I have usecase where dlib based models need to used to perform face detection and face alignment. Is there a way to use these models to perform face detection and alignment as part of pytorch transforms?
Please suggest and possibilities or resources.
In the common use case you would apply any transformation in the
Dataset.__getitem__ method on each sample. Assuming your
dlib transformation works on a single numpy array (or any other object you could transform into a PyTorch tensor) you could just apply it in the
Additionally, you could also write a custom
transform class which expects to initialize internal states in the
__init__ and apply the transformation in its
__call__ method. Take a look at e.g
torchvision.transforms.ToTensor for an example. This would allow you to add this custom transformation into e.g.
torchvision.transforms.Compose, but make sure the expected dtype is used.
Thanks for these details. These are helpful.