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import keras.backend as K
import tensorflow as tf
from keras.layers import *
def resize_images_bilinear(X, height_factor=1, width_factor=1, target_height=None, target_width=None, data_format='default'):
'''Resizes the images contained in a 4D tensor of shape
- [batch, channels, height, width] (for 'channels_first' data_format)
- [batch, height, width, channels] (for 'channels_last' data_format)
by a factor of (height_factor, width_factor). Both factors should be
positive integers.
'''
if data_format == 'default':
data_format = K.image_data_format()
if data_format == 'channels_first':
original_shape = K.int_shape(X)
if target_height and target_width:
new_shape = tf.constant(np.array((target_height, target_width)).astype('int32'))
else:
new_shape = tf.shape(X)[2:]
new_shape *= tf.constant(np.array([height_factor, width_factor]).astype('int32'))
X = K.permute_dimensions(X, [0, 2, 3, 1])
X = tf.image.resize_bilinear(X, new_shape)
X = K.permute_dimensions(X, [0, 3, 1, 2])
if target_height and target_width:
X.set_shape((None, None, target_height, target_width))
else:
X.set_shape((None, None, original_shape[2] * height_factor, original_shape[3] * width_factor))
return X
elif data_format == 'channels_last':
original_shape = K.int_shape(X)
if target_height and target_width:
new_shape = tf.constant(np.array((target_height, target_width)).astype('int32'))
else:
new_shape = tf.shape(X)[1:3]
new_shape *= tf.constant(np.array([height_factor, width_factor]).astype('int32'))
X = tf.image.resize_bilinear(X, new_shape)
if target_height and target_width:
X.set_shape((None, target_height, target_width, None))
else:
X.set_shape((None, original_shape[1] * height_factor, original_shape[2] * width_factor, None))
return X
else:
raise Exception('Invalid data_format: ' + data_format)
class BilinearUpSampling2D(Layer):
def __init__(self, size=(1, 1), target_size=None, data_format='default', **kwargs):
if data_format == 'default':
data_format = K.image_data_format()
self.size = tuple(size)
if target_size is not None:
self.target_size = tuple(target_size)
else:
self.target_size = None
assert data_format in {'channels_last', 'channels_first'}, 'data_format must be in {tf, th}'
self.data_format = data_format
self.input_spec = [InputSpec(ndim=4)]
super(BilinearUpSampling2D, self).__init__(**kwargs)
def compute_output_shape(self, input_shape):
if self.data_format == 'channels_first':
width = int(self.size[0] * input_shape[2] if input_shape[2] is not None else None)
height = int(self.size[1] * input_shape[3] if input_shape[3] is not None else None)
if self.target_size is not None:
width = self.target_size[0]
height = self.target_size[1]
return (input_shape[0],
input_shape[1],
width,
height)
elif self.data_format == 'channels_last':
width = int(self.size[0] * input_shape[1] if input_shape[1] is not None else None)
height = int(self.size[1] * input_shape[2] if input_shape[2] is not None else None)
if self.target_size is not None:
width = self.target_size[0]
height = self.target_size[1]
return (input_shape[0],
width,
height,
input_shape[3])
else:
raise Exception('Invalid data_format: ' + self.data_format)
def call(self, x, mask=None):
if self.target_size is not None:
return resize_images_bilinear(x, target_height=self.target_size[0], target_width=self.target_size[1], data_format=self.data_format)
else:
return resize_images_bilinear(x, height_factor=self.size[0], width_factor=self.size[1], data_format=self.data_format)
def get_config(self):
config = {'size': self.size, 'target_size': self.target_size}
base_config = super(BilinearUpSampling2D, self).get_config()
return dict(list(base_config.items()) + list(config.items())) |