GitLab

* modify processPacket to support fast seek

* add fastSeek to ProcessPacket decoder definition

* add fastseek flag to DecoderParametersStruct

* add fastseek flag to the process packet call

* no default params in C++ implementation

* enable flag in C++ implementation

* make order of parameters more normal

* register new seek with python api

* [somewhat broken] test suite for keyframes using pyav

* revert " changes

* add type annotations to init

* Adding tests

* linter

* Flake doesn't show up :|

* Change from unitest to pytest syntax

* add return type
Co-authored-by: Prabhat Roy <prabhatroy@fb.com>

* Reordering the builders to use proper typing.

* Adding additional meta-data on existing quantized models.

* Fixing meta on unquantized model.

* Adding quantized googlenet builder.

* undo inception move.

* Adding recipe information.

* Aligning exception with all other models.

* Adding prototype preprocessing on video references.

* Adding the rest of model builders on faster_rcnn.

* Port semeion dataset

* Update torchvision/prototype/datasets/_builtin/semeion.py
Co-authored-by: Philip Meier <github.pmeier@posteo.de>

* explicitly convert the image array to torch.uint8

* explicitly convert the image array to torch.uint8
Co-authored-by: Philip Meier <github.pmeier@posteo.de>
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

* Update typing

* Fix bug

* Unblock mypy

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

* Adding multi-weight support to Quantized ResNet.

* Update references script to support testing quantized models with the new API.

* Handle quantized models correctly in ref script.

* Fixing references for quantization.

* Update training references from legacy models.

* Refactoring to share common parts.

* Expose misc ops at package level

* Adding documentation to the ops exposed

* Moving original builder at the bottom of the page to use proper typing.

* Adding multiweight support to inception.

* Update doc.

* change range into uint8

* Same typo in TrivialAugmentWide and RandAugment
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>
Co-authored-by: Prabhat Roy <prabhatroy@fb.com>

* Change enum name for weights contributed by community.

* Adding multiweight support to squeezenet.

* Move model builder at the bottom of the file, so we can use proper typing.

* Adding GoogLeNet with multi-weight support.

* Simplify expression.

* Add logging to torchvision ops

* Hack to make torchscript work

* Bugfix

* Bugfix

* Lint

* mypy... let's silence it

* Fighting with mymy

* One more try

Summary:
This diff enables ImageNet to be usable from within torchvision.

It implements a different codepath for some families of datasets that can be sharded into multiple pieces, and uses it if within fbcode.

For now all previously supported datasets are also supported (MNIST, CIFAR and FashionMNIST), in addition to ImageNet.

Pull Request resolved: https://github.com/pytorch/vision/pull/4776

Reviewed By: datumbox

Differential Revision: D31929684

fbshipit-source-id: 58f9fe2ed6ce731a6b43a38be912983247eff562
(cherry picked from commit b43fead8ea5164e37637ade0e80477e94f993365)

* Adding multiweight support for shufflenetv2 prototype models

* Revert "Adding multiweight support for shufflenetv2 prototype models"

This reverts commit 31fadbee7d1a65cd73ae43dfd4ac6e97e7ca7b01.

* Adding multiweight support for shufflenetv2 prototype models

* Revert "Adding multiweight support for shufflenetv2 prototype models"

This reverts commit 4e3d900f796c1e3e667312087e77956ca4a4c017.

* Adding multiweight support for shufflenetv2 prototype models

* addressing PR comment

* Add mutli-weight support for VideoResNet.

* Fix linter.

* Minor refactoring.

* Update comments.

Co-authored-by: Nicolas Hug <nicolashug@fb.com>

* adding multiweight support for deeplabv3 prototype models

* adding default values for optional params

* fixing bug

* addressing PR comment

* Change test_random_apply

* Change test_random_choice

* Change test_randomness

* took care of RandomVert/HorizFlip

* take care of RandomGrayScale

* minor cleanup

* avoid 0 degree rotation just in case

* Fix flakiness on the TestStochasticDepth test.

* Fix minor bug when p=1.0

* Remove device and dtype setting.

* cache dataset info

* try loading categories file by default
Co-authored-by: Francisco Massa <fvsmassa@gmail.com>

* Adding multi-weight support to LRASPP

* Adding tests for segmentation models.

* Skip segmentation test by default.

Co-authored-by: Prabhat Roy <prabhatroy@fb.com>

* replace np.frombuffer with torch.frombuffer in MNIST prototype

* cleanup

* appease mypy

* more cleanup

* clarify inplace offset

* fix num bytes for floating point data

Summary:
Pull Request resolved: https://github.com/pytorch/vision/pull/4620

Pull Request resolved: https://github.com/pytorch/nestedtensor/pull/455



Fixes missing kernel guards as identified by D30072495

Reviewed By: jingsh, xush6528

Differential Revision: D31553158

fbshipit-source-id: 80de017ba2ddc52e2a684df9b3eae5de84ed49f4
Co-authored-by: Richard Barnes <rbarnes@fb.com>

Summary:
Pull Request resolved: https://github.com/pytorch/vision/pull/4571



Fixing windows build for `torchvision` when `MOBILE` is defined. In `csrc/vision.cpp`, since `PyMODINIT_FUNC` depends on `Python.h` I added the same condition for `PyMODINIT_FUNC` as the one for `import <PyTorch.h>`.

Reviewed By: malfet

Differential Revision: D31488734

fbshipit-source-id: fd52a9ab161288d9c8bf3b9e0ab9da856a4ebadb
Co-authored-by: Mengwei Liu <larryliu@fb.com>

* Update model checkpoint for resnet50.

* Add get_weight method to retrieve weights from name.

* Update the references to support prototype weights.

* Fixing mypy typing.

* Switching to a python 3.6 supported equivalent.

* Add unit-test.

* Add optional num_classes.