【ビルド版】NVIDIA Jetson Nanoで Caffe DeepDreamを GPUパワーで動かしてキモイ絵をモリモリ量産

【2020年版】NVIDIA Jetson Nano、Jetson Xavier NXの便利スクリプト

NVIDIA Jetson Nanoで OpenCV 3をビルドしてインストールする方法、NVCaffe等の OpenCV 4未対応を動かす

src/caffe/util/io.cpp:6:10: fatal error: opencv2/core/core.hpp: No such file or directory
 #include <opencv2/core/core.hpp>
          ^~~~~~~~~~~~~~~~~~~~~~~

jetson@jetson-desktop:~$ python -c "import cv2; print (cv2.__version__)"
4.1.1

jetson@jetson-desktop:~$ ls -l /usr/include/opencv4/opencv2/
drwxrwxr-x 2 root root   4096 12月 17 03:56 calib3d
-rw-r--r-- 1 root root 182567 12月 14 00:46 calib3d.hpp
drwxrwxr-x 7 root root   4096 12月 17 03:56 core
-rw-r--r-- 1 root root 151515 12月 14 00:46 core.hpp
-rw-r--r-- 1 root root   3440 12月 14 02:25 cvconfig.h
drwxrwxr-x 3 root root   4096 12月 17 03:56 dnn
-rw-r--r-- 1 root root   3347 12月 14 00:46 dnn.hpp

jetson@jetson-desktop:~$ ls -l /usr/include/opencv4/opencv2/core/
-rw-r--r-- 1 root root  21525 12月 14 00:46 affine.hpp
-rw-r--r-- 1 root root   2819 12月 14 00:46 async.hpp
-rw-r--r-- 1 root root  26499 12月 14 00:46 base.hpp
-rw-r--r-- 1 root root   1197 12月 14 00:46 bindings_utils.hpp
-rw-r--r-- 1 root root    845 12月 14 00:46 bufferpool.hpp
-rw-r--r-- 1 root root   6975 12月 14 00:46 check.hpp
-rw-r--r-- 1 root root 128652 12月 14 00:46 core_c.h
-rw-r--r-- 1 root root   2366 12月 14 00:46 core.hpp

USE_OPENCV := 0

sed -i 's/# USE_OPENCV/USE_OPENCV/' Makefile.config

jetson@jetson-desktop:~/nvcaffe$ apt-cache search libopencv-core3.2
libopencv-core-dev - development files for libopencv-core3.2
libopencv-core3.2 - computer vision core library

sudo apt-get install -y libopencv-core-dev libopencv-core3.2

jetson@jetson-desktop:~$ sudo ln -s /usr/include/opencv4/opencv2/ /usr/include/opencv2

jetson@jetson-desktop:~$ ls -l /usr/include/opencv2/core/
-rw-r--r-- 1 root root  21525 12月 14 00:46 affine.hpp
-rw-r--r-- 1 root root   2819 12月 14 00:46 async.hpp
-rw-r--r-- 1 root root  26499 12月 14 00:46 base.hpp
-rw-r--r-- 1 root root   1197 12月 14 00:46 bindings_utils.hpp
-rw-r--r-- 1 root root    845 12月 14 00:46 bufferpool.hpp
-rw-r--r-- 1 root root   6975 12月 14 00:46 check.hpp
-rw-r--r-- 1 root root 128652 12月 14 00:46 core_c.h
-rw-r--r-- 1 root root   2366 12月 14 00:46 core.hpp

error: ‘CV_FILLED’ was not declared in this scope

【ビルド版】Raspberry Piで DeepDreamを動かしてキモイ絵をモリモリ量産 Caffe Deep Learning Framework

【インストール版】Raspberry Piで DeepDreamを動かしてキモイ絵をモリモリ量産 Caffe Deep Learning

● Prerequisites
 BLAS via ATLAS, MKL, or OpenBLAS.
 Boost >= 1.55
 protobuf, glog, gflags, hdf5

● Pycaffe interfaces have their own natural needs.
 For Python Caffe: Python 2.7 or Python 3.3+, numpy (>= 1.7), boost-provided boost.python

● CPU-only Caffe
 for cold-brewed CPU-only Caffe uncomment the CPU_ONLY := 1 flag in Makefile.config to configure and build Caffe without CUDA. This is helpful for cloud or cluster deployment.

