partition stringclasses 3
values | func_name stringlengths 1 134 | docstring stringlengths 1 46.9k | path stringlengths 4 223 | original_string stringlengths 75 104k | code stringlengths 75 104k | docstring_tokens listlengths 1 1.97k | repo stringlengths 7 55 | language stringclasses 1
value | url stringlengths 87 315 | code_tokens listlengths 19 28.4k | sha stringlengths 40 40 |
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train | ClientRequest.update_headers | Update request headers. | aiohttp/client_reqrep.py | def update_headers(self, headers: Optional[LooseHeaders]) -> None:
"""Update request headers."""
self.headers = CIMultiDict() # type: CIMultiDict[str]
# add host
netloc = cast(str, self.url.raw_host)
if helpers.is_ipv6_address(netloc):
netloc = '[{}]'.format(netloc)... | def update_headers(self, headers: Optional[LooseHeaders]) -> None:
"""Update request headers."""
self.headers = CIMultiDict() # type: CIMultiDict[str]
# add host
netloc = cast(str, self.url.raw_host)
if helpers.is_ipv6_address(netloc):
netloc = '[{}]'.format(netloc)... | [
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train | ClientRequest.update_cookies | Update request cookies header. | aiohttp/client_reqrep.py | def update_cookies(self, cookies: Optional[LooseCookies]) -> None:
"""Update request cookies header."""
if not cookies:
return
c = SimpleCookie()
if hdrs.COOKIE in self.headers:
c.load(self.headers.get(hdrs.COOKIE, ''))
del self.headers[hdrs.COOKIE]
... | def update_cookies(self, cookies: Optional[LooseCookies]) -> None:
"""Update request cookies header."""
if not cookies:
return
c = SimpleCookie()
if hdrs.COOKIE in self.headers:
c.load(self.headers.get(hdrs.COOKIE, ''))
del self.headers[hdrs.COOKIE]
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train | ClientRequest.update_content_encoding | Set request content encoding. | aiohttp/client_reqrep.py | def update_content_encoding(self, data: Any) -> None:
"""Set request content encoding."""
if not data:
return
enc = self.headers.get(hdrs.CONTENT_ENCODING, '').lower()
if enc:
if self.compress:
raise ValueError(
'compress can n... | def update_content_encoding(self, data: Any) -> None:
"""Set request content encoding."""
if not data:
return
enc = self.headers.get(hdrs.CONTENT_ENCODING, '').lower()
if enc:
if self.compress:
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train | ClientRequest.update_transfer_encoding | Analyze transfer-encoding header. | aiohttp/client_reqrep.py | def update_transfer_encoding(self) -> None:
"""Analyze transfer-encoding header."""
te = self.headers.get(hdrs.TRANSFER_ENCODING, '').lower()
if 'chunked' in te:
if self.chunked:
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"""Analyze transfer-encoding header."""
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train | ClientRequest.update_auth | Set basic auth. | aiohttp/client_reqrep.py | def update_auth(self, auth: Optional[BasicAuth]) -> None:
"""Set basic auth."""
if auth is None:
auth = self.auth
if auth is None:
return
if not isinstance(auth, helpers.BasicAuth):
raise TypeError('BasicAuth() tuple is required instead')
sel... | def update_auth(self, auth: Optional[BasicAuth]) -> None:
"""Set basic auth."""
if auth is None:
auth = self.auth
if auth is None:
return
if not isinstance(auth, helpers.BasicAuth):
raise TypeError('BasicAuth() tuple is required instead')
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train | ClientRequest.write_bytes | Support coroutines that yields bytes objects. | aiohttp/client_reqrep.py | async def write_bytes(self, writer: AbstractStreamWriter,
conn: 'Connection') -> None:
"""Support coroutines that yields bytes objects."""
# 100 response
if self._continue is not None:
await writer.drain()
await self._continue
protocol =... | async def write_bytes(self, writer: AbstractStreamWriter,
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"""Support coroutines that yields bytes objects."""
# 100 response
if self._continue is not None:
await writer.drain()
await self._continue
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train | ClientResponse.start | Start response processing. | aiohttp/client_reqrep.py | async def start(self, connection: 'Connection') -> 'ClientResponse':
"""Start response processing."""
self._closed = False
self._protocol = connection.protocol
self._connection = connection
with self._timer:
while True:
# read response
... | async def start(self, connection: 'Connection') -> 'ClientResponse':
"""Start response processing."""
self._closed = False
self._protocol = connection.protocol
self._connection = connection
with self._timer:
while True:
# read response
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train | ClientResponse.read | Read response payload. | aiohttp/client_reqrep.py | async def read(self) -> bytes:
"""Read response payload."""
if self._body is None:
try:
self._body = await self.content.read()
for trace in self._traces:
await trace.send_response_chunk_received(self._body)
except BaseException:... | async def read(self) -> bytes:
"""Read response payload."""
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await trace.send_response_chunk_received(self._body)
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train | ClientResponse.text | Read response payload and decode. | aiohttp/client_reqrep.py | async def text(self,
encoding: Optional[str]=None, errors: str='strict') -> str:
"""Read response payload and decode."""
if self._body is None:
await self.read()
if encoding is None:
encoding = self.get_encoding()
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train | ClientResponse.json | Read and decodes JSON response. | aiohttp/client_reqrep.py | async def json(self, *, encoding: str=None,
loads: JSONDecoder=DEFAULT_JSON_DECODER,
content_type: Optional[str]='application/json') -> Any:
"""Read and decodes JSON response."""
if self._body is None:
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... | async def json(self, *, encoding: str=None,
loads: JSONDecoder=DEFAULT_JSON_DECODER,
content_type: Optional[str]='application/json') -> Any:
"""Read and decodes JSON response."""
if self._body is None:
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train | StreamResponse.enable_chunked_encoding | Enables automatic chunked transfer encoding. | aiohttp/web_response.py | def enable_chunked_encoding(self, chunk_size: Optional[int]=None) -> None:
"""Enables automatic chunked transfer encoding."""
self._chunked = True
if hdrs.CONTENT_LENGTH in self._headers:
raise RuntimeError("You can't enable chunked encoding when "
"a ... | def enable_chunked_encoding(self, chunk_size: Optional[int]=None) -> None:
"""Enables automatic chunked transfer encoding."""
self._chunked = True
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raise RuntimeError("You can't enable chunked encoding when "
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train | StreamResponse.enable_compression | Enables response compression encoding. | aiohttp/web_response.py | def enable_compression(self,
force: Optional[Union[bool, ContentCoding]]=None
) -> None:
"""Enables response compression encoding."""
# Backwards compatibility for when force was a bool <0.17.
if type(force) == bool:
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# Backwards compatibility for when force was a bool <0.17.
if type(force) == bool:
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train | StreamResponse.set_cookie | Set or update response cookie.
Sets new cookie or updates existent with new value.
Also updates only those params which are not None. | aiohttp/web_response.py | def set_cookie(self, name: str, value: str, *,
expires: Optional[str]=None,
domain: Optional[str]=None,
max_age: Optional[Union[int, str]]=None,
path: str='/',
secure: Optional[str]=None,
httponly: Optional... | def set_cookie(self, name: str, value: str, *,
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domain: Optional[str]=None,
max_age: Optional[Union[int, str]]=None,
path: str='/',
secure: Optional[str]=None,
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train | StreamResponse.del_cookie | Delete cookie.
Creates new empty expired cookie. | aiohttp/web_response.py | def del_cookie(self, name: str, *,
domain: Optional[str]=None,
path: str='/') -> None:
"""Delete cookie.
Creates new empty expired cookie.
"""
# TODO: do we need domain/path here?
self._cookies.pop(name, None)
self.set_cookie(name, '... | def del_cookie(self, name: str, *,
domain: Optional[str]=None,
path: str='/') -> None:
"""Delete cookie.
Creates new empty expired cookie.
"""
# TODO: do we need domain/path here?
self._cookies.pop(name, None)
self.set_cookie(name, '... | [
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train | StreamResponse.last_modified | The value of Last-Modified HTTP header, or None.
This header is represented as a `datetime` object. | aiohttp/web_response.py | def last_modified(self) -> Optional[datetime.datetime]:
"""The value of Last-Modified HTTP header, or None.
This header is represented as a `datetime` object.
"""
httpdate = self._headers.get(hdrs.LAST_MODIFIED)
if httpdate is not None:
timetuple = parsedate(httpdate... | def last_modified(self) -> Optional[datetime.datetime]:
"""The value of Last-Modified HTTP header, or None.
This header is represented as a `datetime` object.
"""
httpdate = self._headers.get(hdrs.LAST_MODIFIED)
if httpdate is not None:
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train | _default_expect_handler | Default handler for Expect header.
Just send "100 Continue" to client.
raise HTTPExpectationFailed if value of header is not "100-continue" | aiohttp/web_urldispatcher.py | async def _default_expect_handler(request: Request) -> None:
"""Default handler for Expect header.
Just send "100 Continue" to client.
raise HTTPExpectationFailed if value of header is not "100-continue"
"""
expect = request.headers.get(hdrs.EXPECT)
if request.version == HttpVersion11:
... | async def _default_expect_handler(request: Request) -> None:
"""Default handler for Expect header.
Just send "100 Continue" to client.
raise HTTPExpectationFailed if value of header is not "100-continue"
"""
expect = request.headers.get(hdrs.EXPECT)
if request.version == HttpVersion11:
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train | ResourceRoute.url_for | Construct url for route with additional params. | aiohttp/web_urldispatcher.py | def url_for(self, *args: str, **kwargs: str) -> URL:
"""Construct url for route with additional params."""
return self._resource.url_for(*args, **kwargs) | def url_for(self, *args: str, **kwargs: str) -> URL:
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train | UrlDispatcher.add_static | Add static files view.
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name: Optional[str]=None,
expect_handler: Optional[_ExpectHandler]=None,
chunk_size: int=256 * 1024,
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train | UrlDispatcher.add_head | Shortcut for add_route with method HEAD | aiohttp/web_urldispatcher.py | def add_head(self, path: str, handler: _WebHandler,
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Shortcut for add_route with method HEAD
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Shortcut for add_route with method HEAD
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train | UrlDispatcher.add_options | Shortcut for add_route with method OPTIONS | aiohttp/web_urldispatcher.py | def add_options(self, path: str, handler: _WebHandler,
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Shortcut for add_route with method OPTIONS
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train | UrlDispatcher.add_get | Shortcut for add_route with method GET, if allow_head is true another
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train | UrlDispatcher.add_post | Shortcut for add_route with method POST | aiohttp/web_urldispatcher.py | def add_post(self, path: str, handler: _WebHandler,
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train | UrlDispatcher.add_put | Shortcut for add_route with method PUT | aiohttp/web_urldispatcher.py | def add_put(self, path: str, handler: _WebHandler,
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Shortcut for add_route with method PUT
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Shortcut for add_route with method PUT
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train | UrlDispatcher.add_patch | Shortcut for add_route with method PATCH | aiohttp/web_urldispatcher.py | def add_patch(self, path: str, handler: _WebHandler,
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Shortcut for add_route with method PATCH
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Shortcut for add_route with method PATCH
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train | UrlDispatcher.add_delete | Shortcut for add_route with method DELETE | aiohttp/web_urldispatcher.py | def add_delete(self, path: str, handler: _WebHandler,
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Shortcut for add_route with method DELETE
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Shortcut for add_route with method DELETE
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train | UrlDispatcher.add_view | Shortcut for add_route with ANY methods for a class-based view | aiohttp/web_urldispatcher.py | def add_view(self, path: str, handler: AbstractView,
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"""
Shortcut for add_route with ANY methods for a class-based view
"""
return self.add_route(hdrs.METH_ANY, path, handler, **kwargs) | def add_view(self, path: str, handler: AbstractView,
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Shortcut for add_route with ANY methods for a class-based view
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train | UrlDispatcher.add_routes | Append routes to route table.
