Merge branch 'master' of github.com:ethanweber/mattport into eng/benchmarking

.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

@@ -1,4 +1,4 @@
-# MattPort
+# Radiance
 
 # Quickstart
 
@@ -6,14 +6,14 @@
 
 ```
 # Clone the repo
-git clone --recurse-submodules git@github.com:ethanweber/mattport.git
+git clone --recurse-submodules git@github.com: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_setup.yaml

@@ -1,13 +1,12 @@
 defaults:
   # This makes the logging module logs colorful
   - data: default_data.yaml
-  - graph: default_graph.yaml
   - logging: default_logging.yaml
   - override hydra/job_logging: colorlog
   - override hydra/hydra_logging: colorlog
   - _self_
 
-seed: 0
+seed: 42
 
 hydra:
   run:
@@ -15,12 +14,10 @@ hydra:
     # Example "outputs/mnist/classifier/baseline/2021-03-10-141516"
     dir: outputs/${experiment_name}/${method_name}/${now:%Y-%m-%d_%H%M%S}
 
-experiment_name: blender_lego
-method_name: vanilla_nerf
-
 # machine options
 machine_config:
   num_gpus: 1
   num_machines: 1
   machine_rank: 0
   dist_url: auto
+

configs/graph/default_graph.yaml

@@ -1,32 +0,0 @@
-# saving model specifications
-model_dir: /tmp/mattport_models/
-steps_per_save: null
-steps_per_test: 100
-max_num_iterations: 1000000
-
-# pretrained loading options
-resume_train:
-  load_dir: # pretrained model directory
-  load_step: # pretrained model's step number
-
-network:
-  _target_: mattport.nerf.graph.vanilla_nerf.NeRFGraph
-  near_plane: 2.0
-  far_plane: 6.0
-  num_coarse_samples: 64
-  num_importance_samples: 128
-
-  loss_coefficients: # loss coefficients
-    rgb_loss_coarse: 1.0
-    rgb_loss_fine: 1.0
-
-# optimizer options for network
-param_groups:
-  fields:
-    optimizer:
-      _target_: torch.optim.RAdam
-      lr: 0.0005
-    scheduler:
-      _target_: mattport.nerf.optimizers.ExponentialDecaySchedule
-      lr_final: 0.000005
-      max_steps: 1000000

configs/graph/instant_ngp_graph.yaml

@@ -1,15 +0,0 @@
-defaults:
-  - default_graph.yaml
-  - _self_
-
-network:
-  _target_: mattport.nerf.graph.instant_ngp.NGPGraph
-  field_implementation: torch # torch, tcnn, ...
-  loss_coefficients: # loss coefficients
-    rgb_loss: 1.0
-
-# optimizer options for network
-param_groups:
-  fields:
-    optimizer:
-      lr: 1e-2

configs/graph/nerfw_graph.yaml

@@ -1,11 +0,0 @@
-defaults:
-  - default_graph.yaml
-  - _self_
-
-network:
-  _target_: mattport.nerf.graph.nerfw.NerfWGraph
-  loss_coefficients: # loss coefficients
-    rgb_loss_coarse: 1.0
-    rgb_loss_fine: 1.0
-    uncertainty_loss: 1.0
-    density_loss: 0.01

configs/graph/semantic_nerf_graph.yaml

@@ -1,10 +0,0 @@
-defaults:
-  - default_graph.yaml
-  - _self_
-
-network:
-  _target_: mattport.nerf.graph.semantic_nerf.SemanticNerfGraph
-  loss_coefficients: # loss coefficients
-    rgb_loss_coarse: 1.0
-    rgb_loss_fine: 1.0
-    semantic_loss_fine: 0.05

configs/instant_ngp.yaml

@@ -1,7 +1,20 @@
 defaults:
-  - default
-  - override graph: instant_ngp_graph
+  - vanilla_nerf
   - _self_
+
 method_name: instant_ngp
 data.dataloader.num_rays_per_batch: 4096
-data.val_num_rays_per_chunk: 16384
+data.val_num_rays_per_chunk: 16384
+
+graph:
+  network:
+    _target_: radiance.nerf.graph.instant_ngp.NGPGraph
+    field_implementation: torch # torch, tcnn, ...
