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97 lines
3.4 KiB
Python
97 lines
3.4 KiB
Python
# Copyright 2022 The Plenoptix Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Multi Layer Perceptron
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"""
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from typing import Optional, Tuple
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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 nerfactory.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: Input layer dimension
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num_layers: Number of network layers
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layer_width: Width of each MLP layer
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out_dim: Ouput layer dimension. Defaults to layer_width
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activation: intermediate layer activation function. Defaults to nn.ReLU.
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out_activation: 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: Optional[Tuple[int]] = None,
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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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if self.skip_connections is not None:
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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 self.skip_connections is not None and 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["bs":..., "in_dim"]) -> TensorType["bs":..., "out_dim"]:
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"""Process input with a multilayer perceptron.
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Args:
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in_tensor: Network input
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Returns:
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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 self.skip_connections is not None and 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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