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# Transforms v2: End-to-end object detection/segmentation example

Note

Try on collab or go to the end to download the full example code.

Object detection and segmentation tasks are natively supported: `torchvision.transforms.v2` enables jointly transforming images, videos, bounding boxes, and masks.

This example showcases an end-to-end instance segmentation training case using Torchvision utils from `torchvision.datasets`, `torchvision.models` and `torchvision.transforms.v2`. Everything covered here can be applied similarly to object detection or semantic segmentation tasks.

    import pathlib

    import torch
    import torch.utils.data

    from torchvision import models, datasets, tv_tensors
    from torchvision.transforms import v2

    torch.manual_seed(0)

    # This loads fake data for illustration purposes of this example. In practice, you'll have
    # to replace this with the proper data.
    # If you're trying to run that on collab, you can download the assets and the
    # helpers from https://github.com/pytorch/vision/tree/main/gallery/
    ROOT = pathlib.Path("../assets") / "coco"
    IMAGES_PATH = str(ROOT / "images")
    ANNOTATIONS_PATH = str(ROOT / "instances.json")
    from helpers import plot

## Dataset preparation

We start off by loading the `CocoDetection` dataset to have a look at what it currently returns.

    dataset = datasets.CocoDetection(IMAGES_PATH, ANNOTATIONS_PATH)

    sample = dataset[0]
    img, target = sample
    print(f"{type(img) = }\n{type(target) = }\n{type(target[0]) = }\n{target[0].keys() = }")

    loading annotations into memory...
    Done (t=0.00s)
    creating index...
    index created!
    type(img) = <class 'PIL.Image.Image'>
    type(target) = <class 'list'>
    type(target[0]) = <class 'dict'>
    target[0].keys() = dict_keys(['segmentation', 'iscrowd', 'image_id', 'bbox', 'category_id', 'id'])

Torchvision datasets preserve the data structure and types as it was intended by the datasets authors. So by default, the output structure may not always be compatible with the models or the transforms.

To overcome that, we can use the `wrap_dataset_for_transforms_v2()` function. For `CocoDetection`, this changes the target structure to a single dictionary of lists:

    dataset = datasets.wrap_dataset_for_transforms_v2(dataset, target_keys=("boxes", "labels", "masks"))

    sample = dataset[0]
    img, target = sample
    print(f"{type(img) = }\n{type(target) = }\n{target.keys() = }")
    print(f"{type(target['boxes']) = }\n{type(target['labels']) = }\n{type(target['masks']) = }")

    type(img) = <class 'PIL.Image.Image'>
    type(target) = <class 'dict'>
    target.keys() = dict_keys(['boxes', 'masks', 'labels'])
    type(target['boxes']) = <class 'torchvision.tv_tensors._bounding_boxes.BoundingBoxes'>
    type(target['labels']) = <class 'torch.Tensor'>
    type(target['masks']) = <class 'torchvision.tv_tensors._mask.Mask'>

We used the `target_keys` parameter to specify the kind of output we’re interested in. Our dataset now returns a target which is dict where the values are TVTensors (all are `torch.Tensor` subclasses). We’re dropped all unncessary keys from the previous output, but if you need any of the original keys e.g. “image_id”, you can still ask for it.

Note

If you just want to do detection, you don’t need and shouldn’t pass “masks” in `target_keys`: if masks are present in the sample, they will be transformed, slowing down your transformations unnecessarily.

As baseline, let’s have a look at a sample without transformations:

    plot([dataset[0], dataset[1]])

## Transforms

Let’s now define our pre-processing transforms. All the transforms know how to handle images, bouding boxes and masks when relevant.

Transforms are typically passed as the `transforms` parameter of the dataset so that they can leverage multi-processing from the `torch.utils.data.DataLoader`.

    transforms = v2.Compose(
        [
            v2.ToImage(),
            v2.RandomPhotometricDistort(p=1),
            v2.RandomZoomOut(fill={tv_tensors.Image: (123, 117, 104), "others": 0}),
            v2.RandomIoUCrop(),
            v2.RandomHorizontalFlip(p=1),
            v2.SanitizeBoundingBoxes(),
            v2.ToDtype(torch.float32, scale=True),
        ]
    )

    dataset = datasets.CocoDetection(IMAGES_PATH, ANNOTATIONS_PATH, transforms=transforms)
    dataset = datasets.wrap_dataset_for_transforms_v2(dataset, target_keys=["boxes", "labels", "masks"])

    loading annotations into memory...
    Done (t=0.00s)
    creating index...
    index created!

A few things are worth noting here:

- We’re converting the PIL image into a `Image` object. This isn’t strictly necessary, but relying on Tensors (here: a Tensor subclass) will generally be faster.

- We are calling `SanitizeBoundingBoxes` to make sure we remove degenerate bounding boxes, as well as their corresponding labels and masks. `SanitizeBoundingBoxes` should be placed at least once at the end of a detection pipeline; it is particularly critical if `RandomIoUCrop` was used.

Let’s look how the sample looks like with our augmentation pipeline in place:

    plot([dataset[0], dataset[1]])

We can see that the color of the images were distorted, zoomed in or out, and flipped. The bounding boxes and the masks were transformed accordingly. And without any further ado, we can start training.

## Data loading and training loop

Below we’re using Mask-RCNN which is an instance segmentation model, but everything we’ve covered in this tutorial also applies to object detection and semantic segmentation tasks.

    data_loader = torch.utils.data.DataLoader(
        dataset,
        batch_size=2,
        # We need a custom collation function here, since the object detection
        # models expect a sequence of images and target dictionaries. The default
        # collation function tries to torch.stack() the individual elements,
        # which fails in general for object detection, because the number of bouding
        # boxes varies between the images of a same batch.
        collate_fn=lambda batch: tuple(zip(*batch)),
    )

    model = models.get_model("maskrcnn_resnet50_fpn_v2", weights=None, weights_backbone=None).train()

    for imgs, targets in data_loader:
        loss_dict = model(imgs, targets)
        # Put your training logic here

        print(f"{[img.shape for img in imgs] = }")
        print(f"{[type(target) for target in targets] = }")
        for name, loss_val in loss_dict.items():
            print(f"{name:<20}{loss_val:.3f}")

    [img.shape for img in imgs] = [torch.Size([3, 512, 512]), torch.Size([3, 409, 493])]
    [type(target) for target in targets] = [<class 'dict'>, <class 'dict'>]
    loss_classifier     4.722
    loss_box_reg        0.006
    loss_mask           0.734
    loss_objectness     0.691
    loss_rpn_box_reg    0.036

## Training References

From there, you can check out the torchvision references where you’ll find the actual training scripts we use to train our models.

Disclaimer The code in our references is more complex than what you’ll need for your own use-cases: this is because we’re supporting different backends (PIL, tensors, TVTensors) and different transforms namespaces (v1 and v2). So don’t be afraid to simplify and only keep what you need.

Total running time of the script: (0 minutes 3.389 seconds)

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