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Samuel Callahan
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MRI-tumor-segmentation
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import tensorflow as tf

from tensorflow.contrib.keras.python.keras.layers import *





def Conv3DWithBN(x, filters, ksize, strides, name, padding='same', dilation_rate=1, center=True, scale=True, decay=0.99):

    x = Conv3D(filters=filters, kernel_size=ksize, strides=strides, padding=padding, dilation_rate=dilation_rate,

                        use_bias=False, kernel_initializer='he_normal', name=name+'_conv')(x)

    x = BatchNormalization(center=center, scale=scale, momentum=decay, name=name+'_bn')(x)

    x = Activation('relu', name=name+'_relu')(x)

    return x





def Conv2DWithBN(x, filters, ksize, strides, name, padding='same', dilation_rate=1, center=True, scale=True, decay=0.99):

    x = Conv2D(filters=filters, kernel_size=ksize, strides=strides, padding=padding, dilation_rate=dilation_rate,

                        use_bias=False, kernel_initializer='he_normal', name=name+'_conv')(x)

    x = BatchNormalization(center=center, scale=scale, momentum=decay, name=name+'_bn')(x)

    x = Activation('relu', name=name+'_relu')(x)

    return x





def Conv1DWithBN(x, filters, ksize, strides, name, padding='same', dilation_rate=1, center=True, scale=True, decay=0.99):

    x = Conv1D(filters=filters, kernel_size=ksize, strides=strides, padding=padding, dilation_rate=dilation_rate,

               use_bias=False, kernel_initializer='he_normal', name=name+'_conv')(x)

    x = BatchNormalization(center=center, scale=scale, momentum=decay, name=name+'_bn')(x)

    x = Activation('relu', name=name+'_relu')(x)

    return x





def DenseWithBN(x, units, name, kernel_regularizer=None, center=True, scale=True, decay=0.99):

    x = Dense(units=units, use_bias=False, kernel_regularizer=kernel_regularizer, name=name+'_weight')(x)

    x = BatchNormalization(center=center, scale=scale, momentum=decay, name=name+'_bias')(x)

    x = Activation('relu', name=name+'_relu')(x)

    return x





def ResNetUnit2D(x, filters, ksize, name, end=False, center=True, scale=True, decay=0.99):

    identity = x



    x = BatchNormalization(center=center, scale=scale, momentum=decay, name=name+'_bn_1')(x)

    x = Activation('relu', name=name+'_relu_1')(x)

    x = Conv2D(filters, kernel_size=ksize, strides=1, padding='same', kernel_initializer='he_normal', name=name+'_conv_1')(x)



    x = BatchNormalization(center=center, scale=scale, momentum=decay, name=name+'_bn_2')(x)

    x = Activation('relu', name=name+'_relu_2')(x)

    x = Conv2D(filters, kernel_size=ksize, strides=1, padding='same', kernel_initializer='he_normal', name=name+'_conv2')(x)



    x = add([x, identity])

    if end:

        x = BatchNormalization(center=center, scale=scale, momentum=decay)(x)

        x = Activation('relu')(x)

    return x





def ResNetUnitIncreasingDims2D(x, filters, ksize, strides, name, begin=False, center=True, scale=True, decay=0.99):

    identity = x



    if not begin:

        x = BatchNormalization(center=center, scale=scale, momentum=decay, name=name+'_bn_1')(x)

        x = Activation('relu', name=name+'_relu_1')(x)

    x = Conv2D(filters, kernel_size=ksize, strides=strides[0], padding='same', kernel_initializer='he_normal', use_bias=False, name=name+'_conv_1')(x)



    x = BatchNormalization(center=center, scale=scale, momentum=decay, name=name+'_bn_2')(x)

    x = Activation('relu', name=name+'_relu_2')(x)

    x = Conv2D(filters, kernel_size=ksize, strides=strides[1], padding='same', kernel_initializer='he_normal', use_bias=False, name=name+'_conv_2')(x)



    identity = Conv2D(filters, kernel_size=1, strides=strides[0], padding='same', kernel_initializer='he_normal', name=name+'_conv_identity')(identity)

    x = add([x, identity])

    return x





def ResNetUnit1D(x, filters, ksize, name, end=False, center=True, scale=True, decay=0.99):

    identity = x



    x = BatchNormalization(center=center, scale=scale, momentum=decay, name=name+'_bn_1')(x)

    x = Activation('relu', name=name+'_relu_1')(x)

    x = Conv1D(filters, kernel_size=ksize, strides=1, padding='same', kernel_initializer='he_normal', use_bias=False, name=name+'_conv_1')(x)



    x = BatchNormalization(center=center, scale=scale, momentum=decay, name=name+'_bn_2')(x)

    x = Activation('relu', name=name+'_relu_2')(x)

    x = Conv1D(filters, kernel_size=ksize, strides=1, padding='same', kernel_initializer='he_normal', use_bias=False, name=name+'_conv_2')(x)



    x = add([x, identity])

    if end:

        x = BatchNormalization(center=center, scale=scale, momentum=decay, name=name+'_bn_3')(x)

        x = Activation('relu', name=name+'_relu_3')(x)

    return x





def ResNetUnitIncreasingDims1D(x, filters, ksize, strides, name, begin=False, center=True, scale=True, decay=0.99):

    '''

    ResNet unit without BottleNeck. 2 layers

    :param x:

    :param filters:

    :param ksize:

    :param strides: list with 2 elements, stride for each layer

    :param begin:

    :return:

    '''



    identity = x



    if not begin:

        x = BatchNormalization(center=center, scale=scale, momentum=decay, name=name+'_bn_1')(x)

        x = Activation('relu', name=name+'_relu_1')(x)

    x = Conv1D(filters, kernel_size=ksize, strides=strides[0], padding='same', kernel_initializer='he_normal', use_bias=False, name=name+'_conv_1')(x)



    x = BatchNormalization(center=center, scale=scale, momentum=decay, name=name+'_bn_2')(x)

    x = Activation('relu', name=name+'_relu_2')(x)

    x = Conv1D(filters, kernel_size=ksize, strides=strides[1], padding='same', kernel_initializer='he_normal', use_bias=False, name=name+'_conv_2')(x)



    identity = Conv1D(filters, kernel_size=1, strides=strides[0], padding='same', kernel_initializer='he_normal', name=name+'_conv_identity')(identity)

    x = add([x, identity])

    return x





def ContextualAvgPooling(x, ksizes, strides):

    '''

    Concatenate input with pooling result

    :param x:

    :param ksizes:

    :param strides:

    :return:

    '''

    out = None

    for ks in ksizes:

        x_pooled = AvgPool1D(pool_size=ks, strides=strides, padding='same')

        if out is None:

            out = x_pooled

        else:

            out = concatenate([out, x_pooled], axis=-1)

    x = concatenate([x, out], axis=-1)

    return x





def ContextualAtrousConv1D(x, filters, ksize, strides, dilation_rates, name):

    """

    Retrieve contextual information with Atrous Convolution

    :param x:

    :param filters:

    :param ksize:

    :param strides:

    :param dilation_rates:

    :return:

    """

    concat = x

    for dr in dilation_rates:

        x_atrous = Conv1DWithBN(x, filters=filters, ksize=ksize, strides=strides, dilation_rate=dr, name=name+'a_conv_s'+str(dr))

        concat = concatenate([concat, x_atrous], axis=-1)

    concat = Conv1DWithBN(concat, filters=256, ksize=ksize, strides=strides, name=name+'atrou_post_conv1')

    return concat





def ContextualAtrousConv3D(x, filters, ksize, strides, dilation_rates, name):

    """

    Retrieve contextual information with Atrous Convolution

    :param x:

    :param filters:

    :param ksize:

    :param strides:

    :param dilation_rates:

    :return:

