def testSelectOpScalarCondition(self):
if test.is_gpu_available(cuda_only=True):
random_seed.set_random_seed(0)
x = random_ops.truncated_normal([1, 784], seed=0)
conv = _two_layer_model(x)
add = math_ops.add(conv, conv)
condition = constant_op.constant(True)
select = gen_math_ops._select(condition, conv, add)
output = array_ops.identity(select)
with session.Session() as sess:
output_val_ref = sess.run(output)
with session.Session(config=_get_config()) as sess:
metadata = config_pb2.RunMetadata()
output_val = sess.run(output, run_metadata=metadata)
nodes = []
num_transposes = 0
for node in metadata.cost_graph.node:
if _is_transpose(node.name):
num_transposes += 1
nodes.append(node.name)
expected_num_transposes = 2
self.assertEqual(expected_num_transposes, num_transposes)
self._assert_trans_nhwc_to_nchw('Conv2D-0', nodes)
self._assert_trans_nchw_to_nhwc('Select-0-0', nodes)
self.assertAllClose(output_val_ref, output_val, atol=1e-3)
def testSelectOpConditionUnknownShape(self):
if test.is_gpu_available(cuda_only=True):
random_seed.set_random_seed(0)
x = random_ops.truncated_normal([1, 784], seed=0)
conv = _two_layer_model(x)
add = math_ops.add(conv, conv)
condition = array_ops.placeholder(dtype='bool')
select = gen_math_ops._select(condition, conv, add)
output = array_ops.identity(select)
condition_val = np.zeros((1, 7, 7, 64))
with session.Session() as sess:
output_val_ref = sess.run(output, feed_dict={condition: condition_val})
with session.Session(config=_get_config()) as sess:
metadata = config_pb2.RunMetadata()
output_val = sess.run(
output, run_metadata=metadata, feed_dict={condition: condition_val})
nodes = []
num_transposes = 0
for node in metadata.cost_graph.node:
if _is_transpose(node.name):
num_transposes += 1
nodes.append(node.name)
expected_num_transposes = 3
self.assertEqual(expected_num_transposes, num_transposes)
self._assert_trans_nhwc_to_nchw('Conv2D-0', nodes)
self.assertAllClose(output_val_ref, output_val, atol=1e-3)
def testSelectOpScalarCondition(self):
if test.is_gpu_available(cuda_only=True):
random_seed.set_random_seed(0)
x = random_ops.truncated_normal([1, 784], seed=0)
conv = _two_layer_model(x)
add = math_ops.add(conv, conv)
condition = constant_op.constant(True)
select = gen_math_ops._select(condition, conv, add)
output = array_ops.identity(select)
with session.Session() as sess:
output_val_ref = sess.run(output)
with session.Session(config=_get_config()) as sess:
metadata = config_pb2.RunMetadata()
output_val = sess.run(output, run_metadata=metadata)
nodes = []
num_transposes = 0
for node in metadata.cost_graph.node:
if node.name.startswith('LayoutOptimizerTranspose'):
num_transposes += 1
nodes.append(node.name)
expected_num_transposes = 2
self.assertEqual(expected_num_transposes, num_transposes)
self.assertIn('LayoutOptimizerTransposeNHWCToNCHW-Conv2D-0', nodes)
self.assertIn('LayoutOptimizerTransposeNCHWToNHWC-Select-0-0', nodes)
self.assertAllClose(output_val_ref, output_val, atol=1e-3)
def testTernaryOp(self):
if test.is_gpu_available(cuda_only=True):
random_seed.set_random_seed(0)
x = random_ops.truncated_normal([1, 784], seed=0)
conv = _two_layer_model(x)
add = math_ops.add(conv, conv)
mean = math_ops.reduce_mean(conv)
condition = math_ops.less(conv, mean)
select = gen_math_ops._select(condition, conv, add)
output = array_ops.identity(select)
with session.Session() as sess:
output_val_ref = sess.run(output)
with session.Session(config=_get_config()) as sess:
metadata = config_pb2.RunMetadata()
output_val = sess.run(output, run_metadata=metadata)
nodes = []
num_transposes = 0
for node in metadata.cost_graph.node:
if node.name.startswith('LayoutOptimizerTranspose'):
num_transposes += 1
nodes.append(node.name)
expected_num_transposes = 3
self.assertEqual(expected_num_transposes, num_transposes)
self.assertIn('LayoutOptimizerTransposeNHWCToNCHW-Conv2D-0', nodes)
self.assertIn('LayoutOptimizerTransposeNCHWToNHWC-Select-0-0', nodes)
self.assertAllClose(output_val_ref, output_val, atol=1e-3)
def testSelectOp(self):
if test.is_gpu_available(cuda_only=True):
random_seed.set_random_seed(0)
x = random_ops.truncated_normal([1, 784], seed=0)
conv = _two_layer_model(x)
add = math_ops.add(conv, conv)
mean = math_ops.reduce_mean(conv)
condition = math_ops.less(conv, mean)
select = gen_math_ops._select(condition, conv, add)
output = array_ops.identity(select)
with session.Session() as sess:
output_val_ref = sess.run(output)
with session.Session(config=_get_config()) as sess:
metadata = config_pb2.RunMetadata()
output_val = sess.run(output, run_metadata=metadata)
nodes = []
num_transposes = 0
for node in metadata.cost_graph.node:
if _is_transpose(node.name):
num_transposes += 1
nodes.append(node.name)
expected_num_transposes = 2
self.assertEqual(expected_num_transposes, num_transposes)
self._assert_trans_nhwc_to_nchw('Conv2D-0', nodes)
self._assert_trans_nchw_to_nhwc('Select-0-0', nodes)
self.assertAllClose(output_val_ref, output_val, atol=1e-3)
