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os\nimport cv2\nimport numpy as np\nimport pandas as pd\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.utils.data import Dataset, DataLoader\nfrom torch.optim.lr_scheduler import CosineAnnealingLR\nfrom sklearn.model_selection import train_test_split\nimport torchvision.transforms as T\nimport torchvision.models as models\nfrom tqdm.auto import tqdm\nimport matplotlib.pyplot as plt\nfrom IPython.display import clear_output","metadata":{"execution":{"iopub.execute_input":"2026-04-12T05:56:19.274830Z","iopub.status.busy":"2026-04-12T05:56:19.274510Z","iopub.status.idle":"2026-04-12T05:56:33.345113Z","shell.execute_reply":"2026-04-12T05:56:33.344374Z"},"papermill":{"duration":14.076112,"end_time":"2026-04-12T05:56:33.346908+00:00","exception":false,"start_time":"2026-04-12T05:56:19.270796+00:00","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"3c68d935","cell_type":"code","source":"# ================= CẤU HÌNH (CONFIG) =================\nclass CFG:\n    seed = 42\n    img_size = 256\n    batch_size = 32 \n    epochs_phase_1 = 5 # Warm-up\n    epochs_phase_2 = 25 # Fine-tuning\n    lr_phase_1 = 1e-3\n    lr_phase_2 = 1e-4\n    weight_decay = 1e-2 # Tăng lên 1e-2 cho AdamW\n    num_classes = 9\n    patience = 7 # Số epoch chờ cho Early Stopping\n    device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n    \n    # Cấu hình đường dẫn\n    train_csv = \"/kaggle/input/datasets/trankimhuu/images-2-datasets-of-big2015/trainLabels.csv\"\n    bytes_dir = \"/kaggle/input/datasets/trankimhuu/images-2-datasets-of-big2015/train/train/train_images_bytes\" \n    asm_dir = \"/kaggle/input/datasets/trankimhuu/images-2-datasets-of-big2015/train/train/train_images_bytes\"\n    test_csv = \"/kaggle/input/competitions/malware-classification/sampleSubmission.csv\"\n    test_bytes_dir = \"/kaggle/input/datasets/trankimhuu/images-2-datasets-of-big2015/test/test/test_images_bytes\"\n    test_asm_dir = \"/kaggle/input/datasets/trankimhuu/images-2-datasets-of-big2015/test/test/test_images_asm\"\n\ndef seed_everything(seed=42):\n    np.random.seed(seed)\n    torch.manual_seed(seed)\n    torch.cuda.manual_seed(seed)\n    torch.backends.cudnn.deterministic = True\n    torch.backends.cudnn.benchmark = False\n\nseed_everything(CFG.seed)","metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","execution":{"iopub.execute_input":"2026-04-12T05:56:33.351780Z","iopub.status.busy":"2026-04-12T05:56:33.351221Z","iopub.status.idle":"2026-04-12T05:56:33.609920Z","shell.execute_reply":"2026-04-12T05:56:33.609308Z"},"papermill":{"duration":0.262564,"end_time":"2026-04-12T05:56:33.611315+00:00","exception":false,"start_time":"2026-04-12T05:56:33.348751+00:00","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"8c513550","cell_type":"code","source":"class MalwareDualDataset(Dataset):\n    def __init__(self, df, bytes_dir, asm_dir, transform=None):\n        self.df = df.reset_index(drop=True)\n        self.bytes_dir = bytes_dir\n        self.asm_dir = asm_dir\n        self.transform = transform\n\n    def __len__(self):\n        return len(self.df)\n\n    def __getitem__(self, idx):\n        file_id = str(self.df.loc[idx, 'Id']) \n        bytes_path = os.path.join(self.bytes_dir, f\"{file_id}.png\")\n        asm_path = os.path.join(self.asm_dir, f\"{file_id}.png\")\n        \n        bytes_img = cv2.imread(bytes_path)\n        