Support image labels in Torch #880
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gheinrich
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@TimZaman do you mind reviewing the last commit? I have rebased the change on top of your data augmentation changes. Thanks! |
| # choose random seed and add to userdata so it gets persisted | ||
| self.userdata['seed'] = random.randint(0, 1000) | ||
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| random.seed(self.userdata['seed']) |
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Ah, usage of the user seed. This reminds me however of the way shuffling, ofc an random op, is done elsewhere in DIGITS: This is a unrelated to this PR though.
In object detection datasets: https://github.com/NVIDIA/DIGITS/blob/master/digits/extensions/data/objectDetection/data.py#L235
and actually also in folder parsing
https://github.com/NVIDIA/DIGITS/blob/master/tools/parse_folder.py#L416
It's not clear to me if that seed from folder parsing is random or if the scope is persistent from somewhere else. In any case, in uploading a folder it might also be nice to be able to randomize or fix the seed, during shuffling in folder parsing.
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Yes I need to save the seed here as I want to generate the same random indices from two separate processes (the process that creates the training DB and the process that creates the validation DB). Indeed this is different from the (many) other seeds that are used in the places you pointed at. Here a new seed is generated every time we create a dataset (however it's done only once per dataset).
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Briefly ran through it and looks fine, but I'd would have to run some different types of data through otherwise; but I recon you have done that yourselves. |
Did you see the image segmentation PR where I show that we can do inference on a bigger image than we used during training? |
To be used with networks where the input and the output are images
Labels were restricted to scalars (classification) or vectors (regression). This change extends supported label types to anything that fits in an LMDB. Small refactoring of datum decoding.
gheinrich
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rebased and updated according to comment #880 (comment) |
Depends on #806 for tests.