Yang Yang
7 years ago
commit
e0e45c052e
@ -0,0 +1,106 @@
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from __future__ import absolute_import
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from __future__ import print_function
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from __future__ import unicode_literals
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import argparse
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import io, re
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import sys, os
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import subprocess
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import platform
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COPYRIGHT = '''
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Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
|
||||
You may obtain a copy of the License at
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||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
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||||
|
||||
Unless required by applicable law or agreed to in writing, software
|
||||
distributed under the License is distributed on an "AS IS" BASIS,
|
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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||||
limitations under the License.
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'''
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LANG_COMMENT_MARK = None
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NEW_LINE_MARK = None
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COPYRIGHT_HEADER = None
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if platform.system() == "Windows":
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NEW_LINE_MARK = "\r\n"
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else:
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NEW_LINE_MARK = '\n'
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COPYRIGHT_HEADER = COPYRIGHT.split(NEW_LINE_MARK)[1]
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p = re.search('(\d{4})', COPYRIGHT_HEADER).group(0)
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process = subprocess.Popen(["date", "+%Y"], stdout=subprocess.PIPE)
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date, err = process.communicate()
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date = date.decode("utf-8").rstrip("\n")
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COPYRIGHT_HEADER = COPYRIGHT_HEADER.replace(p, date)
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def generate_copyright(template, lang='C'):
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if lang == 'Python':
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LANG_COMMENT_MARK = '#'
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else:
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LANG_COMMENT_MARK = "//"
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lines = template.split(NEW_LINE_MARK)
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ans = LANG_COMMENT_MARK + " " + COPYRIGHT_HEADER + NEW_LINE_MARK
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for lino, line in enumerate(lines):
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if lino == 0 or lino == 1 or lino == len(lines) - 1: continue
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ans += LANG_COMMENT_MARK + " " + line + NEW_LINE_MARK
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return ans + "\n"
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def lang_type(filename):
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if filename.endswith(".py"):
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return "Python"
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elif filename.endswith(".h"):
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return "C"
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elif filename.endswith(".hpp"):
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return "C"
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elif filename.endswith(".cc"):
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return "C"
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elif filename.endswith(".cpp"):
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return "C"
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elif filename.endswith(".cu"):
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return "C"
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elif filename.endswith(".cuh"):
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return "C"
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elif filename.endswith(".go"):
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return "C"
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elif filename.endswith(".proto"):
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return "C"
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else:
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print("Unsupported filetype")
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exit(0)
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def main(argv=None):
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parser = argparse.ArgumentParser(
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description='Checker for copyright declaration.')
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parser.add_argument('filenames', nargs='*', help='Filenames to check')
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args = parser.parse_args(argv)
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retv = 0
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for filename in args.filenames:
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first_line = io.open(filename).readline()
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if "COPYRIGHT" in first_line.upper() : continue
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original_contents = io.open(filename).read()
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new_contents = generate_copyright(
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COPYRIGHT, lang_type(filename)) + original_contents
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print('Auto Insert Copyright Header {}'.format(filename))
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retv = 1
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with io.open(filename, 'w') as output_file:
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output_file.write(new_contents)
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return retv
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if __name__ == '__main__':
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exit(main())
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@ -0,0 +1,46 @@
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# Contributor Covenant Code of Conduct
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|
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## Our Pledge
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||||
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In the interest of fostering an open and welcoming environment, we as contributors and maintainers pledge to making participation in our project and our community a harassment-free experience for everyone, regardless of age, body size, disability, ethnicity, gender identity and expression, level of experience, nationality, personal appearance, race, religion, or sexual identity and orientation.
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|
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## Our Standards
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||||
|
||||
Examples of behavior that contributes to creating a positive environment include:
|
||||
|
||||
* Using welcoming and inclusive language
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* Being respectful of differing viewpoints and experiences
|
||||
* Gracefully accepting constructive criticism
|
||||
* Focusing on what is best for the community
|
||||
* Showing empathy towards other community members
|
||||
|
||||
Examples of unacceptable behavior by participants include:
|
||||
|
||||
* The use of sexualized language or imagery and unwelcome sexual attention or advances
|
||||
* Trolling, insulting/derogatory comments, and personal or political attacks
|
||||
* Public or private harassment
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||||
* Publishing others' private information, such as a physical or electronic address, without explicit permission
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||||
* Other conduct which could reasonably be considered inappropriate in a professional setting
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||||
|
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## Our Responsibilities
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Project maintainers are responsible for clarifying the standards of acceptable behavior and are expected to take appropriate and fair corrective action in response to any instances of unacceptable behavior.
|
||||
|
||||
Project maintainers have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, or to ban temporarily or permanently any contributor for other behaviors that they deem inappropriate, threatening, offensive, or harmful.
|
||||
|
||||
## Scope
|
||||
|
||||
This Code of Conduct applies both within project spaces and in public spaces when an individual is representing the project or its community. Examples of representing a project or community include using an official project e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event. Representation of a project may be further defined and clarified by project maintainers.
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||||
|
||||
## Enforcement
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||||
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Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by contacting the project team at paddle-dev@baidu.com. The project team will review and investigate all complaints, and will respond in a way that it deems appropriate to the circumstances. The project team is obligated to maintain confidentiality with regard to the reporter of an incident. Further details of specific enforcement policies may be posted separately.
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||||
Project maintainers who do not follow or enforce the Code of Conduct in good faith may face temporary or permanent repercussions as determined by other members of the project's leadership.
|
||||
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||||
## Attribution
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||||
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This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, available at [http://contributor-covenant.org/version/1/4][version]
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||||
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[homepage]: http://contributor-covenant.org
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[version]: http://contributor-covenant.org/version/1/4/
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@ -0,0 +1,9 @@
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# Advbox
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Advbox is a Python toolbox to create adversarial examples that fool neural networks. It requires Python and paddle.
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## How to use
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1. train a model and save it's parameters. (like fluid_mnist.py)
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2. load the parameters which is trained in step1, then reconstruct the model.(like mnist_tutorial_fgsm.py)
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3. use advbox to generate the adversarial sample.
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@ -0,0 +1,52 @@
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# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
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#
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#Licensed under the Apache License, Version 2.0 (the "License");
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#you may not use this file except in compliance with the License.
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#You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
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#
|
||||
#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.
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||||
"""
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The base model of the model.
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"""
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from abc import ABCMeta, abstractmethod
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class Attack(object):
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"""
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Abstract base class for adversarial attacks. `Attack` represent an adversarial attack
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which search an adversarial example. subclass should implement the _apply() method.
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Args:
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model(Model): an instance of the class advbox.base.Model.
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"""
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__metaclass__ = ABCMeta
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def __init__(self, model):
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self.model = model
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def __call__(self, image_label):
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"""
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Generate the adversarial sample.
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Args:
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image_label(list): The image and label tuple list with one element.
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"""
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adv_img = self._apply(image_label)
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return adv_img
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@abstractmethod
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def _apply(self, image_label):
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"""
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Search an adversarial example.
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Args:
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image_batch(list): The image and label tuple list with one element.
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"""
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raise NotImplementedError
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@ -0,0 +1,87 @@
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# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
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#
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#Licensed under the Apache License, Version 2.0 (the "License");
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||||
#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.
|
||||
"""
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||||
This module provide the attack method for FGSM's implement.
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"""
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from __future__ import division
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import numpy as np
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from collections import Iterable
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from .base import Attack
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class GradientSignAttack(Attack):
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"""
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This attack was originally implemented by Goodfellow et al. (2015) with the
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infinity norm (and is known as the "Fast Gradient Sign Method"). This is therefore called
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the Fast Gradient Method.
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Paper link: https://arxiv.org/abs/1412.6572
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"""
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def _apply(self, image_label, epsilons=1000):
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assert len(image_label) == 1
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pre_label = np.argmax(self.model.predict(image_label))
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min_, max_ = self.model.bounds()
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gradient = self.model.gradient(image_label)
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gradient_sign = np.sign(gradient) * (max_ - min_)
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if not isinstance(epsilons, Iterable):
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epsilons = np.linspace(0, 1, num=epsilons + 1)
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for epsilon in epsilons:
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adv_img = image_label[0][0].reshape(
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gradient_sign.shape) + epsilon * gradient_sign
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adv_img = np.clip(adv_img, min_, max_)
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adv_label = np.argmax(self.model.predict([(adv_img, 0)]))
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if pre_label != adv_label:
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return adv_img
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FGSM = GradientSignAttack
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class IteratorGradientSignAttack(Attack):
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"""
|
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This attack was originally implemented by Alexey Kurakin(Google Brain).
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Paper link: https://arxiv.org/pdf/1607.02533.pdf
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||||
"""
|
||||
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def _apply(self, image_label, epsilons=100, steps=10):
|
||||
"""
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Apply the iterative gradient sign attack.
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Args:
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image_label(list): The image and label tuple list of one element.
|
||||
epsilons(list|tuple|int): The epsilon (input variation parameter).
|
||||
steps(int): The number of iterator steps.
|
||||
Return:
|
||||
numpy.ndarray: The adversarail sample generated by the algorithm.
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||||
"""
|
||||
assert len(image_label) == 1
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pre_label = np.argmax(self.model.predict(image_label))
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gradient = self.model.gradient(image_label)
|
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min_, max_ = self.model.bounds()
|
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|
||||
if not isinstance(epsilons, Iterable):
|
||||
epsilons = np.linspace(0, 1, num=epsilons + 1)
|
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|
||||
for epsilon in epsilons:
|
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adv_img = image_label[0][0].reshape(gradient.shape)
|
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for _ in range(steps):
|
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gradient = self.model.gradient([(adv_img, image_label[0][1])])
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gradient_sign = np.sign(gradient) * (max_ - min_)
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adv_img = adv_img + epsilon * gradient_sign
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adv_img = np.clip(adv_img, min_, max_)
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adv_label = np.argmax(self.model.predict([(adv_img, 0)]))
|
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if pre_label != adv_label:
|
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return adv_img
|
||||
@ -0,0 +1,103 @@
|
||||
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
|
||||
#
|
||||
#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.
|
||||
"""
|
||||
The base model of the model.
|
||||
"""
|
||||
from abc import ABCMeta
|
||||
import abc
|
||||
|
||||
abstractmethod = abc.abstractmethod
|
||||
|
||||
|
||||
class Model(object):
|
||||
"""
|
||||
Base class of model to provide attack.
|
||||
|
||||
|
||||
Args:
|
||||
bounds(tuple): The lower and upper bound for the image pixel.
|
||||
channel_axis(int): The index of the axis that represents the color channel.
|
||||
preprocess(tuple): Two element tuple used to preprocess the input. First
|
||||
substract the first element, then divide the second element.
|
||||
"""
|
||||
__metaclass__ = ABCMeta
|
||||
|
||||
def __init__(self, bounds, channel_axis, preprocess=None):
|
||||
assert len(bounds) == 2
|
||||
assert channel_axis in [0, 1, 2, 3]
|
||||
|
||||
if preprocess is None:
|
||||
preprocess = (0, 1)
|
||||
self._bounds = bounds
|
||||
self._channel_axis = channel_axis
|
||||
self._preprocess = preprocess
|
||||
|
||||
def bounds(self):
|
||||
"""
|
||||
Return the upper and lower bounds of the model.
|
||||
"""
|
||||
return self._bounds
|
||||
|
||||
def channel_axis(self):
|
||||
"""
|
||||
Return the channel axis of the model.
