delete dataset.py

src/dataset.py

@@ -1,65 +0,0 @@
-from PIL import Image
-from torch.utils.data import Dataset
-from torchvision import transforms
-import os 
-
-class ClassifierDataset(Dataset):
-    def __init__(self, root_dir, transform=None):
-        self.root_dir = root_dir
-        self.transform = transform
-
-        self.classes = ['0', '1']
-        self.class_to_idx = {cls: idx for idx, cls in enumerate(self.classes)}
-
-        self.samples = self._make_dataset()
-
-    def _make_dataset(self):
-        samples = []
-        for class_name in self.classes:
-            class_dir = os.path.join(self.root_dir, class_name)
-            for img_name in os.listdir(class_dir):
-                img_path = os.path.join(class_dir, img_name)
-                samples.append((img_path, self.class_to_idx[class_name]))
-        return samples
-
-    def __len__(self):
-        return len(self.samples)
-
-    def __getitem__(self, idx):
-        img_path, label = self.samples[idx]
-        img = Image.open(img_path).convert('L')  # Convert to grayscale
-        if self.transform:
-            img = self.transform(img)
-        return img, label
-    
-
-
-class CustomDataset(Dataset):
-    def __init__(self, root_dir, train_N, train_P, img_res):
-        self.root_dir = root_dir
-        self.train_N = train_N
-        self.train_P = train_P
-        self.img_res = img_res
-        self.transforms = transforms.Compose([
-            transforms.Resize(img_res),
-            transforms.ToTensor(),
-            transforms.Normalize(mean=[0.5], std=[0.5])  # Assuming grayscale images
-        ])
-
-    def __len__(self):
-        return min(len(os.listdir(os.path.join(self.root_dir, self.train_N))),
-                   len(os.listdir(os.path.join(self.root_dir, self.train_P))))
-
-    def __getitem__(self, idx):
-        normal_path = os.path.join(self.root_dir, self.train_N, os.listdir(os.path.join(self.root_dir, self.train_N))[idx])
-        pneumo_path = os.path.join(self.root_dir, self.train_P, os.listdir(os.path.join(self.root_dir, self.train_P))[idx])
-        
-        normal_img = Image.open(normal_path).convert("L")  # Load as grayscale
-        pneumo_img = Image.open(pneumo_path).convert("L")  # Load as grayscale
-        
-        normal_img = self.transforms(normal_img)
-        pneumo_img = self.transforms(pneumo_img)
-        
-        return normal_img, pneumo_img
-
-