def _get_top_k_sequences(self, log_probs, wordpiece_mask, k):
batch_size = log_probs.size()[0]
seq_length = log_probs.size()[1]
beam_search = BeamSearch(self._end_index,
max_steps=seq_length,
beam_size=k,
per_node_beam_size=self._per_node_beam_size)
beam_log_probs = torch.nn.functional.pad(
log_probs, pad=(0, 2, 0, 0, 0, 0), value=-1e7
) # add low log probabilites for start and end tags used in the beam search
start_predictions = beam_log_probs.new_full(
(batch_size, ), fill_value=self._start_index).long()
# Shape: (batch_size, beam_size, seq_length)
top_k_predictions, seq_log_probs = beam_search.search(
start_predictions, {
'log_probs': beam_log_probs,
'wordpiece_mask': wordpiece_mask,
'step_num': beam_log_probs.new_zeros((batch_size, )).long()
}, self.take_step)
# get rid of start and end tags if they slipped in
top_k_predictions[top_k_predictions > 2] = 0
return top_k_predictions
def _init_graph(self):
"""
init graph
"""
self.ys = (self.input_y, None, None)
self.xs = (self.input_x, None)
self.memory = self.model.encode(self.xs, False)[0]
self.logits = self.model.decode(self.xs, self.ys, self.memory, False)[0]
ckpt = self.tf.train.get_checkpoint_state(self.model_dir).all_model_checkpoint_paths[-1]
graph = self.logits.graph
sess_config = self.tf.ConfigProto(allow_soft_placement=True)
sess_config.gpu_options.allow_growth = True
saver = self.tf.train.Saver()
self.sess = self.tf.Session(config=sess_config, graph=graph)
self.sess.run(self.tf.global_variables_initializer())
self.tf.reset_default_graph()
saver.restore(self.sess, ckpt)
self.bs = BeamSearch(self.model,
self.hp.beam_size,
list(self.idx2token.keys())[2],
list(self.idx2token.keys())[3],
self.idx2token,
self.hp.maxlen2,
self.input_x,
self.input_y,
self.logits)
def beam_sample(self, image_features, beam_size=5):
batch_size = image_features.size(0)
beam_searcher = BeamSearch(beam_size, batch_size, 17)
# init the result with zeros and lstm states
states = self.init_hidden_noise(image_features)
states = (states[0].repeat(1, beam_size, 1).cuda(),
states[1].repeat(1, beam_size, 1).cuda())
# embed the start symbol
words_feed = self.embed.word_embeddings([self.embed.START_SYMBOL] * batch_size) \
.repeat(beam_size, 1).unsqueeze(1).cuda()
for i in range(self.max_sentence_length):
hidden, states = self.lstm(words_feed, states)
outputs = self.output_linear(hidden.squeeze(1))
beam_indices, words_indices = beam_searcher.expand_beam(
outputs=outputs)
if len(beam_indices) == 0 or i == 15:
generated_captions = beam_searcher.get_results()[:, 0]
outcaps = self.embed.words_from_indices(
generated_captions.cpu().numpy())
else:
words_feed = torch.stack([
self.embed.word_embeddings_from_indices(words_indices)
]).view(beam_size, 1, -1).cuda()
return " ".join(outcaps) # .split(self.embed.END_SYMBOL)[0]
def beam_search(self, input_seq, beam_size=3, attentionOverrideMap=None, correctionMap=None, unk_map=None,
beam_length=0.5, beam_coverage=0.5,
max_length=MAX_LENGTH):
torch.set_grad_enabled(False)
input_seqs = [indexes_from_sentence(self.input_lang, input_seq)]
input_lengths = [len(seq) for seq in input_seqs]
input_batches = Variable(torch.LongTensor(input_seqs)).transpose(0, 1)
if use_cuda:
input_batches = input_batches.cuda()
self.encoder.train(False)
self.decoder.train(False)
encoder_outputs, encoder_hidden = self.encoder(input_batches, input_lengths, None)
decoder_hidden = encoder_hidden
beam_search = BeamSearch(self.decoder, encoder_outputs, decoder_hidden, self.output_lang, beam_size,
attentionOverrideMap,
correctionMap, unk_map, beam_length=beam_length, beam_coverage=beam_coverage)
result = beam_search.search()
self.encoder.train(True)
self.decoder.train(True)
torch.set_grad_enabled(True)
return result # Return a list of indexes, one for each word in the sentence, plus EOS
def rnn_generate(gen_input_file, model_path, max_gen_len, beam_size,
word_dict_file):
"""
use RNN model to generate sequences.
:param word_id_dict: vocab.
:type word_id_dict: dictionary with content of "{word, id}",
"word" is string type , "id" is int type.
:param num_words: the number of the words to generate.
:type num_words: int
:param beam_size: beam width.
:type beam_size: int
:return: save prediction results to output_file
"""
assert os.path.exists(gen_input_file), "test file does not exist!"
assert os.path.exists(model_path), "trained model does not exist!"
assert os.path.exists(
word_dict_file), "word dictionary file does not exist!"