bytes_img = cv2.cvtColor(bytes_img, cv2.COLOR_BGR2RGB) if bytes_img is not None else np.zeros((CFG.img_size, CFG.img_size, 3), dtype=np.uint8)\n            \n        asm_img = cv2.imread(asm_path)\n        asm_img = cv2.cvtColor(asm_img, cv2.COLOR_BGR2RGB) if asm_img is not None else np.zeros((CFG.img_size, CFG.img_size, 3), dtype=np.uint8)\n\n        if self.transform:\n            bytes_img = self.transform(bytes_img)\n            asm_img = self.transform(asm_img)\n\n        label = int(self.df.loc[idx, 'Class']) - 1 \n        return bytes_img, asm_img, torch.tensor(label, dtype=torch.long)\n\n# CHÚ Ý: Bỏ T.Normalize để giữ nguyên vẹn giá trị phổ pixel của file Bytes như ở model Swin cũ\ntrain_transforms = T.Compose([\n    T.ToTensor(),\n    T.Resize((CFG.img_size, CFG.img_size), antialias=True)\n])\n\nval_transforms = T.Compose([\n    T.ToTensor(),\n    T.Resize((CFG.img_size, CFG.img_size), antialias=True)\n])","metadata":{"execution":{"iopub.execute_input":"2026-04-12T05:56:33.616068Z","iopub.status.busy":"2026-04-12T05:56:33.615524Z","iopub.status.idle":"2026-04-12T05:56:33.623025Z","shell.execute_reply":"2026-04-12T05:56:33.622469Z"},"papermill":{"duration":0.011299,"end_time":"2026-04-12T05:56:33.624393+00:00","exception":false,"start_time":"2026-04-12T05:56:33.613094+00:00","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"426779b0","cell_type":"code","source":"class SEBlock1D(nn.Module):\n    def __init__(self, channels, reduction=16):\n        super().__init__()\n        self.fc1 = nn.Linear(channels, channels // reduction, bias=False)\n        self.relu = nn.ReLU(inplace=True)\n        self.fc2 = nn.Linear(channels // reduction, channels, bias=False)\n        self.sigmoid = nn.Sigmoid()\n\n    def forward(self, x):\n        return x * self.sigmoid(self.fc2(self.relu(self.fc1(x))))\n\nclass MalwareDualModel(nn.Module):\n    def __init__(self, num_classes=CFG.num_classes, pretrained=True, dropout_rate=0.4):\n        super().__init__()\n        # Dùng thư viện torchvision mặc định, tải model từ máy chủ PyTorch (rất nhanh)\n        self.bytes_branch = models.convnext_tiny(pretrained=pretrained)\n        bytes_feat_dim = self.bytes_branch.classifier[2].in_features\n        self.bytes_branch.classifier[2] = nn.Identity() \n\n        self.asm_branch = models.efficientnet_v2_s(pretrained=pretrained)\n        asm_feat_dim = self.asm_branch.classifier[1].in_features\n        self.asm_branch.classifier[1] = nn.Identity() \n\n        total_feat_dim = bytes_feat_dim + asm_feat_dim\n        self.se_fusion = SEBlock1D(channels=total_feat_dim, reduction=16)\n\n        self.classifier = nn.Sequential(\n            nn.Dropout(p=dropout_rate),\n            nn.Linear(total_feat_dim, 512),\n            nn.BatchNorm1d(512),\n            nn.GELU(),\n            nn.Dropout(p=dropout_rate),\n            nn.Linear(512, num_classes)\n        )\n\n    def forward(self, x_bytes, x_asm):\n        feat_bytes = self.bytes_branch(x_bytes)\n        feat_asm = self.asm_branch(x_asm)\n        fused = self.se_fusion(torch.cat((feat_bytes, feat_asm), dim=1))\n        return self.classifier(fused)\n\n    def freeze_backbones(self):\n        for param in self.bytes_branch.parameters(): param.requires_grad = False\n        for param in self.asm_branch.parameters(): param.requires_grad = False\n\n    def unfreeze_backbones(self):\n        for param in self.parameters(): param.requires_grad = True","metadata":{"execution":{"iopub.execute_input":"2026-04-12T05:56:33.628867Z","iopub.status.busy":"2026-04-12T05:56:33.628541Z","iopub.status.idle":"2026-04-12T05:56:33.637719Z","shell.execute_reply":"2026-04-12T05:56:33.637156Z"},"papermill":{"duration":0.01298,"end_time":"2026-04-12T05:56:33.638938+00:00","exception":false,"start_time":"2026-04-12T05:56:33.625958+00:00","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"b2407660","cell_type":"code","source":"class EarlyStopping:\n    def __init__(self, patience=5, min_delta=0.0):\n        self.patience = patience\n        self.min_delta = min_delta\n        self.counter = 0\n        self.best_loss = float('inf')\n        self.early_stop = False\n\n    def __call__(self, val_loss):\n        if val_loss > self.best_loss - self.min_delta:\n            self.counter += 1\n            print(f\"  -> EarlyStopping counter: {self.counter} out of {self.patience}\")\n            if self.counter >= self.patience:\n                self.early_stop = True\n        else:\n            self.best_loss = val_loss\n            self.counter = 0\n\ndef train_one_epoch(model, dataloader, optimizer, criterion, device):\n    model.train()\n    running_loss, correct, total = 0.0, 0, 0\n    pbar = tqdm(dataloader, desc=\"Training\", leave=False)\n    for bytes_imgs, asm_imgs, labels in pbar:\n        bytes_imgs, asm_imgs, labels = bytes_imgs.to(device), asm_imgs.to(device), labels.to(device)\n\n        optimizer.zero_grad()\n        outputs = model(bytes_imgs, asm_imgs)\n        loss = criterion(outputs, labels)\n        loss.backward()\n        optimizer.step()\n\n        running_loss += loss.item()\n        _, predicted = outputs.max(1)\n        total += labels.size(0)\n        correct += predicted.eq(labels).sum().item()\n        pbar.set_postfix({'loss': running_loss/total, 'acc': correct/total})\n        \n    return running_loss / len(dataloader), correct / total\n\ndef validate(model, dataloader, criterion, device):\n    model.eval()\n    running_loss, correct, total = 0.0, 0, 0\n    with torch.no_grad():\n        for bytes_imgs, asm_imgs, labels in dataloader:\n            bytes_imgs, asm_imgs, labels = bytes_imgs.to(device), asm_imgs.to(device), labels.to(device)\n            outputs = model(bytes_imgs, asm_imgs)\n            loss = criterion(outputs, labels)\n\n            running_loss += loss.item()\n            _, predicted = outputs.max(1)\n            total += labels.size(0)\n            correct += predicted.eq(labels).sum().item()\n    return running_loss / len(dataloader), correct / total\n\ndef plot_training_history(history, phase1_epochs):\n    # Đảm bảo trục x khớp với số lượng epoch thực tế (đã xử lý lỗi mismatch)\n    epochs = range(1, len(history['train_loss']) + 1)\n    \n    plt.figure(figsize=(14, 5))\n    \n    plt.subplot(1, 2, 1)\n    plt.plot(epochs, history['train_loss'], 'b-', label='Train Loss')\n    plt.plot(epochs, history['val_loss'], 'r-', label='Val Loss')\n    plt.title('Loss Curve')\n    plt.xlabel('Epochs'); plt.ylabel('Loss')\n    if len(epochs) > phase1_epochs: plt.axvline(x=phase1_epochs, color='green', linestyle=':', label='End Phase 1')\n    plt.legend(); plt.grid(True)\n    \n    plt.subplot(1, 2, 2)\n    plt.plot(epochs, history['train_acc'], 'b-', label='Train Acc')\n    plt.plot(epochs, history['val_acc'], 'r-', label='Val Acc')\n    plt.title('Accuracy Curve')\n    