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"""Functional tests for Transpose op."""
import itertools
import tensorflow.python.platform
import numpy as np
import tensorflow as tf
from tensorflow.python.kernel_tests.gradient_checker import ComputeGradient
class TransposeTest(tf.test.TestCase):
def _np_transpose(self, x, perm):
ret = np.copy(x)
ret = ret.transpose(perm)
return ret
def _compareCpu(self, x, p):
np_ans = self._np_transpose(x, p)
with self.test_session(use_gpu=False):
inx = tf.convert_to_tensor(x)
y = tf.transpose(inx, p)
tf_ans = y.eval()
self.assertAllEqual(np_ans, tf_ans)
self.assertShapeEqual(np_ans, y)
jacob_t = None
# Gradient check on CPU.
xs = list(np.shape(x))
ys = list(np.shape(tf_ans))
if x.dtype == np.float32:
jacob_t, jacob_n = ComputeGradient(inx, xs, y, ys, x, 1e-2)
self.assertAllClose(jacob_t, jacob_n, 1e-3, 1e-3)
elif x.dtype == np.float64:
jacob_t, jacob_n = ComputeGradient(inx, xs, y, ys, x, 1e-2)
self.assertAllClose(jacob_t, jacob_n, 1e-6, 1e-6)
return tf_ans, jacob_t
def _compareGpu(self, x, p):
np_ans = self._np_transpose(x, p)
with self.test_session(use_gpu=True):
inx = tf.convert_to_tensor(x)
y = tf.transpose(inx, p)
tf_ans = y.eval()
self.assertAllEqual(np_ans, tf_ans)
self.assertShapeEqual(np_ans, y)
jacob_t = None
# Gradient check on GPU.
xs = list(np.shape(x))
ys = list(np.shape(tf_ans))
if x.dtype == np.float32:
jacob_t, jacob_n = ComputeGradient(inx, xs, y, ys, x, 1e-2)
self.assertAllClose(jacob_t, jacob_n, 1e-3, 1e-3)
elif x.dtype == np.float64:
jacob_t, jacob_n = ComputeGradient(inx, xs, y, ys, x, 1e-2)
self.assertAllClose(jacob_t, jacob_n, 1e-6, 1e-6)
return tf_ans, jacob_t
def _compare(self, x, use_gpu=False):
n = np.ndim(x)
# generate all permutations of [0, 1, ... n-1] in random order.
all_perm = np.random.permutation(
[p for p in itertools.permutations(range(n))]).astype(np.int32)
for p in all_perm[0:2]:
self._compareCpu(x, p)
if use_gpu:
self._compareGpu(x, p)
def _compare_cpu_gpu(self, x):
n = np.ndim(x)
# generate all permutation of [0, 1, ... n-1] in random order.
all_perm = np.random.permutation(
[p for p in itertools.permutations(range(n))]).astype(np.int32)
for p in all_perm[0:2]:
tf_a_cpu, tf_g_cpu = self._compareCpu(x, p)
tf_a_gpu, tf_g_gpu = self._compareGpu(x, p)
assert tf_g_cpu is not None
assert tf_g_gpu is not None
if x.dtype == np.float32:
self.assertAllClose(tf_a_cpu, tf_a_gpu, 1e-3, 1e-3)
self.assertAllClose(tf_g_cpu, tf_g_gpu, 1e-3, 1e-3)
elif x.dtype == np.float64:
self.assertAllClose(tf_a_cpu, tf_a_gpu, 1e-6, 1e-6)
self.assertAllClose(tf_g_cpu, tf_g_gpu, 1e-6, 1e-6)
def _testCpu(self, x):
self._compare(x, use_gpu=False)
def test1D(self):
self._compareCpu(np.arange(0., 2), [0])
def testNop(self):
self._compareCpu(np.arange(0, 6).reshape([3, 2]).astype(np.float32), [0, 1])
def testSimple(self):
self._compareCpu(np.arange(0, 8).reshape([2, 4]).astype(np.float32),
np.array([1, 0]).astype(np.int32))
def testFloat(self):
self._compare_cpu_gpu(np.arange(0, 21).reshape([3, 7]).astype(np.float32))
self._compare_cpu_gpu(
np.arange(0, 210).reshape([2, 3, 5, 7]).astype(np.float32))
def testDouble(self):
self._compare_cpu_gpu(np.arange(0, 21).reshape([3, 7]).astype(np.float64))
self._compare_cpu_gpu(
np.arange(0, 210).reshape([2, 3, 5, 7]).astype(np.float64))
def testSComplex(self):
self._testCpu(np.complex(1, 2) * np.arange(0, 21).reshape(
[3, 7]).astype(np.complex64))
self._testCpu(np.complex(1, 2) * np.arange(0, 210).reshape(
[2, 3, 5, 7]).astype(np.complex64))
def testInt8(self):
self._testCpu(np.arange(0, 21).reshape([3, 7]).astype(np.int8))
self._testCpu(np.arange(0, 210).reshape([2, 3, 5, 7]).astype(np.int8))
def testInt16(self):
self._testCpu(np.arange(0, 21).reshape([3, 7]).astype(np.int16))
self._testCpu(np.arange(0, 210).reshape([2, 3, 5, 7]).astype(np.int16))
def testInt32(self):
self._testCpu(np.arange(0, 21).reshape([3, 7]).astype(np.int32))
self._testCpu(np.arange(0, 210).reshape([2, 3, 5, 7]).astype(np.int32))
def testInt64(self):
self._testCpu(np.arange(0, 21).reshape([3, 7]).astype(np.int64))
self._testCpu(np.arange(0, 210).reshape([2, 3, 5, 7]).astype(np.int64))
def testTranspose2DAuto(self):
x_np = [[1, 2, 3], [4, 5, 6]]
for use_gpu in [False, True]:
with self.test_session(use_gpu=use_gpu):
x_tf = tf.transpose(x_np).eval()
self.assertAllEqual(x_tf, [[1, 4], [2, 5], [3, 6]])
def testTransposeShapes(self):
self.assertEqual([], tf.transpose(
tf.placeholder(tf.int32, shape=[])).get_shape().dims)
self.assertEqual([100], tf.transpose(
tf.placeholder(tf.int32, shape=[100])).get_shape().dims)
self.assertEqual([37, 100], tf.transpose(
tf.placeholder(tf.int32, shape=[100, 37])).get_shape().dims)
self.assertEqual([100, 37], tf.transpose(
tf.placeholder(tf.int32, shape=[100, 37]), [0, 1]).get_shape().dims)
self.assertEqual([15, 37, 100], tf.transpose(
tf.placeholder(tf.int32, shape=[100, 37, 15])).get_shape().dims)
self.assertEqual([15, 100, 37], tf.transpose(
tf.placeholder(tf.int32,
shape=[100, 37, 15]), [2, 0, 1]).get_shape().dims)
self.assertEqual(tf.TensorShape(None), tf.transpose(
tf.placeholder(tf.int32)).get_shape())
def _testError(self, x, p, err):
with self.test_session():
with self.assertRaisesOpError(err):
tf.transpose(x, p).eval()
def testError(self):
with self.assertRaises(ValueError):
tf.transpose(np.arange(0., 30).reshape([2, 3, 5]), [[0, 1], [2, 3]])
self._testError(np.arange(0., 2 ** 10).reshape([2] * 10),
range(10),
"not implemented")
with self.assertRaises(IndexError):
tf.transpose(np.arange(0., 30).reshape([2, 3, 5]), [0, 1, 3])
self._testError(np.arange(0., 30).reshape([2, 3, 5]),
[0, 1, 1],
"2 is missing")
if __name__ == "__main__":
tf.test.main()
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