Delete src/utils

src/utils/data_loader.py

@@ -1,17 +0,0 @@
-import os
-from torchvision import datasets, transforms
-from torch.utils.data import DataLoader, random_split
-
-def get_dataloaders(data_dir, batch_size=32, val_split=0.2):
-    transform = transforms.Compose([
-        transforms.Resize((128, 128)),
-        transforms.ToTensor(),
-        transforms.Normalize([0.5]*3, [0.5]*3)
-    ])
-    dataset = datasets.ImageFolder(data_dir, transform=transform)
-    val_len = int(len(dataset) * val_split)
-    train_len = len(dataset) - val_len
-    train_set, val_set = random_split(dataset, [train_len, val_len])
-    train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True)
-    val_loader = DataLoader(val_set, batch_size=batch_size)
-    return train_loader, val_loader, dataset.classes

src/utils/evaluate.py

@@ -1,13 +0,0 @@
-from sklearn.metrics import classification_report, confusion_matrix
-import torch
-
-def evaluate_model(model, dataloader, device):
-    model.eval()
-    y_true, y_pred = [], []
-    with torch.no_grad():
-        for x, y in dataloader:
-            x, y = x.to(device), y.to(device)
-            preds = model(x).argmax(dim=1)
-            y_true.extend(y.cpu().numpy())
-            y_pred.extend(preds.cpu().numpy())
-    return classification_report(y_true, y_pred), confusion_matrix(y_true, y_pred)