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* Update KeypointRCNN weights with correct file

* Fix model

* Fix

* add scriptable transform: center_crop

* add test: center_crop

* add scriptable transform: five_crop

* add scriptable transform: five_crop

* add scriptable transform: fix minor issues

The current link to the dataset is dead. The change links to the dataset's author's personal page, which describe the dataset and is also referenced at https://github.com/fyu/lsun.

* Add Deformable Convolution operation.

This adds the deformable convolution operation, as described in Deformable Convolutional Networks (https://arxiv.org/abs/1703.06211).

- The code is based on https://github.com/open-mmlab/mmdetection/blob/master/mmdet/ops/dcn/src/deform_conv_cuda.cpp ; the whole code was modified and refactored to remove redundancies and increase clarity, and to adapt it to torchvision.

- The CPU part is a direct copy of the CUDA code; it might make sense to do follow-up adjustments in the CPU code to simplify it / optimize it, or to reuse functionality between CPU and CUDA..

- We also add tests (with a non-trivial set of parameters); they can be made more robust by randomizing the parameters and executing multiple times.

* Update DeformConv to be more consistent w/ Conv2d

* rename some variables and arguments to match Conv2d;
* add optional bias;
* add weight, offset and bias as module parameters;
* remove the n_parallel_imgs parameter;
* Fix __repr__;
* etc..

Initialization of weight and bias is the same as in Conv2d, and
initialization of offsets to zero is the same as in the paper.

This also includes some other small unrelated fixes/improvements.

* Apply clang-format in DeformConv files.

* Import Optional type annotation

* Remove offset param from DeformConv2d module

- We pass the offset in the forward of DeformConv2d, instead of having
an internal parameter. This adds some complexity to creating the module
(e.g. now you have to worry about the output size, to create the
offset), but it gives more flexibility.
- We also use make_tuple for tuple creation, in an attempt to fix error
w/ older compilers.

* Replace abs by std::abs

Old gcc versions were giving wrong results here, because they would
resolve abs as int -> int, thus causing undesired truncation. Replacing
abs by std::abs should allow for correct overloading of abs as float -> float.

* Reorder declarations for clarity

* Reorder weight and offset args in deform_conv2d

We place offset arg before the weight arg, to be more
consistent with DeformConv2d.forward(input, offset)

* Replace abs by std::abs in DeformConv_cuda

* Add tests for results in script vs eager mode

This copies some logic from `test_jit.py` to check that a TorchScript'ed
model's outputs are the same as outputs from the model in eager mode.

To support differences in TorchScript / eager mode outputs, an
`unwrapper` function can be provided per-model.

* Fix inception, use PYTORCH_TEST_WITH_SLOW

* Update

* Remove assertNestedTensorObjectsEqual

* Add PYTORCH_TEST_WITH_SLOW to CircleCI config

* Add MaskRCNN unwrapper

* fix prec args

* Remove CI changes

* update

* Update

* remove expect changes

* Fix tolerance bug

* Fix breakages

* Fix quantized resnet

* Fix merge errors and simplify code

* DeepLabV3 has been fixed

* Temporarily disable jit compilation

* almost working...

* respond to comments

* add empty tensor op, handle different output types in generalized rcnn

* clean ups

* address comments

* more changes

* it's working!

* torchscript bugs

* add script/ eager test

* eval script model

* fix flake

* division import

* py2 compat

* update test, fix arange bug

* import division statement

* fix linter

* fixes

* changes needed for JIT master

* cleanups

* remove imagelist_to

* requested changes

* Make FPN backwards-compatible and torchscript compatible

We remove support for feature channels=0, but support for it was already a bit limited

* Fix ONNX regression

* code changes to enable onnx export for keypoint rcnn

* add import

* fix copy paste error

* remove changes that induced BC

* Re-enable tests that have been disabled

* Remove outdated comment

* Remove outdated comment

Rename with_bias() to bias(), and output_channels() to out_channels() in C++ conv layer options usage (#1576)

* enable faster rcnn test

* flake8

* smaller image size

* set min/max

* Fix inconsistent NMS implementation

* Improve tests for NMS

* Remove unnecessary using statement

* Add Scriptable Transform: Grayscale

* add scriptable transforms: rgb_to_grayscale

* add scriptable transform: rgb_to_grayscale

* add scriptable transform: rgb_to_grayscale

* add scriptable transform: rgb_to_grayscale

* update code: rgb_to_grayscale

* add test: rgb_to_grayscale

* update parameters: rgb_to_grayscale

* add scriptable transform: rgb_to_grayscale

* update rgb_to_grayscale

* update rgb_to_grayscale

* 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

* Unify video metadata in VideoClips

* Bugfix

* Make tests a bit more robust

* Improve readability of affine transformation code

* Make shear transformation area preserving

The previous shear implementation did not preserve area, and we
implement a version that does.

