Adios mattport

.github/workflows/code_checks.yml

@@ -25,10 +25,10 @@ jobs:
           pip install -r environment/requirements.txt
           pip install -e .
       - name: Run Black
-        run: black mattport/ notebooks/ --check
+        run: black radiance/ notebooks/ --check
       - name: Python Pylint
         run: |
-          pylint mattport tests
+          pylint radiance tests
       - name: Test with pytest
         run: |
           pytest

README.md

@@ -1,19 +1,19 @@
-# MattPort
+# Radiance
 
 # Quickstart
 
 ## Installation: Setup the environment
 
 ```
 # Clone the repo
-git clone --recurse-submodules [email protected]:ethanweber/mattport.git
+git clone --recurse-submodules [email protected]:ethanweber/radiance.git
 
 # Create the python environment
-conda create --name mattport python=3.8.13
-conda activate mattport
+conda create --name radiance python=3.8.13
+conda activate radiance
 pip install -r environment/requirements.txt
 
-# Install mattport as a library
+# Install radiance as a library
 pip install -e .
 
 # Install library with CUDA support. Change setup.py to `USE_CUDA = True` and then
@@ -69,7 +69,7 @@ bash environments/run_jupyter.sh
 
 # Tooling
 
-1. One can use [autoenv](https://github.com/hyperupcall/autoenv) to make setting up the environment and environment variables easier. This will run the `.envrc` file upon entering the `/path/to/mattport` folder. It will also remove the environment parameters upon leaving.
+1. One can use [autoenv](https://github.com/hyperupcall/autoenv) to make setting up the environment and environment variables easier. This will run the `.envrc` file upon entering the `/path/to/radiance` folder. It will also remove the environment parameters upon leaving.
 
 ```
 # Install direnv.
@@ -79,7 +79,7 @@ sudo apt install direnv
 eval "$(direnv hook bash)"
 
 # Populate your .envrc with commands you want to run. Then, run the following to allow updates.
-cd /path/to/mattport
+cd /path/to/radiance
 direnv allow .
 ```
 
@@ -125,12 +125,12 @@ python scripts/benchmarking/run_benchmark.py
 ```
 # nerf-pytorch
 cd external
-python run_nerf.py --config configs/chair.txt --datadir /path/to/mattport/data/blender/chair
+python run_nerf.py --config configs/chair.txt --datadir /path/to/radiance/data/blender/chair
 
