Error when resizing predicted mask

[dancebean]

OK, it seems I found the source of the problem and the solution: by casting the dataType of the ImageSegmentation from long to float!

Try add the following line before calling the pred_mask:

pred_mask = ImageSegment(pred_mask.data.type(torch.float32)

And then:
pred_mask.apply_tfms(tfms[0],size=(1, img.size[0], img.size[1]),resize_method=ResizeMethod.SQUISH)

Ref:
image segment data should be stored internally as floats and the resampling methods can be found here

Labels for image segmentation when we decreasing image size

Thanks, but how can you do interpolation for the labels in this case? A segmentation label should have a discrete value. And if you create 1 pixels from 4, you may have cases when all 4 of them had different segment labels or 2 has one and 2 other has second one. How the algorithm will choose in this case which label to keep for a reduced image?

even though ImageSegment.data returns a long tensor, it stores a float tensor in px , so I need to call ImageSegment on a float tensor instead of a long .

Error when calling transform on SegmentationLabelList

I am trying to load data for a segmentation task, using the very classic following function: class MultiClassSegList(SegmentationLabelList): def open(self, id_rles): image_id, rles = id_rles[0], id_rles[1:] shape = open_image(self.path/image_id).shape[-2:] final_mask = torch.zeros((1, *shape)).long() for k, rle in enumerate(rles): if isinstance(rle, str): mask = open_mask_rle(rle, shape).data.permute(0, 2, 1) …