machine-learning/image-captioning/ImageCaptioning_PythonCode.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "By7_mGNiswoU"
      },
      "source": [
        "# Getting Started"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PCnIaQFL0R4o"
      },
      "outputs": [],
      "source": [
        "!pip install torch transformers rouge_score evaluate datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eK_JAStp4ada",
        "outputId": "3f3c2b9f-d2eb-42ef-acf7-d9696f564042"
      },
      "outputs": [],
      "source": [
        "import requests\n",
        "import torch\n",
        "from PIL import Image\n",
        "from transformers import *\n",
        "from tqdm import tqdm\n",
        "\n",
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X69Lr4FOs0cw"
      },
      "source": [
        "# Using a Trained Model"
      ]
    },
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        },
        "id": "LFkwmHrJ0Vq-",
        "outputId": "881a67e0-9fd7-47da-c030-1c9208186841"
      },
      "outputs": [],
      "source": [
        "# load a fine-tuned image captioning model and corresponding tokenizer and image processor\n",
        "finetuned_model = VisionEncoderDecoderModel.from_pretrained(\"nlpconnect/vit-gpt2-image-captioning\").to(device)\n",
        "finetuned_tokenizer = GPT2TokenizerFast.from_pretrained(\"nlpconnect/vit-gpt2-image-captioning\")\n",
        "finetuned_image_processor = ViTImageProcessor.from_pretrained(\"nlpconnect/vit-gpt2-image-captioning\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hxWYldgKTVw8"
      },
      "outputs": [],
      "source": [
        "import urllib.parse as parse\n",
        "import os\n",
        "\n",
        "# a function to determine whether a string is a URL or not\n",
        "def is_url(string):\n",
        "    try:\n",
        "        result = parse.urlparse(string)\n",
        "        return all([result.scheme, result.netloc, result.path])\n",
        "    except:\n",
        "        return False\n",
        "    \n",
        "# a function to load an image\n",
        "def load_image(image_path):\n",
        "    if is_url(image_path):\n",
        "        return Image.open(requests.get(image_path, stream=True).raw)\n",
        "    elif os.path.exists(image_path):\n",
        "        return Image.open(image_path)\n",
        "        \n",
        "\n",
        "# a function to perform inference\n",
        "def get_caption(model, image_processor, tokenizer, image_path):\n",
        "    image = load_image(image_path)\n",
        "    # preprocess the image\n",
        "    img = image_processor(image, return_tensors=\"pt\").to(device)\n",
        "    # generate the caption (using greedy decoding by default)\n",
        "    output = model.generate(**img)\n",
        "    # decode the output\n",
        "    caption = tokenizer.batch_decode(output, skip_special_tokens=True)[0]\n",
        "    return caption"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 877
        },
        "id": "SkU3ewzUTXz-",
        "outputId": "4263893b-efde-46bf-e8cc-39b372bb78ac"
      },
      "outputs": [],
      "source": [
        "# load displayer\n",
        "from IPython.display import display\n",
        "\n",
        "url = \"http://images.cocodataset.org/test-stuff2017/000000009384.jpg\"\n",
        "display(load_image(url))\n",
        "get_caption(finetuned_model, finetuned_image_processor, finetuned_tokenizer, url)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gJr8IpSzyr3j"
      },
      "source": [
        "# Fine-tuning your Own Image Captioning Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4Y5Vmjmcs7_7"
      },
      "source": [
        "## Loading the Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7Q0--Xzhyrif"
      },
      "outputs": [],
      "source": [
        "# the encoder model that process the image and return the image features\n",
        "# encoder_model = \"WinKawaks/vit-small-patch16-224\"\n",
        "# encoder_model = \"google/vit-base-patch16-224\"\n",
        "# encoder_model = \"google/vit-base-patch16-224-in21k\"\n",
