def __init__(self, **kwargs):
_Factory.__init__(self)
self.template_fn = namedtuple(
"FractionalMaxPool3d",
[
"kernel_size",
"output_size",
"output_ratio",
"return_indices",
"_random_samples",
],
)
self.params = self.template_fn(
kernel_size=TupleParam(
name="kernel_size",
size=3,
limits=((1, 1, 1), (1, 1, 1)),
default=(1, 1, 1),
),
output_size=TupleParam(name="output_size",
size=3,
default=(1, 1, 1)),
output_ratio=Param(name="output_ratio", default=None),
return_indices=BinaryParam(name="return_indices",
default=False,
true_prob=0.5),
_random_samples=Param(name="_random_samples", default=None),
)
for k, v in kwargs.items():
getattr(self.params, k).val = v
def __init__(self, **kwargs):
_Factory.__init__(self)
self.template_fn = namedtuple(
"AvgPool1d",
[
"kernel_size", "stride", "padding", "ceil_mode",
"count_include_pad"
],
)
self.params = self.template_fn(
kernel_size=TupleParam(name="kernel_size",
size=1,
limits=((1, ), (1, )),
default=(1, )),
stride=TupleParam(name="stride",
size=1,
limits=((1, ), (1, )),
default=(1, )),
padding=TupleParam(name="padding",
size=1,
limits=((0, ), (0, )),
default=(0, )),
ceil_mode=BinaryParam(name="ceil_mode",
default=False,
true_prob=0.5),
count_include_pad=BinaryParam(name="count_include_pad",
default=True,
true_prob=0.5),
)
for k, v in kwargs.items():
getattr(self.params, k).val = v
def __init__(self, **kwargs):
_Factory.__init__(self)
self.template_fn = namedtuple(
"ConvTranspose3d",
[
"in_channels",
"out_channels",
"kernel_size",
"stride",
"padding",
"output_padding",
"groups",
"bias",
"dilation",
"padding_mode",
],
)
self.params = self.template_fn(
in_channels=IntParam(name="in_channels", default=1),
out_channels=IntParam(name="out_channels", default=1),
kernel_size=TupleParam(
name="kernel_size",
size=3,
limits=((1, 1, 1), (1, 1, 1)),
default=(1, 1, 1),
),
stride=TupleParam(
name="stride", size=3, limits=((1, 1, 1), (1, 1, 1)), default=(1, 1, 1)
),
padding=TupleParam(
name="padding", size=3, limits=((0, 0, 0), (0, 0, 0)), default=(0, 0, 0)
),
output_padding=TupleParam(
name="output_padding",
size=3,
limits=((0, 0, 0), (0, 0, 0)),
default=(0, 0, 0),
),
groups=IntParam(name="groups", default=1),
bias=BinaryParam(name="bias", default=True, true_prob=0.5),
dilation=TupleParam(
name="dilation",
size=3,
limits=((1, 1, 1), (1, 1, 1)),
default=(1, 1, 1),
),
padding_mode=ChoiceParam(
name="padding_mode", choices=("zeros",), cprobs=(1,), default="zeros"
),
)
for k, v in kwargs.items():
getattr(self.params, k).val = v
def __init__(self, **kwargs):
_Factory.__init__(self)
self.template_fn = namedtuple("AdaptiveAvgPool3d", ["output_size"])
self.params = self.template_fn(output_size=TupleParam(
name="output_size", size=3, default=(1, 1, 1)))
for k, v in kwargs.items():
getattr(self.params, k).val = v
def __init__(self, **kwargs):
_Factory.__init__(self)
self.template_fn = namedtuple(
"MaxPool3d",
[
"kernel_size",
"stride",
"padding",
"dilation",
"return_indices",
"ceil_mode",
],
)
self.params = self.template_fn(
kernel_size=TupleParam(
name="kernel_size",
size=3,
limits=((1, 1, 1), (1, 1, 1)),
default=(1, 1, 1),
),
stride=TupleParam(name="stride",
size=3,
limits=((1, 1, 1), (1, 1, 1)),
default=(1, 1, 1)),
padding=TupleParam(name="padding",
size=3,
limits=((0, 0, 0), (0, 0, 0)),
default=(0, 0, 0)),
dilation=TupleParam(
name="dilation",
size=3,
limits=((1, 1, 1), (1, 1, 1)),
default=(1, 1, 1),
),
return_indices=BinaryParam(name="return_indices",
default=False,
true_prob=0.5),
ceil_mode=BinaryParam(name="ceil_mode",
default=False,
true_prob=0.5),
)
for k, v in kwargs.items():
getattr(self.params, k).val = v
def __init__(self, **kwargs):
_Factory.__init__(self)
self.template_fn = namedtuple("ZeroPad2d", ["padding"])
self.params = self.template_fn(
padding=TupleParam(
name="padding", size=2, limits=((0, 0), (0, 0)), default=(0, 0)
)
)
for k, v in kwargs.items():
getattr(self.params, k).val = v
def __init__(self, **kwargs):
_Factory.__init__(self)
self.template_fn = namedtuple("MaxUnpool1d",
["kernel_size", "stride", "padding"])
self.params = self.template_fn(
kernel_size=TupleParam(name="kernel_size",
size=1,
limits=((1, ), (1, )),
default=(1, )),
stride=TupleParam(name="stride",
size=1,
limits=((1, ), (1, )),
default=(1, )),
padding=TupleParam(name="padding",
size=1,
limits=((0, ), (0, )),
default=(0, )),
)
for k, v in kwargs.items():
getattr(self.params, k).val = v
def __init__(self, **kwargs):
_Factory.__init__(self)
self.template_fn = namedtuple(
"LPPool2d", ["norm_type", "kernel_size", "stride", "ceil_mode"])
self.params = self.template_fn(
norm_type=Param(name="norm_type", default=None),
kernel_size=TupleParam(name="kernel_size",
size=2,
limits=((1, 1), (1, 1)),
default=(1, 1)),
stride=TupleParam(name="stride",
size=2,
limits=((1, 1), (1, 1)),
default=(1, 1)),
ceil_mode=BinaryParam(name="ceil_mode",
default=False,
true_prob=0.5),
)
for k, v in kwargs.items():
getattr(self.params, k).val = v
def __init__(self, **kwargs):
_Factory.__init__(self)
self.template_fn = namedtuple("ConstantPad1d", ["padding", "value"])
self.params = self.template_fn(
padding=TupleParam(
name="padding", size=1, limits=((0,), (0,)), default=(0,)
),
value=FloatParam(name="value", default=0.0),
)
for k, v in kwargs.items():
getattr(self.params, k).val = v
def __init__(self, **kwargs):
_Factory.__init__(self)
self.template_fn = namedtuple("AdaptiveMaxPool2d",
["output_size", "return_indices"])
self.params = self.template_fn(
output_size=TupleParam(name="output_size", size=2, default=(1, 1)),
return_indices=BinaryParam(name="return_indices",
default=False,
true_prob=0.5),
)
for k, v in kwargs.items():
getattr(self.params, k).val = v
