add: fair pseudo label creator

sscma/models/semi/fairpseudolabel.py

@@ -0,0 +1,41 @@
+import torch
+from mmdet.structures.det_data_sample import SampleList
+from mmengine.structures.instance_data import InstanceData
+from sscma.models.detectors.base import BasePseudoLabelCreator
+from sscma.registry import MODELS
+
+
+@MODELS.register_module()
+class FairPseudoLabel(BasePseudoLabelCreator):
+    def __init__(self, *args, **kwargs) -> None:
+        super().__init__(*args, **kwargs)
+
+        self.cls_thr = None
+        self.cls_thr_ig = None
+
+    def generate_pseudo_labels_online(
+        self,
+        teach_pred,
+        student_sample: SampleList,
+    ):
+        if self.cls_thr is not None and self.cls_thr_ig is not None:
+            for idx_sample, pred in enumerate(teach_pred):
+                pseudo_bboxs, pseudo_label = [], []
+                pseudo_bboxs_ig, pseudo_label_ig = [], []
+                gt = InstanceData()
+                ignore = InstanceData()
+                for idx, label in enumerate(pred.pred_instances.labels):
+                    if pred.pred_instances.scores[idx] > self.cls_thr[int(label)]:
+                        pseudo_bboxs.append(pred.pred_instances.bboxes[idx])
+                        pseudo_label.append(label)
+                    elif pred.pred_instances.scores[idx] > self.cls_thr_ig[int(label)]:
+                        pseudo_bboxs_ig.append(pred.pred_instances.bboxes[idx])
+                        pseudo_label_ig.append(label)
+                if len(pseudo_bboxs):
+                    gt.bboxes = torch.concat([i.unsqueeze(0) for i in pseudo_bboxs], dim=0)
+                    gt.labels = torch.concat([i.unsqueeze(0) for i in pseudo_label], dim=0)
+                if len(pseudo_bboxs_ig):
+                    ignore.labels = torch.concat([i.unsqueeze(0) for i in pseudo_label_ig], dim=0)
+                    ignore.bboxes = torch.concat([i.unsqueeze(0) for i in pseudo_bboxs_ig], dim=0)
+                student_sample[idx_sample].gt_instannces = gt
+                student_sample[idx_sample].ignored_instances = ignore