        "encoder_model = \"microsoft/swin-base-patch4-window7-224-in22k\"\n",
        "# the decoder model that process the image features and generate the caption text\n",
        "# decoder_model = \"bert-base-uncased\"\n",
        "# decoder_model = \"prajjwal1/bert-tiny\"\n",
        "decoder_model = \"gpt2\"\n",
        "# load the model\n",
        "model = VisionEncoderDecoderModel.from_encoder_decoder_pretrained(\n",
        "    encoder_model, decoder_model\n",
        ").to(device)"
      ]
    },
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      "outputs": [],
      "source": [
        "# initialize the tokenizer\n",
        "# tokenizer = AutoTokenizer.from_pretrained(decoder_model)\n",
        "tokenizer = GPT2TokenizerFast.from_pretrained(decoder_model)\n",
        "# tokenizer = BertTokenizerFast.from_pretrained(decoder_model)\n",
        "# load the image processor\n",
        "image_processor = ViTImageProcessor.from_pretrained(encoder_model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hTOwe5-OzoY1"
      },
      "outputs": [],
      "source": [
        "if \"gpt2\" in decoder_model:\n",
        "  # gpt2 does not have decoder_start_token_id and pad_token_id\n",
        "  # but has bos_token_id and eos_token_id\n",
        "  tokenizer.pad_token = tokenizer.eos_token # pad_token_id as eos_token_id\n",
        "  model.config.eos_token_id = tokenizer.eos_token_id\n",
        "  model.config.pad_token_id = tokenizer.pad_token_id\n",
        "  # set decoder_start_token_id as bos_token_id\n",
        "  model.config.decoder_start_token_id = tokenizer.bos_token_id\n",
        "else:\n",
        "  # set the decoder start token id to the CLS token id of the tokenizer\n",
        "  model.config.decoder_start_token_id = tokenizer.cls_token_id\n",
        "  # set the pad token id to the pad token id of the tokenizer\n",
        "  model.config.pad_token_id = tokenizer.pad_token_id"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "34BbBMRCW3iF"
      },
      "source": [
        "## Downloading & Loading the Dataset"
      ]
    },
    {
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        },
        "id": "KCdLhUS50d8X",
        "outputId": "451c9242-4b5d-4894-f075-a0ae9e47cea2"
      },
      "outputs": [],
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "max_length = 32 # max length of the captions in tokens\n",
        "coco_dataset_ratio = 50 # 50% of the COCO2014 dataset\n",
        "train_ds = load_dataset(\"HuggingFaceM4/COCO\", split=f\"train[:{coco_dataset_ratio}%]\")\n",
        "valid_ds = load_dataset(\"HuggingFaceM4/COCO\", split=f\"validation[:{coco_dataset_ratio}%]\")\n",
        "test_ds = load_dataset(\"HuggingFaceM4/COCO\", split=\"test\")\n",
        "len(train_ds), len(valid_ds), len(test_ds)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ho4OMahqXEQ_"
      },
      "source": [
        "## Preprocessing the Inputs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 263,
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        },
        "id": "cPmPwr0L3GLS",
        "outputId": "2e83aab3-b534-42cf-df16-6b975b5df84f"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "\n",
        "# remove the images with less than 3 dimensions (possibly grayscale images)\n",
        "train_ds = train_ds.filter(lambda item: np.array(item[\"image\"]).ndim in [3, 4], num_proc=2)\n",
        "valid_ds = valid_ds.filter(lambda item: np.array(item[\"image\"]).ndim in [3, 4], num_proc=2)\n",
        "test_ds = test_ds.filter(lambda item: np.array(item[\"image\"]).ndim in [3, 4], num_proc=2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6reT679g3Rrl"
      },
      "outputs": [],
      "source": [
        "def preprocess(items):\n",
        "  # preprocess the image\n",
        "  pixel_values = image_processor(items[\"image\"], return_tensors=\"pt\").pixel_values.to(device)\n",
        "  # tokenize the caption with truncation and padding\n",
        "  targets = tokenizer([ sentence[\"raw\"] for sentence in items[\"sentences\"] ], \n",