+    loss_coefficients: # loss coefficients
+      rgb_loss: 1.0
+
+  # optimizer options for network
+  param_groups:
+    fields:
+      optimizer:
+        lr: 1e-2

configs/instant_ngp_tcnn.yaml

@@ -1,7 +1,7 @@
 defaults:
-  - default
-  - override graph: instant_ngp_graph
+  - instant_ngp
   - _self_
+  
 method_name: instant_ngp
 data:
   dataloader:

configs/nerfw.yaml

@@ -1,14 +1,21 @@
 defaults:
-  - default
+  - vanilla_nerf
   - override data/dataset: friends_TBBT-big_living_room
-  - override graph: nerfw_graph
   - _self_
 
 experiment_name: friends_TBBT-big_living_room
 method_name: nerfw
 
-network:
-  near_plane: 0.1
-  far_plane: 10.0
-  num_coarse_samples: 64
-  num_importance_samples: 64
+graph:
+  network:
+    _target_: radiance.nerf.graph.nerfw.NerfWGraph
+    near_plane: 0.1
+    far_plane: 10.0
+    num_coarse_samples: 64
+    num_importance_samples: 64
+
+    loss_coefficients: # loss coefficients
+      rgb_loss_coarse: 1.0
+      rgb_loss_fine: 1.0
+      uncertainty_loss: 1.0
+      density_loss: 0.01

configs/semantic_nerf.yaml

@@ -1,14 +1,20 @@
 defaults:
-  - default
+  - vanilla_nerf
   - override data/dataset: friends_TBBT-big_living_room
-  - override graph: semantic_nerf_graph
   - _self_
 
 experiment_name: friends_TBBT-big_living_room
 method_name: semantic_nerf
 
-network:
-  near_plane: 0.1
-  far_plane: 10.0
-  num_coarse_samples: 64
-  num_importance_samples: 64
+graph:
+  network:
+    _target_: radiance.nerf.graph.semantic_nerf.SemanticNerfGraph
+    near_plane: 0.1
+    far_plane: 10.0
+    num_coarse_samples: 64
+    num_importance_samples: 64
+
+    loss_coefficients: # loss coefficients
+      rgb_loss_coarse: 1.0
+      rgb_loss_fine: 1.0
+      semantic_loss_fine: 0.05

configs/tests/test_dryrun.yaml

@@ -1,5 +1,5 @@
 defaults:
-  - default
+  - vanilla_nerf
 
 dry_run: true
 

configs/vanilla_nerf.yaml

@@ -0,0 +1,40 @@
+defaults:
+  - default_setup
+  - _self_
+
+experiment_name: blender_lego
+method_name: vanilla_nerf
+
+graph:
+  # saving model specifications
+  model_dir: /tmp/radiance_models/
+  steps_per_save: null
+  steps_per_test: 100
+  max_num_iterations: 1000000
+
+  # pretrained loading options
+  resume_train:
+    load_dir: # pretrained model directory
+    load_step: # pretrained model's step number
+
+  network:
+    _target_: radiance.nerf.graph.vanilla_nerf.NeRFGraph
+    near_plane: 2.0
+    far_plane: 6.0
+    num_coarse_samples: 64
+    num_importance_samples: 128
+
+    loss_coefficients: # loss coefficients
+      rgb_loss_coarse: 1.0
+      rgb_loss_fine: 1.0
+
+  # optimizer options for network
+  param_groups:
+    fields:
+      optimizer:
+        _target_: torch.optim.RAdam
+        lr: 0.0005
+      scheduler:
+        _target_: radiance.nerf.optimizers.ExponentialDecaySchedule
+        lr_final: 0.000005
+        max_steps: 1000000

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",
 ]

radiance/cuda/sampler.py

@@ -7,13 +7,13 @@ import math
 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 @@ class LLTMFunction(torch.autograd.Function):
 
     @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
 

radiance/nerf/colliders.py

@@ -4,8 +4,8 @@ Ray generator.
 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):

radiance/nerf/dataset/format/blender.py

@@ -10,8 +10,8 @@ import numpy as np
 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(

radiance/nerf/dataset/format/friends.py

@@ -7,10 +7,10 @@ import numpy as np
 
 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):

radiance/nerf/dataset/format/instant_ngp.py

@@ -10,8 +10,8 @@ import imageio
 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(

radiance/nerf/dataset/image_dataset.py

@@ -12,7 +12,7 @@ from PIL import Image
 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):

radiance/nerf/dataset/utils.py

@@ -6,12 +6,12 @@ from typing import Optional, Union
 
 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(

radiance/nerf/field_modules/embedding.py

@@ -5,7 +5,7 @@ Code for embeddings.
