v0.2.1b3

ONNX-Chainer

This is an add-on package for ONNX support by Chainer.

Requirements

• onnx==0.2.1
• chainer>=3.1.0

Installation

See INSTALL.md

Quick Start

import numpy as np
import chainer.links as L
import onnx_chainer

model = L.VGG16Layers()

# Pseudo input
x = np.zeros((1, 3, 224, 224), dtype=np.float32)

onnx_chainer.export(model, x, filename='VGG16.onnx')

Supported Functions

Currently 50 Chainer Functions are supported to export in ONNX format.

Activation

• ELU
• HardSigmoid
• LeakyReLU
• LogSoftmax
• PReLUFunction
• ReLU
• Sigmoid
• Softmax
• Softplus
• Tanh

Array

• Cast
• Concat
• Depth2Space
• Pad ¹²
• Reshape
• Space2Depth
• SplitAxis
• Squeeze
• Tile
• Transpose

Connection

• Convolution2DFunction
• ConvolutionND
• Deconvolution2DFunction
• DeconvolutionND
• EmbedIDFunction ³
• LinearFunction

Math

• Add
• Absolute
• Div
• Mul
• Neg
• PowVarConst
• Sub
• Clip
• Exp
• Identity
• MatMul ⁴
• Maximum
• Minimum
• Sqrt
• SquaredDifference
• Sum

Noise

• Dropout ⁵

Pooling

• AveragePooling2D
• AveragePoolingND
• MaxPooling2D
• MaxPoolingND

Normalization

• BatchNormalization
• FixedBatchNormalization
• LocalResponseNormalization

---

1: mode should be either 'constant', 'reflect', or 'edge'

2: ONNX doesn't support multiple constant values for Pad operation

3: Current ONNX doesn't support ignore_label for EmbedID

4: Current ONNX doesn't support transpose options for matmul ops

5: In test mode, all dropout layers aren't included in the exported file