MLP#

Multi Layer Perceptron

class nerfstudio.field_components.mlp.MLP(in_dim: int, num_layers: int, layer_width: int, out_dim: Optional[int] = None, skip_connections: Optional[Tuple[int]] = None, activation: Optional[Module] = ReLU(), out_activation: Optional[Module] = None, implementation: Literal['tcnn', 'torch'] = 'torch')[source]#

Bases: FieldComponent

Multilayer perceptron

Parameters

  • in_dim – Input layer dimension

  • num_layers – Number of network layers

  • layer_width – Width of each MLP layer

  • out_dim – Output layer dimension. Uses layer_width if None.

  • activation – intermediate layer activation function.

  • out_activation – output activation function.

  • implementation – Implementation of hash encoding. Fallback to torch if tcnn not available.

build_nn_modules() → None[source]#

Initialize the torch version of the multi-layer perceptron.

forward(in_tensor: Float[Tensor, '*bs in_dim']) → Float[Tensor, '*bs out_dim'][source]#

Returns processed tensor

Parameters

in_tensor – Input tensor to process

classmethod get_tcnn_network_config(activation, out_activation, layer_width, num_layers) → dict[source]#

Get the network configuration for tcnn if implemented

pytorch_fwd(in_tensor: Float[Tensor, '*bs in_dim']) → Float[Tensor, '*bs out_dim'][source]#

Process input with a multilayer perceptron.

Parameters

in_tensor – Network input

Returns

MLP network output

class nerfstudio.field_components.mlp.MLPWithHashEncoding(num_levels: int = 16, min_res: int = 16, max_res: int = 1024, log2_hashmap_size: int = 19, features_per_level: int = 2, hash_init_scale: float = 0.001, interpolation: Optional[Literal['Nearest', 'Linear', 'Smoothstep']] = None, num_layers: int = 2, layer_width: int = 64, out_dim: Optional[int] = None, skip_connections: Optional[Tuple[int]] = None, activation: Optional[Module] = ReLU(), out_activation: Optional[Module] = None, implementation: Literal['tcnn', 'torch'] = 'torch')[source]#

Bases: FieldComponent

Multilayer perceptron with hash encoding

Parameters

  • num_levels – Number of feature grids.

  • min_res – Resolution of smallest feature grid.

  • max_res – Resolution of largest feature grid.

  • log2_hashmap_size – Size of hash map is 2^log2_hashmap_size.

  • features_per_level – Number of features per level.

  • hash_init_scale – Value to initialize hash grid.

  • interpolation – Interpolation override for tcnn hashgrid. Not supported for torch unless linear.

  • num_layers – Number of network layers

  • layer_width – Width of each MLP layer

  • out_dim – Output layer dimension. Uses layer_width if None.

  • activation – intermediate layer activation function.

  • out_activation – output activation function.

  • implementation – Implementation of hash encoding. Fallback to torch if tcnn not available.

build_nn_modules() → None[source]#

Initialize the torch version of the MLP with hash encoding.

forward(in_tensor: Float[Tensor, '*bs in_dim']) → Float[Tensor, '*bs out_dim'][source]#

Returns processed tensor

Parameters

in_tensor – Input tensor to process

nerfstudio.field_components.mlp.activation_to_tcnn_string(activation: Optional[Module]) → str[source]#

Converts a torch.nn activation function to a string that can be used to initialize a TCNN activation function.

Parameters

activation – torch.nn activation function

Returns

TCNN activation function string

Return type

str