| """ |
| 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 |
| |
| 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} |
| 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 = '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]) |