| 315 | |
| 316 | @torch.no_grad() |
| 317 | def throughput(data_loader, model, logger): |
| 318 | model.eval() |
| 319 | |
| 320 | for idx, (images, _) in enumerate(data_loader): |
| 321 | images = images.cuda(non_blocking=True) |
| 322 | batch_size = images.shape[0] |
| 323 | for i in range(50): |
| 324 | model(images) |
| 325 | torch.cuda.synchronize() |
| 326 | logger.info(f"throughput averaged with 30 times") |
| 327 | tic1 = time.time() |
| 328 | for i in range(30): |
| 329 | model(images) |
| 330 | torch.cuda.synchronize() |
| 331 | tic2 = time.time() |
| 332 | logger.info(f"batch_size {batch_size} throughput {30 * batch_size / (tic2 - tic1)}") |
| 333 | return |
| 334 | |
| 335 | |
| 336 | if __name__ == '__main__': |