# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from mmcv.cnn import ConvModule, Scale, bias_init_with_prob, normal_init
from mmcv.runner import force_fp32
from mmdet.core import (anchor_inside_flags, build_assigner, build_sampler,
images_to_levels, multi_apply, reduce_mean, unmap)
from mmdet.core.bbox import bbox_overlaps
from ..builder import HEADS, build_loss
from .anchor_head import AnchorHead
EPS = 1e-12
@HEADS.register_module()
class DDODHead(AnchorHead):
"""DDOD head decomposes conjunctions lying in most current one-stage
detectors via label assignment disentanglement, spatial feature
disentanglement, and pyramid supervision disentanglement.
https://arxiv.org/abs/2107.02963
Args:
num_classes (int): Number of categories excluding the
background category.
in_channels (int): Number of channels in the input feature map.
stacked_convs (int): The number of stacked Conv. Default: 4.
conv_cfg (dict): Conv config of ddod head. Default: None.
use_dcn (bool): Use dcn, Same as ATSS when False. Default: True.
norm_cfg (dict): Normal config of ddod head. Default:
dict(type='GN', num_groups=32, requires_grad=True).
loss_iou (dict): Config of IoU loss. Default:
dict(type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0).
"""
def __init__(self,
num_classes,
in_channels,
stacked_convs=4,
conv_cfg=None,
use_dcn=True,
norm_cfg=dict(type='GN', num_groups=32, requires_grad=True),
loss_iou=dict(
type='CrossEntropyLoss',
use_sigmoid=True,
loss_weight=1.0),
**kwargs):
self.stacked_convs = stacked_convs
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
self.use_dcn = use_dcn
super(DDODHead, self).__init__(num_classes, in_channels, **kwargs)
self.sampling = False
if self.train_cfg:
self.cls_assigner = build_assigner(self.train_cfg.assigner)
self.reg_assigner = build_assigner(self.train_cfg.reg_assigner)
sampler_cfg = dict(type='PseudoSampler')
self.sampler = build_sampler(sampler_cfg, context=self)
self.loss_iou = build_loss(loss_iou)
def _init_layers(self):
"""Initialize layers of the head."""
self.relu = nn.ReLU(inplace=True)
self.cls_convs = nn.ModuleList()
self.reg_convs = nn.ModuleList()
for i in range(self.stacked_convs):
chn = self.in_channels if i == 0 else self.feat_channels
self.cls_convs.append(
ConvModule(
chn,
self.feat_channels,
3,
stride=1,
padding=1,
conv_cfg=dict(type='DCN', deform_groups=1)
if i == 0 and self.use_dcn else self.conv_cfg,
norm_cfg=self.norm_cfg))
self.reg_convs.append(
ConvModule(
chn,
self.feat_channels,
3,
stride=1,
padding=1,
conv_cfg=dict(type='DCN', deform_groups=1)
if i == 0 and self.use_dcn else self.conv_cfg,
norm_cfg=self.norm_cfg))
self.atss_cls = nn.Conv2d(
self.feat_channels,
self.num_base_priors * self.cls_out_channels,
3,
padding=1)
self.atss_reg = nn.Conv2d(
self.feat_channels, self.num_base_priors * 4, 3, padding=1)
self.atss_iou = nn.Conv2d(
self.feat_channels, self.num_base_priors * 1, 3, padding=1)
self.scales = nn.ModuleList(
[Scale(1.0) for _ in self.prior_generator.strides])
# we use the global list in loss
self.cls_num_pos_samples_per_level = [
0. for _ in range(len(self.prior_generator.strides))
]
self.reg_num_pos_samples_per_level = [
0. for _ in range(len(self.prior_generator.strides))
]
def init_weights(self):
"""Initialize weights of the head."""
for m in self.cls_convs:
normal_init(m.conv, std=0.01)
for m in self.reg_convs:
normal_init(m.conv, std=0.01)
normal_init(self.atss_reg, std=0.01)
normal_init(self.atss_iou, std=0.01)
bias_cls = bias_init_with_prob(0.01)
normal_init(self.atss_cls, std=0.01, bias=bias_cls)
def forward(self, feats):
"""Forward features from the upstream network.