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train | HttpParser.parse_headers | Parses RFC 5322 headers from a stream.
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train | ClientWebSocketResponse.get_extra_info | extra info from connection transport | aiohttp/client_ws.py | def get_extra_info(self, name: str, default: Any=None) -> Any:
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train | user_agent | Returns an apparently legit user-agent, if not requested one of a specific
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train | BaseParser.pq | `PyQuery <https://pythonhosted.org/pyquery/>`_ representation
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"""`PyQuery <https://pythonhosted.org/pyquery/>`_ representation
of the :class:`Element <Element>` or :class:`HTML <HTML>`.
"""
if self._pq is None:
self._pq = PyQuery(self.lxml)
return self._pq | def pq(self) -> PyQuery:
"""`PyQuery <https://pythonhosted.org/pyquery/>`_ representation
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"""
if self._pq is None:
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train | BaseParser.lxml | `lxml <http://lxml.de>`_ representation of the
:class:`Element <Element>` or :class:`HTML <HTML>`. | requests_html.py | def lxml(self) -> HtmlElement:
"""`lxml <http://lxml.de>`_ representation of the
:class:`Element <Element>` or :class:`HTML <HTML>`.
"""
if self._lxml is None:
try:
self._lxml = soup_parse(self.html, features='html.parser')
except ValueError:
... | def lxml(self) -> HtmlElement:
"""`lxml <http://lxml.de>`_ representation of the
:class:`Element <Element>` or :class:`HTML <HTML>`.
"""
if self._lxml is None:
try:
self._lxml = soup_parse(self.html, features='html.parser')
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train | BaseParser.find | Given a CSS Selector, returns a list of
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:param clean: Whether or not to sanitize the found HTML of ``<script>`` and ``<style>`` tags.
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train | BaseParser.xpath | Given an XPath selector, returns a list of
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:param clean: Whether or not to sanitize the found HTML of ``<script>`` and ``<style>`` tags.
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"""Given an XPath selector, returns a list of
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train | BaseParser.search_all | Search the :class:`Element <Element>` (multiple times) for the given parse
template.
:param template: The Parse template to use. | requests_html.py | def search_all(self, template: str) -> _Result:
"""Search the :class:`Element <Element>` (multiple times) for the given parse
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:param template: The Parse template to use.
"""
return [r for r in findall(template, self.html)] | def search_all(self, template: str) -> _Result:
"""Search the :class:`Element <Element>` (multiple times) for the given parse
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train | BaseParser.links | All found links on page, in as–is form. | requests_html.py | def links(self) -> _Links:
"""All found links on page, in as–is form."""
def gen():
for link in self.find('a'):
try:
href = link.attrs['href'].strip()
if href and not (href.startswith('#') and self.skip_anchors) and not href.startswit... | def links(self) -> _Links:
"""All found links on page, in as–is form."""
def gen():
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try:
href = link.attrs['href'].strip()
if href and not (href.startswith('#') and self.skip_anchors) and not href.startswit... | [
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train | BaseParser._make_absolute | Makes a given link absolute. | requests_html.py | def _make_absolute(self, link):
"""Makes a given link absolute."""
# Parse the link with stdlib.
parsed = urlparse(link)._asdict()
# If link is relative, then join it with base_url.
if not parsed['netloc']:
return urljoin(self.base_url, link)
# Link is abso... | def _make_absolute(self, link):
"""Makes a given link absolute."""
# Parse the link with stdlib.
parsed = urlparse(link)._asdict()
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train | BaseParser.absolute_links | All found links on page, in absolute form
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"""All found links on page, in absolute form
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"""
def gen():
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def gen():
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train | BaseParser.base_url | The base URL for the page. Supports the ``<base>`` tag
(`learn more <https://www.w3schools.com/tags/tag_base.asp>`_). | requests_html.py | def base_url(self) -> _URL:
"""The base URL for the page. Supports the ``<base>`` tag
(`learn more <https://www.w3schools.com/tags/tag_base.asp>`_)."""
# Support for <base> tag.
base = self.find('base', first=True)
if base:
result = base.attrs.get('href', '').strip()... | def base_url(self) -> _URL:
"""The base URL for the page. Supports the ``<base>`` tag
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# Support for <base> tag.
