YOLOv11_Product_Detection_Implementation_Final.ipynb

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        "This notebook contains the implementation of the models to:\n",
        "\n",
        "- Make boundary boxes\n",
        "- Crop the images\n",
        "    - Store the cropped images in a folder, with the name of the shelf_image. All the cropped images should be there\n",
        "- Try to eliminate the non-product images\n",
        "\n",
        "Models:\n",
        "\n",
        "- YOLOv11.n (10MB)\n",
        "- YOLOv5 (148MB)"
      ],
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            "Collecting ultralytics\n",
            "  Downloading ultralytics-8.3.36-py3-none-any.whl.metadata (35 kB)\n",
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            "Collecting ultralytics-thop>=2.0.0 (from ultralytics)\n",
            "  Downloading ultralytics_thop-2.0.12-py3-none-any.whl.metadata (9.4 kB)\n",
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            "Installing collected packages: ultralytics-thop, ultralytics\n",
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            "Cloning into 'dotslash-repo'...\n",
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          ]
        }
      ],
      "source": [
        "# Dependencies and Packages:\n",
        "\n",
        "!pip install ultralytics\n",
        "# from google.colab import drive\n",
        "# drive.mount('/content/drive')\n",
        "# !cp \"/content/drive/MyDrive/YOLOv5_SKU.pt\" /content/\n",
        "!git clone https://github.com/warun7/dotslash-repo.git\n",
        "!cp \"/content/dotslash-repo/YOLOv11_SKU.pt\" /content/\n",
        "!unzip /content/dotslash-repo/smart_cataloging.zip"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Importing the models\n",
        "\n",
        "import torch\n",
        "import ultralytics\n",
        "from ultralytics import YOLO\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gKGwZdkKC1cn",
        "outputId": "86dda25d-0704-4fa7-deff-3d556206a9aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Creating new Ultralytics Settings v0.0.6 file ✅ \n",
            "View Ultralytics Settings with 'yolo settings' or at '/root/.config/Ultralytics/settings.json'\n",
            "Update Settings with 'yolo settings key=value', i.e. 'yolo settings runs_dir=path/to/dir'. For help see https://docs.ultralytics.com/quickstart/#ultralytics-settings.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import numpy as np\n",
        "from PIL import Image\n",
        "from pathlib import Path\n",
        "import cv2\n",
        "from ultralytics import YOLO  # For YOLOv11\n",
        "\n",
        "class YOLOv11Detector:\n",
        "    def __init__(self, weights_path, base_output_dir):\n",
        "        \"\"\"\n",
        "        Initialize YOLOv11 detector\n",
        "\n",
        "        Args:\n",
        "            weights_path (str): Path to YOLOv11 weights\n",
        "            base_output_dir (str): Base directory for outputs\n",
        "        \"\"\"\n",
        "        self.model = self._load_model(weights_path)\n",
        "        self.base_output_dir = base_output_dir\n",
        "        os.makedirs(base_output_dir, exist_ok=True)\n",
        "\n",
        "    def _load_model(self, weights_path):\n",
        "        \"\"\"Load YOLOv11 model with specified weights\"\"\"\n",
        "        model = YOLO(weights_path)\n",
        "        model.conf = 0.25  # confidence threshold\n",
        "        model.iou = 0.45   # NMS IoU threshold\n",
        "        return model\n",
        "\n",
        "    def create_output_directory(self, image_name):\n",
        "        \"\"\"Create output directory structure for an image\"\"\"\n",
        "        image_dir = os.path.join(self.base_output_dir, image_name)\n",
        "        os.makedirs(image_dir, exist_ok=True)\n",
        "        return image_dir\n",
        "\n",
        "    def draw_boxes(self, image, detections):\n",
        "        \"\"\"Draw bounding boxes on image\"\"\"\n",
        "        img = np.array(image)\n",
        "\n",
        "        # Get detections\n",
        "        boxes = detections.boxes.data.cpu().numpy()\n",
        "\n",
        "        for box in boxes:\n",
        "            x1, y1, x2, y2 = map(int, box[:4])\n",
        "            conf = box[4]\n",
        "\n",
        "            # Draw rectangle\n",
        "            cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 2)\n",
        "\n",
        "            # Add confidence score\n",
        "            conf_text = f'{conf:.2f}'\n",
        "            cv2.putText(img, conf_text, (x1, y1-10),\n",
        "                       cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)\n",
        "\n",
        "        return Image.fromarray(img)\n",
        "\n",
        "    def crop_detections(self, image, detections, output_dir, image_name):\n",
        "        \"\"\"Crop and save detected products\"\"\"\n",
        "        saved_paths = []\n",
        "\n",
        "        # Get detections\n",
        "        boxes = detections.boxes.data.cpu().numpy()\n",
        "\n",
        "        for i, box in enumerate(boxes):\n",
        "            x1, y1, x2, y2 = map(int, box[:4])\n",
        "\n",
        "            # Crop detection\n",
        "            cropped = image.crop((x1, y1, x2, y2))\n",
        "\n",
        "            # Save cropped image\n",
        "            save_path = os.path.join(output_dir, f\"{image_name}_P_{i+1}.jpg\")\n",
        "            cropped.save(save_path)\n",
        "            saved_paths.append(save_path)\n",
        "\n",
        "        return saved_paths\n",
        "\n",
        "    def filter_non_products(self, image_path, min_size=50, aspect_ratio_range=(0.5, 2.0)):\n",
        "        \"\"\"Filter out likely non-product images\"\"\"\n",
        "        img = Image.open(image_path)\n",
        "        width, height = img.size\n",
        "\n",
        "        if width < min_size or height < min_size:\n",
        "            return False\n",
        "\n",
        "        aspect_ratio = width / height\n",
        "        if not (aspect_ratio_range[0] <= aspect_ratio <= aspect_ratio_range[1]):\n",
        "            return False\n",
        "\n",
        "        return True\n",
        "\n",
        "    def process_image(self, image_path):\n",
        "        \"\"\"Process single image with YOLOv11\"\"\"\n",
        "        # Load image\n",
        "        image = Image.open(image_path)\n",
        "        image_name = Path(image_path).stem\n",
        "\n",
        "        print(f\"Processing {image_name}...\")\n",
        "\n",
        "        # Create output directory\n",
        "        output_dir = self.create_output_directory(image_name)\n",
        "\n",
        "        # Detect products\n",
        "        detections = self.model(image)[0]\n",
        "\n",
        "        # Save image with bounding boxes\n",
        "        bb_image = self.draw_boxes(image, detections)\n",
        "        bb_path = os.path.join(output_dir, f\"{image_name}_BB.jpg\")\n",
        "        bb_image.save(bb_path)\n",
        "\n",
        "        # Crop and save individual products\n",
        "        cropped_paths = self.crop_detections(image, detections, output_dir, image_name)\n",
        "\n",
        "        # # Filter non-products\n",
        "        # for path in cropped_paths:\n",
        "        #     if not self.filter_non_products(path):\n",
        "        #         os.remove(path)\n",
        "        #         print(f\"Removed likely non-product: {path}\")\n",
        "\n",
        "        print(f\"Finished processing {image_name}\")\n",
