def convert_layer_add(self, layer_config: Dict[str, object], inputs: List[Variable]) -> List[Variable]:
"""
Example:
{'class_name': 'Add',
'config': {'name': 'add_1', 'trainable': True},
'inbound_nodes': [[['conv2d_2', 0, 0, {}], ['conv2d_3', 0, 0, {}]]],
'name': 'add_1'},
:param layer_config:
:param inputs:
:return:
"""
name: str = layer_config["name"]
sum_opr = ElementwiseSum(name)
y, = sum_opr(*inputs)
return [y]
def test_general():
vx1 = np.random.rand(2, 3, 4, 5)
vx2 = np.random.rand(2, 3, 4, 5)
vy = vx1 + vx2
x1 = Variable(vx1.shape, order=OrderNHWC)
x2 = Variable(vx2.shape, order=OrderNHWC)
y, = ElementwiseSum(None)(x1, x2)
generate_kernel_test_case(description=f"ElementwiseSum",
backend=["webgpu", "webassembly", "fallback"],
graph=Graph([x1, x2], [y]),
inputs={
x1: vx1,
x2: vx2
},
expected={y: vy})
def test_every_order():
orders = [
OrderC, OrderNC, OrderCN, OrderNHWC, OrderHWNC, OrderHWCN, OrderNCHW,
OrderCNHW, OrderCHWN
]
for order1, order2 in itertools.product(orders, orders):
if set(order1.axes) != set(order2.axes):
continue
default_order = {1: OrderC, 2: OrderNC, 4: OrderNHWC}
op = ElementwiseSum("op")
x1 = Variable(np.arange(order1.ndim) + 1, default_order[order2.ndim])
x2 = Variable(np.arange(order2.ndim) + 1, default_order[order2.ndim])
x1.change_order(order1)
x2.change_order(order2)
y, = op(x1, x2)
for axis in order1.axes:
assert y.shape_dict[axis] == x1.shape_dict[axis]
def test_invalid_shape2():
op = ElementwiseSum("op")
x1 = Variable((2, 3, 4, 5), OrderNCHW)
x2 = Variable((2, 3, 4, 5), OrderNHWC)
y, = op(x1, x2)
def __call__(self, inputs: List[Variable]) -> List[Variable]:
opr = ElementwiseSum(generate_unique_name(self.cfunc.label))
return list(opr(*inputs))