def check_sequence_rearrange(batch_size,
shape,
reorders,
persample_reorder=True,
op_type="cpu",
layout=""):
pipe = Pipeline(batch_size=batch_size, num_threads=4, device_id=0)
with pipe:
input = fn.external_source(lambda: get_sequences(batch_size, shape),
layout=layout)
frames = input.gpu() if op_type == "gpu" else input
order = fn.external_source(
lambda: reorders) if persample_reorder else reorders
rearranged = fn.sequence_rearrange(frames,
new_order=order,
device=op_type)
pipe.set_outputs(rearranged, input)
pipe.build()
result, input = pipe.run()
if op_type == "gpu":
result = result.as_cpu()
input = to_batch(input, batch_size)
baseline = reorder(input, shape[0], reorders, persample_reorder)
for i in range(batch_size):
np.testing.assert_array_equal(result[i], baseline[i])
def pipe(max_batch_size, input_data, device):
pipe = Pipeline(batch_size=max_batch_size, num_threads=4, device_id=0)
data = fn.external_source(source=input_data, cycle=False, device=device,
layout="FHWC")
processed = fn.sequence_rearrange(data, new_order=[0, 4, 1, 3, 2])
pipe.set_outputs(processed)
return pipe
def test_sequence_rearrange_cpu():
pipe = Pipeline(batch_size=batch_size, num_threads=4, device_id=None)
test_data_shape = [5, 10, 20, 3]
def get_data():
out = [
np.random.randint(0, 255, size=test_data_shape, dtype=np.uint8)
for _ in range(batch_size)
]
return out
data = fn.external_source(source=get_data, layout="FHWC")
processed = fn.sequence_rearrange(data, new_order=[0, 4, 1, 3, 2])
pipe.set_outputs(processed)
pipe.build()
for _ in range(3):
pipe.run()
