def test_model_api(self):
model = MLModel(self.spec)
self.assertIsNotNone(model)
model.author = 'Test author'
self.assertEquals(model.author, 'Test author')
self.assertEquals(model.get_spec().description.metadata.author, 'Test author')
model.license = 'Test license'
self.assertEquals(model.license, 'Test license')
self.assertEquals(model.get_spec().description.metadata.license, 'Test license')
model.short_description = 'Test model'
self.assertEquals(model.short_description, 'Test model')
self.assertEquals(model.get_spec().description.metadata.shortDescription, 'Test model')
model.input_description['feature_1'] = 'This is feature 1'
self.assertEquals(model.input_description['feature_1'], 'This is feature 1')
model.output_description['output'] = 'This is output'
self.assertEquals(model.output_description['output'], 'This is output')
filename = tempfile.mktemp(suffix = '.mlmodel')
model.save(filename)
loaded_model = MLModel(filename)
self.assertEquals(model.author, 'Test author')
self.assertEquals(model.license, 'Test license')
# self.assertEquals(model.short_description, 'Test model')
self.assertEquals(model.input_description['feature_1'], 'This is feature 1')
self.assertEquals(model.output_description['output'], 'This is output')
def test_model_api(self):
model = MLModel(self.spec)
self.assertIsNotNone(model)
model.author = "Test author"
self.assertEqual(model.author, "Test author")
self.assertEqual(model.get_spec().description.metadata.author,
"Test author")
model.license = "Test license"
self.assertEqual(model.license, "Test license")
self.assertEqual(model.get_spec().description.metadata.license,
"Test license")
model.short_description = "Test model"
self.assertEqual(model.short_description, "Test model")
self.assertEqual(
model.get_spec().description.metadata.shortDescription,
"Test model")
model.version = "1.3"
self.assertEqual(model.version, "1.3")
self.assertEqual(model.get_spec().description.metadata.versionString,
"1.3")
model.input_description["feature_1"] = "This is feature 1"
self.assertEqual(model.input_description["feature_1"],
"This is feature 1")
model.output_description["output"] = "This is output"
self.assertEqual(model.output_description["output"], "This is output")
package = tempfile.TemporaryDirectory(suffix=".mlpackage")
package.cleanup()
model.save(package.name)
loaded_model = MLModel(package.name)
self.assertEqual(model.author, "Test author")
self.assertEqual(model.license, "Test license")
self.assertEqual(model.short_description, "Test model")
self.assertEqual(model.input_description["feature_1"],
"This is feature 1")
self.assertEqual(model.output_description["output"], "This is output")
# cleanup
MLModelTest._remove_path(package.name)
def test_model_api(self):
model = MLModel(self.spec)
self.assertIsNotNone(model)
model.author = "Test author"
self.assertEqual(model.author, "Test author")
self.assertEqual(model.get_spec().description.metadata.author,
"Test author")
model.license = "Test license"
self.assertEqual(model.license, "Test license")
self.assertEqual(model.get_spec().description.metadata.license,
"Test license")
model.short_description = "Test model"
self.assertEqual(model.short_description, "Test model")
self.assertEqual(
model.get_spec().description.metadata.shortDescription,
"Test model")
model.version = "1.3"
self.assertEqual(model.version, "1.3")
self.assertEqual(model.get_spec().description.metadata.versionString,
"1.3")
model.input_description["feature_1"] = "This is feature 1"
self.assertEqual(model.input_description["feature_1"],
"This is feature 1")
model.output_description["output"] = "This is output"
self.assertEqual(model.output_description["output"], "This is output")
filename = tempfile.mktemp(suffix=".mlmodel")
model.save(filename)
loaded_model = MLModel(filename)
self.assertEqual(model.author, "Test author")
self.assertEqual(model.license, "Test license")
# self.assertEqual(model.short_description, 'Test model')
self.assertEqual(model.input_description["feature_1"],
"This is feature 1")
self.assertEqual(model.output_description["output"], "This is output")
def test_model_api(self):
model = MLModel(self.spec)
assert model is not None
model.author = "Test author"
assert model.author == "Test author"
assert model.get_spec().description.metadata.author == "Test author"
model.license = "Test license"
assert model.license == "Test license"
assert model.get_spec().description.metadata.license == "Test license"
model.short_description = "Test model"
assert model.short_description == "Test model"
assert model.get_spec(
).description.metadata.shortDescription == "Test model"
model.version = "1.3"
assert model.version == "1.3"
assert model.get_spec().description.metadata.versionString == "1.3"
model.input_description["feature_1"] = "This is feature 1"
assert model.input_description["feature_1"] == "This is feature 1"
model.output_description["output"] = "This is output"
assert model.output_description["output"] == "This is output"
package = tempfile.TemporaryDirectory(suffix=".mlpackage")
package.cleanup()
model.save(package.name)
loaded_model = MLModel(package.name)
assert model.author == "Test author"
assert model.license == "Test license"
assert model.short_description == "Test model"
assert model.input_description["feature_1"] == "This is feature 1"
assert model.output_description["output"] == "This is output"
# cleanup
_remove_path(package.name)
def convert_keras_to_mlmodel(keras_model_path, coreml_model_path):
import importlib.machinery as imm
from coremltools.converters.keras._keras_converter import convertToSpec
from coremltools.models import MLModel, _MLMODEL_FULL_PRECISION, _MLMODEL_HALF_PRECISION
# from coremltools.models.utils import convert_double_to_float_multiarray_type
from keras.models import load_model
from kerassurgeon.operations import delete_layer
sys.path.append(os.path.dirname(sys.argv[4]))
# Import neural network code
NN_file_name = os.path.splitext(os.path.basename(sys.argv[3]))[0]
NN = imm.SourceFileLoader(NN_file_name, sys.argv[3]).load_module()
try:
NN_model_name = NN.Model_Name()
except:
NN_model_name = NN_file_name
try:
NN_model_description = NN.Model_Description()
except:
