sched noise dup code remove

timm/scheduler/plateau_lr.py

@@ -43,7 +43,7 @@ class PlateauLRScheduler(Scheduler):
             min_lr=lr_min
         )
 
-        self.noise_range = noise_range_t
+        self.noise_range_t = noise_range_t
         self.noise_pct = noise_pct
         self.noise_type = noise_type
         self.noise_std = noise_std
@@ -82,25 +82,12 @@ class PlateauLRScheduler(Scheduler):
 
             self.lr_scheduler.step(metric, epoch)  # step the base scheduler
 
-            if self.noise_range is not None:
-                if isinstance(self.noise_range, (list, tuple)):
-                    apply_noise = self.noise_range[0] <= epoch < self.noise_range[1]
-                else:
-                    apply_noise = epoch >= self.noise_range
-                if apply_noise:
+            if self._is_apply_noise(epoch):
                 self._apply_noise(epoch)
                 
+
     def _apply_noise(self, epoch):
-        g = torch.Generator()
-        g.manual_seed(self.noise_seed + epoch)
-        if self.noise_type == 'normal':
-            while True:
-                # resample if noise out of percent limit, brute force but shouldn't spin much
-                noise = torch.randn(1, generator=g).item()
-                if abs(noise) < self.noise_pct:
-                    break
-        else:
-            noise = 2 * (torch.rand(1, generator=g).item() - 0.5) * self.noise_pct
+        noise = self._calculate_noise(epoch)
 
         # apply the noise on top of previous LR, cache the old value so we can restore for normal
         # stepping of base scheduler

timm/scheduler/scheduler.py

@@ -85,12 +85,21 @@ class Scheduler:
             param_group[self.param_group_field] = value
 
     def _add_noise(self, lrs, t):
+        if self._is_apply_noise(t):
+            noise = self._calculate_noise(t)
+            lrs = [v + v * noise for v in lrs]
+        return lrs
+
+    def _is_apply_noise(self, t) -> bool:
+        """Return True if scheduler in noise range."""
         if self.noise_range_t is not None:
             if isinstance(self.noise_range_t, (list, tuple)):
                 apply_noise = self.noise_range_t[0] <= t < self.noise_range_t[1]
             else:
                 apply_noise = t >= self.noise_range_t
-            if apply_noise:
+        return apply_noise
+
+    def _calculate_noise(self, t) -> float:
         g = torch.Generator()
         g.manual_seed(self.noise_seed + t)
         if self.noise_type == 'normal':
@@ -98,8 +107,7 @@ class Scheduler:
                         # resample if noise out of percent limit, brute force but shouldn't spin much
                 noise = torch.randn(1, generator=g).item()
                 if abs(noise) < self.noise_pct:
-                            break
+                    return noise
         else:
             noise = 2 * (torch.rand(1, generator=g).item() - 0.5) * self.noise_pct
-                lrs = [v + v * noise for v in lrs]
-        return lrs
+        return noise