| import torch | |
| from torch import nn | |
| def to(x, device): | |
| if isinstance(x, dict): | |
| for k, v in x.items(): | |
| if isinstance(v, torch.Tensor): | |
| x[k] = v.to(device) | |
| else: | |
| x = x.to(device) | |
| return x | |
| def get_cur_acc(testset, hyps, model, shuffle, iter_index): | |
| from data import split_dataset, build_dataloader | |
| cur_test_batch_dataset = split_dataset(testset, hyps['val_batch_size'], iter_index)[0] | |
| cur_test_batch_dataloader = build_dataloader(cur_test_batch_dataset, hyps['train_batch_size'], hyps['num_workers'], False, shuffle) | |
| cur_acc = model.get_accuracy(cur_test_batch_dataloader) | |
| return cur_acc | |