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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

* [WIP] Minor cleanups on R3d

* Move all models to video/resnet.py

* Remove old files

* Make tests less memory intensive

* Lint

* Fix typo and add pretraing arg to training script

* Move RandomClipSampler to references

* Lint and bugfix

Also add docs

* Copy classification scripts for video classification

* Initial version of video classification

* add version

* Training of r2plus1d_18 on kinetics work

Gives even slightly better results than expected, with 57.336 top1 clip accuracy. But we count some clips twice in this evaluation

* Cleanups on training script

* Lint

* Minor improvements

* Remove some hacks

* Lint

* adding mixed precision training with Apex

* fix APEX default optimization level

* adding python version check for apex

* fix LINT errors and raise exceptions if apex not available

* fixing apex distributed training

* fix throughput calculation: include forward pass

* remove torch.cuda.set_device(args.gpu) as it's already called in init_distributed_mode

* fix linter: new line

* move Apex initialization code back to the beginning of main

* move apex initialization to before lr_scheduler - for peace of mind. Though, doing apex initialization after lr_scheduler seems to work fine as well

* Add loss, sampler, and train script

* Fix train script

* Add argparse

* Fix lint

* Change f strings to .format()

* Remove unused imports

* Change TripletMarginLoss to extend nn.Module

* Load eye uint8 tensors directly on device

* Refactor model.py to backbone=None

* Add docstring for PKSampler

* Refactor evaluate() to take loader as arg instead

* Change eval method to cat embeddings all at once

* Add dataset comments

* Add README.md

* Add tests for sampler

* Refactor threshold finder to helper method

* Refactor targets comment

* Fix lint

* Rename embedding to similarity (More consistent with existing literature)

updated all docstrings and code references for boxes to be consistent with the scheme (x1, y1, x2, y2) (#1110)

* Doc multigpu and propagate data path.

* Use raw doc because of backslash.

* can't work with pytorch-cpu fixed

utils.py can't work with pytorch-cpu because of this line of code `memory=torch.cuda.max_memory_allocated()`

* can't work with pytorch-cpu fixed

utils.py can't work with pytorch-cpu because of this line of code 'memory=torch.cuda.max_memory_allocated()'