[v1.x] ONNX example for CV model inference

example/onnx/README.md

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+
+# MXNet ONNX Export Examples
+
+This folder contains examples that use mx2onnx module to export MXNet models to ONNX format.
+
+Please refer to [this link](https://github.com/apache/incubator-mxnet/tree/v1.x/python/mxnet/onnx#onnx-export-support-for-mxnet)
+for more details.
+
+- cv_model_inference.py.
+    - This is an end-to-end exapmle of onnx export and inference for CV models.
+    - It downloads a pretrained CV model, exports the model to ONNX format, prepares input images and performs inference with ONNXRuntime.
+
+

example/onnx/cv_model_inference.py

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+# 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.
+
+import os
+import json
+import urllib.request
+import mxnet as mx
+import numpy as np
+import gluoncv
+import onnxruntime
+from urllib.parse import urlparse
+from mxnet.gluon.data.vision import transforms
+
+def preprocess_image(imgfile, resize_short=256, crop_size=224,
+                   mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)):
+    # load image
+    img_data = mx.image.imread(imgfile).astype('float32')
+    # normalization and standerdization
+    transform_fn = transforms.Compose([
+        transforms.Resize(resize_short, keep_ratio=True),
+        transforms.CenterCrop(crop_size),
+        transforms.ToTensor(),
+        transforms.Normalize(mean, std)
+    ])
+    # expand batch dimension
+    res = transform_fn(img_data).expand_dims(0)
+    # convert mx ndarray to np ndarray for onnxruntime
+    res = res.asnumpy()
+    return res
+
+# molde path prefix
+prefix = './resnet50_v2'
+# input shape and type
+in_shape = (1, 3, 224, 224)
+in_dtype = 'float32'
+# download model
+gluon_model = gluoncv.model_zoo.get_model('resnet50_v2', pretrained=True)
+gluon_model.hybridize()
+# forward with dummy input and save model
+dummy_input = mx.nd.zeros(in_shape, dtype=in_dtype)
+gluon_model.forward(dummy_input)
+gluon_model.export(prefix, 0)
+
+# mxnet model symbol file
+mx_sym = prefix + '-symbol.json'
+# mxnet model params file
+mx_params = prefix + '-0000.params'
+# onnx model file that will be exported
+onnx_file = prefix + '.onnx'
+# list of shape for all inputs
+in_shapes = [in_shape]
+# list of data type for all inputs
+in_dtypes = [in_dtype]
+# export onnx model
+mx.onnx.export_model(mx_sym, mx_params, in_shapes, in_dtypes, onnx_file)
+
+# # example for dynamic input shape (optional)
+# # None indicating dynamic shape at a certain dimension
+# dynamic_input_shapes = [((None, 3, 224, 224))]
+# mx.onnx.export_model(mx_sym, mx_params, in_shapes, in_dtypes, onnx_file,
+#                      dynamic=True, dynamic_input_shapes=dynamic_input_shapes)
+
+# download and process the input image
+img_dir = './images'
+img_url = 'https://github.com/apache/incubator-mxnet-ci/raw/master/test-data/images/car.jpg'
+fname = os.path.join(img_dir, os.path.basename(urlparse(img_url).path))
+mx.test_utils.download(img_url, fname=fname)
+img_data = preprocess_image(fname)
+
+# create onnxruntime session using the onnx model file
+ses_opt = onnxruntime.SessionOptions()
+ses_opt.log_severity_level = 3
+session = onnxruntime.InferenceSession(onnx_file, ses_opt)
+input_name = session.get_inputs()[0].name
+
+# run onnx inference
+onnx_result = session.run([], {input_name: img_data})[0]
+idx = np.argmax(onnx_result, axis=1).astype('int')[0]
+
+# post processing: map class index to class name
+url = 'https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json'
+urllib.request.urlretrieve(url, './imagenet_class_index.json')
+class_idx = json.load(open('imagenet_class_index.json'))
+idx2label = [class_idx[str(k)][1] for k in range(len(class_idx))]
+print(idx2label[idx])