def model_perplexity(
model,
src,
src_test,
trg,
trg_test,
config,
loss_criterion,
src_valid=None,
trg_valid=None,
verbose=False,
):
"""Compute model perplexity."""
# Get source minibatch
losses = []
for j in xrange(0,
len(src_test['data']) // 100,
config['data']['batch_size']):
input_lines_src, output_lines_src, lens_src, mask_src = get_minibatch(
src_test['data'],
src['word2id'],
j,
config['data']['batch_size'],
config['data']['max_src_length'],
add_start=True,
add_end=True)
input_lines_src = Variable(input_lines_src.data, volatile=True)
output_lines_src = Variable(input_lines_src.data, volatile=True)
mask_src = Variable(mask_src.data, volatile=True)
# Get target minibatch
input_lines_trg_gold, output_lines_trg_gold, lens_src, mask_src = (
get_minibatch(trg_test['data'],
trg['word2id'],
j,
config['data']['batch_size'],
config['data']['max_trg_length'],
add_start=True,
add_end=True))
input_lines_trg_gold = Variable(input_lines_trg_gold.data,
volatile=True)
output_lines_trg_gold = Variable(output_lines_trg_gold.data,
volatile=True)
mask_src = Variable(mask_src.data, volatile=True)
decoder_logit = model(input_lines_src, input_lines_trg_gold)
loss = loss_criterion(
decoder_logit.contiguous().view(-1, decoder_logit.size(2)),
output_lines_trg_gold.view(-1))
losses.append(loss.data[0])
return np.exp(np.mean(losses))
def translate(self):
"""Translate the whole dataset."""
trg_preds = []
trg_gold = []
output_res = open(self.output,'w')
for j in xrange(
0, len(self.src['data']),
self.config['data']['batch_size']
):
"""Decode a single minibatch."""
print('Decoding %d out of %d ' % (j, len(self.src['data'])))
hypotheses, scores = decoder.decode_batch(j)
all_hyp_inds = [[x[0] for x in hyp] for hyp in hypotheses]
all_preds = [
' '.join([trg['id2word'][x] for x in hyp[:-1]])
for hyp in all_hyp_inds
]
# Get target minibatch
input_lines_trg_gold, output_lines_trg_gold, lens_src, mask_src = (
get_minibatch(
self.trg['data'], self.tgt_dict, j,
self.config['data']['batch_size'],
self.config['data']['max_trg_length'],
is_gui=False, add_start=True, add_end=True
)
)
output_lines_trg_gold = output_lines_trg_gold.data.cpu().numpy()
all_gold_inds = [[x for x in hyp] for hyp in output_lines_trg_gold]
all_gold = [
' '.join([trg['id2word'][x] for x in hyp[:-1]])
for hyp in all_gold_inds
]
trg_preds += all_preds
trg_gold += all_gold
output_res.writelines('\n'.join(trg_preds))
bleu_score = get_bleu(trg_preds, trg_gold)
output_res.close()
print('BLEU : %.5f ' % (bleu_score))
def translate(self):
"""Translate the whole dataset."""
trg_preds = []
trg_gold = []
for j in range(
0, len(self.src['data']),
self.config['data']['batch_size']
):
"""Decode a single minibatch."""
print('Decoding %d out of %d ' % (j, len(self.src['data'])))
hypotheses, scores = self.decode_batch(j)
all_hyp_inds = [[x[0] for x in hyp] for hyp in hypotheses]
all_preds = [
' '.join([self.trg['id2word'][x] for x in hyp])
for hyp in all_hyp_inds
]
# Get target minibatch
input_lines_trg_gold, output_lines_trg_gold, lens_src, mask_src = (
get_minibatch(
self.trg['data'], self.tgt_dict, j,
self.config['data']['batch_size'],
self.config['data']['max_trg_length'],
add_start=True, add_end=True, use_cuda=self.use_cuda
)
)
output_lines_trg_gold = output_lines_trg_gold.data.cpu().numpy()
all_gold_inds = [[x for x in hyp] for hyp in output_lines_trg_gold]
all_gold = [
' '.join([self.trg['id2word'][x] for x in hyp])
for hyp in all_gold_inds
]
trg_preds += all_preds
trg_gold += all_gold
print("investigate some preds and golds.....")
print("trg_preds: ", trg_preds[0])
print("trg_gold: ", trg_gold[0])
bleu_score = get_bleu(trg_preds, trg_gold)
# print('BLEU : %.5f ' % (bleu_score))
return bleu_score
def translate(self):
"""Translate the whole dataset."""
trg_preds = []
trg_gold = []
for j in xrange(
0, len(self.src['data']),
self.config['data']['batch_size']
):
"""Decode a single minibatch."""
#print 'Decoding %d out of %d ' % (j, len(self.src['data']))
hypotheses, scores = decoder.decode_batch(j)
all_hyp_inds = [[x[0] for x in hyp] for hyp in hypotheses]
all_preds = [
' '.join([trg['id2word'][x.item()] for x in hyp])
for hyp in all_hyp_inds
]
# Get target minibatch
input_lines_trg_gold, output_lines_trg_gold, lens_src, mask_src = (
get_minibatch(
self.trg['data'], self.tgt_dict, j,
self.config['data']['batch_size'],
self.config['data']['max_trg_length'],
add_start=True, add_end=True
)
)
output_lines_trg_gold = output_lines_trg_gold.data.cpu().numpy()
all_gold_inds = [[x for x in hyp] for hyp in output_lines_trg_gold]
all_gold = [
' '.join([trg['id2word'][x] for x in hyp])
for hyp in all_gold_inds
]
trg_preds += all_preds
trg_gold += all_gold
for p in all_preds:
print p.replace('<s>', '').replace('</s>', '').strip()
def decode_batch(self, idx):
"""Decode a minibatch."""
# Get source minibatch
input_lines_src, output_lines_src, lens_src, mask_src = get_minibatch(
self.src['data'],
self.src_dict,
idx,
self.config['data']['batch_size'],
self.config['data']['max_src_length'],
add_start=True,
add_end=True)
#print(self.src_dict)
'''
lines = [
['<s>'] + line + ['</s>']
for line in self.src['data'][idx:idx + self.config['data']['max_src_length']]
]
lines = [line[:self.config['data']['max_src_length']] for line in lines]
lens = [len(line) for line in lines]
max_len = max(lens)
word2ind = self.src_dict
input_lines = [
[word2ind[w] if w in word2ind else word2ind['<unk>'] for w in line[:-1]] +
[word2ind['<pad>']] * (max_len - len(line))
for line in lines
]
#print(len(input_lines))
#print(input_lines_src[0])
'''
#id2word_src = {v: k for k, v in self.src_dict.iteritems()}
#inp = input_lines_src[0].data.cpu().numpy().tolist()
#print([inv_dict[a] for a in inp])
beam_size = self.beam_size
# (1) run the encoder on the src
context_h, (
context_h_t,
context_c_t) = self.get_hidden_representation(input_lines_src)
context_h = context_h.transpose(0, 1) # Make things sequence first.
# (3) run the decoder to generate sentences, using beam search
batch_size = context_h.size(1)
# Expand tensors for each beam.
context = Variable(context_h.data.repeat(1, beam_size, 1))
#print context.size()
dec_states = [
Variable(context_h_t.data.repeat(1, beam_size, 1)),
Variable(context_c_t.data.repeat(1, beam_size, 1))
]
finite_state_machines = [
FSMBeamSearch(input_lines_src[k][1:]) for k in range(batch_size)
]
beam = [
Beam(beam_size,
self.tgt_dict,
self.id2word_src,
trg['id2word'],
cuda=True) for k in range(batch_size)
]
'''fsms = []
for k in range(batch_size):
tuple = input_lines_src.data[k][1:]
constraint_ids = filter(lambda s: s != self.src_dict["EmptyParameter"], tuple)
fsm = FSMBeamSearch(tuple, constraint_ids, beam_size, self.tgt_dict,
self.id2word_src, trg['id2word'], cuda=True)
fsms.append(fsm)'''
dec_out = self.get_init_state_decoder(dec_states[0].squeeze(0))
dec_states[0] = dec_out
#print(dec_states[0].size())
batch_idx = list(range(batch_size))
remaining_sents = batch_size
for i in range(batch_size):
# initial state is 0000
# and if some parameters are EmptyParameter
# if tuple is (xxx, yyy, EmptyParameter, EmptyParameter)
# then the state will be 1100
state = 0
input_event = [
self.id2word_src[e]
for e in input_lines_src[i].data.cpu().numpy().tolist()
][1:]
#print(input_event)
for j, evt in enumerate(input_event):
if evt == "EmptyParameter" or evt == "<unk>":
state = state | (1 << j)
#this state will be the initial state
# print(i, state, input_event)
finite_state_machines[i].beams[state] = beam[i]
finite_state_machines[i].evt_tokens = input_event
finite_state_machines[i].num_states += 1
finite_state_machines[i].dec_states[state] = [
dec_states[0][i * beam_size:(i + 1) * beam_size, :].clone(),
dec_states[1][:, i * beam_size:(i + 1) * beam_size, :].clone()
]
finite_state_machines[i].context[state] = context[0][
i * beam_size:(i + 1) * beam_size, :].clone()
for i in range(self.config['data']['max_trg_length']):
#print(i)
current_states = []
for b in finite_state_machines:
if not b.done:
current_states += b.get_current_state()
input = torch.stack(current_states).t().contiguous().view(1, -1)
trg_emb = self.model.trg_embedding(Variable(input).transpose(1, 0))
#print trg_emb.size()
#print dec_states[0].size(), dec_states[1].size()
#print context.size()
trg_h, (trg_h_t, trg_c_t) = self.model.decoder(
trg_emb, (dec_states[0].squeeze(0), dec_states[1].squeeze(0)),
context)
dec_states = (trg_h_t.unsqueeze(0), trg_c_t.unsqueeze(0))
dec_out = trg_h_t.squeeze(1).view(-1, self.model.trg_hidden_dim)
#print dec_out.size()
out = F.softmax(self.model.decoder2vocab(dec_out)).unsqueeze(0)
word_lk = out.view(beam_size, len(current_states),
-1).transpose(0, 1).contiguous()
active = []
cur = 0
for b in range(batch_size):
if finite_state_machines[b].done:
continue
idx = batch_idx[b]
#print(idx, len(lines), input_lines_src.size())
cur_state_size = finite_state_machines[b].num_states
# print(b, cur_state_size)
# print(dec_states[0][:,cur*self.beam_size:(cur+cur_state_size)*self.beam_size,:].size(), dec_states[1][:,cur*self.beam_size:(cur+cur_state_size)*self.beam_size,:].size())
# print(context[:,cur*self.beam_size:(cur+cur_state_size)*self.beam_size,:].size())
if not finite_state_machines[b].advance(
word_lk.data[cur:(cur + cur_state_size)],
input_lines_src[idx],
[
dec_states[0][:, cur *
self.beam_size:(cur + cur_state_size) *
self.beam_size, :].clone(),
dec_states[1][:, cur *
self.beam_size:(cur + cur_state_size) *
self.beam_size, :].clone()
], context[:, cur * self.beam_size:(cur + cur_state_size) *
self.beam_size, :].clone()):
active += [b]
cur += cur_state_size