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train | Element.attrs | Returns a dictionary of the attributes of the :class:`Element <Element>`
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"""
if self._attrs is None:
self._attrs = {k: v for k, v in self.element.items()}
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if self._attrs is None:
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train | HTML.next | Attempts to find the next page, if there is one. If ``fetch``
is ``True`` (default), returns :class:`HTML <HTML>` object of
next page. If ``fetch`` is ``False``, simply returns the next URL. | requests_html.py | def next(self, fetch: bool = False, next_symbol: _NextSymbol = DEFAULT_NEXT_SYMBOL) -> _Next:
"""Attempts to find the next page, if there is one. If ``fetch``
is ``True`` (default), returns :class:`HTML <HTML>` object of
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train | HTML._async_render | Handle page creation and js rendering. Internal use for render/arender methods. | requests_html.py | async def _async_render(self, *, url: str, script: str = None, scrolldown, sleep: int, wait: float, reload, content: Optional[str], timeout: Union[float, int], keep_page: bool):
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train | HTML.render | Reloads the response in Chromium, and replaces HTML content
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train | BaseSession.response_hook | Change response enconding and replace it by a HTMLResponse. | requests_html.py | def response_hook(self, response, **kwargs) -> HTMLResponse:
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response.encoding = DEFAULT_ENCODING
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train | add_depth_channel | img_tensor: N, C, H, W | examples/trials/kaggle-tgs-salt/loader.py | def add_depth_channel(img_tensor, pad_mode):
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train | DotAttention.get_pre_compute | :param s: [src_sequence, batch_size, src_dim]
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train | DotAttention.get_prob | :param s: [src_sequence_length, batch_size, src_dim]
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train | DotAttention.get_att | :param s: [src_sequence_length, batch_size, src_dim]
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train | shape | Get shape of variable.
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'''
Get shape of variable.
Return type is tuple.
'''
temp_s = tensor.get_shape()
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train | get_variable | Get variable by name. | examples/trials/weight_sharing/ga_squad/util.py | def get_variable(name, temp_s):
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Get variable by name.
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Get variable by name.
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train | dropout | Dropout except test. | examples/trials/weight_sharing/ga_squad/util.py | def dropout(tensor, drop_prob, is_training):
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train | Timer.get_elapsed | Calculate time span. | examples/trials/weight_sharing/ga_squad/util.py | def get_elapsed(self, restart=True):
'''
Calculate time span.
'''
end = time.time()
span = end - self.__start
if restart:
self.__start = end
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end = time.time()
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train | do_tta_predict | return 18000x128x128 np array | examples/trials/kaggle-tgs-salt/predict.py | def do_tta_predict(args, model, ckp_path, tta_num=4):
'''
return 18000x128x128 np array
'''
model.eval()
preds = []
meta = None
# i is tta index, 0: no change, 1: horizon flip, 2: vertical flip, 3: do both
for flip_index in range(tta_num):
print('flip_index:', flip_index)
... | def do_tta_predict(args, model, ckp_path, tta_num=4):
'''
return 18000x128x128 np array
'''
model.eval()
preds = []
meta = None
# i is tta index, 0: no change, 1: horizon flip, 2: vertical flip, 3: do both
for flip_index in range(tta_num):
print('flip_index:', flip_index)
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train | partition_dataset | Partitioning MNIST | examples/trials/mnist-distributed-pytorch/dist_mnist.py | def partition_dataset():
""" Partitioning MNIST """
dataset = datasets.MNIST(
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train=True,
download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))
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size = dist.get_world_size... | def partition_dataset():
""" Partitioning MNIST """
dataset = datasets.MNIST(
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train=True,
download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))
]))
size = dist.get_world_size... | [
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train | average_gradients | Gradient averaging. | examples/trials/mnist-distributed-pytorch/dist_mnist.py | def average_gradients(model):
""" Gradient averaging. """
size = float(dist.get_world_size())
for param in model.parameters():
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param.grad.data /= size | def average_gradients(model):
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size = float(dist.get_world_size())
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dist.all_reduce(param.grad.data, op=dist.reduce_op.SUM, group=0)
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train | run | Distributed Synchronous SGD Example | examples/trials/mnist-distributed-pytorch/dist_mnist.py | def run(params):
""" Distributed Synchronous SGD Example """
rank = dist.get_rank()
torch.manual_seed(1234)
train_set, bsz = partition_dataset()
model = Net()
model = model
optimizer = optim.SGD(model.parameters(), lr=params['learning_rate'], momentum=params['momentum'])
num_batches = c... | def run(params):
""" Distributed Synchronous SGD Example """
rank = dist.get_rank()
torch.manual_seed(1234)
train_set, bsz = partition_dataset()
model = Net()
model = model
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train | graph_loads | Load graph | examples/trials/ga_squad/graph.py | def graph_loads(graph_json):
'''
Load graph
'''
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Load graph
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train | Layer.set_size | Set size. | examples/trials/ga_squad/graph.py | def set_size(self, graph_id, size):
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train | Layer.clear_size | Clear size | examples/trials/ga_squad/graph.py | def clear_size(self):
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Clear size
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train | Graph.is_topology | valid the topology | examples/trials/ga_squad/graph.py | def is_topology(self, layers=None):
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valid the topology
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valid the topology
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train | Graph.is_legal | Judge whether is legal for layers | examples/trials/ga_squad/graph.py | def is_legal(self, layers=None):
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Judge whether is legal for layers
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train | Graph.mutation | Mutation for a graph | examples/trials/ga_squad/graph.py | def mutation(self, only_add=False):
'''
Mutation for a graph
'''
types = []
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types.append(0)
types.append(1)
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Mutation for a graph
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train | SMACTuner._main_cli | Main function of SMAC for CLI interface
Returns
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instance
optimizer | src/sdk/pynni/nni/smac_tuner/smac_tuner.py | def _main_cli(self):
"""Main function of SMAC for CLI interface
Returns
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instance
optimizer
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self.logger.info("SMAC call: %s" % (" ".join(sys.argv)))
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args, _ = cmd_reader.read_cmd()
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Returns
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instance
optimizer
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train | SMACTuner.update_search_space | TODO: this is urgly, we put all the initialization work in this method, because initialization relies
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NOTE: updating search space is not supported.
