def convert_to_coverage_model():
"""Load non-coverage checkpoint, add initialized extra variables for coverage, and save as new checkpoint"""
tf.logging.info("converting non-coverage model to coverage model..")
# initialize an entire coverage model from scratch
sess = tf.Session(config=util.get_config())
print("initializing everything...")
sess.run(tf.global_variables_initializer())
# load all non-coverage weights from checkpoint
saver = tf.train.Saver([
v for v in tf.global_variables()
if "coverage" not in v.name and "Adagrad" not in v.name
])
print("restoring non-coverage variables...")
curr_ckpt = util.load_ckpt(saver, sess)
print("restored.")
# save this model and quit
new_fname = curr_ckpt + '_cov_init'
print("saving model to %s..." % (new_fname))
new_saver = tf.train.Saver(
) # this one will save all variables that now exist
new_saver.save(sess, new_fname)
print("saved.")
exit()
def restore_best_model():
"""Load bestmodel file from eval directory, add variables for adagrad, and save to train directory"""
tf.logging.info("Restoring bestmodel for training...")
# Initialize all vars in the model
sess = tf.Session(config=util.get_config())
print("Initializing all variables...")
sess.run(tf.global_variables_initializer())
# Restore the best model from eval dir
saver = tf.train.Saver(
[v for v in tf.all_variables() if "Adagrad" not in v.name])
print("Restoring all non-adagrad variables from best model in eval dir...")
curr_ckpt = util.load_ckpt(saver, sess, "eval")
print("Restored %s." % curr_ckpt)
# Save this model to train dir and quit
new_model_name = curr_ckpt.split("/")[-1].replace("bestmodel", "model")
new_fname = os.path.join(cf.log_root, "train", new_model_name)
print("Saving model to %s..." % (new_fname))
new_saver = tf.train.Saver(
) # this saver saves all variables that now exist, including Adagrad variables
new_saver.save(sess, new_fname)
print("Saved.")
exit()
def run_eval(model, batcher, vocab):
"""Repeatedly runs eval iterations, logging to screen and writing summaries. Saves the model with the best loss seen so far."""
model.build_graph()
saver = tf.train.Saver(max_to_keep=3)
sess = tf.Session(config=util.get_config())
# make a subdir of the root dir for eval data
eval_dir = os.path.join(cf.log_root, "eval")
# this is where checkpoints of best models are saved
bestmodel_save_path = os.path.join(eval_dir, 'bestmodel')
summary_writer = tf.summary.FileWriter(eval_dir)
running_avg_loss = 0 # the eval job keeps a smoother, running average loss to tell it when to implement early stopping
best_loss = None # will hold the best loss achieved so far
while True:
_ = util.load_ckpt(saver, sess) # load a new checkpoint
batch = batcher.next_batch() # get the next batch
# run eval on the batch
t0 = time.time()
results = model.run_eval_step(sess, batch)
t1 = time.time()
tf.logging.info('seconds for batch: %.2f', t1 - t0)
# print the loss and coverage loss to screen
loss = results['loss']
tf.logging.info('loss: %f', loss)
if cf.coverage:
coverage_loss = results['coverage_loss']
tf.logging.info("coverage_loss: %f", coverage_loss)
# add summaries
summaries = results['summaries']
train_step = results['global_step']
summary_writer.add_summary(summaries, train_step)
# calculate running avg loss
running_avg_loss = calc_running_avg_loss(np.asscalar(loss),
running_avg_loss,
summary_writer, train_step)
# If running_avg_loss is best so far, save this checkpoint (early stopping).
# These checkpoints will appear as bestmodel-<iteration_number> in the eval dir
if best_loss is None or running_avg_loss < best_loss:
tf.logging.info(
'Found new best model with %.3f running_avg_loss. Saving to %s',
running_avg_loss, bestmodel_save_path)
saver.save(sess,
bestmodel_save_path,
global_step=train_step,
latest_filename='checkpoint_best')
best_loss = running_avg_loss
# flush the summary writer every so often
if train_step % 100 == 0:
summary_writer.flush()
def __init__(self, model, batcher, vocab):
"""Initialize decoder.
