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Resnet Example Uses tf.Datasets now (#960)

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Change Resnet example to use tf.Datasets instead of queues.
This commit is contained in:
Wapaul1
2017-09-20 14:14:04 -07:00
committed by Robert Nishihara
parent 5c70faf76b
commit c26c7553bc
2 changed files with 47 additions and 61 deletions
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examples/resnet/cifar_input.py
+37 -41
View File
@@ -31,24 +31,27 @@ def build_data(data_path, size, dataset):
image_bytes = image_size * image_size * depth
record_bytes = label_bytes + label_offset + image_bytes
data_files = tf.gfile.Glob(data_path)
file_queue = tf.train.string_input_producer(data_files, shuffle=True)
def load_transform(value):
# Convert these examples to dense labels and processed images.
record = tf.reshape(tf.decode_raw(value, tf.uint8), [record_bytes])
label = tf.cast(tf.slice(record, [label_offset], [label_bytes]),
tf.int32)
# Convert from string to [depth * height * width] to
# [depth, height, width].
depth_major = tf.reshape(
tf.slice(record, [label_bytes], [image_bytes]),
[depth, image_size, image_size])
# Convert from [depth, height, width] to [height, width, depth].
image = tf.cast(tf.transpose(depth_major, [1, 2, 0]), tf.float32)
return (image, label)
# Read examples from files in the filename queue.
reader = tf.FixedLengthRecordReader(record_bytes=record_bytes)
_, value = reader.read(file_queue)
# Convert these examples to dense labels and processed images.
record = tf.reshape(tf.decode_raw(value, tf.uint8), [record_bytes])
label = tf.cast(tf.slice(record, [label_offset], [label_bytes]), tf.int32)
# Convert from string to [depth * height * width] to
# [depth, height, width].
depth_major = tf.reshape(tf.slice(record, [label_bytes], [image_bytes]),
[depth, image_size, image_size])
# Convert from [depth, height, width] to [height, width, depth].
image = tf.cast(tf.transpose(depth_major, [1, 2, 0]), tf.float32)
queue = tf.train.shuffle_batch([image, label], size, size, 0,
num_threads=16)
return queue
data_files = tf.gfile.Glob(data_path)
data = tf.contrib.data.FixedLengthRecordDataset(data_files,
record_bytes=record_bytes)
data = data.map(load_transform)
data = data.batch(size)
iterator = data.make_one_shot_iterator()
return iterator.get_next()
def build_input(data, batch_size, dataset, train):
@@ -67,42 +70,35 @@ def build_input(data, batch_size, dataset, train):
Raises:
ValueError: When the specified dataset is not supported.
"""
images_constant = tf.constant(data[0])
labels_constant = tf.constant(data[1])
image_size = 32
depth = 3
num_classes = 10 if dataset == "cifar10" else 100
image, label = tf.train.slice_input_producer([images_constant,
labels_constant],
capacity=16 * batch_size)
if train:
images, labels = data
num_samples = images.shape[0] - images.shape[0] % batch_size
dataset = tf.contrib.data.Dataset.from_tensor_slices(
(images[:num_samples], labels[:num_samples]))
def map_train(image, label):
image = tf.image.resize_image_with_crop_or_pad(image, image_size + 4,
image_size + 4)
image = tf.random_crop(image, [image_size, image_size, 3])
image = tf.image.random_flip_left_right(image)
image = tf.image.per_image_standardization(image)
example_queue = tf.RandomShuffleQueue(
capacity=16 * batch_size,
min_after_dequeue=8 * batch_size,
dtypes=[tf.float32, tf.int32],
shapes=[[image_size, image_size, depth], [1]])
num_threads = 16
else:
return (image, label)
def map_test(image, label):
image = tf.image.resize_image_with_crop_or_pad(image, image_size,
image_size)
image = tf.image.per_image_standardization(image)
example_queue = tf.FIFOQueue(
3 * batch_size,
dtypes=[tf.float32, tf.int32],
shapes=[[image_size, image_size, depth], [1]])
num_threads = 1
return (image, label)
example_enqueue_op = example_queue.enqueue([image, label])
tf.train.add_queue_runner(tf.train.queue_runner.QueueRunner(
example_queue, [example_enqueue_op] * num_threads))