● BLAS
 OpenBLAS: free and open source; this optimized and parallel BLAS could require more effort to install, although it might offer a speedup.
 Install OpenBLAS
 Set BLAS := open in Makefile.config

● Python Caffe (optional)
 The main requirements are numpy and boost.python (provided by boost). pandas is useful too and needed for some examples.
 You can install the dependencies with
 pip install -r requirements.txt

● Compilation with Make
cp Makefile.config.example Makefile.config
# Adjust Makefile.config (for example, if using Anaconda Python, or if cuDNN is desired)
make all
make test
make runtest

● General dependencies
sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compiler
sudo apt-get install --no-install-recommends libboost-all-dev
sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev

● BLAS
 install ATLAS by
sudo apt-get install libatlas-base-dev

 install OpenBLAS by
sudo apt-get install libopenblas-dev

 MKL for better CPU performance.
※ MKLは NVIDIA Jetson Nanoでは関係有りません（Intel CPU専用）

● Python (optional)
 if you use the default Python you will need to
sudo apt-get install python-dev
 package to have the Python headers for building the pycaffe interface.

# glog
wget https://github.com/google/glog/archive/v0.3.3.tar.gz
tar zxvf v0.3.3.tar.gz
cd glog-0.3.3
./configure
make && make install

# gflags
wget https://github.com/schuhschuh/gflags/archive/master.zip
unzip master.zip
cd gflags-master
mkdir build && cd build
export CXXFLAGS="-fPIC" && cmake .. && make VERBOSE=1
make && make install

# lmdb
git clone https://github.com/LMDB/lmdb
cd lmdb/libraries/liblmdb
make && make install

# NVIDIA Jetson Nanoでお決まりの sudo apt-get update
sudo apt-get update

# BVLC/caffeの Gitからソースリスト一式を clone（ダウンロード）する
cd
git clone https://github.com/BVLC/caffe.git --depth 1
cd
cd caffe

# Makefile.configファイルを作成（参考例のファイルからコピー）する
cp Makefile.config.example Makefile.config
# Adjust Makefile.config (for example, if using Anaconda Python, or if cuDNN is desired)

# Makefile.configファイルを Jetson Nano環境に合わせて編集する
nano Makefile.config

# NVIDIA Jetson Nano環境は演算処理に cuDNNが使えるので USE_CUDNN := 1の行のコメント文字＃を取って有効にする
USE_CUDNN := 1

# BLAS（Basic Linear Algebra Subprograms、ベクトルと行列に関する数値演算ライブラリの名称）
# 今回は BLASライブラリとして ATLAS実装（Automatically Tuned Linear Algebra Software）を使用します
# BLAS choice:
# atlas for ATLAS (default)
# mkl for MKL
# open for OpenBlas
BLAS := atlas
# BLAS := open

# hdf5関係で「ファイルが見つからないエラー」が出るのでパスを設定する
# https://support.hdfgroup.org/HDF5/
# Whatever else you find you need goes here.
INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/include/hdf5/serial
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/lib/aarch64-linux-gnu/hdf5/serial

# OpenCVが OpenCV 3なので OPENCV_VERSION := 3のコメント文字＃を取って有効にする
# undefined reference to `cv::imread(cv::String const&, int)'
OPENCV_VERSION := 3

# nvcc fatal   : Unsupported gpu architecture 'compute_20'
# Makefile:604: recipe for target '.build_release/cuda/src/caffe/layers/filter_layer.o' failed
# エラーが出るので CUDA_ARCHの定義で、
# -gencode arch=compute_20,code=sm_20
# -gencode arch=compute_20,code=sm_21
# を削除する

# Check failed: error == cudaSuccess (48 vs. 0)  no kernel image is available for execution on the device
# CUDA_ARCHに -gencode arch=compute_53,code=sm_53を追加する
# 下記では削除する予定の compute_20,code=sm_20を arch=compute_53,code=sm_53に文字列置換している。
CUDA_ARCH := -gencode arch=compute_53,code=sm_53 \

# undefined symbol: _ZN5caffe21PyErrReportAndForwardEv
# WITH_PYTHON_LAYER := 1のコメント文字＃を取って有効にする
# Uncomment to support layers written in Python (will link against Python libs)
WITH_PYTHON_LAYER := 1

