TrOCR_Japanese.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# TROCR - Japanese Language"
      ],
      "metadata": {
        "id": "zglRm5SMqfer"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Required Package"
      ],
      "metadata": {
        "id": "QylQiRX1qoOE"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pkSzlRJq68tH"
      },
      "source": [
        "!pip install -q transformers\n",
        "!pip install -q datasets jiwer"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Importing required libraries"
      ],
      "metadata": {
        "id": "Lx30-YyPq87A"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "import torch\n",
        "from torch.utils.data import Dataset\n",
        "from PIL import Image\n",
        "from transformers import TrOCRProcessor\n",
        "from transformers import VisionEncoderDecoderModel\n",
        "from transformers import Seq2SeqTrainer, Seq2SeqTrainingArguments\n",
        "from transformers import default_data_collator\n",
        "from datasets import load_metric\n"
      ],
      "metadata": {
        "id": "ND7lMTTQrADw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lsTaPrDR7My2"
      },
      "source": [
        "## Prepare data"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Download data using google drive file id\n",
        "!gdown --id 11TqvOaM8ly07EDyO5dRBzJgmD0AiPuvT \n",
        "# Unzip the file- Change the directory according to the training directory\n",
        "!unzip \"/content/dimg.zip\" -d '/content/'\n",
        "BASE_DIR = '/content/新しいフォルダー/'       # Base directory of the unzip folder\n",
        "# generate text from generated images and create df\n",
        "training_files = os.listdir(BASE_DIR)\n",
        "processed_data = pd.DataFrame(training_files, columns = ['file_name'])\n",
        "#processed_data['words'] = processed_data['filename'].str.split('.jpeg', expand = True)[0]\n",
        "processed_data['text'] = processed_data['file_name'].str.split('.', expand = True)[0]\n",
        "df = processed_data.copy()\n",
        "df.head()"
      ],
      "metadata": {
        "id": "IUB5lhljBDiH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qJlVYVal9Ojy"
      },
      "source": [
        "Spliting the data into Train, Test"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6qLVT1TPN8Nt"
      },
      "source": [
        "train_df, test_df = train_test_split(df, test_size=0.2, random_state = 42)\n",
        "# we reset the indices to start from zero\n",
        "train_df.reset_index(drop=True, inplace=True)\n",
        "test_df.reset_index(drop=True, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qO5Q8WYp7DLx"
      },
      "source": [
        "class IAMDataset(Dataset):\n",
        "    def __init__(self, root_dir, df, processor, max_target_length=128):\n",
        "        self.root_dir = root_dir\n",
        "        self.df = df\n",
        "        self.processor = processor\n",
        "        self.max_target_length = max_target_length\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.df)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        # get file name + text \n",
        "        file_name = self.df['file_name'][idx]\n",
        "        text = self.df['text'][idx]\n",
        "        # prepare image (i.e. resize + normalize)\n",
        "        image = Image.open(self.root_dir + file_name).convert(\"RGB\")\n",
        "        pixel_values = self.processor(image, return_tensors=\"pt\").pixel_values\n",
        "        # add labels (input_ids) by encoding the text\n",
        "        labels = self.processor.tokenizer(text, \n",
        "                                          padding=\"max_length\", \n",
        "                                          max_length=self.max_target_length).input_ids\n",
        "        # important: make sure that PAD tokens are ignored by the loss function\n",
        "        labels = [label if label != self.processor.tokenizer.pad_token_id else -100 for label in labels]\n",
        "\n",
        "        encoding = {\"pixel_values\": pixel_values.squeeze(), \"labels\": torch.tensor(labels)}\n",
        "        return encoding"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#from transformers import VisionEncoderDecoderModel\n",
        "\n",
        "#model = VisionEncoderDecoderModel.from_encoder_decoder_pretrained(\"google/vit-base-patch16-224-in21k\", \"cl-tohoku/bert-base-japanese\")\n",
        "\n",
        "# set decoder config to causal lm (only required in case one initializes the decoder with the weights of an encoder-only model)\n",
        "# this will add the randomly initialized cross-attention layers\n",
