# 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. """ Compile ONNX Models =================== **Author**: `Joshua Z. Zhang <https://zhreshold.github.io/>`_ This article is an introductory tutorial to deploy ONNX models with Relay. For us to begin with, ONNX package must be installed. A quick solution is to install protobuf compiler, and .. code-block:: bash pip install onnx --user or please refer to offical site. https://github.com/onnx/onnx """ import onnx import numpy as np import tvm import tvm.relay as relay from tvm.contrib.download import download_testdata ###################################################################### # 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/', '93672b029103648953c4e5ad3ac3aadf346a4cdc/', 'super_resolution_0.2.onnx']) model_path = download_testdata(model_url, 'super_resolution.onnx', module='onnx') # now you have super_resolution.onnx on disk onnx_model = onnx.load(model_path) ###################################################################### # Load a test image # --------------------------------------------- # A single cat dominates the examples! from PIL import Image img_url = 'https://github.com/dmlc/mxnet.js/blob/master/data/cat.png?raw=true' img_path = download_testdata(img_url, 'cat.png', module='data') img = Image.open(img_path).resize((224, 224)) 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, :, :] ###################################################################### # Compile the model with relay # --------------------------------------------- target = 'llvm' input_name = '1' shape_dict = {input_name: x.shape} mod, params = relay.frontend.from_onnx(onnx_model, shape_dict) with relay.build_config(opt_level=1): intrp = relay.build_module.create_executor('graph', mod, tvm.cpu(0), target) ###################################################################### # Execute on TVM # --------------------------------------------- dtype = 'float32' tvm_output = intrp.evaluate()(tvm.nd.array(x.astype(dtype)), **params).asnumpy() ###################################################################### # Display results # --------------------------------------------- # We put input and output image neck to neck from matplotlib import pyplot as plt 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)) plt.show()