GitLab

Summary: Made the config system call open_dict when it calls the tweak function.

Reviewed By: shapovalov

Differential Revision: D38315334

fbshipit-source-id: 5924a92d8d0bf399bbf3788247f81fc990e265e7

Summary:
Stats are logically connected to the training loop, not to the model. Hence, moving to the training loop.

Also removing resume_epoch from OptimizerFactory in favor of a single place - ModelFactory. This removes the need for config consistency checks etc.

Reviewed By: kjchalup

Differential Revision: D38313475

fbshipit-source-id: a1d188a63e28459df381ff98ad8acdcdb14887b7

Summary: Blender data doesn't have depths or crops.

Reviewed By: shapovalov

Differential Revision: D38345583

fbshipit-source-id: a19300daf666bbfd799d0038aeefa14641c559d7

Summary: Simple DataLoaderMapProvider instance

Reviewed By: davnov134

Differential Revision: D38326719

fbshipit-source-id: 58556833e76fae5790d25a59bea0aac4ce046bf1

Summary: fix to the D38275943 (https://github.com/facebookresearch/pytorch3d/commit/597e0259dc43bf4903e9c99f5d61410c1ad75b78).

Reviewed By: bottler

Differential Revision: D38355683

fbshipit-source-id: f326f45279fafa57f24b9211ebd3fda18a518937

Summary: Before this diff, train_stats.py would not be created by default, EXCEPT when resuming training. This makes them appear from start.

Reviewed By: shapovalov

Differential Revision: D38320341

fbshipit-source-id: 8ea5b99ec81c377ae129f58e78dc2eaff94821ad

Summary: Remove the dataset's need to provide the task type.

Reviewed By: davnov134, kjchalup

Differential Revision: D38314000

fbshipit-source-id: 3805d885b5d4528abdc78c0da03247edb9abf3f7

Summary:
Added _NEED_CONTROL
 to JsonIndexDatasetMapProviderV2 and made dataset_tweak_args use it.

Reviewed By: bottler

Differential Revision: D38313914

fbshipit-source-id: 529847571065dfba995b609a66737bd91e002cfe

Summary: Only import it if you ask for it.

Reviewed By: kjchalup

Differential Revision: D38327167

fbshipit-source-id: 3f05231f26eda582a63afc71b669996342b0c6f9

Summary: <see title>

Reviewed By: bottler

Differential Revision: D38314727

fbshipit-source-id: 7178b816a22b06af938a35c5f7bb88404fb1b1c4

Summary: Made eval_batches be set in call to `__init__` not after the construction as they were before

Reviewed By: bottler

Differential Revision: D38275943

fbshipit-source-id: 32737401d1ddd16c284e1851b7a91f8b041c406f

Summary: Currently, seeds are set only inside the train loop. But this does not ensure that the model weights are initialized the same way everywhere which makes all experiments irreproducible. This diff fixes it.

Reviewed By: bottler

Differential Revision: D38315840

fbshipit-source-id: 3d2ecebbc36072c2b68dd3cd8c5e30708e7dd808

Summary: Fixes the MC rasterization bug

Reviewed By: bottler

Differential Revision: D38312234

fbshipit-source-id: 910cf809ef3faff3de7a8d905b0821f395a52edf

Summary: Make a dummy single-scene dataset using the code from generate_cow_renders (used in existing NeRF tutorials)

Reviewed By: kjchalup

Differential Revision: D38116910

fbshipit-source-id: 8db6df7098aa221c81d392e5cd21b0e67f65bd70

Summary:
This large diff rewrites a significant portion of Implicitron's config hierarchy. The new hierarchy, and some of the default implementation classes, are as follows:
```
Experiment
    data_source: ImplicitronDataSource
        dataset_map_provider
        data_loader_map_provider
    model_factory: ImplicitronModelFactory
        model: GenericModel
    optimizer_factory: ImplicitronOptimizerFactory
    training_loop: ImplicitronTrainingLoop
        evaluator: ImplicitronEvaluator
```

1) Experiment (used to be ExperimentConfig) is now a top-level Configurable and contains as members mainly (mostly new) high-level factory Configurables.
2) Experiment's job is to run factories, do some accelerate setup and then pass the results to the main training loop.
3) ImplicitronOptimizerFactory and ImplicitronModelFactory are new high-level factories that create the optimizer, scheduler, model, and stats objects.
4) TrainingLoop is a new configurable that runs the main training loop and the inner train-validate step.
5) Evaluator is a new configurable that TrainingLoop uses to run validation/test steps.
6) GenericModel is not the only model choice anymore. Instead, ImplicitronModelBase (by default instantiated with GenericModel) is a member of Experiment and can be easily replaced by a custom implementation by the user.

