| 103 | |
| 104 | @torch.no_grad() |
| 105 | def evaluate(data_loader, model, device, amp_autocast): |
| 106 | criterion = torch.nn.CrossEntropyLoss() |
| 107 | |
| 108 | metric_logger = utils.MetricLogger(delimiter=" ") |
| 109 | header = 'Test:' |
| 110 | |
| 111 | # switch to evaluation mode |
| 112 | model.eval() |
| 113 | |
| 114 | for images, target in metric_logger.log_every(data_loader, 10, header): |
| 115 | images = images.to(device, non_blocking=True) |
| 116 | target = target.to(device, non_blocking=True) |
| 117 | |
| 118 | # compute output |
| 119 | with amp_autocast(): |
| 120 | output = model(images) |
| 121 | loss = criterion(output, target) |
| 122 | |
| 123 | acc1, acc5 = accuracy(output, target, topk=(1, 5)) |
| 124 | |
| 125 | batch_size = images.shape[0] |
| 126 | metric_logger.update(loss=loss.item()) |
| 127 | metric_logger.meters['acc1'].update(acc1.item(), n=batch_size) |
| 128 | metric_logger.meters['acc5'].update(acc5.item(), n=batch_size) |
| 129 | # gather the stats from all processes |
| 130 | metric_logger.synchronize_between_processes() |
| 131 | print('* Acc@1 {top1.global_avg:.3f} Acc@5 {top5.global_avg:.3f} loss {losses.global_avg:.3f}' |
| 132 | .format(top1=metric_logger.acc1, top5=metric_logger.acc5, losses=metric_logger.loss)) |
| 133 | |
| 134 | return {k: meter.global_avg for k, meter in metric_logger.meters.items()} |