From e13d401b163dd5dc9dc7b7692d27b9e29003ba84 Mon Sep 17 00:00:00 2001 From: Ethan Weber Date: Sat, 1 Oct 2022 16:36:25 -0700 Subject: [PATCH] restructure codebase pre release (#602) * moving a lot * building --- docs/conf.py | 3 +- .../model_components/visualize_encoders.ipynb | 1118 +- .../model_components/visualize_samplers.ipynb | 6 +- .../visualize_spatial_distortions.ipynb | 468850 +++++++-------- docs/guides/models/nerf.md | 6 +- docs/reference/api/config.rst | 2 +- docs/reference/api/data.rst | 12 +- .../embeddings.rst | 2 +- .../encodings.rst | 2 +- .../field_heads.rst | 2 +- .../field_losses.rst | 2 +- .../modules => field_components}/index.rst | 0 .../modules => field_components}/mlp.rst | 2 +- .../spatial_distortions.rst | 2 +- docs/reference/api/fields.rst | 2 +- docs/reference/api/index.rst | 7 +- .../modules => model_components}/index.rst | 3 +- .../ray_losses.rst | 2 +- .../ray_sampler.rst | 2 +- .../renderers.rst} | 2 +- docs/reference/api/{graphs.rst => models.rst} | 0 docs/reference/api/optimizers.rst | 15 +- .../tutorials/pipelines/creating_models.ipynb | 6 +- .../pipelines/creating_pipelines.ipynb | 706 +- nerfstudio/cameras/camera_paths.py | 4 +- .../cameras/{utils.py => camera_utils.py} | 0 nerfstudio/cameras/cameras.py | 2 +- nerfstudio/cameras/rays.py | 8 - .../configs/{base.py => base_config.py} | 56 +- .../configs/{utils.py => config_utils.py} | 0 .../{base_configs.py => model_configs.py} | 41 +- nerfstudio/{datamanagers => data}/__init__.py | 0 .../base.py => data/datamanagers.py} | 30 +- .../dataparsers/__init__.py | 0 .../dataparsers/base_dataparser.py} | 60 +- .../dataparsers/blender_dataparser.py} | 10 +- .../dataparsers/friends_dataparser.py} | 11 +- .../dataparsers/instant_ngp_dataparser.py} | 12 +- .../dataparsers/mipnerf_dataparser.py} | 10 +- .../dataparsers/nerfstudio_dataparser.py} | 12 +- .../dataparsers/record3d_dataparser.py} | 14 +- .../pixel_samplers.py} | 0 .../structs.py => data/scene_box.py} | 60 +- .../utils}/dataloaders.py | 2 +- .../{datamanagers => data/utils}/datasets.py | 4 +- .../{optimizers => engine}/optimizers.py | 41 +- .../{optimizers => engine}/schedulers.py | 22 + nerfstudio/engine/trainer.py | 4 +- .../modules => field_components}/__init__.py | 0 .../modules => field_components}/base.py | 0 .../modules => field_components}/embedding.py | 2 +- .../modules => field_components}/encoding.py | 2 +- .../field_heads.py | 2 +- .../field_losses.py | 0 .../modules => field_components}/mlp.py | 2 +- .../spatial_distortions.py | 0 nerfstudio/fields/{base.py => base_field.py} | 2 +- .../{density_field.py => density_fields.py} | 8 +- nerfstudio/fields/instant_ngp_field.py | 8 +- nerfstudio/fields/nerf_field.py | 10 +- .../{compound_field.py => nerfacto_field.py} | 20 +- nerfstudio/fields/nerfw_field.py | 10 +- nerfstudio/fields/semantic_nerf_field.py | 8 +- .../modules => model_components}/__init__.py | 0 .../{optimizers => model_components}/loss.py | 0 .../ray_generator.py | 0 .../ray_losses.py | 0 .../ray_sampler.py | 0 .../renderers.py | 0 .../scene_colliders.py | 2 +- nerfstudio/models/base.py | 8 +- nerfstudio/models/instant_ngp.py | 10 +- nerfstudio/models/mipnerf.py | 12 +- nerfstudio/models/mipnerf_360.py | 16 +- nerfstudio/models/nerfacto.py | 24 +- nerfstudio/models/nerfw.py | 16 +- nerfstudio/models/semantic_nerf.py | 10 +- nerfstudio/models/tensorf.py | 16 +- nerfstudio/models/vanilla_nerf.py | 12 +- nerfstudio/optimizers/__init__.py | 13 - nerfstudio/pipelines/base.py | 4 +- nerfstudio/pipelines/dynamic_batch.py | 2 +- nerfstudio/renderers/__init__.py | 13 - nerfstudio/utils/callbacks.py | 2 +- nerfstudio/utils/profiler.py | 2 +- nerfstudio/utils/writer.py | 2 +- nerfstudio/viewer/server/viewer_utils.py | 4 +- scripts/eval.py | 2 +- scripts/train.py | 4 +- scripts/viewer/view_dataset.py | 4 +- tests/fields/modules/test_embedding.py | 2 +- tests/fields/modules/test_encodings.py | 2 +- tests/fields/modules/test_field_outputs.py | 2 +- tests/fields/modules/test_mlp.py | 2 +- tests/graphs/modules/test_ray_sampler.py | 4 +- tests/renderers/test_renderers.py | 2 +- tests/test_run_train.py | 10 +- 97 files changed, 235712 insertions(+), 235721 deletions(-) rename docs/reference/api/{fields/modules => field_components}/embeddings.rst (60%) rename docs/reference/api/{fields/modules => field_components}/encodings.rst (60%) rename docs/reference/api/{fields/modules => field_components}/field_heads.rst (60%) rename docs/reference/api/{fields/modules => field_components}/field_losses.rst (60%) rename docs/reference/api/{fields/modules => field_components}/index.rst (100%) rename docs/reference/api/{fields/modules => field_components}/mlp.rst (58%) rename docs/reference/api/{fields/modules => field_components}/spatial_distortions.rst (61%) rename docs/reference/api/{graphs/modules => model_components}/index.rst (85%) rename docs/reference/api/{graphs/modules => model_components}/ray_losses.rst (60%) rename docs/reference/api/{graphs/modules => model_components}/ray_sampler.rst (60%) rename docs/reference/api/{renderers/index.rst => model_components/renderers.rst} (58%) rename docs/reference/api/{graphs.rst => models.rst} (100%) rename nerfstudio/cameras/{utils.py => camera_utils.py} (100%) rename nerfstudio/configs/{base.py => base_config.py} (85%) rename nerfstudio/configs/{utils.py => config_utils.py} (100%) rename nerfstudio/configs/{base_configs.py => model_configs.py} (87%) rename nerfstudio/{datamanagers => data}/__init__.py (100%) rename nerfstudio/{datamanagers/base.py => data/datamanagers.py} (94%) rename nerfstudio/{datamanagers => data}/dataparsers/__init__.py (100%) rename nerfstudio/{datamanagers/dataparsers/base.py => data/dataparsers/base_dataparser.py} (50%) rename nerfstudio/{datamanagers/dataparsers/blender_parser.py => data/dataparsers/blender_dataparser.py} (94%) rename nerfstudio/{datamanagers/dataparsers/friends_parser.py => data/dataparsers/friends_dataparser.py} (96%) rename nerfstudio/{datamanagers/dataparsers/instant_ngp_parser.py => data/dataparsers/instant_ngp_dataparser.py} (93%) rename nerfstudio/{datamanagers/dataparsers/mipnerf_parser.py => data/dataparsers/mipnerf_dataparser.py} (96%) rename nerfstudio/{datamanagers/dataparsers/nerfstudio_parser.py => data/dataparsers/nerfstudio_dataparser.py} (95%) rename nerfstudio/{datamanagers/dataparsers/record3d_parser.py => data/dataparsers/record3d_dataparser.py} (95%) rename nerfstudio/{datamanagers/pixel_sampler.py => data/pixel_samplers.py} (100%) rename nerfstudio/{datamanagers/structs.py => data/scene_box.py} (63%) rename nerfstudio/{datamanagers => data/utils}/dataloaders.py (99%) rename nerfstudio/{datamanagers => data/utils}/datasets.py (96%) rename nerfstudio/{optimizers => engine}/optimizers.py (76%) rename nerfstudio/{optimizers => engine}/schedulers.py (71%) rename nerfstudio/{fields/modules => field_components}/__init__.py (100%) rename nerfstudio/{fields/modules => field_components}/base.py (100%) rename nerfstudio/{fields/modules => field_components}/embedding.py (96%) rename nerfstudio/{fields/modules => field_components}/encoding.py (99%) rename nerfstudio/{fields/modules => field_components}/field_heads.py (99%) rename nerfstudio/{fields/modules => field_components}/field_losses.py (100%) rename nerfstudio/{fields/modules => field_components}/mlp.py (98%) rename nerfstudio/{fields/modules => field_components}/spatial_distortions.py (100%) rename nerfstudio/fields/{base.py => base_field.py} (98%) rename nerfstudio/fields/{density_field.py => density_fields.py} (95%) rename nerfstudio/fields/{compound_field.py => nerfacto_field.py} (95%) rename nerfstudio/{models/modules => model_components}/__init__.py (100%) rename nerfstudio/{optimizers => model_components}/loss.py (100%) rename nerfstudio/{models/modules => model_components}/ray_generator.py (100%) rename nerfstudio/{models/modules => model_components}/ray_losses.py (100%) rename nerfstudio/{models/modules => model_components}/ray_sampler.py (100%) rename nerfstudio/{renderers => model_components}/renderers.py (100%) rename nerfstudio/{models/modules => model_components}/scene_colliders.py (98%) delete mode 100644 nerfstudio/optimizers/__init__.py delete mode 100644 nerfstudio/renderers/__init__.py diff --git a/docs/conf.py b/docs/conf.py index b3bf31ea5..d4ebac9f8 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -15,8 +15,7 @@ import os import sys sys.path.insert(0, os.path.abspath("..")) -import nerfstudio.configs.base -import nerfstudio.datamanagers.base +import nerfstudio.configs.base_config # -- Project information ----------------------------------------------------- diff --git a/docs/guides/model_components/visualize_encoders.ipynb b/docs/guides/model_components/visualize_encoders.ipynb index 3dd590422..86001abd9 100644 --- a/docs/guides/model_components/visualize_encoders.ipynb +++ b/docs/guides/model_components/visualize_encoders.ipynb @@ -1,561 +1,561 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Field Encoders" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "(nerf)=\n", - "## NeRF Positional Encoding\n", - "First introduced in the original NeRF paper. This encoding assumes the inputs are between zero and one and can opperate on any dimensional input." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "tags": [ - "hide-input" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Input Values:\n" - ] + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Field Encoders" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "(nerf)=\n", + "## NeRF Positional Encoding\n", + "First introduced in the original NeRF paper. This encoding assumes the inputs are between zero and one and can opperate on any dimensional input." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Input Values:\n" + ] + }, + { + "data": { + "text/html": [ + "
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Encoded Values:\n" + ] + }, + { + "data": { + "text/html": [ + "
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Encoded Integrate Values:\n", + "Covariance Magnitude: 0.01\n" + ] + }, + { + "data": { + "text/html": [ + "
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Covariance Magnitude: 0.1\n" + ] + }, + { + "data": { + "text/html": [ + "
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Covariance Magnitude: 1\n" + ] + }, + { + "data": { + "text/html": [ + "
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# COLLAPSED\n", + "import torch\n", + "import mediapy as media\n", + "from nerfstudio.field_components import encoding\n", + "\n", + "num_frequencies = 4\n", + "min_freq_exp = 0\n", + "max_freq_exp = 6\n", + "include_input = False\n", + "resolution = 128\n", + "covariance_magnitudes = [0.01, 0.1, 1]\n", + "\n", + "encoder = encoding.NeRFEncoding(\n", + " in_dim=2,\n", + " num_frequencies=num_frequencies,\n", + " min_freq_exp=min_freq_exp,\n", + " max_freq_exp=max_freq_exp,\n", + " include_input=include_input,\n", + ")\n", + "\n", + "x_samples = torch.linspace(0, 1, resolution)\n", + "grid = torch.stack(torch.meshgrid([x_samples, x_samples], indexing=\"ij\"), dim=-1)\n", + "\n", + "encoded_values = encoder(grid)\n", + "\n", + "print(\"Input Values:\")\n", + "media.show_images(torch.moveaxis(grid, 2, 0), cmap=\"plasma\", border=True)\n", + "print(\"Encoded Values:\")\n", + "media.show_images(torch.moveaxis(encoded_values, 2, 0), vmin=-1, vmax=1, cmap=\"plasma\", border=True)\n", + "\n", + "print(\"Encoded Integrate Values:\")\n", + "for covariance_magnitude in covariance_magnitudes:\n", + " print(f\"Covariance Magnitude: {covariance_magnitude}\")\n", + " covs = torch.eye(2)[None, None, :, :] * covariance_magnitude\n", + " encoded_values = encoder(grid, covs=covs)\n", + " media.show_images(torch.moveaxis(encoded_values, 2, 0), vmin=-1, vmax=1, cmap=\"plasma\", border=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "(rff)=\n", + "## Random Fourier Feature (RFF) Encoding\n", + "This encoding assumes the inputs are between zero and one and can opperate on any dimensional input." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Input Values:\n" + ] + }, + { + "data": { + "text/html": [ + "
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Encoded Values:\n" + ] + }, + { + "data": { + "text/html": [ + "
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Encoded Integrate Values:\n", + "Covariance Magnitude: 0.001\n" + ] + }, + { + "data": { + "text/html": [ + "
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Covariance Magnitude: 0.01\n" + ] + }, + { + "data": { + "text/html": [ + "
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Covariance Magnitude: 0.1\n" + ] + }, + { + "data": { + "text/html": [ + "
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# COLLAPSED\n", + "import torch\n", + "import mediapy as media\n", + "from nerfstudio.field_components import encoding\n", + "\n", + "num_frequencies = 8\n", + "scale = 10\n", + "resolution = 128\n", + "covariance_magnitudes = [0.001, 0.01, 0.1]\n", + "\n", + "encoder = encoding.RFFEncoding(in_dim=2, num_frequencies=num_frequencies, scale=scale)\n", + "\n", + "x_samples = torch.linspace(0, 1, resolution)\n", + "grid = torch.stack(torch.meshgrid([x_samples, x_samples], indexing=\"ij\"), dim=-1)\n", + "\n", + "encoded_values = encoder(grid)\n", + "\n", + "print(\"Input Values:\")\n", + "media.show_images(torch.moveaxis(grid, 2, 0), cmap=\"plasma\", border=True)\n", + "print(\"Encoded Values:\")\n", + "media.show_images(torch.moveaxis(encoded_values, 2, 0), cmap=\"plasma\", vmin=-1, vmax=1, border=True)\n", + "\n", + "print(\"Encoded Integrate Values:\")\n", + "for covariance_magnitude in covariance_magnitudes:\n", + " print(f\"Covariance Magnitude: {covariance_magnitude}\")\n", + " covs = torch.eye(2)[None, None, :, :] * covariance_magnitude\n", + " encoded_values = encoder(grid, covs=covs)\n", + " media.show_images(torch.moveaxis(encoded_values, 2, 0), cmap=\"plasma\", vmin=-1, vmax=1, border=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "(hash)=\n", + "## Hash Encoding\n", + "The hash incoding was originally introduced in Instant-NGP. The encoding is optimized during training. This is a visualization of the initialization." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Input Values:\n" + ] + }, + { + "data": { + "text/html": [ + "
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Encoded Values:\n" + ] + }, + { + "data": { + "text/html": [ + "
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# COLLAPSED\n", + "import torch\n", + "import mediapy as media\n", + "from nerfstudio.field_components import encoding\n", + "\n", + "num_levels = 8\n", + "min_res = 2\n", + "max_res = 128\n", + "hash_table_size = 2**4 # Typically much larger tables are used\n", + "\n", + "resolution = 128\n", + "slice = 0\n", + "\n", + "# Fixing features_per_level to 3 for easy RGB visualization. Typical value is 2 in networks\n", + "features_per_level = 3\n", + "\n", + "encoder = encoding.HashEncoding(\n", + " num_levels=num_levels,\n", + " min_res=min_res,\n", + " max_res=max_res,\n", + " hash_table_size=hash_table_size,\n", + " features_per_level=features_per_level,\n", + " hash_init_scale=0.001,\n", + ")\n", + "\n", + "x_samples = torch.linspace(0, 1, resolution)\n", + "grid = torch.stack(torch.meshgrid([x_samples, x_samples, x_samples], indexing=\"ij\"), dim=-1)\n", + "\n", + "encoded_values = encoder(grid)\n", + "\n", + "grid_slice = grid[slice, ...]\n", + "encoded_values_slice = encoded_values[slice, ...]\n", + "\n", + "print(\"Input Values:\")\n", + "media.show_images(torch.moveaxis(grid_slice, 2, 0), cmap=\"plasma\", border=True)\n", + "\n", + "print(\"Encoded Values:\")\n", + "encoded_images = encoded_values_slice.view(resolution, resolution, num_levels, 3)\n", + "encoded_images = torch.moveaxis(encoded_images, 2, 0)\n", + "encoded_images -= torch.min(encoded_images)\n", + "encoded_images /= torch.max(encoded_images)\n", + "media.show_images(encoded_images.detach().numpy(), cmap=\"plasma\", border=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "(spherical)=\n", + "## Spherical Harmonic Encoding\n", + "Encode direction using spherical harmoincs. (Mostly used to encode viewing direction)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Level: 1\n" + ] + }, + { + "data": { + "text/html": [ + "
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Level: 2\n" + ] + }, + { + "data": { + "text/html": [ + "
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Level: 3\n" + ] + }, + { + "data": { + "text/html": [ + "
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Level: 4\n" + ] + }, + { + "data": { + "text/html": [ + "
