We identified that these logging statements can deteriorate performance in certain cases. I propose removing them from the regular renderer implementation and letting individuals re-insert debug logging wherever needed on a case-by-case basis.
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:
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: 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: 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:
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: 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: 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:
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
```
Reviewed By: bottler
Differential Revision: D37775767
Pulled By: d4l3k
fbshipit-source-id: 5f001903985976d7067d1fa0a3102d602790e3e8
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: A new version of json index dataset provider supporting CO3Dv2
Reviewed By: shapovalov
Differential Revision: D37690918
fbshipit-source-id: bf2d5fc9d0f1220259e08661dafc69cdbe6b7f94
Summary:
Implements several changes needed for the CO3Dv2 release:
- FrameData contains crop_bbox_xywh which defines the outline of the image crop corresponding to the image-shaped tensors in FrameData
- revised the definition of a bounding box inside JsonDatasetIndex: bbox_xyxy is [xmin, ymin, xmax, ymax], where xmax, ymax are not inclusive; bbox_xywh = [xmin, ymain, xmax-xmin, ymax-ymin]
- is_filtered for detecting whether the entries of the dataset were somehow filtered
- seq_frame_index_to_dataset_index allows to skip entries that are not present in the dataset
Reviewed By: shapovalov
Differential Revision: D37687547
fbshipit-source-id: 7842756b0517878cc0964fc0935d3c0769454d78
Summary: cu116 builds need to happen in a specific image.
Reviewed By: patricklabatut
Differential Revision: D37680352
fbshipit-source-id: 81bef0642ad832e83e4eba6321287759b3229303
Summary: As part of removing Task, move camera difficulty bin breaks from hard code to the top level.
Reviewed By: davnov134
Differential Revision: D37491040
fbshipit-source-id: f2d6775ebc490f6f75020d13f37f6b588cc07a0b
Summary: As part of removing Task, make the dataset code generate the source cameras for itself. There's a small optimization available here, in that the JsonIndexDataset could avoid loading images.
Reviewed By: shapovalov
Differential Revision: D37313423
fbshipit-source-id: 3e5e0b2aabbf9cc51f10547a3523e98c72ad8755
Summary:
Make ViewMetrics easy to replace by putting them into an OmegaConf dataclass.
Also, re-word a few variable names and fix minor TODOs.
Reviewed By: bottler
Differential Revision: D37327157
fbshipit-source-id: 78d8e39bbb3548b952f10abbe05688409fb987cc
Summary: After landing https://github.com/pytorch/pytorch/pull/69607, that made it an error to use indexing with `cpu_tensor[cuda_indices]`. There was one outstanding test in fbcode that incorrectly used indexing in that way, which is fixed here
Reviewed By: bottler, osalpekar
Differential Revision: D37128838
fbshipit-source-id: 611b6f717b5b5d89fa61fd9ebeb513ad7e65a656
Summary:
David had his code crashed when using frame_annot["meta"] dictionary. Turns out we had a typo.
The tests were passing by chance since all the keys were single-character strings.
Reviewed By: bottler
Differential Revision: D37503987
fbshipit-source-id: c12b0df21116cfbbc4675a0182b9b9e6d62bad2e
Summary: small followup to D37172537 (cba26506b6) and D37209012 (81d63c6382): changing default #harmonics and improving a test
Reviewed By: shapovalov
Differential Revision: D37412357
fbshipit-source-id: 1af1005a129425fd24fa6dd213d69c71632099a0
Summary: The blender synthetic dataset contains object masks in the alpha channel. Provide these in the corresponding dataset.
Reviewed By: shapovalov
Differential Revision: D37344380
fbshipit-source-id: 3ddacad9d667c0fa0ae5a61fb1d2ffc806c9abf3
Summary: Images were coming out in the wrong format.
Reviewed By: shapovalov
Differential Revision: D37291278
fbshipit-source-id: c10871c37dd186982e7abf2071ac66ed583df2e6
Summary: Just register ImplicitronDataSource. We don't use it as pluggable yet here.
Reviewed By: shapovalov
Differential Revision: D37315698
fbshipit-source-id: ac41153383f9ab6b14ac69a3dfdc44aca0d94995
Summary: Document the inputs of idr functions and distinguish n_harmonic_functions to be 0 (simple embedding) versus -1 (no embedding).
Reviewed By: davnov134
Differential Revision: D37209012
fbshipit-source-id: 6e5c3eae54c4e5e8c3f76cad1caf162c6c222d52
Summary: Use generator.permutation instead of choice so that different options for n_known_frames_for_test are nested.
Reviewed By: davnov134
Differential Revision: D37210906
fbshipit-source-id: fd0d34ce62260417c3f63354a3f750aae9998b0d
Summary: Allow specifying a color for non-opaque pixels in LSTMRenderer.
Reviewed By: davnov134
Differential Revision: D37172537
fbshipit-source-id: 6039726678bb7947f7d8cd04035b5023b2d5398c
Summary: Copy code from NeRF for loading LLFF data and blender synthetic data, and create dataset objects for them
Reviewed By: shapovalov
Differential Revision: D35581039
fbshipit-source-id: af7a6f3e9a42499700693381b5b147c991f57e5d