 
 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):

radiance/nerf/field_modules/encoding.py

@@ -9,8 +9,8 @@ import torch.nn.functional as F
 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):

radiance/nerf/field_modules/field_heads.py

@@ -7,7 +7,7 @@ from typing import Optional
 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):

radiance/nerf/field_modules/mlp.py

@@ -7,7 +7,7 @@ import torch
 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):

radiance/nerf/fields/base.py

@@ -8,10 +8,10 @@ from typing import Dict, Optional, Tuple
 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):

radiance/nerf/fields/instant_ngp_field.py

@@ -9,12 +9,12 @@ import torch
 import torch.nn.functional as F
 from torch.nn.parameter import Parameter
 
-from mattport.nerf.field_modules.encoding import Encoding, HashEncoding, SHEncoding
-from mattport.nerf.field_modules.field_heads import FieldHeadNames
-from mattport.nerf.fields.base import Field
-from mattport.nerf.fields.nerf_field import NeRFField
-from mattport.structures.rays import PointSamples
-from mattport.utils.misc import is_not_none
+from radiance.nerf.field_modules.encoding import Encoding, HashEncoding, SHEncoding
+from radiance.nerf.field_modules.field_heads import FieldHeadNames
+from radiance.nerf.fields.base import Field
+from radiance.nerf.fields.nerf_field import NeRFField
+from radiance.structures.rays import PointSamples
+from radiance.utils.misc import is_not_none
 
 try:
     import tinycudann as tcnn

radiance/nerf/fields/nerf_field.py

@@ -7,11 +7,11 @@ import torch
 from torch import nn
 from torchtyping import TensorType
 
-from mattport.nerf.field_modules.encoding import Encoding, Identity
-from mattport.nerf.field_modules.field_heads import DensityFieldHead, FieldHead, FieldHeadNames, RGBFieldHead
-from mattport.nerf.field_modules.mlp import MLP
-from mattport.nerf.fields.base import Field
-from mattport.structures.rays import PointSamples
+from radiance.nerf.field_modules.encoding import Encoding, Identity
+from radiance.nerf.field_modules.field_heads import DensityFieldHead, FieldHead, FieldHeadNames, RGBFieldHead
+from radiance.nerf.field_modules.mlp import MLP
+from radiance.nerf.fields.base import Field
+from radiance.structures.rays import PointSamples
 
 
 class NeRFField(Field):

radiance/nerf/graph/base.py

@@ -11,13 +11,13 @@ from torch import nn
 from torch.nn import Parameter
 from torchtyping import TensorType
 
-from mattport.nerf.dataset.structs import SceneBounds
-from mattport.nerf.ray_generator import RayGenerator
-from mattport.nerf.colliders import AABBBoxCollider
-from mattport.structures.cameras import get_camera_model
-from mattport.structures.rays import RayBundle
-from mattport.utils.misc import get_masked_dict
-from mattport.utils.misc import is_not_none
+from radiance.nerf.dataset.structs import SceneBounds
+from radiance.nerf.ray_generator import RayGenerator
+from radiance.nerf.colliders import AABBBoxCollider
+from radiance.structures.cameras import get_camera_model
+from radiance.structures.rays import RayBundle
+from radiance.utils.misc import get_masked_dict
+from radiance.utils.misc import is_not_none
 
 
 class AbstractGraph(nn.Module):

radiance/nerf/graph/instant_ngp.py

@@ -9,22 +9,22 @@ from torch.nn import Parameter
 from torchmetrics import PeakSignalNoiseRatio, StructuralSimilarityIndexMeasure
 from torchmetrics.image.lpip import LearnedPerceptualImagePatchSimilarity
 
-from mattport.nerf.colliders import AABBBoxCollider
-from mattport.nerf.field_modules.field_heads import FieldHeadNames
-from mattport.nerf.fields.instant_ngp_field import field_implementation_to_class
-from mattport.nerf.graph.base import Graph
-from mattport.nerf.loss import MSELoss
-from mattport.nerf.occupancy_grid import OccupancyGrid
-from mattport.nerf.renderers import AccumulationRenderer, DepthRenderer, RGBRenderer
-from mattport.nerf.sampler import PDFSampler, UniformSampler
-from mattport.structures import colors
-from mattport.structures.rays import RayBundle
-from mattport.utils import visualization, writer
+from radiance.nerf.colliders import AABBBoxCollider
+from radiance.nerf.field_modules.field_heads import FieldHeadNames
+from radiance.nerf.fields.instant_ngp_field import field_implementation_to_class
+from radiance.nerf.graph.base import Graph
+from radiance.nerf.loss import MSELoss
+from radiance.nerf.occupancy_grid import OccupancyGrid
+from radiance.nerf.renderers import AccumulationRenderer, DepthRenderer, RGBRenderer
+from radiance.nerf.sampler import PDFSampler, UniformSampler
+from radiance.structures import colors
+from radiance.structures.rays import RayBundle
+from radiance.utils import visualization, writer
 
 
 class NGPGraph(Graph):
 
-    """NeRF-W graph"""
+    """Instant NGP graph"""
 
     def __init__(self, field_implementation="torch", intrinsics=None, camera_to_world=None, **kwargs) -> None:
         assert field_implementation in field_implementation_to_class

radiance/nerf/graph/nerfw.py

@@ -8,18 +8,18 @@ from typing import Tuple
 import torch
 from torch import nn
 
-from mattport.nerf.field_modules.embedding import Embedding
-from mattport.nerf.field_modules.field_heads import (
+from radiance.nerf.field_modules.embedding import Embedding
+from radiance.nerf.field_modules.field_heads import (
     FieldHeadNames,
     TransientDensityHead,
     TransientRGBHead,
     UncertaintyFieldHead,
 )
-from mattport.nerf.field_modules.mlp import MLP
-from mattport.nerf.graph.vanilla_nerf import NeRFField, NeRFGraph
-from mattport.nerf.renderers import UncertaintyRenderer
-from mattport.structures.rays import RayBundle, RaySamples
-from mattport.utils import visualization, writer
+from radiance.nerf.field_modules.mlp import MLP
+from radiance.nerf.graph.vanilla_nerf import NeRFField, NeRFGraph
+from radiance.nerf.renderers import UncertaintyRenderer
+from radiance.structures.rays import RayBundle, RaySamples
+from radiance.utils import visualization, writer
 
 
 class NerfWField(Field):

radiance/nerf/graph/semantic_nerf.py

@@ -7,12 +7,12 @@ from typing import Tuple
 import torch
 from torch import nn
 
-from mattport.nerf.field_modules.field_heads import FieldHeadNames, SemanticStuffHead
-from mattport.nerf.field_modules.mlp import MLP
-from mattport.nerf.graph.vanilla_nerf import NeRFField, NeRFGraph
-from mattport.nerf.renderers import SemanticRenderer
-from mattport.structures.rays import RayBundle, RaySamples
-from mattport.utils import visualization, writer
+from radiance.nerf.field_modules.field_heads import FieldHeadNames, SemanticStuffHead
+from radiance.nerf.field_modules.mlp import MLP
+from radiance.nerf.graph.vanilla_nerf import NeRFField, NeRFGraph
+from radiance.nerf.renderers import SemanticRenderer
+from radiance.structures.rays import RayBundle, RaySamples
+from radiance.utils import visualization, writer
 
 
 class SemanticNerfField(NeRFField):

radiance/nerf/graph/vanilla_nerf.py

@@ -9,18 +9,18 @@ import torch
 from torch.nn import Parameter
 from torchmetrics import PeakSignalNoiseRatio, StructuralSimilarityIndexMeasure
 from torchmetrics.image.lpip import LearnedPerceptualImagePatchSimilarity
-from mattport.nerf.field_modules.encoding import NeRFEncoding
-from mattport.nerf.field_modules.field_heads import FieldHeadNames
+from radiance.nerf.field_modules.encoding import NeRFEncoding
+from radiance.nerf.field_modules.field_heads import FieldHeadNames
 
-from mattport.nerf.fields.nerf_field import NeRFField
-from mattport.nerf.graph.base import Graph
-from mattport.nerf.loss import MSELoss
-from mattport.nerf.renderers import AccumulationRenderer, DepthRenderer, RGBRenderer
-from mattport.nerf.sampler import PDFSampler, UniformSampler
-from mattport.structures import colors
-from mattport.structures.rays import RayBundle
-from mattport.nerf.colliders import NearFarCollider
-from mattport.utils import visualization, writer
+from radiance.nerf.fields.nerf_field import NeRFField
+from radiance.nerf.graph.base import Graph
+from radiance.nerf.loss import MSELoss
+from radiance.nerf.renderers import AccumulationRenderer, DepthRenderer, RGBRenderer
+from radiance.nerf.sampler import PDFSampler, UniformSampler
+from radiance.structures import colors
+from radiance.structures.rays import RayBundle
+from radiance.nerf.colliders import NearFarCollider
+from radiance.utils import visualization, writer
 
 
 class NeRFGraph(Graph):

radiance/nerf/occupancy_grid.py

@@ -7,7 +7,7 @@ import torch.nn.functional as F
 from torch import nn
 from torchtyping import TensorType
 
-from mattport.utils.misc import is_not_none
+from radiance.utils.misc import is_not_none
 
 
 class OccupancyGrid(nn.Module):

radiance/nerf/optimizers.py

@@ -10,7 +10,7 @@ from omegaconf import DictConfig
 from torch.nn import Parameter
 from torch.optim.lr_scheduler import LambdaLR
 
-from mattport.utils import writer
+from radiance.utils import writer
 
 
 class ExponentialDecaySchedule(LambdaLR):

radiance/nerf/ray_generator.py

@@ -3,8 +3,8 @@ Ray generator.