# load word dictionary
word_2_ids = load_dict(word_dict_file)
try:
UNK_ID = word_2_ids["<unk>"]
except KeyError:
logger.fatal("the word dictionary must contain a <unk> token!")
sys.exit(-1)
# initialize paddle
paddle.init(use_gpu=conf.use_gpu, trainer_count=conf.trainer_count)
# load the trained model
pred_words = rnn_lm(
len(word_2_ids),
conf.emb_dim,
conf.hidden_size,
conf.stacked_rnn_num,
conf.rnn_type,
is_infer=True)
parameters = paddle.parameters.Parameters.from_tar(
gzip.open(model_path, "r"))
inferer = paddle.inference.Inference(
output_layer=pred_words, parameters=parameters)
generator = BeamSearch(inferer, word_dict_file, beam_size, max_gen_len)
# generate text
with open(conf.gen_file, "r") as fin, open(conf.gen_result, "w") as fout:
for idx, line in enumerate(fin):
fout.write("%d\t%s" % (idx, line))
for gen_res in generator.gen_a_sentence([
word_2_ids.get(w, UNK_ID)
for w in line.lower().strip().split()
]):
fout.write("%s\n" % gen_res)
fout.write("\n")
def main():
english = tokenize("data/100ktok.low.en")
spanish = tokenize("data/100ktok.low.es")
training_set, held_out_set, test_set = get_datasets(english, spanish)
translations = get_word_translations("100000_trans.txt")
search = BeamSearch(training_set, held_out_set, translations)
print search.translate(test_set[8])
def main():
"""
Creates a temporary file for the given input which is
used to create a dataset, that is then evaluated on the given model.
The generated summary is printed to standard out.
"""
args, unknown_args = prepare_arg_parser().parse_known_args()
model_file = args.model_file
with suppress_stdout_stderr():
model, _optimizer, vocab, _stats, cfg = train.load_model(
model_file, unknown_args
)
_, filename = tempfile.mkstemp()
try:
with open(filename, "a") as f:
input_ = sys.stdin.read()
article = preprocess.parse(input_)
print(f"{article}\tSUMMARY_STUB", file=f)
with suppress_stdout_stderr():
dataset = Dataset(filename, vocab, cfg)
batch = next(dataset.generator(1, cfg.pointer))
# don't enforce any min lengths (useful for short cmdline summaries")
setattr(cfg, "min_summary_length", 1)
bs = BeamSearch(model, cfg=cfg)
summary = evaluate.batch_to_text(bs, batch)[0]
print(f"SUMMARY:\n{summary}")
finally:
os.remove(filename)
def translate(self, docs):
"""Translate a batch of documents."""
batch_size = docs.inp.size(0)
spt_ids = self.spt_ids_C
decode_strategy = BeamSearch(self.beam_size, batch_size, self.n_best,
self.min_length, self.max_length, spt_ids,
self.eos_mapping)
return self._translate_batch_with_strategy(docs, decode_strategy)
def beam_search(self,
initial_sequence,
forbid_movies=None,
temperature=1,
**kwargs):
"""
Beam search sentence generation
:param initial_sequence: list giving the initial sequence of tokens
:param kwargs: additional parameters to pass to model forward pass (e.g. a conditioning context)
:return:
"""
beam_search = BeamSearch(self.beam_size, initial_sequence,
self.word2id["</s>"])
beams = beam_search.beams
for i in range(self.max_sequence_length):
# compute probabilities for each beam
probabilities = []
for beam in beams:
# add batch_dimension
model_input = Variable(torch.LongTensor(
beam.sequence)).unsqueeze(0)
if self.model.cuda_available:
model_input = model_input.cuda()
beam_forbidden_movies = forbid_movies.union(
beam.mentioned_movies)
prob = self.model(input=model_input,
lengths=[len(beam.sequence)],
log_probabilities=False,
forbid_movies=beam_forbidden_movies,
temperature=temperature,
**kwargs)
# get probabilities for the next token to generate
probabilities.append(prob[0, -1, :].cpu())
# update beams
beams = beam_search.search(probabilities,
n_gram_block=self.n_gram_block)
# replace movie names with the corresponding words
for beam in beams:
if beam.sequence[-1] > len(self.word2id):
# update the list of movies mentioned for preventing repeated recommendations
beam.mentioned_movies.add(beam.sequence[-1] -
len(self.word2id))
beam.sequence[-1:] = replace_movie_with_words(
beam.sequence[-1], self.movieId2name, self.word2id)
return beams
def __init__(self, encoder, decoder,
decoding_style="greedy", special_tokens_dict=None,
max_decoding_steps=128, beam_width=10):
super().__init__()
self.encoder = encoder
self.decoder = decoder
if decoding_style not in ["greedy", "beam_search"]:
print(f"{decoding_style} is not allowed parameter")
decoding_style = "greedy"
self.decoding_style = decoding_style
if special_tokens_dict is None:
self.special_tokens_dict = {"<pad>":0, "<bos>":1, "<eos>":2, "<unk>":3}
else:
self.special_tokens_dict = special_tokens_dict
self.max_decoding_steps = max_decoding_steps
self._beam_search = BeamSearch(self.special_tokens_dict["<eos>"],
max_decoding_steps,
beam_width)
def main():
english = tokenize("data/100ktok.low.en")
spanish = tokenize("data/100ktok.low.es")
training_set, test_set, translated_set = get_datasets(english, spanish)
translations = get_word_translations("3000_trans.txt")
search = BeamSearch(training_set, translations)
test_output = open('trans_beam.txt','w')
true_output = open('trans_true.txt','w')
for i in range(len(test_set)):
print "Translating sentence", i, "..."
