2.9 Multimodal_Models.ipynb

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      "cell_type": "markdown",
      "metadata": {
        "id": "Lg3YEi6BtrwZ"
      },
      "source": [
        "# Multimodal Models\n",
        "When a task requires a model to be able to take as an input more than one type of data, like an image and a sentence, we will call it as **multimodal**. When we think about multimodal models we immediately think about CHatGPT where we can send text, image and audio.<br>\n",
        "Here we will perform image-to-text matching, image captioning, visual Q&A and zero-shot image classification. For the first three tasks we will be using the Bleep model and for the last task we will be using the Clip model from OpenAI."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YtlUnvqK2AEE",
        "outputId": "09e1f8e0-bbc6-44dc-f6ac-08af1669ecfc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
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          ]
        }
      ],
      "source": [
        "!pip install transformers\n",
        "!pip install torch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "jQAFHXCUtyKD"
      },
      "outputs": [],
      "source": [
        "from transformers.utils import logging\n",
        "logging.set_verbosity_error()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "nivu1ZmYvpc1"
      },
      "outputs": [],
      "source": [
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\", message=\"Using the model-agnostic default `max_length`\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "skcPaUW2t1Jr"
      },
      "source": [
        "## Task 1: Image-Text Retrieval"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "Az4lA8sqtzM5"
      },
      "outputs": [],
      "source": [
        "from transformers import BlipForImageTextRetrieval"
      ]
    },
    {
      "cell_type": "code",
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "bE73vqKnt3Qf",
        "outputId": "80a8ee5e-8d0b-4f7d-e7a8-56bbf6edd176"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: 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"
          ]
        },
        {
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            "text/plain": [
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          },
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          "metadata": {},
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        }
      ],
      "source": [
        "model = BlipForImageTextRetrieval.from_pretrained(\n",
        "\"Salesforce/blip-itm-base-coco\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4hlRjtuIt6Vp"
      },
      "source": [
        "More info about [Salesforce/blip-itm-base-coco](https://huggingface.co/Salesforce/blip-itm-base-coco)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "7JMiSfnUt42u"
      },
      "outputs": [],
      "source": [
        "from transformers import AutoProcessor"
      ]
    },
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        "colab": {
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        "id": "gVsyLyBQt8CG",
        "outputId": "153c63ec-fa2e-42cf-fdfb-d9d2d465df74"
      },
      "outputs": [
        {
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          },
          "metadata": {},
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        }
      ],
      "source": [
        "processor = AutoProcessor.from_pretrained(\n",
        "    \"Salesforce/blip-itm-base-coco\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "LlNb_Dywt-PW"
      },
      "outputs": [],
      "source": [
        "from PIL import Image"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "0RW9kViAt_iw"
      },
      "outputs": [],
      "source": [
        "raw_image =  Image.open(\"Images/man&cat.jpg\").convert('RGB')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        },
        "id": "8Jsg_XP_uUVi",
        "outputId": "026d5e43-ba05-4737-aee8-a3f79299e0b5"
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=540x360>"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "raw_image"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3CioPYhcuYK5"
      },
      "source": [
        "Passing the image and text into the processor will modify them in such a way that the model will be able to understand it."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "15J4HCJ9uWuT"
      },
      "outputs": [],
      "source": [
        "text = \"an image of a man holding a cat\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "4dcFOAMzuZei"
      },
      "outputs": [],
      "source": [
        "inputs = processor(images=raw_image,\n",
        "                   text=text,\n",
        "                   return_tensors=\"pt\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PoK1YolZudmf"
      },
      "source": [
        "retrun_tensor is set to pt so that it returns a PyTorch tensor."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Iau_2tK7uaxP",
        "outputId": "9d0e81aa-3bc8-420b-fa44-75239d1e0018"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'pixel_values': tensor([[[[ 2.0772e-01,  1.7853e-01,  1.9312e-01,  ...,  1.8135e+00,\n",
              "            1.8135e+00,  1.8135e+00],\n",
              "          [ 2.0772e-01,  1.7853e-01,  1.9312e-01,  ...,  1.8135e+00,\n",
              "            1.8135e+00,  1.8135e+00],\n",
              "          [ 2.0772e-01,  1.9312e-01,  2.0772e-01,  ...,  1.8135e+00,\n",
              "            1.8135e+00,  1.8135e+00],\n",
              "          ...,\n",
              "          [ 2.9531e-01,  3.2451e-01,  3.2451e-01,  ...,  1.7406e+00,\n",
              "            1.7552e+00,  1.7844e+00],\n",
              "          [ 3.2451e-01,  2.9531e-01,  3.0991e-01,  ...,  1.7552e+00,\n",
              "            1.7552e+00,  1.7844e+00],\n",
              "          [ 3.2451e-01,  2.8071e-01,  2.9531e-01,  ...,  1.7552e+00,\n",
              "            1.7698e+00,  1.7990e+00]],\n",
              "\n",
              "         [[ 1.2387e-01,  1.0887e-01,  1.2387e-01,  ...,  1.9698e+00,\n",
              "            1.9698e+00,  1.9698e+00],\n",
              "          [ 1.2387e-01,  1.0887e-01,  1.2387e-01,  ...,  1.9698e+00,\n",
              "            1.9698e+00,  1.9698e+00],\n",
              "          [ 1.2387e-01,  1.2387e-01,  1.3888e-01,  ...,  1.9698e+00,\n",
              "            1.9698e+00,  1.9698e+00],\n",
              "          ...,\n",
              "          [ 1.9891e-01,  2.2893e-01,  2.2893e-01,  ...,  1.9398e+00,\n",
              "            1.9548e+00,  1.9548e+00],\n",
              "          [ 2.2893e-01,  1.9891e-01,  2.1392e-01,  ...,  1.9548e+00,\n",
              "            1.9548e+00,  1.9548e+00],\n",
              "          [ 2.2893e-01,  1.8391e-01,  1.9891e-01,  ...,  1.9548e+00,\n",
              "            1.9698e+00,  1.9698e+00]],\n",
              "\n",
              "         [[-1.5553e-02, -2.9773e-02, -1.5553e-02,  ...,  2.0748e+00,\n",
              "            2.0748e+00,  2.0748e+00],\n",
              "          [-1.5553e-02, -2.9773e-02, -1.5553e-02,  ...,  2.0748e+00,\n",
              "            2.0748e+00,  2.0748e+00],\n",
              "          [-1.5553e-02, -1.5553e-02, -1.3329e-03,  ...,  2.0748e+00,\n",
              "            2.0748e+00,  2.0748e+00],\n",
              "          ...,\n",
              "          [ 1.2665e-01,  1.5509e-01,  1.5509e-01,  ...,  2.0321e+00,\n",
              "            2.0464e+00,  2.0606e+00],\n",
              "          [ 1.5509e-01,  1.2665e-01,  1.4087e-01,  ...,  2.0464e+00,\n",
              "            2.0464e+00,  2.0606e+00],\n",
              "          [ 1.5509e-01,  1.1243e-01,  1.2665e-01,  ...,  2.0464e+00,\n",
              "            2.0606e+00,  2.0748e+00]]]]), 'input_ids': tensor([[ 101, 2019, 3746, 1997, 1037, 2158, 3173, 1037, 4937,  102]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])}"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inputs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "K00WoEvSuewq"
      },
      "outputs": [],
      "source": [
        "itm_scores = model(**inputs)[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JEgrODECuhmO"
      },
      "source": [
        "**NOTE:** we have to add a double star before passing inputs as the input is a dictionary that contains the arguements."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qtu7biMCugl2",
        "outputId": "4089dc9f-be4b-41ef-d870-78b9ef3231ee"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor([[-1.7542,  1.7517]], grad_fn=<AddmmBackward0>)"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "itm_scores"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OtrAwfl8ukDQ"
      },
      "source": [
        "These numbers in form of tensor are the logits of the model and to convert these values into something that we can understand, we need to pass them into a softmax layer. The output of this number into the softmax will give us the probability."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "hhm6xTCwujAU"
      },
      "outputs": [],
      "source": [
        "import torch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gimqC0UfulXN",
        "outputId": "51c86cd2-db84-4d67-d6ec-60181d0bea17"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor([[0.0291, 0.9709]], grad_fn=<SoftmaxBackward0>)"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "itm_score = torch.nn.functional.softmax(itm_scores,dim=1)\n",
        "itm_score"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z56FNdl-upYf"
      },
      "source": [
        "Now we got a tensor with two values, the first value is the probability that the image and the text are not matched which is very low. And the second value is the probability that they are matched."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tM0j9HjDunBn",
        "outputId": "216c5ad3-e805-4a29-8315-cf8f0607e40f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The image and text are matched with a probability of 0.9709\n"
          ]
        }
      ],
      "source": [
        "print(f\"\"\"The image and text are matched with a probability of {itm_score[0][1]:.4f}\"\"\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "X3SfoIAQuq0U"
      },
      "outputs": [],
      "source": [
        "text2 = \"an image of a cat holding a man\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "3eMthsJduscU"
      },
      "outputs": [],
      "source": [
        "inputs2 = processor(images=raw_image,\n",
        "                   text=text2,\n",
        "                   return_tensors=\"pt\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "1lyN7KdWuttK"
      },
      "outputs": [],
      "source": [
        "itm_scores2 = model(**inputs2)[0]\n",
        "itm_score2 = torch.nn.functional.softmax(itm_scores2,dim=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qL_9cl_Uuv82",
        "outputId": "1d50d76c-8a93-4f9c-89ed-bca8748bc9c9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The image and text are matched with a probability of 0.9864\n"
          ]
        }
      ],
      "source": [
        "print(f\"\"\"The image and text are matched with a probability of {itm_score2[0][1]:.4f}\"\"\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Raz_vo13uzHs"
      },
      "source": [
        "## Task 2: Image Captioning\n",
        "For this task we will use the same Bleep model but with different weights. We ask the model to return the description of the image, we can let the model know the output text should start with \"A man...\"."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "8ZUJwZe7uwUD"
      },
      "outputs": [],
      "source": [
        "from transformers import BlipForConditionalGeneration"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "1d849764398447a2805c42f5b21172fb",
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            "50967af2184643e19d3d46c3e4c0e92c",
            "ace04c21bf674b05a46055ff7e4a5b41",
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            "44c9b16637954bea8f237a16b13c642d",
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            "976086d5caa740afa19370d3b11573d8",
            "6def44030e9d402fbd84f3e464c40e9a",
            "7959fdd02ecb4928a85387e36e7be8cb",
            "52c45bffc131427c8af572d666bc935d"
          ]
        },
        "id": "zSn3-_6qvzUh",
        "outputId": "ccb8a936-8bb0-4a79-ca7e-cfdefce281da"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1d849764398447a2805c42f5b21172fb",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "config.json:   0%|          | 0.00/4.56k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "afc3bf2215474a8d91c3ff7b00d81970",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "pytorch_model.bin:   0%|          | 0.00/990M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "model = BlipForConditionalGeneration.from_pretrained(\n",
        "    \"Salesforce/blip-image-captioning-base\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hzLDax9Dv3UM"
      },
      "source": [
        "Info about [Salesforce/blip-image-captioning-base](https://huggingface.co/Salesforce/blip-image-captioning-base)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "4wy-58YXv1Mq"
      },
      "outputs": [],
      "source": [
        "from transformers import AutoProcessor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 177,
          "referenced_widgets": [
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            "bd76f388e518415bbaed059e986dc5c1",
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            "910b4e9542ea42aba46aa0a78e192fb8",
            "f5ab49ac9cee4945a1abb330a2f2c481",
            "597e88dab98e42b7b51501ca52a15d9b",
            "6dd9d797e25d4d70bcc53076d394f0a6",
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            "1adad8b3321443fca7645467a87e3456",
            "57a0a0a752a44c8daf43d3ea77f59a48",
            "639366f01e2c4d1a9ee18a7625ab97ce",
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            "d245480103054139ad3d44b41aaaf8cc",
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            "809e9069044a483d95dc1246814a2977",
            "de35f35fd0bd439281869ed95957fb21",
            "2781c36606da4881844e8166e5c0accf",
            "836eb7b909084f95926880e15d1f4e07",
            "e3af5d2b9951470c94b862817c1ed47d",
            "c9b5165660d3443291969162233aa7c1",
            "18e80f4dfabe46c6a81f1ad10148bb38",
            "c6d09ed87df74996bdff6f283676ab49",
            "369ea42ed9974f60b9b1ba2b598b0d67"
          ]
        },
        "id": "d-SCBKoSv4if",
        "outputId": "f577f3a2-5b26-479b-e294-721e70f88eef"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f12123771202460a8a3cba80fc4eaa71",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "preprocessor_config.json:   0%|          | 0.00/287 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6dd9d797e25d4d70bcc53076d394f0a6",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/506 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d61a048fe2494a4584fb138d5547cb08",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c6d33ed94c48459bbba14032f2f59e93",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/711k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f3bcf73b4bff4d54991b617c97d59dd1",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/125 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "processor = AutoProcessor.from_pretrained(\n",
        "    \"Salesforce/blip-image-captioning-base\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "74wsu0Gnv7t2"
      },
      "outputs": [],
      "source": [
        "image = raw_image"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WlA92_b3wXyq"
      },
      "source": [
        "### Conditional Image Captioning"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "OkhHxLtiwJpi"
      },
      "outputs": [],
      "source": [
        "text = \"a photograph of\"\n",
        "inputs = processor(image, text, return_tensors=\"pt\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_ApkxTsCwMh-",
        "outputId": "3a603dc3-1ac7-4387-d7dc-acdfe7529ee5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'pixel_values': tensor([[[[ 2.0772e-01,  1.7853e-01,  1.9312e-01,  ...,  1.8135e+00,\n",
              "            1.8135e+00,  1.8135e+00],\n",
              "          [ 2.0772e-01,  1.7853e-01,  1.9312e-01,  ...,  1.8135e+00,\n",
              "            1.8135e+00,  1.8135e+00],\n",
              "          [ 2.0772e-01,  1.9312e-01,  2.0772e-01,  ...,  1.8135e+00,\n",
              "            1.8135e+00,  1.8135e+00],\n",
              "          ...,\n",
              "          [ 2.9531e-01,  3.2451e-01,  3.2451e-01,  ...,  1.7406e+00,\n",
              "            1.7552e+00,  1.7844e+00],\n",
              "          [ 3.2451e-01,  2.9531e-01,  3.0991e-01,  ...,  1.7552e+00,\n",
              "            1.7552e+00,  1.7844e+00],\n",
              "          [ 3.2451e-01,  2.8071e-01,  2.9531e-01,  ...,  1.7552e+00,\n",
              "            1.7698e+00,  1.7990e+00]],\n",
              "\n",
              "         [[ 1.2387e-01,  1.0887e-01,  1.2387e-01,  ...,  1.9698e+00,\n",
              "            1.9698e+00,  1.9698e+00],\n",
              "          [ 1.2387e-01,  1.0887e-01,  1.2387e-01,  ...,  1.9698e+00,\n",
              "            1.9698e+00,  1.9698e+00],\n",
              "          [ 1.2387e-01,  1.2387e-01,  1.3888e-01,  ...,  1.9698e+00,\n",
              "            1.9698e+00,  1.9698e+00],\n",
              "          ...,\n",
              "          [ 1.9891e-01,  2.2893e-01,  2.2893e-01,  ...,  1.9398e+00,\n",
              "            1.9548e+00,  1.9548e+00],\n",
              "          [ 2.2893e-01,  1.9891e-01,  2.1392e-01,  ...,  1.9548e+00,\n",
              "            1.9548e+00,  1.9548e+00],\n",
              "          [ 2.2893e-01,  1.8391e-01,  1.9891e-01,  ...,  1.9548e+00,\n",
              "            1.9698e+00,  1.9698e+00]],\n",
              "\n",
              "         [[-1.5553e-02, -2.9773e-02, -1.5553e-02,  ...,  2.0748e+00,\n",
