import torch
import torch.nn as nn
import torch.optim as optim
import torch.utils.data as Data
torch.manual_seed(1)
IF_CUDA = False
if torch.cuda.is_available():
try:
IF_CUDA = True
except Exception as e: # 防止GPU佔用
print(e)
def argmax(vec):
# return the argmax as a python int
_, idx = torch.max(vec, 1)
return idx.item()
def trunc_pad(_list: list, max_len=100, if_sentence=True): # 設置輸入句子的最大長度
if len(_list) == max_len:
return _list
if len(_list) > max_len:
if if_sentence:
return ["[PAD]"] * 100
else:
return ["O"] * 100
else:
if if_sentence:
_list.extend(["[PAD]"] * (max_len - len(_list)))
return _list
else:
_list.extend(["O"] * (max_len - len(_list)))
return _list
def prepare_sequence(seq, to_ix):
idxs = [to_ix[w] for w in seq]
return torch.tensor(idxs, dtype=torch.long)
# Compute log sum exp in a numerically stable way for the forward algorithm
def log_sum_exp(vec):
max_score = vec[0, argmax(vec)]
max_score_broadcast = max_score.view(1, -1).expand(1, vec.size()[1])
return max_score + \
torch.log(torch.sum(torch.exp(vec - max_score_broadcast)))
def log_sum_exp_bacth(vec):
max_score_vec = torch.max(vec, dim=1)[0]
max_score_broadcast = max_score_vec.view(vec.shape[0], -1).expand(vec.shape[0], vec.size()[1])
return max_score_vec + torch.log(torch.sum(torch.exp(vec - max_score_broadcast), dim=1))
class BiLSTM_CRF(nn.Module):
def __init__(self, vocab_size, tag_to_ix, embedding_dim, hidden_dim):
super(BiLSTM_CRF, self).__init__()
self.embedding_dim = embedding_dim
self.hidden_dim = hidden_dim
self.vocab_size = vocab_size
self.tag_to_ix = tag_to_ix
self.tagset_size = len(tag_to_ix)
self.word_embeds = nn.Embedding(vocab_size, embedding_dim)
self.lstm = nn.LSTM(embedding_dim, hidden_dim // 2,
num_layers=1, bidirectional=True, batch_first=True)
# Maps the output of the LSTM into tag space.
self.hidden2tag = nn.Linear(hidden_dim, self.tagset_size)
# Matrix of transition parameters. Entry i,j is the score of
# transitioning *to* i *from* j.
self.transitions = nn.Parameter(
torch.randn(self.tagset_size, self.tagset_size))
# These two statements enforce the constraint that we never transfer
# to the start tag and we never transfer from the stop tag
self.transitions.data[tag_to_ix[START_TAG], :] = -10000
self.transitions.data[:, tag_to_ix[STOP_TAG]] = -10000
self.hidden = self.init_hidden()
def init_hidden(self, bacth=1):
return (torch.randn(2, bacth, self.hidden_dim // 2),
torch.randn(2, bacth, self.hidden_dim // 2))
def _forward_alg(self, batchfeats):
alpha_list = []
for feats in batchfeats:
# Do the forward algorithm to compute the partition function
init_alphas = torch.full((1, self.tagset_size), -10000.)
# START_TAG has all of the score.
init_alphas[0][self.tag_to_ix[START_TAG]] = 0.
# Wrap in a variable so that we will get automatic backprop
forward_var = init_alphas
# Iterate through the sentence
for feat in feats:
alphas_t = [] # The forward tensors at this timestep
for next_tag in range(self.tagset_size):
# broadcast the emission score: it is the same regardless of
# the previous tag
emit_score = feat[next_tag].view(
1, -1).expand(1, self.tagset_size)
# the ith entry of trans_score is the score of transitioning to
# next_tag from i
trans_score = self.transitions[next_tag].view(1, -1)
# The ith entry of next_tag_var is the value for the
# edge (i -> next_tag) before we do log-sum-exp
next_tag_var = forward_var + trans_score + emit_score
# The forward variable for this tag is log-sum-exp of all the
# scores.
alphas_t.append(log_sum_exp(next_tag_var).view(1))
forward_var = torch.cat(alphas_t).view(1, -1)
terminal_var = forward_var + self.transitions[self.tag_to_ix[STOP_TAG]]
alpha = log_sum_exp(terminal_var)
alpha_list.append(alpha.view(1))
return torch.cat(alpha_list)
def _forward_alg_parallel(self, feats):
# Do the forward algorithm to compute the partition function
if IF_CUDA:
init_alphas = torch.full((feats.shape[0], self.tagset_size), -10000.).cuda()
else:
init_alphas = torch.full((feats.shape[0], self.tagset_size), -10000.)
# START_TAG has all of the score.
init_alphas[:, self.tag_to_ix[START_TAG]] = 0.