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# COLLAPSED\n", + "import torch\n", + "import mediapy as media\n", + "from nerfstudio.field_components import encoding\n", + "\n", + "levels = 4\n", + "\n", + "height = 100\n", + "width = 150\n", + "\n", + "encoder = encoding.SHEncoding(levels=levels)\n", + "\n", + "theta = torch.linspace(-torch.pi, torch.pi, width)\n", + "phi = torch.linspace(0, torch.pi, height)\n", + "[theta, phi] = torch.meshgrid([theta, phi], indexing=\"xy\")\n", + "\n", + "directions = torch.stack([torch.cos(theta) * torch.sin(phi), torch.sin(theta) * torch.sin(phi), torch.cos(phi)], dim=-1)\n", + "\n", + "encoded_values = encoder(directions)\n", + "encoded_values = torch.moveaxis(encoded_values, 2, 0)\n", + "\n", + "for level in range(levels):\n", + " print(f\"Level: {level+1}\")\n", + " media.show_images(encoded_values[level**2 : (level + 1) ** 2, ...], cmap=\"plasma\", border=True)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.8.12 ('nerfactory')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + }, + "vscode": { + "interpreter": { + "hash": "34c28001ff35fb390494047002768a8182dcf55b1b11415165e62ea61557ab83" + } + } }, - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance Magnitude: 0.1\n" - ] - }, - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# COLLAPSED\n", - "import torch\n", - "import mediapy as media\n", - "from nerfstudio.fields.modules import encoding\n", - "\n", - "num_frequencies = 4\n", - "min_freq_exp = 0\n", - "max_freq_exp = 6\n", - "include_input = False\n", - "resolution = 128\n", - "covariance_magnitudes = [0.01, 0.1, 1]\n", - "\n", - "encoder = encoding.NeRFEncoding(\n", - " in_dim=2,\n", - " num_frequencies=num_frequencies,\n", - " min_freq_exp=min_freq_exp,\n", - " max_freq_exp=max_freq_exp,\n", - " include_input=include_input,\n", - ")\n", - "\n", - "x_samples = torch.linspace(0, 1, resolution)\n", - "grid = torch.stack(torch.meshgrid([x_samples, x_samples], indexing=\"ij\"), dim=-1)\n", - "\n", - "encoded_values = encoder(grid)\n", - "\n", - "print(\"Input Values:\")\n", - "media.show_images(torch.moveaxis(grid, 2, 0), cmap=\"plasma\", border=True)\n", - "print(\"Encoded Values:\")\n", - "media.show_images(torch.moveaxis(encoded_values, 2, 0), vmin=-1, vmax=1, cmap=\"plasma\", border=True)\n", - "\n", - "print(\"Encoded Integrate Values:\")\n", - "for covariance_magnitude in covariance_magnitudes:\n", - " print(f\"Covariance Magnitude: {covariance_magnitude}\")\n", - " covs = torch.eye(2)[None, None, :, :] * covariance_magnitude\n", - " encoded_values = encoder(grid, covs=covs)\n", - " media.show_images(torch.moveaxis(encoded_values, 2, 0), vmin=-1, vmax=1, cmap=\"plasma\", border=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "(rff)=\n", - "## Random Fourier Feature (RFF) Encoding\n", - "This encoding assumes the inputs are between zero and one and can opperate on any dimensional input." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "tags": [ - "hide-input" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Input Values:\n" - ] - }, - { - "data": { - "text/html": [ - "
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Encoded Values:\n" - ] - }, - { - "data": { - "text/html": [ - "
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Encoded Integrate Values:\n", - "Covariance Magnitude: 0.001\n" - ] - }, - { - "data": { - "text/html": [ - "
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance Magnitude: 0.01\n" - ] - }, - { - "data": { - "text/html": [ - "
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance Magnitude: 0.1\n" - ] - }, - { - "data": { - "text/html": [ - "
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# COLLAPSED\n", - "import torch\n", - "import mediapy as media\n", - "from nerfstudio.fields.modules import encoding\n", - "\n", - "num_frequencies = 8\n", - "scale = 10\n", - "resolution = 128\n", - "covariance_magnitudes = [0.001, 0.01, 0.1]\n", - "\n", - "encoder = encoding.RFFEncoding(in_dim=2, num_frequencies=num_frequencies, scale=scale)\n", - "\n", - "x_samples = torch.linspace(0, 1, resolution)\n", - "grid = torch.stack(torch.meshgrid([x_samples, x_samples], indexing=\"ij\"), dim=-1)\n", - "\n", - "encoded_values = encoder(grid)\n", - "\n", - "print(\"Input Values:\")\n", - "media.show_images(torch.moveaxis(grid, 2, 0), cmap=\"plasma\", border=True)\n", - "print(\"Encoded Values:\")\n", - "media.show_images(torch.moveaxis(encoded_values, 2, 0), cmap=\"plasma\", vmin=-1, vmax=1, border=True)\n", - "\n", - "print(\"Encoded Integrate Values:\")\n", - "for covariance_magnitude in covariance_magnitudes:\n", - " print(f\"Covariance Magnitude: {covariance_magnitude}\")\n", - " covs = torch.eye(2)[None, None, :, :] * covariance_magnitude\n", - " encoded_values = encoder(grid, covs=covs)\n", - " media.show_images(torch.moveaxis(encoded_values, 2, 0), cmap=\"plasma\", vmin=-1, vmax=1, border=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "(hash)=\n", - "## Hash Encoding\n", - "The hash incoding was originally introduced in Instant-NGP. The encoding is optimized during training. This is a visualization of the initialization." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "tags": [ - "hide-input" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Input Values:\n" - ] - }, - { - "data": { - "text/html": [ - "
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Encoded Values:\n" - ] - }, - { - "data": { - "text/html": [ - "
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# COLLAPSED\n", - "import torch\n", - "import mediapy as media\n", - "from nerfstudio.fields.modules import encoding\n", - "\n", - "num_levels = 8\n", - "min_res = 2\n", - "max_res = 128\n", - "hash_table_size = 2**4 # Typically much larger tables are used\n", - "\n", - "resolution = 128\n", - "slice = 0\n", - "\n", - "# Fixing features_per_level to 3 for easy RGB visualization. Typical value is 2 in networks\n", - "features_per_level = 3\n", - "\n", - "encoder = encoding.HashEncoding(\n", - " num_levels=num_levels,\n", - " min_res=min_res,\n", - " max_res=max_res,\n", - " hash_table_size=hash_table_size,\n", - " features_per_level=features_per_level,\n", - " hash_init_scale=0.001,\n", - ")\n", - "\n", - "x_samples = torch.linspace(0, 1, resolution)\n", - "grid = torch.stack(torch.meshgrid([x_samples, x_samples, x_samples], indexing=\"ij\"), dim=-1)\n", - "\n", - "encoded_values = encoder(grid)\n", - "\n", - "grid_slice = grid[slice, ...]\n", - "encoded_values_slice = encoded_values[slice, ...]\n", - "\n", - "print(\"Input Values:\")\n", - "media.show_images(torch.moveaxis(grid_slice, 2, 0), cmap=\"plasma\", border=True)\n", - "\n", - "print(\"Encoded Values:\")\n", - "encoded_images = encoded_values_slice.view(resolution, resolution, num_levels, 3)\n", - "encoded_images = torch.moveaxis(encoded_images, 2, 0)\n", - "encoded_images -= torch.min(encoded_images)\n", - "encoded_images /= torch.max(encoded_images)\n", - "media.show_images(encoded_images.detach().numpy(), cmap=\"plasma\", border=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "(spherical)=\n", - "## Spherical Harmonic Encoding\n", - "Encode direction using spherical harmoincs. (Mostly used to encode viewing direction)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "tags": [ - "hide-input" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Level: 1\n" - ] - }, - { - "data": { - "text/html": [ - "
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Level: 2\n" - ] - }, - { - "data": { - "text/html": [ - "
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Level: 3\n" - ] - }, - { - "data": { - "text/html": [ - "
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Level: 4\n" - ] - }, - { - "data": { - "text/html": [ - "
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# COLLAPSED\n", - "import torch\n", - "import mediapy as media\n", - "from nerfstudio.fields.modules import encoding\n", - "\n", - "levels = 4\n", - "\n", - "height = 100\n", - "width = 150\n", - "\n", - "encoder = encoding.SHEncoding(levels=levels)\n", - "\n", - "theta = torch.linspace(-torch.pi, torch.pi, width)\n", - "phi = torch.linspace(0, torch.pi, height)\n", - "[theta, phi] = torch.meshgrid([theta, phi], indexing=\"xy\")\n", - "\n", - "directions = torch.stack([torch.cos(theta) * torch.sin(phi), torch.sin(theta) * torch.sin(phi), torch.cos(phi)], dim=-1)\n", - "\n", - "encoded_values = encoder(directions)\n", - "encoded_values = torch.moveaxis(encoded_values, 2, 0)\n", - "\n", - "for level in range(levels):\n", - " print(f\"Level: {level+1}\")\n", - " media.show_images(encoded_values[level**2 : (level + 1) ** 2, ...], cmap=\"plasma\", border=True)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.8.12 ('nerfactory')", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.12" - }, - "vscode": { - "interpreter": { - "hash": "34c28001ff35fb390494047002768a8182dcf55b1b11415165e62ea61557ab83" - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/docs/guides/model_components/visualize_samplers.ipynb b/docs/guides/model_components/visualize_samplers.ipynb index 1c487eee1..fc45475b7 100644 --- a/docs/guides/model_components/visualize_samplers.ipynb +++ b/docs/guides/model_components/visualize_samplers.ipynb @@ -8,7 +8,7 @@ "\n", "## Overview\n", "\n", - "Once we have a set of cameras, we want to cast camera rays associated with each pixel. Along these ray we will sample the _field_ and aggregate the samples to predict the pixels value (ie. color). The parameterization of the samples are described [here](./visualize_samples.ipynb) however we must decide where to place these samples along a ray. For this task we will use a [`Sampler`](../../reference/api/graphs/modules/ray_sampler.rst).\n", + "Once we have a set of cameras, we want to cast camera rays associated with each pixel. Along these ray we will sample the _field_ and aggregate the samples to predict the pixels value (ie. color). The parameterization of the samples are described [here](./visualize_samples.ipynb) however we must decide where to place these samples along a ray. For this task we will use a [`Sampler`](../../reference/api/model_components/ray_sampler.rst).\n", "\n", "In the ideal world we would compute many dense samples along a ray. Unfortunatly each additional sample adds a computation cost to the system as it needs to be processed by the _field_ which is often a neural network. As a result it is common for NeRF methods to use on the order of 100 samples. Therefore we want to optimize where those samples are placed in the scene.\n", "\n", @@ -5115,7 +5115,7 @@ "# COLLAPSED\n", "import torch\n", "from nerfstudio.cameras.rays import RayBundle\n", - "from nerfstudio.models.modules import ray_sampler\n", + "from nerfstudio.model_components import ray_sampler\n", "import plotly.graph_objects as go\n", "from plotly.subplots import make_subplots\n", "\n", @@ -7223,7 +7223,7 @@ "# COLLAPSED\n", "import torch\n", "from nerfstudio.cameras.rays import RayBundle\n", - "from nerfstudio.models.modules import ray_sampler\n", + "from nerfstudio.model_components import ray_sampler\n", "import plotly.graph_objects as go\n", "from plotly.subplots import make_subplots\n", "\n", diff --git a/docs/guides/model_components/visualize_spatial_distortions.ipynb b/docs/guides/model_components/visualize_spatial_distortions.ipynb index ff06cca14..fd8c5f377 100644 --- a/docs/guides/model_components/visualize_spatial_distortions.ipynb +++ b/docs/guides/model_components/visualize_spatial_distortions.ipynb @@ -1,234427 +1,234427 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "# Spatial Distortions\n", - "\n", - "If you are trying to reconstruct an object floating in an empty void, you can stop reading. However if you are trying to reconstruct a scene or object from images, you may wish to consider adding a spatial distortion.\n", - "\n", - "When rendering a target view of a scene, the camera will emit a camera ray for each pixel and query the scene at points along this ray. We can choose where to query these points using different [samplers](visualize_samplers.ipynb). These samplers have some notion of _bounds_ that define where the ray should start and terminate. If you know that everything in your scenes exists within some predefined bounds (ie. a cube that a room fits in) then the sampler will properly sample the entire space. If however the scene is unbounded (ie. an outdoor scene) defining where to stop sampling is challenging. One option to increase the far sampling distance to a large value (ie. 1km). Alternatively we can warp the space into a fixed volume. Below are supported distortions.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "## Scene Contraction\n", - "\n", - "Contract unbounded space into a ball of radius 2. This contraction was proposed in [MipNeRF-360](https://jonbarron.info/mipnerf360/). Samples within the unit ball are not modified, whereas sample outside the unit ball are contracted to fit within the ball of radius 2.\n", - "\n", - "We use the following contraction equation:\n", - "\n", - "$$\n", - " f(x) = \\begin{cases}\n", - " x & ||x|| \\leq 1 \\\\\n", - " (2 - \\frac{1}{||x||})(\\frac{x}{||x||}) & ||x|| > 1\n", - " \\end{cases}\n", - "$$\n", - "\n", - "Below we visualize a ray before and after scene contraction. Visualized are 95% confidence intervals for the multivariate Gaussians for each sample location ([this guide](visualize_samples.ipynb) explains why the samples are represented by Gaussians).\n", - "We are also visualizing both a unit sphere and a radius 2 sphere.\n", - "\n", - "(before)=\n", - "### Before Contraction\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "tags": [ - "remove-cell" - ] - }, - "outputs": [], - "source": [ - "# HIDDEN\n", - "import torch\n", - "import plotly.graph_objects as go\n", - "\n", - "torch.manual_seed(4)\n", - "\n", - "webdocs_layout = go.Layout(\n", - " scene=dict(\n", - " aspectmode=\"data\",\n", - " xaxis=dict(showspikes=False),\n", - " yaxis=dict(showspikes=False),\n", - " zaxis=dict(showspikes=False),\n", - " ),\n", - " scene_camera=dict(up=dict(x=0, y=1, z=0)),\n", - " margin=dict(r=0, b=10, l=0, t=10),\n", - " hovermode=False,\n", - " showlegend=False,\n", - " paper_bgcolor=\"rgba(0,0,0,0)\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "tags": [ - "hide-input" - ] - }, - "outputs": [ - { - "data": { - "text/html": [ - " \n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "# Spatial Distortions\n", + "\n", + "If you are trying to reconstruct an object floating in an empty void, you can stop reading. However if you are trying to reconstruct a scene or object from images, you may wish to consider adding a spatial distortion.\n", + "\n", + "When rendering a target view of a scene, the camera will emit a camera ray for each pixel and query the scene at points along this ray. We can choose where to query these points using different [samplers](visualize_samplers.ipynb). These samplers have some notion of _bounds_ that define where the ray should start and terminate. If you know that everything in your scenes exists within some predefined bounds (ie. a cube that a room fits in) then the sampler will properly sample the entire space. If however the scene is unbounded (ie. an outdoor scene) defining where to stop sampling is challenging. One option to increase the far sampling distance to a large value (ie. 1km). Alternatively we can warp the space into a fixed volume. Below are supported distortions.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## Scene Contraction\n", + "\n", + "Contract unbounded space into a ball of radius 2. This contraction was proposed in [MipNeRF-360](https://jonbarron.info/mipnerf360/). Samples within the unit ball are not modified, whereas sample outside the unit ball are contracted to fit within the ball of radius 2.\n", + "\n", + "We use the following contraction equation:\n", + "\n", + "$$\n", + " f(x) = \\begin{cases}\n", + " x & ||x|| \\leq 1 \\\\\n", + " (2 - \\frac{1}{||x||})(\\frac{x}{||x||}) & ||x|| > 1\n", + " \\end{cases}\n", + "$$\n", + "\n", + "Below we visualize a ray before and after scene contraction. Visualized are 95% confidence intervals for the multivariate Gaussians for each sample location ([this guide](visualize_samples.ipynb) explains why the samples are represented by Gaussians).\n", + "We are also visualizing both a unit sphere and a radius 2 sphere.\n", + "\n", + "(before)=\n", + "### Before Contraction\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "outputs": [], + "source": [ + "# HIDDEN\n", + "import torch\n", + "import plotly.graph_objects as go\n", + "\n", + "torch.manual_seed(4)\n", + "\n", + "webdocs_layout = go.Layout(\n", + " scene=dict(\n", + " aspectmode=\"data\",\n", + " xaxis=dict(showspikes=False),\n", + " yaxis=dict(showspikes=False),\n", + " zaxis=dict(showspikes=False),\n", + " ),\n", + " scene_camera=dict(up=dict(x=0, y=1, z=0)),\n", + " margin=dict(r=0, b=10, l=0, t=10),\n", + " hovermode=False,\n", + " showlegend=False,\n", + " paper_bgcolor=\"rgba(0,0,0,0)\",\n", + ")" + ] + }, + { 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color=vis.get_random_color(idx=i))\n", + "data.append(vis.get_sphere(radius=1.0, color=\"#111111\", opacity=0.05))\n", + "data.append(vis.get_sphere(radius=2.0, color=\"#111111\", opacity=0.05))\n", + "\n", + "fig = go.Figure(data=data, layout=webdocs_layout)\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "### After Contraction\n", + "\n", + "Using a $L_2$ norm" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "marker": { + "color": "black", + "size": 2 + }, + "mode": "markers", + "name": "Means", + "type": "scatter3d", + "x": [ + -0.18384791910648346, + -0.40699541568756104, + -0.6454167366027832, + -0.8779518008232117, + -1.0302002429962158, + -1.1260967254638672, + -1.1910892724990845, + -1.2376961708068848, + 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# COLLAPSED\n", + "from nerfstudio.field_components.spatial_distortions import SceneContraction\n", + "\n", + "data = []\n", + "for i, frustum in enumerate(frustums):\n", + " contracted_gaussian = SceneContraction()(frustum.get_gaussian_blob())\n", + " data += vis.get_gaussian_ellipsoids_list(contracted_gaussian, color=vis.get_random_color(idx=i))\n", + "\n", + "data.append(vis.get_sphere(radius=1.0, color=\"#111111\", opacity=0.05))\n", + "data.append(vis.get_sphere(radius=2.0, color=\"#111111\", opacity=0.05))\n", + "\n", + "fig = go.Figure(data=data, layout=webdocs_layout)\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "Using $L_{\\infty}$ norm will bound the space to a cube of side length 4. This can be useful if downstream encoders opperated on a grid (ie. the hash encoder using in Instant-NGP)." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "marker": { + "color": "black", + "size": 2 + }, + "mode": "markers", + "name": "Means", + "type": "scatter3d", + "x": [ + -0.18384791910648346, + -0.40699541568756104, + -0.6454167366027832, + -0.8877694606781006, + -1.116342306137085, + -1.2734168767929077, + -1.3832396268844604, + -1.4642977714538574, + -1.5265634059906006, + -1.5758836269378662, + -1.6159104108810425, + -1.6490415334701538, + -1.6769161224365234, + -1.700692057609558, + -1.7212109565734863 + ], + "y": [ + 0.21343474090099335, + 0.35488447546958923, + 0.5060160160064697, + 0.6596396565437317, + 0.8032168745994568, + 0.8968518376350403, + 0.9594789743423462, 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# COLLAPSED\n", - "import nerfstudio\n", - "from nerfstudio.utils import plotly as vis\n", - "import torch\n", - "import plotly.graph_objects as go\n", - "\n", - "num_rays = 4\n", - "num_samples = 15\n", - "pixel_area = 0.12\n", - "far = 5\n", - "\n", - "origins = torch.rand((num_rays, 1, 3)) * 1 - 0.5\n", - "directions = torch.randn((num_rays, 1, 3))\n", - "bins = torch.linspace(0.1, far, num_samples + 1)[None, ..., None]\n", - "pixel_area = torch.tensor([pixel_area])[None, None, ...]\n", - "\n", - "directions = directions / directions.norm(dim=-1, keepdim=True)\n", - "\n", - "frustums = nerfstudio.cameras.rays.Frustums(\n", - " origins=origins, directions=directions, starts=bins[:, :-1, :], ends=bins[:, 1:, :], pixel_area=pixel_area\n", - ")\n", - "\n", - "data = []\n", - "for i, frustum in enumerate(frustums):\n", - " data += vis.get_gaussian_ellipsoids_list(frustum.get_gaussian_blob(), color=vis.get_random_color(idx=i))\n", - 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# COLLAPSED\n", - "from nerfstudio.fields.modules.spatial_distortions import SceneContraction\n", - "\n", - "data = []\n", - "for i, frustum in enumerate(frustums):\n", - " contracted_gaussian = SceneContraction()(frustum.get_gaussian_blob())\n", - " data += vis.get_gaussian_ellipsoids_list(contracted_gaussian, color=vis.get_random_color(idx=i))\n", - "\n", - "data.append(vis.get_sphere(radius=1.0, color=\"#111111\", opacity=0.05))\n", - "data.append(vis.get_sphere(radius=2.0, color=\"#111111\", opacity=0.05))\n", - "\n", - "fig = go.Figure(data=data, layout=webdocs_layout)\n", - "fig.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "Using $L_{\\infty}$ norm will bound the space to a cube of side length 4. This can be useful if downstream encoders opperated on a grid (ie. the hash encoder using in Instant-NGP)." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "tags": [ - "hide-input" - ] - }, - "outputs": [ - { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "marker": { - "color": "black", - "size": 2 - }, - "mode": "markers", - "name": "Means", - "type": "scatter3d", - "x": [ - -0.18384791910648346, - -0.40699541568756104, - -0.6454167366027832, - -0.8877694606781006, - -1.116342306137085, - -1.2734168767929077, - -1.3832396268844604, - -1.4642977714538574, - -1.5265634059906006, - -1.5758836269378662, - -1.6159104108810425, - -1.6490415334701538, - -1.6769161224365234, - -1.700692057609558, - -1.7212109565734863 - ], - "y": [ - 0.21343474090099335, - 0.35488447546958923, - 0.5060160160064697, - 0.6596396565437317, - 0.8032168745994568, - 0.8968518376350403, - 0.9594789743423462, - 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# COLLAPSED\n", - "from nerfstudio.fields.modules.spatial_distortions import SceneContraction\n", - "\n", - "data = []\n", - "for i, frustum in enumerate(frustums):\n", - " contracted_gaussian = SceneContraction(order=float(\"inf\"))(frustum.get_gaussian_blob())\n", - " data += vis.get_gaussian_ellipsoids_list(contracted_gaussian, color=vis.get_random_color(idx=i))\n", - "\n", - "data.append(vis.get_cube(side_length=2.0, color=\"#111111\", opacity=0.05))\n", - "data.append(vis.get_cube(side_length=4.0, color=\"#111111\", opacity=0.05))\n", - "\n", - "fig = go.Figure(data=data, layout=webdocs_layout)\n", - "fig.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.8.12 ('nerfactory')", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.12" - }, - "vscode": { - "interpreter": { - "hash": "34c28001ff35fb390494047002768a8182dcf55b1b11415165e62ea61557ab83" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/docs/guides/models/nerf.md b/docs/guides/models/nerf.md index 90a1cf3bf..1744763fd 100644 --- a/docs/guides/models/nerf.md +++ b/docs/guides/models/nerf.md @@ -84,7 +84,7 @@ field_fine = NeRFField(position_encoding=pos_enc, direction_encoding=dir_enc) An extra trick is necessary to make the neural network expressive enough to represent fine details in the scene. The input coordinates $(x,y,z,\theta,\phi)$ need to be encoded to a higher dimensional space prior to being input into the network. You can learn more about encodings [here](../model_components/visualize_encoders.ipynb). ```python -from nerfstudio.fields.modules.encoding import NeRFEncoding +from nerfstudio.field_components.encoding import NeRFEncoding pos_enc = NeRFEncoding( in_dim=3, num_frequencies=10, min_freq_exp=0.0, max_freq_exp=8.0, include_input=True @@ -108,7 +108,7 @@ dir_enc = NeRFEncoding( Now that we have a representation of space, we need some way to render new images of it. To accomplish this, we are going to _project_ a ray from the target pixel and evaluate points along that ray. We then rely on classic volumetric rendering techniques [[Kajiya, 1984]](https://dl.acm.org/doi/abs/10.1145/964965.808594) to composite the points into a predicted color. This compositing is similar to what happens in tools like Photoshop when you layer multiple objects of varying opacity on top of each other. The only difference is that NeRF takes into account the differences in spacing between points. -Rending RGB images is not the only type of output render supported. It is possible to render other output types such as depth and semantics. Additional renderers can be found [Here](../../reference/api/renderers/index.rst). +Rending RGB images is not the only type of output render supported. It is possible to render other output types such as depth and semantics. Additional renderers can be found [Here](../../reference/api/model_components/renderers.rst). Associated nerfstudio code: @@ -142,7 +142,7 @@ How we sample points along rays in space is an important design decision. Variou Associated code: ```python -from nerfstudio.models.modules.ray_sampler import PDFSampler, UniformSampler +from nerfstudio.model_components.ray_sampler import PDFSampler, UniformSampler sampler_uniform = UniformSampler(num_samples=num_coarse_samples) ray_samples_uniform = sampler_uniform(ray_bundle) diff --git a/docs/reference/api/config.rst b/docs/reference/api/config.rst index c7ebb0ab1..d5885a229 100644 --- a/docs/reference/api/config.rst +++ b/docs/reference/api/config.rst @@ -3,6 +3,6 @@ Configs ============ -.. automodule:: nerfstudio.configs.base +.. automodule:: nerfstudio.configs.base_config :members: :show-inheritance: diff --git a/docs/reference/api/data.rst b/docs/reference/api/data.rst index 3ec38920f..a0ac5a2b2 100644 --- a/docs/reference/api/data.rst +++ b/docs/reference/api/data.rst @@ -6,34 +6,34 @@ Data Data Parsers ---------------- -.. automodule:: nerfstudio.datamanagers.dataparsers +.. automodule:: nerfstudio.data.dataparsers :members: :show-inheritance: Datasets ---------------- -.. automodule:: nerfstudio.datamanagers.datasets +.. automodule:: nerfstudio.data.utils.datasets :members: :show-inheritance: Dataloaders ---------------- -.. automodule:: nerfstudio.datamanagers.dataloaders +.. automodule:: nerfstudio.data.utils.dataloaders :members: :show-inheritance: Pixel Samplers ---------------- -.. automodule:: nerfstudio.datamanagers.pixel_sampler +.. automodule:: nerfstudio.data.pixel_samplers :members: :show-inheritance: -Structures +Scene Box ---------------- -.. automodule:: nerfstudio.datamanagers.structs +.. automodule:: nerfstudio.data.scene_box :members: :show-inheritance: \ No newline at end of file diff --git a/docs/reference/api/fields/modules/embeddings.rst b/docs/reference/api/field_components/embeddings.rst similarity index 60% rename from docs/reference/api/fields/modules/embeddings.rst rename to docs/reference/api/field_components/embeddings.rst index fe123e058..f4bd2b677 100644 --- a/docs/reference/api/fields/modules/embeddings.rst +++ b/docs/reference/api/field_components/embeddings.rst @@ -3,6 +3,6 @@ Embeddings =================== -.. automodule:: nerfstudio.fields.modules.embedding +.. automodule:: nerfstudio.field_components.embedding :members: :show-inheritance: \ No newline at end of file diff --git a/docs/reference/api/fields/modules/encodings.rst b/docs/reference/api/field_components/encodings.rst similarity index 60% rename from docs/reference/api/fields/modules/encodings.rst rename to docs/reference/api/field_components/encodings.rst index da175ed48..a498fe66d 100644 --- a/docs/reference/api/fields/modules/encodings.rst +++ b/docs/reference/api/field_components/encodings.rst @@ -3,6 +3,6 @@ Encodings =================== -.. automodule:: nerfstudio.fields.modules.encoding +.. automodule:: nerfstudio.field_components.encoding :members: :show-inheritance: \ No newline at end of file diff --git a/docs/reference/api/fields/modules/field_heads.rst b/docs/reference/api/field_components/field_heads.rst similarity index 60% rename from docs/reference/api/fields/modules/field_heads.rst rename to docs/reference/api/field_components/field_heads.rst index 52da49c44..49abedc9e 100644 --- a/docs/reference/api/fields/modules/field_heads.rst +++ b/docs/reference/api/field_components/field_heads.rst @@ -3,6 +3,6 @@ Field Heads =================== -.. automodule:: nerfstudio.fields.modules.field_heads +.. automodule:: nerfstudio.field_components.field_heads :members: :show-inheritance: \ No newline at end of file diff --git a/docs/reference/api/fields/modules/field_losses.rst b/docs/reference/api/field_components/field_losses.rst similarity index 60% rename from docs/reference/api/fields/modules/field_losses.rst rename to docs/reference/api/field_components/field_losses.rst index b2bbc6110..07984cfdf 100644 --- a/docs/reference/api/fields/modules/field_losses.rst +++ b/docs/reference/api/field_components/field_losses.rst @@ -3,6 +3,6 @@ Field Losses =================== -.. automodule:: nerfstudio.fields.modules.field_losses +.. automodule:: nerfstudio.field_components.field_losses :members: :show-inheritance: \ No newline at end of file diff --git a/docs/reference/api/fields/modules/index.rst b/docs/reference/api/field_components/index.rst similarity index 100% rename from docs/reference/api/fields/modules/index.rst rename to docs/reference/api/field_components/index.rst diff --git a/docs/reference/api/fields/modules/mlp.rst b/docs/reference/api/field_components/mlp.rst similarity index 58% rename from docs/reference/api/fields/modules/mlp.rst rename to docs/reference/api/field_components/mlp.rst index 6f19f72af..80be3a629 100644 --- a/docs/reference/api/fields/modules/mlp.rst +++ b/docs/reference/api/field_components/mlp.rst @@ -3,6 +3,6 @@ MLP =================== -.. automodule:: nerfstudio.fields.modules.mlp +.. automodule:: nerfstudio.field_components.mlp :members: :show-inheritance: \ No newline at end of file diff --git a/docs/reference/api/fields/modules/spatial_distortions.rst b/docs/reference/api/field_components/spatial_distortions.rst similarity index 61% rename from docs/reference/api/fields/modules/spatial_distortions.rst rename to docs/reference/api/field_components/spatial_distortions.rst index 3d56c8540..ee74610d5 100644 --- a/docs/reference/api/fields/modules/spatial_distortions.rst +++ b/docs/reference/api/field_components/spatial_distortions.rst @@ -3,6 +3,6 @@ Spatial Distortions ===================== -.. automodule:: nerfstudio.fields.modules.spatial_distortions +.. automodule:: nerfstudio.field_components.spatial_distortions :members: :show-inheritance: \ No newline at end of file diff --git a/docs/reference/api/fields.rst b/docs/reference/api/fields.rst index 2933b81de..febf65aac 100644 --- a/docs/reference/api/fields.rst +++ b/docs/reference/api/fields.rst @@ -6,7 +6,7 @@ Fields Base ---------------- -.. automodule:: nerfstudio.fields.base +.. automodule:: nerfstudio.fields.base_field :members: :show-inheritance: diff --git a/docs/reference/api/index.rst b/docs/reference/api/index.rst index e9443b495..c1c9ce0b2 100644 --- a/docs/reference/api/index.rst +++ b/docs/reference/api/index.rst @@ -10,10 +10,9 @@ TODO: Explanation of each component config data fields - fields/modules/index - graphs - graphs/modules/index + field_components/index + models + model_components/index optimizers - renderers/index utils/index viewer diff --git a/docs/reference/api/graphs/modules/index.rst b/docs/reference/api/model_components/index.rst similarity index 85% rename from docs/reference/api/graphs/modules/index.rst rename to docs/reference/api/model_components/index.rst index 1e401b782..67c5d56a7 100644 --- a/docs/reference/api/graphs/modules/index.rst +++ b/docs/reference/api/model_components/index.rst @@ -8,4 +8,5 @@ TODO: High level description of graph modules and how they are used. .. toctree:: ray_sampler - ray_losses \ No newline at end of file + ray_losses + renderers \ No newline at end of file diff --git a/docs/reference/api/graphs/modules/ray_losses.rst b/docs/reference/api/model_components/ray_losses.rst similarity index 60% rename from docs/reference/api/graphs/modules/ray_losses.rst rename to docs/reference/api/model_components/ray_losses.rst index bf1092b20..0c174b9c3 100644 --- a/docs/reference/api/graphs/modules/ray_losses.rst +++ b/docs/reference/api/model_components/ray_losses.rst @@ -3,6 +3,6 @@ Ray Losses =================== -.. automodule:: nerfstudio.models.modules.ray_losses +.. automodule:: nerfstudio.model_components.ray_losses :members: :show-inheritance: \ No newline at end of file diff --git a/docs/reference/api/graphs/modules/ray_sampler.rst b/docs/reference/api/model_components/ray_sampler.rst similarity index 60% rename from docs/reference/api/graphs/modules/ray_sampler.rst rename to docs/reference/api/model_components/ray_sampler.rst index 74a5fa3f2..f289ec1ee 100644 --- a/docs/reference/api/graphs/modules/ray_sampler.rst +++ b/docs/reference/api/model_components/ray_sampler.rst @@ -3,6 +3,6 @@ Ray Sampler =================== -.. automodule:: nerfstudio.models.modules.ray_sampler +.. automodule:: nerfstudio.model_components.ray_sampler :members: :show-inheritance: \ No newline at end of file diff --git a/docs/reference/api/renderers/index.rst b/docs/reference/api/model_components/renderers.rst similarity index 58% rename from docs/reference/api/renderers/index.rst rename to docs/reference/api/model_components/renderers.rst index fd0ce325b..76f9301ae 100644 --- a/docs/reference/api/renderers/index.rst +++ b/docs/reference/api/model_components/renderers.rst @@ -3,6 +3,6 @@ Renderers ============ -.. automodule:: nerfstudio.renderers.renderers +.. automodule:: nerfstudio.model_components.renderers :members: :show-inheritance: diff --git a/docs/reference/api/graphs.rst b/docs/reference/api/models.rst similarity index 100% rename from docs/reference/api/graphs.rst rename to docs/reference/api/models.rst diff --git a/docs/reference/api/optimizers.rst b/docs/reference/api/optimizers.rst index c293d2082..ed697daf3 100644 --- a/docs/reference/api/optimizers.rst +++ b/docs/reference/api/optimizers.rst @@ -1,9 +1,18 @@ -.. _optimizers: +.. _engine: -Optimizers +Engine ============ -.. automodule:: nerfstudio.optimizers.optimizers +Optimizers +---------------- + +.. automodule:: nerfstudio.engine.optimizers :members: :show-inheritance: +Schedulers +---------------- + +.. automodule:: nerfstudio.engine.schedulers + :members: + :show-inheritance: diff --git a/docs/tutorials/pipelines/creating_models.ipynb b/docs/tutorials/pipelines/creating_models.ipynb index 0125b022f..e2413b3d1 100644 --- a/docs/tutorials/pipelines/creating_models.ipynb +++ b/docs/tutorials/pipelines/creating_models.ipynb @@ -18,19 +18,19 @@ "\n", "### Modules\n", "\n", - "We can decompose all graphs into respective modules. For instance, we have {ref}`optimizers` which contains modules related to the loss, {ref}`renderers` which contains all rendering modules, {`graph/modules/`}, which includes samplers and ray generators. Additionally, we have {ref}`field_modules`, which will be discussed below.\n", + "We can decompose all graphs into respective modules. For instance, we have `optimizers` which contains modules related to the loss, `renderers` which contains all rendering modules, {`graph/modules/`}, which includes samplers and ray generators. Additionally, we have `field_modules`, which will be discussed below.\n", "In general, modules can be thought of as individual component parts of the graph that can be swapped in/out. We provide most of the standard modules, but if you are creating a new NeRF graph, you may have to define new modules.\n", "\n", "### Fields\n", "\n", - "{ref}`fields` represents the \"space\", aka. the radiance field of the NeRF. Here, we define the field as the part of the network that takes in point samples and any other conditioning, and outputs any of the `FieldHeadNames` (`nerf/field_modules/field_heads.py`).\n", + "`fields` represents the \"space\", aka. the radiance field of the NeRF. Here, we define the field as the part of the network that takes in point samples and any other conditioning, and outputs any of the `FieldHeadNames` (`nerf/field_modules/field_heads.py`).\n", "\n", "All fields are composed of modules continained within the `fields/` directory (e.g. `fields/modules/`). We can think of field modules as modules that actually define or interact with the field.\n", "\n", "To build a NeRF field, we therefore follow these steps:\n", "\n", "1. define any relevant field modules\n", - "2. create a new class that extends the {ref}`fields` base class and use these field modules to compose the full field\n", + "2. create a new class that extends the `fields` base class and use these field modules to compose the full field\n", "3. implement the abstract functions `get_density()` and `get_outputs()`." ] }, diff --git a/docs/tutorials/pipelines/creating_pipelines.ipynb b/docs/tutorials/pipelines/creating_pipelines.ipynb index b02f28900..f6104aa6c 100644 --- a/docs/tutorials/pipelines/creating_pipelines.ipynb +++ b/docs/tutorials/pipelines/creating_pipelines.ipynb @@ -1,354 +1,354 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "# Creating Custom Pipelines\n", - "\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 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", - "We'll show how to make an incremental DataManager where we progressively add cameras to train with from multiple scenes and importance sample rays from pixels with a high loss. The extensible features that we show in this tutorial are the following:\n", - "- a DataManager that incrementally adds cameras\n", - "- a DataManager that uses multiple scenes\n", - "- a Model that is conditioned on the scene\n", - "- a Pipeline that modifies the DataManager's PixelSampler to sample more rays in regions with high loss" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "#### Model Components\n", - "\n", - "> The model renders values from 3D line segments defined by a RayBundle.\n", - "\n", - "We provide Models and Model Components (i.e., Fields and Field Modules) which can be strung together to create a custom model.\n", - "\n", - "
