diff --git a/src/model_modules/advanced_paddings.py b/src/model_modules/advanced_paddings.py
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+++ b/src/model_modules/advanced_paddings.py
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+__author__ = 'Felix Kleinert'
+__date__ = '2020-03-02'
+
+import tensorflow as tf
+import numpy as np
+import keras.backend as K
+
+from keras.layers.convolutional import _ZeroPadding
+from keras.legacy import interfaces
+from keras.utils import conv_utils
+from keras.utils.generic_utils import transpose_shape
+from keras.backend.common import normalize_data_format
+
+
+# class pad_utils:
+# @staticmethod
+# def get_padding_for_same(kernel_size, strides=1):
+# '''
+# This methods calculates the padding size to keep input and output dimensions equal for a given kernel size
+# (STRIDES HAVE TO BE EQUAL TO ONE!)
+# :param kernel_size:
+# :return:
+# '''
+# if strides != 1:
+# raise NotImplementedError("Strides other than 1 not implemented!")
+# ks = np.array(kernel_size, dtype=np.int64)
+# if (d & 0x1 for d in ks):
+# pad = ((ks - 1) / 2).astype(np.int64)
+# # pad = ((pad[0], pad[0]), (pad[1], pad[1]))
+# return pad
+# else:
+# raise NotImplementedError("even kernel size not implemented")
+#
+
+
+
+class ReflectionPadding2D(_ZeroPadding):
+ """Zero-padding layer for 2D input (e.g. picture).
+
+ This layer can add rows and columns of zeros
+ at the top, bottom, left and right side of an image tensor.
+
+ # Arguments
+ padding: int, or tuple of 2 ints, or tuple of 2 tuples of 2 ints.
+ - If int: the same symmetric padding
+ is applied to height and width.
+ - If tuple of 2 ints:
+ interpreted as two different
+ symmetric padding values for height and width:
+ `(symmetric_height_pad, symmetric_width_pad)`.
+ - If tuple of 2 tuples of 2 ints:
+ interpreted as
+ `((top_pad, bottom_pad), (left_pad, right_pad))`
+ data_format: A string,
+ one of `"channels_last"` or `"channels_first"`.
+ The ordering of the dimensions in the inputs.
+ `"channels_last"` corresponds to inputs with shape
+ `(batch, height, width, channels)` while `"channels_first"`
+ corresponds to inputs with shape
+ `(batch, channels, height, width)`.
+ It defaults to the `image_data_format` value found in your
+ Keras config file at `~/.keras/keras.json`.
+ If you never set it, then it will be "channels_last".
+
+ # Input shape
+ 4D tensor with shape:
+ - If `data_format` is `"channels_last"`:
+ `(batch, rows, cols, channels)`
+ - If `data_format` is `"channels_first"`:
+ `(batch, channels, rows, cols)`
+
+ # Output shape
+ 4D tensor with shape:
+ - If `data_format` is `"channels_last"`:
+ `(batch, padded_rows, padded_cols, channels)`
+ - If `data_format` is `"channels_first"`:
+ `(batch, channels, padded_rows, padded_cols)`
+ """
+
+ @interfaces.legacy_zeropadding2d_support
+ def __init__(self,
+ padding=(1, 1),
+ data_format=None,
+ **kwargs):
+ if isinstance(padding, int):
+ normalized_padding = ((padding, padding), (padding, padding))
+ elif hasattr(padding, '__len__'):
+ if len(padding) != 2:
+ raise ValueError('`padding` should have two elements. '
+ 'Found: ' + str(padding))
+ height_padding = conv_utils.normalize_tuple(padding[0], 2,
+ '1st entry of padding')
+ width_padding = conv_utils.normalize_tuple(padding[1], 2,
+ '2nd entry of padding')
+ normalized_padding = (height_padding, width_padding)
+ else:
+ raise ValueError('`padding` should be either an int, '
+ 'a tuple of 2 ints '
+ '(symmetric_height_pad, symmetric_width_pad), '
+ 'or a tuple of 2 tuples of 2 ints '
+ '((top_pad, bottom_pad), (left_pad, right_pad)). '
+ 'Found: ' + str(padding))
+ super(ReflectionPadding2D, self).__init__(normalized_padding,
+ data_format,
+ **kwargs)
+
+ @staticmethod
+ def spatial_2d_padding(x, padding=((1, 1), (1, 1)), data_format=None):
+ """Pads the 2nd and 3rd dimensions of a 4D tensor.
+
+ # Arguments
+ x: Tensor or variable.
+ padding: Tuple of 2 tuples, padding pattern.
+ data_format: string, `"channels_last"` or `"channels_first"`.
+
+ # Returns
+ A padded 4D tensor.
+
+ # Raises
+ ValueError: if `data_format` is neither `"channels_last"` or `"channels_first"`.
+ """
+ assert len(padding) == 2
+ assert len(padding[0]) == 2
+ assert len(padding[1]) == 2
+ data_format = normalize_data_format(data_format)
+
+ pattern = [[0, 0],
+ list(padding[0]),
+ list(padding[1]),
+ [0, 0]]
+ pattern = transpose_shape(pattern, data_format, spatial_axes=(1, 2))
+ return pattern
+
+ def call(self, inputs, mask=None):
+ pattern = self.spatial_2d_padding(inputs, padding=self.padding, data_format=self.data_format)
+ return tf.pad(inputs, pattern, 'REFLECT')
+
+
+if __name__ == '__main__':
+ from keras.models import Model
+ from keras.layers import Conv2D, Flatten, Dense, Input
+ x = np.array(range(2000)).reshape(-1,10,10,1)
+ y = x.mean(axis=(1,2))
+
+ x_input = Input(shape=x.shape[1:])
+ x_out = ReflectionPadding2D(padding=(1,1))(x_input)
+ print(x_out.get_shape())
+ x_out = Conv2D(10, kernel_size=(3,3), activation='relu')(x_out)
+ x_out = Flatten()(x_out)
+ x_out = Dense(1, activation='linear')(x_out)
+
+ model = Model(inputs=x_input, outputs=x_out)
+ model.compile('adam', loss='mse')
+ model.summary()
+ hist = model.fit(x, y, epochs=10)
+
+