Installing Caffe on the Orange Pi 4B
Install dependencies
sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev
libopencv-dev libhdf5-serial-dev protobuf-compiler cmake
sudo apt-get install --no-install-recommends libboost-all-dev
sudo apt-get install libopenblas-dev liblapack-dev libatlas-base-dev
sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev
Download Caffe and modify configuration parameters
git clone https://github.com/BVLC/caffe
cd caffe
cp Makefile.config.example Makefile.config
Modify the parameters of Makefile.config
Change #CPU_ONLY := 1 into CPU_ONLY := 1
Change ＃OPENCV_VERSION := 3 into OPENCV_VERSION := 3
Install Caffe
mkdir build
cd build
cmake ..
Protobuf compiler version doesn't match library version 3.6.1
Okay… Let’s try a sudo apt install protobuf-compiler.
protoc --version
libprotoc 3.6.1
cmake ..
Could NOT find HDF5 (missing: HDF5_LIBRARIES HDF5_INCLUDE_DIRS
My bad, let’s install it.
sudo apt install libhdf5-dev
cmake ..
Could not find a package configuration file provided by "OpenCV" with any
Let’s install openCV, then…
sudo apt install libopencv-dev
cmake ..
[...]
-- Configuring done
-- Generating done
-- Build files have been written to: /home/poddingue/caffe/build
Next step, let’s build it for real. By the way, kiddoes, don’t try this -j6 at home unless you have a good heatsink… Or the machine will stop/hang up/reboot without prior alert.
make all -j6
make pycaffe -j6
make test -j6
make runtest -j6
Run a simple example
Run the caffe example – mnist instance: Mnist is a handwritten digital library. Originally used for handwritten numeral recognition on checks, and it is now the DL’s starting exercise library. The special model for mnist identification is Lenet, which is the earliest CNN model. The training samples of mnist data are 60,000 units, and the test samples are 10,000 units. Each sample is a black and white picture of size 28*28, and the handwritten number is 0-9, so it is divided into 10 categories.
Download the mnist data first
sh data/mnist/get_mnist.sh
cd data/mnist
There are four files in the directory after running the previous command:
ll
total 53676
-rwxr-xr-x 1 poddingue poddingue 408 Jun 22 16:55 get_mnist.sh
-rw-r--r-- 1 poddingue poddingue 7840016 Jul 21 2000 t10k-images-idx3-ubyte # Training set samples
-rw-r--r-- 1 poddingue poddingue 10008 Jul 21 2000 t10k-labels-idx1-ubyte # Corresponding annotation of training set
-rw-r--r-- 1 poddingue poddingue 47040016 Jul 21 2000 train-images-idx3-ubyte # Testing set sample
-rw-r--r-- 1 poddingue poddingue 60008 Jul 21 2000 train-labels-idx1-ubyte # Corresponding annotation of testing set
Converting the data
These data cannot be used directly in Caffe, and need to be converted into LMDB data:
cd ../..
sh examples/mnist/create_mnist.sh
Creating lmdb...
I0622 17:37:06.109530 12691 db_lmdb.cpp:35] Opened lmdb examples/mnist/mnist_train_lmdb
I0622 17:37:06.114603 12691 convert_mnist_data.cpp:88] A total of 60000 items.
I0622 17:37:06.114666 12691 convert_mnist_data.cpp:89] Rows: 28 Cols: 28
I0622 17:37:11.407480 12691 convert_mnist_data.cpp:108] Processed 60000 files.
I0622 17:37:12.192065 12698 db_lmdb.cpp:35] Opened lmdb examples/mnist/mnist_test_lmdb
I0622 17:37:12.194784 12698 convert_mnist_data.cpp:88] A total of 10000 items.
I0622 17:37:12.198009 12698 convert_mnist_data.cpp:89] Rows: 28 Cols: 28
I0622 17:37:12.566424 12698 convert_mnist_data.cpp:108] Processed 10000 files.
