def test_reader_with_limit(self):
ws = workspace.C.Workspace()
session = LocalSession(ws)
""" 1. feed full dataset """
src_init = core.Net('src_init')
src_values = Struct(('label', np.array(range(100))))
src_blobs = NewRecord(src_init, src_values)
src_ds = Dataset(src_blobs)
FeedRecord(src_blobs, src_values, ws)
ws.run(src_init)
""" 2. Read with limit smaller than size of dataset """
dst_init = core.Net('dst_init')
dst_ds = Dataset(src_values.clone_schema())
dst_ds.init_empty(dst_init)
ws.run(dst_init)
with TaskGroup() as tg:
reader = ReaderWithLimit(src_ds.reader(), num_iter=10)
pipe(reader, dst_ds.writer(), num_threads=8)
session.run(tg)
self.assertFalse(ws.blobs[str(reader.data_finished())].fetch())
self.assertEquals(
sorted(ws.blobs[str(dst_ds.content().label())].fetch()), range(10))
""" 3. Read with limit larger than size of dataset """
ws.run(dst_init)
with TaskGroup() as tg:
reader = ReaderWithLimit(src_ds.reader(), num_iter=110)
pipe(reader, dst_ds.writer(), num_threads=8)
session.run(tg)
self.assertEquals(
sorted(ws.blobs[str(dst_ds.content().label())].fetch()),
range(100))
self.assertTrue(ws.blobs[str(reader.data_finished())].fetch())
def test_reader_with_limit(self):
ws = workspace.C.Workspace()
session = LocalSession(ws)
""" 1. feed full dataset """
src_init = core.Net('src_init')
src_values = Struct(('label', np.array(range(100))))
src_blobs = NewRecord(src_init, src_values)
src_ds = Dataset(src_blobs)
FeedRecord(src_blobs, src_values, ws)
ws.run(src_init)
""" 2. Read with limit smaller than size of dataset """
dst_init = core.Net('dst_init')
dst_ds = Dataset(src_values.clone_schema())
dst_ds.init_empty(dst_init)
ws.run(dst_init)
with TaskGroup() as tg:
reader = ReaderWithLimit(src_ds.reader(), num_iter=10)
pipe(reader, dst_ds.writer(), num_threads=8)
session.run(tg)
self.assertFalse(ws.blobs[str(reader.data_finished())].fetch())
self.assertEquals(
sorted(ws.blobs[str(dst_ds.content().label())].fetch()), range(10))
""" 3. Read with limit larger than size of dataset """
ws.run(dst_init)
with TaskGroup() as tg:
reader = ReaderWithLimit(src_ds.reader(), num_iter=110)
pipe(reader, dst_ds.writer(), num_threads=8)
session.run(tg)
self.assertEquals(
sorted(ws.blobs[str(dst_ds.content().label())].fetch()), range(100))
self.assertTrue(ws.blobs[str(reader.data_finished())].fetch())
def build_job(node_id):
all_outputs = []
with Job() as job:
with Node('reader' + str(node_id)):
with job.init_group:
init_net = core.Net('init_net' + str(node_id))
data_arr = Struct(('val', np.array(range(10))))
data = ConstRecord(init_net, data_arr)
ds = Dataset(data, name='dataset' + str(node_id))
full_reader = ds.reader(init_net)
total = init_net.Const([100])
Task(step=init_net)
def inc_total(rec):
net = core.Net('inc_total' + str(node_id))
net.Add([total, rec.val()], [total])
return [net]
epoch_reader = ReaderWithLimit(full_reader, num_iter=3)
pipe(epoch_reader, processor=inc_total)
job.add_stop_signal(epoch_reader.data_finished())
all_outputs.append(total)
total_fetcher = Task(step=core.Net('empty'), outputs=all_outputs)
return job, total_fetcher
def test_dequeue_many(self):
init_net = core.Net('init')
N = 17
NUM_DEQUEUE_RECORDS = 3
src_values = Struct(
('uid', np.array(range(N))),
('value', 0.1 * np.array(range(N))))
expected_dst = Struct(
('uid', 2 * np.array(range(N))),
('value', np.array(N * [0.0])))
with core.NameScope('init'):
