def test_read_train_dev_test():
data_dir = os.path.join(root_dir, "data/ptb")
train_path = os.path.join(data_dir, "train")
dev_path = os.path.join(data_dir, "valid")
test_path = os.path.join(data_dir, "test")
cache_dir = os.path.join(root_dir, "data/ptb/cache")
vocab_size = 20000
if not os.path.exists(cache_dir):
os.mkdir(cache_dir)
train_data_bucket, dev_data_bucket, _buckets, vocab_path = data_util.read_train_dev(
cache_dir, train_path, dev_path, vocab_size, 100, 10)
test_data_bucket, _buckets_test = data_util.read_test(
cache_dir, test_path, vocab_path, vocab_size, 100, 10)
def print_bucket_data(data):
l = [len(x) for x in data]
print l
print "_buckets: {}\n".format(_buckets)
print_bucket_data(train_data_bucket)
print_bucket_data(dev_data_bucket)
print "_buckets_test: {}\n".format(_buckets_test)
print_bucket_data(test_data_bucket)
def dump_lstm():
# dump the hidden states to some where
mylog_section("READ DATA")
test_data_bucket, _buckets, test_data_order = read_test(
FLAGS.data_cache_dir, FLAGS.test_path,
get_vocab_path(FLAGS.data_cache_dir), FLAGS.L, FLAGS.n_bucket)
vocab_path = get_vocab_path(FLAGS.data_cache_dir)
real_vocab_size = get_real_vocab_size(vocab_path)
FLAGS._buckets = _buckets
FLAGS.real_vocab_size = real_vocab_size
test_bucket_sizes = [
len(test_data_bucket[b]) for b in range(len(_buckets))
]
test_total_size = int(sum(test_bucket_sizes))
# reports
mylog_section("REPORT")
mylog("real_vocab_size: {}".format(FLAGS.real_vocab_size))
mylog("_buckets:{}".format(FLAGS._buckets))
mylog("DUMP_LSTM:")
mylog("total: {}".format(test_total_size))
mylog("buckets: {}".format(test_bucket_sizes))
config = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False)
config.gpu_options.allow_growth = FLAGS.allow_growth
with tf.Session(config=config) as sess:
# runtime profile
if FLAGS.profile:
run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
else:
run_options = None
run_metadata = None
mylog_section("MODEL")
mylog("Creating Model")
model = create_model(sess, run_options, run_metadata)
mylog("Init tensors to dump")
model.init_dump_states()
# dump_graph('graph.txt')
mylog_section("All Variables")
show_all_variables()
sess.run(model.dropoutRate.assign(1.0))
start_id = 0
n_steps = 0
batch_size = FLAGS.batch_size
mylog_section("Data Iterators")
dite = DataIterator(model,
test_data_bucket,
len(_buckets),
batch_size,
None,
data_order=test_data_order)
ite = dite.next_original()
fdump = open(FLAGS.dump_file, 'wb')
mylog_section("DUMP_LSTM")
i_sent = 0
for inputs, outputs, weights, bucket_id in ite:
# inputs: [[_GO],[1],[2],[3],[_EOS],[pad_id],[pad_id]]
# positions: [4]
mylog("--- decoding {}/{} sent ---".format(i_sent,
test_total_size))
i_sent += 1
# print(inputs)
# print(outputs)
# print(weights)
# print(bucket_id)
L, states = model.step(sess,
inputs,
outputs,
weights,
bucket_id,
forward_only=True,
dump_lstm=True)
mylog("LOSS: {}".format(L))
sw = StateWrapper()
sw.create(inputs, outputs, weights, states)
sw.save_to_stream(fdump)
# do the following convert:
# inputs: [[pad_id],[1],[2],[pad_id],[pad_id],[pad_id]]
# positions:[2]
fdump.close()
def force_decode():
# force_decode it: generate a file which contains every score and the final score;
mylog_section("READ DATA")
#读入test数据,test不需要新建立词典，直接调用建立好的词典就可以了。
test_data_bucket, _buckets, test_data_order = read_test(
FLAGS.data_cache_dir, FLAGS.test_path,
get_vocab_path(FLAGS.data_cache_dir), FLAGS.L, FLAGS.n_bucket)
vocab_path = get_vocab_path(FLAGS.data_cache_dir)
real_vocab_size = get_real_vocab_size(vocab_path)
FLAGS._buckets = _buckets
FLAGS.real_vocab_size = real_vocab_size
test_bucket_sizes = [
len(test_data_bucket[b]) for b in range(len(_buckets))
]
test_total_size = int(sum(test_bucket_sizes))
# reports
mylog_section("REPORT")
mylog("real_vocab_size: {}".format(FLAGS.real_vocab_size))
mylog("_buckets:{}".format(FLAGS._buckets))
mylog("FORCE_DECODE:")
mylog("total: {}".format(test_total_size))
mylog("bucket_sizes: {}".format(test_bucket_sizes))
config = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False)
config.gpu_options.allow_growth = FLAGS.allow_growth
mylog_section("IN TENSORFLOW")
with tf.Session(config=config) as sess:
# runtime profile
if FLAGS.profile:
run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
else:
run_options = None
run_metadata = None
mylog("Creating Model")
model = create_model(sess, run_options, run_metadata)
mylog_section("All Variables")
show_all_variables()
sess.run(model.dropoutRate.assign(1.0))
batch_size = FLAGS.batch_size
mylog_section("Data Iterators")
dite = DataIterator(model,
test_data_bucket,
len(_buckets),
batch_size,
None,
data_order=test_data_order)
ite = dite.next_original()
fdump = open(FLAGS.score_file, 'w')
i_sent = 0
mylog_section("FORCE_DECODING")
for inputs, outputs, weights, bucket_id in ite:
# inputs: [[_GO],[1],[2],[3],[_EOS],[pad_id],[pad_id]]
# positions: [4]
mylog("--- decoding {}/{} sent ---".format(i_sent,
test_total_size))
i_sent += 1
L = model.step(sess,
inputs,
outputs,
weights,
bucket_id,
forward_only=True,
dump_lstm=False)
mylog("LOSS: {}".format(L))
fdump.write("{}\n".format(L))
fdump.close()
def beam_decode():
# not yet tested:
# known issues:
# should use next_original
mylog("Reading Data...")
