0.0000000 0.0000012 0.0000017 0.0000010 0.0000000
Loading image data/three_28x28.pgm
Performing forward propagation ...
Testing cudnnGetConvolutionForwardAlgorithm_v7 ...
^^^^ CUDNN_STATUS_SUCCESS for Algo 1: -1.000000 time requiring 0 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 0: -1.000000 time requiring 0 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 2: -1.000000 time requiring 0 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 7: -1.000000 time requiring 2057744 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 4: -1.000000 time requiring 184784 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 5: -1.000000 time requiring 178432 memory
^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 6: -1.000000 time requiring 0 memory
^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 3: -1.000000 time requiring 0 memory
Testing cudnnFindConvolutionForwardAlgorithm ...
^^^^ CUDNN_STATUS_SUCCESS for Algo 1: 0.197396 time requiring 0 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 0: 0.205937 time requiring 0 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 2: 0.399844 time requiring 0 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 4: 2.132292 time requiring 184784 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 5: 4.073125 time requiring 178432 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 7: 7.371354 time requiring 2057744 memory
^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 6: -1.000000 time requiring 0 memory
^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 3: -1.000000 time requiring 0 memory
Testing cudnnGetConvolutionForwardAlgorithm_v7 ...
^^^^ CUDNN_STATUS_SUCCESS for Algo 1: -1.000000 time requiring 0 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 0: -1.000000 time requiring 0 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 2: -1.000000 time requiring 0 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 7: -1.000000 time requiring 1433120 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 4: -1.000000 time requiring 2450080 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 5: -1.000000 time requiring 4656640 memory
^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 6: -1.000000 time requiring 0 memory
^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 3: -1.000000 time requiring 0 memory
Testing cudnnFindConvolutionForwardAlgorithm ...
^^^^ CUDNN_STATUS_SUCCESS for Algo 0: 1.505469 time requiring 0 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 2: 1.527083 time requiring 0 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 1: 1.528802 time requiring 0 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 4: 2.178020 time requiring 2450080 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 7: 6.266927 time requiring 1433120 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 5: 10.913073 time requiring 4656640 memory
^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 6: -1.000000 time requiring 0 memory
^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 3: -1.000000 time requiring 0 memory
Resulting weights from Softmax:
0.0000000 0.0000000 0.0000000 0.9999288 0.0000000 0.0000711 0.0000000 0.0000000 0.0000000 0.0000000
Loading image data/five_28x28.pgm
Performing forward propagation ...
Resulting weights from Softmax:
0.0000000 0.0000008 0.0000000 0.0000002 0.0000000 0.9999820 0.0000154 0.0000000 0.0000012 0.0000006
Result of classification: 1 3 5
Test passed!
root@nvidia-desktop:/usr/src/cudnn_samples_v8/mnistCUDNN# cd
