Eyettention_examples_chinese.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "nuclear-dream",
   "metadata": {
    "id": "nuclear-dream"
   },
   "source": [
    "# Eyettention"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "g-FLEOyvYJUG",
   "metadata": {
    "id": "g-FLEOyvYJUG"
   },
   "source": [
    "**Configuration**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "neutral-montreal",
   "metadata": {
    "id": "neutral-montreal"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "#configuration\n",
    "#Chinese\n",
    "cf = {\"model_pretrained\": \"bert-base-chinese\",\n",
    "      \"atten_type\": 'local-g',\n",
    "      #batch computation for training (BSC dataset)\n",
    "      \"max_sn_len\": 27,\n",
    "      \"max_sp_len\": 40\n",
    "     }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "uhLxInukW82r",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "uhLxInukW82r",
    "outputId": "76c33a63-4d13-4496-976d-126fe4ebb1b0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: transformers==4.27.0 in c:\\users\\shuwe\\anaconda3\\envs\\wenwen\\lib\\site-packages (4.27.0)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\shuwe\\appdata\\roaming\\python\\python38\\site-packages (from transformers==4.27.0) (20.9)\n",
      "Requirement already satisfied: numpy>=1.17 in c:\\users\\shuwe\\appdata\\roaming\\python\\python38\\site-packages (from transformers==4.27.0) (1.20.3)\n",
      "Requirement already satisfied: huggingface-hub<1.0,>=0.11.0 in c:\\users\\shuwe\\anaconda3\\envs\\wenwen\\lib\\site-packages (from transformers==4.27.0) (0.15.1)\n",
      "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in c:\\users\\shuwe\\anaconda3\\envs\\wenwen\\lib\\site-packages (from transformers==4.27.0) (0.12.1)\n",
      "Requirement already satisfied: requests in c:\\users\\shuwe\\appdata\\roaming\\python\\python38\\site-packages (from transformers==4.27.0) (2.25.1)\n",
      "Requirement already satisfied: regex!=2019.12.17 in c:\\users\\shuwe\\anaconda3\\envs\\wenwen\\lib\\site-packages (from transformers==4.27.0) (2021.4.4)\n",
      "Requirement already satisfied: tqdm>=4.27 in c:\\users\\shuwe\\anaconda3\\envs\\wenwen\\lib\\site-packages (from transformers==4.27.0) (4.65.0)\n",
      "Requirement already satisfied: pyyaml>=5.1 in c:\\users\\shuwe\\appdata\\roaming\\python\\python38\\site-packages (from transformers==4.27.0) (5.4.1)\n",
      "Requirement already satisfied: filelock in c:\\users\\shuwe\\anaconda3\\envs\\wenwen\\lib\\site-packages (from transformers==4.27.0) (3.0.12)\n",
      "Requirement already satisfied: typing-extensions>=3.7.4.3 in c:\\users\\shuwe\\anaconda3\\envs\\wenwen\\lib\\site-packages (from huggingface-hub<1.0,>=0.11.0->transformers==4.27.0) (4.5.0)\n",
      "Requirement already satisfied: fsspec in c:\\users\\shuwe\\anaconda3\\envs\\wenwen\\lib\\site-packages (from huggingface-hub<1.0,>=0.11.0->transformers==4.27.0) (2023.6.0)\n",
      "Requirement already satisfied: pyparsing>=2.0.2 in c:\\users\\shuwe\\appdata\\roaming\\python\\python38\\site-packages (from packaging>=20.0->transformers==4.27.0) (2.4.7)\n",
      "Requirement already satisfied: colorama in c:\\users\\shuwe\\anaconda3\\envs\\wenwen\\lib\\site-packages (from tqdm>=4.27->transformers==4.27.0) (0.4.4)\n",
      "Requirement already satisfied: chardet<5,>=3.0.2 in c:\\users\\shuwe\\anaconda3\\envs\\wenwen\\lib\\site-packages (from requests->transformers==4.27.0) (3.0.4)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\shuwe\\appdata\\roaming\\python\\python38\\site-packages (from requests->transformers==4.27.0) (2020.12.5)\n",
      "Requirement already satisfied: idna<3,>=2.5 in c:\\users\\shuwe\\appdata\\roaming\\python\\python38\\site-packages (from requests->transformers==4.27.0) (2.10)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\users\\shuwe\\appdata\\roaming\\python\\python38\\site-packages (from requests->transformers==4.27.0) (1.26.5)\n"
     ]
    }
   ],
   "source": [
    "!pip install --upgrade transformers==4.27.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8-8qDf4EXljd",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8-8qDf4EXljd",
    "outputId": "97cffda3-ffde-4231-c80b-0bfbc9f98a68"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "fatal: destination path 'Eyettention' already exists and is not an empty directory.\n"
     ]
    }
   ],
   "source": [
    "# You can also just download the file model.py, which is only used in this tutorial\n",
    "!git clone https://github.com/aeye-lab/Eyettention"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "jbqmeRpRY5Sg",
   "metadata": {
    "id": "jbqmeRpRY5Sg"
   },
   "source": [
    "**Load the model and download pre-trained weights**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "stylish-member",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "stylish-member",
    "outputId": "2307c733-ee9e-4ffb-92ef-f9d973f53fd6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "keeping Bert with pre-trained weights\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at bert-base-chinese were not used when initializing BertModel: ['cls.predictions.transform.dense.bias', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias']\n",
      "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
     ]
    }
   ],
   "source": [
    "from Eyettention import model\n",
    "import torch\n",
    "from torch.utils import model_zoo\n",
    "\n",
    "#DEVICE = 'cuda'\n",
    "DEVICE = 'cpu'\n",
    "\n",
    "pretrained=True\n",
    "\n",
    "#load model\n",
    "dnn = model.Eyettention(cf)\n",
    "dnn.eval()",
    "\n",
    "if pretrained:  \n",
    "    #for Chinese\n",
    "    dnn.load_state_dict(model_zoo.load_url('https://github.com/aeye-lab/Eyettention/releases/download/v1.1/Eyettention_chinese.pth', map_location=torch.device('cpu')))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0DvCfMrZMYg",
   "metadata": {
    "id": "f0DvCfMrZMYg"
   },
   "source": [
    "**Tokenization**\n",
    "<br>\n",
    "**Introduction**: https://huggingface.co/learn/nlp-course/chapter2/4?fw=pt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "severe-findings",
   "metadata": {
    "id": "severe-findings"
   },
   "outputs": [],
   "source": [
    "from transformers import BertTokenizerFast\n",
    "#initialize tokenizer\n",
    "tokenizer = BertTokenizerFast.from_pretrained(cf['model_pretrained'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "funded-validity",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "funded-validity",
    "outputId": "fbc6c028-3049-42ab-8187-6034e787c59d"
   },
   "outputs": [],
   "source": [
    "sentence = '中国选手在男子滑雪比赛中有望蝉联冠军'\n",
    "#add special tokens\n",
    "#text = ('[CLS]' + ' ' + sentence + ' ' + '[SEP]').split()\n",
    "#print(text)\n",
    "tokens = tokenizer(sentence, add_special_tokens=True, max_length=cf['max_sn_len'], padding='max_length')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "vulnerable-figure",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vulnerable-figure",
    "outputId": "9dcba3a3-b8b8-4d77-bed9-3b6059f9cea8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['[CLS]', '中', '国', '选', '手', '在', '男', '子', '滑', '雪', '比', '赛', '中', '有', '望', '蝉', '联', '冠', '军', '[SEP]']\n"
     ]
    }
   ],
   "source": [
    "print(tokenizer.tokenize(sentence, add_special_tokens=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "inappropriate-marketplace",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "inappropriate-marketplace",
    "outputId": "6ca3b171-cd18-4a82-8770-871fd6fad840"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'input_ids': [101, 704, 1744, 6848, 2797, 1762, 4511, 2094, 3998, 7434, 3683, 6612, 704, 3300, 3307, 6068, 5468, 1094, 1092, 102, 0, 0, 0, 0, 0, 0, 0], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0]}\n"
     ]
    }
   ],
   "source": [
    "print(tokens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "intelligent-express",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "intelligent-express",
    "outputId": "3a185313-5e28-450b-cd8c-4b885bac07f0"
   },
   "outputs": [],
   "source": [
    "#we do not need word index in Chinese language since each character is encoded independently. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "Zz6pcnFualMb",
   "metadata": {
    "id": "Zz6pcnFualMb"
   },
   "source": [
    "**Prepare features**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "specified-stomach",
   "metadata": {
    "id": "specified-stomach"
   },
   "outputs": [],
   "source": [
    "def compute_BSC_word_length(sentence, lac):\n",
    "    word_string = lac.run(sentence)\n",
    "    #print(word_string)\n",
    "    word_len = [len(i) for i in word_string]\n",
    "    wl_list = []\n",
    "    for wl in word_len:\n",
    "        wl_list.extend([wl]*wl)\n",
    "    arr = np.asarray(wl_list, dtype=np.float32)\n",
    "    #length of a punctuation is 0, plus an epsilon to avoid division output inf\n",
    "    arr[arr==0] = 1/(0+0.5)\n",
    "    arr[arr!=0] = 1/(arr[arr!=0])\n",
    "    return arr\n",
    "\n",
    "def pad_seq(seqs, max_len, dtype=np.int32, fill_value=np.nan):\n",
    "    padded = np.full((len(seqs), max_len), fill_value=fill_value, dtype=dtype)\n",
    "    for i, seq in enumerate(seqs):\n",
    "        padded[i, 1:(len(seq)+1)] = seq\n",
    "    return padded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "59e9c3cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: LAC in c:\\users\\shuwe\\anaconda3\\envs\\wenwen\\lib\\site-packages (2.1.2)\n"
     ]
    }
   ],
   "source": [
    "#segment sentene into word (not character) to calculate word length features\n",
    "!pip install LAC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ce1a958a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: paddlepaddle==2.5.2 in c:\\users\\shuwe\\anaconda3\\envs\\wenwen\\lib\\site-packages (2.5.2)\n",
      "Requirement already satisfied: astor in c:\\users\\shuwe\\anaconda3\\envs\\wenwen\\lib\\site-packages (from paddlepaddle==2.5.2) (0.8.1)\n",
      "Requirement already satisfied: numpy>=1.13 in c:\\users\\shuwe\\appdata\\roaming\\python\\python38\\site-packages (from paddlepaddle==2.5.2) (1.20.3)\n",
      "Requirement already satisfied: protobuf<=3.20.2,>=3.1.0 in c:\\users\\shuwe\\anaconda3\\envs\\wenwen\\lib\\site-packages (from paddlepaddle==2.5.2) (3.14.0)\n",
      "Requirement already satisfied: opt-einsum==3.3.0 in c:\\users\\shuwe\\anaconda3\\envs\\wenwen\\lib\\site-packages (from paddlepaddle==2.5.2) (3.3.0)\n",
      "Requirement already satisfied: Pillow in c:\\users\\shuwe\\anaconda3\\envs\\wenwen\\lib\\site-packages (from paddlepaddle==2.5.2) (10.1.0)\n",
      "Requirement already satisfied: httpx in c:\\users\\shuwe\\anaconda3\\envs\\wenwen\\lib\\site-packages (from paddlepaddle==2.5.2) (0.26.0)\n",
      "Requirement already satisfied: decorator in c:\\users\\shuwe\\appdata\\roaming\\python\\python38\\site-packages (from paddlepaddle==2.5.2) (4.4.2)\n",
      "Requirement already satisfied: six>=1.9 in c:\\users\\shuwe\\appdata\\roaming\\python\\python38\\site-packages (from protobuf<=3.20.2,>=3.1.0->paddlepaddle==2.5.2) (1.16.0)\n",
      "Requirement already satisfied: sniffio in c:\\users\\shuwe\\anaconda3\\envs\\wenwen\\lib\\site-packages (from httpx->paddlepaddle==2.5.2) (1.2.0)\n",
      "Requirement already satisfied: certifi in c:\\users\\shuwe\\appdata\\roaming\\python\\python38\\site-packages (from httpx->paddlepaddle==2.5.2) (2020.12.5)\n",
      "Requirement already satisfied: idna in c:\\users\\shuwe\\appdata\\roaming\\python\\python38\\site-packages (from httpx->paddlepaddle==2.5.2) (2.10)\n",
      "Requirement already satisfied: httpcore==1.* in c:\\users\\shuwe\\anaconda3\\envs\\wenwen\\lib\\site-packages (from httpx->paddlepaddle==2.5.2) (1.0.2)\n",
      "Requirement already satisfied: anyio in c:\\users\\shuwe\\anaconda3\\envs\\wenwen\\lib\\site-packages (from httpx->paddlepaddle==2.5.2) (2.2.0)\n",
      "Requirement already satisfied: h11<0.15,>=0.13 in c:\\users\\shuwe\\anaconda3\\envs\\wenwen\\lib\\site-packages (from httpcore==1.*->httpx->paddlepaddle==2.5.2) (0.14.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install paddlepaddle==2.5.2\n",
    "from LAC import LAC\n",
    "lac = LAC(mode=\"seg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "proprietary-efficiency",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "proprietary-efficiency",
    "outputId": "a2b21281-ff87-4757-d3ff-88b9702436de"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[nan 0.5 0.5 0.5 0.5 1.  0.5 0.5 0.5 0.5 0.5 0.5 1.  0.5 0.5 0.5 0.5 0.5\n",
      "  0.5 nan nan nan nan nan nan nan nan]]\n"
     ]
    }
   ],
   "source": [
    "#compute the word length feature\n",
    "text_word_len = [compute_BSC_word_length(txt, lac) for txt in [sentence]]\n",
    "text_word_len = pad_seq(text_word_len, cf['max_sn_len'], fill_value=np.nan, dtype=np.float32)\n",
    "print(text_word_len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "severe-industry",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "severe-industry",
