def resize_images(x, height_factor, width_factor, data_format):
"""Resizes the images contained in a 4D tensor.
# Arguments
x: Tensor or variable to resize.
height_factor: Positive integer.
width_factor: Positive integer.
data_format: string, `"channels_last"` or `"channels_first"`.
# Returns
A tensor.
# Raises
ValueError: if `data_format` is neither `"channels_last"` or `"channels_first"`.
"""
if data_format == 'channels_first':
original_shape = K.int_shape(x)
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_nearest_neighbor(x, new_shape)
x = K.permute_dimensions(x, [0, 3, 1, 2])
x.set_shape((None, None, original_shape[2] *
height_factor if original_shape[2] is not None else None,
original_shape[3] *
width_factor if original_shape[3] is not None else None))
return x
elif data_format == 'channels_last':
original_shape = K.int_shape(x)
new_shape = tf.shape(x)[1:3]
new_shape *= tf.constant(
np.array([height_factor, width_factor]).astype('int32'))
x = tf.image.resize_nearest_neighbor(x, new_shape)
x.set_shape(
(None, original_shape[1] *
height_factor if original_shape[1] is not None else None,
original_shape[2] *
width_factor if original_shape[2] is not None else None, None))
return x
else:
raise ValueError('Unknown data_format: ' + str(data_format))
def int_shape(x):
return KC.int_shape(x) if KC.backend() == 'tensorflow' else KC.shape(x)
def __int_shape(self, x):
return KC.int_shape(x) if self.backend == 'tensorflow' else KC.shape(x)
