def insight(self,
data,
spklist,
batch_type="softmax",
output_embeddings=False,
aux_data=None):
"""Just use to debug the network
"""
self.sess.run(tf.global_variables_initializer())
self.sess.run(tf.local_variables_initializer())
assert batch_type == "softmax" or batch_type == "end2end", "The batch_type can only be softmax or end2end"
embeddings_val = None
labels_val = None
self.load()
if output_embeddings:
# If we want to output embeddings, the features should be loaded in order
data_loader = KaldiDataSeqQueue(
data,
spklist,
num_parallel=2,
max_qsize=10,
batch_size=self.params.num_speakers_per_batch *
self.params.num_segments_per_speaker,
min_len=self.params.min_segment_len,
max_len=self.params.max_segment_len,
shuffle=False)
data_loader.start()
tf.logging.info("Generate valid embeddings.")
# In this mode, the embeddings and labels will be saved and output. It needs more memory and takes longer
# to process these values.
while True:
try:
features, labels = data_loader.fetch()
valid_emb_val, valid_labels_val, endpoints_val = self.sess.run(
[self.embeddings, self.valid_labels, self.endpoints],
feed_dict={
self.valid_features: features,
self.valid_labels: labels
})
# acc = np.sum(np.equal(np.argmax(endpoints_val['logits'], axis=1), labels, dtype=np.float)) / float(
# labels.shape[0])
# print("Acc: %f" % acc)
# Save the embeddings and labels
if embeddings_val is None:
embeddings_val = valid_emb_val
labels_val = valid_labels_val
else:
embeddings_val = np.concatenate(
(embeddings_val, valid_emb_val), axis=0)
labels_val = np.concatenate(
(labels_val, valid_labels_val), axis=0)
except DataOutOfRange:
break
data_loader.stop()
if batch_type == "softmax":
data_loader = KaldiDataSeqQueue(
data,
spklist,
num_parallel=2,
max_qsize=10,
batch_size=self.params.num_speakers_per_batch *
self.params.num_segments_per_speaker * 10,
min_len=self.params.min_segment_len,
max_len=self.params.max_segment_len,
shuffle=True)
elif batch_type == "end2end":
# The num_valid_speakers_per_batch and num_valid_segments_per_speaker are only required when
# End2End loss is used. Since we switch the loss function to softmax generalized e2e loss
# when the e2e loss is used.
assert "num_valid_speakers_per_batch" in self.params.dict and "num_valid_segments_per_speaker" in self.params.dict, \
"Valid parameters should be set if E2E loss is selected"
data_loader = KaldiDataRandomQueue(
data,
spklist,
num_parallel=2,
max_qsize=10,
num_speakers=self.params.num_valid_speakers_per_batch,
num_segments=self.params.num_valid_segments_per_speaker,
min_len=self.params.min_segment_len,
max_len=self.params.max_segment_len,
shuffle=True)
else:
raise ValueError
data_loader.start()
while True:
try:
features, labels = data_loader.fetch()
_, endpoints_val = self.sess.run(
[self.valid_ops["valid_loss_op"], self.endpoints],
feed_dict={
self.valid_features: features,
self.valid_labels: labels
})
except DataOutOfRange:
break
data_loader.stop()
loss = self.sess.run(self.valid_ops["valid_loss"])
tf.logging.info(
"Shorter segments are used to test the valid loss (%d-%d)" %
(self.params.min_segment_len, self.params.max_segment_len))
tf.logging.info("Loss: %f" % loss)
# while True:
# try:
# features, labels = data_loader.fetch()
# valid_ops, endpoints_val = self.sess.run([self.valid_ops, self.endpoints], feed_dict={self.valid_features: features,
# self.valid_labels: labels})
# loss = valid_ops["valid_loss"]
# except DataOutOfRange:
# break
# data_loader.stop()
# tf.logging.info("Loss: %f" % loss)
acc = np.sum(
np.equal(np.argmax(endpoints_val['logits'], axis=1),
labels,
dtype=np.float)) / float(labels.shape[0])
print("Acc: %f" % acc)
import pdb
pdb.set_trace()
# from model.test_utils import softmax
# with tf.variable_scope("softmax", reuse=True):
# test = tf.get_variable("output/kernel")
# test_val = self.sess.run(test)
return loss, embeddings_val, labels_val
embeddings_val = None
labels_val = None
data_loader = KaldiDataSeqQueue(args.data_dir,
args.data_spklist,
num_parallel=1,
max_qsize=10,
batch_size=params.num_speakers_per_batch *
params.num_segments_per_speaker,
min_len=params.min_segment_len,
max_len=params.max_segment_len,
shuffle=False)
data_loader.start()
while True:
try:
features, labels = data_loader.fetch()
valid_emb_val, valid_labels_val, _ = trainer.sess.run(
[
trainer.embeddings, trainer.valid_labels,
trainer.valid_ops["valid_loss_op"]
],
feed_dict={
trainer.valid_features: features,
trainer.valid_labels: labels,
trainer.global_step: curr_step
})
# Save the embeddings and labels
if embeddings_val is None:
embeddings_val = valid_emb_val
labels_val = valid_labels_val
else:
def valid(self,
data,
spklist,
batch_type="softmax",
output_embeddings=False,
aux_data=None):
"""Evaluate on the validation set
Args:
data: The training data directory.
spklist: The spklist is a file map speaker name to the index.
batch_type: `softmax` or `end2end`. The batch is `softmax-like` or `end2end-like`.
