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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.
# ==============================================================================
"""Estimator workflow with RevNet train on CIFAR-10."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
from absl import flags
import tensorflow as tf
from tensorflow.contrib.eager.python.examples.revnet import cifar_input
from tensorflow.contrib.eager.python.examples.revnet import main as main_
from tensorflow.contrib.eager.python.examples.revnet import revnet
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)
grads, vars_, logits, loss = model.compute_gradients(
inputs, labels, training=True)
train_op = optimizer.apply_gradients(
zip(grads, vars_), 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 get_input_fn(config, data_dir, split):
"""Get the input function that is required by the `tf.estimator` API.
Args:
config: Customized hyperparameters
data_dir: Directory where the data is stored
split: One of `train`, `validation`, `train_all`, and `test`
Returns:
Input function required by the `tf.estimator` API
"""
data_dir = os.path.join(data_dir, config.dataset)
# Fix split-dependent hyperparameters
if split == "train_all" or split == "train":
data_aug = True
batch_size = config.batch_size
epochs = config.epochs
shuffle = True
prefetch = config.batch_size
else:
data_aug = False
batch_size = config.eval_batch_size
epochs = 1
shuffle = False
prefetch = config.eval_batch_size
def input_fn():
"""Input function required by the `tf.estimator.Estimator` API."""
return cifar_input.get_ds_from_tfrecords(
data_dir=data_dir,
split=split,
data_aug=data_aug,
batch_size=batch_size,
epochs=epochs,
shuffle=shuffle,
prefetch=prefetch,
data_format=config.data_format)
return input_fn
def main(argv):
FLAGS = argv[0] # pylint:disable=invalid-name,redefined-outer-name
tf.logging.set_verbosity(tf.logging.INFO)
# RevNet specific configuration
config = main_.get_config(config_name=FLAGS.config, dataset=FLAGS.dataset)
# Estimator specific configuration
run_config = tf.estimator.RunConfig(
model_dir=FLAGS.train_dir, # Directory for storing checkpoints
tf_random_seed=config.seed,
save_summary_steps=config.log_every,
save_checkpoints_steps=config.log_every,
session_config=None, # Using default
keep_checkpoint_max=100,
keep_checkpoint_every_n_hours=10000, # Using default
log_step_count_steps=config.log_every,
train_distribute=None # Default not use distribution strategy
)
# Construct estimator
revnet_estimator = tf.estimator.Estimator(
model_fn=model_fn,
model_dir=FLAGS.train_dir,
config=run_config,
params={"config": config})
# Construct input functions
train_input_fn = get_input_fn(
config=config, data_dir=FLAGS.data_dir, split="train_all")
eval_input_fn = get_input_fn(
config=config, data_dir=FLAGS.data_dir, split="test")
# Train and evaluate estimator
revnet_estimator.train(input_fn=train_input_fn)
revnet_estimator.evaluate(input_fn=eval_input_fn)
if FLAGS.export:
input_shape = (None,) + config.input_shape
inputs = tf.placeholder(tf.float32, shape=input_shape)
input_fn = tf.estimator.export.build_raw_serving_input_receiver_fn({
"image": inputs
})
revnet_estimator.export_savedmodel(FLAGS.train_dir, input_fn)
if __name__ == "__main__":
flags.DEFINE_string(
"data_dir", default=None, help="Directory to load tfrecords")
flags.DEFINE_string(
"train_dir",
default=None,
help="[Optional] Directory to store the training information")
flags.DEFINE_string(
"dataset",
default="cifar-10",
help="[Optional] The dataset used; either `cifar-10` or `cifar-100`")
flags.DEFINE_boolean(
"export",
default=False,
help="[Optional] Export the model for serving if True")
flags.DEFINE_string(
"config",
default="revnet-38",
help="[Optional] Architecture of network. "
"Other options include `revnet-110` and `revnet-164`")
FLAGS = flags.FLAGS
tf.app.run(main=main, argv=[FLAGS])
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