[draft/onert/python] Tests for inference

runtime/onert/api/python/tests/requrirements.txt

@@ -0,0 +1,6 @@
+# requirements.txt for running tests
+
+onert
+numpy
+pytest
+psutil

runtime/onert/api/python/tests/test_onert_infer_api.py

@@ -0,0 +1,64 @@
+import os
+import pytest
+import numpy as np
+from onert import infer
+from onert.native.libnnfw_api_pybind import tensorinfo
+
+
+@pytest.fixture(scope="module")
+def nnpackage_path():
+    path = os.environ.get("ONERT_TEST_NNPACKAGE")
+    if not path or not os.path.isdir(path):
+        pytest.skip("Set ONERT_TEST_NNPACKAGE env var to a valid nnpackage directory")
+    return path
+
+
+@pytest.fixture(params=["cpu"])
+def session(nnpackage_path, request):
+    return infer.session(nnpackage_path, backends=request.param)
+
+
+def test_get_inputs_tensorinfo(session):
+    infos = session.get_inputs_tensorinfo()
+    assert isinstance(infos, list)
+    assert infos, "There should be at least one input tensorinfo"
+    for info in infos:
+        assert isinstance(info, tensorinfo)
+        assert hasattr(info, "dtype") and hasattr(info, "rank") and hasattr(info, "dims")
+        assert len(info.dims) >= info.rank
+
+
+def test_infer_output_shapes(session):
+    infos = session.get_inputs_tensorinfo()
+    dummy = []
+    for info in infos:
+        shape = tuple([dim if dim > 0 else 1 for dim in info.dims[:info.rank]])
+        dummy.append(np.zeros(shape, dtype=info.dtype))
+    result = session.infer(dummy)
+    outputs = result if isinstance(result, list) else [result]
+    assert all(isinstance(x, np.ndarray) for x in outputs)
+    out_infos = session.get_outputs_tensorinfo()
+    assert len(outputs) == len(out_infos)
+    for arr, info in zip(outputs, out_infos):
+        expected = tuple(info.dims[:info.rank])
+        assert arr.shape == expected
+
+
+def test_infer_with_metrics(session):
+    infos = session.get_inputs_tensorinfo()
+    dummy = []
+    for info in infos:
+        shape = tuple([dim if dim > 0 else 1 for dim in info.dims[:info.rank]])
+        dummy.append(np.zeros(shape, dtype=info.dtype))
+    outputs, metrics = session.infer(dummy, measure=True)
+    assert "run_time_ms" in metrics and isinstance(metrics["run_time_ms"], float)
+
+
+def test_random_dynamic_shape(session):
+    infos = session.get_inputs_tensorinfo()
+    dummy = []
+    for info in infos:
+        dims = [np.random.randint(1, 5) if d == -1 else d for d in info.dims[:info.rank]]
+        dummy.append(np.random.rand(*tuple(dims)).astype(info.dtype))
+    # should run without error
+    session.infer(dummy)