def __init__(self, n_nodes, channels_pp, channels_p, channels, reduction_p,
reduction):
super().__init__()
self.reduction = reduction
self.n_nodes = n_nodes
# If previous cell is reduction cell, current input size does not match with
# output size of cell[k-2]. So the output[k-2] should be reduced by preprocessing.
if reduction_p:
self.preproc0 = ops.FactorizedReduce(channels_pp,
channels,
affine=False)
else:
self.preproc0 = ops.StdConv(channels_pp,
channels,
1,
1,
0,
affine=False)
self.preproc1 = ops.StdConv(channels_p,
channels,
1,
1,
0,
affine=False)
# generate dag
self.mutable_ops = nn.ModuleList()
for depth in range(2, self.n_nodes + 2):
self.mutable_ops.append(
Node(
"{}_n{}".format("reduce" if reduction else "normal",
depth), depth, channels,
2 if reduction else 0))
def __init__(self, node_id, num_prev_nodes, channels,
num_downsample_connect):
super().__init__()
self.ops = nn.ModuleList()
choice_keys = []
for i in range(num_prev_nodes):
stride = 2 if i < num_downsample_connect else 1
choice_keys.append("{}_p{}".format(node_id, i))
self.ops.append(
mutables.LayerChoice([
ops.PoolBN('max', channels, 3, stride, 1, affine=False),
ops.PoolBN('avg', channels, 3, stride, 1, affine=False),
nn.Identity() if stride == 1 else ops.FactorizedReduce(
channels, channels, affine=False),
ops.SepConv(channels, channels, 3, stride, 1,
affine=False),
ops.SepConv(channels, channels, 5, stride, 2,
affine=False),
ops.DilConv(
channels, channels, 3, stride, 2, 2, affine=False),
ops.DilConv(
channels, channels, 5, stride, 4, 2, affine=False)
],
key=choice_keys[-1]))
self.drop_path = ops.DropPath()
self.input_switch = mutables.InputChoice(
choose_from=choice_keys,
n_chosen=2,
key="{}_switch".format(node_id))
def __init__(self, node_id, num_prev_nodes, channels,
num_downsample_connect):
super().__init__()
self.ops = nn.ModuleList()
choice_keys = []
for i in range(num_prev_nodes):
stride = 2 if i < num_downsample_connect else 1
choice_keys.append("{}_p{}".format(node_id, i))
self.ops.append(
LayerChoice(OrderedDict([
("maxpool",
ops.PoolBN('max', channels, 3, stride, 1, affine=False)),
("avgpool",
ops.PoolBN('avg', channels, 3, stride, 1, affine=False)),
("skipconnect", nn.Identity() if stride == 1 else
ops.FactorizedReduce(channels, channels, affine=False)),
("sepconv3x3",
ops.SepConv(channels,
channels,
3,
stride,
1,
affine=False)),
("sepconv5x5",
ops.SepConv(channels,
channels,
5,
stride,
2,
affine=False)),
("dilconv3x3",
ops.DilConv(channels,
channels,
3,
stride,
2,
2,
affine=False)),
("dilconv5x5",
ops.DilConv(channels,
channels,
5,
stride,
4,
2,
affine=False))
]),
label=choice_keys[-1]))
self.drop_path = ops.DropPath()
self.input_switch = InputChoice(n_candidates=len(choice_keys),
n_chosen=2,
label="{}_switch".format(node_id))
def __init__(self, node_id, num_prev_nodes, channels, num_downsample_connect):
'''
Node("{}_n{}".format("reduce" if reduction else "normal", depth),
depth, channels, 2 if reduction else 0)
num_prev_nodes: 之前的节点个数
'''
super().__init__()
self.ops = nn.ModuleList()
choice_keys = [] # 记录 节点+边 组合的名称
for i in range(num_prev_nodes): # 枚举之前的节点
stride = 2 if i < num_downsample_connect else 1
# 统一设置stride
# 如果是reduction cell, stride=2,
# 如果是normal cell, stride=1
choice_keys.append("{}_p{}".format(node_id, i))
self.ops.append(
mutables.LayerChoice(OrderedDict([
("maxpool", ops.PoolBN('max', channels, 3, stride, 1, affine=False)),
("avgpool", ops.PoolBN('avg', channels, 3, stride, 1, affine=False)),
("skipconnect", nn.Identity() if stride == 1 else ops.FactorizedReduce(
channels, channels, affine=False)),
("sepconv3x3", ops.SepConv(channels,
channels, 3, stride, 1, affine=False)),
("sepconv5x5", ops.SepConv(channels,
channels, 5, stride, 2, affine=False)),
("dilconv3x3", ops.DilConv(channels,
channels, 3, stride, 2, 2, affine=False)),
("dilconv5x5", ops.DilConv(channels,
channels, 5, stride, 4, 2, affine=False))
]), key=choice_keys[-1]))
self.drop_path = ops.DropPath() # 以0.2的概率drop path
self.input_switch = mutables.InputChoice( # 控制连接方式， 维护choice_key就是为了这个使用
choose_from=choice_keys, n_chosen=2, key="{}_switch".format(node_id))
