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']