| 238 | |
| 239 | @torch.no_grad() |
| 240 | def evaluate(data_loader, model, device): |
| 241 | criterion = torch.nn.CrossEntropyLoss() |
| 242 | |
| 243 | metric_logger = misc.MetricLogger(delimiter=" ") |
| 244 | header = 'Test:' |
| 245 | |
| 246 | # switch to evaluation mode |
| 247 | model.eval() |
| 248 | |
| 249 | for batch in metric_logger.log_every(data_loader, 500, header): |
| 250 | images = batch[0] |
| 251 | target = batch[-1] |
| 252 | images = images.to(device, non_blocking=True) |
| 253 | target = target.to(device, non_blocking=True) |
| 254 | |
| 255 | # compute output |
| 256 | with torch.cuda.amp.autocast(): |
| 257 | output = model(images) |
| 258 | loss = criterion(output, target) |
| 259 | |
| 260 | acc1, acc5 = accuracy(output, target, topk=(1, 5)) |
| 261 | functional.reset_net(model) |
| 262 | |
| 263 | batch_size = images.shape[0] |
| 264 | metric_logger.update(loss=loss.item()) |
| 265 | metric_logger.meters['acc1'].update(acc1.item(), n=batch_size) |
| 266 | metric_logger.meters['acc5'].update(acc5.item(), n=batch_size) |
| 267 | # gather the stats from all processes |
| 268 | metric_logger.synchronize_between_processes() |
| 269 | print('* Acc@1 {top1.global_avg:.3f} Acc@5 {top5.global_avg:.3f} loss {losses.global_avg:.3f}' |
| 270 | .format(top1=metric_logger.acc1, top5=metric_logger.acc5, losses=metric_logger.loss)) |
| 271 | |
| 272 | return {k: meter.global_avg for k, meter in metric_logger.meters.items()} |