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import os
import cv2
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report, accuracy_score, confusion_matrix
import time
from model import SlowFastNetwork
# --- Configuration ---
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
DATASET_DIR = os.path.join(BASE_DIR, "Dataset")
MODEL_SAVE_PATH = "best_model_slowfast.pth"
# Hyperparameters
IMG_SIZE = 112
SEQ_LEN = 32 # 32 frames for Fast path
ALPHA = 8 # Alpha to define Slow path (32/8 = 4 frames)
BATCH_SIZE = 16
EPOCHS = 80
LEARNING_RATE = 1e-4
PATIENCE = 5
# --- 1. Data Augmentation ---
def augment_video_frames(frames):
"""
Apply augmentation to a sequence of frames.
"""
augmented_frames = []
do_flip = np.random.random() > 0.5
do_rotate = np.random.random() > 0.5
angle = np.random.randint(-15, 15) if do_rotate else 0
brightness = np.random.uniform(0.8, 1.2)
contrast = np.random.uniform(0.8, 1.2)
for frame in frames:
new_frame = frame.copy()
if do_flip:
new_frame = cv2.flip(new_frame, 1)
if do_rotate:
(h, w) = new_frame.shape[:2]
center = (w // 2, h // 2)
M = cv2.getRotationMatrix2D(center, angle, 1.0)
new_frame = cv2.warpAffine(new_frame, M, (w, h))
new_frame = cv2.convertScaleAbs(new_frame, alpha=contrast, beta=(brightness-1)*50)
augmented_frames.append(new_frame)
return np.array(augmented_frames)
# --- Dataset Class ---
class SlowFastDataset(Dataset):
def __init__(self, video_paths, labels, alpha=ALPHA, transform=None, augment=False):
self.video_paths = video_paths
self.labels = labels
self.augment = augment
self.alpha = alpha
def __len__(self):
return len(self.video_paths)
def __getitem__(self, idx):
path = self.video_paths[idx]
label = self.labels[idx]
try:
frames = self._load_video(path)
except Exception as e:
print(f"Error loading {path}: {e}")
frames = np.zeros((SEQ_LEN, IMG_SIZE, IMG_SIZE, 3), dtype=np.uint8)
if self.augment:
frames = augment_video_frames(frames)
# Prepare inputs
# Frames shape: (T, H, W, C)
# Fast Pathway: Use all frames (High Temporal Resolution)
fast_frames = frames
# Slow Pathway: Use strided frames (Low Temporal Resolution, High Spatial focus conceptually)
# Stride = ALPHA
slow_idx = np.arange(0, SEQ_LEN, self.alpha)
slow_frames = frames[slow_idx]
# Preprocessing
fast_input = self._process_frames(fast_frames)
slow_input = self._process_frames(slow_frames)
return slow_input, fast_input, label
def _process_frames(self, frames):
# Convert to Tensor and Channel First
tensor = torch.tensor(frames, dtype=torch.float32)
tensor = tensor / 255.0
tensor = tensor.permute(3, 0, 1, 2) # (C, T, H, W)
return tensor
def _load_video(self, path):
cap = cv2.VideoCapture(path)
frames = []
try:
while True:
ret, frame = cap.read()
if not ret:
break
frame = cv2.resize(frame, (IMG_SIZE, IMG_SIZE))
frames.append(frame)
finally:
cap.release()
if len(frames) == 0:
return np.zeros((SEQ_LEN, IMG_SIZE, IMG_SIZE, 3), dtype=np.uint8)
if len(frames) < SEQ_LEN:
while len(frames) < SEQ_LEN:
frames.append(frames[-1])
elif len(frames) > SEQ_LEN:
# Uniform sampling
indices = np.linspace(0, len(frames)-1, SEQ_LEN, dtype=int)
frames = [frames[i] for i in indices]
return np.array(frames)
# --- 2. Data Splitting ---
def prepare_data():
violence_dir = os.path.join(DATASET_DIR, 'violence')
no_violence_dir = os.path.join(DATASET_DIR, 'no-violence')
if not os.path.exists(violence_dir) or not os.path.exists(no_violence_dir):
raise FileNotFoundError(f"Dataset directories not found.")
violence_files = [os.path.join(violence_dir, f) for f in os.listdir(violence_dir) if f.endswith('.avi') or f.endswith('.mp4')]
no_violence_files = [os.path.join(no_violence_dir, f) for f in os.listdir(no_violence_dir) if f.endswith('.avi') or f.endswith('.mp4')]
X = violence_files + no_violence_files
y = [1] * len(violence_files) + [0] * len(no_violence_files)
X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.30, random_state=42, stratify=y)
X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.50, random_state=42, stratify=y_temp)
return (X_train, y_train), (X_val, y_val), (X_test, y_test)
# --- 4. Early Stopping ---
class EarlyStopping:
def __init__(self, patience=5, verbose=False, path='checkpoint.pth'):
self.patience = patience
self.verbose = verbose
self.counter = 0
self.best_score = None
self.early_stop = False
self.val_loss_min = np.inf
self.path = path
def __call__(self, val_loss, model):
score = -val_loss
if self.best_score is None:
self.best_score = score
self.save_checkpoint(val_loss, model)
elif score < self.best_score:
self.counter += 1
if self.verbose:
print(f'EarlyStopping counter: {self.counter} out of {self.patience}')
if self.counter >= self.patience:
self.early_stop = True
else:
self.best_score = score
self.save_checkpoint(val_loss, model)
self.counter = 0
def save_checkpoint(self, val_loss, model):
if self.verbose:
print(f'Validation loss decreased ({self.val_loss_min:.6f} --> {val_loss:.6f}). Saving model ...')
torch.save(model, self.path)
self.val_loss_min = val_loss
# --- Main Execution ---
if __name__ == "__main__":
start_time = time.time()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Using device: {device}")
try:
(X_train, y_train), (X_val, y_val), (X_test, y_test) = prepare_data()
print(f"Dataset Split Stats:")
print(f"Train: {len(X_train)} samples")
print(f"Val: {len(X_val)} samples")
print(f"Test: {len(X_test)} samples")
except Exception as e:
print(f"Data preparation failed: {e}")
exit(1)
train_dataset = SlowFastDataset(X_train, y_train, augment=True)
val_dataset = SlowFastDataset(X_val, y_val, augment=False)
test_dataset = SlowFastDataset(X_test, y_test, augment=False)
train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=0)
val_loader = DataLoader(val_dataset, batch_size=BATCH_SIZE, shuffle=False, num_workers=0)
test_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=False, num_workers=0)
model = SlowFastNetwork().to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=LEARNING_RATE)
early_stopping = EarlyStopping(patience=PATIENCE, verbose=True, path=MODEL_SAVE_PATH)
print("\nStarting SlowFast Training...")
for epoch in range(EPOCHS):
model.train()
train_loss = 0.0
correct = 0
total = 0
for batch_idx, (slow_in, fast_in, labels) in enumerate(train_loader):
slow_in, fast_in, labels = slow_in.to(device), fast_in.to(device), labels.to(device)
optimizer.zero_grad()
outputs = model(slow_in, fast_in)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
train_loss += loss.item()
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
if batch_idx % 10 == 0:
print(f"Epoch {epoch+1} Batch {batch_idx}/{len(train_loader)} Loss: {loss.item():.4f}", end='\r')
train_acc = 100 * correct / total
avg_train_loss = train_loss / len(train_loader)
# Validation
model.eval()
val_loss = 0.0
correct_val = 0
total_val = 0
with torch.no_grad():
for slow_in, fast_in, labels in val_loader:
slow_in, fast_in, labels = slow_in.to(device), fast_in.to(device), labels.to(device)
outputs = model(slow_in, fast_in)
loss = criterion(outputs, labels)
val_loss += loss.item()
_, predicted = torch.max(outputs.data, 1)
total_val += labels.size(0)
correct_val += (predicted == labels).sum().item()
val_acc = 100 * correct_val / total_val
avg_val_loss = val_loss / len(val_loader)
print(f'\nEpoch [{epoch+1}/{EPOCHS}] '
f'Train Loss: {avg_train_loss:.4f} Acc: {train_acc:.2f}% '
f'Val Loss: {avg_val_loss:.4f} Acc: {val_acc:.2f}%')
early_stopping(avg_val_loss, model)
if early_stopping.early_stop:
print("Early stopping triggered")
break
print("\nLoading best SlowFast model for evaluation...")
if os.path.exists(MODEL_SAVE_PATH):
model = torch.load(MODEL_SAVE_PATH)
else:
print("Warning: Using last epoch model.")
model.eval()
all_preds = []
all_labels = []
print("Evaluating on Test set...")
with torch.no_grad():
for slow_in, fast_in, labels in test_loader:
slow_in, fast_in, labels = slow_in.to(device), fast_in.to(device), labels.to(device)
outputs = model(slow_in, fast_in)
_, predicted = torch.max(outputs.data, 1)
all_preds.extend(predicted.cpu().numpy())
all_labels.extend(labels.cpu().numpy())
print("\n=== SlowFast Model Evaluation Report ===")
print(classification_report(all_labels, all_preds, target_names=['No Violence', 'Violence']))
print("Confusion Matrix:")
print(confusion_matrix(all_labels, all_preds))
acc = accuracy_score(all_labels, all_preds)
print(f"\nFinal Test Accuracy: {acc*100:.2f}%")
elapsed = time.time() - start_time
print(f"\nTotal execution time: {elapsed/60:.2f} minutes")
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