def softmax_make_tuple_pass():
x = Any()
softmax = P.Softmax()
pattern = Call(softmax, [x])
target = Call("make_tuple", [pattern, new_para])
return pattern, target
def bn_pass():
"""
Sub a BN to Softmax.
"""
pattern = Call(P.BatchNorm())
target = Call(P.Softmax())
return pattern, target
def softmax_pass():
x = Any()
pattern = Call(P.Softmax(), [x])
imm = Imm(0)
target_0 = Call("make_tuple", [pattern])
target = Call(Constants.kTupleGetItem, [target_0, imm])
return pattern, target
def softmax_pass():
x = Any()
pattern = Call(P.Softmax(), [x])
imm = Imm(0)
target_0 = Call("make_tuple", [pattern])
target = Call("tuple_getitem", [target_0, imm])
return pattern, target
def softmax_addn_pass():
x = Any()
pattern = Call(P.Softmax(), [x])
weight_tensor = Tensor(np.zeros([42]), mindspore.float16)
new_weight = NewTensor(weight_tensor)
target = Call(P.AddN(), [x, new_weight])
return pattern, target
def softmax_relu_pass():
x = Any()
softmax_pattern = Prim(P.Softmax())
pattern = Call(softmax_pattern, [x])
sigmoid_pattern = Prim(P.Sigmoid())
call_sigmoid = Call(sigmoid_pattern, [x])
relu_pattern = Prim(P.ReLU())
target = Call(relu_pattern, [call_sigmoid])
return pattern, target
def single_bn_pass():
"""
Sub a BN which does NOT take Conv as inputs to ReLU6.
"""
conv2d_prim = Prim("Conv2D")
conv2d = Call(conv2d_prim)
pattern_0 = NoneOf(conv2d)
pattern = Call(P.BatchNorm(), [pattern_0])
target = Call(P.ReLU6(), [pattern_0])
return pattern, target
def softmax_addn_pass():
x = Any()
pattern = Call(P.Softmax(), [x])
default_tensor0 = Tensor(np.ones((4, 4)), mindspore.float32)
default_tensor1 = Tensor(np.ones((4, 4)), mindspore.float32)
new_para_0 = NewParameter("Merlin", default_tensor0)
new_para_1 = NewParameter("Arthur", default_tensor1)
target_0 = Call(P.MatMul(), [new_para_0, new_para_1])
target = Call("make_tuple", [target_0])
return pattern, target
def single_bn_pass():
"""
Sub a BN which does NOT take MatMul as inputs to ReLU6.
"""
matmul = Prim("MatMul")
pattern_0 = NoneOf(matmul)
softmax = P.Softmax()
pattern = Call(softmax, [pattern_0])
relu6 = P.ReLU6()
target = Call(relu6, [pattern_0])
return pattern, target
def softmax_neg_pass():
x = Any()
softmax_pattern = Prim(P.Softmax())
call_softmax = Call(softmax_pattern, [x])
relu_pattern = Prim(P.ReLU())
call_relu = Call(relu_pattern, [x])
pattern = OneOf([call_softmax, call_relu])
neg_ops = Prim(P.Neg())
target = Call(neg_ops, [pattern])
return pattern, target
def softmax_relu_pass():
x = Any()
softmax_pattern = Prim(P.Softmax())
call_softmax = Call(softmax_pattern, [x])
relu_pattern = Prim(P.ReLU())
call_relu = Call(relu_pattern, [x])
pattern = OneOf([call_softmax, call_relu])
relu6_pattern = Prim(P.ReLU6())
target = Call(relu6_pattern, [x])
return pattern, target
def softmax_relu_pass():
x = Any()
sigmoid_softmax_pattern = Prim([P.Sigmoid(), P.Softmax()])
pattern = Call(sigmoid_softmax_pattern, [x])
target = Call(P.ReLU(), [x])
return pattern, target
def softmax_relu_pass():
x = Any()
pattern = Call(P.Softmax(), [x])
target = Call(P.ReLU(), [x])
return pattern, target