Source code for mmrotate.models.detectors.oriented_rcnn
# Copyright (c) OpenMMLab. All rights reserved.
import torch
from ..builder import ROTATED_DETECTORS
from .two_stage import RotatedTwoStageDetector
[docs]@ROTATED_DETECTORS.register_module()
class OrientedRCNN(RotatedTwoStageDetector):
"""Implementation of `Oriented R-CNN for Object Detection.`__
__ https://openaccess.thecvf.com/content/ICCV2021/papers/Xie_Oriented_R-CNN_for_Object_Detection_ICCV_2021_paper.pdf # noqa: E501, E261.
"""
def __init__(self,
backbone,
rpn_head,
roi_head,
train_cfg,
test_cfg,
neck=None,
pretrained=None,
init_cfg=None):
super(OrientedRCNN, 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)
[docs] def forward_dummy(self, img):
"""Used for computing network flops.
See `mmrotate/tools/analysis_tools/get_flops.py`
"""
outs = ()
# backbone
x = self.extract_feat(img)
# rpn
if self.with_rpn:
rpn_outs = self.rpn_head(x)
outs = outs + (rpn_outs, )
proposals = torch.randn(1000, 6).to(img.device)
# roi_head
roi_outs = self.roi_head.forward_dummy(x, proposals)
outs = outs + (roi_outs, )
return outs