def process(self, ellLayers):
"""Appends the ELL equivalent of the current layer to ellLayers."""
# Note that a single CNTK Dense function block is equivalent to the following 3 ELL layers:
# - FullyConnectedLayer
# - BiasLayer
# - ActivationLayer. This layer is sometimes missing, depending on activation type.
#
# Therefore, make sure the output padding characteristics of the last layer reflect the next layer's
# padding requirements.
weightsParameter = utilities.find_parameter_by_name(
self.layer.parameters, 'W', 0)
biasParameter = utilities.find_parameter_by_name(
self.layer.parameters, 'b', 1)
weightsTensor = converters.get_tensor_from_cntk_dense_weight_parameter(
weightsParameter)
biasVector = converters.get_vector_from_cntk_trainable_parameter(
biasParameter)
# Create the ell.neural.LayerParameters for the various ELL layers
firstLayerParameters = ell.neural.LayerParameters(
self.layer.ell_inputShape, self.layer.ell_inputPaddingParameters, self.layer.ell_outputShapeMinusPadding,
ell.neural.NoPadding(), ell.nodes.PortType.smallReal)
middleLayerParameters = ell.neural.LayerParameters(
self.layer.ell_outputShapeMinusPadding, ell.neural.NoPadding(),
self.layer.ell_outputShapeMinusPadding, ell.neural.NoPadding(), ell.nodes.PortType.smallReal)
lastLayerParameters = ell.neural.LayerParameters(
self.layer.ell_outputShapeMinusPadding, ell.neural.NoPadding(),
self.layer.ell_outputShape, self.layer.ell_outputPaddingParameters, ell.nodes.PortType.smallReal)
layerParameters = firstLayerParameters
internalNodes = utilities.get_model_layers(self.layer.block_root)
activationType = utilities.get_ell_activation_type(internalNodes)
# Create the ELL fully connected layer
ellLayers.append(ell.neural.FullyConnectedLayer(
layerParameters, weightsTensor))
# Create the ELL bias layer
if (utilities.is_softmax_activation(internalNodes) or activationType is not None):
layerParameters = middleLayerParameters
else:
layerParameters = lastLayerParameters
ellLayers.append(ell.neural.BiasLayer(layerParameters, biasVector))
# Create the ELL activation layer
if (utilities.is_softmax_activation(internalNodes) or activationType is not None):
layerParameters = lastLayerParameters
# Special case: if this is softmax activation, create an ELL Softmax layer.
# Else, insert an ELL ActivationLayer
if (utilities.is_softmax_activation(internalNodes)):
ellLayers.append(ell.neural.SoftmaxLayer(layerParameters))
else:
if (activationType is not None):
ellLayers.append(ell.neural.ActivationLayer(
layerParameters, activationType))
def process(self, ellLayers):
"""Appends the ELL representation of the current layer to ellLayers."""
# Note that a single CNTK Batch Normalization layer is equivalent to the following 3 ELL layers:
# - BatchNormalizationLayer
# - ScalingLayer
# - BiasLayer
#
# Therefore, make sure the output padding characteristics of the last layer reflect the next layer's
# padding requirements.
scaleVector = converters.get_vector_from_cntk_trainable_parameter(
self.scale)
biasVector = converters.get_vector_from_cntk_trainable_parameter(
self.bias)
meanVector = converters.get_vector_from_cntk_trainable_parameter(
self.mean)
varianceVector = converters.get_vector_from_cntk_trainable_parameter(
self.variance)
# Create the ell.neural.LayerParameters for the various ELL layers
firstLayerParameters = ell.neural.LayerParameters(
self.layer.ell_inputShape, self.layer.ell_inputPaddingParameters,
self.layer.ell_outputShapeMinusPadding, ell.neural.NoPadding(),
ell.nodes.PortType.smallReal)
middleLayerParameters = ell.neural.LayerParameters(
self.layer.ell_outputShapeMinusPadding,
ell.neural.NoPadding(), self.layer.ell_outputShapeMinusPadding,
ell.neural.NoPadding(), ell.nodes.PortType.smallReal)
lastLayerParameters = ell.neural.LayerParameters(
self.layer.ell_outputShapeMinusPadding, ell.neural.NoPadding(),
self.layer.ell_outputShape, self.layer.ell_outputPaddingParameters,
ell.nodes.PortType.smallReal)
# Create the layers
ellLayers.append(
ell.neural.BatchNormalizationLayer(
firstLayerParameters, meanVector, varianceVector, self.epsilon,
ell.neural.EpsilonSummand.variance))
ellLayers.append(
ell.neural.ScalingLayer(middleLayerParameters, scaleVector))
ellLayers.append(ell.neural.BiasLayer(lastLayerParameters, biasVector))
def process(self, ellLayers):
"""Appends the ELL representation of the current layer to ellLayers."""
