def __call__(self, targets, logits, seq_length):
'''
Compute the loss
Creates the operation to compute the Permudation Invariant Training loss
Args:
targets: a dictionary of [batch_size x time x ...] tensor containing
the targets
logits: a dictionary of [batch_size x time x ...] tensor containing
the logits
seq_length: a dictionary of [batch_size] vectors containing
the sequence lengths
Returns:
a scalar value containing the loss
'''
outputs = logits['outputs']
multi_targets = targets['multi_targets']
mix_to_mask = targets['mix_to_mask']
seq_length = seq_length['features']
loss = ops.pit_loss(multi_targets, outputs, mix_to_mask, seq_length,
self.batch_size)
return loss
def __call__(self, targets, logits, seq_length):
"""
Compute the loss
Creates the operation to compute the Permudation Invariant Training loss
Args:
targets: a dictionary of [batch_size x time x ...] tensor containing
the targets
logits: a dictionary of [batch_size x time x ...] tensors containing the logits
seq_length: a dictionary of [batch_size] vectors containing
the sequence lengths
Returns:
loss: a scalar value containing the loss
norm: a scalar value indicating how to normalize the loss
"""
warnings.warn(
'In following versions it will be required to use the PITLoss',
Warning)
multi_targets = targets['multi_targets']
mix_to_mask = targets['mix_to_mask']
seq_length = seq_length['bin_est']
logits = logits['bin_est']
loss, norm = ops.pit_loss(multi_targets,
logits,
mix_to_mask,
seq_length,
self.batch_size,
activation='sigmoid')
return loss, norm
def __call__(self, targets, logits, seq_length):
'''
Compute the loss
Creates the operation to compute the Permudation Invariant Training loss
Args:
targets: a dictionary of [batch_size x time x ...] tensor containing
the targets
logits: a dictionary of [batch_size x time x ...] tensors containing the logits
seq_length: a dictionary of [batch_size] vectors containing
the sequence lengths
Returns:
loss: a scalar value containing the loss
norm: a scalar value indicating how to normalize the loss
'''
multi_targets = targets['multi_targets']
mix_to_mask = targets['mix_to_mask']
seq_length = seq_length['bin_est']
logits = logits['bin_est']
loss, norm = ops.pit_loss(multi_targets,
logits,
mix_to_mask,
seq_length,
self.batch_size,
softmax=False,
rescale_recs=True)
return loss, norm
def __call__(self, targets, logits, seq_length):
"""
Compute the loss
Creates the operation to compute the Permudation Invariant Training loss
Args:
targets: a dictionary of [batch_size x time x ...] tensor containing
the targets
logits: a dictionary of [batch_size x time x ...] tensors containing the logits
seq_length: a dictionary of [batch_size] vectors containing
the sequence lengths
Returns:
loss: a scalar value containing the loss
norm: a scalar value indicating how to normalize the loss
"""
if 'activation' in self.lossconf:
activation = self.lossconf['activation']
else:
activation = 'softmax'
if 'rescale_recs' in self.lossconf:
rescale_recs = self.lossconf['rescale_recs'] == 'True'
else:
rescale_recs = False
if 'overspeakerized' in self.lossconf:
overspeakerized = self.lossconf['overspeakerized'] == 'True'
else:
overspeakerized = False
if 'transpose_order' in self.lossconf:
transpose_order = map(int,
self.lossconf['transpose_order'].split(' '))
else:
transpose_order = False
if 'no_perm' in self.lossconf:
no_perm = self.lossconf['no_perm'] == 'True'
else:
no_perm = False
if 'logits_name' in self.lossconf:
logits_name = self.lossconf['logits_name']
else:
logits_name = 'bin_est'
multi_targets = targets['multi_targets']
mix_to_mask = targets['mix_to_mask']
seq_length = seq_length[logits_name]
logits = logits[logits_name]
if transpose_order:
logits = tf.transpose(logits, transpose_order)
loss, norm = ops.pit_loss(multi_targets,
logits,
mix_to_mask,
seq_length,
self.batch_size,
activation=activation,
rescale_recs=rescale_recs,
overspeakerized=overspeakerized,
no_perm=no_perm)
return loss, norm