# Wrap in a variable so that we will get automatic backprop
forward_var = init_alphas # [1,6]
convert_feats = feats.permute(1, 0, 2)
# Iterate through the sentence
for feat in convert_feats: # feat 6
alphas_t = [] # The forward tensors at this timestep
for next_tag in range(self.tagset_size):
# broadcast the emission score: it is the same regardless of
# the previous tag
emit_score = feat[:, next_tag].view(
feats.shape[0], -1).expand(feats.shape[0], self.tagset_size) # [1,6]
# the ith entry of trans_score is the score of transitioning to
# next_tag from i
trans_score = self.transitions[next_tag].view(1, -1).repeat(feats.shape[0], 1) # [1,6]
# The ith entry of next_tag_var is the value for the
# edge (i -> next_tag) before we do log-sum-exp
next_tag_var = forward_var + trans_score + emit_score # [1,6]
# The forward variable for this tag is log-sum-exp of all the
alphas_t.append(log_sum_exp_bacth(next_tag_var))
forward_var = torch.stack(alphas_t).permute(1, 0)
terminal_var = forward_var + self.transitions[self.tag_to_ix[STOP_TAG]].view(1, -1).repeat(feats.shape[0], 1)
alpha = log_sum_exp_bacth(terminal_var)
return alpha
def _get_lstm_features(self, sentence):
self.hidden = self.init_hidden(bacth=len(sentence))
embeds = self.word_embeds(sentence)
lstm_out, self.hidden = self.lstm(embeds, self.hidden)
lstm_feats = self.hidden2tag(lstm_out)
return lstm_feats
def _score_sentence(self, feats, tags):
# Gives the score of a provided tag sequence
totalsocre_list = []
for feat, tag in zip(feats, tags):
totalscore = torch.zeros(1)
tag = torch.cat([torch.tensor([self.tag_to_ix[START_TAG]], dtype=torch.long), tag])
for i, smallfeat in enumerate(feat):
totalscore = totalscore + \
self.transitions[tag[i + 1], tag[i]] + smallfeat[tag[i + 1]]
totalscore = totalscore + self.transitions[self.tag_to_ix[STOP_TAG], tag[-1]]
totalsocre_list.append(totalscore)
return torch.cat(totalsocre_list)
def _viterbi_decode(self, feats):
backpointers = []
# Initialize the viterbi variables in log space
init_vvars = torch.full((1, self.tagset_size), -10000.)
init_vvars[0][self.tag_to_ix[START_TAG]] = 0
# forward_var at step i holds the viterbi variables for step i-1
forward_var = init_vvars
for feat in feats:
bptrs_t = [] # holds the backpointers for this step
viterbivars_t = [] # holds the viterbi variables for this step
for next_tag in range(self.tagset_size):
# next_tag_var[i] holds the viterbi variable for tag i at the
# previous step, plus the score of transitioning
# from tag i to next_tag.
# We don't include the emission scores here because the max
# does not depend on them (we add them in below)
next_tag_var = forward_var + self.transitions[next_tag]
best_tag_id = argmax(next_tag_var)
bptrs_t.append(best_tag_id)
viterbivars_t.append(next_tag_var[0][best_tag_id].view(1))
# Now add in the emission scores, and assign forward_var to the set
# of viterbi variables we just computed
forward_var = (torch.cat(viterbivars_t) + feat).view(1, -1)
backpointers.append(bptrs_t)
# Transition to STOP_TAG
terminal_var = forward_var + self.transitions[self.tag_to_ix[STOP_TAG]]
best_tag_id = argmax(terminal_var)
path_score = terminal_var[0][best_tag_id]
# Follow the back pointers to decode the best path.
best_path = [best_tag_id]
for bptrs_t in reversed(backpointers):
best_tag_id = bptrs_t[best_tag_id]
best_path.append(best_tag_id)
# Pop off the start tag (we dont want to return that to the caller)
start = best_path.pop()
assert start == self.tag_to_ix[START_TAG] # Sanity check
best_path.reverse()
return path_score, best_path
def _viterbi_decode_predict(self, feats_list):
path_list = []
for feats in feats_list:
backpointers = []
# Initialize the viterbi variables in log space
init_vvars = torch.full((1, self.tagset_size), -10000.)
