PanoHDR-Nerfacto model

nerfstudio/configs/method_configs.py

@@ -66,13 +66,14 @@ from nerfstudio.pipelines.base_pipeline import VanillaPipelineConfig
 from nerfstudio.pipelines.dynamic_batch import DynamicBatchPipelineConfig
 from nerfstudio.plugins.registry import discover_methods
 
-from nerfstudio.lantern.config import Get_lantern_config
+from nerfstudio.lantern.config import Get_lantern_config, Get_PanoHDR_Nerfacto_config
 
 method_configs: Dict[str, TrainerConfig] = {}
 descriptions = {
     "nerfacto": "Recommended real-time model tuned for real captures. This model will be continually updated.",
     "nerfacto-HDR": "Nerfacto that take HDR as input, and output HDR field.",
-    "lantern-nerfacto": "Nerfacto that take HDR as input, and output HDR field.",
+    "lantern-nerfacto": "Nerfacto that train on two exposures.",
+    "panoHDR-nerfacto": "Nerfacto that take HDR as input, and output HDR field.",
     "depth-nerfacto": "Nerfacto with depth supervision.",
     "volinga": "Real-time rendering model from Volinga. Directly exportable to NVOL format at https://volinga.ai/",
     "instant-ngp": "Implementation of Instant-NGP. Recommended real-time model for unbounded scenes.",
@@ -122,6 +123,7 @@ method_configs["nerfacto"] = TrainerConfig(
 )
 
 method_configs["lantern-nerfacto"] = Get_lantern_config()
+method_configs["panoHDR-nerfacto"] = Get_PanoHDR_Nerfacto_config()
 
 method_configs["nerfacto-big"] = TrainerConfig(
     method_name="nerfacto",

nerfstudio/data/dataparsers/nerfstudio_dataparser.py

@@ -294,8 +294,11 @@ class Nerfstudio(DataParser):
         else:
             distortion_params = torch.stack(distort, dim=0)[idx_tensor]
 
-        exposure_tensor = torch.tensor([exposures], dtype=torch.float32).T
-
+        if len(exposures) != 0:
+            exposure_tensor = torch.tensor([exposures], dtype=torch.float32).T
+        else:
+            exposure_tensor = []
+        
         cameras = Cameras(
             fx=fx,
             fy=fy,

nerfstudio/lantern/config.py

@@ -14,6 +14,7 @@ from nerfstudio.engine.schedulers import ExponentialDecaySchedulerConfig
 from nerfstudio.engine.trainer import TrainerConfig
 from nerfstudio.lantern.datamanager import HDRVanillaDataManagerConfig
 from nerfstudio.lantern.model import LanternModelConfig
+from nerfstudio.lantern.model_pano_hdr_nerfacto import PanoHDRNeRFModelConfig
 from nerfstudio.pipelines.base_pipeline import VanillaPipelineConfig
 
 
@@ -50,4 +51,40 @@ def Get_lantern_config():
     },
     viewer=ViewerConfig(num_rays_per_chunk=1 << 15),
     vis="viewer",
+    )
+
+
+def Get_PanoHDR_Nerfacto_config():
+    return TrainerConfig(
+    method_name="panoHDR-nerfacto",
+    steps_per_eval_batch=500,
+    steps_per_save=2000,
+    max_num_iterations=30000,
+    mixed_precision=True,
+    pipeline=VanillaPipelineConfig(
+            datamanager=HDRVanillaDataManagerConfig(
+            dataparser=NerfstudioDataParserConfig(),
+            train_num_rays_per_batch=8192,
+            eval_num_rays_per_batch=8192,
+            # camera_optimizer=CameraOptimizerConfig(
+            #     mode="SO3xR3",
+            #     optimizer=AdamOptimizerConfig(lr=6e-4, eps=1e-8, weight_decay=1e-2),
+            #     scheduler=ExponentialDecaySchedulerConfig(lr_final=6e-6, max_steps=200000),
+            # ),
+            camera_optimizer=None,
+        ),
+        model=PanoHDRNeRFModelConfig(eval_num_rays_per_chunk=1 << 15),
+    ),
+    optimizers={
+        "proposal_networks": {
+            "optimizer": AdamOptimizerConfig(lr=1e-2, eps=1e-15),
+            "scheduler": ExponentialDecaySchedulerConfig(lr_final=0.0001, max_steps=200000),
+        },
+        "fields": {
+            "optimizer": AdamOptimizerConfig(lr=1e-2, eps=1e-15),
+            "scheduler": ExponentialDecaySchedulerConfig(lr_final=0.0001, max_steps=200000),
+        },
+    },
+    viewer=ViewerConfig(num_rays_per_chunk=1 << 15),
+    vis="viewer",
     )

