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from tensorflow.keras.layers import Layer, InputSpec
import keras.utils.conv_utils as conv_utils
import tensorflow as tf
import tensorflow.keras.backend as K
def normalize_data_format(value):
if value is None:
value = K.image_data_format()
data_format = value.lower()
if data_format not in {'channels_first', 'channels_last'}:
raise ValueError('The `data_format` argument must be one of '
'"channels_first", "channels_last". Received: ' +
str(value))
return data_format
class BilinearUpSampling2D(Layer):
def __init__(self, size=(2, 2), data_format=None, **kwargs):
super(BilinearUpSampling2D, self).__init__(**kwargs)
self.data_format = 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(inputs, [height, width], method=tf.image.ResizeMethod.BILINEAR)
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()))
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