test_output.write(' '.join(search.translate(test_set[i])) + "\n")
true_output.write(' '.join(translated_set[i]) + "\n")
test_output.close()
true_output.close()
def main():
english = tokenize("data/100ktok.low.en")
spanish = tokenize("data/100ktok.low.es")
training_set, test_set, translated_set = get_datasets(english, spanish)
translations = get_word_translations("3000_trans.txt")
print "Original Sentence:", ' '.join(test_set[0])
translator = DirectTrans(translations)
print "Direct Translation:", ' '.join(translator.translate(test_set[0]))
test_output = open('trans_beam.txt','w')
true_output = open('trans_true.txt','w')
search = BeamSearch(training_set, translations)
print "Beam Translation:", ' '.join(search.translate(test_set[0]))
print "True Translation:", ' '.join(translated_set[0])
def train(dataset, dataval, y_max_length, steps_per_epoch, vocab, params):
print("training")
print(params.__dict__)
# seq2seq = Seq2seq_attention(len(vocab), params)
seq2seq = Seq2seq_attention(len(vocab),
params,
embedding_matrix=fasttext_embedding(
params, sentences=None))
if params.finetune:
seq2seq.restore_checkpoint()
seq2seq.encoder.embedding.trainable = True
seq2seq.decoder.embedding.trainable = True
seq2seq.decoder.fc1.trainable = True
else:
seq2seq.encoder.embedding.trainable = False
seq2seq.decoder.embedding.trainable = False
seq2seq.decoder.fc1.trainable = False
# it = iter(dataval)
# inp, out = next(it)
beam_search = BeamSearch(seq2seq, params.beam_size, vocab.bos, vocab.eos,
y_max_length)
# seq2seq.compare_input_output(
# inp, vocab, y_max_length, out, beam_search)
seq2seq.summary()
# seq2seq.encoder.summary()
# seq2seq.decoder.summary()
# def my_loss(truth, preds):
# return sum(tf_rouge_l(preds, truth, vocab.eos))
def callback():
print("train set:")
it = iter(dataset.unbatch())
for _ in range(3):
inp, out = next(it)
seq2seq.compare_input_output(inp, vocab, y_max_length, out,
beam_search)
print("validation set:")
it = iter(dataval)
for _ in range(3):
inp, out = next(it)
seq2seq.compare_input_output(inp, vocab, y_max_length, out,
beam_search)
# seq2seq.train_epoch(dataset, epochs, steps_per_epoch, vocab.bos,
# restore_checkpoint=True, dataval=dataval, callback=None)
seq2seq.train_epoch(dataset,
params.epochs,
steps_per_epoch,
vocab.bos,
y_max_length,
restore_checkpoint=params.restore,
dataval=dataval,
epoch_verbosity=params.epoch_verbosity,
callback=callback)
def generate(self, input_variable, batch_size):
input_variable = input_variable.view(batch_size, -1)
encoder_hidden = self.encoder.init_hidden(batch_size)
encoder_outputs, encoder_hidden = self.encoder(input_variable,
encoder_hidden)
decoder_hidden = encoder_hidden
decoder_inputs = [(Variable(torch.LongTensor([[SOS_token]])),
decoder_hidden)] # SOS
beam = BeamSearch(self.vocab_size2, self.beam_size, decoder_hidden)
# loop beam search
for di in xrange(self.target_length):
decoder_outputs = []
for decoder_input, decoder_hidden in decoder_inputs:
decoder_output, decoder_hidden, _ = self.decoder(
decoder_input, decoder_hidden,
encoder_outputs) # SOS + Predict
decoder_outputs.append((decoder_output, decoder_hidden))
decoder_inputs = beam.beam_search(decoder_outputs)
return beam.generate(self.generate_num)
def eva_a_phi(phi):
na, nnh, nh, nw = phi
# choose a dataset to train (mscoco, flickr8k, flickr30k)
dataset = 'mscoco'
data_dir = osp.join(DATA_ROOT, dataset)
from model.ra import Model
# settings
mb = 64 # mini-batch size
lr = 0.0002 # learning rate
# nh = 512 # size of LSTM's hidden size
# nnh = 512 # hidden size of attention mlp
# nw = 512 # size of word embedding vector
# na = 512 # size of the region features after dimensionality reduction
name = 'ra' # model name, just setting it to 'ra' is ok. 'ra'='region attention'
vocab_freq = 'freq5' # use the vocabulary that filtered out words whose frequences are less than 5
print '... loading data {}'.format(dataset)
train_set = Reader(batch_size=mb, data_split='train', vocab_freq=vocab_freq, stage='train',
data_dir=data_dir, feature_file='features_30res.h5', topic_switch='off') # change 0, 1000, 82783
valid_set = Reader(batch_size=1, data_split='val', vocab_freq=vocab_freq, stage='val',
data_dir=data_dir, feature_file='features_30res.h5',
caption_switch='off', topic_switch='off') # change 0, 10, 5000
npatch, nimg = train_set.features.shape[1:]
nout = len(train_set.vocab)
save_dir = '{}-nnh{}-nh{}-nw{}-na{}-mb{}-V{}'.\
format(dataset.lower(), nnh, nh, nw, na, mb, nout)
save_dir = osp.join(SAVE_ROOT, save_dir)
model_file, m = find_last_snapshot(save_dir, resume_training=False)
os.system('cp model/ra.py {}/'.format(save_dir))
logger = Logger(save_dir)
logger.info('... building')
model = Model(name=name, nimg=nimg, nnh=nnh, nh=nh, na=na, nw=nw, nout=nout, npatch=npatch, model_file=model_file)
# start training
bs = BeamSearch([model], beam_size=1, num_cadidates=100, max_length=20)
best = train(model, bs, train_set, valid_set, save_dir, lr,
display=100, starting=m, endding=20, validation=2000, life=10, logger=logger) # change dis1,100; va 2,2000; life 0,10;
average_models(best=best, L=6, model_dir=save_dir, model_name=name+'.h5') # L 1, 6
# evaluation
np.save('data_dir', data_dir)
np.save('save_dir', save_dir)
os.system('python valid_time.py')
scores = np.load('scores.npy')
running_time = np.load('running_time.npy')
print 'cider:', scores[-1], 'B1-4,C:', scores, 'running time:', running_time
return scores, running_time
def __init__(self, model, batch_reader, model_config, data_config, vocab,
data_loader):
self.model = model
self.batch_reader = batch_reader
self.model_config = model_config
self.data_config = data_config
self.vocab = vocab
self.data_loader = data_loader
self.saver = tf.train.Saver()
self.session = tf.Session(config=tf.ConfigProto(
allow_soft_placement=True))
self.restore_model_flag = self.restore_model()
self.bs = BeamSearch(
self.model, self.model_config.beam_size,
self.data_loader.word_to_id(self.data_config.sentence_start),
self.data_loader.word_to_id(self.data_config.sentence_end),
self.model_config.abstract_length)
def transformers_generate_txt(txt, params):
"""
txt- english sent
"""
nlp = spacy.load('en_core_web_sm')
txt = contractions.fix(txt)
enc_input = tf.expand_dims(
tf.constant([tokenize_eng[tok.text.lower()] for tok in nlp(txt)]), 0)
depth = config['transformer']['dmodel'] / config['transformer']['num_heads']
dmodel = config['transformer']['dmodel']
num_blocks = config['transformer']['num_blocks']
num_heads = config['transformer']['num_heads']
transformer = Transformer(num_blocks=num_blocks,
dmodel=dmodel,
depth=depth,
num_heads=num_heads,
inp_vocab_size=config['dataloader']['eng_vocab'],
tar_vocab_size=config['dataloader']['ger_vocab'])
trainer = TrainerTransformer(transformer, config)
trainer.restore_checkpoint(config['transformer']['ckpt_dir'])
dec_input = tf.reshape(tokenize_ger['<sos>'], (1, 1))
att = []
dec_seq_mask = unidirectional_input_mask(enc_input, dec_input)
logits = transformer(enc_input, dec_input, dec_seq_mask=dec_seq_mask)
# beam_search
bs = BeamSearch(config['transformer']['k', 1], trainer.transformer)
sents = bs.call(enc_input, logits, params.dec_max_len)
output = [[detokenize_ger[idx] for idx in sent] for sent in sents]
# att.append(attention_weights)
return [" ".join(sent) for sent in output]
def parse_options():
parser = argparse.ArgumentParser()
Train.add_parse_options(parser)
Encoder.add_parse_options(parser)
AttnDecoder.add_parse_options(parser)
Seq2SeqModel.add_parse_options(parser)
LMModel.add_parse_options(parser)
BeamSearch.add_parse_options(parser)
parser.add_argument("-dev",
default=False,
action="store_true",
help="Get dev set results using the last saved model")
parser.add_argument("-test",
default=False,
action="store_true",
help="Get test results using the last saved model")
args = parser.parse_args()
args = vars(args)
return process_args(args)
def main():
# T=5
n_51 = Node(idx=51)
n_52 = Node(idx=52)
# T=4
n_41 = Node(idx=41, next_nodes=[n_51])
n_42 = Node(idx=42, next_nodes=[n_51])
n_43 = Node(idx=43, next_nodes=[n_52])
# T=3
n_31 = Node(idx=31, next_nodes=[n_41, n_42, n_43])
# T=2
n_21 = Node(idx=21, next_nodes=[n_31])
n_22 = Node(idx=22, next_nodes=[n_31])
