[Docs] update convention (#475)

* update conventions

* fix lint

docs/en/advanced_guides/conventions.md

@@ -1 +1,58 @@
 # Conventions
+
+Please check the following conventions if you would like to modify MMSelfSup as your own project.
+
+## Losses
+
+When the algorithm is implemented, the returned losses is supposed to be `dict` type.
+
+Take `MAE` as an example:
+
+```python
+class MAE(BaseModel):
+    """MAE.
+
+    Implementation of `Masked Autoencoders Are Scalable Vision Learners
+    <https://arxiv.org/abs/2111.06377>`_.
+    """
+
+    def extract_feat(self, inputs: List[torch.Tensor],
+                     **kwarg) -> Tuple[torch.Tensor]:
+        ...
+
+    def loss(self, inputs: List[torch.Tensor],
+             data_samples: List[SelfSupDataSample],
+             **kwargs) -> Dict[str, torch.Tensor]:
+        """The forward function in training.
+
+        Args:
+            inputs (List[torch.Tensor]): The input images.
+            data_samples (List[SelfSupDataSample]): All elements required
+                during the forward function.
+
+        Returns:
+            Dict[str, torch.Tensor]: A dictionary of loss components.
+        """
+        # ids_restore: the same as that in original repo, which is used
+        # to recover the original order of tokens in decoder.
+        latent, mask, ids_restore = self.backbone(inputs[0])
+        pred = self.neck(latent, ids_restore)
+        loss = self.head(pred, inputs[0], mask)
+        losses = dict(loss=loss)
+        return losses
+
+```
+
+The `MAE.loss()` function will be called during model forward to compute the loss and return its value.
+
+By default, only values whose keys contain `'loss'` will be back propagated, if your algorithm need more than one loss value, you could pack losses dict with several keys:
+
+```python
+class YourAlgorithm(BaseModel):
+
+    def loss():
+        ...
+
+        losses['loss_1'] = loss_1
+        losses['loss_2'] = loss_2
+```