vgg.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Downloading datasets and installing packages\n",
    "Uncomment to run; only necessary once for each user.\n",
    "source: https://github.com/Noob-can-Compile/Automatic-Image-Captioning/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #Gets validation data.\n",
    "#\n",
    "#!wget http://images.cocodataset.org/zips/val2014.zip\n",
    "#!unzip val2014.zip -d images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #Gets training data.\n",
    "\n",
    "# !wget  http://images.cocodataset.org/zips/train2014.zip\n",
    "# !unzip train2014.zip -d images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #downloads and unzips train/val annotations.\n",
    "\n",
    "# !wget http://images.cocodataset.org/annotations/annotations_trainval2014.zip\n",
    "# !unzip annotations_trainval2014.zip "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !wget http://images.cocodataset.org/annotations/image_info_test2014.zip\n",
    "# !unzip image_info_test2014.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #downloads and unzips test data\n",
    "# !wget http://images.cocodataset.org/zips/test2014.zip\n",
    "# !unzip 'test2014.zip' -d images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #Install missing packages if modules not loaded\n",
    "# !pip install --user torch==1.7.0+cu110 torchvision==0.8.1+cu110 torchaudio===0.7.0 -f https://download.pytorch.org/whl/torch_stable.html\n",
    "# !pip install nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "B68L-vRriH1y"
   },
   "outputs": [],
   "source": [
    "# #Only use in Colab\n",
    "# from google.colab import files\n",
    "\n",
    "#!cd models\n",
    "\n",
    "#files.download('encoder-1.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Master import list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "import nltk\n",
    "import numpy as np\n",
    "import pickle\n",
    "import os\n",
    "import os.path\n",
    "import random\n",
    "import requests\n",
    "import sys\n",
    "import time\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torchvision.models as models\n",
    "import torch.utils.data as data\n",
    "from torchvision import transforms\n",
    "from pycocotools.coco import COCO\n",
    "from collections import Counter\n",
    "from PIL import Image\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Global definitions\n",
    "Note that filenames will depend on the model and must be changed if you don't want to overwrite an existing log file or encoder/decoder .pkl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "#nltk.download('punkt')\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "#dataloader, encoder, and decoder settings\n",
    "batch_size = 64          # batch size\n",
    "vocab_threshold = 5        # minimum word count threshold\n",
    "vocab_from_file = True    # if True, load existing vocab file\n",
    "embed_size = 300           # dimensionality of image and word embeddings\n",
    "hidden_size = 512          # number of features in hidden state of the RNN decoder\n",
    "\n",
    "#training loop\n",
    "num_epochs = 3             # number of training epochs\n",
    "save_every = 1             # determines frequency of saving model weights\n",
    "print_every = 100          # determines window for printing average loss\n",
    "\n",
    "#BE SURE TO CHANGE THESE FILENAMES IF YOU CHANGE THE CNN\n",
    "log_file = 'training_log_model0vgg16.txt'       # name of file with saved training loss and perplexity\n",
    "loss_file = \"vgg13losses.txt\"\n",
    "perplexity_file = \"vgg13perplexities.txt\"\n",
    "\n",
    "#Specify the saved models to load.\n",
    "encoder_file = 'vgg13encoder-3.pkl'\n",
    "decoder_file = 'vgg13decoder-3.pkl'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Encoder/Decoder definitions\n",
    "This model uses resnet50 as the encoder and an RNN with LSTM as the decoder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "id": "Qn81ycM3c4_E"
   },
   "outputs": [],
   "source": [
    "class EncoderCNN(nn.Module):\n",
    "    def __init__(self, embed_size):\n",
    "        super(EncoderCNN, self).__init__()\n",
    "        resnet = models.vgg13(pretrained=True)\n",
    "        \n",
    "        #print(resnet)\n",
    "        \n",
    "        for param in resnet.parameters():\n",
    "            param.requires_grad_(False)\n",
    "        #resnet.classifier[6] = nn.Linear(512,embed_size)\n",
    "        modules = list(resnet.children())[:-1]\n",
    "        #remove avgpooling\n",
    "        modules = nn.Sequential(*list(resnet.features._modules.values())[:-1])\n",
    "        vggpooling = nn.AdaptiveAvgPool2d(output_size=(1, 1))\n",
    "        \n",
    "        self.resnet = nn.Sequential(*modules, vggpooling)\n",
    "                                       \n",
    "        #print(self.resnet)                               \n",
    "                                       \n",
    "        #self.embed = nn.Linear(resnet.classifier[6].in_features, embed_size)\n",
    "        self.embed = nn.Linear(512, embed_size)\n",
    "    def forward(self, images):\n",
    "        #print(\"resnet: \", self.resnet)\n",
    "        features = self.resnet(images)\n",
    "        #print(\"out of resnet: \", features.shape)\n",
    "        features = features.view(features.size(0), -1)\n",
    "        #print(\"out of view: \", features.shape)\n",
    "        features = self.embed(features)\n",
    "        #print(\"forward output shape: \", features.shape)\n",
    "        return features\n",
    "    \n",
    "\n",
    "class DecoderRNN(nn.Module):\n",
    "    def __init__(self, embed_size, hidden_size, vocab_size, num_layers=1):\n",
    "        super().__init__()\n",
    "        self.embedding_layer = nn.Embedding(vocab_size, embed_size)\n",
    "        \n",
    "        self.lstm = nn.LSTM(input_size = embed_size,hidden_size = hidden_size,\n",
    "                            num_layers = num_layers, batch_first = True)\n",
    "        \n",
    "        self.linear = nn.Linear(hidden_size, vocab_size)\n",
    "    \n",
    "    def forward(self, features, captions):\n",
    "        captions = captions[:, :-1]\n",
    "        embed = self.embedding_layer(captions)\n",
    "        embed = torch.cat((features.unsqueeze(1), embed), dim = 1)\n",
    "        lstm_outputs, _ = self.lstm(embed)\n",
    "        out = self.linear(lstm_outputs)\n",
    "        \n",
    "        return out\n",
    "\n",
    "    def sample(self, inputs, states=None, max_len=20):\n",
    "        \" accepts pre-processed image tensor (inputs) and returns predicted sentence (list of tensor ids of length max_len) \"\n",
    "        output_sentence = []\n",
    "        for i in range(max_len):\n",
    "            lstm_outputs, states = self.lstm(inputs, states)\n",
    "            lstm_outputs = lstm_outputs.squeeze(1)\n",
    "            out = self.linear(lstm_outputs)\n",
    "            last_pick = out.max(1)[1]\n",
    "            output_sentence.append(last_pick.item())\n",
    "            inputs = self.embedding_layer(last_pick).unsqueeze(1)\n",
    "        \n",
    "        return output_sentence"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VYvddgbxzMga"
   },
   "source": [
    "# Dataset and Vocabulary classes\n",
    "Uses COCO dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "id": "-h62qULznoHH"
   },
   "outputs": [],
   "source": [
    "class CoCoDataset(data.Dataset):\n",
    "    \n",
    "    def __init__(self, transform, mode, batch_size, vocab_threshold, vocab_file, start_word, \n",
    "        end_word, unk_word, annotations_file, vocab_from_file, img_folder):\n",
    "        self.transform = transform\n",
    "        self.mode = mode\n",
    "        self.batch_size = batch_size\n",
    "        self.vocab = Vocabulary(vocab_threshold, vocab_file, start_word,\n",
    "            end_word, unk_word, annotations_file, vocab_from_file)\n",
    "        self.img_folder = img_folder\n",
    "        if self.mode == 'train' or self.mode == 'val':\n",
    "            self.coco = COCO(annotations_file)\n",
    "            self.ids = list(self.coco.anns.keys())\n",
    "            print('Obtaining caption lengths...')\n",
    "            all_tokens = [nltk.tokenize.word_tokenize(str(self.coco.anns[self.ids[index]]['caption']).lower()) for index in tqdm(np.arange(len(self.ids)))]\n",
    "            self.caption_lengths = [len(token) for token in all_tokens]\n",
    "        else:\n",
    "            test_info = json.loads(open(annotations_file).read())\n",
    "            self.paths = [item['file_name'] for item in test_info['images']]\n",
    "        \n",
    "    def __getitem__(self, index):\n",
    "        # obtain image and caption if in training mode\n",
    "        if self.mode == 'train' or self.mode == 'val':\n",
    "            ann_id = self.ids[index]\n",
    "            caption = self.coco.anns[ann_id]['caption']\n",
    "            img_id = self.coco.anns[ann_id]['image_id']\n",
    "            path = self.coco.loadImgs(img_id)[0]['file_name']\n",
    "\n",
    "            # Convert image to tensor and pre-process using transform\n",
    "            image = Image.open(os.path.join(self.img_folder, path)).convert('RGB')\n",
    "            image = self.transform(image)\n",
    "\n",
    "            # Convert caption to tensor of word ids.\n",
    "            tokens = nltk.tokenize.word_tokenize(str(caption).lower())\n",
    "            caption = []\n",
    "            caption.append(self.vocab(self.vocab.start_word))\n",
    "            caption.extend([self.vocab(token) for token in tokens])\n",
    "            caption.append(self.vocab(self.vocab.end_word))\n",
    "            caption = torch.Tensor(caption).long()\n",
    "\n",
    "            # return pre-processed image and caption tensors\n",
    "            return image, caption\n",
    "\n",
    "        # obtain image if in test mode\n",
    "        else:\n",
    "            path = self.paths[index]\n",
    "\n",
    "            # Convert image to tensor and pre-process using transform\n",
    "            PIL_image = Image.open(os.path.join(self.img_folder, path)).convert('RGB')\n",
    "            orig_image = np.array(PIL_image)\n",
    "            image = self.transform(PIL_image)\n",
    "\n",
    "            # return original image and pre-processed image tensor\n",
    "            return orig_image, image\n",
    "\n",
    "    def get_train_indices(self):\n",
    "        sel_length = np.random.choice(self.caption_lengths)\n",
    "        all_indices = np.where([self.caption_lengths[i] == sel_length for i in np.arange(len(self.caption_lengths))])[0]\n",
    "        indices = list(np.random.choice(all_indices, size=self.batch_size))\n",
    "        return indices\n",
    "\n",
    "    def __len__(self):\n",
    "        if self.mode == 'train' or self.mode == 'val':\n",
    "            return len(self.ids)\n",
    "        else:\n",
    "            return len(self.paths)\n",
    "\n",
    "class Vocabulary(object):\n",
    "\n",
    "    def __init__(self,\n",
    "        vocab_threshold,\n",
    "        vocab_file='./vocab.pkl',\n",
    "        start_word=\"<start>\",\n",
    "        end_word=\"<end>\",\n",
    "        unk_word=\"<unk>\",\n",
    "        annotations_file='annotations/captions_train2014.json',\n",
    "        vocab_from_file=False):\n",
    "        \"\"\"Initialize the vocabulary.\n",
    "        Args:\n",
    "          vocab_threshold: Minimum word count threshold.\n",
    "          vocab_file: File containing the vocabulary.\n",
    "          start_word: Special word denoting sentence start.\n",
    "          end_word: Special word denoting sentence end.\n",
    "          unk_word: Special word denoting unknown words.\n",
    "          annotations_file: Path for train annotation file.\n",
    "          vocab_from_file: If False, create vocab from scratch & override any existing vocab_file\n",
    "                           If True, load vocab from from existing vocab_file, if it exists\n",
    "        \"\"\"\n",
    "        self.vocab_threshold = vocab_threshold\n",
    "        self.vocab_file = vocab_file\n",
    "        self.start_word = start_word\n",
    "        self.end_word = end_word\n",
    "        self.unk_word = unk_word\n",
    "        self.annotations_file = annotations_file\n",
    "        self.vocab_from_file = vocab_from_file\n",
    "        self.get_vocab()\n",
    "\n",
    "    def get_vocab(self):\n",
    "        \"\"\"Load the vocabulary from file OR build the vocabulary from scratch.\"\"\"\n",
    "        if os.path.exists(self.vocab_file) & self.vocab_from_file:\n",
    "            with open(self.vocab_file, 'rb') as f:\n",
    "                vocab = pickle.load(f)\n",
    "                self.word2idx = vocab.word2idx\n",
    "                self.idx2word = vocab.idx2word\n",
    "            print('Vocabulary successfully loaded from vocab.pkl file!')\n",
    "        else:\n",
    "            self.build_vocab()\n",
    "            with open(self.vocab_file, 'wb') as f:\n",
    "                pickle.dump(self, f)\n",
    "        \n",
    "    def build_vocab(self):\n",
    "        \"\"\"Populate the dictionaries for converting tokens to integers (and vice-versa).\"\"\"\n",
    "        self.init_vocab()\n",
    "        self.add_word(self.start_word)\n",
    "        self.add_word(self.end_word)\n",
    "        self.add_word(self.unk_word)\n",
    "        self.add_captions()\n",
    "\n",
    "    def init_vocab(self):\n",
    "        \"\"\"Initialize the dictionaries for converting tokens to integers (and vice-versa).\"\"\"\n",
    "        self.word2idx = {}\n",
    "        self.idx2word = {}\n",
    "        self.idx = 0\n",
    "\n",
    "    def add_word(self, word):\n",
    "        \"\"\"Add a token to the vocabulary.\"\"\"\n",
    "        if not word in self.word2idx:\n",
    "            self.word2idx[word] = self.idx\n",
    "            self.idx2word[self.idx] = word\n",
    "            self.idx += 1\n",
    "\n",
    "    def add_captions(self):\n",
    "        \"\"\"Loop over training captions and add all tokens to the vocabulary that meet or exceed the threshold.\"\"\"\n",
    "        coco = COCO(self.annotations_file)\n",
    "        counter = Counter()\n",
    "        ids = coco.anns.keys()\n",
    "        for i, id in enumerate(ids):\n",
    "            caption = str(coco.anns[id]['caption'])\n",
    "            tokens = nltk.tokenize.word_tokenize(caption.lower())\n",
    "            counter.update(tokens)\n",
    "\n",
    "            if i % 100000 == 0:\n",
    "                print(\"[%d/%d] Tokenizing captions...\" % (i, len(ids)))\n",
    "\n",
    "        words = [word for word, cnt in counter.items() if cnt >= self.vocab_threshold]\n",
    "\n",
    "        for i, word in enumerate(words):\n",
    "            self.add_word(word)\n",
    "\n",
    "    def __call__(self, word):\n",
    "        if not word in self.word2idx:\n",
    "            return self.word2idx[self.unk_word]\n",
    "        return self.word2idx[word]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.word2idx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kZn24O1XzYKK"
   },
   "source": [
    "# Define dataloader modes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_loader(transform,\n",
    "               mode='train',\n",
    "               batch_size=1,\n",
    "               vocab_threshold=None,\n",
    "               vocab_file='./vocab.pkl',\n",
    "               start_word=\"<start>\",\n",
    "               end_word=\"<end>\",\n",
    "               unk_word=\"<unk>\",\n",
    "               vocab_from_file=True,\n",
    "               num_workers=0,\n",
    "               cocoapi_loc=''):\n",
    "    \"\"\"Returns the data loader.\n",
    "    Args:\n",
    "      transform: Image transform.\n",
    "      mode: One of 'train' or 'test'.\n",
    "      batch_size: Batch size (if in testing mode, must have batch_size=1).\n",
    "      vocab_threshold: Minimum word count threshold.\n",
    "      vocab_file: File containing the vocabulary. \n",
    "      start_word: Special word denoting sentence start.\n",
    "      end_word: Special word denoting sentence end.\n",
    "      unk_word: Special word denoting unknown words.\n",
    "      vocab_from_file: If False, create vocab from scratch & override any existing vocab_file.\n",
    "                       If True, load vocab from from existing vocab_file, if it exists.\n",
    "      num_workers: Number of subprocesses to use for data loading \n",
    "      cocoapi_loc: The location of the folder containing the COCO API: https://github.com/cocodataset/cocoapi\n",
    "    \"\"\"\n",
    "    \n",
    "    assert mode in ['train', 'val', 'test'], \"mode must be one of 'train' or 'test'.\"\n",
    "    #if vocab_from_file==False: assert mode=='train', \"To generate vocab from captions file, must be in training mode (mode='train').\"\n",
    "\n",
    "    # Based on mode (train, val, test), obtain img_folder and annotations_file.\n",
    "    if mode == 'train':\n",
    "        if vocab_from_file==True: assert os.path.exists(vocab_file), \"vocab_file does not exist.  Change vocab_from_file to False to create vocab_file.\"\n",
    "        img_folder = os.path.join(cocoapi_loc, 'images/train2014/')\n",
    "        annotations_file = os.path.join(cocoapi_loc, 'annotations/captions_train2014.json')\n",
    "    if mode == 'test':\n",
    "        assert batch_size==1, \"Please change batch_size to 1 if testing your model.\"\n",
    "        assert os.path.exists(vocab_file), \"Must first generate vocab.pkl from training data.\"\n",
    "        assert vocab_from_file==True, \"Change vocab_from_file to True.\"\n",
    "        img_folder = os.path.join(cocoapi_loc, 'images/test2014/')\n",
    "        annotations_file = os.path.join(cocoapi_loc, 'annotations/image_info_test2014.json')\n",
    "    if mode == 'val':\n",
    "        if vocab_from_file==True: assert os.path.exists(vocab_file), \"vocab_file does not exist.  Change vocab_from_file to False to create vocab_file.\"\n",
    "        img_folder = os.path.join(cocoapi_loc, 'images/val2014/')\n",
    "        annotations_file = os.path.join(cocoapi_loc, 'annotations/captions_val2014.json')\n",
    "\n",
    "    # COCO caption dataset.\n",
    "    dataset = CoCoDataset(transform=transform,\n",
    "                          mode=mode,\n",
    "                          batch_size=batch_size,\n",
    "                          vocab_threshold=vocab_threshold,\n",
    "                          vocab_file=vocab_file,\n",
    "                          start_word=start_word,\n",
    "                          end_word=end_word,\n",
    "                          unk_word=unk_word,\n",
    "                          annotations_file=annotations_file,\n",
    "                          vocab_from_file=vocab_from_file,\n",
    "                          img_folder=img_folder)\n",
    "\n",
    "    if mode == 'train' or mode == 'val':\n",
    "        # Randomly sample a caption length, and sample indices with that length.\n",
    "        indices = dataset.get_train_indices()\n",
    "        # Create and assign a batch sampler to retrieve a batch with the sampled indices.\n",
    "        initial_sampler = data.sampler.SubsetRandomSampler(indices=indices)\n",
    "        # data loader for COCO dataset.\n",
    "        data_loader = data.DataLoader(dataset=dataset, \n",
    "                                      num_workers=num_workers,\n",
    "                                      batch_sampler=data.sampler.BatchSampler(sampler=initial_sampler,\n",
    "                                                                              batch_size=dataset.batch_size,\n",
    "                                                                              drop_last=False))\n",
    "    else:\n",
    "        data_loader = data.DataLoader(dataset=dataset,\n",
    "                                      batch_size=dataset.batch_size,\n",
    "                                      shuffle=True,\n",
    "                                      num_workers=num_workers)\n",
    "\n",
    "    return data_loader"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training\n",
    "## Define training dataloader\n",
    "* define a transform to pre-process the training images\n",
    "* initialize a training dataloader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "iFdg6DZmB84N",
    "outputId": "785c814a-55e1-4207-d1f6-cbbf51ff1804"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading annotations into memory...\n",
      "Done (t=0.63s)\n",
      "creating index...\n",
      "index created!\n",
      "[0/414113] Tokenizing captions...\n",
      "[100000/414113] Tokenizing captions...\n",
      "[200000/414113] Tokenizing captions...\n",
      "[300000/414113] Tokenizing captions...\n",
      "[400000/414113] Tokenizing captions...\n",
      "loading annotations into memory...\n",
      "Done (t=0.61s)\n",
      "creating index...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 875/414113 [00:00<00:47, 8747.07it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "index created!\n",
      "Obtaining caption lengths...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 414113/414113 [00:47<00:00, 8670.56it/s]\n"
     ]
    }
   ],
   "source": [
    "# Define a transform to pre-process the training images.\n",
    "transform_train = transforms.Compose([ \n",
    "    transforms.Resize(256),                          # smaller edge of image resized to 256\n",
    "    transforms.RandomCrop(224),                      # get 224x224 crop from random location\n",
    "    transforms.RandomHorizontalFlip(),               # horizontally flip image with probability=0.5\n",
    "    transforms.ToTensor(),                           # convert the PIL Image to a tensor\n",
    "    transforms.Normalize((0.485, 0.456, 0.406),      # normalize image for pre-trained model\n",
    "                         (0.229, 0.224, 0.225))])\n",
    "\n",
    "# Obtain the training loader.\n",
    "train_loader = get_loader(transform=transform_train,\n",
    "                         mode='train',\n",
    "                         batch_size=batch_size,\n",
    "                         vocab_threshold=vocab_threshold,\n",
    "                         vocab_from_file=False)\n",
    "\n",
    "# Store the size of the vocabulary.\n",
    "vocab_size = len(train_loader.dataset.vocab)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialize encoder and decoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 117,
     "referenced_widgets": [
      "9a9260a86167496ebba04c1a1def834e",
      "3d12a3efc75342f6a316e57bbb055259",
      "dea161f5239443ad9ff5e9011de6a5db",
      "fc2e24ea8bb54c3183e88e6c77c9ed41",
      "187efbfeefe642a5b6db8a3330faa718",
      "bf5a193c0297497d8833d16de0dfa75c",
      "f56d414fb2ce4cc3b4474a2586deb1bd",
      "8da6aba469b749f2ab8cdf0ae28fd54d"
     ]
    },
    "id": "K-ijOTFDR0aa",
