def __init__(self, **kwargs): _Factory.__init__(self) self.template_fn = namedtuple( "GRU", [ "input_size", "hidden_size", "num_layers", "bias", "batch_first", "dropout", "bidirectional", ], ) self.params = self.template_fn( 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("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("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( "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( "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( "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( "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("SELU", ["inplace"]) self.params = self.template_fn( 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( "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("PixelShuffle", ["upscale_factor"]) self.params = self.template_fn( upscale_factor=IntParam(name="upscale_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("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("BasicBlock", ["inplanes", "planes"]) self.params = self.template_fn( inplanes=IntParam(name="inplanes", default=1), planes=IntParam(name="planes", 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("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("Flatten", ["start_dim", "end_dim"]) self.params = self.template_fn( start_dim=IntParam(name="start_dim", default=1), end_dim=IntParam(name="end_dim", 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("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("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("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("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("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("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("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("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("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("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("Linear", ["in_features", "out_features", "bias"]) self.params = self.template_fn( in_features=IntParam(name="in_features", default=1), out_features=IntParam(name="out_features", default=1), 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("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( "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( "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("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