def objective_function(parameters, beta=1.0):
epoch = parameters[0]
weights_h5 = WEIGHTS_H5.format(epoch=epoch)
sequence_embedding = SequenceEmbedding.from_disk(
architecture_yml, weights_h5)
fX = sequence_embedding.transform(X, batch_size=batch_size)
# compute distance between every pair of sequences
y_distance = pdist(fX, metric=distance)
# compute same/different groundtruth
y_true = pdist(y, metric='chebyshev') < 1
# false positive / true positive
fpr, tpr, thresholds = sklearn.metrics.roc_curve(
y_true, -y_distance, pos_label=True, drop_intermediate=True)
fnr = 1. - tpr
far = fpr
thresholds = -thresholds
fscore = 1. - f_measure(1. - fnr, 1. - far, beta=beta)
i = np.nanargmin(fscore)
alphas[epoch] = float(thresholds[i])
return fscore[i]
def speaker_diarization_xp(sequence_embedding, X, y, distance='angular'):
fX = sequence_embedding.transform(X)
# compute distance between every pair of sequences
y_pred = pdist(fX, metric=distance)
# compute same/different groundtruth
y_true = pdist(y, metric='chebyshev') < 1
# return DET curve
return det_curve(y_true, y_pred, distances=True)
def _validate_init_turn(self, protocol_name, subset='development'):
np.random.seed(1337)
protocol = get_protocol(protocol_name,
progress=False,
preprocessors=self.preprocessors_)
batch_generator = SpeechTurnSubSegmentGenerator(
self.feature_extraction_, self.duration, per_label=10, per_turn=5)
batch = next(batch_generator(protocol, subset=subset))
X = np.stack(batch['X'])
y = np.stack(batch['y'])
z = np.stack(batch['z'])
# get list of labels from list of repeated labels:
# z 0 0 0 1 1 1 2 2 2 2 3 3 3 3
# y A A A A A A B B B B B B B B
# becomes
# z 0 0 0 1 1 1 2 2 2 2 3 3 3 3
# y A B
yz = np.vstack([y, z]).T
y = []
for _, yz_ in itertools.groupby(yz, lambda t: t[1]):
yz_ = np.stack(yz_)
y.append(yz_[0, 0])
y = np.array(y).reshape((-1, 1))
# precompute same/different groundtruth
y = pdist(y, metric='equal')
return {'X': X, 'y': y, 'z': z}
def apply(self, fX):
from hdbscan import HDBSCAN
clusterer = HDBSCAN(min_cluster_size=self.min_cluster_size,
min_samples=self.min_samples,
metric='precomputed')
distance_matrix = squareform(pdist(fX, metric=self.metric))
# apply clustering
cluster_labels = clusterer.fit_predict(distance_matrix)
# cluster embedding
n_clusters = np.max(cluster_labels) + 1
if n_clusters < 2:
return np.zeros(fX.shape[0], dtype=np.int)
fC = l2_normalize(
np.vstack([np.sum(fX[cluster_labels == k, :], axis=0)
for k in range(n_clusters)]))
# tag each undefined embedding to closest cluster
undefined = cluster_labels == -1
closest_cluster = np.argmin(
cdist(fC, fX[undefined, :], metric=self.metric), axis=0)
cluster_labels[undefined] = closest_cluster
return cluster_labels
def _validate_init_turn(self, protocol_name, subset='development'):
np.random.seed(1337)
protocol = get_protocol(protocol_name, progress=False,
preprocessors=self.preprocessors_)
batch_generator = SpeechTurnSubSegmentGenerator(
self.feature_extraction_, self.duration,
per_label=10, per_turn=5)
batch = next(batch_generator(protocol, subset=subset))
X = np.stack(batch['X'])
y = np.stack(batch['y'])
z = np.stack(batch['z'])
# get list of labels from list of repeated labels:
# z 0 0 0 1 1 1 2 2 2 2 3 3 3 3
# y A A A A A A B B B B B B B B
# becomes
# z 0 0 0 1 1 1 2 2 2 2 3 3 3 3
# y A B
yz = np.vstack([y, z]).T
y = []
for _, yz_ in itertools.groupby(yz, lambda t: t[1]):
yz_ = np.stack(yz_)
y.append(yz_[0, 0])
y = np.array(y).reshape((-1, 1))
# precompute same/different groundtruth
y = pdist(y, metric='equal')
return {'X': X, 'y': y, 'z': z}
def on_epoch_end(self, epoch, logs={}):
