def test_split(self, tensor_splits, split_as_arg):
# Output Size: 1 - inf
axis, split_info, splits = tensor_splits
split_as_arg = True
if split_as_arg:
input_tensors = [np.concatenate(splits, axis=axis)]
kwargs = dict(axis=axis, split=split_info, num_output=len(splits))
else:
input_tensors = [np.concatenate(splits, axis=axis), split_info]
kwargs = dict(axis=axis, num_output=len(splits))
result = Functional.Split(*input_tensors, **kwargs)
def split_ref(input, split=split_info):
s = np.cumsum([0] + list(split))
return [
np.array(input.take(np.arange(s[i], s[i + 1]), axis=axis))
for i in range(len(split))
]
result_ref = split_ref(*input_tensors)
for i, ref in enumerate(result_ref):
np.testing.assert_array_equal(
result[i], ref, err_msg='Functional Relu result mismatch'
)
def test_concat(self, tensor_splits):
# Input Size: 1 -> inf
axis, _, splits = tensor_splits
concat_result, split_info = Functional.Concat(*splits, axis=axis)
concat_result_ref = np.concatenate(splits, axis=axis)
split_info_ref = np.array([a.shape[axis] for a in splits])
np.testing.assert_array_equal(
concat_result,
concat_result_ref,
err_msg='Functional Concat result mismatch')
np.testing.assert_array_equal(
split_info,
split_info_ref,
err_msg='Functional Concat split info mismatch')
def test_relu(self, X, engine):
X += 0.02 * np.sign(X)
X[X == 0.0] += 0.02
output = Functional.Relu(X)
Y_l = output[0]
Y_d = output["output_0"]
with workspace.WorkspaceGuard("tmp_workspace"):
op = core.CreateOperator("Relu", ["X"], ["Y"], engine=engine)
workspace.FeedBlob("X", X)
workspace.RunOperatorOnce(op)
Y_ref = workspace.FetchBlob("Y")
np.testing.assert_array_equal(
Y_l, Y_ref, err_msg='Functional Relu result mismatch')
np.testing.assert_array_equal(
Y_d, Y_ref, err_msg='Functional Relu result mismatch')