It is very common to have to generate dummy inputs to perform a task (tracing, exporting a model to some backend, testing model outputs, etc). The goal of DummyInputGenerator classes is to make this generation easy and re-usable.
Generates dummy inputs for the supported input names, in the requested framework.
( inputs dim: int )
Concatenates inputs together.
( shape: typing.List[int] value: typing.Union[int, float] = 1 dtype: typing.Optional[typing.Any] = None framework: str = 'pt' )
Generates a constant tensor.
( input_name: str framework: str = 'pt' )
Generates the dummy input matching input_name
for the requested framework.
( input_ dim: int desired_length: typing.Optional[int] = None padding_length: typing.Optional[int] = None value: typing.Union[int, float] = 1 dtype: typing.Optional[typing.Any] = None )
Parameters
int
) —
The dimension along which to pad.
int
, optional) —
The desired length along the dimension after padding.
int
, optional) —
The length to pad along the dimension.
Union[int, float]
, optional, defaults to 1) —
The value to use for padding.
Any
, optional) —
The dtype of the padding.
Pads an input either to the desired length, or by a padding length.
( shape: typing.List[int] min_value: float = 0 max_value: float = 1 framework: str = 'pt' )
Generates a tensor of random floats in the [min_value, max_value] range.
( shape: typing.List[int] max_value: int min_value: int = 0 framework: str = 'pt' )
Generates a tensor of random integers in the [min_value, max_value] range.
(
input_name: str
)
→
bool
Checks whether the DummyInputGenerator
supports the generation of the requested input.
( task: str normalized_config: NormalizedTextConfig batch_size: int = 2 sequence_length: int = 16 num_choices: int = 4 random_batch_size_range: typing.Union[typing.Tuple[int, int], NoneType] = None random_sequence_length_range: typing.Union[typing.Tuple[int, int], NoneType] = None random_num_choices_range: typing.Union[typing.Tuple[int, int], NoneType] = None )
Generates dummy encoder text inputs.
( task: str normalized_config: NormalizedTextConfig batch_size: int = 2 sequence_length: int = 16 num_choices: int = 4 random_batch_size_range: typing.Union[typing.Tuple[int, int], NoneType] = None random_sequence_length_range: typing.Union[typing.Tuple[int, int], NoneType] = None random_num_choices_range: typing.Union[typing.Tuple[int, int], NoneType] = None )
Generates dummy decoder text inputs.
( task: str normalized_config: NormalizedTextConfig batch_size: int = 2 sequence_length: int = 16 random_batch_size_range: typing.Union[typing.Tuple[int, int], NoneType] = None random_sequence_length_range: typing.Union[typing.Tuple[int, int], NoneType] = None )
Generates dummy past_key_values inputs.
( task: str normalized_config: NormalizedSeq2SeqConfig batch_size: int = 2 sequence_length: int = 16 encoder_sequence_length: typing.Optional[int] = None random_batch_size_range: typing.Union[typing.Tuple[int, int], NoneType] = None random_sequence_length_range: typing.Union[typing.Tuple[int, int], NoneType] = None )
Generates dummy past_key_values inputs for seq2seq architectures.
( task: str normalized_config: NormalizedConfig batch_size: int = 2 sequence_length: int = 16 random_batch_size_range: typing.Union[typing.Tuple[int, int], NoneType] = None random_sequence_length_range: typing.Union[typing.Tuple[int, int], NoneType] = None )
Generates dummy bbox inputs.
( task: str normalized_config: NormalizedVisionConfig batch_size: int = 2 num_channels: int = 3 width: int = 224 height: int = 224 )
Generates dummy vision inputs.
( task: str normalized_config: NormalizedConfig batch_size: int = 2 feature_size: int = 80 nb_max_frames: int = 3000 sequence_length: int = 16000 )