import torch.nn as nn
from mmdet.core import bbox2result
from ..builder import DETECTORS, build_backbone, build_head, build_neck
from .base import BaseDetector
[docs]@DETECTORS.register_module()
class SingleStageDetector(BaseDetector):
"""Base class for single-stage detectors.
Single-stage detectors directly and densely predict bounding boxes on the
output features of the backbone+neck.
"""
def __init__(self,
backbone,
neck=None,
bbox_head=None,
train_cfg=None,
test_cfg=None,
pretrained=None):
super(SingleStageDetector, self).__init__()
self.backbone = build_backbone(backbone)
if neck is not None:
self.neck = build_neck(neck)
bbox_head.update(train_cfg=train_cfg)
bbox_head.update(test_cfg=test_cfg)
self.bbox_head = build_head(bbox_head)
self.train_cfg = train_cfg
self.test_cfg = test_cfg
self.init_weights(pretrained=pretrained)
def init_weights(self, pretrained=None):
super(SingleStageDetector, self).init_weights(pretrained)
self.backbone.init_weights(pretrained=pretrained)
if self.with_neck:
if isinstance(self.neck, nn.Sequential):
for m in self.neck:
m.init_weights()
else:
self.neck.init_weights()
self.bbox_head.init_weights()
[docs] def forward_dummy(self, img):
"""Used for computing network flops.
See `mmdetection/tools/get_flops.py`
"""
x = self.extract_feat(img)
outs = self.bbox_head(x)
return outs
[docs] def forward_train(self,
img,
img_metas,
gt_bboxes,
gt_labels,
gt_bboxes_ignore=None):
"""
Args:
img (Tensor): Input images of shape (N, C, H, W).
Typically these should be mean centered and std scaled.
img_metas (list[dict]): A List of image info dict where each dict
has: 'img_shape', 'scale_factor', 'flip', and may also contain
'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
For details on the values of these keys see
:class:`mmdet.datasets.pipelines.Collect`.
gt_bboxes (list[Tensor]): Each item are the truth boxes for each
image in [tl_x, tl_y, br_x, br_y] format.
gt_labels (list[Tensor]): Class indices corresponding to each box
gt_bboxes_ignore (None | list[Tensor]): Specify which bounding
boxes can be ignored when computing the loss.
Returns:
dict[str, Tensor]: A dictionary of loss components.
"""
x = self.extract_feat(img)
outs = self.bbox_head(x)
loss_inputs = outs + (gt_bboxes, gt_labels, img_metas)
losses = self.bbox_head.loss(
*loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore)
return losses
def simple_test(self, img, img_metas, rescale=False):
x = self.extract_feat(img)
outs = self.bbox_head(x)
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[0]
def aug_test(self, imgs, img_metas, rescale=False):
raise NotImplementedError