root@nvidia-desktop:~# cat /usr/include/cudnn_version.h | grep CUDNN_MAJOR -A 2
#define CUDNN_MAJOR 8
#define CUDNN_MINOR 0
#define CUDNN_PATCHLEVEL 0
--
#define CUDNN_VERSION (CUDNN_MAJOR * 1000 + CUDNN_MINOR * 100 + CUDNN_PATCHLEVEL)
#endif /* CUDNN_VERSION_H */
5、安装YOLO v3
1.首先先在官网下载：
git clone git://github.com/pjreddie/darknet.git
2.然后配置：
cd darknet
sudo vim Makefile #修改Makefil
3.接着将Makefile的前三行修改一下：
GPU=1
CUDNN=1
OPENCV=1
4.接着编译：
make -j4
但是从这里开始出错，一直显示：
error:'CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT' undeclared(first use in this function);did you mean 'CUDNN_CONVOLUTION_FWD_ALGO_DIRECT'
查找了各种方法都不能解决，尝试下载AlexeyAB版的darknet。
git clone git://github.com/AlexeyAB/darknet
5.然后配置：
cd darknet/AlexeyAB
sudo vim Makefile #修改Makefil
6.接着将Makefile修改一下：
GPU=1
CUDNN=1
CUDNN_HALF=1
OPENCV=1 #看是否安装opencv
AVX=0
OPENMP=0
LIBSO=0
ZED_CAMERA=0
7.接着编译：
make install
下载权重文件：
这个是yolov3-tiny
wget https://pjreddie.com/media/files/yolov3-tiny.weights
这个是yolov3
wget https://pjreddie.com/media/files/yolov3.weights
两者的区别在于：
yolo3-tiny是yolo3的简化版本，主要区别为：主干网络采用一个7层conv+max网络提取特征（和darknet19类似），嫁接网络采用的是1313、2626的分辨率探测网络，精度比较低。探测精度低一个很重要的原因是tiny的主干网络比较浅（7层），不能提取出更高层次的语义特征。
8.检测一下是否安装成功：
./darknet detector test data/detect.data data/yolov3.cfg data/yolov3.weight
9.运行YOLO v3-tiny:
nvidia@nvidia-desktop:~/darknet/AlexeyAB/darknet$ ./darknet detect cfg/yolov3-tiny.cfg yolov3-tiny.weights data/dog.jpg
CUDA-version: 10020 (10020), cuDNN: 8.0.0, CUDNN_HALF=1, GPU count: 1
CUDNN_HALF=1
OpenCV version: 4.1.1
0 : compute_capability = 530, cudnn_half = 0, GPU: NVIDIA Tegra X1
net.optimized_memory = 0
mini_batch = 1, batch = 1, time_steps = 1, train = 0
layer filters size/strd(dil) input output
0 conv 16 3 x 3/ 1 416 x 416 x 3 -> 416 x 416 x 16 0.150 BF
1 max 2x 2/ 2 416 x 416 x 16 -> 208 x 208 x 16 0.003 BF
2 conv 32 3 x 3/ 1 208 x 208 x 16 -> 208 x 208 x 32 0.399 BF
3 max 2x 2/ 2 208 x 208 x 32 -> 104 x 104 x 32 0.001 BF
4 conv 64 3 x 3/ 1 104 x 104 x 32 -> 104 x 104 x 64 0.399 BF
5 max 2x 2/ 2 104 x 104 x 64 -> 52 x 52 x 64 0.001 BF
6 conv 128 3 x 3/ 1 52 x 52 x 64 -> 52 x 52 x 128 0.399 BF
7 max 2x 2/ 2 52 x 52 x 128 -> 26 x 26 x 128 0.000 BF
8 conv 256 3 x 3/ 1 26 x 26 x 128 -> 26 x 26 x 256 0.399 BF
9 max 2x 2/ 2 26 x 26 x 256 -> 13 x 13 x 256 0.000 BF
10 conv 512 3 x 3/ 1 13 x 13 x 256 -> 13 x 13 x 512 0.399 BF
11 max 2x 2/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.000 BF
12 conv 1024 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF
13 conv 256 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 256 0.089 BF
14 conv 512 3 x 3/ 1 13 x 13 x 256 -> 13 x 13 x 512 0.399 BF
15 conv 255 1 x 1/ 1 13 x 13 x 512 -> 13 x 13 x 255 0.044 BF
16 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, cls_norm: 1.00, scale_x_y: 1.00
17 route 13 -> 13 x 13 x 256
18 conv 128 1 x 1/ 1 13 x 13 x 256 -> 13 x 13 x 128 0.011 BF
19 upsample 2x 13 x 13 x 128 -> 26 x 26 x 128
20 route 19 8 -> 26 x 26 x 384
21 conv 256 3 x 3/ 1 26 x 26 x 384 -> 26 x 26 x 256 1.196 BF
22 conv 255 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 255 0.088 BF
23 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, cls_norm: 1.00, scale_x_y: 1.00
Total BFLOPS 5.571
avg_outputs = 341534
Allocate additional workspace_size = 0.00 MB
Loading weights from yolov3-tiny.weights...
seen 64, trained: 32013 K-images (500 Kilo-batches_64)
Done! Loaded 24 layers from weights-file
Detection layer: 16 - type = 27
Detection layer: 23 - type = 27
data/dog.jpg: Predicted in 2663.842000 milli-seconds.
