def evaluate(model, data_loader, cfg, device, logger=None, **kwargs):
    """ finished, tested
    """
    # cpu_device = torch.device("cpu")
    model.eval()
    # header = 'Test:'

    coco = convert_to_coco_api(data_loader.dataset, bbox_fmt='coco')
    coco_evaluator = CocoEvaluator(coco, iou_types = ["bbox"], bbox_fmt='coco')

    for images, targets in data_loader:
        model_input = [[cv2.resize(img, (cfg.w, cfg.h))] for img in images]
        model_input = np.concatenate(model_input, axis=0)
        model_input = model_input.transpose(0, 3, 1, 2)
        model_input = torch.from_numpy(model_input).div(255.0)
        model_input = model_input.to(device)
        targets = [{k: v.to(device) for k, v in t.items()} for t in targets]

        if torch.cuda.is_available():
            torch.cuda.synchronize()
        model_time = time.time()
        outputs = model(model_input)

        # outputs = [{k: v.to(cpu_device) for k, v in t.items()} for t in outputs]
        model_time = time.time() - model_time

        # outputs = outputs.cpu().detach().numpy()
        res = {}
        # for img, target, output in zip(images, targets, outputs):
        for img, target, boxes, confs in zip(images, targets, outputs[0], outputs[1]):
            img_height, img_width = img.shape[:2]
            # boxes = output[...,:4].copy()  # output boxes in yolo format
            boxes = boxes.squeeze(2).cpu().detach().numpy()
            boxes[...,2:] = boxes[...,2:] - boxes[...,:2] # Transform [x1, y1, x2, y2] to [x1, y1, w, h]
            boxes[...,0] = boxes[...,0]*img_width
            boxes[...,1] = boxes[...,1]*img_height
            boxes[...,2] = boxes[...,2]*img_width
            boxes[...,3] = boxes[...,3]*img_height
            boxes = torch.as_tensor(boxes, dtype=torch.float32)
            # confs = output[...,4:].copy()
            confs = confs.cpu().detach().numpy()
            labels = np.argmax(confs, axis=1).flatten()
            labels = torch.as_tensor(labels, dtype=torch.int64)
            scores = np.max(confs, axis=1).flatten()
            scores = torch.as_tensor(scores, dtype=torch.float32)
            res[target["image_id"].item()] = {
                "boxes": boxes,
                "scores": scores,
                "labels": labels,
            }
        evaluator_time = time.time()
        coco_evaluator.update(res)
        evaluator_time = time.time() - evaluator_time

    # gather the stats from all processes
    coco_evaluator.synchronize_between_processes()

    # accumulate predictions from all images
    coco_evaluator.accumulate()
    coco_evaluator.summarize()

    return coco_evaluator
finished, tested
train.py