def testShapeUtilGetNdims(self):
with self.test_session():
shaper = _ShapeUtil(batch_ndims=0, event_ndims=0)
x = 1
self.assertEqual(shaper.get_sample_ndims(x), 0)
self.assertEqual(shaper.batch_ndims, 0)
self.assertEqual(shaper.event_ndims, 0)
shaper = _ShapeUtil(batch_ndims=1, event_ndims=1)
x = [[[0.0, 1, 2], [3, 4, 5]]]
self.assertAllEqual(shaper.get_ndims(x), 3)
self.assertEqual(shaper.get_sample_ndims(x), 1)
self.assertEqual(shaper.batch_ndims, 1)
self.assertEqual(shaper.event_ndims, 1)
x += [[[6, 7, 8], [9, 10, 11]]]
self.assertAllEqual(shaper.get_ndims(x), 3)
self.assertEqual(shaper.get_sample_ndims(x), 1)
self.assertEqual(shaper.batch_ndims, 1)
self.assertEqual(shaper.event_ndims, 1)
# Test ndims functions work, even despite unfed Tensors.
y = tf.placeholder(tf.float32, shape=(1024, None, 1024))
self.assertAllEqual(shaper.get_ndims(y), 3)
self.assertEqual(shaper.get_sample_ndims(y), 1)
self.assertEqual(shaper.batch_ndims, 1)
self.assertEqual(shaper.event_ndims, 1)
with self.assertRaises(ValueError):
y = tf.placeholder(tf.float32)
shaper.get_ndims(y)
def testShapeUtilGetNdims(self):
with self.test_session():
shaper = _ShapeUtil(batch_ndims=0, event_ndims=0)
x = 1
self.assertEqual(shaper.get_sample_ndims(x), 0)
self.assertEqual(shaper.batch_ndims, 0)
self.assertEqual(shaper.event_ndims, 0)
shaper = _ShapeUtil(batch_ndims=1, event_ndims=1)
x = [[[0., 1, 2], [3, 4, 5]]]
self.assertAllEqual(shaper.get_ndims(x), 3)
self.assertEqual(shaper.get_sample_ndims(x), 1)
self.assertEqual(shaper.batch_ndims, 1)
self.assertEqual(shaper.event_ndims, 1)
x += [[[6, 7, 8], [9, 10, 11]]]
self.assertAllEqual(shaper.get_ndims(x), 3)
self.assertEqual(shaper.get_sample_ndims(x), 1)
self.assertEqual(shaper.batch_ndims, 1)
self.assertEqual(shaper.event_ndims, 1)
# Test ndims functions work, even despite unfed Tensors.
y = tf.placeholder(tf.float32, shape=(1024, None, 1024))
self.assertAllEqual(shaper.get_ndims(y), 3)
self.assertEqual(shaper.get_sample_ndims(y), 1)
self.assertEqual(shaper.batch_ndims, 1)
self.assertEqual(shaper.event_ndims, 1)
with self.assertRaises(ValueError):
y = tf.placeholder(tf.float32)
shaper.get_ndims(y)
def testBijector(self):
with self.test_session():
bijector = _Identity(_ShapeUtil(batch_ndims=1, event_ndims=1))
self.assertEqual(bijector.name, 'Identity')
x = [[[0.], [1]]]
self.assertAllEqual(bijector.forward(x).eval(), x)
self.assertAllEqual(bijector.inverse(x).eval(), x)
self.assertAllEqual(
bijector.inverse_log_det_jacobian(x).eval(), [[0., 0]])
rev, jac = bijector.inverse_and_inverse_log_det_jacobian(x)
self.assertAllEqual(rev.eval(), x)
self.assertAllEqual(jac.eval(), [[0., 0]])
def testBijector(self):
with self.test_session():
bijector = _Identity(_ShapeUtil(batch_ndims=1, event_ndims=1))
self.assertEqual(bijector.name, 'Identity')
x = [[[0.], [1]]]
self.assertAllEqual(bijector.forward(x).eval(), x)
self.assertAllEqual(bijector.inverse(x).eval(), x)
self.assertAllEqual(bijector.inverse_log_det_jacobian(x).eval(),
[[0., 0]])
rev, jac = bijector.inverse_and_inverse_log_det_jacobian(x)
self.assertAllEqual(rev.eval(), x)
self.assertAllEqual(jac.eval(), [[0., 0]])
def testShapeUtilGetShape(self):
with self.test_session() as sess:
shaper = _ShapeUtil(batch_ndims=0, event_ndims=0)
with self.assertRaises(ValueError):
y = tf.placeholder(tf.float32)
shaper.get_sample_shape(y)
with self.assertRaises(ValueError):
y = tf.placeholder(tf.float32)
shaper.get_batch_shape(y)
with self.assertRaises(ValueError):
y = tf.placeholder(tf.float32)
shaper.get_event_shape(y)
shaper = _ShapeUtil(batch_ndims=0, event_ndims=0)
x = 1
self.assertAllEqual(shaper.get_sample_shape(x), [])
self.assertAllEqual(shaper.get_batch_shape(x), [])
self.assertAllEqual(shaper.get_event_shape(x), [])
self.assertAllEqual(shaper.get_shape(x, batch=False), [])
shaper = _ShapeUtil(batch_ndims=1, event_ndims=1)
x = [[[0., 1, 2], [3, 4, 5]]]
self.assertAllEqual(shaper.get_sample_shape(x), [1])
self.assertAllEqual(shaper.get_batch_shape(x), [2])
self.assertAllEqual(shaper.get_event_shape(x), [3])
self.assertAllEqual(shaper.get_shape(x, batch=False), [1, 3])
x += [[[6, 7, 8], [9, 10, 11]]]
self.assertAllEqual(shaper.get_sample_shape(x), [2])
self.assertAllEqual(shaper.get_batch_shape(x), [2])
self.assertAllEqual(shaper.get_event_shape(x), [3])
self.assertAllEqual(shaper.get_shape(x, batch=False), [2, 3])
shaper = _ShapeUtil(batch_ndims=0, event_ndims=1)
x = tf.ones((3, 2))
self.assertAllEqual(shaper.get_shape(x, sample=False), (2, ))
def feed_eval(fun,
build_shape=(None, None, 2),
graph_shape=(3, 4, 2)):
"""Helper to use a deferred-shape tensor eval'ed at graph runtime."""
y = tf.placeholder(tf.int32, shape=build_shape)
y_value = np.ones(graph_shape, dtype=y.dtype.as_numpy_dtype())
return sess.run(fun(y), feed_dict={y: y_value})
shaper = _ShapeUtil(batch_ndims=1, event_ndims=1)
self.assertAllEqual(feed_eval(shaper.get_sample_shape), [3])
self.assertAllEqual(feed_eval(shaper.get_batch_shape), [4])
self.assertAllEqual(feed_eval(shaper.get_event_shape), [2])
self.assertAllEqual(
feed_eval(lambda y: shaper.get_shape(y, batch=False)), [3, 2])
shaper = _ShapeUtil(batch_ndims=0, event_ndims=1)
self.assertAllEqual(
feed_eval(lambda y: shaper.get_shape(y, batch=False),
(None, None), (3, 2)), [3, 2])
self.assertAllEqual(
feed_eval(lambda y: shaper.get_shape(y, sample=False),
(None, None), (3, 2)), [2])
def testShapeUtilGetDims(self):
with self.test_session():
shaper = _ShapeUtil(batch_ndims=0, event_ndims=0)
with self.assertRaises(ValueError):
y = tf.placeholder(tf.float32)
shaper.get_sample_dims(y)
with self.assertRaises(ValueError):
y = tf.placeholder(tf.float32)
shaper.get_batch_dims(y)
with self.assertRaises(ValueError):
y = tf.placeholder(tf.float32)
shaper.get_event_dims(y)
shaper = _ShapeUtil(batch_ndims=0, event_ndims=0)
x = 1
self.assertAllEqual(shaper.get_sample_dims(x), [])
self.assertAllEqual(shaper.get_batch_dims(x), [])
self.assertAllEqual(shaper.get_event_dims(x), [])
self.assertAllEqual(shaper.get_dims(x, sample=False), [])
shaper = _ShapeUtil(batch_ndims=1, event_ndims=2)
x = [[[[0.0, 1], [2, 4]]]]
self.assertAllEqual(shaper.get_sample_dims(x), [0])
self.assertAllEqual(shaper.get_batch_dims(x), [1])
self.assertAllEqual(shaper.get_event_dims(x), [2, 3])
self.assertAllEqual(shaper.get_dims(x, sample=False), [1, 2, 3])
x += x
self.assertAllEqual(shaper.get_sample_dims(x), [0])
self.assertAllEqual(shaper.get_batch_dims(x), [1])
self.assertAllEqual(shaper.get_event_dims(x), [2, 3])
self.assertAllEqual(shaper.get_dims(x, sample=False), [1, 2, 3])
# Test dims functions work, despite unfed Tensors.
y = tf.placeholder(tf.float32, shape=(1024, None, 5, 5))
self.assertAllEqual(shaper.get_sample_dims(y), [0])
self.assertAllEqual(shaper.get_batch_dims(y), [1])
self.assertAllEqual(shaper.get_event_dims(y), [2, 3])
def testShapeUtilGetDims(self):
with self.test_session():
shaper = _ShapeUtil(batch_ndims=0, event_ndims=0)
with self.assertRaises(ValueError):
y = tf.placeholder(tf.float32)
shaper.get_sample_dims(y)
with self.assertRaises(ValueError):
y = tf.placeholder(tf.float32)
shaper.get_batch_dims(y)
with self.assertRaises(ValueError):
y = tf.placeholder(tf.float32)
shaper.get_event_dims(y)
shaper = _ShapeUtil(batch_ndims=0, event_ndims=0)
x = 1
self.assertAllEqual(shaper.get_sample_dims(x), [])
self.assertAllEqual(shaper.get_batch_dims(x), [])
self.assertAllEqual(shaper.get_event_dims(x), [])
self.assertAllEqual(shaper.get_dims(x, sample=False), [])
shaper = _ShapeUtil(batch_ndims=1, event_ndims=2)
x = [[[[0., 1], [2, 4]]]]
self.assertAllEqual(shaper.get_sample_dims(x), [0])
self.assertAllEqual(shaper.get_batch_dims(x), [1])
self.assertAllEqual(shaper.get_event_dims(x), [2, 3])
self.assertAllEqual(shaper.get_dims(x, sample=False), [1, 2, 3])
x += x
self.assertAllEqual(shaper.get_sample_dims(x), [0])
self.assertAllEqual(shaper.get_batch_dims(x), [1])
self.assertAllEqual(shaper.get_event_dims(x), [2, 3])
self.assertAllEqual(shaper.get_dims(x, sample=False), [1, 2, 3])
# Test dims functions work, despite unfed Tensors.
y = tf.placeholder(tf.float32, shape=(1024, None, 5, 5))
self.assertAllEqual(shaper.get_sample_dims(y), [0])
self.assertAllEqual(shaper.get_batch_dims(y), [1])
self.assertAllEqual(shaper.get_event_dims(y), [2, 3])
def testBijector(self):
with self.test_session():
bijector = _Exp(_ShapeUtil(batch_ndims=1, event_ndims=1))
self.assertEqual(bijector.name, 'Exp')
x = [[[1.], [2]]]
self.assertAllClose(bijector.forward(x).eval(),
[[[math.exp(1.)], [math.exp(2.)]]])
self.assertAllClose(bijector.inverse(x).eval(),
[[[math.log(1.)], [math.log(2.)]]])
self.assertAllClose(bijector.inverse_log_det_jacobian(x).eval(),
[[0., -math.log(2.)]])
rev, jac = bijector.inverse_and_inverse_log_det_jacobian(x)
self.assertAllClose(rev.eval(), [[[math.log(1.)], [math.log(2.)]]])
