caffe command line

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.

# 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 -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 a model architecture with the given weights on the first GPU for 10 iterations
# time LeNet training on GPU for the default 50 iterations
caffe time -model examples/mnist/lenet_train_test.prototxt -gpu 0

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

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.Classsifierand caffe.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. The module dir caffe/python/caffe should be installed in your PYTHONPATH for import caffe.