        "        print(f\"Detected {len(cropped_paths)} products\")\n",
        "\n",
        "    def process_directory(self, input_dir):\n",
        "        \"\"\"Process all images in directory\"\"\"\n",
        "        total_images = len([f for f in os.listdir(input_dir)\n",
        "                          if f.lower().endswith(('.jpg', '.jpeg'))])\n",
        "        processed = 0\n",
        "\n",
        "        print(f\"Found {total_images} images to process\")\n",
        "\n",
        "        for image_file in os.listdir(input_dir):\n",
        "            if image_file.lower().endswith(('.jpg', '.jpeg')):\n",
        "                image_path = os.path.join(input_dir, image_file)\n",
        "                self.process_image(image_path)\n",
        "                processed += 1\n",
        "                print(f\"Progress: {processed}/{total_images} images processed\")\n",
        "\n",
        "def main():\n",
        "    # Configuration\n",
        "    input_dir = \"/content/smart_cataloging\"\n",
        "    base_output_dir = \"YOLOv11_results\"\n",
        "    yolov11_weights = \"/content/YOLOv11_SKU.pt\"\n",
        "\n",
        "    # Initialize detector\n",
        "    detector = YOLOv11Detector(\n",
        "        weights_path=yolov11_weights,\n",
        "        base_output_dir=base_output_dir\n",
        "    )\n",
        "\n",
        "    # Process all images\n",
        "    detector.process_directory(input_dir)\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "    main()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hrNUuaisj3jk",
        "outputId": "36ed0c3b-1065-4521-d610-a3dfa2c32e84"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 52 images to process\n",
            "Processing store_22...\n",
            "\n",
            "0: 640x480 175 objects, 46.6ms\n",
            "Speed: 35.2ms preprocess, 46.6ms inference, 751.4ms postprocess per image at shape (1, 3, 640, 480)\n",
            "Finished processing store_22\n",
            "Detected 175 products\n",
            "Progress: 1/52 images processed\n",
            "Processing store_26...\n",
            "\n",
            "0: 640x480 93 objects, 9.7ms\n",
            "Speed: 2.5ms preprocess, 9.7ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 480)\n",
            "Finished processing store_26\n",
            "Detected 93 products\n",
            "Progress: 2/52 images processed\n",
            "Processing store_18...\n",
            "\n",
            "0: 640x480 17 objects, 9.7ms\n",
            "Speed: 2.8ms preprocess, 9.7ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 480)\n",
            "Finished processing store_18\n",
            "Detected 17 products\n",
            "Progress: 3/52 images processed\n",
            "Processing store_47...\n",
            "\n",
            "0: 640x480 110 objects, 9.6ms\n",
            "Speed: 2.7ms preprocess, 9.6ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 480)\n",
            "Finished processing store_47\n",
            "Detected 110 products\n",
            "Progress: 4/52 images processed\n",
            "Processing store_34...\n",
            "\n",
            "0: 480x640 64 objects, 47.5ms\n",
            "Speed: 2.5ms preprocess, 47.5ms inference, 1.5ms postprocess per image at shape (1, 3, 480, 640)\n",
            "Finished processing store_34\n",
            "Detected 64 products\n",
            "Progress: 5/52 images processed\n",
            "Processing store_40...\n",
            "\n",
            "0: 384x640 111 objects, 113.8ms\n",
            "Speed: 2.2ms preprocess, 113.8ms inference, 2.7ms postprocess per image at shape (1, 3, 384, 640)\n",
            "Finished processing store_40\n",
            "Detected 111 products\n",
            "Progress: 6/52 images processed\n",
            "Processing store_11...\n",
            "\n",
            "0: 640x480 105 objects, 11.6ms\n",
            "Speed: 2.0ms preprocess, 11.6ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 480)\n",
            "Finished processing store_11\n",
            "Detected 105 products\n",
            "Progress: 7/52 images processed\n",
            "Processing store_28...\n",
            "\n",
            "0: 640x480 105 objects, 10.0ms\n",
            "Speed: 2.7ms preprocess, 10.0ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 480)\n",
            "Finished processing store_28\n",
            "Detected 105 products\n",
            "Progress: 8/52 images processed\n",
            "Processing store_7...\n",
            "\n",
            "0: 288x640 74 objects, 48.7ms\n",
            "Speed: 1.8ms preprocess, 48.7ms inference, 2.6ms postprocess per image at shape (1, 3, 288, 640)\n",
            "Finished processing store_7\n",
            "Detected 74 products\n",
            "Progress: 9/52 images processed\n",
            "Processing store_8...\n",
            "\n",
            "0: 288x640 63 objects, 10.3ms\n",
            "Speed: 1.7ms preprocess, 10.3ms inference, 1.4ms postprocess per image at shape (1, 3, 288, 640)\n",
            "Finished processing store_8\n",
            "Detected 63 products\n",
            "Progress: 10/52 images processed\n",
            "Processing store_5...\n",
            "\n",
            "0: 288x640 119 objects, 9.7ms\n",
            "Speed: 1.8ms preprocess, 9.7ms inference, 1.4ms postprocess per image at shape (1, 3, 288, 640)\n",
            "Finished processing store_5\n",
            "Detected 119 products\n",
            "Progress: 11/52 images processed\n",
            "Processing store_33...\n",
            "\n",
            "0: 640x480 35 objects, 10.5ms\n",
            "Speed: 2.6ms preprocess, 10.5ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 480)\n",
            "Finished processing store_33\n",
            "Detected 35 products\n",
            "Progress: 12/52 images processed\n",
            "Processing store_36...\n",
            "\n",
            "0: 640x480 67 objects, 9.9ms\n",
            "Speed: 2.7ms preprocess, 9.9ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 480)\n",
            "Finished processing store_36\n",
            "Detected 67 products\n",
            "Progress: 13/52 images processed\n",
            "Processing store_9...\n",
            "\n",
            "0: 288x640 3 objects, 10.7ms\n",
            "Speed: 1.8ms preprocess, 10.7ms inference, 3.4ms postprocess per image at shape (1, 3, 288, 640)\n",
            "Finished processing store_9\n",
            "Detected 3 products\n",
            "Progress: 14/52 images processed\n",
            "Processing store_13...\n",
            "\n",
            "0: 480x640 120 objects, 11.1ms\n",
            "Speed: 2.2ms preprocess, 11.1ms inference, 1.5ms postprocess per image at shape (1, 3, 480, 640)\n",
            "Finished processing store_13\n",
            "Detected 120 products\n",
            "Progress: 15/52 images processed\n",
            "Processing store_12...\n",
            "\n",
            "0: 480x640 125 objects, 14.0ms\n",
            "Speed: 3.0ms preprocess, 14.0ms inference, 2.1ms postprocess per image at shape (1, 3, 480, 640)\n",
            "Finished processing store_12\n",
            "Detected 125 products\n",
            "Progress: 16/52 images processed\n",
            "Processing store_27...\n",
            "\n",
            "0: 640x480 150 objects, 10.8ms\n",
            "Speed: 3.1ms preprocess, 10.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 480)\n",
            "Finished processing store_27\n",
            "Detected 150 products\n",
            "Progress: 17/52 images processed\n",
            "Processing store_30...\n",
            "\n",
            "0: 640x480 150 objects, 10.3ms\n",
            "Speed: 2.8ms preprocess, 10.3ms inference, 1.9ms postprocess per image at shape (1, 3, 640, 480)\n",
            "Finished processing store_30\n",
            "Detected 150 products\n",
            "Progress: 18/52 images processed\n",
            "Processing store_25...\n",
            "\n",
            "0: 640x480 69 objects, 15.2ms\n",
            "Speed: 2.8ms preprocess, 15.2ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 480)\n",
            "Finished processing store_25\n",
            "Detected 69 products\n",
            "Progress: 19/52 images processed\n",
            "Processing store_10...\n",
            "\n",
            "0: 288x640 6 objects, 17.0ms\n",
            "Speed: 2.9ms preprocess, 17.0ms inference, 4.8ms postprocess per image at shape (1, 3, 288, 640)\n",
            "Finished processing store_10\n",
            "Detected 6 products\n",
            "Progress: 20/52 images processed\n",
            "Processing store_15...\n",
            "\n",
            "0: 480x640 97 objects, 13.9ms\n",
            "Speed: 2.7ms preprocess, 13.9ms inference, 1.7ms postprocess per image at shape (1, 3, 480, 640)\n",
            "Finished processing store_15\n",
            "Detected 97 products\n",
            "Progress: 21/52 images processed\n",
            "Processing store_39...\n",
            "\n",
            "0: 384x640 46 objects, 14.2ms\n",
            "Speed: 2.4ms preprocess, 14.2ms inference, 1.6ms postprocess per image at shape (1, 3, 384, 640)\n",
            "Finished processing store_39\n",
            "Detected 46 products\n",
            "Progress: 22/52 images processed\n",
            "Processing store_52...\n",
            "\n",
            "0: 480x640 33 objects, 23.3ms\n",
            "Speed: 6.0ms preprocess, 23.3ms inference, 2.7ms postprocess per image at shape (1, 3, 480, 640)\n",
            "Finished processing store_52\n",
            "Detected 33 products\n",
            "Progress: 23/52 images processed\n",
            "Processing store_6...\n",
            "\n",
            "0: 480x640 115 objects, 12.1ms\n",
            "Speed: 4.7ms preprocess, 12.1ms inference, 1.9ms postprocess per image at shape (1, 3, 480, 640)\n",
            "Finished processing store_6\n",
            "Detected 115 products\n",
            "Progress: 24/52 images processed\n",
            "Processing store_3...\n",
            "\n",
            "0: 480x640 29 objects, 13.1ms\n",
            "Speed: 4.8ms preprocess, 13.1ms inference, 3.1ms postprocess per image at shape (1, 3, 480, 640)\n",
            "Finished processing store_3\n",
            "Detected 29 products\n",
            "Progress: 25/52 images processed\n",
            "Processing store_17...\n",
            "\n",
            "0: 640x480 75 objects, 13.2ms\n",
            "Speed: 5.6ms preprocess, 13.2ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 480)\n",
            "Finished processing store_17\n",
            "Detected 75 products\n",
            "Progress: 26/52 images processed\n",
            "Processing store_42...\n",
            "\n",
            "0: 384x640 59 objects, 15.9ms\n",
            "Speed: 3.3ms preprocess, 15.9ms inference, 4.6ms postprocess per image at shape (1, 3, 384, 640)\n",
            "Finished processing store_42\n",
            "Detected 59 products\n",
            "Progress: 27/52 images processed\n",
            "Processing store_2...\n",
            "\n",
            "0: 480x640 81 objects, 16.8ms\n",
            "Speed: 4.5ms preprocess, 16.8ms inference, 2.0ms postprocess per image at shape (1, 3, 480, 640)\n",
            "Finished processing store_2\n",
            "Detected 81 products\n",
            "Progress: 28/52 images processed\n",
            "Processing store_43...\n",
            "\n",
            "0: 640x480 85 objects, 18.2ms\n",
            "Speed: 4.3ms preprocess, 18.2ms inference, 1.9ms postprocess per image at shape (1, 3, 640, 480)\n",
            "Finished processing store_43\n",
            "Detected 85 products\n",
            "Progress: 29/52 images processed\n",
            "Processing store_31...\n",
            "\n",
            "0: 640x480 83 objects, 16.5ms\n",
            "Speed: 4.5ms preprocess, 16.5ms inference, 3.2ms postprocess per image at shape (1, 3, 640, 480)\n",
            "Finished processing store_31\n",
            "Detected 83 products\n",
            "Progress: 30/52 images processed\n",
            "Processing store_29...\n",
            "\n",
            "0: 640x480 48 objects, 16.4ms\n",
            "Speed: 4.5ms preprocess, 16.4ms inference, 2.2ms postprocess per image at shape (1, 3, 640, 480)\n",
            "Finished processing store_29\n",
            "Detected 48 products\n",
            "Progress: 31/52 images processed\n",
            "Processing store_38...\n",
            "\n",
            "0: 640x480 121 objects, 16.2ms\n",
            "Speed: 4.4ms preprocess, 16.2ms inference, 2.2ms postprocess per image at shape (1, 3, 640, 480)\n",
            "Finished processing store_38\n",
            "Detected 121 products\n",
            "Progress: 32/52 images processed\n",
            "Processing store_1...\n",
            "\n",
            "0: 480x640 64 objects, 17.0ms\n",
            "Speed: 4.4ms preprocess, 17.0ms inference, 4.2ms postprocess per image at shape (1, 3, 480, 640)\n",
            "Finished processing store_1\n",
            "Detected 64 products\n",
            "Progress: 33/52 images processed\n",
            "Processing store_49...\n",
            "\n",
            "0: 480x640 104 objects, 17.0ms\n",
            "Speed: 4.5ms preprocess, 17.0ms inference, 2.1ms postprocess per image at shape (1, 3, 480, 640)\n",
            "Finished processing store_49\n",
            "Detected 104 products\n",
            "Progress: 34/52 images processed\n",
            "Processing store_4...\n",
            "\n",
            "0: 480x640 54 objects, 18.1ms\n",
            "Speed: 4.8ms preprocess, 18.1ms inference, 4.2ms postprocess per image at shape (1, 3, 480, 640)\n",
            "Finished processing store_4\n",
            "Detected 54 products\n",
            "Progress: 35/52 images processed\n",
            "Processing store_48...\n",
            "\n",
            "0: 640x480 89 objects, 10.7ms\n",
            "Speed: 2.8ms preprocess, 10.7ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 480)\n",
            "Finished processing store_48\n",
            "Detected 89 products\n",
            "Progress: 36/52 images processed\n",
            "Processing store_44...\n",
            "\n",
            "0: 640x480 81 objects, 9.9ms\n",
            "Speed: 2.9ms preprocess, 9.9ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 480)\n",
            "Finished processing store_44\n",
            "Detected 81 products\n",
            "Progress: 37/52 images processed\n",
            "Processing store_24...\n",
            "\n",
            "0: 640x480 300 objects, 9.6ms\n",
            "Speed: 2.8ms preprocess, 9.6ms inference, 1.9ms postprocess per image at shape (1, 3, 640, 480)\n",
            "Finished processing store_24\n",
            "Detected 300 products\n",
            "Progress: 38/52 images processed\n",
            "Processing store_51...\n",
            "\n",
            "0: 480x640 61 objects, 9.9ms\n",
            "Speed: 2.7ms preprocess, 9.9ms inference, 1.3ms postprocess per image at shape (1, 3, 480, 640)\n",
            "Finished processing store_51\n",
            "Detected 61 products\n",
            "Progress: 39/52 images processed\n",
            "Processing store_41...\n",
            "\n",
            "0: 640x480 93 objects, 10.3ms\n",
            "Speed: 2.9ms preprocess, 10.3ms inference, 2.2ms postprocess per image at shape (1, 3, 640, 480)\n",
