Interfaces
Caffe has command line, Python, and MATLAB interfaces for day-to-day usage, interfacing with research code, and rapid prototyping. While Caffe is a C++ library at heart and it exposes a modular interface for development, not every occasion calls for custom compilation. The cmdcaffe, pycaffe, and matcaffe interfaces are here for you.
Command Line
The command line interface – cmdcaffe – is the caffe
tool for model training, scoring, and diagnostics. Run caffe
without any arguments for help. This tool and others are found in caffe/build/tools. (The following example calls require completing the LeNet / MNIST example first.)
Training: caffe train
learns models from scratch, resumes learning from saved snapshots, and fine-tunes models to new data and tasks:
- All training requires a solver configuration through the
-solver solver.prototxt
argument. - Resuming requires the
-snapshot model_iter_1000.solverstate
argument to load the solver snapshot. - Fine-tuning requires the
-weights model.caffemodel
argument for the model initialization.
For example, you can run:
# train LeNet
caffe train -solver examples/mnist/lenet_solver.prototxt
# train on GPU 2
caffe train -solver examples/mnist/lenet_solver.prototxt -gpu 2
# resume training from the half-way point snapshot
caffe train -solver examples/mnist/lenet_solver.prototxt -snapshot examples/mnist/lenet_iter_5000.solverstate
For a full example of fine-tuning, see examples/finetuning_on_flickr_style, but the training call alone is
# fine-tune CaffeNet model weights for style recognition
caffe train -solver examples/finetuning_on_flickr_style/solver.prototxt -weights models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel
Testing: caffe test
scores models by running them in the test phase and reports the net output as its score. The net architecture must be properly defined to output an accuracy measure or loss as its output. The per-batch score is reported and then the grand average is reported last.
# score the learned LeNet model on the validation set as defined in the
# model architeture lenet_train_test.prototxt
caffe test -model examples/mnist/lenet_train_test.prototxt -weights examples/mnist/lenet_iter_10000.caffemodel -gpu 0 -iterations 100
Benchmarking: caffe time
benchmarks model execution layer-by-layer through timing and synchronization. This is useful to check system performance and measure relative execution times for models.
# (These example calls require you complete the LeNet / MNIST example first.)
# time LeNet training on CPU for 10 iterations
caffe time -model examples/mnist/lenet_train_test.prototxt -iterations 10
# time LeNet training on GPU for the default 50 iterations
caffe time -model examples/mnist/lenet_train_test.prototxt -gpu 0
# time a model architecture with the given weights on the first GPU for 10 iterations
caffe time -model examples/mnist/lenet_train_test.prototxt -weights examples/mnist/lenet_iter_10000.caffemodel -gpu 0 -iterations 10
Diagnostics: caffe device_query
reports GPU details for reference and checking device ordinals for running on a given device in multi-GPU machines.
# query the first device
caffe device_query -gpu 0
Parallelism: the -gpu
flag to the caffe
tool can take a comma separated list of IDs to run on multiple GPUs. A solver and net will be instantiated for each GPU so the batch size is effectively multiplied by the number of GPUs. To reproduce single GPU training, reduce the batch size in the network definition accordingly.
# train on GPUs 0 & 1 (doubling the batch size)
caffe train -solver examples/mnist/lenet_solver.prototxt -gpu 0,1
# train on all GPUs (multiplying batch size by number of devices)
caffe train -solver examples/mnist/lenet_solver.prototxt -gpu all
Python
The Python interface – pycaffe – is the caffe
module and its scripts in caffe/python. import caffe
to load models, do forward and backward, handle IO, visualize networks, and even instrument model solving. All model data, derivatives, and parameters are exposed for reading and writing.
caffe.Net
is the central interface for loading, configuring, and running models.caffe.Classifier
andcaffe.Detector
provide convenience interfaces for common tasks.caffe.SGDSolver
exposes the solving interface.caffe.io
handles input / output with preprocessing and protocol buffers.caffe.draw
visualizes network architectures.- Caffe blobs are exposed as numpy ndarrays for ease-of-use and efficiency.
Tutorial IPython notebooks are found in caffe/examples: do ipython notebook caffe/examples
to try them. For developer reference docstrings can be found throughout the code.
Compile pycaffe by make pycaffe
.Add the module directory to your $PYTHONPATH
by export PYTHONPATH=/path/to/caffe/python:$PYTHONPATH
or the like for import caffe
.
MATLAB
The MATLAB interface – matcaffe – is the caffe
package in caffe/matlab in which you can integrate Caffe in your Matlab code.
In MatCaffe, you can
- 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 BAIR CaffeNet from Model Zoo to run it).
Build MatCaffe
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 LDLIBRARY_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.
Use MatCaffe
MatCaffe is very similar to PyCaffe in usage.
Examples below shows detailed usages and assumes you have downloaded BAIR CaffeNet from Model Zoo and started matlab
from caffe root folder.
model = './models/bvlc_reference_caffenet/deploy.prototxt';
weights = './models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel';
Set mode and device
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 and access its layers and blobs
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 and backward
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();
Reshape
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]
;
Training
Assume you have created training and validation lmdbs following our ImageNET Tutorial, 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');
Input and output
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.
Clear nets and solvers
Call caffe.reset_all()
to clear all solvers and stand-alone nets you have created.