[MXNET-34] Onnx Module to import onnx models into mxnet

Jenkinsfile

@@ -560,7 +560,18 @@ try {
   }
 
   stage('Integration Test') {
-    parallel 'Python GPU': {
+    parallel 'Onnx CPU': {
+      node('mxnetlinux-cpu') {
+        ws('workspace/it-onnx-cpu') {
+          init_git()
+          unpack_lib('cpu')
+          timeout(time: max_time, unit: 'MINUTES') {
+          	sh "ci/build.py --build --platform ubuntu_cpu /work/runtime_functions.sh integrationtest_ubuntu_cpu_onnx"
+          }
+        }
+      }
+    },
+    'Python GPU': {
       node('mxnetlinux-gpu') {
         ws('workspace/it-python-gpu') {
           init_git()

ci/docker/Dockerfile.build.ubuntu_cpu

@@ -42,6 +42,8 @@ COPY install/ubuntu_mklml.sh /work/
 RUN /work/ubuntu_mklml.sh
 COPY install/ubuntu_caffe.sh /work/
 RUN /work/ubuntu_caffe.sh
+COPY install/ubuntu_onnx.sh /work/
+RUN /work/ubuntu_onnx.sh
 COPY install/ubuntu_docs.sh /work/
 RUN /work/ubuntu_docs.sh
 COPY install/ubuntu_adduser.sh /work/

ci/docker/install/ubuntu_onnx.sh

@@ -0,0 +1,34 @@
+#!/usr/bin/env bash
+
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+######################################################################
+# This script installs ONNX for Python along with all required dependencies 
+# on a Ubuntu Machine.
+# Tested on Ubuntu 16.04 distro.
+######################################################################
+
+set -e
+set -x
+
+echo "Installing libprotobuf-dev and protobuf-compiler ..."
+apt-get install -y libprotobuf-dev protobuf-compiler
+
+echo "Installing pytest, pytest-cov, protobuf, Pillow, ONNX and tabulate ..."
+pip2 install pytest==3.4.0 pytest-cov==2.5.1 protobuf==3.0.0 onnx==1.0.1 Pillow==5.0.0 tabulate==0.7.5
+pip3 install pytest==3.4.0 pytest-cov==2.5.1 protobuf==3.0.0 onnx==1.0.1 Pillow==5.0.0 tabulate==0.7.5

ci/docker/runtime_functions.sh

@@ -386,6 +386,14 @@ unittest_centos7_gpu() {
     python3.6 -m "nose" --with-timer --verbose tests/python/gpu
 }
 
+integrationtest_ubuntu_cpu_onnx() { 
+	set -ex
+	export PYTHONPATH=./python/
+	python example/onnx/super_resolution.py
+	pytest tests/python-pytest/onnx/onnx_backend_test.py
+	pytest tests/python-pytest/onnx/onnx_test.py
+}
+
 integrationtest_ubuntu_gpu_python() {
     set -ex
     export PYTHONPATH=./python/

example/onnx/super_resolution.py

@@ -0,0 +1,84 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""Testing super_resolution model conversion"""
+from __future__ import absolute_import as _abs
+from __future__ import print_function
+from collections import namedtuple
+import logging
+import numpy as np
+from PIL import Image
+import mxnet as mx
+from mxnet.test_utils import download
+import mxnet.contrib.onnx as onnx_mxnet
+
+# set up logger
+logging.basicConfig()
+LOGGER = logging.getLogger()
+LOGGER.setLevel(logging.INFO)
+
+def import_onnx():
+    """Import the onnx model into mxnet"""
+    model_url = 'https://s3.amazonaws.com/onnx-mxnet/examples/super_resolution.onnx'
+    download(model_url, 'super_resolution.onnx', version_tag='"7348c879d16c42bc77e24e270f663524"')
+
+    LOGGER.info("Converting onnx format to mxnet's symbol and params...")
+    sym, params = onnx_mxnet.import_model('super_resolution.onnx')
+    LOGGER.info("Successfully Converted onnx format to mxnet's symbol and params...")
+    return sym, params
+
+def get_test_image():
+    """Download and process the test image"""
+    # Load test image
+    input_image_dim = 224
+    img_url = 'https://s3.amazonaws.com/onnx-mxnet/examples/super_res_input.jpg'
+    download(img_url, 'super_res_input.jpg', version_tag='"02c90a7248e51316b11f7f39dd1b226d"')
+    img = Image.open('super_res_input.jpg').resize((input_image_dim, input_image_dim))
+    img_ycbcr = img.convert("YCbCr")
+    img_y, img_cb, img_cr = img_ycbcr.split()
+    input_image = np.array(img_y)[np.newaxis, np.newaxis, :, :]
+    return input_image, img_cb, img_cr
+
+def perform_inference(sym, params, input_img, img_cb, img_cr):
+    """Perform inference on image using mxnet"""
+    # create module
+    mod = mx.mod.Module(symbol=sym, data_names=['input_0'], label_names=None)
+    mod.bind(for_training=False, data_shapes=[('input_0', input_img.shape)])
+    mod.set_params(arg_params=params, aux_params=None)
+
+    # run inference
+    batch = namedtuple('Batch', ['data'])
+    mod.forward(batch([mx.nd.array(input_img)]))
+
+    # Save the result
+    img_out_y = Image.fromarray(np.uint8(mod.get_outputs()[0][0][0].
+                                         asnumpy().clip(0, 255)), mode='L')
+
+    result_img = Image.merge(
+        "YCbCr", [img_out_y,
+                  img_cb.resize(img_out_y.size, Image.BICUBIC),
+                  img_cr.resize(img_out_y.size, Image.BICUBIC)]).convert("RGB")
+    output_img_dim = 672
+    assert result_img.size == (output_img_dim, output_img_dim)
+    LOGGER.info("Super Resolution example success.")
+    result_img.save("super_res_output.jpg")
+    return result_img
+
+if __name__ == '__main__':
+    MX_SYM, MX_PARAM = import_onnx()
+    INPUT_IMG, IMG_CB, IMG_CR = get_test_image()
+    perform_inference(MX_SYM, MX_PARAM, INPUT_IMG, IMG_CB, IMG_CR)