# for dec_state in dec_states: # iterate over h, c
# # layers x beam*sent x dim
# #print dec_state.size(1), dec_state.size(2), dec_state.size(3)
# state_size = dec_state.size(1) * dec_state.size(3) if self.model.nlayers_trg > 1 else dec_state.size(2)
# sent_states = dec_state.view(
# -1, beam_size, len(current_states), state_size
# )[:, :, idx]
# sent_states.data.copy_(
# sent_states.data.index_select(
# 1,
# beam[b].get_current_origin()
# )
# )
if not active:
break
# in this section, the sentences that are still active are
# compacted so that the decoder is not run on completed sentences
active_idx = torch.cuda.LongTensor([batch_idx[k] for k in active])
batch_idx = {beam: idx for idx, beam in enumerate(active)}
# def update_active(t):
# # select only the remaining active sentences
# view = t.data.view(
# -1, remaining_sents,
# self.model.decoder.hidden_size
# )
# new_size = list(t.size())
# new_size[-2] = new_size[-2] * len(active_idx) \
# // remaining_sents
# return Variable(view.index_select(
# 1, active_idx
# ).view(*new_size))
new_dec_states0 = None
new_dec_states1 = None
new_context = None
#update active dec_states
for k in range(batch_size):
if not finite_state_machines[k].done:
fsm_dec_states0, fsm_dec_states1 = finite_state_machines[
k].get_dec_states()
if new_dec_states0 is None:
new_dec_states0 = fsm_dec_states0.clone()
else:
new_dec_states0 = torch.cat(
(new_dec_states0, fsm_dec_states0.clone()), 1)
if new_dec_states1 is None:
new_dec_states1 = fsm_dec_states1.clone()
else:
new_dec_states1 = torch.cat(
(new_dec_states1.clone(), fsm_dec_states1.clone()),
1)
fsm_context = finite_state_machines[k].get_context()
if new_context is None:
new_context = fsm_context.clone()
else:
new_context = torch.cat(
(new_context, fsm_context.clone()), 1)
dec_states = (new_dec_states0, new_dec_states1)
context = new_context
remaining_sents = len(active)
# (4) package everything up
allHyp, allScores = [], []
n_best = 1
for b in range(batch_size):
hyps, scores = finite_state_machines[b].get_hyp()
hyps = [(hyp[0].item(), ) for hyp in hyps]
allScores += [scores]
allHyp += [hyps]
return allHyp, allScores
def translate(self):
"""Evaluate model."""
preds = []
ground_truths = []
for j in xrange(0, len(self.src['data']),
self.config['data']['batch_size']):
#print 'Decoding : %d out of %d ' % (j, len(self.src['data']))
# Get source minibatch
input_lines_src, output_lines_src, lens_src, mask_src = (
get_minibatch(self.src['data'],
self.src['word2id'],
j,
self.config['data']['batch_size'],
self.config['data']['max_src_length'],
add_start=True,
add_end=True))
input_lines_src = Variable(input_lines_src.data, volatile=True)
output_lines_src = Variable(output_lines_src.data, volatile=True)
mask_src = Variable(mask_src.data, volatile=True)
# Get target minibatch
input_lines_trg_gold, output_lines_trg_gold, lens_src, mask_src = (
get_minibatch(self.trg['data'],
self.trg['word2id'],
j,
self.config['data']['batch_size'],
self.config['data']['max_trg_length'],
add_start=True,
add_end=True))
input_lines_trg_gold = Variable(input_lines_trg_gold.data,
volatile=True)
output_lines_trg_gold = Variable(output_lines_trg_gold.data,
volatile=True)
mask_src = Variable(mask_src.data, volatile=True)
# Initialize target with <s> for every sentence
input_lines_trg = Variable(torch.LongTensor(
[[trg['word2id']['<s>']]
for i in xrange(input_lines_src.size(0))]),
volatile=True).cuda()
# Decode a minibatch greedily add beam search decoding
input_lines_trg = self.decode_minibatch(input_lines_src,
input_lines_trg,
output_lines_trg_gold)
# Copy minibatch outputs to cpu and convert ids to words
input_lines_trg = input_lines_trg.data.cpu().numpy()
input_lines_trg = [[self.trg['id2word'][x] for x in line]
for line in input_lines_trg]
# Do the same for gold sentences
output_lines_trg_gold = output_lines_trg_gold.data.cpu().numpy()
output_lines_trg_gold = [[self.trg['id2word'][x] for x in line]
for line in output_lines_trg_gold]
# Process outputs
for sentence_pred, sentence_real, sentence_real_src in zip(
input_lines_trg, output_lines_trg_gold, output_lines_src):
if '</s>' in sentence_pred:
index = sentence_pred.index('</s>')
else:
index = len(sentence_pred)
preds.append(['<s>'] + sentence_pred[:index + 1])
if '</s>' in sentence_real:
index = sentence_real.index('</s>')
else:
index = len(sentence_real)
ground_truths.append(['<s>'] + sentence_real[:index + 1])
#trg_preds += preds
#trg_gold += all_gold
for p in preds:
print " ".join(p).replace('</s>', '').strip()
def predict(self, semantic_frame):
intent = semantic_frame['diaact']
if 'request' in semantic_frame['diaact']:
slots = [intent] + [
k for k in list(semantic_frame['request_slots'].keys())
]
slot2word = semantic_frame['request_slots']
else:
slots = [intent] + [
k for k in list(semantic_frame['inform_slots'].keys())
]
slot2word = semantic_frame['inform_slots']
src_new = {
'data': [slots],
'word2id': self.src_word2id,
