def _generate_batches(self, recurrent_net, batch_size, max_seqs=-1, batch_variance=0.0, max_seq_length=sys.maxsize):
"""
:param bool recurrent_net: If True, the batch might have a batch seq dimension > 1.
Otherwise, the batch seq dimension is always 1 and multiple seqs will be concatenated.
:param int batch_size: Max number of frames in one batch.
:param int max_seqs: Max number of seqs per batch.
"""
if batch_size == 0: batch_size = sys.maxsize
assert batch_size > 0
ms = max_seqs
bs = batch_size
if max_seqs == -1: max_seqs = float('inf')
assert max_seqs > 0
chunk_size = self.chunk_size
chunk_step = self.chunk_step
if not recurrent_net:
if chunk_size != 0:
print >> log.v4, "Non-recurrent network, chunk size %i:%i ignored" % (chunk_size, chunk_step)
chunk_size = 0
assert batch_variance <= 1.0
if batch_variance > 0.0:
r = (1.0 - self.rnd_batch_variance.random() * batch_variance)
if max_seqs > 0:
max_seqs = max(int(r * ms), 1)
#if batch_size > 0:
# batch_size = max(int(r * bs), 1)
batch = Batch()
for seq_idx, t_start, t_end in self._iterate_seqs(chunk_size=chunk_size, chunk_step=chunk_step):
if recurrent_net:
length = t_end - t_start
if length.max_value() > max_seq_length:
continue
if length.max_value() > batch_size:
print >> log.v4, "warning: sequence length (%i) larger than limit (%i)" % (length.max_value(), batch_size)
dt, ds = batch.try_sequence_as_slice(length)
if ds > 1 and ((dt * ds).max_value() > batch_size or ds > max_seqs):
yield batch
batch = Batch()
if batch_variance > 0.0:
r = (1.0 - self.rnd_batch_variance.random() * batch_variance)
if max_seqs > 0:
max_seqs = max(int(r * ms), 1)
#if batch_size > 0:
# batch_size = max(int(r * bs), 1)
batch.add_sequence_as_slice(seq_idx=seq_idx, seq_start_frame=t_start, length=length)
else: # Not recurrent.
while t_start.max_value() < t_end.max_value():
length = t_end - t_start
num_frames = min(length.max_value(), batch_size - batch.get_all_slices_num_frames())
assert num_frames > 0
batch.add_frames(seq_idx=seq_idx, seq_start_frame=t_start, length=num_frames)
if batch.get_all_slices_num_frames() >= batch_size or batch.get_num_seqs() > max_seqs:
yield batch
batch = Batch()
t_start += num_frames
if batch.get_all_slices_num_frames() > 0:
yield batch
def _generate_batches(self,
recurrent_net,
batch_size,
max_seqs=-1,
seq_drop=0.0,
max_seq_length=sys.maxsize,
used_data_keys=None):
"""
:param bool recurrent_net: If True, the batch might have a batch seq dimension > 1.
Otherwise, the batch seq dimension is always 1 and multiple seqs will be concatenated.
:param int batch_size: Max number of frames in one batch.
:param int max_seqs: Max number of seqs per batch.
:param set(str)|None used_data_keys:
"""
if batch_size == 0: batch_size = sys.maxsize
assert batch_size > 0
if max_seqs == -1: max_seqs = float('inf')
assert max_seqs > 0
assert seq_drop <= 1.0
chunk_size = self.chunk_size
chunk_step = self.chunk_step
if not recurrent_net:
if chunk_size != 0:
print("Non-recurrent network, chunk size %i:%i ignored" %
(chunk_size, chunk_step),
file=log.v4)
chunk_size = 0
batch = Batch()
for seq_idx, t_start, t_end in self._iterate_seqs(
chunk_size=chunk_size,
chunk_step=chunk_step,
used_data_keys=used_data_keys):
if recurrent_net:
length = t_end - t_start
if max_seq_length < 0 and length['classes'] > -max_seq_length:
continue
elif max_seq_length > 0 and length.max_value(
) > max_seq_length:
continue
if length.max_value() > batch_size:
print(
"warning: sequence length (%i) larger than limit (%i)"
% (length.max_value(), batch_size),
file=log.v4)
if self.rnd_seq_drop.random() < seq_drop:
continue
dt, ds = batch.try_sequence_as_slice(length)
if ds > 1 and ((dt * ds).max_value() > batch_size
or ds > max_seqs):
yield batch
batch = Batch()
batch.add_sequence_as_slice(seq_idx=seq_idx,
seq_start_frame=t_start,
length=length)
else: # Not recurrent.
