From 457788d689cc3b8b75e165a1c34d918fa9a07f49 Mon Sep 17 00:00:00 2001 From: Ethan Weber Date: Wed, 8 Jun 2022 09:01:56 -0700 Subject: [PATCH] dataloader logic, new folder structure, flatter configs (#26) * renaming dataloader to data. adding some dataloader classes * restructuring configs * fixed instant ngp tcnn direction rescaling * working through test cases * black and lint and tests --- .gitignore | 3 +- .vscode/launch.json | 6 +- README.md | 36 +- configs/data/default_data.yaml | 19 - .../blender_lego.yaml | 0 .../friends_TBBT-big_living_room.yaml | 0 .../instant_ngp_fox.yaml | 0 configs/default_setup.yaml | 23 - configs/graph_default.yaml | 97 + configs/graph_instant_ngp.yaml | 26 + configs/graph_nerfw.yaml | 34 + configs/graph_semantic_nerf.yaml | 33 + configs/graph_vanilla_nerf.yaml | 4 + configs/instant_ngp.yaml | 20 - configs/instant_ngp_tcnn.yaml | 14 - configs/logging/default_logging.yaml | 13 - configs/nerfw.yaml | 25 - configs/semantic_nerf.yaml | 24 - configs/tests/test_dryrun.yaml | 29 +- configs/vanilla_nerf.yaml | 41 - docs/quickstart/quick_tour.md | 20 +- docs/reference/dataset.rst | 8 +- docs/reference/fields.rst | 6 +- .../modules}/embeddings.rst | 2 +- .../modules}/encodings.rst | 2 +- .../modules}/field_heads.rst | 2 +- .../modules}/field_losses.rst | 2 +- .../modules}/index.rst | 0 .../{field_modules => fields/modules}/mlp.rst | 2 +- docs/reference/graphs.rst | 10 +- docs/reference/graphs/modules/index.rst | 10 + docs/reference/graphs/modules/ray_sampler.rst | 8 + docs/reference/index.rst | 6 +- .../{renderers.rst => renderers/index.rst} | 2 +- docs/reference/samplers.rst | 25 - notebooks/visualize_cameras.ipynb | 39 +- notebooks/visualize_dataset.ipynb | 8046 ++++++++++++++++- notebooks/visualize_encoders.ipynb | 78 +- pyrad/{nerf => }/__init__.py | 0 pyrad/{structures => cameras}/cameras.py | 61 +- pyrad/{structures => cameras}/rays.py | 21 +- pyrad/{nerf/dataset => data}/__init__.py | 0 pyrad/{nerf/dataset => data}/colmap_utils.py | 0 pyrad/data/dataloader.py | 118 + .../{nerf/dataset => data}/format/__init__.py | 0 .../{nerf/dataset => data}/format/blender.py | 12 +- .../{nerf/dataset => data}/format/friends.py | 4 +- .../dataset => data}/format/instant_ngp.py | 2 +- pyrad/{nerf/dataset => data}/format/llff.py | 0 pyrad/{nerf/dataset => data}/image_dataset.py | 2 +- pyrad/{nerf => data}/image_sampler.py | 8 +- pyrad/{nerf => data}/pixel_sampler.py | 13 +- pyrad/{nerf/dataset => data}/structs.py | 4 + pyrad/{nerf/dataset => data}/utils.py | 10 +- pyrad/{nerf/fields => engine}/__init__.py | 0 .../field_losses.py => engine/base.py} | 0 pyrad/{nerf => engine}/trainer.py | 134 +- pyrad/{sfm => fields}/__init__.py | 0 pyrad/{nerf => }/fields/base.py | 4 +- pyrad/{nerf => }/fields/instant_ngp_field.py | 33 +- .../modules}/__init__.py | 0 .../field_modules => fields/modules}/base.py | 0 .../modules}/embedding.py | 2 +- .../modules}/encoding.py | 12 +- .../modules}/field_heads.py | 2 +- .../modules/field_losses.py} | 0 .../field_modules => fields/modules}/mlp.py | 2 +- pyrad/{nerf => }/fields/nerf_field.py | 10 +- pyrad/{nerf => }/fields/nerfw_field.py | 12 +- pyrad/fields/occupancy_fields/base.py | 0 .../occupancy_fields}/occupancy_grid.py | 0 pyrad/graphs/__init__.py | 0 pyrad/{nerf/graph => graphs}/base.py | 58 +- pyrad/{nerf/graph => graphs}/instant_ngp.py | 26 +- pyrad/graphs/modules/__init__.py | 0 .../{nerf => graphs/modules}/ray_generator.py | 4 +- pyrad/{nerf => graphs/modules}/ray_sampler.py | 4 +- .../modules/scene_colliders.py} | 4 +- pyrad/{nerf/graph => graphs}/nerfw.py | 26 +- pyrad/{nerf/graph => graphs}/semantic_nerf.py | 32 +- pyrad/{nerf/graph => graphs}/tensorf.py | 0 pyrad/{nerf/graph => graphs}/vanilla_nerf.py | 25 +- pyrad/optimizers/__init__.py | 0 pyrad/{nerf => optimizers}/loss.py | 0 pyrad/{nerf => optimizers}/optimizers.py | 0 pyrad/renderers/base.py | 0 pyrad/{nerf => renderers}/renderers.py | 0 pyrad/utils/callbacks.py | 4 +- pyrad/{structures => utils}/colors.py | 0 pyrad/utils/math.py | 2 +- pyrad/utils/writer.py | 4 +- pyrad/viewer/plotly.py | 2 +- scripts/benchmarking/run_benchmark.py | 2 +- scripts/run_data_preprocessor.py | 2 +- scripts/run_eval.py | 88 + scripts/run_train.py | 8 +- tests/{structures => cameras}/test_cameras.py | 8 +- .../modules}/test_embedding.py | 2 +- .../modules}/test_encodings.py | 2 +- .../modules}/test_field_outputs.py | 2 +- .../modules}/test_mlp.py | 2 +- tests/{nerf => fields}/test_fields.py | 12 +- .../modules}/test_occupancy_grid.py | 2 +- .../modules/test_ray_sampler.py} | 6 +- tests/{nerf => renderers}/test_renderers.py | 2 +- 105 files changed, 8828 insertions(+), 682 deletions(-) delete mode 100644 configs/data/default_data.yaml rename configs/{data/dataset => dataset_inputs}/blender_lego.yaml (100%) rename configs/{data/dataset => dataset_inputs}/friends_TBBT-big_living_room.yaml (100%) rename configs/{data/dataset => dataset_inputs}/instant_ngp_fox.yaml (100%) delete mode 100644 configs/default_setup.yaml create mode 100644 configs/graph_default.yaml create mode 100644 configs/graph_instant_ngp.yaml create mode 100644 configs/graph_nerfw.yaml create mode 100644 configs/graph_semantic_nerf.yaml create mode 100644 configs/graph_vanilla_nerf.yaml delete mode 100644 configs/instant_ngp.yaml delete mode 100644 configs/instant_ngp_tcnn.yaml delete mode 100644 configs/logging/default_logging.yaml delete mode 100644 configs/nerfw.yaml delete mode 100644 configs/semantic_nerf.yaml delete mode 100644 configs/vanilla_nerf.yaml rename docs/reference/{field_modules => fields/modules}/embeddings.rst (61%) rename docs/reference/{field_modules => fields/modules}/encodings.rst (61%) rename docs/reference/{field_modules => fields/modules}/field_heads.rst (61%) rename docs/reference/{field_modules => fields/modules}/field_losses.rst (61%) rename docs/reference/{field_modules => fields/modules}/index.rst (100%) rename docs/reference/{field_modules => fields/modules}/mlp.rst (60%) create mode 100644 docs/reference/graphs/modules/index.rst create mode 100644 docs/reference/graphs/modules/ray_sampler.rst rename docs/reference/{renderers.rst => renderers/index.rst} (64%) delete mode 100644 docs/reference/samplers.rst rename pyrad/{nerf => }/__init__.py (100%) rename pyrad/{structures => cameras}/cameras.py (85%) rename pyrad/{structures => cameras}/rays.py (94%) rename pyrad/{nerf/dataset => data}/__init__.py (100%) rename pyrad/{nerf/dataset => data}/colmap_utils.py (100%) create mode 100644 pyrad/data/dataloader.py rename pyrad/{nerf/dataset => data}/format/__init__.py (100%) rename pyrad/{nerf/dataset => data}/format/blender.py (83%) rename pyrad/{nerf/dataset => data}/format/friends.py (97%) rename pyrad/{nerf/dataset => data}/format/instant_ngp.py (97%) rename pyrad/{nerf/dataset => data}/format/llff.py (100%) rename pyrad/{nerf/dataset => data}/image_dataset.py (99%) rename pyrad/{nerf => data}/image_sampler.py (92%) rename pyrad/{nerf => data}/pixel_sampler.py (85%) rename pyrad/{nerf/dataset => data}/structs.py (95%) rename pyrad/{nerf/dataset => data}/utils.py (92%) rename pyrad/{nerf/fields => engine}/__init__.py (100%) rename pyrad/{nerf/field_modules/field_losses.py => engine/base.py} (100%) rename pyrad/{nerf => engine}/trainer.py (64%) rename pyrad/{sfm => fields}/__init__.py (100%) rename pyrad/{nerf => }/fields/base.py (96%) rename pyrad/{nerf => }/fields/instant_ngp_field.py (82%) rename pyrad/{nerf/field_modules => fields/modules}/__init__.py (100%) rename pyrad/{nerf/field_modules => fields/modules}/base.py (100%) rename pyrad/{nerf/field_modules => fields/modules}/embedding.py (93%) rename pyrad/{nerf/field_modules => fields/modules}/encoding.py (97%) rename pyrad/{nerf/field_modules => fields/modules}/field_heads.py (99%) rename pyrad/{structures/__init__.py => fields/modules/field_losses.py} (100%) rename pyrad/{nerf/field_modules => fields/modules}/mlp.py (98%) rename pyrad/{nerf => }/fields/nerf_field.py (91%) rename pyrad/{nerf => }/fields/nerfw_field.py (93%) create mode 100644 pyrad/fields/occupancy_fields/base.py rename pyrad/{nerf => fields/occupancy_fields}/occupancy_grid.py (100%) create mode 100644 pyrad/graphs/__init__.py rename pyrad/{nerf/graph => graphs}/base.py (75%) rename pyrad/{nerf/graph => graphs}/instant_ngp.py (88%) create mode 100644 pyrad/graphs/modules/__init__.py rename pyrad/{nerf => graphs/modules}/ray_generator.py (95%) rename pyrad/{nerf => graphs/modules}/ray_sampler.py (98%) rename pyrad/{nerf/colliders.py => graphs/modules/scene_colliders.py} (97%) rename pyrad/{nerf/graph => graphs}/nerfw.py (90%) rename pyrad/{nerf/graph => graphs}/semantic_nerf.py (86%) rename pyrad/{nerf/graph => graphs}/tensorf.py (100%) rename pyrad/{nerf/graph => graphs}/vanilla_nerf.py (90%) create mode 100644 pyrad/optimizers/__init__.py rename pyrad/{nerf => optimizers}/loss.py (100%) rename pyrad/{nerf => optimizers}/optimizers.py (100%) create mode 100644 pyrad/renderers/base.py rename pyrad/{nerf => renderers}/renderers.py (100%) rename pyrad/{structures => utils}/colors.py (100%) create mode 100644 scripts/run_eval.py rename tests/{structures => cameras}/test_cameras.py (84%) rename tests/{nerf/field_modules => fields/modules}/test_embedding.py (77%) rename tests/{nerf/field_modules => fields/modules}/test_encodings.py (98%) rename tests/{nerf/field_modules => fields/modules}/test_field_outputs.py (96%) rename tests/{nerf/field_modules => fields/modules}/test_mlp.py (92%) rename tests/{nerf => fields}/test_fields.py (76%) rename tests/{nerf => graphs/modules}/test_occupancy_grid.py (89%) rename tests/{nerf/test_sampler.py => graphs/modules/test_ray_sampler.py} (89%) rename tests/{nerf => renderers}/test_renderers.py (98%) diff --git a/.gitignore b/.gitignore index 51ac32dce..0e88489f4 100644 --- a/.gitignore +++ b/.gitignore @@ -164,8 +164,9 @@ runs/ outputs/ # Data -data/ +data data_*/ +!pyrad/data !configs/data # Misc diff --git a/.vscode/launch.json b/.vscode/launch.json index 6ee58033a..983bee715 100644 --- a/.vscode/launch.json +++ b/.vscode/launch.json @@ -46,7 +46,8 @@ "program": "scripts/run_train.py", "console": "integratedTerminal", "args": [ - "--config-name=nerfw.yaml", "++data.dataset.use_cache=true" + "--config-name=graph_nerfw.yaml", + "++data.dataset.use_cache=true" ] }, { @@ -56,7 +57,8 @@ "program": "scripts/run_train.py", "console": "integratedTerminal", "args": [ - "--config-name=semantic_nerf.yaml" + "--config-name=graph_semantic_nerf.yaml", + "++data.dataset.use_cache=true" ] }, ] diff --git a/README.md b/README.md index f4f0b3482..6520404dd 100644 --- a/README.md +++ b/README.md @@ -13,17 +13,17 @@

- -* [Quickstart](#quickstart) -* [Walk-through tour](#walk-through-tour) -* [Feature List](#feature-list) -* [Benchmarked Model Architectures](#benchmarked-model-architectures) +- [Quickstart](#quickstart) +- [Walk-through tour](#walk-through-tour) +- [Feature List](#feature-list) +- [Benchmarked Model Architectures](#benchmarked-model-architectures) # Quickstart #### 1. Installation: Setup the environment This repository is tested with cuda 11.3 + ``` # Clone the repo git clone --recurse-submodules git@github.com:ethanweber/pyrad.git @@ -79,14 +79,16 @@ python scripts/run_train.py ++data.dataset.use_cache=true ``` # Walk-through tour + In this quick tour, we will walk you through the core of training and building any NeRFs with pyrad. #### Launching a training job + The entry point for training starts at `scripts/run_train.py`, which spawns instances of our `Trainer()` class (in `nerf/trainer.py`). The `Trainer()` is responsible for setting up the datasets and NeRF graph depending on the config specified. It will then run the usual train/val routine for a config-specified number of iterations. If you are planning on using our codebase to build a new NeRF method or to use an existing implementation, we've abstracted away the training routine in these two files and chances are you will not need to think of them again. #### Graphs, Fields, and Modules -If you are looking to implemnet a new NeRF method or extend an existing one, you only need to edit files in `nerf/graph/`, `nerf/fields/`, `nerf/field_modules/`, `nerf/misc_modules/`. (TODO: restructuring) +If you are looking to implemnet a new NeRF method or extend an existing one, you only need to edit files in `nerf/graph/`, `nerf/fields/`, `nerf/field_modules/`, `nerf/misc_modules/`. (TODO: restructuring) The actual NeRF graph definitions can be found in `nerf/graph/`. For instance, to implement the vanilla NeRF, we create a new class that inherits the abstract Graph class. To fully implement the any new graph class, you will need to implement the following abstract methods defined in the skeleton code below. See also `nerf/graph/vanilla_nerf.py` for the full implementation. @@ -116,7 +118,7 @@ class NeRFGraph(Graph): """ Takes in a Ray Bundle and returns a dictionary of outputs. """ - + def get_loss_dict(self, outputs, batch): """ Computes and returns the losses. @@ -128,16 +130,17 @@ class NeRFGraph(Graph): """ ``` -Note that the graph is composed of fields and modules. +Note that the graph is composed of fields and modules. -**Fields** (`nerf/fields/`) represents the actual radiance field of the NeRF and is composed of field modules (`nerf/field_modules/`). 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`). The **misc. modules** can be any module outside of the field that are needed by the NeRF (e.g. losses, samplers, renderers). +**Fields** (`nerf/fields/`) represents the actual radiance field of the NeRF and is composed of field modules (`nerf/field_modules/`). 