Args:
feats (tuple[Tensor]): Features from the upstream network, each is
a 4D-tensor.
Returns:
tuple: Usually a tuple of classification scores and bbox prediction
cls_scores (list[Tensor]): Classification scores for all scale
levels, each is a 4D-tensor, the channels number is
num_base_priors * num_classes.
bbox_preds (list[Tensor]): Box energies / deltas for all scale
levels, each is a 4D-tensor, the channels number is
num_base_priors * 4.
iou_preds (list[Tensor]): IoU scores for all scale levels,
each is a 4D-tensor, the channels number is
num_base_priors * 1.
"""
return multi_apply(self.forward_single, feats, self.scales)
def forward_single(self, x, scale):
"""Forward feature of a single scale level.
Args:
x (Tensor): Features of a single scale level.
scale (:obj: `mmcv.cnn.Scale`): Learnable scale module to resize
the bbox prediction.
Returns:
tuple:
- cls_score (Tensor): Cls scores for a single scale level \
the channels number is num_base_priors * num_classes.
- bbox_pred (Tensor): Box energies / deltas for a single \
scale level, the channels number is num_base_priors * 4.
- iou_pred (Tensor): Iou for a single scale level, the \
channel number is (N, num_base_priors * 1, H, W).
"""
cls_feat = x
reg_feat = x
for cls_conv in self.cls_convs:
cls_feat = cls_conv(cls_feat)
for reg_conv in self.reg_convs:
reg_feat = reg_conv(reg_feat)
cls_score = self.atss_cls(cls_feat)
# we just follow atss, not apply exp in bbox_pred
bbox_pred = scale(self.atss_reg(reg_feat)).float()
iou_pred = self.atss_iou(reg_feat)
return cls_score, bbox_pred, iou_pred
def loss_cls_single(self, cls_score, labels, label_weights,
reweight_factor, num_total_samples):
"""Compute cls loss of a single scale level.
Args:
cls_score (Tensor): Box scores for each scale level
Has shape (N, num_base_priors * num_classes, H, W).
labels (Tensor): Labels of each anchors with shape
(N, num_total_anchors).
label_weights (Tensor): Label weights of each anchor with shape
(N, num_total_anchors)
reweight_factor (list[int]): Reweight factor for cls and reg
loss.
num_total_samples (int): Number of positive samples that is
reduced over all GPUs.
Returns:
tuple[Tensor]: A tuple of loss components.
"""
cls_score = cls_score.permute(0, 2, 3, 1).reshape(
-1, self.cls_out_channels).contiguous()
labels = labels.reshape(-1)
label_weights = label_weights.reshape(-1)
loss_cls = self.loss_cls(
cls_score, labels, label_weights, avg_factor=num_total_samples)
return reweight_factor * loss_cls,
def loss_reg_single(self, anchors, bbox_pred, iou_pred, labels,
label_weights, bbox_targets, bbox_weights,
reweight_factor, num_total_samples):
"""Compute reg loss of a single scale level.
Args:
anchors (Tensor): Box reference for each scale level with shape
(N, num_total_anchors, 4).
bbox_pred (Tensor): Box energies / deltas for each scale
level with shape (N, num_base_priors * 4, H, W).
iou_pred (Tensor): Iou for a single scale level, the
channel number is (N, num_base_priors * 1, H, W).
labels (Tensor): Labels of each anchors with shape
(N, num_total_anchors).
label_weights (Tensor): Label weights of each anchor with shape
(N, num_total_anchors)
bbox_targets (Tensor): BBox regression targets of each anchor
weight shape (N, num_total_anchors, 4).
bbox_weights (Tensor): BBox weights of all anchors in the
image with shape (N, 4)
reweight_factor (list[int]): Reweight factor for cls and reg
loss.
num_total_samples (int): Number of positive samples that is
reduced over all GPUs.
Returns:
dict[str, Tensor]: A dictionary of loss components.
