machine-learning/visual-question-answering/VisualQuestionAnswering_PythonCodeTutorial.ipynb

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      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x6rzruZmaotA",
        "outputId": "55c2cae1-5a4d-4cb5-f3d1-863ac0e98f86"
      },
      "outputs": [],
      "source": [
        "!pip install -qU transformers"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "HBn28oF_bApo"
      },
      "source": [
        "# BLIP\n",
        "\n"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "s_eFLXZ-bGtT"
      },
      "source": [
        "- https://github.com/huggingface/transformers/blob/main/src/transformers/models/blip/modeling_blip.py\n",
        "- https://huggingface.co/Salesforce/blip-vqa-base/tree/main"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8PfNcIxYa8kz"
      },
      "outputs": [],
      "source": [
        "import requests\n",
        "from PIL import Image\n",
        "from transformers import BlipProcessor, BlipForQuestionAnswering\n",
        "import torch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BXLVku3Jcjrm"
      },
      "outputs": [],
      "source": [
        "# load the image we will test BLIP on\n",
        "img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'\n",
        "image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')\n",
        "image"
      ]
    },
    {
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        "id": "MJZHoYa6a8nJ",
        "outputId": "020751ef-b433-468b-8c8a-a5ea1c9a83d6"
      },
      "outputs": [],
      "source": [
        "# load necessary components: the processor and the model\n",
        "processor = BlipProcessor.from_pretrained(\"Salesforce/blip-vqa-base\")\n",
        "model = BlipForQuestionAnswering.from_pretrained(\"Salesforce/blip-vqa-base\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aEYmYsrCeB8m"
      },
      "outputs": [],
      "source": [
        "def get_answer_blip(model, processor, image, question):\n",
        "    \"\"\"Answers the given question and handles all the preprocessing and postprocessing steps\"\"\"\n",
        "    # preprocess the given image and question\n",
        "    inputs = processor(image, question, return_tensors=\"pt\")\n",
        "    # generate the answer (get output)\n",
        "    out = model.generate(**inputs)\n",
        "    # post-process the output to get human friendly english text\n",
        "    print(processor.decode(out[0], skip_special_tokens=True))\n",
        "    return"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JVB65c-ra8rs",
        "outputId": "5d1c01ef-6c53-42a9-eba9-82a687791d7e"
      },
      "outputs": [],
      "source": [
        "# sample question 1\n",
        "question = \"how many dogs are in the picture?\"\n",
        "get_answer_blip(model, processor, image, question)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yE36DMXxa8yl",
        "outputId": "88d2e84a-079a-4c8a-877c-4405f9d11757"
      },
      "outputs": [],
      "source": [
        "# sample question 2\n",
        "question = \"how will you describe the picture?\"\n",
        "get_answer_blip(model, processor, image, question)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c2HiOLFLa809",
        "outputId": "ff60422e-4741-40c8-c486-ad405aceb52a"
      },
      "outputs": [],
      "source": [
        "# sample question 3\n",
        "question = \"where are they?\"\n",
        "get_answer_blip(model, processor, image, question)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dreS75cKrHeT",
        "outputId": "11d4e51a-7821-48e5-cd94-005a8a39140b"
      },
      "outputs": [],
      "source": [
        "# sample question 4\n",
        "question = \"What are they doing?\"\n",
        "get_answer_blip(model, processor, image, question)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mu7OZMR1rR7Z",
        "outputId": "70528cb7-e2ff-4a4a-db1b-f941d6745bb5"
      },
      "outputs": [],
      "source": [
        "# sample question 5\n",
        "question = \"What the dog is wearing?\"\n",
        "get_answer_blip(model, processor, image, question)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8bBwZxCXa83E"
      },
      "outputs": [],
      "source": [
        "class BLIP_VQA:\n",
        "    \"\"\"Custom implementation of the BLIP model. The code has been adapted from the official transformers implementation\"\"\"\n",
        "\n",
        "    def __init__(self, vision_model, text_encoder, text_decoder, processor):\n",
        "        \"\"\"Initialize various objects\"\"\"\n",
        "        self.vision_model = vision_model\n",
        "        self.text_encoder = text_encoder\n",
        "        self.text_decoder = text_decoder\n",
        "        self.processor = processor\n",
        "\n",
        "    def preprocess(self, img, ques):\n",
        "        \"\"\"preprocess the inputs: image, question\"\"\"\n",
        "        # preprocess using the processor\n",
        "        inputs = self.processor(img, ques, return_tensors='pt')\n",
        "        # store the pixel values of the image, input IDs (i.e., token IDs) of the question and the attention masks separately\n",
        "        pixel_values = inputs['pixel_values']\n",
        "        input_ids = inputs['input_ids']\n",
        "        attention_mask = inputs['attention_mask']\n",
        "\n",
        "        return pixel_values, input_ids, attention_mask\n",
        "\n",
        "\n",
        "    def generate_output(self, pixel_values, input_ids, attention_mask):\n",
        "        \"\"\"Generates output from the preprocessed input\"\"\"\n",
        "\n",
        "        # get the vision outputs (i.e., the image embeds)\n",
        "        vision_outputs = self.vision_model(pixel_values=pixel_values)\n",
        "        img_embeds = vision_outputs[0]\n",
        "\n",
        "        # create attention mask with 1s on all the image embedding positions\n",
        "        img_attention_mask = torch.ones(img_embeds.size()[: -1], dtype=torch.long)\n",
        "\n",
        "        # encode the questions\n",
        "        question_outputs = self.text_encoder(input_ids=input_ids,\n",
        "                                             attention_mask=attention_mask,\n",
        "                                             encoder_hidden_states=img_embeds,\n",
        "                                             encoder_attention_mask=img_attention_mask,\n",
        "                                             return_dict=False)\n",
        "\n",
        "        # create attention mask with 1s on all the question token IDs positions\n",
        "        question_embeds = question_outputs[0]\n",
        "        question_attention_mask = torch.ones(question_embeds.size()[:-1], dtype=torch.long)\n",
        "\n",
        "        # initialize the answers with the beginning-of-sentence IDs (bos ID)\n",
        "        bos_ids = torch.full((question_embeds.size(0), 1), fill_value=30522)\n",
        "\n",
        "        # get output from the decoder. These outputs are the generated IDs\n",
        "        outputs = self.text_decoder.generate(\n",
        "            input_ids=bos_ids,\n",
        "            eos_token_id=102,\n",
        "            pad_token_id=0,\n",
        "            encoder_hidden_states=question_embeds,\n",
        "            encoder_attention_mask=question_attention_mask)\n",
        "\n",
        "        return outputs\n",
        "\n",
        "\n",
        "    def postprocess(self, outputs):\n",
        "        \"\"\"post-process the output generated by the text-decoder\"\"\"\n",
        "\n",
        "        return self.processor.decode(outputs[0], skip_special_tokens=True)\n",
        "\n",
        "\n",
        "    def get_answer(self, image, ques):\n",
        "        \"\"\"Returns human friendly answer to a question\"\"\"\n",
        "\n",
        "        # preprocess\n",
        "        pixel_values, input_ids, attention_mask = self.preprocess(image, ques)\n",
        "        # generate output\n",
        "        outputs = self.generate_output(pixel_values, input_ids, attention_mask)\n",
        "        # post-process\n",
        "        answer = self.postprocess(outputs)\n",
        "        return answer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WBxppK89bhZP"
      },
      "outputs": [],
      "source": [
        "blip_vqa = BLIP_VQA(vision_model=model.vision_model,\n",
        "                    text_encoder=model.text_encoder,\n",
        "                    text_decoder=model.text_decoder,\n",
        "                    processor=processor)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YyASdKlAbhbm",
        "outputId": "060fd21d-2042-418e-88de-e87f4561671d"
      },
      "outputs": [],
      "source": [
        "# sample question 1\n",
        "ques = \"how will you describe the picture?\"\n",
        "print(blip_vqa.get_answer(image, ques))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217
        },
        "id": "BOErJNo1tG6-",
        "outputId": "25b06783-738f-476e-b952-4d8e38e5aa7c"
      },
      "outputs": [],
      "source": [
        "# load another image to test BLIP\n",
        "img_url = \"https://fastly.picsum.photos/id/11/200/200.jpg?hmac=LBGO0uEpEmAVS8NeUXMqxcIdHGIcu0JiOb5DJr4mtUI\"\n",
        "image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')\n",
        "image"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6c4X6eI4tG9N",
        "outputId": "1c7c03d6-28c6-4cc3-9b30-4406410f5492"
      },
      "outputs": [],
      "source": [
        "# sample question 1\n",
        "ques = \"Describe the picture\"\n",
        "print(blip_vqa.get_answer(image, ques))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5fpA0TbVtHAq",
        "outputId": "47ea2820-9ea0-4bf4-b9b7-45941b32ffbb"
      },
      "outputs": [],
      "source": [
        "# sample question 2\n",
        "ques = \"What is the major color present?\"\n",
        "print(blip_vqa.get_answer(image, ques))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5dEIccqnr-uF",
        "outputId": "7816af8c-83f6-4fe8-e968-365ec732bd92"
      },
      "outputs": [],
      "source": [
        "# sample question 3\n",
        "ques = \"How's the weather?\"\n",
        "print(blip_vqa.get_answer(image, ques))"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "73gvmX-Tbk-s"
      },
      "source": [
        "# GIT"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "7EwQAOq-cLH-"
      },
      "source": [
        "- https://github.com/huggingface/transformers/blob/main/src/transformers/models/git/modeling_git.py\n",
        "- https://huggingface.co/microsoft/git-base-textvqa"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "c4Lf7_G5bhju"
      },
      "outputs": [],
      "source": [
        "!pip install -qU transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qY1xeL1oa86Y"
      },
      "outputs": [],
      "source": [
        "from transformers import AutoProcessor, AutoModelForCausalLM\n",
        "from huggingface_hub import hf_hub_download\n",
        "from PIL import Image"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 593,
          "referenced_widgets": [
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        },
        "id": "AgLuCbEyboLn",
        "outputId": "5c14f355-95aa-4eaa-d3e4-524ff497a27c"
      },
      "outputs": [],
      "source": [
        "# load the image we will test GIT on\n",
        "file_path = hf_hub_download(repo_id=\"nielsr/textvqa-sample\", filename=\"bus.png\", repo_type=\"dataset\")\n",
        "image = Image.open(file_path).convert(\"RGB\")\n",
        "image"
      ]
    },
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        },
        "id": "Xyze2yuFl7UD",
        "outputId": "6373ea3f-9076-45cf-c319-74af872647b9"
      },
      "outputs": [],
      "source": [
        "# load necessary components: the processor and the model\n",
        "processor = AutoProcessor.from_pretrained(\"microsoft/git-base-textvqa\")\n",
        "model = AutoModelForCausalLM.from_pretrained(\"microsoft/git-base-textvqa\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PoP6txfhmPI9"
      },
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      "source": [
        "class GIT_VQA:\n",
        "    \"\"\"Custom implementation of the GIT model for Visual Question Answering (VQA) tasks.\"\"\"\n",
        "\n",
        "    def __init__(self, model, processor):\n",
        "        \"\"\"Initializes the model and the processor.\"\"\"\n",
        "        self.model = model\n",
        "        self.processor = processor\n",
        "        return\n",
        "\n",
        "\n",
        "    def preprocess(self, image, question):\n",
        "        \"\"\"Preprocesses the inputs: image, question\"\"\"\n",
