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# Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
from typing import List, Optional, Sequence
import torch
from mmengine.model import BaseModel
from mmengine.structures import BaseDataElement
class BaseClassifier(BaseModel, metaclass=ABCMeta):
"""Base class for classifiers.
Args:
init_cfg (dict, optional): Initialization config dict.
Defaults to None.
data_preprocessor (dict, optional): The config for preprocessing input
data. If None, it will use "BaseDataPreprocessor" as type, see
:class:`mmengine.model.BaseDataPreprocessor` for more details.
Defaults to None.
Attributes:
init_cfg (dict): Initialization config dict.
data_preprocessor (:obj:`mmengine.model.BaseDataPreprocessor`): An
extra data pre-processing module, which processes data from
dataloader to the format accepted by :meth:`forward`.
"""
def __init__(self,
init_cfg: Optional[dict] = None,
data_preprocessor: Optional[dict] = None):
super(BaseClassifier, self).__init__(
init_cfg=init_cfg, data_preprocessor=data_preprocessor)
@property
def with_neck(self) -> bool:
"""Whether the classifier has a neck."""
return hasattr(self, 'neck') and self.neck is not None
@property
def with_head(self) -> bool:
"""Whether the classifier has a head."""
return hasattr(self, 'head') and self.head is not None
@abstractmethod
def forward(self,
inputs: torch.Tensor,
data_samples: Optional[List[BaseDataElement]] = None,
mode: str = 'tensor'):
"""The unified entry for a forward process in both training and test.
The method should accept three modes: "tensor", "predict" and "loss":
- "tensor": Forward the whole network and return tensor or tuple of
tensor without any post-processing, same as a common nn.Module.
- "predict": Forward and return the predictions, which are fully
processed to a list of :obj:`BaseDataElement`.
- "loss": Forward and return a dict of losses according to the given
inputs and data samples.
Note that this method doesn't handle neither back propagation nor
optimizer updating, which are done in the :meth:`train_step`.
Args:
inputs (torch.Tensor): The input tensor with shape (N, C, ...)
in general.
data_samples (List[BaseDataElement], optional): The annotation
data of every samples. It's required if ``mode="loss"``.
Defaults to None.
mode (str): Return what kind of value. Defaults to 'tensor'.
Returns:
The return type depends on ``mode``.
- If ``mode="tensor"``, return a tensor or a tuple of tensor.
- If ``mode="predict"``, return a list of
:obj:`mmengine.BaseDataElement`.
- If ``mode="loss"``, return a dict of tensor.
"""
pass
def extract_feat(self, inputs: torch.Tensor):
"""Extract features from the input tensor with shape (N, C, ...).
The sub-classes are recommended to implement this method to extract
features from backbone and neck.
Args:
inputs (Tensor): A batch of inputs. The shape of it should be
``(num_samples, num_channels, *img_shape)``.
"""
raise NotImplementedError
def extract_feats(self, multi_inputs: Sequence[torch.Tensor],
**kwargs) -> list:
"""Extract features from a sequence of input tensor.
Args:
multi_inputs (Sequence[torch.Tensor]): A sequence of input
tensor. It can be used in augmented inference.
**kwargs: Other keyword arguments accepted by :meth:`extract_feat`.
Returns:
list: Features of every input tensor.
"""
assert isinstance(multi_inputs, Sequence), \
'`extract_feats` is used for a sequence of inputs tensor. If you '\
'want to extract on single inputs tensor, use `extract_feat`.'
return [self.extract_feat(inputs, **kwargs) for inputs in multi_inputs]
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