dog: 81%
bicycle: 38%
car: 71%
truck: 41%
truck: 62%
car: 39%
运行YOLO v3 :
nvidia@nvidia-desktop:~/darknet/AlexeyAB/darknet$ ./darknet detect cfg/yolov3.cfg yolov3.weights data/dog.jpg
CUDA-version: 10020 (10020), cuDNN: 8.0.0, CUDNN_HALF=1, GPU count: 1
CUDNN_HALF=1
OpenCV version: 4.1.1
0 : compute_capability = 530, cudnn_half = 0, GPU: NVIDIA Tegra X1
net.optimized_memory = 0
mini_batch = 1, batch = 1, time_steps = 1, train = 0
layer filters size/strd(dil) input output
0 conv 32 3 x 3/ 1 416 x 416 x 3 -> 416 x 416 x 32 0.299 BF
1 conv 64 3 x 3/ 2 416 x 416 x 32 -> 208 x 208 x 64 1.595 BF
2 conv 32 1 x 1/ 1 208 x 208 x 64 -> 208 x 208 x 32 0.177 BF
3 conv 64 3 x 3/ 1 208 x 208 x 32 -> 208 x 208 x 64 1.595 BF
4 Shortcut Layer: 1, wt = 0, wn = 0, outputs: 208 x 208 x 64 0.003 BF
5 conv 128 3 x 3/ 2 208 x 208 x 64 -> 104 x 104 x 128 1.595 BF
6 conv 64 1 x 1/ 1 104 x 104 x 128 -> 104 x 104 x 64 0.177 BF
7 conv 128 3 x 3/ 1 104 x 104 x 64 -> 104 x 104 x 128 1.595 BF
8 Shortcut Layer: 5, wt = 0, wn = 0, outputs: 104 x 104 x 128 0.001 BF
9 conv 64 1 x 1/ 1 104 x 104 x 128 -> 104 x 104 x 64 0.177 BF
10 conv 128 3 x 3/ 1 104 x 104 x 64 -> 104 x 104 x 128 1.595 BF
11 Shortcut Layer: 8, wt = 0, wn = 0, outputs: 104 x 104 x 128 0.001 BF
12 conv 256 3 x 3/ 2 104 x 104 x 128 -> 52 x 52 x 256 1.595 BF
13 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF
14 conv 256 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF
15 Shortcut Layer: 12, wt = 0, wn = 0, outputs: 52 x 52 x 256 0.001 BF
16 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF
17 conv 256 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF
18 Shortcut Layer: 15, wt = 0, wn = 0, outputs: 52 x 52 x 256 0.001 BF
19 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF
20 conv 256 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF
21 Shortcut Layer: 18, wt = 0, wn = 0, outputs: 52 x 52 x 256 0.001 BF
22 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF
23 conv 256 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF
24 Shortcut Layer: 21, wt = 0, wn = 0, outputs: 52 x 52 x 256 0.001 BF
25 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF
26 conv 256 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF
27 Shortcut Layer: 24, wt = 0, wn = 0, outputs: 52 x 52 x 256 0.001 BF
28 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF
29 conv 256 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF
30 Shortcut Layer: 27, wt = 0, wn = 0, outputs: 52 x 52 x 256 0.001 BF
31 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF
32 conv 256 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF
33 Shortcut Layer: 30, wt = 0, wn = 0, outputs: 52 x 52 x 256 0.001 BF
34 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF
35 conv 256 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF
36 Shortcut Layer: 33, wt = 0, wn = 0, outputs: 52 x 52 x 256 0.001 BF
37 conv 512 3 x 3/ 2 52 x 52 x 256 -> 26 x 26 x 512 1.595 BF
38 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
39 conv 512 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF
40 Shortcut Layer: 37, wt = 0, wn = 0, outputs: 26 x 26 x 512 0.000 BF
41 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
42 conv 512 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF
43 Shortcut Layer: 40, wt = 0, wn = 0, outputs: 26 x 26 x 512 0.000 BF
44 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
45 conv 512 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF
46 Shortcut Layer: 43, wt = 0, wn = 0, outputs: 26 x 26 x 512 0.000 BF
47 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
48 conv 512 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF
49 Shortcut Layer: 46, wt = 0, wn = 0, outputs: 26 x 26 x 512 0.000 BF
50 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
