1 files changed, 0 insertions, 43 deletions
diff --git a/supervised/schedulers.py b/supervised/schedulers.py
deleted file mode 100644
index 7580bf3..0000000
--- a/supervised/schedulers.py
+++ /dev/null
@@ -1,43 +0,0 @@
-import warnings
-
-import numpy as np
-import torch
-
-
-class LinearLR(torch.optim.lr_scheduler._LRScheduler):
-    def __init__(self, optimizer, num_epochs, last_epoch=-1):
-        self.num_epochs = max(num_epochs, 1)
-        super().__init__(optimizer, last_epoch)
-
-    def get_lr(self):
-        res = []
-        for lr in self.base_lrs:
-            res.append(np.maximum(lr * np.minimum(
-                -self.last_epoch * 1. / self.num_epochs + 1., 1.
-            ), 0.))
-        return res
-
-
-class LinearWarmupAndCosineAnneal(torch.optim.lr_scheduler._LRScheduler):
-    def __init__(self, optimizer, warm_up, T_max, last_epoch=-1):
-        self.warm_up = int(warm_up * T_max)
-        self.T_max = T_max - self.warm_up
-        super().__init__(optimizer, last_epoch=last_epoch)
-
-    def get_lr(self):
-        if not self._get_lr_called_within_step:
-            warnings.warn("To get the last learning rate computed by the scheduler, "
-                          "please use `get_last_lr()`.")
-
-        if self.last_epoch == 0:
-            return [lr / (self.warm_up + 1) for lr in self.base_lrs]
-        elif self.last_epoch <= self.warm_up:
-            c = (self.last_epoch + 1) / self.last_epoch
-            return [group['lr'] * c for group in self.optimizer.param_groups]
-        else:
-            # ref: https://github.com/pytorch/pytorch/blob/2de4f245c6b1e1c294a8b2a9d7f916d43380af4b/torch/optim/lr_scheduler.py#L493
-            le = self.last_epoch - self.warm_up
-            return [(1 + np.cos(np.pi * le / self.T_max)) /
-                    (1 + np.cos(np.pi * (le - 1) / self.T_max)) *
-                    group['lr']
-                    for group in self.optimizer.param_groups]