Done.
ll examples/mnist/
total 136
-rw-r--r-- 1 poddingue poddingue 4520 Jun 22 16:55 convert_mnist_data.cpp
-rwxr-xr-x 1 poddingue poddingue 634 Jun 22 16:55 create_mnist.sh
-rw-r--r-- 1 poddingue poddingue 777 Jun 22 16:55 lenet_adadelta_solver.prototxt
-rw-r--r-- 1 poddingue poddingue 778 Jun 22 16:55 lenet_auto_solver.prototxt
-rw-r--r-- 1 poddingue poddingue 6003 Jun 22 16:55 lenet_consolidated_solver.prototxt
-rw-r--r-- 1 poddingue poddingue 871 Jun 22 16:55 lenet_multistep_solver.prototxt
-rw-r--r-- 1 poddingue poddingue 1738 Jun 22 16:55 lenet.prototxt
-rw-r--r-- 1 poddingue poddingue 886 Jun 22 16:55 lenet_solver_adam.prototxt
-rw-r--r-- 1 poddingue poddingue 790 Jun 22 16:55 lenet_solver.prototxt
-rw-r--r-- 1 poddingue poddingue 830 Jun 22 16:55 lenet_solver_rmsprop.prototxt
-rw-r--r-- 1 poddingue poddingue 2282 Jun 22 16:55 lenet_train_test.prototxt
-rw-r--r-- 1 poddingue poddingue 4814 Jun 22 16:55 mnist_autoencoder.prototxt
-rw-r--r-- 1 poddingue poddingue 451 Jun 22 16:55 mnist_autoencoder_solver_adadelta.prototxt
-rw-r--r-- 1 poddingue poddingue 423 Jun 22 16:55 mnist_autoencoder_solver_adagrad.prototxt
-rw-r--r-- 1 poddingue poddingue 466 Jun 22 16:55 mnist_autoencoder_solver_nesterov.prototxt
-rw-r--r-- 1 poddingue poddingue 433 Jun 22 16:55 mnist_autoencoder_solver.prototxt
drwxr--r-- 2 poddingue poddingue 4096 Jun 22 17:37 mnist_test_lmdb
drwxr--r-- 2 poddingue poddingue 4096 Jun 22 17:37 mnist_train_lmdb
-rw-r--r-- 1 poddingue poddingue 11948 Jun 22 16:55 readme.md
-rwxr-xr-x 1 poddingue poddingue 106 Jun 22 16:55 train_lenet_adam.sh
-rwxr-xr-x 1 poddingue poddingue 118 Jun 22 16:55 train_lenet_consolidated.sh
-rwxr-xr-x 1 poddingue poddingue 4517 Jun 22 16:55 train_lenet_docker.sh
-rwxr-xr-x 1 poddingue poddingue 115 Jun 22 16:55 train_lenet_rmsprop.sh
-rwxr-xr-x 1 poddingue poddingue 101 Jun 22 16:55 train_lenet.sh
-rwxr-xr-x 1 poddingue poddingue 120 Jun 22 16:55 train_mnist_autoencoder_adadelta.sh
-rwxr-xr-x 1 poddingue poddingue 119 Jun 22 16:55 train_mnist_autoencoder_adagrad.sh
-rwxr-xr-x 1 poddingue poddingue 120 Jun 22 16:55 train_mnist_autoencoder_nesterov.sh
-rwxr-xr-x 1 poddingue poddingue 117 Jun 22 16:55 train_mnist_autoencoder.sh
The examples/mnist/create_mnist.sh has generated two folders in the examples/mnist/ directory, namely mnist_train_lmdb and mnist_test_lmdb after the LMDB data conversion succeeded. The data.mdb and lock.mdb stored in the folder are the running data we need.
LevelDB Data
If you want to run leveldb data, run the program under examples/siamese/ folder.
./examples/siamese/create_mnist_siamese.sh
Creating leveldb...
