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* 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

* 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

* 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()'

* 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

Allows for easily evaluating the pre-trained models in the modelzoo

* Miscellaneous improvements to the classification reference scritps

* Fix lint

* Add groups support to ResNet

* Kill BaseResNet

* Make it support multi-machine training

Making references/classification/train.py and references/classification/utils.py  compatible with python2  (#831)

* linter fixes

* linter fixes

* Initial version of classification reference training script

* Updates

* Minor updates

* Expose a few more options

* Load optimizer and lr_scheduler when resuming

Also log the learning rate

* Evaluation-only and minor improvements

Identified a bug in the reporting of the results. They need to be reduced between all processes

* Address Soumith's comment

* Fix some approximations on the evaluation metric

* Flake8