init

2022-08-13 17:16:47 +09:00
parent 8ec05b8447
commit f9ffa363fa
5 changed files with 100 additions and 126 deletions
--- a/VitTrainer.py
+++ b/VitTrainer.py
@@ -1,11 +1,13 @@
 # tensor - numpy - PILImage 변환 (https://qlsenddl-lab.tistory.com/37)
+
 import os
 os.environ['KMP_DUPLICATE_LIB_OK']='True'
 import random
 import numpy as np
 import torch
-from datasets import Dataset, load_dataset
+from datasets import Dataset, load_metric, ClassLabel
 from datasets import load_metric
+from transformers import AutoConfig
 from transformers import TrainingArguments, Trainer
 from transformers import ViTForImageClassification
 from torch.utils.data import DataLoader
@@ -44,14 +46,14 @@ class VitTrainer:
            save_strategy="epoch",
            evaluation_strategy="epoch",
            learning_rate=2e-5,
-            per_device_train_batch_size=16,
-            per_device_eval_batch_size=16,
+            per_device_train_batch_size=32,
+            per_device_eval_batch_size=32,
            weight_decay=0.01,
            load_best_model_at_end=True,
            metric_for_best_model="accuracy",
            logging_dir=os.path.join(self.RESOURCE_PATH, 'model', 'logs'),
            remove_unused_columns=False,
-            num_train_epochs=14,
+            num_train_epochs=4,
        )

        return
@@ -117,7 +119,7 @@ class VitTrainer:
        train_ds.set_transform(self.train_transforms)
        val_ds.set_transform(self.val_transforms)

-        train_dataloader = DataLoader(train_ds, collate_fn=self.collate_fn, batch_size=4)
+        train_dataloader = DataLoader(train_ds, collate_fn=self.collate_fn, batch_size=32)

        batch = next(iter(train_dataloader))
        for k,v in batch.items():
@@ -157,7 +159,7 @@ class VitTrainer:
        train_ds.set_transform(self.train_transforms)
        val_ds.set_transform(self.val_transforms)

-        train_dataloader = DataLoader(train_ds, collate_fn=self.collate_fn, batch_size=4)
+        train_dataloader = DataLoader(train_ds, collate_fn=self.collate_fn, batch_size=32)

        batch = next(iter(train_dataloader))
        for k,v in batch.items():
@@ -211,6 +213,14 @@ class VitTrainer:
        train_ds = Dataset.from_dict(train_data)
        val_ds = Dataset.from_dict(val_dsta)

+        features = train_ds.features.copy()
+        features["label"] = ClassLabel(num_classes=self.num_labels, names=["none", "sell", "buy"])
+        def adjust_labels(batch):
+            batch["label"] = [lbl for lbl in batch["label"]]
+            return batch
+        train_ds = train_ds.map(adjust_labels, batched=True, features=features)
+        val_ds = train_ds.map(adjust_labels, batched=True, features=features)
+
        return train_ds, val_ds

 if __name__ == "__main__":
@@ -222,107 +232,5 @@ if __name__ == "__main__":
    stock_code = "252670"
    vitTrainer = VitTrainer(RESOURCE_PATH)

