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from mmdet.core import bbox2result
from ..builder import DETECTORS
from .single_stage import SingleStageDetector
@DETECTORS.register_module()
class DETR(SingleStageDetector):
r"""Implementation of `DETR: End-to-End Object Detection with
Transformers <https://arxiv.org/pdf/2005.12872>`_"""
def __init__(self,
backbone,
bbox_head,
train_cfg=None,
test_cfg=None,
pretrained=None):
super(DETR, self).__init__(backbone, None, bbox_head, train_cfg,
test_cfg, pretrained)
def simple_test(self, img, img_metas, rescale=False):
"""Test function without test time augmentation.
Args:
imgs (list[torch.Tensor]): List of multiple images
img_metas (list[dict]): List of image information.
rescale (bool, optional): Whether to rescale the results.
Defaults to False.
Returns:
list[list[np.ndarray]]: BBox results of each image and classes.
The outer list corresponds to each image. The inner list
corresponds to each class.
"""
batch_size = len(img_metas)
assert batch_size == 1, 'Currently only batch_size 1 for inference ' \
f'mode is supported. Found batch_size {batch_size}.'
x = self.extract_feat(img)
outs = self.bbox_head(x, img_metas)
bbox_list = self.bbox_head.get_bboxes(
*outs, img_metas, rescale=rescale)
bbox_results = [
bbox2result(det_bboxes, det_labels, self.bbox_head.num_classes)
for det_bboxes, det_labels in bbox_list
]
return bbox_results
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