        "#config_decoder.is_decoder = True\n",
        "#config_decoder.add_cross_attention = True"
      ],
      "metadata": {
        "id": "GxgAX72LCWqL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Initialize the training and evaluation datasets"
      ],
      "metadata": {
        "id": "3cxEQ8Evsfpv"
      }
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        "outputId": "f1367263-3a23-4efd-b417-d9fde3deff53"
      },
      "source": [
        "processor = TrOCRProcessor.from_pretrained(\"microsoft/trocr-base-handwritten\")\n",
        "train_dataset = IAMDataset(root_dir=BASE_DIR,\n",
        "                           df=train_df,\n",
        "                           processor=processor)\n",
        "                           \n",
        "eval_dataset = IAMDataset(root_dir=BASE_DIR,\n",
        "                           df=test_df,\n",
        "                           processor=processor)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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          "data": {
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              "Downloading:   0%|          | 0.00/1.09k [00:00<?, ?B/s]"
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              "Downloading:   0%|          | 0.00/878k [00:00<?, ?B/s]"
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              "Downloading:   0%|          | 0.00/772 [00:00<?, ?B/s]"
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PiwZLbMeLCfo",
        "outputId": "23fd8a44-d798-4d7d-fa44-227ba43ac361"
      },
      "source": [
        "print(\"Number of training examples:\", len(train_dataset))\n",
        "print(\"Number of validation examples:\", len(eval_dataset))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of training examples: 753\n",
            "Number of validation examples: 189\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7p8JfQrx-6EM"
      },
      "source": [
        "Display example of dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rwBNrfD78RA7",
        "outputId": "4b67e02b-feec-4d24-c6a3-88061d5e465f"
      },
      "source": [
        "encoding = train_dataset[0]\n",
        "for k,v in encoding.items():\n",
        "  print(k, v.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "pixel_values torch.Size([3, 384, 384])\n",
            "labels torch.Size([128])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lN-3pf6T_uRe"
      },
      "source": [
        "Display Image and label\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 62
        },
        "id": "QzgOFgD4_7Kw",
        "outputId": "3403fd35-2b7f-4f69-d5a6-e3946d9c231e"
      },
      "source": [
        "image = Image.open(train_dataset.root_dir + train_df['file_name'][0]).convert(\"RGB\")\n",
        "image"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=196x45 at 0x7F6B550565D0>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vMtfkDia-8tQ",
        "outputId": "f1e61c51-fef4-4340-eac2-480688ec2091"
      },
      "source": [
        "labels = encoding['labels']\n",
        "labels[labels == -100] = processor.tokenizer.pad_token_id\n",
        "label_str = processor.decode(labels, skip_special_tokens=True)\n",
        "print(label_str)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "のめをせくこ\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XxU7TfoYBvg0"
      },
      "source": [
        "## Train a model\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137,
          "referenced_widgets": [
            "6f1090e974504ee2b3a24fb570f825f2",
            "133209ca45cc4c26b861080e58b1d66a",
            "4454ca979f2b454fada5d0f375a80b74",
            "002d42933c7d4175adf96c96a0f94a01",
            "fd3301a8927a44de8554e3113282488a",
            "f213a34d6f894279bd4539fba429ef09",
            "370abc251e9540d886ade73ade32aedd",
            "267dded615864c498393aa0279194e40",
            "47b71a13adcc4476b3ee8786939f55bf",
            "4bd94d64b8734ad78e9a0227f86835b6",
            "730383d668b24214afe2b6b54acbcf7d",
            "d185b34898a8484fb6790f0a2eb60543",
            "1f70d7008a8a4edf8ead4e8aa148fe84",
            "c22ac58c6bff4027a51246d0dac805c2",
            "d53466f05a3a474fb94ca1b4faebe4ed",
            "4ab89350a0b541fa982d3f33e622f46e",
            "e0f1e16786d14f0cb1194d6d6277126e",
            "6d5fe81c02cf411da15b52b34c81d250",
            "b0117211dbe54179ac3851a3f8b0ee34",
            "7c3a578b904e4daca445a5b4aca23f50",
            "f13965e564dd47bcb04dabb962666ebc",
            "0204e4612cbd412db5c0c918522646be"
          ]
        },
        "id": "bRhvTRrGBIfy",
        "outputId": "1da3bb76-16ad-4eac-82b9-b7a3e5cdc271"
