File size: 8,814 Bytes
d31a75f |
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 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 |
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 torchvision.models.video as models
import time
from model import FeatureFusionNetwork
# --- 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_fusion.pth"
IMG_SIZE = 112
SEQ_LEN = 16
BATCH_SIZE = 16
EPOCHS = 80
LEARNING_RATE = 1e-4
PATIENCE = 5
# --- Dataset ---
class StandardDataset(Dataset):
def __init__(self, video_paths, labels):
self.video_paths = video_paths
self.labels = labels
def __len__(self):
return len(self.video_paths)
def __getitem__(self, idx):
path = self.video_paths[idx]
label = self.labels[idx]
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:
frames = np.zeros((SEQ_LEN, IMG_SIZE, IMG_SIZE, 3), dtype=np.float32)
elif len(frames) < SEQ_LEN:
while len(frames) < SEQ_LEN: frames.append(frames[-1])
elif len(frames) > SEQ_LEN:
indices = np.linspace(0, len(frames)-1, SEQ_LEN, dtype=int)
frames = [frames[i] for i in indices]
frames = np.array(frames, dtype=np.float32) / 255.0
# (T, H, W, C) -> (C, T, H, W)
frames = torch.tensor(frames).permute(3, 0, 1, 2)
return frames, label
# --- Data Preparation ---
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("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)
# --- 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) # FULL MODEL SAVE
self.val_loss_min = val_loss
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 = StandardDataset(X_train, y_train)
val_dataset = StandardDataset(X_val, y_val)
test_dataset = StandardDataset(X_test, y_test)
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 = FeatureFusionNetwork().to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=LEARNING_RATE)
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.1, patience=2)
early_stopping = EarlyStopping(patience=PATIENCE, verbose=True, path=MODEL_SAVE_PATH)
print("\nStarting Feature Fusion Training...")
for epoch in range(EPOCHS):
model.train()
train_loss = 0.0
correct = 0
total = 0
for batch_idx, (inputs, labels) in enumerate(train_loader):
inputs, labels = inputs.to(device), labels.to(device)
optimizer.zero_grad()
outputs = model(inputs)
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)
model.eval()
val_loss = 0.0
correct_val = 0
total_val = 0
with torch.no_grad():
for inputs, labels in val_loader:
inputs, labels = inputs.to(device), labels.to(device)
outputs = model(inputs)
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}%')
scheduler.step(avg_val_loss)
early_stopping(avg_val_loss, model)
if early_stopping.early_stop:
print("Early stopping triggered")
break
print("\nLoading best Fusion model for evaluation...")
if os.path.exists(MODEL_SAVE_PATH):
model = torch.load(MODEL_SAVE_PATH)
else:
print("Warning: Model file not found.")
model.eval()
all_preds = []
all_labels = []
print("Evaluating on Test set...")
with torch.no_grad():
for inputs, labels in test_loader:
inputs, labels = inputs.to(device), labels.to(device)
outputs = model(inputs)
_, predicted = torch.max(outputs.data, 1)
all_preds.extend(predicted.cpu().numpy())
all_labels.extend(labels.cpu().numpy())
print("\n=== Feature Fusion 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")
|