| 75 | |
| 76 | @torch.no_grad() |
| 77 | def evaluate(data_loader, model, device): |
| 78 | criterion = torch.nn.CrossEntropyLoss() |
| 79 | |
| 80 | metric_logger = utils.MetricLogger(delimiter=" ") |
| 81 | header = 'Test:' |
| 82 | |
| 83 | # switch to evaluation mode |
| 84 | model.eval() |
| 85 | |
| 86 | for images, target in metric_logger.log_every(data_loader, 10, header): |
| 87 | images = images.to(device, non_blocking=True) |
| 88 | target = target.to(device, non_blocking=True) |
| 89 | |
| 90 | # compute output |
| 91 | with torch.cuda.amp.autocast(): |
| 92 | output = model(images) |
| 93 | loss = criterion(output, target) |
| 94 | |
| 95 | acc1, acc5 = accuracy(output, target, topk=(1, 5)) |
| 96 | |
| 97 | batch_size = images.shape[0] |
| 98 | metric_logger.update(loss=loss.item()) |
| 99 | metric_logger.meters['acc1'].update(acc1.item(), n=batch_size) |
| 100 | metric_logger.meters['acc5'].update(acc5.item(), n=batch_size) |
| 101 | # gather the stats from all processes |
| 102 | metric_logger.synchronize_between_processes() |
| 103 | print('* Acc@1 {top1.global_avg:.3f} Acc@5 {top5.global_avg:.3f} loss {losses.global_avg:.3f}' |
| 104 | .format(top1=metric_logger.acc1, top5=metric_logger.acc5, losses=metric_logger.loss)) |
| 105 | |
| 106 | return {k: meter.global_avg for k, meter in metric_logger.meters.items()} |