python/mxnet/contrib/__init__.py

@@ -28,5 +28,5 @@
 from . import tensorboard
 
 from . import text
-
+from . import onnx
 from . import io

python/mxnet/contrib/onnx/__init__.py

@@ -0,0 +1,20 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""Module for importing and exporting ONNX models."""
+
+from ._import.import_model import import_model

python/mxnet/contrib/onnx/_import/__init__.py

@@ -0,0 +1,21 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# coding: utf-8
+"""ONNX Import module"""
+from . import import_model
+from . import import_onnx

python/mxnet/contrib/onnx/_import/import_helper.py

@@ -0,0 +1,105 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# coding: utf-8
+# pylint: disable=invalid-name
+"""Operator attributes conversion"""
+from .op_translations import identity, random_uniform, random_normal
+from .op_translations import add, subtract, multiply, divide, absolute, negative, add_n
+from .op_translations import tanh
+from .op_translations import ceil, floor
+from .op_translations import concat
+from .op_translations import leaky_relu, _elu, _prelu, softmax, fully_connected
+from .op_translations import global_avgpooling, global_maxpooling, linalg_gemm
+from .op_translations import sigmoid, pad, relu, matrix_multiplication, batch_norm
+from .op_translations import dropout, local_response_norm, conv, deconv
+from .op_translations import reshape, cast, split, _slice, transpose, squeeze
+from .op_translations import reciprocal, squareroot, power, exponent, _log
+from .op_translations import reduce_max, reduce_mean, reduce_min, reduce_sum
+from .op_translations import reduce_prod, avg_pooling, max_pooling
+from .op_translations import argmax, argmin, maximum, minimum
+
+# convert_map defines maps of ONNX operator names to converter functor(callable)
+# defined in the op_translations module.
+_convert_map = {
+    # Generator Functions
+    'Constant'          : identity,
+    'RandomUniform'     : random_uniform,
+    'RandomNormal'      : random_normal,
+    'RandomUniformLike' : random_uniform,
+    'RandomNormalLike'  : random_normal,
+    # Arithmetic Operators
+    'Add'               : add,
+    'Sub'               : subtract,
+    'Mul'               : multiply,
+    'Div'               : divide,
+    'Abs'               : absolute,
+    'Neg'               : negative,
+    'Sum'               : add_n, #elemwise sum
+    #Hyperbolic functions
+    'Tanh'              : tanh,
+    # Rounding
+    'Ceil'              : ceil,
+    'Floor'             : floor,
+    # Joining and spliting
+    'Concat'            : concat,
+    # Basic neural network functions
+    'Sigmoid'           : sigmoid,
+    'Relu'              : relu,
+    'Pad'               : pad,
+    'MatMul'            : matrix_multiplication, #linalg_gemm2
+    'Conv'              : conv,
+    'ConvTranspose'     : deconv,
+    'BatchNormalization': batch_norm,
+    'SpatialBN'         : batch_norm,
+    'LeakyRelu'         : leaky_relu,
+    'Elu'               : _elu,
+    'PRelu'             : _prelu,
+    'Softmax'           : softmax,
+    'FC'                : fully_connected,
+    'GlobalAveragePool' : global_avgpooling,
+    'GlobalMaxPool'     : global_maxpooling,
+    'Gemm'              : linalg_gemm,
+    'LRN'               : local_response_norm,
+    'Dropout'           : dropout,
+    # Changing shape and type.
+    'Reshape'           : reshape,
+    'Cast'              : cast,
+    'Split'             : split,
+    'Slice'             : _slice,
+    'Transpose'         : transpose,
+    'Squeeze'           : squeeze,
+    #Powers
+    'Reciprocal'        : reciprocal,
+    'Sqrt'              : squareroot,
+    'Pow'               : power,
+    'Exp'               : exponent,
+    'Log'               : _log,
+    # Reduce Functions
+    'ReduceMax'         : reduce_max,
+    'ReduceMean'        : reduce_mean,
+    'ReduceMin'         : reduce_min,
+    'ReduceSum'         : reduce_sum,
+    'ReduceProd'        : reduce_prod,
+    'AveragePool'       : avg_pooling,
+    'MaxPool'           : max_pooling,
+    # Sorting and Searching
+    'ArgMax'            : argmax,
+    'ArgMin'            : argmin,
+    'Max'               : maximum, #elemwise maximum
+    'Min'               : minimum #elemwise minimum
+}

python/mxnet/contrib/onnx/_import/import_model.py

@@ -0,0 +1,46 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# coding: utf-8
+"""import function"""
+# pylint: disable=no-member
+
+from .import_onnx import GraphProto
+
+def import_model(model_file):
+    """Imports the supplied ONNX model file into MXNet symbol and parameters.
+    :parameters model_file
+    ----------
+    model_file : ONNX model file name
+
+    :returns (sym, params)
+    -------
+    sym : mx.symbol
+        Compatible mxnet symbol
+    params : dict of str to mx.ndarray
+        Dict of converted parameters stored in mx.ndarray format
+    """
+    graph = GraphProto()
+
+    # loads model file and returns ONNX protobuf object
+    try:
+        import onnx
+    except ImportError:
+        raise ImportError("Onnx and protobuf need to be installed")
+    model_proto = onnx.load(model_file)
+    sym, params = graph.from_onnx(model_proto.graph)
+    return sym, params