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[Refactor] Refactor SemiBaseDetector and SoftTeacher #8786

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Sep 15, 2022
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[Refactor] Refactor SemiBaseDetector and SoftTeacher #8786

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5f68c26
Refactor SemiBaseDetector and SoftTeacher
Czm369 Sep 13, 2022
841580f
Fix some commits
Czm369 Sep 14, 2022
3b7e63a
refactor semi_base
Czm369 Sep 15, 2022
c6b617f
fix some commits
Czm369 Sep 15, 2022
4546f10
fix some docs
Czm369 Sep 15, 2022
b283fe7
refactor some functions
Czm369 Sep 15, 2022
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54 changes: 29 additions & 25 deletions mmdet/models/detectors/semi_base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -69,6 +69,21 @@ def reweight_loss(losses: dict, weight: float) -> dict:
losses[name] = loss * weight
return losses

@staticmethod
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def rename_loss(prefix: str, losses: dict) -> dict:
"""Rename loss for different branches."""
return {prefix + k: v for k, v in losses.items()}

@staticmethod
def filter_pseudo_instances_by_score(batch_data_samples: SampleList,
thr: float) -> SampleList:
"""Filter invalid pseudo instances by scores."""
for data_samples in batch_data_samples:
if data_samples.gt_instances.bboxes.shape[0] > 0:
data_samples.gt_instances = data_samples.gt_instances[
data_samples.gt_instances.scores > thr]
return batch_data_samples

def loss(self, multi_batch_inputs: Dict[str, Tensor],
multi_batch_data_samples: Dict[str, SampleList]) -> dict:
"""Calculate losses from multi-branch inputs and data samples.
Expand Down Expand Up @@ -113,13 +128,11 @@ def loss_by_gt_instances(self, batch_inputs: Tensor,
Returns:
dict: A dictionary of loss components
"""
gt_loss = {
'sup_' + k: v
for k, v in self.reweight_loss(
self.student.loss(batch_inputs, batch_data_samples),
self.semi_train_cfg.get('sup_weight', 1.)).items()
}
return gt_loss

losses = self.student.loss(batch_inputs, batch_data_samples)
sup_weight = self.semi_train_cfg.get('sup_weight', 1.)
return self.rename_loss('sup_',
self.reweight_loss(losses, sup_weight))

def loss_by_pseudo_instances(self,
batch_inputs: Tensor,
Expand All @@ -141,30 +154,21 @@ def loss_by_pseudo_instances(self,
Returns:
dict: A dictionary of loss components
"""
for data_samples in batch_data_samples:
if data_samples.gt_instances.bboxes.shape[0] > 0:
data_samples.gt_instances = data_samples.gt_instances[
data_samples.gt_instances.scores >
self.semi_train_cfg.cls_pseudo_thr]

batch_data_samples = self.filter_pseudo_instances_by_score(
batch_data_samples, self.semi_train_cfg.cls_pseudo_thr)
losses = self.student.loss(batch_inputs, batch_data_samples)
pseudo_instances_num = sum([
len(data_samples.gt_instances)
for data_samples in batch_data_samples
])
unsup_weight = self.semi_train_cfg.get(
'unsup_weight', 1.) if pseudo_instances_num > 0 else 0.
return self.rename_loss(
'unsup_', self.reweight_loss(losses, unsup_weight))

pseudo_loss = {
'unsup_' + k: v
for k, v in self.reweight_loss(
self.student.loss(batch_inputs, batch_data_samples),
unsup_weight).items()
}
return pseudo_loss

def filter_pseudo_instances(self,
batch_data_samples: SampleList) -> SampleList:
"""Filter invalid pseudo instances from teacher model."""
def filter_pseudo_instances_by_sizes(
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self, batch_data_samples: SampleList) -> SampleList:
"""Filter invalid pseudo instances by sizes from teacher model."""
for data_samples in batch_data_samples:
pseudo_bboxes = data_samples.gt_instances.bboxes
if pseudo_bboxes.shape[0] > 0:
Expand Down Expand Up @@ -198,7 +202,7 @@ def project_pseudo_instances(self, batch_pseudo_instances: SampleList,
data_samples.gt_instances.bboxes,
torch.tensor(data_samples.homography_matrix).to(
self.data_preprocessor.device), data_samples.img_shape)
return self.filter_pseudo_instances(batch_data_samples)
return self.filter_pseudo_instances_by_sizes(batch_data_samples)

def predict(self, batch_inputs: Tensor,
batch_data_samples: SampleList) -> SampleList:
Expand Down
63 changes: 27 additions & 36 deletions mmdet/models/detectors/soft_teacher.py
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Original file line number Diff line number Diff line change
Expand Up @@ -77,12 +77,9 @@ def loss_by_pseudo_instances(self,
x, rpn_results_list, batch_data_samples, batch_info))
losses.update(**self.rcnn_reg_loss_by_pseudo_instances(
x, rpn_results_list, batch_data_samples))
pseudo_loss = {
'unsup_' + k: v
for k, v in self.reweight_loss(
losses, self.semi_train_cfg.get('unsup_weight', 1.)).items()
}
return pseudo_loss
unsup_weight = self.semi_train_cfg.get('unsup_weight', 1.)
return self.rename_loss(
'unsup_', self.reweight_loss(losses, unsup_weight))