    "outputId": "63a1f9da-8484-4d2b-d608-f24f5241424c"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading: \"https://download.pytorch.org/models/vgg13-c768596a.pth\" to /usr4/ec500/brianxu/.cache/torch/hub/checkpoints/vgg13-c768596a.pth\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7d93ae18a068485b834b4b86ddf8c649",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=532194478.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "EncoderCNN(\n",
       "  (resnet): Sequential(\n",
       "    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (1): ReLU(inplace=True)\n",
       "    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (3): ReLU(inplace=True)\n",
       "    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (6): ReLU(inplace=True)\n",
       "    (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (8): ReLU(inplace=True)\n",
       "    (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (11): ReLU(inplace=True)\n",
       "    (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (13): ReLU(inplace=True)\n",
       "    (14): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (15): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (16): ReLU(inplace=True)\n",
       "    (17): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (18): ReLU(inplace=True)\n",
       "    (19): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (20): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (21): ReLU(inplace=True)\n",
       "    (22): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (23): ReLU(inplace=True)\n",
       "    (24): AdaptiveAvgPool2d(output_size=(1, 1))\n",
       "  )\n",
       "  (embed): Linear(in_features=512, out_features=300, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initialize the encoder and move to device\n",
    "encoder = EncoderCNN(embed_size)\n",
    "encoder.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "EnoBqgsNSOw4",
    "outputId": "2c73ea9c-597d-4f97-e2d0-6b8a6fe3ffec"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecoderRNN(\n",
       "  (embedding_layer): Embedding(8852, 300)\n",
       "  (lstm): LSTM(300, 512, batch_first=True)\n",
       "  (linear): Linear(in_features=512, out_features=8852, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initialize the decoder and move to device\n",
    "decoder = DecoderRNN(embed_size, hidden_size, vocab_size)\n",
    "decoder.to(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Trainloader/Encoder/Decoder debugging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sampled indices: [272654, 287666, 235341, 38172, 231039, 46890, 131941, 440, 20227, 362842, 45685, 101515, 45717, 171251, 61618, 115499, 230863, 136484, 219458, 71264, 189236, 31775, 239520, 314780, 87037, 244848, 157291, 235515, 175015, 234720, 243626, 145657, 194378, 25574, 31954, 245847, 260406, 314658, 214422, 357395, 299289, 27792, 217669, 186478, 100499, 88364, 80944, 12915, 190504, 263814, 388905, 324253, 99184, 250340, 139277, 192789, 356087, 112136, 137502, 275603, 317676, 299511, 221645, 221988]\n",
      "images.type: torch.cuda.FloatTensor\n",
      "images.shape: torch.Size([64, 3, 224, 224])\n",
      "captions.type: torch.cuda.LongTensor\n",
      "captions.shape: torch.Size([64, 12])\n"
     ]
    }
   ],
   "source": [
    "# Randomly sample a caption length, and sample indices with that length.\n",
    "indices = train_loader.dataset.get_train_indices()\n",
    "print('sampled indices:', indices)\n",
    "\n",
    "# Create and assign a batch sampler to retrieve a batch with the sampled indices.\n",
    "new_sampler = data.sampler.SubsetRandomSampler(indices=indices)\n",
    "train_loader.batch_sampler.sampler = new_sampler\n",
    "    \n",
    "# Obtain the batch.\n",
    "images, captions = next(iter(train_loader))\n",
    "images = images.to(device)\n",
    "captions = captions.to(device)\n",
    "    \n",
    "print('images.type:',images.type())    \n",
    "print('images.shape:', images.shape)\n",
    "print('captions.type:', captions.type())\n",
    "print('captions.shape:', captions.shape)\n",
    "\n",
    "# (Optional) Uncomment the lines of code below to print the pre-processed images and captions.\n",
    "#print('images:', images)\n",
    "#print('captions:', captions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "type(features): <class 'torch.Tensor'>\n",
      "features.shape: torch.Size([64, 300])\n"
     ]
    }
   ],
   "source": [
    "# Pass the images through the encoder.\n",
    "features = encoder(images)\n",
    "\n",
    "#check feature type and shape\n",
    "print('type(features):', type(features))\n",
    "print('features.shape:', features.shape)\n",
    "\n",
    "assert type(features)==torch.Tensor, \"Encoder output needs to be a PyTorch Tensor.\" \n",
    "assert (features.shape[0]==batch_size) & (features.shape[1]==embed_size), \"The shape of the encoder output is incorrect.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "type(outputs): <class 'torch.Tensor'>\n",
      "outputs.shape: torch.Size([64, 12, 8852])\n",
      "features:  torch.Size([64, 300])\n",
      "captions:  torch.Size([64, 12])\n"
     ]
    }
   ],
   "source": [
    "# Pass the encoder output and captions through the decoder.\n",
    "outputs = decoder(features, captions)\n",
    "\n",
    "print('type(outputs):', type(outputs))\n",
    "print('outputs.shape:', outputs.shape)\n",
    "print(\"features: \", features.shape)\n",
    "print(\"captions: \", captions.shape)\n",
    "\n",
    "assert type(outputs)==torch.Tensor, \"Decoder output needs to be a PyTorch Tensor.\"\n",
    "assert (outputs.shape[0]==batch_size) & (outputs.shape[1]==captions.shape[1]) & (outputs.shape[2]==vocab_size), \"The shape of the decoder output is incorrect.\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define loss, optimizer, steps\n",
    "\n",
    "Loss: Cross Entropy\n",
    "Optimizer: Adam, lr = 0.001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8OUcn19bSekJ",
    "outputId": "2dd3afba-536f-4e99-a80c-e2a40ced28f7"
   },
   "outputs": [],
   "source": [
    "# Define the loss function. \n",
    "criterion = nn.CrossEntropyLoss().cuda() if torch.cuda.is_available() else nn.CrossEntropyLoss()\n",
    "\n",
    "# Specify the learnable parameters of the model.\n",
    "params = list(decoder.parameters()) + list(encoder.embed.parameters())\n",
    "\n",
    "# Define the optimizer.\n",
    "optimizer = torch.optim.Adam(params = params, lr = 0.001)\n",
    "\n",
    "# Set the total number of training steps per epoch.\n",
    "total_step = math.ceil(len(train_loader.dataset.caption_lengths) / train_loader.batch_sampler.batch_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TRAIN (THIS CELL WILL TAKE A LONG TIME)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dpCY0I3lTQEK",
    "outputId": "74e52363-2e23-460c-bcb8-c0348404b668"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [1/3], Step [100/6471], Loss: 3.8945, Perplexity: 49.1303\n",
      "Epoch [1/3], Step [100/6471], Loss: 3.8945, Perplexity: 49.1303\n",
      "Epoch [1/3], Step [115/6471], Loss: 3.8505, Perplexity: 47.0158"
     ]
    }
   ],
   "source": [
    "# Open the training log file.\n",
    "f = open(log_file, 'w')\n",
    "\n",
    "losses = []\n",
    "perplexities = []\n",
    "\n",
    "for epoch in range(1, num_epochs+1):\n",
    "    \n",
    "    for i_step in range(1, total_step+1):        \n",
    "        # Randomly sample a caption length, and sample indices with that length.\n",
    "        indices = train_loader.dataset.get_train_indices()\n",
    "        \n",
    "        # Create and assign a batch sampler to retrieve a batch with the sampled indices.\n",
    "        new_sampler = data.sampler.SubsetRandomSampler(indices=indices)\n",
    "        train_loader.batch_sampler.sampler = new_sampler\n",
    "        \n",