"""
anchors = anchors.reshape(-1, 4)
bbox_pred = bbox_pred.permute(0, 2, 3, 1).reshape(-1, 4)
iou_pred = iou_pred.permute(0, 2, 3, 1).reshape(-1, )
bbox_targets = bbox_targets.reshape(-1, 4)
bbox_weights = bbox_weights.reshape(-1, 4)
labels = labels.reshape(-1)
label_weights = label_weights.reshape(-1)
iou_targets = label_weights.new_zeros(labels.shape)
iou_weights = label_weights.new_zeros(labels.shape)
iou_weights[(bbox_weights.sum(axis=1) > 0).nonzero(
as_tuple=False)] = 1.
# FG cat_id: [0, num_classes -1], BG cat_id: num_classes
bg_class_ind = self.num_classes
pos_inds = ((labels >= 0)
&
(labels < bg_class_ind)).nonzero(as_tuple=False).squeeze(1)
if len(pos_inds) > 0:
pos_bbox_targets = bbox_targets[pos_inds]
pos_bbox_pred = bbox_pred[pos_inds]
pos_anchors = anchors[pos_inds]
pos_decode_bbox_pred = self.bbox_coder.decode(
pos_anchors, pos_bbox_pred)
pos_decode_bbox_targets = self.bbox_coder.decode(
pos_anchors, pos_bbox_targets)
# regression loss
loss_bbox = self.loss_bbox(
pos_decode_bbox_pred,
pos_decode_bbox_targets,
avg_factor=num_total_samples)
iou_targets[pos_inds] = bbox_overlaps(
pos_decode_bbox_pred.detach(),
pos_decode_bbox_targets,
is_aligned=True)
loss_iou = self.loss_iou(
iou_pred,
iou_targets,
iou_weights,
avg_factor=num_total_samples)
else:
loss_bbox = bbox_pred.sum() * 0
loss_iou = iou_pred.sum() * 0
return reweight_factor * loss_bbox, reweight_factor * loss_iou
def calc_reweight_factor(self, labels_list):
"""Compute reweight_factor for regression and classification loss."""
# get pos samples for each level
bg_class_ind = self.num_classes
for ii, each_level_label in enumerate(labels_list):
pos_inds = ((each_level_label >= 0) &
(each_level_label < bg_class_ind)).nonzero(
as_tuple=False).squeeze(1)
self.cls_num_pos_samples_per_level[ii] += len(pos_inds)
# get reweight factor from 1 ~ 2 with bilinear interpolation
min_pos_samples = min(self.cls_num_pos_samples_per_level)
max_pos_samples = max(self.cls_num_pos_samples_per_level)
interval = 1. / (max_pos_samples - min_pos_samples + 1e-10)
reweight_factor_per_level = []
for pos_samples in self.cls_num_pos_samples_per_level:
factor = 2. - (pos_samples - min_pos_samples) * interval
reweight_factor_per_level.append(factor)
return reweight_factor_per_level
@force_fp32(apply_to=('cls_scores', 'bbox_preds', 'iou_preds'))
def loss(self,
cls_scores,
bbox_preds,
iou_preds,
gt_bboxes,
gt_labels,
img_metas,
gt_bboxes_ignore=None):
"""Compute losses of the head.
Args:
cls_scores (list[Tensor]): Box scores for each scale level
Has shape (N, num_base_priors * num_classes, H, W)
bbox_preds (list[Tensor]): Box energies / deltas for each scale
level with shape (N, num_base_priors * 4, H, W)
iou_preds (list[Tensor]): Score factor for all scale level,
each is a 4D-tensor, has shape (batch_size, 1, H, W).
gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
gt_labels (list[Tensor]): class indices corresponding to each box
img_metas (list[dict]): Meta information of each image, e.g.,
image size, scaling factor, etc.
gt_bboxes_ignore (list[Tensor] | None): specify which bounding
boxes can be ignored when computing the loss.
Returns:
dict[str, Tensor]: A dictionary of loss components.