51 conv 512 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF
52 Shortcut Layer: 49, wt = 0, wn = 0, outputs: 26 x 26 x 512 0.000 BF
53 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
54 conv 512 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF
55 Shortcut Layer: 52, wt = 0, wn = 0, outputs: 26 x 26 x 512 0.000 BF
56 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
57 conv 512 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF
58 Shortcut Layer: 55, wt = 0, wn = 0, outputs: 26 x 26 x 512 0.000 BF
59 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
60 conv 512 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF
61 Shortcut Layer: 58, wt = 0, wn = 0, outputs: 26 x 26 x 512 0.000 BF
62 conv 1024 3 x 3/ 2 26 x 26 x 512 -> 13 x 13 x1024 1.595 BF
63 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF
64 conv 1024 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF
65 Shortcut Layer: 62, wt = 0, wn = 0, outputs: 13 x 13 x1024 0.000 BF
66 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF
67 conv 1024 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF
68 Shortcut Layer: 65, wt = 0, wn = 0, outputs: 13 x 13 x1024 0.000 BF
69 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF
70 conv 1024 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF
71 Shortcut Layer: 68, wt = 0, wn = 0, outputs: 13 x 13 x1024 0.000 BF
72 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF
73 conv 1024 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF
74 Shortcut Layer: 71, wt = 0, wn = 0, outputs: 13 x 13 x1024 0.000 BF
75 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF
76 conv 1024 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF
77 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF
78 conv 1024 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF
79 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF
80 conv 1024 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF
81 conv 255 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 255 0.088 BF
82 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, cls_norm: 1.00, scale_x_y: 1.00
83 route 79 -> 13 x 13 x 512
84 conv 256 1 x 1/ 1 13 x 13 x 512 -> 13 x 13 x 256 0.044 BF
85 upsample 2x 13 x 13 x 256 -> 26 x 26 x 256
86 route 85 61 -> 26 x 26 x 768
87 conv 256 1 x 1/ 1 26 x 26 x 768 -> 26 x 26 x 256 0.266 BF
88 conv 512 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF
89 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
90 conv 512 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF
91 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
92 conv 512 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF
93 conv 255 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 255 0.177 BF
94 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, cls_norm: 1.00, scale_x_y: 1.00
95 route 91 -> 26 x 26 x 256
96 conv 128 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 128 0.044 BF
97 upsample 2x 26 x 26 x 128 -> 52 x 52 x 128
98 route 97 36 -> 52 x 52 x 384
99 conv 128 1 x 1/ 1 52 x 52 x 384 -> 52 x 52 x 128 0.266 BF
100 conv 256 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF
101 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF
102 conv 256 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF
103 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF
104 conv 256 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF
105 conv 255 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 255 0.353 BF
106 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, cls_norm: 1.00, scale_x_y: 1.00
Total BFLOPS 65.879
avg_outputs = 532444
Allocate additional workspace_size = 0.00 MB
Loading weights from yolov3.weights...
seen 64, trained: 32013 K-images (500 Kilo-batches_64)
Done! Loaded 107 layers from weights-file
