Nicolas Hug

Thanks for confirming. I understand why this was causing an undesirable warning in the past. Now that we always build `image.so`, I suspect this might have become a non-issue, i.e....

Do you mean to dynamically link against giflib instead of statically? If so, why is that the case? (also let me know about https://github.com/pytorch/vision/pull/8406#discussion_r1691444762 please :) )

Thanks for the report/request @farresti . By any chance, is there a more pythonic way to still enable onnx export here? We try to be reasonably conservative about such changes...

> Could specify which part would you like to be more pythonic please @NicolasHug? I was referring to ```py flow_predictions = torch.zeros((num_flow_updates, batch_size, 2, h, w)) flow_predictions[i] = upsampled_flow ```...

Looks the same as https://github.com/pytorch/vision/issues/7349. Skip the test :(

yeah

Thank you for the feature request @rehno-lindeque . There was also https://github.com/pytorch/vision/issues/4029 opened in the past. So far, we've preferred not exposing such conversion utils in torchvision (see e.g. https://github.com/pytorch/vision/issues/4029#issuecomment-865079727),...

Hi @PierreQuinton , It seems like what you need is a [custom Sampler](https://pytorch.org/docs/stable/data.html#data-loading-order-and-sampler). IIUC, https://github.com/ufoym/imbalanced-dataset-sampler should be pretty close to what you're looking for?

Thanks @PierreQuinton . I'll keep the issue open and rename it for clarity. Ultimately, what is needed to enable that is: - 1) a consistent interface across datasets to access...

This was addressed by https://github.com/pytorch/vision/pull/8200, closing