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ld_atss.py
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ld_atss.py
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import torch
from mmcv.runner import force_fp32
from mmdet.core import (distance2bbox, multi_apply, reduce_mean,
images_to_levels, unmap, anchor_inside_flags)
from ..builder import HEADS, build_loss
from .atss_gfl_head import ATSSGFLHead
EPS = 1e-12
@HEADS.register_module()
class LDATSSHead(ATSSGFLHead):
"""Localization distillation Head. (Short description)
It utilizes the learned bbox distributions to transfer the localization
dark knowledge from teacher to student. Original paper: `Localization
Distillation for Object Detection. <https://arxiv.org/abs/2102.12252>`_
Args:
num_classes (int): Number of categories excluding the background
category.
in_channels (int): Number of channels in the input feature map.
loss_ld (dict): Config of Localization Distillation Loss (LD),
T is the temperature for distillation.
"""
def __init__(self,
num_classes,
in_channels,
loss_ld=dict(
type='LocalizationDistillationLoss',
loss_weight=0.25,
T=10),
loss_kd=None,
**kwargs):
super().__init__(num_classes, in_channels, **kwargs)
self.loss_ld = build_loss(loss_ld)
self.loss_kd = build_loss(loss_kd)
def loss_single(self, anchors, cls_score, bbox_pred, centerness, labels,
label_weights, bbox_targets, stride, soft_targets,
soft_label, assigned_neg, num_total_samples):
"""Compute loss of a single scale level.
Args:
cls_score (Tensor): Box scores for each scale level
Has shape (N, num_anchors * num_classes, H, W).
bbox_pred (Tensor): Box energies / deltas for each scale
level with shape (N, num_anchors * 4, H, W).
anchors (Tensor): Box reference for each scale level with shape
(N, num_total_anchors, 4).
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 wight
shape (N, num_total_anchors, 4).
num_total_samples (int): Number os positive samples that is
reduced over all GPUs.
Returns:
dict[str, Tensor]: A dictionary of loss components.
"""
anchors = anchors.reshape(-1, 4)
cls_score = cls_score.permute(0, 2, 3, 1).reshape(
-1, self.cls_out_channels).contiguous()
soft_label = soft_label.permute(0, 2, 3,
1).reshape(-1, self.cls_out_channels)
bbox_pred = bbox_pred.permute(0, 2, 3,
1).reshape(-1, 4 * (self.reg_max + 1))
centerness = centerness.permute(0, 2, 3, 1).reshape(-1)
bbox_targets = bbox_targets.reshape(-1, 4)
labels = labels.reshape(-1)
label_weights = label_weights.reshape(-1)
soft_targets = soft_targets.permute(0, 2, 3,
1).reshape(-1,
4 * (self.reg_max + 1))
# classification loss
loss_cls = self.loss_cls(
cls_score, labels, label_weights, avg_factor=num_total_samples)
# 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_centerness = centerness[pos_inds]
pos_anchor_centers = self.anchor_center(pos_anchors) / stride[0]
centerness_targets = self.centerness_target(
pos_anchors, pos_bbox_targets)
weight_targets = cls_score.detach().sigmoid()
weight_targets = weight_targets.max(dim=1)[0][pos_inds]
# pos_decode_bbox_pred = self.bbox_coder.decode(
# pos_anchors, pos_bbox_pred)
pos_bbox_pred_corners = self.integral(pos_bbox_pred)
pos_decode_bbox_pred = distance2bbox(pos_anchor_centers,
pos_bbox_pred_corners)
# pos_decode_bbox_targets = self.bbox_coder.decode(
# pos_anchors, pos_bbox_targets)
pos_decode_bbox_targets = pos_bbox_targets / stride[0]
pred_corners = pos_bbox_pred.reshape(-1, self.reg_max + 1)
pos_soft_targets = soft_targets[pos_inds]
soft_corners = pos_soft_targets.reshape(-1, self.reg_max + 1)
loss_ld = self.loss_ld(
pred_corners,
soft_corners,
weight=weight_targets[:, None].expand(-1, 4).reshape(-1),
avg_factor=4.0)
# regression loss
loss_bbox = self.loss_bbox(
pos_decode_bbox_pred,
pos_decode_bbox_targets,
weight=centerness_targets,
avg_factor=1.0)
loss_cls_kd = self.loss_kd(
cls_score[pos_inds],
soft_label[pos_inds],
weight=label_weights[pos_inds],
avg_factor=pos_inds.shape[0])
# centerness loss
loss_centerness = self.loss_centerness(
pos_centerness,
centerness_targets,
avg_factor=num_total_samples)
else:
loss_ld = bbox_pred.sum() * 0
loss_cls_kd = bbox_pred.sum() * 0
loss_bbox = bbox_pred.sum() * 0
loss_centerness = centerness.sum() * 0
centerness_targets = bbox_targets.new_tensor(0.)
assigned_neg = assigned_neg.reshape(-1)
remain_inds = (assigned_neg > 0).nonzero().squeeze(1)
if len(remain_inds) > 0:
neg_pred_corners = bbox_pred[remain_inds].reshape(
-1, self.reg_max + 1)
neg_soft_corners = soft_targets[remain_inds].reshape(
-1, self.reg_max + 1)
weight_targetss = ((cls_score.detach().sigmoid().max(dim=1)[0]) <
0).float()
remain_targets = weight_targetss[remain_inds] + assigned_neg[
remain_inds]
loss_ld_neg = 0.15 * self.loss_ld(
neg_pred_corners,
neg_soft_corners,
weight=remain_targets[:, None].expand(-1, 4).reshape(-1),
avg_factor=4.0)
else:
loss_ld_neg = bbox_pred.sum() * 0
return loss_cls, loss_bbox, loss_centerness, loss_ld, loss_ld_neg, loss_cls_kd,\
centerness_targets.sum()
@force_fp32(apply_to=('cls_scores', 'bbox_preds', 'centernesses'))
def loss(self,
cls_scores,
bbox_preds,
centernesses,
gt_bboxes,
gt_labels,
soft_target,
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]): Box energies / deltas for each scale
level with shape (N, num_anchors * 4, H, W)
centernesses (list[Tensor]): Centerness for each scale
level with shape (N, num_anchors * 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.anchor_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
soft_labels, soft_corners, _ = soft_target
cls_reg_targets = self.get_targets(
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)
if cls_reg_targets is None:
return None
(anchor_list, labels_list, label_weights_list, bbox_targets_list,
bbox_weights_list, num_total_pos, num_total_neg, labels_list_neg,
label_weights_list_neg, bbox_targets_list_neg, bbox_weights_list_neg,
num_total_pos_neg, num_total_neg_neg,
assigned_neg_list) = cls_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)
losses_cls, losses_bbox, loss_centerness, losses_ld, losses_ld_neg, losses_cls_kd,\
bbox_avg_factor = multi_apply(
self.loss_single,
anchor_list,
cls_scores,
bbox_preds,
centernesses,
labels_list,
label_weights_list,
bbox_targets_list,
self.anchor_generator.strides,
soft_corners,
soft_labels,
assigned_neg_list,
num_total_samples=num_total_samples)
bbox_avg_factor = sum(bbox_avg_factor)
bbox_avg_factor = reduce_mean(bbox_avg_factor).item()
if bbox_avg_factor < EPS:
bbox_avg_factor = 1
losses_bbox = list(map(lambda x: x / bbox_avg_factor, losses_bbox))
return dict(
loss_cls=losses_cls,
loss_bbox=losses_bbox,
loss_ld=losses_ld,
loss_ld_neg=losses_ld_neg,
loss_cls_kd=losses_cls_kd,
loss_centerness=loss_centerness)
def forward_train(self,
x,
out_teacher,
img_metas,
gt_bboxes,
gt_labels=None,
gt_bboxes_ignore=None,
proposal_cfg=None,
**kwargs):
"""
Args:
x (list[Tensor]): Features from FPN.
img_metas (list[dict]): Meta information of each image, e.g.,
image size, scaling factor, etc.
gt_bboxes (Tensor): Ground truth bboxes of the image,
shape (num_gts, 4).
gt_labels (Tensor): Ground truth labels of each box,
shape (num_gts,).
gt_bboxes_ignore (Tensor): Ground truth bboxes to be
ignored, shape (num_ignored_gts, 4).
proposal_cfg (mmcv.Config): Test / postprocessing configuration,
if None, test_cfg would be used
Returns:
tuple[dict, list]: The loss components and proposals of each image.
- losses (dict[str, Tensor]): A dictionary of loss components.
- proposal_list (list[Tensor]): Proposals of each image.
"""
outs = self(x)
soft_target = out_teacher
if gt_labels is None:
loss_inputs = outs + (gt_bboxes, soft_target, img_metas)
else:
loss_inputs = outs + (gt_bboxes, gt_labels, soft_target, img_metas)
losses = self.loss(*loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore)
if proposal_cfg is None:
return losses
else:
proposal_list = self.get_bboxes(*outs, img_metas, cfg=proposal_cfg)
return losses, proposal_list
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):
"""Get targets for GFL 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.
