Please save the file in Unicode format to prevent data loss (.\.\src\caffe\util\math_functions.cpp)Ģ) Double-click the error, a dialog box pops up, click OK to jump to the error file, and then save it directly without doing anything else.
MATLAB CAFFE INSTALL CODE
The file contains characters that cannot be represented in the current code page (936). \NugetPackages\boost.1.59.0.0\lib\native\include\boost/format/alt_sstream_impl.hpp :Įrror C2220: Warning is treated as an error-no "object" file was generated (.\.\src\caffe\util\math_functions.cpp) Click on the menu bar Generate-Configuration Manager, select the solution configuration as Release, and the solution platform as 圆4.ġ) Right-click libcaffe and generate. Menu bar Generate-Configuration Managerįind caffe.sln under the caffe-master/windows folder and open it with VS 2013. Simultaneously Cublas.lib file after comment。Ģ. In PropertyGroup Condition CpuOnlyBuildSet to true. 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.$(MatlabDir)\extern\include $(MatlabDir)\toolbox\distcomp\gpu\extern\include $(IncludePath) Output_FC6 = net.blobs(‘fc6’).get_data() 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. If you need to check the weights or the outputs of certain layers you can always do that in Matlab. Scores = mean(scores, 2) % take average scores over 10 crops next you should prepare your image and pass it to the network. check your model to see if you need to modify that. Im_data(center:center+CROPPED_DIM-1,center:center+CROPPED_DIM-1,:) Ĭrops_data(:,:,:,10) = crops_data(end:-1:1, :, :, 5) īe aware that this code uses the imagenet mean file also it performs 10 crops.
% oversample (4 corners, center, and their x-axis flips)Ĭrops_data = zeros(CROPPED_DIM, CROPPED_DIM, 3, 10, ‘single’) Ĭrops_data(:, :, :, n) = im_data(i:i+CROPPED_DIM-1, j:j+CROPPED_DIM-1, : ) Ĭrops_data(:, :, :, n+5) = crops_data(end:-1:1, :, :, n) Im_data = im_data – mean_data % subtract mean_data (already in W x H x C, BGR) Im_data = imresize(im_data,, ‘bilinear’) % resize im_data Im_data = single(im_data) % convert from uint8 to single Im_data = permute(im_data, ) % flip width and height
Im_data = im(:, :, ) % permute channels from RGB to BGR % Convert an image returned by Matlab’s imread to im_data in caffe’s data % is already in W x H x C with BGR channelsĭ = load('./+caffe/imagenet/ilsvrc_2012_mean.mat') % caffe/matlab/+caffe/imagenet/ilsvrc_2012_mean.mat contains mean_data that Please go there to get the most updated version or if you can not get it from the source, here it is you will need the prepare_image function which is available inside the caffehome/matlab/demo/classification_demo.m. 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. Net = caffe.Net(net_model, net_weights, ‘test’) If you have a trained model and you would like to test it, first you need to define your network like: 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) Then, you need to set Caffe mode to either CPU (defualt if not set) or GPU mode. You can do that from your Matlab script using addpath('path/to/cafffe/matlab') If you have Caffe compiled for Matlab (which you can do using make matcaffe ) then you can start following this simple tutorial.įirst you have to make sure Matlab can see caffe/matlab folder which would be something like /home/yourusername/caffe/matlab
0 kommentar(er)