while t_start.max_value() < t_end.max_value():
length = t_end - t_start
num_frames = NumbersDict.min([
length, batch_size - batch.get_all_slices_num_frames()
])
assert num_frames.max_value() > 0
batch.add_frames(seq_idx=seq_idx,
seq_start_frame=t_start,
length=num_frames)
if batch.get_all_slices_num_frames(
) >= batch_size or batch.get_num_seqs() > max_seqs:
yield batch
batch = Batch()
t_start += num_frames
if batch.get_all_slices_num_frames() > 0:
yield batch
def _generate_batches(self,
recurrent_net,
batch_size,
max_seqs=-1,
max_seq_length=sys.maxsize,
min_seq_length=0,
seq_drop=0.0,
max_total_num_seqs=-1,
used_data_keys=None):
"""
:param bool recurrent_net: If True, the batch might have a batch seq dimension > 1.
Otherwise, the batch seq dimension is always 1 and multiple seqs will be concatenated.
:param int batch_size: Max number of frames in one batch.
:param int max_seqs: Max number of seqs per batch.
:param int max_total_num_seqs:
:param int|dict[str,int]|NumbersDict max_seq_length:
:param set(str)|None used_data_keys:
"""
if batch_size == 0:
batch_size = sys.maxsize
assert batch_size > 0
if max_seqs == -1:
max_seqs = float('inf')
if not max_seq_length:
max_seq_length = sys.maxsize
if isinstance(max_seq_length, int) and max_seq_length < 0:
max_seq_length = {"classes": -max_seq_length}
max_seq_length = NumbersDict(max_seq_length)
min_seq_length = NumbersDict(min_seq_length)
assert max_seqs > 0
assert seq_drop <= 1.0
if not max_total_num_seqs or max_total_num_seqs < 0:
max_total_num_seqs = float("inf")
chunk_size = self.chunk_size
chunk_step = self.chunk_step
if not recurrent_net:
if chunk_size != 0:
print("Non-recurrent network, chunk size %s:%s ignored" %
(chunk_size, chunk_step),
file=log.v4)
chunk_size = 0
batch = Batch()
ctx_lr = self._get_context_window_left_right()
total_num_seqs = 0
last_seq_idx = -1
for seq_idx, t_start, t_end in self.iterate_seqs(
chunk_size=chunk_size,
chunk_step=chunk_step,
used_data_keys=used_data_keys):
if seq_idx != last_seq_idx and total_num_seqs >= max_total_num_seqs:
break
if ctx_lr:
t_start -= ctx_lr[0]
t_end += ctx_lr[1]
if recurrent_net:
length = t_end - t_start
if length.any_compare(max_seq_length, (lambda a, b: a > b)):
continue
if length.any_compare(min_seq_length, (lambda a, b: a < b)):
continue
if length.max_value() > batch_size:
print(
"warning: sequence length (%i) larger than limit (%i)"
% (length.max_value(), batch_size),
file=log.v4)
if self.rnd_seq_drop.random() < seq_drop:
continue
dt, ds = batch.try_sequence_as_slice(length)
if ds > 1 and ((dt * ds).max_value() > batch_size
or ds > max_seqs):
yield batch
batch = Batch()
batch.add_sequence_as_slice(seq_idx=seq_idx,
seq_start_frame=t_start,
length=length)
else: # Not recurrent.
while t_start.max_value() < t_end.max_value():
length = t_end - t_start
num_frames = NumbersDict.min([
length, batch_size - batch.get_all_slices_num_frames()
])
assert num_frames.max_value() > 0
batch.add_frames(seq_idx=seq_idx,
seq_start_frame=t_start,
length=num_frames)
if batch.get_all_slices_num_frames(
) >= batch_size or batch.get_num_seqs() > max_seqs:
yield batch
batch = Batch()
t_start += num_frames
if seq_idx != last_seq_idx:
last_seq_idx = seq_idx
total_num_seqs += 1
if batch.get_all_slices_num_frames() > 0:
yield batch
def _generate_batches(self, recurrent_net,
batch_size, max_seqs=-1, max_seq_length=sys.maxsize,
min_seq_length=0, pruning=0.0,
seq_drop=0.0, max_total_num_seqs=-1,
used_data_keys=None):
"""
:param bool recurrent_net: If True, the batch might have a batch seq dimension > 1.