self.assertAllClose(jac.eval(), [[0., -math.log(2.)]])
def testBijector(self):
with self.test_session():
bijector = _Exp(_ShapeUtil(batch_ndims=1, event_ndims=1))
self.assertEqual(bijector.name, 'Exp')
x = [[[1.], [2]]]
self.assertAllClose(
bijector.forward(x).eval(), [[[math.exp(1.)], [math.exp(2.)]]])
self.assertAllClose(
bijector.inverse(x).eval(), [[[math.log(1.)], [math.log(2.)]]])
self.assertAllClose(
bijector.inverse_log_det_jacobian(x).eval(),
[[0., -math.log(2.)]])
rev, jac = bijector.inverse_and_inverse_log_det_jacobian(x)
self.assertAllClose(rev.eval(), [[[math.log(1.)], [math.log(2.)]]])
self.assertAllClose(jac.eval(), [[0., -math.log(2.)]])
def testShapeUtilGetShape(self):
with self.test_session() as sess:
shaper = _ShapeUtil(batch_ndims=0, event_ndims=0)
with self.assertRaises(ValueError):
y = tf.placeholder(tf.float32)
shaper.get_sample_shape(y)
with self.assertRaises(ValueError):
y = tf.placeholder(tf.float32)
shaper.get_batch_shape(y)
with self.assertRaises(ValueError):
y = tf.placeholder(tf.float32)
shaper.get_event_shape(y)
shaper = _ShapeUtil(batch_ndims=0, event_ndims=0)
x = 1
self.assertAllEqual(shaper.get_sample_shape(x), [])
self.assertAllEqual(shaper.get_batch_shape(x), [])
self.assertAllEqual(shaper.get_event_shape(x), [])
self.assertAllEqual(shaper.get_shape(x, batch=False), [])
shaper = _ShapeUtil(batch_ndims=1, event_ndims=1)
x = [[[0.0, 1, 2], [3, 4, 5]]]
self.assertAllEqual(shaper.get_sample_shape(x), [1])
self.assertAllEqual(shaper.get_batch_shape(x), [2])
self.assertAllEqual(shaper.get_event_shape(x), [3])
self.assertAllEqual(shaper.get_shape(x, batch=False), [1, 3])
x += [[[6, 7, 8], [9, 10, 11]]]
self.assertAllEqual(shaper.get_sample_shape(x), [2])
self.assertAllEqual(shaper.get_batch_shape(x), [2])
self.assertAllEqual(shaper.get_event_shape(x), [3])
self.assertAllEqual(shaper.get_shape(x, batch=False), [2, 3])
shaper = _ShapeUtil(batch_ndims=0, event_ndims=1)
x = tf.ones((3, 2))
self.assertAllEqual(shaper.get_shape(x, sample=False), (2,))
def feed_eval(fun, build_shape=(None, None, 2), graph_shape=(3, 4, 2)):
"""Helper to use a deferred-shape tensor eval'ed at graph runtime."""
y = tf.placeholder(tf.int32, shape=build_shape)
y_value = np.ones(graph_shape, dtype=y.dtype.as_numpy_dtype())
return sess.run(fun(y), feed_dict={y: y_value})
shaper = _ShapeUtil(batch_ndims=1, event_ndims=1)
self.assertAllEqual(feed_eval(shaper.get_sample_shape), [3])
self.assertAllEqual(feed_eval(shaper.get_batch_shape), [4])
self.assertAllEqual(feed_eval(shaper.get_event_shape), [2])
self.assertAllEqual(feed_eval(lambda y: shaper.get_shape(y, batch=False)), [3, 2])
shaper = _ShapeUtil(batch_ndims=0, event_ndims=1)
self.assertAllEqual(feed_eval(lambda y: shaper.get_shape(y, batch=False), (None, None), (3, 2)), [3, 2])
self.assertAllEqual(feed_eval(lambda y: shaper.get_shape(y, sample=False), (None, None), (3, 2)), [2])