Modify the inference interface to adapt to standalone and distributed… #96
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SamitHuang
reviewed
Mar 23, 2023
| is_train=False) | ||
| cfg.eval.dataset, | ||
| cfg.eval.loader, | ||
| num_shards=device_num, |
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若改成distributed eval，需要对多卡的评估结果做聚合，需注意的是 f1-score, acc等评估指标仅做多卡平均并不能得到正确的结果，需要对预测结果或评估指标做分布式的更新。
SamitHuang
reviewed
Mar 23, 2023
| epoch_time = time.time() - self.epoch_start_time | ||
| per_step_time = epoch_time * 1000 / cb_params.batch_num | ||
| fps = 1000 * self.batch_size / per_step_time | ||
| msg = 'epoch: [%s/%s] loss: %.6f, epoch time: %.3f s, per step time: %.3f ms, fps: %.2f' % ( |
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这个loss只反映最后一个batch的平均loss，不是整个training data的平均loss，建议复用EvalSaveCallback中的training loss的计算结果，将epoch training loss的在EvalSaveCall的on_train_epoch_end中打印。
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这个losscallback我这边不涉及修改，后面是不是统一修改一下
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