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# Conventions
2022-09-23 17:57:55 +08:00
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
```
