DL_SEQ2SEQ/seq2seq-torch-successful.ipynb

Importy

from __future__ import unicode_literals, print_function, division
from io import open
import unicodedata
import re
import os
import random
import torch
import pandas as pd
import torch.nn as nn
from torch import optim
import torch.nn.functional as F
from torchtext.data.metrics import bleu_score

from torch.utils.data import TensorDataset, DataLoader, RandomSampler
os.environ["CUDA_VISIBLE_DEVICES"] = "0"

print(f'Is CUDA supported by this system? {torch.cuda.is_available()}')
print(f"CUDA version: {torch.version.cuda}")

cuda_id = torch.cuda.current_device()
print(f'ID of current CUDA device: {torch.cuda.current_device()}')

print(f'Name of current CUDA device: {torch.cuda.get_device_name(cuda_id)}')

Is CUDA supported by this system? True
CUDA version: 12.1
ID of current CUDA device: 0
Name of current CUDA device: NVIDIA GeForce GTX 1660 Ti

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(device)

cuda

Konwersja słów na tensory

SOS_token = 0
EOS_token = 1

class Lang:
    def __init__(self, name):
        self.name = name
        self.word2index = {}
        self.word2count = {}
        self.index2word = {0: "SOS", 1: "EOS"}
        self.n_words = 2  # Count SOS and EOS

    def addSentence(self, sentence):
        for word in sentence.split(' '):
            self.addWord(word)

    def addWord(self, word):
        if word not in self.word2index:
            self.word2index[word] = self.n_words
            self.word2count[word] = 1
            self.index2word[self.n_words] = word
            self.n_words += 1
        else:
            self.word2count[word] += 1

Przygotowanie danych

# Turn a Unicode string to plain ASCII, thanks to
# https://stackoverflow.com/a/518232/2809427
def unicodeToAscii(s):
    return ''.join(
        c for c in unicodedata.normalize('NFD', s)
        if unicodedata.category(c) != 'Mn'
    )

# Lowercase, trim, and remove non-letter characters
def normalizeString(s):
    s = unicodeToAscii(s.lower().strip())
    s = re.sub(r"([.!?])", r" \1", s)
    s = re.sub(r"[^a-zA-Z!?]+", r" ", s)
    return s.strip()

Wczytanie danych

def readLangs(lang1, lang2, reverse=False):
    print("Reading lines...")
    # Read the file and split into lines
    lines =  open('data/%s-%s.txt' % (lang1, lang2), encoding='utf-8').\
        read().strip().split('\n')

    # Split every line into pairs and normalize
    pairs = [[normalizeString(s) for s in l.split('\t')[:-1]] for l in lines]

    # Reverse pairs, make Lang instances
    if reverse:
        pairs = [df_filtered(reversed(p)) for p in pairs]
        input_lang = Lang(lang2)
        output_lang = Lang(lang1)
    else:
        input_lang = Lang(lang1)
        output_lang = Lang(lang2)

    return input_lang, output_lang, pairs

Filtracja danych

Ograniczenie zdań do 10 słów oraz zdań zaczynających się od prefiksów

MAX_LENGTH = 10

eng_prefixes = (
    "i am ", "i m ",
    "he is", "he s ",
    "she is", "she s ",
    "you are", "you re ",
    "we are", "we re ",
    "they are", "they re "
)

def filterPair(p):
    return len(p[0].split(' ')) < MAX_LENGTH and \
        len(p[1].split(' ')) < MAX_LENGTH and \
        p[1].startswith(eng_prefixes)


def filterPairs(pairs):
    return [pair for pair in pairs if filterPair(pair)]

def prepareData(lang1, lang2, reverse=False):
    input_lang, output_lang, pairs = readLangs(lang1, lang2, reverse)
    print("Read %s sentence pairs" % len(pairs))
    pairs = filterPairs(pairs)
    print("Trimmed to %s sentence pairs" % len(pairs))
    print("Counting words...")
    for pair in pairs:
        input_lang.addSentence(pair[0])
        output_lang.addSentence(pair[1])
    print("Counted words:")
    print(input_lang.name, input_lang.n_words)
    print(output_lang.name, output_lang.n_words)
    return input_lang, output_lang, pairs

input_lang, output_lang, pairs = prepareData('eng', 'pol' , True)
print(random.choice(pairs))

Reading lines...
Read 49943 sentence pairs
Trimmed to 3613 sentence pairs
Counting words...
Counted words:
pol 3070
eng 1969
['nie umieram', 'i m not dying']

Model

class EncoderRNN(nn.Module):
    def __init__(self, input_size, hidden_size, dropout_p=0.1):
        super(EncoderRNN, self).__init__()
        self.hidden_size = hidden_size

        self.embedding = nn.Embedding(input_size, hidden_size)
        self.gru = nn.GRU(hidden_size, hidden_size, batch_first=True)
        self.dropout = nn.Dropout(dropout_p)

    def forward(self, input):
        embedded = self.dropout(self.embedding(input))
        output, hidden = self.gru(embedded)
        return output, hidden

class DecoderRNN(nn.Module):
    def __init__(self, hidden_size, output_size):
        super(DecoderRNN, self).__init__()
        self.embedding = nn.Embedding(output_size, hidden_size)
        self.gru = nn.GRU(hidden_size, hidden_size, batch_first=True)
        self.out = nn.Linear(hidden_size, output_size)

    def forward(self, encoder_outputs, encoder_hidden, target_tensor=None):
        batch_size = encoder_outputs.size(0)
        decoder_input = torch.empty(batch_size, 1, dtype=torch.long, device=device).fill_(SOS_token)
        decoder_hidden = encoder_hidden
        decoder_outputs = []

        for i in range(MAX_LENGTH):
            decoder_output, decoder_hidden  = self.forward_step(decoder_input, decoder_hidden)
            decoder_outputs.append(decoder_output)

            if target_tensor is not None:
                # Teacher forcing: Feed the target as the next input
                decoder_input = target_tensor[:, i].unsqueeze(1) # Teacher forcing
            else:
                # Without teacher forcing: use its own predictions as the next input
                _, topi = decoder_output.topk(1)
                decoder_input = topi.squeeze(-1).detach()  # detach from history as input

        decoder_outputs = torch.cat(decoder_outputs, dim=1)
        decoder_outputs = F.log_softmax(decoder_outputs, dim=-1)
        return decoder_outputs, decoder_hidden, None # We return `None` for consistency in the training loop

    def forward_step(self, input, hidden):
        output = self.embedding(input)
        output = F.relu(output)
        output, hidden = self.gru(output, hidden)
        output = self.out(output)
        return output, hidden

class BahdanauAttention(nn.Module):
    def __init__(self, hidden_size):
        super(BahdanauAttention, self).__init__()
        self.Wa = nn.Linear(hidden_size, hidden_size)
        self.Ua = nn.Linear(hidden_size, hidden_size)
        self.Va = nn.Linear(hidden_size, 1)

    def forward(self, query, keys):
        scores = self.Va(torch.tanh(self.Wa(query) + self.Ua(keys)))
        scores = scores.squeeze(2).unsqueeze(1)

        weights = F.softmax(scores, dim=-1)
        context = torch.bmm(weights, keys)

        return context, weights

class AttnDecoderRNN(nn.Module):
    def __init__(self, hidden_size, output_size, dropout_p=0.1):
        super(AttnDecoderRNN, self).__init__()
        self.embedding = nn.Embedding(output_size, hidden_size)
        self.attention = BahdanauAttention(hidden_size)
        self.gru = nn.GRU(2 * hidden_size, hidden_size, batch_first=True)
        self.out = nn.Linear(hidden_size, output_size)
        self.dropout = nn.Dropout(dropout_p)

    def forward(self, encoder_outputs, encoder_hidden, target_tensor=None):
        batch_size = encoder_outputs.size(0)
        decoder_input = torch.empty(batch_size, 1, dtype=torch.long, device=device).fill_(SOS_token)
        decoder_hidden = encoder_hidden
        decoder_outputs = []
        attentions = []

        for i in range(MAX_LENGTH):
            decoder_output, decoder_hidden, attn_weights = self.forward_step(
                decoder_input, decoder_hidden, encoder_outputs
            )
            decoder_outputs.append(decoder_output)
            attentions.append(attn_weights)

            if target_tensor is not None:
                # Teacher forcing: Feed the target as the next input
                decoder_input = target_tensor[:, i].unsqueeze(1) # Teacher forcing
            else:
                # Without teacher forcing: use its own predictions as the next input
                _, topi = decoder_output.topk(1)
                decoder_input = topi.squeeze(-1).detach()  # detach from history as input

        decoder_outputs = torch.cat(decoder_outputs, dim=1)
        decoder_outputs = F.log_softmax(decoder_outputs, dim=-1)
        attentions = torch.cat(attentions, dim=1)

        return decoder_outputs, decoder_hidden, attentions


    def forward_step(self, input, hidden, encoder_outputs):
        embedded =  self.dropout(self.embedding(input))

        query = hidden.permute(1, 0, 2)
        context, attn_weights = self.attention(query, encoder_outputs)
        input_gru = torch.cat((embedded, context), dim=2)

        output, hidden = self.gru(input_gru, hidden)
        output = self.out(output)

        return output, hidden, attn_weights

def indexesFromSentence(lang, sentence):
    return [lang.word2index[word] for word in sentence.split(' ')]

def tensorFromSentence(lang, sentence):
    indexes = indexesFromSentence(lang, sentence)
    indexes.append(EOS_token)
    return torch.tensor(indexes, dtype=torch.long, device=device).view(1, -1)

def tensorsFromPair(pair):
    input_tensor = tensorFromSentence(input_lang, pair[0])
    target_tensor = tensorFromSentence(output_lang, pair[1])
    return (input_tensor, target_tensor)

def get_dataloader(batch_size):
    input_lang, output_lang, pairs = prepareData( 'eng', 'pol', True)

    n = len(pairs)
    input_ids = np.zeros((n, MAX_LENGTH), dtype=np.int32)
    target_ids = np.zeros((n, MAX_LENGTH), dtype=np.int32)

    for idx, (inp, tgt) in enumerate(pairs):
        inp_ids = indexesFromSentence(input_lang, inp)
        tgt_ids = indexesFromSentence(output_lang, tgt)
        inp_ids.append(EOS_token)
        tgt_ids.append(EOS_token)
        input_ids[idx, :len(inp_ids)] = inp_ids
        target_ids[idx, :len(tgt_ids)] = tgt_ids

    train_data = TensorDataset(torch.LongTensor(input_ids).to(device),
                               torch.LongTensor(target_ids).to(device))

    train_sampler = RandomSampler(train_data)
    train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=batch_size)
    return input_lang, output_lang, train_dataloader

Trening

def train_epoch(dataloader, encoder, decoder, encoder_optimizer,
          decoder_optimizer, criterion):

    total_loss = 0
    for data in dataloader:
        input_tensor, target_tensor = data

        encoder_optimizer.zero_grad()
        decoder_optimizer.zero_grad()

        encoder_outputs, encoder_hidden = encoder(input_tensor)
        decoder_outputs, _, _ = decoder(encoder_outputs, encoder_hidden, target_tensor)

        loss = criterion(
            decoder_outputs.view(-1, decoder_outputs.size(-1)),
            target_tensor.view(-1)
        )
        loss.backward()

        encoder_optimizer.step()
        decoder_optimizer.step()

        total_loss += loss.item()

    return total_loss / len(dataloader)

import time
import math

def asMinutes(s):
    m = math.floor(s / 60)
    s -= m * 60
    return '%dm %ds' % (m, s)

def timeSince(since, percent):
    now = time.time()
    s = now - since
    es = s / (percent)
    rs = es - s
    return '%s (- %s)' % (asMinutes(s), asMinutes(rs))

import matplotlib.pyplot as plt
plt.switch_backend('agg')
import matplotlib.ticker as ticker
import numpy as np

def showPlot(points):
    plt.figure()
    fig, ax = plt.subplots()
    # this locator puts ticks at regular intervals
    loc = ticker.MultipleLocator(base=0.2)
    ax.yaxis.set_major_locator(loc)
    plt.plot(points)

def train(train_dataloader, encoder, decoder, n_epochs, learning_rate=0.001,
               print_every=100, plot_every=100):
    start = time.time()
    plot_losses = []
    print_loss_total = 0  # Reset every print_every
    plot_loss_total = 0  # Reset every plot_every

    encoder_optimizer = optim.Adam(encoder.parameters(), lr=learning_rate)
    decoder_optimizer = optim.Adam(decoder.parameters(), lr=learning_rate)
    criterion = nn.NLLLoss()

    for epoch in range(1, n_epochs + 1):
        loss = train_epoch(train_dataloader, encoder, decoder, encoder_optimizer, decoder_optimizer, criterion)
        print_loss_total += loss
        plot_loss_total += loss

        if epoch % print_every == 0:
            print_loss_avg = print_loss_total / print_every
            print_loss_total = 0
            print('%s (%d %d%%) %.4f' % (timeSince(start, epoch / n_epochs),
                                        epoch, epoch / n_epochs * 100, print_loss_avg))

        if epoch % plot_every == 0:
            plot_loss_avg = plot_loss_total / plot_every
            plot_losses.append(plot_loss_avg)
            plot_loss_total = 0

    showPlot(plot_losses)

hidden_size = 256
batch_size = 64

input_lang, output_lang, train_dataloader = get_dataloader(batch_size)

encoder = EncoderRNN(input_lang.n_words, hidden_size).to(device)
decoder = AttnDecoderRNN(hidden_size, output_lang.n_words).to(device)

train(train_dataloader, encoder, decoder, 100, print_every=5, plot_every=5)

Reading lines...
Read 49943 sentence pairs
Trimmed to 3613 sentence pairs
Counting words...
Counted words:
pol 3070
eng 1969
0m 7s (- 2m 18s) (5 5%) 1.9851
0m 14s (- 2m 8s) (10 10%) 1.0089
0m 21s (- 1m 59s) (15 15%) 0.5189
0m 28s (- 1m 52s) (20 20%) 0.2294
0m 35s (- 1m 45s) (25 25%) 0.0961
0m 42s (- 1m 38s) (30 30%) 0.0509
0m 50s (- 1m 33s) (35 35%) 0.0355
0m 57s (- 1m 25s) (40 40%) 0.0289
1m 4s (- 1m 18s) (45 45%) 0.0249
1m 11s (- 1m 11s) (50 50%) 0.0228
1m 18s (- 1m 4s) (55 55%) 0.0207
1m 25s (- 0m 57s) (60 60%) 0.0215
1m 32s (- 0m 49s) (65 65%) 0.0249
1m 39s (- 0m 42s) (70 70%) 0.0184
1m 47s (- 0m 35s) (75 75%) 0.0172
1m 55s (- 0m 28s) (80 80%) 0.0166
2m 3s (- 0m 21s) (85 85%) 0.0163
2m 11s (- 0m 14s) (90 90%) 0.0163
2m 18s (- 0m 7s) (95 95%) 0.0176
2m 27s (- 0m 0s) (100 100%) 0.0256

<Figure size 640x480 with 0 Axes>

Ewaluacja

def evaluate(encoder, decoder, sentence, input_lang, output_lang):
    with torch.no_grad():
        input_tensor = tensorFromSentence(input_lang, sentence)

        encoder_outputs, encoder_hidden = encoder(input_tensor)
        decoder_outputs, decoder_hidden, decoder_attn = decoder(encoder_outputs, encoder_hidden)

        _, topi = decoder_outputs.topk(1)
        decoded_ids = topi.squeeze()

        decoded_words = []
        for idx in decoded_ids:
            if idx.item() == EOS_token:
                decoded_words.append('<EOS>')
                break
            decoded_words.append(output_lang.index2word[idx.item()])
    return decoded_words, decoder_attn

def evaluateRandomly(encoder, decoder, n=10):
    for i in range(n):
        pair = random.choice(pairs)
        print('>', pair[0])
        print('=', pair[1])
        output_words, _ = evaluate(encoder, decoder, pair[0], input_lang, output_lang)
        output_sentence = ' '.join(output_words)
        print('<', output_sentence)
        print('')

encoder.eval()
decoder.eval()
evaluateRandomly(encoder, decoder)

> utne sobie drzemke
= i m going to go take a nap
< i m going to go take a nap wallet <EOS>

> nie jestem co do tego pewny to zalezy
= i m not sure about that it depends
< i m not sure about that it depends <EOS>

> nie kupujemy
= we re not buying
< we re not buying <EOS>

> nie jestem g upi
= i m not stupid
< i m not stupid <EOS>

> jestes wymagajacy
= you re demanding
< you re demanding <EOS>

> jestem m ody ale nie az tak
= i m young but i m not that young
< i m young but i m not that young <EOS>

> nie jestem ubrana
= i m not dressed
< i m not dressed <EOS>

> jestem gotowy sie z tym pogodzic
= i m ready to accept it
< i m ready to accept it <EOS>

> jestem pewny ze ona nied ugo wroci
= i m sure that she will come back soon
< i m sure that she will come back soon <EOS>

> w niedziele mam wolne
= i m free on sunday
< i m free on sunday <EOS>

def showAttention(input_sentence, output_words, attentions):
    fig = plt.figure()
    ax = fig.add_subplot(111)
    cax = ax.matshow(attentions.cpu().numpy(), cmap='bone')
    fig.colorbar(cax)

    # Set up axes
    ax.set_xticklabels([''] + input_sentence.split(' ') +
                       ['<EOS>'], rotation=90)
    ax.set_yticklabels([''] + output_words)

    # Show label at every tick
    ax.xaxis.set_major_locator(ticker.MultipleLocator(1))
    ax.yaxis.set_major_locator(ticker.MultipleLocator(1))

    plt.show()


def evaluateAndShowAttention(input_sentence):
    input_sentence = normalizeString(input_sentence)
    output_words, attentions = evaluate(encoder, decoder, input_sentence, input_lang, output_lang)
    print('input =', input_sentence)
    print('output =', ' '.join(output_words))
    showAttention(input_sentence, output_words, attentions[0, :len(output_words), :])

evaluateAndShowAttention('Nie jestem katoliczką')

input = nie jestem katoliczka
output = i m not catholic <EOS>

C:\Users\adamw\AppData\Local\Temp\ipykernel_17652\691622281.py:8: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
  ax.set_xticklabels([''] + input_sentence.split(' ') +
C:\Users\adamw\AppData\Local\Temp\ipykernel_17652\691622281.py:10: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
  ax.set_yticklabels([''] + output_words)

evaluateAndShowAttention('Przykro nam ze to sie zdarzyło')

input = przykro nam ze to sie zdarzy o
output = we re sorry that it happened <EOS>

C:\Users\adamw\AppData\Local\Temp\ipykernel_17652\691622281.py:8: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
  ax.set_xticklabels([''] + input_sentence.split(' ') +
C:\Users\adamw\AppData\Local\Temp\ipykernel_17652\691622281.py:10: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
  ax.set_yticklabels([''] + output_words)

evaluateAndShowAttention('On mówi płynnie po francusku')

input = on mowi p ynnie po francusku
output = he is fluent in french <EOS>

C:\Users\adamw\AppData\Local\Temp\ipykernel_17652\691622281.py:8: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
  ax.set_xticklabels([''] + input_sentence.split(' ') +
C:\Users\adamw\AppData\Local\Temp\ipykernel_17652\691622281.py:10: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
  ax.set_yticklabels([''] + output_words)

BLEU

def filter_rows(row):
    return len(row["eng"].split(' '))<MAX_LENGTH and \
                        len(row["pol"].split(' '))<MAX_LENGTH and \
                        row["eng"].startswith(eng_prefixes)

def evaluateWithTokenization(input_sentence):
    input_sentence = normalizeString(input_sentence)
    output_words, attentions = evaluate(encoder, decoder, input_sentence, input_lang, output_lang)
    if "<EOS>" in output_words:
        output_words.remove("<EOS>")
    return output_words

df = pd.read_csv("data/eng-pol.txt", sep='\t', names=["eng", "pol", "attribution"])
df["eng"] = df["eng"].apply(normalizeString)
df["pol"] = df["pol"].apply(normalizeString)
df_filtered = df.apply(filter_rows, axis=1)
test_df = df[df_filtered].sample(frac=1)
test_df["eng_token"] = test_df["eng"].apply(lambda x: x.split())
test_df["eng_eval"] = test_df["pol"].apply(lambda x: evaluateWithTokenization(x))

references_corpus = test_df["eng_token"].values.tolist()
candidate_corpus = test_df["eng_eval"].values.tolist()
references_corpus = [[el] for el in references_corpus]

bleu_score(candidate_corpus, references_corpus)

0.9301728010177612