n_23 = Node(idx=23, next_nodes=[n_31])
# T=1
n_11 = Node(idx=11, next_nodes=[n_21, n_22])
n_12 = Node(idx=12)
n_13 = Node(idx=13, next_nodes=[n_23])
# T=0
n_root = Node(idx=0, next_nodes=[n_11, n_12, n_13])
# Beam search
n_beam = 3
l_times = 5
beam_search = BeamSearch(n_beam, l_times)
seqs = beam_search.find(n_root)
# Check nodes
for i, seq in enumerate(seqs):
print("Sequence {}".format(i))
for n in seq:
print(n.idx)
def __init__(self, embedder, hidden_dim, num_layers, dropout_p, attention,
beam_width):
super(LstmDecoder, self).__init__()
self._embedder = embedder
self._hidden_dim = hidden_dim
self._num_layers = num_layers
self._attention = attention
decoder_input_dim = self._embedder.get_embed_dim()
decoder_input_dim += self._hidden_dim # for input-feeding
self._lstm = StackedLSTM(decoder_input_dim, self._hidden_dim,
num_layers, dropout_p)
self._output_projection_layer = nn.Linear(
self._hidden_dim, self._embedder.get_vocab_size())
self._start_index = self._embedder.get_init_token_idx()
self._eos_index = self._embedder.get_eos_token_idx()
self._pad_index = self._embedder.get_pad_token_idx()
self._max_decoding_steps = 100
self._beam_search = BeamSearch(self._eos_index,
self._max_decoding_steps, beam_width)
def beamsearch_hamcycle(pred, W, beam_size=2):
N = W.size(-1)
batch_size = W.size(0)
BS = BeamSearch(beam_size, batch_size, N)
trans_probs = pred.gather(1, BS.get_current_state())
for step in range(N-1):
BS.advance(trans_probs, step + 1)
trans_probs = pred.gather(1, BS.get_current_state())
ends = torch.zeros(batch_size, 1).type(dtype_l)
# extract paths
Paths = BS.get_hyp(ends)
# Compute cost of path
Costs = compute_cost_path(Paths, W)
return Costs, Paths
def __init__(self,
config,
train_vocab,
labels_vocab,
is_train=True,
use_attention=True,
beam_search=None):
self.use_attention = use_attention
self.beam_search = None
if beam_search:
self.beam_search = BeamSearch(self)
self.config = config
self.encoder_inputs = None
self.decoder_inputs = None
self.grad_norm = None
self.train_vocab = train_vocab
self.labels_vocab = labels_vocab
self.build(is_train)
def __init__(self,
config,
train_vocab,
is_train=True,
use_attention=True,
beam_search=False,
bidirectional=True,
pointer=True):
self.use_attention = use_attention
self.beam_search = None
if beam_search:
self.beam_search = BeamSearch(self)
self.pointer = pointer
self.bidirectional = bidirectional
self.config = config
self.config.vocab_size = len(train_vocab.tok2id) + 1
self.encoder_inputs = None
self.decoder_inputs = None
self.grad_norm = None
self.vocab = train_vocab
self.build(is_train)
def generate_summaries(model,
dataset,
cfg,
limit=math.inf,
shuffle=False,
pbar=None):
"""
Generate summaries using the given `model` on the given `dataset`.
Expects the given model to be in eval mode.
:param model: Use this model for evaluation
:param dataset: The dataset to evaluate on
:param cfg:
The `Config` used for the given model from which we get
info on whether it uses pointer generation or not.
:param limit: Limit the pairs evaluated to this many
:param shuffle: Whether to shuffle the dataset before yielding batches
:param pbar: Optional pbar (tqdm) to update with progress
"""
batch_size = 1 # beam_search currently only supports batch_size 1
bs = BeamSearch(model, cfg=cfg)
with torch.no_grad():
generator = dataset.generator(batch_size, cfg.pointer, shuffle)
references = []
hypothesis = []
for idx, batch in enumerate(generator):
hyps = batch_to_text(bs, batch)
refs = [" ".join(e.tgt) for e in batch.examples]
hypothesis.extend(hyps)
references.extend(refs)
if batch_size * idx >= limit:
break
if pbar is not None:
pbar.update(batch_size)
pbar.close()
return (hypothesis, references)
def check_train_results(dataval, y_max_length, steps_per_epoch, vocab, params):
print("check_train_results")
seq2seq = Seq2seq_attention(
len(vocab), params, embedding_matrix=fasttext_embedding(params, sentences=None))
it = iter(dataval)
inp, out = next(it)
beam_search = BeamSearch(seq2seq, 9, vocab.bos,
vocab.eos, y_max_length)
# seq2seq.compare_input_output(inp, vocab, y_max_length, out, beam_search)
seq2seq.summary()