\n", - "Models\n", - "
\n", - "
  • Vanilla NeRF Model
    • \n", - "
  • Mip NeRF Model
    • \n", - "
  • Mip NeRF 360 Model
    • \n", - "
  • Instant NGP Model
    • \n", - "
  • NeRF-W Model
    • \n", - "
  • Semantic NeRF Model
    • \n", - "
    \n", - "\n", - "
    \n", - "\n", - "
    \n", - "Model Components\n", - "
    \n", - "Fields:\n", - "
  • Vanilla NeRF Field
    • \n", - "
  • Instant NGP Field
    • \n", - "
    \n", - "Field Modules:\n", - "
  • Encodings
    • \n", - "
  • Embeddings
    • \n", - "
  • Field Heads
    • \n", - "
  • Field Losses
    • \n", - "
  • MLP
    • \n", - "
  • Spatial Distortions
    • \n", - "
    \n", - "\n", - "#### DataManager Components\n", - "\n", - "> The DataManager provides a RayBundle (3D line segments with metadata needed to run the forward pass) and a dictionary of information needed at train-time to supervise the pipeline components.\n", - "\n", - "Similar to the Model components, we provide DataManager components that are strung together to generate RayBundle instances. A RayBundle contains everything needed to generate a ray (e.g., origin and direction, near and far planes) as well as any additional metadata that the Model requires (e.g., camera index, scene index, etc.).\n", - "\n", - "
    \n", - "DataManager Components\n", - "
    \n", - "Datasets Parsers:
    \n", - " Takes a data location and returns a DatsetInputs instance. A DatsetInputs will contain all data needed throughout the pipeline.\n", - "
  • Vanilla Image DataParser
    • \n", - "
    \n", - "Image Datasets:
    \n", - " Takes image filename information and can return images.\n", - "
  • Vanilla Image Dataset
    • \n", - "
    \n", - "Image Dataloader:
    \n", - " Takes an Image Dataset and samples images from it.\n", - "
  • Vanilla Image Dataloader
    • \n", - "
  • Cache Image Dataloader
    • \n", - "
    \n", - "Pixel Samplers:
    \n", - " Takes sampled images and returns pixel locations.\n", - "
  • Vanilla Pixel Sampler
    • \n", - "
    \n", - "Ray Generators:
    \n", - " Takes cameras and pixel locations and returns RayBundle objects.\n", - "
    \n", - "\n", - "Colliders:
    \n", - " Takes RayBundles and clips them according .\n", - "
    " - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "tags": [ - "hide-input" - ] - }, - "outputs": [], - "source": [ - "# COLLAPSED\n", - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", - "from __future__ import annotations\n", - "\n", - "from typing import Dict, Optional, Tuple, List\n", - "import torch\n", - "\n", - "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" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "class CustomDataManagerConfig(DataManagerConfig):\n", - " _target = \"CustomDataManager\"\n", - " scene_names: List[str] = [\"chair\", \"drums\", \"fern\", \"lego\"]\n", - " steps_per_add_cameras: int = 100 # every X iterations, add more cameras\n", - " num_cameras_per_add: int = 10 # number of cameras to add\n", - "\n", - "\n", - "class CustomDataManager(DataManager):\n", - " \"\"\"A custom dataloader that incrementally adds cameras and has multiple scenes.\"\"\"\n", - "\n", - " config: CustomDataManagerConfig\n", - "\n", - " def populate_train_modules(self):\n", - " \"\"\"Populate the train dataloader modules.\"\"\"\n", - " self.scene_name_to_image_sampler = {}\n", - " self.scene_name_to_collider = {} # TODO(ethan): move colliders to the dataloader\n", - " for scene_name in self.config.scene_names:\n", - " dataset_inputs: DatasetInputs = None # TODO\n", - " image_dataset = ImageDataset(dataset_inputs)\n", - " image_sampler = CacheImageDataloader(image_dataset=image_dataset) # a torch dataloader\n", - " self.scene_name_to_image_sampler[scene_name] = image_sampler\n", - " self.train_pixel_sampler = PixelSampler\n", - " self.train_ray_generator = RayGenerator # nn.Module\n", - "\n", - " def populate_eval_modules(self):\n", - " \"\"\"Populate the eval dataloader modules.\"\"\"\n", - " # we'll mostly rely on the train dataloader modules\n", - " self.eval_pixel_sampler = EvalPixelSampler # this will sample entire images\n", - "\n", - " def sample_images(self) -> List[Image]:\n", - " images = []\n", - " for scene_name in self.scene_name_to_image_sampler.keys():\n", - " image_sampler = self.scene_name_to_image_sampler[scene_name]\n", - " x = image_sampler.forward() # note that empty forward calls next()\n", - " images.append(x)\n", - " return images\n", - "\n", - " def next_train(self, step: int) -> Tuple[RayBundle, Dict]:\n", - " \"\"\"Get the next batch of training data by stringing together the train modules.\"\"\"\n", - " # grab some images from each scene\n", - " images = self.sample_images()\n", - " pixels = self.train_pixel_sampler.forward(images)\n", - " ray_bundle = self.train_ray_generator(pixels)\n", - " # shape should be (H, W, :)\n", - " ray_bundle.metadata[\"scene_indices\"] = None # TODO(ethan): populate this with a tensor of scene indices\n", - " dict_ = {}\n", - " return ray_bundle, dict_\n", - "\n", - " def next_eval(self, step: int) -> Tuple[RayBundle, Dict]:\n", - " \"\"\"Get the next batch of eval data by stringing together the eval modules.\"\"\"\n", - " # grab some images from each scene\n", - " images = self.sample_images()\n", - " pixels = self.train_pixel_sampler.forward(images)\n", - " ray_bundle = self.eval_ray_generator(pixels) # notice this is eval and not train\n", - " # shape should be (H, W, :)\n", - " ray_bundle.metadata[\"scene_indices\"] = None # TODO(ethan): populate this with a tensor of scene indices\n", - " dict_ = {}\n", - " return ray_bundle, dict_\n", - "\n", - "\n", - "class CustomNGPModelConfig(ModelConfig):\n", - " _target = \"CustomNGPModel\"\n", - " coarse_field: str = \"temp\"\n", - " fine_field: str = \"temp2\"\n", - "\n", - "\n", - "class CustomNGPModel(Model):\n", - " \"\"\"An instant ngp model modified slightly to output semantics.\"\"\"\n", - "\n", - " config: SceneConditionNGPModelConfig\n", - "\n", - " def populate_modules(self):\n", - " self.ngp_model = NGPModel(coarse_field=self.config.coarse_field, fine_field=self.config.fine_field)\n", - "\n", - " def get_outputs(self, ray_bundle: RayBundle) -> Dict[str, torch.Tensor]:\n", - " # TODO(ethan): pass in batch from the forward function\n", - " # TODO(ethan): rename batch to something else\n", - " outputs = self.ngp_model.forward(ray_bundle)\n", - " outputs[\"semantics\"] = torch.rand_like(outputs[\"rgb\"])\n", - " return outputs\n", - "\n", - " def get_loss_dict(self):\n", - " return {}\n", - "\n", - "\n", - "class CustomPipeline(Pipeline):\n", - " \"\"\"The Instant NGP pipeline.\"\"\"\n", - "\n", - " config: PipelineConfig\n", - "\n", - " def train_step(self, step: int):\n", - " ray_bundle, batch = self.dataloader.next_train(step=step)\n", - " # TODO: maybe run a CNN on the data before passing into model\n", - " model_outputs, loss_dict, metrics_dict = self.model(ray_bundle, batch)\n", - " # TODO: update pixel sampler state with loss map to show the flexibilty of our Pipeline\n", - " self.dataloader.pixel_sampler.update_loss_map()\n", - " return model_outputs, loss_dict, metrics_dict\n", - "\n", - " def eval_step(self):\n", - " ray_bundle, batch = self.dataloader.next_eval(step=step)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "dataloader = DataManager()\n", - "model = SceneConditionNGPModel()\n", - "pipeline = CustomPipeline.from_dataloader_and_model(dataloader=dataloader, model=model)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "# Creating pipelines from a config\n", - "\n", - "Now we show how to create a pipeline from a config, which has the following form:\n", - "\n", - "```python\n", - "@dataclass\n", - "class PipelineConfig:\n", - " \"\"\"Configuration for pipeline instantiation\"\"\"\n", - "\n", - " _target: ClassVar[Type] = Pipeline\n", - " dataloader: DataloaderConfig = MISSING\n", - " model: ModelConfig = MISSING\n", - "```\n", - "\n", - "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`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "import pprint\n", - "import hydra\n", - "\n", - "hydra.core.global_hydra.GlobalHydra.instance().clear()\n", - "from hydra import compose, initialize\n", - "\n", - "initialize(version_base=\"1.2\", config_path=\"../configs/\")\n", - "config_name = \"graph_instant_ngp.yaml\"\n", - "config = compose(config_name)\n", - "pipeline_config = config.pipeline\n", - "pprint.pprint(pipeline_config)\n", - "print(\"----------------------------------------------------\")\n", - "\n", - "# from nerfstudio.pipelines.base import setup_pipeline\n", - "# pipeline = setup_pipeline(pipeline_config, device=\"cuda\")\n", - "pipeline = pipeline_config.setup()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "pipeline.get_train_loss_dict()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.8.13 ('nerfactory')", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.13" - }, - "vscode": { - "interpreter": { - "hash": "100252972d4ee5286ab455ef1c778abde05b4affca9d1be78633d3797abf7e8c" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "# Creating Custom Pipelines\n", + "\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 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", + "We'll show how to make an incremental DataManager where we progressively add cameras to train with from multiple scenes and importance sample rays from pixels with a high loss. The extensible features that we show in this tutorial are the following:\n", + "- a DataManager that incrementally adds cameras\n", + "- a DataManager that uses multiple scenes\n", + "- a Model that is conditioned on the scene\n", + "- a Pipeline that modifies the DataManager's PixelSampler to sample more rays in regions with high loss" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "#### Model Components\n", + "\n", + "> The model renders values from 3D line segments defined by a RayBundle.\n", + "\n", + "We provide Models and Model Components (i.e., Fields and Field Modules) which can be strung together to create a custom model.\n", + "\n", + "
    \n", + "Models\n", + "
    \n", + "
  • Vanilla NeRF Model
    • \n", + "
  • Mip NeRF Model
    • \n", + "
  • Mip NeRF 360 Model
    • \n", + "
  • Instant NGP Model
    • \n", + "
  • NeRF-W Model
    • \n", + "
  • Semantic NeRF Model
    • \n", + "
    \n", + "\n", + "
    \n", + "\n", + "
    \n", + "Model Components\n", + "
    \n", + "Fields:\n", + "
  • Vanilla NeRF Field
    • \n", + "
  • Instant NGP Field
    • \n", + "
    \n", + "Field Modules:\n", + "
  • Encodings
    • \n", + "
  • Embeddings
    • \n", + "
  • Field Heads
    • \n", + "
  • Field Losses
    • \n", + "
  • MLP
    • \n", + "
  • Spatial Distortions
    • \n", + "
    \n", + "\n", + "#### DataManager Components\n", + "\n", + "> The DataManager provides a RayBundle (3D line segments with metadata needed to run the forward pass) and a dictionary of information needed at train-time to supervise the pipeline components.\n", + "\n", + "Similar to the Model components, we provide DataManager components that are strung together to generate RayBundle instances. A RayBundle contains everything needed to generate a ray (e.g., origin and direction, near and far planes) as well as any additional metadata that the Model requires (e.g., camera index, scene index, etc.).\n", + "\n", + "
    \n", + "DataManager Components\n", + "
    \n", + "Datasets Parsers:
    \n", + " Takes a data location and returns a DatsetInputs instance. A DatsetInputs will contain all data needed throughout the pipeline.\n", + "
  • Vanilla Image DataParser
    • \n", + "
    \n", + "Image Datasets:
    \n", + " Takes image filename information and can return images.\n", + "
  • Vanilla Image Dataset
    • \n", + "
    \n", + "Image Dataloader:
    \n", + " Takes an Image Dataset and samples images from it.\n", + "
  • Vanilla Image Dataloader
    • \n", + "
  • Cache Image Dataloader
    • \n", + "
    \n", + "Pixel Samplers:
    \n", + " Takes sampled images and returns pixel locations.\n", + "
  • Vanilla Pixel Sampler
    • \n", + "
    \n", + "Ray Generators:
    \n", + " Takes cameras and pixel locations and returns RayBundle objects.\n", + "
    \n", + "\n", + "Colliders:
    \n", + " Takes RayBundles and clips them according .\n", + "
    " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [ + "hide-input" + ] + }, + "outputs": [], + "source": [ + "# COLLAPSED\n", + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "from __future__ import annotations\n", + "\n", + "from typing import Dict, Optional, Tuple, List\n", + "import torch\n", + "\n", + "from nerfstudio.utils.config import DataManagerConfig, ModelConfig, PipelineConfig, ViewerConfig\n", + "from nerfstudio.data.base import DataManager\n", + "from nerfstudio.data.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" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "class CustomDataManagerConfig(DataManagerConfig):\n", + " _target = \"CustomDataManager\"\n", + " scene_names: List[str] = [\"chair\", \"drums\", \"fern\", \"lego\"]\n", + " steps_per_add_cameras: int = 100 # every X iterations, add more cameras\n", + " num_cameras_per_add: int = 10 # number of cameras to add\n", + "\n", + "\n", + "class CustomDataManager(DataManager):\n", + " \"\"\"A custom dataloader that incrementally adds cameras and has multiple scenes.\"\"\"\n", + "\n", + " config: CustomDataManagerConfig\n", + "\n", + " def populate_train_modules(self):\n", + " \"\"\"Populate the train dataloader modules.\"\"\"\n", + " self.scene_name_to_image_sampler = {}\n", + " self.scene_name_to_collider = {} # TODO(ethan): move colliders to the dataloader\n", + " for scene_name in self.config.scene_names:\n", + " dataset_inputs: DatasetInputs = None # TODO\n", + " image_dataset = ImageDataset(dataset_inputs)\n", + " image_sampler = CacheImageDataloader(image_dataset=image_dataset) # a torch dataloader\n", + " self.scene_name_to_image_sampler[scene_name] = image_sampler\n", + " self.train_pixel_sampler = PixelSampler\n", + " self.train_ray_generator = RayGenerator # nn.Module\n", + "\n", + " def populate_eval_modules(self):\n", + " \"\"\"Populate the eval dataloader modules.\"\"\"\n", + " # we'll mostly rely on the train dataloader modules\n", + " self.eval_pixel_sampler = EvalPixelSampler # this will sample entire images\n", + "\n", + " def sample_images(self) -> List[Image]:\n", + " images = []\n", + " for scene_name in self.scene_name_to_image_sampler.keys():\n", + " image_sampler = self.scene_name_to_image_sampler[scene_name]\n", + " x = image_sampler.forward() # note that empty forward calls next()\n", + " images.append(x)\n", + " return images\n", + "\n", + " def next_train(self, step: int) -> Tuple[RayBundle, Dict]:\n", + " \"\"\"Get the next batch of training data by stringing together the train modules.\"\"\"\n", + " # grab some images from each scene\n", + " images = self.sample_images()\n", + " pixels = self.train_pixel_sampler.forward(images)\n", + " ray_bundle = self.train_ray_generator(pixels)\n", + " # shape should be (H, W, :)\n", + " ray_bundle.metadata[\"scene_indices\"] = None # TODO(ethan): populate this with a tensor of scene indices\n", + " dict_ = {}\n", + " return ray_bundle, dict_\n", + "\n", + " def next_eval(self, step: int) -> Tuple[RayBundle, Dict]:\n", + " \"\"\"Get the next batch of eval data by stringing together the eval modules.\"\"\"\n", + " # grab some images from each scene\n", + " images = self.sample_images()\n", + " pixels = self.train_pixel_sampler.forward(images)\n", + " ray_bundle = self.eval_ray_generator(pixels) # notice this is eval and not train\n", + " # shape should be (H, W, :)\n", + " ray_bundle.metadata[\"scene_indices\"] = None # TODO(ethan): populate this with a tensor of scene indices\n", + " dict_ = {}\n", + " return ray_bundle, dict_\n", + "\n", + "\n", + "class CustomNGPModelConfig(ModelConfig):\n", + " _target = \"CustomNGPModel\"\n", + " coarse_field: str = \"temp\"\n", + " fine_field: str = \"temp2\"\n", + "\n", + "\n", + "class CustomNGPModel(Model):\n", + " \"\"\"An instant ngp model modified slightly to output semantics.