mirror of
https://github.com/nerfstudio-project/nerfstudio.git
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188 lines
7.8 KiB
Python
188 lines
7.8 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 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 pyrad.data.structs import Semantics
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from pyrad.fields.modules.encoding import Encoding, Identity, NeRFEncoding
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from pyrad.fields.modules.field_heads import DensityFieldHead, FieldHeadNames, RGBFieldHead, SemanticFieldHead
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from pyrad.fields.modules.mlp import MLP
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from pyrad.fields.base import Field
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from pyrad.fields.nerf_field import NeRFField
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from pyrad.graphs.vanilla_nerf import NeRFGraph
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from pyrad.renderers.renderers import SemanticRenderer
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from pyrad.cameras.rays import RaySamples, RayBundle
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from pyrad.utils import writer
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class SemanticNerfField(Field):
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"""Semantic-NeRF field"""
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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))
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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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class SemanticNerfGraph(NeRFGraph):
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"""Semantic-NeRF graph"""
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def __init__(self, intrinsics=None, camera_to_world=None, semantics: Semantics = None, **kwargs) -> None:
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self.stuff_classes = semantics.stuff_classes
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self.stuff_colors = semantics.stuff_colors
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super().__init__(intrinsics=intrinsics, camera_to_world=camera_to_world, **kwargs)
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def populate_fields(self):
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"""Set the fields."""
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position_encoding = NeRFEncoding(
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in_dim=3, num_frequencies=10, min_freq_exp=0.0, max_freq_exp=8.0, include_input=True
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)
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direction_encoding = NeRFEncoding(
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in_dim=3, num_frequencies=4, min_freq_exp=0.0, max_freq_exp=4.0, include_input=True
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)
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self.field_coarse = NeRFField(position_encoding=position_encoding, direction_encoding=direction_encoding)
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num_semantic_classes = len(self.stuff_classes)
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self.field_fine = SemanticNerfField(
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num_semantic_classes, position_encoding=position_encoding, direction_encoding=direction_encoding
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)
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def populate_misc_modules(self):
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super().populate_misc_modules()
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self.renderer_semantic = SemanticRenderer()
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self.cross_entropy_loss = nn.CrossEntropyLoss(reduction="mean")
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def get_outputs(self, ray_bundle: RayBundle):
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# uniform sampling
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ray_samples_uniform = self.sampler_uniform(ray_bundle)
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# coarse field
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field_outputs_coarse = self.field_coarse.forward(ray_samples_uniform)
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weights_coarse = ray_samples_uniform.get_weights(field_outputs_coarse[FieldHeadNames.DENSITY])
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rgb_coarse = self.renderer_rgb(
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rgb=field_outputs_coarse[FieldHeadNames.RGB],
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weights=weights_coarse,
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)
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accumulation_coarse = self.renderer_accumulation(weights_coarse)
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depth_coarse = self.renderer_depth(weights_coarse, ray_samples_uniform)
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# pdf sampling
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ray_samples_pdf = self.sampler_pdf(ray_bundle, ray_samples_uniform, weights_coarse)
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# fine field
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field_outputs_fine = self.field_fine.forward(ray_samples_pdf)
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weights_fine = ray_samples_pdf.get_weights(field_outputs_fine[FieldHeadNames.DENSITY])
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rgb_fine = self.renderer_rgb(
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rgb=field_outputs_fine[FieldHeadNames.RGB],
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weights=weights_fine,
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)
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accumulation_fine = self.renderer_accumulation(weights_fine)
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depth_fine = self.renderer_depth(weights_fine, ray_samples_pdf)
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semantic_fine = self.renderer_semantic(field_outputs_fine[FieldHeadNames.SEMANTICS], weights=weights_fine)
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outputs = {
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"rgb_coarse": rgb_coarse,
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"rgb_fine": rgb_fine,
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"accumulation_coarse": accumulation_coarse,
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"accumulation_fine": accumulation_fine,
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"depth_coarse": depth_coarse,
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"depth_fine": depth_fine,
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"semantic_fine": semantic_fine,
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}
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return outputs
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def get_loss_dict(self, outputs, batch):
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image = batch["image"]
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rgb_loss_coarse = self.rgb_loss(image, outputs["rgb_coarse"])
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rgb_loss_fine = self.rgb_loss(image, outputs["rgb_fine"])
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semantic_logits = outputs["semantic_fine"]
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semantic_classes = batch["semantics"][..., 0].long()
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semantic_loss_fine = self.cross_entropy_loss(semantic_logits, semantic_classes)
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loss_dict = {
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"rgb_loss_coarse": rgb_loss_coarse,
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"rgb_loss_fine": rgb_loss_fine,
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"semantic_loss_fine": semantic_loss_fine,
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}
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return loss_dict
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def log_test_image_outputs(self, image_idx, step, batch, outputs):
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super().log_test_image_outputs(image_idx, step, batch, outputs)
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semantic_logits = outputs["semantic_fine"]
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semantic_labels = torch.argmax(torch.nn.functional.softmax(semantic_logits, dim=-1), dim=-1)
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semantic_labels_image = self.stuff_colors[semantic_labels]
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writer.put_image(name=f"semantics/image_idx_{image_idx}", image=semantic_labels_image, step=step)
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mask = batch["mask"].repeat(1, 1, 3)
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writer.put_image(name=f"mask/image_idx_{image_idx}", image=mask, step=step)
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