Source code for niftynet.engine.sampler_random_vector
# -*- coding: utf-8 -*-
"""
Generating sample arrays from random distributions.
"""
from __future__ import absolute_import, print_function, division
import numpy as np
import tensorflow as tf
from niftynet.engine.image_window import ImageWindow, N_SPATIAL
from niftynet.engine.image_window_buffer import InputBatchQueueRunner
from niftynet.layer.base_layer import Layer
[docs]class RandomVectorSampler(Layer, InputBatchQueueRunner):
"""
This class generates two samples from the standard normal
distribution. These two samples are mixed with n
mixing coefficients. The coefficients are generated
by ``np.linspace(0, 1, n_interpolations)``
"""
def __init__(self,
names=('vector',),
vector_size=(100,),
batch_size=10,
n_interpolations=10,
mean=0.0,
stddev=1.0,
repeat=1,
queue_length=10,
name='random_vector_sampler'):
# repeat=None for infinite loops
Layer.__init__(self, name=name)
self.n_interpolations = max(n_interpolations, 1)
self.mean = mean
self.stddev = stddev
self.repeat = repeat
InputBatchQueueRunner.__init__(
self,
capacity=queue_length,
shuffle=False)
tf.logging.info('reading size of preprocessed images')
vector_shapes = {names[0]: vector_size}
vector_dtypes = {names[0]: tf.float32}
self.window = ImageWindow(shapes=vector_shapes,
dtypes=vector_dtypes)
tf.logging.info('initialised window instance')
self._create_queue_and_ops(self.window,
enqueue_size=self.n_interpolations,
dequeue_size=batch_size)
tf.logging.info("initialised sampler output %s ", self.window.shapes)
[docs] def layer_op(self, *args, **kwargs):
"""
This function first draws two samples, and interpolates them
with self.n_interpolations mixing coefficients.
Location coordinates are set to ``np.ones`` for all the vectors.
"""
total_iter = self.repeat if self.repeat is not None else 1
while total_iter:
total_iter = total_iter - 1 if self.repeat is not None else 1
embedding_x = np.random.normal(
self.mean,
self.stddev,
*self.window.shapes[self.window.names[0]][1:])
embedding_y = np.random.normal(
self.mean,
self.stddev,
*self.window.shapes[self.window.names[0]][1:])
steps = np.linspace(0, 1, self.n_interpolations)
output_vectors = []
for (_, mixture) in enumerate(steps):
output_vector = \
embedding_x * mixture + embedding_y * (1 - mixture)
output_vector = output_vector[np.newaxis, ...]
output_vectors.append(output_vector)
output_vectors = np.concatenate(output_vectors, axis=0)
coordinates = np.ones(
(self.n_interpolations, N_SPATIAL * 2 + 1), dtype=np.int32)
output_dict = {}
for name in self.window.names:
coordinates_key = self.window.coordinates_placeholder(name)
image_data_key = self.window.image_data_placeholder(name)
output_dict[coordinates_key] = coordinates
output_dict[image_data_key] = output_vectors
yield output_dict