      },
      "source": [
        "model = VisionEncoderDecoderModel.from_pretrained(\"microsoft/trocr-base-stage1\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/4.11k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6f1090e974504ee2b3a24fb570f825f2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/1.43G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d185b34898a8484fb6790f0a2eb60543"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of VisionEncoderDecoderModel were not initialized from the model checkpoint at microsoft/trocr-base-stage1 and are newly initialized: ['encoder.pooler.dense.bias', 'encoder.pooler.dense.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sNNT1XS_CMgl"
      },
      "source": [
        "# set special tokens used for creating the decoder_input_ids from the labels\n",
        "model.config.decoder_start_token_id = processor.tokenizer.cls_token_id\n",
        "model.config.pad_token_id = processor.tokenizer.pad_token_id\n",
        "# make sure vocab size is set correctly\n",
        "model.config.vocab_size = model.config.decoder.vocab_size\n",
        "\n",
        "# set beam search parameters\n",
        "model.config.eos_token_id = processor.tokenizer.sep_token_id\n",
        "model.config.max_length = 64\n",
        "model.config.early_stopping = True\n",
        "model.config.no_repeat_ngram_size = 3\n",
        "model.config.length_penalty = 2.0\n",
        "model.config.num_beams = 4"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o1G_rkDBzwid"
      },
      "source": [
        "training_args = Seq2SeqTrainingArguments(\n",
        "    predict_with_generate=True,\n",
        "    evaluation_strategy=\"steps\",\n",
        "    per_device_train_batch_size=8,\n",
        "    per_device_eval_batch_size=8,\n",
        "    fp16=True, # for CUDA keep this True\n",
        "    output_dir=\"./\",\n",
        "    num_train_epochs = 120,\n",
        "    logging_steps=2,\n",
        "    save_steps=2000,\n",
        "    eval_steps=1000,\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yoXD3_P10DD4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "054f6861f9004ff4862701d1abbdfb2c",
            "a14c6eac8f0d4cf4bafeb80bdc72ee5d",
            "1b503a90e42d4480a623f9841b31e8f1",
            "b854507560464005b714d786e64243aa",
            "90d27ec2ca854483a1c5b42b7ff866f8",
            "e14aa8c5539f4c8e901045a70baf1788",
            "137b80e4d1cc47e7a89113a3048e322d",
            "7fe270218d324a99a0c8e631779b2c57",
            "e2d646c46d3e404e8b2bf7211dee2b98",
            "8311fcfc94a445d596288ef5f5b29b23",
            "5d9705e66e484c5d8b3848a5d2cd4867"
          ]
        },
        "outputId": "e6b94e03-36c6-4a8f-a26f-e63f971207c5"
      },
      "source": [
        "# Character error rate CER = (S + D + I) / N   > S: substitute, D: Delete, I: insertion, N: total characters\n",
        "cer_metric = load_metric(\"cer\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/2.16k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "054f6861f9004ff4862701d1abbdfb2c"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y36AcnvP0OZw"
      },
      "source": [
        "def compute_metrics(pred):\n",
        "    labels_ids = pred.label_ids\n",
        "    pred_ids = pred.predictions\n",
        "\n",
        "    pred_str = processor.batch_decode(pred_ids, skip_special_tokens=True)\n",
        "    labels_ids[labels_ids == -100] = processor.tokenizer.pad_token_id\n",
        "    label_str = processor.batch_decode(labels_ids, skip_special_tokens=True)\n",
        "\n",
        "    cer = cer_metric.compute(predictions=pred_str, references=label_str)\n",
        "\n",
        "    return {\"cer\": cer}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "mcQMbxi10SDm",
        "outputId": "7616a555-38ed-41c8-bbd3-d17c3db34541"
      },
      "source": [
        "# instantiate trainer\n",
        "trainer = Seq2SeqTrainer(\n",
        "    model=model,\n",
        "    tokenizer=processor.feature_extractor,\n",
        "    args=training_args,\n",
        "    compute_metrics=compute_metrics,\n",
        "    train_dataset=train_dataset,\n",
        "    eval_dataset=eval_dataset,\n",
        "    data_collator=default_data_collator,\n",
        ")\n",
        "trainer.train()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using cuda_amp half precision backend\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:310: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  FutureWarning,\n",
            "***** Running training *****\n",
            "  Num examples = 753\n",
            "  Num Epochs = 120\n",
