def _delete_channels(self,
node,
inputs,
input_masks,
channels=None,
layer_name=None):
"""Delete selected channels of node.outbound_layer. Add it to the graph.
"""
old_layer = node.outbound_layer
old_layer_output = utils.single_element(node.output_tensors)
# Create a mask to propagate the deleted channels to downstream layers
new_delete_mask = self._make_delete_mask(old_layer, channels)
if len(set(channels)) == getattr(old_layer,
utils.get_channels_attr(old_layer)):
self._replace_tensors[old_layer_output] = (None, new_delete_mask)
return None
# If this layer has already been operated on, use the cached copy of
# the new layer. Otherwise, apply the inbound delete mask and
# delete channels to obtain the new layer
if old_layer in self._new_layers_map.keys():
new_layer = self._new_layers_map[old_layer]
else:
temp_layer, new_mask = self._apply_delete_mask(node, input_masks)
# This call is needed to initialise input_shape and output_shape
temp_layer(utils.single_element(inputs))
new_layer = self._delete_channel_weights(temp_layer, channels)
if layer_name:
new_layer.name = layer_name
self._new_layers_map[old_layer] = new_layer
new_output = new_layer(utils.single_element(inputs))
# Replace the original layer's output with the modified layer's output
self._replace_tensors[old_layer_output] = (new_output, new_delete_mask)
def _replace_layer(self, node, inputs, input_masks, new_layer=None):
"""Replace node.outbound_layer with new_layer. Add it to the graph."""
# Call the new layer on the rebuild submodel's inputs
new_output = new_layer(utils.single_element(inputs))
# Replace the original layer's output with the new layer's output
replaced_layer_output = utils.single_element(node.output_tensors)
input_masks = utils.single_element(input_masks)
self._replace_tensors[replaced_layer_output] = (new_output,
input_masks)
def _delete_layer(self, node, inputs, input_masks):
"""Skip adding node.outbound_layer when building the graph."""
# Skip the deleted layer by replacing its outputs with it inputs
if len(inputs) >= 2:
raise ValueError('Cannot insert new layer at node with multiple '
'inbound layers.')
inputs = utils.single_element(inputs)
input_masks = utils.single_element(input_masks)
deleted_layer_output = utils.single_element(node.output_tensors)
self._replace_tensors[deleted_layer_output] = (inputs, input_masks)
def _insert_layer(self, node, inputs, input_masks, new_layer=None):
"""Insert new_layer into the graph before node.outbound_layer."""
# This will not work for nodes with multiple inbound layers
if len(inputs) >= 2:
raise ValueError('Cannot insert new layer at node with multiple '
'inbound layers.')
# Call the new layer on the inbound layer's output
new_output = new_layer(utils.single_element(inputs))
# Replace the inbound layer's output with the new layer's output
old_output = node.input_tensors[0]
input_masks = utils.single_element(input_masks)
self._replace_tensors[old_output] = (new_output, input_masks)
def _apply_delete_mask(self, node, inbound_masks):
"""Apply the inbound delete mask and return the outbound delete mask
When specific channels in a layer or layer instance are deleted, the
mask propagates information about which channels are affected to
downstream layers.
If the layer contains weights, those which were previously connected
to the deleted channels are deleted and outbound masks are set to None
since further downstream layers aren't affected.
If the layer does not contain weights, its output mask is calculated to
reflect any transformations performed by the layer to ensure that
information about the deleted channels is propagated downstream.
Arguments:
node(Node): The node where the delete mask is applied.
inbound_masks: Mask(s) from inbound node(s).
Returns:
new_layer: Pass through `layer` if it has no weights, otherwise a
new `Layer` object with weights corresponding to the
inbound mask deleted.
outbound_mask: Mask corresponding to `new_layer`.
"""
# if delete_mask is None or all values are True, it does not affect
# this layer or any layers above/downstream from it
layer = node.outbound_layer
if all(mask is None for mask in inbound_masks):
new_layer = layer
outbound_mask = None
return new_layer, outbound_mask
# If one or more of the masks are None, replace them with ones.
if any(mask is None for mask in inbound_masks):
inbound_masks = [
np.ones(shape[1:], dtype=bool)
if inbound_masks[i] is None else inbound_masks[i]
for i, shape in enumerate(node.input_shapes)
]
# If the layer is shared and has already been affected by this
# operation, use the cached new layer.
if len(get_inbound_nodes(layer)) > 1 \
and layer in self._replace_layers_map.keys():
return self._replace_layers_map[layer]
output_shape = utils.single_element(node.output_shapes)
input_shape = utils.single_element(node.input_shapes)
data_format = getattr(layer, 'data_format', 'channels_last')
inbound_masks = utils.single_element(inbound_masks)
# otherwise, delete_mask.shape should be: layer.input_shape[1:]
layer_class = layer.__class__.__name__
if layer_class == 'InputLayer':
raise RuntimeError('This should never get here!')
elif layer_class == 'Dense':
if np.all(inbound_masks):
new_layer = layer
else:
weights = layer.get_weights()
weights[0] = weights[0][np.where(inbound_masks)[0], :]
config = layer.get_config()
config['weights'] = weights
new_layer = type(layer).from_config(config)
outbound_mask = None
elif layer_class == 'Flatten':
outbound_mask = np.reshape(inbound_masks, [
-1,
])
new_layer = layer
elif layer_class in ('Conv1D', 'Conv2D', 'Conv3D'):
if np.all(inbound_masks):
new_layer = layer
else:
if data_format == 'channels_first':
inbound_masks = np.swapaxes(inbound_masks, 0, -1)
# Conv layer: trim down inbound_masks to filter shape
k_size = layer.kernel_size
index = [slice(None, 1, None) for _ in k_size]
inbound_masks = inbound_masks[tuple(index + [slice(None)])]
weights = layer.get_weights()
# Delete unused weights to obtain new_weights
# Each deleted channel was connected to all of the channels
# in layer; therefore, the mask must be repeated for each
# channel.
# `delete_mask`'s size: size(weights[0])
delete_mask = np.tile(inbound_masks[..., np.newaxis],
list(k_size) + [1, weights[0].shape[-1]])
new_shape = list(weights[0].shape)
new_shape[-2] = -1 # Weights always have channels_last
weights[0] = np.reshape(weights[0][delete_mask], new_shape)
# Instantiate new layer with new_weights
config = layer.get_config()
config['weights'] = weights
new_layer = type(layer).from_config(config)
outbound_mask = None
elif layer_class in ('Cropping1D', 'Cropping2D', 'Cropping3D',
'MaxPooling1D', 'MaxPooling2D', 'MaxPooling3D',
'AveragePooling1D', 'AveragePooling2D',
'AveragePooling3D'):
index = [slice(None, x, None) for x in output_shape[1:]]
if data_format == 'channels_first':
index[0] = slice(None)
elif data_format == 'channels_last':
index[-1] = slice(None)
else:
raise ValueError('Invalid data format')
outbound_mask = inbound_masks[tuple(index)]
new_layer = layer
elif layer_class in ('UpSampling1D', 'UpSampling2D', 'UpSampling3D',
'ZeroPadding1D', 'ZeroPadding2D',
'ZeroPadding3D'):
# Get slice of mask with all singleton dimensions except
# channels dimension
index = [slice(1)] * (len(input_shape) - 1)
tile_shape = list(output_shape[1:])
if data_format == 'channels_first':
index[0] = slice(None)
tile_shape[0] = 1
elif data_format == 'channels_last':
index[-1] = slice(None)
tile_shape[-1] = 1
else:
raise ValueError('Invalid data format')
channels_vector = inbound_masks[tuple(index)]
# Tile this slice to create the outbound mask
outbound_mask = np.tile(channels_vector, tile_shape)
new_layer = layer
elif layer_class in ('GlobalMaxPooling1D', 'GlobalMaxPooling2D',
'GlobalAveragePooling1D',
'GlobalAveragePooling2D'):
# Get slice of mask with all singleton dimensions except
# channels dimension
index = [0] * (len(input_shape) - 1)
if data_format == 'channels_first':
index[0] = slice(None)
elif data_format == 'channels_last':
index[-1] = slice(None)
else:
raise ValueError('Invalid data format')
channels_vector = inbound_masks[tuple(index)]
# Tile this slice to create the outbound mask
outbound_mask = channels_vector
new_layer = layer
elif layer_class in ('Dropout', 'Activation', 'SpatialDropout1D',
'SpatialDropout2D', 'SpatialDropout3D',
'ActivityRegularization', 'Masking', 'LeakyReLU',
'ELU', 'ThresholdedReLU', 'GaussianNoise',
'GaussianDropout', 'AlphaDropout'):
# Pass-through layers
outbound_mask = inbound_masks
new_layer = layer
elif layer_class == 'Reshape':
outbound_mask = np.reshape(inbound_masks, layer.target_shape)
new_layer = layer
elif layer_class == 'Permute':
outbound_mask = np.transpose(inbound_masks,
[x - 1 for x in layer.dims])
new_layer = layer
elif layer_class == 'RepeatVector':
outbound_mask = np.repeat(np.expand_dims(inbound_masks, 0),
layer.n,
axis=0)
new_layer = layer
elif layer_class == 'Embedding':
# Embedding will always be the first layer so it doesn't need
# to consider the inbound_delete_mask
if inbound_masks is not None:
raise ValueError('Channels cannot be deleted bedore Embedding '
'layers because they change the number of '
'channels.')
