def __init__(self):
super(Net, self).__init__()
self.bias_add_grad = G.BiasAddGrad()
'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', {
'block': P.Gelu(),
'desc_inputs': [[1, 3, 4, 4]],
'desc_bprop': [[1, 3, 4, 4]]}),
('GeluGrad', {
'block': G.GeluGrad(),
'desc_inputs': [[2, 2], [2, 2], [2, 2]],
'desc_bprop': [[2, 2]],
'skip': ['backward']}),
('Tanh', {
'block': P.Tanh(),
'desc_inputs': [[1, 3, 4, 4]],
'desc_bprop': [[1, 3, 4, 4]]}),
def __init__(self):
super(Net, self).__init__()
self.cast = P.Cast()
self.relu = P.ReLU()
self.biasaddgrad = G.BiasAddGrad()
from mindspore.ops import operations as P
from mindspore.ops.operations import _grad_ops as G
Conv = P.Conv2D(out_channel=64,
kernel_size=7,
mode=1,
pad_mode="valid",
pad=0,
stride=1,
dilation=1,
group=1)
Relu = P.ReLU()
Fusion = Primitive('FusionOp')
Reduce = P.ReduceOp()
Biasadd = P.BiasAdd()
Biasaddgrad = G.BiasAddGrad()
Cast = P.Cast()
MatMul = P.MatMul()
Fusion_relu_relu = Primitive('FusionOp_ReLU_ReLU')
Fusion_biasadd = Primitive('FusionOp_ReLU_ReLU_ReLU_BiasAdd_ReLU_ReLU_ReLU')
Fusion_biasaddgrad = Primitive(
'FusionOp_ReLU_ReLU_ReLU_BiasAddGrad_ReLU_ReLU_ReLU')
Fusion_matmul_relu = Primitive('FusionOp_MatMul_ReLU')
Add = P.Add()
Sub = P.Sub()
make_tuple = Primitive('make_tuple')
class FnDict:
