def make_image_input(
model,
input_name,
is_bgr=False,
red_bias=0.0,
blue_bias=0.0,
green_bias=0.0,
gray_bias=0.0,
scale=1.0,
image_format="NHWC",
):
"""
Convert input of type multiarray to type image
Parameters
----------
TODO
Returns
-------
model: MLModel
A coreML MLModel object
Examples
--------
TODO
"""
spec = model.get_spec()
if spec.WhichOneof("Type") not in [
"neuralNetwork",
"neuralNetworkClassifier",
"neuralNetworkRegressor",
]:
raise ValueError(
"Provided model must be of type neuralNetwork, neuralNetworkClassifier or neuralNetworkRegressor"
)
if not isinstance(input_name, list):
input_name = [input_name]
spec_inputs = [i.name for i in spec.description.input]
for name in input_name:
if name not in spec_inputs:
msg = "Provided input_name: {}, is not an existing input to the model"
raise ValueError(msg.format(name))
builder = NeuralNetworkBuilder(spec=spec)
builder.set_pre_processing_parameters(
image_input_names=input_name,
is_bgr=is_bgr,
red_bias=red_bias,
green_bias=green_bias,
blue_bias=blue_bias,
gray_bias=gray_bias,
image_scale=scale,
image_format=image_format,
)
return _get_model(spec)
def make_nn_classifier(
model,
class_labels,
predicted_feature_name=None,
predicted_probabilities_output=None,
):
"""
Convert a model of type "neuralNetwork" to type "neuralNetworkClassifier"
Parameters
----------
TODO
Returns
-------
model: MLModel
A coreML MLModel object
Examples
--------
TODO
"""
spec = model.get_spec()
if spec.WhichOneof("Type") != "neuralNetwork":
raise ValueError('Provided model must be of type "neuralNetwork"')
# convert type to "neuralNetworkClassifier" and copy messages from "neuralNetwork"
nn_spec = _copy.deepcopy(spec.neuralNetwork)
spec.ClearField("neuralNetwork")
for layer in nn_spec.layers:
spec.neuralNetworkClassifier.layers.add().CopyFrom(layer)
for preprocessing in nn_spec.preprocessing:
spec.neuralNetworkClassifier.preprocessing.add().CopyFrom(
preprocessing)
spec.neuralNetworkClassifier.arrayInputShapeMapping = nn_spec.arrayInputShapeMapping
spec.neuralNetworkClassifier.imageInputShapeMapping = nn_spec.imageInputShapeMapping
spec.neuralNetworkClassifier.updateParams.CopyFrom(nn_spec.updateParams)
# set properties related to classifier
builder = NeuralNetworkBuilder(spec=spec)
message = "Class labels must be a list of integers / strings or a file path"
classes_in = class_labels
if isinstance(classes_in, str):
import os
if not os.path.isfile(classes_in):
raise ValueError("Path to class labels (%s) does not exist." %
classes_in)
with open(classes_in, "r") as f:
classes = f.read()
classes = classes.splitlines()
elif isinstance(classes_in, list): # list[int or str]
classes = classes_in
assert all([isinstance(x, \
(int, str)) for x in classes]), message
else:
raise ValueError(message)
kwargs = {}
if predicted_feature_name is not None:
kwargs["predicted_feature_name"] = predicted_feature_name
if predicted_probabilities_output is not None:
kwargs["prediction_blob"] = predicted_probabilities_output
builder.set_class_labels(classes, **kwargs)
return _get_model(spec)