Detection layer: 82 - type = 27
Detection layer: 94 - type = 27
Detection layer: 106 - type = 27
data/dog.jpg: Predicted in 7466.562000 milli-seconds.
bicycle: 99%
dog: 100%
truck: 94%
可以看出YOLO v3-tiny只用了2.6s，而YOLO v3用了7.46s，但从准确度来说，YOLO v3的精准度远远大于YOLO v3-tiny。
TIP：资源不够时应对方案：增加虚拟内存swap
1.先禁用之前的
sudo swapoff /swapfile
2.修改swap 空间的大小为4G
sudo dd if=/dev/zero of=/swapfile bs=1M count=4096
3.设置文件为“swap file”类型
sudo mkswap /swapfile
4.启用swapfile
sudo swapon /swapfile
运行结果：
nvidia@nvidia-desktop:~$ df -hl
文件系统 容量 已用 可用 已用% 挂载点
/dev/mmcblk0p1 30G 22G 6.0G 79% /
none 1.8G 0 1.8G 0% /dev
tmpfs 2.0G 628M 1.4G 32% /dev/shm
tmpfs 2.0G 47M 1.9G 3% /run
tmpfs 5.0M 4.0K 5.0M 1% /run/lock
tmpfs 2.0G 0 2.0G 0% /sys/fs/cgroup
tmpfs 397M 12K 397M 1% /run/user/120
tmpfs 397M 108K 397M 1% /run/user/1000
6、YOLO的常用命令
在终端里，直接运行时Yolo的Darknet的各项命令，/home/nvidia/darknet/cfg/coco.data文件，使用原件：
(1) 单张测试命令
nvidia@nvidia-desktop:~/darknet/AlexeyAB/darknet$ ./darknet detect cfg/yolov3.cfg yolov3.weights data/dog.jpg
(2) 多张测试命令
nvidia@nvidia-desktop:~/darknet/AlexeyAB/darknet$ ./darknet detect cfg/yolov3.cfg yolov3.weights
Enter Image Path: data/dog1.jpg
Enter Image Path: data/dog2.jpg
(3) 改变阈值
YOLO默认阈值0.25，范围[0,1]，可以自行设定：
nvidia@nvidia-desktop:~/darknet/AlexeyAB/darknet$ ./darknet detect cfg/yolov3.cfg yolov3.weights data/dog.jpg -thresh 0
(4) 实时摄像头
实时视频检测需要Darknet with CUDA and OpenCV，-c ，OpenCV默认为0：
nvidia@nvidia-desktop:~/darknet/AlexeyAB/darknet$ ./darknet detector demo cfg/coco.data cfg/yolov3.cfg yolov3.weights
(5) 本地视频检测
nvidia@nvidia-desktop:~/darknet/AlexeyAB/darknet$ ./darknet detector demo cfg/coco.data cfg/yolov3.cfg yolov3.weights <video file>
darknet环境中：
nvidia@nvidia-desktop:~/darknet/AlexeyAB/darknet$ ./darknet detector test cfg/coco.data cfg/yolov3.cfg yolov3.weights data/xxx.mp4
OpenCV 环境中：
nvidia@nvidia-desktop:~/darknet/AlexeyAB/darknet$ python3 object_detection_yolo.py --video=xxx.mp4
(6)在预训练的模型上继续训练
在CPU下训练：
nvidia@nvidia-desktop:~/darknet/AlexeyAB/darknet$ ./darknet detector train cfg/voc.data cfg/yolov3-voc.cfg darknet53.conv.74
在多GPU下训练：
nvidia@nvidia-desktop:~/darknet/AlexeyAB/darknet$ ./darknet detector train cfg/coco.data cfg/yolov3.cfg darknet53.conv.74 -gpus 0,1,2,3
从定点继续训练：
nvidia@nvidia-desktop:~/darknet/AlexeyAB/darknet$ ./darknet detector train cfg/coco.data cfg/yolov3.cfg backup/yolov3.backup -gpus 0,1,2,3
二、运行TensorRT
1、TensorRT介绍
贴一些参考资料：
- TensorRT(1)-介绍.
1.首先在官网下载最新TensorRT7安装包：NVIDIA TensorRT 7.x Download
2.解压
tar xzvf TensorRT-7.0.0.11.Ubuntu-18.04.x86_64-gnu.cuda-10.2.cudnn7.6.tar.gz
3.添加路径
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/home/nvidia/TensorRT-7.0.0.11/lib
4.安装TensorRT
cd TensorRT-7.0.0.11/python
pip3 install tensorrt-7.0.0.11-cp36-none-linux_x86_64.whl
但是在安装完毕之后出现了如下问题：
nvidia@nvidia-desktop:~/TensorRT-7.0.0.11/python$ pip install tensorrt-7.0.0.11-cp36-none-linux_x86_64.whl
Defaulting to user installation because normal site-packages is not writeable
ERROR: tensorrt-7.0.0.11-cp36-none-linux_x86_64.whl is not a supported wheel on this platform.
解决方法：
nvidia@nvidia-desktop:~/TensorRT-7.0.0.11/python$ pip3 --version
pip 20.2.2 from /home/nvidia/.local/lib/python3.6/site-packages/pip (python 3.6)
因为安装了两个版本的python，每次使用切换比较麻烦，所以可以使用如下操作切换系统默认的python：
sudo update-alternatives --install /usr/bin/python python /usr/bin/python2 100
sudo update-alternatives --install /usr/bin/python python /usr/bin/python3 150
此时python版本已经切换为3.6，如果需要切换回来，只需要输入:
sudo update-alternatives --config python
但是再次执行命令还是会出现如上的错误，尝试安装TensorRT6，但是在运行安装的过程中还是发生了如下的命令：
nvidia@nvidia-desktop:~$ pip3 install TensorRT-6.0.1.8/python/tensorrt-6.0.1.8-cp36-none-linux_x86_64.whl
Defaulting to user installation because normal site-packages is not writeable
ERROR: tensorrt-6.0.1.8-cp36-none-linux_x86_64.whl is not a supported wheel on this platform.
