Hi, I’m doing object detection on a custom dataset using transfer learning from a pretrained Faster RCNN model.
I would like to compute validation loss at the end of each epoch. How can this be done?
If I run the code below (model in training mode) I get losses, but dropout isn’t deactivated, so I am wondering how ‘valid’ are these loss values. And running the model in eval mode only returns the predictions.
model.train()
for images, targets in data_loader_val:
    images = [image.to(device) for image in images]
    targets = [{k: v.to(device) for k, v in t.items()} for t in targets]

    with torch.no_grad():
        val_loss_dict = model(images, targets)
        print(val_loss_dict)