def __init__(self,
embbeding_size=128,
classnum=270762,
s=32,
a=1.0,
m=0.3,
b=0.2):
super(CombineMarginFCFp16, self).__init__()
weight_shape = [classnum, embbeding_size]
weight_init = initializer(me_init.ReidXavierUniform(), weight_shape)
self.weight = Parameter(weight_init, name='weight')
self.m = m
self.s = s
self.a = a
self.b = b
self.m_const = Tensor(self.m, dtype=mstype.float16)
self.a_const = Tensor(self.a, dtype=mstype.float16)
self.b_const = Tensor(self.b, dtype=mstype.float16)
self.s_const = Tensor(self.s, dtype=mstype.float16)
self.m_const_zero = Tensor(0, dtype=mstype.float16)
self.a_const_one = Tensor(1, dtype=mstype.float16)
self.normalize = P.L2Normalize(axis=1)
self.fc = P.MatMul(transpose_b=True)
self.onehot = P.OneHot()
self.transpose = P.Transpose()
self.acos = P.ACos()
self.cos = P.Cos()
self.cast = P.Cast()
self.on_value = Tensor(1.0, mstype.float32)
self.off_value = Tensor(0.0, mstype.float32)
def __init__(self, args):
super(CombineMarginFC, self).__init__()
weight_shape = [args.num_classes, args.emb_size]
weight_init = initializer(me_init.ReidXavierUniform(), weight_shape)
self.weight = Parameter(weight_init, name='weight')
self.m = args.margin_m
self.s = args.margin_s
self.a = args.margin_a
self.b = args.margin_b
self.m_const = Tensor(self.m, dtype=mstype.float16)
self.a_const = Tensor(self.a, dtype=mstype.float16)
self.b_const = Tensor(self.b, dtype=mstype.float16)
self.s_const = Tensor(self.s, dtype=mstype.float16)
self.m_const_zero = Tensor(0, dtype=mstype.float16)
self.a_const_one = Tensor(1, dtype=mstype.float16)
self.normalize = P.L2Normalize(axis=1)
self.fc = P.MatMul(transpose_b=True)
self.onehot = P.OneHot()
self.transpose = P.Transpose()
self.acos = P.ACos()
self.cos = P.Cos()
self.cast = P.Cast()
self.on_value = Tensor(1.0, mstype.float32)
self.off_value = Tensor(0.0, mstype.float32)
'desc_bprop': [[2, 3, 3, 5]]}),
('Exp', {
'block': P.Exp(),
'desc_inputs': [[2, 3]],
'desc_bprop': [[2, 3]]}),
('Erf', {
'block': P.Erf(),
'desc_inputs': [Tensor(np.array([-2, -1, 0, 1, 2]).astype(np.float16))],
'desc_bprop': [Tensor(np.array([-2, -1, 0, 1, 2]).astype(np.float16))]}),
('Floor', {
'block': P.Floor(),
'desc_inputs': [[2, 512, 56, 56]],
'desc_bprop': [[2, 512, 56, 56]],
'skip': ['backward']}),
('ACos', {
'block': P.ACos(),
'desc_inputs': [[2, 3]],
'desc_bprop': [[2, 3]]}),
('Acosh', {
'block': P.Acosh(),
'desc_inputs': [Tensor(np.random.rand(4).astype(np.float16))],
'skip': ['backward']}),
('Sin', {
'block': P.Sin(),
'desc_inputs': [[2, 3]],
'desc_bprop': [[2, 3]]}),
('Reciprocal', {
'block': P.Reciprocal(),
'desc_inputs': [[2, 3, 3, 5]],
'desc_bprop': [[2, 3, 3, 5]]}),
('Minimum_0', {
def test_acos_fp16():
x_np = np.array([0.74, 0.04, 0.30, 0.56]).astype(np.float16)
output_ms = P.ACos()(Tensor(x_np))
output_np = np.arccos(x_np)
assert np.allclose(output_ms.asnumpy(), output_np)
'skip': ['backward']}),
# input is not tensor
('Cos0', {
'block': (P.Cos(), {'exception': TypeError, 'error_keywords': ['Cos']}),
'desc_inputs': [5.0],
'skip': ['backward']}),
# input is Tensor(bool)
('Cos1', {
'block': (P.Cos(), {'exception': TypeError, 'error_keywords': ['Cos']}),
'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.bool_))],
'skip': ['backward']}),
# input is not tensor
('ACos0', {
'block': (P.ACos(), {'exception': TypeError, 'error_keywords': ['ACos']}),
'desc_inputs': [5.0],
'skip': ['backward']}),
# input is Tensor(bool)
('ACos1', {
'block': (P.ACos(), {'exception': TypeError, 'error_keywords': ['ACos']}),
'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.bool_))],
'skip': ['backward']}),
# input is not tensor
('Sin0', {
'block': (P.Sin(), {'exception': TypeError, 'error_keywords': ['Sin']}),
'desc_inputs': [5.0],
'skip': ['backward']}),
# input is Tensor(bool)
('Sin1', {
'desc_inputs': [5.0],
'skip': ['backward']
}),
# input is Tensor(bool)
('Cos1', {
'block': (P.Cos(), {
'exception': TypeError,
'error_keywords': ['Cos']
}),
'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.bool_))],
'skip': ['backward']
}),
# input is not tensor
('ACos0', {
'block': (P.ACos(), {
'exception': TypeError,
'error_keywords': ['ACos']
}),
'desc_inputs': [5.0],
'skip': ['backward']
}),
# input is Tensor(bool)
('ACos1', {
'block': (P.ACos(), {
'exception': TypeError,
'error_keywords': ['ACos']
}),
'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.bool_))],
'skip': ['backward']
}),
def __init__(self):
super(NetACos, self).__init__()
self.acos = P.ACos()
