def decorated(*args):
with ops.name_scope("batch") as name:
for a in args:
if not isinstance(a, ops.Tensor):
raise ValueError(
"All arguments to functions decorated with "
"`batch_function` are supposed to be Tensors; "
"found %s" % repr(a))
batched_tensors, batch_index, id_t = gen_batch_ops.batch(
args,
num_batch_threads=num_batch_threads,
max_batch_size=max_batch_size,
batch_timeout_micros=batch_timeout_micros,
max_enqueued_batches=max_enqueued_batches,
allowed_batch_sizes=allowed_batch_sizes,
grad_timeout_micros=grad_timeout_micros,
shared_name=name)
outputs = f(*batched_tensors)
if isinstance(outputs, ops.Tensor):
outputs_list = [outputs]
else:
outputs_list = outputs
with ops.name_scope("unbatch") as unbatch_name:
unbatched = [
gen_batch_ops.unbatch(
t,
batch_index,
id_t,
timeout_micros=unbatch_timeout_micros,
shared_name=unbatch_name + "/" + t.name)
for t in outputs_list
]
if isinstance(outputs, ops.Tensor):
return unbatched[0]
return unbatched
def decorated(*args):
with ops.name_scope("batch") as name:
for a in args:
if not isinstance(a, ops.Tensor):
raise ValueError("All arguments to functions decorated with "
"`batch_function` are supposed to be Tensors; "
"found %s" % repr(a))
batched_tensors, batch_index, id_t = gen_batch_ops.batch(
args,
num_batch_threads=num_batch_threads,
max_batch_size=max_batch_size,
batch_timeout_micros=batch_timeout_micros,
max_enqueued_batches=max_enqueued_batches,
allowed_batch_sizes=allowed_batch_sizes,
grad_timeout_micros=grad_timeout_micros,
shared_name=name)
outputs = f(*batched_tensors)
if isinstance(outputs, ops.Tensor):
outputs_list = [outputs]
else:
outputs_list = outputs
with ops.name_scope("unbatch") as unbatch_name:
unbatched = [
gen_batch_ops.unbatch(t, batch_index, id_t,
timeout_micros=unbatch_timeout_micros,
shared_name=unbatch_name + "/" + t.name)
for t in outputs_list]
if isinstance(outputs, ops.Tensor):
return unbatched[0]
return unbatched