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# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Eager execution workflow with RevNet train on CIFAR-10."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import sys
from absl import flags
import tensorflow as tf
from tqdm import tqdm
from tensorflow.contrib.eager.python.examples.revnet import cifar_input
from tensorflow.contrib.eager.python.examples.revnet import config as config_
from tensorflow.contrib.eager.python.examples.revnet import revnet
tfe = tf.contrib.eager
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()
def warmup(model, config, steps=1):
mock_input = tf.random_normal((config.batch_size,) + config.input_shape)
for _ in range(steps):
model(mock_input, training=False)
def train_one_iter(model,
inputs,
labels,
optimizer,
global_step=None,
verbose=False):
"""Train for one iteration."""
if FLAGS.manual_grad:
if verbose:
print("Using manual gradients")
grads, vars_, loss = model.compute_gradients(inputs, labels)
optimizer.apply_gradients(zip(grads, vars_), global_step=global_step)
else: # For correctness validation
if verbose:
print("Not using manual gradients")
with tf.GradientTape() as tape:
logits, _ = model(inputs, training=True)
loss = model.compute_loss(logits=logits, labels=labels)
grads = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(
zip(grads, model.trainable_variables), global_step=global_step)
return loss.numpy()
def evaluate(model, iterator):
"""Compute accuracy with the given dataset iterator."""
mean_loss = tfe.metrics.Mean()
accuracy = tfe.metrics.Accuracy()
for x, y in tqdm(iterator):
logits, _ = model(x, training=False)
loss = model.compute_loss(logits=logits, labels=y)
accuracy(
labels=tf.cast(y, tf.int64),
predictions=tf.argmax(logits, axis=1, output_type=tf.int64))
mean_loss(loss)
return accuracy.result().numpy(), mean_loss.result().numpy()
if __name__ == "__main__":
flags.DEFINE_string(
"train_dir",
default=None,
help="[Optional] Directory to store the training information")
flags.DEFINE_string(
"data_dir", default=None, help="Directory to load tfrecords")
flags.DEFINE_boolean(
"restore",
default=False,
help="[Optional] Restore the latest checkpoint from `train_dir` if True")
flags.DEFINE_boolean(
"validate",
default=False,
help="[Optional] Use the validation set or not for hyperparameter search")
flags.DEFINE_boolean(
"manual_grad",
default=False,
help="[Optional] Use manual gradient graph to save memory")
FLAGS = flags.FLAGS
tf.app.run(main)
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