:param sample: :param batch_idx: :return: total loss: torch.Tensor, loss_log: dict
(self, sample, batch_idx, optimizer_idx)
| 115 | self.training_losses_meter = {'total_loss': utils.AvgrageMeter()} |
| 116 | |
| 117 | def _training_step(self, sample, batch_idx, optimizer_idx): |
| 118 | """ |
| 119 | |
| 120 | :param sample: |
| 121 | :param batch_idx: |
| 122 | :return: total loss: torch.Tensor, loss_log: dict |
| 123 | """ |
| 124 | raise NotImplementedError |
| 125 | |
| 126 | def training_step(self, sample, batch_idx, optimizer_idx=-1): |
| 127 | loss_ret = self._training_step(sample, batch_idx, optimizer_idx) |