def _split_im2col(graph: Graph, op: Im2Col, v: Variable, v_pair: Sequence[Variable], axis: Axis):
s1 = v_pair[0].shape_dict[axis]
im = op.inputs["im"]
col = op.outputs["col"]
op.remove_all()
if v == col:
"""
before)
im -{Im2Col}- col
after)
+- col_0
im -{PartialIm2Col}-+
+- col_1
"""
col_0, col_1 = PartialIm2Col(None,
ksize=op.ksize, stride=op.stride, padding=op.padding, dilation_rate=op.dilation_rate,
axis=axis, sections=[s1])(im)
OptimizeRule.replace_variable(graph, col_0.transpose(v_pair[0].order), v_pair[0])
OptimizeRule.replace_variable(graph, col_1.transpose(v_pair[1].order), v_pair[1])
elif v == im:
raise NotImplementedError(f"Variable is too large to handle in WebGL backend: {v}")
else:
raise UnexpectedAndPleaseReportError
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 main(k, s, p, d, n, h1, w1, c1, expected_shape_dict: AxisKeyDict[int]):
for order_x in orders4:
op = Im2Col("im2col", ksize=k, stride=s, padding=p, dilation_rate=d)
x = Variable((n, h1, w1, c1), OrderNHWC)
x.change_order(order_x)
y, = op(x)
for axis in y.order.axes:
assert y.shape_dict[axis] == expected_shape_dict[axis]
def test_NHWC():
v_im, v_col = generate_data_311()
im = Variable(v_im.shape, order=OrderNHWC)
col, = Im2Col(None, ksize=3, padding=1, stride=1, dilation_rate=1)(im)
col.change_order(OrderNHWC)
generate_kernel_test_case(description=f"Im2Col output=NHWC",
backend=["webgpu", "webgl", "webassembly"],
graph=Graph([im], [col]),
inputs={im: v_im},
expected={col: v_col})
def test_wide_stride_CNHW():
v_im, v_col = generate_data_212()
col_dummy = ConstantVariable(v_col, order=OrderNHWC)
col_dummy.change_order(OrderCNHW)
im = Variable(v_im.shape, order=OrderNHWC)
col, = Im2Col(None, ksize=2, padding=1, stride=2, dilation_rate=1)(im)
col.change_order(OrderCNHW)
generate_kernel_test_case(description=f"Im2Col output=CNHW stride=2",
backend=["webgpu", "webgl", "webassembly"],
graph=Graph([im], [col]),
inputs={im: v_im},
expected={col: col_dummy.data})
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"]
assert x.order == OrderNHWC
assert y.order == OrderNHWC
assert isinstance(w, ConstantVariable)
flag_changed = True
op.remove_all()
w.change_order(OrderHWCN)
if op.WH != 1 or op.WW != 1 or op.stride != (
1, 1) or op.padding != (0, 0):
im2col = Im2Col(None,
ksize=op.ksize,
stride=op.stride,
padding=op.padding,
dilation_rate=op.dilation_rate)
col, = im2col(x)
col.change_order(OrderNHWC)
else:
col = x
sgemm = Sgemm(
None,
M=col.shape_dict[Axis.N] * col.shape_dict[Axis.H] *
col.shape_dict[Axis.W],
N=w.shape_dict[Axis.N],
K=col.shape_dict[Axis.C],
out_shape=[
col.shape_dict[Axis.N], col.shape_dict[Axis.H],
col.shape_dict[Axis.W], w.shape_dict[Axis.N]
],
out_order=OrderNHWC,
transpose_A=True if col.order == OrderNHWC else False,
transpose_B=True)
new_y, = sgemm(col, w)
new_y.replace(y)
return graph, flag_changed
def _convert_im2col(converter: ChainerConverter, c_op: "chainer.functions.Im2Col"):
x = converter.get_variable(c_op.inputs[0])
if any(not Placeholder.check_resolved(v) for v in x.shape):
raise NotImplementedError("[ChainerConverter] \"GetItem\" for dynamic shape variable is not supported ")
x.order.unify(OrderNCHW)
if c_op.cover_all:
raise NotImplementedError("[ChainerConverter] \"Im2Col\" function with \"cover_all=True\" is not supported")
y, = Im2Col(None,
ksize=(c_op.kh, c_op.kw),
stride=(c_op.sy, c_op.sx),
padding=(c_op.ph, c_op.pw),
dilation_rate=(c_op.dy, c_op.dx))(x)
y = y.combine_axes([Axis.C, Axis.KH, Axis.KW], Axis.C).change_order(OrderNCHW)
converter.set_variable(c_op.outputs[0](), y)
def main(im_shape=[1, 5, 5, 6],
im_order=OrderNHWC,
ksize=3,
stride=1,
padding=1,
dilation_rate=1,
expected_shape_dict: AxisKeyDict[int] = AxisKeyDict(
OrderNHWKKC.axes, [1, 5, 5, 3, 3, 6])):
op = Im2Col(None,
ksize=ksize,
stride=stride,
padding=padding,
dilation_rate=dilation_rate)
x = Variable(im_shape, im_order)
y, = op(x)
for axis in y.order.axes:
assert y.shape_dict[axis] == expected_shape_dict[axis]
def template(im_shape=[2, 3, 4, 5],