# コマンドラインでサクッと Makefile.configファイルを変更する
sed -i 's/^# USE_CUDNN/USE_CUDNN/' Makefile.config
sed -i 's/BLAS :=.*/BLAS := atlas/' Makefile.config
sed -i 's/^INCLUDE_DIRS :=.*/INCLUDE_DIRS := $(PYTHON_INCLUDE) \/usr\/local\/include \/usr\/include\/hdf5\/serial/' Makefile.config
sed -i 's/^LIBRARY_DIRS :=.*/LIBRARY_DIRS := $(PYTHON_LIB) \/usr\/local\/lib \/usr\/lib \/usr\/lib\/aarch64-linux-gnu\/hdf5\/serial/' Makefile.config
sed -i 's/# OPENCV_VERSION/OPENCV_VERSION/' Makefile.config
sed -i 's/arch=compute_20,code=sm_20/arch=compute_53,code=sm_53/' Makefile.config
# sed -i 's/-gencode arch=compute_20,code=sm_21 \\//' Makefile.config
# Makefile.config:41: *** recipe commences before first target.  Stop.
sed -i '/-gencode arch=compute_20,code=sm_21 \\/d' Makefile.config
sed -i 's/# WITH_PYTHON_LAYER/WITH_PYTHON_LAYER/' Makefile.config

# OpenBlas
# sed -i 's/BLAS :=.*/BLAS := open/' Makefile.config

# 編集内容を確認する
grep "BLAS " Makefile.config
grep "CUDA_" Makefile.config
grep "_DIRS :" Makefile.config
grep "OPENCV_" Makefile.config
grep "_CUDNN" Makefile.config
grep "CPU_" Makefile.config
grep "WITH_" Makefile.config

user@user-desktop:~/caffe$ grep "BLAS " Makefile.config
# BLAS choice:
BLAS := atlas

user@user-desktop:~/caffe$ grep "CUDA_" Makefile.config
CUDA_DIR := /usr/local/cuda
# CUDA_DIR := /usr
CUDA_ARCH := -gencode arch=compute_53,code=sm_53 \

user@user-desktop:~/caffe$ grep "_DIRS :" Makefile.config
INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/include/hdf5/serial
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/lib/aarch64-linux-gnu/hdf5/serial

user@user-desktop:~/caffe$ grep "OPENCV_" Makefile.config
OPENCV_VERSION := 3

user@user-desktop:~/caffe$ grep "_CUDNN" Makefile.config
USE_CUDNN := 1

user@user-desktop:~/caffe$ grep "CPU_" Makefile.config
# CPU_ONLY := 1

user@user-desktop:~/caffe$ grep "WITH_" Makefile.config
WITH_PYTHON_LAYER := 1

user@user-desktop:~/caffe$ diff Makefile.config.example Makefile.config
5c5
< # USE_CUDNN := 1
---
> USE_CUDNN := 1
23c23
< # OPENCV_VERSION := 3
---
> OPENCV_VERSION := 3
39,40c39,40
< CUDA_ARCH := -gencode arch=compute_20,code=sm_20 \
<               -gencode arch=compute_20,code=sm_21 \
---
> CUDA_ARCH := -gencode arch=compute_53,code=sm_53 \
>
94c94
< # WITH_PYTHON_LAYER := 1
---
> WITH_PYTHON_LAYER := 1
97,98c97,98
< INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include
< LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib
---
> INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/include/hdf5/serial
> LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/lib/aarch64-linux-gnu/hdf5/serial

sudo apt-get -y install protobuf-compiler libprotoc-dev libboost-dev libgflags-dev libgoogle-glog-dev

# Makefile.config, BLAS := atlas
sudo apt-get -y install libatlas-base-dev
# Makefile.config, BLAS := open
# sudo apt-get -y install libopenblas-dev
sudo apt-get -y install libhdf5-dev libleveldb-dev liblmdb-dev libopencv-dev libsnappy-dev
sudo apt-get -y install libboost-system-dev libboost-filesystem-dev libboost-thread-dev

