Converted tf.keras.layers.Conv2DTranspose to tflite can't run on the gpu-CodePudding

I tried to converting tf2 keras model with Conv2DTranspose layer to tflite.

But after converting to tflite, this model generates automatically Shape and Pack operation. That is a quiet problem for me because these operation can't be supported by mobile gpu. Why tflite works like this and how do I fix it?

Tflite converting code:

def get_model():
    inputs = tf.keras.Input(shape=(64, 64, 3))
    outputs = keras.layers.Conv2DTranspose(3, 3, strides=2, padding="same", activation="relu", name="deconv1")(inputs)
    
    model = keras.Model(inputs=inputs, outputs=outputs, name="custom")

    x = tf.ones((1, 64, 64, 3))
    y = model(x)
    
    return model

def convert_model(saved_model_dir, tflite_save_dir):
    converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)
    converter.optimizations = [tf.lite.Optimize.DEFAULT]
    converter.target_spec.supported_types = [tf.float32]
    tflite_model = converter.convert()

    with open(tflite_save_dir, "wb") as f:
        f.write(tflite_model)


if __name__=="__main__":
    model = get_model()

    current_path = os.path.dirname(os.path.realpath(__file__))
    save_dir = os.path.join(current_path, "custom/1/")
    tf.saved_model.save(model, save_dir)

    tflite_save_dir = os.path.join(current_path, "my_model.tflite")
    convert_model(save_dir, tflite_save_dir)
    test_tflite(tflite_save_dir)

Tflite Model Visualizing:

P.S. tf.compat.v1.layers.conv2d_transpose graph not generate Shape-StridedSlice-Pack operation. That means tf.compat.v1.layers.conv2d_transpose can run on the mobile gpu. Why does this difference occur?

Tflite converting code:

tfv1 = tf.compat.v1

def generate_tflite_model_from_v1(saved_model_dir):

    tflite_save_dir = os.path.join(saved_model_dir, "my_model_tfv1.tflite")

    with tf.Graph().as_default() as graph:

        inputs = tfv1.placeholder(tf.float32, shape=[1, 64, 64, 3])
        x = tfv1.layers.conv2d_transpose(inputs, 3, 3, strides=2, padding="same", name="deconv1")
        outputs = tf.nn.relu(x)

        with graph.as_default(), tfv1.Session(graph=graph) as session:
            session.run(tfv1.global_variables_initializer())
            converter = tfv1.lite.TFLiteConverter.from_session(
                session, input_tensors=[inputs], output_tensors=[outputs])

            with open(tflite_save_dir, "wb") as f:
                f.write(converter.convert())

CodePudding user response：

I found the solution.

It is quite simple: specify the batch size in model's input.

def get_model():
    inputs = tf.keras.Input(shape=(64, 64, 3), batch_size=1)   # specify batch size
    outputs = keras.layers.Conv2DTranspose(3, 3, strides=2, padding="same", activation="relu", name="deconv1")(inputs)
    
    model = keras.Model(inputs=inputs, outputs=outputs, name="custom")

    x = tf.ones((1, 64, 64, 3))
    y = model(x)
    
    return model

def convert_model(saved_model_dir, tflite_save_dir):
    converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)
    converter.optimizations = [tf.lite.Optimize.DEFAULT]
    converter.target_spec.supported_types = [tf.float32]
    tflite_model = converter.convert()

    with open(tflite_save_dir, "wb") as f:
        f.write(tflite_model)


if __name__=="__main__":
    model = get_model()

    current_path = os.path.dirname(os.path.realpath(__file__))
    save_dir = os.path.join(current_path, "custom/1/")
    tf.saved_model.save(model, save_dir)

    tflite_save_dir = os.path.join(current_path, "my_model.tflite")
    convert_model(save_dir, tflite_save_dir)
    test_tflite(tflite_save_dir)

Then, you can get the same model compared to tf1's graph.