\"\"\"\n", + "\n", + " config: SceneConditionNGPModelConfig\n", + "\n", + " def populate_modules(self):\n", + " self.ngp_model = NGPModel(coarse_field=self.config.coarse_field, fine_field=self.config.fine_field)\n", + "\n", + " def get_outputs(self, ray_bundle: RayBundle) -> Dict[str, torch.Tensor]:\n", + " # TODO(ethan): pass in batch from the forward function\n", + " # TODO(ethan): rename batch to something else\n", + " outputs = self.ngp_model.forward(ray_bundle)\n", + " outputs[\"semantics\"] = torch.rand_like(outputs[\"rgb\"])\n", + " return outputs\n", + "\n", + " def get_loss_dict(self):\n", + " return {}\n", + "\n", + "\n", + "class CustomPipeline(Pipeline):\n", + " \"\"\"The Instant NGP pipeline.\"\"\"\n", + "\n", + " config: PipelineConfig\n", + "\n", + " def train_step(self, step: int):\n", + " ray_bundle, batch = self.dataloader.next_train(step=step)\n", + " # TODO: maybe run a CNN on the data before passing into model\n", + " model_outputs, loss_dict, metrics_dict = self.model(ray_bundle, batch)\n", + " # TODO: update pixel sampler state with loss map to show the flexibilty of our Pipeline\n", + " self.dataloader.pixel_sampler.update_loss_map()\n", + " return model_outputs, loss_dict, metrics_dict\n", + "\n", + " def eval_step(self):\n", + " ray_bundle, batch = self.dataloader.next_eval(step=step)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "dataloader = DataManager()\n", + "model = SceneConditionNGPModel()\n", + "pipeline = CustomPipeline.from_dataloader_and_model(dataloader=dataloader, model=model)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "# Creating pipelines from a config\n", + "\n", + "Now we show how to create a pipeline from a config, which has the following form:\n", + "\n", + "```python\n", + "@dataclass\n", + "class PipelineConfig:\n", + " \"\"\"Configuration for pipeline instantiation\"\"\"\n", + "\n", + " _target: ClassVar[Type] = Pipeline\n", + " dataloader: DataloaderConfig = MISSING\n", + " model: ModelConfig = MISSING\n", + "```\n", + "\n", + "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`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import pprint\n", + "import hydra\n", + "\n", + "hydra.core.global_hydra.GlobalHydra.instance().clear()\n", + "from hydra import compose, initialize\n", + "\n", + "initialize(version_base=\"1.2\", config_path=\"../configs/\")\n", + "config_name = \"graph_instant_ngp.yaml\"\n", + "config = compose(config_name)\n", + "pipeline_config = config.pipeline\n", + "pprint.pprint(pipeline_config)\n", + "print(\"----------------------------------------------------\")\n", + "\n", + "# from nerfstudio.pipelines.base import setup_pipeline\n", + "# pipeline = setup_pipeline(pipeline_config, device=\"cuda\")\n", + "pipeline = pipeline_config.setup()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "pipeline.get_train_loss_dict()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.8.13 ('nerfactory')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.13" + }, + "vscode": { + "interpreter": { + "hash": "100252972d4ee5286ab455ef1c778abde05b4affca9d1be78633d3797abf7e8c" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/nerfstudio/cameras/camera_paths.py b/nerfstudio/cameras/camera_paths.py index 1630a124f..80a4679e5 100644 --- a/nerfstudio/cameras/camera_paths.py +++ b/nerfstudio/cameras/camera_paths.py @@ -20,10 +20,10 @@ from typing import Any, Dict, Optional, Tuple import torch -import nerfstudio.cameras.utils as camera_utils import nerfstudio.utils.poses as pose_utils +from nerfstudio.cameras import camera_utils +from nerfstudio.cameras.camera_utils import get_interpolated_poses_many from nerfstudio.cameras.cameras import Cameras -from nerfstudio.cameras.utils import get_interpolated_poses_many from nerfstudio.viewer.server.utils import three_js_perspective_camera_focal_length diff --git a/nerfstudio/cameras/utils.py b/nerfstudio/cameras/camera_utils.py similarity index 100% rename from nerfstudio/cameras/utils.py rename to nerfstudio/cameras/camera_utils.py diff --git a/nerfstudio/cameras/cameras.py b/nerfstudio/cameras/cameras.py index 6e2fbf44f..46c89cde5 100644 --- a/nerfstudio/cameras/cameras.py +++ b/nerfstudio/cameras/cameras.py @@ -26,7 +26,7 @@ import torchvision from torch.nn.functional import normalize from torchtyping import TensorType -from nerfstudio.cameras import utils as camera_utils +from nerfstudio.cameras import camera_utils from nerfstudio.cameras.rays import RayBundle diff --git a/nerfstudio/cameras/rays.py b/nerfstudio/cameras/rays.py index ea54cb769..a6e1a7703 100644 --- a/nerfstudio/cameras/rays.py +++ b/nerfstudio/cameras/rays.py @@ -115,20 +115,12 @@ class RaySamples(TensorDataclass): delta_density = self.deltas * densities alphas = 1 - torch.exp(-delta_density) - # TODO: put both versions of the transmittance calculation somewhere - # mip-nerf version of transmittance calculation: transmittance = torch.cumsum(delta_density[..., :-1, :], dim=-2) transmittance = torch.cat( [torch.zeros((*transmittance.shape[:1], 1, 1), device=densities.device), transmittance], dim=-2 ) transmittance = torch.exp(-transmittance) # [..., "num_samples"] - # most nerf codebases do the following: - # transmittance = torch.cat( - # [torch.ones((*alphas.shape[:1], 1)).to(densities.device), 1.0 - alphas + 1e-10], dim=-1 - # ) - # transmittance = torch.cumprod(transmittance, dim=-1)[..., :-1] # [..., "num_samples"] - weights = alphas * transmittance # [..., "num_samples"] return weights diff --git a/nerfstudio/configs/base.py b/nerfstudio/configs/base_config.py similarity index 85% rename from nerfstudio/configs/base.py rename to nerfstudio/configs/base_config.py index 281fa5524..246b5e403 100644 --- a/nerfstudio/configs/base.py +++ b/nerfstudio/configs/base_config.py @@ -14,6 +14,8 @@ """Base Configs""" +# pylint: disable=wrong-import-position + from __future__ import annotations import logging @@ -22,14 +24,12 @@ from datetime import datetime from pathlib import Path from typing import Any, Dict, List, Literal, Optional, Tuple, Type -import torch import yaml from rich.console import Console -from nerfstudio.configs.utils import to_immutable_dict +from nerfstudio.configs.config_utils import to_immutable_dict # model instances -from nerfstudio.optimizers.schedulers import ExponentialDecaySchedule from nerfstudio.utils import writer @@ -174,54 +174,8 @@ class ViewerConfig(PrintableConfig): """number of rays per chunk to render with viewer""" -# Optimizer related configs -@dataclass -class OptimizerConfig(PrintableConfig): - """Basic optimizer config with RAdam""" - - _target: Type = torch.optim.Adam - lr: float = 0.0005 - eps: float = 1e-08 - - # TODO: somehow make this more generic. i dont like the idea of overriding the setup function - # but also not sure how to go about passing things into predefined torch objects. - def setup(self, params) -> Any: - """Returns the instantiated object using the config.""" - kwargs = vars(self) - del kwargs["_target"] - return self._target(params, **kwargs) - - -@dataclass -class AdamOptimizerConfig(OptimizerConfig): - """Basic optimizer config with Adam""" - - _target: Type = torch.optim.Adam - - -@dataclass -class RAdamOptimizerConfig(OptimizerConfig): - """Basic optimizer config with RAdam""" - - _target: Type = torch.optim.RAdam - - -@dataclass -class SchedulerConfig(InstantiateConfig): - """Basic scheduler config with self-defined exponential decay schedule""" - - _target: Type = ExponentialDecaySchedule - lr_final: float = 0.000005 - max_steps: int = 1000000 - - # TODO: somehow make this more generic. i dont like the idea of overriding the setup function - # but also not sure how to go about passing things into predefined torch objects. - def setup(self, optimizer=None, lr_init=None, **kwargs) -> Any: - """Returns the instantiated object using the config.""" - return self._target(optimizer, lr_init, self.lr_final, self.max_steps) - - -# pylint: disable=wrong-import-position +from nerfstudio.engine.optimizers import OptimizerConfig +from nerfstudio.engine.schedulers import SchedulerConfig from nerfstudio.pipelines.base import VanillaPipelineConfig diff --git a/nerfstudio/configs/utils.py b/nerfstudio/configs/config_utils.py similarity index 100% rename from nerfstudio/configs/utils.py rename to nerfstudio/configs/config_utils.py diff --git a/nerfstudio/configs/base_configs.py b/nerfstudio/configs/model_configs.py similarity index 87% rename from nerfstudio/configs/base_configs.py rename to nerfstudio/configs/model_configs.py index d6ed8fae6..b58761723 100644 --- a/nerfstudio/configs/base_configs.py +++ b/nerfstudio/configs/model_configs.py @@ -22,20 +22,13 @@ from typing import Dict import dcargs -from nerfstudio.configs.base import ( - AdamOptimizerConfig, - Config, - RAdamOptimizerConfig, - SchedulerConfig, - TrainerConfig, - ViewerConfig, -) -from nerfstudio.datamanagers.base import VanillaDataManagerConfig -from nerfstudio.datamanagers.dataparsers.blender_parser import BlenderDataParserConfig -from nerfstudio.datamanagers.dataparsers.friends_parser import FriendsDataParserConfig -from nerfstudio.datamanagers.dataparsers.mipnerf_parser import ( - MipNerf360DataParserConfig, -) +from nerfstudio.configs.base_config import Config, TrainerConfig, ViewerConfig +from nerfstudio.data.datamanagers import VanillaDataManagerConfig +from nerfstudio.data.dataparsers.blender_dataparser import BlenderDataParserConfig +from nerfstudio.data.dataparsers.friends_dataparser import FriendsDataParserConfig +from nerfstudio.data.dataparsers.mipnerf_dataparser import MipNerf360DataParserConfig +from nerfstudio.engine.optimizers import AdamOptimizerConfig, RAdamOptimizerConfig +from nerfstudio.engine.schedulers import SchedulerConfig from nerfstudio.models.base import VanillaModelConfig from nerfstudio.models.instant_ngp import InstantNGPModelConfig from nerfstudio.models.mipnerf import MipNerfModel @@ -48,9 +41,9 @@ from nerfstudio.models.vanilla_nerf import NeRFModel from nerfstudio.pipelines.base import VanillaPipelineConfig from nerfstudio.pipelines.dynamic_batch import DynamicBatchPipelineConfig -base_configs: Dict[str, Config] = {} +model_configs: Dict[str, Config] = {} -base_configs["nerfacto"] = Config( +model_configs["nerfacto"] = Config( method_name="nerfacto", trainer=TrainerConfig(steps_per_eval_batch=500, steps_per_save=2000, mixed_precision=True), pipeline=VanillaPipelineConfig( @@ -73,7 +66,7 @@ base_configs["nerfacto"] = Config( vis=["viewer"], ) -base_configs["instant-ngp"] = Config( +model_configs["instant-ngp"] = Config( method_name="instant-ngp", trainer=TrainerConfig(steps_per_eval_batch=500, steps_per_save=2000, mixed_precision=True), pipeline=DynamicBatchPipelineConfig( @@ -90,7 +83,7 @@ base_configs["instant-ngp"] = Config( vis=["viewer"], ) -base_configs["mipnerf-360"] = Config( +model_configs["mipnerf-360"] = Config( method_name="mipnerf-360", trainer=TrainerConfig(steps_per_eval_batch=200), pipeline=VanillaPipelineConfig( @@ -112,7 +105,7 @@ base_configs["mipnerf-360"] = Config( }, ) -base_configs["mipnerf"] = Config( +model_configs["mipnerf"] = Config( method_name="mipnerf", pipeline=VanillaPipelineConfig( datamanager=VanillaDataManagerConfig(dataparser=BlenderDataParserConfig(), train_num_rays_per_batch=8192), @@ -132,7 +125,7 @@ base_configs["mipnerf"] = Config( }, ) -base_configs["nerfw"] = Config( +model_configs["nerfw"] = Config( method_name="nerfw", pipeline=VanillaPipelineConfig( datamanager=VanillaDataManagerConfig( @@ -149,7 +142,7 @@ base_configs["nerfw"] = Config( ) -base_configs["semantic-nerf"] = Config( +model_configs["semantic-nerf"] = Config( method_name="semantic-nerf", pipeline=VanillaPipelineConfig( datamanager=VanillaDataManagerConfig( @@ -170,7 +163,7 @@ base_configs["semantic-nerf"] = Config( }, ) -base_configs["vanilla-nerf"] = Config( +model_configs["vanilla-nerf"] = Config( method_name="vanilla-nerf", pipeline=VanillaPipelineConfig( datamanager=VanillaDataManagerConfig( @@ -186,7 +179,7 @@ base_configs["vanilla-nerf"] = Config( }, ) -base_configs["tensorf"] = Config( +model_configs["tensorf"] = Config( method_name="tensorf", trainer=TrainerConfig(mixed_precision=True), pipeline=VanillaPipelineConfig( @@ -212,7 +205,7 @@ base_configs["tensorf"] = Config( ) -AnnotatedBaseConfigUnion = dcargs.extras.subcommand_type_from_defaults(base_configs) +AnnotatedBaseConfigUnion = dcargs.extras.subcommand_type_from_defaults(model_configs) """Union[] type over config types, annotated with default instances for use with dcargs.cli(). Allows the user to pick between one of several base configurations, and then override values in it.""" diff --git a/nerfstudio/datamanagers/__init__.py b/nerfstudio/data/__init__.py similarity index 100% rename from nerfstudio/datamanagers/__init__.py rename to nerfstudio/data/__init__.py diff --git a/nerfstudio/datamanagers/base.py b/nerfstudio/data/datamanagers.py similarity index 94% rename from nerfstudio/datamanagers/base.py rename to nerfstudio/data/datamanagers.py index 1bf8881fd..9389dfd17 100644 --- a/nerfstudio/datamanagers/base.py +++ b/nerfstudio/data/datamanagers.py @@ -28,27 +28,23 @@ from torch.nn import Parameter from torch.utils.data.distributed import DistributedSampler from nerfstudio.cameras.rays import RayBundle -from nerfstudio.configs.base import InstantiateConfig -from nerfstudio.datamanagers.dataloaders import ( +from nerfstudio.configs.base_config import InstantiateConfig +from nerfstudio.data.dataparsers.blender_dataparser import BlenderDataParserConfig +from nerfstudio.data.dataparsers.friends_dataparser import FriendsDataParserConfig +from nerfstudio.data.dataparsers.instant_ngp_dataparser import ( + InstantNGPDataParserConfig, +) +from nerfstudio.data.dataparsers.mipnerf_dataparser import MipNerf360DataParserConfig +from nerfstudio.data.dataparsers.nerfstudio_dataparser import NerfstudioDataParserConfig +from nerfstudio.data.dataparsers.record3d_dataparser import Record3DDataParserConfig +from nerfstudio.data.pixel_samplers import PixelSampler +from nerfstudio.data.utils.dataloaders import ( CacheDataloader, FixedIndicesEvalDataloader, RandIndicesEvalDataloader, ) -from nerfstudio.datamanagers.dataparsers.blender_parser import BlenderDataParserConfig -from nerfstudio.datamanagers.dataparsers.friends_parser import FriendsDataParserConfig -from nerfstudio.datamanagers.dataparsers.instant_ngp_parser import ( - InstantNGPDataParserConfig, -) -from nerfstudio.datamanagers.dataparsers.mipnerf_parser import ( - MipNerf360DataParserConfig, -) -from nerfstudio.datamanagers.dataparsers.nerfstudio_parser import ( - NerfstudioDataParserConfig, -) -from nerfstudio.datamanagers.dataparsers.record3d_parser import Record3DDataParserConfig -from nerfstudio.datamanagers.datasets import InputDataset -from nerfstudio.datamanagers.pixel_sampler import PixelSampler -from nerfstudio.models.modules.ray_generator import RayGenerator +from nerfstudio.data.utils.datasets import InputDataset +from nerfstudio.model_components.ray_generator import RayGenerator from nerfstudio.utils.callbacks import TrainingCallback, TrainingCallbackAttributes from nerfstudio.utils.misc import IterableWrapper diff --git a/nerfstudio/datamanagers/dataparsers/__init__.py b/nerfstudio/data/dataparsers/__init__.py similarity index 100% rename from nerfstudio/datamanagers/dataparsers/__init__.py rename to nerfstudio/data/dataparsers/__init__.py diff --git a/nerfstudio/datamanagers/dataparsers/base.py b/nerfstudio/data/dataparsers/base_dataparser.py similarity index 50% rename from nerfstudio/datamanagers/dataparsers/base.py rename to nerfstudio/data/dataparsers/base_dataparser.py index c91a1056e..b32796bc5 100644 --- a/nerfstudio/datamanagers/dataparsers/base.py +++ b/nerfstudio/data/dataparsers/base_dataparser.py @@ -18,10 +18,60 @@ from __future__ import annotations from abc import abstractmethod from dataclasses import dataclass, field -from typing import Type +from pathlib import Path +from typing import Any, Dict, List, Optional, Type -import nerfstudio.configs.base as cfg -from nerfstudio.datamanagers.structs import DatasetInputs +import torch +from torchtyping import TensorType + +import nerfstudio.configs.base_config as cfg +from nerfstudio.cameras.cameras import Cameras +from nerfstudio.configs.config_utils import to_immutable_dict +from nerfstudio.data.scene_box import SceneBounds + + +@dataclass +class Semantics: + """Dataclass for semantic labels.""" + + stuff_filenames: List[Path] + """filenames to load "stuff"/background data""" + stuff_classes: List[str] + """class labels for "stuff" data""" + stuff_colors: torch.Tensor + """color mapping for "stuff" classes""" + thing_filenames: List[Path] + """filenames to load "thing"/foreground data""" + thing_classes: List[str] + """class labels for "thing" data""" + thing_colors: torch.Tensor + """color mapping for "thing" classes""" + + +@dataclass +class DataparserOutputs: + """Dataset inputs for the image dataset and the ray generator.""" + + image_filenames: List[Path] + """Filenames for the images.""" + cameras: Cameras + """Camera object storing collection of camera information in dataset""" + alpha_color: Optional[TensorType[3]] = None + """color of dataset background""" + scene_bounds: SceneBounds = SceneBounds() + """scene bounds of dataset""" + # we support additional input information/formats including mask/depth/semantics. + additional_inputs: Dict[str, Any] = to_immutable_dict({}) + """Dictionary of additional dataset information (e.g. semantics/point clouds/masks). + {input_name: + ... {"func": function to process additional dataset inputs, + ... "kwargs": dictionary of data to pass into "func"} + } + """ + + def as_dict(self) -> dict: + """Returns the dataclass as a dictionary.""" + return vars(self) @dataclass @@ -47,7 +97,7 @@ class DataParser: self.config = config @abstractmethod - def _generate_dataset_inputs(self, split: str = "train") -> DatasetInputs: + def _generate_dataset_inputs(self, split: str = "train") -> DataparserOutputs: """Abstract method that returns the dataset inputs for the given split. Args: @@ -57,7 +107,7 @@ class DataParser: DatasetInputs containing data for the specified dataset and split """ - def get_dataset_inputs(self, split: str = "train") -> DatasetInputs: + def get_dataset_inputs(self, split: str = "train") -> DataparserOutputs: """Returns the dataset inputs for the given split. Args: diff --git a/nerfstudio/datamanagers/dataparsers/blender_parser.py b/nerfstudio/data/dataparsers/blender_dataparser.py similarity index 94% rename from nerfstudio/datamanagers/dataparsers/blender_parser.py rename to nerfstudio/data/dataparsers/blender_dataparser.py index 8e26d77fa..03c01aa53 100644 --- a/nerfstudio/datamanagers/dataparsers/blender_parser.py +++ b/nerfstudio/data/dataparsers/blender_dataparser.py @@ -24,8 +24,12 @@ import numpy as np import torch from nerfstudio.cameras.cameras import Cameras, CameraType -from nerfstudio.datamanagers.dataparsers.base import DataParser, DataParserConfig -from nerfstudio.datamanagers.structs import DatasetInputs, SceneBounds +from nerfstudio.data.dataparsers.base_dataparser import ( + DataParser, + DataParserConfig, + DataparserOutputs, +) +from nerfstudio.data.scene_box import SceneBounds from nerfstudio.utils.colors import get_color from nerfstudio.utils.io import load_from_json @@ -95,7 +99,7 @@ class Blender(DataParser): camera_type=CameraType.PERSPECTIVE, ) - dataset_inputs = DatasetInputs( + dataset_inputs = DataparserOutputs( image_filenames=image_filenames, cameras=cameras, alpha_color=alpha_color_tensor, diff --git a/nerfstudio/datamanagers/dataparsers/friends_parser.py b/nerfstudio/data/dataparsers/friends_dataparser.py similarity index 96% rename from nerfstudio/datamanagers/dataparsers/friends_parser.py rename to nerfstudio/data/dataparsers/friends_dataparser.py index 663bcaedc..f7ed972a7 100644 --- a/nerfstudio/datamanagers/dataparsers/friends_parser.py +++ b/nerfstudio/data/dataparsers/friends_dataparser.py @@ -25,8 +25,13 @@ from PIL import Image from rich.console import Console from nerfstudio.cameras.cameras import Cameras, CameraType -from nerfstudio.datamanagers.dataparsers.base import DataParser, DataParserConfig -from nerfstudio.datamanagers.structs import DatasetInputs, SceneBounds, Semantics +from nerfstudio.data.dataparsers.base_dataparser import ( + DataParser, + DataParserConfig, + DataparserOutputs, + Semantics, +) +from nerfstudio.data.scene_box import SceneBounds from nerfstudio.utils.io import load_from_json CONSOLE = Console() @@ -177,7 +182,7 @@ class Friends(DataParser): ) cameras.rescale_output_resolution(scaling_factor=1.0 / self.config.downscale_factor) - dataset_inputs = DatasetInputs( + dataset_inputs = DataparserOutputs( image_filenames=image_filenames, cameras=cameras, scene_bounds=scene_bounds, diff --git a/nerfstudio/datamanagers/dataparsers/instant_ngp_parser.py b/nerfstudio/data/dataparsers/instant_ngp_dataparser.py similarity index 93% rename from nerfstudio/datamanagers/dataparsers/instant_ngp_parser.py rename to nerfstudio/data/dataparsers/instant_ngp_dataparser.py index 52dee8bbc..5ea545a5f 100644 --- a/nerfstudio/datamanagers/dataparsers/instant_ngp_parser.py +++ b/nerfstudio/data/dataparsers/instant_ngp_dataparser.py @@ -24,10 +24,14 @@ from typing import Type import numpy as np import torch -from nerfstudio.cameras import utils as camera_utils +from nerfstudio.cameras import camera_utils from nerfstudio.cameras.cameras import Cameras, CameraType -from nerfstudio.datamanagers.dataparsers.base import DataParser, DataParserConfig -from nerfstudio.datamanagers.structs import DatasetInputs, SceneBounds +from nerfstudio.data.dataparsers.base_dataparser import ( + DataParser, + DataParserConfig, + DataparserOutputs, +) +from nerfstudio.data.scene_box import SceneBounds from nerfstudio.utils.io import load_from_json @@ -103,7 +107,7 @@ class InstantNGP(DataParser): ) # TODO(ethan): add alpha background color - dataset_inputs = DatasetInputs( + dataset_inputs = DataparserOutputs( image_filenames=image_filenames, cameras=cameras, scene_bounds=scene_bounds, diff --git a/nerfstudio/datamanagers/dataparsers/mipnerf_parser.py b/nerfstudio/data/dataparsers/mipnerf_dataparser.py similarity index 96% rename from nerfstudio/datamanagers/dataparsers/mipnerf_parser.py rename to nerfstudio/data/dataparsers/mipnerf_dataparser.py index 3870c660f..305aca02c 100644 --- a/nerfstudio/datamanagers/dataparsers/mipnerf_parser.py +++ b/nerfstudio/data/dataparsers/mipnerf_dataparser.py @@ -24,8 +24,12 @@ import numpy as np import torch from nerfstudio.cameras.cameras import Cameras, CameraType -from nerfstudio.datamanagers.dataparsers.base import DataParser, DataParserConfig -from nerfstudio.datamanagers.structs import DatasetInputs, SceneBounds +from nerfstudio.data.dataparsers.base_dataparser import ( + DataParser, + DataParserConfig, + DataparserOutputs, +) +from nerfstudio.data.scene_box import SceneBounds @dataclass @@ -151,7 +155,7 @@ class Mipnerf360(DataParser): cameras.rescale_output_resolution(scaling_factor=1.0 / self.config.downscale_factor) - dataset_inputs = DatasetInputs( + dataset_inputs = DataparserOutputs( image_filenames=image_filenames, cameras=cameras, scene_bounds=scene_bounds, diff --git a/nerfstudio/datamanagers/dataparsers/nerfstudio_parser.py b/nerfstudio/data/dataparsers/nerfstudio_dataparser.py similarity index 95% rename from nerfstudio/datamanagers/dataparsers/nerfstudio_parser.py rename to nerfstudio/data/dataparsers/nerfstudio_dataparser.py index f67ba872b..9ff7636db 100644 --- a/nerfstudio/datamanagers/dataparsers/nerfstudio_parser.py +++ b/nerfstudio/data/dataparsers/nerfstudio_dataparser.py @@ -24,10 +24,14 @@ from typing import Literal, Type import numpy as np import torch -from nerfstudio.cameras import utils as camera_utils +from nerfstudio.cameras import camera_utils from nerfstudio.cameras.cameras import CAMERA_MODEL_TO_TYPE, Cameras, CameraType -from nerfstudio.datamanagers.dataparsers.base import DataParser, DataParserConfig -from nerfstudio.datamanagers.structs import DatasetInputs, SceneBounds +from nerfstudio.data.dataparsers.base_dataparser import ( + DataParser, + DataParserConfig, + DataparserOutputs, +) +from nerfstudio.data.scene_box import SceneBounds from nerfstudio.utils.io import load_from_json @@ -153,7 +157,7 @@ class Nerfstudio(DataParser): cameras.rescale_output_resolution(scaling_factor=1.0 / self.config.downscale_factor) - dataset_inputs = DatasetInputs( + dataset_inputs = DataparserOutputs( image_filenames=image_filenames, cameras=cameras, scene_bounds=scene_bounds, diff --git a/nerfstudio/datamanagers/dataparsers/record3d_parser.py b/nerfstudio/data/dataparsers/record3d_dataparser.py similarity index 95% rename from nerfstudio/datamanagers/dataparsers/record3d_parser.py rename to nerfstudio/data/dataparsers/record3d_dataparser.py index a6a20c518..21be800c2 100644 --- a/nerfstudio/datamanagers/dataparsers/record3d_parser.py +++ b/nerfstudio/data/dataparsers/record3d_dataparser.py @@ -23,10 +23,14 @@ import numpy as np import torch from scipy.spatial.transform import Rotation -from nerfstudio.cameras import utils as camera_utils +from nerfstudio.cameras import camera_utils from nerfstudio.cameras.cameras import Cameras, CameraType -from nerfstudio.datamanagers.dataparsers.base import DataParser, DataParserConfig -from nerfstudio.datamanagers.structs import DatasetInputs, SceneBounds +from nerfstudio.data.dataparsers.base_dataparser import ( + DataParser, + DataParserConfig, + DataparserOutputs, +) +from nerfstudio.data.scene_box import SceneBounds from nerfstudio.utils import poses as pose_utils from nerfstudio.utils.io import load_from_json @@ -56,7 +60,7 @@ class Record3D(DataParser): config: Record3DDataParserConfig - def _generate_dataset_inputs(self, split: str = "train") -> DatasetInputs: + def _generate_dataset_inputs(self, split: str = "train") -> DataparserOutputs: image_dir = self.config.data_directory / "rgb" if not image_dir.exists(): @@ -141,7 +145,7 @@ class Record3D(DataParser): camera_type=CameraType.PERSPECTIVE, ) - dataset_inputs = DatasetInputs( + dataset_inputs = DataparserOutputs( image_filenames=image_filenames, cameras=cameras, scene_bounds=scene_bounds, diff --git a/nerfstudio/datamanagers/pixel_sampler.py b/nerfstudio/data/pixel_samplers.py similarity index 100% rename from nerfstudio/datamanagers/pixel_sampler.py rename to nerfstudio/data/pixel_samplers.py diff --git a/nerfstudio/datamanagers/structs.py b/nerfstudio/data/scene_box.py similarity index 63% rename from nerfstudio/datamanagers/structs.py rename to nerfstudio/data/scene_box.py index efd83202d..9bf9319f7 100644 --- a/nerfstudio/datamanagers/structs.py +++ b/nerfstudio/data/scene_box.py @@ -17,43 +17,11 @@ Dataset input structures. """ from dataclasses import dataclass -from pathlib import Path -from typing import Any, Dict, List, Optional, Union +from typing import Dict, Union import torch from torchtyping import TensorType -from nerfstudio.cameras.cameras import Cameras -from nerfstudio.configs.utils import to_immutable_dict - - -@dataclass -class PointCloud: - """Dataclass for a point cloud.""" - - xyz: TensorType["num_points", 3] = None - """the 3D points in point cloud""" - rgb: TensorType["num_points", 3] = None - """the color of each point in point cloud""" - - -@dataclass -class Semantics: - """Dataclass for semantic labels.""" - - stuff_filenames: List[Path] - """filenames to load "stuff"/background data""" - stuff_classes: List[str] - """class labels for "stuff" data""" - stuff_colors: torch.Tensor - """color mapping for "stuff" classes""" - thing_filenames: List[Path] - """filenames to load "thing"/foreground data""" - thing_classes: List[str] - """class labels for "thing" data""" - thing_colors: torch.Tensor - """color mapping for "thing" classes""" - @dataclass class SceneBounds: @@ -122,29 +90,3 @@ class SceneBounds: xyzs = poses[..., :3, -1] aabb = torch.stack([torch.min(xyzs, dim=0)[0], torch.max(xyzs, dim=0)[0]]) return SceneBounds(aabb=aabb * scale_factor) - - -@dataclass -class DatasetInputs: - """Dataset inputs for the image dataset and the ray generator.""" - - image_filenames: List[Path] - """Filenames for the images.""" - cameras: Cameras - """Camera object storing collection of camera information in dataset""" - alpha_color: Optional[TensorType[3]] = None - """color of dataset background""" - scene_bounds: SceneBounds = SceneBounds() - """scene bounds of dataset""" - # we support additional input information/formats including mask/depth/semantics. - additional_inputs: Dict[str, Any] = to_immutable_dict({}) - """Dictionary of additional dataset information (e.g. semantics/point clouds/masks). - {input_name: - ... {"func": function to process additional dataset inputs, - ... "kwargs": dictionary of data to pass into "func"} - } - """ - - def as_dict(self) -> dict: - """Returns the dataclass as a dictionary.""" - return vars(self) diff --git a/nerfstudio/datamanagers/dataloaders.py b/nerfstudio/data/utils/dataloaders.py similarity index 99% rename from nerfstudio/datamanagers/dataloaders.py rename to nerfstudio/data/utils/dataloaders.py index 3b17b5afa..a9bac84dd 100644 --- a/nerfstudio/datamanagers/dataloaders.py +++ b/nerfstudio/data/utils/dataloaders.py @@ -26,7 +26,7 @@ from torch.utils.data.dataloader import DataLoader from nerfstudio.cameras.cameras import Cameras from nerfstudio.cameras.rays import RayBundle -from nerfstudio.datamanagers.datasets import InputDataset +from nerfstudio.data.utils.datasets import InputDataset from nerfstudio.utils.misc import get_dict_to_torch diff --git a/nerfstudio/datamanagers/datasets.py b/nerfstudio/data/utils/datasets.py similarity index 96% rename from nerfstudio/datamanagers/datasets.py rename to nerfstudio/data/utils/datasets.py index 9770df6cb..d19366346 100644 --- a/nerfstudio/datamanagers/datasets.py +++ b/nerfstudio/data/utils/datasets.py @@ -26,7 +26,7 @@ from PIL import Image from torch.utils.data import Dataset from torchtyping import TensorType -from nerfstudio.datamanagers.structs import DatasetInputs +from nerfstudio.data.dataparsers.base_dataparser import DataparserOutputs from nerfstudio.utils.misc import is_not_none @@ -37,7 +37,7 @@ class InputDataset(Dataset): dataset_inputs: description of where and how to read input images. """ - def __init__(self, dataset_inputs: DatasetInputs): + def __init__(self, dataset_inputs: DataparserOutputs): super().__init__() self.dataset_inputs = dataset_inputs diff --git a/nerfstudio/optimizers/optimizers.py b/nerfstudio/engine/optimizers.py similarity index 76% rename from nerfstudio/optimizers/optimizers.py rename to nerfstudio/engine/optimizers.py index 744a698d8..1038646b1 100644 --- a/nerfstudio/optimizers/optimizers.py +++ b/nerfstudio/engine/optimizers.py @@ -17,15 +17,50 @@ Optimizers class. """ from __future__ import annotations -from typing import Any, Dict, List, Union +from dataclasses import dataclass +from typing import Any, Dict, List, Type +import torch from torch.cuda.amp.grad_scaler import GradScaler from torch.nn.parameter import Parameter +from nerfstudio.configs.base_config import PrintableConfig from nerfstudio.utils import writer -def setup_optimizers(config: Union[Dict[str, Any]], param_groups: Dict[str, List[Parameter]]) -> "Optimizers": +# Optimizer related configs +@dataclass +class OptimizerConfig(PrintableConfig): + """Basic optimizer config with RAdam""" + + _target: Type = torch.optim.Adam + lr: float = 0.0005 + eps: float = 1e-08 + + # TODO: somehow make this more generic. i dont like the idea of overriding the setup function + # but also not sure how to go about passing things into predefined torch objects. + def setup(self, params) -> Any: + """Returns the instantiated object using the config.""" + kwargs = vars(self) + del kwargs["_target"] + return self._target(params, **kwargs) + + +@dataclass +class AdamOptimizerConfig(OptimizerConfig): + """Basic optimizer config with Adam""" + + _target: Type = torch.optim.Adam + + +@dataclass +class RAdamOptimizerConfig(OptimizerConfig): + """Basic optimizer config with RAdam""" + + _target: Type = torch.optim.RAdam + + +def setup_optimizers(config: Dict[str, Any], param_groups: Dict[str, List[Parameter]]) -> "Optimizers": """Helper to set up the optimizers Args: @@ -46,7 +81,7 @@ class Optimizers: param_groups: A dictionary of parameter groups to optimize. """ - def __init__(self, config: Union[Dict[str, Any]], param_groups: Dict[str, List[Parameter]]): + def __init__(self, config: Dict[str, Any], param_groups: Dict[str, List[Parameter]]): self.config = config self.optimizers = {} self.schedulers = {} diff --git a/nerfstudio/optimizers/schedulers.py b/nerfstudio/engine/schedulers.py similarity index 71% rename from nerfstudio/optimizers/schedulers.py rename to nerfstudio/engine/schedulers.py index 5863195d5..25effd438 100644 --- a/nerfstudio/optimizers/schedulers.py +++ b/nerfstudio/engine/schedulers.py @@ -14,9 +14,29 @@ """Scheduler Classes""" +from dataclasses import dataclass, field +from typing import Any, Type + import numpy as np from torch.optim.lr_scheduler import LambdaLR +from nerfstudio.configs.base_config import InstantiateConfig + + +@dataclass +class SchedulerConfig(InstantiateConfig): + """Basic scheduler config with self-defined exponential decay schedule""" + + _target: Type = field(default_factory=lambda: ExponentialDecaySchedule) + lr_final: float = 0.000005 + max_steps: int = 1000000 + + # TODO: somehow make this more generic. i dont like the idea of overriding the setup function + # but also not sure how to go about passing things into predefined torch objects. + def setup(self, optimizer=None, lr_init=None, **kwargs) -> Any: + """Returns the instantiated object using the config.""" + return self._target(optimizer, lr_init, self.lr_final, self.max_steps) + class ExponentialDecaySchedule(LambdaLR): """Exponential learning rate decay function. @@ -33,6 +53,8 @@ class ExponentialDecaySchedule(LambdaLR): lr_delay_mult: The multiplier for the learning rate after the delay. """ + config: SchedulerConfig + def __init__(self, optimizer, lr_init, lr_final, max_steps, lr_delay_steps=0, lr_delay_mult=1.0) -> None: def func(step): if lr_delay_steps > 0: diff --git a/nerfstudio/engine/trainer.py b/nerfstudio/engine/trainer.py index efbcce303..eca056173 100644 --- a/nerfstudio/engine/trainer.py +++ b/nerfstudio/engine/trainer.py @@ -28,8 +28,8 @@ import torch from rich import console from torch.cuda.amp.grad_scaler import GradScaler -from nerfstudio.configs import base as cfg -from nerfstudio.optimizers.optimizers import Optimizers, setup_optimizers +from nerfstudio.configs import base_config as cfg +from nerfstudio.engine.optimizers import Optimizers, setup_optimizers from nerfstudio.pipelines.base import VanillaPipeline from nerfstudio.utils import profiler, writer from nerfstudio.utils.callbacks import ( diff --git a/nerfstudio/fields/modules/__init__.py b/nerfstudio/field_components/__init__.py similarity index 100% rename from nerfstudio/fields/modules/__init__.py rename to nerfstudio/field_components/__init__.py diff --git a/nerfstudio/fields/modules/base.py b/nerfstudio/field_components/base.py