GitLab

Summary:
Pull Request resolved: https://github.com/facebookresearch/d2go/pull/199

- `create_fake_detection_data_loader` currently doesn't take `cfg` as input, sometimes we need to test the augmentation that needs more complicated different cfg.
- name is a bit bad, rename it to `create_detection_data_loader_on_toy_dataset`.
- width/height were the resized size previously, we want to change it to the size of data source (image files) and use `cfg` to control resized size.

Update V3:
In V2 there're some test failures, the reason is that V2 is building data loader (via GeneralizedRCNN runner) using actual test config instead of default config before this diff + dataset name change. In V3 we uses the test's runner instead of default runner for the consistency. This reveals some real bugs that we didn't test before.

Reviewed By: omkar-fb

Differential Revision: D35238890

fbshipit-source-id: 28a6037374e74f452f91b494bd455b38d3a48433

Summary:
Pull Request resolved: https://github.com/facebookresearch/d2go/pull/185

The `DiskCachedDatasetFromList` was originally in the `d2go/data/utils.py`, so the class is declared by default. Therefore the clean up call (https://fburl.com/code/cu7hswhx) is always called even when the feature is not enabled. This diff move it to a new place and delay the import, so the clean up won't run.

Reviewed By: tglik

Differential Revision: D34601363

fbshipit-source-id: 734bb9b2c7957d7437ad40c4bfe60a441ec2f23a

Summary:
# TLDR: To use this feature, setting `D2 (https://github.com/facebookresearch/d2go/commit/7992f91324aee6ae59795063a007c6837e60cdb8)GO_DATA.DATASETS.DISK_CACHE.ENABLED` to `True`.

To support larger datasets, one idea is to offload the DatasetFromList from RAM to disk to avoid OOM. `DiskCachedDatasetFromList` is a drop-in replacement for `DatasetFromList`, during `__init__`, it puts serialized list onto the disk and only stores the mapping in the RAM (the mapping could be represented by a list of addresses or even just a single number, eg. every N item is grouped together and N is the fixed number), then the `__getitem__` reads data from disk and deserializes the element. Some more details:
- Originally the RAM cost is `O(s*G*N)` where `s` is average data size, `G` is #GPUs, `N` is dataset size. When diskcache is enabled, depending on the type of mapping, the final RAM cost is constant or O(N) with a very small coefficient; the final disk cost is `O(s*N)`.
- The RAM usage is peaked at preparing stage, the cost is `O(s*N)`, if this becomes bottleneck, we probably need to think about modifying the data loading function (registered in DatasetCatalog). We also change the data loading function to only run on local master process, otherwise RAM will be peaked at `O(s*G*N)` if all processes are loading data at the same time.
- The time overhead of initialization is linear to dataset size, this is capped by disk I/O speed and performance of diskcache library. Benchmark shows it can at least handle 1GB per minute if writing in chucks (much worse if not), which should be fine in most use cases.
- There're also a bit time overhead when reading the data, but this is usually negligible compared with reading files from external storage like manifold.

It's not very easy to integrate this into D2 (https://github.com/facebookresearch/d2go/commit/7992f91324aee6ae59795063a007c6837e60cdb8)/D2 (https://github.com/facebookresearch/d2go/commit/7992f91324aee6ae59795063a007c6837e60cdb8)Go cleanly without patching the code, several approaches:
- Integrate into D2 (https://github.com/facebookresearch/d2go/commit/7992f91324aee6ae59795063a007c6837e60cdb8) directly (modifying D2 (https://github.com/facebookresearch/d2go/commit/7992f91324aee6ae59795063a007c6837e60cdb8)'s `DatasetFromList` and `get_detection_dataset_dicts`): might be the cleanest way, but D2 (https://github.com/facebookresearch/d2go/commit/7992f91324aee6ae59795063a007c6837e60cdb8) doesn't depend on `diskcache` and this is a bit experimental right now.
- D2 (https://github.com/facebookresearch/d2go/commit/7992f91324aee6ae59795063a007c6837e60cdb8)Go uses its own version of [_train_loader_from_config](https://fburl.com/code/0gig5tj2) that wraps the returned `dataset`. It has two issues: 1): it's hard to make the underlying `get_detection_dataset_dicts` only run on local master, partly because building sampler uses `comm.shared_random_seed()`, things can easily go out-of -sync 2): needs some duplicated code for test loader.
- pass new arguments along the way, it requires touching D2 (https://github.com/facebookresearch/d2go/commit/7992f91324aee6ae59795063a007c6837e60cdb8)'s code as well, and we need to carry new arguments in lot of places.

Lots of TODOs:
- Automatically enable this when dataset is larger than certain threshold (need to figure out how to do this in multiple GPUs, some communication is needed if only local master is reading the dataset).
- better cleanups
- figure out the best way of integrating this (patching is a bit hacky) into D2 (https://github.com/facebookresearch/d2go/commit/7992f91324aee6ae59795063a007c6837e60cdb8)/D2 (https://github.com/facebookresearch/d2go/commit/7992f91324aee6ae59795063a007c6837e60cdb8)Go.
- run more benchmarks
- add unit test (maybe also enable integration tests using 2 nodes 2 GPUs for distributed settings)

Reviewed By: sstsai-adl

Differential Revision: D27451187

fbshipit-source-id: 7d329e1a3c3f9ec1fb9ada0298a52a33f2730e15

Reviewed By: zhanghang1989

Differential Revision: D27783989

fbshipit-source-id: f05c11e396a2f62366721b365929b29f05d5bc02

Summary: Separate unit tests into individual folder based on functionality.

Reviewed By: wat3rBro

Differential Revision: D27132567

fbshipit-source-id: 9a8200be530ca14c7ef42191d59795b05b9800cc

Summary: Not d2go.tests is not a library for oss, move utils code to d2go.utils.testing

Reviewed By: zhanghang1989

Differential Revision: D26706933

fbshipit-source-id: 85767b66bbb6c67db05e11823beb4840220b2aa3

fbshipit-source-id: f4a8ba78691d8cf46e003ef0bd2e95f170932778