diff --git a/mmdet/models/anchor_heads/guided_anchor_head.py b/mmdet/models/anchor_heads/guided_anchor_head.py
index 0053cf59146e569ac17395f54393efe4b39fd099..9660f17708b83e0a18fb1e6e69585e636ae23391 100644
--- a/mmdet/models/anchor_heads/guided_anchor_head.py
+++ b/mmdet/models/anchor_heads/guided_anchor_head.py
@@ -36,15 +36,14 @@ class FeatureAdaption(nn.Module):
deformable_groups=4):
super(FeatureAdaption, self).__init__()
offset_channels = kernel_size * kernel_size * 2
- self.conv_offset = nn.Conv2d(2,
- deformable_groups * offset_channels,
- 1,
- bias=False)
- self.conv_adaption = DeformConv(in_channels,
- out_channels,
- kernel_size=kernel_size,
- padding=(kernel_size - 1) // 2,
- deformable_groups=deformable_groups)
+ self.conv_offset = nn.Conv2d(
+ 2, deformable_groups * offset_channels, 1, bias=False)
+ self.conv_adaption = DeformConv(
+ in_channels,
+ out_channels,
+ kernel_size=kernel_size,
+ padding=(kernel_size - 1) // 2,
+ deformable_groups=deformable_groups)
self.relu = nn.ReLU(inplace=True)
def init_weights(self):
@@ -109,20 +108,23 @@ class GuidedAnchorHead(AnchorHead):
target_stds=(1.0, 1.0, 1.0, 1.0),
deformable_groups=4,
loc_filter_thr=0.01,
- loss_loc=dict(type='FocalLoss',
- use_sigmoid=True,
- gamma=2.0,
- alpha=0.25,
- loss_weight=1.0),
- loss_shape=dict(type='IoULoss',
- style='bounded',
- beta=0.2,
- loss_weight=1.0),
- loss_cls=dict(type='CrossEntropyLoss',
- use_sigmoid=True,
- loss_weight=1.0),
- loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
- loss_weight=1.0)):
+ loss_loc=dict(
+ type='FocalLoss',
+ use_sigmoid=True,
+ gamma=2.0,
+ alpha=0.25,
+ loss_weight=1.0),
+ loss_shape=dict(
+ type='IoULoss',
+ style='bounded',
+ beta=0.2,
+ loss_weight=1.0),
+ loss_cls=dict(
+ type='CrossEntropyLoss',
+ use_sigmoid=True,
+ loss_weight=1.0),
+ loss_bbox=dict(
+ type='SmoothL1Loss', beta=1.0, loss_weight=1.0)):
super(AnchorHead, self).__init__()
self.in_channels = in_channels
self.num_classes = num_classes
@@ -258,8 +260,8 @@ class GuidedAnchorHead(AnchorHead):
inside_flags_list.append(inside_flags)
# inside_flag for a position is true if any anchor in this
# position is true
- inside_flags = (torch.stack(inside_flags_list, 0).sum(dim=0) >
- 0)
+ inside_flags = (
+ torch.stack(inside_flags_list, 0).sum(dim=0) > 0)
multi_level_flags.append(inside_flags)
inside_flag_list.append(multi_level_flags)
return approxs_list, inside_flag_list
@@ -347,11 +349,12 @@ class GuidedAnchorHead(AnchorHead):
-1, 2).detach()[mask]
bbox_deltas = anchor_deltas.new_full(squares.size(), 0)
bbox_deltas[:, 2:] = anchor_deltas
- guided_anchors = delta2bbox(squares,
- bbox_deltas,
- self.anchoring_means,
- self.anchoring_stds,
- wh_ratio_clip=1e-6)
+ guided_anchors = delta2bbox(
+ squares,
+ bbox_deltas,
+ self.anchoring_means,
+ self.anchoring_stds,
+ wh_ratio_clip=1e-6)
return guided_anchors, mask
def loss_shape_single(self, shape_pred, bbox_anchors, bbox_gts,
@@ -368,23 +371,26 @@ class GuidedAnchorHead(AnchorHead):
bbox_anchors_ = bbox_anchors[inds]
bbox_gts_ = bbox_gts[inds]
anchor_weights_ = anchor_weights[inds]
- pred_anchors_ = delta2bbox(bbox_anchors_,
- bbox_deltas_,
- self.anchoring_means,
- self.anchoring_stds,
- wh_ratio_clip=1e-6)
- loss_shape = self.loss_shape(pred_anchors_,
- bbox_gts_,
- anchor_weights_,
- avg_factor=anchor_total_num)
+ pred_anchors_ = delta2bbox(
+ bbox_anchors_,
+ bbox_deltas_,
+ self.anchoring_means,
+ self.anchoring_stds,
+ wh_ratio_clip=1e-6)
+ loss_shape = self.loss_shape(
+ pred_anchors_,
+ bbox_gts_,
+ anchor_weights_,
+ avg_factor=anchor_total_num)
return loss_shape
def loss_loc_single(self, loc_pred, loc_target, loc_weight, loc_avg_factor,
cfg):
- loss_loc = self.loss_loc(loc_pred.reshape(-1, 1),
- loc_target.reshape(-1, 1).long(),
- loc_weight.reshape(-1, 1),
- avg_factor=loc_avg_factor)
+ loss_loc = self.loss_loc(
+ loc_pred.reshape(-1, 1),
+ loc_target.reshape(-1, 1).long(),
+ loc_weight.reshape(-1, 1),
+ avg_factor=loc_avg_factor)
return loss_loc
def loss(self,
@@ -418,41 +424,44 @@ class GuidedAnchorHead(AnchorHead):
# 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)
+ 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)
+ 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)
+ 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)
+ 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_cls, losses_bbox = multi_apply(
@@ -467,24 +476,32 @@ class GuidedAnchorHead(AnchorHead):
cfg=cfg)
# get anchor location loss
- losses_loc, = multi_apply(self.loss_loc_single,
- loc_preds,
- loc_targets,
- loc_weights,
- loc_avg_factor=loc_avg_factor,
- cfg=cfg)
+ 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, = 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_bbox=losses_bbox,
- loss_shape=losses_shape,
- loss_loc=losses_loc)
+ 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 get_bboxes(self,
cls_scores,