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https://github.com/nerfstudio-project/nerfstudio.git
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383a6075cc
* initial running code * dozer and poster 6K experiments * code reorganization * removed print * black
113 lines
3.9 KiB
Python
113 lines
3.9 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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Proposal network field.
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"""
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from typing import Optional
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import numpy as np
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from torch.nn.parameter import Parameter
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from torchtyping import TensorType
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from nerfactory.cameras.rays import RaySamples
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from nerfactory.datamanagers.structs import SceneBounds
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from nerfactory.fields.base import Field
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from nerfactory.fields.modules.spatial_distortions import SpatialDistortion
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from nerfactory.utils.activations import trunc_exp
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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 DensityField(Field):
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"""A lightweight density field module.
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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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spatial_distortion: spatial distortion module
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"""
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def __init__(
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self, aabb, num_layers: int = 2, hidden_dim: int = 64, spatial_distortion: Optional[SpatialDistortion] = None
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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.spatial_distortion = spatial_distortion
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num_levels = 16
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# max_res = 2048
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max_res = 4096
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base_res = 16
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growth_factor = 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,
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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": 19,
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"base_resolution": base_res,
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"per_level_scale": growth_factor,
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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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def get_density(self, ray_samples: RaySamples):
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if self.spatial_distortion is not None:
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positions = self.spatial_distortion(ray_samples.frustums.get_positions())
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positions = (positions + 2.0) / 4.0
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else:
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positions = SceneBounds.get_normalized_positions(ray_samples.frustums.get_positions(), self.aabb)
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positions_flat = positions.view(-1, 3)
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# assert all positions are in the range [0, 1]
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# otherwise print min and max values
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# assert torch.all(positions >= 0.0) and torch.all(
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# positions <= 1.0
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# ), f"positions: {positions.min()} {positions.max()}"
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density_before_activation = self.mlp_base(positions_flat).view(*ray_samples.frustums.shape, -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, None
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def get_outputs(self, ray_samples: RaySamples, density_embedding: Optional[TensorType] = None):
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return {}
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