def _prelu_pattern(x):
# MIL operation takes named inputs (instead of positional inputs).
# Here `name` argument is MANDATORY.
neg = mb.mul(x=x, y=-1, name="neg")
relu1 = mb.relu(x=neg, name="relu1")
# use any constant here to match, rank and shape will be verified in "is_var_constraint_satisifed" method
mul = mb.mul(x=relu1, y=np.random.rand(2, 2, 2, 2), name="alpha_mul")
relu2 = mb.relu(x=x, name="relu2")
out = mb.add(x=relu2, y=mul, name="out_op")
return out
def prog(x):
x = mb.relu(x=x, name="relu")
x = mb.transpose(x=x, perm=[0, 3, 1, 2], name="transpose")
x = mb.reduce_mean(x=x, axes=[2, 3], keep_dims=False, name="reduce")
x = mb.log(x=x, name="log")
y = mb.add(x=1, y=2)
return x
def relu_prog(input):
out = mb.relu(x=input, name="output")
return out