            "  Instantaneous batch size per device = 8\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 8\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 11400\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='8075' max='11400' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [ 8075/11400 3:46:18 < 1:33:12, 0.59 it/s, Epoch 84.99/120]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Cer</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>0.488900</td>\n",
              "      <td>0.511758</td>\n",
              "      <td>0.210884</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2000</td>\n",
              "      <td>0.270300</td>\n",
              "      <td>0.326699</td>\n",
              "      <td>0.118562</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3000</td>\n",
              "      <td>0.256300</td>\n",
              "      <td>0.446280</td>\n",
              "      <td>0.111759</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4000</td>\n",
              "      <td>0.035300</td>\n",
              "      <td>0.557071</td>\n",
              "      <td>0.080661</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5000</td>\n",
              "      <td>0.050300</td>\n",
              "      <td>0.752312</td>\n",
              "      <td>0.076774</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6000</td>\n",
              "      <td>0.065800</td>\n",
              "      <td>0.861655</td>\n",
              "      <td>0.103984</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7000</td>\n",
              "      <td>0.000800</td>\n",
              "      <td>1.006792</td>\n",
              "      <td>0.088435</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8000</td>\n",
              "      <td>0.000100</td>\n",
              "      <td>1.135659</td>\n",
              "      <td>0.060253</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "***** Running Evaluation *****\n",
            "  Num examples = 189\n",
            "  Batch size = 8\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 189\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to ./checkpoint-2000\n",
            "Configuration saved in ./checkpoint-2000/config.json\n",
            "Model weights saved in ./checkpoint-2000/pytorch_model.bin\n",
            "Feature extractor saved in ./checkpoint-2000/preprocessor_config.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 189\n",
            "  Batch size = 8\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 189\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to ./checkpoint-4000\n",
            "Configuration saved in ./checkpoint-4000/config.json\n",
            "Model weights saved in ./checkpoint-4000/pytorch_model.bin\n",
            "Feature extractor saved in ./checkpoint-4000/preprocessor_config.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 189\n",
            "  Batch size = 8\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 189\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to ./checkpoint-6000\n",
            "Configuration saved in ./checkpoint-6000/config.json\n",
            "Model weights saved in ./checkpoint-6000/pytorch_model.bin\n",
            "Feature extractor saved in ./checkpoint-6000/preprocessor_config.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 189\n",
            "  Batch size = 8\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 189\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to ./checkpoint-8000\n",
            "Configuration saved in ./checkpoint-8000/config.json\n",
            "Model weights saved in ./checkpoint-8000/pytorch_model.bin\n",
            "Feature extractor saved in ./checkpoint-8000/preprocessor_config.json\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Loading trained Model"
      ],
      "metadata": {
        "id": "mr3z9IZrpVyX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#  model.save_pretrained(\"./models\")\n",
        "#  processor.save_pretrained('./models')\n",
        "#  pro = TrOCRProcessor.from_pretrained(\"./models\")\n",
        "#  processor = TrOCRProcessor.from_pretrained(\"./checkpoint-200\")\n",
        "#  model1 = VisionEncoderDecoderModel.from_pretrained(\"./checkpoint-100\")\n",
        "#  file_name = df['file_name'][8]\n",
        "# text = df['text'][8]\n",
        "# image = Image.open(BASE_DIR + file_name).convert(\"RGB\")\n",
        "# pixel_values = pro(image, return_tensors=\"pt\").pixel_values\n",
        "# pro.batch_decode(model1.generate(pixel_values), skip_special_tokens=True)[0]"
      ],
      "metadata": {
        "id": "R9ziREVZvClU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q8tduj2fBGQK"
      },
