gitignore + first sources
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parent
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commit
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.DS_Store
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.idea
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*.log
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tmp/
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*.py[cod]
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*.egg
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build
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htmlcov
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*/__pycache__/
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import random
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from typing import Tuple
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import torch.nn as nn
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import torch.optim as optim
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import torch.nn.functional as F
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from torch import Tensor
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import torch
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#This file is based on the code developed by Sean Robertson. Go to https://github.com/spro/practical-pytorch for more information.
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class Encoder(nn.Module):
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def __init__(self,
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input_dim: int,
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emb_dim: int,
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enc_hid_dim: int,
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dec_hid_dim: int,
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dropout: float):
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super().__init__()
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self.input_dim = input_dim
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self.emb_dim = emb_dim
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self.enc_hid_dim = enc_hid_dim
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self.dec_hid_dim = dec_hid_dim
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self.dropout = dropout
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self.embedding = nn.Embedding(input_dim, emb_dim)
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self.rnn = nn.GRU(emb_dim, enc_hid_dim, bidirectional=True)
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self.fc = nn.Linear(enc_hid_dim * 2, dec_hid_dim)
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self.dropout = nn.Dropout(dropout)
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def forward(self,
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src: Tensor) -> Tuple[Tensor]:
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embedded = self.dropout(self.embedding(src))
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outputs, hidden = self.rnn(embedded)
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hidden = torch.tanh(self.fc(torch.cat((hidden[-2,:,:], hidden[-1,:,:]), dim = 1)))
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return outputs, hidden
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class Attention(nn.Module):
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def __init__(self,
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enc_hid_dim: int,
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dec_hid_dim: int,
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attn_dim: int):
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super().__init__()
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self.enc_hid_dim = enc_hid_dim
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self.dec_hid_dim = dec_hid_dim
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self.attn_in = (enc_hid_dim * 2) + dec_hid_dim
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self.attn = nn.Linear(self.attn_in, attn_dim)
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def forward(self,
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decoder_hidden: Tensor,
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encoder_outputs: Tensor) -> Tensor:
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src_len = encoder_outputs.shape[0]
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repeated_decoder_hidden = decoder_hidden.unsqueeze(1).repeat(1, src_len, 1)
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encoder_outputs = encoder_outputs.permute(1, 0, 2)
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energy = torch.tanh(self.attn(torch.cat((
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repeated_decoder_hidden,
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encoder_outputs),
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dim = 2)))
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attention = torch.sum(energy, dim=2)
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return F.softmax(attention, dim=1)
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class Decoder(nn.Module):
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def __init__(self,
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output_dim: int,
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emb_dim: int,
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enc_hid_dim: int,
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dec_hid_dim: int,
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dropout: int,
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attention: nn.Module):
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super().__init__()
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self.emb_dim = emb_dim
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self.enc_hid_dim = enc_hid_dim
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self.dec_hid_dim = dec_hid_dim
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self.output_dim = output_dim
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self.dropout = dropout
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self.attention = attention
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self.embedding = nn.Embedding(output_dim, emb_dim)
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self.rnn = nn.GRU((enc_hid_dim * 2) + emb_dim, dec_hid_dim)
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self.out = nn.Linear(self.attention.attn_in + emb_dim, output_dim)
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self.dropout = nn.Dropout(dropout)
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def _weighted_encoder_rep(self,
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decoder_hidden: Tensor,
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encoder_outputs: Tensor) -> Tensor:
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a = self.attention(decoder_hidden, encoder_outputs)
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a = a.unsqueeze(1)
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encoder_outputs = encoder_outputs.permute(1, 0, 2)
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weighted_encoder_rep = torch.bmm(a, encoder_outputs)
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weighted_encoder_rep = weighted_encoder_rep.permute(1, 0, 2)
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return weighted_encoder_rep
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def forward(self,
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input: Tensor,
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decoder_hidden: Tensor,
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encoder_outputs: Tensor) -> Tuple[Tensor]:
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input = input.unsqueeze(0)