 # jaxnerf
 cd external
 conda activate jaxnerf
-python -m jaxnerf.train --data_dir=/path/to/mattport/data/blender/chair --train_dir=/path/to/mattport/outputs/blender_chair_jaxnerf --config=/path/to/mattport/external/jaxnerf/configs/demo --render_every 100
+python -m jaxnerf.train --data_dir=/path/to/radiance/data/blender/chair --train_dir=/path/to/radiance/outputs/blender_chair_jaxnerf --config=/path/to/radiance/external/jaxnerf/configs/demo --render_every 100
 ```
 
 # Speeding up the code

configs/default.yaml

@@ -7,7 +7,7 @@ defaults:
   - override hydra/hydra_logging: colorlog
   - _self_
 
-seed: 0
+seed: 42
 
 hydra:
   run:

configs/graph/default_graph.yaml

@@ -1,5 +1,5 @@
 # saving model specifications
-model_dir: /tmp/mattport_models/
+model_dir: /tmp/radiance_models/
 steps_per_save: null
 steps_per_test: 100
 max_num_iterations: 1000000
@@ -10,7 +10,7 @@ resume_train:
   load_step: # pretrained model's step number
 
 network:
-  _target_: mattport.nerf.graph.vanilla_nerf.NeRFGraph
+  _target_: radiance.nerf.graph.vanilla_nerf.NeRFGraph
   near_plane: 2.0
   far_plane: 6.0
   num_coarse_samples: 64
@@ -27,6 +27,6 @@ param_groups:
       _target_: torch.optim.RAdam
       lr: 0.0005
     scheduler:
-      _target_: mattport.nerf.optimizers.ExponentialDecaySchedule
+      _target_: radiance.nerf.optimizers.ExponentialDecaySchedule
       lr_final: 0.000005
       max_steps: 1000000

configs/graph/instant_ngp_graph.yaml

@@ -3,7 +3,7 @@ defaults:
   - _self_
 
 network:
-  _target_: mattport.nerf.graph.instant_ngp.NGPGraph
+  _target_: radiance.nerf.graph.instant_ngp.NGPGraph
   field_implementation: torch # torch, tcnn, ...
   loss_coefficients: # loss coefficients
     rgb_loss: 1.0

configs/graph/nerfw_graph.yaml

@@ -3,7 +3,7 @@ defaults:
   - _self_
 
 network:
-  _target_: mattport.nerf.graph.nerfw.NerfWGraph
+  _target_: radiance.nerf.graph.nerfw.NerfWGraph
   loss_coefficients: # loss coefficients
     rgb_loss_coarse: 1.0
     rgb_loss_fine: 1.0

configs/graph/semantic_nerf_graph.yaml

@@ -3,7 +3,7 @@ defaults:
   - _self_
 
 network:
-  _target_: mattport.nerf.graph.semantic_nerf.SemanticNerfGraph
+  _target_: radiance.nerf.graph.semantic_nerf.SemanticNerfGraph
   loss_coefficients: # loss coefficients
     rgb_loss_coarse: 1.0
     rgb_loss_fine: 1.0

notebooks/visualize_cameras.ipynb

@@ -14,7 +14,7 @@
    "outputs": [],
    "source": [
     "import torch\n",
-    "from mattport.structures import cameras\n",
+    "from radiance.structures import cameras\n",
     "import plotly.graph_objects as go\n",
     "import mediapy as media"
    ]
@@ -19597,7 +19597,7 @@
    "hash": "024fe9dea615e4e34961116ff517c7e9f5910440e3d2c8eb4560299f11aaf2e7"
   },
   "kernelspec": {
-   "display_name": "Python 3.9.12 ('mattport')",
+   "display_name": "Python 3.9.12 ('radiance')",
    "language": "python",
    "name": "python3"
   },

notebooks/visualize_dataset.ipynb

@@ -20,15 +20,15 @@
     "import torch\n",
     "from torch.utils.data import DataLoader\n",
     "\n",
-    "from mattport.nerf.dataset.image_dataset import ImageDataset, collate_batch\n",
-    "from mattport.nerf.dataset.collate import CollateIterDataset, collate_batch_size_one\n",
-    "from mattport.nerf.dataset.utils import get_dataset_inputs\n",
-    "from mattport.nerf.field_modules.ray_generator import RayGenerator\n",
-    "from mattport.nerf.field_modules.scene_bounds_collider import SceneBoundsCollider\n",
-    "from mattport.structures.rays import RayBundle\n",
-    "from mattport.utils.io import get_absolute_path\n",
-    "from mattport.viewer.plotly import get_line_segments_from_lines\n",
-    "from mattport.structures.cameras import get_camera_model\n",
+    "from radiance.nerf.dataset.image_dataset import ImageDataset, collate_batch\n",
+    "from radiance.nerf.dataset.collate import CollateIterDataset, collate_batch_size_one\n",
+    "from radiance.nerf.dataset.utils import get_dataset_inputs\n",
+    "from radiance.nerf.field_modules.ray_generator import RayGenerator\n",
+    "from radiance.nerf.field_modules.scene_bounds_collider import SceneBoundsCollider\n",
+    "from radiance.structures.rays import RayBundle\n",
+    "from radiance.utils.io import get_absolute_path\n",
+    "from radiance.viewer.plotly import get_line_segments_from_lines\n",
+    "from radiance.structures.cameras import get_camera_model\n",
     "\n",
     "\n",
     "from hydra import compose, initialize\n",
@@ -361,9 +361,9 @@
    "hash": "4a749acc7d255f078aee52e0584cc77b3cb5aaed1b3a7407ec4262c1bf6cb526"
   },
   "kernelspec": {
-   "display_name": "mattport",
+   "display_name": "radiance",
