| 126 | |
| 127 | @torch.no_grad() |
| 128 | def valid(args, config, model, test_loader): |
| 129 | # Validation! |
| 130 | eval_losses = AverageMeter() |
| 131 | acc1_meter = AverageMeter() |
| 132 | acc5_meter = AverageMeter() |
| 133 | |
| 134 | logger.info("***** Running Validation *****") |
| 135 | logger.info(" Num steps = %d", len(test_loader)) |
| 136 | logger.info(" Batch size = %d", args.eval_batch_size) |
| 137 | |
| 138 | model.eval() |
| 139 | all_preds, all_label = [], [] |
| 140 | loss_fct = torch.nn.CrossEntropyLoss() |
| 141 | for step, batch in enumerate(test_loader): |
| 142 | batch = tuple(t.to(args.device) for t in batch) |
| 143 | x, y = batch |
| 144 | |
| 145 | logits, _ = model(x) |
| 146 | |
| 147 | eval_loss = loss_fct(logits, y) |
| 148 | acc1, acc5 = accuracy(logits, y, topk=(1, 5)) |
| 149 | |
| 150 | eval_losses.update(eval_loss.item(), y.size(0)) |
| 151 | acc1_meter.update(acc1.item(), y.size(0)) |
| 152 | acc5_meter.update(acc5.item(), y.size(0)) |
| 153 | |
| 154 | |
| 155 | if step % config.PRINT_FREQ == 0: |
| 156 | logger.info( |
| 157 | f'Test: [{step}/{len(test_loader)}]\t' |
| 158 | f'Loss {eval_losses.val:.4f} ({eval_losses.avg:.4f})\t' |
| 159 | f'Acc@1 {acc1_meter.val:.3f} ({acc1_meter.avg:.3f})\t' |
| 160 | f'Acc@5 {acc5_meter.val:.3f} ({acc5_meter.avg:.3f})') |
| 161 | logger.info(f' * Acc@1 {acc1_meter.avg:.3f} Acc@5 {acc5_meter.avg:.3f}') |
| 162 | |
| 163 | return acc1_meter.avg |
| 164 | |
| 165 | def calib(args, config, model): |
| 166 | """ Calibrate the model """ |