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- # Copyright (c) OpenMMLab. All rights reserved.
- from ..builder import DETECTORS
- from .two_stage import TwoStageDetector
-
-
- @DETECTORS.register_module()
- class CascadeRCNN(TwoStageDetector):
- r"""Implementation of `Cascade R-CNN: Delving into High Quality Object
- Detection <https://arxiv.org/abs/1906.09756>`_"""
-
- def __init__(self,
- backbone,
- neck=None,
- rpn_head=None,
- roi_head=None,
- train_cfg=None,
- test_cfg=None,
- pretrained=None,
- init_cfg=None):
- super(CascadeRCNN, self).__init__(
- backbone=backbone,
- neck=neck,
- rpn_head=rpn_head,
- roi_head=roi_head,
- train_cfg=train_cfg,
- test_cfg=test_cfg,
- pretrained=pretrained,
- init_cfg=init_cfg)
-
- def show_result(self, data, result, **kwargs):
- """Show prediction results of the detector.
-
- Args:
- data (str or np.ndarray): Image filename or loaded image.
- result (Tensor or tuple): The results to draw over `img`
- bbox_result or (bbox_result, segm_result).
-
- Returns:
- np.ndarray: The image with bboxes drawn on it.
- """
- if self.with_mask:
- ms_bbox_result, ms_segm_result = result
- if isinstance(ms_bbox_result, dict):
- result = (ms_bbox_result['ensemble'],
- ms_segm_result['ensemble'])
- else:
- if isinstance(result, dict):
- result = result['ensemble']
- return super(CascadeRCNN, self).show_result(data, result, **kwargs)
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