| 275 | |
| 276 | @torch.no_grad() |
| 277 | def throughput(data_loader, model, logger): |
| 278 | model.eval() |
| 279 | |
| 280 | for idx, (images, _) in enumerate(data_loader): |
| 281 | images = images.cuda(non_blocking=True) |
| 282 | batch_size = images.shape[0] |
| 283 | for i in range(50): |
| 284 | model(images) |
| 285 | torch.cuda.synchronize() |
| 286 | logger.info(f"throughput averaged with 30 times") |
| 287 | tic1 = time.time() |
| 288 | for i in range(30): |
| 289 | model(images) |
| 290 | torch.cuda.synchronize() |
| 291 | tic2 = time.time() |
| 292 | logger.info(f"batch_size {batch_size} throughput {30 * batch_size / (tic2 - tic1)}") |
| 293 | return |
| 294 | |
| 295 | |
| 296 | if __name__ == '__main__': |