def callback():
for _ in range(10):
inp, out = next(it)
seq2seq.compare_input_output(
inp, vocab, y_max_length, out, beam_search)
# seq2seq.train_epoch(dataset, 5, steps_per_epoch, vocab.bos,
# restore_checkpoint=True, dataval=dataval, callback=None)
# seq2seq.train_epoch(dataset, 5, steps_per_epoch, vocab.bos,
# restore_checkpoint=True, dataval=dataval, callback=callback)
seq2seq.restore_checkpoint()
callback()
def _beam_search_decoding(self, imgs, beam_size):
B = imgs.size(0)
# use batch_size*beam_size as new Batch
imgs = tile(imgs, beam_size, dim=0)
enc_outs, hiddens = self.model.encode(imgs)
dec_states, O_t = self.model.init_decoder(enc_outs, hiddens)
new_B = imgs.size(0)
# first decoding step's input
tgt = torch.ones(new_B, 1).long() * START_TOKEN
beam = BeamSearch(beam_size, B)
for t in range(self.max_len):
tgt = beam.current_predictions.unsqueeze(1)
dec_states, O_t, probs = self.step_decoding(
dec_states, O_t, enc_outs, tgt)
log_probs = torch.log(probs)
beam.advance(log_probs)
any_beam_is_finished = beam.is_finished.any()
if any_beam_is_finished:
beam.update_finished()
if beam.done:
break
select_indices = beam.current_origin
if any_beam_is_finished:
# Reorder states
h, c = dec_states
h = h.index_select(0, select_indices)
c = c.index_select(0, select_indices)
dec_states = (h, c)
O_t = O_t.index_select(0, select_indices)
# get results
formulas_idx = torch.stack([hyps[1] for hyps in beam.hypotheses],
dim=0)
results = self._idx2formulas(formulas_idx)
return results
def generate_beamsearch(self,
src: torch.Tensor,
maxlen: int,
bos_index: int,
pad_index: int,
unk_index: int,
eos_index: int,
vocab_size: int,
beam_size: int = 3,
no_repeat_ngram_size: int = 0):
# Obtain device information
device = next(self.parameters()).device
_, batch_size = src.shape
src_key_padding_mask = (src == pad_index).T # batch_size x srclen
memory = self.encode(src, src_key_padding_mask=src_key_padding_mask)
# <BOS> tgt seq for generation
tgt = torch.LongTensor(maxlen, batch_size,
beam_size).fill_(pad_index).to(device)
tgt[0, :, :] = torch.LongTensor(batch_size,
beam_size).fill_(bos_index).to(device)
scores = torch.zeros(batch_size, beam_size, maxlen).to(device)
scores[:, :, 0] = torch.ones(batch_size, beam_size).to(device)
active_beams = [0] # up to beam_size beams.
search = BeamSearch(vocab_size, pad_index, unk_index, eos_index)
ngram_blocking = NgramBlocking(no_repeat_ngram_size)
# After eos
log_probs_after_eos = torch.FloatTensor(batch_size, beam_size,
self.out_vocab_size).fill_(
float("-inf")).cpu()
log_probs_after_eos[:, :, eos_index] = 0.
best_n_indices = tgt.new_full((batch_size, len(active_beams)),
bos_index)
for i in range(1, maxlen):
if (best_n_indices == eos_index).all(
): # if all of last prediction is eos, we can leave the loop
break
# Generate probability for all beams, update probability for all beams (lprobs).
lprobs = torch.zeros(batch_size, len(active_beams),
vocab_size).to(device)
for j in range(len(active_beams)):
tgt_key_padding_mask = (tgt[:i, :,
active_beams[j]] == pad_index
).T # batch_size x len(tgt)
tgt_mask = self.transformer.generate_square_subsequent_mask(
i).to(device)
decode_prob = self.decode(
tgt[:i, :, active_beams[j]],
memory,
tgt_mask=tgt_mask,
tgt_key_padding_mask=tgt_key_padding_mask)
pred_prob = self.linear(decode_prob)
lprobs[:, j, :] = pred_prob[-1, :]
# Update lprobs for n-gram blocking
if no_repeat_ngram_size > 0:
for batch_idx in range(batch_size):
for beam_idx in range(len(active_beams)):
lprobs[batch_idx, beam_idx] = ngram_blocking.update(
i - 1, tgt[:i, batch_idx, beam_idx],
lprobs[batch_idx, beam_idx])
expanded_indices = best_n_indices.detach().cpu().unsqueeze(
-1).expand(
(batch_size, len(active_beams), self.out_vocab_size))
clean_lprobs = torch.where(
expanded_indices == eos_index,
log_probs_after_eos[:, :len(active_beams)],
F.log_softmax(lprobs.detach().cpu(), dim=-1))
# Run the beam search step and select the top-k beams.
best_n_scores, best_n_indices, best_n_beams = search.step(
i, clean_lprobs,
scores.index_select(1, torch.tensor(
active_beams, device=device)).detach().cpu(), beam_size)
