mirror of
https://github.com/nerfstudio-project/nerfstudio.git
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Saurav Maheshkar
de6af6c4e5
* feat(fields): create common get_normalized_directions fn * style: run black * refactor: rename func and update docstring
203 lines
8.2 KiB
Python
203 lines
8.2 KiB
Python
# Copyright 2022 The Nerfstudio 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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Instant-NGP field implementations using tiny-cuda-nn, torch, ....
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"""
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from typing import Dict, Optional, Tuple
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import numpy as np
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import torch
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from nerfacc import ContractionType, contract
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from torch.nn.parameter import Parameter
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from torchtyping import TensorType
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from nerfstudio.cameras.rays import RaySamples
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from nerfstudio.data.scene_box import SceneBox
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from nerfstudio.field_components.activations import trunc_exp
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from nerfstudio.field_components.embedding import Embedding
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from nerfstudio.field_components.field_heads import FieldHeadNames
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from nerfstudio.fields.base_field import Field, shift_directions_for_tcnn
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try:
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import tinycudann as tcnn
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except ImportError:
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# tinycudann module doesn't exist
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pass
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class TCNNInstantNGPField(Field):
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"""TCNN implementation of the Instant-NGP field.
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Args:
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aabb: parameters of scene aabb bounds
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num_layers: number of hidden layers
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hidden_dim: dimension of hidden layers
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geo_feat_dim: output geo feat dimensions
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num_layers_color: number of hidden layers for color network
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hidden_dim_color: dimension of hidden layers for color network
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use_appearance_embedding: whether to use appearance embedding
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num_images: number of images, required if use_appearance_embedding is True
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appearance_embedding_dim: dimension of appearance embedding
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contraction_type: type of contraction
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num_levels: number of levels of the hashmap for the base mlp
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log2_hashmap_size: size of the hashmap for the base mlp
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max_res: maximum resolution of the hashmap for the base mlp
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"""
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def __init__(
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self,
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aabb: TensorType,
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num_layers: int = 2,
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hidden_dim: int = 64,
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geo_feat_dim: int = 15,
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num_layers_color: int = 3,
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hidden_dim_color: int = 64,
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use_appearance_embedding: Optional[bool] = False,
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num_images: Optional[int] = None,
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appearance_embedding_dim: int = 32,
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contraction_type: ContractionType = ContractionType.UN_BOUNDED_SPHERE,
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num_levels: int = 16,
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log2_hashmap_size: int = 19,
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max_res: int = 2048,
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) -> None:
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super().__init__()
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self.aabb = Parameter(aabb, requires_grad=False)
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self.geo_feat_dim = geo_feat_dim
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self.contraction_type = contraction_type
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self.use_appearance_embedding = use_appearance_embedding
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if use_appearance_embedding:
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assert num_images is not None
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self.appearance_embedding_dim = appearance_embedding_dim
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self.appearance_embedding = Embedding(num_images, appearance_embedding_dim)
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# TODO: set this properly based on the aabb
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base_res: int = 16
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per_level_scale = np.exp((np.log(max_res) - np.log(base_res)) / (num_levels - 1))
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self.direction_encoding = tcnn.Encoding(
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n_input_dims=3,
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encoding_config={
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"otype": "SphericalHarmonics",
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"degree": 4,
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},
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)
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self.mlp_base = tcnn.NetworkWithInputEncoding(
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n_input_dims=3,
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n_output_dims=1 + self.geo_feat_dim,
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encoding_config={
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"otype": "HashGrid",
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"n_levels": num_levels,
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"n_features_per_level": 2,
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"log2_hashmap_size": log2_hashmap_size,
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"base_resolution": base_res,
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"per_level_scale": per_level_scale,
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},
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network_config={
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"otype": "FullyFusedMLP",
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"activation": "ReLU",
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"output_activation": "None",
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"n_neurons": hidden_dim,
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"n_hidden_layers": num_layers - 1,
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},
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)
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in_dim = self.direction_encoding.n_output_dims + self.geo_feat_dim
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if self.use_appearance_embedding:
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in_dim += self.appearance_embedding_dim
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self.mlp_head = tcnn.Network(
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n_input_dims=in_dim,
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n_output_dims=3,
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network_config={
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"otype": "FullyFusedMLP",
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"activation": "ReLU",
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"output_activation": "Sigmoid",
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"n_neurons": hidden_dim_color,
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"n_hidden_layers": num_layers_color - 1,
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},
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)
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def get_density(self, ray_samples: RaySamples) -> Tuple[TensorType, TensorType]:
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positions = ray_samples.frustums.get_positions()
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positions_flat = positions.view(-1, 3)
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positions_flat = contract(x=positions_flat, roi=self.aabb, type=self.contraction_type)
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h = self.mlp_base(positions_flat).view(*ray_samples.frustums.shape, -1)
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density_before_activation, base_mlp_out = torch.split(h, [1, self.geo_feat_dim], dim=-1)
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# Rectifying the density with an exponential is much more stable than a ReLU or
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# softplus, because it enables high post-activation (float32) density outputs
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# from smaller internal (float16) parameters.
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density = trunc_exp(density_before_activation.to(positions))
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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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directions = shift_directions_for_tcnn(ray_samples.frustums.directions)
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directions_flat = directions.view(-1, 3)
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d = self.direction_encoding(directions_flat)
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if density_embedding is None:
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positions = SceneBox.get_normalized_positions(ray_samples.frustums.get_positions(), self.aabb)
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h = torch.cat([d, positions.view(-1, 3)], dim=-1)
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else:
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h = torch.cat([d, density_embedding.view(-1, self.geo_feat_dim)], dim=-1)
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if self.use_appearance_embedding:
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if ray_samples.camera_indices is None:
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raise AttributeError("Camera indices are not provided.")
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camera_indices = ray_samples.camera_indices.squeeze()
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if self.training:
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embedded_appearance = self.appearance_embedding(camera_indices)
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else:
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embedded_appearance = torch.zeros(
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(*directions.shape[:-1], self.appearance_embedding_dim), device=directions.device
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)
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h = torch.cat([h, embedded_appearance.view(-1, self.appearance_embedding_dim)], dim=-1)
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rgb = self.mlp_head(h).view(*ray_samples.frustums.directions.shape[:-1], -1).to(directions)
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return {FieldHeadNames.RGB: rgb}
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def get_opacity(self, positions: TensorType["bs":..., 3], step_size) -> TensorType["bs":..., 1]:
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"""Returns the opacity for a position. Used primarily by the occupancy grid.
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Args:
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positions: the positions to evaluate the opacity at.
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step_size: the step size to use for the opacity evaluation.
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"""
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density = self.density_fn(positions)
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## TODO: We should scale step size based on the distortion. Currently it uses too much memory.
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# aabb_min, aabb_max = self.aabb[0], self.aabb[1]
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# if self.contraction_type is not ContractionType.AABB:
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# x = (positions - aabb_min) / (aabb_max - aabb_min)
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# x = x * 2 - 1 # aabb is at [-1, 1]
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# mag = x.norm(dim=-1, keepdim=True)
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# mask = mag.squeeze(-1) > 1
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# dev = (2 * mag - 1) / mag**2 + 2 * x**2 * (1 / mag**3 - (2 * mag - 1) / mag**4)
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# dev[~mask] = 1.0
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# dev = torch.clamp(dev, min=1e-6)
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# step_size = step_size / dev.norm(dim=-1, keepdim=True)
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# else:
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# step_size = step_size * (aabb_max - aabb_min)
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opacity = density * step_size
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return opacity
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