SAM image size for fine-tuning

Intermediate
2

Hi,

Thanks for your interest in my notebook! You could use torch.nn.functional.interpolate to interpolate the predicted masks to the appropriate size before calculating the loss:

from torch import nn

predicted_masks = nn.functional.interpolate(predicted_masks,
                size=(1024, 1024),
                mode='bilinear',
                align_corners=False)

This is also used in the postprocessing method of SamImageProcessor.

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