Better align with nerf

configs/graph/default_graph.yaml

@@ -12,7 +12,7 @@ resume_train:
 # NOTE(ethan): currently setting to nothing b/c we're still designing the config format...
 network:
   _target_: mattport.nerf.graph.vanilla_nerf.NeRFGraph
-  near_plane: 1.0
+  near_plane: 2.0
   far_plane: 6.0
   num_coarse_samples: 64
   num_importance_samples: 128

mattport/nerf/dataset/utils.py

@@ -18,7 +18,7 @@ def get_dataset_inputs_dict(
     dataset_type: str,
     downscale_factor: int = 1,
     alpha_color: Optional[Union[str, list, ListConfig]] = None,
-    splits: Tuple[str] = ("train", "val"),
+    splits: Tuple[str] = ("train", "val", "test"),
 ) -> Dict[str, DatasetInputs]:
     """Returns the dataset inputs, which will be used with an ImageDataset and RayGenerator.
     # TODO: implement the `test` split, which will have depths and normals, etc.

mattport/nerf/graph/vanilla_nerf.py

@@ -48,7 +48,8 @@ def __init__(self, num_layers=8, layer_width=256, skip_connections: Tuple = (4,)
         self.mlp_rgb = MLP(
             in_dim=self.mlp_base.get_out_dim() + self.encoding_dir.get_out_dim(),
             out_dim=self.layer_width // 2,
-            num_layers=1,
+            num_layers=2,
+            layer_width=self.layer_width // 2,
             activation=nn.ReLU(),
         )
         self.field_output_rgb = RGBFieldHead(in_dim=self.mlp_rgb.get_out_dim())
@@ -84,7 +85,7 @@ def __init__(
         self,
         intrinsics=None,
         camera_to_world=None,
-        near_plane=1.0,
+        near_plane=2.0,
         far_plane=6.0,
         num_coarse_samples=64,
         num_importance_samples=128,

mattport/nerf/sampler.py

@@ -125,7 +125,7 @@ def forward(
             u = torch.rand(size=(*cdf.shape[:-1], num_samples), device=cdf.device)
         else:
             u = torch.linspace(0.0, 1.0, steps=num_samples, device=cdf.device)
-            u = torch.expand(size=(*cdf.shape[:-1], num_samples))
+            u = u.expand(size=(*cdf.shape[:-1], num_samples))
 
         u = u.contiguous()
         indicies = torch.searchsorted(cdf, u, right=True)

mattport/nerf/trainer.py

@@ -240,8 +240,10 @@ def train_iteration(self, batch: dict, step: int):
     @profiler.time_function
     def test_image(self, image_idx, step):
         """Test a specific image."""
+        self.graph.eval()
         intrinsics = self.val_image_intrinsics[image_idx]
         camera_to_world = self.val_image_camera_to_world[image_idx]
         outputs = self.graph.get_outputs_for_camera(intrinsics, camera_to_world)
         image = self.val_image_dataset[image_idx]["image"].to(self.device)
         self.graph.log_test_image_outputs(image_idx, step, image, outputs)
+        self.graph.train()