outbound_mask = None
new_layer = layer
elif layer_class in ('Add', 'Multiply', 'Average', 'Maximum'):
# The inputs must be the same size
if not utils.all_equal(inbound_masks):
ValueError(
'{0} layers must have the same size inputs. All '
'inbound nodes must have the same channels deleted'.format(
layer_class))
outbound_mask = inbound_masks[1]
new_layer = layer
elif layer_class == 'Concatenate':
axis = layer.axis
if layer.axis < 0:
axis = axis % len(layer.input_shape[0])
# Below: axis=axis-1 because the mask excludes the batch dimension
outbound_mask = np.concatenate(inbound_masks, axis=axis - 1)
new_layer = layer
elif layer_class in ('SimpleRNN', 'GRU', 'LSTM'):
if np.all(inbound_masks):
new_layer = layer
else:
weights = layer.get_weights()
weights[0] = weights[0][np.where(inbound_masks[0, :])[0], :]
config = layer.get_config()
config['weights'] = weights
new_layer = type(layer).from_config(config)
outbound_mask = None
elif layer_class == 'BatchNormalization':
outbound_mask = inbound_masks
# Get slice of mask with all singleton dimensions except
# channels dimension
index = [0] * (len(input_shape))
index[layer.axis] = slice(None)
index = index[1:]
# TODO: Maybe use channel indices everywhere instead of masks?
channel_indices = np.where(inbound_masks[tuple(index)] == False)[0]
weights = [
np.delete(w, channel_indices, axis=-1)
for w in layer.get_weights()
]
new_layer = BatchNormalization.from_config(layer.get_config())
new_input_shape = list(input_shape)
new_input_shape[new_layer.axis] -= len(channel_indices)
new_layer.build(new_input_shape)
new_layer.set_weights(weights)
else:
# Not implemented:
# - Lambda
# - SeparableConv2D
# - Conv2DTranspose
# - LocallyConnected1D
# - LocallyConnected2D
# - TimeDistributed
# - Bidirectional
# - Dot
# - PReLU
# Warning/error needed for Reshape if channels axis is split
raise ValueError('"{0}" layers are currently '
'unsupported.'.format(layer_class))
if len(get_inbound_nodes(layer)) > 1 and new_layer != layer:
self._replace_layers_map[layer] = (new_layer, outbound_mask)
return new_layer, outbound_mask
def _rebuild_rec(node):
"""Rebuild the graph up to `node` recursively.
Args:
node(Node): Node to rebuild up to.
Returns:
(tuple) containing :
The output tensor of the rebuilt `node`
The output mask of the rebuilt `node`
"""
# TODO: What happens if nodes have multiple output tensors?
# Does that ever happen?
layer = node.outbound_layer
logging.debug('getting inputs for: {0}'.format(layer.name))
node_output = utils.single_element(node.output_tensors)
# First check for conditions to bottom out the recursion
# Check for replaced tensors before any other checks:
# these are created by the surgery methods.
if node_output in self._replace_tensors.keys():
logging.debug(
'bottomed out at replaced output: {0}'.format(node_output))
output, output_mask = self._replace_tensors[node_output]
return output, output_mask
# Next check if the current node has already been rebuilt.
elif node in self._finished_nodes.keys():
logging.debug('reached finished node: {0}'.format(node))
return self._finished_nodes[node]
# Next check if one of the graph_inputs has been reached.
elif node_output in graph_inputs:
logging.debug('bottomed out at a model input')
output_mask = graph_input_masks[graph_inputs.index(
node_output)]
return node_output, output_mask
# Otherwise recursively call this method on the inbound nodes.
else:
inbound_nodes = utils.get_node_inbound_nodes(node)
logging.debug('inbound_layers: {0}'.format(
[node.outbound_layer.name for node in inbound_nodes]))
# Recursively rebuild the model up to `node`s inbound nodes to
# obtain its inputs and input masks
inputs, input_masks = zip(
*[_rebuild_rec(n) for n in inbound_nodes])
if all(i is None for i in inputs):
output = None
output_mask = np.zeros(node.output_shapes[0][1:],
dtype=bool)
elif any(i is None for i in inputs):
if node.outbound_layer.__class__.__name__ != 'Concatenate':
TypeError(
'Inputs can only be missing for concatenate layers.'
)
# remove Nones from inputs list
inputs = [i for i in inputs if i is not None]
new_layer, output_mask = self._apply_delete_mask(
node, input_masks)
if len(inputs) == 1:
output = utils.single_element(list(inputs))
else:
output = new_layer(utils.single_element(list(inputs)))
else:
new_layer, output_mask = self._apply_delete_mask(
node, input_masks)
output = new_layer(utils.single_element(list(inputs)))
# Record that this node has been rebuild
self._finished_nodes[node] = (output, output_mask)
logging.debug('layer complete: {0}'.format(layer.name))
return output, output_mask