# make pycaffe
sudo apt-get -y install libboost-python-dev

# python deepdreamer.py sample.jpg
sudo apt-get -y install python-skimage python-protobuf python-numpy python-pil

# Makefile.config, BLAS := atlas
sudo apt-get -y install libatlas-base-dev protobuf-compiler libprotoc-dev libboost-dev libgflags-dev libgoogle-glog-dev libhdf5-dev libleveldb-dev liblmdb-dev libopencv-dev libsnappy-dev libboost-system-dev libboost-filesystem-dev libboost-thread-dev libboost-python-dev python-skimage python-protobuf python-numpy python-pil

# Makefile.config, BLAS := open
# sudo apt-get -y install libopenblas-dev

user@user-desktop:~/caffe$ nvcc
# nvcc fatal   : No input files specified; use option --help for more information

# Solution
# ~/.bashrcに CUDA関係の環境変数を追加
# /usr/local/cuda-10.0は /usr/local/cudaにシンボリックリンクが存在します
echo 'export CUDA_HOME=/usr/local/cuda' >> ~/.bashrc
echo 'export LD_LIBRARY_PATH=${CUDA_HOME}/lib64:${LD_LIBRARY_PATH}' >> ~/.bashrc
echo 'export PATH=${CUDA_HOME}/bin:${PATH}' >> ~/.bashrc
source ~/.bashrc

# 最大パフォーマンスで動かす
sudo nvpmodel -q
sudo nvpmodel -m 0
sudo nvpmodel -q

sudo /usr/bin/jetson_clocks --show
sudo /usr/bin/jetson_clocks

# ビルド設定を初期化
make clean

# make all -j4で 4 coreを使ってビルド速度を高速化（約 30分）
time make all -j4

# real    29m21.720s
# user    73m4.716s
# sys     7m26.316s

# make testと make runtest
make test -j4
make runtest -j4

# Python用の Caffeの作成と distributeでビルド内容を反映
make pycaffe
make distribute

# ここまで成功すれば Caffeのビルドは完了

# 環境変数 CAFFE_HOMEに PATHを設定する
# export CAFFE_HOME=/home/user/caffe
export CAFFE_HOME=$(pwd)

echo ${CAFFE_HOME}

# パスにビルドで作成した Caffeを追加する
export PYTHONPATH=${CAFFE_HOME}/python:$PYTHONPATH
export LD_LIBRARY_PATH=${CAFFE_HOME}/distribute/lib:$LD_LIBRARY_PATH

user@user-desktop:~/caffe$ pwd
/home/user/caffe

user@user-desktop:~/caffe$ export CAFFE_HOME=/home/user/caffe

user@user-desktop:~/caffe$ echo $CAFFE_HOME
/home/user/caffe

[ RUN      ] AdamSolverTest/2.TestAdamLeastSquaresUpdateWithEverythingShare
F0506 18:24:03.250540 15245 adam_solver.cu:22] Check failed: error == cudaSuccess (48 vs. 0)  no kernel image is available for execution on the device
*** Check failure stack trace: ***
    @       0x7f8e129128  google::LogMessage::Fail()
    @       0x7f8e12af98  google::LogMessage::SendToLog()
    @       0x7f8e128c90  google::LogMessage::Flush()
    @       0x7f8e12b83c  google::LogMessageFatal::~LogMessageFatal()
    @       0x7f8c634b04  caffe::adam_update_gpu<>()
    @       0x7f8c58d284  caffe::AdamSolver<>::ComputeUpdateValue()
    @       0x7f8c5864dc  caffe::SGDSolver<>::ApplyUpdate()
    @       0x7f8c5a0af8  caffe::Solver<>::Step()
    @       0x7f8c5a11d0  caffe::Solver<>::Solve()
    @       0x55690bfce8  _ZN5caffe23GradientBasedSolverTestINS_9GPUDeviceIfEEE21RunLeastSquaresSolverB5cxx11EfffiiibPKc
    @       0x55690d457c  caffe::GradientBasedSolverTest<>::TestLeastSquaresUpdate()
    @       0x55690d5324  caffe::AdamSolverTest_TestAdamLeastSquaresUpdateWithEverythingShare_Test<>::TestBody()
    @       0x55694ed1fc  testing::internal::HandleExceptionsInMethodIfSupported<>()
    @       0x55694e63e4  testing::Test::Run()
    @       0x55694e64ac  testing::TestInfo::Run()
    @       0x55694e65dc  testing::TestCase::Run()
    @       0x55694e6aa8  testing::internal::UnitTestImpl::RunAllTests()
    @       0x55694e6c08  testing::UnitTest::Run()
    @       0x556905ed30  main
    @       0x7f8c07e6e0  __libc_start_main
    @       0x55690674dc  (unknown)
Makefile:542: recipe for target 'runtest' failed
make: *** [runtest] Aborted (core dumped)