nerfstudio/lantern/field_pano_hdr_nerfacto.py

@@ -0,0 +1,103 @@
+"""
+HDR Nerfstudio Field
+
+Currently this subclasses the NerfactoField. Consider subclassing the base Field.
+"""
+
+from typing import Dict, Literal, Optional, Tuple
+
+import torch
+from torch import Tensor, nn
+
+from nerfstudio.cameras.rays import RaySamples
+from nerfstudio.data.scene_box import SceneBox
+from nerfstudio.field_components.activations import trunc_exp
+from nerfstudio.field_components.embedding import Embedding
+from nerfstudio.field_components.encodings import HashEncoding, NeRFEncoding, SHEncoding
+from nerfstudio.field_components.encodings import Encoding, Identity
+from nerfstudio.field_components.field_heads import (
+    FieldHeadNames,
+    PredNormalsFieldHead,
+    SemanticFieldHead,
+    TransientDensityFieldHead,
+    TransientRGBFieldHead,
+    UncertaintyFieldHead,
+)
+from nerfstudio.field_components.mlp import MLP
+from nerfstudio.field_components.spatial_distortions import SpatialDistortion
+from nerfstudio.fields.base_field import Field, get_normalized_directions
+from nerfstudio.fields.nerfacto_field import \
+    NerfactoField  # for subclassing NerfactoField
+
+
+class PanoHDR_NerfactoField(NerfactoField):
+    """Template Field
+
+    Args:
+        aabb: parameters of scene aabb bounds
+        num_images: number of images in the dataset
+    """
+
+    aabb: Tensor
+
+    def __init__(
+        self,
+        aabb: Tensor,
+        num_images: int,
+        num_layers: int = 2,
+        hidden_dim: int = 64,
+        geo_feat_dim: int = 15,
+        num_levels: int = 16,
+        base_res: int = 16,
+        max_res: int = 2048,
+        log2_hashmap_size: int = 19,
+        num_layers_color: int = 3,
+        num_layers_transient: int = 2,
+        features_per_level: int = 2,
+        hidden_dim_color: int = 64,
+        hidden_dim_transient: int = 64,
+        appearance_embedding_dim: int = 32,
+        transient_embedding_dim: int = 16,
+        use_transient_embedding: bool = True,
+        use_semantics: bool = False,
+        num_semantic_classes: int = 100,
+        pass_semantic_gradients: bool = False,
+        use_pred_normals: bool = False,
+        use_average_appearance_embedding: bool = False,
+        spatial_distortion: Optional[SpatialDistortion] = None,
+        implementation: Literal["tcnn", "torch"] = "tcnn",
+        use_appearance_embedding: bool = True,
+    ) -> None:
+        super().__init__(aabb, num_images, num_layers, hidden_dim,
+                         geo_feat_dim, num_levels, base_res, 
+                         max_res, log2_hashmap_size, num_layers_color,
+                         num_layers_transient, features_per_level, hidden_dim_color,
+                         hidden_dim_transient, appearance_embedding_dim, transient_embedding_dim,
+                         use_transient_embedding, use_semantics, num_semantic_classes,
+                         pass_semantic_gradients, use_pred_normals, use_average_appearance_embedding,
+                         spatial_distortion, implementation, use_appearance_embedding)
+        
+        if self.use_appearance_embedding:
+            self.mlp_head = MLP(
+                in_dim=self.direction_encoding.get_out_dim() + self.geo_feat_dim + self.appearance_embedding_dim,
+                num_layers=num_layers_color,
+                layer_width=hidden_dim_color,
+                out_dim= 3,
+                activation=nn.ReLU(),
+                out_activation=nn.ReLU(),
+                # out_activation=nn.Sigmoid(),
+                implementation=implementation,
+            )
+
+        # Define the last part MLP for RGB outputs and Masks
+        else:
+            self.mlp_head = MLP(
+                in_dim=self.direction_encoding.get_out_dim() + self.geo_feat_dim,
+                num_layers=num_layers_color,
+                layer_width=hidden_dim_color,
+                out_dim= 3,
+                activation=nn.ReLU(),
+                out_activation=nn.ReLU(),
+                # out_activation=nn.Sigmoid(),
+                implementation=implementation,
+            )

