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Chainer from/to NNOIR Converter

Project description

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

Project details


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