If the batch is `softmax-like`, each sample are from different speakers;
if the batch is `end2end-like`, the samples are from N speakers with M segments per speaker.
output_embeddings: Set True to output the corresponding embeddings and labels of the valid set.
If output_embeddings, an additional valid metric (e.g. EER) should be computed outside
the function.
aux_data: The auxiliary data directory.
:return: valid_loss, embeddings and labels (None if output_embeddings is False).
"""
# Initialization will reset all the variables in the graph.
# The local variables are also need to be initialized for metrics function.
self.sess.run(tf.global_variables_initializer())
self.sess.run(tf.local_variables_initializer())
assert batch_type == "softmax" or batch_type == "end2end", "The batch_type can only be softmax or end2end"
curr_step = 0
# Load the model. The valid function can only be called after training (of course...)
if os.path.isfile(os.path.join(self.model, "checkpoint")):
curr_step = self.load()
else:
tf.logging.info(
"[Warning] Cannot find model in %s. Random initialization is used in validation."
% self.model)
embeddings_val = None
labels_val = None
num_batches = 0
if output_embeddings:
# If we want to output embeddings, the features should be loaded in order
data_loader = KaldiDataSeqQueue(
data,
spklist,
num_parallel=2,
max_qsize=10,
batch_size=self.params.num_speakers_per_batch *
self.params.num_segments_per_speaker,
min_len=self.params.min_segment_len,
max_len=self.params.max_segment_len,
shuffle=False)
data_loader.start()
tf.logging.info("Generate valid embeddings.")
# In this mode, the embeddings and labels will be saved and output. It needs more memory and takes longer
# to process these values.
while True:
try:
if num_batches % 100 == 0:
tf.logging.info("valid step: %d" % num_batches)
features, labels = data_loader.fetch()
valid_emb_val, valid_labels_val = self.sess.run(
[self.embeddings, self.valid_labels],
feed_dict={
self.valid_features: features,
self.valid_labels: labels,
self.global_step: curr_step
})
# Save the embeddings and labels
if embeddings_val is None:
embeddings_val = valid_emb_val
labels_val = valid_labels_val
else:
embeddings_val = np.concatenate(
(embeddings_val, valid_emb_val), axis=0)
labels_val = np.concatenate(
(labels_val, valid_labels_val), axis=0)
num_batches += 1
except DataOutOfRange:
break
data_loader.stop()
if batch_type == "softmax":
data_loader = KaldiDataSeqQueue(
data,
spklist,
num_parallel=2,
max_qsize=10,
batch_size=self.params.num_speakers_per_batch *
self.params.num_segments_per_speaker,
min_len=self.params.min_segment_len,
max_len=self.params.max_segment_len,
shuffle=True)
elif batch_type == "end2end":
# The num_valid_speakers_per_batch and num_valid_segments_per_speaker are only required when
# End2End loss is used. Since we switch the loss function to softmax generalized e2e loss
# when the e2e loss is used.
assert "num_valid_speakers_per_batch" in self.params.dict and "num_valid_segments_per_speaker" in self.params.dict, \
"Valid parameters should be set if E2E loss is selected"
data_loader = KaldiDataRandomQueue(
data,
spklist,
num_parallel=2,
max_qsize=10,
num_speakers=self.params.num_valid_speakers_per_batch,
num_segments=self.params.num_valid_segments_per_speaker,
min_len=self.params.min_segment_len,
max_len=self.params.max_segment_len,
shuffle=True)
else:
raise ValueError
data_loader.start()
num_batches = 0
for _ in range(self.params.valid_max_iterations):
try:
if num_batches % 100 == 0:
tf.logging.info("valid step: %d" % num_batches)
features, labels = data_loader.fetch()
_ = self.sess.run(self.valid_ops["valid_loss_op"],
feed_dict={
self.valid_features: features,
self.valid_labels: labels,
self.global_step: curr_step
})
num_batches += 1
except DataOutOfRange:
break
data_loader.stop()
loss, summary = self.sess.run(
[self.valid_ops["valid_loss"], self.valid_summary])
# We only save the summary for the last batch.
self.valid_summary_writer.add_summary(summary, curr_step)
# The valid loss is averaged over all the batches.
tf.logging.info("[Validation %d batches] valid loss: %f" %
(num_batches, loss))
# The output embeddings and labels can be used to compute EER or other metrics
return loss, embeddings_val, labels_val