src_blobs = NewRecord(init_net, src_values)
dst_blobs = InitEmptyRecord(init_net, src_values.clone_schema())
counter = init_net.Const(0)
ONE = init_net.Const(1)
def proc1(rec):
with core.NameScope('proc1'):
out = NewRecord(ops, rec)
ops.Add([rec.uid(), rec.uid()], [out.uid()])
out.value.set(blob=rec.value(), unsafe=True)
return out
def proc2(rec):
with core.NameScope('proc2'):
out = NewRecord(ops, rec)
out.uid.set(blob=rec.uid(), unsafe=True)
ops.Sub([rec.value(), rec.value()], [out.value()])
ops.Add([counter, ONE], [counter])
return out
src_ds = Dataset(src_blobs)
dst_ds = Dataset(dst_blobs)
with TaskGroup() as tg:
out1 = pipe(
src_ds.reader(),
output=Queue(
capacity=11, num_dequeue_records=NUM_DEQUEUE_RECORDS),
processor=proc1)
out2 = pipe(out1, processor=proc2)
pipe(out2, dst_ds.writer())
ws = workspace.C.Workspace()
FeedRecord(src_blobs, src_values, ws)
session = LocalSession(ws)
session.run(init_net)
session.run(tg)
output = FetchRecord(dst_blobs, ws=ws)
num_dequeues = ws.blobs[str(counter)].fetch()
self.assertEquals(
num_dequeues, int(math.ceil(float(N) / NUM_DEQUEUE_RECORDS)))
for a, b in zip(output.field_blobs(), expected_dst.field_blobs()):
np.testing.assert_array_equal(a, b)
def test_dequeue_many(self):
init_net = core.Net('init')
N = 17
NUM_DEQUEUE_RECORDS = 3
src_values = Struct(
('uid', np.array(range(N))),
('value', 0.1 * np.array(range(N))))
expected_dst = Struct(
('uid', 2 * np.array(range(N))),
('value', np.array(N * [0.0])))
with core.NameScope('init'):
src_blobs = NewRecord(init_net, src_values)
dst_blobs = InitEmptyRecord(init_net, src_values.clone_schema())
counter = init_net.Const(0)
ONE = init_net.Const(1)
def proc1(rec):
with core.NameScope('proc1'):
out = NewRecord(ops, rec)
ops.Add([rec.uid(), rec.uid()], [out.uid()])
out.value.set(blob=rec.value(), unsafe=True)
return out
def proc2(rec):
with core.NameScope('proc2'):
out = NewRecord(ops, rec)
out.uid.set(blob=rec.uid(), unsafe=True)
ops.Sub([rec.value(), rec.value()], [out.value()])
ops.Add([counter, ONE], [counter])
return out
src_ds = Dataset(src_blobs)
dst_ds = Dataset(dst_blobs)
with TaskGroup() as tg:
out1 = pipe(
src_ds.reader(),
output=Queue(
capacity=11, num_dequeue_records=NUM_DEQUEUE_RECORDS),
processor=proc1)
out2 = pipe(out1, processor=proc2)
pipe(out2, dst_ds.writer())
ws = workspace.C.Workspace()
FeedRecord(src_blobs, src_values, ws)
session = LocalSession(ws)
session.run(init_net)
session.run(tg)
output = FetchRecord(dst_blobs, ws=ws)
num_dequeues = ws.blobs[str(counter)].fetch()
self.assertEquals(
num_dequeues, int(math.ceil(float(N) / NUM_DEQUEUE_RECORDS)))
for a, b in zip(output.field_blobs(), expected_dst.field_blobs()):
np.testing.assert_array_equal(a, b)
def build_pipeline(node_id):
with Node('trainer_%d' % node_id):
with Job.current().init_group, Task():
data_arr = Struct(('val', np.array(list(range(10)))))
data = ConstRecord(ops, data_arr)
ds = Dataset(data, name='dataset:%d' % node_id)
full_reader = ds.reader(ops)
total = ops.Const([100])
def inc_total(rec):
ops.Add([total, rec.val()], [total])
epoch_reader = ReaderWithLimit(full_reader, num_iter=3)
pipe(epoch_reader, processor=inc_total)
Job.current().add_stop_signal(epoch_reader.data_finished())
return [total]
def build_pipeline(node_id):
with Node('trainer_%d' % node_id):
with Job.current().init_group, Task():
data_arr = Struct(('val', np.array(list(range(10)))))
