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"""Tests for tensorflow.ops.data_flow_ops.dynamic_stitch."""
import tensorflow.python.platform
import numpy as np
import tensorflow as tf
class DynamicStitchTest(tf.test.TestCase):
def testScalar(self):
with self.test_session():
indices = [tf.constant(0), tf.constant(1)]
data = [tf.constant(40), tf.constant(60)]
for step in -1, 1:
stitched_t = tf.dynamic_stitch(indices[::step], data)
stitched_val = stitched_t.eval()
self.assertAllEqual([40, 60][::step], stitched_val)
# Dimension 0 is determined by the max index in indices, so we
# can only infer that the output is a vector of some unknown
# length.
self.assertEqual([None], stitched_t.get_shape().as_list())
def testSimpleOneDimensional(self):
with self.test_session():
indices = [tf.constant([0, 4, 7]),
tf.constant([1, 6, 2, 3, 5])]
data = [tf.constant([0, 40, 70]),
tf.constant([10, 60, 20, 30, 50])]
stitched_t = tf.dynamic_stitch(indices, data)
stitched_val = stitched_t.eval()
self.assertAllEqual([0, 10, 20, 30, 40, 50, 60, 70], stitched_val)
# Dimension 0 is determined by the max index in indices, so we
# can only infer that the output is a vector of some unknown
# length.
self.assertEqual([None], stitched_t.get_shape().as_list())
def testSimpleTwoDimensional(self):
with self.test_session():
indices = [tf.constant([0, 4, 7]),
tf.constant([1, 6]),
tf.constant([2, 3, 5])]
data = [tf.constant([[0, 1], [40, 41], [70, 71]]),
tf.constant([[10, 11], [60, 61]]),
tf.constant([[20, 21], [30, 31], [50, 51]])]
stitched_t = tf.dynamic_stitch(indices, data)
stitched_val = stitched_t.eval()
self.assertAllEqual(
[[0, 1], [10, 11], [20, 21], [30, 31],
[40, 41], [50, 51], [60, 61], [70, 71]], stitched_val)
# Dimension 0 is determined by the max index in indices, so we
# can only infer that the output is a matrix with 2 columns and
# some unknown number of rows.
self.assertEqual([None, 2], stitched_t.get_shape().as_list())
def testHigherRank(self):
with self.test_session() as sess:
indices = [tf.constant(6), tf.constant([4, 1]),
tf.constant([[5, 2], [0, 3]])]
data = [tf.constant([61, 62]), tf.constant([[41, 42], [11, 12]]),
tf.constant([[[51, 52], [21, 22]], [[1, 2], [31, 32]]])]
stitched_t = tf.dynamic_stitch(indices, data)
stitched_val = stitched_t.eval()
correct = 10 * np.arange(7)[:, None] + [1, 2]
self.assertAllEqual(correct, stitched_val)
self.assertEqual([None, 2], stitched_t.get_shape().as_list())
# Test gradients
stitched_grad = 7 * stitched_val
grads = tf.gradients(stitched_t, indices + data, stitched_grad)
self.assertEqual(grads[:3], [None] * 3) # Indices have no gradients
for datum, grad in zip(data, sess.run(grads[3:])):
self.assertAllEqual(7 * datum.eval(), grad)
def testErrorIndicesMultiDimensional(self):
indices = [tf.constant([0, 4, 7]),
tf.constant([[1, 6, 2, 3, 5]])]
data = [tf.constant([[0, 40, 70]]),
tf.constant([10, 60, 20, 30, 50])]
with self.assertRaises(ValueError):
tf.dynamic_stitch(indices, data)
def testErrorDataNumDimsMismatch(self):
indices = [tf.constant([0, 4, 7]),
tf.constant([1, 6, 2, 3, 5])]
data = [tf.constant([0, 40, 70]),
tf.constant([[10, 60, 20, 30, 50]])]
with self.assertRaises(ValueError):
tf.dynamic_stitch(indices, data)
def testErrorDataDimSizeMismatch(self):
indices = [tf.constant([0, 4, 5]),
tf.constant([1, 6, 2, 3])]
data = [tf.constant([[0], [40], [70]]),
tf.constant([[10, 11], [60, 61], [20, 21], [30, 31]])]
with self.assertRaises(ValueError):
tf.dynamic_stitch(indices, data)
def testErrorDataAndIndicesSizeMismatch(self):
indices = [tf.constant([0, 4, 7]),
tf.constant([1, 6, 2, 3, 5])]
data = [tf.constant([0, 40, 70]),
tf.constant([10, 60, 20, 30])]
with self.assertRaises(ValueError):
tf.dynamic_stitch(indices, data)
if __name__ == "__main__":
tf.test.main()
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