NN_model_description = None
# Load custom layers if implemented in each Keras model
# Take care the imported NN may not have Custom_Layers() def, so try and catch except.
# The type is a dictionary. The keys are supposed to be same as the corresponding values (=defs).
try:
NN_custom_layers = NN.Custom_Layers()
except:
NN_custom_layers = {}
# Import Train.py to get custom loss and metrics
Train_name = os.path.splitext(os.path.basename(sys.argv[4]))[0]
Train_py = imm.SourceFileLoader(Train_name, sys.argv[4]).load_module()
custom_loss = Train_py.get_loss()
custom_metrics = Train_py.get_metrics()
kpt, kex = os.path.splitext(keras_model_path)
keras_model_path_temp = kpt + '_temp' + kex
print('----------------------------------------------------------')
print('NN model file path: {}'.format(sys.argv[3]))
print('NN model name: {}'.format(NN_model_name))
print('NN model description: {}'.format(NN_model_description))
print('NN custom layers:')
print(NN_custom_layers)
print('Training file path and loss/metrics used:')
print(sys.argv[4])
print(custom_loss)
print(custom_metrics)
print('----------------------------------------------------------')
print('Keras model file: {}'.format(keras_model_path))
print('Keras model file temp: {}'.format(keras_model_path_temp))
print('CoreML model file: {}'.format(coreml_model_path))
print('----------------------------------------------------------')
print('Keras custom layers implemented in AIAS for this code:')
for k in conversion_func_in_AIAS:
print(k)
# Deleting Dropout layers from the Keras model to be converted
# Because the layers will cause unknown conversion failures in coremltools
keras_model = load_model(keras_model_path,
custom_objects=dict(**custom_loss,
**custom_metrics,
**NN_custom_layers),
compile=False)
print('----------------------------------------------------------')
keras_model.summary()
del_prefixs = [
'gaussian_dropout', 'gaussian_noise', 'dropout', 'spatial_dropout2d'
] # Add here to define the layer to be deleted
for del_prefix in del_prefixs:
idp = 1
while True:
try:
layer = keras_model.get_layer('{}_{}'.format(del_prefix, idp))
except:
break
print('Deleting layer: {}_{}'.format(del_prefix, idp))
keras_model = delete_layer(model=keras_model,
layer=layer,
copy=False)
idp += 1
keras_model.summary()
print('Saving temporary Keras model: {}'.format(keras_model_path_temp))
keras_model.save(keras_model_path_temp)
# Construct custom layers and conversion functions
custom_layers = {}
custom_conversion_func = {}
print('----------------------------------------------------------')
if NN_custom_layers is not None:
print('Custom layers in this Keras model:')
for keras_layer_key in NN_custom_layers:
if keras_layer_key in conversion_func_in_AIAS:
print(keras_layer_key + ' - available')
custom_layers[keras_layer_key] = NN_custom_layers[
keras_layer_key]
custom_conversion_func[
keras_layer_key] = conversion_func_in_AIAS[keras_layer_key]
else:
print(keras_layer_key + ' - unavailable')
print('Matched layers and conversion functions for coremltools:')
print(custom_layers)
print(custom_conversion_func)
else:
print('Custom layers not found in this Keras model.')
custom_objects = dict(**custom_loss, **custom_metrics, **custom_layers)
print('----------------------------------------------------------')
print('Custom objects passed into coremltools converter:')
print(custom_objects)
print('----------------------------------------------------------')
# Convert
# Do not change the input_names/output_names because they are used to identify input/output layers in Keras code
spec = convertToSpec(keras_model_path_temp,
input_names='input',
output_names='output',
add_custom_layers=True,
custom_conversion_functions=custom_conversion_func,
custom_objects=custom_objects,
respect_trainable=False) # should be True???
model = MLModel(spec)
# Set descriptions
model.author = 'Takashi Shirakawa'
model.license = '(C) 2019-2020, Takashi Shirakawa. All right reserved.'
model.short_description = NN_model_name + ' for A.I.Segmentation'
model.input_description[
'input'] = 'Input is a square image with 8-bit grayscale per pixel.'
model.output_description[
'output'] = 'Output (segmentation) is supposed to be an image with the same dimension and format.'
# Save mlmodel
model.save(coreml_model_path)
# spec_f = model.get_spec()
# convert_double_to_float_multiarray_type(spec_f)
# model_f = MLModel(spec_f)
# model_f.save(os.path.splitext(coreml_model_path)[0] + ', float_multiarray.mlmodel')
# Show results
spec = model.get_spec()
print('----------------------------------------------------------')
print('Model descriptions:')
print(spec.description)
# print('Model descriptions (float multiarray type):')
# print(spec_f.description)
print('Custom layers:')
for i, layer in enumerate(spec.neuralNetwork.layers):
if layer.HasField('custom'):
print('Layer %d = %s : class name = %s' %
(i + 1, layer.name, layer.custom.className))
# else:
# print('Layer %d = %s' % (i, layer.name))
print('Done.')