nerfstudio/lantern/model.py

@@ -188,10 +188,10 @@ class LanternModel(NerfactoModel):
         validity = self.renderer_rgb(rgb=field_outputs[FieldHeadNames.VALIDITY], weights=weights)
         rgb_fast = self.renderer_rgb(rgb=field_outputs[FieldHeadNames.RGB_FAST], weights=weights)
 
-        if self.config.apply_mu_law:
-            u = 5000.
-            rgb_fast = torch.exp(rgb_fast * torch.log(torch.tensor(u+1.))) - 1.
-            rgb_fast /= u
+        # if self.config.apply_mu_law:
+        #     u = 5000.
+        #     rgb_fast = torch.exp(rgb_fast * torch.log(torch.tensor(u+1.))) - 1.
+        #     rgb_fast /= u
        
         outputs = {
             "rgb": rgb,

nerfstudio/lantern/model_pano_hdr_nerfacto.py

@@ -0,0 +1,294 @@
+"""
+Lantern Model File
+
+Currently this subclasses the Nerfacto model. Consider subclassing from the base Model.
+"""
+from dataclasses import dataclass, field
+from typing import Dict, Tuple, Type
+
+import numpy as np
+import torch
+from torchmetrics.functional import structural_similarity_index_measure
+from torchmetrics.image import PeakSignalNoiseRatio
+from torchmetrics.image.lpip import LearnedPerceptualImagePatchSimilarity
+
+from nerfstudio.cameras.rays import RayBundle, RaySamples
+from nerfstudio.field_components.field_heads import FieldHeadNames
+from nerfstudio.field_components.spatial_distortions import SceneContraction
+from nerfstudio.fields.density_fields import HashMLPDensityField
+from nerfstudio.lantern.field_pano_hdr_nerfacto import PanoHDR_NerfactoField
+from nerfstudio.lantern.renderer import RGBRenderer_HDR
+from nerfstudio.model_components.losses import (
+    MSELoss,
+    interlevel_loss,
+    orientation_loss,
+    pred_normal_loss,
+    scale_gradients_by_distance_squared,
+)
+from nerfstudio.model_components.ray_samplers import (
+    ProposalNetworkSampler,
+    UniformSampler,
+)
+from nerfstudio.model_components.renderers import (
+    AccumulationRenderer,
+    DepthRenderer,
+    NormalsRenderer,
+    SemanticRenderer,
+)
+from nerfstudio.model_components.scene_colliders import NearFarCollider
+from nerfstudio.model_components.shaders import NormalsShader
+from nerfstudio.models.nerfacto import (  # for subclassing Nerfacto model
+    NerfactoModel,
+    NerfactoModelConfig,
+)
+from nerfstudio.utils import colormaps
+
+
+@dataclass
+class PanoHDRNeRFModelConfig(NerfactoModelConfig):
+    """Template Model Configuration.
+
+    Add your custom model config parameters here.
+    """
+
+    _target: Type = field(default_factory=lambda: PanoHDRNeRFModel)
+
+
+class PanoHDRNeRFModel(NerfactoModel):
+    """Template Model."""
+
+    config: PanoHDRNeRFModelConfig
+
+    def populate_modules(self):
+        super().populate_modules()
+
+        if self.config.disable_scene_contraction:
+            scene_contraction = None
+        else:
+            scene_contraction = SceneContraction(order=float("inf"))
+
+        # Fields
+        self.field = PanoHDR_NerfactoField(
+            self.scene_box.aabb,
+            hidden_dim=self.config.hidden_dim,
+            num_levels=self.config.num_levels,
+            max_res=self.config.max_res,
+            base_res=self.config.base_res,
+            features_per_level=self.config.features_per_level,
+            log2_hashmap_size=self.config.log2_hashmap_size,
+            hidden_dim_color=self.config.hidden_dim_color,
