Implementation for cityscapes in proto datasets #5441
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| categories_to_details: FrozenMapping = FrozenMapping( | ||
| { | ||
| "unlabeled": CityscapesClass("unlabeled", 0, 255, "void", 0, False, True, (0, 0, 0)), |
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It seems, all of this information is exposed to the user, but never used internally. Is this just static information or would it make sense to include it in the samples?
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It is used in extra=dict(classname_to_details=self.categories_to_details), and is exposed to the user as in the previously implementation
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My point is: what does the user do with this information? I reckon color is useful to parse a segmentation mask. The category looks like what we called super_category in COCO. So maybe we can just provide two dictionaries in the extra information that map the category to its color and super category respectively?
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CityscapesClass provides more info that just color, category name, super category. It may happen that users could use other fields for visualization purposes and thus new dataset would miss that info.
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In the stable API this is exposed through the undocumented torchvision.datasets.Cityscapes(...).classes attribute. Given that the new API is BC breaking anyway, IMO we should aim to reduce the baggage from the old implementation. I indeed see use cases for returning a color to category mapping (maybe we should have this on the SegmentationMask feature) as well as the category to super category mapping. For the rest I would remove them for now and only put them back if someone requests it. cc @NicolasHug for an opinion.
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It is dataset meta-data, probably coming from https://github.com/mcordts/cityscapesScripts and is used for visualization (this can be seen by searching through the GitHub and exploring repository by repository, https://github.com/search?p=1&q=CityscapesClass&type=Code). Even if this wasn't explicitly documented previously, I do not think that we have to remove that in the new API.
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I would remove them for now and only put them back if someone requests it.
I'm happy with that.
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@pmeier thanks for the review |
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| categories_to_details: FrozenMapping = FrozenMapping( | ||
| { | ||
| "unlabeled": CityscapesClass("unlabeled", 0, 255, "void", 0, False, True, (0, 0, 0)), |
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My point is: what does the user do with this information? I reckon color is useful to parse a segmentation mask. The category looks like what we called super_category in COCO. So maybe we can just provide two dictionaries in the extra information that map the category to its color and super category respectively?
| def _filter_split_images(self, data: Tuple[str, Any], *, req_split: str) -> bool: | ||
| path = Path(data[0]) | ||
| split = path.parent.parts[-2] | ||
| return split == req_split and ".png" == path.suffix |
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I would prefer the granularity given that the performance overhead from this hardly will make any difference. cc @NicolasHug for an opinion.
| if target_type == "polygon": | ||
| enc_data = json.loads(data.read()) | ||
| else: | ||
| enc_data = EncodedImage.from_file(data) |
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Reminder for self: we need an EncodedSegmentationMask feature now that we also have a separate SegementationMask.
Fixes #5353
Related to #5336
Description: