aitech-moj-2023/cw/11_Model_rekurencyjny_z_atencją.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Logo 1](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech1.jpg)\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<h1> Modelowanie Języka</h1>\n",
    "<h2> 10. <i>Model rekurencyjny z atencją</i>  [ćwiczenia]</h2> \n",
    "<h3> Jakub Pokrywka (2022)</h3>\n",
    "</div>\n",
    "\n",
    "![Logo 2](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech2.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "notebook na podstawie:\n",
    "\n",
    "# https://pytorch.org/tutorials/intermediate/seq2seq_translation_tutorial.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import unicode_literals, print_function, division\n",
    "from io import open\n",
    "import unicodedata\n",
    "import string\n",
    "import re\n",
    "import random\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch import optim\n",
    "import torch.nn.functional as F\n",
    "\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "SOS_token = 0\n",
    "EOS_token = 1\n",
    "\n",
    "class Lang:\n",
    "    def __init__(self):\n",
    "        self.word2index = {}\n",
    "        self.word2count = {}\n",
    "        self.index2word = {0: \"SOS\", 1: \"EOS\"}\n",
    "        self.n_words = 2  # Count SOS and EOS\n",
    "\n",
    "    def addSentence(self, sentence):\n",
    "        for word in sentence.split(' '):\n",
    "            self.addWord(word)\n",
    "\n",
    "    def addWord(self, word):\n",
    "        if word not in self.word2index:\n",
    "            self.word2index[word] = self.n_words\n",
    "            self.word2count[word] = 1\n",
    "            self.index2word[self.n_words] = word\n",
    "            self.n_words += 1\n",
    "        else:\n",
    "            self.word2count[word] += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pairs = []\n",
    "with open('data/eng-pol.txt') as f:\n",
    "    for line in f:\n",
    "        eng_line, pol_line = line.lower().rstrip().split('\\t')\n",
    "\n",
    "        eng_line = re.sub(r\"([.!?])\", r\" \\1\", eng_line)\n",
    "        eng_line = re.sub(r\"[^a-zA-Z.!?]+\", r\" \", eng_line)\n",
    "\n",
    "        pol_line = re.sub(r\"([.!?])\", r\" \\1\", pol_line)\n",
    "        pol_line = re.sub(r\"[^a-zA-Z.!?ąćęłńóśźżĄĆĘŁŃÓŚŹŻ]+\", r\" \", pol_line)\n",
    "\n",
    "        pairs.append([eng_line, pol_line])\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pairs[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "MAX_LENGTH = 10\n",
    "eng_prefixes = (\n",
    "    \"i am \", \"i m \",\n",
    "    \"he is\", \"he s \",\n",
    "    \"she is\", \"she s \",\n",
    "    \"you are\", \"you re \",\n",
    "    \"we are\", \"we re \",\n",
    "    \"they are\", \"they re \"\n",
    ")\n",
    "\n",
    "pairs = [p for p in pairs if len(p[0].split(' ')) < MAX_LENGTH and len(p[1].split(' ')) < MAX_LENGTH]\n",
    "pairs = [p for p in pairs if p[0].startswith(eng_prefixes)]\n",
    "\n",
    "eng_lang = Lang()\n",
    "pol_lang = Lang()\n",
    "\n",
    "for pair in pairs:\n",
    "    eng_lang.addSentence(pair[0])\n",
    "    pol_lang.addSentence(pair[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pairs[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pairs[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pairs[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "eng_lang.n_words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pol_lang.n_words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class EncoderRNN(nn.Module):\n",
    "    def __init__(self, input_size, embedding_size, hidden_size):\n",
    "        super(EncoderRNN, self).__init__()\n",
    "        self.embedding_size = 200\n",
    "        self.hidden_size = hidden_size\n",
    "\n",
    "        self.embedding = nn.Embedding(input_size, self.embedding_size)\n",
    "        self.gru = nn.GRU(self.embedding_size, hidden_size)\n",
    "\n",
    "    def forward(self, input, hidden):\n",
    "        embedded = self.embedding(input).view(1, 1, -1)\n",
    "        output = embedded\n",
    "        output, hidden = self.gru(output, hidden)\n",
    "        return output, hidden\n",
    "\n",
    "    def initHidden(self):\n",
    "        return torch.zeros(1, 1, self.hidden_size, device=device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DecoderRNN(nn.Module):\n",
    "    def __init__(self, embedding_size, hidden_size, output_size):\n",
    "        super(DecoderRNN, self).__init__()\n",
    "        self.embedding_size = embedding_size\n",
    "        self.hidden_size = hidden_size\n",
    "\n",
    "        self.embedding = nn.Embedding(output_size, self.embedding_size)\n",
    "        self.gru = nn.GRU(self.embedding_size, hidden_size)\n",
    "        self.out = nn.Linear(hidden_size, output_size)\n",
    "        self.softmax = nn.LogSoftmax(dim=1)\n",
    "\n",
    "    def forward(self, input, hidden):\n",
    "        output = self.embedding(input).view(1, 1, -1)\n",
    "        output = F.relu(output)\n",
    "        output, hidden = self.gru(output, hidden)\n",
    "        output = self.softmax(self.out(output[0]))\n",
    "        return output, hidden\n",
    "\n",
    "    def initHidden(self):\n",
    "        return torch.zeros(1, 1, self.hidden_size, device=device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class AttnDecoderRNN(nn.Module):\n",
    "    def __init__(self, embedding_size, hidden_size, output_size, dropout_p=0.1, max_length=MAX_LENGTH):\n",
    "        super(AttnDecoderRNN, self).__init__()\n",
    "        self.embedding_size = embedding_size\n",
    "        self.hidden_size = hidden_size\n",
    "        self.output_size = output_size\n",
    "        self.dropout_p = dropout_p\n",
    "        self.max_length = max_length\n",
    "\n",
    "        self.embedding = nn.Embedding(self.output_size, self.embedding_size)\n",
    "        self.attn = nn.Linear(self.hidden_size + self.embedding_size, self.max_length)\n",
    "        self.attn_combine = nn.Linear(self.hidden_size + self.embedding_size, self.embedding_size)\n",
    "        self.dropout = nn.Dropout(self.dropout_p)\n",
    "        self.gru = nn.GRU(self.embedding_size, self.hidden_size)\n",
    "        self.out = nn.Linear(self.hidden_size, self.output_size)\n",
    "\n",
    "    def forward(self, input, hidden, encoder_outputs):\n",
    "        embedded = self.embedding(input).view(1, 1, -1)\n",
    "        embedded = self.dropout(embedded)\n",
    "\n",
    "        attn_weights = F.softmax(\n",
    "            self.attn(torch.cat((embedded[0], hidden[0]), 1)), dim=1)\n",
    "        attn_applied = torch.bmm(attn_weights.unsqueeze(0),\n",
    "                                 encoder_outputs.unsqueeze(0))\n",
    "        #import pdb; pdb.set_trace()\n",
    "\n",
    "        output = torch.cat((embedded[0], attn_applied[0]), 1)\n",
    "        output = self.attn_combine(output).unsqueeze(0)\n",
    "\n",
    "        output = F.relu(output)\n",
    "        output, hidden = self.gru(output, hidden)\n",
    "\n",
    "        output = F.log_softmax(self.out(output[0]), dim=1)\n",
    "        return output, hidden, attn_weights\n",
    "\n",
    "    def initHidden(self):\n",
    "        return torch.zeros(1, 1, self.hidden_size, device=device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tensorFromSentence(sentence, lang):\n",
    "    indexes = [lang.word2index[word] for word in sentence.split(' ')]\n",
    "    indexes.append(EOS_token)\n",
    "    return torch.tensor(indexes, dtype=torch.long, device=device).view(-1, 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "teacher_forcing_ratio = 0.5\n",
    "\n",
    "def train_one_batch(input_tensor, target_tensor, encoder, decoder, optimizer, criterion, max_length=MAX_LENGTH):\n",
    "    encoder_hidden = encoder.initHidden()\n",
    "\n",
    "\n",
    "    optimizer.zero_grad()\n",
    "\n",
    "    input_length = input_tensor.size(0)\n",
    "    target_length = target_tensor.size(0)\n",
    "\n",
    "    encoder_outputs = torch.zeros(max_length, encoder.hidden_size, device=device)\n",
    "\n",
    "    loss = 0\n",
    "\n",
    "    for ei in range(input_length):\n",
    "        encoder_output, encoder_hidden = encoder(input_tensor[ei], encoder_hidden)\n",
    "        encoder_outputs[ei] = encoder_output[0, 0]\n",
    "\n",
    "    decoder_input = torch.tensor([[SOS_token]], device=device)\n",
    "\n",
    "    decoder_hidden = encoder_hidden\n",
    "\n",
    "    use_teacher_forcing = True if random.random() < teacher_forcing_ratio else False\n",
    "\n",
    "    if use_teacher_forcing:\n",
    "        for di in range(target_length):\n",
    "            decoder_output, decoder_hidden, decoder_attention = decoder(decoder_input, decoder_hidden, encoder_outputs)\n",
    "            loss += criterion(decoder_output, target_tensor[di])\n",
    "            decoder_input = target_tensor[di]  # Teacher forcing\n",
    "\n",
    "    else:\n",
    "        for di in range(target_length):\n",
    "            decoder_output, decoder_hidden, decoder_attention = decoder(decoder_input, decoder_hidden, encoder_outputs)\n",
    "            topv, topi = decoder_output.topk(1)\n",
    "            decoder_input = topi.squeeze().detach()  # detach from history as input\n",
    "\n",
    "            loss += criterion(decoder_output, target_tensor[di])\n",
    "            if decoder_input.item() == EOS_token:\n",
    "                break\n",
    "\n",
    "    loss.backward()\n",
    "\n",
    "    optimizer.step()\n",
    "\n",
    "    return loss.item() / target_length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def trainIters(encoder, decoder, n_iters, print_every=1000, learning_rate=0.01):\n",
    "    print_loss_total = 0  # Reset every print_every\n",
    "    encoder.train()\n",
    "    decoder.train()\n",
    "\n",
    "    optimizer = optim.SGD(list(encoder.parameters()) + list(decoder.parameters()), lr=learning_rate)\n",
    "    \n",
    "    training_pairs = [random.choice(pairs) for _ in range(n_iters)]\n",
    "    training_pairs = [(tensorFromSentence(p[0], eng_lang), tensorFromSentence(p[1], pol_lang)) for p in training_pairs]\n",
    "    \n",
    "    criterion = nn.NLLLoss()\n",
    "\n",
    "    for i in range(1, n_iters + 1):\n",
    "        training_pair = training_pairs[i - 1]\n",
    "        input_tensor = training_pair[0]\n",
    "        target_tensor = training_pair[1]\n",
    "\n",
    "        loss = train_one_batch(input_tensor,\n",
    "                               target_tensor,\n",
    "                               encoder,\n",
    "                               decoder,\n",
    "                               optimizer,\n",
    "\n",
    "                               criterion)\n",
    "        \n",
    "        print_loss_total += loss\n",
    "\n",
    "        if i % print_every == 0:\n",
    "            print_loss_avg = print_loss_total / print_every\n",
    "            print_loss_total = 0\n",
    "            print(f'iter: {i}, loss: {print_loss_avg}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate(encoder, decoder, sentence, max_length=MAX_LENGTH):\n",
    "    encoder.eval()\n",
    "    decoder.eval()\n",
    "    with torch.no_grad():\n",
    "        input_tensor = tensorFromSentence(sentence, eng_lang)\n",
    "        input_length = input_tensor.size()[0]\n",
    "        encoder_hidden = encoder.initHidden()\n",
    "\n",
    "        encoder_outputs = torch.zeros(max_length, encoder.hidden_size, device=device)\n",
    "\n",
    "        for ei in range(input_length):\n",
    "            encoder_output, encoder_hidden = encoder(input_tensor[ei], encoder_hidden)\n",
    "            encoder_outputs[ei] += encoder_output[0, 0]\n",
    "\n",
    "        decoder_input = torch.tensor([[SOS_token]], device=device)\n",
    "\n",
    "        decoder_hidden = encoder_hidden\n",
    "\n",
    "        decoded_words = []\n",
    "        decoder_attentions = torch.zeros(max_length, max_length)\n",
    "\n",
    "        for di in range(max_length):\n",
    "            decoder_output, decoder_hidden, decoder_attention = decoder(\n",
    "                decoder_input, decoder_hidden, encoder_outputs)\n",
    "            decoder_attentions[di] = decoder_attention.data\n",
    "            topv, topi = decoder_output.data.topk(1)\n",
    "            if topi.item() == EOS_token:\n",
    "                decoded_words.append('<EOS>')\n",
    "                break\n",
    "            else:\n",
    "                decoded_words.append(pol_lang.index2word[topi.item()])\n",
    "\n",
    "            decoder_input = topi.squeeze().detach()\n",
    "\n",
    "        return decoded_words, decoder_attentions[:di + 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluateRandomly(encoder, decoder, n=10):\n",
    "    for i in range(n):\n",
    "        pair = random.choice(pairs)\n",
    "        print('>', pair[0])\n",
    "        print('=', pair[1])\n",
    "        output_words, attentions = evaluate(encoder, decoder, pair[0])\n",
    "        output_sentence = ' '.join(output_words)\n",
    "        print('<', output_sentence)\n",
    "        print('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "embedding_size = 200\n",
    "hidden_size = 256\n",
    "encoder1 = EncoderRNN(eng_lang.n_words, embedding_size, hidden_size).to(device)\n",
    "attn_decoder1 = AttnDecoderRNN(embedding_size, hidden_size, pol_lang.n_words, dropout_p=0.1).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainIters(encoder1, attn_decoder1, 10_000, print_every=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "evaluateRandomly(encoder1, attn_decoder1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## ZADANIE\n",
    "\n",
    "Gonito \"WMT2017 Czech-English machine translation challenge for news \"\n",
    "\n",
    "Proszę wytrenować najpierw model german -> english, a później dotrenować na czech-> english.\n",
    "Można wziąć inicjalizować enkoder od nowa lub nie. Proszę w każdym razie użyć wytrenowanego dekodera."
   ]
  }
 ],
 "metadata": {
  "author": "Jakub Pokrywka",
  "email": "kubapok@wmi.amu.edu.pl",
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "lang": "pl",
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.4"
  },
  "subtitle": "0.Informacje na temat przedmiotu[ćwiczenia]",
  "title": "Ekstrakcja informacji",
  "year": "2021"
 },
 "nbformat": 4,
 "nbformat_minor": 4
}