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