The MATLAB interface -- matcaffe -- is the `caffe` package in caffe/matlab in which you can integrate Caffe in your Matlab code.

* Creating multiple Nets in Matlab

* Do forward and backward computation

* Access any layer within a network, and any parameter blob in a layer

* Get and set data or diff to any blob within a network, not restricting to input blobs or output blobs

* Save a network's parameters to file, and load parameters from file

* Reshape a blob and reshape a network

* Edit network parameter and do network surgery

* Create multiple Solvers in Matlab for training

* Resume training from solver snapshots

* Access train net and test nets in a solver

* Run for a certain number of iterations and give back control to Matlab

* Intermingle arbitrary Matlab code with gradient steps

An ILSVRC image classification demo is in caffe/matlab/demo/classification_demo.m (you need to download BVLC CaffeNet from [Model Zoo](http://caffe.berkeleyvision.org/model_zoo.html) to run it).

Build MatCaffe with `make all matcaffe`. After that, you may test it using `make mattest`.

Common issue: if you run into error messages like `libstdc++.so.6:version 'GLIBCXX_3.4.15' not found` during `make mattest`, then it usually means that your Matlab's runtime libraries do not match your compile-time libraries. You may need to do the following before you start Matlab:

export LD_LIBRARY_PATH=/opt/intel/mkl/lib/intel64:/usr/local/cuda/lib64

export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libstdc++.so.6

Or the equivalent based on where things are installed on your system, and do `make mattest` again to see if the issue is fixed. Note: this issue is sometimes more complicated since during its startup Matlab may overwrite your `LD_LIBRARY_PATH` environment variable. You can run `!ldd ./matlab/+caffe/private/caffe_.mexa64` (the mex extension may differ on your system) in Matlab to see its runtime libraries, and preload your compile-time libraries by exporting them to your `LD_PRELOAD` environment variable.

After successful building and testing, add this package to Matlab search PATH by starting `matlab` from caffe root folder and running the following commands in Matlab command window.

addpath ./matlab

You can save your Matlab search PATH by running `savepath` so that you don't have to run the command above again every time you use MatCaffe.

MatCaffe is very similar to PyCaffe in usage.

Examples below shows detailed usages and assumes you have downloaded BVLC CaffeNet from [Model Zoo](http://caffe.berkeleyvision.org/model_zoo.html) and started `matlab` from caffe root folder.

model = './models/bvlc_reference_caffenet/deploy.prototxt';

weights = './models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel';

**Mode and device should always be set BEFORE you create a net or a solver.**

Use CPU:

caffe.set_mode_cpu();

Use GPU and specify its gpu_id:

caffe.set_mode_gpu();

caffe.set_device(gpu_id);

Create a network:

net = caffe.Net(model, weights, 'test'); % create net and load weights

Or

net = caffe.Net(model, 'test'); % create net but not load weights

net.copy_from(weights); % load weights

which creates `net` object as

Net with properties:

layer_vec: [1x23 caffe.Layer]

blob_vec: [1x15 caffe.Blob]

inputs: {'data'}

outputs: {'prob'}

name2layer_index: [23x1 containers.Map]

name2blob_index: [15x1 containers.Map]

layer_names: {23x1 cell}

blob_names: {15x1 cell}

The two `containers.Map` objects are useful to find the index of a layer or a blob by its name.

You have access to every blob in this network. To fill blob 'data' with all ones:

net.blobs('data').set_data(ones(net.blobs('data').shape));

To multiply all values in blob 'data' by 10:

net.blobs('data').set_data(net.blobs('data').get_data() * 10);

**Be aware that since Matlab is 1-indexed and column-major, the usual 4 blob dimensions in Matlab are `[width, height, channels, num]`, and `width` is the fastest dimension. Also be aware that images are in BGR channels.** Also, Caffe uses single-precision float data. If your data is not single, `set_data` will automatically convert it to single.

