def _benchmark_eager_apply(self,
label,
device_and_format,
defun=False,
execution_mode=None,
compiled=False):
config = config_.get_hparams_imagenet_56()
with tfe.execution_mode(execution_mode):
device, data_format = device_and_format
model = revnet.RevNet(config=config)
if defun:
model.call = tfe.defun(model.call, compiled=compiled)
batch_size = 64
num_burn = 5
num_iters = 10
with tf.device(device):
images, _ = random_batch(batch_size, config)
for _ in range(num_burn):
model(images, training=False)
if execution_mode:
tfe.async_wait()
gc.collect()
start = time.time()
for _ in range(num_iters):
model(images, training=False)
if execution_mode:
tfe.async_wait()
self._report(label, start, num_iters, device, batch_size, data_format)
def test_training_graph(self):
"""Test model training in graph mode."""
with tf.Graph().as_default():
x = tf.random_normal(
shape=(self.config.batch_size,) + self.config.input_shape)
t = tf.random_uniform(
shape=(self.config.batch_size,),
minval=0,
maxval=self.config.n_classes,
dtype=tf.int32)
global_step = tfe.Variable(0., trainable=False)
model = revnet.RevNet(config=self.config)
grads_all, vars_all, _ = model.compute_gradients(x, t, training=True)
optimizer = tf.train.AdamOptimizer(learning_rate=1e-3)
updates = model.get_updates_for(x)
self.assertEqual(len(updates), 192)
with tf.control_dependencies(model.get_updates_for(x)):
train_op = optimizer.apply_gradients(
zip(grads_all, vars_all), global_step=global_step)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for _ in range(1):
sess.run(train_op)
def model_fn(features, labels, mode, params):
"""Function specifying the model that is required by the `tf.estimator` API.
Args:
features: Input images
labels: Labels of images
mode: One of `ModeKeys.TRAIN`, `ModeKeys.EVAL` or 'ModeKeys.PREDICT'
params: A dictionary of extra parameter that might be passed
Returns:
An instance of `tf.estimator.EstimatorSpec`
"""
inputs = features
if isinstance(inputs, dict):
inputs = features["image"]
config = params["config"]
model = revnet.RevNet(config=config)
if mode == tf.estimator.ModeKeys.TRAIN:
global_step = tf.train.get_or_create_global_step()
learning_rate = tf.train.piecewise_constant(
global_step, config.lr_decay_steps, config.lr_list)
optimizer = tf.train.MomentumOptimizer(
learning_rate, momentum=config.momentum)
logits, saved_hidden = model(inputs, training=True)
grads, loss = model.compute_gradients(saved_hidden, labels, training=True)
with tf.control_dependencies(model.get_updates_for(inputs)):
train_op = optimizer.apply_gradients(
zip(grads, model.trainable_variables), global_step=global_step)
return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op)
else:
logits, _ = model(inputs, training=False)
predictions = tf.argmax(logits, axis=1)
probabilities = tf.nn.softmax(logits)
if mode == tf.estimator.ModeKeys.EVAL:
loss = model.compute_loss(labels=labels, logits=logits)
return tf.estimator.EstimatorSpec(
mode=mode,
loss=loss,
eval_metric_ops={
"accuracy":
tf.metrics.accuracy(labels=labels, predictions=predictions)
})
else: # mode == tf.estimator.ModeKeys.PREDICT
result = {
"classes": predictions,
"probabilities": probabilities,
}
return tf.estimator.EstimatorSpec(
mode=mode,
predictions=predictions,
export_outputs={
"classify": tf.estimator.export.PredictOutput(result)
})
def test_training_graph(self):
"""Test model training in graph mode."""