data = ConstRecord(ops, data_arr)
ds = Dataset(data, name='dataset:%d' % node_id)
full_reader = ds.reader(ops)
total = ops.Const([100])
def inc_total(rec):
ops.Add([total, rec.val()], [total])
epoch_reader = ReaderWithLimit(full_reader, num_iter=3)
pipe(epoch_reader, processor=inc_total)
Job.current().add_stop_signal(epoch_reader.data_finished())
return [total]
def test_local_session(self):
init_net = core.Net('init')
src_values = Struct(
('uid', np.array([1, 2, 6])),
('value', np.array([1.4, 1.6, 1.7])))
expected_dst = Struct(
('uid', np.array([2, 4, 12])),
('value', np.array([0.0, 0.0, 0.0])))
with core.NameScope('init'):
src_blobs = NewRecord(init_net, src_values)
dst_blobs = InitEmptyRecord(init_net, src_values.clone_schema())
def proc1(rec):
net = core.Net('proc1')
with core.NameScope('proc1'):
out = NewRecord(net, rec)
net.Add([rec.uid(), rec.uid()], [out.uid()])
out.value.set(blob=rec.value(), unsafe=True)
return [net], out
def proc2(rec):
net = core.Net('proc2')
with core.NameScope('proc2'):
out = NewRecord(net, rec)
out.uid.set(blob=rec.uid(), unsafe=True)
net.Sub([rec.value(), rec.value()], [out.value()])
return [net], out
src_ds = Dataset(src_blobs)
dst_ds = Dataset(dst_blobs)
with TaskGroup() as tg:
out1 = pipe(src_ds.reader(), processor=proc1)
out2 = pipe(out1, processor=proc2)
pipe(out2, dst_ds.writer())
ws = workspace.C.Workspace()
FeedRecord(src_blobs, src_values, ws)
session = LocalSession(ws)
session.run(init_net)
session.run(tg)
output = FetchRecord(dst_blobs, ws=ws)
for a, b in zip(output.field_blobs(), expected_dst.field_blobs()):
np.testing.assert_array_equal(a, b)
def test_local_session(self):
init_net = core.Net('init')
src_values = Struct(
('uid', np.array([1, 2, 6])),
('value', np.array([1.4, 1.6, 1.7])))
expected_dst = Struct(
('uid', np.array([2, 4, 12])),
('value', np.array([0.0, 0.0, 0.0])))
with core.NameScope('init'):
src_blobs = NewRecord(init_net, src_values)
dst_blobs = InitEmptyRecord(init_net, src_values.clone_schema())
def proc1(rec):
net = core.Net('proc1')
with core.NameScope('proc1'):
out = NewRecord(net, rec)
net.Add([rec.uid(), rec.uid()], [out.uid()])
out.value.set(blob=rec.value(), unsafe=True)
return [net], out
def proc2(rec):
net = core.Net('proc2')
with core.NameScope('proc2'):
out = NewRecord(net, rec)
out.uid.set(blob=rec.uid(), unsafe=True)
net.Sub([rec.value(), rec.value()], [out.value()])
return [net], out
src_ds = Dataset(src_blobs)
dst_ds = Dataset(dst_blobs)
with TaskGroup() as tg:
out1 = pipe(src_ds.reader(), processor=proc1)
out2 = pipe(out1, processor=proc2)
pipe(out2, dst_ds.writer())
ws = workspace.C.Workspace()
FeedRecord(src_blobs, src_values, ws)
session = LocalSession(ws)
session.run(init_net)
session.run(tg)
output = FetchRecord(dst_blobs, ws=ws)
for a, b in zip(output.field_blobs(), expected_dst.field_blobs()):
np.testing.assert_array_equal(a, b)
def build_job():
with Node('reader'):
with Job() as job:
with job.init_group:
init_net = core.Net('init_net')
data_arr = Struct(('val', np.array(range(10))))
data = ConstRecord(init_net, data_arr)
ds = Dataset(data)
full_reader = ds.reader(init_net)
total = init_net.Const([100])
Task(step=init_net)
def inc_total(rec):
net = core.Net('inc_total')
net.Add([total, rec.val()], [total])
return [net]
epoch_reader = ReaderWithLimit(full_reader, num_iter=3)
pipe(epoch_reader, processor=inc_total)