        "                      max_length=max_length, padding=\"max_length\", truncation=True, return_tensors=\"pt\").to(device)\n",
        "  return {'pixel_values': pixel_values, 'labels': targets[\"input_ids\"]}\n",
        "\n",
        "\n",
        "# using with_transform to preprocess the dataset during training\n",
        "train_dataset = train_ds.with_transform(preprocess)\n",
        "valid_dataset = valid_ds.with_transform(preprocess)\n",
        "test_dataset  = test_ds.with_transform(preprocess)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CxuxwImK_Zpv"
      },
      "outputs": [],
      "source": [
        "# a function we'll use to collate the batches\n",
        "def collate_fn(batch):\n",
        "    return {\n",
        "        'pixel_values': torch.stack([x['pixel_values'] for x in batch]),\n",
        "        'labels': torch.stack([x['labels'] for x in batch])\n",
        "    }"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rmoafj_dcc5P"
      },
      "source": [
        "## Evaluation Metrics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
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        },
        "id": "_E-OpB1ZYWrQ",
        "outputId": "e503216d-026e-4508-d15a-be933716dc92"
      },
      "outputs": [],
      "source": [
        "import evaluate\n",
        "\n",
        "# load the rouge and bleu metrics\n",
        "rouge = evaluate.load(\"rouge\")\n",
        "bleu = evaluate.load(\"bleu\")\n",
        "  \n",
        "def compute_metrics(eval_pred):\n",
        "  preds = eval_pred.label_ids\n",
        "  labels = eval_pred.predictions\n",
        "  # decode the predictions and labels\n",
        "  pred_str = tokenizer.batch_decode(preds, skip_special_tokens=True)\n",
        "  labels_str = tokenizer.batch_decode(labels, skip_special_tokens=True)\n",
        "  # compute the rouge score\n",
        "  rouge_result = rouge.compute(predictions=pred_str, references=labels_str)\n",
        "  # multiply by 100 to get the same scale as the rouge score\n",
        "  rouge_result = {k: round(v * 100, 4) for k, v in rouge_result.items()}\n",
        "  # compute the bleu score\n",
        "  bleu_result = bleu.compute(predictions=pred_str, references=labels_str)\n",
        "  # get the length of the generated captions\n",
        "  generation_length = bleu_result[\"translation_length\"]\n",
        "  return {\n",
        "        **rouge_result, \n",
        "        \"bleu\": round(bleu_result[\"bleu\"] * 100, 4), \n",
        "        \"gen_len\": bleu_result[\"translation_length\"] / len(preds)\n",
        "  }"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vnZFPXrXbdQV"
      },
      "source": [
        "## Training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0DuyO5kMzuai"
      },
      "outputs": [],
      "source": [
        "num_epochs = 2 # number of epochs\n",
        "batch_size = 16 # the size of batches"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B649R3HIzu5V",
        "outputId": "84554a5a-102a-4aa9-e6c7-a069714da2b0"
      },
      "outputs": [],
      "source": [
        "for item in train_dataset:\n",
        "  print(item[\"labels\"].shape)\n",
        "  print(item[\"pixel_values\"].shape)\n",
        "  break"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9zlpKqTibhgm"
      },
      "source": [
        "### Using the Trainer Class"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kW9_rCYFz1dj",
        "outputId": "1dff9c21-a408-488e-b845-3dfcf9ee02d3"
      },
      "outputs": [],
      "source": [
        "# define the training arguments\n",
        "training_args = Seq2SeqTrainingArguments(\n",
        "    predict_with_generate=True,             # use generate to calculate the loss\n",
        "    num_train_epochs=num_epochs,            # number of epochs\n",
        "    evaluation_strategy=\"steps\",            # evaluate after each eval_steps\n",
        "    eval_steps=2000,                        # evaluate after each 2000 steps\n",
        "    logging_steps=2000,                     # log after each 2000 steps\n",
        "    save_steps=2000,                        # save after each 2000 steps\n",
        "    per_device_train_batch_size=batch_size, # batch size for training\n",
        "    per_device_eval_batch_size=batch_size,  # batch size for evaluation\n",
        "    output_dir=\"vit-swin-base-224-gpt2-image-captioning\", # output directory\n",
        "    # push_to_hub=True # whether you want to push the model to the hub,\n",
        "    # check this guide for more details: https://huggingface.co/transformers/model_sharing.html\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KF_w5AHIz7xq"
      },
      "outputs": [],
      "source": [
        "# instantiate trainer\n",
        "trainer = Seq2SeqTrainer(\n",
        "    model=model,                     # the instantiated 🤗 Transformers model to be trained\n",
        "    tokenizer=image_processor,       # we use the image processor as the tokenizer\n",
        "    args=training_args,              # pass the training arguments\n",
        "    compute_metrics=compute_metrics, # pass the compute metrics function\n",
        "    train_dataset=train_dataset,     # pass the training dataset\n",
        "    eval_dataset=valid_dataset,      # pass the validation dataset\n",
        "    data_collator=collate_fn,        # pass the collate function\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-qOYFJ4oz8-_"
      },
      "outputs": [],
      "source": [
        "from torch.utils.data import DataLoader\n",
        "\n",
        "def get_eval_loader(eval_dataset=None):\n",
        "  return DataLoader(valid_dataset, collate_fn=collate_fn, batch_size=batch_size)\n",
        "\n",
        "def get_test_loader(eval_dataset=None):\n",
        "  return DataLoader(test_dataset, collate_fn=collate_fn, batch_size=batch_size)\n",
        "\n",
        "# override the get_train_dataloader, get_eval_dataloader and\n",
        "# get_test_dataloader methods of the trainer\n",
        "# so that we can properly load the data\n",
        "trainer.get_train_dataloader = lambda: DataLoader(train_dataset, collate_fn=collate_fn, batch_size=batch_size)\n",
        "trainer.get_eval_dataloader = get_eval_loader\n",
        "trainer.get_test_dataloader = get_test_loader"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wajwF15J0CmE"
      },
      "outputs": [],
      "source": [
        "# train the model\n",
        "trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oY-90Fey0EJj"
      },
      "outputs": [],
      "source": [
        "# evaluate on the test_dataset\n",
        "trainer.evaluate(test_dataset)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yb_Kws6lpY98"
      },
      "outputs": [],
      "source": [
        "# if you set the push_to_hub parameter in the trainingarguments\n",
        "# finish the pushing using the below code\n",
        "trainer.push_to_hub()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "wj3qPZKmfWC0",
        "outputId": "9b52ca3b-b985-4d42-a744-a3fa509c509d"
      },
      "outputs": [],
      "source": [
        "# to free up GPU memory\n",
        "import gc\n",
        "# del predictions\n",
        "# del outputs\n",
        "# del labels\n",
        "torch.cuda.empty_cache()\n",
        "gc.collect()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2iRIRkw8bt8n"
      },
      "source": [
        "###Using PyTorch Training Loop"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8bBy0QJV59EV"
      },
      "outputs": [],
      "source": [
        "# alternative way of training: pytorch loop\n",
        "from torch.utils.data import DataLoader\n",
        "\n",
        "# define our data loaders\n",
        "train_dataset_loader = DataLoader(train_dataset, collate_fn=collate_fn, batch_size=batch_size, shuffle=True)\n",
        "valid_dataset_loader = DataLoader(valid_dataset, collate_fn=collate_fn, batch_size=8, shuffle=True)\n",
        "test_dataset_loader = DataLoader(test_dataset, collate_fn=collate_fn, batch_size=8, shuffle=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YEFdsk9dd5m7"
      },
      "outputs": [],
      "source": [
        "from torch.optim import AdamW\n",