im_order=OrderNCHW,
col_order=col_chainer_order,
ksize=(3, 3),
padding=(1, 1),
stride=(1, 1),
dilation=(1, 1),
description: str = ""):
im = Variable(im_shape, im_order)
op = Im2Col(None, ksize, stride, padding, dilation_rate=dilation)
col, = op(im)
col = col.change_order(col_order)
vim = np.random.rand(*(im.shape_dict[a]
for a in OrderNCHW.axes)).astype(np.float32)
vcol = im2col_cpu(vim,
op.KH,
op.KW,
op.SH,
op.SW,
op.PH,
op.PW,
dy=op.DH,
dx=op.DW)
vcol = vcol.transpose(
[col_chainer_order.axes_dict[a] for a in col_order.axes])
vim = vim.transpose([OrderNCHW.axes_dict[a] for a in im_order.axes])
generate_kernel_test_case(
description=f"Im2Col {description}",
backend=["webgpu", "webgl", "webassembly"],
graph=Graph([im], [col]),
inputs={im: vim},
expected={col: vcol},
)
def _convert_separable_conv2d(converter: KerasConverter,
k_op: "keras.layers.SeparableConv2D"):
x = converter.get_variable(converter.get_input_tensor(k_op)[0])
check_data_format(x, k_op.data_format)
axis_c_in = Axis.C
axis_c_out = Axis()
axis_depth_multiplier = Axis()
w_depthwise = converter.convert_to_constant_variable(
k_op.depthwise_kernel,
Order([Axis.KH, Axis.KW, axis_c_in, axis_depth_multiplier]))
w_pointwise = converter.convert_to_constant_variable(
k_op.pointwise_kernel, Order([Axis.KH, Axis.KW, axis_c_in,
axis_c_out]))
w_pointwise = w_pointwise.reshape(
shape=[
x.shape_dict[axis_c_in], k_op.depth_multiplier,
w_pointwise.shape_dict[axis_c_out]
],
order=Order([axis_c_in, axis_depth_multiplier, axis_c_out]))
ksize = tuple(k_op.kernel_size)
stride = tuple(k_op.strides)
dilation_rate = tuple(k_op.dilation_rate)
padding = (parse_padding(k_op.padding, ksize[0], dilation_rate[0]),
parse_padding(k_op.padding, ksize[1], dilation_rate[1]))
if any(p[0] != p[1] for p in padding):
raise NotImplementedError(
"[KerasConverter] \"Different size padding\" is not supported yet")
padding = tuple(p[0] for p in padding)
h, = Im2Col(None,
ksize=ksize,
stride=stride,
padding=padding,
dilation_rate=dilation_rate)(x)
# TODO: Support depth-wise convolution natively
# Currently, depth-wise convolution is not supported natively, and emulated by composition of small convolution operations.
ys = []
for i in range(h.shape_dict[axis_c_in]):
# 1. Depthwise convolution
#
# Ideal | Current implementation
# ----------------------------------+----------------------------------------------------
# h.axes=[N, H, W, KH, KW, C_in] | g_sub.axes=[N, H, W, KH, KW]
# w.axes=[KH, KW, C_in, DM] | w_sub.axes=[KH, KW, DM]
# g.axes=[N, H, W, C_in, DM] | g_sub.axes=[N, H, W, DM]
h_sub, = Slice(
None,
indices=AxisKeyDict(
h.order.axes,
[i if a == axis_c_in else slice(None)
for a in h.order.axes]))(h)
w_depthwise_sub = w_depthwise[:, :, i, :]
g_sub, = Tensordot(None, axes=((Axis.KH, Axis.KW),
(Axis.KH, Axis.KW)))(h_sub,
w_depthwise_sub)
# 2. Pointwise (projection) convolution
#
# Ideal | Current implementation
# ----------------------------------+----------------------------------------------------
# g.axes=[N, H, W, C_in, DM] | g_sub.axes=[N, H, W, DM]
# w.axes=[DM, Cin, C_out] | w_sub.axes=[DM, C_out]
# y.axes=[N, H, W, C_out] | y_sub.axes=[N, H, W, C_out]
w_pointwise_sub = w_pointwise[i, :, :]
y_sub, = Tensordot(None,
axes=((axis_depth_multiplier, ),
(axis_depth_multiplier, )))(g_sub,
w_pointwise_sub)
ys.append(y_sub)
# Sum up all sub convolution results to one
while len(ys) > 1:
ys.append(ys.pop(0) + ys.pop(0))
y = ys[0]
# reinterpret axis "C_out" as C
axes = list(y.order.axes)
i = axes.index(axis_c_out)
axes.pop(i)
axes.insert(i, Axis.C)
y = y.reinterpret_axes(Order(axes))
if k_op.data_format == "channels_last":
y = y.transpose(OrderNHWC)
elif k_op.data_format == "channels_first":
y = y.transpose(OrderNCHW)
else:
raise NotImplementedError(
f"[KerasConverter] Unknown data format: {k_op.data_format}")
if k_op.use_bias:
b = converter.convert_to_constant_variable(k_op.bias, OrderC)
y = y + b
y = do_activation(k_op.activation, y)
converter.set_variable(converter.get_output_tensor(k_op)[0], y)
