feat : TrainConfig 支持 val_split 从训练集自动切分验证集

- val_split 比例从 dataset 中划出验证集，用 random_seed 固定随机切分
- 若 val_dataset 已显式设置则跳过自动切分

astrai/config/train_config.py

@@ -118,6 +118,12 @@ class TrainConfig(BaseConfig):
     val_dataset: Optional[Dataset] = field(
         default=None, metadata={"help": "Dataset for validation."}
     )
+    val_split: Optional[float] = field(
+        default=None,
+        metadata={
+            "help": "Ratio to split from training dataset for validation (e.g. 0.05). Ignored if val_dataset is set."
+        },
+    )
     val_step: int = field(
         default=1000,
         metadata={"help": "Number of optimizer steps between validation runs."},

astrai/trainer/train_context.py

@@ -2,8 +2,9 @@ from dataclasses import dataclass, field
 from pathlib import Path
 from typing import Optional, Self
 
+import torch
 import torch.nn as nn
-from torch.utils.data import DataLoader
+from torch.utils.data import DataLoader, random_split
 
 from astrai.config.train_config import TrainConfig
 from astrai.dataset import ResumableDistributedSampler
@@ -108,15 +109,27 @@ class TrainContextBuilder:
         context.optimizer = cfg.optimizer_fn(model)
         context.scheduler = cfg.scheduler_fn(context.optimizer)
 
+        train_dataset = cfg.dataset
+        val_dataset = cfg.val_dataset
+
+        if val_dataset is None and cfg.val_split is not None:
+            n_total = len(cfg.dataset)
+            n_val = max(1, int(n_total * cfg.val_split))
+            n_train = n_total - n_val
+            generator = torch.Generator().manual_seed(cfg.random_seed)
+            train_dataset, val_dataset = random_split(
+                cfg.dataset, [n_train, n_val], generator=generator
+            )
+
         sampler_offset = context.iteration * cfg.batch_per_device
         sampler = ResumableDistributedSampler(
-            data_source=cfg.dataset,
+            data_source=train_dataset,
             start_epoch=context.epoch,
             start_iter=sampler_offset,
             seed=cfg.random_seed,
         )
         context.dataloader = DataLoader(
-            cfg.dataset,
+            train_dataset,
             batch_size=cfg.batch_per_device,
             sampler=sampler,
             num_workers=cfg.num_workers,
@@ -124,16 +137,16 @@ class TrainContextBuilder:
             prefetch_factor=cfg.prefetch_factor,
         )
 
-        if cfg.val_dataset is not None:
+        if val_dataset is not None:
             val_sampler = ResumableDistributedSampler(
-                data_source=cfg.val_dataset,
+                data_source=val_dataset,
                 start_epoch=0,
                 start_iter=0,
                 seed=cfg.random_seed,
                 shuffle=False,
             )
             context.val_dataloader = DataLoader(
-                cfg.val_dataset,
+                val_dataset,
                 batch_size=cfg.batch_per_device,
                 sampler=val_sampler,
                 num_workers=cfg.num_workers,