GitLab

* Add Flickr8k and Flickr30k Datasets

* Add Flickr to the dataset docs

* Sort ids, glob during construction

* annFile -> ann_file

* Fix undefined variable name bug

* Add SBU Captioned Photo Dataset

* Add SBU to the dataset docs

* Changing to AdaptiveAvgPool2d

* add whitespace

* Update densenet.py

* Update densenet.py

* use predefined function

* cleanup resnet

* fix stride

* revert inplanes since it isnt constant

* lint

* AdaptiveAvgpool2d integrated

The Average Pool Layer on SqueezeNet and ResNet was hardcoded, this was changed, now any the input size is accepted.

* error fixed

* linting fixed

* Revert "move area calculation out of loop (#641)"

This reverts commit 62cbf0bf.

* Revert "Fix FashionMNIST loading MNIST (#640)"

This reverts commit 36215690.

* move area calculation out of loop

No need to calculate area 10 times when it doesn't change.

* lint

Before this would lead FashionMNIST to contain mnist data:

```
MNIST(root, download=True)
FashionMNIST(root, download=True)
```

As MNIST and FashionMNIST are the same classes, the processed
outputs actual ended up to be the same files. This commit now
stores them at different files and also stores the class name when
saving them. I also added md5 sums.

* init hub.py

* rename hub.py -> hub_info.py

* torchvision_hub->torchvision; use alexnet as example since it's smaller

* alexnet -> resnet18

* update repo example structure

* address comment

* add readme as comment

* address comment

* fix lint

* Fix inception v3 input transform for trace & onnx

* Input transform are in-place updates, which produce issues for tracing
and exporting to onnx.

* nit

* cast mnist targer to int

* fix unused variables

* fix syntax in tests

* remove # noqa; rename l variable to line_split; use except OSError

* add W504

* add W504

* add W503,W504 tox.ini

Check if `lambd` is callable instead of checking if its a `types.Lambda` object.

* MNIST and FashionMNIST now have their own 'raw' and 'processed' folders

* mkdir exist_ok

Give a short example of how to build a transform pipeline for a
segmentation task.

Target `float` -> `long`

* ColorJitter Enhancement

* reduce redundancy

* improve functional

* check input in init

* fix ci fail

* Use scandir in _find_classes (#557)

* fix lint

* reduce tqdm version to 4.19 for py2.7 to avoid spurious error

* Update setup.py

* Missed =

* Update wrong version string

- Fix broken update calculation
- Make progress bar use the neat `unit_scale` feature
  of tqdm

* Addresses issue #145 as per @fmessa's suggestion.

* Removed blank line for styling.

* make vision depend on pillow-simd if already installed

* actually make pillow-simd optional