def extract_descriptor(self):
ret = NetworkDescriptor()
topological_node_list = self.topological_order
for u in topological_node_list:
for v, layer_id in self.adj_list[u]:
layer = self.layer_list[layer_id]
if is_layer(layer, 'Conv') and layer.kernel_size not in [1, (1,), (1, 1), (1, 1, 1)]:
ret.add_conv_width(layer_width(layer))
if is_layer(layer, 'Dense'):
ret.add_dense_width(layer_width(layer))
# The position of each node, how many Conv and Dense layers before it.
pos = [0] * len(topological_node_list)
for v in topological_node_list:
layer_count = 0
for u, layer_id in self.reverse_adj_list[v]:
layer = self.layer_list[layer_id]
weighted = 0
if (is_layer(layer, 'Conv') and layer.kernel_size not in [1, (1,), (1, 1), (1, 1, 1)]) \
or is_layer(layer, 'Dense'):
weighted = 1
layer_count = max(pos[u] + weighted, layer_count)
pos[v] = layer_count
for u in topological_node_list:
for v, layer_id in self.adj_list[u]:
if pos[u] == pos[v]:
continue
layer = self.layer_list[layer_id]
if is_layer(layer, 'Concatenate'):
ret.add_skip_connection(pos[u], pos[v], NetworkDescriptor.CONCAT_CONNECT)
if is_layer(layer, 'Add'):
ret.add_skip_connection(pos[u], pos[v], NetworkDescriptor.ADD_CONNECT)
return ret
def extract_descriptor(self):
ret = NetworkDescriptor()
topological_node_list = self.topological_order
for u in topological_node_list:
for v, layer_id in self.adj_list[u]:
layer = self.layer_list[layer_id]
if is_layer(layer, 'Conv') and layer.kernel_size not in [1, (1,), (1, 1), (1, 1, 1)]:
ret.add_conv_width(layer_width(layer))
if is_layer(layer, 'Dense'):
ret.add_dense_width(layer_width(layer))
# The position of each node, how many Conv and Dense layers before it.
pos = [0] * len(topological_node_list)
for v in topological_node_list:
layer_count = 0
for u, layer_id in self.reverse_adj_list[v]:
layer = self.layer_list[layer_id]
weighted = 0
if (is_layer(layer, 'Conv') and layer.kernel_size not in [1, (1,), (1, 1), (1, 1, 1)]) \
or is_layer(layer, 'Dense'):
weighted = 1
layer_count = max(pos[u] + weighted, layer_count)
pos[v] = layer_count
for u in topological_node_list:
for v, layer_id in self.adj_list[u]:
if pos[u] == pos[v]:
continue
layer = self.layer_list[layer_id]
if is_layer(layer, 'Concatenate'):
ret.add_skip_connection(pos[u], pos[v], NetworkDescriptor.CONCAT_CONNECT)
if is_layer(layer, 'Add'):
ret.add_skip_connection(pos[u], pos[v], NetworkDescriptor.ADD_CONNECT)
return ret
def _upper_layer_width(self, u):
for v, layer_id in self.reverse_adj_list[u]:
layer = self.layer_list[layer_id]
if is_layer(layer, 'Conv') or is_layer(layer, 'Dense'):
return layer_width(layer)
elif is_layer(layer, 'Concatenate'):
a = self.layer_id_to_input_node_ids[layer_id][0]
b = self.layer_id_to_input_node_ids[layer_id][1]
return self._upper_layer_width(a) + self._upper_layer_width(b)
else:
return self._upper_layer_width(v)
return self.node_list[0][-1]
def _upper_layer_width(self, u):
for v, layer_id in self.reverse_adj_list[u]:
layer = self.layer_list[layer_id]
if is_layer(layer, 'Conv') or is_layer(layer, 'Dense'):
return layer_width(layer)
elif is_layer(layer, 'Concatenate'):
a = self.layer_id_to_input_node_ids[layer_id][0]
b = self.layer_id_to_input_node_ids[layer_id][1]
return self._upper_layer_width(a) + self._upper_layer_width(b)
else:
return self._upper_layer_width(v)
return self.node_list[0][-1]
def to_wider_model(self, pre_layer_id, n_add):
"""Widen the last dimension of the output of the pre_layer.
Args:
pre_layer_id: The ID of a convolutional layer or dense layer.
n_add: The number of dimensions to add.
"""
self.operation_history.append(('to_wider_model', pre_layer_id, n_add))
pre_layer = self.layer_list[pre_layer_id]
output_id = self.layer_id_to_output_node_ids[pre_layer_id][0]
dim = layer_width(pre_layer)
self.vis = {}
self._search(output_id, dim, dim, n_add)
def to_wider_model(self, pre_layer_id, n_add):
"""Widen the last dimension of the output of the pre_layer.
Args:
pre_layer_id: The ID of a convolutional layer or dense layer.
n_add: The number of dimensions to add.
"""
self.operation_history.append(('to_wider_model', pre_layer_id, n_add))
pre_layer = self.layer_list[pre_layer_id]
output_id = self.layer_id_to_output_node_ids[pre_layer_id][0]
dim = layer_width(pre_layer)
self.vis = {}
self._search(output_id, dim, dim, n_add)
for u in self.topological_order:
for v, layer_id in self.adj_list[u]:
self.node_list[v].shape = self.layer_list[layer_id].output_shape
def to_wider_graph(graph):
weighted_layer_ids = graph.wide_layer_ids()
weighted_layer_ids = list(
filter(
lambda x: layer_width(graph.layer_list[x]) * 2 <= Constant.
MAX_MODEL_WIDTH, weighted_layer_ids))
if len(weighted_layer_ids) == 0:
return None
# n_wider_layer = randint(1, len(weighted_layer_ids))
# wider_layers = sample(weighted_layer_ids, n_wider_layer)
wider_layers = sample(weighted_layer_ids, 1)
for layer_id in wider_layers:
layer = graph.layer_list[layer_id]
if is_layer(layer, 'Conv'):
n_add = layer.filters
else:
n_add = layer.units
graph.to_wider_model(layer_id, n_add)
return graph