              "            2.0748e+00,  2.0748e+00],\n",
              "          [-1.5553e-02, -2.9773e-02, -1.5553e-02,  ...,  2.0748e+00,\n",
              "            2.0748e+00,  2.0748e+00],\n",
              "          [-1.5553e-02, -1.5553e-02, -1.3329e-03,  ...,  2.0748e+00,\n",
              "            2.0748e+00,  2.0748e+00],\n",
              "          ...,\n",
              "          [ 1.2665e-01,  1.5509e-01,  1.5509e-01,  ...,  2.0321e+00,\n",
              "            2.0464e+00,  2.0606e+00],\n",
              "          [ 1.5509e-01,  1.2665e-01,  1.4087e-01,  ...,  2.0464e+00,\n",
              "            2.0464e+00,  2.0606e+00],\n",
              "          [ 1.5509e-01,  1.1243e-01,  1.2665e-01,  ...,  2.0464e+00,\n",
              "            2.0606e+00,  2.0748e+00]]]]), 'input_ids': tensor([[ 101, 1037, 9982, 1997,  102]]), 'attention_mask': tensor([[1, 1, 1, 1, 1]])}"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inputs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wTr5k7vawNY5",
        "outputId": "da57a1c6-f504-48cd-be93-2f68e035e078"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor([[30522,  1037,  9982,  1997,  1037,  2158,  3173,  1037,  4937,   102]])"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "out = model.generate(**inputs)\n",
        "out"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fon4nrIpwPR1",
        "outputId": "0c4eb12c-3340-4834-f29f-d70796160d48"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "a photograph of a man holding a cat\n"
          ]
        }
      ],
      "source": [
        "print(processor.decode(out[0], skip_special_tokens=True))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2A7CmEzrwbYu"
      },
      "source": [
        "### Unconditional Image Captioning"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "TjCqyvxCwRkF"
      },
      "outputs": [],
      "source": [
        "inputs = processor(image,return_tensors=\"pt\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "oF1hEC3ewUay"
      },
      "outputs": [],
      "source": [
        "out = model.generate(**inputs)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C4EHemzRwU1-",
        "outputId": "d81eacbc-f146-4a41-bade-d3171ec01929"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "a man holding a cat\n"
          ]
        }
      ],
      "source": [
        "print(processor.decode(out[0], skip_special_tokens=True))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NYk93RYgy_ll"
      },
      "source": [
        "## Task 3: Visual Question Answering\n",
        "Here we can give the model a picture and we can ask question related to that image."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "H9MkCLDhwV6u"
      },
      "outputs": [],
      "source": [
        "from transformers import BlipForQuestionAnswering"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "6ffba6a6c78a4a16bcbab3b504a7cb6d",
            "21d8f48344394185a5c46af1bccdc97d",
            "d3f6c4227a464d05a9ad05d239a51c4c",
            "7bd336975abb4b2cad5f1f7325cf425e",
            "86614465227c4d9b9460da1c53291a3c",
            "22b29e6bd04d42a6b5f077d28c630304",
            "eace1c17ddc4407f8b5fad9096b54176",
            "5b4573ceb3e3464783baffa40def88fd",
            "17c7ae14021446de92d0f4b3136e9bdb",
            "d8f9083301fd4667b998c1483ffd3d93",
            "23f99bd1071e4cb2a7843ad95381b482",
            "e88d388b12e0468aa855bb8ee8b552ae",
            "240757fa6b684127894ce0a76638c8e9",
            "06ba278769044ee49c5a18d864c198a0",
            "559d7e4cc605494b8a4b944e506b5e85",
            "4d915f1de65046f89bc5cd8281434805",
            "9b6a14b7d9d3460dbdef0ab3175cee5e",
            "b51e1228be8e4508ac0dfe7c30917c7d",
            "c92c33666edb447aa893f5eac8483550",
            "8107f447fad646b39104780e18dd39fa",
            "bc470fb2c2b14c3aa41d90d452847c93",
            "387533ef294f4ba6b7d9d42836ced03c"
          ]
        },
        "id": "j-IWDtv4zSIc",
        "outputId": "5ec57e5d-1fd3-49bc-df97-15c8bf286110"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6ffba6a6c78a4a16bcbab3b504a7cb6d",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "config.json:   0%|          | 0.00/4.56k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e88d388b12e0468aa855bb8ee8b552ae",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/1.54G [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "model = BlipForQuestionAnswering.from_pretrained(\n",
        "    \"Salesforce/blip-vqa-base\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "px0lXlz_zU6t"
      },
      "source": [
        "Info about [Salesforce/blip-vqa-base](https://huggingface.co/Salesforce/blip-vqa-base)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "9bNXmqn-zTo-"
      },
      "outputs": [],
      "source": [
        "from transformers import AutoProcessor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 177,
          "referenced_widgets": [
            "a32afd0f79e5485ba0635a0f1b6db31f",
            "16ab6c9f72394691b423d186032762b2",
            "c33e417737e74abb94c565224fba1267",
            "3cbf67dcbf724b83808010d436fcbe9c",
            "7228b183cca04e8ab5ef933a8d507908",
            "5ca430514d4a4633b23fe3ff4166428f",
            "4d206132a2f9479b9c08c124d9733feb",
            "95b12986645348da89ab22be140677de",
            "197088ce5a5b40f4a149ff52ce20007f",
            "e3761fe9dc23493c886ef52db2575705",
            "826eecc4b8a34630abf6da27934a847b",
            "1dd7d4c341f74678a67724ce5477a1cb",
            "2dae48d0bfe3407c9cdffac5da48e9da",
            "44ab2cab0c3d49fea2a275dc6b9e7a0d",
            "98d6c9ab2f0c4eb193d1cd08e40c5a8b",
            "019bb36416c24eff93f1c27fc2086946",
            "1d390698828b42ac8f1d80583179e2c2",
            "8a9b174bdb3347d68adfaf7a06426837",
            "1688f234b93f425b8a9c3c9c902b46bb",
            "1b6fd8e187984a2da4db980af0fed80c",
            "0993be05496644e1bbe7228d11c622d6",
            "98a3bb09bd4743978e2b05b8870dbfeb",
            "ca5658611b0b483fbc5fa336dbda2c25",
            "f3e7b57a78a248328264bc6aa58fab26",
            "9035b018583c4693a7be5fd01a83ed12",
            "4d44d97d744b43cd82d2289cbc3037fe",
            "1b44fb6c6db44af7bed0846fb05e9342",
            "2de2e60d148a43b090cbde72022d6a88",
            "523695f9b6ff431ab75f3c6b9d77297b",
            "ca2daa58dd9c4f6eb127ad086a342d61",
            "c3358fa3ea434a908c92022875dda6cb",
            "dcc10d90ad71494b961cb4e6d2b4ce66",
            "54572e5ff00449bb960073443940f311",
            "4c62b4ea8a414eed8527d2f054d39f70",
            "053cf1ae72bd47dba7413eb3a685b62e",
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            "4b5296a1cf8f469e98d05d8ba36c163a",
            "aee4da69cb1146568aebf4077a635274",
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            "1eeed69be6ad477b905d74faf9f7e528",
            "2d753eb026704ad9b598b03129134545"
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        },
        "id": "rUyg-8MgzWgl",
        "outputId": "3c049815-8ae8-4b61-a68e-62e144c83113"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a32afd0f79e5485ba0635a0f1b6db31f",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "preprocessor_config.json:   0%|          | 0.00/445 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1dd7d4c341f74678a67724ce5477a1cb",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/592 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ca5658611b0b483fbc5fa336dbda2c25",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4c62b4ea8a414eed8527d2f054d39f70",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/711k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "aee4da69cb1146568aebf4077a635274",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/125 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "processor = AutoProcessor.from_pretrained(\n",
        "    \"Salesforce/blip-vqa-base\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "h1l3d7YzzcW6"
      },
      "outputs": [],
      "source": [
        "question = \"Name the animal present in the picture?\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "6QOZNJ0hzhif"
      },
      "outputs": [],
      "source": [
        "inputs = processor(image, question, return_tensors=\"pt\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "3QIJKtz6zjJy"
      },
      "outputs": [],
      "source": [
        "out = model.generate(**inputs)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6_VXV8grzkCC",
        "outputId": "2d715420-2bcd-4a0c-f066-792f69834656"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "cat\n"
          ]
        }
      ],
      "source": [
        "print(processor.decode(out[0], skip_special_tokens=True))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N2RFRnu6zwg6"
      },
      "source": [
        "## Task 4: Zero-shot Classification\n",
