class TestRunnerMultiGPU(CleverHansTest):
def setUp(self):
super(TestRunnerMultiGPU, self).setUp()
self.sess = tf.Session()
inputs = []
outputs = []
self.niter = 10
niter = self.niter
# A Simple graph with `niter` sub-graphs.
with tf.variable_scope(None, 'runner'):
for i in range(niter):
v = tf.get_variable('v%d' % i, shape=(100, 10))
w = tf.get_variable('w%d' % i, shape=(100, 1))
inputs += [{'v': v, 'w': w}]
outputs += [{'v': v, 'w': w}]
self.runner = RunnerMultiGPU(inputs, outputs, sess=self.sess)
def help_test_runner(self, ninputs, niter):
"""
Tests the MultiGPU runner by feeding in random Tensors for `ninputs`
steps. Then validating the output after `niter-1` steps.
"""
v_val = []
w_val = []
for i in range(ninputs):
v_val += [np.random.rand(100, 10)]
w_val += [np.random.rand(100, 1)]
fvals = self.runner.run({'v': v_val[i], 'w': w_val[i]})
self.assertTrue(len(fvals) == 0)
self.assertFalse(self.runner.is_finished())
for i in range(niter - ninputs - 1):
self.assertFalse(self.runner.is_finished())
fvals = self.runner.run()
self.assertTrue(len(fvals) == 0)
self.assertFalse(self.runner.is_finished())
for i in range(ninputs):
self.assertFalse(self.runner.is_finished())
fvals = self.runner.run()
self.assertTrue('v' in fvals and 'w' in fvals)
self.assertTrue(np.allclose(fvals['v'], v_val[i]))
self.assertTrue(np.allclose(fvals['w'], w_val[i]))
self.assertTrue(self.runner.is_finished())
def test_queue_full(self):
self.help_test_runner(self.niter - 1, self.niter)
def test_queue_half(self):
self.help_test_runner(self.niter // 2, self.niter)
class TestRunnerMultiGPU(CleverHansTest):
def setUp(self):
super(TestRunnerMultiGPU, self).setUp()
self.sess = tf.Session()
inputs = []
outputs = []
self.niter = 10
niter = self.niter
# A Simple graph with `niter` sub-graphs.
with tf.variable_scope(None, 'runner'):
for i in range(niter):
v = tf.get_variable('v%d' % i, shape=(100, 10))
w = tf.get_variable('w%d' % i, shape=(100, 1))
inputs += [{'v': v, 'w': w}]
outputs += [{'v': v, 'w': w}]
self.runner = RunnerMultiGPU(inputs, outputs, sess=self.sess)
def help_test_runner(self, ninputs, niter):
"""
Tests the MultiGPU runner by feeding in random Tensors for `ninputs`
steps. Then validating the output after `niter-1` steps.
"""
v_val = []
w_val = []
for i in range(ninputs):
v_val += [np.random.rand(100, 10)]
w_val += [np.random.rand(100, 1)]
fvals = self.runner.run({'v': v_val[i], 'w': w_val[i]})
self.assertTrue(len(fvals) == 0)
self.assertFalse(self.runner.is_finished())
for i in range(niter-ninputs-1):
self.assertFalse(self.runner.is_finished())
fvals = self.runner.run()
self.assertTrue(len(fvals) == 0)
self.assertFalse(self.runner.is_finished())
for i in range(ninputs):
self.assertFalse(self.runner.is_finished())
fvals = self.runner.run()
self.assertTrue('v' in fvals and 'w' in fvals)
self.assertTrue(np.allclose(fvals['v'], v_val[i]))
self.assertTrue(np.allclose(fvals['w'], w_val[i]))
self.assertTrue(self.runner.is_finished())
def test_queue_full(self):
self.help_test_runner(self.niter-1, self.niter)
def test_queue_half(self):
self.help_test_runner(self.niter//2, self.niter)
def generate_np(self, x_val, **kwargs):
"""
Facilitates testing this attack.
"""
fixed, feedable, hash_key = self.construct_variables(kwargs)
if hash_key not in self.graphs:
with tf.variable_scope(None, 'attack_%d' % len(self.graphs)):
# x is a special placeholder we always want to have
with tf.device('/gpu:0'):
x = tf.placeholder(tf.float32, shape=x_val.shape, name='x')
inputs, outputs = self.generate(x, **kwargs)
from runner import RunnerMultiGPU
runner = RunnerMultiGPU(inputs, outputs, sess=self.sess)
self.graphs[hash_key] = runner
runner = self.graphs[hash_key]
feed_dict = {'x': x_val}
for name in feedable:
feed_dict[name] = feedable[name]
fvals = runner.run(feed_dict)
while not runner.is_finished():
fvals = runner.run()
return fvals['adv_x']
def generate_np(self, x_val, **kwargs):
"""
Facilitates testing this attack.
"""
fixed, feedable, hash_key = self.construct_variables(kwargs)
if hash_key not in self.graphs:
with tf.variable_scope(None, 'attack_%d' % len(self.graphs)):
# x is a special placeholder we always want to have
with tf.device('/gpu:0'):
x = tf.placeholder(tf.float32, shape=x_val.shape, name='x')
inputs, outputs = self.generate(x, **kwargs)
from runner import RunnerMultiGPU
runner = RunnerMultiGPU(inputs, outputs, sess=self.sess)
self.graphs[hash_key] = runner
runner = self.graphs[hash_key]
feed_dict = {'x': x_val}
for name in feedable:
feed_dict[name] = feedable[name]
fvals = runner.run(feed_dict)
while not runner.is_finished():
fvals = runner.run()
return fvals['adv_x']