def _decode_inference_indices(model, sess, output_infer,
output_infer_summary_prefix, inference_indices,
tgt_sos, tgt_eos, bpe_delimiter):
"""Decoding only a specific set of sentences."""
utils.print_out(" decoding to output %s , num sents %d." %
(output_infer, len(inference_indices)))
start_time = time.time()
with codecs.getwriter("utf-8")(tf.gfile.GFile(output_infer,
mode="wb")) as trans_f:
trans_f.write("") # Write empty string to ensure file is created.
for decode_id in inference_indices:
nmt_outputs, infer_summary = model.decode(sess)
# get text translation
assert nmt_outputs.shape[0] == 1
translation = nmt_utils.get_translation(
nmt_outputs,
sent_id=0,
tgt_sos=tgt_sos,
tgt_eos=tgt_eos,
bpe_delimiter=bpe_delimiter)
if infer_summary is not None: # Attention models
image_file = output_infer_summary_prefix + str(
decode_id) + ".png"
utils.print_out(" save attention image to %s*" % image_file)
image_summ = tf.Summary()
image_summ.ParseFromString(infer_summary)
with tf.gfile.GFile(image_file, mode="w") as img_f:
img_f.write(image_summ.value[0].image.encoded_image_string)
trans_f.write("%s\n" % translation)
utils.print_out(b"%s\n" % translation)
utils.print_time(" done", start_time)
def _sample_decode(model, global_step, sess, hparams, iterator, src_data,
tgt_data, iterator_src_placeholder,
iterator_batch_size_placeholder, summary_writer):
"""Pick a sentence and decode."""
decode_id = random.randint(0, len(src_data) - 1)
utils.print_out(" # %d" % decode_id)
iterator_feed_dict = {
iterator_src_placeholder: [src_data[decode_id]],
iterator_batch_size_placeholder: 1,
}
sess.run(iterator.initializer, feed_dict=iterator_feed_dict)
nmt_outputs, attention_summary = model.decode(sess)
if hparams.beam_width > 0:
# get the top translation.
nmt_outputs = nmt_outputs[0]
translation = nmt_utils.get_translation(
nmt_outputs,
sent_id=0,
tgt_eos=hparams.eos,
subword_option=hparams.subword_option)
#utils.print_out(b" src: " + src_data[decode_id])
#utils.print_out(b" ref: " + tgt_data[decode_id])
#utils.print_out(b" nmt: " + translation)
# Summary
if attention_summary is not None:
summary_writer.add_summary(attention_summary, global_step)
def _sample_decode(model, global_step, sess, hparams, iterator, src_data,
tgt_data, iterator_src_placeholder,
iterator_batch_size_placeholder, summary_writer):
"""Pick a sentence and decode."""
iterator_feed_dict = {
iterator_src_placeholder: src_data[-hparams.infer_batch_size:],
iterator_batch_size_placeholder: hparams.infer_batch_size,
}
sess.run(iterator.initializer, feed_dict=iterator_feed_dict)
nmt_outputs, att_w_history, ext_w_history = model.decode(sess)
if hparams.beam_width > 0:
# get the top translation.
nmt_outputs = nmt_outputs[0]
nmt_outputs = np.asarray(nmt_outputs)
outputs = []
for i in range(hparams.infer_batch_size):
tmp = {}
translation = nmt_utils.get_translation(
nmt_outputs,
sent_id=i,
tgt_sos=hparams.sos,
tgt_eos=hparams.eos,
bpe_delimiter=hparams.bpe_delimiter)
if i <= 5:
utils.print_out(" src: %s" %
src_data[-hparams.infer_batch_size + i])
utils.print_out(" ref: %s" %
tgt_data[-hparams.infer_batch_size + i])
utils.print_out(b" nmt: %s" % translation)
tmp['src'] = src_data[-hparams.infer_batch_size + i]
tmp['ref'] = tgt_data[-hparams.infer_batch_size + i]
tmp['nmt'] = translation
if att_w_history is not None:
tmp['attention_head'] = att_w_history[-hparams.infer_batch_size +
i]
if ext_w_history is not None:
for j, ext_head in enumerate(ext_w_history):
tmp['ext_head_{0}'.format(j)] = ext_head[
-hparams.infer_batch_size + i]
outputs.append(tmp)
if hparams.record_w_history:
with open(
hparams.out_dir + '/heads_step_{0}.pickle'.format(global_step),
'wb') as f:
if len(outputs) > 0:
pickle.dump(outputs, f)
def _sample_decode(model, global_step, sess, hparams, iterator, src_data,
tgt_data, lbl_data, iterator_src_placeholder,
iterator_batch_size_placeholder, summary_writer):
"""Pick a sentence and decode."""
decode_id = random.randint(0, len(src_data) - 1)
utils.print_out(" # %d" % decode_id)
iterator_feed_dict = {
iterator_src_placeholder: [src_data[decode_id]],
iterator_batch_size_placeholder: 1,
}
sess.run(iterator.initializer, feed_dict=iterator_feed_dict)
if hparams.task == "joint":
outputs, intent_pred, src_seq_length, attention_summary = model.decode(
sess)
elif hparams.task == "intent":
intent_pred, attention_summary = model.decode(sess)
if hparams.infer_mode == "beam_search":
# get the top translation.
outputs = outputs[0]
utils.print_out(" intent: %s" % lbl_data[decode_id])
utils.print_out(" src: %s" % src_data[decode_id])
if hparams.task == "joint":
translation = nmt_utils.get_translation(
outputs,
src_seq_length,
sent_id=0,
tgt_eos=hparams.eos,
subword_option=hparams.subword_option)
utils.print_out(" slot (ref): %s" % tgt_data[decode_id])
utils.print_out(b" intent (pred): %s" % intent_pred[0])
utils.print_out(b" slot (pred): %s\n" % translation)
elif hparams.task == "intent":
utils.print_out(b" intent (pred): %s" % intent_pred[0])
# Summary
if attention_summary is not None:
summary_writer.add_summary(attention_summary, global_step)
def decode(ckpt_path, inference_input_file, inference_output_file, hparams):
model_creator = get_model_creator(hparams)
infer_model = model_helper.create_infer_model(model_creator, hparams, None)
sess, loaded_infer_model = start_sess_and_load_model(
infer_model, ckpt_path)
# Read data
infer_data = load_data(inference_input_file, hparams)
print("infer_data:", infer_data)
for i in range(len(infer_data)):
sess.run(infer_model.iterator.initializer,
feed_dict={
infer_model.src_placeholder: [infer_data[i]],
infer_model.batch_size_placeholder: 1
})
if hparams.task == "joint":
outputs, intent_pred, src_seq_length, attention_summary = \
loaded_infer_model.decode(sess)
elif hparams.task == "intent":
intent_pred, attention_summary = loaded_infer_model.decode(sess)
if hparams.infer_mode == "beam_search":
# get the top translation.
outputs = outputs[0]
utils.print_out(" src: %s" % infer_data[i])
if hparams.task == "joint":
translation = nmt_utils.get_translation(
outputs,
src_seq_length,
sent_id=0,
tgt_eos=hparams.eos,
subword_option=hparams.subword_option)
utils.print_out(b" intent (pred): %s" % intent_pred[0])
utils.print_out(b" slot (pred): %s\n" % translation)
elif hparams.task == "intent":
utils.print_out(b" intent (pred): %s\n" % intent_pred[0])