lcnn/models/utils.py

# import torch
#
#
# def evaluate(model, data_loader, device):
#     n_threads = torch.get_num_threads()
#     # FIXME remove this and make paste_masks_in_image run on the GPU
#     torch.set_num_threads(1)
#     cpu_device = torch.device("cpu")
#     model.eval()
#     metric_logger = utils.MetricLogger(delimiter="  ")
#     header = "Test:"
#
#     coco = get_coco_api_from_dataset(data_loader.dataset)
#     iou_types = _get_iou_types(model)
#     coco_evaluator = CocoEvaluator(coco, iou_types)
#
#     print(f'start to evaluate!!!')
#     for images, targets in metric_logger.log_every(data_loader, 10, header):
#         images = list(img.to(device) for img in images)
#
#         if torch.cuda.is_available():
#             torch.cuda.synchronize()
#         model_time = time.time()
#         outputs = model(images)
#
#         outputs = [{k: v.to(cpu_device) for k, v in t.items()} for t in outputs]
#         model_time = time.time() - model_time
#
#         res = {target["image_id"]: output for target, output in zip(targets, outputs)}
#         evaluator_time = time.time()
#         coco_evaluator.update(res)
#         evaluator_time = time.time() - evaluator_time
#         metric_logger.update(model_time=model_time, evaluator_time=evaluator_time)
#
#     # gather the stats from all processes
#     metric_logger.synchronize_between_processes()
#     print("Averaged stats:", metric_logger)
#     coco_evaluator.synchronize_between_processes()
#
#     # accumulate predictions from all images
#     coco_evaluator.accumulate()
#     coco_evaluator.summarize()
#     torch.set_num_threads(n_threads)
#     return coco_evaluator