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# Copyright (c) OpenMMLab. All rights reserved.
from mmocr.models.builder import DETECTORS
from .single_stage_text_detector import SingleStageTextDetector
from .text_detector_mixin import TextDetectorMixin
@DETECTORS.register_module()
class DRRG(TextDetectorMixin, SingleStageTextDetector):
"""The class for implementing DRRG text detector. Deep Relational Reasoning
Graph Network for Arbitrary Shape Text Detection.
[https://arxiv.org/abs/2003.07493]
"""
def __init__(self,
backbone,
neck,
bbox_head,
train_cfg=None,
test_cfg=None,
pretrained=None,
show_score=False,
init_cfg=None):
SingleStageTextDetector.__init__(self, backbone, neck, bbox_head,
train_cfg, test_cfg, pretrained,
init_cfg)
TextDetectorMixin.__init__(self, show_score)
def forward_train(self, img, img_metas, **kwargs):
"""
Args:
img (Tensor): Input images of shape (N, C, H, W).
Typically these should be mean centered and std scaled.
img_metas (list[dict]): A List of image info dict where each dict
has: 'img_shape', 'scale_factor', 'flip', and may also contain
'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
For details of the values of these keys see
:class:`mmdet.datasets.pipelines.Collect`.
Returns:
dict[str, Tensor]: A dictionary of loss components.
"""
x = self.extract_feat(img)
gt_comp_attribs = kwargs.pop('gt_comp_attribs')
preds = self.bbox_head(x, gt_comp_attribs)
losses = self.bbox_head.loss(preds, **kwargs)
return losses
def simple_test(self, img, img_metas, rescale=False):
x = self.extract_feat(img)
outs = self.bbox_head.single_test(x)
boundaries = self.bbox_head.get_boundary(*outs, img_metas, rescale)
return [boundaries]
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