similarity index 100% rename from nerfstudio/fields/modules/base.py rename to nerfstudio/field_components/base.py diff --git a/nerfstudio/fields/modules/embedding.py b/nerfstudio/field_components/embedding.py similarity index 96% rename from nerfstudio/fields/modules/embedding.py rename to nerfstudio/field_components/embedding.py index 758b526ea..8280a4ddf 100644 --- a/nerfstudio/fields/modules/embedding.py +++ b/nerfstudio/field_components/embedding.py @@ -20,7 +20,7 @@ Code for embeddings. import torch from torchtyping import TensorType -from nerfstudio.fields.modules.base import FieldModule +from nerfstudio.field_components.base import FieldModule class Embedding(FieldModule): diff --git a/nerfstudio/fields/modules/encoding.py b/nerfstudio/field_components/encoding.py similarity index 99% rename from nerfstudio/fields/modules/encoding.py rename to nerfstudio/field_components/encoding.py index 202f0e827..1e4fedf58 100644 --- a/nerfstudio/fields/modules/encoding.py +++ b/nerfstudio/field_components/encoding.py @@ -25,7 +25,7 @@ import torch.nn.functional as F from torch import nn from torchtyping import TensorType -from nerfstudio.fields.modules.base import FieldModule +from nerfstudio.field_components.base import FieldModule from nerfstudio.utils.math import components_from_spherical_harmonics, expected_sin diff --git a/nerfstudio/fields/modules/field_heads.py b/nerfstudio/field_components/field_heads.py similarity index 99% rename from nerfstudio/fields/modules/field_heads.py rename to nerfstudio/field_components/field_heads.py index ca6eebca8..51aa3d255 100644 --- a/nerfstudio/fields/modules/field_heads.py +++ b/nerfstudio/field_components/field_heads.py @@ -21,7 +21,7 @@ from typing import Callable, Optional, Union from torch import nn from torchtyping import TensorType -from nerfstudio.fields.modules.base import FieldModule +from nerfstudio.field_components.base import FieldModule class FieldHeadNames(Enum): diff --git a/nerfstudio/fields/modules/field_losses.py b/nerfstudio/field_components/field_losses.py similarity index 100% rename from nerfstudio/fields/modules/field_losses.py rename to nerfstudio/field_components/field_losses.py diff --git a/nerfstudio/fields/modules/mlp.py b/nerfstudio/field_components/mlp.py similarity index 98% rename from nerfstudio/fields/modules/mlp.py rename to nerfstudio/field_components/mlp.py index caa385a7d..1d9ad9159 100644 --- a/nerfstudio/fields/modules/mlp.py +++ b/nerfstudio/field_components/mlp.py @@ -21,7 +21,7 @@ import torch from torch import nn from torchtyping import TensorType -from nerfstudio.fields.modules.base import FieldModule +from nerfstudio.field_components.base import FieldModule class MLP(FieldModule): diff --git a/nerfstudio/fields/modules/spatial_distortions.py b/nerfstudio/field_components/spatial_distortions.py similarity index 100% rename from nerfstudio/fields/modules/spatial_distortions.py rename to nerfstudio/field_components/spatial_distortions.py diff --git a/nerfstudio/fields/base.py b/nerfstudio/fields/base_field.py similarity index 98% rename from nerfstudio/fields/base.py rename to nerfstudio/fields/base_field.py index 965d8a95b..e5c4b6be6 100644 --- a/nerfstudio/fields/base.py +++ b/nerfstudio/fields/base_field.py @@ -24,7 +24,7 @@ from torch import nn from torchtyping import TensorType from nerfstudio.cameras.rays import Frustums, RaySamples -from nerfstudio.fields.modules.field_heads import FieldHeadNames +from nerfstudio.field_components.field_heads import FieldHeadNames class Field(nn.Module): diff --git a/nerfstudio/fields/density_field.py b/nerfstudio/fields/density_fields.py similarity index 95% rename from nerfstudio/fields/density_field.py rename to nerfstudio/fields/density_fields.py index c6176e570..06c150d44 100644 --- a/nerfstudio/fields/density_field.py +++ b/nerfstudio/fields/density_fields.py @@ -25,9 +25,9 @@ from torch.nn.parameter import Parameter from torchtyping import TensorType from nerfstudio.cameras.rays import RaySamples -from nerfstudio.datamanagers.structs import SceneBounds -from nerfstudio.fields.base import Field -from nerfstudio.fields.modules.spatial_distortions import SpatialDistortion +from nerfstudio.data.scene_box import SceneBounds +from nerfstudio.field_components.spatial_distortions import SpatialDistortion +from nerfstudio.fields.base_field import Field from nerfstudio.utils.activations import trunc_exp try: @@ -37,7 +37,7 @@ except ImportError: pass -class DensityField(Field): +class HashMLPDensityField(Field): """A lightweight density field module. Args: diff --git a/nerfstudio/fields/instant_ngp_field.py b/nerfstudio/fields/instant_ngp_field.py index 116f0aa47..9042051e6 100644 --- a/nerfstudio/fields/instant_ngp_field.py +++ b/nerfstudio/fields/instant_ngp_field.py @@ -25,10 +25,10 @@ from torch.nn.parameter import Parameter from torchtyping import TensorType from nerfstudio.cameras.rays import RaySamples -from nerfstudio.datamanagers.structs import SceneBounds -from nerfstudio.fields.base import Field -from nerfstudio.fields.modules.embedding import Embedding -from nerfstudio.fields.modules.field_heads import FieldHeadNames +from nerfstudio.data.scene_box import SceneBounds +from nerfstudio.field_components.embedding import Embedding +from nerfstudio.field_components.field_heads import FieldHeadNames +from nerfstudio.fields.base_field import Field from nerfstudio.utils.activations import trunc_exp try: diff --git a/nerfstudio/fields/nerf_field.py b/nerfstudio/fields/nerf_field.py index 4cd58f3c1..62dbf9492 100644 --- a/nerfstudio/fields/nerf_field.py +++ b/nerfstudio/fields/nerf_field.py @@ -22,16 +22,16 @@ from torch import nn from torchtyping import TensorType from nerfstudio.cameras.rays import RaySamples -from nerfstudio.fields.base import Field -from nerfstudio.fields.modules.encoding import Encoding, Identity -from nerfstudio.fields.modules.field_heads import ( +from nerfstudio.field_components.encoding import Encoding, Identity +from nerfstudio.field_components.field_heads import ( DensityFieldHead, FieldHead, FieldHeadNames, RGBFieldHead, ) -from nerfstudio.fields.modules.mlp import MLP -from nerfstudio.fields.modules.spatial_distortions import SpatialDistortion +from nerfstudio.field_components.mlp import MLP +from nerfstudio.field_components.spatial_distortions import SpatialDistortion +from nerfstudio.fields.base_field import Field class NeRFField(Field): diff --git a/nerfstudio/fields/compound_field.py b/nerfstudio/fields/nerfacto_field.py similarity index 95% rename from nerfstudio/fields/compound_field.py rename to nerfstudio/fields/nerfacto_field.py index 0d8bb7f06..9082e2ad8 100644 --- a/nerfstudio/fields/compound_field.py +++ b/nerfstudio/fields/nerfacto_field.py @@ -26,21 +26,21 @@ from torch.nn.parameter import Parameter from torchtyping import TensorType from nerfstudio.cameras.rays import RaySamples -from nerfstudio.datamanagers.structs import SceneBounds -from nerfstudio.fields.base import Field -from nerfstudio.fields.modules.embedding import Embedding -from nerfstudio.fields.modules.encoding import Encoding, HashEncoding, SHEncoding -from nerfstudio.fields.modules.field_heads import ( +from nerfstudio.data.scene_box import SceneBounds +from nerfstudio.field_components.embedding import Embedding +from nerfstudio.field_components.encoding import Encoding, HashEncoding, SHEncoding +from nerfstudio.field_components.field_heads import ( DensityFieldHead, FieldHead, FieldHeadNames, RGBFieldHead, ) -from nerfstudio.fields.modules.mlp import MLP -from nerfstudio.fields.modules.spatial_distortions import ( +from nerfstudio.field_components.mlp import MLP +from nerfstudio.field_components.spatial_distortions import ( SceneContraction, SpatialDistortion, ) +from nerfstudio.fields.base_field import Field from nerfstudio.utils.activations import trunc_exp try: @@ -59,7 +59,7 @@ def get_normalized_directions(directions: TensorType["bs":..., 3]): return (directions + 1.0) / 2.0 -class TCNNCompoundField(Field): +class TCNNNerfactoField(Field): """Compound Field that uses TCNN Args: @@ -200,7 +200,7 @@ class TCNNCompoundField(Field): return {FieldHeadNames.RGB: rgb} -class TorchCompoundField(Field): +class TorchNerfactoField(Field): """ PyTorch implementation of the compound field. """ @@ -291,4 +291,4 @@ class TorchCompoundField(Field): return outputs -field_implementation_to_class = {"tcnn": TCNNCompoundField, "torch": TorchCompoundField} +field_implementation_to_class = {"tcnn": TCNNNerfactoField, "torch": TorchNerfactoField} diff --git a/nerfstudio/fields/nerfw_field.py b/nerfstudio/fields/nerfw_field.py index 864b9a6ea..99df63a02 100644 --- a/nerfstudio/fields/nerfw_field.py +++ b/nerfstudio/fields/nerfw_field.py @@ -21,10 +21,9 @@ from torch import nn from torchtyping import TensorType from nerfstudio.cameras.rays import RaySamples -from nerfstudio.fields.base import Field -from nerfstudio.fields.modules.embedding import Embedding -from nerfstudio.fields.modules.encoding import Encoding, Identity -from nerfstudio.fields.modules.field_heads import ( +from nerfstudio.field_components.embedding import Embedding +from nerfstudio.field_components.encoding import Encoding, Identity +from nerfstudio.field_components.field_heads import ( DensityFieldHead, FieldHeadNames, RGBFieldHead, @@ -32,7 +31,8 @@ from nerfstudio.fields.modules.field_heads import ( TransientRGBFieldHead, UncertaintyFieldHead, ) -from nerfstudio.fields.modules.mlp import MLP +from nerfstudio.field_components.mlp import MLP +from nerfstudio.fields.base_field import Field class VanillaNerfWField(Field): diff --git a/nerfstudio/fields/semantic_nerf_field.py b/nerfstudio/fields/semantic_nerf_field.py index 550ae7817..b37b4f182 100644 --- a/nerfstudio/fields/semantic_nerf_field.py +++ b/nerfstudio/fields/semantic_nerf_field.py @@ -22,15 +22,15 @@ from torch import nn from torchtyping import TensorType from nerfstudio.cameras.rays import RaySamples -from nerfstudio.fields.base import Field -from nerfstudio.fields.modules.encoding import Encoding, Identity -from nerfstudio.fields.modules.field_heads import ( +from nerfstudio.field_components.encoding import Encoding, Identity +from nerfstudio.field_components.field_heads import ( DensityFieldHead, FieldHeadNames, RGBFieldHead, SemanticFieldHead, ) -from nerfstudio.fields.modules.mlp import MLP +from nerfstudio.field_components.mlp import MLP +from nerfstudio.fields.base_field import Field class SemanticNerfField(Field): diff --git a/nerfstudio/models/modules/__init__.py b/nerfstudio/model_components/__init__.py similarity index 100% rename from nerfstudio/models/modules/__init__.py rename to nerfstudio/model_components/__init__.py diff --git a/nerfstudio/optimizers/loss.py b/nerfstudio/model_components/loss.py similarity index 100% rename from nerfstudio/optimizers/loss.py rename to nerfstudio/model_components/loss.py diff --git a/nerfstudio/models/modules/ray_generator.py b/nerfstudio/model_components/ray_generator.py similarity index 100% rename from nerfstudio/models/modules/ray_generator.py rename to nerfstudio/model_components/ray_generator.py diff --git a/nerfstudio/models/modules/ray_losses.py b/nerfstudio/model_components/ray_losses.py similarity index 100% rename from nerfstudio/models/modules/ray_losses.py rename to nerfstudio/model_components/ray_losses.py diff --git a/nerfstudio/models/modules/ray_sampler.py b/nerfstudio/model_components/ray_sampler.py similarity index 100% rename from nerfstudio/models/modules/ray_sampler.py rename to nerfstudio/model_components/ray_sampler.py diff --git a/nerfstudio/renderers/renderers.py b/nerfstudio/model_components/renderers.py similarity index 100% rename from nerfstudio/renderers/renderers.py rename to nerfstudio/model_components/renderers.py diff --git a/nerfstudio/models/modules/scene_colliders.py b/nerfstudio/model_components/scene_colliders.py similarity index 98% rename from nerfstudio/models/modules/scene_colliders.py rename to nerfstudio/model_components/scene_colliders.py index 88cc4786a..180970761 100644 --- a/nerfstudio/models/modules/scene_colliders.py +++ b/nerfstudio/model_components/scene_colliders.py @@ -23,7 +23,7 @@ from torch import nn from torchtyping import TensorType from nerfstudio.cameras.rays import RayBundle -from nerfstudio.datamanagers.structs import SceneBounds +from nerfstudio.data.scene_box import SceneBounds class SceneBoundsCollider(nn.Module): diff --git a/nerfstudio/models/base.py b/nerfstudio/models/base.py index 7ec21adb2..46a979656 100644 --- a/nerfstudio/models/base.py +++ b/nerfstudio/models/base.py @@ -28,10 +28,10 @@ from torch import nn from torch.nn import Parameter from nerfstudio.cameras.rays import RayBundle -from nerfstudio.configs.base import InstantiateConfig -from nerfstudio.configs.utils import to_immutable_dict -from nerfstudio.datamanagers.structs import SceneBounds -from nerfstudio.models.modules.scene_colliders import NearFarCollider +from nerfstudio.configs.base_config import InstantiateConfig +from nerfstudio.configs.config_utils import to_immutable_dict +from nerfstudio.data.scene_box import SceneBounds +from nerfstudio.model_components.scene_colliders import NearFarCollider from nerfstudio.utils.callbacks import TrainingCallback, TrainingCallbackAttributes diff --git a/nerfstudio/models/instant_ngp.py b/nerfstudio/models/instant_ngp.py index c0bcae19d..153c54b86 100644 --- a/nerfstudio/models/instant_ngp.py +++ b/nerfstudio/models/instant_ngp.py @@ -30,16 +30,16 @@ from torchmetrics.functional import structural_similarity_index_measure from torchmetrics.image.lpip import LearnedPerceptualImagePatchSimilarity from nerfstudio.cameras.rays import RayBundle +from nerfstudio.field_components.field_heads import FieldHeadNames from nerfstudio.fields.instant_ngp_field import TCNNInstantNGPField -from nerfstudio.fields.modules.field_heads import FieldHeadNames -from nerfstudio.models.base import Model, ModelConfig -from nerfstudio.models.modules.ray_sampler import VolumetricSampler -from nerfstudio.optimizers.loss import MSELoss -from nerfstudio.renderers.renderers import ( +from nerfstudio.model_components.loss import MSELoss +from nerfstudio.model_components.ray_sampler import VolumetricSampler +from nerfstudio.model_components.renderers import ( AccumulationRenderer, DepthRenderer, RGBRenderer, ) +from nerfstudio.models.base import Model, ModelConfig from nerfstudio.utils import colors, visualization from nerfstudio.utils.callbacks import ( TrainingCallback, diff --git a/nerfstudio/models/mipnerf.py b/nerfstudio/models/mipnerf.py index d6a946da8..9180af324 100644 --- a/nerfstudio/models/mipnerf.py +++ b/nerfstudio/models/mipnerf.py @@ -26,17 +26,17 @@ from torchmetrics.functional import structural_similarity_index_measure from torchmetrics.image.lpip import LearnedPerceptualImagePatchSimilarity from nerfstudio.cameras.rays import RayBundle -from nerfstudio.fields.modules.encoding import NeRFEncoding -from nerfstudio.fields.modules.field_heads import FieldHeadNames +from nerfstudio.field_components.encoding import NeRFEncoding +from nerfstudio.field_components.field_heads import FieldHeadNames from nerfstudio.fields.nerf_field import NeRFField -from nerfstudio.models.base import Model, ModelConfig -from nerfstudio.models.modules.ray_sampler import PDFSampler, UniformSampler -from nerfstudio.optimizers.loss import MSELoss -from nerfstudio.renderers.renderers import ( +from nerfstudio.model_components.loss import MSELoss +from nerfstudio.model_components.ray_sampler import PDFSampler, UniformSampler +from nerfstudio.model_components.renderers import ( AccumulationRenderer, DepthRenderer, RGBRenderer, ) +from nerfstudio.models.base import Model, ModelConfig from nerfstudio.utils import colors, misc, visualization diff --git a/nerfstudio/models/mipnerf_360.py b/nerfstudio/models/mipnerf_360.py index a2cf57dd3..8455ad1ee 100644 --- a/nerfstudio/models/mipnerf_360.py +++ b/nerfstudio/models/mipnerf_360.py @@ -27,19 +27,19 @@ from torchmetrics.functional import structural_similarity_index_measure from torchmetrics.image.lpip import LearnedPerceptualImagePatchSimilarity from nerfstudio.cameras.rays import RayBundle -from nerfstudio.fields.modules.encoding import NeRFEncoding -from nerfstudio.fields.modules.field_heads import FieldHeadNames -from nerfstudio.fields.modules.spatial_distortions import SceneContraction +from nerfstudio.field_components.encoding import NeRFEncoding +from nerfstudio.field_components.field_heads import FieldHeadNames +from nerfstudio.field_components.spatial_distortions import SceneContraction from nerfstudio.fields.nerf_field import NeRFField -from nerfstudio.models.base import Model, ModelConfig -from nerfstudio.models.modules.ray_losses import distortion_loss -from nerfstudio.models.modules.ray_sampler import PDFSampler, UniformSampler -from nerfstudio.optimizers.loss import MSELoss -from nerfstudio.renderers.renderers import ( +from nerfstudio.model_components.loss import MSELoss +from nerfstudio.model_components.ray_losses import distortion_loss +from nerfstudio.model_components.ray_sampler import PDFSampler, UniformSampler +from nerfstudio.model_components.renderers import ( AccumulationRenderer, DepthRenderer, RGBRenderer, ) +from nerfstudio.models.base import Model, ModelConfig from nerfstudio.utils import colors, misc, visualization diff --git a/nerfstudio/models/nerfacto.py b/nerfstudio/models/nerfacto.py index 19b7513f0..bd0cf2e49 100644 --- a/nerfstudio/models/nerfacto.py +++ b/nerfstudio/models/nerfacto.py @@ -29,19 +29,19 @@ from torchmetrics.functional import structural_similarity_index_measure from torchmetrics.image.lpip import LearnedPerceptualImagePatchSimilarity from nerfstudio.cameras.rays