def __init__(self, **kwargs):
_Factory.__init__(self)
self.template_fn = namedtuple(
"SyncBatchNorm",
[
"num_features",
"eps",
"momentum",
"affine",
"track_running_stats",
"process_group",
],
)
self.params = self.template_fn(
num_features=Param(name="num_features", default=None),
eps=FloatParam(name="eps", default=1e-05),
momentum=FloatParam(name="momentum", default=0.1),
affine=BinaryParam(name="affine", default=False, true_prob=0.5),
track_running_stats=BinaryParam(name="track_running_stats",
default=False,
true_prob=0.5),
process_group=Param(name="process_group", 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("RReLU", ["lower", "upper", "inplace"])
self.params = self.template_fn(
lower=FloatParam(name="lower", default=0.125),
upper=FloatParam(name="upper", default=0.3333333333333333),
inplace=BinaryParam(name="inplace", 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("CrossMapLRN2d",
["size", "alpha", "beta", "k"])
self.params = self.template_fn(
size=Param(name="size", default=None),
alpha=FloatParam(name="alpha", default=0.0001),
beta=FloatParam(name="beta", default=0.75),
k=IntParam(name="k", default=1),
)
for k, v in kwargs.items():
getattr(self.params, k).val = v
def __init__(self, **kwargs):
_Factory.__init__(self)
self.template_fn = namedtuple("Softshrink", ["lambd"])
self.params = self.template_fn(
lambd=FloatParam(name="lambd", default=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("CosineSimilarity", ["dim", "eps"])
self.params = self.template_fn(
dim=IntParam(name="dim", default=1),
eps=FloatParam(name="eps", default=1e-08),
)
for k, v in kwargs.items():
getattr(self.params, k).val = v
def __init__(self, **kwargs):
_Factory.__init__(self)
self.template_fn = namedtuple("FeatureAlphaDropout", ["p", "inplace"])
self.params = self.template_fn(
p=FloatParam(name="p", default=0.5),
inplace=BinaryParam(name="inplace", 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("Softplus", ["beta", "threshold"])
self.params = self.template_fn(
beta=FloatParam(name="beta", default=1),
threshold=IntParam(name="threshold", default=20),
)
for k, v in kwargs.items():
getattr(self.params, k).val = v
def __init__(self, **kwargs):
_Factory.__init__(self)
self.template_fn = namedtuple("PReLU", ["num_parameters", "init"])
self.params = self.template_fn(
num_parameters=IntParam(name="num_parameters", default=1),
init=FloatParam(name="init", default=0.25),
)
for k, v in kwargs.items():
getattr(self.params, k).val = v
def __init__(self, **kwargs):
_Factory.__init__(self)
self.template_fn = namedtuple("CELU", ["alpha", "inplace"])
self.params = self.template_fn(
alpha=FloatParam(name="alpha", default=1.0),
inplace=BinaryParam(name="inplace", 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("LeakyReLU",
["negative_slope", "inplace"])
self.params = self.template_fn(
negative_slope=FloatParam(name="negative_slope", default=0.01),
inplace=BinaryParam(name="inplace", 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("PairwiseDistance", ["p", "eps", "keepdim"])
self.params = self.template_fn(
p=IntParam(name="p", default=2.0),
eps=FloatParam(name="eps", default=1e-06),
keepdim=BinaryParam(name="keepdim", 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(
"GroupNorm", ["num_groups", "num_channels", "eps", "affine"])
self.params = self.template_fn(
num_groups=Param(name="num_groups", default=None),
num_channels=Param(name="num_channels", default=None),
eps=FloatParam(name="eps", default=1e-05),
affine=BinaryParam(name="affine", 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(
"LayerNorm", ["normalized_shape", "eps", "elementwise_affine"])
self.params = self.template_fn(
normalized_shape=Param(name="normalized_shape", default=None),
eps=FloatParam(name="eps", default=1e-05),
elementwise_affine=BinaryParam(name="elementwise_affine",
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(
"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(
"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