Make mean teacher algorithm consider distributed training

src/otx/algorithms/detection/adapters/mmdet/models/detectors/mean_teacher.py

@@ -14,6 +14,7 @@
 from mmdet.core.mask.structures import BitmapMasks
 from mmdet.models import DETECTORS, build_detector
 from mmdet.models.detectors import BaseDetector
+from torch import distributed as dist
 
 from otx.utils.logger import get_logger
 
@@ -182,22 +183,29 @@ def forward_train(
         pseudo_bboxes, pseudo_labels, pseudo_masks, pseudo_ratio = self.generate_pseudo_labels(
             teacher_outputs, device=current_device, img_meta=ul_img_metas, **kwargs
         )
-        if self.filter_empty_annotations:
-            non_empty = [bool(len(i)) for i in pseudo_labels]
-            pseudo_bboxes = [pb for i, pb in enumerate(pseudo_bboxes) if non_empty[i]]
-            pseudo_labels = [pl for i, pl in enumerate(pseudo_labels) if non_empty[i]]
-            pseudo_masks = [pm for i, pm in enumerate(pseudo_masks) if non_empty[i]]
-            ul_img_metas = [im for i, im in enumerate(ul_img_metas) if non_empty[i]]
-            ul_img = ul_img[non_empty]
-        else:
-            non_empty = [True]
-        if self.visualize:
-            self._visual_online(ul_img, pseudo_bboxes, pseudo_labels)
+        non_empty = [bool(len(i)) for i in pseudo_labels] if self.filter_empty_annotations else [True]
+        get_unlabeled_loss = pseudo_ratio >= self.min_pseudo_label_ratio and any(non_empty)
+
+        if dist.is_initialized():
+            reduced_get_unlabeled_loss = torch.tensor(int(get_unlabeled_loss)).to(current_device)
+            dist.all_reduce(reduced_get_unlabeled_loss)
+            if dont_have_to_train := not get_unlabeled_loss and reduced_get_unlabeled_loss > 0:
+                get_unlabeled_loss = True
+                non_empty[0] = True
+
         losses.update(ps_ratio=torch.tensor([pseudo_ratio], device=current_device))
 
         # Unsupervised loss
         # Compute only if min_pseudo_label_ratio is reached
-        if pseudo_ratio >= self.min_pseudo_label_ratio and any(non_empty):
+        if get_unlabeled_loss:
+            if self.filter_empty_annotations:
+                pseudo_bboxes = [pb for i, pb in enumerate(pseudo_bboxes) if non_empty[i]]
+                pseudo_labels = [pl for i, pl in enumerate(pseudo_labels) if non_empty[i]]
+                pseudo_masks = [pm for i, pm in enumerate(pseudo_masks) if non_empty[i]]
+                ul_img_metas = [im for i, im in enumerate(ul_img_metas) if non_empty[i]]
+                ul_img = ul_img[non_empty]
+                if self.visualize:
+                    self._visual_online(ul_img, pseudo_bboxes, pseudo_labels)
             if self.bg_loss_weight >= 0.0:
                 self.model_s.bbox_head.bg_loss_weight = self.bg_loss_weight
             if self.model_t.with_mask:
@@ -214,7 +222,10 @@ def forward_train(
                 if ul_loss_name.startswith("loss_"):
                     ul_loss = ul_losses[ul_loss_name]
                     target_loss = ul_loss_name.split("_")[-1]
-                    if self.unlabeled_loss_weights[target_loss] == 0:
+                    if dist.is_initialized():
+                        if dont_have_to_train:
+                            self.unlabeled_loss_weights[target_loss] = 0
+                    elif self.unlabeled_loss_weights[target_loss] == 0:
                         continue
                     self._update_unlabeled_loss(losses, ul_loss, ul_loss_name, self.unlabeled_loss_weights[target_loss])
         return losses

tests/e2e/cli/detection/test_detection.py

@@ -348,8 +348,6 @@ def test_otx_eval(self, template, tmp_dir_path):
     def test_otx_multi_gpu_train_semisl(self, template, tmp_dir_path):
         if not (Path(template.model_template_path).parent / "semisl").is_dir():
             pytest.skip(f"Semi-SL training type isn't available for {template.name}")
-        if template.name == "ResNeXt101-ATSS":
-            pytest.skip(f"Issue#2705: multi-gpu training e2e test failure for {template.name}")
         tmp_dir_path = tmp_dir_path / "detection/test_multi_gpu_semisl"
         args_semisl_multigpu = copy.deepcopy(args_semisl)
         args_semisl_multigpu["--gpus"] = "0,1"