How to use the coremltools.converters function in coremltools

def test_glm_regressor(self):
        X, y = make_regression(n_features=4, random_state=0)

        lr = LinearRegression()
        lr.fit(X, y)
        lr_coreml = coremltools.converters.sklearn.convert(lr)
        lr_onnx = convert(lr_coreml.get_spec())
        self.assertTrue(lr_onnx is not None)
        dump_data_and_model(X.astype(numpy.float32), lr, lr_onnx, basename="CmlLinearRegression-Dec4")

        svr = LinearSVR()
        svr.fit(X, y)
        svr_coreml = coremltools.converters.sklearn.convert(svr)
        svr_onnx = convert(svr_coreml.get_spec())
        self.assertTrue(svr_onnx is not None)
        dump_data_and_model(X.astype(numpy.float32), svr, svr_onnx, basename="CmlLinearSvr-Dec4")

def test_flatten(self):
        N, C, H, W, D = 2, 3, 1, 2, 2
        x = create_tensor(N, C, H, W)

        keras_model = Sequential()
        keras_model.add(Flatten(input_shape=(H, W, C)))
        keras_model.add(Dense(D))
        keras_model.compile(optimizer='adagrad', loss='mse')

        try:
            coreml_model = coremltools.converters.keras.convert(keras_model)        
        except (ImportError, AttributeError):
            warnings.warn("Issue in coremltools.")
            return
        onnx_model = onnxmltools.convert_coreml(coreml_model)
        self.assertIsNotNone(onnx_model)

        if not self._no_available_inference_engine():
            y_reference = keras_model.predict(np.transpose(x, [0, 2, 3, 1]))
            y_produced = evaluate_deep_model(onnx_model, x).reshape(N, D)
            self.assertTrue(np.allclose(y_reference, y_produced))

import coremltools

DNN_ml_model = coremltools.converters.keras.convert('car_detection_keras_DNN_model.h5')
DNN_ml_model.author = 'Ashis Laha'
DNN_ml_model.description = 'Use for Car Detection'
DNN_ml_model.save('car_detection_keras_DNN.mlmodel')
print(DNN_ml_model)


DNN_ml_model = coremltools.converters.keras.convert('car_detection_keras_CNN_model.h5')
DNN_ml_model.author = 'Ashis Laha'
DNN_ml_model.description = 'Use for Car Detection'
DNN_ml_model.save('car_detection_keras_CNN.mlmodel')
print(DNN_ml_model)

def convert(model_path, prototxt_path, label_path, red=-123, green=-117, blue=-104, 
                scale=1.0, bgr=False, output_model_name='sku_cls_model_noise.mlmodel'):
    """
    Args: red, grenn, blue should be nagative: like -123, -117, -104
        output_model_name: default, sku_cls_model_noise.mlmodel
        label_path: Classes are sorted by incrementing。 
            liby_0, 0
            liby_1, 1
            liby_2, 2
            liby_3, 3
            liby_4, 4
    Returns: auto-saved coreml model in source path.
    """

    coreml_model = coremltools.converters.caffe.convert((
        model_path, prototxt_path),
        image_input_names='data',
        is_bgr=bgr,
        image_scale=scale,
        red_bias= red*scale,
        green_bias=green*scale,
        blue_bias=blue*scale,
        class_labels=label_path,
    )
    coreml_model.save(os.path.join(os.path.dirname(model_path), output_model_name))
    print("finished converte {0} to {1}".format(os.path.basename(model_path), output_model_name))
    return

def export_coreml_location_model(model):
    cm = coremltools.converters.keras.convert(
        model, input_names=['accel_gyro_stream'], output_names=['touch_predictions'])
    cm.author = 'Tomas Reimers & Greg Foster'
    cm.license = 'MIT'
    cm.short_description = ''
    cm.input_description['accel_gyro_stream'] = 'An array of time indexed sensor data'
    cm.output_description['touch_predictions'] = 'Was the screen touched or not?'
    cm.save('location_model.mlmodel')

def export_coreml_touch_model(model):
    cm = coremltools.converters.keras.convert(
        model, input_names=['touch_windows'], output_names=['touch_predictions'])
    cm.author = 'Tomas Reimers & Greg Foster'
    cm.license = 'MIT'
    cm.short_description = ''
    cm.input_description['touch_windows'] = 'An array of arrays of time indexed sensor data'
    cm.output_description['touch_predictions'] = 'Where was the screen touched?'
    cm.save('touch_model.mlmodel')

warped = img_matlab[int(boundingboxes[0][1]):int(boundingboxes[0][3]),
                        int(boundingboxes[0][0]):int(boundingboxes[0][2])]
    print(int(boundingboxes[0][1]), int(boundingboxes[0][3]), int(boundingboxes[0][0]), int(boundingboxes[0][2]))
    return warped


### Step 1: tensorflow to caffemodel
tf_model_dir = '/home/logview/workspace/projects/FaceAll/20170512-110547'
convertTf2Caffe(tf_model_dir, 'InceptionResnet_Model', EMBEDDING_SIZE)


### Step 2: caffemodel to CoreML
### use parameter (image_input_names='data') ==> input CVPixelBufferRef in iOS
### (without image_input_names='data') ==> input MLMultiArray in iOS
if(EMBEDDING_SIZE==512):
    coreml_model = coremltools.converters.caffe.convert(('InceptionResnet_Model/inception_resnet_v1_conv1x1.caffemodel', 'InceptionResnet_Model/resnetInception-512.prototxt'), is_bgr=False)
    coreml_model.save('InceptionResnet_Model/InceptionResnet.mlmodel')
else:
    coreml_model = coremltools.converters.caffe.convert(('InceptionResnet_Model/inception_resnet_v1_conv1x1.caffemodel', 'InceptionResnet_Model/resnetInception-128.prototxt'), is_bgr=False)
    coreml_model.save('InceptionResnet_Model/InceptionResnet.mlmodel')


### Step 3: calculate embedding from tensorflow model
imgPath = '4550.jpg'
img = cv2.imread(imgPath)   # BGR
crop = mtcnnDetect(img)     # RGB
calcTFVector(crop, tf_model_dir)


### Step 4: calculate embedding from caffe model
imgPath = '4550.jpg'
img = cv2.imread(imgPath)