'id2word': self.src_id2word
}
trg_new = {
'data': [['人生', '好', '困難', '到底', '該', '怎麼辦', '呢']],
'word2id': self.trg_word2id,
'id2word': self.trg_id2word
}
preds = []
input_lines_src, output_lines_src, lens_src, mask_src = get_minibatch(
src_new['data'],
src_new['word2id'],
0,
1,
10,
add_start=True,
add_end=True)
input_lines_trg_gold, output_lines_trg_gold, lens_trg, mask_trg = get_minibatch(
trg_new['data'],
trg_new['word2id'],
0,
1,
20,
add_start=True,
add_end=True)
# Initialize target with <s> for every sentence
input_lines_trg = Variable(
torch.LongTensor([[self.trg_word2id['<s>']]
for i in range(input_lines_src.size(0))]))
input_lines_trg = decode_minibatch(self.config, self.model,
input_lines_src, input_lines_trg,
output_lines_trg_gold)
# Copy minibatch outputs to cpu and convert ids to words
input_lines_trg = input_lines_trg.data.cpu().numpy()
input_lines_trg = [[self.trg_id2word[x] for x in line]
for line in input_lines_trg]
output_lines_trg_gold = output_lines_trg_gold.data.cpu().numpy()
output_lines_trg_gold = [[self.trg_id2word[x] for x in line]
for line in output_lines_trg_gold]
# Process outputs
for sentence_pred, sentence_real, sentence_real_src in zip(
input_lines_trg, output_lines_trg_gold, output_lines_src):
if '</s>' in sentence_pred:
index = sentence_pred.index('</s>')
else:
index = len(sentence_pred)
preds.append([
slot2word[word] if word in slot2word.keys() else word
for word in sentence_pred[:index + 1]
])
"""
print("Predict: {}".format(' '.join(sentence_pred[:index + 1])))
if '</s>' in sentence_real:
index = sentence_real.index('</s>')
else:
index = len(sentence_real)
print("RealAns: {}".format(' '.join(['<s>'] + sentence_real[:index + 1])))
print('===========================================')
"""
return ''.join(preds[0][1:-1])
def decode_batch(self, idx):
"""Decode a minibatch."""
# Get source minibatch
input_lines_src, output_lines_src, lens_src, mask_src = get_minibatch(
self.src['data'],
self.src_dict,
idx,
self.config['data']['batch_size'],
self.config['data']['max_src_length'],
add_start=True,
add_end=True)
beam_size = self.beam_size
# (1) run the encoder on the src
context_h, (
context_h_t,
context_c_t) = self.get_hidden_representation(input_lines_src)
context_h = context_h.transpose(0, 1) # Make things sequence first.
# (3) run the decoder to generate sentences, using beam search
batch_size = context_h.size(1)
# Expand tensors for each beam.
context = Variable(context_h.data.repeat(1, beam_size, 1))
dec_states = [
Variable(context_h_t.data.repeat(1, beam_size, 1)),
Variable(context_c_t.data.repeat(1, beam_size, 1))
]
beam = [
Beam(beam_size, self.tgt_dict, cuda=True)
for k in range(batch_size)
]
dec_out = self.get_init_state_decoder(dec_states[0].squeeze(0))
dec_states[0] = dec_out
batch_idx = list(range(batch_size))
remaining_sents = batch_size
for i in range(self.config['data']['max_trg_length']):
input = torch.stack([
b.get_current_state() for b in beam if not b.done
]).t().contiguous().view(1, -1)
trg_emb = self.model.trg_embedding(Variable(input).transpose(1, 0))
trg_h, (trg_h_t, trg_c_t) = self.model.decoder(
trg_emb, (dec_states[0].squeeze(0), dec_states[1].squeeze(0)),
context)
dec_states = (trg_h_t.unsqueeze(0), trg_c_t.unsqueeze(0))
dec_out = trg_h_t.squeeze(1)
out = F.softmax(self.model.decoder2vocab(dec_out)).unsqueeze(0)
word_lk = out.view(beam_size, remaining_sents,
-1).transpose(0, 1).contiguous()
active = []
for b in range(batch_size):
if beam[b].done:
continue
idx = batch_idx[b]
if not beam[b].advance(word_lk.data[idx]):
active += [b]
for dec_state in dec_states: # iterate over h, c
# layers x beam*sent x dim
sent_states = dec_state.view(-1, beam_size,
remaining_sents,
dec_state.size(2))[:, :, idx]
sent_states.data.copy_(
sent_states.data.index_select(
1, beam[b].get_current_origin()))
if not active:
break
# in this section, the sentences that are still active are
# compacted so that the decoder is not run on completed sentences
active_idx = torch.cuda.LongTensor([batch_idx[k] for k in active])
batch_idx = {beam: idx for idx, beam in enumerate(active)}
def update_active(t):
# select only the remaining active sentences
view = t.data.view(-1, remaining_sents,
self.model.decoder.hidden_size)
new_size = list(t.size())
new_size[-2] = new_size[-2] * len(active_idx) \
// remaining_sents
return Variable(
view.index_select(1, active_idx).view(*new_size))
dec_states = (update_active(dec_states[0]),
update_active(dec_states[1]))
dec_out = update_active(dec_out)
context = update_active(context)
remaining_sents = len(active)
# (4) package everything up
allHyp, allScores = [], []
n_best = 1
for b in range(batch_size):
scores, ks = beam[b].sort_best()
allScores += [scores[:n_best]]
hyps = zip(*[beam[b].get_hyp(k) for k in ks[:n_best]])
allHyp += [hyps]
return allHyp, allScores
def translate(self):
"""Evaluate model."""
