Add default schedulers

1 files changed, 1 insertions, 23 deletions

diff --git a/supervised/baseline.py b/supervised/baseline.py
index 9ad107c..9c18f9f 100644
--- a/supervised/baseline.py
+++ b/supervised/baseline.py
@@ -1,10 +1,10 @@
 import argparse
 import os
-import sys
 from dataclasses import dataclass
 from pathlib import Path
 from typing import Iterable, Callable
 
+import sys
 import torch
 import yaml
 from torch.utils.data import Dataset
@@ -15,7 +15,6 @@ path = str(Path(Path(__file__).parent.absolute()).parent.absolute())
 sys.path.insert(0, path)
 
 from libs.datautils import Clip
-from libs.schedulers import LinearLR
 from libs.utils import Trainer, BaseConfig
 from libs.logging import BaseBatchLogRecord, BaseEpochLogRecord, Loggers
 from models import CIFARResNet50
@@ -103,10 +102,6 @@ class SupBaselineConfig(BaseConfig):
         betas: tuple[float, float] | None
         weight_decay: float
 
-    @dataclass
-    class SchedConfig(BaseConfig.SchedConfig):
-        sched: str | None
-
 
 class SupBaselineTrainer(Trainer):
     def __init__(self, **kwargs):
@@ -201,23 +196,6 @@ class SupBaselineTrainer(Trainer):
 
             yield f"{model_name}_optim", optimizer
 
-    @staticmethod
-    def _configure_scheduler(
-            optims: Iterable[tuple[str, torch.optim.Optimizer]],
-            last_iter: int,
-            num_iters: int,
-            sched_config: SupBaselineConfig.SchedConfig
-    ) -> Iterable[tuple[str, torch.optim.lr_scheduler._LRScheduler]
-                  | tuple[str, None]]:
-        for optim_name, optim in optims:
-            if sched_config.sched == 'linear':
-                sched = LinearLR(optim, num_iters, last_epoch=last_iter)
-            elif sched_config.sched is None:
-                sched = None
-            else:
-                raise NotImplementedError(f"Unimplemented scheduler: {sched_config.sched}")
-            yield f"{optim_name}_sched", sched
-
     def train(self, num_iters: int, loss_fn: Callable, logger: Loggers, device: torch.device):
         model = self.models['model']
         optim = self.optims['model_optim']