# 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 CoreML Models ===================== **Author**: `Joshua Z. Zhang <https://zhreshold.github.io/>`_ This article is an introductory tutorial to deploy CoreML models with NNVM. For us to begin with, coremltools module is required to be installed. A quick solution is to install via pip .. code-block:: bash pip install -U coremltools --user or please refer to official site https://github.com/apple/coremltools """ import nnvm import tvm import coremltools as cm import numpy as np from PIL import Image from tvm.contrib.download import download_testdata ###################################################################### # Load pretrained CoreML model # ---------------------------- # We will download and load a pretrained mobilenet classification network # provided by apple in this example model_url = 'https://docs-assets.developer.apple.com/coreml/models/MobileNet.mlmodel' model_file = 'mobilenet.mlmodel' model_path = download_testdata(model_url, model_file, module='coreml') # now you mobilenet.mlmodel on disk mlmodel = cm.models.MLModel(model_path) # we can load the graph as NNVM compatible model sym, params = nnvm.frontend.from_coreml(mlmodel) ###################################################################### # 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)) #x = np.transpose(img, (2, 0, 1))[np.newaxis, :] image = np.asarray(img) image = image.transpose((2, 0, 1)) x = image[np.newaxis, :] ###################################################################### # Compile the model on NNVM # --------------------------- # We should be familiar with the process right now. import nnvm.compiler target = 'cuda' shape_dict = {'image': x.shape} with nnvm.compiler.build_config(opt_level=2, add_pass=['AlterOpLayout']): graph, lib, params = nnvm.compiler.build(sym, target, shape_dict, params=params) ###################################################################### # 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 m.set_input('image', tvm.nd.array(x.astype(dtype))) m.set_input(**params) # execute m.run() # get outputs tvm_output = m.get_output(0) top1 = np.argmax(tvm_output.asnumpy()[0]) ##################################################################### # Look up synset name # ------------------- # Look up prediction top 1 index in 1000 class synset. synset_url = ''.join(['https://gist.githubusercontent.com/zhreshold/', '4d0b62f3d01426887599d4f7ede23ee5/raw/', '596b27d23537e5a1b5751d2b0481ef172f58b539/', 'imagenet1000_clsid_to_human.txt']) synset_name = 'imagenet1000_clsid_to_human.txt' synset_path = download_testdata(synset_url, synset_name, module='data') with open(synset_path) as f: synset = eval(f.read()) print('Top-1 id', top1, 'class name', synset[top1])