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`). The **misc. modules** can be any module outside of the field that are needed by the NeRF (e.g. losses, samplers, renderers). -To get started on a new NeRF implementation, you simply have to define all relevant modules and populate them in the graph. +To get started on a new NeRF implementation, you simply have to define all relevant modules and populate them in the graph. #### Dataset population TODO(ethan) -#### Config -Now that you have the graph and dataset all set up, you're ready to create the config that you pass into our run train routine. Our config system is powered by [Hydra](https://hydra.cc/). All Hydra and machine related arguments are stored in `configs/default_setup.yaml`, as well as the defaults list. +#### Config + +Now that you have the graph and dataset all set up, you're ready to create the config that you pass into our run train routine. Our config system is powered by [Hydra](https://hydra.cc/). All Hydra and machine related arguments are stored in `configs/default_setup.yaml`, as well as the defaults list. To set up the graph config, create a new yaml under `configs/`. ``` @@ -152,7 +155,7 @@ method_name: vanilla_nerf graph: network: - _target_: pyrad.nerf.graph.vanilla_nerf.NeRFGraph # set the target to the graph you defined + _target_: pyrad.graph.vanilla_nerf.NeRFGraph # set the target to the graph you defined # @@ -166,10 +169,12 @@ Once you have the config properly set up, you can begin training! Note, you can python scripts/run_train.py --config-name vanilla_nerf ``` - # Feature List + #### :metal: [Hydra config structure](#) + #### :metal: [Logging, debugging utilities](#) + #### :metal: [Benchmarking, other tooling](#) #### :metal: Running other repos with our data @@ -186,6 +191,7 @@ python -m jaxnerf.train --data_dir=/path/to/pyrad/data/blender/chair --train_dir ``` #### :metal: Speeding up the code + Documentation for running the code with CUDA. Please see https://github.com/NVlabs/tiny-cuda-nn for how to install tiny-cuda-nn. @@ -199,7 +205,6 @@ To run instant-ngp with tcnn, you can do the following. This is with the fox dat python scripts/run_train.py --config-name=instant_ngp_tcnn.yaml data/dataset=instant_ngp_fox ``` - #### :metal: Setting up Jupyter ``` @@ -208,6 +213,7 @@ bash environments/run_jupyter.sh ``` # Benchmarked Model Architectures + | Method | PSNR | | --------------------------------------------------------------------------------- | ------------------------ | | [NeRF](https://arxiv.org/abs/2003.08934) | :hourglass_flowing_sand: | diff --git a/configs/data/default_data.yaml b/configs/data/default_data.yaml deleted file mode 100644 index 839c8b24e..000000000 --- a/configs/data/default_data.yaml +++ /dev/null @@ -1,19 +0,0 @@ -defaults: - # dataset options - - dataset: blender_lego - -# validation options -val_image_indices: [0, 10, 20] # which images to evaluate on -val_downscale_factor: 4 -val_num_rays_per_chunk: 4096 - -image_dataset: - _target_: pyrad.nerf.dataset.image_dataset.ImageDataset - -image_sampler: - num_images_to_sample_from: 8 # 0 - num_times_to_repeat_images: 100 # -1 - move_to_graph_device: true # false - -pixel_sampler: - num_rays_per_batch: 1024 diff --git a/configs/data/dataset/blender_lego.yaml b/configs/dataset_inputs/blender_lego.yaml similarity index 100% rename from configs/data/dataset/blender_lego.yaml rename to configs/dataset_inputs/blender_lego.yaml diff --git a/configs/data/dataset/friends_TBBT-big_living_room.yaml b/configs/dataset_inputs/friends_TBBT-big_living_room.yaml similarity index 100% rename from configs/data/dataset/friends_TBBT-big_living_room.yaml rename to configs/dataset_inputs/friends_TBBT-big_living_room.yaml diff --git a/configs/data/dataset/instant_ngp_fox.yaml b/configs/dataset_inputs/instant_ngp_fox.yaml similarity index 100% rename from configs/data/dataset/instant_ngp_fox.yaml rename to configs/dataset_inputs/instant_ngp_fox.yaml diff --git a/configs/default_setup.yaml b/configs/default_setup.yaml deleted file mode 100644 index 7fc61d012..000000000 --- a/configs/default_setup.yaml +++ /dev/null @@ -1,23 +0,0 @@ -defaults: - # This makes the logging module logs colorful - - data: default_data.yaml - - logging: default_logging.yaml - - override hydra/job_logging: colorlog - - override hydra/hydra_logging: colorlog - - _self_ - -seed: 42 - -hydra: - run: - # Configure output dir of each experiment programmatically from the arguments - # Example "outputs/mnist/classifier/baseline/2021-03-10-141516" - dir: outputs/${experiment_name}/${method_name}/${now:%Y-%m-%d_%H%M%S} - -# machine options -machine_config: - num_gpus: 1 - num_machines: 1 - machine_rank: 0 - dist_url: auto - diff --git a/configs/graph_default.yaml b/configs/graph_default.yaml new file mode 100644 index 000000000..3f86c327e --- /dev/null +++ b/configs/graph_default.yaml @@ -0,0 +1,97 @@ +defaults: + - override hydra/job_logging: colorlog + - override hydra/hydra_logging: colorlog + - _self_ + +machine_config: + seed: 42 + num_gpus: 1 + num_machines: 1 + machine_rank: 0 + dist_url: auto + +logging: + steps_per_log: 10 + max_buffer_size: 20 + # writer logs losses and images per iteration + writer: + TensorboardWriter: + save_dir: "./" + LocalWriter: + save_dir: "./" + stats_to_track: + [ITER_LOAD_TIME, ITER_TRAIN_TIME, RAYS_PER_SEC, CURR_TEST_PSNR] + max_log_size: 10 # if 0, logs everything with no erasing + # profiler logs run times of functions and prints at end of training + enable_profiler: True + +experiment_name: blender_lego +method_name: vanilla_nerf + +# hydra settings +hydra: + run: + dir: outputs/${experiment_name}/${method_name}/${now:%Y-%m-%d_%H%M%S} + +data: + # Train dataloading. + dataset_inputs_train: + data_directory: data/blender/lego + dataset_format: blender + downscale_factor: 1 + alpha_color: white + image_dataset_train: + _target_: pyrad.data.image_dataset.ImageDataset + dataloader_train: + _target_: pyrad.data.dataloader.TrainDataloader + image_sampler: + _target_: pyrad.data.image_sampler.CacheImageSampler + num_images_to_sample_from: 8 + num_times_to_repeat_images: 100 + pixel_sampler: + _target_: pyrad.data.pixel_sampler.PixelSampler + num_rays_per_batch: 1024 + # Eval dataloading. + dataset_inputs_eval: + data_directory: data/blender/lego + dataset_format: blender + downscale_factor: 1 # NOTE: this might be the only value that changes + alpha_color: white + image_dataset_eval: + _target_: pyrad.data.image_dataset.ImageDataset + dataloader_eval: + _target_: pyrad.data.dataloader.FixedIndicesEvalDataloader + image_indices: [0] # which images to evaluate on + num_rays_per_chunk: 4096 + # dataloader_eval: + # _target_: pyrad.data.dataloader.RandIndicesEvalDataloader + # num_rays_per_chunk: 4096 + +graph: + model_dir: /tmp/pyrad_models/ + steps_per_save: 100 + steps_per_test: 1000 + max_num_iterations: 1000000 + resume_train: + load_dir: # pretrained model directory + load_step: # pretrained model's step number + network: + _target_: pyrad.graphs.vanilla_nerf.NeRFGraph + collider_config: + _target_: pyrad.graphs.modules.scene_colliders.NearFarCollider + near_plane: 2.0 + far_plane: 6.0 + num_coarse_samples: 64 + num_importance_samples: 128 + loss_coefficients: + rgb_loss_coarse: 1.0 + rgb_loss_fine: 1.0 + param_groups: + fields: + optimizer: + _target_: torch.optim.RAdam + lr: 0.0005 + scheduler: + _target_: pyrad.optimizers.optimizers.ExponentialDecaySchedule + lr_final: 0.000005 + max_steps: 1000000 diff --git a/configs/graph_instant_ngp.yaml b/configs/graph_instant_ngp.yaml new file mode 100644 index 000000000..1b55b5ba6 --- /dev/null +++ b/configs/graph_instant_ngp.yaml @@ -0,0 +1,26 @@ +defaults: + - graph_default + - _self_ + +method_name: instant_ngp +data: + pixel_sampler: + num_rays_per_batch: 4096 # for training + dataloader_eval: + num_rays_per_chunk: 16384 # for evaluation + +graph: + network: + _target_: pyrad.graphs.instant_ngp.NGPGraph + collider_config: + _target_: pyrad.graphs.modules.scene_colliders.AABBBoxCollider + field_implementation: tcnn # torch, tcnn, ... + loss_coefficients: # loss coefficients + rgb_loss: 1.0 + + # optimizer options for network + param_groups: + fields: + optimizer: + lr: 1e-2 + scheduler: null diff --git a/configs/graph_nerfw.yaml b/configs/graph_nerfw.yaml new file mode 100644 index 000000000..cf15b5e67 --- /dev/null +++ b/configs/graph_nerfw.yaml @@ -0,0 +1,34 @@ +defaults: + - graph_default + - _self_ + +experiment_name: friends_TBBT-big_living_room +method_name: nerfw + +# the data +data: + image_dataset_train: + _target_: pyrad.data.image_dataset.PanopticImageDataset + image_dataset_eval: + _target_: pyrad.data.image_dataset.PanopticImageDataset + dataset_inputs_train: + data_directory: data/friends/TBBT-big_living_room + dataset_format: friends + downscale_factor: 1 + dataset_inputs_eval: + data_directory: data/friends/TBBT-big_living_room + dataset_format: friends + downscale_factor: 1 + +graph: + network: + _target_: pyrad.graphs.nerfw.NerfWGraph + collider_config: + _target_: pyrad.graphs.modules.scene_colliders.AABBBoxCollider + num_coarse_samples: 64 + num_importance_samples: 64 + loss_coefficients: + rgb_loss_coarse: 1.0 + rgb_loss_fine: 1.0 + uncertainty_loss: 1.0 + density_loss: 0.01 diff --git a/configs/graph_semantic_nerf.yaml b/configs/graph_semantic_nerf.yaml new file mode 100644 index 000000000..f41ce90a2 --- /dev/null +++ b/configs/graph_semantic_nerf.yaml @@ -0,0 +1,33 @@ +defaults: + - graph_default + - _self_ + +experiment_name: friends_TBBT-big_living_room +method_name: nerfw + +# the data +data: + image_dataset_train: + _target_: pyrad.data.image_dataset.PanopticImageDataset + image_dataset_eval: + _target_: pyrad.data.image_dataset.PanopticImageDataset + dataset_inputs_train: + data_directory: data/friends/TBBT-big_living_room + dataset_format: friends + downscale_factor: 1 + dataset_inputs_eval: + data_directory: data/friends/TBBT-big_living_room + dataset_format: friends + downscale_factor: 1 + +graph: + network: + _target_: pyrad.graphs.semantic_nerf.SemanticNerfGraph + collider_config: + _target_: pyrad.graphs.modules.scene_colliders.AABBBoxCollider + num_coarse_samples: 64 + num_importance_samples: 64 + loss_coefficients: + rgb_loss_coarse: 1.0 + rgb_loss_fine: 1.0 + semantic_loss_fine: 0.05 diff --git a/configs/graph_vanilla_nerf.yaml b/configs/graph_vanilla_nerf.yaml new file mode 100644 index 000000000..d9ce55f91 --- /dev/null +++ b/configs/graph_vanilla_nerf.yaml @@ -0,0 +1,4 @@ +defaults: + - graph_default + - _self_ + diff --git a/configs/instant_ngp.yaml b/configs/instant_ngp.yaml deleted file mode 100644 index 43c62888d..000000000 --- a/configs/instant_ngp.yaml +++ /dev/null @@ -1,20 +0,0 @@ -defaults: - - vanilla_nerf - - _self_ - -method_name: instant_ngp -data.pixel_sampler.num_rays_per_batch: 4096 -data.val_num_rays_per_chunk: 16384 - -graph: - network: - _target_: pyrad.nerf.graph.instant_ngp.NGPGraph - field_implementation: torch # torch, tcnn, ... - loss_coefficients: # loss coefficients - rgb_loss: 1.0 - - # optimizer options for network - param_groups: - fields: - optimizer: - lr: 1e-2 \ No newline at end of file diff --git a/configs/instant_ngp_tcnn.yaml b/configs/instant_ngp_tcnn.yaml deleted file mode 100644 index bf16c7d40..000000000 --- a/configs/instant_ngp_tcnn.yaml +++ /dev/null @@ -1,14 +0,0 @@ -defaults: - - instant_ngp - - _self_ - -method_name: instant_ngp -data: - pixel_sampler: - num_rays_per_batch: 4096 - val_downscale_factor: 1 - val_num_rays_per_chunk: 16384 -graph: - network: - field_implementation: tcnn - steps_per_test: 10000 diff --git a/configs/logging/default_logging.yaml b/configs/logging/default_logging.yaml deleted file mode 100644 index d1873abec..000000000 --- a/configs/logging/default_logging.yaml +++ /dev/null @@ -1,13 +0,0 @@ -steps_per_log: 10 -max_buffer_size: 20 -# writer logs losses and images per iteration -writer: - TensorboardWriter: - save_dir: './' - LocalWriter: - save_dir: './' - stats_to_track: [ITER_LOAD_TIME, ITER_TRAIN_TIME, RAYS_PER_SEC, CURR_TEST_PSNR] - max_log_size: 10 # if 0, logs everything with no erasing - -# profiler logs run times of functions and prints at end of training -enable_profiler: True \ No newline at end of file diff --git a/configs/nerfw.yaml b/configs/nerfw.yaml deleted file mode 100644 index f91c37911..000000000 --- a/configs/nerfw.yaml +++ /dev/null @@ -1,25 +0,0 @@ -defaults: - - vanilla_nerf - - override data/dataset: friends_TBBT-big_living_room - - _self_ - -experiment_name: friends_TBBT-big_living_room -method_name: nerfw - -data: - image_dataset: - # "image", "mask", "semantics" - _target_: pyrad.nerf.dataset.image_dataset.PanopticImageDataset - -graph: - network: - _target_: pyrad.nerf.graph.nerfw.NerfWGraph - collider_config: - _target_: pyrad.nerf.colliders.AABBBoxCollider - num_coarse_samples: 64 - num_importance_samples: 64 - loss_coefficients: # loss coefficients - rgb_loss_coarse: 1.0 - rgb_loss_fine: 1.0 - uncertainty_loss: 1.0 - density_loss: 0.01 diff --git a/configs/semantic_nerf.yaml b/configs/semantic_nerf.yaml deleted file mode 100644 index 4e24131f7..000000000 --- a/configs/semantic_nerf.yaml +++ /dev/null @@ -1,24 +0,0 @@ -defaults: - - vanilla_nerf - - override data/dataset: friends_TBBT-big_living_room - - _self_ - -experiment_name: friends_TBBT-big_living_room -method_name: semantic_nerf - -data: - image_dataset: - # "image", "mask", "semantics" - _target_: pyrad.nerf.dataset.image_dataset.PanopticImageDataset - -graph: - network: - _target_: pyrad.nerf.graph.semantic_nerf.SemanticNerfGraph - collider_config: - _target_: pyrad.nerf.colliders.AABBBoxCollider - num_coarse_samples: 64 - num_importance_samples: 64 - loss_coefficients: # loss coefficients - rgb_loss_coarse: 1.0 - rgb_loss_fine: 1.0 - semantic_loss_fine: 0.05 diff --git a/configs/tests/test_dryrun.yaml b/configs/tests/test_dryrun.yaml index 8d3ced9f4..aafb23c16 100644 --- a/configs/tests/test_dryrun.yaml +++ b/configs/tests/test_dryrun.yaml @@ -1,24 +1,29 @@ defaults: - - vanilla_nerf - -dry_run: true + - graph_default machine_config: num_gpus: 0 data: - dataset: - dataset_format: blender + dataset_inputs_train: data_directory: tests/data/lego_test - downscale_factor: 8 - val_image_indices: [0] - image_sampler: - num_images_to_sample_from: 1 - pixel_sampler: - num_rays_per_batch: 4 + dataset_format: blender + downscale_factor: 16 + dataloader_train: + image_sampler: + num_images_to_sample_from: 1 + pixel_sampler: + num_rays_per_batch: 4 + dataset_inputs_eval: + data_directory: tests/data/lego_test + dataset_format: blender + downscale_factor: 16 graph: - max_num_iterations: 5 + max_num_iterations: 2 + network: + num_coarse_samples: 4 + num_importance_samples: 4 logging: writer: diff --git a/configs/vanilla_nerf.yaml b/configs/vanilla_nerf.yaml deleted file mode 100644 index d98181e2a..000000000 --- a/configs/vanilla_nerf.yaml +++ /dev/null @@ -1,41 +0,0 @@ -defaults: - - default_setup - - _self_ - -experiment_name: blender_lego -method_name: vanilla_nerf - -graph: - # saving model specifications - model_dir: /tmp/pyrad_models/ - steps_per_save: null - steps_per_test: 1000 - max_num_iterations: 1000000 - - # pretrained loading options - resume_train: - load_dir: # pretrained model directory - load_step: # pretrained model's step number - - network: - _target_: pyrad.nerf.graph.vanilla_nerf.NeRFGraph - collider_config: - _target_: pyrad.nerf.colliders.NearFarCollider - near_plane: 2.0 - far_plane: 6.0 - num_coarse_samples: 64 - num_importance_samples: 128 - loss_coefficients: # loss coefficients - rgb_loss_coarse: 1.0 - rgb_loss_fine: 1.0 - - # optimizer options for network - param_groups: - fields: - optimizer: - _target_: torch.optim.RAdam - lr: 0.0005 - scheduler: - _target_: pyrad.nerf.optimizers.ExponentialDecaySchedule - lr_final: 0.000005 - max_steps: 1000000 diff --git a/docs/quickstart/quick_tour.md b/docs/quickstart/quick_tour.md index 20cb7ef3e..f5364e508 100644 --- a/docs/quickstart/quick_tour.md +++ b/docs/quickstart/quick_tour.md @@ -1,12 +1,14 @@ # Walk-through tour + In this quick tour, we will walk you through the core of training and building any NeRFs with pyrad. #### Launching a training job + The entry point for training starts at `scripts/run_train.py`, which spawns instances of our `Trainer()` class (in `nerf/trainer.py`). The `Trainer()` is responsible for setting up the datasets and NeRF graph depending on the config specified. It will then run the usual train/val routine for a config-specified number of iterations. If you are planning on using our codebase to build a new NeRF method or to use an existing implementation, we've abstracted away the training routine in these two files and chances are you will not need to think of them again. #### Graphs, Fields, and Modules -If you are looking to implemnet a new NeRF method or extend an existing one, you only need to edit files in `nerf/graph/`, `nerf/fields/`, `nerf/field_modules/`, `nerf/misc_modules/`. (TODO: restructuring) +If you are looking to implemnet a new NeRF method or extend an existing one, you only need to edit files in `nerf/graph/`, `nerf/fields/`, `nerf/field_modules/`, `nerf/misc_modules/`. (TODO: restructuring) The actual NeRF graph definitions can be found in `nerf/graph/`. For instance, to implement the vanilla NeRF, we create a new class that inherits the abstract Graph class. To fully implement the any new graph class, you will need to implement the following abstract methods defined in the skeleton code below. See also `nerf/graph/vanilla_nerf.py` for the full implementation. @@ -36,7 +38,7 @@ class NeRFGraph(Graph): """ Takes in a Ray Bundle and returns a dictionary of outputs. """ - + def get_loss_dict(self, outputs, batch): """ Computes and returns the losses. @@ -48,16 +50,17 @@ class NeRFGraph(Graph): """ ``` -Note that the graph is composed of fields and modules. +Note that the graph is composed of fields and modules. -**Fields** (`nerf/fields/`) represents the actual radiance field of the NeRF and is composed of field modules (`nerf/field_modules/`). 