def __init__(self):
super(NetAcosh, self).__init__()
self.acosh = P.Acosh()
'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', {
'block': P.Minimum(),
'desc_inputs': [[2, 3, 3, 5], [3, 3, 5]],
'desc_bprop': [[2, 3, 3, 5]]}),
('Maximum', {
'desc_inputs': [5.0, Tensor(np.ones([3, 4]).astype(np.float32))],
'skip': ['backward']}),
# input two tensors, but element types are not same
('FloorMod1', {
'block': (P.FloorMod(), {'exception': TypeError, 'error_keywords': ['FloorMod']}),
'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.float32))],
'skip': ['backward']}),
# input two tensors, their shapes do not match
('FFloorMod2', {
'block': (P.FloorMod(), {'exception': ValueError, 'error_keywords': ['FloorMod']}),
'desc_inputs': [Tensor(np.ones([3, 5]).astype(np.float32)), Tensor(np.ones([3, 4]).astype(np.float32))],
'skip': ['backward']}),
# input x is Tensor(int32), not Tensor(float)
('Acosh1', {
'block': (P.Acosh(),
{'exception': TypeError, 'error_keywords': ['Acosh']}),
'desc_inputs': [Tensor(np.ones([2, 3]).astype(np.bool_))],
'skip': ['backward']}),
# input is not tensor
('Equal0', {
'block': (P.Equal(), {'exception': TypeError, 'error_keywords': ['Equal']}),
'desc_inputs': [5.0, Tensor(np.ones([3, 4]).astype(np.float32))],
'skip': ['backward']}),
# type of x and y not match
('Equal1', {
'block': (P.Equal(), {'exception': TypeError, 'error_keywords': ['Equal']}),
'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.float32))],
'skip': ['backward']}),
# shape of x and y not match
def __init__(self, strategy1, strategy2):
super().__init__()
self.matmul = P.MatMul().set_strategy(strategy1)
self.acosh = P.Acosh().set_strategy(strategy2)
self.matmul2 = P.MatMul().set_strategy(strategy1)
def test_acosh_fp16():
x_np = np.random.rand(4, 2).astype(np.float16) * 10 + 1
output_ms = P.Acosh()(Tensor(x_np))
output_np = np.arccosh(x_np.astype(np.float32)).astype(np.float16)
assert np.allclose(output_ms.asnumpy(), output_np, 1e-3, 1e-3)