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