""" 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 ```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 def download(url, path, overwrite=False): import os if os.path.isfile(path) and not overwrite: print('File {} existed, skip.'.format(path)) return print('Downloading from url {} to {}'.format(url, path)) try: import urllib.request urllib.request.urlretrieve(url, path) except: import urllib urllib.urlretrieve(url, path) ###################################################################### # 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' download(model_url, model_file) # now you mobilenet.mlmodel on disk mlmodel = cm.models.MLModel(model_file) # 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' download(img_url, 'cat.png') img = Image.open('cat.png').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} 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 output_shape = (1000,) tvm_output = m.get_output(0, tvm.nd.empty(output_shape, dtype)).asnumpy() top1 = np.argmax(tvm_output) ##################################################################### # Look up synset name # ------------------- # Look up prdiction 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 = 'synset.txt' download(synset_url, synset_name) with open(synset_name) as f: synset = eval(f.read()) print('Top-1 id', top1, 'class name', synset[top1])