# Take the top results, more optimization can be done here, e.g., avoid <eos> beams.
best_n_scores = best_n_scores[:, :beam_size]
best_n_indices = best_n_indices[:, :beam_size]
best_n_beams = best_n_beams[:, :beam_size]
# update results
tgt = tgt.gather(
2,
best_n_beams.unsqueeze(0).expand(maxlen, batch_size,
-1).to(device))
tgt[i, :, :] = best_n_indices
scores[:, :, i] = best_n_scores
active_beams = range(beam_size)
return tgt[:, :, 0]
def generate(self, title='晚安', genre=3):
if (self.model is None):
raise Exception("has no model")
temperature = 1
topk = 15
context_tokens = []
assert genre in [0, 1, 2, 3]
text_genre_list = ['五言绝句', '七言绝句', '五言律诗', '七言律诗']
genre_code_list = ['wuyanjue', 'qiyanjue', 'wuyanlv', 'qiyanlv']
text_genre = text_genre_list[genre]
genre_code = genre_code_list[genre]
ids = self.title_to_ids[text_genre]
context_tokens.append(ids)
context_tokens.append(100)
context_tokens.extend(
self.tokenizer.convert_tokens_to_ids(
self.tokenizer.tokenize(title)))
context_tokens.append(4282) # 4282 is #
out = None
while out is None:
#generator = CheckedGenerator(model=self.model,
#context=context_tokens,
#tokenizer=self.tokenizer,
#checker=self.checker,
#genre=genre_code,
#temperature=temperature,
#top_k=topk, device=self.device)
# BaseGenerator
#generator = BaseGenerator(model=self.model,
#context=context_tokens,
#tokenizer=self.tokenizer,
#temperature=temperature,
#top_k=topk,
#device=self.device)
#out = generator.sample_sequence()
# BeamSearch
generator = BeamSearch(model=self.model,
context=context_tokens,
tokenizer=self.tokenizer,
temperature=temperature,
beam_size=3,
mode=2,
genre=genre,
top_k=topk,
device=self.device)
out = generator.beam_sequence()
out = out.tolist()
text = self.tokenizer.convert_ids_to_tokens(out[0])
text = text[:-1]
text = ''.join(text)
text = text.split('#')[-1]
return text
logging.info('# init data')
training_iter = train_batches.make_one_shot_iterator()
val_iter = eval_batches.make_initializable_iterator()
logging.info("# Load model")
m = Transformer(hp)
# get op
loss, train_op, global_step, train_summaries = m.train(xs, ys)
y_hat, eval_summaries = m.eval(xs, ys)
token2idx, idx2token = _load_vocab(hp.vocab)
bs = BeamSearch(m, hp.beam_size,
list(idx2token.keys())[2],
list(idx2token.keys())[3], idx2token, hp.maxlen2, m.x,
m.decoder_inputs, m.logits)
logging.info("# Session")
saver = tf.train.Saver(max_to_keep=hp.num_epochs)
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
ckpt = tf.train.latest_checkpoint(hp.logdir)
if ckpt is None:
logging.info("Initializing from scratch")
sess.run(tf.global_variables_initializer())
save_variable_specs(os.path.join(hp.logdir, "specs"))
else:
saver.restore(sess, ckpt)
summary_writer = tf.summary.FileWriter(hp.logdir, sess.graph)
def beam_search(enc_output, enc_bias, source_length):
"""
beam_search
"""
max_len = layers.fill_constant(
shape=[1], dtype='int64', value=max_out_len)
step_idx = layers.fill_constant(
shape=[1], dtype='int64', value=0)
cond = layers.less_than(x=step_idx, y=max_len)
while_op = layers.While(cond)
caches_batch_size = batch_size * beam_size
init_score = np.zeros([1, beam_size]).astype('float32')
init_score[:, 1:] = -INF
initial_log_probs = layers.assign(init_score)
alive_log_probs = layers.expand(initial_log_probs, [batch_size, 1])
# alive seq [batch_size, beam_size, 1]
initial_ids = layers.zeros([batch_size, 1, 1], 'float32')
alive_seq = layers.expand(initial_ids, [1, beam_size, 1])
alive_seq = layers.cast(alive_seq, 'int64')
enc_output = layers.unsqueeze(enc_output, axes=[1])
enc_output = layers.expand(enc_output, [1, beam_size, 1, 1])
enc_output = layers.reshape(enc_output, [caches_batch_size, -1, d_model])
tgt_src_attn_bias = layers.unsqueeze(enc_bias, axes=[1])
tgt_src_attn_bias = layers.expand(tgt_src_attn_bias, [1, beam_size, n_head, 1, 1])
enc_bias_shape = layers.shape(tgt_src_attn_bias)
tgt_src_attn_bias = layers.reshape(tgt_src_attn_bias, [-1, enc_bias_shape[2],
enc_bias_shape[3], enc_bias_shape[4]])
beam_search = BeamSearch(beam_size, batch_size, decode_alpha, trg_vocab_size, d_model)
caches = [{
"k": layers.fill_constant(
shape=[caches_batch_size, 0, d_model],
dtype=enc_output.dtype,
value=0),
"v": layers.fill_constant(
shape=[caches_batch_size, 0, d_model],
dtype=enc_output.dtype,
value=0)
} for i in range(n_layer)]
finished_seq = layers.zeros_like(alive_seq)
finished_scores = layers.fill_constant([batch_size, beam_size],
dtype='float32', value=-INF)
finished_flags = layers.fill_constant([batch_size, beam_size],
dtype='float32', value=0)
with while_op.block():
pos = layers.fill_constant([caches_batch_size, 1, 1], dtype='int64', value=1)
pos = layers.elementwise_mul(pos, step_idx, axis=0)
alive_seq_1 = layers.reshape(alive_seq, [caches_batch_size, -1])
alive_seq_2 = alive_seq_1[:, -1:]
alive_seq_2 = layers.unsqueeze(alive_seq_2, axes=[1])
logits = wrap_decoder(
trg_vocab_size, max_in_len, n_layer, n_head, d_key,
