Source code for niftynet.layer.linear_resize

# -*- coding: utf-8 -*-
from __future__ import absolute_import, print_function

import tensorflow as tf

from niftynet.layer.base_layer import Layer
from niftynet.layer.layer_util import expand_spatial_params
from niftynet.layer.layer_util import infer_spatial_rank

[docs]class LinearResizeLayer(Layer): """ Resize 2D/3D images using ``tf.image.resize_bilinear`` (without trainable parameters). """
[docs] def __init__(self, new_size, name='trilinear_resize'): """ :param new_size: integer or a list of integers set the output 2D/3D spatial shape. If the parameter is an integer ``d``, it'll be expanded to ``(d, d)`` and ``(d, d, d)`` for 2D and 3D inputs respectively. :param name: layer name string """ super(LinearResizeLayer, self).__init__(name=name) self.new_size = new_size
[docs] def layer_op(self, input_tensor): """ Resize the image by linearly interpolating the input using TF ``resize_bilinear`` function. :param input_tensor: 2D/3D image tensor, with shape: ``batch, X, Y, [Z,] Channels`` :return: interpolated volume """ input_spatial_rank = infer_spatial_rank(input_tensor) assert input_spatial_rank in (2, 3), \ "linearly interpolation layer can only be applied to " \ "2D/3D images (4D or 5D tensor)." self.new_size = expand_spatial_params(self.new_size, input_spatial_rank) if input_spatial_rank == 2: return tf.image.resize_bilinear(input_tensor, self.new_size) b_size, x_size, y_size, z_size, c_size = \ input_tensor.shape.as_list() x_size_new, y_size_new, z_size_new = self.new_size if (x_size == x_size_new) and (y_size == y_size_new) and ( z_size == z_size_new): # already in the target shape return input_tensor # resize y-z squeeze_b_x = tf.reshape( input_tensor, [-1, y_size, z_size, c_size]) resize_b_x = tf.image.resize_bilinear( squeeze_b_x, [y_size_new, z_size_new]) resume_b_x = tf.reshape( resize_b_x, [b_size, x_size, y_size_new, z_size_new, c_size]) # resize x # first reorient reoriented = tf.transpose(resume_b_x, [0, 3, 2, 1, 4]) # squeeze and 2d resize squeeze_b_z = tf.reshape( reoriented, [-1, y_size_new, x_size, c_size]) resize_b_z = tf.image.resize_bilinear( squeeze_b_z, [y_size_new, x_size_new]) resume_b_z = tf.reshape( resize_b_z, [b_size, z_size_new, y_size_new, x_size_new, c_size]) output_tensor = tf.transpose(resume_b_z, [0, 3, 2, 1, 4]) return output_tensor