        "Here we will use the CLIP model from OpenAI to classify image. Zero-shot means the model will be able to classify the image from among the list of any labels that we give it. Here we don't have to fine-tune the model to recognize specific categories. Example pass a list consisiting labels like: planes, cats, dogs, trees, etc and the model will choose the most likely label."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "TzebvipQzk7T"
      },
      "outputs": [],
      "source": [
        "from transformers import CLIPModel"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
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          ]
        },
        "id": "j72cv5US0XPg",
        "outputId": "8256e45f-0a64-401c-8374-84f56218a62c"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d4f3b89a69c148e69d59f2015e38bef0",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "config.json:   0%|          | 0.00/4.52k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5f6a9acaf0e8449faea4486b5f99d596",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/1.71G [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "model = CLIPModel.from_pretrained(\n",
        "    \"openai/clip-vit-large-patch14\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "OxEvseOg0Ymt"
      },
      "outputs": [],
      "source": [
        "from transformers import AutoProcessor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 209,
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        },
        "id": "cxShiF4q0ZuC",
        "outputId": "1b9ffcc1-9251-4607-a17c-01b840806c0e"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a0564e1725174f03bdf9981ebbf0dd48",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "preprocessor_config.json:   0%|          | 0.00/316 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6c33efa30a9c423a8351cc33fcb33bda",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/905 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "89818850ea634d2a91095e30fd3df578",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "vocab.json:   0%|          | 0.00/961k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "ea440702cc9041df8a2ecba8164942b0",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "merges.txt:   0%|          | 0.00/525k [00:00<?, ?B/s]"
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "14d2bafbdcc44e14b1797bcb5c3ce93f",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/2.22M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6e74b169f9584046bcd3846cce1da32d",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/389 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "processor = AutoProcessor.from_pretrained(\n",
        "    \"openai/clip-vit-large-patch14\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HQMbm-R40cVz"
      },
      "source": [
        "More info about [openai/clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "id": "EX75WsPI0bLO"
      },
      "outputs": [],
      "source": [
        "labels = [\"a photo of a cat\", \"a photo of a dog\", \"a photo of a man\", \"a photo of a human\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "id": "nJ6xbX8U0e_X"
      },
      "outputs": [],
      "source": [
        "inputs = processor(text=labels,\n",
        "                   images=image,\n",
        "                   return_tensors=\"pt\",\n",
        "                   padding=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AuOhu2Db02jf"
      },
      "source": [
        "Here we add padding in case the label lengths are not the same."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qL8SbLDi0mIG",
        "outputId": "63f2f643-e8fe-454e-f37a-ef825e9ca78a"
      },
      "outputs": [
        {
          "data": {
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              "CLIPOutput(loss=None, logits_per_image=tensor([[22.0966, 17.0880, 20.5824, 20.6752]], grad_fn=<TBackward0>), logits_per_text=tensor([[22.0966],\n",
              "        [17.0880],\n",
              "        [20.5824],\n",
              "        [20.6752]], grad_fn=<MulBackward0>), text_embeds=tensor([[-0.0031,  0.0028,  0.0291,  ...,  0.0227,  0.0088,  0.0132],\n",
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              "       grad_fn=<DivBackward0>), image_embeds=tensor([[-1.4012e-02,  2.5559e-02,  2.6974e-02, -6.0873e-02,  1.5789e-02,\n",
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              "         ...,\n",
              "         [ 5.8302e-01, -1.3717e-01,  2.1518e+00,  ..., -1.0520e+00,\n",
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              "           8.1141e-01,  5.5702e-01]],\n",
              "\n",
