| 12 | |
| 13 | @torch.no_grad() |
| 14 | def evaluate(data_loader, model, device, amp=True): |
| 15 | criterion = torch.nn.CrossEntropyLoss() |
| 16 | |
| 17 | metric_logger = utils.MetricLogger(delimiter=" ") |
| 18 | header = 'Test:' |
| 19 | |
| 20 | # switch to evaluation mode |
| 21 | model.eval() |
| 22 | |
| 23 | for images, target in metric_logger.log_every(data_loader, 10, header): |
| 24 | images = images.to(device, non_blocking=True) |
| 25 | target = target.to(device, non_blocking=True) |
| 26 | # compute output |
| 27 | if amp: |
| 28 | with torch.cuda.amp.autocast(): |
| 29 | output = model(images) |
| 30 | loss = criterion(output, target) |
| 31 | else: |
| 32 | output = model(images) |
| 33 | loss = criterion(output, target) |
| 34 | |
| 35 | acc1, acc5 = accuracy(output, target, topk=(1, 5)) |
| 36 | |
| 37 | batch_size = images.shape[0] |
| 38 | metric_logger.update(loss=loss.item()) |
| 39 | metric_logger.meters['acc1'].update(acc1.item(), n=batch_size) |
| 40 | metric_logger.meters['acc5'].update(acc5.item(), n=batch_size) |
| 41 | # gather the stats from all processes |
| 42 | metric_logger.synchronize_between_processes() |
| 43 | print('* Acc@1 {top1.global_avg:.3f} Acc@5 {top5.global_avg:.3f} loss {losses.global_avg:.3f}' |
| 44 | .format(top1=metric_logger.acc1, top5=metric_logger.acc5, losses=metric_logger.loss)) |
| 45 | |
| 46 | return {k: meter.global_avg for k, meter in metric_logger.meters.items()} |