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