解决mmsegmentation日志无法输出验证损失

一、新版更新mmengine、mmcv后无法正常输出验证损失

1、继续使用最新版参考[Feature] Support calculating loss during validation #1503

• 因为mmengine目前没有可以选择输入验证损失的入口。根据mmengine/runner/loops.py中该PR提交的代码需要将验证损失计算放在（model.forward用mode='predict'或predict）中，并将通过将val_loss添加到BaseDataElement中，输出在其他参数之后。
• mmsegmentation将模式的选择放在mmseg/models/segmentors/base.py的forward函数中，可以选择在这里的predict中添加代码

        if mode == 'loss':
            return self.loss(inputs, data_samples)
        elif mode == 'predict':
            return self.predict(inputs, data_samples)
        elif mode == 'tensor':
            return self._forward(inputs, data_samples)
        else:
            raise RuntimeError(f'Invalid mode "{mode}". '
                               'Only supports loss, predict and tensor mode')

• 我之前因为修改过程中有点问题直接在mmseg/models/segmentors/encoder_decoder.py中进行了修改（建议在base.py中修改）

•     def predict(self,
                  inputs: Tensor,
                  data_samples: OptSampleList = None) -> SampleList:
          """Predict results from a batch of inputs and data samples with post-
          processing.
  
          Args:
              inputs (Tensor): Inputs with shape (N, C, H, W).
              data_samples (List[:obj:`SegDataSample`], optional): The seg data
                  samples. It usually includes information such as `metainfo`
                  and `gt_sem_seg`.
  
          Returns:
              list[:obj:`SegDataSample`]: Segmentation results of the
              input images. Each SegDataSample usually contain:
  
              - ``pred_sem_seg``(PixelData): Prediction of semantic segmentation.
              - ``seg_logits``(PixelData): Predicted logits of semantic
                  segmentation before normalization.
          """
          if data_samples is not None:
              batch_img_metas = [
                  data_sample.metainfo for data_sample in data_samples
              ]
          else:
              batch_img_metas = [
                  dict(
                      ori_shape=inputs.shape[2:],
                      img_shape=inputs.shape[2:],
                      pad_shape=inputs.shape[2:],
                      padding_size=[0, 0, 0, 0])
              ] * inputs.shape[0]
  
          seg_logits = self.inference(inputs, batch_img_metas)
          predictions = self.postprocess_result(seg_logits, data_samples)
  
          # 添加验证损失
          if data_samples is not None and all(hasattr(ds, 'gt_sem_seg') for ds in data_samples): # 避免测试过程被调用
              losses = self.loss(inputs, data_samples)  # 调用 loss 方法计算损失
              val_loss = BaseDataElement(loss=losses)
              return predictions + [val_loss]  # 将损失附加在结果后
          return predictions
  
          # return self.postprocess_result(seg_logits, data_samples)

2、降级mmcv1.x版本参考Fix validation loss logging #1494

内置验证损失