+            hidden_dim_transient=self.config.hidden_dim_transient,
+            spatial_distortion=scene_contraction,
+            num_images=self.num_train_data,
+            use_pred_normals=self.config.predict_normals,
+            use_average_appearance_embedding=self.config.use_average_appearance_embedding,
+            appearance_embedding_dim=self.config.appearance_embed_dim,
+            implementation=self.config.implementation,
+        )
+
+        self.density_fns = []
+        num_prop_nets = self.config.num_proposal_iterations
+        # Build the proposal network(s)
+        self.proposal_networks = torch.nn.ModuleList()
+        if self.config.use_same_proposal_network:
+            assert len(self.config.proposal_net_args_list) == 1, "Only one proposal network is allowed."
+            prop_net_args = self.config.proposal_net_args_list[0]
+            network = HashMLPDensityField(
+                self.scene_box.aabb,
+                spatial_distortion=scene_contraction,
+                **prop_net_args,
+                implementation=self.config.implementation,
+            )
+            self.proposal_networks.append(network)
+            self.density_fns.extend([network.density_fn for _ in range(num_prop_nets)])
+        else:
+            for i in range(num_prop_nets):
+                prop_net_args = self.config.proposal_net_args_list[min(i, len(self.config.proposal_net_args_list) - 1)]
+                network = HashMLPDensityField(
+                    self.scene_box.aabb,
+                    spatial_distortion=scene_contraction,
+                    **prop_net_args,
+                    implementation=self.config.implementation,
+                )
+                self.proposal_networks.append(network)
+            self.density_fns.extend([network.density_fn for network in self.proposal_networks])
+
+        # Samplers
+        def update_schedule(step):
+            return np.clip(
+                np.interp(step, [0, self.config.proposal_warmup], [0, self.config.proposal_update_every]),
+                1,
+                self.config.proposal_update_every,
+            )
+
+        # Change proposal network initial sampler if uniform
+        initial_sampler = None  # None is for piecewise as default (see ProposalNetworkSampler)
+        if self.config.proposal_initial_sampler == "uniform":
+            initial_sampler = UniformSampler(single_jitter=self.config.use_single_jitter)
+
+        self.proposal_sampler = ProposalNetworkSampler(
+            num_nerf_samples_per_ray=self.config.num_nerf_samples_per_ray,
+            num_proposal_samples_per_ray=self.config.num_proposal_samples_per_ray,
+            num_proposal_network_iterations=self.config.num_proposal_iterations,
+            single_jitter=self.config.use_single_jitter,
+            update_sched=update_schedule,
+            initial_sampler=initial_sampler,
+        )
+
+        # Collider
+        self.collider = NearFarCollider(near_plane=self.config.near_plane, far_plane=self.config.far_plane)
+
+        # renderers
+        self.renderer_rgb = RGBRenderer_HDR(background_color=self.config.background_color)
+        self.renderer_accumulation = AccumulationRenderer()
+        self.renderer_depth = DepthRenderer()
+        self.renderer_normals = NormalsRenderer()
+        self.renderer_validity = SemanticRenderer()
+
