from_onnx.py 4.28 KB
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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.
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"""
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Compile ONNX Models
===================
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**Author**: `Joshua Z. Zhang <https://zhreshold.github.io/>`_

This article is an introductory tutorial to deploy ONNX models with NNVM.

For us to begin with, onnx module is required to be installed.

A quick solution is to install protobuf compiler, and
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.. code-block:: bash

    pip install onnx --user

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or please refer to offical site.
https://github.com/onnx/onnx
"""
import nnvm
import tvm
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from tvm.contrib.download import download_testdata
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import onnx
import numpy as np

######################################################################
# Load pretrained ONNX model
# ---------------------------------------------
# The example super resolution model used here is exactly the same model in onnx tutorial
# http://pytorch.org/tutorials/advanced/super_resolution_with_caffe2.html
# we skip the pytorch model construction part, and download the saved onnx model
model_url = ''.join(['https://gist.github.com/zhreshold/',
                     'bcda4716699ac97ea44f791c24310193/raw/',
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                     '93672b029103648953c4e5ad3ac3aadf346a4cdc/',
                     'super_resolution_0.2.onnx'])
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model_path = download_testdata(model_url, 'super_resolution.onnx', module='onnx')
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# now you have super_resolution.onnx on disk
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onnx_model = onnx.load_model(model_path)
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# we can load the graph as NNVM compatible model
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sym, params = nnvm.frontend.from_onnx(onnx_model)
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######################################################################
# Load a test image
# ---------------------------------------------
# A single cat dominates the examples!
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from PIL import Image
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img_url = 'https://github.com/dmlc/mxnet.js/blob/master/data/cat.png?raw=true'
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img_path = download_testdata(img_url, 'cat.png', module='data')
img = Image.open(img_path).resize((224, 224))
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img_ycbcr = img.convert("YCbCr")  # convert to YCbCr
img_y, img_cb, img_cr = img_ycbcr.split()
x = np.array(img_y)[np.newaxis, np.newaxis, :, :]
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######################################################################
# Compile the model on NNVM
# ---------------------------------------------
# We should be familiar with the process right now.
import nnvm.compiler
target = 'cuda'
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# assume first input name is data
input_name = sym.list_input_names()[0]
shape_dict = {input_name: x.shape}
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with nnvm.compiler.build_config(opt_level=3):
    graph, lib, params = nnvm.compiler.build(sym, target, shape_dict, params=params)
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######################################################################
# Execute on TVM
# ---------------------------------------------
# The process is no different from other example
from tvm.contrib import graph_runtime
ctx = tvm.gpu(0)
dtype = 'float32'
m = graph_runtime.create(graph, lib, ctx)
# set inputs
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m.set_input(input_name, tvm.nd.array(x.astype(dtype)))
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m.set_input(**params)
# execute
m.run()
# get outputs
output_shape = (1, 1, 672, 672)
tvm_output = m.get_output(0, tvm.nd.empty(output_shape, dtype)).asnumpy()

######################################################################
# Display results
# ---------------------------------------------
# We put input and output image neck to neck
from matplotlib import pyplot as plt
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out_y = Image.fromarray(np.uint8((tvm_output[0, 0]).clip(0, 255)), mode='L')
out_cb = img_cb.resize(out_y.size, Image.BICUBIC)
out_cr = img_cr.resize(out_y.size, Image.BICUBIC)
result = Image.merge('YCbCr', [out_y, out_cb, out_cr]).convert('RGB')
canvas = np.full((672, 672*2, 3), 255)
canvas[0:224, 0:224, :] = np.asarray(img)
canvas[:, 672:, :] = np.asarray(result)
plt.imshow(canvas.astype(np.uint8))
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plt.show()