layers.py

from keras.engine.topology import Layer, InputSpec
import keras.utils.conv_utils as conv_utils
import tensorflow as tf
import keras.backend as K

class BilinearUpSampling2D(Layer):
    def __init__(self, size=(2, 2), data_format=None, **kwargs):
        super(BilinearUpSampling2D, self).__init__(**kwargs)
        self.data_format = K.normalize_data_format(data_format)
        self.size = conv_utils.normalize_tuple(size, 2, 'size')
        self.input_spec = InputSpec(ndim=4)

    def compute_output_shape(self, input_shape):
        if self.data_format == 'channels_first':
            height = self.size[0] * input_shape[2] if input_shape[2] is not None else None
            width = self.size[1] * input_shape[3] if input_shape[3] is not None else None
            return (input_shape[0],
                    input_shape[1],
                    height,
                    width)
        elif self.data_format == 'channels_last':
            height = self.size[0] * input_shape[1] if input_shape[1] is not None else None
            width = self.size[1] * input_shape[2] if input_shape[2] is not None else None
            return (input_shape[0],
                    height,
                    width,
                    input_shape[3])

    def call(self, inputs):
        input_shape = K.shape(inputs)
        if self.data_format == 'channels_first':
            height = self.size[0] * input_shape[2] if input_shape[2] is not None else None
            width = self.size[1] * input_shape[3] if input_shape[3] is not None else None
        elif self.data_format == 'channels_last':
            height = self.size[0] * input_shape[1] if input_shape[1] is not None else None
            width = self.size[1] * input_shape[2] if input_shape[2] is not None else None
        
        return tf.image.resize_images(inputs, [height, width], method=tf.image.ResizeMethod.BILINEAR, align_corners=True)

    def get_config(self):
        config = {'size': self.size, 'data_format': self.data_format}
        base_config = super(BilinearUpSampling2D, self).get_config()
        return dict(list(base_config.items()) + list(config.items()))