Source code for mmdet.models.dense_heads.tood_head
# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import ConvModule, Scale, bias_init_with_prob, normal_init
from mmcv.ops import deform_conv2d
from mmcv.runner import force_fp32
from mmdet.core import (anchor_inside_flags, build_assigner, distance2bbox,
images_to_levels, multi_apply, reduce_mean, unmap)
from mmdet.core.utils import filter_scores_and_topk
from ..builder import HEADS, build_loss
from .atss_head import ATSSHead
class TaskDecomposition(nn.Module):
"""Task decomposition module in task-aligned predictor of TOOD.
Args:
feat_channels (int): Number of feature channels in TOOD head.
stacked_convs (int): Number of conv layers in TOOD head.
la_down_rate (int): Downsample rate of layer attention.
conv_cfg (dict): Config dict for convolution layer.
norm_cfg (dict): Config dict for normalization layer.
"""
def __init__(self,
feat_channels,
stacked_convs,
la_down_rate=8,
conv_cfg=None,
norm_cfg=None):
super(TaskDecomposition, self).__init__()
self.feat_channels = feat_channels
self.stacked_convs = stacked_convs
self.in_channels = self.feat_channels * self.stacked_convs
self.norm_cfg = norm_cfg
self.layer_attention = nn.Sequential(
nn.Conv2d(self.in_channels, self.in_channels // la_down_rate, 1),
nn.ReLU(inplace=True),
nn.Conv2d(
self.in_channels // la_down_rate,
self.stacked_convs,
1,
padding=0), nn.Sigmoid())
self.reduction_conv = ConvModule(
self.in_channels,
self.feat_channels,
1,
stride=1,
padding=0,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
bias=norm_cfg is None)
def init_weights(self):
for m in self.layer_attention.modules():
if isinstance(m, nn.Conv2d):
normal_init(m, std=0.001)
normal_init(self.reduction_conv.conv, std=0.01)
def forward(self, feat, avg_feat=None):
b, c, h, w = feat.shape
if avg_feat is None:
avg_feat = F.adaptive_avg_pool2d(feat, (1, 1))
weight = self.layer_attention(avg_feat)
# here we first compute the product between layer attention weight and
# conv weight, and then compute the convolution between new conv weight
# and feature map, in order to save memory and FLOPs.
conv_weight = weight.reshape(
b, 1, self.stacked_convs,
1) * self.reduction_conv.conv.weight.reshape(
1, self.feat_channels, self.stacked_convs, self.feat_channels)
conv_weight = conv_weight.reshape(b, self.feat_channels,
self.in_channels)
feat = feat.reshape(b, self.in_channels, h * w)
feat = torch.bmm(conv_weight, feat).reshape(b, self.feat_channels, h,
w)
if self.norm_cfg is not None:
feat = self.reduction_conv.norm(feat)
feat = self.reduction_conv.activate(feat)
return feat
[docs]@HEADS.register_module()
class TOODHead(ATSSHead):
"""TOODHead used in `TOOD: Task-aligned One-stage Object Detection.
<https://arxiv.org/abs/2108.07755>`_.
TOOD uses Task-aligned head (T-head) and is optimized by Task Alignment
Learning (TAL).
Args:
num_dcn (int): Number of deformable convolution in the head.
Default: 0.
anchor_type (str): If set to `anchor_free`, the head will use centers
to regress bboxes. If set to `anchor_based`, the head will
regress bboxes based on anchors. Default: `anchor_free`.
initial_loss_cls (dict): Config of initial loss.
Example:
>>> self = TOODHead(11, 7)
>>> feats = [torch.rand(1, 7, s, s) for s in [4, 8, 16, 32, 64]]
>>> cls_score, bbox_pred = self.forward(feats)
>>> assert len(cls_score) == len(self.scales)
"""
def __init__(self,
num_classes,
in_channels,
num_dcn=0,
anchor_type='anchor_free',
initial_loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
activated=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
**kwargs):
assert anchor_type in ['anchor_free', 'anchor_based']
self.num_dcn = num_dcn
self.anchor_type = anchor_type
self.epoch = 0 # which would be update in SetEpochInfoHook!
super(TOODHead, self).__init__(num_classes, in_channels, **kwargs)
if self.train_cfg:
self.initial_epoch = self.train_cfg.initial_epoch
self.initial_assigner = build_assigner(
self.train_cfg.initial_assigner)
self.initial_loss_cls = build_loss(initial_loss_cls)
self.assigner = self.initial_assigner
self.alignment_assigner = build_assigner(self.train_cfg.assigner)
self.alpha = self.train_cfg.alpha
self.beta = self.train_cfg.beta
def _init_layers(self):
"""Initialize layers of the head."""
