diff --git a/mmdet/core/anchor/anchor_target.py b/mmdet/core/anchor/anchor_target.py
index 2dae8315189afc46011e3fcd39eb722cc220a695..7a5bf4ec1a78005e6187b4d258d51e986a4d3238 100644
--- a/mmdet/core/anchor/anchor_target.py
+++ b/mmdet/core/anchor/anchor_target.py
@@ -11,6 +11,7 @@ def anchor_target(anchor_list,
target_means,
target_stds,
cfg,
+ gt_bboxes_ignore_list=None,
gt_labels_list=None,
label_channels=1,
sampling=True,
@@ -41,6 +42,8 @@ def anchor_target(anchor_list,
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_labels, all_label_weights, all_bbox_targets, all_bbox_weights,
@@ -49,6 +52,7 @@ def anchor_target(anchor_list,
anchor_list,
valid_flag_list,
gt_bboxes_list,
+ gt_bboxes_ignore_list,
gt_labels_list,
img_metas,
target_means=target_means,
@@ -90,6 +94,7 @@ def images_to_levels(target, num_level_anchors):
def anchor_target_single(flat_anchors,
valid_flags,
gt_bboxes,
+ gt_bboxes_ignore,
gt_labels,
img_meta,
target_means,
@@ -108,11 +113,11 @@ def anchor_target_single(flat_anchors,
if sampling:
assign_result, sampling_result = assign_and_sample(
- anchors, gt_bboxes, None, None, cfg)
+ anchors, gt_bboxes, gt_bboxes_ignore, None, cfg)
else:
bbox_assigner = build_assigner(cfg.assigner)
- assign_result = bbox_assigner.assign(anchors, gt_bboxes, None,
- gt_labels)
+ assign_result = bbox_assigner.assign(anchors, gt_bboxes,
+ gt_bboxes_ignore, gt_labels)
bbox_sampler = PseudoSampler()
sampling_result = bbox_sampler.sample(assign_result, anchors,
gt_bboxes)
diff --git a/mmdet/models/anchor_heads/anchor_head.py b/mmdet/models/anchor_heads/anchor_head.py
index 57a7d6419ff13e8f88a8efdfa7e49983ea2cc340..d3ab2d213e734a067e7cb10c486084e85dfb1837 100644
--- a/mmdet/models/anchor_heads/anchor_head.py
+++ b/mmdet/models/anchor_heads/anchor_head.py
@@ -169,8 +169,14 @@ class AnchorHead(nn.Module):
avg_factor=num_total_samples)
return loss_cls, loss_reg
- def loss(self, cls_scores, bbox_preds, gt_bboxes, gt_labels, img_metas,
- cfg):
+ def loss(self,
+ cls_scores,
+ bbox_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.anchor_generators)
@@ -186,6 +192,7 @@ class AnchorHead(nn.Module):
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)
diff --git a/mmdet/models/anchor_heads/rpn_head.py b/mmdet/models/anchor_heads/rpn_head.py
index b913fc7f27a323ab17d77603cbbc273d33cc933c..fe9d5c3a4f3dd5f5e49a4d3f3117e3f404f2b477 100644
--- a/mmdet/models/anchor_heads/rpn_head.py
+++ b/mmdet/models/anchor_heads/rpn_head.py
@@ -34,9 +34,21 @@ class RPNHead(AnchorHead):
rpn_bbox_pred = self.rpn_reg(x)
return rpn_cls_score, rpn_bbox_pred
- def loss(self, cls_scores, bbox_preds, gt_bboxes, img_metas, cfg):
- losses = super(RPNHead, self).loss(cls_scores, bbox_preds, gt_bboxes,
- None, img_metas, cfg)
+ def loss(self,
+ cls_scores,
+ bbox_preds,
+ gt_bboxes,
+ img_metas,
+ cfg,
+ gt_bboxes_ignore=None):
+ losses = super(RPNHead, self).loss(
+ cls_scores,
+ bbox_preds,
+ gt_bboxes,
+ None,
+ img_metas,
+ cfg,
+ gt_bboxes_ignore=gt_bboxes_ignore)
return dict(
loss_rpn_cls=losses['loss_cls'], loss_rpn_reg=losses['loss_reg'])
diff --git a/mmdet/models/anchor_heads/ssd_head.py b/mmdet/models/anchor_heads/ssd_head.py
index ed1b27bc148b461fc6c7ead7d02880c3d0003b54..1f704f74cdc8d527bb227a8bc502eab9ae872061 100644
--- a/mmdet/models/anchor_heads/ssd_head.py
+++ b/mmdet/models/anchor_heads/ssd_head.py
@@ -130,8 +130,14 @@ class SSDHead(AnchorHead):
avg_factor=num_total_samples)
return loss_cls[None], loss_reg
- def loss(self, cls_scores, bbox_preds, gt_bboxes, gt_labels, img_metas,
- cfg):
+ def loss(self,
+ cls_scores,
+ bbox_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.anchor_generators)
@@ -145,6 +151,7 @@ class SSDHead(AnchorHead):
self.target_means,
self.target_stds,
cfg,
+ gt_bboxes_ignore_list=gt_bboxes_ignore,
gt_labels_list=gt_labels,
label_channels=1,
sampling=False,
diff --git a/mmdet/models/detectors/cascade_rcnn.py b/mmdet/models/detectors/cascade_rcnn.py