 """
 from torch import nn
 from torchtyping import TensorType
-from mattport.structures.rays import RayBundle
-from mattport.structures.cameras import get_camera_model
+from radiance.structures.rays import RayBundle
+from radiance.structures.cameras import get_camera_model
 
 
 class RayGenerator(nn.Module):

radiance/nerf/renderers.py

@@ -8,7 +8,7 @@ import torch
 from torch import nn
 from torchtyping import TensorType
 
-from mattport.utils.math import components_from_spherical_harmonics
+from radiance.utils.math import components_from_spherical_harmonics
 
 
 class RGBRenderer(nn.Module):
@@ -17,7 +17,7 @@ class RGBRenderer(nn.Module):
     def __init__(self, background_color: Optional[TensorType[3]] = None) -> None:
         """
         Args:
-            background_color (TensorType[3], optional): Background color as RGB. Defaults to black.
+            background_color (TensorType[3], optional): Background color as RGB. Defaults to random.
         """
         super().__init__()
         self.background_color = background_color
@@ -34,14 +34,17 @@ class RGBRenderer(nn.Module):
         Args:
             rgb (TensorType[..., "num_samples", -1]): RGB for each sample
             weights (TensorType[..., "num_samples"]): Weights for each sample
+            background_color (TensorType[3], optional): Background color as RGB. Defaults to random.
 
         Returns:
             TensorType[..., 3]: Outputs rgb values.
         """
         rgb = torch.sum(weights[..., None] * rgb, dim=-2)
 
-        if background_color is not None:
-            rgb = rgb + background_color.to(weights.device)[None, ...] * (1.0 - torch.sum(weights, dim=-1)[..., None])
+        if background_color is None:
+            background_color = torch.rand_like(rgb).to(rgb.device)
+
+        rgb = rgb + background_color.to(weights.device) * (1.0 - torch.sum(weights, dim=-1)[..., None])
 
         return rgb
 
@@ -74,7 +77,7 @@ class SHRenderer(nn.Module):
     ) -> None:
         """
         Args:
-            background_color (TensorType[3], optional): Background color as RGB. Defaults to black.
+            background_color (TensorType[3], optional): Background color as RGB. Defaults to random.
             activation (Optional[nn.Module], optional): Output activation. Defaults to Sigmoid().
