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# Copyright 2016 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 for DecodeJpegOp."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import time
from six.moves import xrange
from tensorflow.python.client import session
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import image_ops
from tensorflow.python.ops import io_ops
from tensorflow.python.ops import variable_scope
from tensorflow.python.ops import variables
from tensorflow.python.platform import test
prefix_path = 'third_party/tensorflow/core/lib/jpeg/testdata'
class DecodeJpegBenchmark(test.Benchmark):
"""Evaluate tensorflow DecodeJpegOp performance."""
def _evalDecodeJpeg(self,
image_name,
parallelism,
num_iters,
crop_during_decode=None,
crop_window=None,
tile=None):
"""Evaluate DecodeJpegOp for the given image.
TODO(tanmingxing): add decoding+cropping as well.
Args:
image_name: a string of image file name (without suffix).
parallelism: the number of concurrent decode_jpeg ops to be run.
num_iters: number of iterations for evaluation.
crop_during_decode: If true, use fused DecodeAndCropJpeg instead of
separate decode and crop ops. It is ignored if crop_window is None.
crop_window: if not None, crop the decoded image. Depending on
crop_during_decode, cropping could happen during or after decoding.
tile: if not None, tile the image to composite a larger fake image.
Returns:
The duration of the run in seconds.
"""
ops.reset_default_graph()
image_file_path = os.path.join(prefix_path, image_name)
if tile is None:
image_content = variable_scope.get_variable(
'image_%s' % image_name,
initializer=io_ops.read_file(image_file_path))
else:
single_image = image_ops.decode_jpeg(
io_ops.read_file(image_file_path), channels=3, name='single_image')
# Tile the image to composite a new larger image.
tiled_image = array_ops.tile(single_image, tile)
image_content = variable_scope.get_variable(
'tiled_image_%s' % image_name,
initializer=image_ops.encode_jpeg(tiled_image))
with session.Session() as sess:
sess.run(variables.global_variables_initializer())
images = []
for _ in xrange(parallelism):
if crop_window is None:
# No crop.
image = image_ops.decode_jpeg(image_content, channels=3)
elif crop_during_decode:
# combined decode and crop.
image = image_ops.decode_and_crop_jpeg(
image_content, crop_window, channels=3)
else:
# separate decode and crop.
image = image_ops.decode_jpeg(image_content, channels=3)
image = image_ops.crop_to_bounding_box(
image,
offset_height=crop_window[0],
offset_width=crop_window[1],
target_height=crop_window[2],
target_width=crop_window[3])
images.append(image)
r = control_flow_ops.group(*images)
for _ in xrange(3):
# Skip warm up time.
sess.run(r)
start_time = time.time()
for _ in xrange(num_iters):
sess.run(r)
return time.time() - start_time
def benchmarkDecodeJpegSmall(self):
"""Evaluate single DecodeImageOp for small size image."""
num_iters = 10
crop_window = [10, 10, 50, 50]
for parallelism in [1, 100]:
duration_decode = self._evalDecodeJpeg('small.jpg', parallelism,
num_iters)
duration_decode_crop = self._evalDecodeJpeg('small.jpg', parallelism,
num_iters, False, crop_window)
duration_decode_after_crop = self._evalDecodeJpeg(
'small.jpg', parallelism, num_iters, True, crop_window)
self.report_benchmark(
name='decode_jpeg_small_p%d' % (parallelism),
iters=num_iters,
wall_time=duration_decode)
self.report_benchmark(
name='decode_crop_jpeg_small_p%d' % (parallelism),
iters=num_iters,
wall_time=duration_decode_crop)
self.report_benchmark(
name='decode_after_crop_jpeg_small_p%d' % (parallelism),
iters=num_iters,
wall_time=duration_decode_after_crop)
def benchmarkDecodeJpegMedium(self):
"""Evaluate single DecodeImageOp for medium size image."""
num_iters = 10
crop_window = [10, 10, 50, 50]
for parallelism in [1, 100]:
duration_decode = self._evalDecodeJpeg('medium.jpg', parallelism,
num_iters)
duration_decode_crop = self._evalDecodeJpeg('medium.jpg', parallelism,
num_iters, False, crop_window)
duration_decode_after_crop = self._evalDecodeJpeg(
'medium.jpg', parallelism, num_iters, True, crop_window)
self.report_benchmark(
name='decode_jpeg_medium_p%d' % (parallelism),
iters=num_iters,
wall_time=duration_decode)
self.report_benchmark(
name='decode_crop_jpeg_medium_p%d' % (parallelism),
iters=num_iters,
wall_time=duration_decode_crop)
self.report_benchmark(
name='decode_after_crop_jpeg_medium_p%d' % (parallelism),
iters=num_iters,
wall_time=duration_decode_after_crop)
def benchmarkDecodeJpegLarge(self):
"""Evaluate single DecodeImageOp for large size image."""
num_iters = 10
crop_window = [10, 10, 50, 50]
tile = [4, 4, 1]
for parallelism in [1, 100]:
# Tile the medium size image to composite a larger fake image.
duration_decode = self._evalDecodeJpeg('medium.jpg', parallelism,
num_iters, tile)
duration_decode_crop = self._evalDecodeJpeg(
'medium.jpg', parallelism, num_iters, False, crop_window, tile)
duration_decode_after_crop = self._evalDecodeJpeg(
'medium.jpg', parallelism, num_iters, True, crop_window, tile)
self.report_benchmark(
name='decode_jpeg_large_p%d' % (parallelism),
iters=num_iters,
wall_time=duration_decode)
self.report_benchmark(
name='decode_crop_jpeg_large_p%d' % (parallelism),
iters=num_iters,
wall_time=duration_decode_crop)
self.report_benchmark(
name='decode_after_crop_jpeg_large_p%d' % (parallelism),
iters=num_iters,
wall_time=duration_decode_after_crop)
if __name__ == '__main__':
test.main()
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