def __init__(self, genotype, C_prev_prev, C_prev, C, k=9, d=1):
super(Cell, self).__init__()
self.preprocess0 = BasicConv([C_prev_prev, C], 'relu', 'batch', bias=False)
self.preprocess1 = BasicConv([C_prev, C], 'relu', 'batch', bias=False)
self.dilated_knn_graph = DilatedKnn2d(k=k, dilation=d)
op_names, indices = zip(*genotype.normal)
concat = genotype.normal_concat
self._compile(C, op_names, indices, concat)
def __init__(self, steps, multiplier, C_prev_prev, C_prev, C, k=9, d=1):
super(Cell, self).__init__()
self.preprocess0 = BasicConv([C_prev_prev, C], 'relu', 'batch', bias=False)
self.preprocess1 = BasicConv([C_prev, C], 'relu', 'batch', bias=False)
self._steps = steps
self._multiplier = multiplier
self.dilated_knn_graph = DilatedKnn2d(k=k, dilation=d)
self._ops = nn.ModuleList()
self._bns = nn.ModuleList()
for i in range(self._steps):
for j in range(2 + i):
stride = 1
op = MixedOp(C, stride)
self._ops.append(op)