# Solution
# -gencode arch=compute_53,code=sm_53を追加する
CUDA_ARCH := -gencode arch=compute_53,code=sm_53 \

# NVIDIA Jetson Nano CUDA Version
CUDA Device Query (Runtime API) version (CUDART static linking)
Device 0: "NVIDIA Tegra X1"
  CUDA Driver Version / Runtime Version          10.0 / 10.0
  CUDA Capability Major/Minor version number:    5.3

user@user-desktop:~/caffe$ ./.build_release/tools/caffe -version
caffe version 1.0.0

user@user-desktop:~/caffe$ ls -l ./.build_release/tools/caffe
lrwxrwxrwx 1 user user 9  5月  6 23:59 ./.build_release/tools/caffe -> caffe.bin

user@user-desktop:~/caffe$ ls -l ./.build_release/tools/caffe.bin
-rwxrwxr-x 1 user user 138560  5月  6 23:59 ./.build_release/tools/caffe.bin

user@user-desktop:~/caffe$ ./.build_release/tools/caffe
caffe: command line brew
usage: caffe <command> <args>

commands:
  train           train or finetune a model
  test            score a model
  device_query    show GPU diagnostic information
  time            benchmark model execution time

  Flags from tools/caffe.cpp:
    -gpu (Optional; run in GPU mode on given device IDs separated by ','.Use
      '-gpu all' to run on all available GPUs. The effective training batch
      size is multiplied by the number of devices.) type: string default: ""
    -iterations (The number of iterations to run.) type: int32 default: 50
    -level (Optional; network level.) type: int32 default: 0
    -model (The model definition protocol buffer text file.) type: string
      default: ""
    -phase (Optional; network phase (TRAIN or TEST). Only used for 'time'.)
      type: string default: ""
    -sighup_effect (Optional; action to take when a SIGHUP signal is received:
      snapshot, stop or none.) type: string default: "snapshot"
    -sigint_effect (Optional; action to take when a SIGINT signal is received:
      snapshot, stop or none.) type: string default: "stop"
    -snapshot (Optional; the snapshot solver state to resume training.)
      type: string default: ""
    -solver (The solver definition protocol buffer text file.) type: string
      default: ""
    -stage (Optional; network stages (not to be confused with phase), separated
      by ','.) type: string default: ""
    -weights (Optional; the pretrained weights to initialize finetuning,
      separated by ','. Cannot be set simultaneously with snapshot.)
      type: string default: ""

Raspberry Piに Jupyter Notebookをインストールして拡張子 ipynb形式の IPythonを動かす

# Python版の Google Deep Dream kesara/deepdreamerをダウンロードする
cd
git clone https://github.com/kesara/deepdreamer
cd deepdreamer

# 学習データ bvlc_googlenet.caffemodelをダウンロードする
wget http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel

# deploy.prototxtをダウンロードする
# https://github.com/BVLC/caffe/tree/master/models/bvlc_googlenet
wget https://raw.githubusercontent.com/BVLC/caffe/master/models/bvlc_googlenet/deploy.prototxt

# deploy.prototxtの一番最後に force_backward: trueの行を追加する
# nano deploy.prototxt
# force_backward: true

# コマンドラインでサクッと force_backward: trueの行を追加する
echo "force_backward: true" >> deploy.prototxt

# tailコマンドで最後に追加された事を確認する
tail deploy.prototxt

user@user-desktop:~/deepdreamer$ python deepdreamer.py sample.jpg

Traceback (most recent call last):
  File "deepdreamer.py", line 9, in <module>
    from deepdreamer.deepdreamer import deepdream, deepdream_video, list_layers
  File "/home/user/deepdreamer/deepdreamer/deepdreamer.py", line 12, in <module>
    from deepdreamer.images2gif import writeGif
ImportError: No module named images2gif

nano ./deepdreamer/deepdreamer.py

● 12行目をコメントにする。
# from deepdreamer.images2gif import writeGif

● 196～198行目をコメントにする。
#         writeGif(
#             "{}.gif".format(img_path), frames, duration=duration,
#             repeat=loop)

