onnx-chainer 1.2.2a2

Convert a Chainer model into ONNX

# ONNX-Chainer

This is an add-on package for ONNX support by Chainer.
ONNX-Chainer supports opset version <= 7.

## Tested environment

- ONNX >=1.2.1
- Chainer 4.4.0
- Python 2.7.14, 3.5.5, 3.6.5

### Compatibility tests

- with MXNet v1.3.0b20180830
- with TVM v0.4

## Installation

### On Ubuntu 14.04/16.04

**Please install Chainer first.**

```bash
pip install chainer
pip install onnx-chainer
```

## Run Test

### 1. Build Docker images

```bash
cd docker
bash build_docker.sh
```

### 2. Run tests

```bash
bash docker/run_tests.sh 3.5 4.4.0 # -> python 3.5, chainer 4.4.0
```

## Quick Start

First, install [ChainerCV](https://github.com/chainer/chainercv) to get the pre-trained models.

```python
import numpy as np

import chainer
import chainercv.links as C
import onnx_chainer

model = C.VGG16(pretrained_model='imagenet')

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

onnx_chainer.export(model, x, filename='vgg16.onnx')
```

## Load models from MXNet

Please install [MXNet 1.3.0b20180830](https://github.com/apache/incubator-mxnet) or newer one via pip: `pip install mxnet --pre`, then try the following code:

```python
import collections

import mxnet
import numpy as np

import chainer
import chainer.functions as F
import chainercv.links as C
import onnx_chainer

# Prepare an input tensor
x = np.random.rand(1, 3, 224, 224).astype(np.float32) * 255

# Run the model on the data
with chainer.using_config('train', False):
chainer_out = model(x).array

# Export Chainer model into ONNX
onnx_chainer.export(model, x, fn)

# Load ONNX model into MXNet symbol
sym, arg, aux = mxnet.contrib.onnx.import_model(fn)

# Find the name of input tensor
data_names = [graph_input for graph_input in sym.list_inputs()
if graph_input not in arg and graph_input not in aux]
data_shapes = [(data_names[0], x.shape)]

# Create MXNet model
mod = mxnet.mod.Module(
symbol=sym, data_names=data_names, context=mxnet.cpu(),
label_names=None)
mod.bind(
for_training=False, data_shapes=data_shapes,
label_shapes=None)
mod.set_params(
arg_params=arg, aux_params=aux, allow_missing=True,
allow_extra=True)

# Create input data
Batch = collections.namedtuple('Batch', ['data'])
input_data = Batch([mxnet.nd.array(x)])

# Forward computation using MXNet
mod.forward(input_data)

# Retrieve the output of forward result
mxnet_out = mod.get_outputs()[0].asnumpy()

# Check the prediction results are same
assert np.argmax(chainer_out) == np.argmax(mxnet_out)

# Check both outputs have same values
np.testing.assert_almost_equal(chainer_out, mxnet_out, decimal=5)
```

## Compile the Chainer model via ONNX

Please install [TVM v0.4](https://github.com/dmlc/tvm/releases/tag/v0.4) first. You can find hwo to build it in this [Dockerfile](docker/Dockerfile).

```python
import collections

import numpy as np
import onnx

import chainer
import chainer.functions as F
import chainercv.links as C
import nnvm
import onnx_chainer
import tvm

model = C.ResNet50(pretrained_model='imagenet', arch='he')
# Change cover_all option to False to match the default behavior of MXNet's pooling
model.pool1 = lambda x: F.max_pooling_2d(x, ksize=3, stride=2, cover_all=False)
save_as_onnx_then_import_from_nnvm(model, 'resnet50.onnx')

# Prepare an input tensor
x = np.random.rand(1, 3, 224, 224).astype(np.float32) * 255

# Run the model on the data
with chainer.using_config('train', False):
chainer_out = model(x).array

# Export Chainer model into ONNX
onnx_chainer.export(model, x, fn)

# Load the saved ONNX file using ONNX module
model_onnx = onnx.load(fn)

# Convert the ONNX model object into NNVM symbol
sym, params = nnvm.frontend.from_onnx(model_onnx)

# Choose the compilation target
target = 'llvm'

# Extract the name of input variable in the ONNX graph
input_name = sym.list_input_names()[0]
shape_dict = {input_name: x.shape}

# Compile the model using NNVM
graph, lib, params = nnvm.compiler.build(
sym, target, shape_dict, params=params,
dtype={input_name: 'float32'})

# Convert the compiled model into TVM module
module = tvm.contrib.graph_runtime.create(graph, lib, tvm.cpu(0))

# Set the input tensor x
module.set_input(input_name, tvm.nd.array(x))
module.set_input(**params)

# Run the model
module.run()

# Retrieve the inference result
out_shape = (1, 1000)
output = tvm.nd.empty(out_shape, ctx=tvm.cpu(0))
nnvm_output = module.get_output(0, output).asnumpy()

# Check both outputs have same values
np.testing.assert_almost_equal(chainer_out, nnvm_output, decimal=5)
```

## Options of `export` function

```
Export function for chainer.Chain in ONNX format.

This function performs a forward computation of the given
:class:`~chainer.Chain`, ``model``, by passing the given argments ``args``
directly. It means, the output :class:`~chainer.Variable` object ``y`` to
make the computational graph will be created by:

y = model(*args)

Args:
model (~chainer.Chain): The model object you want to export in ONNX
format. It should have :meth:`__call__` method because the second
argment ``args`` is directly given to the model by the ``[]``
accessor.
args (list or dict): The argments which are given to the model
directly.
filename (str or file-like object): The filename used for saving the
resulting ONNX model. If None, nothing is saved to the disk.
export_params (bool): If True, this function exports all the parameters
included in the given model at the same time. If False, the
exported ONNX model doesn't include any parameter values.
graph_name (str): A string to be used for the ``name`` field of the
graph in the exported ONNX model.
save_text (bool): If True, the text format of the output ONNX model is
also saved with ``.txt`` extention.
opset_version (int): The operator set version of ONNX. If not specified
or ``None`` is given, the latest opset version of the onnx module
is used. If an integer is given, it will be ensured that all the
operator version in the exported ONNX file is less than this value.

Returns:
A ONNX model object.
```

## Supported Functions

Currently 51 Chainer Functions are supported to export in ONNX format.

### Activation

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

### Array

- Cast
- Concat
- Copy
- Depth2Space
- Pad ^[1](#pad1)^[2](#pad2)
- Reshape
- Space2Depth
- SplitAxis
- Squeeze
- Tile
- Transpose

### Connection

- Convolution2DFunction
- ConvolutionND
- Deconvolution2DFunction
- DeconvolutionND
- EmbedIDFunction ^[3](#embed1)
- LinearFunction

### Math

- Add
- AddConstant
- Absolute
- Div
- Mul
- Neg
- PowVarConst
- Sub
- Clip
- Exp
- Identity
- MatMul
- Maximum
- Minimum
- Sqrt
- Sum

### Noise

- Dropout ^{[4](#dropout1)}

### Pooling

- AveragePooling2D
- AveragePoolingND
- MaxPooling2D
- MaxPoolingND

### Normalization

- BatchNormalization
- FixedBatchNormalization
- LocalResponseNormalization

---

<a name="pad1">1</a>: mode should be either 'constant', 'reflect', or 'edge'<br />
<a name="pad2">2</a>: ONNX doesn't support multiple constant values for Pad operation<br />
<a name="embed1">3</a>: Current ONNX doesn't support ignore_label for EmbedID<br />
<a name="dropout1">4</a>: In test mode, all dropout layers aren't included in the exported file<br />