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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.
# ==============================================================================
"""Tests and Benchmarks for Densenet model under graph execution."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import time
import numpy as np
import tensorflow as tf
from tensorflow.contrib.eager.python.examples.densenet import densenet
def data_format():
return 'channels_first' if tf.test.is_gpu_available() else 'channels_last'
def image_shape(batch_size):
if data_format() == 'channels_first':
return [batch_size, 3, 224, 224]
return [batch_size, 224, 224, 3]
def random_batch(batch_size):
images = np.random.rand(*image_shape(batch_size)).astype(np.float32)
num_classes = 1000
labels = np.random.randint(
low=0, high=num_classes, size=[batch_size]).astype(np.int32)
one_hot = np.zeros((batch_size, num_classes)).astype(np.float32)
one_hot[np.arange(batch_size), labels] = 1.
return images, one_hot
class DensenetGraphTest(tf.test.TestCase):
def testApply(self):
depth = 7
growth_rate = 2
num_blocks = 3
output_classes = 10
num_layers_in_each_block = -1
batch_size = 1
with tf.Graph().as_default():
images = tf.placeholder(tf.float32, image_shape(None))
model = densenet.DenseNet(depth, growth_rate, num_blocks,
output_classes, num_layers_in_each_block,
data_format(), bottleneck=True, compression=0.5,
weight_decay=1e-4, dropout_rate=0,
pool_initial=False, include_top=True)
predictions = model(images, training=False)
init = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
np_images, _ = random_batch(batch_size)
out = sess.run(predictions, feed_dict={images: np_images})
self.assertAllEqual([batch_size, output_classes], out.shape)
class DensenetBenchmark(tf.test.Benchmark):
def __init__(self):
self.depth = 121
self.growth_rate = 32
self.num_blocks = 4
self.output_classes = 1000
self.num_layers_in_each_block = [6, 12, 24, 16]
def _report(self, label, start, num_iters, batch_size):
avg_time = (time.time() - start) / num_iters
dev = 'gpu' if tf.test.is_gpu_available() else 'cpu'
name = 'graph_%s_%s_batch_%d_%s' % (label, dev, batch_size, data_format())
extras = {'examples_per_sec': batch_size / avg_time}
self.report_benchmark(
iters=num_iters, wall_time=avg_time, name=name, extras=extras)
def benchmark_graph_apply(self):
with tf.Graph().as_default():
images = tf.placeholder(tf.float32, image_shape(None))
model = densenet.DenseNet(self.depth, self.growth_rate, self.num_blocks,
self.output_classes,
self.num_layers_in_each_block, data_format(),
bottleneck=True, compression=0.5,
weight_decay=1e-4, dropout_rate=0,
pool_initial=True, include_top=True)
predictions = model(images, training=False)
init = tf.global_variables_initializer()
batch_size = 64
with tf.Session() as sess:
sess.run(init)
np_images, _ = random_batch(batch_size)
num_burn, num_iters = (3, 30)
for _ in range(num_burn):
sess.run(predictions, feed_dict={images: np_images})
start = time.time()
for _ in range(num_iters):
sess.run(predictions, feed_dict={images: np_images})
self._report('apply', start, num_iters, batch_size)
def benchmark_graph_train(self):
for batch_size in [16, 32, 64]:
with tf.Graph().as_default():
np_images, np_labels = random_batch(batch_size)
dataset = tf.data.Dataset.from_tensors((np_images, np_labels)).repeat()
(images, labels) = dataset.make_one_shot_iterator().get_next()
model = densenet.DenseNet(self.depth, self.growth_rate, self.num_blocks,
self.output_classes,
self.num_layers_in_each_block, data_format(),
bottleneck=True, compression=0.5,
weight_decay=1e-4, dropout_rate=0,
pool_initial=True, include_top=True)
logits = model(images, training=True)
loss = tf.losses.softmax_cross_entropy(
logits=logits, onehot_labels=labels)
optimizer = tf.train.GradientDescentOptimizer(learning_rate=1.0)
train_op = optimizer.minimize(loss)
init = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
(num_burn, num_iters) = (5, 10)
for _ in range(num_burn):
sess.run(train_op)
start = time.time()
for _ in range(num_iters):
sess.run(train_op)
self._report('train', start, num_iters, batch_size)
if __name__ == '__main__':
tf.test.main()
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