You also have access to every layer, so you can do network surgery. For example, to multiply conv1 parameters by 10:

net.params('conv1', 1).set_data(net.params('conv1', 1).get_data() * 10); % set weights

net.params('conv1', 2).set_data(net.params('conv1', 2).get_data() * 10); % set bias

Alternatively, you can use

net.layers('conv1').params(1).set_data(net.layers('conv1').params(1).get_data() * 10);

net.layers('conv1').params(2).set_data(net.layers('conv1').params(2).get_data() * 10);

To save the network you just modified:

net.save('my_net.caffemodel');

To get a layer's type (string):

layer_type = net.layers('conv1').type;

Forward pass can be done using `net.forward` or `net.forward_prefilled`. Function `net.forward` takes in a cell array of N-D arrays containing data of input blob(s) and outputs a cell array containing data from output blob(s). Function `net.forward_prefilled` uses existing data in input blob(s) during forward pass, takes no input and produces no output. After creating some data for input blobs like `data = rand(net.blobs('data').shape);` you can run

res = net.forward({data});

prob = res{1};

Or

net.blobs('data').set_data(data);

net.forward_prefilled();

prob = net.blobs('prob').get_data();

Backward is similar using `net.backward` or `net.backward_prefilled` and replacing `get_data` and `set_data` with `get_diff` and `set_diff`. After creating some gradients for output blobs like `prob_diff = rand(net.blobs('prob').shape);` you can run

res = net.backward({prob_diff});

data_diff = res{1};

Or

net.blobs('prob').set_diff(prob_diff);

net.backward_prefilled();

data_diff = net.blobs('data').get_diff();

**However, the backward computation above doesn't get correct results, because Caffe decides that the network does not need backward computation. To get correct backward results, you need to set `'force_backward: true'` in your network prototxt.**

After performing forward or backward pass, you can also get the data or diff in internal blobs. For example, to extract pool5 features after forward pass:

pool5_feat = net.blobs('pool5').get_data();

Assume you want to run 1 image at a time instead of 10:

net.blobs('data').reshape([227 227 3 1]); % reshape blob 'data'

net.reshape();

Then the whole network is reshaped, and now `net.blobs('prob').shape` should be `[1000 1]`;

Assume you have created training and validation lmdbs following our [ImageNET Tutorial](http://caffe.berkeleyvision.org/gathered/examples/imagenet.html), to create a solver and train on ILSVRC 2012 classification dataset:

solver = caffe.Solver('./models/bvlc_reference_caffenet/solver.prototxt');

which creates `solver` object as

Solver with properties:

net: [1x1 caffe.Net]

test_nets: [1x1 caffe.Net]

To train:

solver.solve();

Or train for only 1000 iterations (so that you can do something to its net before training more iterations)

solver.step(1000);

To get iteration number:

iter = solver.iter();

To get its network:

train_net = solver.net;

test_net = solver.test_nets(1);

To resume from a snapshot "your_snapshot.solverstate":

solver.restore('your_snapshot.solverstate');

`caffe.io` class provides basic input functions `load_image` and `read_mean`. For example, to read ILSVRC 2012 mean file (assume you have downloaded imagenet example auxiliary files by running `./data/ilsvrc12/get_ilsvrc_aux.sh`):

mean_data = caffe.io.read_mean('./data/ilsvrc12/imagenet_mean.binaryproto');

To read Caffe's example image and resize to `[width, height]` and suppose we want `width = 256; height = 256;`

im_data = caffe.io.load_image('./examples/images/cat.jpg');

im_data = imresize(im_data, [width, height]); % resize using Matlab's imresize

**Keep in mind that `width` is the fastest dimension and channels are BGR, which is different from the usual way that Matlab stores an image.** If you don't want to use `caffe.io.load_image` and prefer to load an image by yourself, you can do

im_data = imread('./examples/images/cat.jpg'); % read image

im_data = im_data(:, :, [3, 2, 1]); % convert from RGB to BGR

im_data = permute(im_data, [2, 1, 3]); % permute width and height

im_data = single(im_data); % convert to single precision

Also, you may take a look at caffe/matlab/demo/classification_demo.m to see how to prepare input by taking crops from an image.

We show in caffe/matlab/hdf5creation how to read and write HDF5 data with Matlab. We do not provide extra functions for data output as Matlab itself is already quite powerful in output.