d_value, d_model, d_inner_hid, prepostprocess_dropout,
attention_dropout, relu_dropout, preprocess_cmd,
postprocess_cmd, weight_sharing, embedding_sharing,
dec_inputs=(alive_seq_2, alive_seq_2, pos, None, tgt_src_attn_bias),
enc_output=enc_output, caches=caches, is_train=False, params_type=params_type)
alive_seq_2, alive_log_probs_2, finished_seq_2, finished_scores_2, finished_flags_2, caches_2 = \
beam_search.inner_func(step_idx, logits, alive_seq_1, alive_log_probs, finished_seq,
finished_scores, finished_flags, caches, enc_output,
tgt_src_attn_bias)
layers.increment(x=step_idx, value=1.0, in_place=True)
finish_cond = beam_search.is_finished(step_idx, source_length, alive_log_probs_2,
finished_scores_2, finished_flags_2)
layers.assign(alive_seq_2, alive_seq)
layers.assign(alive_log_probs_2, alive_log_probs)
layers.assign(finished_seq_2, finished_seq)
layers.assign(finished_scores_2, finished_scores)
layers.assign(finished_flags_2, finished_flags)
for i in xrange(len(caches_2)):
layers.assign(caches_2[i]["k"], caches[i]["k"])
layers.assign(caches_2[i]["v"], caches[i]["v"])
layers.logical_and(x=cond, y=finish_cond, out=cond)
finished_flags = layers.reduce_sum(finished_flags, dim=1, keep_dim=True) / beam_size
finished_flags = layers.cast(finished_flags, 'bool')
mask = layers.cast(layers.reduce_any(input=finished_flags, dim=1, keep_dim=True), 'float32')
mask = layers.expand(mask, [1, beam_size])
mask2 = 1.0 - mask
finished_seq = layers.cast(finished_seq, 'float32')
alive_seq = layers.cast(alive_seq, 'float32')
#print mask
finished_seq = layers.elementwise_mul(finished_seq, mask, axis=0) + \
layers.elementwise_mul(alive_seq, mask2, axis = 0)
finished_seq = layers.cast(finished_seq, 'int32')
finished_scores = layers.elementwise_mul(finished_scores, mask, axis=0) + \
layers.elementwise_mul(alive_log_probs, mask2)
finished_seq.persistable = True
finished_scores.persistable = True
return finished_seq, finished_scores
class Prediction:
def __init__(self, args):
"""
:param model_dir: model dir path
:param vocab_file: vocab file path
"""
self.tf = import_tf(0)
self.args = args
self.model_dir = args.logdir
self.vocab_file = args.vocab
self.token2idx, self.idx2token = _load_vocab(args.vocab)
hparams = Hparams()
parser = hparams.parser
self.hp = parser.parse_args()
self.model = Transformer(self.hp)
self._add_placeholder()
self._init_graph()
def _init_graph(self):
"""
init graph
"""
self.ys = (self.input_y, None, None)
self.xs = (self.input_x, None)
self.memory = self.model.encode(self.xs, False)[0]
self.logits = self.model.decode(self.xs, self.ys, self.memory, False)[0]
ckpt = self.tf.train.get_checkpoint_state(self.model_dir).all_model_checkpoint_paths[-1]
graph = self.logits.graph
sess_config = self.tf.ConfigProto(allow_soft_placement=True)
sess_config.gpu_options.allow_growth = True
saver = self.tf.train.Saver()
self.sess = self.tf.Session(config=sess_config, graph=graph)
self.sess.run(self.tf.global_variables_initializer())
self.tf.reset_default_graph()
saver.restore(self.sess, ckpt)
self.bs = BeamSearch(self.model,
self.hp.beam_size,
list(self.idx2token.keys())[2],
list(self.idx2token.keys())[3],
self.idx2token,
self.hp.maxlen2,
self.input_x,
self.input_y,
self.logits)
def predict(self, content):
"""
abstract prediction by beam search
:param content: article content
:return: prediction result
"""
input_x = content.split()
while len(input_x) < self.args.maxlen1: input_x.append('<pad>')
input_x = input_x[:self.args.maxlen1]
input_x = [self.token2idx.get(s, self.token2idx['<unk>']) for s in input_x]
memory = self.sess.run(self.memory, feed_dict={self.input_x: [input_x]})
return self.bs.search(self.sess, input_x, memory[0])
def _add_placeholder(self):
"""
add tensorflow placeholder
"""
self.input_x = self.tf.placeholder(dtype=self.tf.int32, shape=[None, self.args.maxlen1], name='input_x')
self.input_y = self.tf.placeholder(dtype=self.tf.int32, shape=[None, None], name='input_y')
from model.gnic import Model
model_list = ['mscoco-gnic-nh512-nw512-mb64-V8843/gnic.h5.merge']
models = [Model(model_file=osp.join(SAVE_ROOT, m)) for m in model_list]
valid_set = Reader(batch_size=1,
data_split='test',
vocab_freq='freq5',
stage='test',
data_dir=data_dir,
feature_file='features_1res.h5',
caption_switch='off',
topic_switch='off',
head=0,
tail=1000)
bs = BeamSearch(models, beam_size=3, num_cadidates=500, max_length=20)
scores = validate(bs, valid_set)
if task == 'ss':
from model.ss import Model
model_list = ['mscoco-nh512-nw512-mb64-V8843/ss.h5.merge']
models = [Model(model_file=osp.join(SAVE_ROOT, m)) for m in model_list]
valid_set = Reader(batch_size=1,
data_split='test',
vocab_freq='freq5',
stage='val',
data_dir=data_dir,
feature_file='features_1res.h5',
topic_type='pred',
topic_file='lda_topics.h5',
def __init__(self):
self.rev_vocab = self.load_char_vocab()
self.beam_search = BeamSearch(self.ckpt_file, self.rev_vocab)