The formula used was verified with the following sympy code:

from sympy import Matrix, cos, sin, tan, simplify
from sympy.abc import x, y, phi

Xs = Matrix(
        [[1, -tan(x)],
         [0, 1]]
        )

Ys = Matrix(
        [[1, 0],
         [-tan(y), 1]]
        )

R = Matrix(
        [[cos(phi), -sin(phi)],
         [sin(phi), cos(phi)]]
        )

RSS = Matrix(
        [[cos(phi - y)/cos(y), -cos(phi - y)*tan(x)/cos(y) - sin(phi)],
         [sin(phi - y)/cos(y), -sin(phi - y)*tan(x)/cos(y) + cos(phi)]])

print(simplify(R * Ys * Xs - RSS))

One thing that is not clear (and could be tested) is whether avoiding
the explicit products and calculations in _get_inverse_affine_matrix
really gives performance benefits - compared to doing the explicit
calculation done in _test_transformation.

* Use np.matmul instead of @

The @ syntax is not supported in Python 2.

* 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

* Add adjustment operations for RGB Tensor Images.

Right now, we have operations on PIL images, but we want to have a version of the opeartions that act directly on Tensor images.

Here, we add such operations for adjust_brightness, adjust_contrast and adjust_saturation.

In PIL, those functions are implemented by generating an degenerate image from the first, and then interpolating them together.
- https://github.com/python-pillow/Pillow/blob/master/src/PIL/ImageEnhance.py
- https://github.com/python-pillow/Pillow/blob/master/src/libImaging/Blend.c

A few caveats:
* Since PIL operates on uint8, and the tensor operations might be on float, we can get slightly different values because of int truncation.
* We assume here the images are RGB; in particular, to handle an alpha channel, we need to check whether it is present, in which case we copy it to the final image.

* Keep dtype and use broadcast in adjust operations

- We make our operations have input.dtype == output.dtype, at the cost of
adding a few type checks and branches.

- By using Tensor broadcast, we can simplify the calls to _blend.

* Use is_floating_point to check dtype.

* Remove unpacking in tuple

It seems Python 2 does not support this type of unpacking, so it broke
Python 2 builds. This should fix it.

* Add from __future__ import division for Python 2

* Removed unnecessary class variables.

* The integrity of dataset files is now being checked right after the download finished. Thus making sure that a corrupt file is not being extracted. In case of corruption we throw a RuntimeError.

* Added missing md5 hashes to MNIST, FashionMNIST, KMNIST, EMNIST and QMNIST datasets.

* Removed printing of error message when integrity check failed.
Reformulated error message.

* Reformatted code to be lint conform.

* Fixed formatting in utils.py

* Unify video backend interfaces

* Remove reference cycle

* Make functions private and enable tests on OSX

* Disable test if video_reader backend not available

* Lint

* Fix import after refactoring

* Fix lint

* extend DistributedSampler to support group_size

* Fix lint

* remove download process

* address comments

* fix logic error

* bug fixes

* removed unused import

* add docstrings

* flake8

* remove download BC

* fix test

* removed unused code

* flake 8

* add MD5 verification before extraction

* add mock to test

* * unify _verify_archive() method and function
* remove force flag for parse_*_archive functions
* cleanup

* flake8

* Make crop torchscriptable

Relevant #1375

* Invert x and y axis

* fix lint

* Add crop test

* revert deletion of space in functional

* add import random

* add dimension in doc

* add import

* fix flake8

* change to self.assert*

* convert to uint8

* assertTrue

* lint

* doc-build fixed

* deprecation fixes

* deprecation updates

* onnx esport faster rcnn

* test

* address PR comments

* revert unbind workaround

* disable tests for older versions of pytorch

* vflip and hflip tensor

* vflip and hflip tensor

* changes made

* lint

* lint

* lint failing

* added PSRoiAlign and PSRoiPool with C++ autograd and torch ops

* fixed linter errors

* fixed linter errors 2

* fixed linter errors 3

* Handle corrupted video headers in io

* Catch exceptions while decoding partly-corrupted files

* Add more tests