def get_batch_loss_and_error_signal(self, target, log_posteriors, seq_lengths):
"""
:param str target: e.g. "classes". not yet passed over to Sprint.
:param numpy.ndarray log_posteriors: 3d (time,batch,label)
:param numpy.ndarray seq_lengths: 1d (batch)
:rtype (numpy.ndarray, numpy.ndarray)
:returns (loss, error_signal). error_signal has the same shape as posteriors.
loss is a 1d-array (batch).
Note that this accesses some global references, like global current seg info.
"""
assert seq_lengths.ndim == 1
assert log_posteriors.ndim == 3
n_batch = seq_lengths.shape[0]
assert n_batch == log_posteriors.shape[1]
import Device
index = Device.get_current_seq_index(target) # (time,batch)
assert index.ndim == 2
assert index.shape[1] == n_batch
assert (numpy.sum(index, axis=0) == seq_lengths).all()
tags = Device.get_current_seq_tags()
assert len(tags) == n_batch
batch_loss = numpy.zeros((n_batch,), dtype="float32")
batch_error_signal = numpy.zeros_like(log_posteriors, dtype="float32")
# Very simple parallelism. We must avoid any form of multi-threading
# because this can be problematic with Theano.
# See: https://groups.google.com/forum/#!msg/theano-users/Pu4YKlZKwm4/eNcAegzaNeYJ
# We also try to keep it simple here.
for bb in range(0, n_batch, self.max_num_instances):
for i in range(self.max_num_instances):
b = bb + i
if b >= n_batch: break
instance = self._get_instance(i)
instance.get_loss_and_error_signal__send(
seg_name=tags[b], seg_len=seq_lengths[b], posteriors=log_posteriors[:seq_lengths[b], b])
for i in range(self.max_num_instances):
b = bb + i
if b >= n_batch: break
instance = self._get_instance(i)
seg_name, loss, error_signal = instance.get_loss_and_error_signal__read()
assert seg_name == tags[b]
batch_loss[b] = loss
batch_error_signal[:seq_lengths[b], b] = error_signal
return batch_loss, batch_error_signal