    "        # Obtain the batch.\n",
    "        images, captions = next(iter(train_loader))\n",
    "\n",
    "        # Move batch of images and captions to GPU if CUDA is available.\n",
    "        images = images.to(device)\n",
    "        captions = captions.to(device)\n",
    "        \n",
    "        # Zero the gradients.\n",
    "        decoder.zero_grad()\n",
    "        encoder.zero_grad()\n",
    "        \n",
    "        # Pass the inputs through the CNN-RNN model.\n",
    "        features = encoder(images)\n",
    "        outputs = decoder(features, captions)\n",
    "        \n",
    "        # Calculate the batch loss.\n",
    "        loss = criterion(outputs.view(-1, vocab_size), captions.view(-1))\n",
    "        if i_step % 100 == 0:\n",
    "            losses.append(loss)\n",
    "            perplexities.append(np.exp(loss.item()))\n",
    "        \n",
    "        # Backward pass.\n",
    "        loss.backward()\n",
    "        \n",
    "        # Update the parameters in the optimizer.\n",
    "        optimizer.step()\n",
    "            \n",
    "        # Get training statistics.\n",
    "        stats = 'Epoch [%d/%d], Step [%d/%d], Loss: %.4f, Perplexity: %5.4f' % (epoch, num_epochs, i_step, total_step, loss.item(), np.exp(loss.item()))\n",
    "        \n",
    "        # Print training statistics (on same line).\n",
    "        print(\"\\r\" + stats, end=\"\")\n",
    "        sys.stdout.flush()\n",
    "        \n",
    "        # Print training statistics to file.\n",
    "        f.write(stats + '\\n')\n",
    "        f.flush()\n",
    "        \n",
    "        # Print training statistics (on different line).\n",
    "        if i_step % print_every == 0:\n",
    "            print(\"\\n\" + stats, end=\"\\n\")\n",
    "            \n",
    "    # Save the weights.\n",
    "    if epoch % save_every == 0:\n",
    "        torch.save(decoder.state_dict(), os.path.join('./models', 'decoder-%d.pkl' % epoch))\n",
    "        torch.save(encoder.state_dict(), os.path.join('./models', 'encoder-%d.pkl' % epoch))\n",
    "\n",
    "# Close the training log file.\n",
    "with open(loss_file, \"wb\") as buttz1:   #Pickling\n",
    "    pickle.dump(losses, buttz1)\n",
    "\n",
    "with open(perplexity_file, \"wb\") as buttz2:   #Pickling\n",
    "    pickle.dump(perplexities, buttz2)\n",
    "\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the loss and perplexity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final loss: 2.12180233001709\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2b64ff5f3290>]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "with open(loss_file, 'rb') as ls:\n",
    "                los_ses = pickle.load(ls)\n",
    "print(\"Final loss:\", los_ses[-1].item())\n",
    "ploss=[i.item() for i in los_ses]\n",
    "plt.title('VGG13 + RNN')\n",
    "plt.plot(ploss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final perplexity: 8.346166486397664\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2b6512d42f90>]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "with open(perplexity_file, 'rb') as ps:\n",
    "                perplex_ses = pickle.load(ps)\n",
    "print(\"Final perplexity:\", perplex_ses[-1].item())\n",
    "plex=[i.item() for i in perplex_ses]\n",
    "plt.title('VGG13 + RNN Perplexities')\n",
    "plt.plot(plex)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing\n",
    "\n",
    "## Define testing dataloader\n",
    "* define a transform to pre-process the testing images\n",
    "* initialize a testing dataloader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6y6OCxMKsJX9",
    "outputId": "8fd8aaee-a229-48e1-82f7-6b4a1c8eeb12"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vocabulary successfully loaded from vocab.pkl file!\n"
     ]
    }
   ],
   "source": [
    "# Define a transform to pre-process the testing images.\n",
    "transform_test = transforms.Compose([transforms.Resize(256),\n",
    "                                     transforms.CenterCrop(224),\n",
    "                                     transforms.ToTensor(),\n",
    "                                     transforms.Normalize((0.485, 0.456, 0.406),\n",
    "                                                          (0.229, 0.224, 0.225))\n",
    "                                    ])\n",
    "\n",
    "# Create the data loader.\n",
    "test_loader = get_loader(transform=transform_test,    \n",
    "                         mode='test')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load pretrained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "id": "uwYSz37AzRdl",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecoderRNN(\n",
       "  (embedding_layer): Embedding(8852, 300)\n",
       "  (lstm): LSTM(300, 512, batch_first=True)\n",
       "  (linear): Linear(in_features=512, out_features=8852, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get vocabulary size\n",
    "vocab_size = len(test_loader.dataset.vocab)\n",
    "\n",
    "# Initialize the encoder and decoder, and set each to inference mode.\n",
    "encoder.eval()\n",
    "decoder = DecoderRNN(embed_size, hidden_size, vocab_size)\n",
    "decoder.eval()\n",
    "\n",
    "# Load the trained weights.\n",
    "encoder.load_state_dict(torch.load(os.path.join('./models', encoder_file)))\n",
    "decoder.load_state_dict(torch.load(os.path.join('./models', decoder_file)))\n",
    "#uncomment this if you aren't using a GPU\n",
    "#encoder.load_state_dict(torch.load(os.path.join('./models', encoder_file),map_location=torch.device('cpu')))\n",
    "#decoder.load_state_dict(torch.load(os.path.join('./models', decoder_file),map_location=torch.device('cpu')))\n",
    "\n",
    "encoder.to(device)\n",
    "decoder.to(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Display a single image + caption prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_prediction(encoder, decoder, test_loader):\n",
    "    orig_image, image = next(iter(test_loader))\n",
    "    #print('orig_image ' , orig_image.shape)\n",
    "    print('image: ' , image.shape)\n",
    "    plt.imshow(np.squeeze(orig_image))\n",
    "    plt.title('Sample Image')\n",
    "    plt.show()\n",
    "    image = image.to(device)\n",
    "    features = encoder(image).unsqueeze(1)\n",
    "    output = decoder.sample(features)    \n",
    "    sentence = clean_sentence(output, test_loader)\n",
    "    print(sentence)\n",
    "\n",
    "def clean_sentence(output, data_loader):\n",
    "    words_sequence = []\n",
    "    for i in output:\n",
    "        if (i == 1):\n",
    "            continue\n",
    "        words_sequence.append(data_loader.dataset.vocab.idx2word[i])\n",
    "    words_sequence = words_sequence[1:-1] \n",
    "    sentence = ' '.join(words_sequence) \n",
    "    sentence = sentence.capitalize()    \n",
    "    return sentence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 298
    },
    "id": "ww8VWlnr0Wz5",
    "outputId": "e0ff6eed-f10e-4b9a-9f92-2e589e43592a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image:  torch.Size([1, 3, 224, 224])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A group of people standing around a table with food .\n"
     ]
    }
   ],
   "source": [
    "get_prediction(encoder, decoder, test_loader)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate BLEU score\n",
    "Calculate the BLEU score of the model over the entire dataset by averaging the BLEU scores over several batches.\n",
    "\n",
    "### Define a data loader\n",
    "This data loader should return images and their corresponding ground-truth captions from the validation dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vocabulary successfully loaded from vocab.pkl file!\n",
      "loading annotations into memory...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 790/202654 [00:00<00:25, 7899.05it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done (t=0.31s)\n",
      "creating index...\n",
      "index created!\n",
      "Obtaining caption lengths...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 202654/202654 [00:23<00:00, 8665.56it/s]\n"
     ]
    }
   ],
   "source": [
    "transform_val = transforms.Compose([transforms.Resize(256),\n",
    "                                     transforms.CenterCrop(224),\n",
    "                                     transforms.ToTensor(),\n",
    "                                     transforms.Normalize((0.485, 0.456, 0.406),\n",
    "                                                          (0.229, 0.224, 0.225))\n",
    "                                    ])\n",
    "val_loader = get_loader(transform=transform_val,\n",
    "                         mode='val',batch_size=batch_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_sentence_bleu(output, data_loader):\n",
    "    words_sequence = []\n",
    "    for i in output:\n",
    "        i = int(i)\n",
    "        if (i == 1):\n",
    "            continue\n",
    "        words_sequence.append(data_loader.dataset.vocab.idx2word[i])\n",
    "    words_sequence = words_sequence[1:-1] \n",
    "#   sentence = ' '.join(words_sequence) \n",
    "#   sentence = sentence.capitalize()    \n",
    "    return words_sequence\n",
    "\n",
    "def get_avg_bleu(encoder, decoder, dataloader, device):\n",
    "    '''Determine the average BLEU score across the entire dataset\n",
    "    '''\n",
    "    with torch.no_grad():\n",
    "        total_score1 = 0\n",
    "        total_score2 = 0\n",
    "        total_score3 = 0\n",
    "        total_score4 = 0\n",
    "        total_num = 0\n",
    "        for data in tqdm(dataloader):\n",
    "            torch.cuda.empty_cache()\n",
    "            print(\"STARTING NEW BATCH\")\n",
    "            #load images and reference captions from the dataset\n",
    "            images, captions_ref = data\n",
    "            images = images.to(device)\n",
    "            print('captions_ref: ',captions_ref.type())\n",
    "            # Get caption predictions for the current batch of validation images\n",
    "            captions_cand = get_batch_candidates(encoder, decoder, images)\n",
    "            \n",
    "            #calculate the bleu score for the entire batch\n",
    "            print(captions_ref.shape)\n",
    "            refs = []\n",
    "            cands = []\n",
    "            for captions in captions_ref:\n",
    "                #captions = captions.float()\n",
    "                #print(captions.type())\n",
    "                refs.append(clean_sentence_bleu(captions, dataloader))\n",
    "                \n",
    "            for captions_2 in captions_cand:\n",
    "                cands.append(clean_sentence_bleu(captions_2, dataloader))\n",
    "#             print(refs.size())\n",
    "#             print(cands.size())\n",
    "            #batch_score = get_batch_bleu(captions_ref, captions_cand)\n",
    "            batch_score1, batch_score2, batch_score3, batch_score4 = get_batch_bleu(refs, cands)\n",
    "            #increment total_score and total_num\n",
    "            total_score1 = total_score1 + batch_score1\n",
    "            total_score2 = total_score2 + batch_score2\n",
    "            total_score3 = total_score3 + batch_score3\n",
    "            total_score4 = total_score4 + batch_score4\n",
    "            \n",
    "            total_num = total_num + dataloader.dataset.batch_size\n",
    "        \n",
    "            \n",
    "            #print('Total Num: ',total_num)\n",
    "            \n",
    "        avg_score1 = total_score1/total_num\n",
    "        avg_score2 = total_score2/total_num\n",
    "        avg_score3 = total_score3/total_num\n",
    "        avg_score4 = total_score4/total_num\n",
    "        \n",
    "        return avg_score1, avg_score2, avg_score3, avg_score4\n",
    "\n",
    "def get_batch_candidates(encoder, decoder, images):\n",
    "    '''Gets predictions for the current batch of images\n",
    "    '''\n",
    "    with torch.no_grad():\n",
    "        captions_cand = []\n",
    "\n",
    "        features_batch = encoder(images).unsqueeze(1)\n",
    "\n",
    "        for features in features_batch:\n",
    "            new_cand = decoder.sample(features.unsqueeze(0))\n",
    "            captions_cand.append(new_cand)\n",
    "\n",
    "        return torch.Tensor(captions_cand)\n",
    "\n",
    "def get_batch_bleu(captions_ref, captions_cand):\n",
    "    '''Compute the total BLEU score over elements in a batch\n",
    "    '''\n",
    "    with torch.no_grad():\n",
    "        scores1 = 0\n",
    "        scores2 = 0\n",
    "        scores3 = 0\n",
    "        scores4 = 0\n",
    "        for i in range(len(captions_ref)):\n",
    "            #print(len(ref))\n",
    "            captions_cand[i] = [x for x in captions_cand[i] if x != '.']\n",
    "            print('captions_ref= ',captions_ref[i])\n",
    "            print('captions_cand= ',captions_cand[i])\n",
    "            scores1 += nltk.translate.bleu_score.sentence_bleu([captions_ref[i]], captions_cand[i], weights=(1, 0, 0, 0))\n",
    "            scores2 += nltk.translate.bleu_score.sentence_bleu([captions_ref[i]], captions_cand[i], weights=(0.5, 0.5, 0, 0))\n",
    "            scores3 += nltk.translate.bleu_score.sentence_bleu([captions_ref[i]], captions_cand[i], weights=(0.33, 0.33, 0.33, 0.33))\n",
    "            scores4 += nltk.translate.bleu_score.sentence_bleu([captions_ref[i]], captions_cand[i], weights=(0.25, 0.25, 0.25, 0.25))\n",
    "            print('Score : ',nltk.translate.bleu_score.sentence_bleu([captions_ref[i]], captions_cand[i],weights=(1, 0, 0, 0)))\n",
    "            print('Score : ',nltk.translate.bleu_score.sentence_bleu([captions_ref[i]], captions_cand[i],weights=(0.5, 0.5, 0, 0)))\n",
    "            print('Score : ',nltk.translate.bleu_score.sentence_bleu([captions_ref[i]], captions_cand[i],weights=(0.33, 0.33, 0.33, 0)))\n",
    "            print('Score : ',nltk.translate.bleu_score.sentence_bleu([captions_ref[i]], captions_cand[i],weights=(0.25, 0.25, 0.25, 0.25)))\n",
    "        return scores1, scores2, scores3, scores4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  0%|          | 0/1 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "captions_ref:  torch.LongTensor\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:02<00:00,  2.66s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([64, 11])\n",
      "captions_ref=  ['a', 'group', 'of', 'young', 'men', 'are', 'playing', 'soccer']\n",
      "captions_cand=  ['a', 'group', 'of', 'young', 'boys', 'playing', 'soccer', 'in', 'a', 'field']\n",
      "Score :  0.6\n",
      "Score :  0.5163977794943222\n",
      "Score :  0.4091569047284194\n",
      "Score :  0.31239399369202553\n",
      "captions_ref=  ['there', 'are', 'many', 'people', 'standing', 'together', 'y', 'a']\n",
      "captions_cand=  ['a', 'group', 'of', 'people', 'standing', 'around', 'a', 'table', 'with', 'umbrellas']\n",
      "Score :  0.3\n",
      "Score :  0.18257418583505533\n",
      "Score :  9.708980935455592e-103\n",
      "Score :  6.373704167435469e-155\n",
      "captions_ref=  ['a', 'sheep', 'stands', 'on', 'the', 'hillside', 'with', 'rocks']\n",
      "captions_cand=  ['a', 'brown', 'and', 'white', 'cow', 'standing', 'on', 'top', 'of', 'a', 'lush', 'green', 'field']\n",
      "Score :  0.15384615384615385\n",
      "Score :  5.850803834986753e-155\n",
      "Score :  4.7971789246899324e-204\n",
      "Score :  1.1409851298103347e-231\n",