"""
featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
assert len(featmap_sizes) == self.prior_generator.num_levels
device = cls_scores[0].device
anchor_list, valid_flag_list = self.get_anchors(
featmap_sizes, img_metas, device=device)
label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
# calculate common vars for cls and reg assigners at once
targets_com = self.process_predictions_and_anchors(
anchor_list, valid_flag_list, cls_scores, bbox_preds, img_metas,
gt_bboxes_ignore)
(anchor_list, valid_flag_list, num_level_anchors_list, cls_score_list,
bbox_pred_list, gt_bboxes_ignore_list) = targets_com
# classification branch assigner
cls_targets = self.get_cls_targets(
anchor_list,
valid_flag_list,
num_level_anchors_list,
cls_score_list,
bbox_pred_list,
gt_bboxes,
img_metas,
gt_bboxes_ignore_list=gt_bboxes_ignore_list,
gt_labels_list=gt_labels,
label_channels=label_channels)
if cls_targets is None:
return None
(cls_anchor_list, labels_list, label_weights_list, bbox_targets_list,
bbox_weights_list, num_total_pos, num_total_neg) = cls_targets
num_total_samples = reduce_mean(
torch.tensor(num_total_pos, dtype=torch.float,
device=device)).item()
num_total_samples = max(num_total_samples, 1.0)
reweight_factor_per_level = self.calc_reweight_factor(labels_list)
cls_losses_cls, = multi_apply(
self.loss_cls_single,
cls_scores,
labels_list,
label_weights_list,
reweight_factor_per_level,
num_total_samples=num_total_samples)
# regression branch assigner
reg_targets = self.get_reg_targets(
anchor_list,
valid_flag_list,
num_level_anchors_list,
cls_score_list,
bbox_pred_list,
gt_bboxes,
img_metas,
gt_bboxes_ignore_list=gt_bboxes_ignore_list,
gt_labels_list=gt_labels,
label_channels=label_channels)
if reg_targets is None:
return None
(reg_anchor_list, labels_list, label_weights_list, bbox_targets_list,
bbox_weights_list, num_total_pos, num_total_neg) = reg_targets
num_total_samples = reduce_mean(
torch.tensor(num_total_pos, dtype=torch.float,
device=device)).item()
num_total_samples = max(num_total_samples, 1.0)
reweight_factor_per_level = self.calc_reweight_factor(labels_list)
reg_losses_bbox, reg_losses_iou = multi_apply(
self.loss_reg_single,
reg_anchor_list,
bbox_preds,
iou_preds,
labels_list,
label_weights_list,
bbox_targets_list,
bbox_weights_list,
reweight_factor_per_level,
num_total_samples=num_total_samples)
return dict(
loss_cls=cls_losses_cls,
loss_bbox=reg_losses_bbox,
loss_iou=reg_losses_iou)
def process_predictions_and_anchors(self, anchor_list, valid_flag_list,
cls_scores, bbox_preds, img_metas,
gt_bboxes_ignore_list):
"""Compute common vars for regression and classification targets.
Args:
anchor_list (list[Tensor]): anchors of each image.
valid_flag_list (list[Tensor]): Valid flags of each image.
cls_scores (list[Tensor]): Classification scores for all scale
levels, each is a 4D-tensor, the channels number is
num_base_priors * num_classes.
bbox_preds (list[Tensor]): Box energies / deltas for all scale
levels, each is a 4D-tensor, the channels number is
num_base_priors * 4.
img_metas (list[dict]): Meta information of each image, e.g.,
image size, scaling factor, etc.
gt_bboxes_ignore_list (list[Tensor] | None): specify which bounding
boxes can be ignored when computing the loss.
Return:
tuple[Tensor]: A tuple of common loss vars.
