From 3c0e9e8ac1be6e88551ecc1304acfb1c75d4d311 Mon Sep 17 00:00:00 2001
From: Falko Habel <falko.habel@gmx.de>
Date: Tue, 28 Jan 2025 17:18:21 +0100
Subject: [PATCH] fixed savings

---
 run.py                          | 27 +++++++++++++++++++++++++++
 src/aiia/pretrain/pretrainer.py |  6 +-----
 2 files changed, 28 insertions(+), 5 deletions(-)
 create mode 100644 run.py

diff --git a/run.py b/run.py
new file mode 100644
index 0000000..fb20e63
--- /dev/null
+++ b/run.py
@@ -0,0 +1,27 @@
+data_path1 = "/root/training_data/vision-dataset/images_pretrain.parquet"
+data_path2 = "/root/training_data/vision-dataset/vector_img_pretrain.parquet"
+    
+from aiia.model import AIIABase
+from aiia.model.config import AIIAConfig
+from aiia.pretrain import Pretrainer
+
+# Create your model
+config = AIIAConfig(model_name="AIIA-Base-512x20k")
+model = AIIABase(config)
+
+# Initialize pretrainer with the model
+pretrainer = Pretrainer(model, learning_rate=config.learning_rate, config=config)
+
+# List of dataset paths
+dataset_paths = [
+    data_path1,
+    data_path2
+]
+
+# Start training with multiple datasets
+pretrainer.train(
+    dataset_paths=dataset_paths,
+    num_epochs=10,
+    batch_size=2,
+    sample_size=10000
+)
\ No newline at end of file
diff --git a/src/aiia/pretrain/pretrainer.py b/src/aiia/pretrain/pretrainer.py
index fa84fcb..30ebc92 100644
--- a/src/aiia/pretrain/pretrainer.py
+++ b/src/aiia/pretrain/pretrainer.py
@@ -186,7 +186,7 @@ class Pretrainer:
             
             if val_loss < best_val_loss:
                 best_val_loss = val_loss
-                self.save_model("AIIA-base-512")
+                self.model.save("AIIA-base-512")
                 print("Best model saved!")
 
         self.save_losses('losses.csv')
@@ -214,10 +214,6 @@ class Pretrainer:
         print(f"Validation Loss: {avg_val_loss:.4f}")
         return avg_val_loss
 
-    def save_model(self, path):
-        """Save the model and projection head."""
-        self.model.save(path)
-        torch.save(self.projection_head.state_dict(), f"{path}_projection_head.pth")
 
     def save_losses(self, csv_file):
         """Save training and validation losses to a CSV file."""