every 5k steps; examples doc

docs/source/examples/ngp.rst

 Instant-NGP
 ====================
 
-See code at our github repository: https://github.com/KAIR-BAIR/nerfacc/tree/master/examples/
+See code `examples/train_ngp_nerf.py` at our `github repository`_ for details.
 
 Benchmarks
 ------------
 
-We trained on NeRF-Synthetic trainval set using TITAN RTX, and evaluated on testset.
-Note Instant-NGP's results are taken from the paper, which is trained on a Nvidia 3090,
-with random background trick using alpha channel.
+Here we trained a NGP Nerf model on the NeRF-Synthetic dataset. We follow the same
+settings with the paper, which uses trainval split for training and test split for
+evaluation. Our experiments are conducted on a single NVIDIA TITAN RTX GPU.
 
-+----------------------+----------+----------+------------+-------+--------+
-|                      | Lego     | Mic      | Materials  |Chair  |Hotdog  |
-|                      |          |          |            |       |        |
-+======================+==========+==========+============+=======+========+
-| Paper (PSNR: 5min)   | 36.39    | 36.22    |  29.78     |  35.00| 37.40  |
-+----------------------+----------+----------+------------+-------+--------+
-| Ours  (PSNR)         | 36.61    | 37.45    | 30.15      | 36.06 | 38.17  |
-+----------------------+----------+----------+------------+-------+--------+
-| Ours  (Training time)| 300s     | 272s     | 258s       | 331s  | 287s   |
-+----------------------+----------+----------+------------+-------+--------+
+.. note::
+    
+    The paper makes use of the alpha channel in the images to apply random background
+    augmentation during training. Yet we only uses a constant white background.
+
++----------------------+----------+----------+------------+-------+--------+--------+--------+--------+
+|                      | Lego     | Mic      | Materials  |Chair  |Hotdog  | Ficus  | Drums  | Ship   |
+|                      |          |          |            |       |        |        |        |        |
++======================+==========+==========+============+=======+========+========+========+========+
+| Paper (PSNR: 5min)   | 36.39    | 36.22    | 29.78      | 35.00 | 37.40  | 33.51  | 26.02  | 31.10  |
++----------------------+----------+----------+------------+-------+--------+--------+--------+--------+
+| Ours  (PSNR)         | 36.61    | 37.45    | 30.15      | 36.10 | 38.17  |        | 25.83  |        |
++----------------------+----------+----------+------------+-------+--------+--------+--------+--------+
+| Ours  (Training time)| 300s     | 272s     | 258s       | 311s  | 287s   |        | 249s   |        |
++----------------------+----------+----------+------------+-------+--------+--------+--------+--------+
+
+.. _`github repository`: : https://github.com/KAIR-BAIR/nerfacc/
\ No newline at end of file

docs/source/examples/vanilla.rst

+Vanilla Nerf 
+====================

examples/train_mlp_dnerf.py

@@ -170,7 +170,7 @@ if __name__ == "__main__":
             optimizer.step()
             scheduler.step()
 
-            if step % 100 == 0:
+            if step % 5000 == 0:
                 elapsed_time = time.time() - tic
                 loss = F.mse_loss(rgb[alive_ray_mask], pixels[alive_ray_mask])
                 print(

examples/train_mlp_nerf.py

@@ -196,7 +196,7 @@ if __name__ == "__main__":
             optimizer.step()
             scheduler.step()
 
-            if step % 100 == 0:
+            if step % 5000 == 0:
                 elapsed_time = time.time() - tic
                 loss = F.mse_loss(rgb[alive_ray_mask], pixels[alive_ray_mask])
                 print(

examples/train_ngp_nerf.py

@@ -201,7 +201,7 @@ if __name__ == "__main__":
             optimizer.step()
             scheduler.step()
 
-            if step % 100 == 0:
+            if step % 5000 == 0:
                 elapsed_time = time.time() - tic
                 loss = F.mse_loss(rgb[alive_ray_mask], pixels[alive_ray_mask])
                 print(