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os\nimport pandas as pd\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.utils.data import Dataset, DataLoader\nfrom torchvision import transforms\nfrom torchvision.transforms import InterpolationMode\nfrom PIL import Image\nimport timm\nfrom tqdm.notebook import tqdm","metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","execution":{"iopub.execute_input":"2026-04-06T02:15:49.141374Z","iopub.status.busy":"2026-04-06T02:15:49.140812Z","iopub.status.idle":"2026-04-06T02:16:02.998573Z","shell.execute_reply":"2026-04-06T02:16:02.997623Z"},"papermill":{"duration":13.863137,"end_time":"2026-04-06T02:16:03.000602+00:00","exception":false,"start_time":"2026-04-06T02:15:49.137465+00:00","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"01d7322d","cell_type":"code","source":"# --- CẤU HÌNH ĐƯỜNG DẪN ---\nTEST_IMG_DIR = \"/kaggle/input/datasets/trankimhuu/images-datasets-of-big2015/test_images/test_images/kaggle/working/test_images\"\nSAMPLE_SUB_PATH = \"/kaggle/input/competitions/malware-classification/sampleSubmission.csv\"\n# Đường dẫn tới file trọng số của Version 3\nMODEL_WEIGHTS_PATH = \"/kaggle/input/notebooks/huylhn1810/levit-192-ver3/best_levit_model_v3.pth\"\nOUTPUT_SUBMISSION_PATH = \"/kaggle/working/submission_ver3_final.csv\"\n\nBATCH_SIZE = 32\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\nprint(f\"Đang sử dụng thiết bị: {device}\")","metadata":{"execution":{"iopub.execute_input":"2026-04-06T02:16:03.004940Z","iopub.status.busy":"2026-04-06T02:16:03.004543Z","iopub.status.idle":"2026-04-06T02:16:03.256689Z","shell.execute_reply":"2026-04-06T02:16:03.255826Z"},"papermill":{"duration":0.255946,"end_time":"2026-04-06T02:16:03.258183+00:00","exception":false,"start_time":"2026-04-06T02:16:03.002237+00:00","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"0e12b26a","cell_type":"code","source":"class LeViT_Malware(nn.Module):\n    def __init__(self, model_name='levit_192', num_classes=9, pretrained=False):\n        super().__init__()\n        self.backbone = timm.create_model(model_name, pretrained=pretrained, num_classes=0)\n        num_features = self.backbone.num_features\n        \n        # Cấu hình Head đồng bộ với Version 3\n        self.head = nn.Sequential(\n            nn.BatchNorm1d(num_features),\n            nn.Dropout(p=0.2), \n            nn.Linear(num_features, 256), \n            nn.GELU(),\n            nn.BatchNorm1d(256),\n            nn.Dropout(p=0.1), \n            nn.Linear(256, num_classes)\n        )\n\n    def forward(self, x):\n        features = self.backbone(x)\n        return self.head(features)","metadata":{"execution":{"iopub.execute_input":"2026-04-06T02:16:03.262310Z","iopub.status.busy":"2026-04-06T02:16:03.261839Z","iopub.status.idle":"2026-04-06T02:16:03.267234Z","shell.execute_reply":"2026-04-06T02:16:03.266571Z"},"papermill":{"duration":0.008907,"end_time":"2026-04-06T02:16:03.268594+00:00","exception":false,"start_time":"2026-04-06T02:16:03.259687+00:00","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"ea26401e","cell_type":"code","source":"class MalwareTestDataset(Dataset):\n    def __init__(self, sample_sub_df, img_dir, transform=None):\n        self.sample_sub_df = sample_sub_df\n        self.img_dir = img_dir\n        self.transform = transform\n\n    def __len__(self):\n        return len(self.sample_sub_df)\n\n    def __getitem__(self, idx):\n        img_id = str(self.sample_sub_df.iloc[idx]['Id'])\n        img_name = img_id + \".png\" \n        img_path = os.path.join(self.img_dir, img_name)\n        \n        image = Image.open(img_path).convert('RGB')\n        if self.transform:\n            image = self.transform(image)\n        return image, img_id\n\n# Đồng bộ hóa với Version 3: dùng InterpolationMode.NEAREST\ntest_transform = transforms.Compose([\n    transforms.Resize((224, 224), interpolation=InterpolationMode.NEAREST),\n    transforms.ToTensor(),\n    # Sử dụng chuẩn ImageNet hoặc thay bằng Mean/Std thực tế bạn đã tính ở V3\n    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n])","metadata":{"execution":{"iopub.execute_input":"2026-04-06T02:16:03.272876Z","iopub.status.busy":"2026-04-06T02:16:03.272273Z","iopub.status.idle":"2026-04-06T02:16:03.278656Z","shell.execute_reply":"2026-04-06T02:16:03.278062Z"},"papermill":{"duration":0.010266,"end_time":"2026-04-06T02:16:03.280246+00:00","exception":false,"start_time":"2026-04-06T02:16:03.269980+00:00","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"6d139ebd","cell_type":"code","source":"def run_inference():\n    print(\"Đang nạp dữ liệu mẫu...\")\n    sample_sub = pd.read_csv(SAMPLE_SUB_PATH)\n    \n    test_dataset = MalwareTestDataset(sample_sub, TEST_IMG_DIR, transform=test_transform)\n    test_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=False, num_workers=2)\n\n    print(\"Đang nạp mô hình và trọng số V3...\")\n    model = LeViT_Malware(pretrained=False).to(device)\n    \n    # Nạp trọng số với chế độ strict=True để đảm bảo khớp hoàn toàn\n    state_dict = torch.load(MODEL_WEIGHTS_PATH, map_location=device, weights_only=True)\n    model.load_state_dict(state_dict)\n    \n    model.eval()\n\n    all_predictions = []\n    all_ids = []\n\n    print(\"Bắt đầu dự đoán trên tập Test...\")\n    with torch.no_grad():\n        for images, img_ids in tqdm(test_loader, desc=\"Inference\"):\n            images = images.to(device)\n            logits = model(images)\n            probs = F.softmax(logits, dim=1)\n            \n            all_predictions.extend(probs.cpu().numpy())\n            all_ids.extend(img_ids)\n\n    print(\"\\nĐang đóng gói file submission...\")\n    submission_df = pd.DataFrame(all_predictions, columns=[f'Prediction{i}' for i in range(1, 10)])\n    submission_df.insert(0, 'Id', all_ids) \n\n    submission_df.to_csv(OUTPUT_SUBMISSION_PATH, index=False)\n    print(f\"Thành công! File đã lưu tại: {OUTPUT_SUBMISSION_PATH}\")\n","metadata":{"execution":{"iopub.execute_input":"2026-04-06T02:16:03.284071Z","iopub.status.busy":"2026-04-06T02:16:03.283839Z","iopub.status.idle":"2026-04-06T02:16:03.290673Z","shell.execute_reply":"2026-04-06T02:16:03.290067Z"},"papermill":{"duration":0.010362,"end_time":"2026-04-06T02:16:03.291976+00:00","exception":false,"start_time":"2026-04-06T02:16:03.281614+00:00","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"01875d31","cell_type":"code","source":"if __name__ == '__main__':\n    run_inference()","metadata":{"execution":{"iopub.execute_input":"2026-04-06T02:16:03.295904Z","iopub.status.busy":"2026-04-06T02:16:03.295593Z","iopub.status.idle":"2026-04-06T02:17:14.920275Z","shell.execute_reply":"2026-04-06T02:17:14.919084Z"},"papermill":{"duration":71.628536,"end_time":"2026-04-06T02:17:14.921946+00:00","exception":false,"start_time":"2026-04-06T02:16:03.293410+00:00","status":"completed"},"tags":[]},"outputs":[],"execution_count":null}]}