| 297 | |
| 298 | @torch.no_grad() |
| 299 | def throughput(data_loader, model, logger): |
| 300 | model.eval() |
| 301 | |
| 302 | for _, (images, _) in enumerate(data_loader): |
| 303 | images = images.cuda(non_blocking=True) |
| 304 | batch_size = images.shape[0] |
| 305 | for i in range(50): |
| 306 | model(images) |
| 307 | torch.cuda.synchronize() |
| 308 | logger.info(f"throughput averaged with 30 times") |
| 309 | tic1 = time.time() |
| 310 | for i in range(30): |
| 311 | model(images) |
| 312 | torch.cuda.synchronize() |
| 313 | tic2 = time.time() |
| 314 | logger.info(f"batch_size {batch_size} throughput {30 * batch_size / (tic2 - tic1)}") |
| 315 | return |
| 316 | if __name__ == '__main__': |
| 317 | main() |