Parameters
----------
search_space:
searc... | src/sdk/pynni/nni/smac_tuner/smac_tuner.py | def update_search_space(self, search_space):
"""TODO: this is urgly, we put all the initialization work in this method, because initialization relies
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NOTE: updating search space is not supported.
Parameters
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"""TODO: this is urgly, we put all the initialization work in this method, because initialization relies
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train | SMACTuner.receive_trial_result | receive_trial_result
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parameter_id: int
parameter id
parameters:
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value:
value
Raises
------
RuntimeError
Received parameter id not in total_data | src/sdk/pynni/nni/smac_tuner/smac_tuner.py | def receive_trial_result(self, parameter_id, parameters, value):
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Parameters
----------
parameter_id: int
parameter id
parameters:
parameters
value:
value
Raises
------
Run... | def receive_trial_result(self, parameter_id, parameters, value):
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parameter id
parameters:
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value:
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train | SMACTuner.convert_loguniform_categorical | Convert the values of type `loguniform` back to their initial range
Also, we convert categorical:
categorical values in search space are changed to list of numbers before,
those original values will be changed back in this function
Parameters
----------
challenge... | src/sdk/pynni/nni/smac_tuner/smac_tuner.py | def convert_loguniform_categorical(self, challenger_dict):
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Also, we convert categorical:
categorical values in search space are changed to list of numbers before,
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train | SMACTuner.generate_parameters | generate one instance of hyperparameters
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parameter_id: int
parameter id
Returns
-------
list
new generated parameters | src/sdk/pynni/nni/smac_tuner/smac_tuner.py | def generate_parameters(self, parameter_id):
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Parameters
----------
parameter_id: int
parameter id
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list
new generated parameters
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parameter_id: int
parameter id
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list
new generated parameters
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train | SMACTuner.generate_multiple_parameters | generate mutiple instances of hyperparameters
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list of new generated parameters | src/sdk/pynni/nni/smac_tuner/smac_tuner.py | def generate_multiple_parameters(self, parameter_id_list):
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train | lovasz_grad | Computes gradient of the Lovasz extension w.r.t sorted errors
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Computes gradient of the Lovasz extension w.r.t sorted errors
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train | iou_binary | IoU for foreground class
binary: 1 foreground, 0 background | examples/trials/kaggle-tgs-salt/lovasz_losses.py | def iou_binary(preds, labels, EMPTY=1., ignore=None, per_image=True):
"""
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binary: 1 foreground, 0 background
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train | iou | Array of IoU for each (non ignored) class | examples/trials/kaggle-tgs-salt/lovasz_losses.py | def iou(preds, labels, C, EMPTY=1., ignore=None, per_image=False):
"""
Array of IoU for each (non ignored) class
"""
if not per_image:
preds, labels = (preds,), (labels,)
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iou = []
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"""
Array of IoU for each (non ignored) class
"""
if not per_image:
preds, labels = (preds,), (labels,)
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train | lovasz_hinge | Binary Lovasz hinge loss
logits: [B, H, W] Variable, logits at each pixel (between -\infty and +\infty)
labels: [B, H, W] Tensor, binary ground truth masks (0 or 1)
per_image: compute the loss per image instead of per batch
ignore: void class id | examples/trials/kaggle-tgs-salt/lovasz_losses.py | def lovasz_hinge(logits, labels, per_image=True, ignore=None):
"""
Binary Lovasz hinge loss
logits: [B, H, W] Variable, logits at each pixel (between -\infty and +\infty)
labels: [B, H, W] Tensor, binary ground truth masks (0 or 1)
per_image: compute the loss per image instead of per batch
... | def lovasz_hinge(logits, labels, per_image=True, ignore=None):
"""
Binary Lovasz hinge loss
logits: [B, H, W] Variable, logits at each pixel (between -\infty and +\infty)
labels: [B, H, W] Tensor, binary ground truth masks (0 or 1)
per_image: compute the loss per image instead of per batch
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train | lovasz_hinge_flat | Binary Lovasz hinge loss
logits: [P] Variable, logits at each prediction (between -\infty and +\infty)
labels: [P] Tensor, binary ground truth labels (0 or 1)
ignore: label to ignore | examples/trials/kaggle-tgs-salt/lovasz_losses.py | def lovasz_hinge_flat(logits, labels):
"""
Binary Lovasz hinge loss
logits: [P] Variable, logits at each prediction (between -\infty and +\infty)
labels: [P] Tensor, binary ground truth labels (0 or 1)
ignore: label to ignore
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# only void pixels, the gra... | def lovasz_hinge_flat(logits, labels):
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Binary Lovasz hinge loss
logits: [P] Variable, logits at each prediction (between -\infty and +\infty)
labels: [P] Tensor, binary ground truth labels (0 or 1)
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train | flatten_binary_scores | Flattens predictions in the batch (binary case)
Remove labels equal to 'ignore' | examples/trials/kaggle-tgs-salt/lovasz_losses.py | def flatten_binary_scores(scores, labels, ignore=None):
"""
Flattens predictions in the batch (binary case)
Remove labels equal to 'ignore'
"""
scores = scores.view(-1)
labels = labels.view(-1)
if ignore is None:
return scores, labels
valid = (labels != ignore)
vscores = scor... | def flatten_binary_scores(scores, labels, ignore=None):