但是在按照上面步骤之后，发生了如下问题：
nvidia@nvidia-desktop:~$ python
Python 3.6.9 (default, Jul 17 2020, 12:50:27)
[GCC 8.4.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorrt
Traceback (most recent call last):
File "" , line 1, in <module>
File "/home/nvidia/.local/lib/python3.6/site-packages/tensorrt/__init__.py", line 1, in <module>
from .tensorrt import *
ImportError: /home/nvidia/.local/lib/python3.6/site-packages/tensorrt/tensorrt.so: cannot open shared object file: No such file or directory
查找了半天结果是因为TensorFlow没有配置好，转头开始配置TensorFlow。这里一旦运行TensorFlow但是又出现了下面的错误：
>>> import tensorflow
Traceback (most recent call last):
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/pywrap_tensorflow.py", line 58, in <module>
from tensorflow.python.pywrap_tensorflow_internal import *
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/pywrap_tensorflow_internal.py", line 28, in <module>
_pywrap_tensorflow_internal = swig_import_helper()
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/pywrap_tensorflow_internal.py", line 24, in swig_import_helper
_mod = imp.load_module('_pywrap_tensorflow_internal', fp, pathname, description)
File "/usr/lib/python3.6/imp.py", line 243, in load_module
return load_dynamic(name, filename, file)
File "/usr/lib/python3.6/imp.py", line 343, in load_dynamic
return _load(spec)
ImportError: libcublas.so.10.0: cannot open shared object file: No such file or directory
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "" , line 1, in <module>
File "/usr/local/lib/python3.6/dist-packages/tensorflow/__init__.py", line 24, in <module>
from tensorflow.python import pywrap_tensorflow # pylint: disable=unused-import
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/__init__.py", line 49, in <module>
from tensorflow.python import pywrap_tensorflow
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/pywrap_tensorflow.py", line 74, in <module>
raise ImportError(msg)
ImportError: Traceback (most recent call last):
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/pywrap_tensorflow.py", line 58, in <module>
from tensorflow.python.pywrap_tensorflow_internal import *
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/pywrap_tensorflow_internal.py", line 28, in <module>
_pywrap_tensorflow_internal = swig_import_helper()
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/pywrap_tensorflow_internal.py", line 24, in swig_import_helper
_mod = imp.load_module('_pywrap_tensorflow_internal', fp, pathname, description)
File "/usr/lib/python3.6/imp.py", line 243, in load_module
return load_dynamic(name, filename, file)
File "/usr/lib/python3.6/imp.py", line 343, in load_dynamic
return _load(spec)
ImportError: libcublas.so.10.0: cannot open shared object file: No such file or directory
Failed to load the native TensorFlow runtime.
See https://www.tensorflow.org/install/errors
for some common reasons and solutions. Include the entire stack trace
above this error message when asking for help.
三、运行TensorFlow
1、TensorFlow简介
TensorFlow是一个采用数据流图，用于数值计算的开源软件库。可用于机器学习和深度神经网络方面的研究，按着个系统的通性使其也可广泛的用于其他领域。通过使用TensorFlow人们可以快速的入门神经网络，大大降低深度学习的开发成本和开发难度，具有灵活性 、可移植性、多语言支持、性能最优化等特点。
2、安装TensorFlow
在TensorFlow官网中的搜索框搜索TensorFlow，下载tf_gpu-2.2.0 + nv20.6-py3，然后安装。
nvidia@nvidia-desktop:~$ sudo -H pip3 install ~/下载/tensorflow-1.15.3+nv20.7-cp36-cp36m-linux_aarch64.whl
Processing ./下载/tensorflow-1.15.3+nv20.7-cp36-cp36m-linux_aarch64.whl
Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15.3+nv20.7)
Requirement already satisfied: keras-applications>=1.0.8 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15.3+nv20.7)
Requirement already satisfied: wheel>=0.26; python_version >= "3" in /usr/lib/python3/dist-packages (from tensorflow==1.15.3+nv20.7)
Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15.3+nv20.7)
Collecting tensorflow-estimator==1.15.1 (from tensorflow==1.15.3+nv20.7)
Using cached https://files.pythonhosted.org/packages/de/62/2ee9cd74c9fa2fa450877847ba560b260f5d0fb70ee0595203082dafcc9d/tensorflow_estimator-1.15.1-py2.py3-none-any.whl
Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15.3+nv20.7)
Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15.3+nv20.7)
