mirror of
https://github.com/nerfstudio-project/nerfstudio.git
synced 2026-11-13 11:24:00 +08:00
Ethan Weber
457788d689
* renaming dataloader to data. adding some dataloader classes * restructuring configs * fixed instant ngp tcnn direction rescaling * working through test cases * black and lint and tests
82 lines
2.8 KiB
Python
82 lines
2.8 KiB
Python
"""
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Multi Layer Perceptron
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"""
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from typing import Tuple, Optional
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import torch
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from torch import nn
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from torchtyping import TensorType
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from pyrad.fields.modules.base import FieldModule
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class MLP(FieldModule):
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"""Multilayer perceptron
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Args:
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in_dim (int): Input layer dimension
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num_layers (int): Number of network layers
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layer_width (int): Width of each MLP layer
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out_dim (int, optional): Ouput layer dimension. Defaults to layer_width
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activation (Optional[nn.Module], optional): intermediate layer activation function. Defaults to nn.ReLU.
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out_activation (Optional[nn.Module], optional): output activation function. Defaults to None.
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"""
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def __init__(
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self,
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in_dim: int,
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num_layers: int,
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layer_width: int,
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out_dim: Optional[int] = None,
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skip_connections: Tuple[int] = (),
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activation: Optional[nn.Module] = nn.ReLU(),
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out_activation: Optional[nn.Module] = None,
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) -> None:
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super().__init__()
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self.in_dim = in_dim
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assert self.in_dim > 0
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self.out_dim = out_dim if out_dim is not None else layer_width
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self.num_layers = num_layers
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self.layer_width = layer_width
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self.skip_connections = skip_connections
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self.activation = activation
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self.out_activation = out_activation
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self.net = None
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self.build_nn_modules()
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def build_nn_modules(self) -> None:
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"""Initialize multi-layer perceptron."""
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layers = []
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if self.num_layers == 1:
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layers.append(nn.Linear(self.in_dim, self.out_dim))
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else:
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for i in range(self.num_layers - 1):
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if i == 0:
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assert i not in list(self.skip_connections), "Skip connection at layer 0 doesn't make sense."
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layers.append(nn.Linear(self.in_dim, self.layer_width))
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elif i in self.skip_connections:
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layers.append(nn.Linear(self.layer_width + self.in_dim, self.layer_width))
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else:
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layers.append(nn.Linear(self.layer_width, self.layer_width))
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layers.append(nn.Linear(self.layer_width, self.out_dim))
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self.layers = nn.ModuleList(layers)
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def forward(self, in_tensor: TensorType[..., "in_dim"]) -> TensorType[..., "out_dim"]:
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"""Process input with a multilayer perceptron.
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Args:
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in_tensor (TensorType[..., "in_dim]): Network input
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Returns:
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TensorType[..., "out_dim"]: MLP network output
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"""
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x = in_tensor
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for i, layer in enumerate(self.layers):
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if i in self.skip_connections:
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x = torch.cat([in_tensor, x], -1)
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x = layer(x)
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if self.activation:
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x = self.activation(x)
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return x
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