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45742a7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 | import os
import sys
import numpy as np
import tensorflow as tf
from sklearn.metrics import classification_report, confusion_matrix
import matplotlib.pyplot as plt
import seaborn as sns
# 1. --- Import config and generator ---
try:
import config
from video_data_generator import VideoDataGenerator
except ImportError:
print("Error: Could not import config.py or video_data_generator.py.")
sys.exit(1)
# Suppress TensorFlow logs
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
def plot_confusion_matrix(y_true, y_pred, save_path):
"""Plots and saves a confusion matrix."""
cm = confusion_matrix(y_true, y_pred)
plt.figure(figsize=(8, 6))
sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',
xticklabels=['Fake', 'Real'], # Swapped for correct labels
yticklabels=['Fake', 'Real'])
plt.title('Confusion Matrix (Video Model)')
plt.ylabel('Actual Label')
plt.xlabel('Predicted Label')
plt.savefig(save_path)
print(f"Confusion matrix saved to {save_path}")
def main():
print("--- Starting Video Model Evaluation ---")
# 1. Load our test data using the VideoDataGenerator
print("Creating Test Data Generator...")
test_gen = VideoDataGenerator(
data_dir=config.TEST_SEQ_DIR,
batch_size=config.VIDEO_BATCH_SIZE, # Use the video batch size
sequence_length=config.SEQUENCE_LENGTH,
img_size=config.TARGET_IMAGE_SIZE,
shuffle=False # IMPORTANT: Must be False for evaluation
)
# 2. Load our BEST fine-tuned video model
model_path = os.path.join(config.MODEL_DIR, "finetuned_video_model.h5")
if not os.path.exists(model_path):
print(f"Error: Fine-tuned model not found at {model_path}")
return
print(f"Loading final model from {model_path}...")
model = tf.keras.models.load_model(model_path)
# 3. Get model's performance on the test set
print("Evaluating model on test set...")
# 'test_gen.n' isn't defined, so we just use the generator directly
results = model.evaluate(test_gen, verbose=1)
print("\n--- Test Set Evaluation Metrics ---")
print(f"Test Loss: {results[0]:.4f}")
print(f"Test Accuracy: {results[1]:.4f}")
print(f"Test AUC: {results[2]:.4f}")
# 4. Get predictions for the entire test set
print("Generating predictions for classification report...")
y_pred_probs = model.predict(test_gen, verbose=1)
y_pred_classes = (y_pred_probs > 0.5).astype(int)
# 5. Get the true labels
# We get them from the generator's dataframe
y_true = test_gen.df['label'].map({'fake': 0, 'real': 1}).values
# We must slice y_true to match the number of predictions
# This handles any batches that were dropped
y_true = y_true[:len(y_pred_classes)]
# 6. Print Classification Report
print("\n--- Classification Report (Video Model) ---")
print(classification_report(y_true, y_pred_classes, target_names=['fake', 'real']))
# 7. Save Confusion Matrix
cm_path = os.path.join(config.RESULTS_DIR, "video_confusion_matrix.png")
plot_confusion_matrix(y_true, y_pred_classes, cm_path)
if __name__ == "__main__":
os.makedirs(config.RESULTS_DIR, exist_ok=True)
main() |