If you have Caffe compiled for Matlab (which you can do using
make matcaffe
) then you can start following this simple tutorial.I am using Matlab r2014a. But Caffe’s MATLAB interface works also with versions 2015a, 2014b, 2013a/b, and 2012b. Here's a simple script that loads up some default images and runs them through the imagenet classifier (matcaffedemo). However, I should warn you that Caffe's Matlab interface doesn't support as rich a feature set as the Python/C interfa.
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First you have to make sure Matlab can see
you can do that from your Matlab script using
Then, you need to set Caffe mode to either CPU (defualt if not set) or GPU mode. If you have Caffe compiled for GPU use it , it would be faster (unless you have a small GPU with limited memory then you would choose CPU for large models that will not git in the GPU)
caffe/matlab
folder which would be something like /home/yourusername/caffe/matlab
you can do that from your Matlab script using
addpath('path/to/cafffe/matlab');
Then, you need to set Caffe mode to either CPU (defualt if not set) or GPU mode. If you have Caffe compiled for GPU use it , it would be faster (unless you have a small GPU with limited memory then you would choose CPU for large models that will not git in the GPU)
caffe.set_mode_gpu();
gpu_id = 0; % we will use the first gpu
caffe.set_device(gpu_id);
% or you can use
caffe.set_mode_cpu();
gpu_id = 0; % we will use the first gpu
caffe.set_device(gpu_id);
% or you can use
caffe.set_mode_cpu();
If you have a trained model and you would like to test it, first you need to define your network like:
net_weights = [‘path/to/yourmodel.caffemodel’];
net_model = [‘path/to/your_deploy.prototxt’];
net = caffe.Net(net_model, net_weights, ‘test’);
net_model = [‘path/to/your_deploy.prototxt’];
net = caffe.Net(net_model, net_weights, ‘test’);
make sure your deploy file has the same layer names as the actual model , Matlab will not give you an error ! it will just ignore the weights of that layer. next step is to prepare your image. you will need the prepare_image function which is available inside the caffehome/matlab/demo/classification_demo.m. Please go there to get the most updated version or if you can not get it from the source , here it is
function crops_data = prepare_image(im)
% ------------------------------------------------------------------------
% caffe/matlab/+caffe/imagenet/ilsvrc_2012_mean.mat contains mean_data that
% is already in W x H x C with BGR channels
d = load('./+caffe/imagenet/ilsvrc_2012_mean.mat');
mean_data = d.mean_data;
IMAGE_DIM = 256;
CROPPED_DIM = 227;
% Convert an image returned by Matlab’s imread to im_data in caffe’s data
% format: W x H x C with BGR channels
im_data = im(:, :, [3, 2, 1]); % permute channels from RGB to BGR
im_data = permute(im_data, [2, 1, 3]); % flip width and height
im_data = single(im_data); % convert from uint8 to single
im_data = imresize(im_data, [IMAGE_DIM IMAGE_DIM], ‘bilinear’); % resize im_data
im_data = im_data – mean_data; % subtract mean_data (already in W x H x C, BGR)
% format: W x H x C with BGR channels
im_data = im(:, :, [3, 2, 1]); % permute channels from RGB to BGR
im_data = permute(im_data, [2, 1, 3]); % flip width and height
im_data = single(im_data); % convert from uint8 to single
im_data = imresize(im_data, [IMAGE_DIM IMAGE_DIM], ‘bilinear’); % resize im_data
im_data = im_data – mean_data; % subtract mean_data (already in W x H x C, BGR)
% oversample (4 corners, center, and their x-axis flips)
crops_data = zeros(CROPPED_DIM, CROPPED_DIM, 3, 10, ‘single’);
indices = [0 IMAGE_DIM-CROPPED_DIM] + 1;
n = 1;
for i = indices
for j = indices
crops_data(:, :, :, n) = im_data(i:i+CROPPED_DIM-1, j:j+CROPPED_DIM-1, : );
crops_data(:, :, :, n+5) = crops_data(end:-1:1, :, :, n);
n = n + 1;
end
end
center = floor(indices(2) / 2) + 1;
crops_data(:,:,:,5) = …
im_data(center:center+CROPPED_DIM-1,center:center+CROPPED_DIM-1,:);
crops_data(:,:,:,10) = crops_data(end:-1:1, :, :, 5);
crops_data = zeros(CROPPED_DIM, CROPPED_DIM, 3, 10, ‘single’);
indices = [0 IMAGE_DIM-CROPPED_DIM] + 1;
n = 1;
for i = indices
for j = indices
crops_data(:, :, :, n) = im_data(i:i+CROPPED_DIM-1, j:j+CROPPED_DIM-1, : );
crops_data(:, :, :, n+5) = crops_data(end:-1:1, :, :, n);
n = n + 1;
end
end
center = floor(indices(2) / 2) + 1;
crops_data(:,:,:,5) = …
im_data(center:center+CROPPED_DIM-1,center:center+CROPPED_DIM-1,:);
crops_data(:,:,:,10) = crops_data(end:-1:1, :, :, 5);
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Be aware that this code uses the imagenet mean file also it performs 10 crops . check your model to see if you need to modify that. next you should prepare your image and pass it to the network.
input_data = {prepare_image(im)};
scores = net.forward(input_data);
scores = scores{1};
scores = mean(scores, 2); % take average scores over 10 crops
scores = net.forward(input_data);
scores = scores{1};
scores = mean(scores, 2); % take average scores over 10 crops
Caffe Matlab Install
If you need to check the weights or the outputs of certain layers you can always do that in Matlab. Be ware that weights are the network weights and they are independent of the input, while the output is the network activation for this particular input you have just passed to the network.
weights_FC6 = net.params(‘fc6’,1).get_data();
output_FC6 = net.blobs(‘fc6’).get_data();
output_FC6 = net.blobs(‘fc6’).get_data();
Caffe Scale Layer
Caffe Matlab
Empires and puzzles heroes list. If you think there is something missing in this tutorial please comment with your request and i will add it to the tutorial as soon as possible.