"""
num_imgs = len(img_metas)
assert len(anchor_list) == len(valid_flag_list) == num_imgs
# anchor number of multi levels
num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]]
num_level_anchors_list = [num_level_anchors] * num_imgs
anchor_list_ = []
valid_flag_list_ = []
# concat all level anchors and flags to a single tensor
for i in range(num_imgs):
assert len(anchor_list[i]) == len(valid_flag_list[i])
anchor_list_.append(torch.cat(anchor_list[i]))
valid_flag_list_.append(torch.cat(valid_flag_list[i]))
# compute targets for each image
if gt_bboxes_ignore_list is None:
gt_bboxes_ignore_list = [None for _ in range(num_imgs)]
num_levels = len(cls_scores)
cls_score_list = []
bbox_pred_list = []
mlvl_cls_score_list = [
cls_score.permute(0, 2, 3, 1).reshape(
num_imgs, -1, self.num_base_priors * self.cls_out_channels)
for cls_score in cls_scores
]
mlvl_bbox_pred_list = [
bbox_pred.permute(0, 2, 3, 1).reshape(num_imgs, -1,
self.num_base_priors * 4)
for bbox_pred in bbox_preds
]
for i in range(num_imgs):
mlvl_cls_tensor_list = [
mlvl_cls_score_list[j][i] for j in range(num_levels)
]
mlvl_bbox_tensor_list = [
mlvl_bbox_pred_list[j][i] for j in range(num_levels)
]
cat_mlvl_cls_score = torch.cat(mlvl_cls_tensor_list, dim=0)
cat_mlvl_bbox_pred = torch.cat(mlvl_bbox_tensor_list, dim=0)
cls_score_list.append(cat_mlvl_cls_score)
bbox_pred_list.append(cat_mlvl_bbox_pred)
return (anchor_list_, valid_flag_list_, num_level_anchors_list,
cls_score_list, bbox_pred_list, gt_bboxes_ignore_list)
def get_cls_targets(self,
anchor_list,
valid_flag_list,
num_level_anchors_list,
cls_score_list,
bbox_pred_list,
gt_bboxes_list,
img_metas,
gt_bboxes_ignore_list=None,
gt_labels_list=None,
label_channels=1,
unmap_outputs=True):
"""Get cls targets for DDOD head.
This method is almost the same as `AnchorHead.get_targets()`.
Besides returning the targets as the parent method does,
it also returns the anchors as the first element of the
returned tuple.
Args:
anchor_list (list[Tensor]): anchors of each image.
valid_flag_list (list[Tensor]): Valid flags of each image.
num_level_anchors_list (list[Tensor]): Number of anchors of each
scale level of all image.
cls_score_list (list[Tensor]): Classification scores for all scale
levels, each is a 4D-tensor, the channels number is
num_base_priors * num_classes.
bbox_pred_list (list[Tensor]): Box energies / deltas for all scale
levels, each is a 4D-tensor, the channels number is
num_base_priors * 4.
gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image.
img_metas (list[dict]): Meta information of each image, e.g.,
image size, scaling factor, etc.
gt_bboxes_ignore_list (list[Tensor] | None): specify which bounding
boxes can be ignored when computing the loss.
gt_labels_list (list[Tensor]): class indices corresponding to
each box.
label_channels (int): Channel of label.
unmap_outputs (bool): Whether to map outputs back to the original
set of anchors.
Return:
tuple[Tensor]: A tuple of cls targets components.
"""
(all_anchors, all_labels, all_label_weights, all_bbox_targets,
all_bbox_weights, pos_inds_list, neg_inds_list) = multi_apply(
self._get_target_single,
anchor_list,
valid_flag_list,
cls_score_list,
bbox_pred_list,
num_level_anchors_list,
gt_bboxes_list,
gt_bboxes_ignore_list,
gt_labels_list,
img_metas,
label_channels=label_channels,
unmap_outputs=unmap_outputs,
is_cls_assigner=True)
# no valid anchors
if any([labels is None for labels in all_labels]):
return None
# sampled anchors of all images
num_total_pos = sum([max(inds.numel(), 1) for inds in pos_inds_list])
num_total_neg = sum([max(inds.numel(), 1) for inds in neg_inds_list])
# split targets to a list w.r.t. multiple levels
anchors_list = images_to_levels(all_anchors, num_level_anchors_list[0])
labels_list = images_to_levels(all_labels, num_level_anchors_list[0])
label_weights_list = images_to_levels(all_label_weights,
num_level_anchors_list[0])
bbox_targets_list = images_to_levels(all_bbox_targets,
num_level_anchors_list[0])
bbox_weights_list = images_to_levels(all_bbox_weights,
num_level_anchors_list[0])
return (anchors_list, labels_list, label_weights_list,
bbox_targets_list, bbox_weights_list, num_total_pos,
num_total_neg)
def get_reg_targets(self,
anchor_list,
valid_flag_list,
num_level_anchors_list,
cls_score_list,
bbox_pred_list,
gt_bboxes_list,
img_metas,
gt_bboxes_ignore_list=None,
gt_labels_list=None,
label_channels=1,
unmap_outputs=True):
"""Get reg targets for DDOD head.
