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# Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
from typing import Dict, Tuple, Union
import torch
from mmengine.model import BaseModel
from torch import Tensor
from mmocr.utils.typing_utils import (DetSampleList, OptConfigType,
OptDetSampleList, OptMultiConfig)
ForwardResults = Union[Dict[str, torch.Tensor], DetSampleList,
Tuple[torch.Tensor], torch.Tensor]
class BaseTextDetector(BaseModel, metaclass=ABCMeta):
"""Base class for detectors.
Args:
data_preprocessor (dict or ConfigDict, optional): The pre-process
config of :class:`BaseDataPreprocessor`. it usually includes,
``pad_size_divisor``, ``pad_value``, ``mean`` and ``std``.
init_cfg (dict or ConfigDict, optional): the config to control the
initialization. Defaults to None.
"""
def __init__(self,
data_preprocessor: OptConfigType = None,
init_cfg: OptMultiConfig = None):
super().__init__(
data_preprocessor=data_preprocessor, init_cfg=init_cfg)
@property
def with_neck(self) -> bool:
"""bool: whether the detector has a neck"""
return hasattr(self, 'neck') and self.neck is not None
def forward(self,
inputs: torch.Tensor,
data_samples: OptDetSampleList = None,
mode: str = 'tensor') -> ForwardResults:
"""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:`TextDetDataSample`.
- "loss": Forward and return a dict of losses according to the given
inputs and data samples.
Note that this method doesn't handle either back propagation or
parameter update, which are supposed to be done in :meth:`train_step`.
Args:
inputs (torch.Tensor): The input tensor with shape
(N, C, ...) in general.
data_samples (list[:obj:`TextDetDataSample`], optional): A batch of
data samples that contain annotations and predictions.
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:`TextDetDataSample`.
- If ``mode="loss"``, return a dict of tensor.
"""
if mode == 'loss':
return self.loss(inputs, data_samples)
elif mode == 'predict':
return self.predict(inputs, data_samples)
elif mode == 'tensor':
return self._forward(inputs, data_samples)
else:
raise RuntimeError(f'Invalid mode "{mode}". '
'Only supports loss, predict and tensor mode')
@abstractmethod
def loss(self, inputs: Tensor,
data_samples: DetSampleList) -> Union[dict, tuple]:
"""Calculate losses from a batch of inputs and data samples."""
pass
@abstractmethod
def predict(self, inputs: Tensor,
data_samples: DetSampleList) -> DetSampleList:
"""Predict results from a batch of inputs and data samples with post-
processing."""
pass
@abstractmethod
def _forward(self, inputs: Tensor, data_samples: OptDetSampleList = None):
"""Network forward process.
Usually includes backbone, neck and head forward without any post-
processing.
"""
pass
@abstractmethod
def extract_feat(self, inputs: Tensor):
"""Extract features from images."""
pass
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