|
||||
"""
|
||||
return self._channel_axis
|
||||
|
||||
def _process_input(self, input_):
|
||||
res = input_
|
||||
sub, div = self._preprocess
|
||||
if sub != 0:
|
||||
res = input_ - sub
|
||||
assert div != 0
|
||||
if div != 1:
|
||||
res /= div
|
||||
return res
|
||||
|
||||
@abstractmethod
|
||||
def predict(self, image_batch):
|
||||
"""
|
||||
Calculate the prediction of the image batch.
|
||||
|
||||
Args:
|
||||
image_batch(numpy.ndarray): image batch of shape (batch_size, height, width, channels).
|
||||
|
||||
Return:
|
||||
numpy.ndarray: predictions of the images with shape (batch_size, num_of_classes).
|
||||
"""
|
||||
raise NotImplementedError
|
||||
|
||||
@abstractmethod
|
||||
def num_classes(self):
|
||||
"""
|
||||
Determine the number of the classes
|
||||
|
||||
Return:
|
||||
int: the number of the classes
|
||||
"""
|
||||
raise NotImplementedError
|
||||
|
||||
@abstractmethod
|
||||
def gradient(self, image_batch):
|
||||
"""
|
||||
Calculate the gradient of the cross-entropy loss w.r.t the image.
|
||||
|
||||
Args:
|
||||
image_batch(list): The image and label tuple list.
|
||||
|
||||
Return:
|
||||
numpy.ndarray: gradient of the cross-entropy loss w.r.t the image with
|
||||
the shape (height, width, channel).
|
||||
"""
|
||||
raise NotImplementedError
|
||||
@ -0,0 +1,114 @@
|
||||
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
|
||||
#
|
||||
#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.
|
||||
from __future__ import absolute_import
|
||||
|
||||
import numpy as np
|
||||
import paddle.v2 as paddle
|
||||
import paddle.v2.fluid as fluid
|
||||
from paddle.v2.fluid.framework import program_guard
|
||||
|
||||
from .base import Model
|
||||
|
||||
|
||||
class PaddleModel(Model):
|
||||
"""
|
||||
Create a PaddleModel instance.
|
||||
When you need to generate a adversarial sample, you should construct an instance of PaddleModel.
|
||||
|
||||
Args:
|
||||
program(paddle.v2.fluid.framework.Program): The program of the model which generate the adversarial sample.
|
||||
input_name(string): The name of the input.
|
||||
logits_name(string): The name of the logits.
|
||||
predict_name(string): The name of the predict.
|
||||
cost_name(string): The name of the loss in the program.
|
||||
"""
|
||||
|
||||
def __init__(self,
|
||||
program,
|
||||
input_name,
|
||||
logits_name,
|
||||
predict_name,
|
||||
cost_name,
|
||||
bounds,
|
||||
channel_axis=3,
|
||||
preprocess=None):
|
||||
super(PaddleModel, self).__init__(
|
||||
bounds=bounds, channel_axis=channel_axis, preprocess=preprocess)
|
||||
|
||||
if preprocess is None:
|
||||
preprocess = (0, 1)
|
||||
|
||||
self._program = program
|
||||
self._place = fluid.CPUPlace()
|
||||
self._exe = fluid.Executor(self._place)
|
||||
|
||||
self._input_name = input_name
|
||||
self._logits_name = logits_name
|
||||
self._predict_name = predict_name
|
||||
self._cost_name = cost_name
|
||||
|
||||
# gradient
|
||||
loss = self._program.block(0).var(self._cost_name)
|
||||
param_grads = fluid.backward.append_backward(
|
||||
loss, parameter_list=[self._input_name])
|
||||
self._gradient = dict(param_grads)[self._input_name]
|
||||
|
||||
def predict(self, image_batch):
|
||||
"""
|
||||
Predict the label of the image_batch.
|
||||
|
||||
Args:
|
||||
image_batch(list): The image and label tuple list.
|
||||
Return:
|
||||
numpy.ndarray: predictions of the images with shape (batch_size, num_of_classes).