# keep track of current time
now = datetime.datetime.now().isoformat()
embedding = self.extract_embedding(self.model)
fX = embedding.predict(self.X_)
distance = pdist(fX, metric=self.distance)
prefix = self.log_dir + '/{subset}.plot.{epoch:04d}'.format(
subset=self.subset, epoch=epoch)
# plot distance distribution every 20 epochs (and 10 first epochs)
xlim = get_range(metric=self.distance)
if (epoch < 10) or (epoch % 20 == 0):
plot_distributions(self.y_,
distance,
prefix,
xlim=xlim,
ymax=3,
nbins=100,
dpi=75)
# plot DET curve once every 20 epochs (and 10 first epochs)
if (epoch < 10) or (epoch % 20 == 0):
eer = plot_det_curve(self.y_,
distance,
prefix,
distances=True,
dpi=75)
else:
_, _, _, eer = det_curve(self.y_, distance, distances=True)
# store equal error rate in file
mode = 'a' if epoch else 'w'
path = self.log_dir + '/{subset}.eer.txt'.format(subset=self.subset)
with open(path, mode=mode) as fp:
fp.write(self.EER_TEMPLATE_.format(epoch=epoch, eer=eer, now=now))
fp.flush()
# plot eer = f(epoch)
self.eer_.append(eer)
best_epoch = np.argmin(self.eer_)
best_value = np.min(self.eer_)
fig = plt.figure()
plt.plot(self.eer_, 'b')
plt.plot([best_epoch], [best_value], 'bo')
plt.plot([0, epoch], [best_value, best_value], 'k--')
plt.grid(True)
plt.xlabel('epoch')
plt.ylabel('EER on {subset}'.format(subset=self.subset))
TITLE = 'EER = {best_value:.5g} on {subset} @ epoch #{best_epoch:d}'
title = TITLE.format(best_value=best_value,
best_epoch=best_epoch,
subset=self.subset)
plt.title(title)
plt.tight_layout()
path = self.log_dir + '/{subset}.eer.png'.format(subset=self.subset)
plt.savefig(path, dpi=75)
plt.close(fig)
def __init__(self, glue, protocol, subset, log_dir):
super(SpeakerDiarizationValidation, self).__init__()
self.subset = subset
self.distance = glue.distance
self.extract_embedding = glue.extract_embedding
self.log_dir = log_dir
np.random.seed(1337)
# initialize fixed duration sequence generator
if glue.min_duration is None:
# initialize fixed duration sequence generator
generator = FixedDurationSequences(glue.feature_extractor,
duration=glue.duration,
step=glue.step,
batch_size=-1)
else:
# initialize variable duration sequence generator
generator = VariableDurationSequences(
glue.feature_extractor,
max_duration=glue.duration,
min_duration=glue.min_duration,
batch_size=-1)
# randomly select (at most) 100 sequences from each label to ensure
# all labels have (more or less) the same weight in the evaluation
file_generator = getattr(protocol, subset)()
X, y = zip(*generator(file_generator))
X = np.vstack(X)
y = np.hstack(y)
unique, y, counts = np.unique(y,
return_inverse=True,
return_counts=True)
n_labels = len(unique)
indices = []
for label in range(n_labels):
i = np.random.choice(np.where(y == label)[0],
size=min(100, counts[label]),
replace=False)
indices.append(i)
indices = np.hstack(indices)
X, y = X[indices], y[indices, np.newaxis]
# precompute same/different groundtruth
self.y_ = pdist(y, metric='chebyshev') < 1
self.X_ = X
self.EER_TEMPLATE_ = '{epoch:04d} {now} {eer:5f}\n'
self.eer_ = []
def apply(self, current_file):
# initial segmentation
speech_turns = super().apply(current_file)
# initialize the hypothesized annotation
hypothesis = Annotation(uri=current_file['uri'])
if len(speech_turns) < 1:
return hypothesis
# this only happens during pipeline training
if 'annotation' in current_file:
# number of speech turns in reference
reference = current_file['annotation']
n_turns_true = len(list(reference.itertracks()))
# number of speech turns in hypothesis
uem = get_annotated(current_file)
n_turns_pred = len(speech_turns.crop(uem))
# don't even bother trying to cluster those speech turns
# as there are too many of those...
if n_turns_pred > 20 * n_turns_true:
return None
# get raw (sliding window) embeddings
emb = self.emb_(current_file)
# get one embedding per speech turn
# FIXME don't l2_normalize for any metric
fX = l2_normalize(
np.vstack([
np.sum(emb.crop(t, mode='loose'), axis=0) for t in speech_turns
]))
# apply clustering
try:
affinity = -squareform(pdist(fX, metric=self.metric))
clusters = self.cls_.fit_predict(affinity)
except MemoryError as e:
# cannot compute affinity propagation
return None
for speech_turn, cluster in zip(speech_turns, clusters):
# HACK find why fit_predict returns NaN sometimes and fix it.
cluster = -1 if np.isnan(cluster) else cluster
hypothesis[speech_turn] = cluster
return hypothesis
def compute_similarity_matrix(self, parent=None):
clusters = list(self._models)
n_clusters = len(clusters)
X = np.vstack([self[cluster][0] for cluster in clusters])
nX = l2_normalize(X)
similarities = -squareform(pdist(nX, metric=self.distance))
matrix = ValueSortedDict()
for i, j in itertools.combinations(range(n_clusters), 2):
matrix[clusters[i], clusters[j]] = similarities[i, j]
matrix[clusters[j], clusters[i]] = similarities[j, i]
return matrix
def apply(self, current_file):
# initial segmentation
speech_turns = super().apply(current_file)
# initialize the hypothesized annotation
hypothesis = Annotation(uri=current_file['uri'])
if len(speech_turns) < 1:
return hypothesis
# this only happens during pipeline training
if 'annotation' in current_file:
# number of speech turns in reference
reference = current_file['annotation']
n_turns_true = len(list(reference.itertracks()))
# number of speech turns in hypothesis
uem = get_annotated(current_file)
n_turns_pred = len(speech_turns.crop(uem))
# don't even bother trying to cluster those speech turns
# as there are too many of those...
if n_turns_pred > 20 * n_turns_true:
return None
# get raw (sliding window) embeddings
emb = self.emb_(current_file)
# get one embedding per speech turn
# FIXME don't l2_normalize for any metric
fX = l2_normalize(np.vstack(
[np.sum(emb.crop(t, mode='loose'), axis=0) for t in speech_turns]))
# apply clustering
try:
affinity = -squareform(pdist(fX, metric=self.metric))
clusters = self.cls_.fit_predict(affinity)
except MemoryError as e:
# cannot compute affinity propagation
return None
for speech_turn, cluster in zip(speech_turns, clusters):
# HACK find why fit_predict returns NaN sometimes and fix it.
cluster = -1 if np.isnan(cluster) else cluster
hypothesis[speech_turn] = cluster
return hypothesis
def _validate_init_segment(self, protocol_name, subset='development'):
np.random.seed(1337)
protocol = get_protocol(protocol_name, progress=False,
preprocessors=self.preprocessors_)
batch_generator = SpeechSegmentGenerator(
self.feature_extraction_, per_label=10, duration=self.duration)
batch = next(batch_generator(protocol, subset=subset))
X = np.stack(batch['X'])
y = np.stack(batch['y']).reshape((-1, 1))
# precompute same/different groundtruth
y = pdist(y, metric='equal')
return {'X': X, 'y': y}
def _validate_epoch_segment(self, epoch, protocol_name,
subset='development',
validation_data=None):
model = self.load_model(epoch).to(self.device)
model.eval()
sequence_embedding = SequenceEmbedding(
model, self.feature_extraction_,
batch_size=self.batch_size, device=self.device)
fX = sequence_embedding.apply(validation_data['X'])
y_pred = pdist(fX, metric=self.metric)
_, _, _, eer = det_curve(validation_data['y'], y_pred,
distances=True)
return {'EER.{0:g}s'.format(self.duration): {'minimize': True,
'value': eer}}
def _validate_init_segment(self, protocol_name, subset='development'):
np.random.seed(1337)
protocol = get_protocol(protocol_name,
progress=False,
preprocessors=self.preprocessors_)
batch_generator = SpeechSegmentGenerator(self.feature_extraction_,
per_label=10,
duration=self.duration)
batch = next(batch_generator(protocol, subset=subset))
