fix(khaosz/trainer): 修正训练步数计算逻辑

khaosz/trainer/callback.py

@@ -154,6 +154,11 @@ class SchedulerCallback(TrainerCallback):
     def on_train_begin(self, trainer: 'Trainer', **kwargs):
         checkpoint = cast(Checkpoint, kwargs.get('checkpoint'))
         self.current_iter = len(checkpoint.loss_list)
+
+        for group in trainer.train_config.optimizer.param_groups:
+            if "initial_lr" not in group:
+                group["initial_lr"] = group["lr"] 
+
         self.schedule_config.validate()
         lambda_scheduler_fn = SchedulerFactory.load_schedule_fn(
             self.schedule_config

khaosz/trainer/trainer.py

@@ -1,4 +1,5 @@
 import torch
+import itertools
 from typing import Optional, List
 from torch.utils.data import DataLoader, RandomSampler
 
@@ -38,15 +39,20 @@ class Trainer:
             SchedulerCallback(self.schedule_config),
         ]
         
-    def _create_dataloader(self) -> DataLoader:
+    def _create_dataloader(self, start_index: int = 0) -> DataLoader:
         seed = self.train_config.random_seed
         generator = torch.Generator().manual_seed(seed)
         sampler = RandomSampler(self.train_config.dataset, generator=generator)
-        return DataLoader(
+        dataloader = DataLoader(
             self.train_config.dataset, 
             batch_size=self.train_config.batch_size, 
             sampler=sampler
         )
+        
+        if start_index > 0:
+            dataloader = itertools.islice(dataloader, start_index, None)
+        
+        return dataloader
 
     def _call_callbacks(self, method_name: str, **kwargs):
         for callback in self.callbacks:
@@ -58,34 +64,29 @@ class Trainer:
         self,
         train_checkpoint: Optional[Checkpoint] = None
     ) -> Checkpoint:
-        assert self.schedule_config.schedule_type in ["cosine", "sgdr"]
-        
+                
         if train_checkpoint:
             self.checkpoint = train_checkpoint
             self.train_config.optimizer.load_state_dict(train_checkpoint.optim_state)
-        
-        self.checkpoint.optim_state = self.train_config.optimizer.state_dict()
+        else:
+            self.checkpoint.optim_state = self.train_config.optimizer.state_dict()
+            
         current_iter = len(self.checkpoint.loss_list)
-
-        for group in self.train_config.optimizer.param_groups:
-            if "initial_lr" not in group:
-                group["initial_lr"] = group["lr"] 
+        total_steps_per_epoch = len(self.train_config.dataset) // self.train_config.batch_size
+        total_reamining_steps = total_steps_per_epoch * self.train_config.n_epoch - current_iter
         
-        reamining_steps = self.train_config.n_epoch - current_iter
-        total_steps = len(self.train_config.dataset) // self.train_config.batch_size
-        remaining_epochs = (reamining_steps + total_steps - 1) // total_steps
+        current_epochs = total_reamining_steps // total_steps_per_epoch 
+        current_steps = total_reamining_steps % total_steps_per_epoch
+
         # train
         self._call_callbacks('on_train_begin', checkpoint=self.checkpoint)
+        self.checkpoint.model.train()
         
         try:
-            for epoch in range(remaining_epochs):
-                self.checkpoint.model.train()
-                
+            for epoch in range(current_epochs):
                 # epoch
                 self._call_callbacks('on_epoch_begin', epoch=epoch)
-                
-                dataloader = self._create_dataloader()
-                
+                dataloader = self._create_dataloader(start_index=current_steps)
                 for batch in dataloader:
                     # batch
                     self._call_callbacks('on_batch_begin', batch=batch)
@@ -110,5 +111,4 @@ class Trainer:
 
         finally:
             self._call_callbacks('on_train_end', checkpoint=self.checkpoint)
-        
-        return self.checkpoint
+            return self.checkpoint