def testMapStructureOverPlaceholders(self):
# We must drop into a graph context to avoid eager mode. Placeholders
# and feed_dicts do not work with eager mode.
with tf.Graph().as_default() as g:
inp_a = (tf.compat.v1.placeholder(tf.float32, shape=[3, 4]),
tf.compat.v1.placeholder(tf.float32, shape=[3, 7]))
inp_b = (tf.compat.v1.placeholder(tf.float32, shape=[3, 4]),
tf.compat.v1.placeholder(tf.float32, shape=[3, 7]))
output = nest.map_structure(lambda x1, x2: x1 + x2, inp_a, inp_b)
nest.assert_same_structure(output, inp_a)
self.assertShapeEqual(np.zeros((3, 4)), output[0])
self.assertShapeEqual(np.zeros((3, 7)), output[1])
feed_dict = {
inp_a: (np.random.randn(3, 4), np.random.randn(3, 7)),
inp_b: (np.random.randn(3, 4), np.random.randn(3, 7))
}
with self.session(graph=g) as sess:
output_np = sess.run(output, feed_dict=feed_dict)
self.assertAllClose(output_np[0],
feed_dict[inp_a][0] + feed_dict[inp_b][0])
self.assertAllClose(output_np[1],
feed_dict[inp_a][1] + feed_dict[inp_b][1])
def run_and_report(self, s1, s2, name):
burn_iter, test_iter = 100, 30000
for _ in range(burn_iter):
nest.assert_same_structure(s1, s2)
t0 = time.time()
for _ in range(test_iter):
nest.assert_same_structure(s1, s2)
t1 = time.time()
self.report_benchmark(iters=test_iter, wall_time=(t1 - t0) / test_iter,
name=name)
def testMapStructureWithStrings(self):
inp_a = NestTest.ABTuple(a="foo", b=("bar", "baz"))
inp_b = NestTest.ABTuple(a=2, b=(1, 3))
out = nest.map_structure(lambda string, repeats: string * repeats,
inp_a, inp_b)
self.assertEqual("foofoo", out.a)
self.assertEqual("bar", out.b[0])
self.assertEqual("bazbazbaz", out.b[1])
nt = NestTest.ABTuple(a=("something", "something_else"),
b="yet another thing")
rev_nt = nest.map_structure(lambda x: x[::-1], nt)
# Check the output is the correct structure, and all strings are reversed.
nest.assert_same_structure(nt, rev_nt)
self.assertEqual(nt.a[0][::-1], rev_nt.a[0])
self.assertEqual(nt.a[1][::-1], rev_nt.a[1])
self.assertEqual(nt.b[::-1], rev_nt.b)
def testMapStructureOverPlaceholders(self):
inp_a = (tf.placeholder(tf.float32, shape=[3, 4]),
tf.placeholder(tf.float32, shape=[3, 7]))
inp_b = (tf.placeholder(tf.float32, shape=[3, 4]),
tf.placeholder(tf.float32, shape=[3, 7]))
output = nest.map_structure(lambda x1, x2: x1 + x2, inp_a, inp_b)
nest.assert_same_structure(output, inp_a)
self.assertShapeEqual(np.zeros((3, 4)), output[0])
self.assertShapeEqual(np.zeros((3, 7)), output[1])
feed_dict = {
inp_a: (np.random.randn(3, 4), np.random.randn(3, 7)),
inp_b: (np.random.randn(3, 4), np.random.randn(3, 7))
}
with self.cached_session() as sess:
output_np = sess.run(output, feed_dict=feed_dict)
self.assertAllClose(output_np[0],
feed_dict[inp_a][0] + feed_dict[inp_b][0])
self.assertAllClose(output_np[1],
feed_dict[inp_a][1] + feed_dict[inp_b][1])
def testHeterogeneousComparison(self):
nest.assert_same_structure({"a": 4}, _CustomMapping(a=3))
nest.assert_same_structure(_CustomMapping(b=3), {"b": 4})