X = np.stack(batch['X'])
y = np.stack(batch['y']).reshape((-1, 1))
# precompute same/different groundtruth
y = pdist(y, metric='equal')
return {'X': X, 'y': y}
def _validate_epoch_turn(self,
epoch,
protocol_name,
subset='development',
validation_data=None):
model = self.load_model(epoch).to(self.device)
model.eval()
sequence_embedding = SequenceEmbedding(model,
self.feature_extraction_,
batch_size=self.batch_size,
device=self.device)
fX = sequence_embedding.apply(validation_data['X'])
z = validation_data['z']
# iterate over segments, speech turn by speech turn
fX_avg = []
nz = np.vstack([np.arange(len(z)), z]).T
for _, nz_ in itertools.groupby(nz, lambda t: t[1]):
# (n, 2) numpy array where
# * n is the number of segments in current speech turn
# * dim #0 is the index of segment in original batch
# * dim #1 is the index of speech turn (used for grouping)
nz_ = np.stack(nz_)
# compute (and stack) average embedding over all segments
# of current speech turn
indices = nz_[:, 0]
fX_avg.append(np.mean(fX[indices], axis=0))
fX = np.vstack(fX_avg)
y_pred = pdist(fX, metric=self.metric)
_, _, _, eer = det_curve(validation_data['y'], y_pred, distances=True)
metrics = {}
metrics['EER.turn'] = {'minimize': True, 'value': eer}
return metrics
def _validate_epoch_turn(self, epoch, protocol_name,
subset='development',
validation_data=None):
model = self.load_model(epoch).to(self.device)
model.eval()
sequence_embedding = SequenceEmbedding(
model, self.feature_extraction_,
batch_size=self.batch_size, device=self.device)
fX = sequence_embedding.apply(validation_data['X'])
z = validation_data['z']
# iterate over segments, speech turn by speech turn
fX_avg = []
nz = np.vstack([np.arange(len(z)), z]).T
for _, nz_ in itertools.groupby(nz, lambda t: t[1]):
# (n, 2) numpy array where
# * n is the number of segments in current speech turn
# * dim #0 is the index of segment in original batch
# * dim #1 is the index of speech turn (used for grouping)
nz_ = np.stack(nz_)
# compute (and stack) average embedding over all segments
# of current speech turn
indices = nz_[:, 0]
fX_avg.append(np.mean(fX[indices], axis=0))
fX = np.vstack(fX_avg)
y_pred = pdist(fX, metric=self.metric)
_, _, _, eer = det_curve(validation_data['y'], y_pred,
distances=True)
metrics = {}
metrics['EER.turn'] = {'minimize': True, 'value': eer}
return metrics
def _validate_epoch_segment(self,
epoch,
protocol_name,
subset='development',
validation_data=None):
model = self.load_model(epoch).to(self.device)
model.eval()
sequence_embedding = SequenceEmbedding(model,
self.feature_extraction_,
batch_size=self.batch_size,
device=self.device)
fX = sequence_embedding.apply(validation_data['X'])
y_pred = pdist(fX, metric=self.metric)
_, _, _, eer = det_curve(validation_data['y'], y_pred, distances=True)
return {
'EER.{0:g}s'.format(self.duration): {
'minimize': True,
'value': eer
}
}
def batch_loss(self, batch, model, device, writer=None, **kwargs):
lengths = torch.tensor([len(x) for x in batch['X']])
variable_lengths = len(set(lengths)) > 1
if variable_lengths:
sorted_lengths, sort = torch.sort(lengths, descending=True)
_, unsort = torch.sort(sort)
sequences = [torch.tensor(batch['X'][i],
dtype=torch.float32,
device=device) for i in sort]
padded = pad_sequence(sequences, batch_first=True, padding_value=0)
packed = pack_padded_sequence(padded, sorted_lengths,
batch_first=True)
batch['X'] = packed
else:
batch['X'] = torch.tensor(np.stack(batch['X']),
dtype=torch.float32,
device=device)
# forward pass
fX = model(batch['X'])
if variable_lengths:
fX = fX[unsort]
# log embedding norms
if writer is not None:
norm_npy = np.linalg.norm(self.to_numpy(fX), axis=1)
self.log_norm_.append(norm_npy)
batch['fX'] = fX
batch = self.aggregate(batch)
fX = batch['fX']
y = batch['y']
# pre-compute pairwise distances
distances = self.pdist(fX)
# sample triplets
triplets = getattr(self, 'batch_{0}'.format(self.sampling))
anchors, positives, negatives = triplets(y, distances)