preds = []
ground_truths = []
out_put = open(self.output,'w')
for j in xrange(0, len(self.src['data']),self.config['data']['batch_size']):
print('Decoding : %d out of %d ' % (j, len(self.src['data'])))
# Get source minibatch
input_lines_src, output_lines_src, lens_src, mask_src = (
get_minibatch(
self.src['data'], self.src['word2id'], j,
self.config['data']['batch_size'],
self.config['data']['max_src_length'],
is_gui=False, add_start=True, add_end=True
)
)
#if input_lines_src.size(0) != self.config['data']['batch_size']:
# break
input_lines_src = Variable(input_lines_src.data, volatile=True)
output_lines_src = Variable(output_lines_src.data, volatile=True)
mask_src = Variable(mask_src.data, volatile=True)
# Get target minibatch
input_lines_trg_gold, output_lines_trg_gold, lens_src, mask_src = (
get_minibatch(
self.trg['data'], self.trg['word2id'], j,
self.config['data']['batch_size'],
self.config['data']['max_trg_length'],
is_gui=False, add_start=True, add_end=True
)
)
input_lines_trg_gold = Variable(input_lines_trg_gold.data, volatile=True)
output_lines_trg_gold = Variable(output_lines_trg_gold.data, volatile=True)
mask_src = Variable(mask_src.data, volatile=True)
input_lines_gui, output_lines_gui, lens_gui, mask_gui, input_type_gui = get_minibatch(
self.gui['data'], self.gui['word2id'], j, self.config['data']['batch_size'],
self.config['data']['max_gui_length'], is_gui=True, add_start=True, add_end=True, line_types=self.gui['type']
)
input_lines_gui = Variable(input_lines_gui.data, volatile=True)
output_lines_gui = Variable(output_lines_gui.data, volatile=True)
input_type_gui = Variable(input_type_gui.data, volatile=True)
mask_gui = Variable(mask_gui.data, volatile=True)
# Initialize target with <s> for every sentence
input_lines_trg = Variable(torch.LongTensor(
[
[trg['word2id']['<s>']]
for i in xrange(input_lines_src.size(0))
]
), volatile=True).cuda()
# Decode a minibatch greedily __TODO__ add beam search decoding
input_lines_trg = self.decode_minibatch(
input_lines_src, input_lines_trg,
input_lines_gui,
input_type_gui,
output_lines_trg_gold
)
# Copy minibatch outputs to cpu and convert ids to words
input_lines_trg = input_lines_trg.data.cpu().numpy()
input_lines_trg = [
[self.trg['id2word'][x] for x in line]
for line in input_lines_trg
]
# Do the same for gold sentences
output_lines_trg_gold = output_lines_trg_gold.data.cpu().numpy()
output_lines_trg_gold = [
[self.trg['id2word'][x] for x in line]
for line in output_lines_trg_gold
]
# Process outputs
for sentence_pred, sentence_real, sentence_real_src in zip(
input_lines_trg,
output_lines_trg_gold,
output_lines_src
):
if '</s>' in sentence_pred:
index = sentence_pred.index('</s>')
else:
index = len(sentence_pred)
preds.append(sentence_pred[:index + 1])
out_put.writelines(' '.join(sentence_pred[1:index]) + '\n')
print('Predicted : %s ' % (' '.join(sentence_pred[1:index])))
if '</s>' in sentence_real:
index = sentence_real.index('</s>')
else:
index = len(sentence_real)
ground_truths.append(['<s>'] + sentence_real[:index + 1])
print('-----------------------------------------------')
print('Real : %s ' % (' '.join(sentence_real[:index])))
print('===============================================')
#print(preds)
bleu_score = get_bleu(preds, ground_truths)
print('BLEU score : %.5f ' % (bleu_score))
out_put.close()
def main(args):
data_utils.download_and_extract_tar(DATA_URL, args.data_dir)
data_utils.download_and_extract_tar(tf_utils.INCEPTION_URL, args.model_dir)
dataset = data_utils.build_dataset_object(os.path.join(
args.data_dir, 'Images'),
test_percent=0.1,
training_percent=0.1,
force_rebuild=args.force_rebuild)
model_path = (os.path.join(args.model_dir, 'classify_image_graph_def.pb'))
pretrained_model = tf_utils.PretrainedModel(model_path)
features_size = pretrained_model.features_tensor.get_shape()[0]
num_labels = len(dataset['label_to_index'])
input_tensor, label_tensor, train_step, mean_loss, accuracy = \
add_new_layer(features_size, num_labels)
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(tf.initialize_all_variables())
checkpoint_state = tf.train.get_checkpoint_state(args.model_dir)
if checkpoint_state and checkpoint_state.model_checkpoint_path:
print('Loading checkpoint')
saver.restore(sess, checkpoint_state.model_checkpoint_path)
print('Setting up validation')
images_validation, labels_validation = \
pretrained_model.run(sess, dataset['validation'], dataset['label_to_index'], args.model_dir)
for i in range(args.steps):
images = data_utils.get_minibatch(dataset['train'],
args.batch_size)
features, labels = \
pretrained_model.run(sess, images, dataset['label_to_index'], args.model_dir)
loss, _ = sess.run([mean_loss, train_step],
feed_dict={
input_tensor: features,
label_tensor: labels
})
if args.verbose:
sys.stdout.write('\rStep: %i - Loss: %f' % (i + 1, loss))
sys.stdout.flush()
if (i + 1) % args.checkpoint_interval == 0:
print()
print('Saving checkpoint - Step: %i' % (i + 1))
checkpoint_path = os.path.join(args.model_dir,
'model.checkpoint')
saver.save(sess, checkpoint_path, global_step=i + 1)
print('Running validation')
validation_accuracy = sess.run(accuracy,
feed_dict={
input_tensor:
images_validation,
label_tensor:
labels_validation
})
print('Validation accuracy: %f%%' %
(float(validation_accuracy) * 100.0))
print()
print('Saving model')
saver.save(sess, os.path.join(args.model_dir, 'model.graph'))
print('Running test')
images_test, labels_test = \
pretrained_model.run(sess, dataset['test'], dataset['label_to_index'], args.model_dir)
test_accuracy = sess.run(accuracy,
feed_dict={
input_tensor: images_test,
label_tensor: labels_test
})
print('Test accuracy: %f%%' % (float(test_accuracy) * 100.0))
def decode_batch(self, idx):
"""Decode a minibatch."""