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`). The **misc. modules** can be any module outside of the field that are needed by the NeRF (e.g. losses, samplers, renderers). +**Fields** (`nerf/fields/`) represents the actual radiance field of the NeRF and is composed of field modules (`nerf/field_modules/`). 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`). The **misc. modules** can be any module outside of the field that are needed by the NeRF (e.g. losses, samplers, renderers). -To get started on a new NeRF implementation, you simply have to define all relevant modules and populate them in the graph. +To get started on a new NeRF implementation, you simply have to define all relevant modules and populate them in the graph. #### Dataset population TODO(ethan) -#### Config -Now that you have the graph and dataset all set up, you're ready to create the config that you pass into our run train routine. Our config system is powered by [Hydra](https://hydra.cc/). All Hydra and machine related arguments are stored in `configs/default_setup.yaml`, as well as the defaults list. +#### Config + +Now that you have the graph and dataset all set up, you're ready to create the config that you pass into our run train routine. Our config system is powered by [Hydra](https://hydra.cc/). All Hydra and machine related arguments are stored in `configs/default_setup.yaml`, as well as the defaults list. To set up the graph config, create a new yaml under `configs/`. ``` @@ -72,7 +75,7 @@ method_name: vanilla_nerf graph: network: - _target_: pyrad.nerf.graph.vanilla_nerf.NeRFGraph # set the target to the graph you defined + _target_: pyrad.graph.vanilla_nerf.NeRFGraph # set the target to the graph you defined # @@ -85,4 +88,3 @@ Once you have the config properly set up, you can begin training! Note, you can ``` python scripts/run_train.py --config-name vanilla_nerf ``` - diff --git a/docs/reference/dataset.rst b/docs/reference/dataset.rst index e2099728c..62049e341 100644 --- a/docs/reference/dataset.rst +++ b/docs/reference/dataset.rst @@ -6,27 +6,27 @@ Dataset Colmap Utils ---------------- -.. automodule:: pyrad.nerf.dataset.colmap_utils +.. automodule:: pyrad.data.colmap_utils :members: :show-inheritance: Image Dataset ---------------- -.. automodule:: pyrad.nerf.dataset.image_dataset +.. automodule:: pyrad.data.image_dataset :members: :show-inheritance: Structures ---------------- -.. automodule:: pyrad.nerf.dataset.structs +.. automodule:: pyrad.data.structs :members: :show-inheritance: Utils ---------------- -.. automodule:: pyrad.nerf.dataset.utils +.. automodule:: pyrad.data.utils :members: :show-inheritance: \ No newline at end of file diff --git a/docs/reference/fields.rst b/docs/reference/fields.rst index 8990bebd3..b1805635b 100644 --- a/docs/reference/fields.rst +++ b/docs/reference/fields.rst @@ -6,20 +6,20 @@ Fields Base ---------------- -.. automodule:: pyrad.nerf.fields.base +.. automodule:: pyrad.fields.base :members: :show-inheritance: Instant NGP ---------------- -.. automodule:: pyrad.nerf.fields.instant_ngp_field +.. automodule:: pyrad.fields.instant_ngp_field :members: :show-inheritance: NeRF ---------------- -.. automodule:: pyrad.nerf.fields.nerf_field +.. automodule:: pyrad.fields.nerf_field :members: :show-inheritance: \ No newline at end of file diff --git a/docs/reference/field_modules/embeddings.rst b/docs/reference/fields/modules/embeddings.rst similarity index 61% rename from docs/reference/field_modules/embeddings.rst rename to docs/reference/fields/modules/embeddings.rst index 4dabbbb2c..7d0e0ad13 100644 --- a/docs/reference/field_modules/embeddings.rst +++ b/docs/reference/fields/modules/embeddings.rst @@ -3,6 +3,6 @@ Embeddings =================== -.. automodule:: pyrad.nerf.field_modules.embedding +.. automodule:: pyrad.fields.modules.embedding :members: :show-inheritance: \ No newline at end of file diff --git a/docs/reference/field_modules/encodings.rst b/docs/reference/fields/modules/encodings.rst similarity index 61% rename from docs/reference/field_modules/encodings.rst rename to docs/reference/fields/modules/encodings.rst index d7a900f6a..78a0430d6 100644 --- a/docs/reference/field_modules/encodings.rst +++ b/docs/reference/fields/modules/encodings.rst @@ -3,6 +3,6 @@ Encodings =================== -.. automodule:: pyrad.nerf.field_modules.encoding +.. automodule:: pyrad.fields.modules.encoding :members: :show-inheritance: \ No newline at end of file diff --git a/docs/reference/field_modules/field_heads.rst b/docs/reference/fields/modules/field_heads.rst similarity index 61% rename from docs/reference/field_modules/field_heads.rst rename to docs/reference/fields/modules/field_heads.rst index a2e9d8247..79f331047 100644 --- a/docs/reference/field_modules/field_heads.rst +++ b/docs/reference/fields/modules/field_heads.rst @@ -3,6 +3,6 @@ Field Heads =================== -.. automodule:: pyrad.nerf.field_modules.field_heads +.. automodule:: pyrad.fields.modules.field_heads :members: :show-inheritance: \ No newline at end of file diff --git a/docs/reference/field_modules/field_losses.rst b/docs/reference/fields/modules/field_losses.rst similarity index 61% rename from docs/reference/field_modules/field_losses.rst rename to docs/reference/fields/modules/field_losses.rst index f2aa12430..19ef0ea2a 100644 --- a/docs/reference/field_modules/field_losses.rst +++ b/docs/reference/fields/modules/field_losses.rst @@ -3,6 +3,6 @@ Field Losses =================== -.. automodule:: pyrad.nerf.field_modules.field_losses +.. automodule:: pyrad.fields.modules.field_losses :members: :show-inheritance: \ No newline at end of file diff --git a/docs/reference/field_modules/index.rst b/docs/reference/fields/modules/index.rst similarity index 100% rename from docs/reference/field_modules/index.rst rename to docs/reference/fields/modules/index.rst diff --git a/docs/reference/field_modules/mlp.rst b/docs/reference/fields/modules/mlp.rst similarity index 60% rename from docs/reference/field_modules/mlp.rst rename to docs/reference/fields/modules/mlp.rst index a71fcd7f0..3c99faa60 100644 --- a/docs/reference/field_modules/mlp.rst +++ b/docs/reference/fields/modules/mlp.rst @@ -3,6 +3,6 @@ MLP =================== -.. automodule:: pyrad.nerf.field_modules.mlp +.. automodule:: pyrad.fields.modules.mlp :members: :show-inheritance: \ No newline at end of file diff --git a/docs/reference/graphs.rst b/docs/reference/graphs.rst index a2cd06fdc..715cbe93c 100644 --- a/docs/reference/graphs.rst +++ b/docs/reference/graphs.rst @@ -6,34 +6,34 @@ Graphs Base ---------------- -.. automodule:: pyrad.nerf.graph.base +.. automodule:: pyrad.graphs.base :members: :show-inheritance: Instant NGP ---------------- -.. automodule:: pyrad.nerf.graph.instant_ngp +.. automodule:: pyrad.graphs.instant_ngp :members: :show-inheritance: Semantic NeRF ---------------- -.. automodule:: pyrad.nerf.graph.semantic_nerf +.. automodule:: pyrad.graphs.semantic_nerf :members: :show-inheritance: TensoRF ---------------- -.. automodule:: pyrad.nerf.graph.tensorf +.. automodule:: pyrad.graphs.tensorf :members: :show-inheritance: NeRF ---------------- -.. automodule:: pyrad.nerf.graph.vanilla_nerf +.. automodule:: pyrad.graphs.vanilla_nerf :members: :show-inheritance: \ No newline at end of file diff --git a/docs/reference/graphs/modules/index.rst b/docs/reference/graphs/modules/index.rst new file mode 100644 index 000000000..cfeea81a5 --- /dev/null +++ b/docs/reference/graphs/modules/index.rst @@ -0,0 +1,10 @@ +.. _graph_modules: + +Graph Modules +=================== + +TODO: High level description of graph modules and how they are used. + +.. toctree:: + + ray_sampler \ No newline at end of file diff --git a/docs/reference/graphs/modules/ray_sampler.rst b/docs/reference/graphs/modules/ray_sampler.rst new file mode 100644 index 000000000..b74f292f3 --- /dev/null +++ b/docs/reference/graphs/modules/ray_sampler.rst @@ -0,0 +1,8 @@ +.. _ray_sampler: + +Ray Sampler +=================== + +.. automodule:: pyrad.graphs.modules.ray_sampler + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/reference/index.rst b/docs/reference/index.rst index 7b45421be..0091353d1 100644 --- a/docs/reference/index.rst +++ b/docs/reference/index.rst @@ -9,11 +9,11 @@ TODO: Explanation of each component :hidden: dataset - samplers - field_modules/index - renderers fields + fields/modules/index graphs + graphs/modules/index + renderers/index Hydra config structure ======================== diff --git a/docs/reference/renderers.rst b/docs/reference/renderers/index.rst similarity index 64% rename from docs/reference/renderers.rst rename to docs/reference/renderers/index.rst index a18d6f437..432650b15 100644 --- a/docs/reference/renderers.rst +++ b/docs/reference/renderers/index.rst @@ -3,6 +3,6 @@ Renderers ============ -.. automodule:: pyrad.nerf.renderers +.. automodule:: pyrad.renderers.renderers :members: :show-inheritance: diff --git a/docs/reference/samplers.rst b/docs/reference/samplers.rst deleted file mode 100644 index 62cc35476..000000000 --- a/docs/reference/samplers.rst +++ /dev/null @@ -1,25 +0,0 @@ -.. _samplers: - -Samplers -============ - -Image Samplers -------------------------------- - -.. automodule:: pyrad.nerf.image_sampler - :members: - :show-inheritance: - -Pixel Samplers -------------------------------- - -.. automodule:: pyrad.nerf.pixel_sampler - :members: - :show-inheritance: - -Ray Samplers -------------------------------- - -.. automodule:: pyrad.nerf.ray_sampler - :members: - :show-inheritance: \ No newline at end of file diff --git a/notebooks/visualize_cameras.ipynb b/notebooks/visualize_cameras.ipynb index d04cce46a..6f0ca0a31 100644 --- a/notebooks/visualize_cameras.ipynb +++ b/notebooks/visualize_cameras.ipynb @@ -14,7 +14,7 @@ "outputs": [], "source": [ "import torch\n", - "from pyrad.structures import cameras\n", + "from pyrad.cameras import cameras\n", "import plotly.graph_objects as go\n", "import mediapy as media" ] @@ -34,7 +34,7 @@ " print(\"Image UV coordinates\")\n", " media.show_image(coords, width=400)\n", "\n", - " ray_bundle = camera.generate_camera_rays()\n", + " ray_bundle = camera.get_camera_ray_bundle()\n", "\n", " print(\"Pixel Area\")\n", " media.show_image(ray_bundle.pixel_area, width=400, cmap=\"hot\")\n", @@ -13030,6 +13030,15 @@ } ], "layout": { + "autosize": true, + "scene": { + "aspectmode": "auto", + "aspectratio": { + "x": 1.3148144863956661, + "y": 0.8080168870630272, + "z": 0.9412718749263642 + } + }, "template": { "data": { "bar": [ @@ -13849,9 +13858,9 @@ } }, "text/html": [ - "
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", + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "layout = go.Layout(\n", " autosize=False,\n", @@ -239,6 +8169,14 @@ "metadata": {}, "outputs": [], "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f1cd7183-c514-4f87-a8cc-e14228228e24", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/notebooks/visualize_encoders.ipynb b/notebooks/visualize_encoders.ipynb index b09536150..644a999ca 100644 --- a/notebooks/visualize_encoders.ipynb +++ b/notebooks/visualize_encoders.ipynb @@ -9,24 +9,13 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 1, "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 6;\n var nbb_unformatted_code = \"import torch\\nimport mediapy as media\\nfrom pyrad.nerf.field_modules import encoding\";\n var nbb_formatted_code = \"import torch\\nimport mediapy as media\\nfrom pyrad.nerf.field_modules import encoding\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import torch\n", "import mediapy as media\n", - "from pyrad.nerf.field_modules import encoding" + "from pyrad.fields.modules import encoding" ] }, { @@ -39,7 +28,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -79,16 +68,6 @@ }, "metadata": {}, "output_type": "display_data" - }, - { - "data": { - "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 7;\n var nbb_unformatted_code = \"num_frequencies = 4\\nmin_freq_exp = 0\\nmax_freq_exp = 6\\ninclude_input = True\\nresolution = 128\\n\\nencoder = 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\\nx_samples = torch.linspace(0, 1, resolution)\\ngrid = torch.stack(torch.meshgrid([x_samples, x_samples], indexing=\\\"ij\\\"), dim=-1)\\n\\nencoded_values = encoder(grid)\\n\\nprint(\\\"Input Values:\\\")\\nmedia.show_images(torch.moveaxis(grid, 2, 0), cmap=\\\"plasma\\\", border=True)\\nprint(\\\"Encoded Values:\\\")\\nmedia.show_images(torch.moveaxis(encoded_values, 2, 0), cmap=\\\"plasma\\\", border=True)\";\n var nbb_formatted_code = \"num_frequencies = 4\\nmin_freq_exp = 0\\nmax_freq_exp = 6\\ninclude_input = True\\nresolution = 128\\n\\nencoder = 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\\nx_samples = torch.linspace(0, 1, resolution)\\ngrid = torch.stack(torch.meshgrid([x_samples, x_samples], indexing=\\\"ij\\\"), dim=-1)\\n\\nencoded_values = encoder(grid)\\n\\nprint(\\\"Input Values:\\\")\\nmedia.show_images(torch.moveaxis(grid, 2, 0), cmap=\\\"plasma\\\", border=True)\\nprint(\\\"Encoded Values:\\\")\\nmedia.show_images(torch.moveaxis(encoded_values, 2, 0), cmap=\\\"plasma\\\", border=True)\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ @@ -127,7 +106,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -159,7 +138,7 @@ { "data": { "text/html": [ - "
" + "
" ], "text/plain": [ "" @@ -167,16 +146,6 @@ }, "metadata": {}, "output_type": "display_data" - }, - { - "data": { - "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 8;\n var nbb_unformatted_code = \"num_frequencies = 8\\nscale = 10\\nresolution = 128\\n\\nencoder = encoding.RFFEncoding(in_dim=2, num_frequencies=num_frequencies, scale=scale)\\n\\nx_samples = torch.linspace(0, 1, resolution)\\ngrid = torch.stack(torch.meshgrid([x_samples, x_samples], indexing=\\\"ij\\\"), dim=-1)\\n\\nencoded_values = encoder(grid)\\n\\nprint(\\\"Input Values:\\\")\\nmedia.show_images(torch.moveaxis(grid, 2, 0), cmap=\\\"plasma\\\", border=True)\\nprint(\\\"Encoded Values:\\\")\\nmedia.show_images(torch.moveaxis(encoded_values, 2, 0), cmap=\\\"plasma\\\", border=True)\";\n var nbb_formatted_code = \"num_frequencies = 8\\nscale = 10\\nresolution = 128\\n\\nencoder = encoding.RFFEncoding(in_dim=2, num_frequencies=num_frequencies, scale=scale)\\n\\nx_samples = torch.linspace(0, 1, resolution)\\ngrid = torch.stack(torch.meshgrid([x_samples, x_samples], indexing=\\\"ij\\\"), dim=-1)\\n\\nencoded_values = encoder(grid)\\n\\nprint(\\\"Input Values:\\\")\\nmedia.show_images(torch.moveaxis(grid, 2, 0), cmap=\\\"plasma\\\", border=True)\\nprint(\\\"Encoded Values:\\\")\\nmedia.show_images(torch.moveaxis(encoded_values, 2, 0), cmap=\\\"plasma\\\", border=True)\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ @@ -207,7 +176,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -239,7 +208,7 @@ { "data": { "text/html": [ - "
" + "
" ], "text/plain": [ "" @@ -247,16 +216,6 @@ }, "metadata": {}, "output_type": "display_data" - }, - { - "data": { - "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 9;\n var nbb_unformatted_code = \"num_levels = 8\\nmin_res = 2\\nmax_res = 128\\nhash_table_size = 2**4 # Typically much larger tables are used\\n\\nresolution = 128\\nslice = 0\\n\\n# Fixing features_per_level to 3 for easy RGB visualization. Typical value is 2 in networks\\nfeatures_per_level = 3\\n\\nencoder = 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\\nx_samples = torch.linspace(0, 1, resolution)\\ngrid = torch.stack(torch.meshgrid([x_samples, x_samples, x_samples], indexing=\\\"ij\\\"), dim=-1)\\n\\nencoded_values = encoder(grid)\\n\\ngrid_slice = grid[slice, ...]\\nencoded_values_slice = encoded_values[slice, ...]\\n\\nprint(\\\"Input Values:\\\")\\nmedia.show_images(torch.moveaxis(grid_slice, 2, 0), cmap=\\\"plasma\\\", border=True)\\n\\nprint(\\\"Encoded Values:\\\")\\nencoded_images = encoded_values_slice.view(resolution, resolution, num_levels, 3)\\nencoded_images = torch.moveaxis(encoded_images, 2, 0)\\nencoded_images -= torch.min(encoded_images)\\nencoded_images /= torch.max(encoded_images)\\nmedia.show_images(encoded_images.detach().numpy(), cmap=\\\"plasma\\\", border=True)\";\n var nbb_formatted_code = \"num_levels = 8\\nmin_res = 2\\nmax_res = 128\\nhash_table_size = 2**4 # Typically much larger tables are used\\n\\nresolution = 128\\nslice = 0\\n\\n# Fixing features_per_level to 3 for easy RGB visualization. Typical value is 2 in networks\\nfeatures_per_level = 3\\n\\nencoder = 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\\nx_samples = torch.linspace(0, 1, resolution)\\ngrid = torch.stack(\\n torch.meshgrid([x_samples, x_samples, x_samples], indexing=\\\"ij\\\"), dim=-1\\n)\\n\\nencoded_values = encoder(grid)\\n\\ngrid_slice = grid[slice, ...]\\nencoded_values_slice = encoded_values[slice, ...]\\n\\nprint(\\\"Input Values:\\\")\\nmedia.show_images(torch.moveaxis(grid_slice, 2, 0), cmap=\\\"plasma\\\", border=True)\\n\\nprint(\\\"Encoded Values:\\\")\\nencoded_images = encoded_values_slice.view(resolution, resolution, num_levels, 3)\\nencoded_images = torch.moveaxis(encoded_images, 2, 0)\\nencoded_images -= torch.min(encoded_images)\\nencoded_images /= torch.max(encoded_images)\\nmedia.show_images(encoded_images.detach().numpy(), cmap=\\\"plasma\\\", border=True)\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ @@ -309,7 +268,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -387,16 +346,6 @@ }, "metadata": {}, "output_type": "display_data" - }, - { - "data": { - "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 10;\n var nbb_unformatted_code = \"levels = 4\\n\\nheight = 100\\nwidth = 150\\n\\nencoder = encoding.SHEncoding(levels=levels)\\n\\ntheta = torch.linspace(-torch.pi, torch.pi, width)\\nphi = torch.linspace(0, torch.pi, height)\\n[theta, phi] = torch.meshgrid([theta, phi], indexing=\\\"xy\\\")\\n\\ndirections = torch.stack([torch.cos(theta) * torch.sin(phi), torch.sin(theta) * torch.sin(phi), torch.cos(phi)], dim=-1)\\n\\nencoded_values = encoder(directions)\\nencoded_values = torch.moveaxis(encoded_values, 2, 0)\\n\\nfor 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)\";\n var nbb_formatted_code = \"levels = 4\\n\\nheight = 100\\nwidth = 150\\n\\nencoder = encoding.SHEncoding(levels=levels)\\n\\ntheta = torch.linspace(-torch.pi, torch.pi, width)\\nphi = torch.linspace(0, torch.pi, height)\\n[theta, phi] = torch.meshgrid([theta, phi], indexing=\\\"xy\\\")\\n\\ndirections = torch.stack(\\n [\\n torch.cos(theta) * torch.sin(phi),\\n torch.sin(theta) * torch.sin(phi),\\n torch.cos(phi),\\n ],\\n dim=-1,\\n)\\n\\nencoded_values = encoder(directions)\\nencoded_values = torch.moveaxis(encoded_values, 2, 0)\\n\\nfor level in range(levels):\\n print(f\\\"Level: {level+1}\\\")\\n media.show_images(\\n encoded_values[level**2 : (level + 1) ** 2, ...], cmap=\\\"plasma\\\", border=True\\n )\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ @@ -434,7 +383,7 @@ "hash": "024fe9dea615e4e34961116ff517c7e9f5910440e3d2c8eb4560299f11aaf2e7" }, "kernelspec": { - "display_name": "Python 3.9.11 ('pyrad')", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -448,10 +397,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" - }, - "orig_nbformat": 4 + "version": "3.8.13" + } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/pyrad/nerf/__init__.py b/pyrad/__init__.py similarity index 100% rename from pyrad/nerf/__init__.py rename to pyrad/__init__.py diff --git a/pyrad/structures/cameras.py b/pyrad/cameras/cameras.py similarity index 85% rename from pyrad/structures/cameras.py rename to pyrad/cameras/cameras.py index 2ccd69e09..86468835e 100644 --- a/pyrad/structures/cameras.py +++ b/pyrad/cameras/cameras.py @@ -9,22 +9,20 @@ import torch from torch.nn.functional import normalize from torchtyping import TensorType -from pyrad.structures.rays import RayBundle - - -@dataclass -class Rays: - """Camera rays. Can be arbitrary dimension""" - - origin: TensorType[..., 3] - direction: TensorType[..., 3] +from pyrad.cameras.rays import RayBundle +from pyrad.utils.misc import is_not_none class Camera: """Base Camera. Intended to be subclassed""" - def __init__(self, camera_to_world: Optional[TensorType[3, 4]] = torch.eye(4)[:3]) -> None: + def __init__( + self, + camera_to_world: Optional[TensorType[3, 4]] = torch.eye(4)[:3], + camera_index: int = None, + ) -> None: self.camera_to_world = camera_to_world + self.camera_index = camera_index @abstractmethod def get_num_intrinsics_params(self) -> int: @@ -98,7 +96,7 @@ class Camera: """ return - def generate_camera_rays(self, device=None) -> RayBundle: + def get_camera_ray_bundle(self, device=None) -> RayBundle: """Generate rays for the camera. Returns: @@ -114,6 +112,8 @@ class Camera: camera_to_world = self.camera_to_world.unsqueeze(0).repeat(height, width, 1, 1).to(device) # (num_rays, 3, 4) coords = self.get_image_coords().to(device) ray_bundle = self.generate_rays(intrinsics, camera_to_world, coords) + if is_not_none(self.camera_index): + ray_bundle.set_camera_indices(camera_index=self.camera_index) return ray_bundle @@ -121,9 +121,15 @@ class PinholeCamera(Camera): """Pinhole camera model.""" def __init__( - self, cx: float, cy: float, fx: float, fy: float, camera_to_world: Optional[TensorType[3, 4]] = torch.eye(4)[:3] + self, + cx: float, + cy: float, + fx: float, + fy: float, + camera_to_world: Optional[TensorType[3, 4]] = torch.eye(4)[:3], + camera_index: int = None, ): - super().__init__(camera_to_world) + super().__init__(camera_to_world, camera_index) self.cx = cx self.cy = cy self.fx = fx @@ -209,8 +215,15 @@ class PinholeCamera(Camera): class SimplePinholeCamera(PinholeCamera): """Simple Pinhole Camera model.""" - def __init__(self, cx: float, cy: float, f: float, camera_to_world: Optional[TensorType[3, 4]] = torch.eye(4)[:3]): - super().__init__(cx, cy, f, f, camera_to_world) + def __init__( + self, + cx: float, + cy: float, + f: float, + camera_to_world: Optional[TensorType[3, 4]] = torch.eye(4)[:3], + camera_index: int = None, + ): + super().__init__(cx, cy, f, f, camera_to_world, camera_index) @classmethod def fx_index(cls): @@ -224,8 +237,14 @@ class SimplePinholeCamera(PinholeCamera): class EquirectangularCamera(Camera): """Equirectangular (360 degree) camera model.""" - def __init__(self, height: int, width: int, camera_to_world: Optional[TensorType[3, 4]] = torch.eye(4)[:3]): - super().__init__(camera_to_world) + def __init__( + self, + height: int, + width: int, + camera_to_world: Optional[TensorType[3, 4]] = torch.eye(4)[:3], + camera_index: int = None, + ): + super().__init__(camera_to_world, camera_index) self.height = height self.width = width @@ -308,3 +327,11 @@ def get_camera_model(num_intrinsics_params: int) -> Type[Camera]: if num_intrinsics_params == 4: return PinholeCamera raise NotImplementedError + + +def get_camera(intrinsics, camera_to_world, camera_index): + assert len(intrinsics.shape) == 1, "The intrinsics object should be a flat tensor." + num_intrinsics_params = len(intrinsics) + camera_class = get_camera_model(num_intrinsics_params) + camera = camera_class(*intrinsics.tolist(), camera_to_world=camera_to_world, camera_index=camera_index) + return camera diff --git a/pyrad/structures/rays.py b/pyrad/cameras/rays.py similarity index 94% rename from pyrad/structures/rays.py rename to pyrad/cameras/rays.py index 1df0b226a..6df16a730 100644 --- a/pyrad/structures/rays.py +++ b/pyrad/cameras/rays.py @@ -40,12 +40,13 @@ class Frustums: Returns: Frustums: A size 1 frustum with meaningless values. """ + device = cls.origins.device return Frustums( - origins=torch.ones((1, 3)), - directions=torch.ones((1, 3)), - frustum_starts=torch.ones((1, 1)), - frustum_ends=torch.ones((1, 1)) + 1, - pixel_area=torch.ones((1, 1)), + origins=torch.ones((1, 3), device=device), + directions=torch.ones((1, 3), device=device), + frustum_starts=torch.ones((1, 1), device=device), + frustum_ends=torch.ones((1, 1), device=device) + 1, + pixel_area=torch.ones((1, 1), device=device), ) def apply_masks(self, mask: TensorType) -> "Frustums": @@ -185,6 +186,7 @@ class RayBundle: nears: Optional[TensorType["num_rays"]] = None fars: Optional[TensorType["num_rays"]] = None valid_mask: Optional[TensorType["num_rays"]] = None + num_rays_per_chunk: int = None def move_to_device(self, device: torch.device) -> None: """Move bundle data to a device. @@ -277,9 +279,10 @@ class RayBundle: Returns: RaySamples: Samples projected along ray. """ + device = self.origins.device num_samples = bins.shape[-1] - 1 - valid_mask = torch.ones((bins.shape[0], num_samples), dtype=torch.bool, device=bins.device) + valid_mask = torch.ones((bins.shape[0], num_samples), dtype=torch.bool, device=device) dists = bins[:, 1:] - bins[:, :-1] # [N_rays, N_samples] deltas = dists * torch.norm(self.directions[:, None, :], dim=-1) @@ -292,9 +295,9 @@ class RayBundle: frustums = Frustums( origins=self.origins.unsqueeze(1).repeat(1, num_samples, 1), directions=self.directions.unsqueeze(1).repeat(1, num_samples, 1), - frustum_starts=bins[:, :-1, None], - frustum_ends=bins[:, 1:, None], - pixel_area=self.pixel_area.unsqueeze(1).repeat(1, num_samples, 1), + frustum_starts=bins[:, :-1, None].to(device), + frustum_ends=bins[:, 1:, None].to(device), + pixel_area=self.pixel_area.unsqueeze(1).repeat(1, num_samples, 1).to(device), ) ray_samples = RaySamples( diff --git a/pyrad/nerf/dataset/__init__.py b/pyrad/data/__init__.py similarity index 100% rename from pyrad/nerf/dataset/__init__.py rename to pyrad/data/__init__.py diff --git a/pyrad/nerf/dataset/colmap_utils.py b/pyrad/data/colmap_utils.py similarity index 100% rename from pyrad/nerf/dataset/colmap_utils.py rename to pyrad/data/colmap_utils.py diff --git a/pyrad/data/dataloader.py b/pyrad/data/dataloader.py new file mode 100644 index 000000000..55d620ee0 --- /dev/null +++ b/pyrad/data/dataloader.py @@ -0,0 +1,118 @@ +""" +Data loader. +""" + +import random +from typing import Dict, List, Tuple, Union +from omegaconf import ListConfig + +from torchtyping import TensorType + +from pyrad.cameras.cameras import get_camera +from pyrad.cameras.rays import RayBundle +from pyrad.data.image_dataset import ImageDataset +from pyrad.data.image_sampler import ImageSampler +from pyrad.data.pixel_sampler import PixelSampler +from pyrad.utils.misc import get_dict_to_torch + + +class TrainDataloader: + """Training dataloader base class.""" + + def __init__(self, image_sampler: ImageSampler, pixel_sampler: PixelSampler): + self.image_sampler = image_sampler + self.pixel_sampler = pixel_sampler + self.iter_image_sampler = iter(self.image_sampler) + self.count = 0 + + def __iter__(self): + self.count = 0 + return self + + def __next__(self) -> Tuple[TensorType["num_rays", 3], Dict]: + self.count += 1 + image_batch = next(self.iter_image_sampler) + batch = self.pixel_sampler.sample(image_batch) + ray_indices = batch["indices"] + return ray_indices, batch + + +class EvalDataloader: # pylint: disable=too-few-public-methods + """Evaluation dataloader base class""" + + def __init__( + self, image_dataset: ImageDataset, intrinsics, camera_to_world, num_rays_per_chunk: int, device="cpu", **kwargs + ): + super().__init__() + self.image_dataset = image_dataset + self.intrinsics = intrinsics + self.camera_to_world = camera_to_world + self.num_rays_per_chunk = num_rays_per_chunk + self.device = device + self.kwargs = kwargs + + def get_data_from_image_idx(self, image_idx) -> Tuple[RayBundle, Dict]: + """Returns the data for a specific image index.""" + intrinsics = self.intrinsics[image_idx].to(self.device) + camera_to_world = self.camera_to_world[image_idx].to(self.device) + camera = get_camera(intrinsics, camera_to_world, camera_index=image_idx) + ray_bundle = camera.get_camera_ray_bundle(device=self.device) + ray_bundle.num_rays_per_chunk = self.num_rays_per_chunk + batch = self.image_dataset[image_idx] + batch = get_dict_to_torch(batch, device=self.device) + return ray_bundle, batch + + +class FixedIndicesEvalDataloader(EvalDataloader): + """Dataloader that returns a fixed set of indices.""" + + def __init__( + self, + image_dataset: ImageDataset, + intrinsics, + camera_to_world, + num_rays_per_chunk: int, + image_indices: Union[List[int], ListConfig], + device="cpu", + **kwargs + ): + super().__init__(image_dataset, intrinsics, camera_to_world, num_rays_per_chunk, device, **kwargs) + self.image_indices = image_indices + self.num_rays_per_chunk = num_rays_per_chunk + self.count = 0 + + def __iter__(self): + self.count = 0 + return self + + def __next__(self): + if self.count < len(self.image_indices): + image_idx = self.image_indices[self.count] + ray_bundle, batch = self.get_data_from_image_idx(image_idx) + ray_bundle.num_rays_per_chunk = self.num_rays_per_chunk + self.count += 1 + return ray_bundle, batch + raise StopIteration + + +class RandIndicesEvalDataloader(EvalDataloader): + """Dataloader that returns random images.""" + + def __init__( + self, image_dataset: ImageDataset, intrinsics, camera_to_world, num_rays_per_chunk: int, device="cpu", **kwargs + ): + super().