All the new Configurables are children of ReplaceableBase, and can be easily replaced with custom implementations.

In addition, I added support for the exponential LR schedule, updated the config files and the test, as well as added a config file that reproduces NERF results and a test to run the repro experiment.

Reviewed By: bottler

Differential Revision: D37723227

fbshipit-source-id: b36bee880d6aa53efdd2abfaae4489d8ab1e8a27

Summary:
This is an internal change in the config systen. It allows redefining a pluggable implementation with new default values. This is useful in notebooks / interactive use. For example, this now works.

        class A(ReplaceableBase):
            pass

        registry.register
        class B(A):
            i: int = 4

        class C(Configurable):
            a: A
            a_class_type: str = "B"

            def __post_init__(self):
                run_auto_creation(self)

        expand_args_fields(C)

        registry.register
        class B(A):
            i: int = 5

        c = C()

        assert c.a.i == 5

Reviewed By: shapovalov

Differential Revision: D38219371

fbshipit-source-id: 72911a9bd3426d3359cf8802cc016fc7f6d7713b

Summary:
Adding MeshRasterizerOpenGL, a faster alternative to MeshRasterizer. The new rasterizer follows the ideas from "Differentiable Surface Rendering via non-Differentiable Sampling".

The new rasterizer 20x faster on a 2M face mesh (try pose optimization on Nefertiti from https://www.cs.cmu.edu/~kmcrane/Projects/ModelRepository/!). The larger the mesh, the larger the speedup.

There are two main disadvantages:
* The new rasterizer works with an OpenGL backend, so requires pycuda.gl and pyopengl installed (though we avoided writing any C++ code, everything is in Python!)
* The new rasterizer is non-differentiable. However, you can still differentiate the rendering function if you use if with the new SplatterPhongShader which we recently added to PyTorch3D (see the original paper cited above).

Reviewed By: patricklabatut, jcjohnson

Differential Revision: D37698816

fbshipit-source-id: 54d120639d3cb001f096237807e54aced0acda25

Summary: Needed to properly change devices during OpenGL rasterization.

Reviewed By: jcjohnson

Differential Revision: D37698568

fbshipit-source-id: 38968149d577322e662d3b5d04880204b0a7be29

Summary:
EGLContext is a utility to render with OpenGL without an attached display (that is, without a monitor).

DeviceContextManager allows us to avoid unnecessary context creations and releases. See docstrings for more info.

Reviewed By: jcjohnson

Differential Revision: D36562551

fbshipit-source-id: eb0d2a2f85555ee110e203d435a44ad243281d2c

Summary: Error when sending an unbatched FrameData through GM.

Reviewed By: shapovalov

Differential Revision: D38036286

fbshipit-source-id: b8d280c61fbbefdc112c57ccd630ab3ccce7b44e

Summary: Avoid calculating all_train_cameras before it is needed, because it is slow in some datasets.

Reviewed By: shapovalov

Differential Revision: D38037157

fbshipit-source-id: 95461226655cde2626b680661951ab17ebb0ec75

Summary: lint issues (mostly flake) in implicitron

Reviewed By: patricklabatut

Differential Revision: D37920948

fbshipit-source-id: 8cb3c2a2838d111c80a211c98a404c210d4649ed

Summary: We especially need omegaconf when testing impicitron.

Reviewed By: patricklabatut

Differential Revision: D37921440

fbshipit-source-id: 4e66fde35aa29f60eabd92bf459cd584cfd7e5ca

Summary:
X-link: https://github.com/fairinternal/pytorch3d/pull/39

Blender and LLFF cameras were sending screen space focal length and principal point to a camera init function expecting NDC

Reviewed By: shapovalov

Differential Revision: D37788686

fbshipit-source-id: 2ddf7436248bc0d174eceb04c288b93858138582

Summary: Add the conditioning types to the repro yaml files. In particular, this fixes test_conditioning_type.