with tf.Graph().as_default():
config = config_.get_hparams_cifar_38()
config.add_hparam("n_classes", 10)
config.add_hparam("dataset", "cifar-10")
x = tf.random_normal(
shape=(self.config.batch_size,) + self.config.input_shape)
t = tf.random_uniform(
shape=(self.config.batch_size,),
minval=0,
maxval=self.config.n_classes,
dtype=tf.int32)
global_step = tf.Variable(0., trainable=False)
model = revnet.RevNet(config=config)
_, saved_hidden = model(x)
grads, _ = model.compute_gradients(saved_hidden=saved_hidden, labels=t)
optimizer = tf.train.AdamOptimizer(learning_rate=1e-3)
train_op = optimizer.apply_gradients(
zip(grads, model.trainable_variables), global_step=global_step)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for _ in range(1):
sess.run(train_op)
def setUp(self):
super(RevnetTest, self).setUp()
config = config_.get_hparams_imagenet_56()
shape = (config.batch_size, ) + config.input_shape
self.model = revnet.RevNet(config=config)
self.x = tf.random_normal(shape=shape)
self.t = tf.random_uniform(shape=[config.batch_size],
minval=0,
maxval=config.n_classes,
dtype=tf.int32)
self.config = config
def test_train_step_defun(self):
self.model.call = tfe.defun(self.model.call)
logits, _ = self.model(self.x, training=True)
loss = self.model.compute_loss(logits=logits, labels=self.t)
optimizer = tf.train.AdamOptimizer(learning_rate=1e-3)
for _ in range(3):
loss_ = self.model.train_step(self.x, self.t, optimizer, report=True)
self.assertTrue(loss_.numpy() <= loss.numpy())
loss = loss_
# Initialize new model, so that other tests are not affected
self.model = revnet.RevNet(config=self.config)
def setUp(self):
super(RevNetTest, self).setUp()
config = config_.get_hparams_cifar_38()
# Reconstruction could cause numerical error, use double precision for tests
config.dtype = tf.float64
config.fused = False # Fused batch norm does not support tf.float64
shape = (config.batch_size, ) + config.input_shape
self.model = revnet.RevNet(config=config)
self.x = tf.random_normal(shape=shape, dtype=tf.float64)
self.t = tf.random_uniform(shape=[config.batch_size],
minval=0,
maxval=config.n_classes,
dtype=tf.int64)
self.config = config
def setUp(self):
super(RevNetTest, self).setUp()
config = config_.get_hparams_cifar_38()
config.add_hparam("n_classes", 10)
config.add_hparam("dataset", "cifar-10")
# Reconstruction could cause numerical error, use double precision for tests
config.dtype = tf.float64
config.fused = False # Fused batch norm does not support tf.float64
# Reduce the batch size for tests because the OSS version runs
# in constrained GPU environment with 1-2GB of memory.
config.batch_size = 2
shape = (config.batch_size,) + config.input_shape
self.model = revnet.RevNet(config=config)
self.x = tf.random_normal(shape=shape, dtype=tf.float64)
self.t = tf.random_uniform(
shape=[config.batch_size],
minval=0,
maxval=config.n_classes,
dtype=tf.int64)
self.config = config
def _benchmark_eager_train(self,
label,
make_iterator,
device_and_format,
defun=False,
execution_mode=None,
compiled=False):
config = config_.get_hparams_imagenet_56()
config.add_hparam("n_classes", 1000)
config.add_hparam("dataset", "ImageNet")
with tfe.execution_mode(execution_mode):
device, data_format = device_and_format
for batch_size in self._train_batch_sizes():
(images, labels) = random_batch(batch_size, config)
model = revnet.RevNet(config=config)
optimizer = tf.train.GradientDescentOptimizer(0.1)
if defun:
model.call = tfe.defun(model.call)
num_burn = 3
num_iters = 10
with tf.device(device):
iterator = make_iterator((images, labels))
for _ in range(num_burn):
(images, labels) = iterator.next()
train_one_iter(model, images, labels, optimizer)
if execution_mode:
tfe.async_wait()
self._force_device_sync()
gc.collect()
start = time.time()
for _ in range(num_iters):
(images, labels) = iterator.next()
train_one_iter(model, images, labels, optimizer)
if execution_mode:
tfe.async_wait()
self._force_device_sync()
self._report(label, start, num_iters, device, batch_size,
data_format)
def main(_):
"""Eager execution workflow with RevNet trained on CIFAR-10."""