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embedded = self.dropout(self.embedding(input))
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weighted_encoder_rep = self._weighted_encoder_rep(decoder_hidden,
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encoder_outputs)
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rnn_input = torch.cat((embedded, weighted_encoder_rep), dim = 2)
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output, decoder_hidden = self.rnn(rnn_input, decoder_hidden.unsqueeze(0))
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embedded = embedded.squeeze(0)
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output = output.squeeze(0)
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weighted_encoder_rep = weighted_encoder_rep.squeeze(0)
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output = self.out(torch.cat((output,
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weighted_encoder_rep,
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embedded), dim = 1))
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return output, decoder_hidden.squeeze(0)
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class Seq2Seq(nn.Module):
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def __init__(self,
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encoder: nn.Module,
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decoder: nn.Module,
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device: torch.device):
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super().__init__()
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self.encoder = encoder
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self.decoder = decoder
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self.device = device
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def forward(self,
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src: Tensor,
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trg: Tensor,
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teacher_forcing_ratio: float = 0.5) -> Tensor:
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batch_size = src.shape[1]
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max_len = trg.shape[0]
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trg_vocab_size = self.decoder.output_dim
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outputs = torch.zeros(max_len, batch_size, trg_vocab_size).to(self.device)
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encoder_outputs, hidden = self.encoder(src)
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# first input to the decoder is the <sos> token
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output = trg[0,:]
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for t in range(1, max_len):
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output, hidden = self.decoder(output, hidden, encoder_outputs)
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outputs[t] = output
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teacher_force = random.random() < teacher_forcing_ratio
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top1 = output.max(1)[1]
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output = (trg[t] if teacher_force else top1)
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return outputs
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from torchtext.data.utils import _basic_english_normalize
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import torch
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import random
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from argparse import ArgumentParser
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from model import *
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from spacy.tokenizer import Tokenizer
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from spacy.lang.eu import Basque
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nlp = Basque()
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def tokenizer(s):
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return list(map(lambda x: x.text, nlp(s)))
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parser = ArgumentParser(description='Azpitituluetan oinarritutako elkarrizketa \
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sistemaren proba')
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parser.add_argument('-decoding_strategy', type=str, default='top1', choices=['top1', 'topk', 'multinomial'])
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args = parser.parse_args()
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def decode(logits, decoding_strategy='max', k=3, temp=0.4):
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if decoding_strategy=='top1':
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target = logits.max(1)[1]
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elif decoding_strategy=='topk':
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target = logits.topk(k)[1][0][random.randint(0, k-1)].unsqueeze(-1)
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else:
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target = torch.multinomial(logits.squeeze().div(temp).exp().cpu(), 1)
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return target
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def evaluate(sentence):
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with torch.no_grad():
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sentence = '<sos> ' + sentence + ' <eos>'
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sent_len = len(sentence.split())
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sentence = torch.Tensor([text_field.vocab.stoi[i] for i in sentence.lower().split()]).long().view(sent_len, 1)
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target = torch.Tensor([text_field.vocab.stoi['<sos>']]).long()
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output_sentence = ''
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encoder_outputs, hidden = model.encoder(sentence)
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for t in range(MAX_LENGTH):
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# first input to the decoder is the <sos> token
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output, hidden = model.decoder(target, hidden, encoder_outputs)
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target = decode(output, decoding_strategy)
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word = text_field.vocab.itos[target.numpy()[0]]
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if word == '<eos>':
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return output_sentence
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else:
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output_sentence = output_sentence + ' ' + word
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return output_sentence
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#Load model and fields
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text_field = torch.load('../model/text_field.Field')
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model = torch.load('../model/model.pt', map_location=torch.device('cpu'))
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torch.nn.Module.dump_patches = True
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MAX_LENGTH = 10
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#Print welcome message
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print('-----------------------------------------')
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print(' Ongi etorri elkarrizketara.')
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print("Idatz ezazu 'Agur' elkarrizketa bukatzeko.")
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print('------------------------------------------')
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#Main system loop
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user = input('- ')
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model.eval()
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decoding_strategy = args.decoding_strategy
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while user != 'Agur' and user != 'Agur':
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sentence = evaluate(' '.join(tokenizer(user)))
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print('-' + sentence.strip().capitalize())
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user = input('-')
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sentence = evaluate(' '.join(tokenizer(user)))
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print('-' + sentence.strip().capitalize())
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from torchtext.data import Field, BucketIterator, TabularDataset
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import torch
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from torchtext import data
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from model import Seq2Seq, Encoder, Decoder, Attention
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import math
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import time
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from tqdm import tqdm
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import torch.nn as nn
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import torch.optim as optim
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import torch.nn.functional as F
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from spacy.tokenizer import Tokenizer
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from spacy.lang.eu import Basque
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nlp = Basque()
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def tokenizer(s):
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return list(map(lambda x: x.text, nlp(s)))
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text_field = Field(init_token = '<sos>',
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eos_token = '<eos>',lower=True,
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tokenize=tokenizer, tokenizer_language='eu')
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fields = [('query', text_field), ('answer', text_field)]
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train_data = TabularDataset(path='../data/eu_train.tsv', format='tsv', fields=fields)
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text_field.build_vocab(train_data, min_freq=5)
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print("Vocabulary has been built")
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print("Vocab len is {}".format(len(text_field.vocab)))
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#Save the text field for testing
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torch.save(text_field, '../model/text_field.Field')
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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BATCH_SIZE = 32
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train_iterator = BucketIterator(
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dataset=train_data,
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batch_size=BATCH_SIZE,
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sort_key=lambda x: data.interleave_keys(len(x.query), len(x.answer)),
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device=device)
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#Tamainak egokitu zuen beharretara
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INPUT_DIM = len(text_field.vocab)
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OUTPUT_DIM = len(text_field.vocab)
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ENC_EMB_DIM = 32
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DEC_EMB_DIM = 32
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ENC_HID_DIM = 64
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DEC_HID_DIM = 64
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ATTN_DIM = 8
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ENC_DROPOUT = 0.5
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DEC_DROPOUT = 0.5
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enc = Encoder(INPUT_DIM, ENC_EMB_DIM, ENC_HID_DIM, DEC_HID_DIM, ENC_DROPOUT)
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attn = Attention(ENC_HID_DIM, DEC_HID_DIM, ATTN_DIM)
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dec = Decoder(OUTPUT_DIM, DEC_EMB_DIM, ENC_HID_DIM, DEC_HID_DIM, DEC_DROPOUT, attn)
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model = Seq2Seq(enc, dec, device).to(device)
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def init_weights(m: nn.Module):
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for name, param in m.named_parameters():
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if 'weight' in name:
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nn.init.normal_(param.data, mean=0, std=0.01)
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else:
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nn.init.constant_(param.data, 0)
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model.apply(init_weights)
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optimizer = optim.Adam(model.parameters())
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def count_parameters(model: nn.Module):
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return sum(p.numel() for p in model.parameters() if p.requires_grad)
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print(f'The model has {count_parameters(model):,} trainable parameters')
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PAD_IDX = text_field.vocab.stoi['<pad>']
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criterion = nn.CrossEntropyLoss(ignore_index=PAD_IDX)
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def train(model: nn.Module,
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iterator: BucketIterator,
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optimizer: optim.Optimizer,
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criterion: nn.Module,
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clip: float):
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model.train()
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epoch_loss = 0
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for _, batch in tqdm(enumerate(iterator),total=len(iterator)):
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src = batch.query
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trg = batch.answer
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optimizer.zero_grad()
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output = model(src, trg)
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output = output[1:].view(-1, output.shape[-1])
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trg = trg[1:].view(-1)
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loss = criterion(output, trg)
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loss.backward()
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torch.nn.utils.clip_grad_norm_(model.parameters(), clip)
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optimizer.step()
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epoch_loss += loss.item()
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return epoch_loss / len(iterator)
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def epoch_time(start_time: int,
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end_time: int):
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elapsed_time = end_time - start_time
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elapsed_mins = int(elapsed_time / 60)
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elapsed_secs = int(elapsed_time - (elapsed_mins * 60))
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return elapsed_mins, elapsed_secs
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N_EPOCHS = 10
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CLIP = 1
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best_valid_loss = float('inf')
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for epoch in tqdm(range(N_EPOCHS)):
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start_time = time.time()
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train_loss = train(model, train_iterator, optimizer, criterion, CLIP)
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end_time = time.time()
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epoch_mins, epoch_secs = epoch_time(start_time, end_time)
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print(f'Epoch: {epoch+1:02} | Time: {epoch_mins}m {epoch_secs}s')
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print(f'\tTrain Loss: {train_loss:.3f} | Train PPL: {math.exp(train_loss):7.3f}')
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# Save checkpoint
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torch.save(model, '../model/model.pt')
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