test_data_bucket, _buckets, test_data_order = read_test(
FLAGS.data_cache_dir, FLAGS.test_path,
get_vocab_path(FLAGS.data_cache_dir), FLAGS.L, FLAGS.n_bucket)
vocab_path = get_vocab_path(FLAGS.data_cache_dir)
real_vocab_size = get_real_vocab_size(vocab_path)
FLAGS._buckets = _buckets
FLAGS.real_vocab_size = real_vocab_size
test_bucket_sizes = [
len(test_data_bucket[b]) for b in range(len(_buckets))
]
test_total_size = int(sum(test_bucket_sizes))
# reports
mylog("real_vocab_size: {}".format(FLAGS.real_vocab_size))
mylog("_buckets:{}".format(FLAGS._buckets))
mylog("BEAM_DECODE:")
mylog("total: {}".format(test_total_size))
mylog("buckets: {}".format(test_bucket_sizes))
config = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False)
config.gpu_options.allow_growth = FLAGS.allow_growth
with tf.Session(config=config) as sess:
# runtime profile
if FLAGS.profile:
run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
else:
run_options = None
run_metadata = None
mylog("Creating Model")
model = create_model(sess, run_options, run_metadata)
mylog("before init_beam_decoder()")
show_all_variables()
model.init_beam_decoder(beam_size=FLAGS.beam_size,
max_steps=FLAGS.beam_step)
model.init_beam_variables(sess)
mylog("after init_beam_decoder()")
show_all_variables()
sess.run(model.dropoutRate.assign(1.0))
start_id = 0
n_steps = 0
batch_size = FLAGS.batch_size
dite = DataIterator(model, test_data_bucket, len(_buckets), batch_size,
None)
ite = dite.next_sequence(stop=True, test=True)
i_sent = 0
for inputs, positions, valids, bucket_id in ite:
# user : [0]
# inputs: [[_GO],[1],[2],[3],[_EOS],[pad_id],[pad_id]]
# positions: [4]
print("--- decoding {}/{} sent ---".format(i_sent, n_total_user))
i_sent += 1
# do the following convert:
# inputs: [[pad_id],[1],[2],[pad_id],[pad_id],[pad_id]]
# positions:[2]
PAD_ID = 0
last_history = inputs[positions[0]]
inputs_beam = [last_history * FLAGS.beam_size]
inputs[positions[0]] = list([PAD_ID] * FLAGS.beam_size)
inputs[positions[0] - 1] = list([PAD_ID] * FLAGS.beam_size)
positions[0] = positions[0] - 2 if positions[0] >= 2 else 0
scores = [0.0] * FLAGS.beam_size
sentences = [[] for x in range(FLAGS.beam_size)]
beam_parent = range(FLAGS.beam_size)
for i in range(FLAGS.beam_step):
if i == 0:
top_value, top_index = model.beam_step(
sess,
index=i,
word_inputs_history=inputs,
sequence_length=positions,
word_inputs_beam=inputs_beam)
else:
top_value, top_index = model.beam_step(
sess,
index=i,
word_inputs_beam=inputs_beam,
beam_parent=beam_parent)
# expand
global_queue = []
if i == 0:
nrow = 1
else:
nrow = top_index[0].shape[0]
for row in range(nrow):
for col in range(top_index[0].shape[1]):
score = scores[row] + np.log(top_value[0][row, col])
word_index = top_index[0][row, col]
beam_index = row
if FLAGS.no_repeat:
if not word_index in sentences[beam_index]:
global_queue.append(
(score, beam_index, word_index))
else:
global_queue.append(
(score, beam_index, word_index))
global_queue = sorted(global_queue, key=lambda x: -x[0])
inputs_beam = []
beam_parent = []
scores = []
temp_sentences = []
if FLAGS.print_beam:
print("--------- Step {} --------".format(i))
for j, (score, beam_index, word_index) in enumerate(
global_queue[:FLAGS.beam_size]):
if FLAGS.print_beam:
print("Beam:{} Father:{} word:{} score:{}".format(
j, beam_index, word_index, score))
beam_parent.append(beam_index)
inputs_beam.append(word_index)
scores.append(score)
temp_sentences.append(sentences[beam_index] + [word_index])
inputs_beam = [inputs_beam]
sentences = temp_sentences
if FLAGS.print_beam:
print(sentences)
