Refactor effdet dataloaders wrapper and fix the image sizes passed to effdet #630
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potipot
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…factor_dataloaders
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## master #630 +/- ##
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+ Coverage 85.63% 85.68% +0.04%
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Files 149 149
Lines 3245 3242 -3
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+ Misses 466 464 -2
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I'll take a look at this today =) |
Co-authored-by: Lucas Vazquez <lucasgouvaz@gmail.com>
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So as I changed |
| targets["bbox"].append(bboxes) | ||
| # convert to tensors | ||
| batch_images = torch.stack(batch_images) | ||
| batch_bboxes = [tensor(bboxes, dtype=torch.float32) for bboxes in batch_bboxes] |
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It's a bit more verbose, but I like the fact that we're passing dtype explicitly here, specially because as you pointed out effdet complains if the dtype is not what it expects
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Oh this missing space in COCOMetric test keeps popping up, its really annoying, sometimes it passes others it doesn't. Any idea how to fix this for good? |
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I know what this is related to, the original The tests are done using Again, pycocotools causing problems -.-' |
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Yes, I do have it locally. Waiting for resolution of those errors before merge? |
For the errors just remove the spaces |
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I think we are set. |
I started working on a refactor of effdet dataloaders. I noticed many common parts in the
build_train_batch,build_valid_batchandbuild_infer_batchand came up with an idea to unify this API. There is a main loop over all records in order to extract certain information about the image tensor: image itself, bboxes and labels, image sizes and scales. Whether we want to extract certain parameters depends on the train/valid/infer part.Main change is the
process_recordfunction that will be used in single iteration over all records (instead of doing it twice now, second time if we need to extract additional info not handled by the basebuild_train_batchfunction).I also removed the tensor casting from the
process_recordfunction in order to improve code readability. The idea is to handle to_tensor casting bycollate_records.This is a draft PR. Looking forward for your thoughts and suggestions!