__init__(image_dataset, intrinsics, camera_to_world, num_rays_per_chunk, device, **kwargs) + self.count = 0 + + def __iter__(self): + self.count = 0 + return self + + def __next__(self): + if self.count < 1: + image_indices = range(len(self.camera_to_world)) + image_idx = random.choice(image_indices) + ray_bundle, batch = self.get_data_from_image_idx(image_idx) + self.count += 1 + return ray_bundle, batch + raise StopIteration diff --git a/pyrad/nerf/dataset/format/__init__.py b/pyrad/data/format/__init__.py similarity index 100% rename from pyrad/nerf/dataset/format/__init__.py rename to pyrad/data/format/__init__.py diff --git a/pyrad/nerf/dataset/format/blender.py b/pyrad/data/format/blender.py similarity index 83% rename from pyrad/nerf/dataset/format/blender.py rename to pyrad/data/format/blender.py index f436f8bc2..b5d3739c5 100644 --- a/pyrad/nerf/dataset/format/blender.py +++ b/pyrad/data/format/blender.py @@ -10,12 +10,16 @@ import numpy as np import torch from torchtyping import TensorType -from pyrad.nerf.dataset.structs import DatasetInputs, SceneBounds +from pyrad.data.structs import DatasetInputs, SceneBounds from pyrad.utils.io import load_from_json def load_blender_data( - basedir: str, downscale_factor: int = 1, alpha_color: Optional[TensorType[3]] = None, split: str = "train" + basedir: str, + downscale_factor: int = 1, + alpha_color: Optional[TensorType[3]] = None, + split: str = "train", + scale_factor: float = 0.33, ) -> DatasetInputs: """Processes the a blender dataset directory. Some of this code comes from https://github.com/yenchenlin/nerf-pytorch/blob/master/load_blender.py#L37. @@ -25,6 +29,7 @@ def load_blender_data( downscale_factor (int, optional): How much to downscale images. Defaults to 1.0. alpha_color (TensorType[3], optional): Sets transparent regions to specified color, otherwise black. split (str, optional): Which dataset split to generate. + scale_factor (float): How much to scale the camera origins by. Returns: DatasetInputs @@ -54,7 +59,8 @@ def load_blender_data( intrinsics *= torch.tensor([cx, cy, focal_length]) # in x,y,z order - scene_bounds = SceneBounds(aabb=torch.tensor([[-2, -2, -2], [2, 2, 2]], dtype=torch.float32)) + camera_to_world[..., 3] *= scale_factor + scene_bounds = SceneBounds(aabb=torch.tensor([[-0.5, -0.5, -0.5], [0.5, 0.5, 0.5]], dtype=torch.float32)) dataset_inputs = DatasetInputs( image_filenames=image_filenames, diff --git a/pyrad/nerf/dataset/format/friends.py b/pyrad/data/format/friends.py similarity index 97% rename from pyrad/nerf/dataset/format/friends.py rename to pyrad/data/format/friends.py index 344947f5b..cf24d84a6 100644 --- a/pyrad/nerf/dataset/format/friends.py +++ b/pyrad/data/format/friends.py @@ -7,8 +7,8 @@ import os import numpy as np import torch -from pyrad.nerf.dataset.colmap_utils import read_cameras_binary, read_images_binary, read_pointsTD_binary -from pyrad.nerf.dataset.structs import DatasetInputs, PointCloud, SceneBounds, Semantics +from pyrad.data.colmap_utils import read_cameras_binary, read_images_binary, read_pointsTD_binary +from pyrad.data.structs import DatasetInputs, PointCloud, SceneBounds, Semantics from pyrad.utils import profiler from pyrad.utils.io import load_from_json diff --git a/pyrad/nerf/dataset/format/instant_ngp.py b/pyrad/data/format/instant_ngp.py similarity index 97% rename from pyrad/nerf/dataset/format/instant_ngp.py rename to pyrad/data/format/instant_ngp.py index d645d9a94..bb692ef4b 100644 --- a/pyrad/nerf/dataset/format/instant_ngp.py +++ b/pyrad/data/format/instant_ngp.py @@ -10,7 +10,7 @@ import imageio import numpy as np import torch -from pyrad.nerf.dataset.structs import DatasetInputs, SceneBounds +from pyrad.data.structs import DatasetInputs, SceneBounds from pyrad.utils.io import load_from_json diff --git a/pyrad/nerf/dataset/format/llff.py b/pyrad/data/format/llff.py similarity index 100% rename from pyrad/nerf/dataset/format/llff.py rename to pyrad/data/format/llff.py diff --git a/pyrad/nerf/dataset/image_dataset.py b/pyrad/data/image_dataset.py similarity index 99% rename from pyrad/nerf/dataset/image_dataset.py rename to pyrad/data/image_dataset.py index fcdbf56e9..4b2d72bdb 100644 --- a/pyrad/nerf/dataset/image_dataset.py +++ b/pyrad/data/image_dataset.py @@ -12,7 +12,7 @@ import torch from PIL import Image from torchtyping import TensorType -from pyrad.nerf.dataset.structs import Semantics +from pyrad.data.structs import Semantics from pyrad.utils.misc import is_not_none diff --git a/pyrad/nerf/image_sampler.py b/pyrad/data/image_sampler.py similarity index 92% rename from pyrad/nerf/image_sampler.py rename to pyrad/data/image_sampler.py index adbd29f3d..aeb9e866a 100644 --- a/pyrad/nerf/image_sampler.py +++ b/pyrad/data/image_sampler.py @@ -38,13 +38,15 @@ class CacheImageSampler(ImageSampler): def __init__( self, dataset: torch.utils.data.Dataset, - num_samples_to_collate: int, + num_images_to_sample_from: int, num_times_to_repeat_images: int = 0, device: Union[torch.device, str] = "cpu", ): super().__init__() self.dataset = dataset - self.num_samples_to_collate = num_samples_to_collate + self.num_images_to_sample_from = ( + len(self.dataset) if num_images_to_sample_from == 0 else num_images_to_sample_from + ) self.num_times_to_repeat_images = num_times_to_repeat_images self.device = device @@ -57,7 +59,7 @@ class CacheImageSampler(ImageSampler): def _get_batch_list(self): """Returns a list of batches from the dataset attribute.""" - indices = random.sample(range(len(self.dataset)), k=self.num_samples_to_collate) + indices = random.sample(range(len(self.dataset)), k=self.num_images_to_sample_from) batch_list = [self.dataset.__getitem__(idx) for idx in indices] return batch_list diff --git a/pyrad/nerf/pixel_sampler.py b/pyrad/data/pixel_sampler.py similarity index 85% rename from pyrad/nerf/pixel_sampler.py rename to pyrad/data/pixel_sampler.py index fc053c43c..a3eb5f1f9 100644 --- a/pyrad/nerf/pixel_sampler.py +++ b/pyrad/data/pixel_sampler.py @@ -13,7 +13,7 @@ def collate_image_dataset_batch(batch, num_rays_per_batch, keep_full_image: bool Returns a collated batch which is input to the Graph. It will sample only within the valid 'mask' if it's specified. """ - + device = batch["image"].device num_images, image_height, image_width, _ = batch["image"].shape # only sample within the mask, if the mask is in the batch @@ -23,16 +23,17 @@ def collate_image_dataset_batch(batch, num_rays_per_batch, keep_full_image: bool indices = nonzero_indices[chosen_indices] else: indices = torch.floor( - torch.rand((num_rays_per_batch, 3)) * torch.tensor([num_images, image_height, image_width]) + torch.rand((num_rays_per_batch, 3), device=device) + * torch.tensor([num_images, image_height, image_width], device=device) ).long() c, y, x = [i.flatten() for i in torch.split(indices, 1, dim=-1)] - pixels = batch["image"][c, y, x] + image = batch["image"][c, y, x] if "mask" in batch: mask = batch["mask"][c, y, x] if "semantics" in batch: semantics = batch["semantics"][c, y, x] - assert pixels.shape == (num_rays_per_batch, 3), pixels.shape + assert image.shape == (num_rays_per_batch, 3), image.shape # Needed to correct the random indices to their actual camera idx locations. local_indices = indices.clone() @@ -40,7 +41,7 @@ def collate_image_dataset_batch(batch, num_rays_per_batch, keep_full_image: bool collated_batch = { "local_indices": local_indices, # local to the batch returned "indices": indices, # with the abs camera indices - "pixels": pixels, + "image": image, } if "mask" in batch: collated_batch["mask"] = mask @@ -48,7 +49,7 @@ def collate_image_dataset_batch(batch, num_rays_per_batch, keep_full_image: bool collated_batch["semantics"] = semantics if keep_full_image: - collated_batch["image"] = batch["image"] + collated_batch["full_image"] = batch["image"] return collated_batch diff --git a/pyrad/nerf/dataset/structs.py b/pyrad/data/structs.py similarity index 95% rename from pyrad/nerf/dataset/structs.py rename to pyrad/data/structs.py index 74518c0ee..3be74c363 100644 --- a/pyrad/nerf/dataset/structs.py +++ b/pyrad/data/structs.py @@ -88,3 +88,7 @@ class DatasetInputs: def load_from_folder_name(self, data_directory: str): """Load the saved dataset inputs.""" raise NotImplementedError + + def as_dict(self) -> dict: + """Returns the dataclass as a dictionary.""" + return vars(self) diff --git a/pyrad/nerf/dataset/utils.py b/pyrad/data/utils.py similarity index 92% rename from pyrad/nerf/dataset/utils.py rename to pyrad/data/utils.py index c6f00bc28..d9b9033e3 100644 --- a/pyrad/nerf/dataset/utils.py +++ b/pyrad/data/utils.py @@ -7,11 +7,11 @@ from typing import Optional, Union from omegaconf import ListConfig -from pyrad.nerf.dataset.format.blender import load_blender_data -from pyrad.nerf.dataset.format.friends import load_friends_data -from pyrad.nerf.dataset.format.instant_ngp import load_instant_ngp_data -from pyrad.nerf.dataset.structs import DatasetInputs -from pyrad.structures.colors import get_color +from pyrad.data.format.blender import load_blender_data +from pyrad.data.format.friends import load_friends_data +from pyrad.data.format.instant_ngp import load_instant_ngp_data +from pyrad.data.structs import DatasetInputs +from pyrad.utils.colors import get_color from pyrad.utils.io import get_absolute_path, load_from_pkl, make_dir, write_to_pkl from pyrad.utils.misc import get_hash_str_from_dict diff --git a/pyrad/nerf/fields/__init__.py b/pyrad/engine/__init__.py similarity index 100% rename from pyrad/nerf/fields/__init__.py rename to pyrad/engine/__init__.py diff --git a/pyrad/nerf/field_modules/field_losses.py b/pyrad/engine/base.py similarity index 100% rename from pyrad/nerf/field_modules/field_losses.py rename to pyrad/engine/base.py diff --git a/pyrad/nerf/trainer.py b/pyrad/engine/trainer.py similarity index 64% rename from pyrad/nerf/trainer.py rename to pyrad/engine/trainer.py index 56507b5bd..a59559caf 100644 --- a/pyrad/nerf/trainer.py +++ b/pyrad/engine/trainer.py @@ -1,7 +1,6 @@ """ Code to train model. """ -import copy import logging import os from time import time @@ -12,14 +11,14 @@ import torch.distributed as dist from omegaconf import DictConfig from torch.nn.parallel import DistributedDataParallel as DDP -from pyrad.nerf.dataset.utils import DatasetInputs, get_dataset_inputs_from_dataset_config -from pyrad.nerf.image_sampler import CacheImageSampler -from pyrad.nerf.optimizers import Optimizers -from pyrad.nerf.pixel_sampler import PixelSampler +from pyrad.cameras.rays import RayBundle +from pyrad.data.dataloader import EvalDataloader, TrainDataloader +from pyrad.data.utils import DatasetInputs, get_dataset_inputs_from_dataset_config +from pyrad.optimizers.optimizers import Optimizers from pyrad.utils import profiler, writer from pyrad.utils.callbacks import update_occupancy from pyrad.utils.decorators import check_main_thread -from pyrad.utils.misc import get_dict_to_torch, instantiate_from_dict_config +from pyrad.utils.misc import instantiate_from_dict_config logging.getLogger("PIL").setLevel(logging.WARNING) @@ -40,12 +39,8 @@ class Trainer: self.local_rank = local_rank self.world_size = world_size # dataset variables - self.train_image_dataset = None - self.train_image_sampler = None - self.train_pixel_sampler = None - self.val_image_dataset = None - self.val_image_intrinsics = None - self.val_image_camera_to_world = None + self.dataloader_train = None + self.dataloader_eval = None # model variables self.graph = None self.optimizers = None @@ -57,51 +52,45 @@ class Trainer: def setup(self, test_mode=False): """Setup the Trainer by calling other setup functions.""" - dataset_inputs_train = get_dataset_inputs_from_dataset_config(**self.config.data.dataset, split="train") - if test_mode: - dataset_inputs_eval = get_dataset_inputs_from_dataset_config(**self.config.data.dataset, split="test") - else: - config_data_dataset_val = copy.deepcopy(self.config.data.dataset) - config_data_dataset_val.downscale_factor = self.config.data.val_downscale_factor - dataset_inputs_eval = get_dataset_inputs_from_dataset_config(**config_data_dataset_val, split="val") + dataset_inputs_train = get_dataset_inputs_from_dataset_config( + **self.config.data.dataset_inputs_train, split="train" + ) + eval_split = "test" if test_mode else "val" + dataset_inputs_eval = get_dataset_inputs_from_dataset_config( + **self.config.data.dataset_inputs_eval, split=eval_split + ) self.setup_dataset_train(dataset_inputs_train) self.setup_dataset_eval(dataset_inputs_eval) self.setup_graph(dataset_inputs_train) - @profiler.time_function - def setup_dataset_eval(self, dataset_inputs: DatasetInputs): - """Helper method to load test or val dataset based on test/train mode""" - self.val_image_dataset = instantiate_from_dict_config( - self.config.data.image_dataset, - image_filenames=dataset_inputs.image_filenames, - downscale_factor=dataset_inputs.downscale_factor, - semantics=dataset_inputs.semantics, - alpha_color=dataset_inputs.alpha_color, - ) - self.val_image_intrinsics = dataset_inputs.intrinsics - self.val_image_camera_to_world = dataset_inputs.camera_to_world - @profiler.time_function def setup_dataset_train(self, dataset_inputs: DatasetInputs): """_summary_""" - self.train_image_dataset = instantiate_from_dict_config( - self.config.data.image_dataset, - image_filenames=dataset_inputs.image_filenames, - downscale_factor=dataset_inputs.downscale_factor, - semantics=dataset_inputs.semantics, - alpha_color=dataset_inputs.alpha_color, - ) # ImageDataset - self.train_image_sampler = CacheImageSampler( - self.train_image_dataset, - num_samples_to_collate=len(self.train_image_dataset) - if self.config.data.image_sampler.num_images_to_sample_from == 0 - else self.config.data.image_sampler.num_images_to_sample_from, - num_times_to_repeat_images=self.config.data.image_sampler.num_times_to_repeat_images, - device=self.device if self.config.data.image_sampler.move_to_graph_device else "cpu", - ) # ImageSampler - self.train_pixel_sampler = PixelSampler( - num_rays_per_batch=self.config.data.pixel_sampler.num_rays_per_batch, keep_full_image=False - ) # PixelSampler + # ImageDataset + image_dataset_train = instantiate_from_dict_config( + self.config.data.image_dataset_train, **dataset_inputs.as_dict() + ) + # ImageSampler + image_sampler_train = instantiate_from_dict_config( + self.config.data.dataloader_train.image_sampler, dataset=image_dataset_train, device=self.device + ) + # PixelSampler + pixel_sampler_train = instantiate_from_dict_config(self.config.data.dataloader_train.pixel_sampler) + # Dataloader + self.dataloader_train = TrainDataloader(image_sampler_train, pixel_sampler_train) + + @profiler.time_function + def setup_dataset_eval(self, dataset_inputs: DatasetInputs): + """Helper method to load test or val dataset based on test/train mode""" + image_dataset_eval = instantiate_from_dict_config( + self.config.data.image_dataset_eval, **dataset_inputs.as_dict() + ) + self.dataloader_eval = instantiate_from_dict_config( + self.config.data.dataloader_eval, + image_dataset=image_dataset_eval, + device=self.device, + **dataset_inputs.as_dict(), + ) @profiler.time_function def setup_graph(self, dataset_inputs: DatasetInputs): @@ -110,14 +99,7 @@ class Trainer: Args: dataset_inputs (DatasetInputs): The inputs which will be used to define the camera parameters. """ - self.graph = instantiate_from_dict_config( - self.config.graph.network, - intrinsics=dataset_inputs.intrinsics, - camera_to_world=dataset_inputs.camera_to_world, - scene_bounds=dataset_inputs.scene_bounds, - stuff_classes=dataset_inputs.semantics.stuff_classes, - stuff_colors=dataset_inputs.semantics.stuff_colors, - ) + self.graph = instantiate_from_dict_config(self.config.graph.network, **dataset_inputs.as_dict()) self.graph.to(self.device) self.setup_optimizers() # NOTE(ethan): can