This method is almost the same as `AnchorHead.get_targets()` when
is_cls_assigner is False. Besides returning the targets as the parent
method does, it also returns the anchors as the first element of the
returned tuple.
Args:
anchor_list (list[Tensor]): anchors of each image.
valid_flag_list (list[Tensor]): Valid flags of each image.
num_level_anchors (int): Number of anchors of each scale level.
cls_scores (list[Tensor]): Classification scores for all scale
levels, each is a 4D-tensor, the channels number is
num_base_priors * num_classes.
bbox_preds (list[Tensor]): Box energies / deltas for all scale
levels, each is a 4D-tensor, the channels number is
num_base_priors * 4.
gt_labels_list (list[Tensor]): class indices corresponding to
each box.
img_metas (list[dict]): Meta information of each image, e.g.,
image size, scaling factor, etc.
gt_bboxes_ignore_list (list[Tensor] | None): specify which bounding
boxes can be ignored when computing the loss.
Return:
tuple[Tensor]: A tuple of reg targets components.
"""
(all_anchors, all_labels, all_label_weights, all_bbox_targets,
all_bbox_weights, pos_inds_list, neg_inds_list) = multi_apply(
self._get_target_single,
anchor_list,
valid_flag_list,
cls_score_list,
bbox_pred_list,
num_level_anchors_list,
gt_bboxes_list,
gt_bboxes_ignore_list,
gt_labels_list,
img_metas,
label_channels=label_channels,
unmap_outputs=unmap_outputs,
is_cls_assigner=False)
# no valid anchors
if any([labels is None for labels in all_labels]):
return None
# sampled anchors of all images
num_total_pos = sum([max(inds.numel(), 1) for inds in pos_inds_list])
num_total_neg = sum([max(inds.numel(), 1) for inds in neg_inds_list])
# split targets to a list w.r.t. multiple levels
anchors_list = images_to_levels(all_anchors, num_level_anchors_list[0])
labels_list = images_to_levels(all_labels, num_level_anchors_list[0])
label_weights_list = images_to_levels(all_label_weights,
num_level_anchors_list[0])
bbox_targets_list = images_to_levels(all_bbox_targets,
num_level_anchors_list[0])
bbox_weights_list = images_to_levels(all_bbox_weights,
num_level_anchors_list[0])
return (anchors_list, labels_list, label_weights_list,
bbox_targets_list, bbox_weights_list, num_total_pos,
num_total_neg)
def _get_target_single(self,
flat_anchors,
valid_flags,
cls_scores,
bbox_preds,
num_level_anchors,
gt_bboxes,
gt_bboxes_ignore,
gt_labels,
img_meta,
label_channels=1,
unmap_outputs=True,
is_cls_assigner=True):
"""Compute regression, classification targets for anchors in a single
image.
Args:
flat_anchors (Tensor): Multi-level anchors of the image,
which are concatenated into a single tensor of shape
(num_base_priors, 4).
valid_flags (Tensor): Multi level valid flags of the image,
which are concatenated into a single tensor of
shape (num_base_priors,).
cls_scores (Tensor): Classification scores for all scale
levels of the image.
bbox_preds (Tensor): Box energies / deltas for all scale
levels of the image.
num_level_anchors (list[int]): Number of anchors of each
scale level.
gt_bboxes (Tensor): Ground truth bboxes of the image,
shape (num_gts, 4).
gt_bboxes_ignore (Tensor): Ground truth bboxes to be
ignored, shape (num_ignored_gts, ).
gt_labels (Tensor): Ground truth labels of each box,
shape (num_gts, ).
img_meta (dict): Meta info of the image.
label_channels (int): Channel of label. Default: 1.
unmap_outputs (bool): Whether to map outputs back to the original
set of anchors. Default: True.
is_cls_assigner (bool): Classification or regression.
Default: True.