|
||||
"""
|
||||
feeder = fluid.DataFeeder(
|
||||
feed_list=[self._input_name, self._logits_name],
|
||||
place=self._place,
|
||||
program=self._program)
|
||||
predict_var = self._program.block(0).var(self._predict_name)
|
||||
predict = self._exe.run(self._program,
|
||||
feed=feeder.feed(image_batch),
|
||||
fetch_list=[predict_var])
|
||||
return predict
|
||||
|
||||
def num_classes(self):
|
||||
"""
|
||||
Calculate the number of classes of the output label.
|
||||
|
||||
Return:
|
||||
int: the number of classes
|
||||
"""
|
||||
predict_var = self._program.block(0).var(self._predict_name)
|
||||
assert len(predict_var.shape) == 2
|
||||
return predict_var.shape[1]
|
||||
|
||||
def gradient(self, image_batch):
|
||||
"""
|
||||
Calculate the gradient of the loss w.r.t the input.
|
||||
|
||||
Args:
|
||||
image_batch(list): The image and label tuple list.
|
||||
Return:
|
||||
list: The list of the gradient of the image.
|
||||
"""
|
||||
feeder = fluid.DataFeeder(
|
||||
feed_list=[self._input_name, self._logits_name],
|
||||
place=self._place,
|
||||
program=self._program)
|
||||
|
||||
grad, = self._exe.run(self._program,
|
||||
feed=feeder.feed(image_batch),
|
||||
fetch_list=[self._gradient])
|
||||
return grad
|
||||
@ -0,0 +1,99 @@
|
||||
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
|
||||
#
|
||||
#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.
|
||||
"""
|
||||
CNN on mnist data using fluid api of paddlepaddle
|
||||
"""
|
||||
import paddle.v2 as paddle
|
||||
import paddle.v2.fluid as fluid
|
||||
|
||||
|
||||
def mnist_cnn_model(img):
|
||||
"""
|
||||
Mnist cnn model
|
||||
|
||||
Args:
|
||||
img(Varaible): the input image to be recognized
|
||||
|
||||
Returns:
|
||||
Variable: the label prediction
|
||||
"""
|
||||
conv_pool_1 = fluid.nets.simple_img_conv_pool(
|
||||
input=img,
|
||||
num_filters=20,
|
||||
filter_size=5,
|
||||
pool_size=2,
|
||||
pool_stride=2,
|
||||
act='relu')
|
||||
|
||||
conv_pool_2 = fluid.nets.simple_img_conv_pool(
|
||||
input=conv_pool_1,
|
||||
num_filters=50,
|
||||
filter_size=5,
|
||||
pool_size=2,
|
||||
pool_stride=2,
|
||||
act='relu')
|
||||
|
||||
logits = fluid.layers.fc(input=conv_pool_2, size=10, act='softmax')
|
||||
return logits
|
||||
|
||||
|
||||
def main():
|
||||
"""
|
||||
Train the cnn model on mnist datasets
|
||||
"""
|
||||
img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32')
|
||||
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
|
||||
logits = mnist_cnn_model(img)
|
||||
cost = fluid.layers.cross_entropy(input=logits, label=label)
|
||||
avg_cost = fluid.layers.mean(x=cost)
|
||||
optimizer = fluid.optimizer.Adam(learning_rate=0.01)
|
||||
optimizer.minimize(avg_cost)
|
||||
|
||||
accuracy = fluid.evaluator.Accuracy(input=logits, label=label)
|
||||
|
||||
BATCH_SIZE = 50
|
||||
PASS_NUM = 3
|
||||
ACC_THRESHOLD = 0.98
|
||||
LOSS_THRESHOLD = 10.0
|
||||
train_reader = paddle.batch(
|
||||
paddle.reader.shuffle(
|
||||
paddle.dataset.mnist.train(), buf_size=500),
|
||||
batch_size=BATCH_SIZE)
|
||||
|
||||
place = fluid.CPUPlace()
|
||||
exe = fluid.Executor(place)
|
||||
feeder = fluid.DataFeeder(feed_list=[img, label], place=place)
|
||||
exe.run(fluid.default_startup_program())
|
||||
|
||||
for pass_id in range(PASS_NUM):
|
||||
accuracy.reset(exe)
|
||||
for data in train_reader():
|
||||
loss, acc = exe.run(fluid.default_main_program(),
|
||||
feed=feeder.feed(data),
|
||||
fetch_list=[avg_cost] + accuracy.metrics)
|
||||
pass_acc = accuracy.eval(exe)
|
||||
print("pass_id=" + str(pass_id) + " acc=" + str(acc) + " pass_acc="
|
||||
+ str(pass_acc))
|
||||
if loss < LOSS_THRESHOLD and pass_acc > ACC_THRESHOLD:
|
||||
break
|
||||
|
||||
pass_acc = accuracy.eval(exe)
|
||||
print("pass_id=" + str(pass_id) + " pass_acc=" + str(pass_acc))
|
||||
fluid.io.save_params(
|
||||
exe, dirname='./mnist', main_program=fluid.default_main_program())
|
||||
print('train mnist done')
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
@ -0,0 +1,100 @@
|
||||
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
|
||||
#
|
||||
#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.
