Merge pull request #21 from daniel-unyi-42/main

Synchronize tile naming conventions

scripts/train_model.py

@@ -3,7 +3,7 @@
 import argparse
 from pathlib import Path
 import torch
-import lightning as L
+from pytorch_lightning import Trainer
 from torch_geometric.loader import DataLoader
 from segger.data.utils import SpatialTranscriptomicsDataset  # Updated dataset class
 from segger.models.segger_model import Segger
@@ -57,7 +57,7 @@ def main(args):
     litsegger = LitSegger(model=model)
 
     # Initialize the PyTorch Lightning trainer
-    trainer = L.Trainer(
+    trainer = Trainer(
         accelerator=args.accelerator,
         strategy=args.strategy,
         precision=args.precision,

src/segger/data/io.py

@@ -815,7 +815,7 @@ def _process_tile(self, tile_params: Tuple) -> None:
 
             # Save the tile data to the appropriate directory based on split
             if self.verbose: print(f"Saving data for tile at (x_min: {x_loc}, y_min: {y_loc})...")
-            filename = f"tiles_x{x_loc}_y{y_loc}_{x_size}_{y_size}.pt"
+            filename = f"tiles_x={x_loc}_y={y_loc}_w={x_size}_h={y_size}.pt"
             if prob > val_prob + test_prob:
                 torch.save(data, processed_dir / 'train_tiles' / 'processed' / filename)
             elif prob > test_prob:

src/segger/data/parquet/pyg_dataset.py

@@ -37,7 +37,7 @@ def processed_file_names(self) -> List[str]:
         Returns:
             List[str]: List of processed file names.
         """
-        paths = glob.glob(f'{self.processed_dir}/x=*_y=*_w=*_h=*.pt')
+        paths = glob.glob(f'{self.processed_dir}/tiles_x=*_y=*_w=*_h=*.pt')
         file_names = list(map(os.path.basename, paths))
         return file_names
 

src/segger/data/parquet/sample.py

@@ -785,7 +785,7 @@ def uid(self) -> str:
         'x=100_y=200_w=50_h=50'
         """
         x_min, y_min, x_max, y_max = map(int, self.extents.bounds)
-        uid = f'x={x_min}_y={y_min}_w={x_max-x_min}_h={y_max-y_min}'
+        uid = f'tiles_x={x_min}_y={y_min}_w={x_max-x_min}_h={y_max-y_min}'
         return uid