removed bilstm
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William Falcon
parent
18eaa59c28
commit
64827b7029
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import torch.nn as nn
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import numpy as np
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from test_tube import HyperOptArgumentParser
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import torch
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from torch.autograd import Variable
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from sklearn.metrics import confusion_matrix, f1_score
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from torch.nn import functional as F
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class BiLSTMPack(nn.Module):
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"""
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Sample model to show how to define a template
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"""
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def __init__(self, hparams):
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# init superclass
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super(BiLSTMPack, self).__init__(hparams)
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self.hidden = None
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# trigger tag building
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self.ner_tagset = {'O': 0, 'I-Bio': 1}
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self.nb_tags = len(self.ner_tagset)
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# build model
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print('building model...')
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if hparams.model_load_weights_path is None:
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self.__build_model()
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print('model built')
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else:
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self = BiLSTMPack.load(hparams.model_load_weights_path, hparams.on_gpu, hparams)
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print('model loaded from: {}'.format(hparams.model_load_weights_path))
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def __build_model(self):
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"""
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Layout model
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:return:
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"""
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# design the number of final units
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self.output_dim = self.hparams.nb_lstm_units
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# when it's bidirectional our weights double
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if self.hparams.bidirectional:
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self.output_dim *= 2
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# total number of words
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total_words = len(self.tng_dataloader.dataset.words_token_to_idx)
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# word embeddings
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self.word_embedding = nn.Embedding(
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num_embeddings=total_words + 1,
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embedding_dim=self.hparams.embedding_dim,
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padding_idx=0
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)
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# design the LSTM
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self.lstm = nn.LSTM(
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self.hparams.embedding_dim,
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self.hparams.nb_lstm_units,
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num_layers=self.hparams.nb_lstm_layers,
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bidirectional=self.hparams.bidirectional,
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dropout=self.hparams.drop_prob,
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batch_first=True,
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)
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# map to tag space
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self.fc_out = nn.Linear(self.output_dim, self.out_dim)
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self.hidden_to_tag = nn.Linear(self.output_dim, self.nb_tags)
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def init_hidden(self, batch_size):
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# the weights are of the form (nb_layers * 2 if bidirectional, batch_size, nb_lstm_units)
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mult = 2 if self.hparams.bidirectional else 1
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hidden_a = torch.randn(self.hparams.nb_layers * mult, batch_size, self.nb_rnn_units)
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hidden_b = torch.randn(self.hparams.nb_layers * mult, batch_size, self.nb_rnn_units)
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if self.hparams.on_gpu:
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hidden_a = hidden_a.cuda()
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hidden_b = hidden_b.cuda()
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hidden_a = Variable(hidden_a)
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hidden_b = Variable(hidden_b)
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return (hidden_a, hidden_b)
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def forward(self, model_in):
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# layout data (expand it, etc...)
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# x = sequences
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x, seq_lengths = model_in
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batch_size, seq_len = x.size()
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# reset RNN hidden state
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self.hidden = self.init_hidden(batch_size)
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# embed
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x = self.word_embedding(x)
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# run through rnn using packed sequences
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x = torch.nn.utils.rnn.pack_padded_sequence(x, seq_lengths, batch_first=True)
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x, self.hidden = self.lstm(x, self.hidden)
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x, _ = torch.nn.utils.rnn.pad_packed_sequence(x, batch_first=True)
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# if asked for only last state, use the h_n which is the same as out(t=n)
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if not self.return_sequence:
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# pull out hidden states
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# h_n = (nb_directions * nb_layers, batch_size, emb_size)
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nb_directions = 2 if self.bidirectional else 1
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(h_n, _) = self.hidden
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# reshape to make indexing easier
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# forward = 0, backward = 1 (of nb_directions)
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h_n = h_n.view(self.nb_layers, nb_directions, batch_size, self.nb_rnn_units)
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# pull out last forward
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forward_h_n = h_n[-1, 0, :, :]
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x = forward_h_n
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# if bidirectional, also pull out the last hidden of backward network
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if self.bidirectional:
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backward_h_n = h_n[-1, 1, :, :]
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x = torch.cat([forward_h_n, backward_h_n], dim=1)
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# project to tag space
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x = x.contiguous()
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x = x.view(-1, self.output_dim)
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x = self.hidden_to_tag(x)
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return x
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def loss(self, model_out):
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# cross entropy loss
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logits, y = model_out
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y, y_lens = y
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# flatten y and logits
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y = y.view(-1)
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logits = logits.view(-1, self.nb_tags)
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# calculate a mask to remove padding tokens
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mask = (y >= 0).float()
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# count how many tokens we have
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num_tokens = int(torch.sum(mask).data[0])
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# pick the correct values and mask out
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logits = logits[range(logits.shape[0]), y] * mask
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# compute the ce loss
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ce_loss = -torch.sum(logits)/num_tokens
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return ce_loss
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def pull_out_last_embedding(self, x, seq_lengths, batch_size, on_gpu):
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# grab only the last activations from the non-padded ouput
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x_last = torch.zeros([batch_size, 1, x.size(-1)])
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for i, seq_len in enumerate(seq_lengths):
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x_last[i, :, :] = x[i, seq_len-1, :]
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# put on gpu when requested
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if on_gpu:
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x_last = x_last.cuda()
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# turn into torch var
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x_last = Variable(x_last)
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return x_last
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