        "\n",
        "# define the optimizer\n",
        "optimizer = AdamW(model.parameters(), lr=1e-5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Avv1tI09d9Al"
      },
      "outputs": [],
      "source": [
        "# start tensorboard\n",
        "%load_ext tensorboard\n",
        "%tensorboard --logdir ./image-captioning/tensorboard"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Pt5YYNKjiJ4x"
      },
      "outputs": [],
      "source": [
        "# Training loop\n",
        "from torch.utils.tensorboard import SummaryWriter\n",
        "\n",
        "summary_writer = SummaryWriter(log_dir=\"./image-captioning/tensorboard\")\n",
        "\n",
        "# print some statistics before training\n",
        "# number of training steps\n",
        "n_train_steps = num_epochs * len(train_dataset_loader)\n",
        "# number of validation steps\n",
        "n_valid_steps = len(valid_dataset_loader)\n",
        "# current training step\n",
        "current_step = 0\n",
        "# logging, eval & save steps\n",
        "save_steps = 1000"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "H-BsInrQeAZ2",
        "outputId": "87f1f09a-32a2-4313-da06-dbdb867fa093"
      },
      "outputs": [],
      "source": [
        "for epoch in range(num_epochs):\n",
        "    # set the model to training mode\n",
        "    model.train()\n",
        "    # initialize the training loss\n",
        "    train_loss = 0\n",
        "    for batch in tqdm(train_dataset_loader, \"Training\", total=len(train_dataset_loader), leave=False):\n",
        "      if current_step % save_steps == 0:\n",
        "        ### evaluation code ###\n",
        "        # evaluate on the validation set\n",
        "        # if the current step is a multiple of the save steps\n",
        "        print()\n",
        "        print(f\"Validation at step {current_step}...\")\n",
        "        print()\n",
        "        # set the model to evaluation mode\n",
        "        model.eval()\n",
        "        # initialize our lists that store the predictions and the labels\n",
        "        predictions, labels = [], []\n",
        "        # initialize the validation loss\n",
        "        valid_loss = 0\n",
        "        for batch in valid_dataset_loader:\n",
        "            # get the batch\n",
        "            pixel_values = batch[\"pixel_values\"]\n",
        "            label_ids = batch[\"labels\"]\n",
        "            # forward pass\n",
        "            outputs = model(pixel_values=pixel_values, labels=label_ids)\n",
        "            # get the loss\n",
        "            loss = outputs.loss\n",
        "            valid_loss += loss.item()\n",
        "            # free the GPU memory\n",
        "            logits = outputs.logits.detach().cpu()\n",
        "            # add the predictions to the list\n",
        "            predictions.extend(logits.argmax(dim=-1).tolist())\n",
        "            # add the labels to the list\n",
        "            labels.extend(label_ids.tolist())\n",
        "        # make the EvalPrediction object that the compute_metrics function expects\n",
        "        eval_prediction = EvalPrediction(predictions=predictions, label_ids=labels)\n",
        "        # compute the metrics\n",
        "        metrics = compute_metrics(eval_prediction)\n",
        "        # print the stats\n",
        "        print()\n",
        "        print(f\"Epoch: {epoch}, Step: {current_step}, Train Loss: {train_loss / save_steps:.4f}, \" + \n",
        "              f\"Valid Loss: {valid_loss / n_valid_steps:.4f}, BLEU: {metrics['bleu']:.4f}, \" + \n",
        "              f\"ROUGE-1: {metrics['rouge1']:.4f}, ROUGE-2: {metrics['rouge2']:.4f}, ROUGE-L: {metrics['rougeL']:.4f}\")\n",
        "        print()\n",
        "        # log the metrics\n",
        "        summary_writer.add_scalar(\"valid_loss\", valid_loss / n_valid_steps, global_step=current_step)\n",
        "        summary_writer.add_scalar(\"bleu\", metrics[\"bleu\"], global_step=current_step)\n",