index 705a078a01819f28d8d2a28bac8afa45ed05504c..6df9e22689d6a423a6e948623b50b779c11ad0b1 100644
--- a/mmdet/models/detectors/cascade_rcnn.py
+++ b/mmdet/models/detectors/cascade_rcnn.py
@@ -109,8 +109,8 @@ class CascadeRCNN(BaseDetector, RPNTestMixin):
img,
img_meta,
gt_bboxes,
- gt_bboxes_ignore,
gt_labels,
+ gt_bboxes_ignore=None,
gt_masks=None,
proposals=None):
x = self.extract_feat(img)
@@ -121,7 +121,8 @@ class CascadeRCNN(BaseDetector, RPNTestMixin):
rpn_outs = self.rpn_head(x)
rpn_loss_inputs = rpn_outs + (gt_bboxes, img_meta,
self.train_cfg.rpn)
- rpn_losses = self.rpn_head.loss(*rpn_loss_inputs)
+ rpn_losses = self.rpn_head.loss(
+ *rpn_loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore)
losses.update(rpn_losses)
proposal_inputs = rpn_outs + (img_meta, self.test_cfg.rpn)
diff --git a/mmdet/models/detectors/rpn.py b/mmdet/models/detectors/rpn.py
index b882024c97ff01dcdb80a0e5f18f4f4003e82572..36b92d0dafd9c920e73501c2d5bec5cf4efb4b06 100644
--- a/mmdet/models/detectors/rpn.py
+++ b/mmdet/models/detectors/rpn.py
@@ -38,7 +38,11 @@ class RPN(BaseDetector, RPNTestMixin):
x = self.neck(x)
return x
- def forward_train(self, img, img_meta, gt_bboxes=None):
+ def forward_train(self,
+ img,
+ img_meta,
+ gt_bboxes=None,
+ gt_bboxes_ignore=None):
if self.train_cfg.rpn.get('debug', False):
self.rpn_head.debug_imgs = tensor2imgs(img)
@@ -46,7 +50,8 @@ class RPN(BaseDetector, RPNTestMixin):
rpn_outs = self.rpn_head(x)
rpn_loss_inputs = rpn_outs + (gt_bboxes, img_meta, self.train_cfg.rpn)
- losses = self.rpn_head.loss(*rpn_loss_inputs)
+ losses = self.rpn_head.loss(
+ *rpn_loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore)
return losses
def simple_test(self, img, img_meta, rescale=False):
diff --git a/mmdet/models/detectors/single_stage.py b/mmdet/models/detectors/single_stage.py
index 17fe6fba6c11641d5b3e0de720dcb5f182eafc82..6f73b34a976e76f1a5a117c42c9de2e81f41f9ff 100644
--- a/mmdet/models/detectors/single_stage.py
+++ b/mmdet/models/detectors/single_stage.py
@@ -42,11 +42,17 @@ class SingleStageDetector(BaseDetector):
x = self.neck(x)
return x
- def forward_train(self, img, img_metas, gt_bboxes, gt_labels):
+ def forward_train(self,
+ img,
+ img_metas,
+ gt_bboxes,
+ gt_labels,
+ gt_bboxes_ignore=None):
x = self.extract_feat(img)
outs = self.bbox_head(x)
loss_inputs = outs + (gt_bboxes, gt_labels, img_metas, self.train_cfg)
- losses = self.bbox_head.loss(*loss_inputs)
+ losses = self.bbox_head.loss(
+ *loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore)
return losses
def simple_test(self, img, img_meta, rescale=False):
diff --git a/mmdet/models/detectors/two_stage.py b/mmdet/models/detectors/two_stage.py
index 68f734f29ecdaa7e2e1ae1eac00565eb2e8a61f4..3dca618acfb6070a1798ef03141ab94423b52deb 100644
--- a/mmdet/models/detectors/two_stage.py
+++ b/mmdet/models/detectors/two_stage.py
@@ -81,8 +81,8 @@ class TwoStageDetector(BaseDetector, RPNTestMixin, BBoxTestMixin,
img,
img_meta,
gt_bboxes,
- gt_bboxes_ignore,
gt_labels,
+ gt_bboxes_ignore=None,
gt_masks=None,
proposals=None):
x = self.extract_feat(img)
@@ -94,7 +94,8 @@ class TwoStageDetector(BaseDetector, RPNTestMixin, BBoxTestMixin,
rpn_outs = self.rpn_head(x)
rpn_loss_inputs = rpn_outs + (gt_bboxes, img_meta,
self.train_cfg.rpn)
- rpn_losses = self.rpn_head.loss(*rpn_loss_inputs)
+ rpn_losses = self.rpn_head.loss(
+ *rpn_loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore)
losses.update(rpn_losses)
proposal_inputs = rpn_outs + (img_meta, self.test_cfg.rpn)
@@ -108,6 +109,8 @@ class TwoStageDetector(BaseDetector, RPNTestMixin, BBoxTestMixin,
bbox_sampler = build_sampler(
self.train_cfg.rcnn.sampler, context=self)
num_imgs = img.size(0)
+ if gt_bboxes_ignore is None:
+ gt_bboxes_ignore = [None for _ in range(num_imgs)]
sampling_results = []
for i in range(num_imgs):
assign_result = bbox_assigner.assign(