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