init_vvars[0][self.tag_to_ix[START_TAG]] = 0
# forward_var at step i holds the viterbi variables for step i-1
forward_var = init_vvars
for feat in feats:
bptrs_t = [] # holds the backpointers for this step
viterbivars_t = [] # holds the viterbi variables for this step
for next_tag in range(self.tagset_size):
# next_tag_var[i] holds the viterbi variable for tag i at the
# previous step, plus the score of transitioning
# from tag i to next_tag.
# We don't include the emission scores here because the max
# does not depend on them (we add them in below)
next_tag_var = forward_var + self.transitions[next_tag]
best_tag_id = argmax(next_tag_var)
bptrs_t.append(best_tag_id)
viterbivars_t.append(next_tag_var[0][best_tag_id].view(1))
# Now add in the emission scores, and assign forward_var to the set
# of viterbi variables we just computed
forward_var = (torch.cat(viterbivars_t) + feat).view(1, -1)
backpointers.append(bptrs_t)
# Transition to STOP_TAG
terminal_var = forward_var + self.transitions[self.tag_to_ix[STOP_TAG]]
best_tag_id = argmax(terminal_var)
path_score = terminal_var[0][best_tag_id]
# Follow the back pointers to decode the best path.
best_path = [best_tag_id]
for bptrs_t in reversed(backpointers):
best_tag_id = bptrs_t[best_tag_id]
best_path.append(best_tag_id)
# Pop off the start tag (we dont want to return that to the caller)
start = best_path.pop()
assert start == self.tag_to_ix[START_TAG] # Sanity check
best_path.reverse()
path_list.append(best_path)
return path_list
def neg_log_likelihood(self, sentence, tags):
feats = self._get_lstm_features(sentence)
forward_score = self._forward_alg_parallel(feats)
gold_score = self._score_sentence(feats, tags)
return torch.sum(forward_score - gold_score)
def forward(self, sentence): # dont confuse this with _forward_alg above.
# Get the emission scores from the BiLSTM
lstm_feats = self._get_lstm_features(sentence)
# Find the best path, given the features.
score, tag_seq = self._viterbi_decode(lstm_feats)
return score, tag_seq
def predict(self, sentence):
lstm_feats = self._get_lstm_features(sentence)
# Find the best path, given the features.
tag_seq_list = self._viterbi_decode_predict(lstm_feats)
return tag_seq_list
START_TAG = "<START>"
STOP_TAG = "<STOP>"
EMBEDDING_DIM = 128
HIDDEN_DIM = 100
BATCH_SIZE = 32 # 批訓練的數據個數
num_epochs = 100
with open("/home/wgh/desktop/loc.txt", encoding='utf8') as f:
word = []
label = []
word_set = []
label_set = []
data = f.readline().strip()
while data:
data = data.strip()
SP = data.split(' ')
if (len(SP) == 2):
word.append(SP[0])
label.append(SP[1])
else:
word_set.append(word)
word = []
label_set.append(label)
label = []
data = f.readline()
tag_to_ix = {"[PAD]": 0, "B-PRO": 1, "I-PRO": 2, "O": 3, START_TAG: 4, STOP_TAG: 5}
word_to_ix = {"[PAD]": 0}
for item_st in range(len(word_set)):
word_set[item_st] = trunc_pad(word_set[item_st], if_sentence=True)
label_set[item_st] = trunc_pad(label_set[item_st], if_sentence=False)
for sentence in word_set:
for word in sentence:
if word not in word_to_ix:
word_to_ix[word] = len(word_to_ix)
for i in range(len(word_set)):
word_set[i] = [word_to_ix[t] for t in word_set[i]]
label_set[i] = [tag_to_ix[t] for t in label_set[i]]
model = BiLSTM_CRF(len(word_to_ix), tag_to_ix, EMBEDDING_DIM, HIDDEN_DIM)
optimizer = optim.SGD(model.parameters(), lr=0.01, weight_decay=1e-4)
# 先轉換成 torch 能識別的 Dataset
torch_dataset = Data.TensorDataset(torch.tensor(word_set, dtype=torch.long), torch.tensor(label_set, dtype=torch.long))
# 把 dataset 放入 DataLoader
loader = Data.DataLoader(
dataset=torch_dataset, # torch TensorDataset format
batch_size=BATCH_SIZE, # mini batch size
shuffle=True, #
num_workers=2, # 多線程來讀數據
)
# Make up some training data
# Make sure prepare_sequence from earlier in the LSTM section is loaded
for epoch in range(num_epochs):
for step, (batch_x, batch_y) in enumerate(loader):
print(str(epoch) + '/' + str(num_epochs) + ':' + ' ' + str(step) + '/' + str(len(word_set) // BATCH_SIZE))
model.zero_grad()
loss = model.neg_log_likelihood(torch.tensor(batch_x, dtype=torch.long),
torch.tensor(batch_y, dtype=torch.long))
print(loss)
loss.backward()
optimizer.step()
torch.save(model, 'Model/bilstm_crf.pkl')
# model = torch.load('Model/bilstm_crf.pkl')
# Check predictions after training
with torch.no_grad():
precheck_sent = torch.tensor([word_set[0], word_set[1]], dtype=torch.long)
print(label_set[0], label_set[1])
print(model.predict(precheck_sent.view(len(precheck_sent), -1)))
