def __init__(self, dim, fixed_neigh=False):
super().__init__()
self.fixed_neigh = fixed_neigh
self.broad_ones = P.Ones()((1, 1, dim), ms.int32)
if fixed_neigh:
self.gatherd = None
else:
self.gatherd = P.GatherD()
def __init__(self, fixed_atoms=False, dim=3):
super().__init__()
self.fixed_atoms = fixed_atoms
self.reducesum = P.ReduceSum()
self.pow = P.Pow()
# self.concat = P.Concat()
# self.pack = P.Pack()
self.gatherd = P.GatherD()
self.norm = nn.Norm(-1)
self.gather_neighbors = GatherNeighbors(dim, fixed_atoms)
def __init__(self, weight=None, gamma=2.0, reduction='mean'):
super(FocalLoss, self).__init__(reduction=reduction)
self.gamma = validator.check_value_type("gamma", gamma, [float])
if weight is not None and not isinstance(weight, Tensor):
raise TypeError("The type of weight should be Tensor, but got {}.".format(type(weight)))
self.weight = weight
self.expand_dims = P.ExpandDims()
self.gather_d = P.GatherD()
self.squeeze = P.Squeeze(axis=1)
self.tile = P.Tile()
self.cast = P.Cast()
def __init__(self, dim=1):
super(NetGatherD, self).__init__()
self.gatherd = P.GatherD()
self.dim = int(dim)
def __init__(self, dim=0):
super(GatherDNet, self).__init__()
self.gather_d = P.GatherD()
self.dim = dim
def __init__(self, dim=0):
super(Net, self).__init__()
self.op = P.GatherD()
self.dim = dim