Convert MobileNet from Keras to CoreML

Question

I'm using Keras 2.1.3 and I want to convert MobileNet to CoreML:

from keras.applications import MobileNet
from keras.applications.mobilenet import relu6
from keras.applications.mobilenet import DepthwiseConv2D

import coremltools.converters.keras as k

def save_model():
    model = MobileNet(input_shape=(128,128,3), include_top=False)
    model.save('temp.h5')

def convert():
    model = k.convert('temp.h5',
                      input_names=['input'],
                      output_names=['output'],
                      model_precision='float16',
                      custom_conversion_functions={'relu6': relu6, 'DepthwiseConv2D': DepthwiseConv2D})
    model.save('temp.model')

save_model()
convert()

This gives an error: ValueError: Unknown activation function:relu6

Inure

Answer 1

Here is solution based on https://github.com/apple/coremltools/issues/38

from keras.applications import MobileNet
import keras

import coremltools.converters.keras as k
from keras.utils.generic_utils import CustomObjectScope

def save_model():
    model = MobileNet(input_shape=(128,128,3), include_top=False)
    model.save('temp.h5')

def convert():
    with CustomObjectScope({'relu6': keras.applications.mobilenet.relu6,
                            'DepthwiseConv2D': keras.applications.mobilenet.DepthwiseConv2D}):
        model = k.convert("temp.h5",
                          input_names=['input'],
                          output_names=['output'],
                          model_precision='float16')

    model.save('temp.mlmodel')

save_model()
convert()

Inure

Answer 2

for Keras 2.2.4 and Tensorflow 1.12.0 I found a solution.

Save model weights & architecture like:

model_json = model.to_json()
open('architecture.json', 'w').write(model_json)
model.save_weights('weights.h5', overwrite=True)

And for converting a model to CoreML .mlmodel I use:

import coremltools

from keras.layers import DepthwiseConv2D, ReLU
from pathlib import Path
from keras.models import model_from_json
from tensorflow.python.keras.utils.generic_utils import CustomObjectScope

model_architecture = './Networks/architecture.json'
model_weights = './Networks/weights.h5'

model_structure = Path(model_architecture).read_text()

with CustomObjectScope({'relu6': ReLU ,'DepthwiseConv2D': DepthwiseConv2D}):
    model = model_from_json(model_structure)
    model.load_weights(model_weights)

    output_labels = ['0', '1', '2', '3', '4', '5', '6']
    coreml_model = coremltools.converters.keras.convert(
        model, input_names=['image'], output_names=['output'],
        class_labels=output_labels, image_input_names='image')

    coreml_model.save('ModelX.mlmodel')

Zoba