def test_load_constant_converter(self): value = numpy.ndarray(shape=(1, 1, 2)) value[:] = [[[-95, 95]]] shape = value.shape input_dim = (1, 2, 3, 4) inputs = [('input', datatypes.Array(*input_dim))] outputs = [('const', datatypes.Array(*shape)), ('output', datatypes.Array(*input_dim))] builder = NeuralNetworkBuilder(inputs, outputs) builder.add_load_constant(name='LoadConstant', output_name='const', constant_value=value, shape=shape) builder.add_permute(name='Permute', input_name='input', output_name='output', dim=(0, 1, 2, 3)) model_onnx = convert_coreml(builder.spec) self.assertTrue(model_onnx is not None)
def test_load_constant_converter(self): value = numpy.ndarray(shape=(1, 1, 2)) value[:] = [[[-95, 95]]] shape = value.shape inputs = [('const', datatypes.Array(*shape))] outputs = [('const', datatypes.Array(*shape))] builder = NeuralNetworkBuilder(inputs, outputs) builder.add_load_constant(name='LoadConstant', output_name='const', constant_value=value, shape=shape) context = ConvertContext() node = LoadConstantLayerConverter.convert( context, builder.spec.neuralNetwork.layers[0], ['input'], ['output']) self.assertTrue(node is not None)