def __init__(self):
ChoiceParam.__init__(
self,
name="Activation",
choices=[Sigmoid(), Tanh(), ReLU(),
SELU()],
cprobs=[i / 4 for i in range(1, 5)],
is_random=False,
)
def __init__(self, **kwargs):
_Factory.__init__(self)
self.template_fn = namedtuple(
"EmbeddingBag",
[
"num_embeddings",
"embedding_dim",
"max_norm",
"norm_type",
"scale_grad_by_freq",
"mode",
"sparse",
"_weight",
],
)
self.params = self.template_fn(
num_embeddings=IntParam(name="num_embeddings", default=1),
embedding_dim=IntParam(name="embedding_dim", default=1),
max_norm=Param(name="max_norm", default=None),
norm_type=IntParam(name="norm_type", default=2.0),
scale_grad_by_freq=BinaryParam(name="scale_grad_by_freq",
default=False,
true_prob=0.5),
mode=ChoiceParam(name="mode",
choices=("mean", ),
cprobs=(1, ),
default="mean"),
sparse=BinaryParam(name="sparse", default=False, true_prob=0.5),
_weight=Param(name="_weight", 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(
"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("MultiLabelMarginLoss",
["size_average", "reduce", "reduction"])
self.params = self.template_fn(
size_average=Param(name="size_average", default=None),
reduce=Param(name="reduce", default=None),
reduction=ChoiceParam(name="reduction",
choices=("mean", ),
cprobs=(1, ),
default="mean"),
)
for k, v in kwargs.items():
getattr(self.params, k).val = v
def __init__(self, **kwargs):
_Factory.__init__(self)
self.template_fn = namedtuple(
"RNNCell", ["input_size", "hidden_size", "bias", "nonlinearity"])
self.params = self.template_fn(
input_size=IntParam(name="input_size", default=1),
hidden_size=Param(name="hidden_size", default=None),
bias=BinaryParam(name="bias", default=True, true_prob=0.5),
nonlinearity=ChoiceParam(name="nonlinearity",
choices=("tanh", ),
cprobs=(1, ),
default="tanh"),
)
for k, v in kwargs.items():
getattr(self.params, k).val = v
def __init__(self, **kwargs):
_Factory.__init__(self)
self.template_fn = namedtuple(
"Upsample", ["size", "scale_factor", "mode", "align_corners"])
self.params = self.template_fn(
size=Param(name="size", default=None),
scale_factor=IntParam(name="scale_factor", default=1),
mode=ChoiceParam(name="mode",
choices=("nearest", ),
cprobs=(1, ),
default="nearest"),
align_corners=Param(name="align_corners", 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("CTCLoss",
["blank", "reduction", "zero_infinity"])
self.params = self.template_fn(
blank=IntParam(name="blank", default=0),
reduction=ChoiceParam(name="reduction",
choices=("mean", ),
cprobs=(1, ),
default="mean"),
zero_infinity=BinaryParam(name="zero_infinity",
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(
"CosineEmbeddingLoss",
["margin", "size_average", "reduce", "reduction"])
self.params = self.template_fn(
margin=IntParam(name="margin", default=0.0),
size_average=Param(name="size_average", default=None),
reduce=Param(name="reduce", default=None),
reduction=ChoiceParam(name="reduction",
choices=("mean", ),
cprobs=(1, ),
default="mean"),
)
for k, v in kwargs.items():
getattr(self.params, k).val = v
def __init__(self, **kwargs):
_Factory.__init__(self)
self.template_fn = namedtuple(
"NLLLoss",
["weight", "size_average", "ignore_index", "reduce", "reduction"])
self.params = self.template_fn(
weight=Param(name="weight", default=None),
size_average=Param(name="size_average", default=None),
ignore_index=IntParam(name="ignore_index", default=-100),
reduce=Param(name="reduce", default=None),
reduction=ChoiceParam(name="reduction",
choices=("mean", ),
cprobs=(1, ),
default="mean"),
)
for k, v in kwargs.items():
getattr(self.params, k).val = v
def __init__(self, **kwargs):
_Factory.__init__(self)
self.template_fn = namedtuple(
"TransformerDecoderLayer",
['d_model', 'nhead', 'dim_feedforward', 'dropout', 'activation'])
self.params = self.template_fn(
d_model=Param(name="d_model", default=None),
nhead=Param(name="nhead", default=None),
dim_feedforward=IntParam(name="dim_feedforward", default=2048),
dropout=FloatParam(name="dropout", default=0.1),
activation=ChoiceParam(name="activation",
choices=("relu", ),
cprobs=(1, ),
default="relu"),
)
for k, v in kwargs.items():
getattr(self.params, k).val = v
def __init__(self, **kwargs):
_Factory.__init__(self)
self.template_fn = namedtuple(
"BCEWithLogitsLoss",
["weight", "size_average", "reduce", "reduction", "pos_weight"],
)
self.params = self.template_fn(
weight=Param(name="weight", default=None),
size_average=Param(name="size_average", default=None),
reduce=Param(name="reduce", default=None),
reduction=ChoiceParam(name="reduction",
choices=("mean", ),
cprobs=(1, ),
default="mean"),
pos_weight=Param(name="pos_weight", 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(
"MultiMarginLoss",
["p", "margin", "weight", "size_average", "reduce", "reduction"],
)
self.params = self.template_fn(
p=IntParam(name="p", default=1),
margin=IntParam(name="margin", default=1.0),
weight=Param(name="weight", default=None),
size_average=Param(name="size_average", default=None),
reduce=Param(name="reduce", default=None),
reduction=ChoiceParam(name="reduction",
choices=("mean", ),
cprobs=(1, ),
default="mean"),
)
for k, v in kwargs.items():
getattr(self.params, k).val = v
def __init__(self, **kwargs):
_Factory.__init__(self)
self.template_fn = namedtuple(
"TripletMarginLoss",
[
"margin", "p", "eps", "swap", "size_average", "reduce",
"reduction"
],
)
self.params = self.template_fn(
margin=IntParam(name="margin", default=1.0),
p=IntParam(name="p", default=2.0),
eps=FloatParam(name="eps", default=1e-06),
swap=BinaryParam(name="swap", default=False, true_prob=0.5),
size_average=Param(name="size_average", default=None),
reduce=Param(name="reduce", default=None),
reduction=ChoiceParam(name="reduction",
choices=("mean", ),
cprobs=(1, ),
default="mean"),
)
for k, v in kwargs.items():
getattr(self.params, k).val = v
def __init__(self, **kwargs):
_Factory.__init__(self)
self.template_fn = namedtuple(
"PoissonNLLLoss",
[
"log_input", "full", "size_average", "eps", "reduce",
"reduction"
],
)
self.params = self.template_fn(
log_input=BinaryParam(name="log_input",
default=True,
true_prob=0.5),
full=BinaryParam(name="full", default=False, true_prob=0.5),
size_average=Param(name="size_average", default=None),
eps=FloatParam(name="eps", default=1e-08),
reduce=Param(name="reduce", default=None),
reduction=ChoiceParam(name="reduction",
choices=("mean", ),
cprobs=(1, ),
default="mean"),
)
for k, v in kwargs.items():
getattr(self.params, k).val = v