def update(self, labels, preds):
check_label_shapes(labels, preds)
if self.is_logging:
log = LogUtil().getlogger()
labelUtil = LabelUtil()
self.batch_loss = 0.
# log.info(self.audio_paths)
host_name = socket.gethostname()
for label, pred in zip(labels, preds):
label = label.asnumpy()
pred = pred.asnumpy()
seq_length = len(pred) / int(
int(self.batch_size) / int(self.num_gpu))
for i in range(int(int(self.batch_size) / int(self.num_gpu))):
l = remove_blank(label[i])
p = []
probs = []
for k in range(int(seq_length)):
p.append(
np.argmax(pred[
k * int(int(self.batch_size) / int(self.num_gpu)) +
i]))
probs.append(
pred[k * int(int(self.batch_size) / int(self.num_gpu))
+ i])
p = pred_best(p)
l_distance = levenshtein_distance(l, p)
# l_distance = editdistance.eval(labelUtil.convert_num_to_word(l).split(" "), res)
self.total_n_label += len(l)
self.total_l_dist += l_distance
this_cer = float(l_distance) / float(len(l))
if self.is_logging and this_cer > 0.4:
log.info("%s label: %s " %
(host_name, labelUtil.convert_num_to_word(l)))
log.info(
"%s pred : %s , cer: %f (distance: %d/ label length: %d)"
% (host_name, labelUtil.convert_num_to_word(p),
this_cer, l_distance, len(l)))
# log.info("ctc_loss: %.2f" % ctc_loss(l, pred, i, int(seq_length), int(self.batch_size), int(self.num_gpu)))
self.num_inst += 1
self.sum_metric += this_cer
# if self.is_epoch_end:
# loss = ctc_loss(l, pred, i, int(seq_length), int(self.batch_size), int(self.num_gpu))
# self.batch_loss += loss
# if self.is_logging:
# log.info("loss: %f " % loss)
self.total_ctc_loss += 0 # self.batch_loss
def update(self, labels, preds):
check_label_shapes(labels, preds)
if self.is_logging:
log = LogUtil().getlogger()
labelUtil = LabelUtil()
self.batch_loss = 0.
shouldPrint = True
host_name = socket.gethostname()
for label, pred in zip(labels, preds):
label = label.asnumpy()
pred = pred.asnumpy()
seq_length = len(pred) / int(
int(self.batch_size) / int(self.num_gpu))
# sess = tf.Session()
for i in range(int(int(self.batch_size) / int(self.num_gpu))):
l = remove_blank(label[i])
# p = []
probs = []
for k in range(int(seq_length)):
# p.append(np.argmax(pred[k * int(int(self.batch_size) / int(self.num_gpu)) + i]))
probs.append(
pred[k * int(int(self.batch_size) / int(self.num_gpu))
+ i])
# p = pred_best(p)
probs = np.array(probs)
st = time.time()
beam_size = 20
results = ctc_beam_decode(self.scorer, beam_size,
labelUtil.byList, probs)
log.info("decode by ctc_beam cost %.2f result: %s" %
(time.time() - st, "\n".join(results)))
res_str1 = ctc_greedy_decode(probs, labelUtil.byList)
log.info("decode by pred_best: %s" % res_str1)
# max_time_steps = int(seq_length)
# input_log_prob_matrix_0 = np.log(probs) # + 2.0
#
# # len max_time_steps array of batch_size x depth matrices
# inputs = ([
# input_log_prob_matrix_0[t, :][np.newaxis, :] for t in range(max_time_steps)]
# )
#
# inputs_t = [ops.convert_to_tensor(x) for x in inputs]
# inputs_t = array_ops.stack(inputs_t)
#
# st = time.time()
# # run CTC beam search decoder in tensorflow
# decoded, log_probabilities = tf.nn.ctc_beam_search_decoder(inputs_t,
# [max_time_steps],
# beam_width=10,
# top_paths=3,
# merge_repeated=False)
# tf_decoded, tf_log_probs = sess.run([decoded, log_probabilities])
# st1 = time.time() - st
# for index in range(3):
# tf_result = ''.join([labelUtil.byIndex.get(i + 1, ' ') for i in tf_decoded[index].values])
# print("%.2f elpse %.2f, %s" % (tf_log_probs[0][index], st1, tf_result))
l_distance = editdistance.eval(
labelUtil.convert_num_to_word(l).split(" "), res_str1)
# l_distance_beam = editdistance.eval(labelUtil.convert_num_to_word(l).split(" "), beam_result[0][1])
l_distance_beam_cpp = editdistance.eval(
labelUtil.convert_num_to_word(l).split(" "), results[0])
self.total_n_label += len(l)
# self.total_l_dist_beam += l_distance_beam
self.total_l_dist_beam_cpp += l_distance_beam_cpp
self.total_l_dist += l_distance
this_cer = float(l_distance) / float(len(l))
if self.is_logging:
# log.info("%s label: %s " % (host_name, labelUtil.convert_num_to_word(l)))
# log.info("%s pred : %s , cer: %f (distance: %d/ label length: %d)" % (
# host_name, labelUtil.convert_num_to_word(p), this_cer, l_distance, len(l)))
log.info("%s label: %s " %
(host_name, labelUtil.convert_num_to_word(l)))
log.info(
"%s pred : %s , cer: %f (distance: %d/ label length: %d)"
% (host_name, res_str1, this_cer, l_distance, len(l)))
# log.info("%s predb: %s , cer: %f (distance: %d/ label length: %d)" % (
# host_name, " ".join(beam_result[0][1]), float(l_distance_beam) / len(l), l_distance_beam,
# len(l)))
log.info(
"%s predc: %s , cer: %f (distance: %d/ label length: %d)"
% (host_name, " ".join(
results[0]), float(l_distance_beam_cpp) / len(l),
l_distance_beam_cpp, len(l)))
self.total_ctc_loss += self.batch_loss
self.placeholder = res_str1 + "\n" + "\n".join(results)