      "source": [
        "## Predictions\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def text_prediction(file_name, BASE_DIR, device, model, processor):\n",
        "  image = Image.open(BASE_DIR + file_name).convert(\"RGB\")\n",
        "  pixel_values = processor(image, return_tensors=\"pt\").pixel_values\n",
        "  generated_ids = model.generate(pixel_values.to(device))\n",
        "  generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]\n",
        "  return generated_text\n"
      ],
      "metadata": {
        "id": "U2a6q0Ne4L1n"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "model.to(device)\n",
        "pred = []\n",
        "for i in df.index:\n",
        "  pred.append(text_prediction(df['file_name'][i], BASE_DIR, device, model, processor))"
      ],
      "metadata": {
        "id": "R6UipKqG3ntl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predicted_df = df.copy()\n",
        "predicted_df['predicted text'] = pred"
      ],
      "metadata": {
        "id": "pTOkJEoHU9WH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predicted_df.head(100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "WuEp-m6--PU3",
        "outputId": "33ca0780-f968-47e0-a94b-62d2fb4038cf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      file_name     text predicted text\n",
              "0     あねんや.jpeg     あねんや           わふぬわ\n",
              "1    よさせにきぬ.png   よさせにきぬ        わそふねにみわ\n",
              "2    えいせうにお.png   えいせうにお        ちふぬうけねむ\n",
              "3     めぬそつさ.png    めぬそつさ         ややふへねつ\n",
              "4      やいしお.png     やいしお            ゆふぬ\n",
              "..          ...      ...            ...\n",
              "95  たみなろいまれ.png  たみなろいまれ        ねそわぬせそつ\n",
              "96     むおのて.png     むおのて          つへぬわの\n",
              "97  さろさめそひけ.png  さろさめそひけ       されゆろむねそね\n",
              "98    つせそのふ.png    つせそのふ         のふおののむ\n",
              "99    におるかつ.png    におるかつ         にふわねむり\n",
              "\n",
              "[100 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3e89cb00-8e0d-47aa-8292-a369647497c0\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>file_name</th>\n",
              "      <th>text</th>\n",
              "      <th>predicted text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>あねんや.jpeg</td>\n",
              "      <td>あねんや</td>\n",
              "      <td>わふぬわ</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>よさせにきぬ.png</td>\n",
              "      <td>よさせにきぬ</td>\n",
              "      <td>わそふねにみわ</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>えいせうにお.png</td>\n",
              "      <td>えいせうにお</td>\n",
              "      <td>ちふぬうけねむ</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>めぬそつさ.png</td>\n",
              "      <td>めぬそつさ</td>\n",
              "      <td>ややふへねつ</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>やいしお.png</td>\n",
              "      <td>やいしお</td>\n",
              "      <td>ゆふぬ</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>たみなろいまれ.png</td>\n",
              "      <td>たみなろいまれ</td>\n",
              "      <td>ねそわぬせそつ</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>むおのて.png</td>\n",
              "      <td>むおのて</td>\n",
              "      <td>つへぬわの</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>さろさめそひけ.png</td>\n",
              "      <td>さろさめそひけ</td>\n",
              "      <td>されゆろむねそね</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>つせそのふ.png</td>\n",
              "      <td>つせそのふ</td>\n",
              "      <td>のふおののむ</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>におるかつ.png</td>\n",
              "      <td>におるかつ</td>\n",
              "      <td>にふわねむり</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3e89cb00-8e0d-47aa-8292-a369647497c0')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3e89cb00-8e0d-47aa-8292-a369647497c0 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3e89cb00-8e0d-47aa-8292-a369647497c0');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predicted_df.to_csv('predictions.csv')"
      ],
      "metadata": {
        "id": "d9RJawIy_pPp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Loading Trained Model"
      ],
      "metadata": {
        "id": "pdHVxFghVVBG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import VisionEncoderDecoderModel\n",
        "\n",
        "model1 = VisionEncoderDecoderModel.from_pretrained(\"./trained_model\")"
      ],
      "metadata": {
        "id": "g1THE7eZVWJO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "model.to(device)"
      ],
      "metadata": {
        "id": "IUuEzkPSVZOJ"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}