def _convert_global_average_pooling1d(converter: KerasConverter, k_op: "keras.layers.GlobalAveragePooling1D"):
x = converter.get_variable(converter.get_input_tensor(k_op)[0])
y = x.reshape([x.shape[0], x.shape[1], 1, x.shape[2]], OrderNHWC)
y, = AveragePooling2D(None, ksize=(x.shape[1], 1), stride=(1, 1), padding=(0, 0))(y)
# flatten without changing memory layout
z = y.reshape([y.shape[0], mul(y.shape[1:])], OrderNC)
converter.set_variable(converter.get_output_tensor(k_op)[0], z)
def convert_layer_global_average_pooling2d(converter: KerasConverter, k_op: "keras.layers.GlobalAveragePooling2D"):
x = converter.get_variable(converter.get_input_tensor(k_op)[0])
check_data_format(x, k_op.data_format)
y, = AveragePooling2D(None, ksize=(x.shape_dict[Axis.H], x.shape_dict[Axis.W]), stride=(1, 1), padding=(0, 0))(x)
# flatten without changing memory layout
z = y.reshape([y.shape[0], mul(y.shape[1:])], OrderNC)
converter.set_variable(converter.get_output_tensor(k_op)[0], z)
def __call__(self, inputs: List[Variable]) -> Tuple[Variable]:
conv_opr = AveragePooling2D(generate_unique_name(self.cfunc.label),
ksize=(self.cfunc.kh, self.cfunc.kw),
stride=(self.cfunc.sy, self.cfunc.sx),
padding=(self.cfunc.ph, self.cfunc.pw))
opr_out, = conv_opr(inputs[0])
opr_out.change_order(OrderNCHW)
return opr_out,
def _convert_global_average_pooling1d(converter: KerasConverter, k_op: keras.layers.GlobalAveragePooling1D):
x = converter.get_variable(converter.get_input_tensor(k_op)[0])
# FIXME: More effective implementation
y, = Reshape(None, in_order=OrderNTC, out_order=OrderNHWC, out_shape=[x.shape[0], x.shape[1], 1, x.shape[2]])(x)
y, = AveragePooling2D(None, ksize=(x.shape[1], 1), stride=(1, 1), padding=(0, 0))(y)
# flatten without changing memory layout
z, = Reshape(None, in_order=y.order, out_order=OrderNC, out_shape=[y.shape[0], mul(y.shape[1:])])(y)
converter.set_variable(converter.get_output_tensor(k_op)[0], z)
def _convert_average_pooling2d(converter: ChainerConverter,
c_op: chainer.functions.AveragePooling2D):
x = converter.get_variable(c_op.inputs[0])
pool_opr = AveragePooling2D(None,
ksize=(c_op.kh, c_op.kw),
stride=(c_op.sy, c_op.sx),
padding=(c_op.ph, c_op.pw))
y, = pool_opr(x)
converter.set_variable(c_op.outputs[0](), y)
def _convert_average_pooling1d(converter: KerasConverter, k_op: "keras.layers.AveragePooling1D"):
x = converter.get_variable(converter.get_input_tensor(k_op)[0])
y = x.reshape([x.shape[0], x.shape[1], 1, x.shape[2]], OrderNHWC)
ksize = (k_op.pool_size[0], 1)
stride = (k_op.strides[0], 1)
padding = (parse_padding(k_op.padding, ksize[0], 1)[0], 0)
y, = AveragePooling2D(None, ksize=ksize, stride=stride, padding=padding)(y)
z = y.reshape([y.shape[0], y.shape[1], y.shape[3]], OrderNTC)
converter.set_variable(converter.get_output_tensor(k_op)[0], z)
def main(k, s, p, n, h1, w1, c1, expected_shape_dict: Dict[Axis, int]):
orders_x = [OrderNHWC, OrderHWNC, OrderHWCN, OrderNCHW, OrderCNHW, OrderCHWN]
for order_x in orders_x:
op = AveragePooling2D(None, ksize=k, stride=s, padding=p)
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 _convert_average_pooling2d(converter: ChainerConverter,
c_op: "chainer.functions.AveragePooling2D"):
x = converter.get_variable(c_op.inputs[0])
x.order.unify(OrderNCHW)
pool_opr = AveragePooling2D(None,
ksize=(c_op.kh, c_op.kw),
stride=(c_op.sy, c_op.sx),
padding=(c_op.ph, c_op.pw),
cover_all=c_op.cover_all)
y, = pool_opr(x)
converter.set_variable(c_op.outputs[0](), y)
def convert_layer_global_average_pooling2d(converter: KerasConverter, k_op: keras.layers.GlobalAveragePooling2D):
x = converter.get_variable(converter.get_input_tensor(k_op)[0])
if k_op.data_format == "channels_first":
assert x.order == OrderNCHW
elif k_op.data_format == "channels_last":
assert x.order == OrderNHWC
else:
raise ValueError(f"[KerasConverter] Unknown data format: {k_op.data_format}")
y, = AveragePooling2D(None, ksize=(x.shape_dict[Axis.H], x.shape_dict[Axis.W]), stride=(1, 1), padding=(0, 0))(x)
# flatten without changing memory layout
z, = Reshape(None, in_order=y.order, out_order=OrderNC, out_shape=[y.shape[0], mul(y.shape[1:])])(y)
converter.set_variable(converter.get_output_tensor(k_op)[0], z)
def _convert_max_pooling2d(converter: KerasConverter, k_op: "keras.layers.AveragePooling2D"):
x = converter.get_variable(converter.get_input_tensor(k_op)[0])
check_data_format(x, k_op.data_format)
padding = (
parse_padding(k_op.padding, k_op.pool_size[0], 1),
parse_padding(k_op.padding, k_op.pool_size[1], 1)
)
x, padding = convert_odd_padding_to_concat(x, padding=padding)
divide_without_padding = any(p > 0 for p in padding)
# handling tensorflow style padding https://github.com/mil-tokyo/webdnn/issues/694
y, = AveragePooling2D(None, ksize=k_op.pool_size, stride=k_op.strides, padding=padding, cover_all=False,
divide_without_padding=divide_without_padding)(x)
converter.set_variable(converter.get_output_tensor(k_op)[0], y)
def _convert_average_pooling2d(converter: ChainerConverter, c_op: "chainer.functions.AveragePooling2D"):
x = converter.get_variable(c_op.inputs[0])
x.order.unify(OrderNCHW)
pool_opr = AveragePooling2D(None,
ksize=(c_op.kh, c_op.kw),
stride=(c_op.sy, c_op.sx),
padding=(c_op.ph, c_op.pw))
y, = pool_opr(x)
if ((y.shape_dict[Axis.H] + c_op.ph * 2 - c_op.kh) % c_op.sy != 0) or ((y.shape_dict[Axis.W] + c_op.pw * 2 - c_op.kw) % c_op.sx != 0):
console.warning(
"[AveragePooling2D] AveragePooling2D in chainer is performed as cover_all=False mode. "
"However, AveragePooling2D in WebDNN is always calculated as cover_all=True mode. "
"Therefore the result may be difference from chainer's output.")
converter.set_variable(c_op.outputs[0](), y)
def mean_handler(converter: TensorFlowConverter, tf_op: "tf.Operation"):
# FIXME: currently supports only the operation is meaning global average pooling.
# (1, 7, 7, 2048) -> (1, 1, 1, 2048)
assert tf_op.get_attr("keep_dims") is True
in_var = converter.get_variable(tf_op.inputs[0])
unify_order(in_var.order, OrderNHWC) # FIXME: assuming input order as NHWC
out_tf_var = tf_op.outputs[0]
in_shape = in_var.shape
out_shape = [s.value for s in out_tf_var.shape.dims]
assert len(in_shape) == len(out_shape)
assert out_shape[1] == 1
assert out_shape[2] == 1
assert out_shape[0] == in_shape[0]
assert out_shape[3] == in_shape[3]
out_var, = AveragePooling2D(None, ksize=tuple(in_shape[1:3]), stride=tuple(in_shape[1:3]), padding=(0, 0))(in_var)
converter.set_variable(out_tf_var, out_var)
def _convert_max_pooling2d(converter: KerasConverter,
k_op: "keras.layers.AveragePooling2D"):
x = converter.get_variable(converter.get_input_tensor(k_op)[0])
check_data_format(x, k_op.data_format)
ksize = tuple(k_op.pool_size)
stride = tuple(k_op.strides)
padding = (parse_padding(k_op.padding, ksize[0],
1), parse_padding(k_op.padding, ksize[1], 1))
if k_op.padding == "same":
console.warning(
"[KerasConverter] keras.layers.AveragePooling computes average by dividing number of valid elements in window "
"(without padding element), but WebDNN divides it by the number of elements including padding element, so different "
"result will be generated on the edge.")
y, = AveragePooling2D(None, ksize=ksize, stride=stride, padding=padding)(x)
converter.set_variable(converter.get_output_tensor(k_op)[0], y)
def _convert_average_pooling1d(converter: KerasConverter, k_op: keras.layers.AveragePooling1D):
x = converter.get_variable(converter.get_input_tensor(k_op)[0])
# FIXME: More effective implementation
y, = Reshape(None, in_order=x.order, out_order=OrderNHWC, out_shape=[x.shape[0], x.shape[1], 1, x.shape[2]])(x)
if k_op.padding == "valid":
padding = (0, 0)
elif k_op.padding == "same":
padding = (k_op.pool_size[0] // 2, k_op.pool_size[0] // 2)
else:
raise NotImplementedError(f"Unknown padding: {k_op.padding}")
y, = AveragePooling2D(None, ksize=(k_op.pool_size[0], 1), stride=(1, 1), padding=padding)(y)
z, = Reshape(None, in_order=y.order, out_order=OrderNTC, out_shape=[y.shape[0], y.shape[1], y.shape[3]])(y)
converter.set_variable(converter.get_output_tensor(k_op)[0], z)
def _convert_max_pooling2d(converter: KerasConverter,
k_op: "keras.layers.AveragePooling2D"):
x = converter.get_variable(converter.get_input_tensor(k_op)[0])
if k_op.data_format == "channels_first":
x.order.unify(OrderNCHW)
elif k_op.data_format == "channels_last":
x.order.unify(OrderNHWC)
else:
raise ValueError(
f"[KerasConverter] Unknown data format: {k_op.data_format}")
ksize = tuple(k_op.pool_size)
stride = tuple(k_op.strides)
if k_op.padding == "valid":
padding = (0, 0)
elif k_op.padding == "same":
# https://www.tensorflow.org/api_guides/python/nn#convolution
if x.shape_dict[Axis.H] % stride[0] == 0:
pad_h = max(ksize[0] - stride[0], 0)
else:
pad_h = max(ksize[0] - (x.shape_dict[Axis.H] % stride[0]), 0)
if x.shape_dict[Axis.W] % stride[1] == 0:
pad_w = max(ksize[1] - stride[1], 0)
else:
pad_w = max(ksize[1] - (x.shape_dict[Axis.W] % stride[1]), 0)
padding = (pad_h // 2, pad_w // 2)
console.warning(
"[KerasConverter] keras.layers.AveragePooling computes average by dividing number of valid elements in window "
"(without padding element), but WebDNN divides it by the number of elements including padding element, so different "
"result will be generated on the edge.")