            "Finished processing store_41\n",
            "Detected 93 products\n",
            "Progress: 40/52 images processed\n",
            "Processing store_16...\n",
            "\n",
            "0: 640x480 10 objects, 9.9ms\n",
            "Speed: 2.8ms preprocess, 9.9ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 480)\n",
            "Finished processing store_16\n",
            "Detected 10 products\n",
            "Progress: 41/52 images processed\n",
            "Processing store_23...\n",
            "\n",
            "0: 480x640 93 objects, 14.5ms\n",
            "Speed: 3.4ms preprocess, 14.5ms inference, 1.7ms postprocess per image at shape (1, 3, 480, 640)\n",
            "Finished processing store_23\n",
            "Detected 93 products\n",
            "Progress: 42/52 images processed\n",
            "Processing store_37...\n",
            "\n",
            "0: 640x480 67 objects, 10.7ms\n",
            "Speed: 2.8ms preprocess, 10.7ms inference, 2.3ms postprocess per image at shape (1, 3, 640, 480)\n",
            "Finished processing store_37\n",
            "Detected 67 products\n",
            "Progress: 43/52 images processed\n",
            "Processing store_46...\n",
            "\n",
            "0: 640x480 61 objects, 9.5ms\n",
            "Speed: 2.6ms preprocess, 9.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 480)\n",
            "Finished processing store_46\n",
            "Detected 61 products\n",
            "Progress: 44/52 images processed\n",
            "Processing store_14...\n",
            "\n",
            "0: 288x640 59 objects, 11.1ms\n",
            "Speed: 1.7ms preprocess, 11.1ms inference, 1.4ms postprocess per image at shape (1, 3, 288, 640)\n",
            "Finished processing store_14\n",
            "Detected 59 products\n",
            "Progress: 45/52 images processed\n",
            "Processing store_20...\n",
            "\n",
            "0: 640x480 35 objects, 10.2ms\n",
            "Speed: 2.7ms preprocess, 10.2ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 480)\n",
            "Finished processing store_20\n",
            "Detected 35 products\n",
            "Progress: 46/52 images processed\n",
            "Processing store_19...\n",
            "\n",
            "0: 640x480 51 objects, 13.8ms\n",
            "Speed: 4.7ms preprocess, 13.8ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 480)\n",
            "Finished processing store_19\n",
            "Detected 51 products\n",
            "Progress: 47/52 images processed\n",
            "Processing store_50...\n",
            "\n",
            "0: 480x640 116 objects, 11.0ms\n",
            "Speed: 2.8ms preprocess, 11.0ms inference, 2.4ms postprocess per image at shape (1, 3, 480, 640)\n",
            "Finished processing store_50\n",
            "Detected 116 products\n",
            "Progress: 48/52 images processed\n",
            "Processing store_45...\n",
            "\n",
            "0: 480x640 80 objects, 10.6ms\n",
            "Speed: 2.7ms preprocess, 10.6ms inference, 1.5ms postprocess per image at shape (1, 3, 480, 640)\n",
            "Finished processing store_45\n",
            "Detected 80 products\n",
            "Progress: 49/52 images processed\n",
            "Processing store_35...\n",
            "\n",
            "0: 640x480 105 objects, 16.3ms\n",
            "Speed: 4.6ms preprocess, 16.3ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 480)\n",
            "Finished processing store_35\n",
            "Detected 105 products\n",
            "Progress: 50/52 images processed\n",
            "Processing store_32...\n",
            "\n",
            "0: 640x480 88 objects, 9.8ms\n",
            "Speed: 2.8ms preprocess, 9.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 480)\n",
            "Finished processing store_32\n",
            "Detected 88 products\n",
            "Progress: 51/52 images processed\n",
            "Processing store_21...\n",
            "\n",
            "0: 640x480 161 objects, 9.8ms\n",
            "Speed: 2.7ms preprocess, 9.8ms inference, 1.9ms postprocess per image at shape (1, 3, 640, 480)\n",
            "Finished processing store_21\n",
            "Detected 161 products\n",
            "Progress: 52/52 images processed\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!zip YOLOv11_results_all -r /content/YOLOv11_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xPMH5oEMkR-b",
        "outputId": "a03bd43b-827a-4562-b602-4827924939d3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  adding: content/YOLOv11_results/ (stored 0%)\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Trying OCR:"
      ],
      "metadata": {
        "id": "cBuiglobo1K5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load model directly\n",
        "from transformers import AutoProcessor, AutoModelForImageTextToText\n",
        "\n",
        "processor = AutoProcessor.from_pretrained(\"huz-relay/idefics2-8b-ocr\")\n",
        "model = AutoModelForImageTextToText.from_pretrained(\"huz-relay/idefics2-8b-ocr\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 770,
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        },
        "id": "e29ZuZjEo0_I",
        "outputId": "31b5b065-0d61-45de-91b8-7cd60f08f7ed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "processor_config.json:   0%|          | 0.00/483 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ebf61bbdc0874f31a3eecbfebda2a4e3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Chat templates should be in a 'chat_template.json' file but found key='chat_template' in the processor's config. Make sure to move your template to its own file.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "preprocessor_config.json:   0%|          | 0.00/460 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/1.64k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "f79df4e7ca72401eb7db289286cd16d0"
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          "metadata": {}
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        {
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            ],
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          "metadata": {}
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          "metadata": {}
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            "application/vnd.jupyter.widget-view+json": {
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            }
          },
          "metadata": {}
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "436337f1c0ad48b3807760a3c07eb0fb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading shards:   0%|          | 0/7 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1ea0fd4935624b9c9b68aad678dfd9c7"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "8e1eedac2aa648ca825ddcf1a9780a87"
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          },
          "metadata": {}
        },
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              "model_id": "da626811333c4dd7af752b730ec42c6d"
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          "metadata": {}
        },
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              "model_id": "9a06340d342f4a2ab6332f9b1b146053"
            }
          },
          "metadata": {}
        },
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          "metadata": {}
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              "model_id": "527b4e163eae4b87afc39839b2056232"
            }
          },
          "metadata": {}
        },
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              "model_id": "70b31a3e22fa4d8eb05051285a0fbed2"
            }
          },
