def _search(self, u, start_dim, total_dim, n_add):
"""Search the graph for widening the layers.
Args:
u: The starting node identifier.
start_dim: The position to insert the additional dimensions.
total_dim: The total number of dimensions the layer has before widening.
n_add: The number of dimensions to add.
"""
if (u, start_dim, total_dim, n_add) in self.vis:
return
self.vis[(u, start_dim, total_dim, n_add)] = True
for v, layer_id in self.adj_list[u]:
layer = self.layer_list[layer_id]
if is_layer(layer, 'Conv'):
new_layer = wider_next_conv(layer, start_dim, total_dim, n_add,
self.weighted)
self._replace_layer(layer_id, new_layer)
elif is_layer(layer, 'Dense'):
new_layer = wider_next_dense(layer, start_dim, total_dim,
n_add, self.weighted)
self._replace_layer(layer_id, new_layer)
elif is_layer(layer, 'BatchNormalization'):
new_layer = wider_bn(layer, start_dim, total_dim, n_add,
self.weighted)
self._replace_layer(layer_id, new_layer)
self._search(v, start_dim, total_dim, n_add)
elif is_layer(layer, 'Concatenate'):
if self.layer_id_to_input_node_ids[layer_id][1] == u:
# u is on the right of the concat
# next_start_dim += next_total_dim - total_dim
left_dim = self._upper_layer_width(
self.layer_id_to_input_node_ids[layer_id][0])
next_start_dim = start_dim + left_dim
next_total_dim = total_dim + left_dim
else:
next_start_dim = start_dim
next_total_dim = total_dim + self._upper_layer_width(
self.layer_id_to_input_node_ids[layer_id][1])
self._search(v, next_start_dim, next_total_dim, n_add)
else:
self._search(v, start_dim, total_dim, n_add)
for v, layer_id in self.reverse_adj_list[u]:
layer = self.layer_list[layer_id]
if is_layer(layer, 'Conv'):
new_layer = wider_pre_conv(layer, n_add, self.weighted)
self._replace_layer(layer_id, new_layer)
elif is_layer(layer, 'Dense'):
new_layer = wider_pre_dense(layer, n_add, self.weighted)
self._replace_layer(layer_id, new_layer)
elif is_layer(layer, 'Concatenate'):
continue
else:
self._search(v, start_dim, total_dim, n_add)
def _search(self, u, start_dim, total_dim, n_add):
"""Search the graph for widening the layers.
Args:
u: The starting node identifier.
start_dim: The position to insert the additional dimensions.
total_dim: The total number of dimensions the layer has before widening.
n_add: The number of dimensions to add.
"""
if (u, start_dim, total_dim, n_add) in self.vis:
return
self.vis[(u, start_dim, total_dim, n_add)] = True
for v, layer_id in self.adj_list[u]:
layer = self.layer_list[layer_id]
if is_layer(layer, 'Conv'):
new_layer = wider_next_conv(layer, start_dim, total_dim, n_add, self.weighted)
self._replace_layer(layer_id, new_layer)
elif is_layer(layer, 'Dense'):
new_layer = wider_next_dense(layer, start_dim, total_dim, n_add, self.weighted)
self._replace_layer(layer_id, new_layer)
elif is_layer(layer, 'BatchNormalization'):
new_layer = wider_bn(layer, start_dim, total_dim, n_add, self.weighted)
self._replace_layer(layer_id, new_layer)
self._search(v, start_dim, total_dim, n_add)
elif is_layer(layer, 'Concatenate'):
if self.layer_id_to_input_node_ids[layer_id][1] == u:
# u is on the right of the concat
# next_start_dim += next_total_dim - total_dim
left_dim = self._upper_layer_width(self.layer_id_to_input_node_ids[layer_id][0])
next_start_dim = start_dim + left_dim
next_total_dim = total_dim + left_dim
else:
next_start_dim = start_dim
next_total_dim = total_dim + self._upper_layer_width(self.layer_id_to_input_node_ids[layer_id][1])
self._search(v, next_start_dim, next_total_dim, n_add)
else:
self._search(v, start_dim, total_dim, n_add)
for v, layer_id in self.reverse_adj_list[u]:
layer = self.layer_list[layer_id]
if is_layer(layer, 'Conv'):
new_layer = wider_pre_conv(layer, n_add, self.weighted)
self._replace_layer(layer_id, new_layer)
elif is_layer(layer, 'Dense'):
new_layer = wider_pre_dense(layer, n_add, self.weighted)
self._replace_layer(layer_id, new_layer)
elif is_layer(layer, 'Concatenate'):
continue
else:
self._search(v, start_dim, total_dim, n_add)
def _wider_next_conv(self, layer, start_dim, total_dim, n_add):
return wider_next_conv(layer, start_dim, total_dim, n_add)
