b4910f5fc768b1304099295a8d8d2d8d08474469,src/graph_transpiler/webdnn/graph/operators/unpooling_2d.py,Unpooling2D,__call__,#Unpooling2D#Any#,43

Before Change

    def __call__(self, x: Variable):
        self.append_input("x", x)
        return self.exec()

    def exec(self):
        x = self.inputs["x"]
        x_shape_dict = x.shape_dict

After Change

        self.parameters["padding"] = to_tuple(padding)
        self.parameters["outsize"] = to_tuple(outsize)

    def __call__(self, x: Variable):
        x_shape_dict = x.shape_dict
        N = x_shape_dict[Axis.N]
        H2 = self.outsize[0]
        W2 = self.outsize[1]
        C2 = x_shape_dict[Axis.C]
        y = Variable([N, H2, W2, C2], OrderNHWC)
        y.change_order(x.order)  // output same order as input to preserve following reshape semantics

        for axis in x.order.axes:
            if axis == Axis.H or axis == Axis.W:
                continue

            self.attributes.add(Tensorwise(axis))

        self.append_input("x", x)
        self.append_output("y", y)
        return y,

    @property
    def ksize(self) -> Tuple[int, int]:
        return self.parameters["ksize"]

Instances