mmcls/models/classifiers/base.py

# 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]