def predictor_from_cntk_model(modelFile, plotModel=False):
"""Loads a CNTK model and returns an ell.neural.NeuralNetworkPredictor"""
_logger = logger.get()
_logger.info("Loading...")
z = load_model(modelFile)
_logger.info("\nFinished loading.")
if plotModel:
filename = os.path.join(os.path.dirname(modelFile), os.path.basename(modelFile) + ".svg")
cntk_utilities.plot_model(z, filename)
_logger.info("Pre-processing...")
modelLayers = cntk_utilities.get_model_layers(z)
# Get the relevant CNTK layers that we will convert to ELL
layersToConvert = cntk_layers.get_filtered_layers_list(modelLayers)
_logger.info("\nFinished pre-processing.")
predictor = None
try:
# Create a list of ELL layers from the CNTK layers
ellLayers = cntk_layers.convert_cntk_layers_to_ell_layers(
layersToConvert)
# Create an ELL neural network predictor from the layers
predictor = ell.neural.NeuralNetworkPredictor(ellLayers)
except BaseException as exception:
_logger.error("Error occurred attempting to convert cntk layers to ELL layers: " + str(exception))
raise exception
return predictor
def run(self):
self.report = open("report.md", "w")
self.report.write("# Comparison Results\n")
self.report.write("**model**: %s\n\n" % (self.model_file))
if self.image_file is not None:
self.image = self.load_image(self.image_file)
self.report.write("**image**: %s\n\n" % (self.image_file))
self.cntk_model = cntk.load_model(self.model_file)
modelLayers = cntk_utilities.get_model_layers(self.cntk_model)
# Get the relevant CNTK layers that we will convert to ELL
layersToConvert = cntk_layers.get_filtered_layers_list(modelLayers)
self.logger.info(
"----------------------------------------------------------------------------------"
)
if self.layers:
for layer in layersToConvert:
self.compare_layer(layer)
else:
self.compare_model(layersToConvert)
self.print_top_result()
self.report.close()
