Image_captioning.ipynb

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      "execution_count": null,
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        "id": "4aOXDwhyH978",
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      },
      "outputs": [
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          "name": "stdout",
          "text": [
            "Collecting transformers\n",
            "  Downloading transformers-4.31.0-py3-none-any.whl (7.4 MB)\n",
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            "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1 (from transformers)\n",
            "  Downloading tokenizers-0.13.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
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            "\u001b[?25hCollecting safetensors>=0.3.1 (from transformers)\n",
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            "Installing collected packages: tokenizers, safetensors, huggingface-hub, transformers\n",
            "Successfully installed huggingface-hub-0.16.4 safetensors-0.3.1 tokenizers-0.13.3 transformers-4.31.0\n"
          ]
        }
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      "source": [
        "!pip install transformers"
      ]
    },
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      "cell_type": "code",
      "source": [
        "from transformers import VisionEncoderDecoderModel, ViTFeatureExtractor, AutoTokenizer\n",
        "import torch\n",
        "from PIL import Image"
      ],
      "metadata": {
        "id": "Xws-nsxBIN0U"
      },
      "execution_count": null,
      "outputs": []
    },
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      "source": [
        "model = VisionEncoderDecoderModel.from_pretrained(\"nlpconnect/vit-gpt2-image-captioning\")"
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            "text/plain": [
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        "feature_extractor = ViTFeatureExtractor.from_pretrained(\"nlpconnect/vit-gpt2-image-captioning\")\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"nlpconnect/vit-gpt2-image-captioning\")"
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)rocessor_config.json:   0%|          | 0.00/228 [00:00<?, ?B/s]"
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          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/models/vit/feature_extraction_vit.py:28: FutureWarning: The class ViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please use ViTImageProcessor instead.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/241 [00:00<?, ?B/s]"
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      "cell_type": "code",
      "source": [
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\" )\n",
        "model.to(device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nzkFLpkBJygu",
        "outputId": "ce0c4f85-5e32-4078-f134-687b55ec6187"
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "VisionEncoderDecoderModel(\n",
              "  (encoder): ViTModel(\n",
              "    (embeddings): ViTEmbeddings(\n",
              "      (patch_embeddings): ViTPatchEmbeddings(\n",
              "        (projection): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16))\n",
              "      )\n",
              "      (dropout): Dropout(p=0.0, inplace=False)\n",
              "    )\n",
              "    (encoder): ViTEncoder(\n",
              "      (layer): ModuleList(\n",
              "        (0-11): 12 x ViTLayer(\n",
              "          (attention): ViTAttention(\n",
              "            (attention): ViTSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.0, inplace=False)\n",
              "            )\n",
              "            (output): ViTSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.0, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): ViTIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): ViTOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (dropout): Dropout(p=0.0, inplace=False)\n",
              "          )\n",
              "          (layernorm_before): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "          (layernorm_after): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (layernorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "    (pooler): ViTPooler(\n",
              "      (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "      (activation): Tanh()\n",
              "    )\n",
              "  )\n",
              "  (decoder): GPT2LMHeadModel(\n",
              "    (transformer): GPT2Model(\n",
              "      (wte): Embedding(50257, 768)\n",
              "      (wpe): Embedding(1024, 768)\n",
              "      (drop): Dropout(p=0.1, inplace=False)\n",
              "      (h): ModuleList(\n",
              "        (0-11): 12 x GPT2Block(\n",
              "          (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "          (attn): GPT2Attention(\n",
              "            (c_attn): Conv1D()\n",
              "            (c_proj): Conv1D()\n",
              "            (attn_dropout): Dropout(p=0.1, inplace=False)\n",
              "            (resid_dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "          (crossattention): GPT2Attention(\n",
              "            (c_attn): Conv1D()\n",
              "            (q_attn): Conv1D()\n",
              "            (c_proj): Conv1D()\n",
              "            (attn_dropout): Dropout(p=0.1, inplace=False)\n",
              "            (resid_dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (ln_cross_attn): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "          (mlp): GPT2MLP(\n",
              "            (c_fc): Conv1D()\n",
              "            (c_proj): Conv1D()\n",
              "            (act): NewGELUActivation()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (ln_f): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "    )\n",
              "    (lm_head): Linear(in_features=768, out_features=50257, bias=False)\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max_length = 16\n",
        "num_beams = 4\n",
        "\n",
        "gen_kwargs = {\"max_length\": max_length, \"num_beams\": num_beams}"
      ],
      "metadata": {
        "id": "0224e1mHKNdP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def predict_step(image_paths):\n",
        "  images = []\n",
        "  for image_path in image_paths:\n",
        "    i_image = Image.open(image_path)\n",
        "    if i_image.mode != \"RGB\":\n",
        "      i_image = i_image.convert(mode = \"RGB\")\n",
        "    images.append(i_image)\n",
        "\n",
        "  pixel_values = feature_extractor(images= images, return_tensors='pt').pixel_values\n",
        "  pixel_values = pixel_values.to(device)\n",
        "\n",
        "  output_ids = model.generate(pixel_values, **gen_kwargs)\n",
        "\n",
        "  preds = tokenizer.batch_decode(output_ids, skip_special_tokens= True)\n",
        "  preds = [pred.strip() for pred in preds]\n",
        "\n",
        "  return preds"
      ],
      "metadata": {
        "id": "aC2Jx9BEKiiS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predict_step(['singer.jpg'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zjsmy0k7MWHN",
        "outputId": "d4441319-7cc9-468c-c1ee-4961b56c275c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['a crowd of people standing in front of a microphone']"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    }
  ]
}