def _test_scalar(device, as_tensors):
"""Test propagation of scalars from external source"""
batch_size = 4
src_pipe = Pipeline(batch_size, 1, 0)
src_ext = fn.external_source(
source=lambda i:
[np.float32(i * 10 + i + 1) for i in range(batch_size)],
device=device)
src_pipe.set_outputs(src_ext)
src_pipe.build()
dst_pipe = Pipeline(batch_size,
1,
0,
exec_async=False,
exec_pipelined=False)
dst_pipe.set_outputs(fn.external_source(name="ext", device=device))
dst_pipe.build()
for iter in range(3):
src = src_pipe.run()
data = src[0]
if as_tensors:
data = [data[i] for i in range(len(data))]
dst_pipe.feed_input("ext", data)
dst = dst_pipe.run()
check_batch(src[0], dst[0], batch_size, 0, 0, "")
def test_external_source_with_serialized_pipe():
@pipeline_def
def serialized_pipe():
return fn.external_source(name="es")
pipe = serialized_pipe(batch_size=10, num_threads=3, device_id=0)
serialized_str = pipe.serialize()
deserialized_pipe = Pipeline(10, 4, 0)
deserialized_pipe.deserialize_and_build(serialized_str)
deserialized_pipe.feed_input("es", np.zeros([10, 10]))
def _test_feed_input(device):
src_pipe, batch_size = build_src_pipe(device)
dst_pipe = Pipeline(batch_size,
1,
0,
exec_async=False,
exec_pipelined=False)
dst_pipe.set_outputs(fn.external_source(name="ext", device=device))
dst_pipe.build()
for iter in range(3):
out1 = src_pipe.run()
dst_pipe.feed_input("ext", out1[0])
out2 = dst_pipe.run()
check_batch(out2[0], out1[0], batch_size, 0, 0, "XY")
def test_layout_changing():
src_pipe = Pipeline(1, 1, 0)
src_pipe.set_outputs(fn.external_source(name="input"))
src_pipe.build()
src_pipe.feed_input("input", [np.zeros((1))], layout="W")
src_pipe.feed_input("input", [np.zeros((1))], layout="H")
