add test

test/pytest/test_transpose_concat.py

@@ -54,3 +54,37 @@ def test_accuracy(data, keras_model, hls_model):
     y_hls4ml = hls_model.predict(X).reshape(y_keras.shape)
     # "accuracy" of hls4ml predictions vs keras
     np.testing.assert_allclose(y_keras, y_hls4ml, rtol=0, atol=1e-04, verbose=True)
+
+
+@pytest.fixture(scope='module')
+def keras_model_highdim():
+    inp = Input(shape=(2, 3, 4, 5, 6), name='input_1')
+    out = Permute((3, 5, 4, 1, 2))(inp)
+    model = Model(inputs=inp, outputs=out)
+    return model
+
+
+@pytest.fixture(scope='module')
+def data_highdim():
+    X = np.random.randint(-128, 127, (100, 2, 3, 4, 5, 6)) / 128
+    X = X.astype(np.float32)
+    return X
+
+
+@pytest.mark.parametrize('io_type', ['io_stream', 'io_parallel'])
+@pytest.mark.parametrize('backend', ['Vivado', 'Vitis'])
+def test_highdim_permute(data_highdim, keras_model_highdim, io_type, backend):
+    X = data_highdim
+    model = keras_model_highdim
+
+    model_hls = hls4ml.converters.convert_from_keras_model(
+        model,
+        io_type=io_type,
+        backend=backend,
+        output_dir=str(test_root_path / f'hls4mlprj_highdim_transpose_{backend}_{io_type}'),
+    )
+    model_hls.compile()
+    y_keras = model.predict(X)
+    y_hls4ml = model_hls.predict(X).reshape(y_keras.shape)  # type: ignore
+
+    assert np.all(y_keras == y_hls4ml)