def __init__(self, temp=0.1):
super(LossNet, self).__init__()
self.concat = P.Concat()
self.exp = P.Exp()
self.t = P.Transpose()
self.diag_part = P.DiagPart()
self.matmul = P.MatMul()
self.sum = P.ReduceSum()
self.sum_keep_dim = P.ReduceSum(keep_dims=True)
self.log = P.Log()
self.mean = P.ReduceMean()
self.shape = P.Shape()
self.eye = P.Eye()
self.temp = temp
'block': NetForUnpackInput(P.Unpack(axis=0)),
'desc_inputs':[[2, 4]],
'desc_bprop':[[4], [4]],
}),
('Unpack_1', {
'block': NetForUnpackInput(P.Unpack(axis=-1)),
'desc_inputs':[Tensor(np.array([[1, 1, 1]], np.float32))],
'desc_bprop':[[1], [1], [1]],
}),
('Diag', {
'block': P.Diag(),
'desc_inputs': [[4]],
'desc_bprop': [[4, 4]],
}),
('DiagPart', {
'block': P.DiagPart(),
'desc_inputs': [[4, 4]],
'desc_bprop': [[4]],
}),
('SpaceToBatch_1', {
'block': P.SpaceToBatch(2, [[0, 0], [0, 0]]),
'desc_inputs': [[1, 3, 2, 2]],
'desc_bprop': [[4, 3, 1, 1]],
}),
('SpaceToBatch_2', {
'block': P.SpaceToBatch(2, [[1, 1], [0, 4]]),
'desc_inputs': [[1, 3, 2, 2]],
'desc_bprop': [[4, 3, 2, 4]],
}),
('BatchToSpace_1', {
'block': P.BatchToSpace(2, [[0, 0], [0, 0]]),