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Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

Replace tabs with spaces, add newlines to files and replace whitelist with allowlist

* removing the tab?

* initial commit

* Addressing Victor's comments
Co-authored-by: vfdev <vfdev.5@gmail.com>

* [WIP] Update ref example video classification

* [WIP] Updated video classification ref example

* Replaced mem format conversion functions by classes

* add a README for training object detection models

* replaced np.asarray with np.array to avoid warning messages

* added data-path for flexibility

* fixed a typo

* Add rough implementation of RetinaNet.

* Move AnchorGenerator to a seperate file.

* Move box similarity to Matcher.

* Expose extra blocks in FPN.

* Expose retinanet in __init__.py.

* Use P6 and P7 in FPN for retinanet.

* Use parameters from retinanet for anchor generation.

* General fixes for retinanet model.

* Implement loss for retinanet heads.

* Output reshaped outputs from retinanet heads.

* Add postprocessing of detections.

* Small fixes.

* Remove unused argument.

* Remove python2 invocation of super.

* Add postprocessing for additional outputs.

* Add missing import of ImageList.

* Remove redundant import.

* Simplify class correction.

* Fix pylint warnings.

* Remove the label adjustment for background class.

* Set default score threshold to 0.05.

* Add weight initialization for regression layer.

* Allow training on images with no annotations.

* Use smooth_l1_loss with beta value.

* Add more typehints for TorchScript conversions.

* Fix linting issues.

* Fix type hints in postprocess_detections.

* Fix type annotations for TorchScript.

* Fix inconsistency with matched_idxs.

* Add retinanet model test.

* Add missing JIT annotations.

* Remove redundant model construction

Make tests pass

* Fix bugs during training on newer PyTorch and unused params in DDP

Needs cleanup and to add back support for images with no annotations

* Cleanup resnet_fpn_backbone

* Use L1 loss for regression

Gives 1mAP improvement over smooth l1

* Disable support for images with no annotations

Need to fix distributed first

* Fix retinanet tests

Need to deduplicate those box checks

* Fix Lint

* Add pretrained model

* Add training info for retinanet
Co-authored-by: Hans Gaiser <hansg91@gmail.com>
Co-authored-by: Hans Gaiser <hans.gaiser@robovalley.com>
Co-authored-by: Hans Gaiser <hans.gaiser@robohouse.com>

Co-authored-by: dmitrysarov <d.shaulskiy@gmail.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
```

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* Update README.md

added some clarity to get the examples executable.  Waiting to hear back if instructions should mention to setup COCO dataset

* Update README.md

* Update README.md

* Update README.md

* Update README.md

* Update README.md

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}
```

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adding slight clarification to evaluation logic area, regarding images

* remove sys.version_info == 2

* remove sys.version_info < 3

* remove from __future__ imports

* add default parameters to README

* fix vgg_*_bn

usage and performance are from the vision0.5 release notes.

* fix a little bug about resume

When resuming, we need to start from the last epoch not 0.

* the second way for resuming

the second way for resuming

Bugfix on GroupedBatchSampler for corner case where there are not enough examples in a category to form a batch (#1677)

* Generalised for custom dataset

* Typo, redundant code, sensible default

* Args for name of train and val dir

* add quantized models

* Modify mobilenet.py documentation and clean up comments
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* Move fuse_model method to QuantizableInvertedResidual and clean up args documentation
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* Restore relu settings to default in resnet.py
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* Fix missing return in forward
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* Fix missing return in forwards
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* Change pretrained -> pretrained_float_models
Replace InvertedResidual with block

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

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* Replace forward method with simple function assignment

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* Fix error in arguments for resnet18

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* pretrained_float_model argument missing for mobilenet

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* 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

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* 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

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* 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

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

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* 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.

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* Fix minor errors in train_quantization.py

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* 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

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* Add model urls

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

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* Move setting qengine prior to convert.

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* Fix lint error

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* Add readme.md

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* Readme.md

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* Fix lint

* Initial version of README for classification reference scripts

* More context

* move sampler into TV core. Update UniformClipSampler

* Fix reference training script

* Skip test if pyav not available

* change interpolation from round() to floor() as round(0.5) behaves differently between py2 and py3

Fix reference training script for Mask R-CNN for PyTorch 1.2 (during evaluation after epoch, mask datatype became bool, pycocotools expects uint8) (#1413)

* explain lr and batch size in references/detection/train.py

* fix typo