How to delete tensors from pytorch graph?
I'm doing predictions on images for object detection in a for loop.
I've actually ran into the same issue with tensorflow and hoped I could solve it with pytorch.
At least now it seems I have found out what the issue is (naively assuming it's the same for tensorflow)
I predict like this
 model = detection.fasterrcnn_resnet50_fpn(pretrained=True, 
    progress=True,pretrained_backbone=True).to(DEVICE)
    for i in tqdm(range(train.shape[0])):
        image = cv2.imread(train_img_paths[i])
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
        image = image.transpose((2, 0, 1))
        image = image / 255.0
        image = np.expand_dims(image, axis=0)
        image = torch.FloatTensor(image)
        image = image.to(DEVICE)
        predictions = model(image)[0]

Now through the garbage collector I found that each and every image stays in the graph.
Is there away to avoid it?
I have not been able to use dataloader or dataset with the detection models (same with tensorflow hub)

Don't forget when you're doing testing to turn gradient accumulation off. You can do this by either wrapping your code like:
with torch.no_grad():
     model.eval()
     out = model(x)

or if your code is a function, using a decorator to do the same thing:
@torch.no_grad()
def model_proc(model,x):
    model.eval()
    return model(x)