"""
Flattens predictions in the batch (binary case)
Remove labels equal to 'ignore'
"""
scores = scores.view(-1)
labels = labels.view(-1)
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return scores, labels
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train | binary_xloss | Binary Cross entropy loss
logits: [B, H, W] Variable, logits at each pixel (between -\infty and +\infty)
labels: [B, H, W] Tensor, binary ground truth masks (0 or 1)
ignore: void class id | examples/trials/kaggle-tgs-salt/lovasz_losses.py | def binary_xloss(logits, labels, ignore=None):
"""
Binary Cross entropy loss
logits: [B, H, W] Variable, logits at each pixel (between -\infty and +\infty)
labels: [B, H, W] Tensor, binary ground truth masks (0 or 1)
ignore: void class id
"""
logits, labels = flatten_binary_scores(logi... | def binary_xloss(logits, labels, ignore=None):
"""
Binary Cross entropy loss
logits: [B, H, W] Variable, logits at each pixel (between -\infty and +\infty)
labels: [B, H, W] Tensor, binary ground truth masks (0 or 1)
ignore: void class id
"""
logits, labels = flatten_binary_scores(logi... | [
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train | lovasz_softmax | Multi-class Lovasz-Softmax loss
probas: [B, C, H, W] Variable, class probabilities at each prediction (between 0 and 1)
labels: [B, H, W] Tensor, ground truth labels (between 0 and C - 1)
only_present: average only on classes present in ground truth
per_image: compute the loss per image instead ... | examples/trials/kaggle-tgs-salt/lovasz_losses.py | def lovasz_softmax(probas, labels, only_present=False, per_image=False, ignore=None):
"""
Multi-class Lovasz-Softmax loss
probas: [B, C, H, W] Variable, class probabilities at each prediction (between 0 and 1)
labels: [B, H, W] Tensor, ground truth labels (between 0 and C - 1)
only_present: av... | def lovasz_softmax(probas, labels, only_present=False, per_image=False, ignore=None):
"""
Multi-class Lovasz-Softmax loss
probas: [B, C, H, W] Variable, class probabilities at each prediction (between 0 and 1)
labels: [B, H, W] Tensor, ground truth labels (between 0 and C - 1)
only_present: av... | [
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train | lovasz_softmax_flat | Multi-class Lovasz-Softmax loss
probas: [P, C] Variable, class probabilities at each prediction (between 0 and 1)
labels: [P] Tensor, ground truth labels (between 0 and C - 1)
only_present: average only on classes present in ground truth | examples/trials/kaggle-tgs-salt/lovasz_losses.py | def lovasz_softmax_flat(probas, labels, only_present=False):
"""
Multi-class Lovasz-Softmax loss
probas: [P, C] Variable, class probabilities at each prediction (between 0 and 1)
labels: [P] Tensor, ground truth labels (between 0 and C - 1)
only_present: average only on classes present in grou... | def lovasz_softmax_flat(probas, labels, only_present=False):
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Multi-class Lovasz-Softmax loss
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labels: [P] Tensor, ground truth labels (between 0 and C - 1)
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train | flatten_probas | Flattens predictions in the batch | examples/trials/kaggle-tgs-salt/lovasz_losses.py | def flatten_probas(probas, labels, ignore=None):
"""
Flattens predictions in the batch
"""
B, C, H, W = probas.size()
probas = probas.permute(0, 2, 3, 1).contiguous().view(-1, C) # B * H * W, C = P, C
labels = labels.view(-1)
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valid = (lab... | def flatten_probas(probas, labels, ignore=None):
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Flattens predictions in the batch
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probas = probas.permute(0, 2, 3, 1).contiguous().view(-1, C) # B * H * W, C = P, C
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if ignore is None:
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train | xloss | Cross entropy loss | examples/trials/kaggle-tgs-salt/lovasz_losses.py | def xloss(logits, labels, ignore=None):
"""
Cross entropy loss
"""
return F.cross_entropy(logits, Variable(labels), ignore_index=255) | def xloss(logits, labels, ignore=None):
"""
Cross entropy loss
"""
return F.cross_entropy(logits, Variable(labels), ignore_index=255) | [
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train | mean | nanmean compatible with generators. | examples/trials/kaggle-tgs-salt/lovasz_losses.py | def mean(l, ignore_nan=False, empty=0):
"""
nanmean compatible with generators.
"""
l = iter(l)
if ignore_nan:
l = ifilterfalse(np.isnan, l)
try:
n = 1
acc = next(l)
except StopIteration:
if empty == 'raise':
raise ValueError('Empty mean')
... | def mean(l, ignore_nan=False, empty=0):
"""
nanmean compatible with generators.
"""
l = iter(l)
if ignore_nan:
l = ifilterfalse(np.isnan, l)
try:
n = 1
acc = next(l)
except StopIteration:
if empty == 'raise':
raise ValueError('Empty mean')
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train | main_loop | main loop logic for trial keeper | tools/nni_trial_tool/trial_keeper.py | def main_loop(args):
'''main loop logic for trial keeper'''
if not os.path.exists(LOG_DIR):
os.makedirs(LOG_DIR)
stdout_file = open(STDOUT_FULL_PATH, 'a+')
stderr_file = open(STDERR_FULL_PATH, 'a+')
trial_keeper_syslogger = RemoteLogger(args.nnimanager_ip, args.nnimanager_port, 'tr... | def main_loop(args):
'''main loop logic for trial keeper'''
if not os.path.exists(LOG_DIR):
os.makedirs(LOG_DIR)
stdout_file = open(STDOUT_FULL_PATH, 'a+')
stderr_file = open(STDERR_FULL_PATH, 'a+')
trial_keeper_syslogger = RemoteLogger(args.nnimanager_ip, args.nnimanager_port, 'tr... | [
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train | ShuffleBlock.forward | Channel shuffle: [N,C,H,W] -> [N,g,C/g,H,W] -> [N,C/g,g,H,w] -> [N,C,H,W] | examples/trials/cifar10_pytorch/models/shufflenet.py | def forward(self, x):
'''Channel shuffle: [N,C,H,W] -> [N,g,C/g,H,W] -> [N,C/g,g,H,w] -> [N,C,H,W]'''
N,C,H,W = x.size()
g = self.groups
return x.view(N,g,C/g,H,W).permute(0,2,1,3,4).contiguous().view(N,C,H,W) | def forward(self, x):
'''Channel shuffle: [N,C,H,W] -> [N,g,C/g,H,W] -> [N,C/g,g,H,w] -> [N,C,H,W]'''
N,C,H,W = x.size()
g = self.groups
return x.view(N,g,C/g,H,W).permute(0,2,1,3,4).contiguous().view(N,C,H,W) | [
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train | load_embedding | return embedding for a specific file by given file path. | examples/trials/ga_squad/trial.py | def load_embedding(path):
'''
return embedding for a specific file by given file path.