Requirement already satisfied: gast==0.2.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15.3+nv20.7)
Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15.3+nv20.7)
Requirement already satisfied: google-pasta>=0.1.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15.3+nv20.7)
Requirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15.3+nv20.7)
Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15.3+nv20.7)
Collecting opt-einsum>=2.3.2 (from tensorflow==1.15.3+nv20.7)
Using cached https://files.pythonhosted.org/packages/bc/19/404708a7e54ad2798907210462fd950c3442ea51acc8790f3da48d2bee8b/opt_einsum-3.3.0-py3-none-any.whl
Collecting tensorboard<1.16.0,>=1.15.0 (from tensorflow==1.15.3+nv20.7)
Using cached https://files.pythonhosted.org/packages/1e/e9/d3d747a97f7188f48aa5eda486907f3b345cd409f0a0850468ba867db246/tensorboard-1.15.0-py3-none-any.whl
Collecting numpy<2.0,>=1.16.0 (from tensorflow==1.15.3+nv20.7)
Using cached https://files.pythonhosted.org/packages/2c/2f/7b4d0b639a42636362827e611cfeba67975ec875ae036dd846d459d52652/numpy-1.19.1.zip
Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15.3+nv20.7)
Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.6.1->tensorflow==1.15.3+nv20.7)
Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.8->tensorflow==1.15.3+nv20.7)
Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.16.0,>=1.15.0->tensorflow==1.15.3+nv20.7)
Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.16.0,>=1.15.0->tensorflow==1.15.3+nv20.7)
Requirement already satisfied: importlib-metadata; python_version < "3.8" in /usr/local/lib/python3.6/dist-packages (from markdown>=2.6.8->tensorboard<1.16.0,>=1.15.0->tensorflow==1.15.3+nv20.7)
Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata; python_version < "3.8"->markdown>=2.6.8->tensorboard<1.16.0,>=1.15.0->tensorflow==1.15.3+nv20.7)
Building wheels for collected packages: numpy
Running setup.py bdist_wheel for numpy ... error
Complete output from command /usr/bin/python3 -u -c "import setuptools, tokenize;__file__='/tmp/pip-build-1g5p2fm1/numpy/setup.py';f=getattr(tokenize, 'open', open)(__file__);code=f.read().replace('\r\n', '\n');f.close();exec(compile(code, __file__, 'exec'))" bdist_wheel -d /tmp/tmpca5hou2rpip-wheel- --python-tag cp36:
Running from numpy source directory.
Cythonizing sources
Processing numpy/random/_bounded_integers.pxd.in
Processing numpy/random/_philox.pyx
Traceback (most recent call last):
File "/tmp/pip-build-1g5p2fm1/numpy/tools/cythonize.py", line 59, in process_pyx
from Cython.Compiler.Version import version as cython_version
ModuleNotFoundError: No module named 'Cython'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/tmp/pip-build-1g5p2fm1/numpy/tools/cythonize.py", line 235, in <module>
main()
File "/tmp/pip-build-1g5p2fm1/numpy/tools/cythonize.py", line 231, in main
find_process_files(root_dir)
File "/tmp/pip-build-1g5p2fm1/numpy/tools/cythonize.py", line 222, in find_process_files
process(root_dir, fromfile, tofile, function, hash_db)
File "/tmp/pip-build-1g5p2fm1/numpy/tools/cythonize.py", line 188, in process
processor_function(fromfile, tofile)
File "/tmp/pip-build-1g5p2fm1/numpy/tools/cythonize.py", line 64, in process_pyx
raise OSError('Cython needs to be installed in Python as a module')
OSError: Cython needs to be installed in Python as a module
Traceback (most recent call last):
File "" , line 1, in <module>
File "/tmp/pip-build-1g5p2fm1/numpy/setup.py", line 499, in <module>
setup_package()
File "/tmp/pip-build-1g5p2fm1/numpy/setup.py", line 479, in setup_package
generate_cython()
File "/tmp/pip-build-1g5p2fm1/numpy/setup.py", line 274, in generate_cython
raise RuntimeError("Running cythonize failed!")
RuntimeError: Running cythonize failed!
----------------------------------------
Failed building wheel for numpy
Running setup.py clean for numpy
Complete output from command /usr/bin/python3 -u -c "import setuptools, tokenize;__file__='/tmp/pip-build-1g5p2fm1/numpy/setup.py';f=getattr(tokenize, 'open', open)(__file__);code=f.read().replace('\r\n', '\n');f.close();exec(compile(code, __file__, 'exec'))" clean --all:
Running from numpy source directory.
