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import os
import random
from src.config import Config
from src.dataset import DeepfakeDataset
def test_dataloading():
print("Testing Data Loading & Splitting Logic...")
Config.setup()
print(f"Data Path: {Config.TRAIN_DATA_PATH}")
# 1. Test Scan
paths, labels = DeepfakeDataset.scan_directory(Config.TRAIN_DATA_PATH)
total_files = len(paths)
print(f"Total images found: {total_files}")
if total_files == 0:
print("[FAIL] No images found! Check path.")
return
# 2. Simulate Split Logic
combined = list(zip(paths, labels))
random.shuffle(combined)
split_idx = int(len(combined) * 0.8)
train_data = combined[:split_idx]
val_data = combined[split_idx:]
print(f"Train Split: {len(train_data)} images")
print(f"Val Split: {len(val_data)} images")
# 3. Test Dataset Initialization
try:
train_paths, train_labels = zip(*train_data)
ds = DeepfakeDataset(file_paths=list(train_paths), labels=list(train_labels), phase='train')
print(f"[Pass] Dataset initialized with {len(ds)} samples.")
# Test Get Item
img, lbl = ds[0]
print(f"[Pass] Loaded sample image. Shape: {img.shape}, Label: {lbl}")
except Exception as e:
print(f"[FAIL] Dataset initialization or loading error: {e}")
return
print("\nSUCCESS: Data loading verification passed!")
if __name__ == "__main__":
test_dataloading()
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