-    train_ds, val_ds = vitTrainer.getData(stock_code, sDate="20220701", eDate="20220731")
+    train_ds, val_ds = vitTrainer.getData(stock_code, sDate="20220809", eDate="20220812")
    vitTrainer.train(train_ds, val_ds, model_path)
-
-    """
-    print("ym: 2020-07")
-    train_ds, val_ds = vitTrainer.getData(stock_code, sDate="20200701", eDate="20200731")
-    vitTrainer.train(train_ds, val_ds, model_path)
-
-    print ("ym: 2020-08")
-    train_ds, val_ds = vitTrainer.getData(stock_code, sDate="20200725", eDate="20200831")
-    vitTrainer.finetunning(train_ds, val_ds, model_path)
-
-    print("ym: 2020-09")
-    train_ds, val_ds = vitTrainer.getData(stock_code, sDate="20200825", eDate="20200931")
-    vitTrainer.finetunning(train_ds, val_ds, model_path)
-
-    print("ym: 2020-10")
-    train_ds, val_ds = vitTrainer.getData(stock_code, sDate="20200925", eDate="20201031")
-    vitTrainer.finetunning(train_ds, val_ds, model_path)
-
-    print("ym: 2020-11")
-    train_ds, val_ds = vitTrainer.getData(stock_code, sDate="20201025", eDate="20201131")
-    vitTrainer.finetunning(train_ds, val_ds, model_path)
-
-    print("ym: 2020-12")
-    train_ds, val_ds = vitTrainer.getData(stock_code, sDate="20201125", eDate="20201231")
-    vitTrainer.finetunning(train_ds, val_ds, model_path)
-
-    print("ym: 2021-01")
-    train_ds, val_ds = vitTrainer.getData(stock_code, sDate="20201225", eDate="20210131")
-    vitTrainer.finetunning(train_ds, val_ds, model_path)
-
-    print("ym: 2021-02")
-    train_ds, val_ds = vitTrainer.getData(stock_code, sDate="20210125", eDate="20210231")
-    vitTrainer.finetunning(train_ds, val_ds, model_path)
-
-    print("ym: 2021-03")
-    train_ds, val_ds = vitTrainer.getData(stock_code, sDate="20210225", eDate="20210331")
-    vitTrainer.finetunning(train_ds, val_ds, model_path)
-
-    print("ym: 2021-04")
-    train_ds, val_ds = vitTrainer.getData(stock_code, sDate="20210325", eDate="20210431")
-    vitTrainer.finetunning(train_ds, val_ds, model_path)
-
-    print("ym: 2021-05")
-    train_ds, val_ds = vitTrainer.getData(stock_code, sDate="20210425", eDate="20210531")
-    vitTrainer.finetunning(train_ds, val_ds, model_path)
-
-    print("ym: 2021-06")
-    train_ds, val_ds = vitTrainer.getData(stock_code, sDate="20210525", eDate="20210631")
-    vitTrainer.finetunning(train_ds, val_ds, model_path)
-
-    print("ym: 2021-07")
-    train_ds, val_ds = vitTrainer.getData(stock_code, sDate="20210625", eDate="20210731")
-    vitTrainer.finetunning(train_ds, val_ds, model_path)
-
-    print("ym: 2021-08")
-    train_ds, val_ds = vitTrainer.getData(stock_code, sDate="20210725", eDate="20210831")
-    vitTrainer.finetunning(train_ds, val_ds, model_path)
-
-    print("ym: 2021-09")
-    train_ds, val_ds = vitTrainer.getData(stock_code, sDate="20210825", eDate="20210931")
-    vitTrainer.finetunning(train_ds, val_ds, model_path)
-
-    print("ym: 2021-10")
-    train_ds, val_ds = vitTrainer.getData(stock_code, sDate="20210925", eDate="20212031")
-    vitTrainer.finetunning(train_ds, val_ds, model_path)
-
-    print("ym: 2021-11")
-    train_ds, val_ds = vitTrainer.getData(stock_code, sDate="20211025", eDate="20211131")
-    vitTrainer.finetunning(train_ds, val_ds, model_path)
-
-    print("ym: 2021-12")
-    train_ds, val_ds = vitTrainer.getData(stock_code, sDate="20211125", eDate="20211231")
-    vitTrainer.finetunning(train_ds, val_ds, model_path)
-
-    print("ym: 2022-01")
-    train_ds, val_ds = vitTrainer.getData(stock_code, sDate="20211225", eDate="20220131")
-    vitTrainer.finetunning(train_ds, val_ds, model_path)
-
-    print("ym: 2022-02")
-    train_ds, val_ds = vitTrainer.getData(stock_code, sDate="20220125", eDate="20220231")
-    vitTrainer.finetunning(train_ds, val_ds, model_path)
-
-    print("ym: 2022-03")
-    train_ds, val_ds = vitTrainer.getData(stock_code, sDate="20220225", eDate="20220331")
-    vitTrainer.finetunning(train_ds, val_ds, model_path)
-
-    print("ym: 2022-04")
-    train_ds, val_ds = vitTrainer.getData(stock_code, sDate="20220325", eDate="20220431")
-    vitTrainer.finetunning(train_ds, val_ds, model_path)
-
-    print("ym: 2022-05")
-    train_ds, val_ds = vitTrainer.getData(stock_code, sDate="20220425", eDate="20220531")
-    vitTrainer.finetunning(train_ds, val_ds, model_path)
-
-    print("ym: 2022-06")
-    train_ds, val_ds = vitTrainer.getData(stock_code, sDate="20220525", eDate="20220631")
-    vitTrainer.finetunning(train_ds, val_ds, model_path)
-
-    print("ym: 2022-07")
-    train_ds, val_ds = vitTrainer.getData(stock_code, sDate="20220625", eDate="20220731")
-    vitTrainer.finetunning(train_ds, val_ds, model_path)
-    """