def testSingleTrainingStepOnTPU(self, model, normalization, architecture,
penalty_type):
if not self.isValidModel(model, normalization, architecture,
penalty_type):
return
parameters = self.parameters.copy()
parameters.update(params.GetDatasetParameters("cifar10"))
parameters.update({
"use_tpu": True,
"discriminator_normalization": normalization,
"architecture": architecture,
"penalty_type": penalty_type,
"disc_iters": 1,
"training_steps": 1,
})
dataset_content = test_utils.load_fake_dataset(parameters).repeat()
model_dir = os.path.join(FLAGS.test_tmpdir, model.__name__,
normalization, architecture, penalty_type)
config = tf.contrib.tpu.RunConfig(
model_dir=model_dir,
tpu_config=tf.contrib.tpu.TPUConfig(iterations_per_loop=1))
print("Testing loss %s, regularizer %s, with architecture %s." %
(model, penalty_type, architecture))
gan = model(runtime_info=FakeRuntimeInfo(model_dir),
dataset_content=dataset_content,
parameters=parameters)
gan.train_with_estimator(config)
def testGANBuildsAndImageShapeIsOk(self, model, normalization,
architecture, penalty_type):
if not self.isValidModel(model, normalization, architecture,
penalty_type):
return
parameters = self.parameters.copy()
parameters.update(params.GetDatasetParameters("celeba"))
parameters.update({
"architecture": architecture,
"penalty_type": penalty_type,
"discriminator_normalization": normalization,
})
dataset_content = test_utils.load_fake_dataset(parameters).repeat()
config = tf.ConfigProto(allow_soft_placement=True)
tf.reset_default_graph()
with tf.Session(config=config):
kwargs = dict(runtime_info=FakeRuntimeInfo(),
dataset_content=dataset_content,
parameters=parameters)
print("Testing loss %s, regularizer %s, with architecture %s." %
(model, penalty_type, architecture))
gan = model(**kwargs)
gan.build_model()
self.assertEqual(gan.fake_images.get_shape(), [64, 64, 64, 3])
def testDiscItersIsUsedCorrectly(self, disc_iters):
parameters = self.parameters.copy()
parameters.update(params.GetDatasetParameters("cifar10"))
parameters.update({
"batch_size": 2,
"training_steps": 10,
"save_checkpoint_steps": 1,
"disc_iters": disc_iters,
"architecture": consts.RESNET_CIFAR,
"penalty_type": consts.NO_PENALTY,
"discriminator_normalization": consts.NO_NORMALIZATION,
})
dataset_content = test_utils.load_fake_dataset(parameters).repeat()
task_workdir = os.path.join(FLAGS.test_tmpdir, str(disc_iters))
with tf.Graph().as_default(), tf.Session() as sess:
gan = GAN_PENALTY(
runtime_info=FakeRuntimeInfo(task_workdir),
dataset_content=dataset_content,
parameters=parameters)
gan.build_model()
gan.train(sess)
# Read checkpoints for each training step. If the weight in the generator
# changed we trained the generator during that step.
previous_values = {}
generator_trained = []
for step in range(0, parameters["training_steps"] + 1):
basename = os.path.join(
task_workdir, "GAN_PENALTY.model-{}".format(step))
self.assertTrue(tf.gfile.Exists(basename + ".index"))
ckpt = tf.train.load_checkpoint(basename)
if step == 0:
for name in ckpt.get_variable_to_shape_map():
previous_values[name] = ckpt.get_tensor(name)
continue
d_trained = False
g_trained = False
for name in ckpt.get_variable_to_shape_map():
t = ckpt.get_tensor(name)
diff = np.abs(previous_values[name] - t).max()
previous_values[name] = t
if "discriminator" in name and diff > 1e-10:
d_trained = True
elif "generator" in name and diff > 1e-10:
if name.endswith('moving_mean') or name.endswith('moving_variance'):
# Note: Even when we don't train the generator the batch norm
# values still get updated.
continue
tf.logging.info(
"step %d: %s changed up to %f", step, name, diff)
g_trained = True
self.assertTrue(d_trained) # Discriminator is trained every step.
generator_trained.append(g_trained)
self.assertEqual(generator_trained,
GENERATOR_TRAINED_IN_STEPS[disc_iters - 1])
def testL2Regularization(self, architecture):
parameters = self.parameters.copy()
parameters.update(params.GetDatasetParameters("celeba"))
parameters.update({
"architecture": architecture,
"penalty_type": consts.L2_PENALTY,
"discriminator_normalization": consts.NO_NORMALIZATION,
})
dataset_content = test_utils.load_fake_dataset(parameters).repeat()
config = tf.ConfigProto(allow_soft_placement=True)
tf.reset_default_graph()
with tf.Session(config=config):
kwargs = dict(
runtime_info=FakeRuntimeInfo(),
dataset_content=dataset_content,
parameters=parameters)
gan = GAN_PENALTY(**kwargs)
gan.build_model()
def testDiscItersIsUsedCorrectly(self, disc_iters, use_tpu):
if disc_iters > 1 and use_tpu:
return
parameters = self.parameters.copy()
parameters.update(params.GetDatasetParameters("cifar10"))
parameters.update({
"use_tpu": use_tpu,
"discriminator_normalization": consts.NO_NORMALIZATION,
"architecture": consts.RESNET_CIFAR,
"penalty_type": consts.NO_PENALTY,
"disc_iters": disc_iters,
"training_steps": 5,
})
dataset_content = test_utils.load_fake_dataset(parameters).repeat()
model_dir = os.path.join(FLAGS.test_tmpdir, str(disc_iters))
config = tf.contrib.tpu.RunConfig(
model_dir=model_dir,
save_checkpoints_steps=1,
keep_checkpoint_max=1000,
tpu_config=tf.contrib.tpu.TPUConfig(iterations_per_loop=1))
gan = GAN_PENALTY(runtime_info=FakeRuntimeInfo(model_dir),
dataset_content=dataset_content,
parameters=parameters)
gan.train_with_estimator(config)
# Read checkpoints for each training step. If the weight in the generator
# changed we trained the generator during that step.
previous_values = {}
generator_trained = []
for step in range(0, parameters["training_steps"] + 1):
basename = os.path.join(model_dir, "model.ckpt-{}".format(step))
self.assertTrue(tf.gfile.Exists(basename + ".index"))
ckpt = tf.train.load_checkpoint(basename)
if step == 0:
for name in ckpt.get_variable_to_shape_map():
previous_values[name] = ckpt.get_tensor(name)
continue
d_trained = False
g_trained = False
for name in ckpt.get_variable_to_shape_map():
t = ckpt.get_tensor(name)
diff = np.abs(previous_values[name] - t).max()
previous_values[name] = t
if "discriminator" in name and diff > 1e-10:
d_trained = True
elif "generator" in name and diff > 1e-10:
if name.endswith("moving_mean") or name.endswith(
"moving_variance"):
# Note: Even when we don't train the generator the batch norm
# values still get updated.
continue
tf.logging.info("step %d: %s changed up to %f", step, name,
diff)
g_trained = True
self.assertTrue(d_trained) # Discriminator is trained every step.
generator_trained.append(g_trained)
self.assertEqual(generator_trained,
GENERATOR_TRAINED_IN_STEPS[disc_iters - 1])