         """
         super().__init__()

radiance/nerf/sampler.py

@@ -9,8 +9,8 @@ import torch
 from torch import nn
 from torchtyping import TensorType
 
-from mattport.nerf.occupancy_grid import OccupancyGrid
-from mattport.structures.rays import RayBundle, RaySamples
+from radiance.nerf.occupancy_grid import OccupancyGrid
+from radiance.structures.rays import RayBundle, RaySamples
 
 
 class Sampler(nn.Module):

radiance/nerf/trainer.py

@@ -14,14 +14,14 @@ from omegaconf import DictConfig
 from torch.nn.parallel import DistributedDataParallel as DDP
 from torch.utils.data import DataLoader
 
-from mattport.nerf.dataset.collate import CollateIterDataset, collate_batch_size_one
-from mattport.nerf.dataset.image_dataset import ImageDataset, collate_batch
-from mattport.nerf.dataset.utils import DatasetInputs, get_dataset_inputs
-from mattport.nerf.optimizers import Optimizers
-from mattport.utils import profiler, writer
-from mattport.utils.callbacks import update_occupancy
-from mattport.utils.decorators import check_main_thread
-from mattport.utils.misc import get_dict_to_torch
+from radiance.nerf.dataset.collate import CollateIterDataset, collate_batch_size_one
+from radiance.nerf.dataset.image_dataset import ImageDataset, collate_batch
+from radiance.nerf.dataset.utils import DatasetInputs, get_dataset_inputs
+from radiance.nerf.optimizers import Optimizers
+from radiance.utils import profiler, writer
+from radiance.utils.callbacks import update_occupancy
+from radiance.utils.decorators import check_main_thread
+from radiance.utils.misc import get_dict_to_torch
 
 logging.getLogger("PIL").setLevel(logging.WARNING)
 

radiance/structures/cameras.py

@@ -9,7 +9,7 @@ import torch
 from torch.nn.functional import normalize
 from torchtyping import TensorType
 
-from mattport.structures.rays import RayBundle
+from radiance.structures.rays import RayBundle
 
 
 @dataclass

radiance/structures/rays.py

@@ -8,7 +8,7 @@ from typing import Optional
 import torch
 from torchtyping import TensorType
 
-from mattport.utils.misc import is_not_none
+from radiance.utils.misc import is_not_none
 
 
 @dataclass

radiance/utils/callbacks.py

@@ -2,8 +2,8 @@
 Callback functions for training iterations
 """
 
-from mattport.nerf.graph.base import Graph
-from mattport.nerf.occupancy_grid import OccupancyGrid
+from radiance.nerf.graph.base import Graph
+from radiance.nerf.occupancy_grid import OccupancyGrid
 
 
 def update_occupancy(graph: Graph):

radiance/utils/decorators.py

@@ -3,7 +3,7 @@ Decorator definitions
 """
 from typing import Callable, List
 
-from mattport.utils import comms
+from radiance.utils import comms
 
 
 def decorate_all(decorators: List[Callable]) -> Callable:

radiance/utils/profiler.py

@@ -7,8 +7,8 @@ from typing import Callable
 
 from omegaconf import DictConfig
 
-from mattport.utils import comms
-from mattport.utils.decorators import check_main_thread, check_profiler_enabled, decorate_all
+from radiance.utils import comms
+from radiance.utils.decorators import check_main_thread, check_profiler_enabled, decorate_all
 
 PROFILER = []
 

radiance/utils/writer.py

@@ -15,8 +15,8 @@ from omegaconf import DictConfig, ListConfig
 from torch.utils.tensorboard import SummaryWriter
 from torchtyping import TensorType
 
-import mattport.utils.writer
-from mattport.utils.decorators import check_main_thread, decorate_all
+import radiance.utils.writer
+from radiance.utils.decorators import check_main_thread, decorate_all
 
 to8b = lambda x: (255 * torch.clamp(x, min=0, max=1)).to(torch.uint8)
 EVENT_WRITERS = []
@@ -150,7 +150,7 @@ def setup_event_writers(config: DictConfig) -> None:
     """Initialization of all event writers specified in config"""
     logging_configs = config.logging.writer
     for writer_type in logging_configs:
-        writer_class = getattr(mattport.utils.writer, writer_type)
+        writer_class = getattr(radiance.utils.writer, writer_type)
