Add an example in a script and README file.

README.md

@@ -23,6 +23,53 @@ pip install .
 
 to install deepCABAC extension.
 
+## Examples
+
+### Encoding Pytorch model weights
+```
+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)
+```
+
+### Decoding Pytorch model weights
+```
+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)
+```
+
 ### Debugging
 
 If you want to debug the module, on Ubuntu with gdb you can use:

examples/example.py

@@ -0,0 +1,55 @@
+import torch
+import deepCABAC
+import torchvision.models as models
+import numpy as np
+from tqdm import tqdm
+
+
+def main():
+    # encoding
+    model = models.resnet18(pretrained=True)
+    encoder = deepCABAC.Encoder()
+
+    interv = 0.1
+    stepsize = 2**(-0.5*15)
+    stepsize_other = 2**(-0.5*19)
+    _lambda = 0.
+
+    for name, param in tqdm(model.state_dict().items()):
+        if '.num_batches_tracked' in name:
+            continue
+        param = param.cpu().numpy()
+        if '.weight' in name:
+            encoder.encodeWeightsRD(param, interv, stepsize, _lambda)
+        else:
+            encoder.encodeWeightsRD(param, interv, stepsize_other, _lambda)
+
+    stream = encoder.finish().tobytes()
+    print("Compressed size: {:2f} MB".format(1e-6 * len(stream)))
+    with open('weights.bin', 'wb') as f:
+        f.write(stream)
+
+    # decoding
+    model = models.resnet18(pretrained=False)
+    decoder = deepCABAC.Decoder()
+
+    with open('weights.bin', 'rb') as f:
+        stream = f.read()
+
+    decoder.getStream(np.frombuffer(stream, dtype=np.uint8))
+    state_dict = model.state_dict()
+
+    for name in tqdm(state_dict.keys()):
+        if '.num_batches_tracked' in name:
+            continue
+        param = decoder.decodeWeights()
+        state_dict[name] = torch.tensor(param)
+    decoder.finish()
+
+    model.load_state_dict(state_dict)
+
+    # evaluate(model)
+
+
+if __name__ == '__main__':
+    main()