(self)
| 17 | |
| 18 | @torch.no_grad() |
| 19 | def run_eval(self): |
| 20 | self.eval_mode() |
| 21 | self.before_all_iter(self.hooks_dict[self._mode]) |
| 22 | all_results = [] |
| 23 | for batch_idx, batch_data in tqdm( |
| 24 | enumerate(self.datas[self._mode].dataloader)): |
| 25 | self.before_iter(self.hooks_dict[self._mode]) |
| 26 | if self.sample_args: |
| 27 | batch_data.update(self.sample_args.get_lowercase_dict()) |
| 28 | with torch.autocast(device_type='cuda', |
| 29 | enabled=self.use_amp, |
| 30 | dtype=self.dtype): |
| 31 | results = self.run_step_eval(transfer_data_to_cuda(batch_data), |
| 32 | batch_idx, |
| 33 | step=self.total_iter, |
| 34 | rank=we.rank) |
| 35 | all_results.extend(results) |
| 36 | self.after_iter(self.hooks_dict[self._mode]) |
| 37 | log_data, log_label = self.save_results(all_results) |
| 38 | self.register_probe({'eval_label': log_label}) |
| 39 | self.register_probe({ |
| 40 | 'eval_image': |
| 41 | ProbeData(log_data, |
| 42 | is_image=True, |
| 43 | build_html=True, |
| 44 | build_label=log_label) |
| 45 | }) |
| 46 | self.after_all_iter(self.hooks_dict[self._mode]) |
| 47 | |
| 48 | @torch.no_grad() |
| 49 | def run_test(self): |
no test coverage detected