"""
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)]
'''
(all_anchors, all_labels, all_label_weights, all_bbox_targets,
all_bbox_weights, pos_inds_list, neg_inds_list, all_assigned_neg, assigned_neg_inds_list) = multi_apply(
self._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_anchors, all_labels, all_label_weights, all_bbox_targets,
all_bbox_weights, pos_inds_list, neg_inds_list, all_labels_neg,
all_label_weights_neg, all_bbox_targets_neg, all_bbox_weights_neg,
pos_inds_list_neg, neg_inds_list_neg, all_assigned_neg) = multi_apply(
self._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)
# 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)
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)
bbox_weights_list = images_to_levels(all_bbox_weights,
num_level_anchors)
assigned_neg_list = images_to_levels(all_assigned_neg,
num_level_anchors)
# sampled anchors of all images
num_total_pos_neg = sum(
[max(inds.numel(), 1) for inds in pos_inds_list_neg])
num_total_neg_neg = sum(
[max(inds.numel(), 1) for inds in neg_inds_list_neg])
# split targets to a list w.r.t. multiple levels
labels_list_neg = images_to_levels(all_labels_neg, num_level_anchors)
label_weights_list_neg = images_to_levels(all_label_weights_neg,
num_level_anchors)
bbox_targets_list_neg = images_to_levels(all_bbox_targets_neg,
num_level_anchors)
bbox_weights_list_neg = images_to_levels(all_bbox_weights_neg,
num_level_anchors)
return (anchors_list, labels_list, label_weights_list,
bbox_targets_list, bbox_weights_list, num_total_pos,
num_total_neg, labels_list_neg, label_weights_list_neg,
bbox_targets_list_neg, bbox_weights_list_neg,
num_total_pos_neg, num_total_neg_neg, assigned_neg_list)
def _get_target_single(self,
flat_anchors,
valid_flags,
num_level_anchors,
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:
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,).
num_level_anchors Tensor): 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, 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).
bbox_weights (Tensor): BBox weights of all anchors in the
image with shape (N, 4).
pos_inds (Tensor): Indices of postive 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)
assign_result = self.assigner.assign(anchors, num_level_anchors_inside,
gt_bboxes, gt_bboxes_ignore,
gt_labels)
sampling_result = self.sampler.sample(assign_result, anchors,
gt_bboxes)
assign_result_neg, assigned_neg = self.assigner.assign_neg(
anchors, num_level_anchors_inside, gt_bboxes, gt_bboxes_ignore,
gt_labels)
sampling_result_neg = self.sampler.sample(assign_result_neg, anchors,
gt_bboxes)
num_valid_anchors = anchors.shape[0]
bbox_targets = torch.zeros_like(anchors)
bbox_weights = torch.zeros_like(anchors)
bbox_targets_neg = torch.zeros_like(anchors)
bbox_weights_neg = torch.zeros_like(anchors)
labels = anchors.new_full((num_valid_anchors, ),
self.num_classes,
dtype=torch.long)
labels_neg = anchors.new_full((num_valid_anchors, ),
self.num_classes,
dtype=torch.long)
label_weights = anchors.new_zeros(num_valid_anchors, dtype=torch.float)
label_weights_neg = anchors.new_zeros(
num_valid_anchors, dtype=torch.float)
pos_inds = sampling_result.pos_inds
neg_inds = sampling_result.neg_inds
pos_inds_neg = sampling_result_neg.pos_inds
neg_inds_neg = sampling_result_neg.neg_inds
if len(pos_inds) > 0:
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
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
if len(pos_inds_neg) > 0:
pos_bbox_targets_neg = sampling_result_neg.pos_gt_bboxes
bbox_targets_neg[pos_inds_neg, :] = pos_bbox_targets_neg
bbox_weights_neg[pos_inds_neg, :] = 1.0
if gt_labels is None:
# Only rpn gives gt_labels as None
# Foreground is the first class
labels_neg[pos_inds_neg] = 0
else:
labels_neg[pos_inds_neg] = gt_labels[
sampling_result_neg.pos_assigned_gt_inds]
if self.train_cfg.pos_weight <= 0:
label_weights_neg[pos_inds_neg] = 1.0
else:
label_weights_neg[pos_inds_neg] = self.train_cfg.pos_weight
if len(neg_inds_neg) > 0:
label_weights_neg[neg_inds_neg] = 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)
assigned_neg = unmap(assigned_neg, num_total_anchors, inside_flags)
labels_neg = unmap(
labels_neg,
num_total_anchors,
inside_flags,
fill=self.num_classes)
label_weights_neg = unmap(label_weights_neg, num_total_anchors,
inside_flags)
bbox_targets_neg = unmap(bbox_targets_neg, num_total_anchors,
inside_flags)
bbox_weights_neg = unmap(bbox_weights_neg, num_total_anchors,
inside_flags)
return (anchors, labels, label_weights, bbox_targets, bbox_weights,
pos_inds, neg_inds, labels_neg, label_weights_neg,
bbox_targets_neg, bbox_weights_neg, pos_inds_neg, neg_inds_neg,
assigned_neg)