tutorials/frontend/from_coreml.py

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"""
Compile CoreML Models
=====================
**Author**: `Joshua Z. Zhang <https://zhreshold.github.io/>`_, \
            `Kazutaka Morita <https://github.com/kazum>`_, \
            `Zhao Wu <https://github.com/FrozenGene>`_

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

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 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/master/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_runtime

ctx = tvm.cpu(0)
dtype = "float32"
m = graph_runtime.GraphModule(lib["default"](ctx))
# 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.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())
# You should see the following result: Top-1 id 282 class name tiger cat
print("Top-1 id", top1, "class name", synset[top1])