    "language": "python",
-   "name": "mattport"
+   "name": "radiance"
   },
   "language_info": {
    "codemirror_mode": {

notebooks/visualize_encoders.ipynb

@@ -14,7 +14,7 @@
    "outputs": [
     {
      "data": {
-      "application/javascript": "\n            setTimeout(function() {\n                var nbb_cell_id = 6;\n                var nbb_unformatted_code = \"import torch\\nimport mediapy as media\\nfrom mattport.nerf.field_modules import encoding\";\n                var nbb_formatted_code = \"import torch\\nimport mediapy as media\\nfrom mattport.nerf.field_modules import encoding\";\n                var nbb_cells = Jupyter.notebook.get_cells();\n                for (var i = 0; i < nbb_cells.length; ++i) {\n                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n                             nbb_cells[i].set_text(nbb_formatted_code);\n                        }\n                        break;\n                    }\n                }\n            }, 500);\n            ",
+      "application/javascript": "\n            setTimeout(function() {\n                var nbb_cell_id = 6;\n                var nbb_unformatted_code = \"import torch\\nimport mediapy as media\\nfrom radiance.nerf.field_modules import encoding\";\n                var nbb_formatted_code = \"import torch\\nimport mediapy as media\\nfrom radiance.nerf.field_modules import encoding\";\n                var nbb_cells = Jupyter.notebook.get_cells();\n                for (var i = 0; i < nbb_cells.length; ++i) {\n                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n                             nbb_cells[i].set_text(nbb_formatted_code);\n                        }\n                        break;\n                    }\n                }\n            }, 500);\n            ",
       "text/plain": [
        "<IPython.core.display.Javascript object>"
       ]
@@ -26,7 +26,7 @@
    "source": [
     "import torch\n",
     "import mediapy as media\n",
-    "from mattport.nerf.field_modules import encoding"
+    "from radiance.nerf.field_modules import encoding"
    ]
   },
   {
@@ -434,7 +434,7 @@
    "hash": "024fe9dea615e4e34961116ff517c7e9f5910440e3d2c8eb4560299f11aaf2e7"
   },
   "kernelspec": {
-   "display_name": "Python 3.9.11 ('mattport')",
+   "display_name": "Python 3.9.11 ('radiance')",
    "language": "python",
    "name": "python3"
   },

pyproject.toml

@@ -11,7 +11,7 @@ line-length = 120
 max-line-length = 120
 generated-members = ["numpy.*", "torch.*", "cv2.*", "cv.*"]
 good-names-rgxs = "^[_a-zA-Z][_a-z0-9]?$"
-ignore = ["mattport/cuda", "tests/cuda", "nerfw.py", "semantic_nerf.py"]
+ignore = ["radiance/cuda", "tests/cuda", "nerfw.py", "semantic_nerf.py"]
 
 disable = [
   "too-many-arguments",
@@ -23,7 +23,7 @@ disable = [
 
 #pytest
 [tool.pytest.ini_options]
-addopts = "--typeguard-packages=mattport --torchtyping-patch-typeguard --disable-warnings --ignore=tests/cuda"
+addopts = "--typeguard-packages=radiance --torchtyping-patch-typeguard --disable-warnings --ignore=tests/cuda"
 testpaths = [
     "tests",
 ]

mattport/cuda/sampler.py → radiance/cuda/sampler.py

@@ -7,13 +7,13 @@
 import torch
 from torch import nn
 
-import mattport_cuda
+import radiance_cuda
 
 
 class LLTMFunction(torch.autograd.Function):
     @staticmethod
     def forward(ctx, input, weights, bias, old_h, old_cell):
-        outputs = mattport_cuda.forward(input, weights, bias, old_h, old_cell)
+        outputs = radiance_cuda.forward(input, weights, bias, old_h, old_cell)
         new_h, new_cell = outputs[:2]
         variables = outputs[1:] + [weights]
         ctx.save_for_backward(*variables)
@@ -22,7 +22,7 @@ def forward(ctx, input, weights, bias, old_h, old_cell):
 
     @staticmethod
     def backward(ctx, grad_h, grad_cell):
-        outputs = mattport_cuda.backward(grad_h.contiguous(), grad_cell.contiguous(), *ctx.saved_variables)
+        outputs = radiance_cuda.backward(grad_h.contiguous(), grad_cell.contiguous(), *ctx.saved_variables)
         d_old_h, d_input, d_weights, d_bias, d_old_cell, d_gates = outputs
         return d_input, d_weights, d_bias, d_old_h, d_old_cell
 

mattport/nerf/colliders.py → radiance/nerf/colliders.py

@@ -4,8 +4,8 @@
 import torch
 from torch import nn
 from torchtyping import TensorType
-from mattport.nerf.dataset.structs import SceneBounds
-from mattport.structures.rays import RayBundle
+from radiance.nerf.dataset.structs import SceneBounds
+from radiance.structures.rays import RayBundle
 
 
 class SceneBoundsCollider(nn.Module):

mattport/nerf/dataset/format/blender.py → radiance/nerf/dataset/format/blender.py

