def __init__(self, layer):
if not layer.is_block:
raise ValueError(
"Error: Convolution layer node is not in block node")
self.op_name = 'Convolution'
# initialize weights and input characteristics
self.input_parameter = layer.arguments[0]
self.weights_parameter = utilities.find_parameter_by_name(
layer.parameters, 'W', 0)
self.bias_parameter = utilities.find_parameter_by_name(
layer.parameters, 'b', 1)
# Get the hyper-parameters for the convolution.
# They are on the convolution node inside this block.
convolution_nodes = depth_first_search(
layer.block_root,
lambda x: utilities.op_name_equals(x, 'Convolution'))
self.attributes = convolution_nodes[0].attributes
self.convolution_method = 0
self.input_shape = self.input_parameter.shape
super().__init__(layer)
nodes = utilities.get_model_layers(layer.block_root)
if utilities.is_softmax_activation(nodes):
self.additional_layer_text = 'softmax'
else:
activation_type = utilities.get_cntk_activation_name(nodes)
if activation_type:
self.additional_layer_text = activation_type
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 __init__(self, layer):
self.op_name = 'BatchNormalization'
self.scale = utilities.find_parameter_by_name(layer.parameters,
'scale', 0)
self.bias = utilities.find_parameter_by_name(layer.parameters, 'bias',
1)
self.mean = utilities.find_parameter_by_name(layer.constants,
'aggregate_mean', 0)
self.variance = utilities.find_parameter_by_name(
layer.constants, 'aggregate_variance', 1)
# The default CNTK epsilon
self.epsilon = 1e-5
super().__init__(layer)
def clone_cntk_layer(self, feature):
"""Returns a clone of the CNTK layer for per-layer forward prop validation"""
weightsParameter = utilities.find_parameter_by_name(
self.layer.parameters, 'W', 0)
biasParameter = utilities.find_parameter_by_name(
self.layer.parameters, 'b', 1)
internalNodes = utilities.get_model_layers(self.layer.block_root)
activationType = utilities.get_cntk_activation_op(internalNodes)
includeBias = biasParameter is not None
layer = Dense(self.layer.shape, activation=activationType, bias=includeBias)(feature)
layer.parameters[0].value = weightsParameter.value
if includeBias:
layer.parameters[1].value = biasParameter.value
return layer
def __init__(self, layer):
if layer.is_block:
raise ValueError(
"Error: Binary Convolution layer node is in block node")
self.op_name = 'BinaryConvolution'
# Convolution function (ASSUME part of a Binary Convolution layer)
# - Weights is 4-dimensional (filters, channels, rows, columns)
# - Input is 3-dimensional (channels, rows, columns)
# - Bias is a separate layer and not processed by this class
# - Activation is a separate layer and not processed by this class
if len(layer.inputs[0].shape) == 3:
self.input_parameter = layer.inputs[0]
weights_input = layer.inputs[1]
else:
self.input_parameter = layer.inputs[1]
weights_input = layer.inputs[0]
self.weights_parameter = utilities.find_parameter_by_name(
weights_input.owner.parameters, 'filter')
self.attributes = layer.attributes
# Determine the binarization method used for weights based on the
# name attributes of the UserFunctions defined in the custom_functions.py
# used during training.
# Until we can find a better heuristic, assume that the custom function names
# don't change across models.
function_name = weights_input.owner.name
if function_name == 'Sign':
self.convolution_method = ell.neural.BinaryConvolutionMethod.bitwise
self.weights_scale = ell.neural.BinaryWeightsScale.none
else:
raise ValueError("Error: unrecognized binarization function: " +
function_name)
self.input_shape = self.input_parameter.shape
super().__init__(layer)
def __init__(self, layer):
self.op_name = 'PReLU'
super().__init__(layer)
self.prelu_parameter = utilities.find_parameter_by_name(
self.layer.parameters, 'prelu', 0)