GitLab

* 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

* Fix PIL link

* Make normalize more efficient

* fix a bug described in issue #488

* improve doc described in issue #488

* add arguments in RandomCrop as proposed by vfdev-5 in PR #489

This reverts commit 11da4f17.

* Add classes metadata to MNIST and FashionMNIST

* Add `targets` property to MNIST and FashionMNIST

* Add class metadata to CIFAR10/CIFAR100

* Add `targets` property to CIFAR10/CIFAR100

* Add targets attribute to DatasetFolder

This reverts commit 972b80c9.

* cifar10.meta['label_names']

* cifar100.meta['fine_label_names']

* cifar100.meta['coarse_label_names']

Improve `test_pad_with_non_constant_padding_modes` to avoid data multiplication in `transforms.ToPILImage()` on float data:
```python
img = torch.zeros(3, 27, 27)  # Float32
img[:, :, 0] = 1   # we add 1 and not 255
img = transforms.ToPILImage()(img)  # This converts 1 to 255 due to [pic = pic.mul(255).byte()](https://github.com/pytorch/vision/blob/master/torchvision/transforms/functional.py#L107)
```
and thus test's correct values are [..., 200, 1, 0]