Compile CoreML Models

Author: Joshua Z. Zhang, Kazutaka Morita, Zhao Wu

This article is an introductory tutorial to deploy CoreML models with Relay.

To begin, we must install coremltools:

pip install coremltools

or please refer to official site https://github.com/apple/coremltools

import tvm
from tvm import te
import tvm.relay as relay
from tvm.contrib.download import download_testdata
import coremltools as cm
import numpy as np
from PIL import Image

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 have mobilenet.mlmodel on disk
mlmodel = cm.models.MLModel(model_path)

Load a test image

A single cat dominates the examples!

img_url = "https://github.com/dmlc/mxnet.js/blob/main/data/cat.png?raw=true"
img_path = download_testdata(img_url, "cat.png", module="data")
img = Image.open(img_path).resize((224, 224))
# Mobilenet.mlmodel's input is BGR format
img_bgr = np.array(img)[:, :, ::-1]
x = np.transpose(img_bgr, (2, 0, 1))[np.newaxis, :]

Compile the model on Relay

We should be familiar with the process right now.

target = "llvm"
shape_dict = {"image": x.shape}

# Parse CoreML model and convert into Relay computation graph
mod, params = relay.frontend.from_coreml(mlmodel, shape_dict)

with tvm.transform.PassContext(opt_level=3):
    lib = relay.build(mod, target, params=params)

Execute on TVM

The process is no different from other example

from tvm.contrib import graph_executor

dev = tvm.cpu(0)
dtype = "float32"
m = graph_executor.GraphModule(lib["default"](dev))
# set inputs
m.set_input("image", tvm.nd.array(x.astype(dtype)))
# execute
m.run()
# get outputs
tvm_output = m.get_output(0)
top1 = np.argmax(tvm_output.numpy()[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())
# You should see the following result: Top-1 id 282 class name tiger cat
print("Top-1 id", top1, "class name", synset[top1])