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https://github.com/open-mmlab/mmclassification.git
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* remove basehead * add moco series * add byol simclr simsiam * add ut * update configs * add simsiam hook * add and refactor beit * update ut * add cae * update extract_feat * refactor cae * add mae * refactor data preprocessor * update heads * add maskfeat * add milan * add simmim * add mixmim * fix lint * fix ut * fix lint * add eva * add densecl * add barlowtwins * add swav * fix lint * update readtherdocs rst * update docs * update * Decrease UT memory usage * Fix docstring * update DALLEEncoder * Update model docs * refactor dalle encoder * update docstring * fix ut * fix config error * add val_cfg and test_cfg * refactor clip generator * fix lint * pass check * fix ut * add lars * update type of BEiT in configs * Use MMEngine style momentum in EMA. * apply mmpretrain solarize --------- Co-authored-by: mzr1996 <mzr1996@163.com>
32 lines
711 B
Python
32 lines
711 B
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import torch
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from mmengine.model import BaseModule
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from mmpretrain.registry import MODELS
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@MODELS.register_module()
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class SwAVHead(BaseModule):
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"""Head for SwAV Pre-training.
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Args:
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loss (dict): Config dict for module of loss functions.
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"""
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def __init__(self, loss: dict) -> None:
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super().__init__()
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self.loss_module = MODELS.build(loss)
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def loss(self, pred: torch.Tensor) -> torch.Tensor:
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"""Generate loss.
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Args:
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pred (torch.Tensor): NxC input features.
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Returns:
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torch.Tensor: The SwAV loss.
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"""
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loss = self.loss_module(pred)
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return loss
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