         writer_config = logging_configs[writer_type]
         if writer_type == "LocalWriter":
             curr_writer = writer_class(writer_config.save_dir, writer_config.stats_to_track, writer_config.max_log_size)

radiance/viewer/plotly.py

@@ -6,7 +6,7 @@ import numpy as np
 import plotly.graph_objects as go
 import torch
 
-from mattport.structures.rays import RayBundle
+from radiance.structures.rays import RayBundle
 
 
 def color_str(color):

scripts/benchmarking/launch_train_blender.sh

@@ -43,7 +43,7 @@ for dataset in ${DATASETS[@]}; do
            data.dataset.data_directory=data/blender/${dataset} \
            data.dataset.downscale_factor=1 \
            experiment_name=blender_${dataset}_${tag} \
-           graph.model_dir=mattport_models/ \
+           graph.model_dir=radiance_models/ \
            graph.steps_per_save=25000 \
            graph.max_num_iterations=2000000 \
            logging.enable_profiler=False &

scripts/benchmarking/run_benchmark.py

@@ -12,12 +12,12 @@ from hydra.core.global_hydra import GlobalHydra
 from omegaconf import DictConfig
 from tqdm import tqdm
 
-from mattport.nerf.trainer import Trainer
+from radiance.nerf.trainer import Trainer
 
 BENCH = {
     "method": "vanilla_nerf",
     "hydra_base_dir": "outputs/",
-    "benchmark_date": "05-24-2022",
+    "benchmark_date": "05-26-2022",
     "object_list": ["mic", "ficus", "chair", "hotdog", "materials", "drums", "ship", "lego"],
 }
 

scripts/run_train.py

@@ -3,6 +3,7 @@ run_train_nerf.py
 """
 import datetime
 import logging
+import os
 import random
 import socket
 from datetime import timedelta
@@ -15,9 +16,10 @@ import torch
 import torch.distributed as dist
 import torch.multiprocessing as mp
 from omegaconf import DictConfig
+import yaml
 
-from mattport.nerf.trainer import Trainer
-from mattport.utils import comms, profiler
+from radiance.nerf.trainer import Trainer
+from radiance.utils import comms, profiler
 from time import time
 
 logging.basicConfig(format="[%(filename)s:%(lineno)d] %(message)s", level=logging.DEBUG)
@@ -198,9 +200,17 @@ def launch(
             profiler.flush_profiler(config.logging)
 
 
-@hydra.main(config_path="../configs", config_name="default.yaml")
+@hydra.main(config_path="../configs", config_name="vanilla_nerf.yaml")
 def main(config: DictConfig):
     """Main function."""
+    unrolled_path = os.path.join(os.getcwd(), ".hydra/config.yaml")
+    if os.path.exists(unrolled_path):
+        with open(unrolled_path, "r", encoding="utf8") as f:
+            unrolled_config = yaml.safe_load(f)
+        logger = logging.getLogger(__name__)
+        logger.info("Printing current config setup")
+        print(yaml.dump(unrolled_config, sort_keys=False, default_flow_style=False))
+
     launch(
         _train,
         config.machine_config.num_gpus,

setup.cfg

@@ -1,7 +1,7 @@
 [metadata]
-name = mattport
+name = radiance
 version = 0.0.0
-url = https://github.com/ethanweber/mattport.git
+url = https://github.com/ethanweber/radiance.git
 description = differentiable volume rendering package
 long_description = file: README.md
 

setup.py

@@ -13,12 +13,12 @@ if USE_CUDA:
 
         ext_modules = [
             CUDAExtension(
-                name="mattport_cuda",
+                name="radiance_cuda",
                 sources=[
-                    "mattport/cuda/mattport_cuda.cpp",
-                    "mattport/cuda/mattport_cuda_kernel.cu",
+                    "radiance/cuda/radiance_cuda.cpp",
+                    "radiance/cuda/radiance_cuda_kernel.cu",
                 ],
-                include_dirs=[os.path.join("mattport", "cuda", "include")],
+                include_dirs=[os.path.join("radiance", "cuda", "include")],
                 extra_compile_args={
                     "cxx": [],
                     "nvcc": [],

tests/cuda/test_mattport_cuda.py

@@ -2,9 +2,9 @@
 Test the CUDA bindings.
