Rename to nerfstudio (#589)

docs/tutorials/pipelines/creating_models.ipynb

@@ -6,7 +6,7 @@
             "source": [
                 "# Developing Custom Models\n",
                 "\n",
-                "In this quick tour, we will walk you through the core of training and building any NeRFs with nerfactory.\n",
+                "In this quick tour, we will walk you through the core of training and building any NeRFs with nerfstudio.\n",
                 "\n",
                 "The entry point for training starts at `scripts/train.py`, which spawns instances of our `Trainer()` class (in `engine/trainer.py`). The `Trainer()` is responsible for setting up the datasets and NeRF graph depending on the config specified. It will then run the usual train/val routine for a config-specified number of iterations. If you are planning on using our codebase to build a new NeRF method or to use an existing implementation, we've abstracted away the training routine in these two files and chances are you will not need to think of them again.\n",
                 "\n",
@@ -178,7 +178,7 @@
                 "  # <insert any dataset related overrides here>\n",
                 "\n",
                 "graph:\n",
-                "  _target_: nerfactory.graph.vanilla_nerf.NeRFGraph # set the target to the graph you defined\n",
+                "  _target_: nerfstudio.graph.vanilla_nerf.NeRFGraph # set the target to the graph you defined\n",
                 "  # <insert any graph related overrides here>\n",
                 "\n",
                 "optimizers:\n",
@@ -193,7 +193,7 @@
                 "python scripts/train.py --config-name vanilla_nerf\n",
                 "```\n",
                 "\n",
-                "And now you have a brand-new NeRF in training! For testing and visualizing, simply refer to steps 4-5 in the [quickstart guide](https://github.com/plenoptix/nerfactory#quickstart).\n",
+                "And now you have a brand-new NeRF in training! For testing and visualizing, simply refer to steps 4-5 in the [quickstart guide](https://github.com/plenoptix/nerfstudio#quickstart).\n",
                 "\n",
                 "To help you get started, we also provide additional training tools such as profiling, logging, and debugging. Please refer to our [features guide](../../tooling/index.rst)."
             ]

docs/tutorials/pipelines/creating_pipelines.ipynb

@@ -8,9 +8,9 @@
    "source": [
     "# Creating Custom Pipelines\n",
     "\n",
-    "> Our goal is to enable Nerfactory users to implement an entire NeRF method by only editing one file.\n",
+    "> Our goal is to enable nerfstudio users to implement an entire NeRF method by only editing one file.\n",
     "\n",
-    "Here we explain how to create custom pipelines with nerfactory. Pipelines are composed of two components, namely a DataManager and a Model. The pipeline is responsible for routing RayBundle instances returned from the DataManager into the Model.\n",
+    "Here we explain how to create custom pipelines with nerfstudio. Pipelines are composed of two components, namely a DataManager and a Model. The pipeline is responsible for routing RayBundle instances returned from the DataManager into the Model.\n",
     "\n",
     "A NeRF method is implemented as a Pipeline. Users can create a Pipeline with a DataManager and Model. We provide default implementations for these components prefixed with the name Vanilla (e.g., `VanillaPipeline`, `VanillaDataManager` which inherit from the `Pipeline` and `DataManager` class, respectively) and specific Models (e.g., `InstantNGPModel` or `TensoRFModel` which inherit from the `Model` class). With most NeRF methods, `VanillaPipeline` and `VanillaDataManager` can be used and only the model needs to be changed. We provide many already implmented Models for people to get started with 3D reconstruction. However, in this tutorial we will demonstrate how to customize each of the Pipeline, DataManager, and Model with an example.\n",
     "\n",
@@ -116,13 +116,13 @@
     "from typing import Dict, Optional, Tuple, List\n",
     "import torch\n",
     "\n",
-    "from nerfactory.utils.config import DataManagerConfig, ModelConfig, PipelineConfig, ViewerConfig\n",
-    "from nerfactory.datamanagers.base import DataManager\n",
-    "from nerfactory.datamanagers.structs import DatasetInputs\n",
-    "from nerfactory.models.base import Model\n",
-    "from nerfactory.pipelines.base import Pipeline\n",
-    "from nerfactory.models.instant_ngp import NGPModel\n",
-    "from nerfactory.cameras.rays import RayBundle"
+    "from nerfstudio.utils.config import DataManagerConfig, ModelConfig, PipelineConfig, ViewerConfig\n",
+    "from nerfstudio.datamanagers.base import DataManager\n",
+    "from nerfstudio.datamanagers.structs import DatasetInputs\n",
+    "from nerfstudio.models.base import Model\n",
+    "from nerfstudio.pipelines.base import Pipeline\n",
+    "from nerfstudio.models.instant_ngp import NGPModel\n",
+    "from nerfstudio.cameras.rays import RayBundle"
    ]
   },
   {
@@ -268,7 +268,7 @@
     "    model: ModelConfig = MISSING\n",
     "```\n",
     "\n",
-    "See `nerfactory/utils/config.py` for more details. In this example, we will simply load from an existing configuration from `configs/graph_instant_ngp.yaml`."
+    "See `nerfstudio/utils/config.py` for more details. In this example, we will simply load from an existing configuration from `configs/graph_instant_ngp.yaml`."
    ]
   },
   {
@@ -292,7 +292,7 @@
     "pprint.pprint(pipeline_config)\n",
     "print(\"----------------------------------------------------\")\n",
     "\n",
-    "# from nerfactory.pipelines.base import setup_pipeline\n",
+    "# from nerfstudio.pipelines.base import setup_pipeline\n",
     "# pipeline = setup_pipeline(pipeline_config, device=\"cuda\")\n",
     "pipeline = pipeline_config.setup()"
    ]