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# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# Same functions as in onnxfns1.py, using autocast and default-attribute-values
from onnxscript import script
from onnxscript.onnx_opset import opset18 as op
@script()
def Relu(X):
zero = op.CastLike(0, X)
return op.Max(X, zero)
@script()
def Selu(
X,
alpha: float = 1.67326319217681884765625,
gamma: float = 1.05070102214813232421875,
):
neg = gamma * (alpha * op.Exp(X) - alpha)
pos = gamma * X
return op.Where(X <= 0.0, neg, pos)
@script()
def Elu(X, alpha: float = 1.0):
return op.Where(X < 0.0, alpha * (op.Exp(X) - 1.0), X)
@script()
def Elu05(X):
return op.Where(X < 0.0, 0.5 * (op.Exp(X) - 1.0), X)
@script()
def ThresholdedRelu(X, alpha: float = 1.0):
zero = op.CastLike(0, X)
return op.Where(X > alpha, X, zero)
@script()
def LeakyRelu(X, alpha: float = 0.01):
return op.Where(X < 0.0, alpha * X, X)
@script()
def PRelu(X, slope):
# future-work: capturing extra requirements such as:
# slope must be unidirectionally broadcastable to X's shape.
return op.Where(X < 0.0, slope * X, X)
@script()
def HardSigmoid(X, alpha: float = 0.2, beta: float = 0.5):
zero = op.CastLike(0, X)
one = op.CastLike(1, X)
return op.Max(zero, op.Min(one, alpha * X + beta))
@script()
def Shrink(x, lambd: float = 0.5, bias: float = 0.0):
zero = op.CastLike(0, x)
return op.Where(x < -lambd, x + bias, op.Where(x > lambd, x - bias, zero))
@script()
def Softplus(X):
return op.Log(op.Exp(X) + 1.0)
@script()
def Softsign(X):
return X / (1.0 + op.Abs(X))
@script()
def Clip(input, min, max):
# TODO: default values specified for min/max
return op.Where(input < min, min, op.Where(input > max, max, input))
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