          "metadata": {}
        },
        {
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            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "a4a7eda2b43b4cadbc812b196c0fe34f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c2e4be536d9b4a84bec0d759840b89f8"
            }
          },
          "metadata": {}
        },
        {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0176f65a62ea4ccb87928ef57b0dbe2a"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 144
        },
        "id": "2M9KW4CgzGyf",
        "outputId": "d96a5231-8703-491c-919c-d9062f652c30"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'model' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-a87813ffe34d>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'model' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from PIL import Image\n",
        "from transformers import AutoProcessor, AutoModelForImageTextToText\n",
        "\n",
        "def perform_ocr(image_path, model, processor, prompt=\"The following is a product image, extract the product name from this image:\", max_new_tokens=512):\n",
        "    \"\"\"\n",
        "    Perform OCR on an image using IDEFICS2 model.\n",
        "\n",
        "    Args:\n",
        "        image_path (str): Path to the image file\n",
        "        model: The loaded IDEFICS2 model\n",
        "        processor: The loaded IDEFICS2 processor\n",
        "        prompt (str): Prompt to guide the text extraction\n",
        "        max_new_tokens (int): Maximum number of tokens to generate\n",
        "\n",
        "    Returns:\n",
        "        str: Extracted text from the image\n",
        "    \"\"\"\n",
        "    # Load and preprocess the image\n",
        "    image = Image.open(image_path)\n",
        "\n",
        "    # Prepare inputs\n",
        "    inputs = processor(\n",
        "        prompt,\n",
        "        images=image,\n",
        "        return_tensors=\"pt\",\n",
        "        truncation=True,\n",
        "        max_length=2048\n",
        "    )\n",
        "\n",
        "    # Generate text\n",
        "    with torch.no_grad():\n",
        "        outputs = model.generate(\n",
        "            **inputs,\n",
        "            max_new_tokens=max_new_tokens,\n",
        "            do_sample=False\n",
        "        )\n",
        "\n",
        "    # Decode the generated text\n",
        "    generated_text = processor.batch_decode(outputs, skip_special_tokens=True)[0]\n",
        "\n",
        "    # Remove the prompt from the generated text\n",
        "    if generated_text.startswith(prompt):\n",
        "        generated_text = generated_text[len(prompt):].strip()\n",
        "\n",
        "    return generated_text\n",
        "\n",
        "# Example usage\n",
        "if __name__ == \"__main__\":\n",
        "    # Load model and processor (you already have this part)\n",
        "    # processor = AutoProcessor.from_pretrained(\"huz-relay/idefics2-8b-ocr\")\n",
        "    # model = AutoModelForImageTextToText.from_pretrained(\"huz-relay/idefics2-8b-ocr\")\n",
        "\n",
        "    # Example usage\n",
        "    image_path = \"/content/YOLOv11_results/store_29/store_29_P_26.jpg\"\n",
        "    extracted_text = perform_ocr(image_path, model, processor)\n",
        "    print(\"Extracted text:\", extracted_text)\n",
        "\n",
        "    # Example with custom prompt\n",
        "    custom_prompt = \"The following is a product image, extract the product name from this image\"\n",
        "    extracted_text = perform_ocr(image_path, model, processor, prompt=custom_prompt)\n",
        "    print(\"Extracted text with custom prompt:\", extracted_text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 341
        },
        "id": "Jj7k9GZto3WA",
        "outputId": "e5df15da-8868-47f9-b324-84cdb8a9f5d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "Idefics2Processor.__call__() got multiple values for argument 'images'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-6-7a636fcfc6e8>\u001b[0m in \u001b[0;36m<cell line: 49>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     54\u001b[0m     \u001b[0;31m# Example usage\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     55\u001b[0m     \u001b[0mimage_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"/content/YOLOv11_results/store_29/store_29_P_26.jpg\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 56\u001b[0;31m     \u001b[0mextracted_text\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mperform_ocr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprocessor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     57\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Extracted text:\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mextracted_text\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     58\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-6-7a636fcfc6e8>\u001b[0m in \u001b[0;36mperform_ocr\u001b[0;34m(image_path, model, processor, prompt, max_new_tokens)\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m     \u001b[0;31m# Prepare inputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m     inputs = processor(\n\u001b[0m\u001b[1;32m     24\u001b[0m         \u001b[0mprompt\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m         \u001b[0mimages\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: Idefics2Processor.__call__() got multiple values for argument 'images'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from PIL import Image\n",
        "from transformers import AutoProcessor, AutoModelForImageTextToText\n",
        "\n",
        "def perform_ocr(image_path, model, processor, prompt=\"Extract all text from this image:\", max_new_tokens=512):\n",
        "    \"\"\"\n",
        "    Perform OCR on an image using IDEFICS2 model.\n",
        "\n",
        "    Args:\n",
        "        image_path (str): Path to the image file\n",
        "        model: The loaded IDEFICS2 model\n",
        "        processor: The loaded IDEFICS2 processor\n",
        "        prompt (str): Prompt to guide the text extraction\n",
        "        max_new_tokens (int): Maximum number of tokens to generate\n",
        "\n",
        "    Returns:\n",
        "        str: Extracted text from the image\n",
        "    \"\"\"\n",
        "    # Load and preprocess the image\n",
        "    image = Image.open(image_path)\n",
        "\n",
        "    # Prepare inputs - format text and images correctly\n",
        "    inputs = processor(\n",
        "       text=[prompt],\n",
        "       images=image,\n",
        "       return_tensors=\"pt\",\n",
        "       truncation=True,\n",
        "       max_length=2048\n",
        "   )\n",
        "    # Generate text\n",
        "    with torch.no_grad():\n",
        "        outputs = model.generate(\n",
        "            **inputs,\n",
        "            max_new_tokens=max_new_tokens,\n",
        "            do_sample=False\n",
        "        )\n",
        "\n",
        "    # Decode the generated text\n",
        "    generated_text = processor.batch_decode(outputs, skip_special_tokens=True)[0]\n",
        "\n",
        "    # Remove the prompt from the generated text if it appears at the start\n",
        "    if generated_text.startswith(prompt):\n",
        "        generated_text = generated_text[len(prompt):].strip()\n",
        "\n",
        "    return generated_text\n",
        "\n",
        "# Example usage\n",
        "if __name__ == \"__main__\":\n",
        "    # Load image and try OCR\n",
        "    image_path = \"/content/Everes_masala.jpg\"\n",
        "\n",
        "    # Different prompts to try\n",
        "    prompts = [\n",
