simple_trainer.py -> simple_trainer.py default

EXPLORATION.md

@@ -26,7 +26,7 @@
 | `--absgrad --grow_grad2d 2e-4`               | 8m30s    | 0.018s/im | 2.21 GB | 0.6251 | 20.68 | 0.587 | 0.89M |
 | `--absgrad --grow_grad2d 2e-4` (30k)         | --       | 0.030s/im | 5.25 GB | 0.7442 | 24.12 | 0.291 | 2.62M |
 
-Note: default args means running `CUDA_VISIBLE_DEVICES=0 python simple_trainer.py --data_dir <DATA_DIR>` with:
+Note: default args means running `CUDA_VISIBLE_DEVICES=0 python simple_trainer.py default --data_dir <DATA_DIR>` with:
 
 - Garden ([Source](https://jonbarron.info/mipnerf360/)): `--result_dir results/garden`
 - U1 (a.k.a University 1 from [Source](https://localrf.github.io/)): `--result_dir results/u1 --data_factor 1 --grow_scale3d 0.001`

docs/source/examples/colmap.rst

@@ -3,7 +3,7 @@ Fit a COLMAP Capture
 
 .. currentmodule:: gsplat
 
-The :code:`examples/simple_trainer.py` script allows you train a 
+The :code:`examples/simple_trainer.py default` script allows you train a 
 `3D Gaussian Splatting <https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/>`_ 
 model for novel view synthesis, on a COLMAP processed capture. This script follows the
 exact same logic with the `official implementation 
@@ -15,7 +15,7 @@ Simply run the script under `examples/`:
 
 .. code-block:: bash
 
-    CUDA_VISIBLE_DEVICES=0 python simple_trainer.py \
+    CUDA_VISIBLE_DEVICES=0 python simple_trainer.py default \
         --data_dir data/360_v2/garden/ --data_factor 4 \
         --result_dir ./results/garden
 

docs/source/examples/large_scale.rst

@@ -35,7 +35,7 @@ The code for this example can be found under `examples/`:
 .. code-block:: bash
 
     # First train a 3DGS model
-    CUDA_VISIBLE_DEVICES=0 python simple_trainer.py \
+    CUDA_VISIBLE_DEVICES=0 python simple_trainer.py default \
         --data_dir data/360_v2/garden/ --data_factor 4 \
         --result_dir ./results/garden
 

docs/source/tests/eval.rst

@@ -17,7 +17,7 @@ Evaluation
     | gsplat-30k (4 GPUs) | 28.91 | 0.871 | 0.135 | **2.0 GB**       | **11m28s** |
     +---------------------+-------+-------+-------+------------------+------------+
 
-This repo comes with a standalone script (:code:`examples/simple_trainer.py`) that reproduces 
+This repo comes with a standalone script (:code:`examples/simple_trainer.py default`) that reproduces 
 the `Gaussian Splatting <https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/>`_ with
 exactly the same performance on PSNR, SSIM, LPIPS, and converged number of Gaussians. 
 Powered by `gsplat`'s efficient CUDA implementation, the training takes up to 

examples/benchmarks/basic.sh

@@ -11,14 +11,14 @@ do
     echo "Running $SCENE"
 
     # train without eval
-    CUDA_VISIBLE_DEVICES=0 python simple_trainer.py --eval_steps -1 --disable_viewer --data_factor $DATA_FACTOR \
+    CUDA_VISIBLE_DEVICES=0 python simple_trainer.py default --eval_steps -1 --disable_viewer --data_factor $DATA_FACTOR \
         --data_dir data/360_v2/$SCENE/ \
         --result_dir $RESULT_DIR/$SCENE/
 
     # run eval and render
     for CKPT in $RESULT_DIR/$SCENE/ckpts/*;
     do
-        CUDA_VISIBLE_DEVICES=0 python simple_trainer.py --disable_viewer --data_factor $DATA_FACTOR \
+        CUDA_VISIBLE_DEVICES=0 python simple_trainer.py default --disable_viewer --data_factor $DATA_FACTOR \
             --data_dir data/360_v2/$SCENE/ \
             --result_dir $RESULT_DIR/$SCENE/ \
             --ckpt $CKPT

examples/benchmarks/basic_4gpus.sh

@@ -11,7 +11,7 @@ do
     echo "Running $SCENE"
 
     # train and eval at the last step
-    CUDA_VISIBLE_DEVICES=0,1,2,3 python simple_trainer.py --eval_steps -1 --disable_viewer --data_factor $DATA_FACTOR \
+    CUDA_VISIBLE_DEVICES=0,1,2,3 python simple_trainer.py default --eval_steps -1 --disable_viewer --data_factor $DATA_FACTOR \
         # 4 GPUs is effectively 4x batch size so we scale down the steps by 4x as well.
         # "--packed" reduces the data transfer between GPUs, which leads to faster training. 
         --steps_scaler 0.25 --packed \