rename files, add gitignore for data folder in lerf

projects/lerf/.gitignore

@@ -0,0 +1 @@
+!lerf/data

projects/lerf/README.md

@@ -0,0 +1,2 @@
+# LERF Implementation
+TODO

projects/lerf/lerf/lerf_config.py

@@ -17,7 +17,6 @@ from nerfstudio.plugins.types import MethodSpecification
 """
 Swap out the network config to use OpenCLIP or CLIP here.
 """
-from lerf.encoders.slip_encoder import SLIPNetworkConfig
 from lerf.encoders.clip_encoder import CLIPNetworkConfig
 from lerf.encoders.openclip_encoder import OpenCLIPNetworkConfig
 
@@ -47,6 +46,10 @@ lerf_method = MethodSpecification(
             network=OpenCLIPNetworkConfig(
                 clip_model_type="ViT-B-16", clip_model_pretrained="laion2b_s34b_b88k", clip_n_dims=512
             ),
+            #  You can swap the type of input encoder by specifying different NetworkConfigs, the one below uses OpenAI CLIP, the one above uses OpenCLIP
+            # network=CLIPNetworkConfig(
+            #     clip_model_type="ViT-B/16", clip_n_dims=512
+            # )
         ),
         optimizers={
             "proposal_networks": {
@@ -160,50 +163,3 @@ lerf_method_lite = MethodSpecification(
     ),
     description="A lightweight version of LERF designed to work on smaller GPUs",
 )
-
-lerf_openai_method = MethodSpecification(
-    config=TrainerConfig(
-        method_name="lerf-openai",
-        steps_per_eval_batch=500,
-        steps_per_save=2000,
-        max_num_iterations=30000,
-        mixed_precision=True,
-        pipeline=LERFPipelineConfig(
-            datamanager=LERFDataManagerConfig(
-                dataparser=NerfstudioDataParserConfig(train_split_fraction=0.99),
-                train_num_rays_per_batch=4096,
-                eval_num_rays_per_batch=4096,
-                camera_optimizer=CameraOptimizerConfig(
-                    mode="SO3xR3", optimizer=AdamOptimizerConfig(lr=6e-4, eps=1e-8, weight_decay=1e-2)
-                ),
-            ),
-            model=LERFModelConfig(
-                eval_num_rays_per_chunk=1 << 15,
-                hashgrid_sizes=(19, 19),
-                hashgrid_layers=(12, 12),
-                hashgrid_resolutions=((16, 128), (128, 512)),
-                num_lerf_samples=24,
-            ),
-            network=CLIPNetworkConfig(
-                clip_model_type="ViT-B/16", clip_n_dims=512
-            )
-        ),
-        optimizers={
-            "proposal_networks": {
-                "optimizer": AdamOptimizerConfig(lr=1e-2, eps=1e-15),
-                "scheduler": None,
-            },
-            "fields": {
-                "optimizer": RAdamOptimizerConfig(lr=1e-2, eps=1e-15),
-                "scheduler": ExponentialDecaySchedulerConfig(lr_final=1e-3, max_steps=30000),
-            },
-            "lerf": {
-                "optimizer": RAdamOptimizerConfig(lr=1e-2, eps=1e-15),
-                "scheduler": ExponentialDecaySchedulerConfig(lr_final=1e-3, max_steps=7000),
-            },
-        },
-        viewer=ViewerConfig(num_rays_per_chunk=1 << 15),
-        vis="viewer",
-    ),
-    description="Base config for CLIP-400M (openai)",
-)

projects/lerf/pyproject.toml

@@ -5,5 +5,4 @@ version = "0.1.0"
 [project.entry-points.'nerfstudio.method_configs']
 lerf = 'lerf.lerf_config:lerf_method'
 lerf_big = 'lerf.lerf_config:lerf_method_big'
-lerf_lite = 'lerf.lerf_config:lerf_method_lite'
-lerf_openai = 'lerf.lerf_config:lerf_openai_method'
+lerf_lite = 'lerf.lerf_config:lerf_method_lite'