    "outputId": "93474602-f510-4841-c612-dca9de230a9b"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'wget' is not recognized as an internal or external command,\n",
      "operable program or batch file.\n"
     ]
    }
   ],
   "source": [
    "#Download precomputed sn_word_len mean and std (from CELER dataset) for feature normalization\n",
    "import pickle\n",
    "!wget https://github.com/aeye-lab/Eyettention/releases/download/v1.1/feature_norm_BSC.pickle\n",
    "path = 'feature_norm_BSC.pickle'\n",
    "file_to_read = open(path, \"rb\")\n",
    "loaded_dictionary = pickle.load(file_to_read)\n",
    "sn_word_len_mean = loaded_dictionary['sn_word_len_mean'].numpy()\n",
    "sn_word_len_std = loaded_dictionary['sn_word_len_std'].numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "dried-biodiversity",
   "metadata": {
    "id": "dried-biodiversity"
   },
   "outputs": [],
   "source": [
    "#normalize word length feature\n",
    "text_word_len = (text_word_len - sn_word_len_mean)/sn_word_len_std\n",
    "text_word_len = np.nan_to_num(text_word_len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "according-decimal",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "according-decimal",
    "outputId": "e88d7af1-1d6a-4d65-86cc-f6bb50dd3c2a"
   },
   "outputs": [],
   "source": [
    "tokens['input_ids'] = torch.tensor([tokens['input_ids']]).to(DEVICE)\n",
    "tokens['attention_mask'] = torch.tensor([tokens['attention_mask']]).to(DEVICE)\n",
    "word_len = torch.tensor([text_word_len.squeeze()]).to(DEVICE)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "Nrm-Rq4Ya2TX",
   "metadata": {
    "id": "Nrm-Rq4Ya2TX"
   },
   "source": [
    "**Generate synthetic scanpath**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "novel-monaco",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 74
    },
    "id": "novel-monaco",
    "outputId": "8169a529-a161-4e48-d324-0c4399d8ff70"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LabelEncoder()"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "#prepare the saccade range classes for model predictions, cf[\"max_sn_len\"]-1 -> stop class\n",
    "le = LabelEncoder()\n",
    "le.fit(np.append(np.arange(-cf[\"max_sn_len\"]+3, cf[\"max_sn_len\"]-1), cf[\"max_sn_len\"]-1))\n",
    "#le.classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "mathematical-dimension",
   "metadata": {
    "id": "mathematical-dimension"
   },
   "outputs": [],
   "source": [
    "syn_scanpath, density_pred = dnn.scanpath_generation(sn_emd=tokens['input_ids'],\n",
    "                                                     sn_mask=tokens['attention_mask'],\n",
    "                                                     word_ids_sn=None,\n",
    "                                                     sn_word_len = word_len,\n",
    "                                                     le=le,\n",
    "                                                     max_pred_len=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "bacterial-cardiff",
   "metadata": {
    "id": "bacterial-cardiff"
   },
   "outputs": [],
   "source": [
    "def post_process_scanpath(syn_scanpath, sn_len):\n",
    "    syn_scanpath = syn_scanpath.detach().to('cpu').numpy()\n",
    "    max_sp_len = syn_scanpath.shape[1]\n",
    "\n",
    "    #Find the number \"sn_len+1\" -> the stop class\n",
    "    stop_indx = []\n",
    "    for i in range(syn_scanpath.shape[0]):\n",
    "        stop = np.where(syn_scanpath[i,:]==(sn_len[i]+1))[0]\n",
    "        if len(stop)==0:#no stop class can be find -> exceeds the maximum length of the generated scanpath\n",
    "            stop_indx.append(max_sp_len-1)\n",
    "        else:\n",
    "            stop_indx.append(stop[0])\n",
    "\n",
    "    #Truncating data after the stop class\n",
    "    syn_scanpath_cut = [syn_scanpath[i][1:stop_indx[i]] for i in range(syn_scanpath.shape[0])]\n",
    "    return syn_scanpath_cut"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "southern-cycling",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "southern-cycling",
    "outputId": "32c519fa-5251-4869-fc78-242bae3cc5f3"
   },
   "outputs": [],
   "source": [
    "sn_len = (torch.sum(tokens['attention_mask'], axis=1) - 2).detach().to('cpu').numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "563c5aa6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1  2  4  6  9 12 16]\n"
