def predictor_from_cntk_model_using_new_engine(modelFile, plotModel=True):
"""
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)
try:
_logger.info("Pre-processing...")
# Get the relevant nodes from CNTK that make up the model
importer_nodes = cntk_utilities.Utilities.get_model_nodes(z)
_logger.info("\nFinished pre-processing.")
# Create an ImporterModel from the CNTK nodes
importer_model = import_nodes(importer_nodes)
# Use the common importer engine to drive conversion of the
# ImporterModel to ELL layers
importer_engine = common.importer.ImporterEngine()
ell_layers = importer_engine.convert(importer_model)
# Create an ELL neural network predictor from the layers
predictor = ell.neural.NeuralNetworkPredictor(ell_layers)
except BaseException as exception:
_logger.error("Error occurred attempting to convert cntk layers to ELL layers: " + str(exception))
raise exception
return predictor
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 map_from_cntk_model_using_new_engine(modelFile, step_interval_msec=None, lag_threshold_msec=None, plot_model=False,
verify_model={"audio": False, "vision": False}):
"""
Loads a CNTK model and returns an ell.model.Map
"""
_logger = logger.get()
_logger.info("Loading...")
cntk_model = load_model(modelFile)
_logger.info("\nFinished loading.")
if plot_model:
filename = os.path.join(os.path.dirname(modelFile), os.path.basename(modelFile) + ".svg")
cntk_utilities.plot_model(cntk_model, filename)
try:
_logger.info("Pre-processing...")
# Get the relevant nodes from CNTK that make up the model
cntk_nodes = cntk_utilities.Utilities.get_model_nodes(cntk_model)
_logger.info("\nFinished pre-processing.")
# Create an ImporterModel from the CNTK nodes
importer_model = import_nodes(cntk_nodes)
# Use the common importer engine to drive conversion of the
# ImporterModel to ELL layers
importer_engine = common.importer.ImporterEngine(step_interval_msec=step_interval_msec,
lag_threshold_msec=lag_threshold_msec)
ell_map = importer_engine.convert_nodes(importer_model)
except BaseException as exception:
_logger.error("Error occurred attempting to convert cntk layers to ELL model: " + str(exception))
raise exception
if verify_model["audio"]:
_logger.info("Verification of audio models is not supported at this time, skipping verification")
elif verify_model["vision"]:
testing_info = {"apply_softmax": False}
try:
ordered_importer_nodes, node_mapping = importer_engine.get_importer_node_to_ell_mapping()
# RESTORE:
# verify_ell_nodes_in_vision_model(ell_map, cntk_model, cntk_nodes, ordered_importer_nodes, node_mapping,
# testing_info)
verify_compiled_ell_nodes_in_vision_model(modelFile, cntk_model, cntk_nodes, ordered_importer_nodes,
verify_model=verify_model)
verify_ell_output_in_vision_model(ell_map, cntk_model, testing_info)
except BaseException as exception:
_logger.error("Error occurred verifying imported model")
basename, ext = os.path.splitext(modelFile)
ell_map.Save(basename + ".ell.verification_failed")
raise exception
return ell_map