 """
 import torch
-from mattport.cuda.sampler import Sampler
+from radiance.cuda.sampler import Sampler
 import time
-import mattport_cuda
+import radiance_cuda
 import os
 
 
@@ -78,7 +78,7 @@ def test_benchmark():
     print("PYTORCH_SAMPLE_TIME:", PYTORCH_SAMPLE_TIME)
 
     start = time.time()
-    time_steps_cu, samples_cu, time_steps_mask_cu = mattport_cuda.sample_uniformly_along_ray_bundle(
+    time_steps_cu, samples_cu, time_steps_mask_cu = radiance_cuda.sample_uniformly_along_ray_bundle(
         origins, directions, nears, fars, offsets, max_num_samples
     )
     CUDA_SAMPLE_TIME = time.time() - start

tests/nerf/dataloader/test_ray_generator.py

@@ -7,12 +7,12 @@ import torch
 from torch.utils.data import DataLoader
 from tqdm import tqdm
 
-from mattport.nerf.dataset.collate import CollateIterDataset, collate_batch_size_one
-from mattport.nerf.dataset.image_dataset import ImageDataset, collate_batch
-from mattport.nerf.dataset.utils import get_dataset_inputs
-from mattport.nerf.ray_generator import RayGenerator
-from mattport.utils.io import get_absolute_path
-from mattport.viewer.plotly import visualize_dataset
+from radiance.nerf.dataset.collate import CollateIterDataset, collate_batch_size_one
+from radiance.nerf.dataset.image_dataset import ImageDataset, collate_batch
+from radiance.nerf.dataset.utils import get_dataset_inputs
+from radiance.nerf.ray_generator import RayGenerator
+from radiance.utils.io import get_absolute_path
+from radiance.viewer.plotly import visualize_dataset
 
 
 def visualize_batch(batch):

tests/nerf/field_modules/test_embedding.py

@@ -1,7 +1,7 @@
 """
 Embedding tests
 """
-from mattport.nerf.field_modules.embedding import Embedding
+from radiance.nerf.field_modules.embedding import Embedding
 
 
 def test_indexing():

tests/nerf/field_modules/test_encodings.py

@@ -3,7 +3,7 @@ Encoding Tests
 """
 import pytest
 import torch
-from mattport.nerf.field_modules import encoding
+from radiance.nerf.field_modules import encoding
 
 
 def test_scaling_and_offset():

tests/nerf/field_modules/test_field_outputs.py

@@ -5,7 +5,7 @@ import torch
 from torch import nn
 import pytest
 
-from mattport.nerf.field_modules.field_heads import (
+from radiance.nerf.field_modules.field_heads import (
     DensityFieldHead,
     FieldHead,
     FieldHeadNames,

tests/nerf/field_modules/test_mlp.py

@@ -4,7 +4,7 @@ MLP Test
 import torch
 from torch import nn
 
-from mattport.nerf.field_modules import MLP
+from radiance.nerf.field_modules import MLP
 
 
 def test_mlp():

tests/nerf/test_fields.py

@@ -3,9 +3,9 @@ Test the fields
 """
 import torch
 
-from mattport.nerf.field_modules.field_heads import FieldHeadNames
-from mattport.nerf.fields.instant_ngp_field import TCNNInstantNGPField
-from mattport.structures.rays import PointSamples
+from radiance.nerf.field_modules.field_heads import FieldHeadNames
+from radiance.nerf.fields.instant_ngp_field import TCNNInstantNGPField
+from radiance.structures.rays import PointSamples
 
 
 def test_tcnn_instant_ngp_field():

tests/nerf/test_occupancy_grid.py

@@ -3,7 +3,7 @@ Code to test the occupancy grid.
 """
 
 import torch
-from mattport.nerf.occupancy_grid import OccupancyGrid
+from radiance.nerf.occupancy_grid import OccupancyGrid
 
 
 def test_occupancy_grid():

tests/nerf/test_renderers.py

@@ -4,7 +4,7 @@ Test renderers
 import pytest
 import torch
 
-from mattport.nerf import renderers
+from radiance.nerf import renderers
 
 
 def test_rgb_renderer():
@@ -21,7 +21,7 @@ def test_rgb_renderer():
     assert torch.max(rgb) > 0.9
 
     rgb = rgb_renderer(rgb=rgb_samples * 0, weights=weights)
-    assert torch.max(rgb) == pytest.approx(0)
+    assert torch.max(rgb) == pytest.approx(0, abs=1e-6)
 
 
 def test_sh_renderer():

tests/nerf/test_sampler.py

@@ -2,10 +2,10 @@
 Test samplers
 """
 import torch
-from mattport.nerf.sampler import PDFSampler, UniformSampler
+from radiance.nerf.sampler import PDFSampler, UniformSampler
 
-from mattport.structures.rays import RayBundle
-from mattport.nerf.colliders import NearFarCollider
+from radiance.structures.rays import RayBundle
+from radiance.nerf.colliders import NearFarCollider
 
 
 def test_uniform_sampler():

tests/structures/test_cameras.py

@@ -4,8 +4,8 @@ Test the camera classes.
 
 import torch
 
-from mattport.structures.cameras import PinholeCamera
-from mattport.structures.rays import RayBundle
+from radiance.structures.cameras import PinholeCamera
+from radiance.structures.rays import RayBundle
 
 
 def test_pinhole_camera():