def __init__(self, channel=1, w=0.25, strategy_=None, strategy1_=None):
super(PReLU, self).__init__()
self.add = P.TensorAdd(strategy=strategy1_)
self.prelu = P.PReLU(strategy=strategy_)
def __init__(self, strategy):
super().__init__()
self.prelu = P.PReLU().set_strategy(strategy)
def __init__(self):
super().__init__()
self.prelu = P.PReLU()
def __init__(self):
super(PReLUNet, self).__init__()
self.prelu = P.PReLU()
self.w = Tensor(np.ones(3, np.float32))
'desc_inputs': [[4, 128, 1024]],
'desc_bprop': [[4, 128, 1024]]}),
('ReLU', {
'block': P.ReLU(),
'desc_inputs': [[64, 64, 112, 112]],
'desc_bprop': [[64, 64, 112, 112]]}),
('SeqConvBnRelu', {
'block': SeqConvBnRelu(3, 64),
'desc_inputs': [[64, 3, 112, 112]],
'desc_bprop': [[64, 64, 112, 112]]}),
('PReluCell', {
'block': nn.PReLU(1, [np.float32(0.25)]),
'desc_inputs': [[128, 64, 112, 112]],
'desc_bprop': [[128, 64, 112, 112]]}),
('PRelu', {
'block': P.PReLU(),
'desc_inputs': [[128, 64, 112, 112], [64,]],
'desc_bprop': [[128, 64, 112, 112]]}),
('Cos', {
'block': P.Cos(),
'desc_inputs': [[8, 16]],
'desc_bprop': [[8, 16]]}),
('ACos', {
'block': P.ACos(),
'desc_inputs': [[8, 16]],
'desc_bprop': [[8, 16]]}),
('Exp', {
'block': P.Exp(),
'desc_inputs': [[256, 8]],
'desc_bprop': [[256, 8]]}),
('Pow', {
def __init__(self, strategy1, strategy2):
super().__init__()
self.matmul = P.MatMul().set_strategy(strategy1)
self.prelu = P.PReLU().set_strategy(strategy2)
def __init__(self):
super(NetPReLU, self).__init__()
self.prelu = P.PReLU()
('ScatterNdUpdate', {
'block': (P.ScatterNdUpdate(), {
'exception': TypeError
}),
'desc_inputs': (Tensor(np.ones(
(2, 3), np.float32)), Tensor(np.ones(
(2, 2), np.int32)), Tensor(np.ones((2, ), np.float32))),
'desc_bprop': [[2, 3]]
}),
('Pack', {
'block': (NetForPackInput(P.Pack()), {
'exception': ValueError
}),
'desc_inputs': [[2, 2]],
'desc_bprop': [[1, 2, 2]]
}),
('PReLU', {
'block': (P.PReLU(), {
'exception': ValueError
}),
'desc_inputs': [[2], [1]],
'desc_bprop': [[1]]
}),
]
@mindspore_test(
pipeline_for_compile_forward_ge_graph_for_case_by_case_config_exception)
def test_check_exception():
return raise_set
def __init__(self, channel=1, w=0.25, strategy1=None, strategy2=None):
super().__init__()
self.norm = P.L2Normalize().shard(strategy1)
self.prelu = P.PReLU().shard(strategy2)
self.w = Parameter(initializer(w, [channel,]), name='w')
def __init__(self, channel=1, w=0.25):
super().__init__()
self.norm = P.L2Normalize(axis=1)
self.prelu = P.PReLU()
self.w = Parameter(initializer(w, [channel,]), name='w')
'desc_bprop': [[2, 3, 3, 5]]}),
('Shape_error', {
'block': (P.Shape(), {'exception': TypeError}),
'desc_inputs': [(64, 1)],
'desc_bprop': [[64]]}),
('Flatten_Error', {
'block': (NetForFlatten0D(), {'exception': ValueError}),
'desc_inputs': [Tensor(np.array(0).astype(np.int32))],
'desc_bprop': [Tensor(np.array(0).astype(np.int32))]}),
('ScatterNdUpdate', {
'block': (P.ScatterNdUpdate(), {'exception': TypeError}),
'desc_inputs': (Tensor(np.ones((2, 3), np.float32)),
Tensor(np.ones((2, 2), np.int32)),
Tensor(np.ones((2,), np.float32))),
'desc_bprop': [[2, 3]]}),
('Pack', {
'block': (NetForPackInput(P.Pack()), {'exception': ValueError}),
'desc_inputs': [[2, 2]],
'desc_bprop': [[1, 2, 2]]}),
('PReLU', {
'block': (P.PReLU(), {'exception': ValueError}),
'desc_inputs': [[2], [1]],
'desc_bprop': [[1]]}),
]
@mindspore_test(pipeline_for_compile_forward_ge_graph_for_case_by_case_config_exception)
def test_check_exception():
return raise_set