     ]
    }
   ],
   "source": [
    "syn_scanpath = post_process_scanpath(syn_scanpath, sn_len)\n",
    "print(syn_scanpath[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "imposed-passing",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "imposed-passing",
    "outputId": "2400559b-3d51-461b-8df2-19f1ccc621da"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['中', '国', '手', '男', '雪', '中', '联']\n"
     ]
    }
   ],
   "source": [
    "fixated_character = [sentence[idx-1] for idx in syn_scanpath[0]] #python index start from 0, our model prediction index start from 1\n",
    "print(fixated_character)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "Q9ojBr5rba-R",
   "metadata": {
    "id": "Q9ojBr5rba-R"
   },
   "source": [
    "**Prediction visualization**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "dental-knock",
   "metadata": {
    "id": "dental-knock"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "plt.rcParams[\"font.sans-serif\"]=['SimHei']\n",
    "def visualize_distribution(syn_scanpath, density_pred, timestep, text):\n",
    "    #visualize density map\n",
    "    plt.figure(figsize = (25,5))\n",
    "    sac_range = np.append(np.arange(-cf[\"max_sn_len\"]+3, cf[\"max_sn_len\"]-1), cf[\"max_sn_len\"]-1)\n",
    "\n",
    "    #current gaze position\n",
    "    cur_loc = syn_scanpath[timestep]\n",
    "    #convert the saccade range classes to the target word indexes\n",
    "    target_loc = sac_range + cur_loc\n",
    "\n",
    "    prob_valid = density_pred[np.where((0 <= target_loc) & (target_loc <= (len(text)-2)))]#exclude SEP token\n",
    "    prob_valid = np.append(prob_valid, density_pred[-1])\n",
    "\n",
    "    plt.subplot(2, 1, 2)\n",
    "    sns.heatmap(prob_valid[np.newaxis, :], annot=np.array(text)[np.newaxis,:], fmt=\"\", linewidths=2, cmap=\"YlGnBu\", xticklabels=False, yticklabels=False, cbar=True, annot_kws={\"fontsize\":15}, cbar_kws = dict(use_gridspec=False,location=\"top\"))\n",
    "    y = 3\n",
    "    for past_ts in range(timestep):\n",
    "        dy = 0\n",
    "        dx = syn_scanpath[past_ts+1] - syn_scanpath[past_ts]\n",
    "        #if refixation, display on next line\n",
    "        if dx <= 0:\n",
    "            dy = -0.5\n",
    "            plt.arrow(syn_scanpath[past_ts]+0.5, y, dx, dy, color='b', ec ='blue', head_width=0.2)\n",
    "            y = y + dy\n",
    "        else:\n",
    "            plt.arrow(syn_scanpath[past_ts]+0.5, y, dx, dy, color='b', ec ='blue', head_width=0.2)\n",
    "\n",
    "    plt.arrow(syn_scanpath[timestep]+0.5, y, (syn_scanpath[timestep+1]-syn_scanpath[timestep]), 0, color='r', ec ='red', head_width=0.2)\n",
    "    plt.xlim([0, len(text)])\n",
    "    plt.ylim([0, 3.5])\n",
    "    ax = plt.gca()\n",
    "    ax.axes.get_xaxis().set_visible(False)\n",
    "    ax.axes.get_yaxis().set_visible(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "cross-commercial",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 130
    },
    "id": "cross-commercial",
    "outputId": "66a6e06d-7473-412f-9dba-10b9db130b9a"
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 1800x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn_char = [c for c in sentence]\n",
    "sn_char = [''] + sn_char + ['SEP']\n",
    "sample_index=0\n",
    "timestep = 2\n",
    "visualize_distribution(syn_scanpath=syn_scanpath[sample_index],\n",
    "                       density_pred=density_pred[timestep][sample_index].detach().numpy(),\n",
    "                       timestep=timestep,\n",
    "                       text=sn_char)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "orange-persian",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 130
    },
    "id": "orange-persian",
    "outputId": "df44ad8a-8a17-4f20-9c1d-3dea8be063a7"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_index=0\n",
    "timestep = 3\n",
    "visualize_distribution(syn_scanpath=syn_scanpath[sample_index],\n",
    "                       density_pred=density_pred[timestep][sample_index].detach().numpy(),\n",
    "                       timestep=timestep,\n",
    "                       text=sn_char)"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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