+        # shaders
+        self.normals_shader = NormalsShader()
+
+        # TODO losses
+        self.rgb_loss = MSELoss()
+
+        # metrics, psnr
+        self.psnr = PeakSignalNoiseRatio(data_range=1.0)
+        self.ssim = structural_similarity_index_measure
+        self.lpips = LearnedPerceptualImagePatchSimilarity(normalize=True)
+
+
+    def get_outputs(self, ray_bundle: RayBundle):
+        ray_samples: RaySamples
+        ray_samples, weights_list, ray_samples_list = self.proposal_sampler(ray_bundle, density_fns=self.density_fns)
+
+        field_outputs = self.field.forward(ray_samples, compute_normals=self.config.predict_normals)
+        if self.config.use_gradient_scaling:
+            field_outputs = scale_gradients_by_distance_squared(field_outputs, ray_samples)
+
+        weights = ray_samples.get_weights(field_outputs[FieldHeadNames.DENSITY])
+        weights_list.append(weights)
+        ray_samples_list.append(ray_samples)
+
+        rgb = self.renderer_rgb(rgb=field_outputs[FieldHeadNames.RGB], weights=weights)
+        depth = self.renderer_depth(weights=weights, ray_samples=ray_samples)
+        accumulation = self.renderer_accumulation(weights=weights)
+       
+        outputs = {
+            "rgb": rgb,
+            "accumulation": accumulation,
+            "depth": depth,
+        }
+        
+        if self.config.predict_normals:
+            normals = self.renderer_normals(normals=field_outputs[FieldHeadNames.NORMALS], weights=weights)
+            pred_normals = self.renderer_normals(field_outputs[FieldHeadNames.PRED_NORMALS], weights=weights)
+            outputs["normals"] = self.normals_shader(normals)
+            outputs["pred_normals"] = self.normals_shader(pred_normals)
+        # These use a lot of GPU memory, so we avoid storing them for eval.
+        if self.training:
+            outputs["weights_list"] = weights_list
+            outputs["ray_samples_list"] = ray_samples_list
+
+        if self.training and self.config.predict_normals:
+            outputs["rendered_orientation_loss"] = orientation_loss(
+                weights.detach(), field_outputs[FieldHeadNames.NORMALS], ray_bundle.directions
+            )
+
+            outputs["rendered_pred_normal_loss"] = pred_normal_loss(
+                weights.detach(),
+                field_outputs[FieldHeadNames.NORMALS].detach(),
+                field_outputs[FieldHeadNames.PRED_NORMALS],
+            )
+
+        for i in range(self.config.num_proposal_iterations):
+            outputs[f"prop_depth_{i}"] = self.renderer_depth(weights=weights_list[i], ray_samples=ray_samples_list[i])
+
+        return outputs
+    
+    
+    def get_image_metrics_and_images(
+        self, outputs: Dict[str, torch.Tensor], batch: Dict[str, torch.Tensor]
+        ) -> Tuple[Dict[str, float], Dict[str, torch.Tensor]]:
+        
+        gt_rgb = batch["image"].to(self.device)
+        predicted_rgb = outputs["rgb"]  # Blended with background (black if random background)
+        
+        gt_rgb = self.renderer_rgb.blend_background(gt_rgb)
+        acc = colormaps.apply_colormap(outputs["accumulation"])
+        depth = colormaps.apply_depth_colormap(
+            outputs["depth"],
+            accumulation=outputs["accumulation"],
+        )
+        gt_rgb = batch["mask"] * gt_rgb
+        predicted_rgb = batch["mask"] * predicted_rgb
+        