Args:
model: a Seq2SeqAttentionModel object.
batcher: a Batcher object.
vocab: Vocabulary object
"""
self._model = model
self._model.build_graph()
self._batcher = batcher
self._vocab = vocab
self._saver = tf.train.Saver()
self._sess = tf.Session(config=util.get_config())
# Load an initial checkpoint to use for decoding
ckpt_path = util.load_ckpt(self._saver, self._sess)
if cf.single_pass:
# Make a descriptive decode directory name
# this is something of the form "ckpt-123456"
ckpt_name = "ckpt-" + ckpt_path.split('-')[-1]
self._decode_dir = os.path.join(cf.log_root,
get_decode_dir_name(ckpt_name))
if os.path.exists(self._decode_dir):
raise Exception(
"single_pass decode directory %s should not already exist"
% self._decode_dir)
else: # Generic decode dir name
self._decode_dir = os.path.join(cf.log_root, "decode")
# Make the decode dir if necessary
if not os.path.exists(self._decode_dir): os.mkdir(self._decode_dir)
if cf.single_pass:
# Make the dirs to contain output written in the correct format for pyrouge
self._rouge_ref_dir = os.path.join(self._decode_dir, "reference")
if not os.path.exists(self._rouge_ref_dir):
os.mkdir(self._rouge_ref_dir)
self._rouge_dec_dir = os.path.join(self._decode_dir, "decoded")
if not os.path.exists(self._rouge_dec_dir):
os.mkdir(self._rouge_dec_dir)
def decode(self):
"""
Decode examples until data is exhausted (if cf.single_pass) and return,
or decode indefinitely, loading latest checkpoint at regular intervals
"""
t0 = time.time()
start_time = t0
counter = 0
while True:
# 1 example repeated across batch
batch = self._batcher.next_batch()
if batch is None: # finished decoding dataset in single_pass mode
assert cf.single_pass, "Dataset exhausted, but we are not in single_pass mode"
tf.logging.info(
"Decoder has finished reading dataset for single_pass, using %d seconds.",
time.time() - start_time)
tf.logging.info(
"Output has been saved in %s and %s. Now starting ROUGE eval...",
self._rouge_ref_dir, self._rouge_dec_dir)
#todo: need to update for rouge
# results_dict = rouge_eval(self._rouge_ref_dir,
# self._rouge_dec_dir)
# rouge_log(results_dict, self._decode_dir)
return
original_context = batch.original_contexts[0] # string
original_query = batch.original_querys[0]
original_summarization = batch.original_summarizations[0] # string
#original_abstract_sents = batch.original_abstracts_sents[
# 0] # list of strings
context_withunks = data.show_art_oovs(original_context, self._vocab)
abstract_withunks = data.show_abs_oovs(
original_summarization, self._vocab,
(batch.art_oovs[0] if cf.pointer_gen else None)) # string
# Run beam search to get best Hypothesis
best_hyp = beam_search.run_beam_search(self._sess, self._model,
self._vocab, batch)
# export_path = os.path.join(cf.export_dir,str(cf.export_version))
# Extract the output ids from the hypothesis and convert back to words
output_ids = [int(t) for t in best_hyp.tokens[1:]]
decoded_words = data.outputids2words(
output_ids, self._vocab, (batch.art_oovs[0]
if cf.pointer_gen else None))
# Remove the [STOP] token from decoded_words, if necessary
try:
# index of the (first) [STOP] symbol
fst_stop_idx = decoded_words.index(data.MARK_EOS)
decoded_words = decoded_words[:fst_stop_idx]
except ValueError:
decoded_words = decoded_words
decoded_output = ''.join(decoded_words) # single string
if cf.single_pass:
#todo: need to check
# write ref summary and decoded summary to file, to eval with pyrouge later
self.write_result(original_context, original_summarization,
decoded_words, counter)
# self.write_for_eval(original_summarization, output_ids,
# counter)
counter += 1 # this is how many examples we've decoded
else:
# log output to screen
print_results(context_withunks, abstract_withunks,
decoded_output)
# write info to .json file for visualization tool
self.write_for_attnvis(context_withunks, abstract_withunks,
decoded_words, best_hyp.attn_dists)
# Check if SECS_UNTIL_NEW_CKPT has elapsed;
# if so return so we can load a new checkpoint
t1 = time.time()
if t1 - t0 > SECS_UNTIL_NEW_CKPT:
tf.logging.info(
'We\'ve been decoding with same checkpoint for %i seconds. Time to load new checkpoint',
t1 - t0)
_ = util.load_ckpt(self._saver, self._sess)
t0 = time.time()