this be before DDP? @@ -188,12 +170,10 @@ class Trainer: """_summary_""" train_start = time() num_iterations = self.config.graph.max_num_iterations - iter_train_image_sampler = iter(self.train_image_sampler) + iter_dataloader_train = iter(self.dataloader_train) for step in range(self.start_step, self.start_step + num_iterations): data_start = time() - image_batch = next(iter_train_image_sampler) - image_batch = get_dict_to_torch(image_batch, device=self.device) - batch = self.train_pixel_sampler.sample(image_batch) + ray_indices, batch = next(iter_dataloader_train) writer.put_time( name=writer.EventName.ITER_LOAD_TIME, start_time=data_start, @@ -202,14 +182,14 @@ class Trainer: avg_over_iters=True, ) iter_start = time() - loss_dict = self.train_iteration(batch, step, _callback=[update_occupancy]) + loss_dict = self.train_iteration(ray_indices, batch, step, _callback=[update_occupancy]) writer.put_time( name=writer.EventName.RAYS_PER_SEC, start_time=iter_start, end_time=time(), step=step, avg_over_iters=True, - avg_over_batch=batch["indices"].shape[0], + avg_over_batch=ray_indices.shape[0], ) writer.put_time( name=writer.EventName.ITER_TRAIN_TIME, @@ -225,8 +205,7 @@ class Trainer: if step != 0 and self.config.graph.steps_per_save and step % self.config.graph.steps_per_save == 0: self.save_checkpoint(self.config.graph.model_dir, step) if step % self.config.graph.steps_per_test == 0: - for image_idx in self.config.data.val_image_indices: - _ = self.test_image(image_idx=image_idx, step=step) + self.eval_with_dataloader(self.dataloader_eval, step=step) self._write_out_storage(step) writer.put_time( @@ -245,10 +224,9 @@ class Trainer: writer.write_out_storage() @profiler.time_function - def train_iteration(self, batch: dict, step: int, _callback: List[Callable] = None): + def train_iteration(self, ray_indices, batch: dict, step: int, _callback: List[Callable] = None): """Run one iteration with a batch of inputs.""" - ray_indices = batch["indices"] - _, loss_dict = self.graph.forward(ray_indices, batch=batch, step=step) + _, loss_dict = self.graph.forward(ray_indices, batch=batch) loss = loss_dict["aggregated_loss"] self.optimizers.zero_grad_all() loss.backward() @@ -260,18 +238,16 @@ class Trainer: return loss_dict @profiler.time_function - def test_image(self, image_idx, step): + def test_image(self, camera_ray_bundle: RayBundle, batch: dict, step: int = None): """Test a specific image.""" self.graph.eval() - intrinsics = self.val_image_intrinsics[image_idx].to(self.device) - camera_to_world = self.val_image_camera_to_world[image_idx].to(self.device) - chunk_size = self.config.data.val_num_rays_per_chunk - training_camera_index = image_idx # TODO(ethan): change this because training and test should not be the same - outputs = self.graph.get_outputs_for_camera( - intrinsics, camera_to_world, chunk_size=chunk_size, training_camera_index=training_camera_index - ) - batch = self.val_image_dataset[image_idx] - batch = get_dict_to_torch(batch, device=self.device) + image_idx = int(camera_ray_bundle.camera_indices[0, 0]) + outputs = self.graph.get_outputs_for_camera_ray_bundle(camera_ray_bundle) psnr = self.graph.log_test_image_outputs(image_idx, step, batch, outputs) self.graph.train() return psnr + + def eval_with_dataloader(self, dataloader: EvalDataloader, step: int = None): + """Run evaluation with a given dataloader.""" + for camera_ray_bundle, batch in dataloader: + self.test_image(camera_ray_bundle, batch, step=step) diff --git a/pyrad/sfm/__init__.py b/pyrad/fields/__init__.py similarity index 100% rename from pyrad/sfm/__init__.py rename to pyrad/fields/__init__.py diff --git a/pyrad/nerf/fields/base.py b/pyrad/fields/base.py similarity index 96% rename from pyrad/nerf/fields/base.py rename to pyrad/fields/base.py index da229801f..28009ffb7 100644 --- a/pyrad/nerf/fields/base.py +++ b/pyrad/fields/base.py @@ -8,9 +8,9 @@ from typing import Dict, Optional, Tuple from torch import nn import torch from torchtyping import TensorType -from pyrad.nerf.field_modules.field_heads import FieldHeadNames +from pyrad.fields.modules.field_heads import FieldHeadNames -from pyrad.structures.rays import Frustums, PointSamples +from pyrad.cameras.rays import Frustums, PointSamples from pyrad.utils.misc import is_not_none diff --git a/pyrad/nerf/fields/instant_ngp_field.py b/pyrad/fields/instant_ngp_field.py similarity index 82% rename from pyrad/nerf/fields/instant_ngp_field.py rename to pyrad/fields/instant_ngp_field.py index ec9a184bc..7b0619a80 100644 --- a/pyrad/nerf/fields/instant_ngp_field.py +++ b/pyrad/fields/instant_ngp_field.py @@ -9,12 +9,11 @@ import torch import torch.nn.functional as F from torch.nn.parameter import Parameter -from pyrad.nerf.field_modules.encoding import Encoding, HashEncoding, SHEncoding -from pyrad.nerf.field_modules.field_heads import FieldHeadNames -from pyrad.nerf.fields.base import Field -from pyrad.nerf.fields.nerf_field import NeRFField -from pyrad.structures.rays import PointSamples -from pyrad.utils.misc import is_not_none +from pyrad.fields.modules.encoding import Encoding, HashEncoding, SHEncoding +from pyrad.fields.modules.field_heads import FieldHeadNames +from pyrad.fields.base import Field +from pyrad.fields.nerf_field import NeRFField +from pyrad.cameras.rays import PointSamples try: import tinycudann as tcnn @@ -30,6 +29,11 @@ def get_normalized_positions(positions, aabb): return positions +def get_normalized_directions(directions): + """SH encoding must be in the range [0, 1]""" + return (directions + 1.0) / 2.0 + + class TCNNInstantNGPField(Field): """NeRF Field""" @@ -38,7 +42,6 @@ class TCNNInstantNGPField(Field): ) -> None: super().__init__() - # TODO: make this a parameter that is put to the correct device self.aabb = Parameter(aabb, requires_grad=False) self.geo_feat_dim = geo_feat_dim @@ -92,26 +95,24 @@ class TCNNInstantNGPField(Field): def get_density(self, point_samples: PointSamples): """Computes and returns the densities.""" - # TODO: add valid_mask masking! - # remap positions to range 0 to 1 based on the aabb - positions = get_normalized_positions(point_samples.positions, self.aabb) + positions = get_normalized_positions(point_samples.frustums.get_positions(), self.aabb) positions_flat = positions.view(-1, 3) dtype = positions_flat.dtype x = self.position_encoding(positions_flat) - h = self.mlp_base(x).view(*point_samples.positions.shape[:-1], -1).to(dtype) + h = self.mlp_base(x).view(*point_samples.frustums.get_positions().shape[:-1], -1).to(dtype) density_before_activation, base_mlp_out = torch.split(h, [1, self.geo_feat_dim], dim=-1) density = F.softplus(density_before_activation) return density, base_mlp_out def get_outputs(self, point_samples: PointSamples, density_embedding=None): # TODO: add valid_mask masking! - assert is_not_none(density_embedding) - directions = point_samples.directions + # tcnn requires directions in the range [0,1] + directions = get_normalized_directions(point_samples.frustums.directions) directions_flat = directions.view(-1, 3) dtype = directions_flat.dtype d = self.direction_encoding(directions_flat) h = torch.cat([d, density_embedding.view(-1, self.geo_feat_dim)], dim=-1) - h = self.mlp_head(h).view(*point_samples.directions.shape[:-1], -1).to(dtype) + h = self.mlp_head(h).view(*point_samples.frustums.directions.shape[:-1], -1).to(dtype) rgb = torch.sigmoid(h) return {FieldHeadNames.RGB: rgb} @@ -145,7 +146,9 @@ class TorchInstantNGPField(NeRFField): def get_density(self, point_samples: PointSamples): normalized_point_samples = point_samples - normalized_point_samples.positions = get_normalized_positions(normalized_point_samples.positions, self.aabb) + normalized_point_samples.positions = get_normalized_positions( + normalized_point_samples.frustums.get_positions(), self.aabb + ) return super().get_density(normalized_point_samples) diff --git a/pyrad/nerf/field_modules/__init__.py b/pyrad/fields/modules/__init__.py similarity index 100% rename from pyrad/nerf/field_modules/__init__.py rename to pyrad/fields/modules/__init__.py diff --git a/pyrad/nerf/field_modules/base.py b/pyrad/fields/modules/base.py similarity index 100% rename from pyrad/nerf/field_modules/base.py rename to pyrad/fields/modules/base.py diff --git a/pyrad/nerf/field_modules/embedding.py b/pyrad/fields/modules/embedding.py similarity index 93% rename from pyrad/nerf/field_modules/embedding.py rename to pyrad/fields/modules/embedding.py index d265a0dc5..1fc29bb14 100644 --- a/pyrad/nerf/field_modules/embedding.py +++ b/pyrad/fields/modules/embedding.py @@ -5,7 +5,7 @@ Code for embeddings. import torch from torchtyping import TensorType -from pyrad.nerf.field_modules.base import FieldModule +from pyrad.fields.modules.base import FieldModule class Embedding(FieldModule): diff --git a/pyrad/nerf/field_modules/encoding.py b/pyrad/fields/modules/encoding.py similarity index 97% rename from pyrad/nerf/field_modules/encoding.py rename to pyrad/fields/modules/encoding.py index 75c058ecf..b28fb4ddf 100644 --- a/pyrad/nerf/field_modules/encoding.py +++ b/pyrad/fields/modules/encoding.py @@ -9,7 +9,7 @@ import torch.nn.functional as F from torch import nn from torchtyping import TensorType -from pyrad.nerf.field_modules.base import FieldModule +from pyrad.fields.modules.base import FieldModule from pyrad.utils.math import components_from_spherical_harmonics @@ -200,11 +200,17 @@ class HashEncoding(Encoding): """Hash tensor using method described in Instant-NGP Args: - in_tensor (TensorType[..., "num_levels", 3]): Tensor to be hashed + in_tensor (TensorType[..., "num_levels", 3]): Tensor to be hashed Returns: - TensorType[..., "num_levels"]: Hashed tensor + TensorType[..., "num_levels"]: Hashed tensor """ + + # min_val = torch.min(in_tensor) + # max_val = torch.max(in_tensor) + # assert min_val >= 0.0 + # assert max_val <= 1.0 + in_tensor = in_tensor * torch.tensor([1, 2654435761, 805459861]).to(in_tensor.device) x = torch.bitwise_xor(in_tensor[..., 0], in_tensor[..., 1]) x = torch.bitwise_xor(x, in_tensor[..., 2]) diff --git a/pyrad/nerf/field_modules/field_heads.py b/pyrad/fields/modules/field_heads.py similarity index 99% rename from pyrad/nerf/field_modules/field_heads.py rename to pyrad/fields/modules/field_heads.py index 6ea53c6f8..18f558f85 100644 --- a/pyrad/nerf/field_modules/field_heads.py +++ b/pyrad/fields/modules/field_heads.py @@ -7,7 +7,7 @@ from typing import Optional from torch import nn from torchtyping import TensorType -from pyrad.nerf.field_modules.base import FieldModule +from pyrad.fields.modules.base import FieldModule class FieldHeadNames(Enum): diff --git a/pyrad/structures/__init__.py b/pyrad/fields/modules/field_losses.py similarity index 100% rename from pyrad/structures/__init__.py rename to pyrad/fields/modules/field_losses.py diff --git a/pyrad/nerf/field_modules/mlp.py b/pyrad/fields/modules/mlp.py similarity index 98% rename from pyrad/nerf/field_modules/mlp.py rename to pyrad/fields/modules/mlp.py index 5065ef39d..c464ab3bb 100644 --- a/pyrad/nerf/field_modules/mlp.py +++ b/pyrad/fields/modules/mlp.py @@ -7,7 +7,7 @@ import torch from torch import nn from torchtyping import TensorType -from pyrad.nerf.field_modules.base import FieldModule +from pyrad.fields.modules.base import FieldModule class MLP(FieldModule): diff --git a/pyrad/nerf/fields/nerf_field.py b/pyrad/fields/nerf_field.py similarity index 91% rename from pyrad/nerf/fields/nerf_field.py rename to pyrad/fields/nerf_field.py index faeb5dd0f..63faf25eb 100644 --- a/pyrad/nerf/fields/nerf_field.py +++ b/pyrad/fields/nerf_field.py @@ -7,11 +7,11 @@ import torch from torch import nn from torchtyping import TensorType -from pyrad.nerf.field_modules.encoding import Encoding, Identity -from pyrad.nerf.field_modules.field_heads import DensityFieldHead, FieldHead, FieldHeadNames, RGBFieldHead -from pyrad.nerf.field_modules.mlp import MLP -from pyrad.nerf.fields.base import Field -from pyrad.structures.rays import PointSamples +from pyrad.fields.modules.encoding import Encoding, Identity +from pyrad.fields.modules.field_heads import DensityFieldHead, FieldHead, FieldHeadNames, RGBFieldHead +from pyrad.fields.modules.mlp import MLP +from pyrad.fields.base import Field +from pyrad.cameras.rays import PointSamples class NeRFField(Field): diff --git a/pyrad/nerf/fields/nerfw_field.py b/pyrad/fields/nerfw_field.py similarity index 93% rename from pyrad/nerf/fields/nerfw_field.py rename to pyrad/fields/nerfw_field.py index 78fad2162..2a62d7d61 100644 --- a/pyrad/nerf/fields/nerfw_field.py +++ b/pyrad/fields/nerfw_field.py @@ -6,9 +6,9 @@ import torch from torch import nn from torchtyping import TensorType -from pyrad.nerf.field_modules.embedding import Embedding -from pyrad.nerf.field_modules.encoding import Encoding, Identity -from pyrad.nerf.field_modules.field_heads import ( +from pyrad.fields.modules.embedding import Embedding +from pyrad.fields.modules.encoding import Encoding, Identity +from pyrad.fields.modules.field_heads import ( DensityFieldHead, FieldHeadNames, RGBFieldHead, @@ -16,9 +16,9 @@ from pyrad.nerf.field_modules.field_heads import ( TransientRGBFieldHead, UncertaintyFieldHead, ) -from pyrad.nerf.field_modules.mlp import MLP -from pyrad.nerf.fields.base import Field -from pyrad.structures.rays import PointSamples +from pyrad.fields.modules.mlp import MLP +from pyrad.fields.base import Field +from pyrad.cameras.rays import PointSamples class VanillaNerfWField(Field): diff --git a/pyrad/fields/occupancy_fields/base.py b/pyrad/fields/occupancy_fields/base.py new file mode 100644 index 000000000..e69de29bb diff --git a/pyrad/nerf/occupancy_grid.py b/pyrad/fields/occupancy_fields/occupancy_grid.py similarity index 100% rename from pyrad/nerf/occupancy_grid.py rename to pyrad/fields/occupancy_fields/occupancy_grid.py diff --git a/pyrad/graphs/__init__.py b/pyrad/graphs/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/pyrad/nerf/graph/base.py b/pyrad/graphs/base.py similarity index 75% rename from pyrad/nerf/graph/base.py rename to pyrad/graphs/base.py index f18f9b700..6a090a5c8 100644 --- a/pyrad/nerf/graph/base.py +++ b/pyrad/graphs/base.py @@ -11,10 +11,10 @@ from torch import nn from torch.nn import Parameter from torchtyping import TensorType -from pyrad.nerf.dataset.structs import SceneBounds -from pyrad.nerf.ray_generator import RayGenerator -from pyrad.structures.cameras import get_camera_model -from pyrad.structures.rays import RayBundle +from pyrad.cameras.cameras import Camera +from pyrad.cameras.rays import RayBundle +from pyrad.data.structs import SceneBounds +from pyrad.graphs.modules.ray_generator import RayGenerator from pyrad.utils.misc import get_masked_dict, instantiate_from_dict_config, is_not_none @@ -31,12 +31,7 @@ class AbstractGraph(nn.Module): return self.device_indicator_param.device @abstractmethod - def forward( - self, - ray_indices: TensorType["num_rays", 3], - batch: Union[str, Dict[str, torch.tensor]] = None, - step: int = None, - ): + def forward(self, ray_indices: TensorType["num_rays", 3], batch: Union[str, Dict[str, torch.tensor]] = None): """Process starting with ray indices. Turns them into rays, then performs volume rendering.""" @@ -88,16 +83,9 @@ class Graph(AbstractGraph): def