if FLAGS.data_dir is None:
raise ValueError("No supplied data directory")
if not os.path.exists(FLAGS.data_dir):
raise ValueError("Data directory {} does not exist".format(
FLAGS.data_dir))
tf.enable_eager_execution()
config = config_.get_hparams_cifar_38()
model = revnet.RevNet(config=config)
ds_train = cifar_input.get_ds_from_tfrecords(
data_dir=FLAGS.data_dir,
split="train",
data_aug=True,
batch_size=config.batch_size,
epochs=config.epochs,
shuffle=config.shuffle,
data_format=config.data_format,
dtype=config.dtype,
prefetch=config.prefetch)
ds_validation = cifar_input.get_ds_from_tfrecords(
data_dir=FLAGS.data_dir,
split="validation",
data_aug=False,
batch_size=config.eval_batch_size,
epochs=1,
data_format=config.data_format,
dtype=config.dtype,
prefetch=config.prefetch)
ds_test = cifar_input.get_ds_from_tfrecords(
data_dir=FLAGS.data_dir,
split="test",
data_aug=False,
batch_size=config.eval_batch_size,
epochs=1,
data_format=config.data_format,
dtype=config.dtype,
prefetch=config.prefetch)
global_step = tfe.Variable(1, trainable=False)
def learning_rate(
): # TODO(lxuechen): Remove once cl/201089859 is in place
return tf.train.piecewise_constant(global_step, config.lr_decay_steps,
config.lr_list)
optimizer = tf.train.MomentumOptimizer(learning_rate, momentum=0.9)
checkpoint = tf.train.Checkpoint(optimizer=optimizer,
model=model,
optimizer_step=global_step)
if FLAGS.train_dir:
summary_writer = tf.contrib.summary.create_file_writer(FLAGS.train_dir)
if FLAGS.restore:
latest_path = tf.train.latest_checkpoint(FLAGS.train_dir)
checkpoint.restore(latest_path)
for x, y in ds_train:
loss = train_one_iter(model, x, y, optimizer, global_step=global_step)
if global_step % config.log_every == 0:
it_validation = ds_validation.make_one_shot_iterator()
it_test = ds_test.make_one_shot_iterator()
acc_validation = evaluate(model, it_validation)
acc_test = evaluate(model, it_test)
print("Iter {}, "
"train loss {}, "
"validation accuracy {}, "
"test accuracy {}".format(global_step.numpy(), loss,
acc_validation, acc_test))
if FLAGS.train_dir:
with summary_writer.as_default():
with tf.contrib.summary.always_record_summaries():
tf.contrib.summary.scalar("Validation accuracy",
acc_validation)
tf.contrib.summary.scalar("Test accuracy", acc_test)
tf.contrib.summary.scalar("Training loss", loss)
if global_step.numpy() % config.save_every == 0 and FLAGS.train_dir:
checkpoint.save(file_prefix=FLAGS.train_dir + "ckpt")
def model_fn(features, labels, mode, params):
"""Model function required by the `tf.contrib.tpu.TPUEstimator` API.
Args:
features: Input images
labels: Labels of images
mode: One of `ModeKeys.TRAIN`, `ModeKeys.EVAL` or 'ModeKeys.PREDICT'
params: A dictionary of extra parameter that might be passed
Returns:
An instance of `tf.contrib.tpu.TPUEstimatorSpec`
"""
revnet_config = params["revnet_config"]
model = revnet.RevNet(config=revnet_config)
inputs = features
if isinstance(inputs, dict):
inputs = features["image"]
if revnet_config.data_format == "channels_first":
assert not FLAGS.transpose_input # channels_first only for GPU
inputs = tf.transpose(inputs, [0, 3, 1, 2])
if FLAGS.transpose_input and mode != tf.estimator.ModeKeys.PREDICT:
inputs = tf.transpose(inputs, [3, 0, 1, 2]) # HWCN to NHWC
# Normalize the image to zero mean and unit variance.
inputs -= tf.constant(MEAN_RGB, shape=[1, 1, 3], dtype=inputs.dtype)
inputs /= tf.constant(STDDEV_RGB, shape=[1, 1, 3], dtype=inputs.dtype)
if mode == tf.estimator.ModeKeys.TRAIN:
global_step = tf.train.get_or_create_global_step()
learning_rate = tf.train.piecewise_constant(
global_step, revnet_config.lr_decay_steps, revnet_config.lr_list)
optimizer = tf.train.MomentumOptimizer(learning_rate,
revnet_config.momentum)
if FLAGS.use_tpu:
optimizer = tf.contrib.tpu.CrossShardOptimizer(optimizer)
logits, saved_hidden = model(inputs, training=True)
grads, loss = model.compute_gradients(saved_hidden,
labels,
training=True)
with tf.control_dependencies(model.get_updates_for(inputs)):
train_op = optimizer.apply_gradients(zip(
grads, model.trainable_variables),
global_step=global_step)
if not FLAGS.skip_host_call:
# To log the loss, current learning rate, and epoch for Tensorboard, the
# summary op needs to be run on the host CPU via host_call. host_call
# expects [batch_size, ...] Tensors, thus reshape to introduce a batch
# dimension. These Tensors are implicitly concatenated to
# [params['batch_size']].
gs_t = tf.reshape(global_step, [1])
loss_t = tf.reshape(loss, [1])
lr_t = tf.reshape(learning_rate, [1])
host_call = (_host_call_fn, [gs_t, loss_t, lr_t])
return tf.contrib.tpu.TPUEstimatorSpec(mode=mode,
loss=loss,
train_op=train_op,
host_call=host_call)
elif mode == tf.estimator.ModeKeys.EVAL:
logits, _ = model(inputs, training=False)
loss = model.compute_loss(labels=labels, logits=logits)
return tf.contrib.tpu.TPUEstimatorSpec(mode=mode,
loss=loss,
eval_metrics=(_metric_fn,
[labels, logits]))
else: # Predict or export
logits, _ = model(inputs, training=False)
predictions = {
"classes": tf.argmax(logits, axis=1),
"probabilities": tf.nn.softmax(logits),
}
return tf.contrib.tpu.TPUEstimatorSpec(
mode=mode,
predictions=predictions,
export_outputs={
"classify": tf.estimator.export.PredictOutput(predictions)
})
def model_fn(features, labels, mode, params):
"""Model function required by the `tf.contrib.tpu.TPUEstimator` API.
Args:
features: Input images
labels: Labels of images
mode: One of `ModeKeys.TRAIN`, `ModeKeys.EVAL` or 'ModeKeys.PREDICT'
params: A dictionary of extra parameter that might be passed
Returns:
An instance of `tf.contrib.tpu.TPUEstimatorSpec`
"""
inputs = features
if isinstance(inputs, dict):
inputs = features["image"]
FLAGS = params["FLAGS"] # pylint:disable=invalid-name,redefined-outer-name
config = params["config"]
model = revnet.RevNet(config=config)
if mode == tf.estimator.ModeKeys.TRAIN:
global_step = tf.train.get_or_create_global_step()
learning_rate = tf.train.piecewise_constant(
global_step, config.lr_decay_steps, config.lr_list)
optimizer = tf.train.MomentumOptimizer(
learning_rate, momentum=config.momentum)
if FLAGS.use_tpu:
optimizer = tf.contrib.tpu.CrossShardOptimizer(optimizer)
# Define gradients
grads, vars_, logits, loss = model.compute_gradients(
inputs, labels, training=True)
train_op = optimizer.apply_gradients(
zip(grads, vars_), global_step=global_step)
names = [v.name for v in model.variables]
tf.logging.warn("{}".format(names))
return tf.contrib.tpu.TPUEstimatorSpec(
mode=tf.estimator.ModeKeys.TRAIN, loss=loss, train_op=train_op)
if mode == tf.estimator.ModeKeys.EVAL:
logits, _ = model(inputs, training=False)
loss = model.compute_loss(labels=labels, logits=logits)
def metric_fn(labels, logits):
predictions = tf.argmax(logits, axis=1)
accuracy = tf.metrics.accuracy(labels=labels, predictions=predictions)
return {
"accuracy": accuracy,
}
return tf.contrib.tpu.TPUEstimatorSpec(
mode=mode, loss=loss, eval_metrics=(metric_fn, [labels, logits]))
if mode == tf.estimator.ModeKeys.PREDICT:
logits, _ = model(inputs, training=False)
predictions = {
"classes": tf.argmax(logits, axis=1),
"probabilities": tf.nn.softmax(logits),
}
return tf.contrib.tpu.TPUEstimatorSpec(
mode=mode,
predictions=predictions,
export_outputs={
"classify": tf.estimator.export.PredictOutput(predictions)
})
def main(_):
"""Eager execution workflow with RevNet trained on CIFAR-10."""
tf.enable_eager_execution()
config = get_config(config_name=FLAGS.config, dataset=FLAGS.dataset)
ds_train, ds_train_one_shot, ds_validation, ds_test = get_datasets(
data_dir=FLAGS.data_dir, config=config)
model = revnet.RevNet(config=config)
global_step = tf.train.get_or_create_global_step() # Ensure correct summary
global_step.assign(1)
learning_rate = tf.train.piecewise_constant(
global_step, config.lr_decay_steps, config.lr_list)
optimizer = tf.train.MomentumOptimizer(
learning_rate, momentum=config.momentum)
checkpointer = tf.train.Checkpoint(
optimizer=optimizer, model=model, optimizer_step=global_step)
if FLAGS.use_defun:
model.call = tfe.defun(model.call)
model.compute_gradients = tfe.defun(model.compute_gradients)
model.get_moving_stats = tfe.defun(model.get_moving_stats)
model.restore_moving_stats = tfe.defun(model.restore_moving_stats)
global apply_gradients # pylint:disable=global-variable-undefined
apply_gradients = tfe.defun(apply_gradients)
if FLAGS.train_dir:
summary_writer = tf.contrib.summary.create_file_writer(FLAGS.train_dir)
if FLAGS.restore:
latest_path = tf.train.latest_checkpoint(FLAGS.train_dir)
checkpointer.restore(latest_path)
print("Restored latest checkpoint at path:\"{}\" "
"with global_step: {}".format(latest_path, global_step.numpy()))
sys.stdout.flush()
for x, y in ds_train:
train_one_iter(model, x, y, optimizer, global_step=global_step)
if global_step.numpy() % config.log_every == 0:
it_test = ds_test.make_one_shot_iterator()
acc_test, loss_test = evaluate(model, it_test)
if FLAGS.validate:
it_train = ds_train_one_shot.make_one_shot_iterator()
it_validation = ds_validation.make_one_shot_iterator()
acc_train, loss_train = evaluate(model, it_train)
acc_validation, loss_validation = evaluate(model, it_validation)
print("Iter {}, "
"training set accuracy {:.4f}, loss {:.4f}; "
"validation set accuracy {:.4f}, loss {:.4f}; "
"test accuracy {:.4f}, loss {:.4f}".format(
global_step.numpy(), acc_train, loss_train, acc_validation,
loss_validation, acc_test, loss_test))
else:
print("Iter {}, test accuracy {:.4f}, loss {:.4f}".format(
global_step.numpy(), acc_test, loss_test))
sys.stdout.flush()
if FLAGS.train_dir:
with summary_writer.as_default():
with tf.contrib.summary.always_record_summaries():
tf.contrib.summary.scalar("Test accuracy", acc_test)
tf.contrib.summary.scalar("Test loss", loss_test)
if FLAGS.validate:
tf.contrib.summary.scalar("Training accuracy", acc_train)
tf.contrib.summary.scalar("Training loss", loss_train)
tf.contrib.summary.scalar("Validation accuracy", acc_validation)
tf.contrib.summary.scalar("Validation loss", loss_validation)
if global_step.numpy() % config.save_every == 0 and FLAGS.train_dir:
saved_path = checkpointer.save(
file_prefix=os.path.join(FLAGS.train_dir, "ckpt"))
print("Saved checkpoint at path: \"{}\" "
"with global_step: {}".format(saved_path, global_step.numpy()))
sys.stdout.flush()
def main(_):
"""Eager execution workflow with RevNet trained on CIFAR-10."""
