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I have successfully compiled and run the new RPI 2 jpcnn but had a slight problem. As previously noted in issue 36, I had a problem with the original RPI code on a B2. Successfully Compiled jpcnn on RPI B2 using your B2 instructions but my B2 locked up when running the test and needed to be hard booted. After looking at the problem I determined that my previous RPI B+ install,sh had copied /usr/lib/libjpcnn;so and /usr/include/libjpcnn.h. I replaced these from new source files and all works fine now. I am looking forward to doing some testing with my robot. Since the GPU is not required for jpcnn then it should be easier to use camera in parallel. I have used multiprocessor on my robot opencv program and am hoping to put jpcnn in another thread to do object recognition.
Thanks for your excellent work. Greatly Appreciated
Claude ...
I created a new install.sh in ~/projects/DeepBeliefSDK/source per below
0.015300 soccer ball
0.020866 standard poodle
0.169138 boxer
0.038989 corgi
0.213237 Staffordshire bullterrier
0.024005 greyhound
0.012390 English setter
0.013972 miniature poodle
0.016022 tennis ball
0.025065 dalmatian
0.394529 golden retriever
Classification took 4637 milliseconds
The text was updated successfully, but these errors were encountered:
pageauc
changed the title
Installed and tested RPI 2 jpcnn. problem with previous /usr/lib/libjpcnn.so and include
Installed and tested RPI 2 jpcnn Had problem with previous /usr/lib/libjpcnn.so and include
Jun 27, 2015
pageauc
changed the title
Installed and tested RPI 2 jpcnn Had problem with previous /usr/lib/libjpcnn.so and include
compiled and tested RPI 2 jpcnn Had problem with previous /usr/lib/libjpcnn.so and include
Jun 27, 2015
pageauc
changed the title
compiled and tested RPI 2 jpcnn Had problem with previous /usr/lib/libjpcnn.so and include
Compiled and tested RPI 2 jpcnn Had problem with previous /usr/lib/libjpcnn.so and include
Jun 27, 2015
The explanation of what I did is at the top of this issue. Basically copied certain files so python so would be from the compiled version. Here is a summary of the script I wrote
I created a new install.sh in ~/projects/DeepBeliefSDK/source per below
I have successfully compiled and run the new RPI 2 jpcnn but had a slight problem. As previously noted in issue 36, I had a problem with the original RPI code on a B2. Successfully Compiled jpcnn on RPI B2 using your B2 instructions but my B2 locked up when running the test and needed to be hard booted. After looking at the problem I determined that my previous RPI B+ install,sh had copied /usr/lib/libjpcnn;so and /usr/include/libjpcnn.h. I replaced these from new source files and all works fine now. I am looking forward to doing some testing with my robot. Since the GPU is not required for jpcnn then it should be easier to use camera in parallel. I have used multiprocessor on my robot opencv program and am hoping to put jpcnn in another thread to do object recognition.
Thanks for your excellent work. Greatly Appreciated
Claude ...
I created a new install.sh in ~/projects/DeepBeliefSDK/source per below
!/bin/sh
echo "Installing libjpcnn library files"
cp libjpcnn.so /usr/lib/
cp src/include/libjpcnn.h /usr/include/
echo "Done"
Then ran
sudo ./install.sh.
FYI I also Updated to the latest 4.0 kernel
http://news.softpedia.com/news/raspberry-pi-s-default-firmware-updated-to-linux-kernel-4-0-485088.shtml
updated firmware using commands
sudo apt-get update
sudo apt-get upgrade
sudo rpi-update
sudo reboot
sudo apt-get update
sudo apt-get upgrade
RPI B2 is overclocked per /boot/config.txt entries below.