# Get source minibatch
input_lines_src, output_lines_src, lens_src, mask_src = get_minibatch(
self.src['data'],
self.src_dict,
idx,
self.config['data']['batch_size'],
self.config['data']['max_src_length'],
add_start=True,
add_end=True)
#print(self.src_dict)
'''
lines = [
['<s>'] + line + ['</s>']
for line in self.src['data'][idx:idx + self.config['data']['max_src_length']]
]
lines = [line[:self.config['data']['max_src_length']] for line in lines]
lens = [len(line) for line in lines]
max_len = max(lens)
word2ind = self.src_dict
input_lines = [
[word2ind[w] if w in word2ind else word2ind['<unk>'] for w in line[:-1]] +
[word2ind['<pad>']] * (max_len - len(line))
for line in lines
]
#print(len(input_lines))
#print(input_lines_src[0])
'''
#id2word_src = {v: k for k, v in self.src_dict.iteritems()}
#inp = input_lines_src[0].data.cpu().numpy().tolist()
#print([inv_dict[a] for a in inp])
beam_size = self.beam_size
# (1) run the encoder on the src
context_h, (
context_h_t,
context_c_t) = self.get_hidden_representation(input_lines_src)
context_h = context_h.transpose(0, 1) # Make things sequence first.
# (3) run the decoder to generate sentences, using beam search
batch_size = context_h.size(1)
# Expand tensors for each beam.
context = Variable(context_h.data.repeat(1, beam_size, 1))
#print context.size()
dec_states = [
Variable(context_h_t.data.repeat(1, beam_size, 1)),
Variable(context_c_t.data.repeat(1, beam_size, 1))
]
beam = [
MonteCarlo(beam_size,
self.tgt_dict,
self.id2word_src,
trg['id2word'],
self.config['data']['max_trg_length'],
self.model,
cuda=True) for k in range(batch_size)
]
dec_out = self.get_init_state_decoder(dec_states[0].squeeze(0))
dec_states[0] = dec_out
#print(dec_states[0].size())
batch_idx = list(range(batch_size))
remaining_sents = batch_size
for i in range(self.config['data']['max_trg_length']):
#print(i)
input = torch.stack([
b.get_current_state() for b in beam if not b.done
]).t().contiguous().view(1, -1)
trg_emb = self.model.trg_embedding(Variable(input).transpose(1, 0))
#print trg_emb.size()
#print dec_states[0].size(), dec_states[1].size()
#print context.size()
trg_h, (trg_h_t, trg_c_t) = self.model.decoder(
trg_emb, (dec_states[0].squeeze(0), dec_states[1].squeeze(0)),
context)
dec_states = (trg_h_t.unsqueeze(0), trg_c_t.unsqueeze(0))
dec_out = trg_h_t.squeeze(1).view(-1, self.model.trg_hidden_dim)
#print dec_out.size()
out = F.softmax(self.model.decoder2vocab(dec_out)).unsqueeze(0)
word_lk = out.view(beam_size, remaining_sents,
-1).transpose(0, 1).contiguous()
active = []
cur = 0
for b in range(batch_size):
if beam[b].done:
continue
idx = batch_idx[b]
#print(idx, len(lines), input_lines_src.size())
if not beam[b].advance(
word_lk.data[idx], input_lines_src[idx], [
dec_states[0][:, cur * self.beam_size:(cur + 1) *
self.beam_size, :],
dec_states[1][:, cur * self.beam_size:(cur + 1) *
self.beam_size, :]
], context[:, cur * self.beam_size:(cur + 1) *
self.beam_size, :]):
active += [b]
cur += 1
for dec_state in dec_states: # iterate over h, c
# layers x beam*sent x dim
#print dec_state.size(1), dec_state.size(2), dec_state.size(3)
state_size = dec_state.size(1) * dec_state.size(
3) if self.model.nlayers_trg > 1 else dec_state.size(2)
sent_states = dec_state.view(-1, beam_size,
remaining_sents,
state_size)[:, :, idx]
sent_states.data.copy_(
sent_states.data.index_select(
1, beam[b].get_current_origin()))
if not active:
break
# in this section, the sentences that are still active are
# compacted so that the decoder is not run on completed sentences
active_idx = torch.cuda.LongTensor([batch_idx[k] for k in active])
batch_idx = {beam: idx for idx, beam in enumerate(active)}
def update_active(t):
# select only the remaining active sentences
view = t.data.view(-1, remaining_sents,
self.model.decoder.hidden_size)
new_size = list(t.size())
new_size[-2] = new_size[-2] * len(active_idx) \
// remaining_sents
return Variable(
view.index_select(1, active_idx).view(*new_size))
dec_states = (update_active(dec_states[0]),
update_active(dec_states[1]))
dec_out = update_active(dec_out)
context = update_active(context)
remaining_sents = len(active)
# (4) package everything up
allHyp, allScores = [], []
n_best = 1
for b in range(batch_size):
scores, ks = beam[b].sort_best()
#print(ks)
allScores += [scores[:n_best]]
hyps = zip(*[beam[b].get_hyp(k) for k in ks[:n_best]])
#print "Element in batch " + str(hyps)
#print(hyps)
allHyp += [hyps]
return allHyp, allScores
def evaluate_model(
model, src, src_test, trg,
trg_test, config, src_valid=None, trg_valid=None,
verbose=True, metric='bleu', use_cuda=False
):
"""Evaluate model.
