vid to nerf and hleitchnerf added

Moving into a seperate branch for playing with nerfacto (using hleitchnerf)

.vs/ProjectSettings.json

@@ -0,0 +1,3 @@
+{
+  "CurrentProjectSetting": null
+}

.vs/PythonSettings.json

@@ -0,0 +1,3 @@
+{
+  "TestFramework": "Pytest"
+}

experimenting.py

@@ -0,0 +1,5 @@
+from torchtyping import TensorType
+
+test = TensorType["Num_verts", 3]
+
+print(test)

nerfstudio/models/hleitchnerf.py

@@ -0,0 +1,351 @@
+# Copyright 2022 The Nerfstudio Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+NeRF implementation that combines many recent advancements.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import Dict, List, Tuple, Type
+
+import numpy as np
+import torch
+from torch.nn import Parameter
+from torchmetrics import PeakSignalNoiseRatio
+from torchmetrics.functional import structural_similarity_index_measure
+from torchmetrics.image.lpip import LearnedPerceptualImagePatchSimilarity
+from typing_extensions import Literal
+
+from nerfstudio.cameras.rays import RayBundle
+from nerfstudio.engine.callbacks import (
+    TrainingCallback,
+    TrainingCallbackAttributes,
+    TrainingCallbackLocation,
+)
+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.fields.nerfacto_field import TCNNNerfactoField
+from nerfstudio.model_components.losses import (
+    MSELoss,
+    distortion_loss,
+    interlevel_loss,
+    orientation_loss,
+    pred_normal_loss,
+)
+from nerfstudio.model_components.ray_samplers import ProposalNetworkSampler
+from nerfstudio.model_components.renderers import (
+    AccumulationRenderer,
+    DepthRenderer,
+    NormalsRenderer,
+    RGBRenderer,
+)
+from nerfstudio.model_components.scene_colliders import NearFarCollider
+from nerfstudio.models.base_model import Model, ModelConfig
+from nerfstudio.utils import colormaps
+
+
+@dataclass
+class NerfactoModelConfig(ModelConfig):
+    """Nerfacto Model Config"""
+
+    _target: Type = field(default_factory=lambda: NerfactoModel)
+    near_plane: float = 0.05
+    """How far along the ray to start sampling."""
+    far_plane: float = 1000.0
+    """How far along the ray to stop sampling."""
+    background_color: Literal["background", "last_sample"] = "last_sample"
+    """Whether to randomize the background color."""
+    num_levels: int = 16
+    """Number of levels of the hashmap for the base mlp."""
+    max_res: int = 1024
+    """Maximum resolution of the hashmap for the base mlp."""
+    log2_hashmap_size: int = 19
+    """Size of the hashmap for the base mlp"""
+    num_proposal_samples_per_ray: Tuple[int] = (256, 96)
+    """Number of samples per ray for the proposal network."""
+    num_nerf_samples_per_ray: int = 48
+    """Number of samples per ray for the nerf network."""
+    proposal_update_every: int = 5
+    """Sample every n steps after the warmup"""
+    proposal_warmup: int = 5000
+    """Scales n from 1 to proposal_update_every over this many steps"""
+    num_proposal_iterations: int = 2
+    """Number of proposal network iterations."""
+    use_same_proposal_network: bool = False
+    """Use the same proposal network. Otherwise use different ones."""
+    proposal_net_args_list: List[Dict] = field(
+        default_factory=lambda: [
+            {"hidden_dim": 16, "log2_hashmap_size": 17, "num_levels": 5, "max_res": 64},
+            {"hidden_dim": 16, "log2_hashmap_size": 17, "num_levels": 5, "max_res": 256},
+        ]
+    )
+    """Arguments for the proposal density fields."""
+    interlevel_loss_mult: float = 1.0
+    """Proposal loss multiplier."""
+    distortion_loss_mult: float = 0.002
+    """Distortion loss multiplier."""
+    orientation_loss_mult: float = 0.0001
+    """Orientation loss multipier on computed noramls."""
+    pred_normal_loss_mult: float = 0.001
+    """Predicted normal loss multiplier."""
+    use_proposal_weight_anneal: bool = True
+    """Whether to use proposal weight annealing."""
+    use_average_appearance_embedding: bool = True
+    """Whether to use average appearance embedding or zeros for inference."""
+    proposal_weights_anneal_slope: float = 10.0
+    """Slope of the annealing function for the proposal weights."""
+    proposal_weights_anneal_max_num_iters: int = 1000
+    """Max num iterations for the annealing function."""
+    use_single_jitter: bool = True
+    """Whether use single jitter or not for the proposal networks."""
+    predict_normals: bool = False
+    """Whether to predict normals or not."""
+
+
+class NerfactoModel(Model):
+    """Nerfacto model
+
+    Args:
+        config: Nerfacto configuration to instantiate model
+    """
+
+    config: NerfactoModelConfig
+
+    def populate_modules(self):
+        """Set the fields and modules."""
+        super().populate_modules()
+
+        scene_contraction = SceneContraction(order=float("inf"))
+
+        # Fields
+        self.field = TCNNNerfactoField(
+            self.scene_box.aabb,
+            num_levels=self.config.num_levels,
+            max_res=self.config.max_res,
+            log2_hashmap_size=self.config.log2_hashmap_size,
+            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,
+        )
+
+        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)
+            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,
+                )
+                self.proposal_networks.append(network)
+            self.density_fns.extend([network.density_fn for network in self.proposal_networks])
+
+        # Samplers
+        update_schedule = lambda step: np.clip(
+            np.interp(step, [0, self.config.proposal_warmup], [0, self.config.proposal_update_every]),
+            1,
+            self.config.proposal_update_every,
+        )
+        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,
