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5ca40aa Dec 11, 2017
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import sys, os
import numpy as np
import keras
from keras.models import *
from keras.layers import *
from keras.preprocessing.image import load_img, img_to_array
from keras import backend as K
import coremltools
from coremltools.proto import NeuralNetwork_pb2
# This is a custom activation function.
def swish(x):
return K.sigmoid(x) * x
# Create a silly model that has our custom activation function as a new layer.
def create_model():
inp = Input(shape=(256, 256, 3))
x = Conv2D(6, (3, 3), padding="same")(inp)
#x = Activation(swish)(x) # doesn't work! :-(
x = Lambda(swish)(x)
x = GlobalAveragePooling2D()(x)
x = Dense(10, activation="softmax")(x)
return Model(inp, x)
# Build the model.
model = create_model()
model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
model.summary()
# Here is where you would train the model... To keep things simple, we don't
# actually do any training but give the model a fixed set of random weights.
# These weights do not mean anything; they're just here to test that Core ML
# gives the same output as the Keras model.
W = model.get_weights()
np.random.seed(12345)
for i in range(len(W)):
W[i] = np.random.randn(*(W[i].shape)) * 2 - 1
model.set_weights(W)
# Test the model with an image. We'll do the same thing with Core ML in the
# iOS app and it should give the same output for the same input.
img = load_img("floortje.png", target_size=(256, 256))
img = np.expand_dims(img_to_array(img), 0)
pred = model.predict(img)
print("Predicted output:")
print(pred)
# The conversion function for Lambda layers.
def convert_lambda(layer):
# Only convert this Lambda layer if it is for our swish function.
if layer.function == swish:
params = NeuralNetwork_pb2.CustomLayerParams()
# The name of the Swift or Obj-C class that implements this layer.
params.className = "Swish"
# The desciption is shown in Xcode's mlmodel viewer.
params.description = "A fancy new activation function"
# Set configuration parameters
# params.parameters["someNumber"].intValue = 100
# params.parameters["someString"].stringValue = "Hello, world!"
# Add some random weights
# my_weights = params.weights.add()
# my_weights.floatValue.extend(np.random.randn(10).astype(float))
return params
else:
return None
print("\nConverting the model:")
# Convert the model to Core ML.
coreml_model = coremltools.converters.keras.convert(
model,
input_names="image",
image_input_names="image",
output_names="output",
add_custom_layers=True,
custom_conversion_functions={ "Lambda": convert_lambda })
# This is the alternative method of filling in the CustomLayerParams:
# grab the layer and change its properties directly.
#layer = coreml_model._spec.neuralNetwork.layers[1]
#layer.custom.className = "Swish"
# Look at the layers in the converted Core ML model.
print("\nLayers in the converted model:")
for i, layer in enumerate(coreml_model._spec.neuralNetwork.layers):
if layer.HasField("custom"):
print("Layer %d = %s --> custom layer = %s" % (i, layer.name, layer.custom.className))
else:
print("Layer %d = %s" % (i, layer.name))
# Fill in the metadata and save the model.
coreml_model.author = "AuthorMcAuthorName"
coreml_model.license = "Public Domain"
coreml_model.short_description = "Playing with custom Core ML layers"
coreml_model.input_description["image"] = "Input image"
coreml_model.output_description["output"] = "The predictions"
coreml_model.save("NeuralMcNeuralNet.mlmodel")