# コマンドラインでサクッと deepdreamer/deepdreamer.pyを編集する
# 196～198行目を削除する
sed '196,198d' -i ./deepdreamer/deepdreamer.py
# 12行目を削除する
sed '12d' -i ./deepdreamer/deepdreamer.py

user@user-desktop:~/deepdreamer$ python deepdreamer.py sample.jpg

cd
cd deepdreamer
wget https://github.com/google/deepdream/raw/master/flowers.jpg

python deepdreamer.py flowers.jpg


user@user-desktop:~/deepdreamer$ ls -l fl*
-rw-rw-r-- 1 user user 30570  5月  7 00:46 flowers.jpg
-rw-rw-r-- 1 user user 22887  5月  7 20:48 flowers.jpg_0.jpg
-rw-rw-r-- 1 user user 22420  5月  7 20:50 flowers.jpg_1.jpg
-rw-rw-r-- 1 user user 22073  5月  7 20:51 flowers.jpg_2.jpg
-rw-rw-r-- 1 user user 21882  5月  7 20:53 flowers.jpg_3.jpg

optional arguments:
  --gpuid GPUID         enable GPU with id GPUID (default: disabled)

cp flowers.jpg gfl.jpg
python deepdreamer.py --gpuid 0 gfl.jpg

user@user-desktop:~/deepdreamer$ ls -l gfl*
-rw-rw-r-- 1 user user 30570  5月  7 20:56 gfl.jpg
-rw-rw-r-- 1 user user 22500  5月  7 20:56 gfl.jpg_0.jpg
-rw-rw-r-- 1 user user 22277  5月  7 20:56 gfl.jpg_1.jpg
-rw-rw-r-- 1 user user 22126  5月  7 20:56 gfl.jpg_2.jpg
-rw-rw-r-- 1 user user 22010  5月  7 20:56 gfl.jpg_3.jpg
-rw-rw-r-- 1 user user 22079  5月  7 20:56 gfl.jpg_4.jpg
-rw-rw-r-- 1 user user 21995  5月  7 20:56 gfl.jpg_5.jpg
-rw-rw-r-- 1 user user 22021  5月  7 20:57 gfl.jpg_6.jpg
-rw-rw-r-- 1 user user 22137  5月  7 20:57 gfl.jpg_7.jpg

wget https://github.com/google/deepdream/raw/master/sky1024px.jpg

python deepdreamer.py --gpuid 0 sky1024px.jpg

user@user-desktop:~/deepdreamer$ ls -l sk*
-rw-rw-r-- 1 user user  65049  5月  7 21:00 sky1024px.jpg
-rw-rw-r-- 1 user user 108857  5月  7 21:01 sky1024px.jpg_0.jpg
-rw-rw-r-- 1 user user 125632  5月  7 21:01 sky1024px.jpg_1.jpg
-rw-rw-r-- 1 user user 134555  5月  7 21:02 sky1024px.jpg_2.jpg
-rw-rw-r-- 1 user user 139890  5月  7 21:02 sky1024px.jpg_3.jpg
-rw-rw-r-- 1 user user 143883  5月  7 21:03 sky1024px.jpg_4.jpg
-rw-rw-r-- 1 user user 146022  5月  7 21:03 sky1024px.jpg_5.jpg

user@user-desktop:~/caffe$ ls -l ${CAFFE_HOME}/distribute/lib
-rw-rw-r-- 1 user user 27615930  5月  7 00:44 libcaffe.a
lrwxrwxrwx 1 user user       17  5月  7 00:44 libcaffe.so -> libcaffe.so.1.0.0
-rw-r--r-- 1 user user 13695208  5月  7 00:44 libcaffe.so.1.0.0

user@user-desktop:~/caffe$ ls -l ${CAFFE_HOME}/python
drwxrwxr-x 5 user user 4096  5月  7 00:46 caffe
-rwxrwxr-x 1 user user 4262  5月  6 17:18 classify.py
-rw-rw-r-- 1 user user 1523  5月  6 17:18 CMakeLists.txt
-rwxrwxr-x 1 user user 5734  5月  6 17:18 detect.py
-rwxrwxr-x 1 user user 2254  5月  6 17:18 draw_net.py
-rw-rw-r-- 1 user user  261  5月  6 17:18 requirements.txt
-rw-rw-r-- 1 user user 3145  5月  6 17:18 train.py