|
||||
"""
|
||||
FGSM demos on mnist using advbox tool.
|
||||
"""
|
||||
import paddle.v2 as paddle
|
||||
import paddle.v2.fluid as fluid
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
|
||||
from advbox.models.paddle import PaddleModel
|
||||
from advbox.attacks.gradientsign import GradientSignAttack
|
||||
|
||||
|
||||
def cnn_model(img):
|
||||
"""
|
||||
Mnist cnn model
|
||||
Args:
|
||||
img(Varaible): the input image to be recognized
|
||||
Returns:
|
||||
Variable: the label prediction
|
||||
"""
|
||||
#conv1 = fluid.nets.conv2d()
|
||||
conv_pool_1 = fluid.nets.simple_img_conv_pool(
|
||||
input=img,
|
||||
num_filters=20,
|
||||
filter_size=5,
|
||||
pool_size=2,
|
||||
pool_stride=2,
|
||||
act='relu')
|
||||
|
||||
conv_pool_2 = fluid.nets.simple_img_conv_pool(
|
||||
input=conv_pool_1,
|
||||
num_filters=50,
|
||||
filter_size=5,
|
||||
pool_size=2,
|
||||
pool_stride=2,
|
||||
act='relu')
|
||||
|
||||
logits = fluid.layers.fc(input=conv_pool_2, size=10, act='softmax')
|
||||
return logits
|
||||
|
||||
|
||||
def main():
|
||||
"""
|
||||
Advbox demo which demonstrate how to use advbox.
|
||||
"""
|
||||
IMG_NAME = 'img'
|
||||
LABEL_NAME = 'label'
|
||||
|
||||
img = fluid.layers.data(name=IMG_NAME, shape=[1, 28, 28], dtype='float32')
|
||||
# gradient should flow
|
||||
img.stop_gradient = False
|
||||
label = fluid.layers.data(name=LABEL_NAME, shape=[1], dtype='int64')
|
||||
logits = cnn_model(img)
|
||||
cost = fluid.layers.cross_entropy(input=logits, label=label)
|
||||
avg_cost = fluid.layers.mean(x=cost)
|
||||
|
||||
place = fluid.CPUPlace()
|
||||
exe = fluid.Executor(place)
|
||||
|
||||
BATCH_SIZE = 1
|
||||
train_reader = paddle.batch(
|
||||
paddle.reader.shuffle(
|
||||
paddle.dataset.mnist.train(), buf_size=500),
|
||||
batch_size=BATCH_SIZE)
|
||||
feeder = fluid.DataFeeder(
|
||||
feed_list=[IMG_NAME, LABEL_NAME],
|
||||
place=place,
|
||||
program=fluid.default_main_program())
|
||||
|
||||
fluid.io.load_params(
|
||||
exe, "./mnist/", main_program=fluid.default_main_program())
|
||||
|
||||
# advbox demo
|
||||
m = PaddleModel(fluid.default_main_program(), IMG_NAME, LABEL_NAME,
|
||||
logits.name, avg_cost.name, (-1, 1))
|
||||
att = GradientSignAttack(m)
|
||||
for data in train_reader():
|
||||
# fgsm attack
|
||||
adv_img = att(data)
|
||||
plt.imshow(n[0][0], cmap='Greys_r')
|
||||
plt.show()
|
||||
#np.save('adv_img', adv_img)
|
||||
break
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