import RayBundle -from nerfstudio.fields.compound_field import TCNNCompoundField -from nerfstudio.fields.density_field import DensityField -from nerfstudio.fields.modules.field_heads import FieldHeadNames -from nerfstudio.fields.modules.spatial_distortions import SceneContraction -from nerfstudio.models.base import Model, ModelConfig -from nerfstudio.models.modules.ray_sampler import ProposalNetworkSampler -from nerfstudio.models.modules.scene_colliders import NearFarCollider -from nerfstudio.optimizers.loss import MSELoss, distortion_loss, interlevel_loss -from nerfstudio.renderers.renderers import ( +from nerfstudio.field_components.field_heads import FieldHeadNames +from nerfstudio.field_components.spatial_distortions import SceneContraction +from nerfstudio.fields.density_fields import HashMLPDensityField +from nerfstudio.fields.nerfacto_field import TCNNNerfactoField +from nerfstudio.model_components.loss import MSELoss, distortion_loss, interlevel_loss +from nerfstudio.model_components.ray_sampler import ProposalNetworkSampler +from nerfstudio.model_components.renderers import ( AccumulationRenderer, DepthRenderer, RGBRenderer, ) +from nerfstudio.model_components.scene_colliders import NearFarCollider +from nerfstudio.models.base import Model, ModelConfig from nerfstudio.utils import colors, visualization from nerfstudio.utils.callbacks import ( TrainingCallback, @@ -104,7 +104,7 @@ class NerfactoModel(Model): # Fields if self.config.use_appearance_conditioning: - self.field = TCNNCompoundField( + self.field = TCNNNerfactoField( self.scene_bounds.aabb, spatial_distortion=scene_contraction, num_images=self.num_train_data, @@ -116,12 +116,12 @@ class NerfactoModel(Model): # Build the proposal network(s) self.proposal_networks = torch.nn.ModuleList() if self.config.use_same_proposal_network: - network = DensityField(self.scene_bounds.aabb, spatial_distortion=scene_contraction) + network = HashMLPDensityField(self.scene_bounds.aabb, spatial_distortion=scene_contraction) self.proposal_networks.append(network) self.density_fns = [network.density_fn for _ in range(self.config.num_proposal_network_iterations)] else: for _ in range(self.config.num_proposal_network_iterations): - network = DensityField(self.scene_bounds.aabb, spatial_distortion=scene_contraction) + network = HashMLPDensityField(self.scene_bounds.aabb, spatial_distortion=scene_contraction) self.proposal_networks.append(network) self.density_fns = [network.density_fn for network in self.proposal_networks] diff --git a/nerfstudio/models/nerfw.py b/nerfstudio/models/nerfw.py index 7e6959bfc..a2e2f5940 100644 --- a/nerfstudio/models/nerfw.py +++ b/nerfstudio/models/nerfw.py @@ -27,21 +27,21 @@ from torchmetrics.functional import structural_similarity_index_measure from torchmetrics.image.lpip import LearnedPerceptualImagePatchSimilarity from nerfstudio.cameras.rays import RayBundle -from nerfstudio.configs.utils import to_immutable_dict -from nerfstudio.fields.modules.encoding import NeRFEncoding -from nerfstudio.fields.modules.field_heads import FieldHeadNames +from nerfstudio.configs.config_utils import to_immutable_dict +from nerfstudio.field_components.encoding import NeRFEncoding +from nerfstudio.field_components.field_heads import FieldHeadNames from nerfstudio.fields.nerf_field import NeRFField from nerfstudio.fields.nerfw_field import VanillaNerfWField -from nerfstudio.models.base import Model, VanillaModelConfig -from nerfstudio.models.modules.ray_sampler import PDFSampler, UniformSampler -from nerfstudio.models.modules.scene_colliders import AABBBoxCollider -from nerfstudio.optimizers.loss import MSELoss -from nerfstudio.renderers.renderers import ( +from nerfstudio.model_components.loss import MSELoss +from nerfstudio.model_components.ray_sampler import PDFSampler, UniformSampler +from nerfstudio.model_components.renderers import ( AccumulationRenderer, DepthRenderer, RGBRenderer, UncertaintyRenderer, ) +from nerfstudio.model_components.scene_colliders import AABBBoxCollider +from nerfstudio.models.base import Model, VanillaModelConfig from nerfstudio.utils import colors, misc, visualization diff --git a/nerfstudio/models/semantic_nerf.py b/nerfstudio/models/semantic_nerf.py index 967d10beb..3bdccadec 100644 --- a/nerfstudio/models/semantic_nerf.py +++ b/nerfstudio/models/semantic_nerf.py @@ -23,15 +23,15 @@ import torch from torch import nn from nerfstudio.cameras.rays import RayBundle -from nerfstudio.datamanagers.structs import Semantics -from nerfstudio.fields.modules.encoding import NeRFEncoding -from nerfstudio.fields.modules.field_heads import FieldHeadNames +from nerfstudio.data.dataparsers.base_dataparser import Semantics +from nerfstudio.field_components.encoding import NeRFEncoding +from nerfstudio.field_components.field_heads import FieldHeadNames from nerfstudio.fields.nerf_field import NeRFField from nerfstudio.fields.semantic_nerf_field import SemanticNerfField +from nerfstudio.model_components.renderers import SemanticRenderer +from nerfstudio.model_components.scene_colliders import AABBBoxCollider from nerfstudio.models.base import ModelConfig -from nerfstudio.models.modules.scene_colliders import AABBBoxCollider from nerfstudio.models.vanilla_nerf import NeRFModel -from nerfstudio.renderers.renderers import SemanticRenderer from nerfstudio.utils import misc diff --git a/nerfstudio/models/tensorf.py b/nerfstudio/models/tensorf.py index 275ebf1d1..56a994a7f 100644 --- a/nerfstudio/models/tensorf.py +++ b/nerfstudio/models/tensorf.py @@ -29,19 +29,19 @@ from torchmetrics.functional import structural_similarity_index_measure from torchmetrics.image.lpip import LearnedPerceptualImagePatchSimilarity from nerfstudio.cameras.rays import RayBundle -from nerfstudio.configs.utils import to_immutable_dict -from nerfstudio.fields.modules.encoding import TensorVMEncoding -from nerfstudio.fields.modules.field_heads import FieldHeadNames +from nerfstudio.configs.config_utils import to_immutable_dict +from nerfstudio.engine.optimizers import Optimizers +from nerfstudio.field_components.encoding import TensorVMEncoding +from nerfstudio.field_components.field_heads import FieldHeadNames from nerfstudio.fields.nerf_field import NeRFField -from nerfstudio.models.base import Model, VanillaModelConfig -from nerfstudio.models.modules.ray_sampler import PDFSampler, UniformSampler -from nerfstudio.optimizers.loss import L1Loss, MSELoss -from nerfstudio.optimizers.optimizers import Optimizers -from nerfstudio.renderers.renderers import ( +from nerfstudio.model_components.loss import L1Loss, MSELoss +from nerfstudio.model_components.ray_sampler import PDFSampler, UniformSampler +from nerfstudio.model_components.renderers import ( AccumulationRenderer, DepthRenderer, RGBRenderer, ) +from nerfstudio.models.base import Model, VanillaModelConfig from nerfstudio.utils import colors, misc, visualization from nerfstudio.utils.callbacks import ( TrainingCallback, diff --git a/nerfstudio/models/vanilla_nerf.py b/nerfstudio/models/vanilla_nerf.py index 0b0bd9734..1b02e3e2d 100644 --- a/nerfstudio/models/vanilla_nerf.py +++ b/nerfstudio/models/vanilla_nerf.py @@ -27,17 +27,17 @@ from torchmetrics.functional import structural_similarity_index_measure from torchmetrics.image.lpip import LearnedPerceptualImagePatchSimilarity from nerfstudio.cameras.rays import RayBundle -from nerfstudio.fields.modules.encoding import NeRFEncoding -from nerfstudio.fields.modules.field_heads import FieldHeadNames +from nerfstudio.field_components.encoding import NeRFEncoding +from nerfstudio.field_components.field_heads import FieldHeadNames from nerfstudio.fields.nerf_field import NeRFField -from nerfstudio.models.base import Model, ModelConfig -from nerfstudio.models.modules.ray_sampler import PDFSampler, UniformSampler -from nerfstudio.optimizers.loss import MSELoss -from nerfstudio.renderers.renderers import ( +from nerfstudio.model_components.loss import MSELoss +from nerfstudio.model_components.ray_sampler import PDFSampler, UniformSampler +from nerfstudio.model_components.renderers import ( AccumulationRenderer, DepthRenderer, RGBRenderer, ) +from nerfstudio.models.base import Model, ModelConfig from nerfstudio.utils import colors, misc, visualization diff --git a/nerfstudio/optimizers/__init__.py b/nerfstudio/optimizers/__init__.py deleted file mode 100644 index 02655775e..000000000 --- a/nerfstudio/optimizers/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -# Copyright 2022 The Plenoptix Team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. diff --git a/nerfstudio/pipelines/base.py b/nerfstudio/pipelines/base.py index d9fb4d725..58b17c6fe 100644 --- a/nerfstudio/pipelines/base.py +++ b/nerfstudio/pipelines/base.py @@ -36,8 +36,8 @@ from torch import nn from torch.nn import Parameter from torch.nn.parallel import DistributedDataParallel as DDP -from nerfstudio.configs import base as cfg -from nerfstudio.datamanagers.base import ( +from nerfstudio.configs import base_config as cfg +from nerfstudio.data.datamanagers import ( DataManager, VanillaDataManager, VanillaDataManagerConfig, diff --git a/nerfstudio/pipelines/dynamic_batch.py b/nerfstudio/pipelines/dynamic_batch.py index 8c2bdebbf..c76e6b7d0 100644 --- a/nerfstudio/pipelines/dynamic_batch.py +++ b/nerfstudio/pipelines/dynamic_batch.py @@ -21,7 +21,7 @@ from typing import Type import torch -from nerfstudio.datamanagers.base import VanillaDataManager +from nerfstudio.data.datamanagers import VanillaDataManager from nerfstudio.pipelines.base import VanillaPipeline, VanillaPipelineConfig diff --git a/nerfstudio/renderers/__init__.py b/nerfstudio/renderers/__init__.py deleted file mode 100644 index 02655775e..000000000 --- a/nerfstudio/renderers/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -# Copyright 2022 The Plenoptix Team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. diff --git a/nerfstudio/utils/callbacks.py b/nerfstudio/utils/callbacks.py index 3a8dce769..960836b54 100644 --- a/nerfstudio/utils/callbacks.py +++ b/nerfstudio/utils/callbacks.py @@ -21,7 +21,7 @@ from enum import Enum, auto from inspect import signature from typing import Callable, Dict, List, Optional, Tuple -from nerfstudio.configs.base import TrainerConfig +from nerfstudio.configs.base_config import TrainerConfig @dataclass diff --git a/nerfstudio/utils/profiler.py b/nerfstudio/utils/profiler.py index 4b840d618..58211e92c 100644 --- a/nerfstudio/utils/profiler.py +++ b/nerfstudio/utils/profiler.py @@ -21,7 +21,7 @@ import logging import time from typing import Callable -from nerfstudio.configs import base as cfg +from nerfstudio.configs import base_config as cfg from nerfstudio.utils import comms from nerfstudio.utils.decorators import ( check_main_thread, diff --git a/nerfstudio/utils/writer.py b/nerfstudio/utils/writer.py index 02b622443..5b6005f91 100644 --- a/nerfstudio/utils/writer.py +++ b/nerfstudio/utils/writer.py @@ -30,7 +30,7 @@ from rich import console from torch.utils.tensorboard import SummaryWriter from torchtyping import TensorType -from nerfstudio.configs import base as cfg +from nerfstudio.configs import base_config as cfg from nerfstudio.utils.decorators import check_main_thread, decorate_all from nerfstudio.utils.misc import human_format diff --git a/nerfstudio/viewer/server/viewer_utils.py b/nerfstudio/viewer/server/viewer_utils.py index d6e36134b..ce67f609e 100644 --- a/nerfstudio/viewer/server/viewer_utils.py +++ b/nerfstudio/viewer/server/viewer_utils.py @@ -30,8 +30,8 @@ from rich.console import Console from nerfstudio.cameras.cameras import Cameras from nerfstudio.cameras.rays import RayBundle -from nerfstudio.configs import base as cfg -from nerfstudio.datamanagers.datasets import InputDataset +from nerfstudio.configs import base_config as cfg +from nerfstudio.data.utils.datasets import InputDataset from nerfstudio.models.base import Model from nerfstudio.utils import profiler, visualization, writer from nerfstudio.utils.io import load_from_json, write_to_json diff --git a/scripts/eval.py b/scripts/eval.py index ce9bbd161..9bfe8c338 100755 --- a/scripts/eval.py +++ b/scripts/eval.py @@ -28,7 +28,7 @@ from typing_extensions import assert_never from nerfstudio.cameras.camera_paths import get_path_from_json, get_spiral_path from nerfstudio.cameras.cameras import Cameras -from nerfstudio.configs import base as cfg +from nerfstudio.configs import base_config as cfg from nerfstudio.pipelines.base import Pipeline from nerfstudio.utils.rich import ItersPerSecColumn diff --git a/scripts/train.py b/scripts/train.py index 27e024cf1..fbabee9c3 100755 --- a/scripts/train.py +++ b/scripts/train.py @@ -19,8 +19,8 @@ import torch.distributed as dist import torch.multiprocessing as mp import yaml -from nerfstudio.configs import base as cfg -from nerfstudio.configs.base_configs import AnnotatedBaseConfigUnion +from nerfstudio.configs import base_config as cfg +from nerfstudio.configs.model_configs import AnnotatedBaseConfigUnion from nerfstudio.engine.trainer import train_loop from nerfstudio.utils import comms, profiler diff --git a/scripts/viewer/view_dataset.py b/scripts/viewer/view_dataset.py index 4353422b2..f5ee45940 100644 --- a/scripts/viewer/view_dataset.py +++ b/scripts/viewer/view_dataset.py @@ -12,8 +12,8 @@ import dcargs import torch from nerfstudio.configs import base as cfg -from nerfstudio.datamanagers.base import AnnotatedDataParserUnion -from nerfstudio.datamanagers.datasets import InputDataset +from nerfstudio.data.base import AnnotatedDataParserUnion +from nerfstudio.data.datasets import InputDataset from nerfstudio.viewer.server import viewer_utils logging.basicConfig(format="[%(filename)s:%(lineno)d] %(message)s", level=logging.INFO) diff --git a/tests/fields/modules/test_embedding.py b/tests/fields/modules/test_embedding.py index 52a8fb773..11ba7d5a9 100644 --- a/tests/fields/modules/test_embedding.py +++ b/tests/fields/modules/test_embedding.py @@ -1,7 +1,7 @@ """ Embedding tests """ -from nerfstudio.fields.modules.embedding import Embedding +from nerfstudio.field_components.embedding import Embedding def test_indexing(): diff --git a/tests/fields/modules/test_encodings.py b/tests/fields/modules/test_encodings.py index 497970841..3b8b350d9 100644 --- a/tests/fields/modules/test_encodings.py +++ b/tests/fields/modules/test_encodings.py @@ -4,7 +4,7 @@ Encoding Tests import pytest import torch -from nerfstudio.fields.modules import encoding +from nerfstudio.field_components import encoding def test_scaling_and_offset(): diff --git a/tests/fields/modules/test_field_outputs.py b/tests/fields/modules/test_field_outputs.py index 27e7789a6..08aa236a2 100644 --- a/tests/fields/modules/test_field_outputs.py +++ b/tests/fields/modules/test_field_outputs.py @@ -5,7 +5,7 @@ import pytest import torch from torch import nn -from nerfstudio.fields.modules.field_heads import ( +from nerfstudio.field_components.field_heads import ( DensityFieldHead, FieldHead, FieldHeadNames, diff --git a/tests/fields/modules/test_mlp.py b/tests/fields/modules/test_mlp.py index 89055a66e..e54c38a54 100644 --- a/tests/fields/modules/test_mlp.py +++ b/tests/fields/modules/test_mlp.py @@ -4,7 +4,7 @@ MLP Test import torch from torch import nn -from nerfstudio.fields.modules import MLP +from nerfstudio.field_components import MLP def test_mlp(): diff --git a/tests/graphs/modules/test_ray_sampler.py b/tests/graphs/modules/test_ray_sampler.py index b927d5cbc..577334421 100644 --- a/tests/graphs/modules/test_ray_sampler.py +++ b/tests/graphs/modules/test_ray_sampler.py @@ -5,14 +5,14 @@ Test samplers import torch from nerfstudio.cameras.rays import RayBundle -from nerfstudio.models.modules.ray_sampler import ( +from nerfstudio.model_components.ray_sampler import ( LinearDisparitySampler, LogSampler, PDFSampler, SqrtSampler, UniformSampler, ) -from nerfstudio.models.modules.scene_colliders import NearFarCollider +from nerfstudio.model_components.scene_colliders import NearFarCollider def test_uniform_sampler(): diff --git a/tests/renderers/test_renderers.py b/tests/renderers/test_renderers.py index bcc811ed5..053b4936d 100644 --- a/tests/renderers/test_renderers.py +++ b/tests/renderers/test_renderers.py @@ -5,7 +5,7 @@ import pytest import torch from nerfstudio.cameras.rays import Frustums, RaySamples -from nerfstudio.renderers import renderers +from nerfstudio.model_components import renderers def test_rgb_renderer(): diff --git a/tests/test_run_train.py b/tests/test_run_train.py index cae0906b6..170e56909 100644 --- a/tests/test_run_train.py +++ b/tests/test_run_train.py @@ -9,9 +9,9 @@ from pathlib import Path import pytest -from nerfstudio.configs.base import Config -from nerfstudio.configs.base_configs import base_configs -from nerfstudio.datamanagers.dataparsers.blender_parser import BlenderDataParserConfig +from nerfstudio.configs.base_config import Config +from nerfstudio.configs.model_configs import model_configs +from nerfstudio.data.dataparsers.blender_dataparser import BlenderDataParserConfig from nerfstudio.engine.trainer import train_loop BLACKLIST = ["base", "semantic-nerf", "mipnerf-360", "instant-ngp", "nerfacto"] @@ -48,13 +48,13 @@ def set_reduced_config(config: Config): @pytest.mark.filterwarnings("ignore::DeprecationWarning") def test_train(): """test run train script works properly""" - all_config_names = base_configs.keys() + all_config_names = model_configs.keys() for config_name in all_config_names: if config_name in BLACKLIST: print("skipping", config_name) continue print(f"testing run for: {config_name}") - config = base_configs[config_name] + config = model_configs[config_name] config = set_reduced_config(config) train_loop(local_rank=0, world_size=0, config=config)