:param model: the model object
:param src:
:param src_test:
:param trg:
:param trg_test:
:param config: the config object
:param src_valid:
:param trg_valid:
:param verbose:
:param metric:
:param use_cuda:
:return:
"""
preds = []
ground_truths = []
for j in range(0, len(src_test['data']), config['data']['batch_size']):
# Get source minibatch
input_lines_src, output_lines_src, lens_src, _ = get_minibatch(
src_test['data'], src['word2id'], j, config['data']['batch_size'],
config['data']['max_src_length'], add_start=True, add_end=True,
use_cuda=use_cuda)
# Get target minibatch
input_lines_trg_gold, output_lines_trg_gold, lens_src, _ = (
get_minibatch(
trg_test['data'], trg['word2id'], j,
config['data']['batch_size'], config['data']['max_trg_length'],
add_start=True, add_end=True, use_cuda=use_cuda
))
# Initialize target with <s> for every sentence
input_lines_trg = Variable(torch.LongTensor(
[
[trg['word2id']['<s>']]
for i in range(input_lines_src.size(0))
]
))
if use_cuda:
input_lines_trg = input_lines_trg.cuda()
# print("input_lines_src: ", input_lines_src.size(), "input_lines_trg: ", input_lines_trg.size())
# input_lines_src: [80, 49], "input_lines_trg: " [80, 1]
# Decode a minibatch greedily __TODO__ add beam search decoding
input_lines_trg = decode_minibatch(
config, model, input_lines_src,
input_lines_trg, output_lines_trg_gold,
use_cuda=use_cuda
)
# save gpu memory(in vain)
input_lines_src = input_lines_src.data.cpu().numpy()
del input_lines_src
output_lines_src = output_lines_src.data.cpu().numpy()
input_lines_trg_gold = input_lines_trg_gold.data.cpu().numpy()
del input_lines_trg_gold
# Copy minibatch outputs to cpu and convert ids to words
input_lines_trg = input_lines_trg.data.cpu().numpy()
input_lines_trg = [
[trg['id2word'][x] for x in line]
for line in input_lines_trg
]
# Do the same for gold sentences
output_lines_trg_gold = output_lines_trg_gold.data.cpu().numpy()
output_lines_trg_gold = [
[trg['id2word'][x] for x in line]
for line in output_lines_trg_gold
]
print("input_lines_trg: ", input_lines_trg[0])
print("the length of a sent", len(input_lines_trg[0]))
# Process outputs
for sentence_pred, sentence_real, sentence_real_src in zip(
input_lines_trg,
output_lines_trg_gold,
output_lines_src
):
# 去除开始和结束符， 构造完整的句子sentence, 以便计算bleu值
if '<s>' in sentence_pred:
index = sentence_pred.index('<s>')
sentence_pred = sentence_pred[index+1:]
if '</s>' in sentence_pred:
index = sentence_pred.index('</s>')
sentence_pred = sentence_pred[:index]
preds.append(sentence_pred)
if '<s>' in sentence_real:
index = sentence_real.index('<s>')
sentence_real = sentence_real[index+1:]
if '</s>' in sentence_real:
index = sentence_real.index('</s>')
sentence_real = sentence_real[: index]
ground_truths.append(sentence_real)
print("call the get_bleu method to calc bleu score.....")
print("preds: ", preds[0])
print("ground_truths: ", ground_truths[0])
return get_bleu(preds, ground_truths)
batch_index = range(0, len(src['data']), batch_size)
for i in xrange(1000):
losses = []
#flag_continue = False
shuffle(batch_index)
# flag_continue = False
for j in batch_index:
#---------------------------------------------------------------
# get mini batch
input_lines_src, _, lens_src, mask_src = get_minibatch(src['data'],
src['word2id'],
j,
batch_size,
max_length_src,
is_gui=False,
add_start=True,
add_end=False)
#if input_lines_src.size(0) != batch_size:
#flag_continue = True
#break
input_lines_trg, output_lines_trg, lens_trg, mask_trg = get_minibatch(
trg['data'],
trg['word2id'],
j,
batch_size,
max_length_trg,
is_gui=False,
def evaluate_model(
model, src, src_test, trg,
trg_test, config, src_valid=None, trg_valid=None,
verbose=True, metric='bleu'
):
"""Evaluate model."""