# compute loss for each triplet
losses, deltas, _, _ = self.triplet_loss(
distances, anchors, positives, negatives,
return_delta=True)
if writer is not None:
pdist_npy = self.to_numpy(distances)
delta_npy = self.to_numpy(deltas)
same_speaker = pdist(y.reshape((-1, 1)), metric='equal')
self.log_positive_.append(pdist_npy[np.where(same_speaker)])
self.log_negative_.append(pdist_npy[np.where(~same_speaker)])
self.log_delta_.append(delta_npy)
# average over all triplets
return torch.mean(losses)
def computeDistMat(self, X, metric='angular'):
dist=pdist(X, metric=metric)
return dist
def computeLogDistMat(self, X, metric='angular'):
dist=pdist(X, metric=metric)
distMat = squareform((dist))*(-1.0)
return distMat
def test(protocol, tune_dir, test_dir, subset, beta=1.0):
batch_size = 32
try:
os.makedirs(test_dir)
except Exception as e:
pass
train_dir = os.path.dirname(os.path.dirname(tune_dir))
# -- DURATIONS --
duration, min_duration, step, heterogeneous = \
path_to_duration(os.path.basename(train_dir))
config_dir = os.path.dirname(os.path.dirname(os.path.dirname(train_dir)))
config_yml = config_dir + '/config.yml'
with open(config_yml, 'r') as fp:
config = yaml.load(fp)
# -- PREPROCESSORS --
for key, preprocessor in config.get('preprocessors', {}).items():
preprocessor_name = preprocessor['name']
preprocessor_params = preprocessor.get('params', {})
preprocessors = __import__('pyannote.audio.preprocessors',
fromlist=[preprocessor_name])
Preprocessor = getattr(preprocessors, preprocessor_name)
protocol.preprocessors[key] = Preprocessor(**preprocessor_params)
# -- FEATURE EXTRACTION --
feature_extraction_name = config['feature_extraction']['name']
features = __import__('pyannote.audio.features',
fromlist=[feature_extraction_name])
FeatureExtraction = getattr(features, feature_extraction_name)
feature_extraction = FeatureExtraction(
**config['feature_extraction'].get('params', {}))
distance = config['glue'].get('params', {}).get('distance', 'sqeuclidean')
# -- HYPER-PARAMETERS --
tune_yml = tune_dir + '/tune.yml'
with open(tune_yml, 'r') as fp:
tune = yaml.load(fp)
architecture_yml = train_dir + '/architecture.yml'
WEIGHTS_H5 = train_dir + '/weights/{epoch:04d}.h5'
weights_h5 = WEIGHTS_H5.format(epoch=tune['epoch'])
sequence_embedding = SequenceEmbedding.from_disk(
architecture_yml, weights_h5)
X, y = generate_test(protocol, subset, feature_extraction,
duration, min_duration=min_duration, step=step)
fX = sequence_embedding.transform(X, batch_size=batch_size)
y_distance = pdist(fX, metric=distance)
y_true = pdist(y, metric='chebyshev') < 1
fpr, tpr, thresholds = sklearn.metrics.roc_curve(
y_true, -y_distance, pos_label=True, drop_intermediate=True)
frr = 1. - tpr
far = fpr
thresholds = -thresholds
eer_index = np.where(far > frr)[0][0]
eer = .25 * (far[eer_index-1] + far[eer_index] +
frr[eer_index-1] + frr[eer_index])
fscore = 1. - f_measure(1. - frr, 1. - far, beta=beta)
opt_i = np.nanargmin(fscore)
opt_alpha = float(thresholds[opt_i])
opt_far = far[opt_i]
opt_frr = frr[opt_i]
opt_fscore = fscore[opt_i]
alpha = tune['alpha']
actual_i = np.searchsorted(thresholds, alpha)
actual_far = far[actual_i]
actual_frr = frr[actual_i]
actual_fscore = fscore[actual_i]
save_to = test_dir + '/' + subset
plot_distributions(y_true, y_distance, save_to)
eer = plot_det_curve(y_true, -y_distance, save_to)
plot_precision_recall_curve(y_true, -y_distance, save_to)
with open(save_to + '.txt', 'w') as fp:
fp.write('# cond. thresh far frr fscore eer\n')
TEMPLATE = '{condition} {alpha:.5f} {far:.5f} {frr:.5f} {fscore:.5f} {eer:.5f}\n'
fp.write(TEMPLATE.format(condition='optimal',
alpha=opt_alpha,
far=opt_far,
frr=opt_frr,
fscore=opt_fscore,
eer=eer))
fp.write(TEMPLATE.format(condition='actual ',
alpha=alpha,
far=actual_far,
frr=actual_frr,
fscore=actual_fscore,
eer=eer))
def cluster_(self, fX):
"""Compute complete dendrogram
Parameters
----------
fX : (n_items, dimension) np.array
Embeddings.
