def loss_first(self,
loc_preds,
bbox_preds,
gt_bboxes,
img_metas,
cfg,
gt_labels=None,
gt_bboxes_ignore=None):
featmap_sizes = [featmap.size()[-2:] for featmap in bbox_preds]
assert len(featmap_sizes) == len(self.anchor_generators)
device = bbox_preds[0].device
# get loc targets
loc_targets, loc_weights, loc_avg_factor = ga_loc_target(
gt_bboxes,
featmap_sizes,
self.anchor_scales,
self.anchor_strides,
center_ratio=cfg.center_ratio,
ignore_ratio=cfg.ignore_ratio)
anchor_list, valid_flag_list = self.get_anchors(featmap_sizes,
img_metas,
device=device)
# self.num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]]
# get anchor targets
sampling = False if self.cls_focal_loss else True
label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
cls_reg_targets = anchor_target(anchor_list,
valid_flag_list,
gt_bboxes,
img_metas,
self.target_means,
self.target_stds,
cfg,
gt_bboxes_ignore_list=gt_bboxes_ignore,
gt_labels_list=gt_labels,
label_channels=label_channels,
sampling=sampling)
if cls_reg_targets is None:
return None
(labels_list, label_weights_list, bbox_targets_list, bbox_weights_list,
num_total_pos, num_total_neg) = cls_reg_targets
num_total_samples = (num_total_pos if self.cls_focal_loss else
num_total_pos + num_total_neg)
losses_bbox_first = []
# get bbox regression losses on the first stage
for i in range(len(bbox_preds)):
loss_bbox = self.loss_first_single(
bbox_preds[i],
bbox_targets_list[i],
bbox_weights_list[i],
num_total_samples=num_total_samples,
cfg=cfg)
losses_bbox_first.append(loss_bbox)
# get anchor location loss
losses_loc = []
for i in range(len(loc_preds)):
loss_loc = self.loss_loc_single(loc_preds[i],
loc_targets[i],
loc_weights[i],
loc_avg_factor=loc_avg_factor,
cfg=cfg)
losses_loc.append(loss_loc)
return dict(loss_rpn_bbox_first=losses_bbox_first, loss_loc=losses_loc)
def loss(self,
cls_scores,
bbox_preds,
shape_preds,
loc_preds,
gt_bboxes,
gt_labels,
img_metas,
cfg,
gt_bboxes_ignore=None):
featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
assert len(featmap_sizes) == len(self.approx_generators)
loc_targets, loc_weights, loc_avg_factor = ga_loc_target(
gt_bboxes,
featmap_sizes,
self.octave_base_scale,
self.anchor_strides,
center_ratio=cfg.center_ratio,
ignore_ratio=cfg.ignore_ratio)
(approxs_list, valid_flag_list, base_approxs_list,
guided_anchors_list) = self.get_anchors(featmap_sizes, shape_preds,
img_metas)
sampling = False if not hasattr(cfg, 'ga_sampler') else True
shape_targets = ga_shape_target(approxs_list,
valid_flag_list,
base_approxs_list,
gt_bboxes,
img_metas,
self.approxs_per_octave,
cfg,
sampling=sampling)
if shape_targets is None:
return None
(bbox_anchors_list, bbox_gts_list, anchor_weights_list,
all_inside_flags, anchor_fg_num, anchor_ng_num) = shape_targets
anchor_total_num = (anchor_fg_num if not sampling else anchor_fg_num +
anchor_ng_num)
sampling = False if self.cls_focal_loss else True
label_channels = self.cls_out_channels if self.cls_sigmoid_loss else 1
cls_reg_targets = anchor_target(guided_anchors_list,
all_inside_flags,
gt_bboxes,
img_metas,
self.target_means,
self.target_stds,
cfg,
gt_bboxes_ignore_list=gt_bboxes_ignore,
gt_labels_list=gt_labels,
label_channels=label_channels,
sampling=sampling)
if cls_reg_targets is None:
return None
(labels_list, label_weights_list, bbox_targets_list, bbox_weights_list,
num_total_pos, num_total_neg) = cls_reg_targets
num_total_samples = (num_total_pos if self.cls_focal_loss else
num_total_pos + num_total_neg)
losses_cls, losses_reg = multi_apply(
self.loss_single,
cls_scores,
bbox_preds,
labels_list,
label_weights_list,
bbox_targets_list,
bbox_weights_list,
num_total_samples=num_total_samples,
cfg=cfg)
losses_loc, = multi_apply(self.loss_loc_single,
loc_preds,
loc_targets,
loc_weights,
loc_avg_factor=loc_avg_factor,
cfg=cfg)
losses_shape, = multi_apply(self.loss_shape_single,
shape_preds,
bbox_anchors_list,
bbox_gts_list,
anchor_weights_list,
anchor_total_num=anchor_total_num)
return dict(loss_cls=losses_cls,
loss_reg=losses_reg,
loss_shape=losses_shape,
loss_loc=losses_loc)
def loss(self,
cls_scores,
bbox_preds,
shape_preds,
