nnoir-chainer

Chainer Model from/to NNOIR converter

ATTENTION: This library goes into the maintenance phase too.

Install

pip install nnoir-chainer

Example

Import NNOIR

import chainer
from nnoir_chainer import NNOIRFunction
m = NNOIRFunction('nnoir_file_path')
x = chainer.Variable(np_array)
with chainer.using_config('train', False):
    y = m(x)
    print(y)

Export NNOIR

m = model.CNN()
chainer.serializers.load_npz('cnn.model', L.Classifier(m))
with chainer.using_config('train', False):
    x = chainer.Variable(np.zeros((1, 28*28)).astype(np.float32))
    y = m(x)
    g = nnoir_chainer.Graph(m, (x,), (y,))
    result = g.to_nnoir()
    with open('model.nnoir', 'w') as f:
        f.buffer.write(result)

These layers are supported by nnoir-chainer exporter.

• chainer.links
  • BatchNormalization
  • Bias
  • Linear
  • Convolution2D (DepthwiseConvolution2D, DilatedConvolution2D)
  • Scale
  • Swish
• chainer.function
  • Add
  • AddConstant
  • AveragePooling2D
  • ClippedReLU
  • Concat
  • Dropout
  • ELU
  • LeakyReLU
  • MaxPooling2D
  • Mul
  • MulConstant
  • Pad
  • ReLU
  • Reshape
  • Sigmoid
  • Softmax
  • Sub
  • Sum
  • Tanh
  • Transpose
  • Unpooling2D