biasVector = converters.get_vector_from_cntk_trainable_parameter(
self.layer.parameters[0])
# Create the ell.neural.LayerParameters for the ELL layer
layerParameters = ell.neural.LayerParameters(
self.layer.ell_inputShape, self.layer.ell_inputPaddingParameters, self.layer.ell_outputShape,
self.layer.ell_outputPaddingParameters, ell.nodes.PortType.smallReal)
# Create the ELL bias layer
ellLayers.append(ell.neural.BiasLayer(layerParameters, biasVector))
def process(self, ellLayers):
"""Helper to convert a convolutional layer to the ELL equivalent."""
# Note that a single CNTK Convolutional function block is equivalent to the following 3 ELL layers:
# - ConvolutionalLayer
# - BiasLayer. This layer is sometimes missing, depending on whether bias is included.
# - ActivationLayer. This layer is sometimes missing, depending on activation type.
#
# Therefore, make sure the output padding characteristics of the last layer reflect the next layer's
# padding requirements.
weightsTensor = converters.get_tensor_from_cntk_convolutional_weight_parameter(
self.weights_parameter)
internalNodes = utilities.get_model_layers(self.layer.block_root)
activationType = utilities.get_ell_activation_type(internalNodes)
isSoftmaxActivation = utilities.is_softmax_activation(internalNodes)
hasActivation = isSoftmaxActivation or activationType != None
hasBias = self.bias_parameter != None
# Create the ell.neural.LayerParameters for the various ELL layers
onlyLayerParameters = ell.neural.LayerParameters(
self.layer.ell_inputShape, self.layer.ell_inputPaddingParameters,
self.layer.ell_outputShape, self.layer.ell_outputPaddingParameters,
ell.nodes.PortType.smallReal)
firstLayerParameters = ell.neural.LayerParameters(
self.layer.ell_inputShape, self.layer.ell_inputPaddingParameters,
self.layer.ell_outputShapeMinusPadding, ell.neural.NoPadding(),
ell.nodes.PortType.smallReal)
middleLayerParameters = ell.neural.LayerParameters(
self.layer.ell_outputShapeMinusPadding,
ell.neural.NoPadding(), self.layer.ell_outputShapeMinusPadding,
ell.neural.NoPadding(), ell.nodes.PortType.smallReal)
lastLayerParameters = ell.neural.LayerParameters(
self.layer.ell_outputShapeMinusPadding, ell.neural.NoPadding(),
self.layer.ell_outputShape, self.layer.ell_outputPaddingParameters,
ell.nodes.PortType.smallReal)
# Choose the layer parameters for the convolutional layer. If there is
# bias or activation, then the convolution is the first of two or more,
# otherwise it is the only layer
if hasActivation or hasBias:
layerParameters = firstLayerParameters
else:
layerParameters = onlyLayerParameters
# Fill in the convolutional parameters
weightsShape = self.weights_parameter.shape
receptiveField = weightsShape[2]
stride = self.attributes['strides'][2]
filterBatchSize = layerParameters.outputShape.channels
convolutionalParameters = ell.neural.ConvolutionalParameters(
receptiveField, stride, self.convolution_method, filterBatchSize)
# Create the ELL convolutional layer
ellLayers.append(
ell.neural.ConvolutionalLayer(layerParameters,
convolutionalParameters,
weightsTensor))
# Create the ELL bias layer
if hasBias:
if hasActivation:
layerParameters = middleLayerParameters
else:
layerParameters = lastLayerParameters
biasVector = converters.get_vector_from_cntk_trainable_parameter(
self.bias_parameter)
ellLayers.append(ell.neural.BiasLayer(layerParameters, biasVector))
# Create the ELL activation layer
if hasActivation:
layerParameters = lastLayerParameters
# Special case: if this is softmax activation, create an ELL Softmax layer.
# Else, insert an ELL ActivationLayer
if (isSoftmaxActivation):
ellLayers.append(ell.neural.SoftmaxLayer(layerParameters))
else:
ellLayers.append(
ell.neural.ActivationLayer(layerParameters,
activationType))
