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CodingTil · Oct 30, 2023 · 61c9543 · 61c9543
1 parent b1d8556
commit 61c9543
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Showing 2 changed files with 10 additions and 11 deletions.
diff --git a/eiuie/fusion_model.py b/eiuie/fusion_model.py
@@ -43,9 +43,7 @@ def forward(self, x):
 
 
 class EarlyStopping:
-    def __init__(
-        self, patience=5, verbose=False, delta=0, trace_func=print
-    ):
+    def __init__(self, patience=5, verbose=False, delta=0, trace_func=print):
         """
         Parameters
         ----------
@@ -170,7 +168,7 @@ def _get_latest_checkpoint(self) -> Optional[str]:
 
         if "best_model.pt" in checkpoint_files:
             return "best_model.pt"
-        
+
         # Sort based on epoch number
         checkpoint_files.sort(key=lambda x: int(x.split("_")[-1].split(".")[0]))
 

diff --git a/eiuie/pixel_dataset.py b/eiuie/pixel_dataset.py
@@ -7,36 +7,37 @@
 
 FILE = "data/pixel_dataset.ds"
 
+
 class PixelDataset(Dataset):
     def __init__(self, batch_size=1, chunk_size=10000, use_fraction=1.0):
         # Ensure use_fraction is within valid bounds
         use_fraction = max(0.0, min(1.0, use_fraction))
-        
+
         # Use numpy's memory mapping
         raw_data = np.memmap(FILE, dtype=np.uint8, mode="r").reshape(-1, 15)
-        
+
         # Randomly select a fraction of the data if use_fraction < 1.0
         if use_fraction < 1.0:
             n_samples = int(len(raw_data) * use_fraction)
             idxs = np.random.choice(len(raw_data), n_samples, replace=False)
             raw_data = raw_data[idxs]
-        
+
         self.data_array = np.zeros_like(raw_data, dtype=np.float32)
         n_rows = raw_data.shape[0]
 
         # Convert each set of BGR values to HSI in chunks
         for start_idx in range(0, n_rows, chunk_size):
             end_idx = min(start_idx + chunk_size, n_rows)
             chunk_bgr = raw_data[start_idx:end_idx]
-            
+
             hsi_data_list = []
             for i in range(0, chunk_bgr.shape[1], 3):
-                bgr_img = chunk_bgr[:, i:i+3].reshape(-1, 1, 3)
+                bgr_img = chunk_bgr[:, i : i + 3].reshape(-1, 1, 3)
                 hsi_img = bm.BGR2HSI(bgr_img)
                 hsi_data_list.append(hsi_img.reshape(-1, 3))
-                
+
             self.data_array[start_idx:end_idx] = np.concatenate(hsi_data_list, axis=1)
-            
+
         self.batch_size = batch_size
 
     def __len__(self) -> int: