def testBatchNorm(self):
decorator = ops.ConfigurableOps()
kwargs = dict(center=False, scale=False)
decorator_regular_output = decorator.batch_norm(self.inputs, **kwargs)
decorator_zero_output = decorator.batch_norm(ops.VANISHED, **kwargs)
tf_output = tf.contrib.layers.batch_norm(self.inputs, **kwargs)
self.assertAllEqual(decorator_regular_output, tf_output)
self.assertTrue(ops.is_vanished(decorator_zero_output))
def testTowerVanishes(self, parameterization):
depth = self.inputs.shape.as_list()[3]
decorator = ops.ConfigurableOps(parameterization=parameterization)
net = decorator.conv2d(
self.inputs, num_outputs=12, kernel_size=3, scope='first')
net = decorator.conv2d(
net, num_outputs=depth, kernel_size=1, scope='second')
self.assertTrue(ops.is_vanished(net))
def testDefaultToZero(self):
parameterization = {'first/Conv2D': 3}
decorator = ops.ConfigurableOps(
parameterization=parameterization, fallback_rule='zero')
first = decorator.conv2d(
self.inputs, num_outputs=12, kernel_size=3, scope='first')
second = decorator.conv2d(self.inputs, 13, kernel_size=1, scope='second')
self.assertEqual(3, first.shape.as_list()[3])
self.assertTrue(ops.is_vanished(second))
self.assertEqual(0, decorator.constructed_ops['second/Conv2D'])
def testBatchNorm(self):
decorator = ops.ConfigurableOps()
kwargs = dict(center=False, scale=False)
decorator_regular_output = decorator.batch_norm(self.inputs, **kwargs)
decorator_zero_output = decorator.batch_norm(ops.VANISHED, **kwargs)
tf_output = layers.batch_norm(self.inputs, **kwargs)
with self.cached_session():
tf.global_variables_initializer().run()
self.assertAllEqual(decorator_regular_output, tf_output)
self.assertTrue(ops.is_vanished(decorator_zero_output))
def testPool(self):
decorator = ops.ConfigurableOps()
empty = ops.VANISHED
pool_kwargs = dict(kernel_size=2, stride=2, padding='same', scope='pool')
for fn_name in ['max_pool2d', 'avg_pool2d']:
decorator_pool_fn = getattr(decorator, fn_name)
decorator_regular_output = decorator_pool_fn(self.inputs, **pool_kwargs)
decorator_zero_output = decorator_pool_fn(empty, **pool_kwargs)
tf_pool_fn = getattr(layers, fn_name)
tf_output = tf_pool_fn(self.inputs, **pool_kwargs)
self.assertAllEqual(decorator_regular_output, tf_output)
self.assertTrue(ops.is_vanished(decorator_zero_output))
def testConcatHijack(self):
decorator = ops.ConfigurableOps()
module = Fake()
inputs = tf.ones([2, 3, 3, 5])
empty = ops.VANISHED
with self.assertRaises(ValueError):
# empty will generate an error before the hijack.
_ = module.concat([inputs, empty], 3).shape.as_list()
# hijacking:
ops.hijack_module_functions(decorator, module)
# Verifying success of hijack.
self.assertAllEqual(
module.concat([inputs, empty], 3).shape.as_list(), [2, 3, 3, 5])
self.assertTrue(ops.is_vanished(module.concat([empty, empty], 3)))
self.assertAllEqual(
module.concat([inputs, empty, inputs], 3).shape.as_list(),
[2, 3, 3, 10])