get_outputs(self, ray_bundle: RayBundle): """Takes in a Ray Bundle and returns a dictionary of outputs.""" - def forward( - self, - ray_indices: TensorType["num_rays", 3], - batch: Union[str, Dict[str, torch.tensor]] = None, - step: int = None, - ): - - # get the rays - original_ray_bundle = self.ray_generator.forward(ray_indices) # RayBundle - intersected_ray_bundle = self.collider(original_ray_bundle) + def forward_after_ray_generator(self, ray_bundle: RayBundle, batch: Union[str, Dict[str, torch.tensor]] = None): + """Run forward starting with a ray bundle.""" + intersected_ray_bundle = self.collider(ray_bundle) if isinstance(batch, type(None)): # during inference, keep all rays @@ -113,6 +101,11 @@ class Graph(AbstractGraph): aggregated_loss_dict = self.get_aggregated_loss_dict(loss_dict) return outputs, aggregated_loss_dict + def forward(self, ray_indices: TensorType["num_rays", 3], batch: Union[str, Dict[str, torch.tensor]] = None): + """Run the forward starting with ray indices.""" + ray_bundle = self.ray_generator.forward(ray_indices) + return self.forward_after_ray_generator(ray_bundle, batch=batch) + @abstractmethod def get_loss_dict(self, outputs, batch) -> Dict[str, torch.tensor]: """Computes and returns the losses.""" @@ -128,32 +121,29 @@ class Graph(AbstractGraph): return aggregated_loss_dict @torch.no_grad() - def get_outputs_for_camera(self, intrinsics, camera_to_world, chunk_size=1024, training_camera_index=0): + def get_outputs_for_camera_ray_bundle(self, camera_ray_bundle: RayBundle): """Takes in camera parameters and computes the output of the graph.""" - assert len(intrinsics.shape) == 1 - num_intrinsics_params = len(intrinsics) - camera_class = get_camera_model(num_intrinsics_params) - camera = camera_class(*intrinsics.tolist(), camera_to_world=camera_to_world) - camera_ray_bundle = camera.generate_camera_rays(device=self.get_device()) - # TODO(ethan): decide how to properly handle the image indices for validation images - camera_ray_bundle.set_camera_indices(camera_index=training_camera_index) + assert is_not_none(camera_ray_bundle.num_rays_per_chunk) image_height, image_width = camera_ray_bundle.origins.shape[:2] - num_rays = len(camera_ray_bundle) outputs = {} outputs_lists = defaultdict(list) - for i in range(0, num_rays, chunk_size): + for i in range(0, num_rays, camera_ray_bundle.num_rays_per_chunk): start_idx = i - end_idx = i + chunk_size - original_ray_bundle = camera_ray_bundle.get_row_major_sliced_ray_bundle(start_idx, end_idx) - intersected_ray_bundle = self.collider(original_ray_bundle) - outputs = self.get_outputs(intersected_ray_bundle) + end_idx = i + camera_ray_bundle.num_rays_per_chunk + ray_bundle = camera_ray_bundle.get_row_major_sliced_ray_bundle(start_idx, end_idx) + outputs = self.forward_after_ray_generator(ray_bundle) for output_name, output in outputs.items(): outputs_lists[output_name].append(output) for output_name, outputs_list in outputs_lists.items(): outputs[output_name] = torch.cat(outputs_list).view(image_height, image_width, -1) return outputs + def get_outputs_for_camera(self, camera: Camera): + """Get the graph outputs for a Camera.""" + camera_ray_bundle = camera.get_camera_ray_bundle(device=self.get_device()) + return self.get_outputs_for_camera_ray_bundle(camera_ray_bundle) + @abstractmethod def log_test_image_outputs(self, image_idx, step, batch, outputs): """Writes the test image outputs.""" diff --git a/pyrad/nerf/graph/instant_ngp.py b/pyrad/graphs/instant_ngp.py similarity index 88% rename from pyrad/nerf/graph/instant_ngp.py rename to pyrad/graphs/instant_ngp.py index 56165f986..68ea4250e 100644 --- a/pyrad/nerf/graph/instant_ngp.py +++ b/pyrad/graphs/instant_ngp.py @@ -9,16 +9,15 @@ from torch.nn import Parameter from torchmetrics import PeakSignalNoiseRatio, StructuralSimilarityIndexMeasure from torchmetrics.image.lpip import LearnedPerceptualImagePatchSimilarity -from pyrad.nerf.colliders import AABBBoxCollider -from pyrad.nerf.field_modules.field_heads import FieldHeadNames -from pyrad.nerf.fields.instant_ngp_field import field_implementation_to_class -from pyrad.nerf.graph.base import Graph -from pyrad.nerf.loss import MSELoss -from pyrad.nerf.occupancy_grid import OccupancyGrid -from pyrad.nerf.renderers import AccumulationRenderer, DepthRenderer, RGBRenderer -from pyrad.nerf.ray_sampler import PDFSampler, UniformSampler -from pyrad.structures import colors -from pyrad.structures.rays import RayBundle +from pyrad.fields.modules.field_heads import FieldHeadNames +from pyrad.fields.instant_ngp_field import field_implementation_to_class +from pyrad.graphs.base import Graph +from pyrad.optimizers.loss import MSELoss +from pyrad.fields.occupancy_fields.occupancy_grid import OccupancyGrid +from pyrad.renderers.renderers import AccumulationRenderer, DepthRenderer, RGBRenderer +from pyrad.graphs.modules.ray_sampler import PDFSampler, UniformSampler +from pyrad.utils import colors +from pyrad.cameras.rays import RayBundle from pyrad.utils import visualization, writer @@ -32,9 +31,6 @@ class NGPGraph(Graph): self.field = None super().__init__(intrinsics=intrinsics, camera_to_world=camera_to_world, **kwargs) - def populate_collider(self): - self.collider = AABBBoxCollider(self.scene_bounds) - def populate_fields(self): """Set the fields.""" # torch or tiny-cuda-nn version @@ -112,8 +108,8 @@ class NGPGraph(Graph): def get_loss_dict(self, outputs, batch): device = self.get_device() - pixels = batch["pixels"].to(device) - rgb_loss = self.rgb_loss(pixels, outputs["rgb"]) + image = batch["image"].to(device) + rgb_loss = self.rgb_loss(image, outputs["rgb"]) loss_dict = {"rgb_loss": rgb_loss} return loss_dict diff --git a/pyrad/graphs/modules/__init__.py b/pyrad/graphs/modules/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/pyrad/nerf/ray_generator.py b/pyrad/graphs/modules/ray_generator.py similarity index 95% rename from pyrad/nerf/ray_generator.py rename to pyrad/graphs/modules/ray_generator.py index 23516695a..0d76e1711 100644 --- a/pyrad/nerf/ray_generator.py +++ b/pyrad/graphs/modules/ray_generator.py @@ -3,8 +3,8 @@ Ray generator. """ from torch import nn from torchtyping import TensorType -from pyrad.structures.rays import RayBundle -from pyrad.structures.cameras import get_camera_model +from pyrad.cameras.rays import RayBundle +from pyrad.cameras.cameras import get_camera_model class RayGenerator(nn.Module): diff --git a/pyrad/nerf/ray_sampler.py b/pyrad/graphs/modules/ray_sampler.py similarity index 98% rename from pyrad/nerf/ray_sampler.py rename to pyrad/graphs/modules/ray_sampler.py index 64b1a03e2..575e67ff9 100644 --- a/pyrad/nerf/ray_sampler.py +++ b/pyrad/graphs/modules/ray_sampler.py @@ -9,8 +9,8 @@ import torch from torch import nn from torchtyping import TensorType -from pyrad.nerf.occupancy_grid import OccupancyGrid -from pyrad.structures.rays import RayBundle, RaySamples +from pyrad.fields.occupancy_fields.occupancy_grid import OccupancyGrid +from pyrad.cameras.rays import RayBundle, RaySamples class Sampler(nn.Module): diff --git a/pyrad/nerf/colliders.py b/pyrad/graphs/modules/scene_colliders.py similarity index 97% rename from pyrad/nerf/colliders.py rename to pyrad/graphs/modules/scene_colliders.py index 818291e21..a4ef6fb78 100644 --- a/pyrad/nerf/colliders.py +++ b/pyrad/graphs/modules/scene_colliders.py @@ -4,8 +4,8 @@ Ray generator. import torch from torch import nn from torchtyping import TensorType -from pyrad.nerf.dataset.structs import SceneBounds -from pyrad.structures.rays import RayBundle +from pyrad.data.structs import SceneBounds +from pyrad.cameras.rays import RayBundle class SceneBoundsCollider(nn.Module): diff --git a/pyrad/nerf/graph/nerfw.py b/pyrad/graphs/nerfw.py similarity index 90% rename from pyrad/nerf/graph/nerfw.py rename to pyrad/graphs/nerfw.py index d8fd9e518..86a0b8e2e 100644 --- a/pyrad/nerf/graph/nerfw.py +++ b/pyrad/graphs/nerfw.py @@ -6,16 +6,16 @@ import torch from torchmetrics import PeakSignalNoiseRatio, StructuralSimilarityIndexMeasure from torchmetrics.image.lpip import LearnedPerceptualImagePatchSimilarity -from pyrad.nerf.field_modules.encoding import NeRFEncoding -from pyrad.nerf.field_modules.field_heads import FieldHeadNames -from pyrad.nerf.fields.nerf_field import NeRFField -from pyrad.nerf.fields.nerfw_field import VanillaNerfWField -from pyrad.nerf.graph.base import Graph -from pyrad.nerf.loss import MSELoss -from pyrad.nerf.ray_sampler import PDFSampler, UniformSampler -from pyrad.nerf.renderers import AccumulationRenderer, DepthRenderer, RGBRenderer, UncertaintyRenderer -from pyrad.structures import colors -from pyrad.structures.rays import RayBundle +from pyrad.fields.modules.encoding import NeRFEncoding +from pyrad.fields.modules.field_heads import FieldHeadNames +from pyrad.fields.nerf_field import NeRFField +from pyrad.fields.nerfw_field import VanillaNerfWField +from pyrad.graphs.base import Graph +from pyrad.optimizers.loss import MSELoss +from pyrad.graphs.modules.ray_sampler import PDFSampler, UniformSampler +from pyrad.renderers.renderers import AccumulationRenderer, DepthRenderer, RGBRenderer, UncertaintyRenderer +from pyrad.utils import colors +from pyrad.cameras.rays import RayBundle from pyrad.utils import visualization, writer @@ -151,13 +151,13 @@ class NerfWGraph(Graph): def get_loss_dict(self, outputs, batch): device = outputs["rgb_coarse"].device - pixels = batch["pixels"].to(device) + image = batch["image"].to(device) rgb_coarse = outputs["rgb_coarse"] rgb_fine = outputs["rgb_fine"] density_transient = outputs["density_transient"] betas = outputs["uncertainty"] - rgb_loss_coarse = 0.5 * ((pixels - rgb_coarse) ** 2).sum(-1).mean() - rgb_loss_fine = 0.5 * (((pixels - rgb_fine) ** 2).sum(-1) / (betas[..., 0] ** 2)).mean() + rgb_loss_coarse = 0.5 * ((image - rgb_coarse) ** 2).sum(-1).mean() + rgb_loss_fine = 0.5 * (((image - rgb_fine) ** 2).sum(-1) / (betas[..., 0] ** 2)).mean() uncertainty_loss = 0.5 * (3 + torch.log(betas)).mean() density_loss = density_transient.mean() diff --git a/pyrad/nerf/graph/semantic_nerf.py b/pyrad/graphs/semantic_nerf.py similarity index 86% rename from pyrad/nerf/graph/semantic_nerf.py rename to pyrad/graphs/semantic_nerf.py index fb7146533..0bf752372 100644 --- a/pyrad/nerf/graph/semantic_nerf.py +++ b/pyrad/graphs/semantic_nerf.py @@ -7,15 +7,16 @@ from typing import Dict, Optional, Tuple import torch from torch import nn from torchtyping import TensorType +from pyrad.data.structs import Semantics -from pyrad.nerf.field_modules.encoding import Encoding, Identity, NeRFEncoding -from pyrad.nerf.field_modules.field_heads import DensityFieldHead, FieldHeadNames, RGBFieldHead, SemanticFieldHead -from pyrad.nerf.field_modules.mlp import MLP -from pyrad.nerf.fields.base import Field -from pyrad.nerf.fields.nerf_field import NeRFField -from pyrad.nerf.graph.vanilla_nerf import NeRFGraph -from pyrad.nerf.renderers import SemanticRenderer -from pyrad.structures.rays import PointSamples, RayBundle +from pyrad.fields.modules.encoding import Encoding, Identity, NeRFEncoding +from pyrad.fields.modules.field_heads import DensityFieldHead, FieldHeadNames, RGBFieldHead, SemanticFieldHead +from pyrad.fields.modules.mlp import MLP +from pyrad.fields.base import Field +from pyrad.fields.nerf_field import NeRFField +from pyrad.graphs.vanilla_nerf import NeRFGraph +from pyrad.renderers.renderers import SemanticRenderer +from pyrad.cameras.rays import PointSamples, RayBundle from pyrad.utils import writer @@ -61,7 +62,7 @@ class SemanticNerfField(Field): ) def get_density(self, point_samples: PointSamples): - encoded_xyz = self.position_encoding(point_samples.positions) + encoded_xyz = self.position_encoding(point_samples.frustums.get_positions()) base_mlp_out = self.mlp_base(encoded_xyz) density = self.field_head_density(base_mlp_out) return density, base_mlp_out @@ -83,10 +84,9 @@ class SemanticNerfField(Field): class SemanticNerfGraph(NeRFGraph): """Semantic-NeRF graph""" - def __init__(self, intrinsics=None, camera_to_world=None, stuff_classes=None, stuff_colors=None, **kwargs) -> None: - assert stuff_classes is not None - self.stuff_classes = stuff_classes - self.stuff_colors = stuff_colors + def __init__(self, intrinsics=None, camera_to_world=None, semantics: Semantics = None, **kwargs) -> None: + self.stuff_classes = semantics.stuff_classes + self.stuff_colors = semantics.stuff_colors super().__init__(intrinsics=intrinsics, camera_to_world=camera_to_world, **kwargs) def populate_fields(self): @@ -149,9 +149,9 @@ class SemanticNerfGraph(NeRFGraph): return outputs def get_loss_dict(self, outputs, batch): - pixels = batch["pixels"] - rgb_loss_coarse = self.rgb_loss(pixels, outputs["rgb_coarse"]) - rgb_loss_fine = self.rgb_loss(pixels, outputs["rgb_fine"]) + image = batch["image"] + rgb_loss_coarse = self.rgb_loss(image, outputs["rgb_coarse"]) + rgb_loss_fine = self.rgb_loss(image, outputs["rgb_fine"]) semantic_logits = outputs["semantic_fine"] semantic_classes = batch["semantics"][..., 0].long() semantic_loss_fine = self.cross_entropy_loss(semantic_logits, semantic_classes) diff --git a/pyrad/nerf/graph/tensorf.py b/pyrad/graphs/tensorf.py similarity index 100% rename from pyrad/nerf/graph/tensorf.py rename to pyrad/graphs/tensorf.py diff --git a/pyrad/nerf/graph/vanilla_nerf.py b/pyrad/graphs/vanilla_nerf.py similarity index 90% rename from pyrad/nerf/graph/vanilla_nerf.py rename to pyrad/graphs/vanilla_nerf.py index 2f19a712e..9ae52d098 100644 --- a/pyrad/nerf/graph/vanilla_nerf.py +++ b/pyrad/graphs/vanilla_nerf.py @@ -10,15 +10,15 @@ from torch.nn import Parameter from torchmetrics import PeakSignalNoiseRatio, StructuralSimilarityIndexMeasure from torchmetrics.image.lpip import LearnedPerceptualImagePatchSimilarity -from pyrad.nerf.field_modules.encoding import NeRFEncoding -from pyrad.nerf.field_modules.field_heads import FieldHeadNames -from pyrad.nerf.fields.nerf_field import NeRFField -from pyrad.nerf.graph.base import Graph -from pyrad.nerf.loss import MSELoss -from pyrad.nerf.ray_sampler import PDFSampler, UniformSampler -from pyrad.nerf.renderers import AccumulationRenderer, DepthRenderer, RGBRenderer -from pyrad.structures import colors -from pyrad.structures.rays import RayBundle +from pyrad.fields.modules.encoding import NeRFEncoding +from pyrad.fields.modules.field_heads import FieldHeadNames +from pyrad.fields.nerf_field import NeRFField +from pyrad.graphs.base import Graph +from pyrad.optimizers.loss import MSELoss +from pyrad.graphs.modules.ray_sampler import PDFSampler, UniformSampler +from pyrad.renderers.renderers import AccumulationRenderer, DepthRenderer, RGBRenderer +from pyrad.utils import colors +from pyrad.cameras.rays import RayBundle from pyrad.utils import visualization, writer @@ -117,10 +117,9 @@ class NeRFGraph(Graph): return outputs def get_loss_dict(self, outputs, batch): - device = outputs["rgb_coarse"].device - pixels = batch["pixels"].to(device) - rgb_loss_coarse = self.rgb_loss(pixels, outputs["rgb_coarse"]) - rgb_loss_fine = self.rgb_loss(pixels, outputs["rgb_fine"]) + image = batch["image"] + rgb_loss_coarse = self.rgb_loss(image, outputs["rgb_coarse"]) + rgb_loss_fine = self.rgb_loss(image, outputs["rgb_fine"]) loss_dict = {"rgb_loss_coarse": rgb_loss_coarse, "rgb_loss_fine": rgb_loss_fine} return loss_dict