def test_multiarray_to_image_input_util_HWC_format(self):
H, W, C = 1, 1, 3
input_features = [("data", datatypes.Array(H, W, C))]
output_features = [("out", datatypes.Array(H, W, C))]
builder = NeuralNetworkBuilder(
input_features, output_features, disable_rank5_shape_mapping=True
)
builder.add_activation("linear", "LINEAR", "data", "out")
spec = builder.spec
mlmodel = MLModel(spec)
mlmodel = make_image_input(
mlmodel,
"data",
red_bias=-5,
green_bias=-6,
blue_bias=-2.5,
scale=10.0,
image_format="NHWC",
)
x = np.array([4, 2, 5], dtype=np.uint8)
x = np.reshape(x, (H, W, C))
pil_img = PIL.Image.fromarray(x)
y = mlmodel.predict({"data": pil_img}, useCPUOnly=True)["out"]
self.assertEqual(y.shape, (H, W, C))
np.testing.assert_almost_equal(y.flatten(), [35.0, 14.0, 47.5])
def test_rename_image_input(self):
input_features = [("data", datatypes.Array(3, 1, 1))]
output_features = [("out", datatypes.Array(3, 1, 1))]
builder = NeuralNetworkBuilder(
input_features, output_features, disable_rank5_shape_mapping=True
)
builder.add_activation("linear", "LINEAR", "data", "out")
spec = builder.spec
# make an image input
mlmodel = make_image_input(MLModel(spec), "data", image_format="NCHW", scale=2.0)
# rename the input
spec = mlmodel.get_spec()
rename_feature(spec, "data", "new_input_name")
mlmodel = MLModel(spec)
# test
x = np.array([4, 5, 6], dtype=np.uint8).reshape(1, 1, 3)
pil_img = PIL.Image.fromarray(x)
out = mlmodel.predict({"new_input_name": pil_img}, useCPUOnly=True)['out']
np.testing.assert_equal(out, np.array([8.0, 10.0, 12.0]).reshape(3, 1, 1))
