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"""Tests for tensorflow.kernels.sparse_op."""
import tensorflow.python.platform
import numpy as np
import tensorflow as tf
def _SparseToDense(sparse_indices, output_size, sparse_values,
default_value):
return tf.sparse_to_dense(sparse_indices, output_size,
sparse_values, default_value)
class SparseToDenseTest(tf.test.TestCase):
def testInt(self):
with self.test_session(use_gpu=False):
tf_ans = _SparseToDense([1, 3], [5], 1, 0).eval()
np_ans = np.array([0, 1, 0, 1, 0]).astype(np.int32)
self.assertAllClose(np_ans, tf_ans)
def testFloat(self):
with self.test_session(use_gpu=False):
tf_ans = _SparseToDense([1, 3], [5], 1.0, 0.0).eval()
np_ans = np.array([0, 1, 0, 1, 0]).astype(np.float32)
self.assertAllClose(np_ans, tf_ans)
def testString(self):
with self.test_session(use_gpu=False):
tf_ans = _SparseToDense([1, 3], [5], "a", "b").eval()
np_ans = np.array(["b", "a", "b", "a", "b"]).astype(np.string_)
self.assertAllEqual(np_ans, tf_ans)
def testSetValue(self):
with self.test_session(use_gpu=False):
tf_ans = _SparseToDense([1, 3], [5], [1, 2], -1).eval()
np_ans = np.array([-1, 1, -1, 2, -1]).astype(np.int32)
self.assertAllClose(np_ans, tf_ans)
def testSetSingleValue(self):
with self.test_session(use_gpu=False):
tf_ans = _SparseToDense([1, 3], [5], 1, -1).eval()
np_ans = np.array([-1, 1, -1, 1, -1]).astype(np.int32)
self.assertAllClose(np_ans, tf_ans)
def test2d(self):
# pylint: disable=bad-whitespace
with self.test_session(use_gpu=False):
tf_ans = _SparseToDense([[1, 3], [2, 0]], [3, 4], 1, -1).eval()
np_ans = np.array([[-1, -1, -1, -1],
[-1, -1, -1, 1],
[ 1, -1, -1, -1]]).astype(np.int32)
self.assertAllClose(np_ans, tf_ans)
def test3d(self):
with self.test_session(use_gpu=False):
tf_ans = _SparseToDense([[1, 3, 0], [2, 0, 1]], [3, 4, 2], 1, -1).eval()
np_ans = np.ones((3, 4, 2), dtype=np.int32) * -1
np_ans[1, 3, 0] = 1
np_ans[2, 0, 1] = 1
self.assertAllClose(np_ans, tf_ans)
def testBadShape(self):
with self.test_session():
with self.assertRaisesWithPredicateMatch(
ValueError, lambda e: ("Input shape should be a vector" == str(e))):
_SparseToDense([1, 3], [[5], [3]], 1, -1)
def testBadValue(self):
with self.test_session():
dense = _SparseToDense([1, 3], [5], [[5], [3]], -1)
with self.assertRaisesOpError(
r"sparse_values has incorrect shape \[2,1\], "
r"should be \[\] or \[2\]"):
dense.eval()
def testBadNumValues(self):
with self.test_session():
dense = _SparseToDense([1, 3], [5], [1, 2, 3], -1)
with self.assertRaisesOpError(
r"sparse_values has incorrect shape \[3\], should be \[\] or \[2\]"):
dense.eval()
def testBadDefault(self):
with self.test_session():
dense = _SparseToDense([1, 3], [5], [1, 2], [1, 2])
with self.assertRaisesOpError("default_value should be a scalar"):
dense.eval()
def testShapeInferenceKnownShape(self):
with self.test_session(use_gpu=False):
indices = tf.placeholder(tf.int64)
shape = [4, 5, 6]
output = tf.sparse_to_dense(indices, shape, 1, 0)
self.assertEqual(output.get_shape(), [4, 5, 6])
shape = tf.placeholder(tf.int64, shape=(3,))
output = tf.sparse_to_dense(indices, shape, 1, 0)
self.assertEqual(output.get_shape().as_list(), [None, None, None])
def testShapeInferenceUnknownShape(self):
with self.test_session(use_gpu=False):
indices = tf.placeholder(tf.int64)
shape = tf.placeholder(tf.int64)
output = tf.sparse_to_dense(indices, shape, 1, 0)
self.assertEqual(output.get_shape().ndims, None)
if __name__ == "__main__":
tf.test.main()
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