        "        # process the image to get pixel values\n",
        "        pixel_values = self.processor(images=image, return_tensors=\"pt\").pixel_values\n",
        "\n",
        "        # process the question to get input IDs, but do not add special tokens\n",
        "        input_ids = self.processor(text=question, add_special_tokens=False).input_ids\n",
        "\n",
        "        # add the CLS token at the beginning of the input_ids and format for model input\n",
        "        input_ids = [self.processor.tokenizer.cls_token_id] + input_ids\n",
        "        input_ids = torch.tensor(input_ids).unsqueeze(0)\n",
        "\n",
        "        return pixel_values, input_ids\n",
        "\n",
        "\n",
        "    def generate(self, pixel_values, input_ids):\n",
        "        \"\"\"Generates the output from the preprocessed inputs.\"\"\"\n",
        "\n",
        "        # generate output using the model with a maximum length of 50 tokens\n",
        "        outputs = self.model.generate(pixel_values=pixel_values, input_ids=input_ids, max_length=50)\n",
        "        return outputs\n",
        "\n",
        "\n",
        "    def postprocess(self, outputs):\n",
        "        \"\"\"Post-processes the output generated by the model.\"\"\"\n",
        "\n",
        "        # decode the output, ignoring special tokens\n",
        "        answer = self.processor.batch_decode(outputs, skip_special_tokens=True)\n",
        "        return answer\n",
        "\n",
        "\n",
        "    def get_answer(self, image, question):\n",
        "        \"\"\"Returns human friendly answer to a question\"\"\"\n",
        "\n",
        "        # preprocess\n",
        "        pixel_values, input_ids = self.preprocess(image, question)\n",
        "        # generate output\n",
        "        outputs = self.generate(pixel_values, input_ids)\n",
        "        # post-process\n",
        "        answer = self.postprocess(outputs)\n",
        "        return answer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YXXaDaQZqpen"
      },
      "outputs": [],
      "source": [
        "# create a GIT instance\n",
        "git_vqa = GIT_VQA(model=model, processor=processor)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9HT3VFLsboQE",
        "outputId": "5f5e7a77-a40f-448d-84e3-d3bbfe594eb8"
      },
      "outputs": [],
      "source": [
        "# sample question 1\n",
        "question = \"what does the front of the bus say at the top?\"\n",
        "answer = git_vqa.get_answer(image, question)\n",
        "print(answer)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lcj5yO2sboT2",
        "outputId": "65301084-2148-402f-c641-8bd774e5308c"
      },
      "outputs": [],
      "source": [
        "# sample question 2\n",
        "question = \"what are all the colors present on the bus?\"\n",
        "answer = git_vqa.get_answer(image, question)\n",
        "print(answer)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PBqTU4qwboXV",
        "outputId": "a36cf954-da7c-42d6-a1ef-179058fc0270"
      },
      "outputs": [],
      "source": [
        "# sample question 3\n",
        "question = \"How many wheels you see in the bus?\"\n",
        "answer = git_vqa.get_answer(image, question)\n",
        "print(answer)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "cBdRCN28b4FQ",
        "outputId": "22a64008-64c7-439c-ad3f-2c4a4295dba0"
      },
      "outputs": [],
      "source": [
        "# load another image to test BLIP\n",
        "img_url = \"https://fastly.picsum.photos/id/110/500/500.jpg?hmac=wSHhLFNyJ6k3uM94s6etGQ0WWhmwbdUSiZ9ZDL5Hh2Q\"\n",
        "image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')\n",
        "image"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-CD02X_ub4Ho",
        "outputId": "74fe9c3f-3e1d-4627-9fd0-c8cf166e4942"
      },
      "outputs": [],
      "source": [
        "# sample question 1\n",
        "question = \"Is it night in the image?\"\n",
        "answer = git_vqa.get_answer(image, question)\n",
        "print(answer)"
      ]
    },
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      "execution_count": null,
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        "id": "uK_KAVyEb4LC"
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