def __init__(self,
stem,
cells,
head,
init_channels,
num_classes,
auxiliary,
search,
aux_size=8,
auxiliary_layer=13,
drop_path_prob=0):
"""Create layers."""
super(DartsNetwork, self).__init__()
self.is_search = search
self._auxiliary = auxiliary
self.drop_path_prob = drop_path_prob
if auxiliary:
self._aux_size = aux_size
self._auxiliary_layer = auxiliary_layer
# Build stems part
self.pre_stems = ClassFactory.get_instance(ClassType.NETWORK, stem)
# Build cells part
c_curr = self.pre_stems.output_channel
self.cells_ = Cells(cells,
c_curr,
init_channels,
auxiliary=auxiliary,
auxiliary_layer=auxiliary_layer)
# output params
self.len_alpha = self.cells_.len_alpha
self.num_ops = self.cells_.num_ops
self.steps = self.cells_.steps
c_prev, c_aux = self.cells_.output_channels()
if not search and auxiliary:
self.auxiliary_head = AuxiliaryHead(c_aux, num_classes, aux_size)
# head
self.head = ClassFactory.get_instance(ClassType.NETWORK,
head,
base_channel=c_prev,
num_classes=num_classes)
# Initialize architecture parameters
self.set_parameters(
'alphas_normal',
1e-3 * ops.random_normal(self.len_alpha, self.num_ops))
self.set_parameters(
'alphas_reduce',
1e-3 * ops.random_normal(self.len_alpha, self.num_ops))
self.cell_list = self.cells_.children()
self.name_list = []
for tmp_cell in self.cells_.children():
self.name_list.append(tmp_cell.__class__.__name__)
def __init__(self,
stem,
cells,
head,
init_channels,
num_classes,
auxiliary,
search,
aux_size=8,
auxiliary_layer=13,
drop_path_prob=0):
"""Create layers."""
super(DartsNetwork, self).__init__()
self.is_search = search
self._auxiliary = auxiliary
self.drop_path_prob = drop_path_prob
self._cells = cells
if auxiliary:
self._aux_size = aux_size
self._auxiliary_layer = auxiliary_layer
# Build stems part
self.pre_stems = ClassFactory.get_instance(ClassType.SEARCH_SPACE,
stem)
# Build cells part
c_curr = self.pre_stems.output_channel
self.cells_ = Cells(cells,
c_curr,
init_channels,
auxiliary=auxiliary,
auxiliary_layer=auxiliary_layer)
# output params
self.len_alpha = self.cells_.len_alpha
self.num_ops = self.cells_.num_ops
self.steps = self.cells_.steps
c_prev, c_aux = self.cells_.output_channels()
if not search and auxiliary:
self.auxiliary_head = AuxiliaryHead(c_aux, num_classes, aux_size)
# head
self.head = ClassFactory.get_instance(ClassType.SEARCH_SPACE,
head,
base_channel=c_prev,
num_classes=num_classes)
self.initializer()