| 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): |
| 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) |
|
|
| 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) |
|
|
