def test_intermediate_to_keras(pmml_file, keras_model):
"""
Test conversion between intermediate form and keras
Compare the loaded PMML file to @keras_model
"""
print("\n--- Test Intermediate to Keras (%s) ---" % pmml_file)
intermediate = DeepNetwork(filename=pmml_file)
new_keras_model = intermediate.get_keras_model()
new_config = new_keras_model.get_config()
old_config = keras_model.get_config()
def convert_torch_to_pmml(torch_model,
output_path,
weights_path,
description,
debug=True):
"""
Convert a torch model to PMML
"""
print("\nGenerating model: %s" % description)
class_map = load_imagenet_classes()
pmml = convert(torch_model, class_map=class_map, description=description)
pmml.save_pmml(output_path, weights_path=weights_path, save_weights=False)
if debug:
print("Checking model: %s" % output_path)
intermediate = DeepNetwork(filename=output_path)
old_config = torch_model.get_config()
new_config = intermediate.get_keras_model().get_config()
directory = os.path.dirname(output_path)
dump_config(old_config, new_config, directory)
test_prediction(output_path, "assets/cat.jpg")