Call `caffe.reset_all()` to clear all solvers and stand-alone nets you have created.

classdef test_net < matlab.unittest.TestCase

properties

num_output

model_file

net

end

methods (Static)

function model_file = simple_net_file(num_output)

model_file = tempname();

fid = fopen(model_file, 'w');

fprintf(fid, [ ...

'name: "testnet" force_backward: true\n' ...

'layer { type: "DummyData" name: "data" top: "data" top: "label"\n' ...

'dummy_data_param { num: 5 channels: 2 height: 3 width: 4\n' ...

' num: 5 channels: 1 height: 1 width: 1\n' ...

' data_filler { type: "gaussian" std: 1 }\n' ...

' data_filler { type: "constant" } } }\n' ...

'layer { type: "Convolution" name: "conv" bottom: "data" top: "conv"\n' ...

' convolution_param { num_output: 11 kernel_size: 2 pad: 3\n' ...

' weight_filler { type: "gaussian" std: 1 }\n' ...

' bias_filler { type: "constant" value: 2 } }\n' ...

' param { decay_mult: 1 } param { decay_mult: 0 }\n' ...

' }\n' ...

'layer { type: "InnerProduct" name: "ip" bottom: "conv" top: "ip"\n' ...

' inner_product_param { num_output: ' num2str(num_output) ...

' weight_filler { type: "gaussian" std: 2.5 }\n' ...

' bias_filler { type: "constant" value: -3 } } }\n' ...

'layer { type: "SoftmaxWithLoss" name: "loss" bottom: "ip" bottom: "label"\n' ...

' top: "loss" }' ]);

fclose(fid);

end

end

methods

function self = test_net()

self.num_output = 13;

self.model_file = caffe.test.test_net.simple_net_file(self.num_output);

self.net = caffe.Net(self.model_file, 'train');

% also make sure get_solver runs

caffe.get_net(self.model_file, 'train');

% fill in valid labels

self.net.blobs('label').set_data(randi( ...

self.num_output - 1, self.net.blobs('label').shape));

delete(self.model_file);

end

end

methods (Test)

function self = test_blob(self)

self.net.blobs('data').set_data(10 * ones(self.net.blobs('data').shape));

self.verifyEqual(self.net.blobs('data').get_data(), ...

10 * ones(self.net.blobs('data').shape, 'single'));

self.net.blobs('data').set_diff(-2 * ones(self.net.blobs('data').shape));

self.verifyEqual(self.net.blobs('data').get_diff(), ...

-2 * ones(self.net.blobs('data').shape, 'single'));

original_shape = self.net.blobs('data').shape;

self.net.blobs('data').reshape([6 5 4 3 2 1]);

self.verifyEqual(self.net.blobs('data').shape, [6 5 4 3 2 1]);

self.net.blobs('data').reshape(original_shape);

self.net.reshape();

end

function self = test_layer(self)

self.verifyEqual(self.net.params('conv', 1).shape, [2 2 2 11]);

self.verifyEqual(self.net.layers('conv').params(2).shape, 11);

self.verifyEqual(self.net.layers('conv').type(), 'Convolution');

end

function test_forward_backward(self)

self.net.forward_prefilled();

self.net.backward_prefilled();

end

function test_inputs_outputs(self)

self.verifyEqual(self.net.inputs, cell(0, 1))

self.verifyEqual(self.net.outputs, {'loss'});

end

function test_save_and_read(self)

weights_file = tempname();

self.net.save(weights_file);

model_file2 = caffe.test.test_net.simple_net_file(self.num_output);

net2 = caffe.Net(model_file2, 'train');

net2.copy_from(weights_file);

net3 = caffe.Net(model_file2, weights_file, 'train');

delete(model_file2);

delete(weights_file);

for l = 1:length(self.net.layer_vec)

for i = 1:length(self.net.layer_vec(l).params)

self.verifyEqual(self.net.layer_vec(l).params(i).get_data(), ...

net2.layer_vec(l).params(i).get_data());

self.verifyEqual(self.net.layer_vec(l).params(i).get_data(), ...

net3.layer_vec(l).params(i).get_data());

end

end

end

end