'''
EMBEDDING_DIM = 300
embedding_dict = {}
with open(path, 'r', encoding='utf-8') as file:
pairs = [line.strip('\r\n').split() for line in file.readlines()]
for pair in pairs:
if l... | def load_embedding(path):
'''
return embedding for a specific file by given file path.
'''
EMBEDDING_DIM = 300
embedding_dict = {}
with open(path, 'r', encoding='utf-8') as file:
pairs = [line.strip('\r\n').split() for line in file.readlines()]
for pair in pairs:
if l... | [
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train | generate_predict_json | Generate json by prediction. | examples/trials/ga_squad/trial.py | def generate_predict_json(position1_result, position2_result, ids, passage_tokens):
'''
Generate json by prediction.
'''
predict_len = len(position1_result)
logger.debug('total prediction num is %s', str(predict_len))
answers = {}
for i in range(predict_len):
sample_id = ids[i]
... | def generate_predict_json(position1_result, position2_result, ids, passage_tokens):
'''
Generate json by prediction.
'''
predict_len = len(position1_result)
logger.debug('total prediction num is %s', str(predict_len))
answers = {}
for i in range(predict_len):
sample_id = ids[i]
... | [
"Generate",
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"by",
"prediction",
"."
] | Microsoft/nni | python | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/examples/trials/ga_squad/trial.py#L252-L269 | [
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train | generate_data | Generate data | examples/trials/ga_squad/trial.py | def generate_data(path, tokenizer, char_vcb, word_vcb, is_training=False):
'''
Generate data
'''
global root_path
qp_pairs = data.load_from_file(path=path, is_training=is_training)
tokenized_sent = 0
# qp_pairs = qp_pairs[:1000]1
for qp_pair in qp_pairs:
tokenized_sent += 1
... | def generate_data(path, tokenizer, char_vcb, word_vcb, is_training=False):
'''
Generate data
'''
global root_path
qp_pairs = data.load_from_file(path=path, is_training=is_training)
tokenized_sent = 0
# qp_pairs = qp_pairs[:1000]1
for qp_pair in qp_pairs:
tokenized_sent += 1
... | [
"Generate",
"data"
] | Microsoft/nni | python | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/examples/trials/ga_squad/trial.py#L272-L299 | [
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train | f1_score | Calculate the f1 score. | examples/trials/ga_squad/evaluate.py | def f1_score(prediction, ground_truth):
'''
Calculate the f1 score.
'''
prediction_tokens = normalize_answer(prediction).split()
ground_truth_tokens = normalize_answer(ground_truth).split()
common = Counter(prediction_tokens) & Counter(ground_truth_tokens)
num_same = sum(common.values())
... | def f1_score(prediction, ground_truth):
'''
Calculate the f1 score.
'''
prediction_tokens = normalize_answer(prediction).split()
ground_truth_tokens = normalize_answer(ground_truth).split()
common = Counter(prediction_tokens) & Counter(ground_truth_tokens)
num_same = sum(common.values())
... | [
"Calculate",
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"f1",
"score",
"."
] | Microsoft/nni | python | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/examples/trials/ga_squad/evaluate.py#L63-L76 | [
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train | _evaluate | Evaluate function. | examples/trials/ga_squad/evaluate.py | def _evaluate(dataset, predictions):
'''
Evaluate function.
'''
f1_result = exact_match = total = 0
count = 0
for article in dataset:
for paragraph in article['paragraphs']:
for qa_pair in paragraph['qas']:
total += 1
if qa_pair['id'] not in pr... | def _evaluate(dataset, predictions):
'''
Evaluate function.
'''
f1_result = exact_match = total = 0
count = 0
for article in dataset:
for paragraph in article['paragraphs']:
for qa_pair in paragraph['qas']:
total += 1
if qa_pair['id'] not in pr... | [
"Evaluate",
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] | Microsoft/nni | python | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/examples/trials/ga_squad/evaluate.py#L94-L116 | [
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train | evaluate | Evaluate. | examples/trials/ga_squad/evaluate.py | def evaluate(data_file, pred_file):
'''
Evaluate.
'''
expected_version = '1.1'
with open(data_file) as dataset_file:
dataset_json = json.load(dataset_file)
if dataset_json['version'] != expected_version:
print('Evaluation expects v-' + expected_version +
... | def evaluate(data_file, pred_file):
'''
Evaluate.
'''
expected_version = '1.1'
with open(data_file) as dataset_file:
dataset_json = json.load(dataset_file)
if dataset_json['version'] != expected_version:
print('Evaluation expects v-' + expected_version +
... | [
"Evaluate",
"."