self.relu = nn.ReLU(inplace=True)
self.inter_convs = nn.ModuleList()
for i in range(self.stacked_convs):
if i < self.num_dcn:
conv_cfg = dict(type='DCNv2', deform_groups=4)
else:
conv_cfg = self.conv_cfg
chn = self.in_channels if i == 0 else self.feat_channels
self.inter_convs.append(
ConvModule(
chn,
self.feat_channels,
3,
stride=1,
padding=1,
conv_cfg=conv_cfg,
norm_cfg=self.norm_cfg))
self.cls_decomp = TaskDecomposition(self.feat_channels,
self.stacked_convs,
self.stacked_convs * 8,
self.conv_cfg, self.norm_cfg)
self.reg_decomp = TaskDecomposition(self.feat_channels,
self.stacked_convs,
self.stacked_convs * 8,
self.conv_cfg, self.norm_cfg)
self.tood_cls = nn.Conv2d(
self.feat_channels,
self.num_base_priors * self.cls_out_channels,
3,
padding=1)
self.tood_reg = nn.Conv2d(
self.feat_channels, self.num_base_priors * 4, 3, padding=1)
self.cls_prob_module = nn.Sequential(
nn.Conv2d(self.feat_channels * self.stacked_convs,
self.feat_channels // 4, 1), nn.ReLU(inplace=True),
nn.Conv2d(self.feat_channels // 4, 1, 3, padding=1))
self.reg_offset_module = nn.Sequential(
nn.Conv2d(self.feat_channels * self.stacked_convs,
self.feat_channels // 4, 1), nn.ReLU(inplace=True),
nn.Conv2d(self.feat_channels // 4, 4 * 2, 3, padding=1))
self.scales = nn.ModuleList(
[Scale(1.0) for _ in self.prior_generator.strides])
[docs] def init_weights(self):
"""Initialize weights of the head."""
bias_cls = bias_init_with_prob(0.01)
for m in self.inter_convs:
normal_init(m.conv, std=0.01)
for m in self.cls_prob_module:
if isinstance(m, nn.Conv2d):
normal_init(m, std=0.01)
for m in self.reg_offset_module:
if isinstance(m, nn.Conv2d):
normal_init(m, std=0.001)
normal_init(self.cls_prob_module[-1], std=0.01, bias=bias_cls)
self.cls_decomp.init_weights()
self.reg_decomp.init_weights()
normal_init(self.tood_cls, std=0.01, bias=bias_cls)
normal_init(self.tood_reg, std=0.01)
[docs] 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_anchors * num_classes.
bbox_preds (list[Tensor]): Decoded box for all scale levels,
each is a 4D-tensor, the channels number is
num_anchors * 4. In [tl_x, tl_y, br_x, br_y] format.
"""
cls_scores = []
bbox_preds = []
for idx, (x, scale, stride) in enumerate(
zip(feats, self.scales, self.prior_generator.strides)):
b, c, h, w = x.shape
anchor = self.prior_generator.single_level_grid_priors(
(h, w), idx, device=x.device)
anchor = torch.cat([anchor for _ in range(b)])
# extract task interactive features
inter_feats = []
for inter_conv in self.inter_convs:
x = inter_conv(x)
inter_feats.append(x)
feat = torch.cat(inter_feats, 1)
# task decomposition
avg_feat = F.adaptive_avg_pool2d(feat, (1, 1))
cls_feat = self.cls_decomp(feat, avg_feat)
reg_feat = self.reg_decomp(feat, avg_feat)
# cls prediction and alignment
cls_logits = self.tood_cls(cls_feat)
cls_prob = self.cls_prob_module(feat)
cls_score = (cls_logits.sigmoid() * cls_prob.sigmoid()).sqrt()
# reg prediction and alignment
if self.anchor_type == 'anchor_free':
reg_dist = scale(self.tood_reg(reg_feat).exp()).float()
reg_dist = reg_dist.permute(0, 2, 3, 1).reshape(-1, 4)
reg_bbox = distance2bbox(
self.anchor_center(anchor) / stride[0],
reg_dist).reshape(b, h, w, 4).permute(0, 3, 1,
2) # (b, c, h, w)
elif self.anchor_type == 'anchor_based':
reg_dist = scale(self.tood_reg(reg_feat)).float()
reg_dist = reg_dist.permute(0, 2, 3, 1).reshape(-1, 4)
reg_bbox = self.bbox_coder.decode(anchor, reg_dist).reshape(
b, h, w, 4).permute(0, 3, 1, 2) / stride[0]
else:
raise NotImplementedError(
f'Unknown anchor type: {self.anchor_type}.'
f'Please use `anchor_free` or `anchor_based`.')
reg_offset = self.reg_offset_module(feat)
bbox_pred = self.deform_sampling(reg_bbox.contiguous(),
reg_offset.contiguous())
cls_scores.append(cls_score)
bbox_preds.append(bbox_pred)
return tuple(cls_scores), tuple(bbox_preds)
[docs] def deform_sampling(self, feat, offset):
"""Sampling the feature x according to offset.