        "        \"Extract all text from this image:\",\n",
        "        \"What text can you read in this image? Extract all text:\",\n",
        "        \"Please transcribe all visible text in this image, including any small text:\"\n",
        "    ]\n",
        "\n",
        "    # Try with default prompt first\n",
        "    try:\n",
        "        extracted_text = perform_ocr(image_path, model, processor)\n",
        "        print(\"Extracted text:\", extracted_text)\n",
        "    except Exception as e:\n",
        "        print(f\"Error with default prompt: {str(e)}\")\n",
        "\n",
        "    # Try with different prompts\n",
        "    for prompt in prompts:\n",
        "        try:\n",
        "            print(f\"\\nTrying with prompt: {prompt}\")\n",
        "            extracted_text = perform_ocr(image_path, model, processor, prompt=prompt)\n",
        "            print(\"Extracted text:\", extracted_text)\n",
        "        except Exception as e:\n",
        "            print(f\"Error with prompt '{prompt}': {str(e)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8MtYMWYuu3M2",
        "outputId": "4c4169e3-a719-450e-dfee-bf5b3876085c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Error with default prompt: The number of images in the text [0] and images  [1] should be the same.\n",
            "\n",
            "Trying with prompt: Extract all text from this image:\n",
            "Error with prompt 'Extract all text from this image:': The number of images in the text [0] and images  [1] should be the same.\n",
            "\n",
            "Trying with prompt: What text can you read in this image? Extract all text:\n",
            "Error with prompt 'What text can you read in this image? Extract all text:': The number of images in the text [0] and images  [1] should be the same.\n",
            "\n",
            "Trying with prompt: Please transcribe all visible text in this image, including any small text:\n",
            "Error with prompt 'Please transcribe all visible text in this image, including any small text:': The number of images in the text [0] and images  [1] should be the same.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True"
      ],
      "metadata": {
        "id": "n0pQy-oFyJY5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = model.to(\"cuda\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "nVWiPp-RwVX6",
        "outputId": "f0094bfa-5e41-479c-af5a-fc1d4a8f6b0a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "OutOfMemoryError",
          "evalue": "CUDA out of memory. Tried to allocate 224.00 MiB. GPU 0 has a total capacity of 14.75 GiB of which 211.06 MiB is free. Process 4277 has 14.54 GiB memory in use. Of the allocated memory 14.16 GiB is allocated by PyTorch, and 236.22 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mOutOfMemoryError\u001b[0m                          Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-22-ddb4345499c6>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"cuda\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py\u001b[0m in \u001b[0;36mto\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   3155\u001b[0m                     \u001b[0;34m\" `dtype` by passing the correct `torch_dtype` argument.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3156\u001b[0m                 )\n\u001b[0;32m-> 3157\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3158\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3159\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mhalf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36mto\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1338\u001b[0m                     \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1339\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1340\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconvert\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1341\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1342\u001b[0m     def register_full_backward_pre_hook(\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_apply\u001b[0;34m(self, fn, recurse)\u001b[0m\n\u001b[1;32m    898\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrecurse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    899\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchildren\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 900\u001b[0;31m                 \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    901\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    902\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mcompute_should_use_set_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensor_applied\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_apply\u001b[0;34m(self, fn, recurse)\u001b[0m\n\u001b[1;32m    898\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrecurse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    899\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchildren\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 900\u001b[0;31m                 \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    901\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    902\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mcompute_should_use_set_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensor_applied\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_apply\u001b[0;34m(self, fn, recurse)\u001b[0m\n\u001b[1;32m    898\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrecurse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    899\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchildren\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 900\u001b[0;31m                 \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    901\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    902\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mcompute_should_use_set_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensor_applied\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_apply\u001b[0;34m(self, fn, recurse)\u001b[0m\n\u001b[1;32m    898\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrecurse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    899\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchildren\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 900\u001b[0;31m                 \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    901\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    902\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mcompute_should_use_set_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensor_applied\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_apply\u001b[0;34m(self, fn, recurse)\u001b[0m\n\u001b[1;32m    898\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrecurse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    899\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchildren\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 900\u001b[0;31m                 \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    901\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    902\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mcompute_should_use_set_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensor_applied\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_apply\u001b[0;34m(self, fn, recurse)\u001b[0m\n\u001b[1;32m    898\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrecurse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    899\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchildren\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 900\u001b[0;31m                 \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    