plt.xlabel('Epochs'); plt.ylabel('Accuracy')\n    if len(epochs) > phase1_epochs: plt.axvline(x=phase1_epochs, color='green', linestyle=':')\n    plt.legend(); plt.grid(True)\n    \n    plt.tight_layout()\n    plt.show()","metadata":{"execution":{"iopub.execute_input":"2026-04-12T05:56:33.643078Z","iopub.status.busy":"2026-04-12T05:56:33.642870Z","iopub.status.idle":"2026-04-12T05:56:33.654185Z","shell.execute_reply":"2026-04-12T05:56:33.653520Z"},"papermill":{"duration":0.015069,"end_time":"2026-04-12T05:56:33.655563+00:00","exception":false,"start_time":"2026-04-12T05:56:33.640494+00:00","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"4359c9b4","cell_type":"code","source":"print(\"--- 1. ĐỌC VÀ CHIA DỮ LIỆU ---\")\ndf_train = pd.read_csv(CFG.train_csv) \n\ntrain_df, val_df = train_test_split(\n    df_train, test_size=0.25, random_state=CFG.seed, shuffle=True, stratify=df_train['Class']\n)\ntrain_df, val_df = train_df.reset_index(drop=True), val_df.reset_index(drop=True)\n\ntrain_loader = DataLoader(MalwareDualDataset(train_df, CFG.bytes_dir, CFG.asm_dir, train_transforms), batch_size=CFG.batch_size, shuffle=True, num_workers=4)\nval_loader = DataLoader(MalwareDualDataset(val_df, CFG.bytes_dir, CFG.asm_dir, val_transforms), batch_size=CFG.batch_size, shuffle=False, num_workers=4)\n\nmodel = MalwareDualModel(pretrained=True).to(CFG.device)\n\n# Sử dụng Cross Entropy tiêu chuẩn với Label Smoothing 0.05\ncriterion = nn.CrossEntropyLoss(label_smoothing=0.05)\n\nhistory = {'train_loss': [], 'train_acc': [], 'val_loss': [], 'val_acc': []}\n\n# ================= PHASE 1 =================\nprint(\"\\n--- PHASE 1: WARM-UP (Đóng băng Backbone) ---\")\nmodel.freeze_backbones()\n# Đã đổi thành AdamW\noptimizer_phase1 = torch.optim.AdamW(filter(lambda p: p.requires_grad, model.parameters()), lr=CFG.lr_phase_1, weight_decay=CFG.weight_decay)\n\nfor epoch in range(CFG.epochs_phase_1):\n    train_loss, train_acc = train_one_epoch(model, train_loader, optimizer_phase1, criterion, CFG.device)\n    val_loss, val_acc = validate(model, val_loader, criterion, CFG.device)\n    \n    history['train_loss'].append(train_loss); history['train_acc'].append(train_acc)\n    history['val_loss'].append(val_loss);     history['val_acc'].append(val_acc)\n    \n    print(f\"Epoch {epoch+1}/{CFG.epochs_phase_1} - Val Loss: {val_loss:.4f} | Val Acc: {val_acc:.4f}\")\n\n# ================= PHASE 2 =================\nprint(\"\\n--- PHASE 2: FINE-TUNING (Mở khóa toàn bộ) ---\")\nmodel.unfreeze_backbones()\n# Đã đổi thành AdamW\noptimizer_phase2 = torch.optim.AdamW(model.parameters(), lr=CFG.lr_phase_2, weight_decay=CFG.weight_decay)\nscheduler = CosineAnnealingLR(optimizer_phase2, T_max=CFG.epochs_phase_2)\nearly_stopping = EarlyStopping(patience=CFG.patience, min_delta=1e-4)\n\nbest_val_loss = float('inf')\n\nfor epoch in range(CFG.epochs_phase_2):\n    train_loss, train_acc = train_one_epoch(model, train_loader, optimizer_phase2, criterion, CFG.device)\n    val_loss, val_acc = validate(model, val_loader, criterion, CFG.device)\n    scheduler.step()\n    \n    history['train_loss'].append(train_loss); history['train_acc'].append(train_acc)\n    history['val_loss'].append(val_loss);     history['val_acc'].append(val_acc)\n    \n    print(f\"Epoch Phase 2 - {epoch+1}/{CFG.epochs_phase_2} - Val Loss: {val_loss:.4f} | Val Acc: {val_acc:.4f}\")\n    \n    if val_loss < best_val_loss:\n        best_val_loss = val_loss\n        torch.save(model.state_dict(), 'best_dual_model.pth')\n        print(\"--> Đã lưu Best Model!