'desc_bprop': [[1, 1, 1]]}),
('Sum_6', {
'block': P.ReduceSum(),
'desc_const': [()],
'desc_inputs': [[2, 3, 4]],
'desc_bprop': [[1]]}),
('Sign', {
'block': P.Sign(),
'desc_inputs': [[3]],
'desc_bprop': [[3]]}),
('Round', {
'block': P.Round(),
'desc_inputs': [[3]],
'desc_bprop': [[3]]}),
('Atan2', {
'block': P.Atan2(),
'desc_inputs': [Tensor(np.array([0, 1]).astype(np.float32)),
Tensor(np.array([1, 1]).astype(np.float32))],
'desc_bprop': [[2]]})
]
test_case_nn_ops = [
('BiasAdd', {
'block': P.BiasAdd(),
'desc_inputs': [[1, 3, 3, 3], [3]],
'desc_bprop': [[1, 3, 3, 3]]}),
('BiasAddGrad', {
'block': G.BiasAddGrad(),
'desc_inputs': [[1, 3, 3, 3]],
'skip': ['backward']}),
('Gelu', {
'skip': ['backward']}),
# input is not tensor
('Round0', {
'block': (P.Round(), {'exception': TypeError, 'error_keywords': ['Round']}),
'desc_inputs': [5.0],
'skip': ['backward']}),
# input is Tensor(bool)
('Round1', {
'block': (P.Round(), {'exception': TypeError, 'error_keywords': ['Round']}),
'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.bool_))],
'skip': ['backward']}),
# one input is scalar, and another is Tensor(float32)
('Atan20', {
'block': (P.Atan2(), {'exception': TypeError, 'error_keywords': ['Atan2']}),
'desc_inputs': [5.0, Tensor(np.ones([3, 4]).astype(np.float32))],
'skip': ['backward']}),
# input two tensors, but element types are not same
('Atan21', {
'block': (P.Atan2(), {'exception': TypeError, 'error_keywords': ['Atan2']}),
'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
('Atan22', {
'block': (P.Atan2(), {'exception': ValueError, 'error_keywords': ['Atan2']}),
'desc_inputs': [Tensor(np.ones([3, 5]).astype(np.float32)), Tensor(np.ones([3, 4]).astype(np.float32))],
'skip': ['backward']}),
]
'desc_inputs': [5.0],
'skip': ['backward']
}),
# input is Tensor(bool)
('Round1', {
'block': (P.Round(), {
'exception': TypeError,
'error_keywords': ['Round']
}),
'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.bool_))],
'skip': ['backward']
}),
# input two tensors, their shapes do not match
('Atan22', {
'block': (P.Atan2(), {
'exception': ValueError,
'error_keywords': ['Atan2']
}),
'desc_inputs': [
Tensor(np.ones([3, 5]).astype(np.float32)),
Tensor(np.ones([3, 4]).astype(np.float32))
],
'skip': ['backward']
}),
]
test_case_math_ops = [
# input two tensors, but element types are not same
('TensorAdd1', {
'block':
def __init__(self, strategy1, strategy2):
super().__init__()
self.matmul = P.MatMul().shard(strategy1)
self.atan2 = P.Atan2().shard(strategy2)
'desc_const': [()],
'desc_inputs': [[2, 3, 4]],
'desc_bprop': [[1]]
}), ('Sign', {
'block': P.Sign(),
'desc_inputs': [[3]],
'desc_bprop': [[3]]
}),
('Round', {
'block': P.Round(),
'desc_inputs': [[3]],
'desc_bprop': [[3]]
}),
('Atan2', {
'block':
P.Atan2(),
'desc_inputs': [
Tensor(np.array([0, 1]).astype(np.float32)),
Tensor(np.array([1, 1]).astype(np.float32))
],
'desc_bprop': [[2]]
})
]
test_case_nn_ops = [
('BiasAdd', {
'block': P.BiasAdd(),
'desc_inputs': [[1, 3, 3, 3], [3]],
'desc_bprop': [[1, 3, 3, 3]]
}),
('BiasAddGrad', {
def __init__(self):
super(NetAtan2, self).__init__()
self.atan2 = P.Atan2()