+        combined_rgb = torch.cat([gt_rgb, predicted_rgb], dim=1)
+        combined_acc = torch.cat([acc], dim=1)
+        combined_depth = torch.cat([depth], dim=1)
+
+        # Switch images from [H, W, C] to [1, C, H, W] for metrics computations
+        # gt_rgb_tonemapped = torch.clamp(colormaps.apply_colormap(gt_rgb), 0, 1)
+        gt_rgb_tonemapped = torch.clamp(gt_rgb, 0, 1)
+        # predicted_rgb_tonemapped = torch.clamp(colormaps.apply_colormap(predicted_rgb), 0, 1)
+        predicted_rgb_tonemapped = torch.clamp(predicted_rgb, 0, 1)
+        predicted_rgb_tonemapped = torch.nan_to_num(predicted_rgb_tonemapped, nan=0.0)
+        gt_rgb_tonemapped = torch.moveaxis(gt_rgb_tonemapped, -1, 0)[None, ...]
+        predicted_rgb_tonemapped = torch.moveaxis(predicted_rgb_tonemapped, -1, 0)[None, ...]
+        gt_rgb = torch.moveaxis(gt_rgb, -1, 0)[None, ...]
+        predicted_rgb = torch.moveaxis(predicted_rgb, -1, 0)[None, ...]
+
+        psnr = self.psnr(gt_rgb, predicted_rgb)
+        ssim = self.ssim(gt_rgb, predicted_rgb)
+        # tonemapping before computing lpips
+        lpips = self.lpips(gt_rgb_tonemapped, predicted_rgb_tonemapped)
+
+        # all of these metrics will be logged as scalars
+        metrics_dict = {"psnr": float(psnr.item()), "ssim": float(ssim)}  # type: ignore
+        metrics_dict["lpips"] = float(lpips)
+
+        images_dict = {"img": combined_rgb, "accumulation": combined_acc, "depth": combined_depth}
+
+        for i in range(self.config.num_proposal_iterations):
+            key = f"prop_depth_{i}"
+            prop_depth_i = colormaps.apply_depth_colormap(
+                outputs[key],
+                accumulation=outputs["accumulation"],
+            )
+            images_dict[key] = prop_depth_i
+
+        return metrics_dict, images_dict
+ 
+    
+    def get_loss_dict(self, outputs, batch, metrics_dict=None):
+        loss_dict = {}
+        weights_for_RGB_loss = None
+
+        image = batch["image"].to(self.device)
+        
+        pred_rgb, gt_rgb = self.renderer_rgb.blend_background_for_loss_computation(
+            pred_image=outputs["rgb"],
+            pred_accumulation=outputs["accumulation"],
+            gt_image=image,
+        )
+        
+        gt_rgb = gt_rgb ** (1 / 2.2)
+        loss_dict["rgb_loss"] = self.rgb_loss(gt_rgb, pred_rgb)
+        
+        if self.training:
+            loss_dict["interlevel_loss"] = self.config.interlevel_loss_mult * interlevel_loss(
+                outputs["weights_list"], outputs["ray_samples_list"]
+            )
+            assert metrics_dict is not None and "distortion" in metrics_dict
+            loss_dict["distortion_loss"] = self.config.distortion_loss_mult * metrics_dict["distortion"]
+            # loss_dict["fg"] = (torch.pow(1 - torch.sum( outputs["weights_list"][-1], dim=0), 2)).mean()
+            if self.config.predict_normals:
+                # orientation loss for computed normals
+                loss_dict["orientation_loss"] = self.config.orientation_loss_mult * torch.mean(
+                    outputs["rendered_orientation_loss"]
+                )
+
+                # ground truth supervision for normals
+                loss_dict["pred_normal_loss"] = self.config.pred_normal_loss_mult * torch.mean(
+                    outputs["rendered_pred_normal_loss"]
+                )
+        return loss_dict