diff --git a/pyrad/optimizers/__init__.py b/pyrad/optimizers/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/pyrad/nerf/loss.py b/pyrad/optimizers/loss.py similarity index 100% rename from pyrad/nerf/loss.py rename to pyrad/optimizers/loss.py diff --git a/pyrad/nerf/optimizers.py b/pyrad/optimizers/optimizers.py similarity index 100% rename from pyrad/nerf/optimizers.py rename to pyrad/optimizers/optimizers.py diff --git a/pyrad/renderers/base.py b/pyrad/renderers/base.py new file mode 100644 index 000000000..e69de29bb diff --git a/pyrad/nerf/renderers.py b/pyrad/renderers/renderers.py similarity index 100% rename from pyrad/nerf/renderers.py rename to pyrad/renderers/renderers.py diff --git a/pyrad/utils/callbacks.py b/pyrad/utils/callbacks.py index 578886104..e0963cd67 100644 --- a/pyrad/utils/callbacks.py +++ b/pyrad/utils/callbacks.py @@ -2,8 +2,8 @@ Callback functions for training iterations """ -from pyrad.nerf.graph.base import Graph -from pyrad.nerf.occupancy_grid import OccupancyGrid +from pyrad.graphs.base import Graph +from pyrad.fields.occupancy_fields.occupancy_grid import OccupancyGrid def update_occupancy(graph: Graph): diff --git a/pyrad/structures/colors.py b/pyrad/utils/colors.py similarity index 100% rename from pyrad/structures/colors.py rename to pyrad/utils/colors.py diff --git a/pyrad/utils/math.py b/pyrad/utils/math.py index 87b571ee1..5bf1767f7 100644 --- a/pyrad/utils/math.py +++ b/pyrad/utils/math.py @@ -8,7 +8,7 @@ def components_from_spherical_harmonics(levels: int, directions: TensorType[..., """ Args: levels (int): Number of spherical harmonic levels to compute. - coeffs (TensorType[..., "coeffs"]): Spherical hamonic coefficients + directions (TensorType[..., 3]): Spherical hamonic coefficients Returns: TensorType[..., "components"]: Value for each component. diff --git a/pyrad/utils/writer.py b/pyrad/utils/writer.py index 66f4384b6..f83a32db9 100644 --- a/pyrad/utils/writer.py +++ b/pyrad/utils/writer.py @@ -5,6 +5,7 @@ Generic Writer class import enum import os +import sys from abc import abstractmethod from typing import Dict @@ -16,7 +17,6 @@ from omegaconf import DictConfig, ListConfig from torch.utils.tensorboard import SummaryWriter from torchtyping import TensorType -import pyrad.utils.writer from pyrad.utils.decorators import check_main_thread, decorate_all to8b = lambda x: (255 * torch.clamp(x, min=0, max=1)).to(torch.uint8) @@ -136,7 +136,7 @@ def setup_event_writers(config: DictConfig) -> None: """Initialization of all event writers specified in config""" logging_configs = config.logging.writer for writer_type in logging_configs: - writer_class = getattr(pyrad.utils.writer, writer_type) + writer_class = getattr(sys.modules[__name__], writer_type) writer_config = logging_configs[writer_type] if writer_type == "LocalWriter": curr_writer = writer_class(writer_config.save_dir, writer_config.stats_to_track, writer_config.max_log_size) diff --git a/pyrad/viewer/plotly.py b/pyrad/viewer/plotly.py index 98a0128da..f6838f4c0 100644 --- a/pyrad/viewer/plotly.py +++ b/pyrad/viewer/plotly.py @@ -6,7 +6,7 @@ import numpy as np import plotly.graph_objects as go import torch -from pyrad.structures.rays import RayBundle +from pyrad.cameras.rays import RayBundle def color_str(color): diff --git a/scripts/benchmarking/run_benchmark.py b/scripts/benchmarking/run_benchmark.py index 0e4e169ea..2ffcc743d 100644 --- a/scripts/benchmarking/run_benchmark.py +++ b/scripts/benchmarking/run_benchmark.py @@ -12,7 +12,7 @@ from hydra.core.global_hydra import GlobalHydra from omegaconf import DictConfig from tqdm import tqdm -from pyrad.nerf.trainer import Trainer +from pyrad.engine.trainer import Trainer BENCH = { "method": "vanilla_nerf", diff --git a/scripts/run_data_preprocessor.py b/scripts/run_data_preprocessor.py index fe20976f7..8e2b941b5 100644 --- a/scripts/run_data_preprocessor.py +++ b/scripts/run_data_preprocessor.py @@ -7,7 +7,7 @@ Script to process different dataset types into our format. from hydra import compose, initialize import json -from pyrad.nerf.dataset.utils import get_dataset_inputs +from pyrad.data.utils import get_dataset_inputs from pyrad.utils.io import get_absolute_path, make_dir, write_to_pkl from pyrad.utils.misc import get_hash_str_from_dict diff --git a/scripts/run_eval.py b/scripts/run_eval.py new file mode 100644 index 000000000..4dec9adb5 --- /dev/null +++ b/scripts/run_eval.py @@ -0,0 +1,88 @@ +""" +run_eval.py +""" +import json +import logging +import os +from datetime import date + +import torch +from hydra import compose, initialize +from hydra.core.global_hydra import GlobalHydra +from omegaconf import DictConfig +from tqdm import tqdm + +from pyrad.engine.trainer import Trainer +from pyrad.cameras.cameras import Camera + +BENCH = { + "method": "vanilla_nerf", + "hydra_base_dir": "outputs/", + "benchmark_date": "05-26-2022", + "object_list": ["mic", "ficus", "chair", "hotdog", "materials", "drums", "ship", "lego"], +} + + +def load_best_ckpt(hydra_dir: str, config: DictConfig) -> str: + """helper function to update config with latest checkpoint in specified hydra_dir""" + model_dir = os.path.join(hydra_dir, config.graph.model_dir) + latest_ckpt = os.listdir(model_dir) + latest_ckpt.sort() + latest_ckpt = latest_ckpt[-1] + step = os.path.splitext(latest_ckpt)[0].split("-")[-1] + config.graph.resume_train.load_dir = model_dir + config.graph.resume_train.load_step = int(step) + config.logging.writer.TensorboardWriter.save_dir = "/tmp/" + return os.path.join(model_dir, latest_ckpt) + + +def main(): + """Main function.""" + # benchmarks = {} + # hydra_base_dir = BENCH["hydra_base_dir"] + # method = BENCH["method"] + # benchmark_date = BENCH["benchmark_date"] + # for dataset in tqdm(BENCH["object_list"]): + + # hydra_dir = f"{hydra_base_dir}/blender_{dataset}_{benchmark_date}/{method}/" + # basename = os.listdir(hydra_dir)[0] + # hydra_dir = f"{hydra_dir}/{basename}" + # initialize(config_path=os.path.join("../../", hydra_dir, ".hydra/")) + # config = compose("configs/flat.yaml") + + with initialize(version_base=None, config_path="../configs"): + config = compose(config_name="flat.yaml") + trainer = Trainer(config, local_rank=0, world_size=1) + trainer.setup(test_mode=False) + + # create a camera trajectory + print("Running eval.") + # camera = Camera() + # trainer.graph.eval() + # camera_ray_bundle = camera.get_camera_ray_bundle(device=trainer.device) + image_idx = 0 + camera_ray_bundle, batch = trainer.dataloader_eval.get_data_from_image_idx(image_idx) + image_height, image_width = camera_ray_bundle.origins.shape[:2] + print(image_height, image_width) + outputs = trainer.graph.get_outputs_for_camera_ray_bundle(camera_ray_bundle) + + # print(outputs) + + # avg_psnr = 0 + # for step, image_idx in enumerate(config.data.val_image_indices): + # with torch.no_grad(): + # psnr = trainer.test_image(image_idx=image_idx, step=step) + # avg_psnr = (step * avg_psnr + psnr) / (step + 1) + # benchmarks[dataset] = (avg_psnr, ckpt) + # GlobalHydra.instance().clear() + + # benchmark_info = {"bench": BENCH, "results": benchmarks} + # timestamp = date.today().strftime("%b-%d-%Y") + # json_file = os.path.join(BENCH["hydra_base_dir"], f"{timestamp}.json") + # with open(json_file, "w", encoding="utf8") as f: + # json.dump(benchmark_info, f, indent=2) + # logging.info("saved benchmark results to %s", json_file) + + +if __name__ == "__main__": + main() diff --git a/scripts/run_train.py b/scripts/run_train.py index b51b78f81..9610f098e 100644 --- a/scripts/run_train.py +++ b/scripts/run_train.py @@ -19,7 +19,7 @@ import torch.multiprocessing as mp from omegaconf import DictConfig import yaml -from pyrad.nerf.trainer import Trainer +from pyrad.engine.trainer import Trainer from pyrad.utils import comms, profiler logging.basicConfig(format="[%(filename)s:%(lineno)d] %(message)s", level=logging.DEBUG) @@ -102,7 +102,7 @@ def _distributed_worker( assert num_gpus_per_machine <= torch.cuda.device_count() torch.cuda.set_device(local_rank) - _set_random_seed(config.seed + global_rank) + _set_random_seed(config.machine_config.seed + global_rank) comms.synchronize(world_size) output = main_func(local_rank, world_size, config) @@ -151,11 +151,11 @@ def launch( world_size = num_machines * num_gpus_per_machine if world_size == 0: # Using only CPU and one process. - _set_random_seed(config.seed) + _set_random_seed(config.machine_config.seed) main_func(local_rank=0, world_size=0, config=config) elif world_size == 1: # Using one gpu and one process. - _set_random_seed(config.seed) + _set_random_seed(config.machine_config.seed) try: main_func(local_rank=0, world_size=1, config=config) except KeyboardInterrupt: diff --git a/tests/structures/test_cameras.py b/tests/cameras/test_cameras.py similarity index 84% rename from tests/structures/test_cameras.py rename to tests/cameras/test_cameras.py index a689baa20..8e4313a3c 100644 --- a/tests/structures/test_cameras.py +++ b/tests/cameras/test_cameras.py @@ -4,14 +4,14 @@ Test the camera classes. import torch -from pyrad.structures.cameras import EquirectangularCamera, PinholeCamera -from pyrad.structures.rays import RayBundle +from pyrad.cameras.cameras import EquirectangularCamera, PinholeCamera +from pyrad.cameras.rays import RayBundle def test_pinhole_camera(): """Test that the pinhole camera model works.""" pinhole_camera = PinholeCamera(cx=400.0, cy=400.0, fx=10.0, fy=10.0) - camera_ray_bundle = pinhole_camera.generate_camera_rays() + camera_ray_bundle = pinhole_camera.get_camera_ray_bundle() assert isinstance(camera_ray_bundle, RayBundle) assert torch.allclose(camera_ray_bundle.origins[0], torch.tensor([0.0, 0.0, 0.0])) @@ -26,7 +26,7 @@ def test_pinhole_camera(): def test_equirectangular_camera(): """Test that the equirectangular camera model works.""" equirectangular_camera = EquirectangularCamera(height=400, width=400) - camera_ray_bundle = equirectangular_camera.generate_camera_rays() + camera_ray_bundle = equirectangular_camera.get_camera_ray_bundle() assert isinstance(camera_ray_bundle, RayBundle) assert torch.allclose(camera_ray_bundle.origins[0], torch.tensor([0.0, 0.0, 0.0])) diff --git a/tests/nerf/field_modules/test_embedding.py b/tests/fields/modules/test_embedding.py similarity index 77% rename from tests/nerf/field_modules/test_embedding.py rename to tests/fields/modules/test_embedding.py index c8c682b77..749ead2d9 100644 --- a/tests/nerf/field_modules/test_embedding.py +++ b/tests/fields/modules/test_embedding.py @@ -1,7 +1,7 @@ """ Embedding tests """ -from pyrad.nerf.field_modules.embedding import Embedding +from pyrad.fields.modules.embedding import Embedding def test_indexing(): diff --git a/tests/nerf/field_modules/test_encodings.py b/tests/fields/modules/test_encodings.py similarity index 98% rename from tests/nerf/field_modules/test_encodings.py rename to tests/fields/modules/test_encodings.py index 921dc37f1..ff9c2965c 100644 --- a/tests/nerf/field_modules/test_encodings.py +++ b/tests/fields/modules/test_encodings.py @@ -3,7 +3,7 @@ Encoding Tests """ import pytest import torch -from pyrad.nerf.field_modules import encoding +from pyrad.fields.modules import encoding def test_scaling_and_offset(): diff --git a/tests/nerf/field_modules/test_field_outputs.py b/tests/fields/modules/test_field_outputs.py similarity index 96% rename from tests/nerf/field_modules/test_field_outputs.py rename to tests/fields/modules/test_field_outputs.py index b7fe775ec..8d7a0f69e 100644 --- a/tests/nerf/field_modules/test_field_outputs.py +++ b/tests/fields/modules/test_field_outputs.py @@ -5,7 +5,7 @@ import torch from torch import nn import pytest -from pyrad.nerf.field_modules.field_heads import ( +from pyrad.fields.modules.field_heads import ( DensityFieldHead, FieldHead, FieldHeadNames, diff --git a/tests/nerf/field_modules/test_mlp.py b/tests/fields/modules/test_mlp.py similarity index 92% rename from tests/nerf/field_modules/test_mlp.py rename to tests/fields/modules/test_mlp.py index 25a071f7a..15bfeb1f4 100644 --- a/tests/nerf/field_modules/test_mlp.py +++ b/tests/fields/modules/test_mlp.py @@ -4,7 +4,7 @@ MLP Test import torch from torch import nn -from pyrad.nerf.field_modules import MLP +from pyrad.fields.modules import MLP def test_mlp(): diff --git a/tests/nerf/test_fields.py b/tests/fields/test_fields.py similarity index 76% rename from tests/nerf/test_fields.py rename to tests/fields/test_fields.py index 57acc7f6c..ad76fa4e0 100644 --- a/tests/nerf/test_fields.py +++ b/tests/fields/test_fields.py @@ -3,9 +3,9 @@ Test the fields """ import torch -from pyrad.nerf.field_modules.field_heads import FieldHeadNames -from pyrad.nerf.fields.instant_ngp_field import TCNNInstantNGPField -from pyrad.structures.rays import Frustums, PointSamples +from pyrad.fields.modules.field_heads import FieldHeadNames +from pyrad.fields.instant_ngp_field import TCNNInstantNGPField +from pyrad.cameras.rays import Frustums, PointSamples def test_tcnn_instant_ngp_field(): @@ -31,9 +31,9 @@ def test_tcnn_instant_ngp_field(): frustums = Frustums( origins=positions, directions=directions, - frustum_starts=torch.zeros((*directions.shape[:-1], 1)), - frustum_ends=torch.zeros((*directions.shape[:-1], 1)), - pixel_area=torch.ones((*directions.shape[:-1], 1)), + frustum_starts=torch.zeros((*directions.shape[:-1], 1), device=device), + frustum_ends=torch.zeros((*directions.shape[:-1], 1), device=device), + pixel_area=torch.ones((*directions.shape[:-1], 1), device=device), ) point_samples = PointSamples(frustums=frustums) field_outputs = field.forward(point_samples) diff --git a/tests/nerf/test_occupancy_grid.py b/tests/graphs/modules/test_occupancy_grid.py similarity index 89% rename from tests/nerf/test_occupancy_grid.py rename to tests/graphs/modules/test_occupancy_grid.py index 4e10ed380..5110ee0bf 100644 --- a/tests/nerf/test_occupancy_grid.py +++ b/tests/graphs/modules/test_occupancy_grid.py @@ -3,7 +3,7 @@ Code to test the occupancy grid. """ import torch -from pyrad.nerf.occupancy_grid import OccupancyGrid +from pyrad.fields.occupancy_fields.occupancy_grid import OccupancyGrid def test_occupancy_grid(): diff --git a/tests/nerf/test_sampler.py b/tests/graphs/modules/test_ray_sampler.py similarity index 89% rename from tests/nerf/test_sampler.py rename to tests/graphs/modules/test_ray_sampler.py index 2daaf8b8d..6bca80045 100644 --- a/tests/nerf/test_sampler.py +++ b/tests/graphs/modules/test_ray_sampler.py @@ -2,10 +2,10 @@ Test samplers """ import torch -from pyrad.nerf.ray_sampler import PDFSampler, UniformSampler +from pyrad.graphs.modules.ray_sampler import PDFSampler, UniformSampler -from pyrad.structures.rays import RayBundle -from pyrad.nerf.colliders import NearFarCollider +from pyrad.cameras.rays import RayBundle +from pyrad.graphs.modules.scene_colliders import NearFarCollider def test_uniform_sampler(): diff --git a/tests/nerf/test_renderers.py b/tests/renderers/test_renderers.py similarity index 98% rename from tests/nerf/test_renderers.py rename to tests/renderers/test_renderers.py index 2c6a0ca6a..67f780b57 100644 --- a/tests/nerf/test_renderers.py +++ b/tests/renderers/test_renderers.py @@ -4,7 +4,7 @@ Test renderers import pytest import torch -from pyrad.nerf import renderers +from pyrad.renderers import renderers def test_rgb_renderer():