@torch.no_grad()
def get_pseudo_instances(
Expand All @@ -106,10 +103,10 @@ def get_pseudo_instances(

for data_samples, results in zip(batch_data_samples, results_list):
data_samples.gt_instances = results
if data_samples.gt_instances.bboxes.shape[0] > 0:
data_samples.gt_instances = data_samples.gt_instances[
data_samples.gt_instances.scores >
self.semi_train_cfg.pseudo_label_initial_score_thr]

batch_data_samples = self.filter_pseudo_instances_by_score(
batch_data_samples,
self.semi_train_cfg.pseudo_label_initial_score_thr)

reg_uncs_list = self.compute_uncertainty_with_aug(
x, batch_data_samples)
Expand Down Expand Up @@ -151,12 +148,8 @@ def rpn_loss_by_pseudo_instances(self, x: Tuple[Tensor],
"""

rpn_data_samples = copy.deepcopy(batch_data_samples)
for data_samples in rpn_data_samples:
if data_samples.gt_instances.bboxes.shape[0] > 0:
data_samples.gt_instances = data_samples.gt_instances[
data_samples.gt_instances.scores >
self.semi_train_cfg.rpn_pseudo_thr]

rpn_data_samples = self.filter_pseudo_instances_by_score(
rpn_data_samples, self.semi_train_cfg.rpn_pseudo_thr)
proposal_cfg = self.student.train_cfg.get('rpn_proposal',
self.student.test_cfg.rpn)
# set cat_id of gt_labels to 0 in RPN
Expand Down Expand Up @@ -196,11 +189,8 @@ def rcnn_cls_loss_by_pseudo_instances(self, x: Tuple[Tensor],
"""
rpn_results_list = copy.deepcopy(unsup_rpn_results_list)
cls_data_samples = copy.deepcopy(batch_data_samples)
for data_samples in cls_data_samples:
if data_samples.gt_instances.bboxes.shape[0] > 0:
data_samples.gt_instances = data_samples.gt_instances[
data_samples.gt_instances.scores >
self.semi_train_cfg.cls_pseudo_thr]
cls_data_samples = self.filter_pseudo_instances_by_score(
cls_data_samples, self.semi_train_cfg.cls_pseudo_thr)

outputs = unpack_gt_instances(cls_data_samples)
batch_gt_instances, batch_gt_instances_ignore, _ = outputs
Expand All @@ -212,7 +202,6 @@ def rcnn_cls_loss_by_pseudo_instances(self, x: Tuple[Tensor],
# rename rpn_results.bboxes to rpn_results.priors
rpn_results = rpn_results_list[i]
rpn_results.priors = rpn_results.pop('bboxes')

assign_result = self.student.roi_head.bbox_assigner.assign(
rpn_results, batch_gt_instances[i],
batch_gt_instances_ignore[i])
Expand All @@ -226,19 +215,21 @@ def rcnn_cls_loss_by_pseudo_instances(self, x: Tuple[Tensor],
selected_bboxes = [res.priors for res in sampling_results]
rois = bbox2roi(selected_bboxes)
bbox_results = self.student.roi_head._bbox_forward(x, rois)
# cls_reg_targets is a tuple of labels, label_weights,
# and bbox_targets, bbox_weights
cls_reg_targets = self.student.roi_head.bbox_head.get_targets(
sampling_results, self.student.train_cfg.rcnn)

selected_results_list = []
for bboxes, data_samples, homography_matrix, img_shape in zip(
for bboxes, data_samples, teacher_matrix, teacher_img_shape in zip(
selected_bboxes, batch_data_samples,
batch_info['homography_matrix'], batch_info['img_shape']):
selected_results_list.append(
InstanceData(
bboxes=bbox_project(
bboxes, homography_matrix @ torch.tensor(
data_samples.homography_matrix).inverse().to(
self.data_preprocessor.device), img_shape)))
student_matrix = torch.tensor(
data_samples.homography_matrix, device=teacher_matrix.device)
homography_matrix = teacher_matrix @ student_matrix.inverse()
projected_bboxes = bbox_project(bboxes, homography_matrix,
teacher_img_shape)
selected_results_list.append(InstanceData(bboxes=projected_bboxes))

with torch.no_grad():
results_list = self.teacher.roi_head.predict_bbox(
Expand All @@ -249,18 +240,18 @@ def rcnn_cls_loss_by_pseudo_instances(self, x: Tuple[Tensor],
rescale=False)
bg_score = torch.cat(
[results.scores[:, -1] for results in results_list])
# cls_reg_targets[0] is labels
neg_inds = cls_reg_targets[
0] == self.student.roi_head.bbox_head.num_classes
# cls_reg_targets[1] is label_weights
cls_reg_targets[1][neg_inds] = bg_score[neg_inds].detach()
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losses = self.student.roi_head.bbox_head.loss(
bbox_results['cls_score'],
bbox_results['bbox_pred'],
rois,
*cls_reg_targets,
reduction_override='none')
losses['loss_cls'] = losses['loss_cls'].sum() / max(
cls_reg_targets[1].sum(), 1.0)
bbox_results['cls_score'], bbox_results['bbox_pred'], rois,
*cls_reg_targets)
# cls_reg_targets[1] is label_weights
losses['loss_cls'] = losses['loss_cls'] * len(
cls_reg_targets[1]) / max(sum(cls_reg_targets[1]), 1.0)
return losses

def rcnn_reg_loss_by_pseudo_instances(
Expand Down