Args:
feat (Tensor): Feature
offset (Tensor): Spatial offset for for feature sampliing
"""
# it is an equivalent implementation of bilinear interpolation
b, c, h, w = feat.shape
weight = feat.new_ones(c, 1, 1, 1)
y = deform_conv2d(feat, offset, weight, 1, 0, 1, c, c)
return y
[docs] def anchor_center(self, anchors):
"""Get anchor centers from anchors.
Args:
anchors (Tensor): Anchor list with shape (N, 4), "xyxy" format.
Returns:
Tensor: Anchor centers with shape (N, 2), "xy" format.
"""
anchors_cx = (anchors[:, 2] + anchors[:, 0]) / 2
anchors_cy = (anchors[:, 3] + anchors[:, 1]) / 2
return torch.stack([anchors_cx, anchors_cy], dim=-1)
[docs] def loss_single(self, anchors, cls_score, bbox_pred, labels, label_weights,
bbox_targets, alignment_metrics, stride):
"""Compute loss of a single scale level.
Args:
anchors (Tensor): Box reference for each scale level with shape
(N, num_total_anchors, 4).
cls_score (Tensor): Box scores for each scale level
Has shape (N, num_anchors * num_classes, H, W).
bbox_pred (Tensor): Decoded bboxes for each scale
level with shape (N, num_anchors * 4, 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 with
shape (N, num_total_anchors, 4).
alignment_metrics (Tensor): Alignment metrics with shape
(N, num_total_anchors).
stride (tuple[int]): Downsample stride of the feature map.
Returns:
dict[str, Tensor]: A dictionary of loss components.
"""
assert stride[0] == stride[1], 'h stride is not equal to w stride!'
anchors = anchors.reshape(-1, 4)
cls_score = cls_score.permute(0, 2, 3, 1).reshape(
-1, self.cls_out_channels).contiguous()
bbox_pred = bbox_pred.permute(0, 2, 3, 1).reshape(-1, 4)
bbox_targets = bbox_targets.reshape(-1, 4)
labels = labels.reshape(-1)
alignment_metrics = alignment_metrics.reshape(-1)
label_weights = label_weights.reshape(-1)
targets = labels if self.epoch < self.initial_epoch else (
labels, alignment_metrics)
cls_loss_func = self.initial_loss_cls \
if self.epoch < self.initial_epoch else self.loss_cls
loss_cls = cls_loss_func(
cls_score, targets, label_weights, avg_factor=1.0)
# 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().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 = pos_bbox_pred
pos_decode_bbox_targets = pos_bbox_targets / stride[0]
# regression loss
pos_bbox_weight = self.centerness_target(
pos_anchors, pos_bbox_targets
) if self.epoch < self.initial_epoch else alignment_metrics[
pos_inds]
loss_bbox = self.loss_bbox(
pos_decode_bbox_pred,
pos_decode_bbox_targets,
weight=pos_bbox_weight,
avg_factor=1.0)
else:
loss_bbox = bbox_pred.sum() * 0
pos_bbox_weight = bbox_targets.new_tensor(0.)
return loss_cls, loss_bbox, alignment_metrics.sum(
), pos_bbox_weight.sum()
[docs] @force_fp32(apply_to=('cls_scores', 'bbox_preds'))
def loss(self,
cls_scores,
bbox_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_anchors * num_classes, H, W)
bbox_preds (list[Tensor]): Decoded box for each scale
level with shape (N, num_anchors * 4, H, W) in
[tl_x, tl_y, br_x, br_y] format.
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.