      "captions_ref=  ['a', 'helicopter', 'is', 'flying', 'in', 'the', 'clear', 'sky']\n",
      "captions_cand=  ['a', 'group', 'of', 'people', 'flying', 'kites', 'in', 'a', 'field']\n",
      "Score :  0.3333333333333333\n",
      "Score :  8.612150057732663e-155\n",
      "Score :  6.19152043543562e-204\n",
      "Score :  1.384292958842266e-231\n",
      "captions_ref=  ['a', 'man', 'and', 'woman', 'sitting', 'on', 'a', 'park']\n",
      "captions_cand=  ['a', 'man', 'sitting', 'on', 'a', 'bench', 'overlooking', 'a', 'large', 'body', 'of', 'water']\n",
      "Score :  0.4166666666666667\n",
      "Score :  0.337099931231621\n",
      "Score :  0.22820266830716382\n",
      "Score :  3.9876353728947065e-78\n",
      "captions_ref=  ['this', 'kitchen', 'has', 'white', 'cabinets', 'and', 'wooden', 'counter']\n",
      "captions_cand=  ['a', 'kitchen', 'with', 'a', 'large', 'window', 'and', 'a', 'large', 'window']\n",
      "Score :  0.2\n",
      "Score :  6.6709427497276e-155\n",
      "Score :  5.231030151567236e-204\n",
      "Score :  1.2183324802375697e-231\n",
      "captions_ref=  ['three', 'restaurant', 'employees', 'behind', 'a', 'counter', 'preparing', 'food']\n",
      "captions_cand=  ['a', 'man', 'in', 'a', 'kitchen', 'preparing', 'to', 'serve', 'a', 'meal']\n",
      "Score :  0.2\n",
      "Score :  6.6709427497276e-155\n",
      "Score :  5.231030151567236e-204\n",
      "Score :  1.2183324802375697e-231\n",
      "captions_ref=  ['a', 'train', 'is', 'pulling', 'into', 'the', 'train', 'station']\n",
      "captions_cand=  ['a', 'train', 'is', 'parked', 'on', 'the', 'tracks', 'near', 'a', 'platform']\n",
      "Score :  0.4\n",
      "Score :  0.2981423969999719\n",
      "Score :  0.22651656746142707\n",
      "Score :  3.965294799986402e-78\n",
      "captions_ref=  ['a', 'pizza', 'with', 'a', 'cheesy', 'topping', 'on', 'it']\n",
      "captions_cand=  ['a', 'pizza', 'sitting', 'on', 'top', 'of', 'a', 'pan', 'covered', 'in', 'cheese']\n",
      "Score :  0.36363636363636365\n",
      "Score :  0.19069251784911848\n",
      "Score :  9.991803243213663e-103\n",
      "Score :  6.513869329968086e-155\n",
      "captions_ref=  ['a', 'kitchen', 'with', 'tiled', 'floors', 'and', 'wallpapered', 'walls']\n",
      "captions_cand=  ['a', 'kitchen', 'with', 'a', 'refrigerator', 'and', 'a', 'table']\n",
      "Score :  0.5\n",
      "Score :  0.3779644730092272\n",
      "Score :  0.29129144784395894\n",
      "Score :  4.797597231912944e-78\n",
      "captions_ref=  ['a', 'vase', 'with', 'designs', 'of', 'horses', 'and', 'men']\n",
      "captions_cand=  ['a', 'yellow', 'and', 'black', 'bird', 'sitting', 'on', 'a', 'table']\n",
      "Score :  0.2222222222222222\n",
      "Score :  7.031791076575252e-155\n",
      "Score :  5.41610649555647e-204\n",
      "Score :  1.2508498911928379e-231\n",
      "captions_ref=  ['small', 'dog', 'sitting', 'on', 'display', 'behind', 'the', 'window']\n",
      "captions_cand=  ['a', 'dog', 'sitting', 'in', 'a', 'chair', 'next', 'to', 'a', 'window']\n",
      "Score :  0.3\n",
      "Score :  0.18257418583505533\n",
      "Score :  9.708980935455592e-103\n",
      "Score :  6.373704167435469e-155\n",
      "captions_ref=  ['a', 'couple', 'of', '<unk>', 'look', 'to', 'help', 'a']\n",
      "captions_cand=  ['a', 'group', 'of', 'people', 'standing', 'next', 'to', 'a', 'train']\n",
      "Score :  0.4444444444444444\n",
      "Score :  9.94445430826712e-155\n",
      "Score :  6.808118302749002e-204\n",
      "Score :  1.4875195904069663e-231\n",
      "captions_ref=  ['a', '<unk>', '<unk>', 'bathroom', 'is', 'laid', 'out', 'with']\n",
      "captions_cand=  ['a', 'bathroom', 'with', 'a', 'sink', 'and', 'a', 'mirror', 'the', 'toilet']\n",
      "Score :  0.3\n",
      "Score :  8.170202920075766e-155\n",
      "Score :  5.979947053917884e-204\n",
      "Score :  1.3483065280626046e-231\n",
      "captions_ref=  ['an', 'elephant', 'is', 'walking', 'along', 'with', 'another', 'elephant']\n",
      "captions_cand=  ['a', 'large', 'elephant', 'standing', 'next', 'to', 'a', 'tree']\n",
      "Score :  0.12500000000000003\n",
      "Score :  5.273843307431707e-155\n",
      "Score :  4.4794802798562165e-204\n",
      "Score :  1.0832677820940877e-231\n",
      "captions_ref=  ['an', 'airplane', 'flying', 'in', 'a', 'cloudy', 'blue', 'sky']\n",
      "captions_cand=  ['a', 'large', 'propeller', 'plane', 'flying', 'through', 'a', 'cloudy', 'sky']\n",
      "Score :  0.4444444444444444\n",
      "Score :  0.23570226039551587\n",
      "Score :  1.149168560061151e-102\n",
      "Score :  7.241926111174567e-155\n",
      "captions_ref=  ['a', 'picture', 'of', 'a', 'pizza', 'that', 'was', 'delivered']\n",
      "captions_cand=  ['a', 'pizza', 'sitting', 'on', 'top', 'of', 'a', 'white', 'plate']\n",
      "Score :  0.4444444444444444\n",
      "Score :  0.33333333333333337\n",
      "Score :  1.4445202495768514e-102\n",
      "Score :  8.612150057732663e-155\n",
      "captions_ref=  ['a', 'pool', 'with', 'several', 'visitors', 'sitting', 'beside', 'it']\n",
      "captions_cand=  ['a', 'man', 'is', 'sitting', 'on', 'a', 'bench', 'with', 'a', 'dog']\n",
      "Score :  0.3\n",
      "Score :  8.170202920075766e-155\n",
      "Score :  5.979947053917884e-204\n",
      "Score :  1.3483065280626046e-231\n",
      "captions_ref=  ['an', 'older', 'man', 'holding', 'on', 'to', 'a', 'train']\n",
      "captions_cand=  ['a', 'man', 'standing', 'next', 'to', 'a', 'train', 'on', 'a', 'train', 'track']\n",
      "Score :  0.45454545454545453\n",
      "Score :  0.30151134457776363\n",
      "Score :  0.21950296237497788\n",
      "Score :  3.8719283242189536e-78\n",
      "captions_ref=  ['a', 'black', 'cat', 'sits', 'in', 'a', 'cream', 'tub']\n",
      "captions_cand=  ['a', 'cat', 'laying', 'on', 'top', 'of', 'a', 'white', 'bed']\n",
      "Score :  0.3333333333333333\n",
      "Score :  8.612150057732663e-155\n",
      "Score :  6.19152043543562e-204\n",
      "Score :  1.384292958842266e-231\n",
      "captions_ref=  ['an', 'old', 'toilet', 'sitting', 'next', 'to', 'other', 'debris']\n",
      "captions_cand=  ['a', 'bathroom', 'with', 'a', 'toilet', 'and', 'a', 'sink']\n",
      "Score :  0.12500000000000003\n",
      "Score :  5.273843307431707e-155\n",
      "Score :  4.4794802798562165e-204\n",
      "Score :  1.0832677820940877e-231\n",
      "captions_ref=  ['this', 'living', 'room', 'is', 'painted', 'yellow', 'with', 'beige']\n",
      "captions_cand=  ['a', 'living', 'room', 'with', 'a', 'large', 'window', 'and', 'a', 'chair']\n",
      "Score :  0.3\n",
      "Score :  0.18257418583505533\n",
      "Score :  9.708980935455592e-103\n",
      "Score :  6.373704167435469e-155\n",
      "captions_ref=  ['a', 'clean', 'bathroom', 'with', 'black', 'marble', 'counter', 'top']\n",
      "captions_cand=  ['a', 'kitchen', 'with', 'a', 'large', 'window', 'and', 'a', 'large', 'window']\n",
      "Score :  0.2\n",
      "Score :  6.6709427497276e-155\n",
      "Score :  5.231030151567236e-204\n",
      "Score :  1.2183324802375697e-231\n",
      "captions_ref=  ['a', 'plate', 'of', 'food', 'that', 'is', 'on', 'a']\n",
      "captions_cand=  ['a', 'sandwich', 'with', 'meat', 'and', 'lettuce', 'on', 'a', 'plate']\n",
      "Score :  0.4444444444444444\n",
      "Score :  0.33333333333333337\n",
      "Score :  1.4445202495768514e-102\n",
      "Score :  8.612150057732663e-155\n",
      "captions_ref=  ['a', 'stuffed', 'animal', 'is', 'inside', 'of', 'a', 'microwave']\n",
      "captions_cand=  ['a', 'black', 'and', 'white', 'cat', 'sitting', 'on', 'a', 'desk']\n",
      "Score :  0.2222222222222222\n",
      "Score :  7.031791076575252e-155\n",
      "Score :  5.41610649555647e-204\n",
      "Score :  1.2508498911928379e-231\n",