loc_preds,
gt_bboxes,
gt_labels,
img_metas,
cfg,
gt_bboxes_ignore=None):
featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
assert len(featmap_sizes) == len(self.approx_generators)
# get loc targets
loc_targets, loc_weights, loc_avg_factor = ga_loc_target(
gt_bboxes,
featmap_sizes,
self.octave_base_scale,
self.anchor_strides,
center_ratio=cfg.center_ratio,
ignore_ratio=cfg.ignore_ratio)
# get sampled approxes
approxs_list, inside_flag_list = self.get_sampled_approxs(
featmap_sizes, img_metas, cfg)
# get squares and guided anchors
squares_list, guided_anchors_list, _ = self.get_anchors(
featmap_sizes, shape_preds, loc_preds, img_metas)
# get shape targets
sampling = False if not hasattr(cfg, 'ga_sampler') else True
shape_targets = ga_shape_target(approxs_list,
inside_flag_list,
squares_list,
gt_bboxes,
img_metas,
self.approxs_per_octave,
cfg,
sampling=sampling)
if shape_targets is None:
return None
(bbox_anchors_list, bbox_gts_list, anchor_weights_list, anchor_fg_num,
anchor_bg_num) = shape_targets
anchor_total_num = (anchor_fg_num if not sampling else anchor_fg_num +
anchor_bg_num)
# get anchor targets
sampling = False if self.cls_focal_loss else True
label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
cls_reg_targets = anchor_target(guided_anchors_list,
inside_flag_list,
gt_bboxes,
img_metas,
self.target_means,
self.target_stds,
cfg,
gt_bboxes_ignore_list=gt_bboxes_ignore,
gt_labels_list=gt_labels,
label_channels=label_channels,
sampling=sampling)
if cls_reg_targets is None:
return None
# (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list,
# num_total_pos, num_total_neg) = cls_reg_targets
(labels_list, label_weights_list, bbox_targets_list, bbox_weights_list,
num_total_pos, num_total_neg, level_anchor_list) = cls_reg_targets
num_total_samples = (num_total_pos if self.cls_focal_loss else
num_total_pos + num_total_neg)
# added by WSK
num_total_samples_cls = (num_total_pos_cls if self.cls_focal_loss else
num_total_pos_cls + num_total_neg_cls)
# get classification and bbox regression losses
losses_cls, losses_bbox = multi_apply(
self.loss_single,
cls_scores,
bbox_preds,
labels_list,
label_weights_list,
bbox_targets_list,
bbox_weights_list,
level_anchor_list, # added by Shengkai Wu
bbox_targets_cls_list, # added by Shengkai Wu
bbox_weights_cls_list, # added by Shengkai Wu
num_total_samples_cls=num_total_samples_cls, # added by Shengkai Wu
num_total_samples=num_total_samples,
gt_bboxes=gt_bboxes, # added by Shengkai Wu
cfg=cfg)
# get anchor location loss
losses_loc = []
for i in range(len(loc_preds)):
loss_loc = self.loss_loc_single(loc_preds[i],
loc_targets[i],
loc_weights[i],
loc_avg_factor=loc_avg_factor,
cfg=cfg)
losses_loc.append(loss_loc)
# get anchor shape loss
losses_shape = []
for i in range(len(shape_preds)):
loss_shape = self.loss_shape_single(
shape_preds[i],
bbox_anchors_list[i],
bbox_gts_list[i],
anchor_weights_list[i],
anchor_total_num=anchor_total_num)
losses_shape.append(loss_shape)
return dict(loss_cls=losses_cls,
loss_bbox=losses_bbox,
loss_shape=losses_shape,
loss_loc=losses_loc)
def loss(self,
cls_scores,
bbox_preds,
shape_preds,
loc_preds,
gt_bboxes,
gt_labels,
img_metas,
cfg,
gt_bboxes_ignore=None):
featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
assert len(featmap_sizes) == len(self.approx_generators)
device = cls_scores[0].device
# get loc targets
loc_targets, loc_weights, loc_avg_factor = ga_loc_target(
gt_bboxes,
featmap_sizes,
self.octave_base_scale,
self.anchor_strides,
center_ratio=cfg.center_ratio,
ignore_ratio=cfg.ignore_ratio)
# get sampled approxes
approxs_list, inside_flag_list = self.get_sampled_approxs(
featmap_sizes, img_metas, cfg, device=device)
# get squares and guided anchors
squares_list, guided_anchors_list, _ = self.get_anchors(
featmap_sizes, shape_preds, loc_preds, img_metas, device=device)
# get shape targets
sampling = False if not hasattr(cfg, 'ga_sampler') else True
shape_targets = ga_shape_target(
approxs_list,
inside_flag_list,
squares_list,
gt_bboxes,
img_metas,
self.approxs_per_octave,
cfg,
sampling=sampling)
if shape_targets is None:
return None
(bbox_anchors_list, bbox_gts_list, anchor_weights_list, anchor_fg_num,
anchor_bg_num) = shape_targets
anchor_total_num = (
anchor_fg_num if not sampling else anchor_fg_num + anchor_bg_num)
# get anchor targets
sampling = False if self.cls_focal_loss else True
label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
# get classification and bbox regression losses
cfg['ga_sampler']['pos_fraction'] = 0.5 # 0.55
cfg['sampler']['pos_fraction'] = 0.5 # 0.55
cls_reg_targets = anchor_target(
guided_anchors_list,
inside_flag_list,
gt_bboxes,
img_metas,
self.target_means,
self.target_stds,
cfg,
gt_bboxes_ignore_list=gt_bboxes_ignore,
gt_labels_list=gt_labels,
label_channels=label_channels,
sampling=sampling)
if cls_reg_targets is None:
return None
(labels_list, label_weights_list, bbox_targets_list, bbox_weights_list,
num_total_pos, num_total_neg) = cls_reg_targets
num_total_samples = (
num_total_pos if self.cls_focal_loss else num_total_pos +
num_total_neg)
losses_cls, losses_bbox = multi_apply(
self.loss_single,
cls_scores,
bbox_preds,
labels_list,
label_weights_list,
bbox_targets_list,
bbox_weights_list,
num_total_samples=num_total_samples,
cfg=cfg)
# get anchor location loss
losses_loc = []
for i in range(len(loc_preds)):
loss_loc = self.loss_loc_single(
loc_preds[i],
loc_targets[i],
loc_weights[i],
loc_avg_factor=loc_avg_factor,
cfg=cfg)
losses_loc.append(loss_loc)
# get anchor shape loss
losses_shape = []
for i in range(len(shape_preds)):
loss_shape = self.loss_shape_single(
shape_preds[i],
bbox_anchors_list[i],
bbox_gts_list[i],
anchor_weights_list[i],
anchor_total_num=anchor_total_num)
losses_shape.append(loss_shape)
if 1:
# get sampled approxes
approxs_list, inside_flag_list = self.get_sampled_approxs(
featmap_sizes, img_metas, cfg, device=device)
# get squares and guided anchors
squares_list, guided_anchors_list, _ = self.get_anchors(
featmap_sizes, shape_preds, loc_preds, img_metas, device=device)
# get shape targets
sampling = False if not hasattr(cfg, 'ga_sampler') else True
shape_targets = ga_shape_target(
approxs_list,
inside_flag_list,
squares_list,
gt_bboxes,
img_metas,
self.approxs_per_octave,
cfg,
sampling=sampling)
if shape_targets is None:
return None
(bbox_anchors_list, bbox_gts_list, anchor_weights_list, anchor_fg_num,
anchor_bg_num) = shape_targets
anchor_total_num = (
anchor_fg_num if not sampling else anchor_fg_num + anchor_bg_num)
# get anchor targets
sampling = False if self.cls_focal_loss else True
label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
lvls = len(guided_anchors_list)
bboxes_lst = [[] for k in range(lvls)]
for z in range(lvls):
for k in range(len(guided_anchors_list[z])):
bbox_pred = bbox_preds[k][z].permute(1, 2, 0).reshape(-1, 4)
a = guided_anchors_list[z][k]
# print(a.shape,bbox_pred.shape)
bboxes = delta2bbox(guided_anchors_list[z][k], bbox_pred, self.target_means,
self.target_stds)
bboxes_lst[z].append(bboxes.clone())
zz = 0
# label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
# label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
cfg['ga_sampler']['pos_fraction'] = 0.55 # 0.55
cfg['sampler']['pos_fraction'] = 0.6 # 0.55
cls_reg_targets = anchor_target(
guided_anchors_list,
inside_flag_list,
gt_bboxes,
img_metas,
self.target_means,
self.target_stds,
cfg,
gt_bboxes_ignore_list=gt_bboxes_ignore,
gt_labels_list=gt_labels,
label_channels=label_channels,
sampling=sampling)
if cls_reg_targets is None:
return None
(labels_list, label_weights_list, bbox_targets_list, bbox_weights_list,
num_total_pos, num_total_neg) = cls_reg_targets
num_total_samples = (
num_total_pos if self.cls_focal_loss else num_total_pos +
num_total_neg)
# get classification and bbox regression losses
losses_cls2, losses_bbox2 = multi_apply(
self.loss_single,
cls_scores,
bbox_preds,
labels_list,
label_weights_list,
bbox_targets_list,
bbox_weights_list,
num_total_samples=num_total_samples,
cfg=cfg)
zz=0
for k in range(lvls):
losses_cls[k] = losses_cls[k] / 2 + losses_cls2[k] / 2
return dict(
loss_cls=losses_cls,
loss_bbox=losses_bbox,
loss_shape=losses_shape,
loss_loc=losses_loc)