Reviewed By: shapovalov

Differential Revision: D37914537

fbshipit-source-id: 621390f329d9da662d915eb3b7bc709206a20552

Summary: For debugging, introduce PYTORCH3D_NO_ACCELERATE env var.

Reviewed By: shapovalov

Differential Revision: D37885393

fbshipit-source-id: de080080c0aa4b6d874028937083a0113bb97c23

Summary:
I tried to run `experiment.py` and `pytorch3d_implicitron_runner` and faced the failure with this traceback: https://www.internalfb.com/phabricator/paste/view/P515734086

It seems to be due to the new release of OmegaConf (version=2.2.2) which requires different typing. This fix helped to overcome it.

Reviewed By: bottler

Differential Revision: D37881644

fbshipit-source-id: be0cd4ced0526f8382cea5bdca9b340e93a2fba2

Summary:
EPnP fails the test when the number of points is below 6. As suggested, quadratic option is in theory to deal with as few as 4 points (so num_pts_thresh=3 is set). And when num_pts > num_pts_thresh=4, skip_q is False.

To avoid bumping num_pts_thresh while passing all the original tests, check_output is set to False when num_pts < 6, similar to the logic in Line 123-127.  It makes sure that the algo doesn't crash.

Reviewed By: shapovalov

Differential Revision: D37804438

fbshipit-source-id: 74576d63a9553e25e3ec344677edb6912b5f9354

Summary: New linter warning is complaining about `raise` inside `except`.

Reviewed By: kjchalup

Differential Revision: D37819264

fbshipit-source-id: 56ad5d0558ea39e1125f3c76b43b7376aea2bc7c

Summary: Removing 1 from the crop mask does not seem sensible.

Reviewed By: bottler, shapovalov

Differential Revision: D37843680

fbshipit-source-id: 70cec80f9ea26deac63312da62b9c8af27d2a010

Summary:
1. Random sampling of num batches without replacement not supported.
2.Providers should implement the interface for the training loop to work.

Reviewed By: bottler, davnov134

Differential Revision: D37815388

fbshipit-source-id: 8a2795b524e733f07346ffdb20a9c0eb1a2b8190

Summary: Fixing comments on D37592429 (https://github.com/facebookresearch/pytorch3d/commit/0dce883241ae638b9fa824f34fca9590d5f0782c)

Reviewed By: shapovalov

Differential Revision: D37752367

fbshipit-source-id: 40aa7ee4dc0c5b8b7a84a09d13a3933a9e3afedd

Summary: Accelerate is an additional implicitron dependency, so document it.

Reviewed By: shapovalov

Differential Revision: D37786933

fbshipit-source-id: 11024fe604107881f8ca29e17cb5cbfe492fc7f9

Summary:
1. Respecting `visdom_show_preds` parameter when it is False.
2. Clipping the images pre-visualisation, which is important for methods like SRN that are not arare of pixel value range.

Reviewed By: bottler

Differential Revision: D37786439

fbshipit-source-id: 8dbb5104290bcc5c2829716b663cae17edc911bd

Summary: one more bugfix in JsonIndexDataset

Reviewed By: bottler

Differential Revision: D37789138

fbshipit-source-id: 2fb2bda7448674091ff6b279175f0bbd16ff7a62

Summary: After recent accelerate change D37543870 (https://github.com/facebookresearch/pytorch3d/commit/aa8b03f31dc2a178f8d7da457df28f19b5917009), update interactive trainer test.

Reviewed By: shapovalov

Differential Revision: D37785932

fbshipit-source-id: 9211374323b6cfd80f6c5ff3a4fc1c0ca04b54ba

Summary:
This fixes a indexing bug in HardDepthShader and adds proper unit tests for both of the DepthShaders. This bug was introduced when updating the shader sizes and discovered when I switched my local model onto pytorch3d trunk instead of the patched copy.

Pull Request resolved: https://github.com/facebookresearch/pytorch3d/pull/1252

Test Plan:
Unit test + custom model code

```
pytest tests/test_shader.py
```

![image](https://user-images.githubusercontent.com/909104/178397456-f478d0e0-9f6c-467a-a85b-adb4c47adfee.png)

Reviewed By: bottler

Differential Revision: D37775767

Pulled By: d4l3k

fbshipit-source-id: 5f001903985976d7067d1fa0a3102d602790e3e8

renderer: add support for rendering high dimensional textures for classification/segmentation use cases (#1248)

Summary:
For 3D segmentation problems it's really useful to be able to train the models from multiple viewpoints using Pytorch3D as the renderer. Currently due to hardcoded assumptions in a few spots the mesh renderer only supports rendering RGB (3 dimensional) data. You can encode the classification information as 3 channel data but if you have more than 3 classes you're out of luck.

This relaxes the assumptions to make rendering semantic classes work with `HardFlatShader` and `AmbientLights` with no diffusion/specular. The other shaders/lights don't make any sense for classification since they mutate the texture values in some way.

This only requires changes in `Materials` and `AmbientLights`. The bulk of the code is the unit test.

Pull Request resolved: https://github.com/facebookresearch/pytorch3d/pull/1248

Test Plan: Added unit test that renders a 5 dimensional texture and compare dimensions 2-5 to a stored picture.

Reviewed By: bottler

Differential Revision: D37764610

Pulled By: d4l3k

fbshipit-source-id: 031895724d9318a6f6bab5b31055bb3f438176a5

Summary:
## Changes:
- Added Accelerate Library and refactored experiment.py to use it
- Needed to move `init_optimizer` and `ExperimentConfig` to a separate file to be compatible with submitit/hydra
- Needed to make some modifications to data loaders etc to work well with the accelerate ddp wrappers
- Loading/saving checkpoints incorporates an unwrapping step so remove the ddp wrapped model

## Tests

Tested with both `torchrun` and `submitit/hydra` on two gpus locally. Here are the commands:

**Torchrun**

Modules loaded:
```sh
1) anaconda3/2021.05   2) cuda/11.3   3) NCCL/2.9.8-3-cuda.11.3   4) gcc/5.2.0. (but unload gcc when using submit)
```

```sh
torchrun --nnodes=1 --nproc_per_node=2 experiment.py --config-path ./configs --config-name repro_singleseq_nerf_test
```

**Submitit/Hydra Local test**

```sh
~/pytorch3d/projects/implicitron_trainer$ HYDRA_FULL_ERROR=1 python3.9 experiment.py --config-name repro_singleseq_nerf_test --multirun --config-path ./configs  hydra/launcher=submitit_local hydra.launcher.gpus_per_node=2 hydra.launcher.tasks_per_node=2 hydra.launcher.nodes=1
```

**Submitit/Hydra distributed test**

```sh
~/implicitron/pytorch3d$ python3.9 experiment.py --config-name repro_singleseq_nerf_test --multirun --config-path ./configs  hydra/launcher=submitit_slurm hydra.launcher.gpus_per_node=8 hydra.launcher.tasks_per_node=8 hydra.launcher.nodes=1 hydra.launcher.partition=learnlab hydra.launcher.timeout_min=4320
```

## TODOS:
- Fix distributed evaluation: currently this doesn't work as the input format to the evaluation function is not suitable for gathering across gpus (needs to be nested list/tuple/dicts of objects that satisfy `is_torch_tensor`) and currently `frame_data`  contains `Cameras` type.
- Refactor the `accelerator` object to be accessible by all functions instead of needing to pass it around everywhere? Maybe have a `Trainer` class and add it as a method?
- Update readme with installation instructions for accelerate and also commands for running jobs with torchrun and submitit/hydra

X-link: https://github.com/fairinternal/pytorch3d/pull/37

Reviewed By: davnov134, kjchalup

Differential Revision: D37543870

Pulled By: bottler

fbshipit-source-id: be9eb4e91244d4fe3740d87dafec622ae1e0cf76

Summary: remove erroneous RandomDataLoaderMapProvider

Reviewed By: davnov134

Differential Revision: D37751116

fbshipit-source-id: cf3b555dc1e6304425914d1522b4f70407b498bf