keras -> mlmodel: coreml object has no attribute 'convert' - keras

Question

I am trying to convert my keras model into mlmodel using coreml. However, it is saying that coremltools module has no attribute 'convert'.
AttributeError: 'module' object has no attribute 'convert'
My coremltools, keras, tensorflow(tensorflow-gpu) modules are all up to date.
I am also using python 2.7.10.
I've used windows and mac, in which, neither worked. However, caffe.convert is working using a caffe model.
Code:
coreml_model = coremltools.converters.keras.convert(MODEL_PATH)
As per the documentation, I expected the converters.keras.convert method to be available in coremltools.
Documentation: https://apple.github.io/coremltools/generated/coremltools.converters.keras.convert.html
Please help, thanks in advance!
Edit:
import coremltools
# from keras.models import load_model
import keras
import sys
from keras.applications import MobileNet
from keras.utils.generic_utils import CustomObjectScope
with CustomObjectScope({'relu6': keras.applications.MobileNet.relu6, 'DepthwiseConv2D': keras.applications.mobilenet.DepthwiseConv2D}):
model = load_model('weights.hdf5')
MODEL_PATH = "data/model_wide_cifar-10_fruits_model.h5"
def main():
""" Takes in keras model and convert to .mlmodel"""
print(sys.version)
# Load in keras model.
# model = load_model(MODEL_PATH)
# load labels
labels=[]
label_handler = open("fruit-labels.txt", 'r')
for label in label_handler:
labels.append(label.rstrip())
label_handler.close()
print("[INFO] Labels: {0}".format(labels))
# Convert to .mlmodel
coreml_model = coremltools.converters.keras.convert(
model=MODEL_PATH,
input_names="image",
output_names="image",
class_labels=labels)
labels = 'fruit-labels.txt'
# Save .mlmodel
coreml_model.utils.save_spec('fruitclassifier.mlmodel')

Answer 1

The solution is to use virtualenv. Follow the instructions from the coremltools README:
Installation
We recommend using virtualenv to use, install, or build coremltools. Be
sure to install virtualenv using your system pip.
pip install virtualenv
The method for installing coremltools follows the
standard python package installation steps.
To create a Python virtual environment called pythonenv follow these steps:
# Create a folder for virtualenv
mkdir virtualenvs
cd virtualenvs
# Create a Python virtual environment for your Core ML project
virtualenv coremltools
To activate your new virtual environment and install coremltools in this environment, follow these steps:
# Active your virtual environment
source coremltools/bin/activate
# Install coremltools in the new virtual environment, pythonenv
pip install --upgrade pip
pip install -U coremltools==3.0b5
Install keras and tensorflow
pip install keras tensorflow
Now make sure it works. With the coremltools environment activated, run
>>> python
Python 3.7.4 (v3.7.4:e09359112e, Sep 5 2019, 14:54:52)
>>> import coremltools
>>> coremltools.converters.keras.convert()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: convert() missing 1 required positional argument: 'model'
coremltools documentation
Credit to this guys issue: https://github.com/apple/coremltools/issues/440

Answer 2

Communist Hacker's answer does not work for my current setup:
tensorflow 2.4.1
coremltools 4.1
Python 3.8.7
However, after reviewing the documentation for coremltools here, I was able to fix it by removing keras from the function and the call now works:
import coremltools
coreml_model = coremltools.converters.convert(model,
input_names="inputname",
output_names="outputname")
Running the above command now produces this in my Jupyter notebook:
Running TensorFlow Graph Passes: 100%|██████████| 5/5 [00:00<00:00, 37.53 passes/s]
Converting Frontend ==> MIL Ops: 100%|██████████| 6/6 [00:00<00:00, 5764.05 ops/s]
Running MIL optimization passes: 100%|██████████| 17/17 [00:00<00:00, 5633.05 passes/s]
Translating MIL ==> MLModel Ops: 100%|██████████| 3/3 [00:00<00:00, 6864.65 ops/s]
Note - I am a complete noob with this, so I probably described things
incorrectly.