preds = []
ground_truths = []
for j in xrange(0, len(src_test['data']), config['data']['batch_size']):
# Get source minibatch
input_lines_src, output_lines_src, lens_src, mask_src = get_minibatch(
src_test['data'], src['word2id'], j, config['data']['batch_size'],
config['data']['max_src_length'], add_start=True, add_end=True
)
# Get target minibatch
input_lines_trg_gold, output_lines_trg_gold, lens_src, mask_src = (
get_minibatch(
trg_test['data'], trg['word2id'], j,
config['data']['batch_size'], config['data']['max_trg_length'],
add_start=True, add_end=True
)
)
# Initialize target with <s> for every sentence
input_lines_trg = Variable(torch.LongTensor(
[
[trg['word2id']['<s>']]
for i in xrange(input_lines_src.size(0))
]
))
# Decode a minibatch greedily __TODO__ add beam search decoding
input_lines_trg = decode_minibatch(
config, model, input_lines_src,
input_lines_trg, output_lines_trg_gold
)
# Copy minibatch outputs to cpu and convert ids to words
input_lines_trg = input_lines_trg.data.cpu().numpy()
input_lines_trg = [
[trg['id2word'][x] for x in line]
for line in input_lines_trg
]
# Do the same for gold sentences
output_lines_trg_gold = output_lines_trg_gold.data.cpu().numpy()
output_lines_trg_gold = [
[trg['id2word'][x] for x in line]
for line in output_lines_trg_gold
]
# Process outputs
for sentence_pred, sentence_real, sentence_real_src in zip(
input_lines_trg,
output_lines_trg_gold,
output_lines_src
):
if '</s>' in sentence_pred:
index = sentence_pred.index('</s>')
else:
index = len(sentence_pred)
preds.append(['<s>'] + sentence_pred[:index + 1])
if verbose:
print ' '.join(['<s>'] + sentence_pred[:index + 1])
if '</s>' in sentence_real:
index = sentence_real.index('</s>')
else:
index = len(sentence_real)
if verbose:
print ' '.join(['<s>'] + sentence_real[:index + 1])
if verbose:
print '--------------------------------------'
ground_truths.append(['<s>'] + sentence_real[:index + 1])
return get_bleu(preds, ground_truths)
def evaluate_model(model,
src,
src_test,
trg,
trg_test,
config,
src_valid=None,
trg_valid=None,
verbose=True,
metric='bleu'):
"""Evaluate model."""
preds = []
ground_truths = []
for j in xrange(0, len(src_test['data']), config['data']['batch_size']):
input_lines_src, output_lines_src, lens_src, mask_src = get_minibatch(
src_test['data'],
src['word2id'],
j,
config['data']['batch_size'],
config['data']['max_src_length'],
add_start=True,
add_end=True)
input_lines_trg_gold, output_lines_trg_gold, lens_src, mask_src = get_minibatch(
trg_test['data'],
trg['word2id'],
j,
config['data']['batch_size'],
config['data']['max_src_length'],
add_start=True,
add_end=True)
input_lines_trg = Variable(
torch.LongTensor([[trg['word2id']['<s>']]
for i in xrange(input_lines_src.size(0))
])).cuda()
for i in xrange(config['data']['max_src_length']):
decoder_logit = model(input_lines_src, input_lines_trg)
word_probs = model.decode(decoder_logit)
decoder_argmax = word_probs.data.cpu().numpy().argmax(axis=-1)
next_preds = Variable(torch.from_numpy(decoder_argmax[:,
-1])).cuda()
input_lines_trg = torch.cat(
(input_lines_trg, next_preds.unsqueeze(1)), 1)
input_lines_trg = input_lines_trg.data.cpu().numpy()
input_lines_trg = [[trg['id2word'][x] for x in line]
for line in input_lines_trg]
output_lines_trg_gold = output_lines_trg_gold.data.cpu().numpy()
output_lines_trg_gold = [[trg['id2word'][x] for x in line]
for line in output_lines_trg_gold]
for sentence_pred, sentence_real, sentence_real_src in zip(
input_lines_trg, output_lines_trg_gold, output_lines_src):
if '</s>' in sentence_pred:
index = sentence_pred.index('</s>')
else:
index = len(sentence_pred)
preds.append(['<s>'] + sentence_pred[:index + 1])
if verbose:
print ' '.join(['<s>'] + sentence_pred[:index + 1])
if '</s>' in sentence_real:
index = sentence_real.index('</s>')
else:
index = len(sentence_real)
if verbose:
print ' '.join(['<s>'] + sentence_real[:index + 1])
if verbose:
print '--------------------------------------'
ground_truths.append(['<s>'] + sentence_real[:index + 1])
if '</s>' in sentence_real_src:
index = sentence_real_src.index('</s>')
else:
index = len(sentence_real_src)
return get_bleu(preds, ground_truths)
lr = config['training']['lrate']
optimizer = optim.Adam(model.parameters(), lr=lr)
elif config['training']['optimizer'] == 'adadelta':
optimizer = optim.Adadelta(model.parameters())
elif config['training']['optimizer'] == 'sgd':
lr = config['training']['lrate']
optimizer = optim.SGD(model.parameters(), lr=lr)
else:
raise NotImplementedError("Learning method not recommend for task")
for i in range(1000):
losses = []
for j in range(0, len(src['data']), batch_size):
input_lines_src, _, lens_src, mask_src = get_minibatch(
src['data'], src['word2id'], j,
batch_size, max_length_src, add_start=True, add_end=False
)
input_lines_trg, output_lines_trg, lens_trg, mask_trg = get_minibatch(
trg['data'], trg['word2id'], j,
batch_size, max_length_trg, add_start=True, add_end=True
)
decoder_logit = model(input_lines_src, input_lines_trg)
optimizer.zero_grad()
loss = loss_criterion(
decoder_logit.contiguous().view(-1, vocab_size),
output_lines_trg.view(-1)
)
losses.append(loss.data[0])
"""
optimizer = optim.Adam(model.parameters(), lr=config['training']['lrate'])
epoch = 1000
for i in range(epoch):
losses = []
for j in range(0, len(src_train['data']), batch_size):
slots = src_train['data'][j][1:]
shuffle(slots)
src_train['data'][j][1:] = slots
input_lines_src, output_lines_src, lens_src, mask_src = get_minibatch(src_train['data'], src_train['word2id'], j, batch_size, config['data']['max_src_length'])
input_lines_trg, output_lines_trg, lens_trg, mask_trg = get_minibatch(trg_train['data'], trg_train['word2id'], j, batch_size, config['data']['max_trg_length'])
decoder_logit = model(input_lines_src, input_lines_trg)
optimizer.zero_grad()
loss = loss_criterion(
decoder_logit.contiguous().view(-1, trg_vocab_size),
output_lines_trg.view(-1)
)
losses.append(loss.data[0])
loss.backward()
optimizer.step()