def __init__(self, **kwargs):
_Factory.__init__(self)
self.template_fn = namedtuple(
"RNNBase",
[
"mode",
"input_size",
"hidden_size",
"num_layers",
"bias",
"batch_first",
"dropout",
"bidirectional",
],
)
self.params = self.template_fn(
mode=Param(name="mode", default=None),
input_size=IntParam(name="input_size", default=1),
hidden_size=Param(name="hidden_size", default=None),
num_layers=IntParam(name="num_layers", default=1),
bias=BinaryParam(name="bias", default=True, true_prob=0.5),
batch_first=BinaryParam(name="batch_first",
default=False,
true_prob=0.5),
dropout=IntParam(name="dropout", default=0.0),
bidirectional=BinaryParam(name="bidirectional",
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(
"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(
"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(
"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(
"MultiheadAttention",
[
"embed_dim",
"num_heads",
"dropout",
"bias",
"add_bias_kv",
"add_zero_attn",
"kdim",
"vdim",
],
)
self.params = self.template_fn(
embed_dim=Param(name="embed_dim", default=None),
num_heads=Param(name="num_heads", default=None),
dropout=IntParam(name="dropout", default=0.0),
bias=BinaryParam(name="bias", default=True, true_prob=0.5),
add_bias_kv=BinaryParam(name="add_bias_kv",
default=False,
true_prob=0.5),
add_zero_attn=BinaryParam(name="add_zero_attn",
default=False,
true_prob=0.5),
kdim=Param(name="kdim", default=None),
vdim=Param(name="vdim", 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("Threshold",
["threshold", "value", "inplace"])
self.params = self.template_fn(
threshold=Param(name="threshold", default=None),
value=Param(name="value", default=None),
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("TransformerEncoder",
['encoder_layer', 'num_layers', 'norm'])
self.params = self.template_fn(
encoder_layer=Param(name="encoder_layer", default=None),
num_layers=Param(name="num_layers", default=None),
norm=Param(name="norm", 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(
"RNNCellBase", ["input_size", "hidden_size", "bias", "num_chunks"])
self.params = self.template_fn(
input_size=IntParam(name="input_size", default=1),
hidden_size=Param(name="hidden_size", default=None),
bias=Param(name="bias", default=None),
num_chunks=Param(name="num_chunks", 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(
"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(
"Hardtanh",
["min_val", "max_val", "inplace", "min_value", "max_value"])
self.params = self.template_fn(
min_val=IntParam(name="min_val", default=-1.0),
max_val=IntParam(name="max_val", default=1.0),
inplace=BinaryParam(name="inplace", default=False, true_prob=0.5),
min_value=Param(name="min_value", default=None),
max_value=Param(name="max_value", 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("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(
"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(
"AdaptiveLogSoftmaxWithLoss",
["in_features", "n_classes", "cutoffs", "div_value", "head_bias"],
)
self.params = self.template_fn(
in_features=IntParam(name="in_features", default=1),
n_classes=Param(name="n_classes", default=None),
cutoffs=Param(name="cutoffs", default=None),
div_value=IntParam(name="div_value", default=4.0),
head_bias=BinaryParam(name="head_bias", 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(
"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(
"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(
"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("UpsamplingBilinear2d",
["size", "scale_factor"])
self.params = self.template_fn(
size=Param(name="size", default=None),
scale_factor=IntParam(name="scale_factor", 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("GRUCell",
["input_size", "hidden_size", "bias"])
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),
)
for k, v in kwargs.items():
getattr(self.params, k).val = v
def __init__(self, **kwargs):
_Factory.__init__(self)
self.template_fn = namedtuple(
"Unfold", ["kernel_size", "dilation", "padding", "stride"])
self.params = self.template_fn(
kernel_size=Param(name="kernel_size", default=None),
dilation=IntParam(name="dilation", default=1),
padding=IntParam(name="padding", default=0),
stride=IntParam(name="stride", 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("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(
"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(
"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
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(
"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(
"AvgPool3d",
[
"kernel_size",
"stride",
"padding",
"ceil_mode",
"count_include_pad",
"divisor_override",
],
)
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)),
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),
divisor_override=Param(name="divisor_override", 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("LogSoftmax", ["dim"])
self.params = self.template_fn(dim=Param(name="dim", default=None))
for k, v in kwargs.items():
getattr(self.params, k).val = v