+        )
+
+        # Collider
+        self.collider = NearFarCollider(near_plane=self.config.near_plane, far_plane=self.config.far_plane)
+
+        # renderers
+        self.renderer_rgb = RGBRenderer(background_color=self.config.background_color)
+        self.renderer_accumulation = AccumulationRenderer()
+        self.renderer_depth = DepthRenderer()
+        self.renderer_normals = NormalsRenderer()
+
+        # losses
+        self.rgb_loss = MSELoss()
+
+        # metrics
+        self.psnr = PeakSignalNoiseRatio(data_range=1.0)
+        self.ssim = structural_similarity_index_measure
+        self.lpips = LearnedPerceptualImagePatchSimilarity()
+
+    def get_param_groups(self) -> Dict[str, List[Parameter]]:
+        param_groups = {}
+        param_groups["proposal_networks"] = list(self.proposal_networks.parameters())
+        param_groups["fields"] = list(self.field.parameters())
+        return param_groups
+
+    def get_training_callbacks(
+        self, training_callback_attributes: TrainingCallbackAttributes
+    ) -> List[TrainingCallback]:
+        callbacks = []
+        if self.config.use_proposal_weight_anneal:
+            # anneal the weights of the proposal network before doing PDF sampling
+            N = self.config.proposal_weights_anneal_max_num_iters
+
+            def set_anneal(step):
+                # https://arxiv.org/pdf/2111.12077.pdf eq. 18
+                train_frac = np.clip(step / N, 0, 1)
+                bias = lambda x, b: (b * x) / ((b - 1) * x + 1)
+                anneal = bias(train_frac, self.config.proposal_weights_anneal_slope)
+                self.proposal_sampler.set_anneal(anneal)
+
+            callbacks.append(
+                TrainingCallback(
+                    where_to_run=[TrainingCallbackLocation.BEFORE_TRAIN_ITERATION],
+                    update_every_num_iters=1,
+                    func=set_anneal,
+                )
+            )
+            callbacks.append(
+                TrainingCallback(
+                    where_to_run=[TrainingCallbackLocation.AFTER_TRAIN_ITERATION],
+                    update_every_num_iters=1,
+                    func=self.proposal_sampler.step_cb,
+                )
+            )
+        return callbacks
+
+    def get_outputs(self, ray_bundle: RayBundle):
+        ray_samples, weights_list, ray_samples_list = self.proposal_sampler(ray_bundle, density_fns=self.density_fns)
+        field_outputs = self.field(ray_samples, compute_normals=self.config.predict_normals)
+        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:
+            outputs["normals"] = self.renderer_normals(normals=field_outputs[FieldHeadNames.NORMALS], weights=weights)
+            outputs["pred_normals"] = self.renderer_normals(field_outputs[FieldHeadNames.PRED_NORMALS], weights=weights)
+
+        # 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_metrics_dict(self, outputs, batch):
+        metrics_dict = {}
+        image = batch["image"].to(self.device)
+        metrics_dict["psnr"] = self.psnr(outputs["rgb"], image)
+        if self.training:
+            metrics_dict["distortion"] = distortion_loss(outputs["weights_list"], outputs["ray_samples_list"])
+        return metrics_dict
+
+    def get_loss_dict(self, outputs, batch, metrics_dict=None):
+        loss_dict = {}
+        image = batch["image"].to(self.device)
+        loss_dict["rgb_loss"] = self.rgb_loss(image, outputs["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"]
+            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
+
+    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]]:
+
+        image = batch["image"].to(self.device)
+        rgb = outputs["rgb"]
+        acc = colormaps.apply_colormap(outputs["accumulation"])
+        depth = colormaps.apply_depth_colormap(
+            outputs["depth"],
+            accumulation=outputs["accumulation"],
+        )
+
+        combined_rgb = torch.cat([image, 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
+        image = torch.moveaxis(image, -1, 0)[None, ...]
+        rgb = torch.moveaxis(rgb, -1, 0)[None, ...]
+
+        psnr = self.psnr(image, rgb)
+        ssim = self.ssim(image, rgb)
+        lpips = self.lpips(image, rgb)
+
+        # 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}
+
+        # normals to RGB for visualization. TODO: use a colormap
+        if "normals" in outputs:
+            images_dict["normals"] = (outputs["normals"] + 1.0) / 2.0
+        if "pred_normals" in outputs:
+            images_dict["pred_normals"] = (outputs["pred_normals"] + 1.0) / 2.0
+
+        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

scripts/train.py

@@ -245,6 +245,7 @@ def entrypoint():
     """Entrypoint for use with pyproject scripts."""
     # Choose a base configuration and override values.
     tyro.extras.set_accent_color("bright_yellow")
+    print("train has been entered")
     main(
         tyro.cli(
             AnnotatedBaseConfigUnion,

vidtonerf.py

@@ -53,5 +53,5 @@ def recursiveSearch(directory, videoList):
         os.system(f"ns-eval --load-config {directory}\\{method}\\{timestamp}\\config.yml --output-path {directory}")
         print("Evaluation Finished")
         os.system(
-            f"ns-render --load-config {directory}\\{method}\\{timestamp}\\config.yml --traj filename --camera-path-filename C:\\Users\\hleit\\Documents\\nerfstudio\\CamPath\\DecayCamPath.json --seconds 15"
+            f"ns-render --load-config {directory}\\{method}\\{timestamp}\\config.yml --traj filename --camera-path-filename C:\\Users\\hleit\\Documents\\nerfstudio\\CamPath\\DecayCamPath.json --seconds 15 --output-path {directory}\\{method}\\{timestamp}.mp4"
         )