901\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    902\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mcompute_should_use_set_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensor_applied\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_apply\u001b[0;34m(self, fn, recurse)\u001b[0m\n\u001b[1;32m    925\u001b[0m             \u001b[0;31m# `with torch.no_grad():`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    926\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mno_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 927\u001b[0;31m                 \u001b[0mparam_applied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparam\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    928\u001b[0m             \u001b[0mp_should_use_set_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompute_should_use_set_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparam\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparam_applied\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    929\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36mconvert\u001b[0;34m(t)\u001b[0m\n\u001b[1;32m   1324\u001b[0m                         \u001b[0mmemory_format\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconvert_to_format\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1325\u001b[0m                     )\n\u001b[0;32m-> 1326\u001b[0;31m                 return t.to(\n\u001b[0m\u001b[1;32m   1327\u001b[0m                     \u001b[0mdevice\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1328\u001b[0m                     \u001b[0mdtype\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_floating_point\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_complex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mOutOfMemoryError\u001b[0m: CUDA out of memory. Tried to allocate 224.00 MiB. GPU 0 has a total capacity of 14.75 GiB of which 211.06 MiB is free. Process 4277 has 14.54 GiB memory in use. Of the allocated memory 14.16 GiB is allocated by PyTorch, and 236.22 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import requests\n",
        "import torch\n",
        "from PIL import Image\n",
        "from io import BytesIO\n",
        "\n",
        "from transformers import AutoProcessor, AutoModelForVision2Seq\n",
        "from transformers.image_utils import load_image\n",
        "\n",
        "# DEVICE = \"cuda:0\"\n",
        "\n",
        "# Note that passing the image urls (instead of the actual pil images) to the processor is also possible\n",
        "image1 = load_image(\"/content/Everest_SM.jpeg\")\n",
        "image2 = load_image(\"/content/Everest_PB.jpg\")\n",
        "image3 = load_image(\"/content/Everes_masala.jpg\")\n",
        "\n"
      ],
      "metadata": {
        "id": "01YwXdP5wkCJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# processor = AutoProcessor.from_pretrained(\"HuggingFaceM4/idefics2-8b-base\")\n",
        "# model = AutoModelForVision2Seq.from_pretrained(\n",
        "#     \"HuggingFaceM4/idefics2-8b-base\",\n",
        "# ).to(DEVICE)\n",
        "\n",
        "# Create inputs\n",
        "prompts = [\n",
        "  \"<image>The following is a product image, extract the product name from this image.<image>The following is a product image, extract the product name from this image,\",\n",
        "  \"The following is a product image, extract the product name from this image<image>\",\n",
        "]\n",
        "images = [[image1, image2], [image3]]\n",
        "inputs = processor(text=prompts, images=images, padding=True, return_tensors=\"pt\")\n",
        "inputs = {k: v for k, v in inputs.items()}\n",
        "\n",
        "\n",
        "# Generate\n",
        "generated_ids = model.generate(**inputs, max_new_tokens=500)\n",
        "generated_texts = processor.batch_decode(generated_ids, skip_special_tokens=True)\n",
        "\n",
        "print(generated_texts)\n",
        "# ['In this image, we can see the city of New York, and more specifically the Statue of Liberty. In this image, we can see the city of Chicago, and more specifically the skyscrapers of the city.', 'In which city is that bridge located? The Golden Gate Bridge is a suspension bridge spanning the Golden Gate, the one-mile-wide (1.6 km) strait connecting San Francisco Bay and the Pacific Ocean. The structure links the American city of San Francisco, California — the northern tip of the San Francisco Peninsula — to Marin County, carrying both U.S. Route 101 and California State Route 1 across the strait. The bridge is one of the most internationally recognized symbols of San Francisco, California, and the United States. It has been declared one of the Wonders of the Modern World by the American Society of Civil Engineers.\\n\\nThe Golden Gate Bridge is a suspension bridge spanning the Golden Gate, the one-mile-wide (1.6 km) strait connecting San Francisco Bay and the Pacific Ocean. The structure links the American city of San Francisco, California — the northern tip of the San Francisco Peninsula — to Marin County, carrying both U.S. Route 101 and California State Route 1 across the strait. The bridge is one of the most internationally recognized symbols of San Francisco, California, and the United States. It has been declared one of the Wonders of the Modern World by the American Society of Civil Engineers.\\n\\nThe Golden Gate Bridge is a suspension bridge spanning the Golden Gate, the one-mile-wide (1.6 km) strait connecting San Francisco Bay and the Pacific Ocean. The structure links the American city of San Francisco, California — the northern tip of the San Francisco Peninsula — to Marin County, carrying both U.S. Route 101 and California State Route 1 across the strait. The bridge is one of the most internationally recognized symbols of San Francisco, California, and the United States. It has been declared one of the Wonders of the Modern World by the American Society of Civil Engineers.\\n\\nThe Golden Gate Bridge is a suspension bridge spanning the Golden Gate, the one-mile-wide (1.6 km) strait connecting San Francisco Bay and the Pacific Ocean. The structure links the American city of San Francisco, California — the northern tip of the San Francisco Peninsula — to Marin County, carrying both U.S. Route 101 and California State Route 1 across the strait. The bridge is one of the most internationally recognized symbols of San Francisco, California, and']\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "F7EOVjzSvDTk",
        "outputId": "b55aa61d-2687-4bb7-befe-6bfc06968a46"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:2097: UserWarning: You are calling .generate() with the `input_ids` being on a device type different than your model's device. `input_ids` is on cpu, whereas the model is on cuda. You may experience unexpected behaviors or slower generation. Please make sure that you have put `input_ids` to the correct device by calling for example input_ids = input_ids.to('cuda') before running `.generate()`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "RuntimeError",