Done.
ll examples/siamese/
total 204
-rw-r--r-- 1 poddingue poddingue 4368 Jun 22 16:55 convert_mnist_siamese_data.cpp
-rwxr-xr-x 1 poddingue poddingue 617 Jun 22 16:55 create_mnist_siamese.sh
-rw-r--r-- 1 poddingue poddingue 158921 Jun 22 16:55 mnist_siamese.ipynb
-rw-r--r-- 1 poddingue poddingue 1488 Jun 22 16:55 mnist_siamese.prototxt
-rw-r--r-- 1 poddingue poddingue 810 Jun 22 16:55 mnist_siamese_solver.prototxt
drwxr-xr-x 2 poddingue poddingue 4096 Jun 22 17:39 mnist_siamese_test_leveldb
drwxr-xr-x 2 poddingue poddingue 4096 Jun 22 17:39 mnist_siamese_train_leveldb
-rw-r--r-- 1 poddingue poddingue 4907 Jun 22 16:55 mnist_siamese_train_test.prototxt
-rw-r--r-- 1 poddingue poddingue 5949 Jun 22 16:55 readme.md
-rwxr-xr-x 1 poddingue poddingue 125 Jun 22 16:55 train_mnist_siamese.sh
Next modify the configuration file lenet_solver.prototxt
vi ./examples/mnist/lenet_solver.prototxt
As needed, set the maximum number of iterations at 100 and the last line, solver_mode, to CPU:
# The train/test net protocol buffer definition
net: "examples/mnist/lenet_train_test.prototxt"
# test_iter specifies how many forward passes the test should carry out.
# In the case of MNIST, we have test batch size 100 and 100 test iterations,
# covering the full 10,000 testing images.
test_iter: 100
# Carry out testing every 500 training iterations.
test_interval: 500
# The base learning rate, momentum and the weight decay of the network.
base_lr: 0.01
momentum: 0.9
weight_decay: 0.0005
# The learning rate policy
lr_policy: "inv"
gamma: 0.0001
power: 0.75
# Display every 100 iterations
display: 100
# The maximum number of iterations
max_iter: 10000
# snapshot intermediate results
snapshot: 5000
snapshot_prefix: "examples/mnist/lenet"
# solver mode: CPU or GPU
solver_mode: CPU
Finally, run this example:
root@OrangePi:~/caffe# time sh examples/mnist/train_lenet.sh I0110 06:28:06.117972 25078 caffe.cpp:197] Use CPU. I0110 06:28:06.118988 25078 solver.cpp:45] Initializing solver from parameters: test_iter: 100 test_interval: 500 base_lr: 0.01 display: 100 XUNLONG Software Co,. Ltd All rights reserved 36 www.orangepi.com max_iter: 10000 lr_policy: “inv” gamma: 0.0001 power: 0.75 momentum: 0.9 weight_decay: 0.0005 snapshot: 5000 snapshot_prefix: “examples/mnist/lenet” solver_mode: CPU net: “examples/mnist/lenet_train_test.prototxt” …… …… …… I0110 07:25:37.016746 25078 sgd_solver.cpp:284] Snapshotting solver state to binary proto file examples/mnist/lenet_iter_10000.solverstate I0110 07:25:37.146054 25078 solver.cpp:327] Iteration 10000, loss = 0.00200602 I0110 07:25:37.146195 25078 solver.cpp:347] Iteration 10000, Testing net (#0) I0110 07:25:54.299634 25080 data_layer.cpp:73] Restarting data prefetching from start. I0110 07:25:55.010598 25078 solver.cpp:414] Test net output #0: accuracy = 0.9913 I0110 07:25:55.010757 25078 solver.cpp:414] Test net output #1: loss = 0.0273203 (* 1 = 0.0273203 loss) I0110 07:25:55.010777 25078 solver.cpp:332] Optimization Done. I0110 07:25:55.010793 25078 caffe.cpp:250] Optimization Done. real 57m48.984s user 58m6.479s sys 0m1.977s ⑦Operation conclusion According to the above results, the running time of the CPU is about 58 minutes, with an accuracy of about 99%.