Otherwise, the batch seq dimension is always 1 and multiple seqs will be concatenated.
:param int|dict[str,int]|NumbersDict batch_size: Max number of frames in one batch.
:param int max_seqs: Max number of seqs per batch.
:param int max_total_num_seqs:
:param int|dict[str,int]|NumbersDict max_seq_length:
:param set(str)|None used_data_keys:
"""
if not batch_size:
batch_size = sys.maxsize
batch_size = NumbersDict(batch_size)
assert not batch_size.any_compare(NumbersDict(0), (lambda a, b: a <= b))
if max_seqs == -1:
max_seqs = float('inf')
if not max_seq_length:
max_seq_length = sys.maxsize
if isinstance(max_seq_length, int) and max_seq_length < 0:
max_seq_length = {"classes": -max_seq_length}
max_seq_length = NumbersDict(max_seq_length)
min_seq_length = NumbersDict(min_seq_length)
assert max_seqs > 0
assert seq_drop <= 1.0
if not max_total_num_seqs or max_total_num_seqs < 0:
max_total_num_seqs = float("inf")
chunk_size = self.chunk_size
chunk_step = self.chunk_step
if not recurrent_net:
if chunk_size != 0:
print("Non-recurrent network, chunk size %s:%s ignored" % (chunk_size, chunk_step), file=log.v4)
chunk_size = 0
batch = Batch()
ctx_lr = self._get_context_window_left_right()
total_num_seqs = 0
last_seq_idx = -1
avg_weight = sum([v[0] for v in self.weights.values()]) / (len(self.weights.keys()) or 1)
for idx in self.weights:
self.weights[idx][1] = random() * avg_weight * pruning
self.weights[idx][0] *= (1. + pruning)
for seq_idx, t_start, t_end in self.iterate_seqs(
chunk_size=chunk_size, chunk_step=chunk_step, used_data_keys=used_data_keys):
if not self.sample(seq_idx):
continue
if total_num_seqs > max_total_num_seqs:
break
if ctx_lr:
t_start -= ctx_lr[0]
t_end += ctx_lr[1]
if recurrent_net:
length = t_end - t_start
if length.any_compare(max_seq_length, (lambda a, b: a > b)):
continue
if length.any_compare(min_seq_length, (lambda a, b: a < b)):
continue
if length.any_compare(batch_size, (lambda a, b: a > b)):
print("warning: sequence length (%r) larger than limit (%r)" % (length, batch_size), file=log.v4)
if self.rnd_seq_drop.random() < seq_drop:
continue
dt, ds = batch.try_sequence_as_slice(length)
if ds > 1 and ((dt * ds).any_compare(batch_size, (lambda a, b: a > b)) or ds > max_seqs):
yield batch
batch = Batch()
batch.add_sequence_as_slice(seq_idx=seq_idx, seq_start_frame=t_start, length=length)
else: # Not recurrent.
while t_start.max_value() < t_end.max_value():
length = t_end - t_start
num_frames = NumbersDict.min(
[length, batch_size.copy_like(length) - batch.get_all_slices_num_frames().copy_like(length)])
assert num_frames.max_value() > 0
batch.add_frames(seq_idx=seq_idx, seq_start_frame=t_start, length=num_frames)
if (batch.get_all_slices_num_frames().any_compare(batch_size, (lambda a, b: a >= b))
or batch.get_num_seqs() > max_seqs):
yield batch
batch = Batch()
t_start += num_frames
if seq_idx != last_seq_idx:
last_seq_idx = seq_idx
total_num_seqs += 1
if batch.get_all_slices_num_frames().max_value() > 0:
yield batch