          "evalue": "Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu! (when checking argument for argument index in method wrapper_CUDA__index_select)",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-19-97066784e74c>\u001b[0m in \u001b[0;36m<cell line: 17>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;31m# Generate\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m \u001b[0mgenerated_ids\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_new_tokens\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m500\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m \u001b[0mgenerated_texts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprocessor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbatch_decode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgenerated_ids\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskip_special_tokens\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py\u001b[0m in \u001b[0;36mdecorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    114\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    115\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mctx_factory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 116\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    118\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self, inputs, generation_config, logits_processor, stopping_criteria, prefix_allowed_tokens_fn, synced_gpus, assistant_model, streamer, negative_prompt_ids, negative_prompt_attention_mask, **kwargs)\u001b[0m\n\u001b[1;32m   2213\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2214\u001b[0m             \u001b[0;31m# 12. run sample (it degenerates to greedy search when `generation_config.do_sample=False`)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2215\u001b[0;31m             result = self._sample(\n\u001b[0m\u001b[1;32m   2216\u001b[0m                 \u001b[0minput_ids\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2217\u001b[0m                 \u001b[0mlogits_processor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mprepared_logits_processor\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py\u001b[0m in \u001b[0;36m_sample\u001b[0;34m(self, input_ids, logits_processor, stopping_criteria, generation_config, synced_gpus, streamer, **model_kwargs)\u001b[0m\n\u001b[1;32m   3204\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3205\u001b[0m             \u001b[0;31m# forward pass to get next token\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3206\u001b[0;31m             \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mmodel_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3207\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3208\u001b[0m             \u001b[0;31m# synced_gpus: don't waste resources running the code we don't need; kwargs must be updated before skipping\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1734\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiled_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1735\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1736\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1737\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1738\u001b[0m     \u001b[0;31m# torchrec tests the code consistency with the following code\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1745\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1746\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1747\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1748\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1749\u001b[0m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/models/idefics2/modeling_idefics2.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input_ids, attention_mask, position_ids, past_key_values, inputs_embeds, pixel_values, pixel_attention_mask, image_hidden_states, labels, use_cache, output_attentions, output_hidden_states, return_dict, num_logits_to_keep)\u001b[0m\n\u001b[1;32m   1622\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1623\u001b[0m         \u001b[0;31m# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1624\u001b[0;31m         outputs = self.model(\n\u001b[0m\u001b[1;32m   1625\u001b[0m             \u001b[0minput_ids\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_ids\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1626\u001b[0m             \u001b[0mattention_mask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattention_mask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1734\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiled_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1735\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1736\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1737\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1738\u001b[0m     \u001b[0;31m# torchrec tests the code consistency with the following code\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1745\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1746\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1747\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1748\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1749\u001b[0m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/models/idefics2/modeling_idefics2.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input_ids, attention_mask, position_ids, past_key_values, inputs_embeds, pixel_values, pixel_attention_mask, image_hidden_states, use_cache, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m   1386\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1387\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0minputs_embeds\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1388\u001b[0;31m             \u001b[0minputs_embeds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_input_embeddings\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_ids\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1389\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1390\u001b[0m         \u001b[0;31m# START VISUAL INPUTS INTEGRATION\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1734\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiled_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1735\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1736\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1737\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1738\u001b[0m     \u001b[0;31m# torchrec tests the code consistency with the following code\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1745\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1746\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1747\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1748\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1749\u001b[0m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/sparse.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    188\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    189\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 190\u001b[0;31m         return F.embedding(\n\u001b[0m\u001b[1;32m    191\u001b[0m             \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    192\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/functional.py\u001b[0m in \u001b[0;36membedding\u001b[0;34m(input, weight, padding_idx, max_norm, norm_type, scale_grad_by_freq, sparse)\u001b[0m\n\u001b[1;32m   2549\u001b[0m         \u001b[0;31m# remove once script supports set_grad_enabled\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2550\u001b[0m         \u001b[0m_no_grad_embedding_renorm_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_norm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnorm_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2551\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0membedding\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpadding_idx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscale_grad_by_freq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msparse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2552\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2553\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mRuntimeError\u001b[0m: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu! (when checking argument for argument index in method wrapper_CUDA__index_select)"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "sKR9g8snx7Jk"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}