/
gen_cc_md.py
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/
gen_cc_md.py
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# Copyright 2015 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.
# ==============================================================================
"""Convert Doxygen .xml files to MarkDown (.md files)."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import re
from BeautifulSoup import BeautifulStoneSoup
import tensorflow as tf
ANCHOR_RE = re.compile(r'\W+')
PAGE_TEMPLATE = '''# `{0} {1}`
{2}
###Member Details
{3}'''
INDEX_TEMPLATE = '''# TensorFlow C++ Session API reference documentation
TensorFlow's public C++ API includes only the API for executing graphs, as of
version 0.5. To control the execution of a graph from C++:
1. Build the computation graph using the [Python API](../python/).
1. Use [`tf.train.write_graph()`](../python/train.md#write_graph) to
write the graph to a file.
1. Load the graph using the C++ Session API. For example:
```c++
// Reads a model graph definition from disk, and creates a session object you
// can use to run it.
Status LoadGraph(string graph_file_name, Session** session) {
GraphDef graph_def;
TF_RETURN_IF_ERROR(
ReadBinaryProto(Env::Default(), graph_file_name, &graph_def));
TF_RETURN_IF_ERROR(NewSession(SessionOptions(), session));
TF_RETURN_IF_ERROR((*session)->Create(graph_def));
return Status::OK();
}
```
1. Run the graph with a call to `session->Run()`
## Env
@@Env
@@RandomAccessFile
@@WritableFile
@@EnvWrapper
## Session
@@Session
@@SessionOptions
## Status
@@Status
@@Status::State
## Tensor
@@Tensor
@@TensorShape
@@TensorShapeDim
@@TensorShapeUtils
@@PartialTensorShape
@@PartialTensorShapeUtils
@@TF_Buffer
## Thread
@@Thread
@@ThreadOptions
'''
FLAGS = tf.flags.FLAGS
tf.flags.DEFINE_string('src_dir', None,
'Directory containing the doxygen output.')
tf.flags.DEFINE_string('out_dir', None,
'Directory to which docs should be written.')
def member_definition(member_elt):
def_text = ''
def_elt = member_elt.find('definition')
if def_elt:
def_text = def_elt.text
return def_text
def member_sig(member_elt):
def_text = member_definition(member_elt)
argstring_text = ''
argstring = member_elt.find('argsstring')
if argstring:
argstring_text = argstring.text
sig = def_text + argstring_text
return sig
def anchorize(name):
return ANCHOR_RE.sub('_', name)
def element_text(member_elt, elt_name):
"""Extract all `para` text from (`elt_name` in) `member_elt`."""
text = []
if elt_name:
elt = member_elt.find(elt_name)
else:
elt = member_elt
if elt:
paras = elt.findAll('para')
for p in paras:
text.append(p.getText(separator=u' ').strip())
return '\n\n'.join(text)
def full_member_entry(member_elt):
"""Generate the description of `member_elt` for "Member Details"."""
anchor = '{#' + anchorize(member_definition(member_elt)) + '}'
full_entry = '#### `%s` %s' % (member_sig(member_elt), anchor)
complete_descr = element_text(member_elt, 'briefdescription') + '\n\n'
complete_descr += element_text(member_elt, 'detaileddescription')
if complete_descr:
full_entry += '\n\n' + complete_descr
return full_entry
def brief_member_entry(member_elt):
"""Generate the description of `member_elt` for the "Member Summary"."""
brief_item = ''
brief_descr = element_text(member_elt, 'briefdescription')
if brief_descr:
brief_item = '\n * ' + brief_descr
sig = member_sig(member_elt)
memdef = member_definition(member_elt)
linkified_sig = '[`{0}`](#{1})'.format(sig, anchorize(memdef))
return '* ' + linkified_sig + brief_item
def all_briefs(members):
briefs = [brief_member_entry(member_elt) for member_elt in members]
return '\n'.join(briefs)
def all_fulls(members):
fulls = [full_member_entry(member_elt) for member_elt in members]
return '\n\n'.join(fulls)
def page_overview(class_elt):
"""Returns the contents of the .md file for `class_elt`."""
