GitLab

Change documentation of perspective(). The doc was about an old version that used directly transformation parameters

* Raise error during downloading

* Fix py2 error and lint

* create imagenet fakedata on-the-fly

* flake8

* Minor test refactorings (#1011)

* Make tests work on fbcode

* Lint

* Fix rebase error

* Properly use get_file_path_2

* Fix wrong use of get_file_path_2 again

* Missing import

* create imagenet fakedata on-the-fly

* Make tests work on fbcode

* Lint

* Fix rebase error

* Properly use get_file_path_2

* Fix wrong use of get_file_path_2 again

* Missing import

* added test for CIFAR10(0)

* create CIFAR data on-the-fly

* flake8

* fixed typo

* removed falsely added import

* Added general reader for sn3 tensors in "pascalvincent" format

* Added class QMNIST into mnist.py

* QMNIST dataset: make some pt files smaller

* Change request from fmassa.

* read_sn3_pascalvincent_tensor: cse

* read_sn3_pascalvincent_tensor: check file size (when strict!=False)

* Fix lint

* More lint

* Add documentation and expose QMNIST to dataset namespace

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

* CelebA: track attr names, support split="all", code cleanup

* fix typo

* Added the existing code

* Added squeezenet and fixed some stuff in the other models

* Wrote DenseNet and a part of InceptionV3

Going to clean and check all of the models and finish inception

* Fixed some errors in the models

Next step is writing inception and comparing with python code again.

* Completed inception and changed models directory

* Fixed and wrote some stuff

* fixed maxpoool2d and avgpool2d and adaptiveavgpool2d

* Fixed a few stuff

Moved cmakelists to root and changed the namespace to vision and wrote weight initialization in inception

* Added models namespace and changed cmakelists

the project is now installable

* Removed some comments

* Changed style to pytorch style, added some comments and fixed some minor errors

* Removed truncated normal init

* Changed classes to structs and fixed a few errors

* Replaced modelsimpl structs with functional wherever possible

* Changed adaptive average pool from struct to function

* Wrote a max_pool2d wrapper and added some comments

* Replaced xavier init with kaiming init

* Fixed an error in kaiming inits

* Added model conversion and tests

* Fixed a typo in alexnet and removed tests from cmake

* Made an extension of tests and added module names to Densenet

* Added python tests

* Added MobileNet and GoogLeNet models

* Added tests and conversions for new models and fixed a few errors

* Updated Alexnet ad VGG

* Updated Densenet, Squeezenet and Inception

* Added ResNexts and their conversions

* Added tests for ResNexts

* Wrote tools nessesary to write ShuffleNet

* Added ShuffleNetV2

* Fixed some errors in ShuffleNetV2

* Added conversions for shufflenetv2

* Fixed the errors in test_models.cpp

* Updated setup.py

* Fixed flake8 error on test_cpp_models.py

* Changed view to reshape in forward of ResNet

* Updated ShuffleNetV2

* Split extensions to tests and ops

* Fixed test extension

* Fixed image path in test_cpp_models.py

* Fixed image path in test_cpp_models.py

* Fixed a few things in test_cpp_models.py

* Put the test models in evaluation mode

* Fixed registering error in GoogLeNet

* Updated setup.py

* write test_cpp_models.py with unittest

* Fixed a problem with pytest in test_cpp_models.py

* Fixed a lint problem

Changes made in 7716aba5 broke calls to this method.

* GPU efficient Densenets

* removed `import math`

* Changed 'efficient' to 'memory_efficient'

* Add tests

* Bugfix in test

* Fix lint

* Remove unecessary formatting

* allow user to define residual settings

* 4spaces

* linting errors

* backward compatible, and added test

* 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

* updating docs for randomperspective

* my

* ci

* added fake data

* fixed fake data

* renamed extract and download methods and added functionality

* added raw fake data

* refactored imagenet and added test

* flake8

* added fake devkit and mocked download_url

* reversed uncommenting

* added mock to CI

* fixed tests for imagefolder

* flake8

Add 'import torch' in example

* add more hub models

* fix lint

* make C extension lazy-import

* add lazy loading to roi_pool

* modif of the STL10 loader

* missing space

* Fix STL10 repr

* Do not inherit from Cifar10

* Make it safer to inherit from VisionDataset

* WIP

* WIP: minor improvements

* Add tests

* Fix typo

* Use download_and_extract on caltech, cifar and omniglot

* Add a print message during extraction

* Remove EMNIST from test

* add USPS dataset

* minor fixes

* Improvements to the USPS dataset

Add it to the documentation, expose it to torchvision.datasets
and inherit from VisionDataset

Consider the difference of the division operator between Python 2.x and Python 3.x.

* Allow forcing GPU build with FORCE_CUDA=1

This is convenient to e.g. build with GPU support inside a docker image

* Document FORCE_CUDA

* #944
  MSBuild Compile time casting Error

* #944
 MSBuild Error  static_cast<Long> to static_cast<int64_t>

* Add eval.py
 Not Work find_contours

* Remove unnecessary file

* Lint

Updated nms_cuda signature to accept detections and scores as separate tensors.
This also required updating the indexing in the NMS CUDA kernel.

Also made the iou_threshold parameter name consistent across implementations.

This reverts commit 06b9b0fc.