job.add_stop_signal(epoch_reader.data_finished())
total_fetcher = Task(step=core.Net('empty'), outputs=[total])
return job, total_fetcher
def test_record_queue(self):
num_prod = 8
num_consume = 3
schema = Struct(
('floats', Map(Scalar(np.int32), Scalar(np.float32))), )
contents_raw = [
[1, 2, 3], # len
[11, 21, 22, 31, 32, 33], # key
[1.1, 2.1, 2.2, 3.1, 3.2, 3.3], # value
]
contents = from_blob_list(schema, contents_raw)
ds = Dataset(schema)
net = core.Net('init')
ds.init_empty(net)
content_blobs = NewRecord(net, contents)
FeedRecord(content_blobs, contents)
writer = ds.writer(init_net=net)
writer.write_record(net, content_blobs)
reader = ds.reader(init_net=net)
# prepare receiving dataset
rec_dataset = Dataset(contents, name='rec')
rec_dataset.init_empty(init_net=net)
rec_dataset_writer = rec_dataset.writer(init_net=net)
workspace.RunNetOnce(net)
queue = RecordQueue(contents, num_threads=num_prod)
def process(net, fields):
new_fields = []
for f in fields.field_blobs():
new_f = net.Copy(f)
new_fields.append(new_f)
new_fields = from_blob_list(fields, new_fields)
return new_fields
q_reader, q_step, q_exit, fields = queue.build(reader, process)
producer_step = core.execution_step('producer', [q_step, q_exit])
consumer_steps = []
for i in range(num_consume):
name = 'queue_reader_' + str(i)
net_consume = core.Net(name)
should_stop, fields = q_reader.read_record(net_consume)
step_consume = core.execution_step(name, net_consume)
name = 'dataset_writer_' + str(i)
net_dataset = core.Net(name)
rec_dataset_writer.write(net_dataset, fields.field_blobs())
step_dataset = core.execution_step(name, net_dataset)
step = core.execution_step('consumer_' + str(i),
[step_consume, step_dataset],
should_stop_blob=should_stop)
consumer_steps.append(step)
consumer_step = core.execution_step('consumers',
consumer_steps,
concurrent_substeps=True)
work_steps = core.execution_step('work',
[producer_step, consumer_step],
concurrent_substeps=True)
plan = core.Plan('test')
plan.AddStep(work_steps)
core.workspace.RunPlan(plan)
data = workspace.FetchBlobs(rec_dataset.get_blobs())
self.assertEqual(6, sum(data[0]))
self.assertEqual(150, sum(data[1]))
self.assertAlmostEqual(15, sum(data[2]), places=5)
def test_record_queue(self):
num_prod = 8
num_consume = 3
schema = Struct(
('floats', Map(
Scalar(np.int32),
Scalar(np.float32))),
)
contents_raw = [
[1, 2, 3], # len
[11, 21, 22, 31, 32, 33], # key
[1.1, 2.1, 2.2, 3.1, 3.2, 3.3], # value
]
contents = from_blob_list(schema, contents_raw)
ds = Dataset(schema)
net = core.Net('init')
ds.init_empty(net)
content_blobs = NewRecord(net, contents)
FeedRecord(content_blobs, contents)
writer = ds.writer(init_net=net)
writer.write_record(net, content_blobs)
reader = ds.reader(init_net=net)
# prepare receiving dataset
rec_dataset = Dataset(contents, name='rec')
rec_dataset.init_empty(init_net=net)
rec_dataset_writer = rec_dataset.writer(init_net=net)
workspace.RunNetOnce(net)
queue = RecordQueue(contents, num_threads=num_prod)
def process(net, fields):
new_fields = []
for f in fields.field_blobs():
new_f = net.Copy(f)
new_fields.append(new_f)
new_fields = from_blob_list(fields, new_fields)
return new_fields
q_reader, q_step, q_exit, fields = queue.build(reader, process)
producer_step = core.execution_step('producer', [q_step, q_exit])
consumer_steps = []
for i in range(num_consume):
name = 'queue_reader_' + str(i)
net_consume = core.Net(name)
