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 compare_model(self, layers):
ellLayers = cntk_layers.convert_cntk_layers_to_ell_layers(layers)
# Create an ELL neural network predictor from the layers
predictor = ell.neural.NeuralNetworkPredictor(ellLayers)
shape = predictor.GetInputShape()
self.input_shape = (shape.channels, shape.rows, shape.columns
) # to CNTK (channel, rows, columns) order
self.data = self.get_input_data()
if (len(self.cntk_model.arguments) > 1):
output = np.zeros(self.cntk_model.arguments[1].shape).astype(
np.float32)
predictions = self.cntk_model.eval({
self.cntk_model.arguments[0]: [self.data],
self.cntk_model.arguments[1]:
output
})
else:
predictions = self.cntk_model.eval(
{self.cntk_model.arguments[0]: [self.data.ravel()]})
size = 0
output = None
if isinstance(predictions, dict):
for key in self.cntk_model.outputs:
shape = key.shape
if len(shape) > 0:
s = np.max(shape)
if (s > size):
size = s
output = predictions[key][0] / 100
else:
output = predictions[0]
self.verify_ell("Softmax", predictor, self.data, output)
self.data = output # make this the input to the next layer.
self.save_layer_outputs("Softmax")