This code implements the encoding and decoding process of DeepCABAC as described in (insert paper link).
Python binding of the C++ Implementation of deepCABAC using pybind11.
Tested on
- Ubuntu 18.04, gcc 7.4
- Windows 7, Visual Studio 2019
- Windows 10, Visual Studio 2019
- OS X Mojave 10.14, Xcode 10.2.1
You need python >= 3.6 with working pip:
From the root of this repository, run
pip install .
to install deepCABAC extension.
import deepCABAC
import torchvision.models as models
model = models.resnet50(pretrained=True)
encoder = deepCABAC.Encoder()
interv = 0.1
stepsize = 15
_lambda = 0.
for name, param in model.state_dict().items():
if '.weight' in name:
encoder.encodeWeightsRD( weights, interv, stepsize, _lambda )
else:
encoder.encodeWeightsRD( weights, interv, stepsize + 4, _lambda )
stream = encoder.finish()
with open('weights.bin', 'wb') as f:
f.write(stream)
import deepCABAC
import torchvision.models as models
model = models.resnet50(pretrained=False)
decoder = deepCABAC.Decoder()
with open('weights.bin', 'rb') as f:
stream = f.read()
decoder.getStream(stream)
state_dict = model.state_dict()
for name in state_dict.keys():
state_dict[name] = torch.tensor(decA.decodeWeights())
decoder.finish()
model.load_state_dict(state_dict)
# evaluate(model)
If you want to debug the module, on Ubuntu with gdb you can use:
CFLAGS='-Wall -O0 -g' pip install .
Find simple usage example in ./Tests.
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