        "        summary_writer.add_scalar(\"rouge1\", metrics[\"rouge1\"], global_step=current_step)\n",
        "        summary_writer.add_scalar(\"rouge2\", metrics[\"rouge2\"], global_step=current_step)\n",
        "        summary_writer.add_scalar(\"rougeL\", metrics[\"rougeL\"], global_step=current_step)\n",
        "        # save the model\n",
        "        model.save_pretrained(f\"./image-captioning/checkpoint-{current_step}\")\n",
        "        tokenizer.save_pretrained(f\"./image-captioning/checkpoint-{current_step}\")\n",
        "        image_processor.save_pretrained(f\"./image-captioning/checkpoint-{current_step}\")\n",
        "        # get the model back to train mode\n",
        "        model.train()\n",
        "        # reset the train and valid loss\n",
        "        train_loss, valid_loss = 0, 0\n",
        "      ### training code below ###\n",
        "      # get the batch & convert to tensor\n",
        "      pixel_values = batch[\"pixel_values\"]\n",
        "      labels = batch[\"labels\"]\n",
        "      # forward pass\n",
        "      outputs = model(pixel_values=pixel_values, labels=labels)\n",
        "      # get the loss\n",
        "      loss = outputs.loss\n",
        "      # backward pass\n",
        "      loss.backward()\n",
        "      # update the weights\n",
        "      optimizer.step()\n",
        "      # zero the gradients\n",
        "      optimizer.zero_grad()\n",
        "      # log the loss\n",
        "      loss_v = loss.item()\n",
        "      train_loss += loss_v\n",
        "      # increment the step\n",
        "      current_step += 1\n",
        "      # log the training loss\n",
        "      summary_writer.add_scalar(\"train_loss\", loss_v, global_step=current_step)\n",
        "        "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zdb0aS7dn4Cq"
      },
      "outputs": [],
      "source": [
        "# load the best model, change the checkpoint number to the best checkpoint\n",
        "# if the last checkpoint is the best, then ignore this cell\n",
        "best_checkpoint = 3000\n",
        "best_model = VisionEncoderDecoderModel.from_pretrained(f\"./image-captioning/checkpoint-{best_checkpoint}\").to(device)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_xATNp3btNB8"
      },
      "source": [
        "# Models Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HVoQi-_AgnZ4"
      },
      "outputs": [],
      "source": [
        "def get_evaluation_metrics(model, dataset):\n",
        "  model.eval()\n",
        "  # define our dataloader\n",
        "  dataloader = DataLoader(dataset, collate_fn=collate_fn, batch_size=batch_size)\n",
        "  # number of testing steps\n",
        "  n_test_steps = len(dataloader)\n",
        "  # initialize our lists that store the predictions and the labels\n",
        "  predictions, labels = [], []\n",
        "  # initialize the test loss\n",
        "  test_loss = 0.0\n",
        "  for batch in tqdm(dataloader, \"Evaluating\"):\n",
        "      # get the batch\n",
        "      pixel_values = batch[\"pixel_values\"]\n",
        "      label_ids = batch[\"labels\"]\n",
        "      # forward pass\n",
        "      outputs = model(pixel_values=pixel_values, labels=label_ids)\n",
        "      # outputs = model.generate(pixel_values=pixel_values, max_length=max_length)\n",
        "      # get the loss\n",
        "      loss = outputs.loss\n",
        "      test_loss += loss.item()\n",
        "      # free the GPU memory\n",
        "      logits = outputs.logits.detach().cpu()\n",
        "      # add the predictions to the list\n",
        "      predictions.extend(logits.argmax(dim=-1).tolist())\n",
        "      # add the labels to the list\n",
        "      labels.extend(label_ids.tolist())\n",
        "  # make the EvalPrediction object that the compute_metrics function expects\n",
        "  eval_prediction = EvalPrediction(predictions=predictions, label_ids=labels)\n",
        "  # compute the metrics\n",
        "  metrics = compute_metrics(eval_prediction)\n",