Returns
-------
dendrogram : list of (i, j, distance) tuples
Dendrogram.
"""
N = len(fX)
# clusters contain the identifier of each cluster
clusters = SortedSet(np.arange(N))
# labels[i] = c means ith item belongs to cluster c
labels = np.array(np.arange(N))
squared = squareform(pdist(fX, metric=self.metric))
distances = ValueSortedDict()
for i, j in itertools.combinations(range(N), 2):
distances[i, j] = squared[i, j]
dendrogram = []
for _ in range(N-1):
# find most similar clusters
(c_i, c_j), d = distances.peekitem(index=0)
# keep track of this iteration
dendrogram.append((c_i, c_j, d))
# index of clusters in 'clusters' and 'fX'
i = clusters.index(c_i)
j = clusters.index(c_j)
# merge items of cluster c_j into cluster c_i
labels[labels == c_j] = c_i
# update c_i representative
fX[i] += fX[j]
# remove c_j cluster
fX[j:-1, :] = fX[j+1:, :]
fX = fX[:-1]
# remove distances to c_j cluster
for c in clusters[:j]:
distances.pop((c, c_j))
for c in clusters[j+1:]:
distances.pop((c_j, c))
clusters.remove(c_j)
if len(clusters) < 2:
continue
# compute distance to new c_i cluster
new_d = cdist(fX[i, :].reshape((1, -1)), fX, metric=self.metric).squeeze()
for c_k, d in zip(clusters, new_d):
if c_k < c_i:
distances[c_k, c_i] = d
elif c_k > c_i:
distances[c_i, c_k] = d
return dendrogram
def validate(self,
protocol_name,
subset='development',
aggregate=False,
every=1,
start=0):
# prepare paths
validate_dir = self.VALIDATE_DIR.format(train_dir=self.train_dir_,
protocol=protocol_name)
validate_txt = self.VALIDATE_TXT.format(
validate_dir=validate_dir,
subset=subset,
aggregate='aggregate.' if aggregate else '')
validate_png = self.VALIDATE_PNG.format(
validate_dir=validate_dir,
subset=subset,
aggregate='aggregate.' if aggregate else '')
validate_eps = self.VALIDATE_EPS.format(
validate_dir=validate_dir,
subset=subset,
aggregate='aggregate.' if aggregate else '')
# create validation directory
mkdir_p(validate_dir)
# Build validation set
if aggregate:
X, n, y = self._validation_set_z(protocol_name, subset=subset)
else:
X, y = self._validation_set_y(protocol_name, subset=subset)
# list of equal error rates, and epoch to process
eers, epoch = SortedDict(), start
desc_format = ('Best EER = {best_eer:.2f}% @ epoch #{best_epoch:d} ::'
' EER = {eer:.2f}% @ epoch #{epoch:d} :')
progress_bar = tqdm(unit='epoch')
with open(validate_txt, mode='w') as fp:
# watch and evaluate forever
while True:
# last completed epochs
completed_epochs = self.get_epochs(self.train_dir_) - 1
if completed_epochs < epoch:
time.sleep(60)
