

         labels = [
             {k: v for k, v in t.items()} for t in batch["labels"]
         ]  # these are in DETR format, resized + normalized
         # forward pass
         outputs = model(pixel_values=pixel_values, pixel_mask=pixel_mask)
         orig_target_sizes = torch.stack([target["orig_size"] for target in labels], dim=0)
         results = im_processor.post_process(outputs, orig_target_sizes)  # convert outputs of model to Pascal VOC format (xmin, ymin, xmax, ymax)
         module.add(prediction=results, reference=labels)
         del batch