描述
Hello,
🚀 Feature
I think it would be more generic to have transform(https://github.com/pytorch/vision/blob/3d65fc6723f1e0709916f24d819d6e17a925b394/torchvision/models/detection/faster_rcnn.py#L231) as a function that can be modified by users rather than a default one.
Motivation
I am applying transformations separately as a part of data augmentation, which includes cropping and resizing. Hence I would prefer to not do the twice while retraining FasterRCNN.
Pitch
I would like to have a fixed size input to be fed into the network for variable-sized images. At present, I do this by resizing the images separately as a part of DataLoader and adjust the parameters of GeneralizedRCNNTransform accordingly.
Alternatives
My present way of using FasterRCNN is an alternative. Since my set of transformations are pre-defined, I have to apply hacks such as setting mean to 0., std to 1. and altering min and max sizes to my default value(this would mean that scale=1 and interpolation would return the same image.
Additional context
While the input to the network is fixed size, I apply many other augmentations such as mirror, random cropping etc, inspired by SSD based networks. Hence I would prefer to do all augmentation in a separate place once instead of twice.
Thank you!
Edit : If you think this would be a meaningful change, I will be happy to send a Pull Request.