def __init__(self, arch, prev_layers, channels, reduction, layer_id, layers, steps, drop_path_keep_prob=None):
super(Cell, self).__init__()
#print(prev_layers)
assert len(prev_layers) == 2
self.arch = arch
self.reduction = reduction
self.layer_id = layer_id
self.layers = layers
self.steps = steps
self.drop_path_keep_prob = drop_path_keep_prob
self.ops = nn.ModuleList()
self.nodes = len(arch) // 4
self.used = [0] * (self.nodes + 2)
# maybe calibrate size
prev_layers = [list(prev_layers[0]), list(prev_layers[1])]
self.maybe_calibrate_size = MaybeCalibrateSize(prev_layers, channels)
prev_layers = self.maybe_calibrate_size.out_shape
stride = 2 if self.reduction else 1
for i in range(self.nodes):
x_id, x_op, y_id, y_op = arch[4*i], arch[4*i+1], arch[4*i+2], arch[4*i+3]
x_shape, y_shape = prev_layers[x_id], prev_layers[y_id]
node = Node(x_id, x_op, y_id, y_op, x_shape, y_shape, channels, stride, drop_path_keep_prob, layer_id, layers, steps)
self.ops.append(node)
self.used[x_id] += 1
self.used[y_id] += 1
prev_layers.append(node.out_shape)
self.concat = [i for i in range(self.nodes+2) if self.used[i] == 0]
out_hw = min([shape[0] for i, shape in enumerate(prev_layers) if i in self.concat])
self.final_combine = FinalCombine(prev_layers, out_hw, channels, self.concat)
self.out_shape = [out_hw, out_hw, channels * len(self.concat)]
def __init__(self,
search_space,
prev_layers,
nodes,
channels,
reduction,
layer_id,
layers,
steps,
drop_path_keep_prob=None):
super(Cell, self).__init__()
self.search_space = search_space
assert len(prev_layers) == 2
print(prev_layers)
self.reduction = reduction
self.layer_id = layer_id
self.layers = layers
self.steps = steps
self.drop_path_keep_prob = drop_path_keep_prob
self.ops = nn.ModuleList()
self.nodes = nodes
# maybe calibrate size
prev_layers = [list(prev_layers[0]), list(prev_layers[1])]
self.maybe_calibrate_size = MaybeCalibrateSize(prev_layers, channels)
prev_layers = self.maybe_calibrate_size.out_shape
stride = 2 if self.reduction else 1
for i in range(self.nodes):
node = Node(search_space, prev_layers, channels, stride,
drop_path_keep_prob, i, layer_id, layers, steps)
self.ops.append(node)
prev_layers.append(node.out_shape)
out_hw = min([shape[0] for i, shape in enumerate(prev_layers)])
if reduction:
self.fac_1 = FactorizedReduce(prev_layers[0][-1], channels,
prev_layers[0])
self.fac_2 = FactorizedReduce(prev_layers[1][-1], channels,
prev_layers[1])
self.final_combine_conv = WSReLUConvBN(self.nodes + 2, channels,
channels, 1)
self.out_shape = [out_hw, out_hw, channels]