Implement ListDataset to iterate over files

This issue is the part of #85

Design

ListDataset should be very simple. It can iterate over files, apply transforms, and return tensors.

from torch.utils.data import Dataset
import torch

class ListDataset(Dataset):
    def __init__(self, filenames_list, transforms, f_imread):
        ...

    def __getitem__(self, idx) -> torch.Tensor:
        ...

Please, don’t forget to support different types of transforms. Now we use transforms from albumentations and default torchvision transforms, you can check it in oml.datasets.base.BaseDataset.

Transforms are responsible for augmentations of the original images, resizing and normalizing.

Test

Iterate over the files in mock dataset with dataloader and check shapes of batches.

Mock dataset can be found in

from oml.const import MOCK_DATASET_PATH
print(MOCK_DATASET_PATH / 'images')

If you have no mock dataset locally, use make download_mock_dataset from the root of the project.

[churnikov]

@AlekseySh

Thank you for the approval and merge!

I saw on email that I forgot to add ListDataset to documentation. I fixed it in this pull request https://github.com/OML-Team/open-metric-learning/pull/224

[churnikov]

https://github.com/OML-Team/open-metric-learning/pull/210