should_stop, fields = q_reader.read_record(net_consume)
step_consume = core.execution_step(name, net_consume)
name = 'dataset_writer_' + str(i)
net_dataset = core.Net(name)
rec_dataset_writer.write(net_dataset, fields.field_blobs())
step_dataset = core.execution_step(name, net_dataset)
step = core.execution_step(
'consumer_' + str(i),
[step_consume, step_dataset],
should_stop_blob=should_stop)
consumer_steps.append(step)
consumer_step = core.execution_step(
'consumers', consumer_steps, concurrent_substeps=True)
work_steps = core.execution_step(
'work', [producer_step, consumer_step], concurrent_substeps=True)
plan = core.Plan('test')
plan.AddStep(work_steps)
core.workspace.RunPlan(plan)
data = workspace.FetchBlobs(rec_dataset.get_blobs())
self.assertEqual(6, sum(data[0]))
self.assertEqual(150, sum(data[1]))
self.assertAlmostEqual(15, sum(data[2]), places=5)
class CachedReader(Reader):
"""
Reader with persistent in-file cache.
Example usage:
cached_reader = CachedReader(reader)
build_cache_step = cached_reader.build_cache('/tmp/cache.db')
with LocalSession() as session:
session.run(build_cache_step)
Every time new reader is created, it's expected that build_cache will be
called before setup_ex and usage of the reader. build_cache will check
existence of provided file path and in case it's missing will initialize it
by reading data from original reader. All consequent attempts to read will
ignore original reader (i.e. no additional data will be read from it).
"""
def __init__(self, reader, db_type='leveldb', name='cached_reader'):
super(CachedReader, self).__init__(reader.schema())
self.original_reader = reader
self.cache_path = None
self.ds_reader = None
self.ds = Dataset(self._schema, name)
self.db_type = db_type
self.name = name
self.field_names = self._schema.field_names()
def setup_ex(self, init_net, finish_net):
assert self.cache_path, 'build_cache must be called first'
self._init_dataset(init_net)
self._load_from_file(init_net)
self.ds_reader = self.ds.reader(init_net, batch_size=100)
def read(self, read_net):
assert self.ds_reader, 'setup must be called first'
return self.ds_reader.read(read_net)
def has_cache(self):
return self.cache_path and os.path.exists(self.cache_path)
def build_cache(self, cache_path, overwrite=False):
if not self.has_cache() or overwrite:
self.cache_path = cache_path
if self.has_cache() and not overwrite:
# cache already exists, no need to rebuild it
return core.execution_step('build_step', [])
init_net = core.Net('init')
self._init_dataset(init_net)
with Cluster(), core.NameScope(self.name), TaskGroup() as copy_tg:
pipe(self.original_reader, self.ds.writer(), num_threads=16)
copy_step = copy_tg.to_task().get_step()
save_net = core.Net('save')
self._save_to_file(save_net)
return core.execution_step('build_cache', [init_net, copy_step, save_net])
def _init_dataset(self, init_net):
with core.NameScope(self.name):
self.ds.init_empty(init_net)
def _save_to_file(self, net):
net.Save(
self.ds.content().field_blobs(),
[],
db=self.cache_path,
db_type=self.db_type,
blob_name_overrides=self.field_names,
absolute_path=True,
)
def _load_from_file(self, net):
net.Load(
[],
self.ds.content().field_blobs(),
db=self.cache_path,
db_type=self.db_type,
absolute_path=True,
source_blob_names=self.field_names,
)
class DBFileReader(Reader):
default_name_suffix = 'db_file_reader'
"""Reader reads from a DB file.