`setup.py clean` is not supported, use one of the following instead:
- `git clean -xdf` (cleans all files)
- `git clean -Xdf` (cleans all versioned files, doesn't touch
files that aren't checked into the git repo)
Add `--force` to your command to use it anyway if you must (unsupported).
----------------------------------------
Failed cleaning build dir for numpy
Failed to build numpy
Installing collected packages: tensorflow-estimator, numpy, opt-einsum, tensorboard, tensorflow
Found existing installation: tensorflow-estimator 1.13.0
Uninstalling tensorflow-estimator-1.13.0:
Successfully uninstalled tensorflow-estimator-1.13.0
Found existing installation: numpy 1.13.3
Not uninstalling numpy at /usr/lib/python3/dist-packages, outside environment /usr
Running setup.py install for numpy ... error
Complete output from command /usr/bin/python3 -u -c "import setuptools, tokenize;__file__='/tmp/pip-build-1g5p2fm1/numpy/setup.py';f=getattr(tokenize, 'open', open)(__file__);code=f.read().replace('\r\n', '\n');f.close();exec(compile(code, __file__, 'exec'))" install --record /tmp/pip-d81_pz_r-record/install-record.txt --single-version-externally-managed --compile:
Running from numpy source directory.
Note: if you need reliable uninstall behavior, then install
with pip instead of using `setup.py install`:
- `pip install .` (from a git repo or downloaded source
release)
- `pip install numpy` (last NumPy release on PyPi)
Cythonizing sources
numpy/random/_bounded_integers.pxd.in has not changed
Processing numpy/random/_philox.pyx
Traceback (most recent call last):
File "/tmp/pip-build-1g5p2fm1/numpy/tools/cythonize.py", line 59, in process_pyx
from Cython.Compiler.Version import version as cython_version
ModuleNotFoundError: No module named 'Cython'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/tmp/pip-build-1g5p2fm1/numpy/tools/cythonize.py", line 235, in <module>
main()
File "/tmp/pip-build-1g5p2fm1/numpy/tools/cythonize.py", line 231, in main
find_process_files(root_dir)
File "/tmp/pip-build-1g5p2fm1/numpy/tools/cythonize.py", line 222, in find_process_files
process(root_dir, fromfile, tofile, function, hash_db)
File "/tmp/pip-build-1g5p2fm1/numpy/tools/cythonize.py", line 188, in process
processor_function(fromfile, tofile)
File "/tmp/pip-build-1g5p2fm1/numpy/tools/cythonize.py", line 64, in process_pyx
raise OSError('Cython needs to be installed in Python as a module')
OSError: Cython needs to be installed in Python as a module
Traceback (most recent call last):
File "" , line 1, in <module>
File "/tmp/pip-build-1g5p2fm1/numpy/setup.py", line 499, in <module>
setup_package()
File "/tmp/pip-build-1g5p2fm1/numpy/setup.py", line 479, in setup_package
generate_cython()
File "/tmp/pip-build-1g5p2fm1/numpy/setup.py", line 274, in generate_cython
raise RuntimeError("Running cythonize failed!")
RuntimeError: Running cythonize failed!
----------------------------------------
Can't rollback numpy, nothing uninstalled.
Command "/usr/bin/python3 -u -c "import setuptools, tokenize;__file__='/tmp/pip-build-1g5p2fm1/numpy/setup.py';f=getattr(tokenize, 'open', open)(__file__);code=f.read().replace('\r\n', '\n');f.close();exec(compile(code, __file__, 'exec'))" install --record /tmp/pip-d81_pz_r-record/install-record.txt --single-version-externally-managed --compile" failed with error code 1 in /tmp/pip-build-1g5p2fm1/numpy/
查找错误：
1、RuntimeError: Running cythonize failed!.重新编译安装cython：
sudo apt cython
2、 ImportError: libcublas.so.10.0: cannot open shared object file: No such file or directory.在其官网上从源代码构建TensorFlow可以查看各版本对应的CUDA和cudnn，察其原因是因为TensorFlow与CUDA版本不匹配。
感觉是JetPack版本下载的有问题，导致CUDA和cudnn的版本与TensorRT和TensorFlow所支持的版本不匹配，准备刷个机重新再来