    """

    concat = None

    for dr in dilation_rates:

        x_atrous = Conv3DWithBN(x, filters=filters, ksize=ksize, strides=strides, dilation_rate=dr, name=name+'a_conv_s'+str(dr))

        if concat is None:

            concat = x_atrous

        else:

            concat = concatenate([concat, x_atrous], axis=-1)

    concat = Conv3DWithBN(concat, filters=filters, ksize=ksize, strides=strides, name=name+'atrou_post_conv1')

    return concat





def SharedAtrousConv1D(x, SharedConvs, PostConv):

    concat = x

    for SharedConv in SharedConvs:

        x_atrous = SharedConv(x)

        x_atrous = BatchNormalization()(x_atrous)

        x_atrous = Activation('relu')(x_atrous)

        concat = concatenate([concat, x_atrous], axis=-1)

    concat = PostConv(concat)

    concat = BatchNormalization()(concat)

    concat = Activation('relu')(concat)

    return concat





def densenet_block3d(x, k, rep):

    dense_input = x

    for i in range(rep):

        x_dense = Conv3D(filters=k, kernel_size=3, strides=1, padding='same', activation='relu')(dense_input)

        dense_input = concatenate([dense_input, x_dense])

    return dense_input





def DenseNetTransit(x, rate=1, name=None):

    if rate != 1:

        out_features = x.get_shape().as_list()[-1] * rate

        x = BatchNormalization(center=True, scale=True, name=name + '_bn')(x)

        x = Activation('relu', name=name + '_relu')(x)

        x = Conv3D(filters=out_features, kernel_size=1, strides=1, padding='same', kernel_initializer='he_normal',

                   use_bias=False, name=name + '_conv')(x)

    x = AveragePooling3D(pool_size=2, strides=2, padding='same')(x)

    return x





def DenseNetUnit3D(x, growth_rate, ksize, rep, bn_decay=0.99, name=None):

    for i in range(rep):

        concat = x

        x = BatchNormalization(center=True, scale=True, momentum=bn_decay, name=name+'_bn_rep_'+str(i))(x)

        x = Activation('relu')(x)

        x = Conv3D(filters=growth_rate, kernel_size=ksize, padding='same',

                   kernel_initializer='glorot_normal', use_bias=False, name=name+'_conv_rep_'+str(i))(x)

        x = concatenate([concat, x])

    return x





class BilinearUpsampling3D(Layer):

    """

    Wrapping 1D BilinearUpsamling as a Keras layer

    Input: 3D Tensor (batch, dim, channels)

    """

    def __init__(self, size, **kwargs):

        self.size = size

        super(BilinearUpsampling3D, self).__init__(**kwargs)



    def build(self, input_shape):

        super(BilinearUpsampling3D,self).build(input_shape)



    def call(self, x, mask=None):

        x = tf.expand_dims(x, axis=2)

        x = tf.image.resize_bilinear(x, [self.size, 1])

        x = tf.squeeze(x, axis=2)

        return x



    def get_output_shape_for(self, input_shape):

        return (input_shape[0], self.size, input_shape[2])





# def SharedAtrousConv1D(x, SharedConvs, PostConv):

#     concat = None

#     for SharedConv in SharedConvs:

#         x_atrous = SharedConv(x)

#         x_atrous = BatchNormalization()(x_atrous)

#         x_atrous = Activation('relu')(x_atrous)

#         if concat is None:

#             concat = x_atrous

#         else:

#             concat = concatenate([concat, x_atrous], axis=-1)

#     concat = PostConv(concat)

#     concat = BatchNormalization()(concat)

#     concat = Activation('relu')(concat)

#     return concat





class BilinearUpsampling1D(Layer):

    """

    Wrapping 1D BilinearUpsamling as a Keras layer

    Input: 3D Tensor (batch, dim, channels)

    """

    def __init__(self, size, **kwargs):

        self.size = size

        super(BilinearUpsampling1D, self).__init__(**kwargs)



    def build(self, input_shape):

        super(BilinearUpsampling1D,self).build(input_shape)



    def call(self, x, mask=None):

        x = tf.expand_dims(x, axis=2)

        x = tf.image.resize_bilinear(x, [self.size, 1])

        x = tf.squeeze(x, axis=2)

        return x



    def get_output_shape_for(self, input_shape):

        return (input_shape[0], self.size, input_shape[2])