1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
|
"""Functional tests for Split Op."""
import tensorflow.python.platform
import numpy as np
import tensorflow as tf
class SplitOpTest(tf.test.TestCase):
def _compare(self, x, dim, num, use_gpu):
np_ans = np.split(x, num, dim)
with self.test_session(use_gpu=use_gpu) as sess:
tf_ans = tf.split(dim, num, x)
out = sess.run(tf_ans)
self.assertEqual(num, len(np_ans))
self.assertEqual(num, len(np_ans))
self.assertEqual(num, len(out))
for i in range(num):
self.assertAllEqual(np_ans[i], out[i])
self.assertShapeEqual(np_ans[i], tf_ans[i])
def _testSplitRows(self, use_gpu):
inp = np.random.rand(4, 4).astype("f")
self._compare(inp, 0, 4, use_gpu)
def testSplitRowsAll(self):
self._testSplitRows(use_gpu=False)
self._testSplitRows(use_gpu=True)
def _testSplitCols(self, use_gpu):
inp = np.random.rand(4, 4).astype("f")
self._compare(inp, 1, 4, use_gpu)
def testSplitColsAll(self):
self._testSplitRows(use_gpu=False)
self._testSplitCols(use_gpu=True)
def _testEmpty(self, x, dim, num, expected_shape):
with self.test_session() as sess:
tf_ans = tf.split(dim, num, x)
out = sess.run(tf_ans)
self.assertEqual(x.size, 0)
self.assertEqual(len(out), num)
for i in range(num):
self.assertEqual(out[i].shape, expected_shape)
self.assertEqual(expected_shape, tf_ans[i].get_shape())
def testEmpty(self):
# Note: np.split returns a rank-0 empty ndarray
# if the input ndarray is empty.
inp = np.random.rand(8, 0, 21).astype("f")
self._testEmpty(inp, 0, 2, (4, 0, 21))
self._testEmpty(inp, 0, 4, (2, 0, 21))
self._testEmpty(inp, 1, 4, (8, 0, 21))
self._testEmpty(inp, 2, 3, (8, 0, 7))
self._testEmpty(inp, 2, 7, (8, 0, 3))
def testIdentity(self):
inp = np.random.rand(2, 2, 2).astype("f")
for use_gpu in [False, True]:
self._compare(inp, 0, 1, use_gpu)
self._compare(inp, 1, 1, use_gpu)
self._compare(inp, 2, 1, use_gpu)
def testSplitDim0(self):
for use_gpu in [False, True]:
self._compare(np.random.rand(6, 10, 18).astype("f"), 0, 3, use_gpu)
self._compare(np.random.rand(6, 7, 18).astype("f"), 0, 3, use_gpu)
self._compare(np.random.rand(6, 7, 9).astype("f"), 0, 3, use_gpu)
def _RunAndVerify(self, use_gpu):
# Random dims of rank 5
shape = np.random.randint(0, 5, size=5)
split_dim = np.random.randint(0, 5)
num_split = np.random.randint(2, 8)
shape[split_dim] = np.random.randint(2, 5) * num_split
inp = np.random.rand(*shape).astype("f")
with self.test_session(use_gpu=use_gpu) as sess:
result = sess.run(tf.split(split_dim, num_split, inp))
slices = [slice(0, x) for x in shape]
offset = 0
length = shape[split_dim] / num_split
for i in range(num_split):
slices[split_dim] = slice(offset, offset + length)
offset += length
self.assertAllEqual(result[i], inp[slices])
def testRandom(self):
for _ in range(5):
self._RunAndVerify(use_gpu=False)
self._RunAndVerify(use_gpu=True)
def _testGradientsSimple(self, use_gpu):
inp = np.random.rand(4, 4).astype("f")
with self.test_session(use_gpu=use_gpu):
inp_tensor = tf.convert_to_tensor(inp)
s = tf.split(1, 4, inp_tensor)
inp_grads = [np.random.rand(4, 1).astype("f") for _ in range(4)]
grad_tensors = [tf.constant(x) for x in inp_grads]
grad = tf.gradients(s, [inp_tensor], grad_tensors)[0]
result = grad.eval()
for i in range(4):
self.assertAllEqual(result[:, i:i+1], inp_grads[i])
def testGradientsAll(self):
self._testGradientsSimple(use_gpu=False)
self._testGradientsSimple(use_gpu=True)
def testShapeFunctionEdgeCases(self):
# split_dim greater than rank of input.
with self.assertRaises(ValueError):
tf.split(2, 4, [[0, 1], [2, 3]])
# num_split does not evenly divide the size in split_dim.
with self.assertRaisesRegexp(ValueError, "should evenly divide"):
tf.split(0, 3, [0, 1, 2, 3])
# Unknown split_dim.
splits = tf.split(tf.placeholder(tf.int32),
4, [[0, 1, 2, 3]])
for s in splits:
self.assertEqual([None, None], s.get_shape().as_list())
# Unknown split_dim and input shape.
splits = tf.split(tf.placeholder(tf.int32),
4, tf.placeholder(tf.float32))
for s in splits:
self.assertEqual(None, s.get_shape().ndims)
if __name__ == "__main__":
tf.test.main()
|