# Read "batch" labels + images from the example queue.
images, labels = example_queue.dequeue_many(batch_size)
dataset = dataset.map(map_train if train else map_test)
dataset = dataset.batch(batch_size)
dataset = dataset.repeat()
if train:
dataset = dataset.shuffle(buffer_size=16 * batch_size)
images, labels = dataset.make_one_shot_iterator().get_next()
images = tf.reshape(images, [batch_size, image_size, image_size, depth])
labels = tf.reshape(labels, [batch_size, 1])
indices = tf.reshape(tf.range(0, batch_size, 1), [batch_size, 1])
labels = tf.sparse_to_dense(
examples/resnet/resnet_main.py
+10 -20
View File
@@ -15,9 +15,11 @@ import tensorflow as tf
import cifar_input
import resnet_model
# Tensorflow must be at least version 1.0.0 for the example to work.
if int(tf.__version__.split(".")[0]) < 1:
raise Exception("Your Tensorflow version is less than 1.0.0. Please "
# Tensorflow must be at least version 1.2.0 for the example to work.
tf_major = int(tf.__version__.split(".")[0])
tf_minor = int(tf.__version__.split(".")[1])
if (tf_major < 1) or (tf_major == 1 and tf_minor < 2):
raise Exception("Your Tensorflow version is less than 1.2.0. Please "
"update Tensorflow to the latest version.")
parser = argparse.ArgumentParser(description="Run the ResNet example.")
@@ -50,12 +52,9 @@ def get_data(path, size, dataset):
# This only uses the cpu.
os.environ["CUDA_VISIBLE_DEVICES"] = ""
with tf.device("/cpu:0"):
queue = cifar_input.build_data(path, size, dataset)
dataset = cifar_input.build_data(path, size, dataset)
sess = tf.Session()
coord = tf.train.Coordinator()
tf.train.start_queue_runners(sess, coord=coord)
images, labels = sess.run(queue)
coord.request_stop()
images, labels = sess.run(dataset)
sess.close()
return images, labels
@@ -86,12 +85,9 @@ class ResNetTrainActor(object):
# Only a single actor in this case.
tf.set_random_seed(1)
input_images = data[0]
input_labels = data[1]
with tf.device("/gpu:0" if num_gpus > 0 else "/cpu:0"):
# Build the model.
images, labels = cifar_input.build_input([input_images,
input_labels],
images, labels = cifar_input.build_input(data,
hps.batch_size, dataset,
False)
self.model = resnet_model.ResNet(hps, images, labels, "train")
@@ -100,8 +96,6 @@ class ResNetTrainActor(object):
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
self.model.variables.set_session(sess)
self.coord = tf.train.Coordinator()
tf.train.start_queue_runners(sess, coord=self.coord)
init = tf.global_variables_initializer()
sess.run(init)
self.steps = 10
@@ -123,6 +117,7 @@ class ResNetTrainActor(object):
@ray.remote
class ResNetTestActor(object):
def __init__(self, data, dataset, eval_batch_count, eval_dir):
os.environ["CUDA_VISIBLE_DEVICES"] = ""
hps = resnet_model.HParams(
batch_size=100,
num_classes=100 if dataset == "cifar100" else 10,
@@ -134,12 +129,9 @@ class ResNetTestActor(object):
relu_leakiness=0.1,
optimizer="mom",
num_gpus=0)
input_images = data[0]
input_labels = data[1]
with tf.device("/cpu:0"):
# Builds the testing network.
images, labels = cifar_input.build_input([input_images,
input_labels],
images, labels = cifar_input.build_input(data,
hps.batch_size, dataset,
False)
self.model = resnet_model.ResNet(hps, images, labels, "eval")
@@ -148,8 +140,6 @@ class ResNetTestActor(object):
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
self.model.variables.set_session(sess)
self.coord = tf.train.Coordinator()
tf.train.start_queue_runners(sess, coord=self.coord)
init = tf.global_variables_initializer()
sess.run(init)