if FLAGS.data_dir is None:
raise ValueError("No supplied data directory")
if not os.path.exists(FLAGS.data_dir):
raise ValueError("Data directory {} does not exist".format(
FLAGS.data_dir))
tf.enable_eager_execution()
config = config_.get_hparams_cifar_38()
if FLAGS.validate:
# 40k Training set
ds_train = cifar_input.get_ds_from_tfrecords(
data_dir=FLAGS.data_dir,
split="train",
data_aug=True,
batch_size=config.batch_size,
epochs=config.epochs,
shuffle=config.shuffle,
data_format=config.data_format,
dtype=config.dtype,
prefetch=config.batch_size)
# 10k Training set
ds_validation = cifar_input.get_ds_from_tfrecords(
data_dir=FLAGS.data_dir,
split="validation",
data_aug=False,
batch_size=config.eval_batch_size,
epochs=1,
shuffle=False,
data_format=config.data_format,
dtype=config.dtype,
prefetch=config.eval_batch_size)
else:
# 50k Training set
ds_train = cifar_input.get_ds_from_tfrecords(
data_dir=FLAGS.data_dir,
split="train_all",
data_aug=True,
batch_size=config.batch_size,
epochs=config.epochs,
shuffle=config.shuffle,
data_format=config.data_format,
dtype=config.dtype,
prefetch=config.batch_size)
# Always compute loss and accuracy on whole training and test set
ds_train_one_shot = cifar_input.get_ds_from_tfrecords(
data_dir=FLAGS.data_dir,
split="train_all",
data_aug=False,
batch_size=config.eval_batch_size,
epochs=1,
shuffle=False,
data_format=config.data_format,
dtype=config.dtype,
prefetch=config.eval_batch_size)
ds_test = cifar_input.get_ds_from_tfrecords(
data_dir=FLAGS.data_dir,
split="test",
data_aug=False,
batch_size=config.eval_batch_size,
epochs=1,
shuffle=False,
data_format=config.data_format,
dtype=config.dtype,
prefetch=config.eval_batch_size)
model = revnet.RevNet(config=config)
global_step = tfe.Variable(1, trainable=False)
learning_rate = tf.train.piecewise_constant(global_step,
config.lr_decay_steps,
config.lr_list)
optimizer = tf.train.MomentumOptimizer(learning_rate,
momentum=config.momentum)
checkpointer = tf.train.Checkpoint(optimizer=optimizer,
model=model,
optimizer_step=global_step)
if FLAGS.train_dir:
summary_writer = tf.contrib.summary.create_file_writer(FLAGS.train_dir)
if FLAGS.restore:
latest_path = tf.train.latest_checkpoint(FLAGS.train_dir)
checkpointer.restore(latest_path)
print("Restored latest checkpoint at path:\"{}\" "
"with global_step: {}".format(latest_path,
global_step.numpy()))
sys.stdout.flush()
warmup(model, config)
for x, y in ds_train:
loss = train_one_iter(model, x, y, optimizer, global_step=global_step)
if global_step.numpy() % config.log_every == 0:
it_train = ds_train_one_shot.make_one_shot_iterator()
acc_train, loss_train = evaluate(model, it_train)
it_test = ds_test.make_one_shot_iterator()
acc_test, loss_test = evaluate(model, it_test)
if FLAGS.validate:
it_validation = ds_validation.make_one_shot_iterator()
acc_validation, loss_validation = evaluate(
model, it_validation)
print("Iter {}, "
"training set accuracy {:.4f}, loss {:.4f}; "
"validation set accuracy {:.4f}, loss {:4.f}"
"test accuracy {:.4f}, loss {:.4f}".format(
global_step.numpy(), acc_train, loss_train,
acc_validation, loss_validation, acc_test,
loss_test))
else:
print("Iter {}, "
"training set accuracy {:.4f}, loss {:.4f}; "
"test accuracy {:.4f}, loss {:.4f}".format(
global_step.numpy(), acc_train, loss_train, acc_test,
loss_test))
sys.stdout.flush()
if FLAGS.train_dir:
with summary_writer.as_default():
with tf.contrib.summary.always_record_summaries():
tf.contrib.summary.scalar("Training loss", loss)
tf.contrib.summary.scalar("Test accuracy", acc_test)
if FLAGS.validate:
tf.contrib.summary.scalar("Validation accuracy",
acc_validation)
if global_step.numpy() % config.save_every == 0 and FLAGS.train_dir:
saved_path = checkpointer.save(
file_prefix=os.path.join(FLAGS.train_dir, "ckpt"))
print("Saved checkpoint at path: \"{}\" "
"with global_step: {}".format(saved_path,
global_step.numpy()))
sys.stdout.flush()
