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# Copyright (c) OpenMMLab. All rights reserved.
from ..builder import HEADS
from .ascend_anchor_head import AscendAnchorHead
from .retina_head import RetinaHead
@HEADS.register_module()
class AscendRetinaHead(RetinaHead, AscendAnchorHead):
r"""An anchor-based head used in `RetinaNet
<https://arxiv.org/pdf/1708.02002.pdf>`_.
The head contains two subnetworks. The first classifies anchor boxes and
the second regresses deltas for the anchors.
Example:
>>> import torch
>>> self = RetinaHead(11, 7)
>>> x = torch.rand(1, 7, 32, 32)
>>> cls_score, bbox_pred = self.forward_single(x)
>>> # Each anchor predicts a score for each class except background
>>> cls_per_anchor = cls_score.shape[1] / self.num_anchors
>>> box_per_anchor = bbox_pred.shape[1] / self.num_anchors
>>> assert cls_per_anchor == (self.num_classes)
>>> assert box_per_anchor == 4
"""
def __init__(self,
num_classes,
in_channels,
stacked_convs=4,
conv_cfg=None,
norm_cfg=None,
anchor_generator=dict(
type='AnchorGenerator',
octave_base_scale=4,
scales_per_octave=3,
ratios=[0.5, 1.0, 2.0],
strides=[8, 16, 32, 64, 128]),
init_cfg=dict(
type='Normal',
layer='Conv2d',
std=0.01,
override=dict(
type='Normal',
name='retina_cls',
std=0.01,
bias_prob=0.01)),
**kwargs):
super(AscendRetinaHead, self).__init__(
num_classes=num_classes,
in_channels=in_channels,
stacked_convs=stacked_convs,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
anchor_generator=anchor_generator,
init_cfg=init_cfg,
**kwargs)
def get_targets(self,
anchor_list,
valid_flag_list,
gt_bboxes_list,
img_metas,
gt_bboxes_ignore_list=None,
gt_labels_list=None,
label_channels=1,
unmap_outputs=True,
return_sampling_results=False,
return_level=True):
"""Compute regression and classification targets for anchors in
multiple images.
Args:
anchor_list (list[list[Tensor]]): Multi level anchors of each
image. The outer list indicates images, and the inner list
corresponds to feature levels of the image. Each element of
the inner list is a tensor of shape (num_anchors, 4).
valid_flag_list (list[list[Tensor]]): Multi level valid flags of
each image. The outer list indicates images, and the inner list
corresponds to feature levels of the image. Each element of
the inner list is a tensor of shape (num_anchors, )
gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image.
img_metas (list[dict]): Meta info of each image.
gt_bboxes_ignore_list (list[Tensor]): Ground truth bboxes to be
ignored.
gt_labels_list (list[Tensor]): Ground truth labels of each box.
label_channels (int): Channel of label.
unmap_outputs (bool): Whether to map outputs back to the original
set of anchors.
return_sampling_results (bool): Whether to return the result of
sample.
return_level (bool): Whether to map outputs back to the levels
of feature map sizes.
Returns:
tuple: Usually returns a tuple containing learning targets.
- labels_list (list[Tensor]): Labels of each level.
- label_weights_list (list[Tensor]): Label weights of each
level.
- bbox_targets_list (list[Tensor]): BBox targets of each level.
- bbox_weights_list (list[Tensor]): BBox weights of each level.
- num_total_pos (int): Number of positive samples in all
images.
- num_total_neg (int): Number of negative samples in all
images.
additional_returns: This function enables user-defined returns from
`self._get_targets_single`. These returns are currently refined
to properties at each feature map (i.e. having HxW dimension).
The results will be concatenated after the end
"""
return AscendAnchorHead.get_targets(
self, anchor_list, valid_flag_list, gt_bboxes_list, img_metas,
gt_bboxes_ignore_list, gt_labels_list, label_channels,
unmap_outputs, return_sampling_results, return_level)
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