MLAB - Source Code

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import tensorflow as tf


def conv_layer(x, filters, stride=1):
    """Convolutional block

    Args:
        x (tf.Keras.Layer): Previous Keras layer
        filters (int): The dimensionality of the output space (i.e. the number of output filters in the convolution).
        stride (int, optional): An integer, specifying the strides of the convolution along the height and width. Defaults to 1.

    Returns:
        tf.Keras.Layer: The model with a convolutional block at the top.
    """
    x = tf.keras.layers.Conv2D(filters, kernel_size=(3,3), strides=(stride,stride), padding='same', use_bias=False)(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.ReLU()(x)
    
    return x


def depthwise_conv_layer(x, filters, stride=1, depth_multiplier=1):
    """Depth-wise convolutional block

    Args:
        x (tf.Keras.Layer): Previous Keras layer
        filters (int): The dimensionality of the output space (i.e. the number of output filters in the convolution).
        stride (int, optional): An integer, specifying the strides of the convolution along the height and width. Defaults to 1.
        depth_multiplier (int, optional): An integer, specifying the depth_multiplier of the convolution layer. Defaults to 1.

    Returns:
        tf.Keras.Layer: The model with a depth-wise convolutional block at the top.
    """
    x = tf.keras.layers.DepthwiseConv2D(kernel_size=(3,3), strides=(stride,stride), padding='same', depth_multiplier=depth_multiplier, use_bias=False)(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.ReLU()(x)


    x = tf.keras.layers.Conv2D(filters, kernel_size=(1,1), strides=(1,1), padding='same', use_bias=False)(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.ReLU()(x)

    return x


def mobilenet_vitis(input_tensor=None, include_top=True, weight_path=None, return_tensor=False, classes=1000, classifier_activation="softmax", alpha=1.0, depth_multiplier=1):
    """Creates and returns the ResNet152 CNN architecture.

    Args:
        input_tensor: optional keras layer, like an input tensor.
        include_top: whether to include the top layers or top.
        weight_path: If not none, these weights will be loaded.
        return_tensor: Whether to return the network as tensor or as `tf.keras.model` (if true, weights will not be loaded).
        classes: By default the number of classes are 1000 (ImageNet). Only important `include_top=True`.
        classifier_activation: By default softmax (ImageNet). Only important `include_top=True`.
        alpha (float, optional): controls the width of the network.
            - If `alpha` < 1.0, proportionally decreases the number
                of filters in each layer.
            - If `alpha` > 1.0, proportionally increases the number
                of filters in each layer.
            - If `alpha` = 1, default number of filters from the paper
                 are used at each layer.
        depth_multiplier (int, optional): The number of depthwise convolution output channels
            for each input channel. Defaults to 1.

    Returns:
        The CNN architecture as `tf.keras.model` if `return_tensor=False`, otherwise as `tf.keras.layers`.
    """
    if input_tensor is None:
        input_tensor = tf.keras.layers.Input(shape=(224,224,3))

    x = tf.keras.layers.ZeroPadding2D()(input_tensor)
    x = conv_layer(x, int(32 * alpha), 2)
    x = depthwise_conv_layer(x, int(64 * alpha), depth_multiplier=depth_multiplier)
    x = tf.keras.layers.ZeroPadding2D()(x)
    x = depthwise_conv_layer(x, int(128 * alpha), 2, depth_multiplier=depth_multiplier)
    x = depthwise_conv_layer(x, int(128 * alpha), depth_multiplier=depth_multiplier)
    x = tf.keras.layers.ZeroPadding2D()(x)
    x = depthwise_conv_layer(x, int(256 * alpha), 2, depth_multiplier=depth_multiplier)
    x = depthwise_conv_layer(x, int(256 * alpha), depth_multiplier=depth_multiplier)
    x = tf.keras.layers.ZeroPadding2D()(x)
    x = depthwise_conv_layer(x, int(512 * alpha), 2, depth_multiplier=depth_multiplier)

    x = depthwise_conv_layer(x, int(512 * alpha), depth_multiplier=depth_multiplier)
    x = depthwise_conv_layer(x, int(512 * alpha), depth_multiplier=depth_multiplier)
    x = depthwise_conv_layer(x, int(512 * alpha), depth_multiplier=depth_multiplier)
    x = depthwise_conv_layer(x, int(512 * alpha), depth_multiplier=depth_multiplier)
    x = depthwise_conv_layer(x, int(512 * alpha), depth_multiplier=depth_multiplier)

    x = tf.keras.layers.ZeroPadding2D()(x)
    x = depthwise_conv_layer(x, int(1024 * alpha), 2, depth_multiplier=depth_multiplier)
    x = depthwise_conv_layer(x, int(1024 * alpha), 2, depth_multiplier=depth_multiplier)

    if include_top is True:
        x = tf.keras.layers.GlobalAveragePooling2D()(x)
        x = tf.keras.layers.Flatten()(x)
        x = tf.keras.layers.Dense(classes, activation=classifier_activation, name="predictions")(x)

    if return_tensor:
        return x
    
    model = tf.keras.Model(input_tensor, x, name="mobilenet")
    if weight_path is not None:
        model.load_weights(weight_path)

    return model