mirror of
https://github.com/nerfstudio-project/nerfstudio.git
synced 2026-11-11 02:24:38 +08:00
96 lines
3.4 KiB
Python
96 lines
3.4 KiB
Python
# Copyright 2022 The Plenoptix Team. All rights reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
"""
|
|
Multi Layer Perceptron
|
|
"""
|
|
from typing import Optional, Tuple
|
|
|
|
import torch
|
|
from torch import nn
|
|
from torchtyping import TensorType
|
|
|
|
from nerfactory.fields.modules.base import FieldModule
|
|
|
|
|
|
class MLP(FieldModule):
|
|
"""Multilayer perceptron
|
|
|
|
Args:
|
|
in_dim (int): Input layer dimension
|
|
num_layers (int): Number of network layers
|
|
layer_width (int): Width of each MLP layer
|
|
out_dim (int, optional): Ouput layer dimension. Defaults to layer_width
|
|
activation (Optional[nn.Module], optional): intermediate layer activation function. Defaults to nn.ReLU.
|
|
out_activation (Optional[nn.Module], optional): output activation function. Defaults to None.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
in_dim: int,
|
|
num_layers: int,
|
|
layer_width: int,
|
|
out_dim: Optional[int] = None,
|
|
skip_connections: Tuple[int] = (),
|
|
activation: Optional[nn.Module] = nn.ReLU(),
|
|
out_activation: Optional[nn.Module] = None,
|
|
) -> None:
|
|
|
|
super().__init__()
|
|
self.in_dim = in_dim
|
|
assert self.in_dim > 0
|
|
self.out_dim = out_dim if out_dim is not None else layer_width
|
|
self.num_layers = num_layers
|
|
self.layer_width = layer_width
|
|
self.skip_connections = skip_connections
|
|
self.activation = activation
|
|
self.out_activation = out_activation
|
|
self.net = None
|
|
self.build_nn_modules()
|
|
|
|
def build_nn_modules(self) -> None:
|
|
"""Initialize multi-layer perceptron."""
|
|
layers = []
|
|
if self.num_layers == 1:
|
|
layers.append(nn.Linear(self.in_dim, self.out_dim))
|
|
else:
|
|
for i in range(self.num_layers - 1):
|
|
if i == 0:
|
|
assert i not in list(self.skip_connections), "Skip connection at layer 0 doesn't make sense."
|
|
layers.append(nn.Linear(self.in_dim, self.layer_width))
|
|
elif i in self.skip_connections:
|
|
layers.append(nn.Linear(self.layer_width + self.in_dim, self.layer_width))
|
|
else:
|
|
layers.append(nn.Linear(self.layer_width, self.layer_width))
|
|
layers.append(nn.Linear(self.layer_width, self.out_dim))
|
|
self.layers = nn.ModuleList(layers)
|
|
|
|
def forward(self, in_tensor: TensorType[..., "in_dim"]) -> TensorType[..., "out_dim"]:
|
|
"""Process input with a multilayer perceptron.
|
|
|
|
Args:
|
|
in_tensor (TensorType[..., "in_dim]): Network input
|
|
|
|
Returns:
|
|
TensorType[..., "out_dim"]: MLP network output
|
|
"""
|
|
x = in_tensor
|
|
for i, layer in enumerate(self.layers):
|
|
if i in self.skip_connections:
|
|
x = torch.cat([in_tensor, x], -1)
|
|
x = layer(x)
|
|
if self.activation:
|
|
x = self.activation(x)
|
|
return x
|