"""
num_imgs = len(img_metas)
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
flatten_cls_scores = torch.cat([
cls_score.permute(0, 2, 3, 1).reshape(num_imgs, -1,
self.cls_out_channels)
for cls_score in cls_scores
], 1)
flatten_bbox_preds = torch.cat([
bbox_pred.permute(0, 2, 3, 1).reshape(num_imgs, -1, 4) * stride[0]
for bbox_pred, stride in zip(bbox_preds,
self.prior_generator.strides)
], 1)
cls_reg_targets = self.get_targets(
flatten_cls_scores,
flatten_bbox_preds,
anchor_list,
valid_flag_list,
gt_bboxes,
img_metas,
gt_bboxes_ignore_list=gt_bboxes_ignore,
gt_labels_list=gt_labels,
label_channels=label_channels)
(anchor_list, labels_list, label_weights_list, bbox_targets_list,
alignment_metrics_list) = cls_reg_targets
losses_cls, losses_bbox,\
cls_avg_factors, bbox_avg_factors = multi_apply(
self.loss_single,
anchor_list,
cls_scores,
bbox_preds,
labels_list,
label_weights_list,
bbox_targets_list,
alignment_metrics_list,
self.prior_generator.strides)
cls_avg_factor = reduce_mean(sum(cls_avg_factors)).clamp_(min=1).item()
losses_cls = list(map(lambda x: x / cls_avg_factor, losses_cls))
bbox_avg_factor = reduce_mean(
sum(bbox_avg_factors)).clamp_(min=1).item()
losses_bbox = list(map(lambda x: x / bbox_avg_factor, losses_bbox))
return dict(loss_cls=losses_cls, loss_bbox=losses_bbox)
def _get_bboxes_single(self,
cls_score_list,
bbox_pred_list,
score_factor_list,
mlvl_priors,
img_meta,
cfg,
rescale=False,
with_nms=True,
**kwargs):
"""Transform outputs of a single image into bbox predictions.
Args:
cls_score_list (list[Tensor]): Box scores from all scale
levels of a single image, each item has shape
(num_priors * num_classes, H, W).
bbox_pred_list (list[Tensor]): Box energies / deltas from
all scale levels of a single image, each item has shape
(num_priors * 4, H, W).
score_factor_list (list[Tensor]): Score factor from all scale
levels of a single image, each item has shape
(num_priors * 1, H, W).
mlvl_priors (list[Tensor]): Each element in the list is
the priors of a single level in feature pyramid. In all
anchor-based methods, it has shape (num_priors, 4). In
all anchor-free methods, it has shape (num_priors, 2)
when `with_stride=True`, otherwise it still has shape
(num_priors, 4).
img_meta (dict): Image meta info.
cfg (mmcv.Config): Test / postprocessing configuration,
if None, test_cfg would be used.
rescale (bool): If True, return boxes in original image space.
Default: False.
with_nms (bool): If True, do nms before return boxes.
Default: True.
Returns:
tuple[Tensor]: Results of detected bboxes and labels. If with_nms
is False and mlvl_score_factor is None, return mlvl_bboxes and
mlvl_scores, else return mlvl_bboxes, mlvl_scores and
mlvl_score_factor. Usually with_nms is False is used for aug
test. If with_nms is True, then return the following format
- det_bboxes (Tensor): Predicted bboxes with shape \
[num_bboxes, 5], where the first 4 columns are bounding \
box positions (tl_x, tl_y, br_x, br_y) and the 5-th \
column are scores between 0 and 1.
- det_labels (Tensor): Predicted labels of the corresponding \
box with shape [num_bboxes].
"""
cfg = self.test_cfg if cfg is None else cfg
nms_pre = cfg.get('nms_pre', -1)
mlvl_bboxes = []
mlvl_scores = []
mlvl_labels = []
for cls_score, bbox_pred, priors, stride in zip(
cls_score_list, bbox_pred_list, mlvl_priors,
self.prior_generator.strides):
assert cls_score.size()[-2:] == bbox_pred.size()[-2:]
bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, 4) * stride[0]
scores = cls_score.permute(1, 2,
0).reshape(-1, self.cls_out_channels)
# After https://github.com/open-mmlab/mmdetection/pull/6268/,
# this operation keeps fewer bboxes under the same `nms_pre`.
# There is no difference in performance for most models. If you
# find a slight drop in performance, you can set a larger
# `nms_pre` than before.
results = filter_scores_and_topk(
scores, cfg.score_thr, nms_pre,
dict(bbox_pred=bbox_pred, priors=priors))
scores, labels, keep_idxs, filtered_results = results
bboxes = filtered_results['bbox_pred']
mlvl_bboxes.append(bboxes)
mlvl_scores.append(scores)
mlvl_labels.append(labels)
return self._bbox_post_process(mlvl_scores, mlvl_labels, mlvl_bboxes,
img_meta['scale_factor'], cfg, rescale,
with_nms, None, **kwargs)
[docs] def get_targets(self,
cls_scores,
bbox_preds,
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):
"""Compute regression and classification targets for anchors in
multiple images.
