def optimize(self, graph: Graph) -> Tuple[Graph, bool]:
flag_changed = False
for op in traverse.filter_nodes(traverse.listup_operators(graph),
Convolution2D): # type: Convolution2D
x = op.inputs["x"]
w = op.inputs["w"]
y = op.outputs["y"]
flag_changed = True
op.remove_all()
a_filter, a_kh, a_kw = Axis(), Axis(), Axis()
w, = ReinterpretAxis(None,
in_order=OrderNHWC,
out_order=Order(
[Axis.C, a_kh, a_kw, a_filter]))(w)
if op.WH == 1 and op.WW == 1 and op.stride == (
1, 1) and op.padding == (0, 0):
# Projection
col, = ReinterpretAxis(
None,
in_order=OrderNHWC,
out_order=Order([Axis.N, Axis.H, Axis.W, a_filter]))(x)
new_y, = Tensordot(None,
[[a_filter], [a_kh, a_kw, a_filter]])(col,
w)
elif op.WH == x.shape_dict[Axis.H] and op.WW == x.shape_dict[
Axis.W] and op.padding == (0, 0):
# Global convolution
col, = ReinterpretAxis(None,
in_order=OrderNHWC,
out_order=Order(
[Axis.N, a_kh, a_kw, a_filter]))(x)
new_y, = Tensordot(
None, [[[a_kh, a_kw, a_filter], [a_kh, a_kw, a_filter]],
[a_kh, a_kw, a_filter]])(col, w)
else:
# General convolution
col, = Im2Col(None,
ksize=op.ksize,
stride=op.stride,
padding=op.padding,
dilation_rate=op.dilation_rate)(x)
col, = ReinterpretAxis(
None,
in_order=OrderNHWC,
out_order=Order([Axis.N, Axis.H, Axis.W, a_filter]))(col)
new_y, = Tensordot(None,
[[a_filter], [a_kh, a_kw, a_filter]])(col,
w)
new_y = new_y.transpose(y.order)
OptimizeRule.replace_variable(graph, new_y, y)
return graph, flag_changed
def optimize(self, graph: Graph) -> Tuple[Graph, bool]:
flag_changed = False
for op in traverse.filter_nodes(
traverse.listup_operators(graph),
Deconvolution2D): # type: Deconvolution2D
x = op.inputs["x"]
w = op.inputs["w"]
y = op.outputs["y"]
flag_changed = True
op.remove_all()
a_filter, a_kh, a_kw = Axis(), Axis(), Axis()
w, = ReinterpretAxis(None,
in_order=OrderNHWC,
out_order=Order(
[Axis.C, a_kh, a_kw, a_filter]))(w)
x, = ReinterpretAxis(None,
in_order=OrderNHWC,
out_order=Order(
[Axis.N, Axis.H, Axis.W, a_filter]))(x)
col, = Tensordot(None, axes=a_filter)(x, w)
col = col.transpose(
Order([Axis.N, Axis.H, Axis.W, a_kh, a_kw, Axis.C]))
col = col.reshape(shape=[*col.shape[0:3],
mul(col.shape[3:6])],
order=OrderNHWC)
new_y, = Col2Im(None,
ksize=op.ksize,
stride=op.stride,
padding=op.padding)(col)
OptimizeRule.replace_variable(graph, new_y.transpose_like(y), y)
return graph, flag_changed