else:
raise ValueError(f"[KerasConverter] Unknown padding: {k_op.padding}")
y, = AveragePooling2D(None, ksize=ksize, stride=stride, padding=padding)(x)
converter.set_variable(converter.get_output_tensor(k_op)[0], y)
def _convert_max_pooling2d(converter: KerasConverter,
k_op: "keras.layers.AveragePooling2D"):
x = converter.get_variable(converter.get_input_tensor(k_op)[0])
x, padding = convolution_handler_preprocess(x,
ksize=k_op.pool_size,
padding=k_op.padding,
dilation_rate=(1, 1),
data_format=k_op.data_format)
if any(p > 0 for p in padding):
console.warning(
"[KerasConverter] keras.layers.AveragePooling computes average by dividing number of valid elements in window "
"(without padding element), but WebDNN divides it by the number of elements including padding element, so different "
"result will be generated on the edge.")
y, = AveragePooling2D(None,
ksize=k_op.pool_size,
stride=k_op.strides,
padding=padding,
cover_all=False)(x)
converter.set_variable(converter.get_output_tensor(k_op)[0], y)
def template(x_order=OrderNHWC, y_order=OrderNHWC, description: str = ""):
vx = np.random.rand(2, 4, 6, 8)
vy = np.empty((2, 2, 3, 8))
KH, KW = (2, 2)
SH, SW = (2, 2)
PH, PW = (0, 0)
for n, h2, w2, c in itertools.product(range(vy.shape[0]),
range(vy.shape[1]),
range(vy.shape[2]),
range(vy.shape[3])):
v = 0
for (kh, kw) in itertools.product(range(KH), range(KW)):
h1 = (h2 * SH - PH) + kh
w1 = (w2 * SW - PW) + kw
v += 0 if (h1 < 0 or h1 >= 4 or w1 < 0 or w1 >= 6) else vx[n, h1,
w1, c]
vy[n, h2, w2, c] = v / (KH * KW)
x = Variable(vx.shape, order=x_order)
y, = AveragePooling2D(None,
ksize=(KH, KW),
stride=(SH, SW),
padding=(PH, PW))(x)
y.change_order(y_order)
generate_kernel_test_case(
description=f"Average Pooling {description}",
backend=["webgpu", "webgl", "webassembly", "fallback"],
graph=Graph([x], [y]),
inputs={
x: np.transpose(vx, [OrderNHWC.axes_dict[a] for a in x.order.axes])
},
expected={
y: np.transpose(vy, [OrderNHWC.axes_dict[a] for a in y.order.axes])
},
)
def convert_layer_averagepooling2d(self, layer_config: Dict[str, object], inputs: List[Variable]) -> List[
Variable]:
"""
Example:
{'class_name': 'AveragePooling2D',
'config': {'data_format': 'channels_last',
'name': 'avg_pool',
'padding': 'valid',
'pool_size': [7, 7],
'strides': [7, 7],
'trainable': True},
'inbound_nodes': [[['activation_49', 0, 0, {}]]],
'name': 'avg_pool'},
:param layer_config:
:param inputs:
:return:
"""
assert len(inputs) == 1
input = inputs[0]
name: str = layer_config["name"]
ksize: Tuple[int, int] = tuple(layer_config["pool_size"])
stride: Tuple[int, int] = tuple(layer_config["strides"])
padding_keras: str = layer_config["padding"] # valid or same
if padding_keras == "valid":
padding = (0, 0)
elif padding_keras == "same":
padding = (ksize[0] // 2, ksize[1] // 2)
else:
raise ValueError("Unknown padding")
ksize: Tuple[int, int] = (input.shape_dict[Axis.H], input.shape_dict[Axis.W])
average_pooling_2d_opr = AveragePooling2D(name,
ksize=ksize,
stride=stride,
padding=padding)
y, = average_pooling_2d_opr(input)
return [y]