GroundingDINO_GPU.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FSsZq7VTDBsj",
        "outputId": "16bf2aff-047c-4c06-a780-986a36486a6c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'GroundingDINO'...\n",
            "remote: Enumerating objects: 308, done.\u001b[K\n",
            "remote: Counting objects: 100% (81/81), done.\u001b[K\n",
            "remote: Compressing objects: 100% (58/58), done.\u001b[K\n",
            "remote: Total 308 (delta 39), reused 31 (delta 23), pack-reused 227\u001b[K\n",
            "Receiving objects: 100% (308/308), 12.35 MiB | 18.82 MiB/s, done.\n",
            "Resolving deltas: 100% (143/143), done.\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/IDEA-Research/GroundingDINO.git"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd GroundingDINO"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0Vb1jtz4DhfA",
        "outputId": "03a177c0-92ed-4e07-ec65-1868724dc299"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/GroundingDINO\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q -e ."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vi4eziepDl4w",
        "outputId": "64578dbe-17e0-4b22-c501-0068a5a5fc8a"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.0/7.0 MB\u001b[0m \u001b[31m41.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m200.9/200.9 kB\u001b[0m \u001b[31m17.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m549.1/549.1 kB\u001b[0m \u001b[31m31.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m224.5/224.5 kB\u001b[0m \u001b[31m14.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m103.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir weights"
      ],
      "metadata": {
        "id": "vb5pXuVVDsKH"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%cd weights"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "omTHMd9MDuwH",
        "outputId": "26f66f82-fa0b-40eb-c98b-8df81eeec294"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/GroundingDINO/weights\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!wget -q https://github.com/IDEA-Research/GroundingDINO/releases/download/v0.1.0-alpha/groundingdino_swint_ogc.pth"
      ],
      "metadata": {
        "id": "kYRRBXFCDw_a"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%cd .."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n6LJ9i3yDznx",
        "outputId": "95a109d5-edca-46a8-eb40-0687f533d940"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/GroundingDINO\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://djl.ai/examples/src/test/resources/dog_bike_car.jpg"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tdu6EoUJD153",
        "outputId": "fa868d6a-6a07-49a6-8809-778e0a475e24"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-05-04 14:10:34--  https://djl.ai/examples/src/test/resources/dog_bike_car.jpg\n",
            "Resolving djl.ai (djl.ai)... 185.199.111.153, 185.199.110.153, 185.199.109.153, ...\n",
            "Connecting to djl.ai (djl.ai)|185.199.111.153|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 163759 (160K) [image/jpeg]\n",
            "Saving to: ‘dog_bike_car.jpg’\n",
            "\n",
            "dog_bike_car.jpg    100%[===================>] 159.92K  --.-KB/s    in 0.03s   \n",
            "\n",
            "2023-05-04 14:10:34 (4.64 MB/s) - ‘dog_bike_car.jpg’ saved [163759/163759]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python demo/inference_on_a_image.py \\\n",
        "-c groundingdino/config/GroundingDINO_SwinT_OGC.py \\\n",
        "-p weights/groundingdino_swint_ogc.pth \\\n",
        "-i dog_bike_car.jpg \\\n",
        "-o \"output\" \\\n",
        "-t \"car . dog . bike\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vZzKQ_I-FflG",
        "outputId": "1f387f4f-a50c-44d3-9685-dc683c7442fd"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3483.)\n",
            "  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n",
            "final text_encoder_type: bert-base-uncased\n",
            "Downloading (…)okenizer_config.json: 100% 28.0/28.0 [00:00<00:00, 168kB/s]\n",
            "Downloading (…)lve/main/config.json: 100% 570/570 [00:00<00:00, 3.87MB/s]\n",
            "Downloading (…)solve/main/vocab.txt: 100% 232k/232k [00:00<00:00, 3.76MB/s]\n",
            "Downloading (…)/main/tokenizer.json: 100% 466k/466k [00:00<00:00, 6.00MB/s]\n",
            "Downloading pytorch_model.bin: 100% 440M/440M [00:02<00:00, 214MB/s]\n",
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.bias']\n",
            "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "_IncompatibleKeys(missing_keys=[], unexpected_keys=['label_enc.weight'])\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py:862: FutureWarning: The `device` argument is deprecated and will be removed in v5 of Transformers.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:31: UserWarning: None of the inputs have requires_grad=True. Gradients will be None\n",
            "  warnings.warn(\"None of the inputs have requires_grad=True. Gradients will be None\")\n",
            "2023-05-04 14:10:50.856211: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
          ]
        }
      ]
    }
  ]
}