Dev

.gitignore

@@ -2,4 +2,4 @@ data/*
 outputs/*
 seathru_nerf.egg-info/*
 .vscode/*
-*/__pycache__/*
+*/__pycache__/*

README.md

@@ -100,6 +100,8 @@ To train your first subsea NeRF, please download the dataset available [here](ht
 ns-train seathru-nerf-lite --vis viewer+wandb colmap --data <path_to_dataset> --images-path images_wb
 ```
 
+(If you get an error with the colmap path, create a directory `colmap` in the dataset folder and move the `sparse` folder into it.)
+
 You can then track the training process on [wandb](https://wandb.ai/site) or watch it live via the [nerfstudio viewer](https://docs.nerf.studio/en/latest/quickstart/viewer_quickstart.html).
 
 When training is completed (takes around 1hr for the SeaThru-NeRF dataset scenes with a RTX3060 Laptop GPU), the trained model will be saved to an `./outputs/` folder.

seathru/seathru_config.py

@@ -3,7 +3,6 @@
 from nerfstudio.pipelines.base_pipeline import VanillaPipelineConfig
 from nerfstudio.data.datamanagers.base_datamanager import VanillaDataManagerConfig
 from nerfstudio.data.dataparsers.nerfstudio_dataparser import NerfstudioDataParserConfig
-from nerfstudio.cameras.camera_optimizers import CameraOptimizerConfig
 from nerfstudio.engine.schedulers import ExponentialDecaySchedulerConfig
 from nerfstudio.engine.optimizers import AdamOptimizerConfig
 from nerfstudio.configs.base_config import ViewerConfig
@@ -24,13 +23,6 @@
                 dataparser=NerfstudioDataParserConfig(),
                 train_num_rays_per_batch=16384,
                 eval_num_rays_per_batch=4096,
-                camera_optimizer=CameraOptimizerConfig(
-                    mode="off",
-                    optimizer=AdamOptimizerConfig(lr=6e-4, eps=1e-8, weight_decay=1e-2),
-                    scheduler=ExponentialDecaySchedulerConfig(
-                        lr_final=6e-6, max_steps=500000
-                    ),
-                ),
             ),
             model=SeathruModelConfig(eval_num_rays_per_chunk=1 << 15),
         ),
@@ -47,6 +39,13 @@
                     lr_final=1e-5, max_steps=500000, warmup_steps=1024
                 ),
             },
+            "camera_opt": {
+                "mode": "off",
+                "optimizer": AdamOptimizerConfig(lr=6e-4, eps=1e-8, weight_decay=1e-2),
+                "scheduler": ExponentialDecaySchedulerConfig(
+                    lr_final=6e-6, max_steps=500000
+                ),
+            },
         },
         viewer=ViewerConfig(num_rays_per_chunk=1 << 15),
         vis="viewer",
@@ -67,13 +66,6 @@
                 dataparser=NerfstudioDataParserConfig(),
                 train_num_rays_per_batch=8192,
                 eval_num_rays_per_batch=4096,
-                camera_optimizer=CameraOptimizerConfig(
-                    mode="off",
-                    optimizer=AdamOptimizerConfig(lr=6e-4, eps=1e-8, weight_decay=1e-2),
-                    scheduler=ExponentialDecaySchedulerConfig(
-                        lr_final=6e-6, max_steps=500000
-                    ),
-                ),
             ),
             model=SeathruModelConfig(
                 eval_num_rays_per_chunk=1 << 15,
@@ -116,6 +108,13 @@
                     lr_final=1e-5, max_steps=500000, warmup_steps=1024
                 ),
             },
+            "camera_opt": {
+                "mode": "off",
+                "optimizer": AdamOptimizerConfig(lr=6e-4, eps=1e-8, weight_decay=1e-2),
+                "scheduler": ExponentialDecaySchedulerConfig(
+                    lr_final=6e-6, max_steps=500000
+                ),
+            },
         },
         viewer=ViewerConfig(num_rays_per_chunk=1 << 15),
         vis="viewer",

seathru/seathru_field.py

@@ -186,8 +186,8 @@ def get_density(self, ray_samples: RaySamples) -> Tuple[Tensor, Tensor]:
         density = density * selector[..., None]
         return density, bottleneck_vector
 
-    def get_outputs(
-        self, ray_samples: RaySamples, density_embedding: Tensor
+    def get_outputs(  # type: ignore
+        self, ray_samples: RaySamples, density_embedding: Optional[Tensor] = None
     ) -> Dict[SeathruHeadNames, Tensor]:
         """Compute outputs of object and medium networks (except object density).
 

seathru/seathru_model.py

@@ -154,7 +154,7 @@ class SeathruModel(Model):
         config: SeaThru-NeRF configuration to instantiate the model with.
     """
 
-    config: SeathruModelConfig
+    config: SeathruModelConfig  # type: ignore
 
     def populate_modules(self):
         """Setup the fields and modules."""
@@ -349,7 +349,7 @@ def bias(x, b):
 
         return callbacks
 
-    def get_outputs(self, ray_bundle: RayBundle) -> Dict[str, torch.Tensor]:
+    def get_outputs(self, ray_bundle: RayBundle) -> Dict[str, torch.Tensor]:  # type: ignore
         """Get outputs from the model.
 
         Args: