GitLab

* Adding multi-weight support to Quantized ResNet.

* Update references script to support testing quantized models with the new API.

* Handle quantized models correctly in ref script.

* Fixing references for quantization.

Co-authored-by: Nicolas Hug <nicolashug@fb.com>

* Add type to default argument

To resolve issue #4694

* Resolve issue #4694

Add missing types on argument parser

* Update with ufmt

formatted with ufmt

* Updated with review

Updated with review

* Update type of arguments

Add train.py from video_classification, similarity and train_quantization.py
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

* Update EMA every X iters.

* Adding AdamW optimizer.

* Adjusting EMA decay scheme.

* Support custom weight decay for Normalization layers.

* Fix identation bug.

* Change EMA adjustment.

* Quality of life changes to faciliate testing

* ufmt format

* Fixing imports.

* Adding FixRes improvement.

* Support EMA in store_model_weights.

* Adding interpolation values.

* Change train_crop_size.

* Add interpolation option.

* Removing hardcoded interpolation and sizes from the scripts.

* Fixing linter.

* Incorporating feedback from code review.

* add ufmt as code formatter

* cleanup

* quote ufmt requirement

* split imports into more groups

* regenerate circleci config

* fix CI

* clarify local testing utils section

* use ufmt pre-commit hook

* split relative imports into local category

* Revert "split relative imports into local category"

This reverts commit f2e224cde2008c56c9347c1f69746d39065cdd51.

* pin black and usort dependencies

* fix local test utils detection

* fix ufmt rev

* add reference utils to local category

* fix usort config

* remove custom categories sorting

* Run pre-commit without fixing flake8

* got a double import in merge
Co-authored-by: Nicolas Hug <nicolashug@fb.com>

* Add submitit script, partition param and parser on its own method.

* Fix method names, handle add_help correctly and refactoring.

* Delete run_with_submitit.py file

* Refactoring mobilenetv3 to make code reusable.

* Adding quantizable MobileNetV3 architecture.

* Fix bug on reference script.

* Moving documentation of quantized models in the right place.

* Update documentation.

* Workaround for loading correct weights of quant model.

* Update weight URL and readme.

* Adding eval.

* remove unused imports after manual review

* Update torchvision/datasets/voc.py
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

* remove two more instances
Co-authored-by: Ben Weinstein <benweinstein@Bens-MacBook-Pro.local>
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

Summary:

We've made two recent changes to QAT in PyTorch core:
1. add support for SyncBatchNorm
2. make eager mode QAT prepare scripts respect device affinity

This PR updates the torchvision QAT reference script to take
advantage of both of these.  This should be landed after
https://github.com/pytorch/pytorch/pull/39337 (the last PT
fix) to avoid compatibility issues.

Test Plan:

```
python -m torch.distributed.launch
  --nproc_per_node 8
  --use_env
  references/classification/train_quantization.py
  --data-path {imagenet1k_subset}
  --output-dir {tmp}
  --sync-bn
```

Reviewers:

Subscribers:

Tasks:

Tags:

Summary:

Redo of https://github.com/pytorch/vision/pull/2191

Makes the classification QAT tutorial not crash when used
with DDP. There were two issues:

1. the model was moved to GPU before the observers were added, and they
are created on CPU. In the context of this repo, the fix is to finalize
the model before moving to GPU. We can potentially follow up with a
better error message in the future, in a separate PR.
2. the QAT conversion was running on the DDP'ed model, which had various
problems. The fix is to unwrap the model from DDP before cloning it for
evaluation.

There is still work to do on verifying that BN is working correctly in
QAT + DDP, but saving that for a separate PR.

Test Plan:

```
python -m torch.distributed.launch --use_env references/classification/train_quantization.py --data-path {path_to_imagenet_1k} --output_dir {output_dir}
```

Reviewers:

Subscribers:

Tasks:

Tags:

* remove sys.version_info == 2

* remove sys.version_info < 3

* remove from __future__ imports

* add quantized models

* Modify mobilenet.py documentation and clean up comments
Summary:

Test Plan:

Reviewers:

Subscribers:

Tasks:

Tags:

* Move fuse_model method to QuantizableInvertedResidual and clean up args documentation
Summary:

Test Plan:

Reviewers:

Subscribers:

Tasks:

Tags:

* Restore relu settings to default in resnet.py
Summary:

Test Plan:

Reviewers:

Subscribers:

Tasks:

Tags:

* Fix missing return in forward
Summary:

Test Plan:

Reviewers:

Subscribers:

Tasks:

Tags:

* Fix missing return in forwards
Summary:

Test Plan:

Reviewers:

Subscribers:

Tasks:

Tags:

* Change pretrained -> pretrained_float_models
Replace InvertedResidual with block

Summary:

Test Plan:

Reviewers:

Subscribers:

Tasks:

Tags:

* Update tests to follow similar structure to test_models.py, allowing for modular testing

Summary:

Test Plan:

Reviewers:

Subscribers:

Tasks:

Tags:

* Replace forward method with simple function assignment

Summary:

Test Plan:

Reviewers:

Subscribers:

Tasks:

Tags:

* Fix error in arguments for resnet18

Summary:

Test Plan:

Reviewers:

Subscribers:

Tasks:

Tags:

* pretrained_float_model argument missing for mobilenet

Summary:

Test Plan:

Reviewers:

Subscribers:

Tasks:

Tags:

* reference script for quantization aware training and post training quantization

* reference script for quantization aware training and post training quantization

* set pretrained_float_model as False and explicitly provide float model

Summary:

Test Plan:

Reviewers:

Subscribers:

Tasks:

Tags:

* Address review comments:
1. Replace forward with _forward
2. Use pretrained models in reference train/eval script
3. Modify test to skip if fbgemm is not supported

Summary:

Test Plan:

Reviewers:

Subscribers:

Tasks:

Tags:

* Fix lint errors.
Use _forward for common code between float and quantized models
Clean up linting for reference train scripts
Test over all quantizable models

Summary:

Test Plan:

Reviewers:

Subscribers:

Tasks:

Tags:

* Update default values for args in quantization/train.py

Summary:

Test Plan:

Reviewers:

Subscribers:

Tasks:

Tags:

* Update models to conform to new API with quantize argument
Remove apex in training script, add post training quant as an option
Add support for separate calibration data set.

Summary:

Test Plan:

Reviewers:

Subscribers:

Tasks:

Tags:

* Fix minor errors in train_quantization.py

Summary:

Test Plan:

Reviewers:

Subscribers:

Tasks:

Tags:

* Remove duplicate file

* Bugfix

* Minor improvements on the models

* Expose print_freq to evaluate

* Minor improvements on train_quantization.py

* Ensure that quantized models are created and run on the specified backends
Fix errors in test only mode

Summary:

Test Plan:

Reviewers:

Subscribers:

Tasks:

Tags:

* Add model urls

* Fix errors in quantized model tests.
Speedup creation of random quantized model by removing histogram observers

Summary:

Test Plan:

Reviewers:

Subscribers:

Tasks:

Tags:

* Move setting qengine prior to convert.

Summary:

Test Plan:

Reviewers:

Subscribers:

Tasks:

Tags:

* Fix lint error

Summary:

Test Plan:

Reviewers:

Subscribers:

Tasks:

Tags:

* Add readme.md

Summary:

Test Plan:

Reviewers:

Subscribers:

Tasks:

Tags:

* Readme.md

Summary:

Test Plan:

Reviewers:

Subscribers:

Tasks:

Tags:

* Fix lint