Add Semi-SL Instance Segmentation (#2444)

* added semisl MT. Loss not working.

* added recipie. Unbiased teacher works

* added MT

* exps contin

* proceed with experiments

* fix errors in forward

* change hyperparams. Add clip for testing

* some exps

* change hyperparams

* added per class thrsh

* minor:

* exps

* add switching parameter for thrsh

* din thrsh

* added DEMA

* added dinam thrsh

* removed dinam

* final round exps

* added MT and semi-sl for ResNet

* added semisl stage. Remove old otx

* training launches. Merged code with OD task.

* fix pre-commit

* added tests for Semi-SL IS

* fix detection resolution

* added unit test for MT

* overwrite iter params in semi-sl config. Return configuration.ymal back

* added semisl for effnet. Hovewer it still doesn't work

* changed teacher forward method. Fixed pre-commit

* fix unit tests

* fixed detection issues. Moved data pipeline

* minor

* fixed det unit test configure

* rename file

* revert detection scaling back

* rename semisl data

* some changes in unit test for focal loss

* fixed pre-commit. returned incremental part back

* rename selfsl in semisl

* rename MeanTeacherHook

* return yolox data_pipeline

* fix pre-commit

* added one more unit test

* fix pre-commit

* reply comments

src/otx/algorithms/common/adapters/mmcv/hooks/__init__.py

@@ -37,6 +37,7 @@
 from .ib_loss_hook import IBLossHook
 from .logger_hook import LoggerReplaceHook, OTXLoggerHook
 from .loss_dynamics_tracking_hook import LossDynamicsTrackingHook
+from .mean_teacher_hook import MeanTeacherHook
 from .mem_cache_hook import MemCacheHook
 from .model_ema_v2_hook import ModelEmaV2Hook
 from .no_bias_decay_hook import NoBiasDecayHook
@@ -51,7 +52,6 @@
 from .semisl_cls_hook import SemiSLClsHook
 from .task_adapt_hook import TaskAdaptHook
 from .two_crop_transform_hook import TwoCropTransformHook
-from .unbiased_teacher_hook import UnbiasedTeacherHook
 
 __all__ = [
     "AdaptiveRepeatDataHook",
@@ -87,7 +87,7 @@
     "SemiSLClsHook",
     "TaskAdaptHook",
     "TwoCropTransformHook",
-    "UnbiasedTeacherHook",
+    "MeanTeacherHook",
     "MemCacheHook",
     "LossDynamicsTrackingHook",
 ]

src/otx/algorithms/common/adapters/mmcv/hooks/custom_model_ema_hook.py

@@ -25,6 +25,26 @@ def __init__(self, momentum=0.0002, epoch_momentum=0.0, interval=1, **kwargs):
         self.epoch_momentum = epoch_momentum
         self.interval = interval
 
+    def before_run(self, runner):
+        """To resume model with it's ema parameters more friendly.
+
+        Register ema parameter as ``named_buffer`` to model
+        """
+        if is_module_wrapper(runner.model):
+            model = runner.model.module.model_s if hasattr(runner.model.module, "model_s") else runner.model.module
+        else:
+            model = runner.model.model_s if hasattr(runner.model, "model_s") else runner.model
+        self.param_ema_buffer = {}
+        self.model_parameters = dict(model.named_parameters(recurse=True))
+        for name, value in self.model_parameters.items():
+            # "." is not allowed in module's buffer name
+            buffer_name = f"ema_{name.replace('.', '_')}"
+            self.param_ema_buffer[name] = buffer_name
+            model.register_buffer(buffer_name, value.data.clone())
+        self.model_buffers = dict(model.named_buffers(recurse=True))
+        if self.checkpoint is not None:
+            runner.resume(self.checkpoint)
+
     def before_train_epoch(self, runner):
         """Update the momentum."""
         if self.epoch_momentum > 0.0:

src/otx/algorithms/common/adapters/mmcv/hooks/dual_model_ema_hook.py

@@ -76,7 +76,7 @@ def before_run(self, runner):
 
     def before_train_epoch(self, runner):
         """Momentum update."""
-        if runner.epoch == self.start_epoch:
+        if runner.epoch + 1 == self.start_epoch:
             self._copy_model()
             self.enabled = True
 
@@ -110,21 +110,24 @@ def _get_model(self, runner):
     def _copy_model(self):
         with torch.no_grad():
             for name, src_param in self.src_params.items():
-                dst_param = self.dst_params[name]
-                dst_param.data.copy_(src_param.data)
+                if not name.startswith("ema_"):
+                    dst_param = self.dst_params[name]
+                    dst_param.data.copy_(src_param.data)
 
     def _ema_model(self):
         momentum = min(self.momentum, 1.0)
         with torch.no_grad():
             for name, src_param in self.src_params.items():
-                dst_param = self.dst_params[name]
-                dst_param.data.copy_(dst_param.data * (1 - momentum) + src_param.data * momentum)
+                if not name.startswith("ema_"):
+                    dst_param = self.dst_params[name]
+                    dst_param.data.copy_(dst_param.data * (1 - momentum) + src_param.data * momentum)
 
     def _diff_model(self):
         diff_sum = 0.0
         with torch.no_grad():
             for name, src_param in self.src_params.items():
-                dst_param = self.dst_params[name]
-                diff = ((src_param - dst_param) ** 2).sum()
-                diff_sum += diff
+                if not name.startswith("ema_"):
+                    dst_param = self.dst_params[name]
+                    diff = ((src_param - dst_param) ** 2).sum()
+                    diff_sum += diff
         return diff_sum

src/otx/algorithms/common/adapters/mmcv/hooks/unbiased_teacher_hook.py → src/otx/algorithms/common/adapters/mmcv/hooks/mean_teacher_hook.py

@@ -14,29 +14,27 @@
 
 
 @HOOKS.register_module()
-class UnbiasedTeacherHook(DualModelEMAHook):
-    """UnbiasedTeacherHook for semi-supervised learnings."""
+class MeanTeacherHook(DualModelEMAHook):
+    """MeanTeacherHook for semi-supervised learnings."""
 
-    def __init__(self, min_pseudo_label_ratio=0.1, **kwargs):
+    def __init__(self, **kwargs):
         super().__init__(**kwargs)
-        self.min_pseudo_label_ratio = min_pseudo_label_ratio
         self.unlabeled_loss_enabled = False
 
     def before_train_epoch(self, runner):
         """Enable unlabeled loss if over start epoch."""
-        super().before_train_epoch(runner)
-
         if runner.epoch + 1 < self.start_epoch:
             return
         if self.unlabeled_loss_enabled:
             return
 
+        super().before_train_epoch(runner)
+
         average_pseudo_label_ratio = self._get_average_pseudo_label_ratio(runner)
         logger.info(f"avr_ps_ratio: {average_pseudo_label_ratio}")
-        if average_pseudo_label_ratio > self.min_pseudo_label_ratio:
-            self._get_model(runner).enable_unlabeled_loss()
-            self.unlabeled_loss_enabled = True
-            logger.info("---------- Enabled unlabeled loss")
+        self._get_model(runner).enable_unlabeled_loss(True)
+        self.unlabeled_loss_enabled = True
+        logger.info("---------- Enabled unlabeled loss and EMA smoothing")
 
     def after_train_iter(self, runner):
         """Update ema parameter every self.interval iterations."""
@@ -46,7 +44,6 @@ def after_train_iter(self, runner):
 
         if runner.epoch + 1 < self.start_epoch or self.unlabeled_loss_enabled is False:
             # Just copy parameters before enabled
-            self._copy_model()
             return
 
         # EMA

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

@@ -14,8 +14,8 @@
 from .custom_vfnet_detector import CustomVFNet
 from .custom_yolox_detector import CustomYOLOX
 from .l2sp_detector_mixin import L2SPDetectorMixin
+from .mean_teacher import MeanTeacher
 from .sam_detector_mixin import SAMDetectorMixin
-from .unbiased_teacher import UnbiasedTeacher
 
 __all__ = [
     "CustomATSS",
@@ -29,6 +29,6 @@
     "CustomYOLOX",
     "L2SPDetectorMixin",
     "SAMDetectorMixin",
-    "UnbiasedTeacher",
     "CustomMaskRCNNTileOptimized",
+    "MeanTeacher",
 ]