Config

class dataset2vec.config.Dataset2VecConfig(*, activation_cls: ~typing.Type[~torch.nn.modules.module.Module] = <class 'torch.nn.modules.activation.ReLU'>, f_dense_hidden_size: int = 32, f_res_hidden_size: int = 32, f_res_n_layers: int = 3, f_block_repetitions: int = 7, f_out_size: int = 32, g_layers_sizes: list[int] = [32, 16, 8], h_dense_hidden_size: int = 16, h_res_hidden_size: int = 16, h_res_n_layers: int = 3, h_block_repetitions: int = 3, output_size: int = 16)

Bases: BaseModel

Configuration of the Dataset2Vec encoder

activation_cls: Type[Module]

Class of the activation function used in entire network.

f_block_repetitions: int

Number of building blocks of the first stage.

f_dense_hidden_size: int

Size of the hidden layers of the first stage.

f_out_size: int

Dimensionality of the output of the first starge.

f_res_hidden_size: int

Size of the hidden layers of the residual blocks of the first stage.

f_res_n_layers: int

Number of the layers of the residual block of the first stage.

g_layers_sizes: list[int]

Sizes of the layers of the feed forward network in the second stage.

h_block_repetitions: int

Number of building blocks of the third stage.

h_dense_hidden_size: int

Size of the hidden layers of the feed forward net of the third stage.

h_res_hidden_size: int

Size of the hidden layers of the residual blocks of the third stage.

h_res_n_layers: int

Number of layers of the residual block of the third stage.

output_size: int

Output dimensionality of the encoder.

class dataset2vec.config.OptimizerConfig(*, gamma: float = 1, optimizer_cls: ~typing.Type[~torch.optim.optimizer.Optimizer] = <class 'torch.optim.adam.Adam'>, learning_rate: float = 0.0001, weight_decay: float = 0.0001)

Bases: BaseModel

Configuration of the Dataset2Vec training

gamma: float

Scaling parameter for the calculation of the probability.

learning_rate: float

Learning rate.

optimizer_cls: Type[Optimizer]

Class of the optimizer.

weight_decay: float

Weight decay.