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106 lines
4.0 KiB
Python
106 lines
4.0 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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Semantic NeRF field implementation.
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"""
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from typing import Dict, 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.cameras.rays import RaySamples
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from nerfactory.fields.base import Field
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from nerfactory.fields.modules.encoding import Encoding, Identity
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from nerfactory.fields.modules.field_heads import (
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DensityFieldHead,
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FieldHeadNames,
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RGBFieldHead,
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SemanticFieldHead,
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)
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from nerfactory.fields.modules.mlp import MLP
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class SemanticNerfField(Field):
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"""Semantic-NeRF field
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Args:
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num_semantic_classes: Number of distinct semantic classes.
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position_encoding: Position encoder.
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direction_encoding: Direction encoder.
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base_mlp_num_layers: Number of layers for base MLP.
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base_mlp_layer_width: Width of base MLP layers.
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head_mlp_num_layers: Number of layer for ourput head MLP.
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head_mlp_layer_width: Width of output head MLP layers.
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skip_connections: Where to add skip connection in base MLP.
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"""
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def __init__(
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self,
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num_semantic_classes: int,
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position_encoding: Encoding = Identity(in_dim=3),
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direction_encoding: Encoding = Identity(in_dim=3),
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base_mlp_num_layers: int = 8,
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base_mlp_layer_width: int = 256,
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head_mlp_num_layers: int = 2,
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head_mlp_layer_width: int = 128,
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skip_connections: Tuple[int] = (4,),
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) -> None:
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super().__init__()
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self.num_semantic_classes = num_semantic_classes
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self.position_encoding = position_encoding
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self.direction_encoding = direction_encoding
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self.mlp_base = MLP(
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in_dim=self.position_encoding.get_out_dim(),
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num_layers=base_mlp_num_layers,
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layer_width=base_mlp_layer_width,
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skip_connections=skip_connections,
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)
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self.mlp_head = MLP(
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in_dim=self.mlp_base.get_out_dim() + self.direction_encoding.get_out_dim(),
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num_layers=head_mlp_num_layers,
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layer_width=head_mlp_layer_width,
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)
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self.mlp_semantic = MLP(
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in_dim=self.mlp_head.get_out_dim(),
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layer_width=self.mlp_head.layer_width // 2,
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num_layers=1,
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activation=nn.ReLU(),
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)
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self.field_head_density = DensityFieldHead(in_dim=self.mlp_base.get_out_dim())
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self.field_head_rgb = RGBFieldHead(in_dim=self.mlp_head.get_out_dim())
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self.field_head_semantic = SemanticFieldHead(
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in_dim=self.mlp_semantic.get_out_dim(), num_classes=self.num_semantic_classes
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)
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def get_density(self, ray_samples: RaySamples):
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encoded_xyz = self.position_encoding(ray_samples.frustums.get_positions())
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base_mlp_out = self.mlp_base(encoded_xyz)
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density = self.field_head_density(base_mlp_out)
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return density, base_mlp_out
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def get_outputs(
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self, ray_samples: RaySamples, density_embedding: Optional[TensorType] = None
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) -> Dict[FieldHeadNames, TensorType]:
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encoded_dir = self.direction_encoding(ray_samples.frustums.directions)
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mlp_out = self.mlp_head(torch.cat([encoded_dir, density_embedding], dim=-1)) # type: ignore
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outputs = {}
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# rgb
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outputs[self.field_head_rgb.field_head_name] = self.field_head_rgb(mlp_out)
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# semantic
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mlp_out_sem = self.mlp_semantic(mlp_out)
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outputs[self.field_head_semantic.field_head_name] = self.field_head_semantic(mlp_out_sem)
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return outputs
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