Spaces:
Runtime error
Runtime error
File size: 6,656 Bytes
51f6859 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 |
# Copyright (c) OpenMMLab. All rights reserved.
from mmdet.core import bbox2roi
from ..builder import HEADS
from ..losses.pisa_loss import carl_loss, isr_p
from .standard_roi_head import StandardRoIHead
@HEADS.register_module()
class PISARoIHead(StandardRoIHead):
r"""The RoI head for `Prime Sample Attention in Object Detection
<https://arxiv.org/abs/1904.04821>`_."""
def forward_train(self,
x,
img_metas,
proposal_list,
gt_bboxes,
gt_labels,
gt_bboxes_ignore=None,
gt_masks=None):
"""Forward function for training.
Args:
x (list[Tensor]): List of multi-level img features.
img_metas (list[dict]): 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
`mmdet/datasets/pipelines/formatting.py:Collect`.
proposals (list[Tensors]): List of region proposals.
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 (list[Tensor], optional): Specify which bounding
boxes can be ignored when computing the loss.
gt_masks (None | Tensor) : True segmentation masks for each box
used if the architecture supports a segmentation task.
Returns:
dict[str, Tensor]: a dictionary of loss components
"""
# assign gts and sample proposals
if self.with_bbox or self.with_mask:
num_imgs = len(img_metas)
if gt_bboxes_ignore is None:
gt_bboxes_ignore = [None for _ in range(num_imgs)]
sampling_results = []
neg_label_weights = []
for i in range(num_imgs):
assign_result = self.bbox_assigner.assign(
proposal_list[i], gt_bboxes[i], gt_bboxes_ignore[i],
gt_labels[i])
sampling_result = self.bbox_sampler.sample(
assign_result,
proposal_list[i],
gt_bboxes[i],
gt_labels[i],
feats=[lvl_feat[i][None] for lvl_feat in x])
# neg label weight is obtained by sampling when using ISR-N
neg_label_weight = None
if isinstance(sampling_result, tuple):
sampling_result, neg_label_weight = sampling_result
sampling_results.append(sampling_result)
neg_label_weights.append(neg_label_weight)
losses = dict()
# bbox head forward and loss
if self.with_bbox:
bbox_results = self._bbox_forward_train(
x,
sampling_results,
gt_bboxes,
gt_labels,
img_metas,
neg_label_weights=neg_label_weights)
losses.update(bbox_results['loss_bbox'])
# mask head forward and loss
if self.with_mask:
mask_results = self._mask_forward_train(x, sampling_results,
bbox_results['bbox_feats'],
gt_masks, img_metas)
losses.update(mask_results['loss_mask'])
return losses
def _bbox_forward(self, x, rois):
"""Box forward function used in both training and testing."""
# TODO: a more flexible way to decide which feature maps to use
bbox_feats = self.bbox_roi_extractor(
x[:self.bbox_roi_extractor.num_inputs], rois)
if self.with_shared_head:
bbox_feats = self.shared_head(bbox_feats)
cls_score, bbox_pred = self.bbox_head(bbox_feats)
bbox_results = dict(
cls_score=cls_score, bbox_pred=bbox_pred, bbox_feats=bbox_feats)
return bbox_results
def _bbox_forward_train(self,
x,
sampling_results,
gt_bboxes,
gt_labels,
img_metas,
neg_label_weights=None):
"""Run forward function and calculate loss for box head in training."""
rois = bbox2roi([res.bboxes for res in sampling_results])
bbox_results = self._bbox_forward(x, rois)
bbox_targets = self.bbox_head.get_targets(sampling_results, gt_bboxes,
gt_labels, self.train_cfg)
# neg_label_weights obtained by sampler is image-wise, mapping back to
# the corresponding location in label weights
if neg_label_weights[0] is not None:
label_weights = bbox_targets[1]
cur_num_rois = 0
for i in range(len(sampling_results)):
num_pos = sampling_results[i].pos_inds.size(0)
num_neg = sampling_results[i].neg_inds.size(0)
label_weights[cur_num_rois + num_pos:cur_num_rois + num_pos +
num_neg] = neg_label_weights[i]
cur_num_rois += num_pos + num_neg
cls_score = bbox_results['cls_score']
bbox_pred = bbox_results['bbox_pred']
# Apply ISR-P
isr_cfg = self.train_cfg.get('isr', None)
if isr_cfg is not None:
bbox_targets = isr_p(
cls_score,
bbox_pred,
bbox_targets,
rois,
sampling_results,
self.bbox_head.loss_cls,
self.bbox_head.bbox_coder,
**isr_cfg,
num_class=self.bbox_head.num_classes)
loss_bbox = self.bbox_head.loss(cls_score, bbox_pred, rois,
*bbox_targets)
# Add CARL Loss
carl_cfg = self.train_cfg.get('carl', None)
if carl_cfg is not None:
loss_carl = carl_loss(
cls_score,
bbox_targets[0],
bbox_pred,
bbox_targets[2],
self.bbox_head.loss_bbox,
**carl_cfg,
num_class=self.bbox_head.num_classes)
loss_bbox.update(loss_carl)
bbox_results.update(loss_bbox=loss_bbox)
return bbox_results
|