overview_brief = ''
overview_details = ''
briefs = class_elt.findAll('briefdescription', recursive=False)
if briefs:
overview_brief = element_text(briefs[0], None)
details = class_elt.findAll('detaileddescription', recursive=False)
if details:
overview_details = element_text(details[0], None)
return overview_brief + '\n\n' + overview_details
def page_with_name(pages, name):
def match(n):
for i in xrange(len(pages)):
if pages[i].get_name() == n:
return i
return None
return match(name) or match('tensorflow::' + name)
def get_all_indexed_pages():
all_pages = set()
lines = INDEX_TEMPLATE.split('\n')
for i in range(len(lines)):
if lines[i].startswith('@@'):
name = lines[i][2:]
all_pages.add(name)
return all_pages
def index_page(pages):
"""Create the index page linking to `pages` using INDEX_TEMPLATE."""
pages = pages[:]
lines = INDEX_TEMPLATE.split('\n')
all_md_files = []
for i in range(len(lines)):
if lines[i].startswith('@@'):
name = lines[i][2:]
page_index = page_with_name(pages, name)
if page_index is None:
raise ValueError('Missing page with name: ' + name)
lines[i] = '* [{0}]({1})'.format(
pages[page_index].get_name(), pages[page_index].get_md_filename())
all_md_files.append(pages[page_index].get_md_filename())
pages.pop(page_index)
return '\n'.join(lines)
def page_in_name_list(page, names):
for name in names:
if page.get_name() == name or page.get_name() == 'tensorflow::' + name:
return True
return False
class Page(object):
"""Holds the MarkDown converted contents of a .xml page."""
def __init__(self, xml_path, deftype):
self.type = deftype
xml_file = open(xml_path)
xml = xml_file.read()
xml = xml.replace('<computeroutput>', '`').replace('</computeroutput>', '`')
# TODO(josh11b): Should not use HTML entities inside ```...```.
soup = BeautifulStoneSoup(
xml, convertEntities=BeautifulStoneSoup.HTML_ENTITIES)
self.name = soup.find('compoundname').text
print('Making page with name ' + self.name + ' (from ' + xml_path + ')')
members = soup('memberdef', prot='public')
fulls = all_fulls(members)
self.overview = page_overview(soup.find('compounddef'))
self.page_text = PAGE_TEMPLATE.format(
self.type, self.name, self.overview, fulls)
def get_text(self):
return self.page_text
def get_name(self):
return self.name
def get_short_name(self):
parse = self.get_name().split('::')
return parse[len(parse)-1]
def get_type(self):
return self.type
def get_md_filename(self):
capitalized_type = self.get_type()[0].upper() + self.get_type()[1:]
return capitalized_type + anchorize(self.get_short_name()) + '.md'
def main(unused_argv):
print('Converting in ' + FLAGS.src_dir)
pages = []
all_pages = get_all_indexed_pages()
xml_files = os.listdir(FLAGS.src_dir)
for fname in xml_files:
if len(fname) < 6: continue
newpage = None
if fname[0:5] == 'class':
newpage = Page(os.path.join(FLAGS.src_dir, fname), 'class')
elif fname[0:6] == 'struct':
newpage = Page(os.path.join(FLAGS.src_dir, fname), 'struct')
if newpage is not None and page_in_name_list(newpage, all_pages):
pages.append(newpage)
md_filename = newpage.get_md_filename()
print('Writing ' + md_filename)
md_file = open(os.path.join(FLAGS.out_dir, md_filename), 'w')
print(newpage.get_text(), file=md_file)
index_text = index_page(pages)
index_md_file = open(os.path.join(FLAGS.out_dir, 'index.md'), 'w')
print(index_text, file=index_md_file)
return 0
if __name__ == '__main__':
tf.app.run()