Returns:
tuple: N is the number of total anchors in the image.
- labels (Tensor): Labels of all anchors in the image with \
shape (N, ).
- label_weights (Tensor): Label weights of all anchor in the \
image with shape (N, ).
- bbox_targets (Tensor): BBox targets of all anchors in the \
image with shape (N, 4).
- bbox_weights (Tensor): BBox weights of all anchors in the \
image with shape (N, 4)
- pos_inds (Tensor): Indices of positive anchor with shape \
(num_pos, ).
- neg_inds (Tensor): Indices of negative anchor with shape \
(num_neg, ).
"""
inside_flags = anchor_inside_flags(flat_anchors, valid_flags,
img_meta['img_shape'][:2],
self.train_cfg.allowed_border)
if not inside_flags.any():
return (None, ) * 7
# assign gt and sample anchors
anchors = flat_anchors[inside_flags, :]
num_level_anchors_inside = self.get_num_level_anchors_inside(
num_level_anchors, inside_flags)
bbox_preds_valid = bbox_preds[inside_flags, :]
cls_scores_valid = cls_scores[inside_flags, :]
assigner = self.cls_assigner if is_cls_assigner else self.reg_assigner
# decode prediction out of assigner
bbox_preds_valid = self.bbox_coder.decode(anchors, bbox_preds_valid)
assign_result = assigner.assign(anchors, num_level_anchors_inside,
gt_bboxes, gt_bboxes_ignore, gt_labels,
cls_scores_valid, bbox_preds_valid)
sampling_result = self.sampler.sample(assign_result, anchors,
gt_bboxes)
num_valid_anchors = anchors.shape[0]
bbox_targets = torch.zeros_like(anchors)
bbox_weights = torch.zeros_like(anchors)
labels = anchors.new_full((num_valid_anchors, ),
self.num_classes,
dtype=torch.long)
label_weights = anchors.new_zeros(num_valid_anchors, dtype=torch.float)
pos_inds = sampling_result.pos_inds
neg_inds = sampling_result.neg_inds
if len(pos_inds) > 0:
if hasattr(self, 'bbox_coder'):
pos_bbox_targets = self.bbox_coder.encode(
sampling_result.pos_bboxes, sampling_result.pos_gt_bboxes)
else:
# used in VFNetHead
pos_bbox_targets = sampling_result.pos_gt_bboxes
bbox_targets[pos_inds, :] = pos_bbox_targets
bbox_weights[pos_inds, :] = 1.0
if gt_labels is None:
# Only rpn gives gt_labels as None
# Foreground is the first class since v2.5.0
labels[pos_inds] = 0
else:
labels[pos_inds] = gt_labels[
sampling_result.pos_assigned_gt_inds]
if self.train_cfg.pos_weight <= 0:
label_weights[pos_inds] = 1.0
else:
label_weights[pos_inds] = self.train_cfg.pos_weight
if len(neg_inds) > 0:
label_weights[neg_inds] = 1.0
# map up to original set of anchors
if unmap_outputs:
num_total_anchors = flat_anchors.size(0)
anchors = unmap(anchors, num_total_anchors, inside_flags)
labels = unmap(
labels, num_total_anchors, inside_flags, fill=self.num_classes)
label_weights = unmap(label_weights, num_total_anchors,
inside_flags)
bbox_targets = unmap(bbox_targets, num_total_anchors, inside_flags)
bbox_weights = unmap(bbox_weights, num_total_anchors, inside_flags)
return (anchors, labels, label_weights, bbox_targets, bbox_weights,
pos_inds, neg_inds)
def get_num_level_anchors_inside(self, num_level_anchors, inside_flags):
"""Get the anchors of each scale level inside.
Args:
num_level_anchors (list[int]): Number of anchors of each
scale level.
inside_flags (Tensor): Multi level inside flags of the image,
which are concatenated into a single tensor of
shape (num_base_priors,).
Returns:
list[int]: Number of anchors of each scale level inside.
"""
split_inside_flags = torch.split(inside_flags, num_level_anchors)
num_level_anchors_inside = [
int(flags.sum()) for flags in split_inside_flags
]
return num_level_anchors_inside