Rewrite transforms v2 e2e example #7881
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NicolasHug
commented
Aug 24, 2023
| @@ -1,5 +1,8 @@ | |||
| import matplotlib.pyplot as plt | |||
| from torchvision.utils import draw_bounding_boxes | |||
| import torch | |||
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Changes in this file are only needed to support masks
NicolasHug
commented
Aug 24, 2023
| @@ -90,7 +90,7 @@ | |||
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| from torchvision import datapoints # we'll describe this a bit later, bare with us | |||
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| bboxes = datapoints.BoundingBoxes( | |||
| boxes = datapoints.BoundingBoxes( | |||
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Dataset wrapper uses "boxes" instead of "bboxes". I don't have a pref, but we should align (hence the changes in this example)
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The reason the dataset wrapper uses "boxes" is because our models require that:
vision/torchvision/models/detection/ssd.py
Lines 333 to 341 in f7c7bdf
NicolasHug
commented
Aug 24, 2023
| ``torchvision.transforms.v2`` enables jointly transforming images, videos, bounding boxes, and masks. This example | ||
| showcases an end-to-end object detection training using the stable ``torchvision.datasets`` and ``torchvision.models`` | ||
| as well as the new ``torchvision.transforms.v2`` v2 API. | ||
| Object detection and segmentation tasks are natively supported: |
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Using "segmentation" loosely here (i.e. both instance and semantic). It's made more accurate a bit later in the example, the main point being that it doesn't matter which one you're targetting, the transforms work the same.
vfdev-5
reviewed
Aug 24, 2023
| ``torchvision.transforms.v2`` enables jointly transforming images, videos, | ||
| bounding boxes, and masks. | ||
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| This example showcases an end-to-end instance segmentation training case using |
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Suggested change
| This example showcases an end-to-end instance segmentation training case using | |
| This example showcases an end-to-end object detection / instance segmentation training case using |
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I think it's fine to keep as-is considering the sentence just below. Technically, it is an instance segmentation model.
pmeier
approved these changes
Aug 25, 2023
gallery/v2_transforms/helpers.py
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| img = draw_bounding_boxes(img, boxes, colors="yellow", width=3) | ||
| if masks is not None: | ||
| img = draw_segmentation_masks(img, masks.to(torch.bool), alpha=.65) |
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Should we maybe also make the masks yellow or some other color? I fell in the current version the masks is not completely obvious on the image.
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hmmm looks like the color param is broken, I tried colors="green" and colors=["green"] * masks.shape[0], none of which had an effect. Might be worth looking into but I'll merge
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maybe I was looking at the wrong output - it actually works fine and the masks are green (and very obvious)
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Sep 6, 2023
Summary: (Note: this ignores all push blocking failures!) Reviewed By: matteobettini Differential Revision: D48900411 fbshipit-source-id: 2880b47b7029d33e09883243651ddffaed8f5d94
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This is a light rewrite despite the seemingly big diff. I just extended it a little to include masks and to be a bit more descriptive / detailed.