Caffe to TensorFlow

Convert Caffe models to TensorFlow.

Usage with standalone model

1 - Install caffe-tensorflow

git clone https://github.com/linkfluence/caffe-tensorflow
# Optional: create a Python 2.7 env and activate it
# This fork has only be tested with Python 2.7

2 - (Optional) Switch to Tensorflow CPU

You might bump into memory issues if you don't have enough memory. In this case just uninstall tensorflow-gpu and install tensorflow

3 - Convert your model

python convert.py --caffemodel ./model.caffemodel ./model.prototxt --data-output-path ./output.mat --code-output-path ./output.py --standalone-output-path ./standalone.pb`

4 - (Optional) Re-install Tensorflow GPU

5- Use the standalone.pb file

It contains the weights and the architecture of the network.

Usage

Run convert.py to convert an existing Caffe model to TensorFlow.

Make sure you're using the latest Caffe format (see the notes section for more info).

The output consists of two files:

1. A data file (in NumPy's native format) containing the model's learned parameters.
2. A Python class that constructs the model's graph.

Alternatively, you can save a standalone GraphDef model file containing the model's graph and learned parameters.

Examples

See the examples folder for more details.

Verification

The following converted models have been verified on the ILSVRC2012 validation set using validate.py.

Model	Top 5 Accuracy
ResNet 152	92.92%
ResNet 101	92.63%
ResNet 50	92.02%
VGG 16	89.88%
GoogLeNet	89.06%
Network in Network	81.21%
CaffeNet	79.93%
AlexNet	79.84%

Notes

• Only the new Caffe model format is supported. If you have an old model, use the upgrade_net_proto_text and upgrade_net_proto_binary tools that ship with Caffe to upgrade them first. Also make sure you're using a fairly recent version of Caffe.

• It appears that Caffe and TensorFlow cannot be concurrently invoked (CUDA conflicts - even with set_mode_cpu). This makes it a two-stage process: first extract the parameters with convert.py, then import it into TensorFlow.

• Caffe is not strictly required. If PyCaffe is found in your PYTHONPATH, and the USE_PYCAFFE environment variable is set, it will be used. Otherwise, a fallback will be used. However, the fallback uses the pure Python-based implementation of protobuf, which is astoundingly slow (~1.5 minutes to parse the VGG16 parameters). The experimental CPP protobuf backend doesn't particularly help here, since it runs into the file size limit (Caffe gets around this by overriding this limit in C++). A cleaner solution here would be to implement the loader as a C++ module.

• Only a subset of Caffe layers and accompanying parameters are currently supported.

• Not all Caffe models can be converted to TensorFlow. For instance, Caffe supports arbitrary padding whereas TensorFlow's support is currently restricted to SAME and VALID.

• The border values are handled differently by Caffe and TensorFlow. However, these don't appear to affect things too much.

• Image rescaling can affect the ILSVRC2012 top 5 accuracy listed above slightly. VGG16 expects isotropic rescaling (anisotropic reduces accuracy to 88.45%) whereas BVLC's implementation of GoogLeNet expects anisotropic (isotropic reduces accuracy to 87.7%).

• The support class kaffe.tensorflow.Network has no internal dependencies. It can be safely extracted and deployed without the rest of this library.

• The ResNet model uses 1x1 convolutions with a stride of 2. This is currently only supported in the master branch of TensorFlow (the latest release at time of writing being v0.8.0, which does not support it).