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# Copyright (c) OpenMMLab. All rights reserved.
from typing import Dict, List
import torch
from mmpretrain.registry import MODELS
from mmpretrain.structures import DataSample
from .base import BaseSelfSupervisor
@MODELS.register_module()
class SwAV(BaseSelfSupervisor):
"""SwAV.
Implementation of `Unsupervised Learning of Visual Features by Contrasting
Cluster Assignments <https://arxiv.org/abs/2006.09882>`_.
The queue is built in ``mmpretrain/engine/hooks/swav_hook.py``.
"""
def loss(self, inputs: List[torch.Tensor], data_samples: List[DataSample],
**kwargs) -> Dict[str, torch.Tensor]:
"""Forward computation during training.
Args:
inputs (List[torch.Tensor]): The input images.
data_samples (List[DataSample]): All elements required
during the forward function.
Returns:
Dict[str, torch.Tensor]: A dictionary of loss components.
"""
assert isinstance(inputs, list)
# multi-res forward passes
idx_crops = torch.cumsum(
torch.unique_consecutive(
torch.tensor([input.shape[-1] for input in inputs]),
return_counts=True)[1], 0)
start_idx = 0
output = []
for end_idx in idx_crops:
_out = self.backbone(torch.cat(inputs[start_idx:end_idx]))
output.append(_out)
start_idx = end_idx
output = self.neck(output)
loss = self.head.loss(output)
losses = dict(loss=loss)
return losses
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