@ -0,0 +1,78 @@
|
||||
# Cluster Training Benchmark
|
||||
|
||||
## Setup
|
||||
|
||||
- Platform
|
||||
- Kubernetes: v1.6.2
|
||||
- Linux Kernel: v3.10.0
|
||||
|
||||
- Resource
|
||||
- CPU: 10 Cores per Pod
|
||||
- Memory: 5GB per Pod
|
||||
|
||||
- Docker Image
|
||||
|
||||
We use different base Docker Image to run the benchmark on Kubernetes:
|
||||
- PaddlePaddle v2: paddlepaddle/paddle:0.11.0
|
||||
- PaddlePaddle Fluid: paddlepaddle/paddle:[commit-id]
|
||||
- TensorFlow: tensorflow/tensorflow:1.5.0-rc0
|
||||
|
||||
- Model
|
||||
vgg16 is used in this benchmark.
|
||||
|
||||
## Cases
|
||||
|
||||
- Variable
|
||||
- Batch Size of training data.
|
||||
- PServer count of the training job.
|
||||
- The number of trainers.
|
||||
|
||||
- Invariant
|
||||
- The resource of trainer/pserver Pod.
|
||||
|
||||
### Measure the Performance for Different Batch Size
|
||||
|
||||
- PServer Count: 40
|
||||
- Trainer Count: 100
|
||||
- Metrics: mini-batch / sec
|
||||
|
||||
| Batch Size | 32 | 64 | 128 | 256 |
|
||||
| -- | -- | -- | -- | -- |
|
||||
| PaddlePaddle Fluid | - | - | - | - |
|
||||
| PaddlePaddle v2 | - | - | - | - |
|
||||
| TensorFlow | - | - | - | - |
|
||||
|
||||
### Measure the Performance for Different PServer Count
|
||||
|
||||
- Trainer Count: 100
|
||||
- Batch Size: 64
|
||||
- Metrics: mini-batch / sec
|
||||
|
||||
| PServer Count | 10 | 20 | 40 | 60 |
|
||||
| -- | -- | -- | -- | -- |
|
||||
| PaddlePaddle Fluid | - | - | - | - |
|
||||
| PaddlePaddle v2 | - | - | - | - |
|
||||
| TensorFlow | - | - | - | - |
|
||||
|
||||
### Measure Parallel Efficiency By Increasing Trainer Count
|
||||
|
||||
- PServer Count: 20
|
||||
- Batch Size: 64
|
||||
- Metrics:
|
||||
|
||||
$S = \div(T1, TN)$
|
||||
|
||||
which S is the ratio of T1 over TN, training time of 1 and N trainers.
|
||||
The parallel efficiency is:
|
||||
|
||||
$E = \div(S, N)$
|
||||
|
||||
| Trainer Counter | 1 | 10 | 20 | 30 | 40 | 50 | 60 | 70 | 80 | 90 | 100 |
|
||||
| -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- |
|
||||
| PaddlePaddle Fluid | - | - | - | - | - | - | - | - | - | - | - |
|
||||
| PaddlePaddle v2 | - | - | - | - | - | - | - | - | - | - | - | - |
|
||||
| TensorFlow | - | - | - | - | - | - | - | - | - | - | - | - | - |
|
||||
|
||||
## Reproduce the benchmark
|
||||
|
||||
TODO
|
||||
Some files were not shown because too many files have changed in this diff Show More
Loading…
Reference in new issue