def f(x):
nonlocal buf
neg = ops.Elemwise(mode="negate")
(x, ) = apply(neg, x)
buf = x.numpy()
(x, ) = apply(neg, x)
return x
def f(x):
nonlocal buf
neg = ops.Elemwise(Elemwise.Mode.NEGATE)
(x, ) = apply(neg, x)
buf = x.numpy()
(x, ) = apply(neg, x)
return x
def f(x):
neg = ops.Elemwise(Elemwise.Mode.NEGATE)
(x, ) = apply(neg, x)
with exclude_from_trace():
if i % 2:
(x, ) = apply(neg, x)
(x, ) = apply(neg, x)
return x
def f(x):
neg = ops.Elemwise(mode="negate")
(x, ) = apply(neg, x)
with exclude_from_trace():
if i % 2:
(x, ) = apply(neg, x)
(x, ) = apply(neg, x)
return x
def test_opdef_serialization():
with TemporaryFile() as f:
x = builtin.Elemwise(mode="Add")
pickle.dump(x, f)
f.seek(0)
load_x = pickle.load(f)
assert x == load_x
with TemporaryFile() as f:
x = builtin.Convolution(stride_h=9, compute_mode="float32")
x.strategy = (builtin.Convolution.Strategy.PROFILE
| builtin.Convolution.Strategy.HEURISTIC
| builtin.Convolution.Strategy.REPRODUCIBLE)
pickle.dump(x, f)
f.seek(0)
load_x = pickle.load(f)
assert x.strategy == load_x.strategy
assert x == load_x
def f(a, b):
op = ops.Elemwise(Elemwise.Mode.ADD)
(y, ) = apply(op, a, b)
return y
def f(x):
op = ops.Elemwise(Elemwise.Mode.NEGATE)
(y, ) = apply(op, x)
return y
def f(x):
op = ops.Elemwise(Elemwise.Mode.MUL)
(y, ) = apply(op, x, p)
return y
def f(a, b):
op = ops.Elemwise(mode="add")
(y, ) = apply(op, a, b)
return y
def f(x):
op = ops.Elemwise(mode="negate")
(y, ) = apply(op, x)
return y
def f(x):
op = ops.Elemwise(mode="mul")
(y, ) = apply(op, x, p)
return y
def forward(self, x):
out = x.shape
out = apply(builtin.Elemwise(mode="ADD"), out, Tensor(1))
return out