Args:
cls_scores (Tensor): Classification predictions of images,
a 3D-Tensor with shape [num_imgs, num_priors, num_classes].
bbox_preds (Tensor): Decoded bboxes predictions of one image,
a 3D-Tensor with shape [num_imgs, num_priors, 4] in [tl_x,
tl_y, br_x, br_y] format.
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.
Returns:
tuple: a tuple containing learning targets.
- anchors_list (list[list[Tensor]]): Anchors of each level.
- 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.
- norm_alignment_metrics_list (list[Tensor]): Normalized
alignment metrics of each level.
"""
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
# 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[i] = torch.cat(anchor_list[i])
valid_flag_list[i] = 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)]
if gt_labels_list is None:
gt_labels_list = [None for _ in range(num_imgs)]
# anchor_list: list(b * [-1, 4])
if self.epoch < self.initial_epoch:
(all_anchors, all_labels, all_label_weights, all_bbox_targets,
all_bbox_weights, pos_inds_list, neg_inds_list) = multi_apply(
super()._get_target_single,
anchor_list,
valid_flag_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)
all_assign_metrics = [
weight[..., 0] for weight in all_bbox_weights
]
else:
(all_anchors, all_labels, all_label_weights, all_bbox_targets,
all_assign_metrics) = multi_apply(
self._get_target_single,
cls_scores,
bbox_preds,
anchor_list,
valid_flag_list,
gt_bboxes_list,
gt_bboxes_ignore_list,
gt_labels_list,
img_metas,
label_channels=label_channels,
unmap_outputs=unmap_outputs)
# no valid anchors
if any([labels is None for labels in all_labels]):
return None
# split targets to a list w.r.t. multiple levels
anchors_list = images_to_levels(all_anchors, num_level_anchors)
labels_list = images_to_levels(all_labels, num_level_anchors)
label_weights_list = images_to_levels(all_label_weights,
num_level_anchors)
bbox_targets_list = images_to_levels(all_bbox_targets,
num_level_anchors)
norm_alignment_metrics_list = images_to_levels(all_assign_metrics,
num_level_anchors)
return (anchors_list, labels_list, label_weights_list,
bbox_targets_list, norm_alignment_metrics_list)
def _get_target_single(self,
cls_scores,
bbox_preds,
flat_anchors,
valid_flags,
gt_bboxes,
gt_bboxes_ignore,
gt_labels,
img_meta,
label_channels=1,
unmap_outputs=True):
"""Compute regression, classification targets for anchors in a single
image.
Args:
cls_scores (list(Tensor)): Box scores for each image.
bbox_preds (list(Tensor)): Box energies / deltas for each image.
flat_anchors (Tensor): Multi-level anchors of the image, which are
concatenated into a single tensor of shape (num_anchors ,4)
valid_flags (Tensor): Multi level valid flags of the image,
which are concatenated into a single tensor of
shape (num_anchors,).
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, 4).
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.
unmap_outputs (bool): Whether to map outputs back to the original
set of anchors.
Returns:
tuple: N is the number of total anchors in the image.
anchors (Tensor): All anchors in the image with shape (N, 4).
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).
norm_alignment_metrics (Tensor): Normalized alignment metrics
of all priors in the image with shape (N,).
"""
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, :]
assign_result = self.alignment_assigner.assign(
cls_scores[inside_flags, :], bbox_preds[inside_flags, :], anchors,
gt_bboxes, gt_bboxes_ignore, gt_labels, self.alpha, self.beta)
assign_ious = assign_result.max_overlaps
assign_metrics = assign_result.assign_metrics
sampling_result = self.sampler.sample(assign_result, anchors,
gt_bboxes)
num_valid_anchors = anchors.shape[0]
bbox_targets = 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)
norm_alignment_metrics = 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:
# point-based
pos_bbox_targets = sampling_result.pos_gt_bboxes
bbox_targets[pos_inds, :] = pos_bbox_targets
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
class_assigned_gt_inds = torch.unique(
sampling_result.pos_assigned_gt_inds)
for gt_inds in class_assigned_gt_inds:
gt_class_inds = pos_inds[sampling_result.pos_assigned_gt_inds ==
gt_inds]
pos_alignment_metrics = assign_metrics[gt_class_inds]
pos_ious = assign_ious[gt_class_inds]
pos_norm_alignment_metrics = pos_alignment_metrics / (
pos_alignment_metrics.max() + 10e-8) * pos_ious.max()
norm_alignment_metrics[gt_class_inds] = pos_norm_alignment_metrics
# 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)
norm_alignment_metrics = unmap(norm_alignment_metrics,
num_total_anchors, inside_flags)
return (anchors, labels, label_weights, bbox_targets,
norm_alignment_metrics)