@@ -10,8 +10,8 @@
 import torch
 from torchtyping import TensorType
 
-from mattport.nerf.dataset.structs import DatasetInputs, SceneBounds
-from mattport.utils.io import load_from_json
+from radiance.nerf.dataset.structs import DatasetInputs, SceneBounds
+from radiance.utils.io import load_from_json
 
 
 def load_blender_data(

mattport/nerf/dataset/format/friends.py → radiance/nerf/dataset/format/friends.py

@@ -7,10 +7,10 @@
 
 import torch
 
-from mattport.nerf.dataset.colmap_utils import read_cameras_binary, read_images_binary, read_pointsTD_binary
-from mattport.nerf.dataset.structs import DatasetInputs, PointCloud, SceneBounds, Semantics
-from mattport.utils import profiler
-from mattport.utils.io import load_from_json
+from radiance.nerf.dataset.colmap_utils import read_cameras_binary, read_images_binary, read_pointsTD_binary
+from radiance.nerf.dataset.structs import DatasetInputs, PointCloud, SceneBounds, Semantics
+from radiance.utils import profiler
+from radiance.utils.io import load_from_json
 
 
 def get_aabb_and_transform(basedir):

mattport/nerf/dataset/format/instant_ngp.py → radiance/nerf/dataset/format/instant_ngp.py

@@ -10,8 +10,8 @@
 import numpy as np
 import torch
 
-from mattport.nerf.dataset.structs import DatasetInputs, SceneBounds
-from mattport.utils.io import load_from_json
+from radiance.nerf.dataset.structs import DatasetInputs, SceneBounds
+from radiance.utils.io import load_from_json
 
 
 def load_instant_ngp_data(

mattport/nerf/dataset/image_dataset.py → radiance/nerf/dataset/image_dataset.py

@@ -12,7 +12,7 @@
 from torch.utils.data import default_collate
 from torchtyping import TensorType
 
-from mattport.nerf.dataset.structs import Semantics
+from radiance.nerf.dataset.structs import Semantics
 
 
 def collate_batch(batch_list, num_rays_per_batch, keep_full_image: bool = False):

mattport/nerf/dataset/utils.py → radiance/nerf/dataset/utils.py

@@ -6,12 +6,12 @@
 
 from omegaconf import ListConfig
 
-from mattport.nerf.dataset.format.blender import load_blender_data
-from mattport.nerf.dataset.format.friends import load_friends_data
-from mattport.nerf.dataset.format.instant_ngp import load_instant_ngp_data
-from mattport.nerf.dataset.structs import DatasetInputs
-from mattport.structures.colors import get_color
-from mattport.utils.io import get_absolute_path
+from radiance.nerf.dataset.format.blender import load_blender_data
+from radiance.nerf.dataset.format.friends import load_friends_data
+from radiance.nerf.dataset.format.instant_ngp import load_instant_ngp_data
+from radiance.nerf.dataset.structs import DatasetInputs
+from radiance.structures.colors import get_color
+from radiance.utils.io import get_absolute_path
 
 
 def get_dataset_inputs(

mattport/nerf/field_modules/embedding.py → radiance/nerf/field_modules/embedding.py

@@ -5,7 +5,7 @@
 
 import torch
 from torchtyping import TensorType
-from mattport.nerf.field_modules.base import FieldModule
+from radiance.nerf.field_modules.base import FieldModule
 
 
 class Embedding(FieldModule):

mattport/nerf/field_modules/encoding.py → radiance/nerf/field_modules/encoding.py

@@ -9,8 +9,8 @@
 from torch import nn
 from torchtyping import TensorType
 
-from mattport.nerf.field_modules.base import FieldModule
-from mattport.utils.math import components_from_spherical_harmonics
+from radiance.nerf.field_modules.base import FieldModule
+from radiance.utils.math import components_from_spherical_harmonics
 
 
 class Encoding(FieldModule):

mattport/nerf/field_modules/field_heads.py → radiance/nerf/field_modules/field_heads.py

@@ -7,7 +7,7 @@
 from torch import nn
 from torchtyping import TensorType
 
-from mattport.nerf.field_modules.base import FieldModule
+from radiance.nerf.field_modules.base import FieldModule
 
 
 class FieldHeadNames(Enum):

mattport/nerf/field_modules/mlp.py → radiance/nerf/field_modules/mlp.py

@@ -7,7 +7,7 @@
 from torch import nn
 from torchtyping import TensorType
 
-from mattport.nerf.field_modules.base import FieldModule
+from radiance.nerf.field_modules.base import FieldModule
 
 
 class MLP(FieldModule):

mattport/nerf/fields/base.py → radiance/nerf/fields/base.py

@@ -8,10 +8,10 @@
 from torch import nn
 import torch
 from torchtyping import TensorType
-from mattport.nerf.field_modules.field_heads import FieldHeadNames
+from radiance.nerf.field_modules.field_heads import FieldHeadNames
 
-from mattport.structures.rays import PointSamples
-from mattport.utils.misc import is_not_none
+from radiance.structures.rays import PointSamples
+from radiance.utils.misc import is_not_none
 
 
 class Field(nn.Module):