\")\n        \n    early_stopping(val_loss)\n    if early_stopping.early_stop:\n        print(f\"[!] EARLY STOPPING KÍCH HOẠT! Mô hình ngừng cải thiện.\")\n        break # Break ở đây đảm bảo history đã lưu xong, vẽ chart sẽ không bị lỗi mảng\n\n# Vẽ biểu đồ tổng kết\nplot_training_history(history, CFG.epochs_phase_1)","metadata":{"execution":{"iopub.execute_input":"2026-04-12T05:56:33.659805Z","iopub.status.busy":"2026-04-12T05:56:33.659592Z","iopub.status.idle":"2026-04-12T08:45:38.554777Z","shell.execute_reply":"2026-04-12T08:45:38.553797Z"},"papermill":{"duration":10145.033092,"end_time":"2026-04-12T08:45:38.690208+00:00","exception":false,"start_time":"2026-04-12T05:56:33.657116+00:00","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"ab9fa4bb","cell_type":"code","source":"class MalwareTestDataset(Dataset):\n    def __init__(self, df, bytes_dir, asm_dir, transform=None):\n        self.df = df.reset_index(drop=True)\n        self.bytes_dir, self.asm_dir, self.transform = bytes_dir, asm_dir, transform\n\n    def __len__(self): return len(self.df)\n\n    def __getitem__(self, idx):\n        file_id = str(self.df.loc[idx, 'Id']) \n        bytes_img = cv2.imread(os.path.join(self.bytes_dir, f\"{file_id}.png\"))\n        bytes_img = cv2.cvtColor(bytes_img, cv2.COLOR_BGR2RGB) if bytes_img is not None else np.zeros((CFG.img_size, CFG.img_size, 3), dtype=np.uint8)\n            \n        asm_img = cv2.imread(os.path.join(self.asm_dir, f\"{file_id}.png\"))\n        asm_img = cv2.cvtColor(asm_img, cv2.COLOR_BGR2RGB) if asm_img is not None else np.zeros((CFG.img_size, CFG.img_size, 3), dtype=np.uint8)\n\n        if self.transform:\n            bytes_img, asm_img = self.transform(bytes_img), self.transform(asm_img)\n        return bytes_img, asm_img, file_id\n\nprint(\"\\n--- INFERENCE TẬP TEST ---\")\ndf_test = pd.read_csv(CFG.test_csv) \n\ntest_loader = DataLoader(MalwareTestDataset(df_test, CFG.test_bytes_dir, CFG.test_asm_dir, val_transforms), batch_size=CFG.batch_size*2, shuffle=False, num_workers=4)\n\n# Nhớ gọi pretrained=False lúc test\ninfer_model = MalwareDualModel(pretrained=False).to(CFG.device)\ninfer_model.load_state_dict(torch.load('best_dual_model.pth', map_location=CFG.device)) \n\ninfer_model.eval()\nall_probs, all_ids = [], []\n\nwith torch.no_grad():\n    for bytes_imgs, asm_imgs, file_ids in tqdm(test_loader, desc=\"Dự đoán\"):\n        logits = infer_model(bytes_imgs.to(CFG.device), asm_imgs.to(CFG.device))\n        all_probs.extend(F.softmax(logits, dim=1).cpu().numpy())\n        all_ids.extend(file_ids)\n\nsubmission_df = pd.DataFrame(all_probs, columns=[f'Prediction{i}' for i in range(1, 10)])\nsubmission_df.insert(0, 'Id', all_ids)\nsubmission_df.to_csv(\"dual_model_submission.csv\", index=False)\n\nprint(\"Đã lưu file: dual_model_submission.csv\")\ndisplay(submission_df.head())","metadata":{"execution":{"iopub.execute_input":"2026-04-12T08:45:39.427668Z","iopub.status.busy":"2026-04-12T08:45:39.426806Z","iopub.status.idle":"2026-04-12T08:48:02.626129Z","shell.execute_reply":"2026-04-12T08:48:02.625406Z"},"papermill":{"duration":143.616821,"end_time":"2026-04-12T08:48:02.627741+00:00","exception":false,"start_time":"2026-04-12T08:45:39.010920+00:00","status":"completed"},"tags":[]},"outputs":[],"execution_count":null}]}