def test_scalar_constant():
pipe = Pipeline(batch_size = 1, num_threads = 1, device_id = 0)
image1 = np.array([
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12]], dtype=np.uint8)[:,:,np.newaxis]
image2 = np.array([
[10, 20],
[30, 40],
[50, 60]], dtype=np.uint8)[:,:,np.newaxis]
batches = [[image1], [image2]]
inputs = fn.external_source(lambda: batches, 2, layout = "HWC")
rotated = fn.rotate(inputs, angle = types.ScalarConstant(90))
pipe.set_outputs(*rotated, types.ScalarConstant(90))
pipe.build()
outs = pipe.run()
arr1 = outs[0].at(0)
arr2 = outs[1].at(0)
arr3 = outs[2].at(0)
ref1 = np.array([
[4, 8, 12],
[3, 7, 11],
[2, 6, 10],
[1, 5, 9]])[:,:,np.newaxis]
ref2 = np.array([
[20, 40, 60],
[10, 30, 50]], dtype=np.uint8)[:,:,np.newaxis]
ref3 = np.array(90)
assert(np.array_equal(arr1, ref1))
assert(np.array_equal(arr2, ref2))
assert(np.array_equal(arr3, ref3))
def test_pipe():
data = fn.external_source(source=get_data)
shape = types.ScalarConstant(10)
anchor = types.ScalarConstant(5)
if device != 'cpu':
data = data.gpu()
sliced = fn.slice(data, start = anchor, shape = shape, axes=[0], device=device)
return data, sliced, shape, anchor