【ビルド版】NVIDIA Jetson Nanoで NVIDIA版 Caffe NVCaffeを GPUパワーで動かしてキモイ絵をモリモリ量産

nano Makefile.config
BLAS := open

# Makefile.config, BLAS := open
sudo apt-get -y install libopenblas-dev

# 環境変数で OpenBLASライブラリを 4コアで動かす指定をする
export OPENBLAS_NUM_THREADS=4

# Setting the number of threads using environment variables
# The priorities are OPENBLAS_NUM_THREADS > GOTO_NUM_THREADS > OMP_NUM_THREADS .
# https://github.com/xianyi/OpenBLAS
export OPENBLAS_NUM_THREADS=4
export GOTO_NUM_THREADS=${OPENBLAS_NUM_THREADS}
export OMP_NUM_THREADS=${OPENBLAS_NUM_THREADS}

cd
cd deepdreamer

cp flowers.jpg bl4.jpg
python deepdreamer.py bl4.jpg

user@user-desktop:~/deepdreamer$ ls -l bl4*
-rw-rw-r-- 1 user user 30570  5月  8 23:02 bl4.jpg
-rw-rw-r-- 1 user user 22653  5月  8 23:02 bl4.jpg_0.jpg
-rw-rw-r-- 1 user user 22216  5月  8 23:03 bl4.jpg_1.jpg
-rw-rw-r-- 1 user user 21993  5月  8 23:03 bl4.jpg_2.jpg
-rw-rw-r-- 1 user user 21974  5月  8 23:03 bl4.jpg_3.jpg
-rw-rw-r-- 1 user user 21888  5月  8 23:04 bl4.jpg_4.jpg
-rw-rw-r-- 1 user user 21911  5月  8 23:04 bl4.jpg_5.jpg
-rw-rw-r-- 1 user user 21944  5月  8 23:04 bl4.jpg_6.jpg
-rw-rw-r-- 1 user user 21954  5月  8 23:05 bl4.jpg_7.jpg
-rw-rw-r-- 1 user user 21917  5月  8 23:05 bl4.jpg_8.jpg
-rw-rw-r-- 1 user user 21896  5月  8 23:05 bl4.jpg_9.jpg
-rw-rw-r-- 1 user user 21756  5月  8 23:06 bl4.jpg_10.jpg
-rw-rw-r-- 1 user user 21718  5月  8 23:06 bl4.jpg_11.jpg
-rw-rw-r-- 1 user user 21654  5月  8 23:06 bl4.jpg_12.jpg
-rw-rw-r-- 1 user user 21589  5月  8 23:07 bl4.jpg_13.jpg
-rw-rw-r-- 1 user user 21476  5月  8 23:07 bl4.jpg_14.jpg
-rw-rw-r-- 1 user user 21363  5月  8 23:07 bl4.jpg_15.jpg
-rw-rw-r-- 1 user user 21302  5月  8 23:08 bl4.jpg_16.jpg
-rw-rw-r-- 1 user user 21356  5月  8 23:08 bl4.jpg_17.jpg
-rw-rw-r-- 1 user user 21430  5月  8 23:08 bl4.jpg_18.jpg

# -- CUDA detected: 10.2
# -- Found cuDNN: ver. ??? found (include: /usr/include, library: /usr/lib/aarch64-linux-gnu/libcudnn.so)
# CMake Error at cmake/Cuda.cmake:256 (message):
#   cuDNN version >3 is required.
# Call Stack (most recent call first):
#   cmake/Cuda.cmake:284 (detect_cuDNN)
#   cmake/Dependencies.cmake:85 (include)
#   CMakeLists.txt:49 (include)

# Caffe Configuration
# 読み込むファイルを cudnn.hから cudnn_version.hに変更する
# file(READ ${CUDNN_INCLUDE}/cudnn.h CUDNN_VERSION_FILE_CONTENTS)
sed -i -e "s/cudnn.h/cudnn_version.h/g" ../cmake/Cuda.cmake

cat /usr/include/cudnn_version.h | grep CUDNN
# #ifndef CUDNN_VERSION_H_
# #define CUDNN_VERSION_H_
# #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 */

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