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- import tqdm
- import torch
- from block.metric_get import metric
- def val_get(args, val_dataloader, model, loss, ema, data_len):
- tqdm_len = (data_len - 1) // (args.batch // args.device_number) + 1
- tqdm_show = tqdm.tqdm(total=tqdm_len)
- with torch.no_grad():
- model = ema.ema if args.ema else model.eval()
- pred_all = [] # 记录所有预测
- true_all = [] # 记录所有标签
- for index, (image_batch, true_batch) in enumerate(val_dataloader):
- image_batch = image_batch.to(args.device, non_blocking=args.latch)
- pred_batch = model(image_batch).detach().cpu()
- loss_batch = loss(pred_batch, true_batch)
- pred_all.extend(pred_batch)
- true_all.extend(true_batch)
- tqdm_show.set_postfix({'val_loss': loss_batch.item()}) # 添加显示
- tqdm_show.update(1) # 更新进度条
- # tqdm
- tqdm_show.close()
- # 计算指标
- pred_all = torch.stack(pred_all, dim=0)
- true_all = torch.stack(true_all, dim=0)
- loss_all = loss(pred_all, true_all).item()
- accuracy, precision, recall, m_ap = metric(pred_all, true_all, args.class_threshold)
- print(f'\n| 验证 | val_loss:{loss_all:.4f} | 阈值:{args.class_threshold:.2f} | val_accuracy:{accuracy:.4f} |'
- f' val_precision:{precision:.4f} | val_recall:{recall:.4f} | val_m_ap:{m_ap:.4f} |')
- return loss_all, accuracy, precision, recall, m_ap
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