continue
# if last completed epoch has already been processed
# go back to first epoch that hasn't been processed yet
process_epoch = epoch if completed_epochs in eers \
else completed_epochs
# do not validate this epoch if it has been done before...
if process_epoch == epoch and epoch in eers:
epoch += every
progress_bar.update(every)
continue
weights_h5 = LoggingCallback.WEIGHTS_H5.format(
log_dir=self.train_dir_, epoch=process_epoch)
# this is needed for corner case when training is started from
# an epoch > 0
if not isfile(weights_h5):
time.sleep(60)
continue
# sleep 5 seconds to let the checkpoint callback finish
time.sleep(5)
embedding = keras.models.load_model(
weights_h5, custom_objects=CUSTOM_OBJECTS, compile=False)
if aggregate:
def embed(X):
func = K.function([
embedding.get_layer(name='input').input,
K.learning_phase()
], [embedding.get_layer(name='internal').output])
return func([X, 0])[0]
else:
embed = embedding.predict
# embed all validation sequences
fX = embed(X)
if aggregate:
indices = np.hstack([[0], np.cumsum(n)])
fX = np.stack([
np.sum(np.sum(fX[i:j], axis=0), axis=0)
for i, j in pairwise(indices)
])
fX = l2_normalize(fX)
# compute pairwise distances
y_pred = pdist(fX, metric=self.approach_.metric)
# compute pairwise groundtruth
y_true = pdist(y, metric='chebyshev') < 1
# estimate equal error rate
_, _, _, eer = det_curve(y_true, y_pred, distances=True)
eers[process_epoch] = eer
# save equal error rate to file
fp.write(
self.VALIDATE_TXT_TEMPLATE.format(epoch=process_epoch,
eer=eer))
fp.flush()
# keep track of best epoch so far
best_epoch = eers.iloc[np.argmin(eers.values())]
best_eer = eers[best_epoch]
progress_bar.set_description(
desc_format.format(epoch=process_epoch,
eer=100 * eer,
best_epoch=best_epoch,
best_eer=100 * best_eer))
# plot
fig = plt.figure()
plt.plot(eers.keys(), eers.values(), 'b')
plt.plot([best_epoch], [best_eer], 'bo')
plt.plot([eers.iloc[0], eers.iloc[-1]], [best_eer, best_eer],
'k--')
plt.grid(True)
plt.xlabel('epoch')
plt.ylabel('EER on {subset}'.format(subset=subset))
TITLE = '{best_eer:.5g} @ epoch #{best_epoch:d}'
title = TITLE.format(best_eer=best_eer,
best_epoch=best_epoch,
subset=subset)
plt.title(title)
plt.tight_layout()
plt.savefig(validate_png, dpi=75)
plt.savefig(validate_eps)
plt.close(fig)
# go to next epoch
if epoch == process_epoch:
epoch += every
progress_bar.update(every)
else:
progress_bar.update(0)
progress_bar.close()
def batch_loss(self, batch, model, device, writer=None, **kwargs):
lengths = torch.tensor([len(x) for x in batch['X']])
variable_lengths = len(set(lengths)) > 1
if variable_lengths:
sorted_lengths, sort = torch.sort(lengths, descending=True)
_, unsort = torch.sort(sort)
sequences = [
torch.tensor(batch['X'][i], dtype=torch.float32, device=device)
for i in sort
]
padded = pad_sequence(sequences, batch_first=True, padding_value=0)
packed = pack_padded_sequence(padded,
sorted_lengths,
batch_first=True)
batch['X'] = packed
else:
batch['X'] = torch.tensor(np.stack(batch['X']),
dtype=torch.float32,
device=device)
# forward pass
fX = model(batch['X'])
if variable_lengths:
fX = fX[unsort]
# log embedding norms
if writer is not None:
norm_npy = np.linalg.norm(self.to_numpy(fX), axis=1)
self.log_norm_.append(norm_npy)
batch['fX'] = fX
batch = self.aggregate(batch)
fX = batch['fX']
y = batch['y']
# pre-compute pairwise distances
distances = self.pdist(fX)
# sample triplets
triplets = getattr(self, 'batch_{0}'.format(self.sampling))
anchors, positives, negatives = triplets(y, distances)
# compute loss for each triplet
losses, deltas, _, _ = self.triplet_loss(distances,
anchors,
positives,
negatives,
return_delta=True)
if writer is not None:
pdist_npy = self.to_numpy(distances)
delta_npy = self.to_numpy(deltas)
same_speaker = pdist(y.reshape((-1, 1)), metric='equal')
self.log_positive_.append(pdist_npy[np.where(same_speaker)])
self.log_negative_.append(pdist_npy[np.where(~same_speaker)])
self.log_delta_.append(delta_npy)
# average over all triplets
return torch.mean(losses)