        "  # add the test_loss to the metrics\n",
        "  metrics[\"test_loss\"] = test_loss / n_test_steps\n",
        "  return metrics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "Xb2cd9NdH_TF",
        "outputId": "f7d429c8-50b7-45cf-a314-3d8014f4ca46"
      },
      "outputs": [],
      "source": [
        "metrics = get_evaluation_metrics(best_model, test_dataset)\n",
        "metrics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "XHSi26dziV8n",
        "outputId": "9449a7b6-da56-4d30-c100-f2bdc613770f"
      },
      "outputs": [],
      "source": [
        "finetuned_metrics = get_evaluation_metrics(finetuned_model, test_dataset)\n",
        "finetuned_metrics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "dHqdq8zPICJQ"
      },
      "outputs": [],
      "source": [
        "# using the pipeline API\n",
        "image_captioner = pipeline(\"image-to-text\", model=\"Abdou/vit-swin-base-224-gpt2-image-captioning\")\n",
        "image_captioner.model = image_captioner.model.to(device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "hv53JzzKIFww",
        "outputId": "2479b343-a0b1-4f19-f000-ca43431995a1"
      },
      "outputs": [],
      "source": [
        "get_evaluation_metrics(image_captioner.model, test_dataset)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nkn3OvtAtUV2"
      },
      "source": [
        "# Performing Inference"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Fz4394QgIJCV"
      },
      "outputs": [],
      "source": [
        "def show_image_and_captions(url):\n",
        "  # get the image and display it\n",
        "  display(load_image(url))\n",
        "  # get the captions on various models\n",
        "  our_caption = get_caption(best_model, image_processor, tokenizer, url)\n",
        "  finetuned_caption = get_caption(finetuned_model, finetuned_image_processor, finetuned_tokenizer, url)\n",
        "  pipeline_caption = get_caption(image_captioner.model, image_processor, tokenizer, url)\n",
        "  # print the captions\n",
        "  print(f\"Our caption: {our_caption}\")\n",
        "  print(f\"nlpconnect/vit-gpt2-image-captioning caption: {finetuned_caption}\")\n",
        "  print(f\"Abdou/vit-swin-base-224-gpt2-image-captioning caption: {pipeline_caption}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 986
        },
        "id": "5jUGOrTdINAY",
        "outputId": "4b45568f-12e3-45e6-f8c3-c5a3d86a6f2c"
      },
      "outputs": [],
      "source": [
        "show_image_and_captions(\"http://images.cocodataset.org/test-stuff2017/000000000001.jpg\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "kO2q1J-6ILod",
        "outputId": "c97f388d-8a96-4f68-cfb8-fb50e5e7b735"
      },
      "outputs": [],
      "source": [
        "show_image_and_captions(\"http://images.cocodataset.org/test-stuff2017/000000000019.jpg\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "wXTk8fvCIKLx",
        "outputId": "a261b678-5038-4f31-fa52-82269898075b"
      },
      "outputs": [],
      "source": [
        "show_image_and_captions(\"http://images.cocodataset.org/test-stuff2017/000000000128.jpg\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "ATPcqJAVIQc3",
        "outputId": "874f402b-9556-4270-a6c8-7a6bbb0bfbaa"
      },
      "outputs": [],
      "source": [
        "show_image_and_captions(\"http://images.cocodataset.org/test-stuff2017/000000003072.jpg\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "hkl4vLorIR5e",
        "outputId": "57e13469-5f5b-4978-861d-aa8c8508bce5"
      },
      "outputs": [],
      "source": [
        "show_image_and_captions(\"http://images.cocodataset.org/test-stuff2017/000000003324.jpg\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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        "outputId": "8bfca0b1-41e0-451f-88ee-497e47245c5c"
      },
      "outputs": [],
      "source": [
        "show_image_and_captions(\"http://images.cocodataset.org/test-stuff2017/000000003720.jpg\")"
      ]
    },
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      "cell_type": "code",
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