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"""Tests for tensorflow.ops.reshape_op."""
import tensorflow.python.platform
import numpy as np
import tensorflow as tf
from tensorflow.python.kernel_tests import gradient_checker as gc
class ReshapeTest(tf.test.TestCase):
def _testReshape(self, x, y, use_gpu=False):
with self.test_session(use_gpu=use_gpu):
np_ans = x.reshape(y)
tf_ans = tf.reshape(x, y)
out = tf_ans.eval()
self.assertEqual(tf_ans.get_shape(), out.shape)
self.assertShapeEqual(np_ans, tf_ans)
def _testBothReshape(self, x, y):
self._testReshape(x, y, False)
self._testReshape(x, y, True)
def testFloatBasic(self):
x = np.arange(1., 7.).reshape([1, 6]).astype(np.float32)
self._testBothReshape(x, [2, 3])
def testDoubleBasic(self):
x = np.arange(1., 7.).reshape([1, 6]).astype(np.float64)
self._testBothReshape(x, [2, 3])
def testInt32Basic(self):
x = np.arange(1., 7.).reshape([1, 6]).astype(np.int32)
self._testBothReshape(x, [2, 3])
def testSComplexBasic(self):
x = np.arange(1., 7.).reshape([1, 6]).astype(np.complex64)
self._testBothReshape(x, [2, 3])
def testFloatReshapeThreeDimensions(self):
x = np.arange(1., 28.).reshape([1, 27]).astype(np.float32)
self._testBothReshape(x, [3, 3, 3])
def testFloatUnspecifiedDimOnly(self):
x = np.arange(1., 7.).reshape([6]).astype(np.float32)
self._testBothReshape(x, [-1])
def testFloatUnspecifiedDimBegin(self):
x = np.arange(1., 7.).reshape([6]).astype(np.float32)
self._testBothReshape(x, [-1, 2])
def testFloatUnspecifiedDimEnd(self):
x = np.arange(1., 7.).reshape([6]).astype(np.float32)
self._testBothReshape(x, [3, -1])
# TODO(vrv): Add tests for failure conditions once python test_util
# reports errors.
def testFloatReshapeGradThreeDimensions(self):
x = np.arange(1., 25.).reshape([1, 24]).astype(np.float32)
s = list(np.shape(x))
with self.test_session():
input_tensor = tf.constant(x, shape=[2, 3, 4])
reshape_out = tf.reshape(input_tensor, [1, 8, 3])
err = gc.ComputeGradientError(input_tensor, s,
reshape_out, s, x_init_value=x)
print "Reshape gradient error = " % err
self.assertLess(err, 1e-3)
def testFloatEmpty(self):
x = np.empty((0, 0, 0, 0), dtype=np.float32)
self._testBothReshape(x, [1, 2, 3, 0])
self._testBothReshape(x, [1, 0, 0, 4])
self._testBothReshape(x, [0, 0, 0, 0])
self._testBothReshape(x, [1, 2, 0])
self._testBothReshape(x, [0, 0, 0])
self._testBothReshape(x, [1, -1, 5])
def testErrors(self):
x = tf.constant(0.0, shape=[1, 0, 3])
with self.assertRaisesRegexp(
ValueError, "cannot infer the missing input size"):
tf.reshape(x, [0, -1, 5])
y = tf.constant(0.0, shape=[23, 29, 31])
with self.assertRaisesRegexp(ValueError, "isn't divisible by 17"):
tf.reshape(y, [17, -1])
def testPartialShapes(self):
x = tf.placeholder(tf.float32)
# Unknown input shape, partial new shape.
y = tf.reshape(x, [1, 1, -1, 1])
self.assertEqual([1, 1, None, 1], y.get_shape().as_list())
# Unknown input shape, unknown new shape.
y = tf.reshape(x, tf.placeholder(tf.int32))
self.assertEqual(None, y.get_shape().ndims)
# Unknown input shape, known rank for new shape.
y = tf.reshape(x, tf.placeholder(tf.int32, shape=(3,)))
self.assertEqual([None, None, None], y.get_shape().as_list())
if __name__ == "__main__":
tf.test.main()
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