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# ETH3DStereo

class torchvision.datasets.ETH3DStereo(root: str, split: str = 'train', transforms: Optional[Callable] = None)

  ETH3D Low-Res Two-View dataset.

  The dataset is expected to have the following structure:

      root
          ETH3D
              two_view_training
                  scene1
                      im1.png
                      im0.png
                      images.txt
                      cameras.txt
                      calib.txt
                  scene2
                      im1.png
                      im0.png
                      images.txt
                      cameras.txt
                      calib.txt
                  ...
              two_view_training_gt
                  scene1
                      disp0GT.pfm
                      mask0nocc.png
                  scene2
                      disp0GT.pfm
                      mask0nocc.png
                  ...
              two_view_testing
                  scene1
                      im1.png
                      im0.png
                      images.txt
                      cameras.txt
                      calib.txt
                  scene2
                      im1.png
                      im0.png
                      images.txt
                      cameras.txt
                      calib.txt
                  ...

  Parameters:

    - root (string) – Root directory of the ETH3D Dataset.

    - split (string, optional) – The dataset split of scenes, either “train” (default) or “test”.

    - transforms (callable, optional) – A function/transform that takes in a sample and returns a transformed version.

  Special-members:

  __getitem__(index: int) → Tuple[Image, Image, Optional[ndarray]]

    Return example at given index.

    Parameters:

      index (int) – The index of the example to retrieve

    Returns:

      A 4-tuple with `(img_left, img_right, disparity, valid_mask)`. The disparity is a numpy array of shape (1, H, W) and the images are PIL images. `valid_mask` is implicitly `None` if the `transforms` parameter does not generate a valid mask. Both `disparity` and `valid_mask` are `None` if the dataset split is test.

    Return type:

      tuple

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