Example usage:
db_file_reader = DBFileReader(db_path='/tmp/cache.db', db_type='LevelDB')
Args:
db_path: str.
db_type: str. DB type of file. A db_type is registed by
`REGISTER_CAFFE2_DB(<db_type>, <DB Class>)`.
name: str or None. Name of DBFileReader.
Optional name to prepend to blobs that will store the data.
Default to '<db_name>_<default_name_suffix>'.
batch_size: int.
How many examples are read for each time the read_net is run.
"""
def __init__(
self,
db_path,
db_type,
name=None,
batch_size=100,
):
assert db_path is not None, "db_path can't be None."
assert db_type in C.registered_dbs(), \
"db_type [{db_type}] is not available. \n" \
"Choose one of these: {registered_dbs}.".format(
db_type=db_type,
registered_dbs=C.registered_dbs(),
)
self.db_path = db_path
self.db_type = db_type
self.name = name or '{db_name}_{default_name_suffix}'.format(
db_name=self._extract_db_name_from_db_path(),
default_name_suffix=self.default_name_suffix,
)
self.batch_size = batch_size
# Before self._init_reader_schema(...),
# self.db_path and self.db_type are required to be set.
super(DBFileReader, self).__init__(self._init_reader_schema())
self.ds = Dataset(self._schema, self.name + '_dataset')
self.ds_reader = None
def _init_name(self, name):
return name or self._extract_db_name_from_db_path(
) + '_db_file_reader'
def _init_reader_schema(self):
"""Restore a reader schema from the DB file.
Here it is assumed that:
1). Each field of the schema have corresponding blobs
stored in the DB file.
2). Each blob loaded from the DB file corresponds to
a field of the schema.
Load a set of blobs from a DB file. From names of these blobs,
restore the DB file schema using `from_column_list(...)`.
Returns:
schema: schema.Struct. Used in Reader.__init__(...).
"""
assert os.path.exists(self.db_path), \
'db_path [{db_path}] does not exist'.format(db_path=self.db_path)
with core.NameScope(self.name):
# blob_prefix is for avoiding name conflict in workspace
blob_prefix = scope.CurrentNameScope()
workspace.RunOperatorOnce(
core.CreateOperator(
'Load',
[],
[],
absolute_path=True,
db=self.db_path,
db_type=self.db_type,
load_all=True,
add_prefix=blob_prefix,
)
)
col_names = [
blob_name[len(blob_prefix):] for blob_name in workspace.Blobs()
if blob_name.startswith(blob_prefix)
]
schema = from_column_list(col_names)
return schema
def setup_ex(self, init_net, finish_net):
"""From the Dataset, create a _DatasetReader and setup a init_net.
Make sure the _init_field_blobs_as_empty(...) is only called once.
Because the underlying NewRecord(...) creats blobs by calling
NextScopedBlob(...), so that references to previously-initiated
empty blobs will be lost, causing accessibility issue.
"""
if self.ds_reader:
self.ds_reader.setup_ex(init_net, finish_net)
else:
self._init_field_blobs_as_empty(init_net)
self._feed_field_blobs_from_db_file(init_net)
self.ds_reader = self.ds.reader(
init_net,
batch_size=self.batch_size,
)
def read(self, read_net):
assert self.ds_reader, 'setup_ex must be called first'
return self.ds_reader.read(read_net)
def _init_field_blobs_as_empty(self, init_net):
"""Initialize dataset field blobs by creating an empty record"""
with core.NameScope(self.name):
self.ds.init_empty(init_net)
def _feed_field_blobs_from_db_file(self, net):
"""Load from the DB file at db_path and feed dataset field blobs"""
assert os.path.exists(self.db_path), \
'db_path [{db_path}] does not exist'.format(db_path=self.db_path)
net.Load(
[],
self.ds.get_blobs(),
db=self.db_path,
db_type=self.db_type,
absolute_path=True,
source_blob_names=self.ds.field_names(),
)
def _extract_db_name_from_db_path(self):
"""Extract DB name from DB path
E.g. given self.db_path=`/tmp/sample.db`,
it returns `sample`.
Returns:
db_name: str.
"""
return os.path.basename(self.db_path).rsplit('.', 1)[0]