      "captions_ref=  ['a', 'girl', 'on', 'a', 'skateboard', 'watches', 'skateboarding', 'action']\n",
      "captions_cand=  ['a', 'young', 'boy', 'holding', 'a', 'tennis', 'racquet', 'on', 'a', 'tennis', 'court', 'a', 'game']\n",
      "Score :  0.23076923076923078\n",
      "Score :  0.1386750490563073\n",
      "Score :  8.097343424563915e-103\n",
      "Score :  5.554837769749797e-155\n",
      "captions_ref=  ['several', 'people', 'with', 'numbers', 'going', 'downhill', 'on', 'skis']\n",
      "captions_cand=  ['a', 'group', 'of', 'people', 'riding', 'skis', 'on', 'a', 'snowy', 'slope']\n",
      "Score :  0.3\n",
      "Score :  8.170202920075766e-155\n",
      "Score :  5.979947053917884e-204\n",
      "Score :  1.3483065280626046e-231\n",
      "captions_ref=  ['two', 'animals', 'in', 'a', 'field', 'next', 'to', 'some']\n",
      "captions_cand=  ['a', 'clock', 'tower', 'in', 'a', 'garden', 'with', 'a', 'sky', 'background']\n",
      "Score :  0.2\n",
      "Score :  0.14907119849998599\n",
      "Score :  8.493047105004868e-103\n",
      "Score :  5.7592918561109494e-155\n",
      "captions_ref=  ['a', 'stone', 'bench', 'sits', 'near', 'some', 'fallen', 'trees']\n",
      "captions_cand=  ['a', 'giraffe', 'standing', 'next', 'to', 'a', 'tree', 'in', 'a', 'field']\n",
      "Score :  0.10000000000000002\n",
      "Score :  4.717068855239749e-155\n",
      "Score :  4.16147533319354e-204\n",
      "Score :  1.0244914152188952e-231\n",
      "captions_ref=  ['flock', 'of', 'ducks', 'flying', 'over', 'a', 'sandy', 'beach']\n",
      "captions_cand=  ['a', 'flock', 'of', 'birds', 'standing', 'on', 'top', 'of', 'a', 'body', 'of', 'water']\n",
      "Score :  0.25\n",
      "Score :  0.1507556722888818\n",
      "Score :  8.556266030080004e-103\n",
      "Score :  5.791739854583281e-155\n",
      "captions_ref=  ['a', 'person', 'that', 'is', 'snowboarding', 'in', 'some', 'snow']\n",
      "captions_cand=  ['a', 'man', 'riding', 'skis', 'down', 'a', 'snow', 'covered', 'slope']\n",
      "Score :  0.2222222222222222\n",
      "Score :  7.031791076575252e-155\n",
      "Score :  5.41610649555647e-204\n",
      "Score :  1.2508498911928379e-231\n",
      "captions_ref=  ['a', 'bus', 'with', 'people', 'is', 'driving', 'down', 'the']\n",
      "captions_cand=  ['a', 'bus', 'is', 'parked', 'in', 'front', 'of', 'a', 'building']\n",
      "Score :  0.3333333333333333\n",
      "Score :  0.20412414523193154\n",
      "Score :  1.0450906266575576e-102\n",
      "Score :  6.7393716283177006e-155\n",
      "captions_ref=  ['a', 'single', 'propeller', 'silver', 'airplane', 'in', 'the', 'sky']\n",
      "captions_cand=  ['a', 'large', 'commercial', 'airplane', 'flying', 'in', 'the', 'sky']\n",
      "Score :  0.625\n",
      "Score :  0.42257712736425823\n",
      "Score :  0.3135508904498039\n",
      "Score :  5.072841446586652e-78\n",
      "captions_ref=  ['a', 'young', 'girl', 'eating', 'a', 'slice', 'of', 'pizza']\n",
      "captions_cand=  ['a', 'man', 'holding', 'a', 'slice', 'of', 'pizza', 'with', 'a', 'bite', 'out', 'of', 'it']\n",
      "Score :  0.38461538461538464\n",
      "Score :  0.31008683647302115\n",
      "Score :  0.2630627225467731\n",
      "Score :  0.2044800736021839\n",
      "captions_ref=  ['a', 'whole', 'chicken', 'in', 'roasting', 'in', 'an', 'oven']\n",
      "captions_cand=  ['a', 'plate', 'of', 'donuts', 'with', 'a', 'knife', 'and', 'fork']\n",
      "Score :  0.11111111111111109\n",
      "Score :  4.972227154133551e-155\n",
      "Score :  4.308710317117189e-204\n",
      "Score :  1.0518351895246305e-231\n",
      "captions_ref=  ['a', 'giraffe', '<unk>', 'it', \"'s\", 'head', 'towards', 'a']\n",
      "captions_cand=  ['a', 'giraffe', 'standing', 'in', 'a', 'field', 'with', 'a', 'tree', 'in', 'the', 'background']\n",
      "Score :  0.25\n",
      "Score :  0.1507556722888818\n",
      "Score :  8.556266030080004e-103\n",
      "Score :  5.791739854583281e-155\n",
      "captions_ref=  ['a', 'large', 'white', 'airplane', 'on', 'a', 'open', 'road']\n",
      "captions_cand=  ['a', 'large', 'passenger', 'jet', 'sitting', 'on', 'top', 'of', 'an', 'airport', 'runway']\n",
      "Score :  0.2727272727272727\n",
      "Score :  0.1651445647689541\n",
      "Score :  9.086865300538255e-103\n",
      "Score :  6.061838450024688e-155\n",
      "captions_ref=  ['six', 'different', 'cellphones', 'on', 'a', 'white', 'textured', 'surface']\n",
      "captions_cand=  ['a', 'bunch', 'of', 'different', 'types', 'of', 'cell', 'phones', 'on', 'a', 'table']\n",
      "Score :  0.2727272727272727\n",
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      "captions_ref=  ['a', 'photo', 'looking', 'down', 'into', 'a', 'toilet', 'bowl']\n",
      "captions_cand=  ['a', 'toilet', 'with', 'a', 'lid', 'and', 'a', '<unk>']\n",
      "Score :  0.375\n",
      "Score :  0.23145502494313785\n",
      "Score :  1.1354594457738457e-102\n",
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      "captions_ref=  ['small', 'computer', 'area', 'with', 'a', 'small', 'wooden', 'desk']\n",
      "captions_cand=  ['a', 'desk', 'with', 'a', 'monitor', 'a', 'keyboard', 'and', 'a', 'monitor', 'a']\n",
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      "captions_ref=  ['an', 'elephant', 'walking', 'alone', 'in', 'a', 'grassy', 'area']\n",
      "captions_cand=  ['an', 'elephant', 'standing', 'in', 'a', 'field', 'with', 'a', 'tree', 'in', 'the', 'background']\n",
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      "Score :  0.25\n",
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      "captions_ref=  ['food', 'is', 'being', 'prepared', 'in', 'a', 'toaster', 'oven']\n",
      "captions_cand=  ['a', 'grill', 'with', 'a', 'lot', 'of', 'donuts', 'on', 'it']\n",
      "Score :  0.11111111111111109\n",
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      "captions_ref=  ['a', 'woman', 'is', 'striking', 'a', 'ball', 'with', 'a']\n",
      "captions_cand=  ['a', 'woman', 'is', 'playing', 'tennis', 'on', 'a', 'court']\n",
      "Score :  0.5\n",
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      "captions_ref=  ['man', 'with', 'helmet', 'walking', 'toward', 'man', 'on', 'motorcycle']\n",
      "captions_cand=  ['a', 'man', 'and', 'a', 'woman', 'riding', 'a', 'motorcycle', 'down', 'a', 'street']\n",
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      "captions_ref=  ['two', 'zebras', 'standing', 'on', 'a', 'gravel', 'road', 'look']\n",
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      "Score :  0.4\n",
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      "captions_cand=  ['a', 'woman', 'with', 'a', 'hat', 'and', 'glasses', 'is', 'holding', 'a', 'parasol']\n",
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      "captions_ref=  ['the', 'two', 'monitors', 'are', 'sitting', 'among', 'the', 'laptops']\n",
      "captions_cand=  ['a', 'computer', 'desk', 'with', 'a', 'laptop', 'computer', 'on', 'it', 'it', 'the', 'same', 'direction']\n",
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      "Score :  0\n",
      "Score :  0\n",
      "Score :  0\n",
      "Score :  0\n",
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      "captions_ref=  ['group', 'of', 'giraffes', 'mingling', 'in', 'a', 'stand', 'of']\n",
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      "Score :  0.625\n",
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      "captions_ref=  ['people', 'are', 'watching', 'a', 'movie', 'on', 'their', 'laptop']\n",
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   "source": [
    "bleu_score1, bleu_score2, bleu_score3, bleu_score4 = get_avg_bleu(encoder, decoder, val_loader, device)\n",
    "print([bleu_score1, bleu_score2, bleu_score3, bleu_score4])"
   ]
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