arm_freq=1000
sdram_freq=500
core_freq=500
over_voltage=2
gpu_mem=128
Here is the output from the test that took 4637 milliseconds
pi@dawn-robot ~/projects/DeepBeliefSDK/source $ ./jpcnn -i data/dog.jpg -n ../networks/jetpac.ntwk -t -m s -d
JPCNN Network with 28 layers
Node ConvNode - conv1 - _kernelWidth=11, _kernelCount=96, _marginSize=0, _sampleStride=4, _kernels->_dims=(96, 363), _bias->_dims=(96, 1)
Node ReluNode - conv1_neuron.1 -
Node ReluNode - conv1_neuron -
Node PoolNode - pool1 - _patchWidth=3, _stride=2, _mode=max
Node NormalizeNode - rnorm1 - _windowSize=5, _k=1.000000, _alpha=0.000020, _beta=0.750000
Node GConvNode - conv2 - _kernelsCount = 256, _subnodes = Node ConvNode - conv2 - _kernelWidth=5, _kernelCount=128, _marginSize=2, _sampleStride=1, _kernels->_dims=(128, 1200), _bias->_dims=(128, 1) Node ConvNode - conv2 - _kernelWidth=5, _kernelCount=128, _marginSize=2, _sampleStride=1, _kernels->_dims=(128, 1200), _bias->_dims=(128, 1)
Node ReluNode - conv2_neuron.1 -
Node ReluNode - conv2_neuron -
Node PoolNode - pool2 - _patchWidth=3, _stride=2, _mode=max
Node NormalizeNode - rnorm2 - _windowSize=5, _k=1.000000, _alpha=0.000020, _beta=0.750000
Node ConvNode - conv3 - _kernelWidth=3, _kernelCount=384, _marginSize=1, _sampleStride=1, _kernels->_dims=(384, 2304), _bias->_dims=(384, 1)
Node ReluNode - conv3_neuron.1 -
Node ReluNode - conv3_neuron -
Node GConvNode - conv4 - _kernelsCount = 384, _subnodes = Node ConvNode - conv4 - _kernelWidth=3, _kernelCount=192, _marginSize=1, _sampleStride=1, _kernels->_dims=(192, 1728), _bias->_dims=(192, 1) Node ConvNode - conv4 - _kernelWidth=3, _kernelCount=192, _marginSize=1, _sampleStride=1, _kernels->_dims=(192, 1728), _bias->_dims=(192, 1)
Node ReluNode - conv4_neuron.1 -
Node ReluNode - conv4_neuron -
Node GConvNode - conv5 - _kernelsCount = 256, _subnodes = Node ConvNode - conv5 - _kernelWidth=3, _kernelCount=128, _marginSize=1, _sampleStride=1, _kernels->_dims=(128, 1728), _bias->_dims=(128, 1) Node ConvNode - conv5 - _kernelWidth=3, _kernelCount=128, _marginSize=1, _sampleStride=1, _kernels->_dims=(128, 1728), _bias->_dims=(128, 1)
Node ReluNode - conv5_neuron.1 -
Node ReluNode - conv5_neuron -
Node PoolNode - pool5 - _patchWidth=3, _stride=2, _mode=max
Node NeuronNode - fc6 - _outputsCount=4096, _useBias=1, _weights->_dims=(4096, 9216)
Node ReluNode - fc6_neuron.1 -
Node ReluNode - fc6_neuron -
Node NeuronNode - fc7 - _outputsCount=4096, _useBias=1, _weights->_dims=(4096, 4096)
Node ReluNode - fc7_neuron.1 -
Node ReluNode - fc7_neuron -
Node NeuronNode - fc8 - _outputsCount=999, _useBias=1, _weights->_dims=(999, 4096)
Node MaxNode - probs -
0.015300 soccer ball
0.020866 standard poodle
0.169138 boxer
0.038989 corgi
0.213237 Staffordshire bullterrier
0.024005 greyhound
0.012390 English setter
0.013972 miniature poodle
0.016022 tennis ball
0.025065 dalmatian
0.394529 golden retriever
Classification took 4637 milliseconds
The text was updated successfully, but these errors were encountered: