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* quantizable googlenet

* Minor improvements

* Rename basic_conv2d with conv_block plus additional fixes

* More renamings and fixes

* Bugfix

* Fix missing import for mypy

* Add pretrained weights

* Support Exporting Mask Rcnn to ONNX

* update tetst

* add control flow test

* fix

* update test and fix img_shape

* 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

* onnx esport faster rcnn

* test

* address PR comments

* revert unbind workaround

* disable tests for older versions of pytorch

* Support Exporting RPN to ONNX

* address PR comments

* fix cat

* add flatten

* replace cat by stack

* update test to run only on rpn module

* use tolerate_small_mismatch

* add support for video resnet models, restructure script test to just ignore RCNN models

* switch back to testing subset of the models

* make googlnet scriptable

* Remove typing import in favor of torch.jit.annotations

* add inceptionnet

* flake fixes

* fix asssert true

* add import division for torchscript

* fix script compilation

* fix flake, py2 division error

* fix py2 division error

* Add initial mnasnet impl

* Remove all type hints, comply with PyTorch overall style

* Expose models

* Remove avgpool from features() and add separately

* Fix python3-only stuff, replace subclasses with functions

* fix __all__

* Fix typo

* Remove conditional dropout

* Make dropout functional

* Addressing @fmassa's feedback, round 1

* Replaced adaptive avgpool with mean on H and W to prevent collapsing the batch dimension

* Partially address feedback

* YAPF

* Removed redundant class vars

* Update urls to releases

* Add information to models.rst

* Replace init with kaiming_normal_ in fan-out mode

* Use load_state_dict_from_url

* Fix depth scaling on first 2 layers

* Restore initialization

* Match reference implementation initialization for dense layer

* Meant to use Kaiming

* Remove spurious relu

* Point to the newest 0.5 checkpoint

* Latest pretrained checkpoint

* Restore 1.0 checkpoint

* YAPF

* Implement backwards compat as suggested by Soumith

* Update checkpoint URL

* Move warnings up

* Record a couple more function parameters

* Update comment

* Set the correct version such that if the BC-patched model is saved, it could be reloaded with BC patching again

* Set a member var, not class var

* Update mnasnet.py

Remove unused member var as per review.

* Update the path to weights

* script_fcn_resnet

* Make old models load

* DeepLabV3 also got torchscript-ready

* make densenet scriptable

* make py2 compat

* use torch List polyfill

* fix unpacking for checkpointing

* fewer changes to _Denseblock

* improve error message

* print traceback

* add typing dependency

* add typing dependency to travis too

* Make loading old checkpoints work

* Make AnchorGenerator support half precision

* Add test for fasterrcnn with double

* convert gt_boxes to right dtype

* Support Exporting GeneralizedRCNNTransform

* refactor code to address comments

* update tests

* address comments

* revert min_size to test CI

* re-revert min_size

* make shufflenet scriptable

* make resnet18 scriptable

* set downsample to identity instead of __constants__ api

* use __constants__ for downsample instead of identity

* import tensor to fix flake

* use torch.Tensor type annotation instead of import

`self.fc1(x)` converts the shape of `x` into "N x 1024", and `self.fc2(x)` converts into "N x num_classes".

By adding `print(x.shape)` under each comment line, the console displays as follows (batch_size is 1):

```text
torch.Size([1, 2048])
torch.Size([1, 1024])
torch.Size([1, 1024])
torch.Size([1, 1024])
torch.Size([1, 1000])
```

* Add docs for video models

* Fix docstrings for resnet and vgg

* [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

* [0.4_video] models - initial commit

* addressing fmassas inline comments

* pep8 and flake8

* simplify "hacks"

* sorting out latest comments

* nitpick

* Updated tests and constructors

* Added docstrings - ready to merge

* in_channels_stage2 from backbone.inplanes

* remove type for backward compatible

Fix the old flatten method which use the size(0) to caculate the batch size, the old method will intruduce Gather opertion in the onnx output, which will faild parsed by tensorRT 5.0 (#1134)

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

* add float32 to keypoint_rcnn docs

* add float32 to faster_rcnn docs

* add float32 to mask_rcnn

* Update faster_rcnn.py

* Update keypoint_rcnn.py

* Update mask_rcnn.py

* Update faster_rcnn.py

* make keypoints float

* make masks uint8

* Update keypoint_rcnn.py

* make labels Int64

* make labels Int64

* make labels Int64

* Add checks for boxes, labels, masks, keypoints

* update mask dim

* remove dtype

* check only if targets is not None

* account for targets being a list

* update target to be list of dict

* Update faster_rcnn.py

* Update keypoint_rcnn.py

* allow boxes to be of float16 type as well

* remove checks on mask

* Add memory_efficient args to densenet docs

This PR adds the arg `memory_efficient` to densenet docs and also mentions the relevant publication.

* linting

This PR updates the docs of MnasNet

Fixes #1047.

* Add paper references to VGG

* Add paper references to ResNet and its variants

* fix transform for rcnns so original images are unchanged

* transform does not change input list anymore

transform does not change input list anymore. Improve code according to reviewer comment

* transform for maskrcnn no longer modify input list

transform for maskrcnn no longer modify input list. improve code according to comment

* transform for maskrcnn no longer modifies input list

* Fixed width multiplier

Layer channels are now rounded to a multiple of 8, as per the official tensorflow implementation.  I found this fix when looking through: https://github.com/d-li14/mobilenetv2.pytorch

* Channel multiple now a user configurable option

The official tensorflow slim mobilenet v2 implementation rounds the number of channels in each layer to a multiple of 8.  This is now user configurable - 1 turns off rounding

* Fixed whitespace error

Fixed error: ./torchvision/models/mobilenet.py:152:1: W293 blank line contains whitespace

* add wide resnet

* add docstring for wide resnet

* update WRN-50-2 model

* add docs

* extend WRN docstring

* use pytorch storage for WRN

* fix rebase

* fix typo in docs

* Add initial mnasnet impl

* Remove all type hints, comply with PyTorch overall style

* Expose models

* Remove avgpool from features() and add separately

* Fix python3-only stuff, replace subclasses with functions

* fix __all__

* Fix typo

* Remove conditional dropout

* Make dropout functional

* Addressing @fmassa's feedback, round 1

* Replaced adaptive avgpool with mean on H and W to prevent collapsing the batch dimension

* Partially address feedback

* YAPF

* Removed redundant class vars

* Update urls to releases

* Add information to models.rst

* Replace init with kaiming_normal_ in fan-out mode

* Use load_state_dict_from_url

I grepped the repo for Ouputs and these were the only occurences

This PR uses a protected method for loading and initializing the segmentation models. Relevant #875