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If you want to benchmark an existing full-precision network structure

If you just want to benchmark the latency of a BNN instead of deploying it, you can determine which convolutions in the input ONNX model are binary by passing "--binary-list filename" command-line argument. Each line of the file is the output name of a convolution.

For example, you have a full-precision model named "model.onnx". In the model, you want three convolutions, whose outputs are "34", "36", "55" respectively, to be binary convolutions, and test how fast the model with three binary convolutions will be. In this case, you should first create a text file with

34

36

55

After creating the text file (Let's assume the file is named "my_binary_convs"), you can convert the model by

./onnx2bnn model.onnx model.dab --binary-list my_binary_convs

Once the command finishes, you will get a BNN model named model.dab.

If you want to train and export a dabnn-compatible ONNX model

If you want to train and deploy a BNN on real device, the following instructions are what you needed.

Binary convolutions are not supported natively by training frameworks (e.g., TensorFlow, PyTorch, MXNet). To implement correct and dabnn-compatible binary convolutions by self, there is something needed attention:

  1. The input of binary convolutions should only be +1/-1, but the padding value of convolution is 0.

  2. PyTorch doesn't support export ONNX sign operator until PyTorch 1.2.

Therefore, we provide a "standard" PyTorch implementation which is compatible with dabnn and produces a correct result. The implementations TensorFlow, MXNet and other training frameworks should be similar.

How does dabnn recognize binary convolutions in ONNX model

The converter onnx2bnn has three mode in terms of how it recognizes binary convolutions:

  • Aggressive (default). In this mode, onnx2bnn will mark all convolutions whose weights consist of only +1 or -1 as binary convolutions. The aggressive mode is for the existing BNN models which do not have the correct padding value (-1 rather than 0). Note: The output of the generated dabnn model is different from that of the ONNX model since the padding value is 0 instead of -1.
  • Moderate. This mode is for our "standard" implementation -- A Conv operator with binary weight and following a -1 Pad operator.
  • Strict. In this mode, onnx2bnn only recognizes the following natural and correct "pattern" of binary convolutions: A Conv operator, whose input is got from a Sign op and a Pad op (the order doesn't matter), and weight is got from a Sign op.

For now "Aggressive" is the default mode. To enable moderate or strict mode, pass "--moderate" or "--strict" command-line argument to onnx2bnn.