] | Microsoft/nni | python | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/examples/trials/ga_squad/evaluate.py#L118-L135 | [
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train | evaluate_with_predictions | Evalutate with predictions/ | examples/trials/ga_squad/evaluate.py | def evaluate_with_predictions(data_file, predictions):
'''
Evalutate with predictions/
'''
expected_version = '1.1'
with open(data_file) as dataset_file:
dataset_json = json.load(dataset_file)
if dataset_json['version'] != expected_version:
print('Evaluation expects v-' +... | def evaluate_with_predictions(data_file, predictions):
'''
Evalutate with predictions/
'''
expected_version = '1.1'
with open(data_file) as dataset_file:
dataset_json = json.load(dataset_file)
if dataset_json['version'] != expected_version:
print('Evaluation expects v-' +... | [
"Evalutate",
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] | Microsoft/nni | python | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/examples/trials/ga_squad/evaluate.py#L137-L150 | [
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train | send | Send command to Training Service.
command: CommandType object.
data: string payload. | src/sdk/pynni/nni/protocol.py | def send(command, data):
"""Send command to Training Service.
command: CommandType object.
data: string payload.
"""
global _lock
try:
_lock.acquire()
data = data.encode('utf8')
assert len(data) < 1000000, 'Command too long'
msg = b'%b%06d%b' % (command.value, len... | def send(command, data):
"""Send command to Training Service.
command: CommandType object.
data: string payload.
"""
global _lock
try:
_lock.acquire()
data = data.encode('utf8')
assert len(data) < 1000000, 'Command too long'
msg = b'%b%06d%b' % (command.value, len... | [
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".",
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"payload",
"."
] | Microsoft/nni | python | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/src/sdk/pynni/nni/protocol.py#L56-L71 | [
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train | receive | Receive a command from Training Service.
Returns a tuple of command (CommandType) and payload (str) | src/sdk/pynni/nni/protocol.py | def receive():
"""Receive a command from Training Service.
Returns a tuple of command (CommandType) and payload (str)
"""
header = _in_file.read(8)
logging.getLogger(__name__).debug('Received command, header: [%s]' % header)
if header is None or len(header) < 8:
# Pipe EOF encountered
... | def receive():
"""Receive a command from Training Service.
Returns a tuple of command (CommandType) and payload (str)
"""
header = _in_file.read(8)
logging.getLogger(__name__).debug('Received command, header: [%s]' % header)
if header is None or len(header) < 8:
# Pipe EOF encountered
... | [
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")",
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"(",
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] | Microsoft/nni | python | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/src/sdk/pynni/nni/protocol.py#L74-L89 | [
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train | json2space | Change json to search space in hyperopt.
Parameters
----------
in_x : dict/list/str/int/float
The part of json.
name : str
name could be ROOT, TYPE, VALUE or INDEX. | src/sdk/pynni/nni/hyperopt_tuner/hyperopt_tuner.py | def json2space(in_x, name=ROOT):
"""
Change json to search space in hyperopt.
Parameters
----------
in_x : dict/list/str/int/float
The part of json.
name : str
name could be ROOT, TYPE, VALUE or INDEX.
"""
out_y = copy.deepcopy(in_x)
if isinstance(in_x, dict):
... | def json2space(in_x, name=ROOT):
"""
Change json to search space in hyperopt.
Parameters
----------
in_x : dict/list/str/int/float
The part of json.
name : str
name could be ROOT, TYPE, VALUE or INDEX.
"""
out_y = copy.deepcopy(in_x)
if isinstance(in_x, dict):
... | [
"Change",
"json",
"to",
"search",
"space",
"in",
"hyperopt",
"."
] | Microsoft/nni | python | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/src/sdk/pynni/nni/hyperopt_tuner/hyperopt_tuner.py#L52-L85 | [
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train | json2parameter | Change json to parameters. | src/sdk/pynni/nni/hyperopt_tuner/hyperopt_tuner.py | def json2parameter(in_x, parameter, name=ROOT):
"""
Change json to parameters.
"""
out_y = copy.deepcopy(in_x)
if isinstance(in_x, dict):
if TYPE in in_x.keys():
_type = in_x[TYPE]
name = name + '-' + _type
if _type == 'choice':
_index = pa... | def json2parameter(in_x, parameter, name=ROOT):
"""
Change json to parameters.
"""
out_y = copy.deepcopy(in_x)
if isinstance(in_x, dict):
if TYPE in in_x.keys():
_type = in_x[TYPE]
name = name + '-' + _type
if _type == 'choice':
_index = pa... | [
"Change",
"json",
"to",
"parameters",
"."
] | Microsoft/nni | python | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/src/sdk/pynni/nni/hyperopt_tuner/hyperopt_tuner.py#L88-L116 | [
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"(... | c7cc8db32da8d2ec77a382a55089f4e17247ce41 |
train | _add_index | change parameters in NNI format to parameters in hyperopt format(This function also support nested dict.).
For example, receive parameters like:
{'dropout_rate': 0.8, 'conv_size': 3, 'hidden_size': 512}
Will change to format in hyperopt, like:
{'dropout_rate': 0.8, 'conv_size': {'_index': 1, '_v... | src/sdk/pynni/nni/hyperopt_tuner/hyperopt_tuner.py | def _add_index(in_x, parameter):
"""
change parameters in NNI format to parameters in hyperopt format(This function also support nested dict.).
For example, receive parameters like:
{'dropout_rate': 0.8, 'conv_size': 3, 'hidden_size': 512}
Will change to format in hyperopt, like:
{'dropo... | def _add_index(in_x, parameter):
"""
change parameters in NNI format to parameters in hyperopt format(This function also support nested dict.).
For example, receive parameters like:
{'dropout_rate': 0.8, 'conv_size': 3, 'hidden_size': 512}
Will change to format in hyperopt, like:
{'dropo... | [
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