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              "          -3.0660e-01,  6.7454e-02],\n",
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              "         ...,\n",
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              "\n",
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              "         ...,\n",
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              "\n",
              "        [[-3.8838e-01,  2.2944e-02, -5.2197e-02,  ..., -4.8988e-01,\n",
              "          -3.0660e-01,  6.7454e-02],\n",
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              "           9.7681e-01,  6.6517e-01],\n",
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              "          -1.1519e+00, -3.3113e-01],\n",
              "         ...,\n",
              "         [ 5.8302e-01, -1.3717e-01,  2.1518e+00,  ..., -1.0520e+00,\n",
              "          -1.5292e-01,  9.6757e-02],\n",
              "         [-1.5582e+00,  2.0724e-01,  5.5926e-01,  ..., -1.4402e+00,\n",
              "           8.1228e-01, -9.3731e-01],\n",
              "         [-4.4888e-01, -3.4523e-01,  3.3116e-01,  ...,  9.4688e-02,\n",
              "           1.2973e+00,  9.3016e-01]]], grad_fn=<NativeLayerNormBackward0>), pooler_output=tensor([[-1.2166e+00, -5.1482e-01,  4.8002e-01,  ..., -1.3832e-01,\n",
              "          8.1141e-01,  5.5702e-01],\n",
              "        [-9.5111e-01,  7.5059e-03,  1.3867e-01,  ...,  6.0323e-04,\n",
              "          2.2237e-01,  8.9224e-01],\n",
              "        [-6.5651e-01, -4.7202e-01, -2.6003e-01,  ..., -7.9789e-01,\n",
              "          3.6016e-01,  6.9163e-02],\n",
              "        [-4.4888e-01, -3.4523e-01,  3.3116e-01,  ...,  9.4688e-02,\n",
              "          1.2973e+00,  9.3016e-01]], grad_fn=<IndexBackward0>), hidden_states=None, attentions=None), vision_model_output=BaseModelOutputWithPooling(last_hidden_state=tensor([[[ 0.3057, -0.4834,  0.2767,  ...,  0.1574, -0.4413, -0.2416],\n",
              "         [ 0.8527,  0.1066, -0.3700,  ...,  0.8211,  0.0849, -0.2295],\n",
              "         [ 1.2008,  0.5247,  0.2992,  ...,  0.3441,  0.2552, -0.4080],\n",
              "         ...,\n",
              "         [-0.1033,  0.3884,  0.1657,  ...,  0.1202,  0.2235, -0.2113],\n",
              "         [ 0.2376, -0.1696,  0.2197,  ...,  0.3786,  0.1543, -0.4765],\n",
              "         [ 0.4960,  0.0392,  0.0810,  ...,  0.2781,  0.2965, -0.6423]]],\n",
              "       grad_fn=<AddBackward0>), pooler_output=tensor([[ 0.5936, -0.8104,  0.4890,  ...,  0.5566, -0.8311, -0.4705]],\n",
              "       grad_fn=<NativeLayerNormBackward0>), hidden_states=None, attentions=None))"
            ]
          },
          "execution_count": 49,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "outputs = model(**inputs)\n",
        "outputs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x9NnCdKd0nCP",
        "outputId": "3bbacb46-8a65-4474-eea8-68ce345d6ddb"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor([[22.0966, 17.0880, 20.5824, 20.6752]], grad_fn=<TBackward0>)"
            ]
          },
          "execution_count": 50,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "outputs.logits_per_image"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1lcOiBfX0qhZ",
        "outputId": "d6c5f06d-066f-49cb-9ef3-f92be6c41315"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor([0.6812, 0.0046, 0.1498, 0.1644], grad_fn=<SelectBackward0>)"
            ]
          },
          "execution_count": 51,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "probs = outputs.logits_per_image.softmax(dim=1)[0]\n",
        "probs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HD5V3Z6M0r8P",
        "outputId": "9a9458e5-396f-4d0a-89d6-7200f40a797b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "label: a photo of a cat - probability of 0.6812\n",
            "label: a photo of a dog - probability of 0.0046\n",
            "label: a photo of a man - probability of 0.1498\n",
            "label: a photo of a human - probability of 0.1644\n"
          ]
        }
      ],
      "source": [
        "probs = list(probs)\n",
        "for i in range(len(labels)):\n",
        "  print(f\"label: {labels[i]} - probability of {probs[i].item():.4f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "id": "sL6XIUSI0tfn"
      },
      "outputs": [],
      "source": []
    }
  ],
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