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Source code for mmocr.models.textdet.detectors.drrg

# 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


[docs]@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)
[docs] 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
[docs] 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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