fix: 修复数据集注册和加载逻辑，优化代码格式，确保一致性

src/dataset_library/RefCOCODataset.py

@@ -25,8 +25,10 @@ class RefCOCODataset(Dataset):
         self.vis_processor = vis_processor
         self.text_processor = text_processor
         self.split = split
+
     def load_data(self):
         from .format import dataset_dir
+
         ds = load_dataset("lmms-lab/RefCOCO", cache_dir=dataset_dir)  # type: ignore
         self.data = ds[self.split]  # type: ignore
 
@@ -118,12 +120,14 @@ def test_RefCOCO():
     assert len(dataset) > 0
     assert len(dataset[0]["chat"]) > 0
 
+
 dataset = {
     "train": RefCOCODataset(split="val"),
     "test": RefCOCODataset(split="test"),
     "generation": RefCOCODatasetForGeneration(split="test"),
-        }
+}
 from .factory import register_dataset
+
 register_dataset(
     dataset_name="refcoco",
     dataset=dataset,

src/dataset_library/RefCOCOPlusDataset.py

@@ -28,6 +28,7 @@ class RefCOCOplusDataset(Dataset):
 
     def load_data(self):
         from .format import dataset_dir
+
         ds = load_dataset("lmms-lab/RefCOCOplus", cache_dir=dataset_dir)  # type: ignore
         self.data = ds[self.split]  # type: ignore
 
@@ -119,12 +120,14 @@ def test_RefCOCOplus():
     assert len(dataset) > 0
     assert len(dataset[0]["chat"]) > 0
 
+
 dataset = {
     "train": RefCOCOplusDataset(split="val"),
     "test": RefCOCOplusDataset(split="testA"),
     "generation": RefCOCOplusDatasetForGeneration(split="testA"),
-        }
+}
 from .factory import register_dataset
+
 register_dataset(
     dataset_name="refcocoplus",
     dataset=dataset,

src/dataset_library/RefCOCOgDataset.py

@@ -28,6 +28,7 @@ class RefCOCOgDataset(Dataset):
 
     def load_data(self):
         from .format import dataset_dir
+
         ds = load_dataset("lmms-lab/RefCOCOg", cache_dir=dataset_dir)  # type: ignore
         self.data = ds[self.split]  # type: ignore
 
@@ -119,12 +120,14 @@ def test_RefCOCOg():
     assert len(dataset) > 0
     assert len(dataset[0]["chat"]) > 0
 
+
 dataset = {
     "train": RefCOCOgDataset(split="val"),
     "test": RefCOCOgDataset(split="test"),
     "generation": RefCOCOgDatasetForGeneration(split="test"),
-        }
+}
 from .factory import register_dataset
+
 register_dataset(
     dataset_name="refcocog",
     dataset=dataset,

src/dataset_library/ScienceQADataset.py

@@ -22,7 +22,8 @@ class ScienceQADataset(Dataset):
 
     def load_data(self):
         from .format import dataset_dir
-        ds = load_dataset("derek-thomas/ScienceQA",cache_dir=dataset_dir) # type: ignore
+
+        ds = load_dataset("derek-thomas/ScienceQA", cache_dir=dataset_dir)  # type: ignore
         self.data = ds[self.split]  # type: ignore
 
     def __len__(self):
@@ -119,12 +120,14 @@ def test_scienceQA():
     assert len(dataset) > 0
     assert len(dataset[0]["chat"]) > 0
 
+
 dataset = {
     "train": ScienceQADataset(split="train"),
     "test": ScienceQADataset(split="test"),
     "generation": ScienceQADatasetForGeneration(split="test"),
 }
 from .factory import register_dataset
+
 register_dataset(
     dataset_name="scienceqa",
     dataset=dataset,

src/dataset_library/factory.py

@@ -1,5 +1,5 @@
 from torch.utils.data import Dataset
-from typing import Literal,List
+from typing import Literal, List
 from pathlib import Path
 from dataset_library.format import dataset_dir
 
@@ -58,7 +58,6 @@ from .VizWizDataset import (
 )
 
 
-
 def get_dataset(
     dataset_name: str, base_path=dataset_dir
 ) -> dict[Literal["train", "test", "generation"], Dataset]:

src/dataset_library/test_dataset.py

@@ -4,6 +4,7 @@ from .factory import get_dataset, MAPPING_NAME_TO_DATASET
 # Get all registered dataset names for parameterization
 dataset_names = list(MAPPING_NAME_TO_DATASET.keys())
 
+
 @pytest.mark.parametrize("dataset_name", dataset_names)
 def test_registered_datasets(dataset_name):
     dataset = get_dataset(dataset_name)

src/dataset_library/vis_processor.py

@@ -1,4 +1,6 @@
 from PIL import Image
+
+
 def size_processor(image: Image.Image):
     width, height = image.size
     if width > 500 or height > 500:

src/peft_library/regularizations/__init__.py

@@ -1,10 +1,10 @@
-
 class RegularizationMethod:
     """RegularizationMethod implement regularization strategies.
     RegularizationMethod is a callable.
     The method `update` is called to update the loss, typically at the end
     of an experience.
     """
+
     def pre_adapt(self, agent, exp):
         pass  # implementation may be empty if adapt is not needed
 
@@ -13,3 +13,7 @@ class RegularizationMethod:
 
     def __call__(self, *args, **kwargs):
         raise NotImplementedError()
+
+
+from .ewc import EWC
+from .lwf import LWF

src/peft_library/regularizations/ewc.py

@@ -1,6 +1,7 @@
 from . import RegularizationMethod
 import torch
 
+
 class EWC(RegularizationMethod):
     """Learning Without Forgetting.
 
@@ -24,14 +25,19 @@ class EWC(RegularizationMethod):
         Knowledge distillation uses only units corresponding
         to old classes. 
         """
-    def adapt(self, output,model, **kwargs):
+
+    def adapt(self, output, model, **kwargs):
         ewc_loss = 0
         for n, p in model.named_parameters():
             if p.requires_grad:
                 dev = p.device
-                l = self.EWC_lambda * self.fisher[n].to(dev) * (p.data - self.optpar[n].to(dev)).pow(2)
+                l = (
+                    self.EWC_lambda
+                    * self.fisher[n].to(dev)
+                    * (p.data - self.optpar[n].to(dev)).pow(2)
+                )
                 ewc_loss += l.sum()
-        output['loss'] += ewc_loss
+        output["loss"] += ewc_loss
         return output
 
     def init_epoch(self, model):
@@ -42,6 +48,7 @@ class EWC(RegularizationMethod):
             if p.requires_grad:
                 fisher[n] = torch.zeros(p.data.shape)
                 optpar[n] = p.clone().cpu().data
+
     def update_fisher(self, model):
         """Update the fisher information for the given question id."""
         for n, p in model.module.base_model.model.named_parameters():

src/peft_library/regularizations/lwf.py

@@ -1,6 +1,7 @@
 from . import RegularizationMethod
 import torch
 
+
 class LWF(RegularizationMethod):
     """Learning Without Forgetting.
 
@@ -23,6 +24,7 @@ class LWF(RegularizationMethod):
         Knowledge distillation uses only units corresponding
         to old classes. 
         """
+
     def adapt(self, output, **kwargs):
         def modified_kl_div(old, new):
             return -torch.mean(torch.sum(old * torch.log(new), 1))
@@ -37,16 +39,27 @@ class LWF(RegularizationMethod):
 
         previous_keys = self.previous_logits.keys()
 
-        for index, question_id in enumerate(iterable=kwargs['question_ids']):
+        for index, question_id in enumerate(iterable=kwargs["question_ids"]):
             if question_id in previous_keys:
                 previous_logits = self.previous_logits[question_id]
-                current_logits = output['logits'][index]
+                current_logits = output["logits"][index]
                 short_index = min(len(previous_logits), len(current_logits))
                 previous_logits = previous_logits[:short_index]
                 current_logits = current_logits[:short_index]
-                lwf_loss.append(modified_kl_div(old=smooth(logits=soft(previous_logits).to(current_logits.device), temp=2, dim=1),new=smooth(logits=soft(current_logits), temp=2, dim=1)))
+                lwf_loss.append(
+                    modified_kl_div(
+                        old=smooth(
+                            logits=soft(previous_logits).to(current_logits.device),
+                            temp=2,
+                            dim=1,
+                        ),
+                        new=smooth(logits=soft(current_logits), temp=2, dim=1),
+                    )
+                )
         if len(lwf_loss) > 0:
-            output['loss'] += self.LWF_lambda *  torch.stack(tensors=lwf_loss, dim=0).sum(dim=0)
+            output["loss"] += self.LWF_lambda * torch.stack(
+                tensors=lwf_loss, dim=0
+            ).sum(dim=0)
         return output
 
     def update_previous_logits(self, question_id, logits):

src/train.py

@@ -15,6 +15,8 @@ from trl import (
 from utils.trainer import ContinualTrainer
 from utils.args import ContinualScriptArguments, ContinualModelConfig
 import logging
+from typing import TYPE_CHECKING
+
 
 logging.basicConfig(level=logging.INFO)
 
@@ -24,15 +26,16 @@ if __name__ == "__main__":
         (ContinualScriptArguments, TrainingArguments, ContinualModelConfig)
     )
     script_args, training_args, model_args = parser.parse_args_and_config()
-    # for type hint
-    if 0 == 1:
-        script_args = ContinualScriptArguments()
-        training_args = TrainingArguments()
-        model_args = ContinualModelConfig()
     training_args.gradient_checkpointing_kwargs = dict(use_reentrant=False)
     training_args.remove_unused_columns = False
     training_args.dataset_kwargs = {"skip_prepare_dataset": True}
 
+    # for type hint
+    if 1 == 0:
+        script_args = ContinualScriptArguments
+        training_args = TrainingArguments
+        model_args = ContinualModelConfig
+
     from model_library.factory import get_model
 
     model, processor, collate_fn_for_train, collate_fn_for_evaluate = get_model(
@@ -68,6 +71,8 @@ if __name__ == "__main__":
     else:
         peft_config = None
 
+    from peft import get_peft_model
+
     if accelerator.is_local_main_process:
         print(model)
 

src/utils/args.py

@@ -31,4 +31,3 @@ class ContiunalRegularizationArguments:
     # LWF
     lwf_lambda: float = 0.0
     lwf_enable: bool = False
-    

src/utils/trainer.py

@@ -15,6 +15,7 @@ from transformers import (
     TrainingArguments,
 )
 from .args import ContiunalRegularizationArguments
+from peft_library.regularizations import EWC, LWF
 
 
 class ContinualTrainer(Trainer):
@@ -26,7 +27,7 @@ class ContinualTrainer(Trainer):
         train_dataset,
         eval_dataset,
         accelerator,
-        regularization_args:ContiunalRegularizationArguments=None,
+        regularization_args: ContiunalRegularizationArguments = None,
     ):
         self.accelerator = accelerator
         super().__init__(model, args, data_collator, train_dataset, eval_dataset)
@@ -38,7 +39,6 @@ class ContinualTrainer(Trainer):
         if regularization_args.lwf_enable:
             self.lwf_lambda = regularization_args.lwf_lambda
 
-
     def create_accelerator_and_postprocess(self):
 
         if self.accelerator is not None: