INSTRUCTION stringlengths 1 8.43k | RESPONSE stringlengths 75 104k |
|---|---|
Update request headers. | 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)... |
Update request cookies header. | 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]
... |
Set request content encoding. | 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... |
Analyze transfer - encoding header. | 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:
raise ValueError(
'chunked can not be set '
'if ... |
Set basic auth. | 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... |
Support coroutines that yields bytes objects. | 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 =... |
Start response processing. | 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
... |
Read response payload. | 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:... |
Read response payload and decode. | 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()
return self._body.decode(enco... |
Read and decodes JSON response. | 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:
await self.read()
if content_type:
... |
Enables automatic chunked transfer encoding. | 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 ... |
Enables response compression encoding. | 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:
force = Conte... |
Set or update response cookie. | 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... |
Delete cookie. | 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, '... |
The value of Last - Modified HTTP header or None. | 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... |
Default handler for Expect header. | 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:
... |
Construct url for route with additional params. | def url_for(self, *args: str, **kwargs: str) -> URL:
"""Construct url for route with additional params."""
return self._resource.url_for(*args, **kwargs) |
Add static files view. | def add_static(self, prefix: str, path: PathLike, *,
name: Optional[str]=None,
expect_handler: Optional[_ExpectHandler]=None,
chunk_size: int=256 * 1024,
show_index: bool=False, follow_symlinks: bool=False,
append_version: bo... |
Shortcut for add_route with method HEAD | def add_head(self, path: str, handler: _WebHandler,
**kwargs: Any) -> AbstractRoute:
"""
Shortcut for add_route with method HEAD
"""
return self.add_route(hdrs.METH_HEAD, path, handler, **kwargs) |
Shortcut for add_route with method OPTIONS | def add_options(self, path: str, handler: _WebHandler,
**kwargs: Any) -> AbstractRoute:
"""
Shortcut for add_route with method OPTIONS
"""
return self.add_route(hdrs.METH_OPTIONS, path, handler, **kwargs) |
Shortcut for add_route with method GET if allow_head is true another route is added allowing head requests to the same endpoint | def add_get(self, path: str, handler: _WebHandler, *,
name: Optional[str]=None, allow_head: bool=True,
**kwargs: Any) -> AbstractRoute:
"""
Shortcut for add_route with method GET, if allow_head is true another
route is added allowing head requests to the same endp... |
Shortcut for add_route with method POST | def add_post(self, path: str, handler: _WebHandler,
**kwargs: Any) -> AbstractRoute:
"""
Shortcut for add_route with method POST
"""
return self.add_route(hdrs.METH_POST, path, handler, **kwargs) |
Shortcut for add_route with method PUT | def add_put(self, path: str, handler: _WebHandler,
**kwargs: Any) -> AbstractRoute:
"""
Shortcut for add_route with method PUT
"""
return self.add_route(hdrs.METH_PUT, path, handler, **kwargs) |
Shortcut for add_route with method PATCH | def add_patch(self, path: str, handler: _WebHandler,
**kwargs: Any) -> AbstractRoute:
"""
Shortcut for add_route with method PATCH
"""
return self.add_route(hdrs.METH_PATCH, path, handler, **kwargs) |
Shortcut for add_route with method DELETE | def add_delete(self, path: str, handler: _WebHandler,
**kwargs: Any) -> AbstractRoute:
"""
Shortcut for add_route with method DELETE
"""
return self.add_route(hdrs.METH_DELETE, path, handler, **kwargs) |
Shortcut for add_route with ANY methods for a class - based view | def add_view(self, path: str, handler: AbstractView,
**kwargs: Any) -> AbstractRoute:
"""
Shortcut for add_route with ANY methods for a class-based view
"""
return self.add_route(hdrs.METH_ANY, path, handler, **kwargs) |
Append routes to route table. | def add_routes(self, routes: Iterable[AbstractRouteDef]) -> None:
"""Append routes to route table.
Parameter should be a sequence of RouteDef objects.
"""
for route_def in routes:
route_def.register(self) |
Parses RFC 5322 headers from a stream. | def parse_headers(
self,
lines: List[bytes]
) -> Tuple['CIMultiDictProxy[str]',
RawHeaders,
Optional[bool],
Optional[str],
bool,
bool]:
"""Parses RFC 5322 headers from a stream.
Line continuations are... |
extra info from connection transport | def get_extra_info(self, name: str, default: Any=None) -> Any:
"""extra info from connection transport"""
conn = self._response.connection
if conn is None:
return default
transport = conn.transport
if transport is None:
return default
return transp... |
Returns an apparently legit user - agent if not requested one of a specific style. Defaults to a Chrome - style User - Agent. | def user_agent(style=None) -> _UserAgent:
"""Returns an apparently legit user-agent, if not requested one of a specific
style. Defaults to a Chrome-style User-Agent.
"""
global useragent
if (not useragent) and style:
useragent = UserAgent()
return useragent[style] if style else DEFAULT_... |
Bytes representation of the HTML content. ( learn more <http:// www. diveintopython3. net/ strings. html > _ ). | def raw_html(self) -> _RawHTML:
"""Bytes representation of the HTML content.
(`learn more <http://www.diveintopython3.net/strings.html>`_).
"""
if self._html:
return self._html
else:
return etree.tostring(self.element, encoding='unicode').strip().encode(se... |
Unicode representation of the HTML content ( learn more <http:// www. diveintopython3. net/ strings. html > _ ). | def html(self) -> _BaseHTML:
"""Unicode representation of the HTML content
(`learn more <http://www.diveintopython3.net/strings.html>`_).
"""
if self._html:
return self.raw_html.decode(self.encoding, errors='replace')
else:
return etree.tostring(self.eleme... |
The encoding string to be used extracted from the HTML and: class: HTMLResponse <HTMLResponse > headers. | def encoding(self) -> _Encoding:
"""The encoding string to be used, extracted from the HTML and
:class:`HTMLResponse <HTMLResponse>` headers.
"""
if self._encoding:
return self._encoding
# Scan meta tags for charset.
if self._html:
self._encoding ... |
PyQuery <https:// pythonhosted. org/ pyquery/ > _ representation of the: class: Element <Element > or: class: HTML <HTML >. | def pq(self) -> PyQuery:
"""`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 |
lxml <http:// lxml. de > _ representation of the: class: Element <Element > or: class: HTML <HTML >. | 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:
... |
Given a CSS Selector returns a list of: class: Element <Element > objects or a single one. | def find(self, selector: str = "*", *, containing: _Containing = None, clean: bool = False, first: bool = False, _encoding: str = None) -> _Find:
"""Given a CSS Selector, returns a list of
:class:`Element <Element>` objects or a single one.
:param selector: CSS Selector to use.
:param c... |
Given an XPath selector returns a list of: class: Element <Element > objects or a single one. | def xpath(self, selector: str, *, clean: bool = False, first: bool = False, _encoding: str = None) -> _XPath:
"""Given an XPath selector, returns a list of
:class:`Element <Element>` objects or a single one.
:param selector: XPath Selector to use.
:param clean: Whether or not to sanitiz... |
Search the: class: Element <Element > ( multiple times ) for the given parse template. | def search_all(self, template: str) -> _Result:
"""Search the :class:`Element <Element>` (multiple times) for the given parse
template.
:param template: The Parse template to use.
"""
return [r for r in findall(template, self.html)] |
All found links on page in as–is form. | 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... |
Makes a given link absolute. | 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... |
All found links on page in absolute form ( learn more <https:// www. navegabem. com/ absolute - or - relative - links. html > _ ). | def absolute_links(self) -> _Links:
"""All found links on page, in absolute form
(`learn more <https://www.navegabem.com/absolute-or-relative-links.html>`_).
"""
def gen():
for link in self.links:
yield self._make_absolute(link)
return set(gen()) |
The base URL for the page. Supports the <base > tag ( learn more <https:// www. w3schools. com/ tags/ tag_base. asp > _ ). | 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()... |
Returns a dictionary of the attributes of the: class: Element <Element > ( learn more <https:// www. w3schools. com/ tags/ ref_attributes. asp > _ ). | def attrs(self) -> _Attrs:
"""Returns a dictionary of the attributes of the :class:`Element <Element>`
(`learn more <https://www.w3schools.com/tags/ref_attributes.asp>`_).
"""
if self._attrs is None:
self._attrs = {k: v for k, v in self.element.items()}
# Split c... |
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. | 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
next page. If ``fetch`` is ``False``, simply returns the next URL.
... |
Handle page creation and js rendering. Internal use for render/ arender methods. | 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):
""" Handle page creation and js rendering. Internal use for render/arender methods. """
try:
page = await self.bro... |
Reloads the response in Chromium and replaces HTML content with an updated version with JavaScript executed. | def render(self, retries: int = 8, script: str = None, wait: float = 0.2, scrolldown=False, sleep: int = 0, reload: bool = True, timeout: Union[float, int] = 8.0, keep_page: bool = False):
"""Reloads the response in Chromium, and replaces HTML content
with an updated version, with JavaScript executed.
... |
Change response enconding and replace it by a HTMLResponse. | def response_hook(self, response, **kwargs) -> HTMLResponse:
""" Change response enconding and replace it by a HTMLResponse. """
if not response.encoding:
response.encoding = DEFAULT_ENCODING
return HTMLResponse._from_response(response, self) |
If a browser was created close it first. | def close(self):
""" If a browser was created close it first. """
if hasattr(self, "_browser"):
self.loop.run_until_complete(self._browser.close())
super().close() |
Partial original request func and run it in a thread. | def request(self, *args, **kwargs):
""" Partial original request func and run it in a thread. """
func = partial(super().request, *args, **kwargs)
return self.loop.run_in_executor(self.thread_pool, func) |
Pass in all the coroutines you want to run it will wrap each one in a task run it and wait for the result. Return a list with all results this is returned in the same order coros are passed in. | def run(self, *coros):
""" Pass in all the coroutines you want to run, it will wrap each one
in a task, run it and wait for the result. Return a list with all
results, this is returned in the same order coros are passed in. """
tasks = [
asyncio.ensure_future(coro()) ... |
img_tensor: N C H W | def add_depth_channel(img_tensor, pad_mode):
'''
img_tensor: N, C, H, W
'''
img_tensor[:, 1] = get_depth_tensor(pad_mode)
img_tensor[:, 2] = img_tensor[:, 0] * get_depth_tensor(pad_mode) |
: param s: [ src_sequence batch_size src_dim ]: return: [ src_sequence batch_size. hidden_dim ] | def get_pre_compute(self, s):
'''
:param s: [src_sequence, batch_size, src_dim]
:return: [src_sequence, batch_size. hidden_dim]
'''
hidden_dim = self.hidden_dim
src_dim = s.get_shape().as_list()[-1]
assert src_dim is not None, 'src dim must be defined'
W =... |
: param s: [ src_sequence_length batch_size src_dim ]: param h: [ batch_size tgt_dim ] or [ tgt_sequence_length batch_size tgt_dim ]: param mask: [ src_sequence_length batch_size ] \ or [ tgt_sequence_length src_sequence_length batch_sizse ]: param pre_compute: [ src_sequence_length batch_size hidden_dim ]: return: [ s... | def get_prob(self, src, tgt, mask, pre_compute, return_logits=False):
'''
:param s: [src_sequence_length, batch_size, src_dim]
:param h: [batch_size, tgt_dim] or [tgt_sequence_length, batch_size, tgt_dim]
:param mask: [src_sequence_length, batch_size]\
or [tgt_sequence_lengt... |
: param s: [ src_sequence_length batch_size src_dim ]: param prob: [ src_sequence_length batch_size ] \ or [ tgt_sequence_length src_sequence_length batch_size ]: return: [ batch_size src_dim ] or [ tgt_sequence_length batch_size src_dim ] | def get_att(self, s, prob):
'''
:param s: [src_sequence_length, batch_size, src_dim]
:param prob: [src_sequence_length, batch_size]\
or [tgt_sequence_length, src_sequence_length, batch_size]
:return: [batch_size, src_dim] or [tgt_sequence_length, batch_size, src_dim]
... |
Get shape of variable. Return type is tuple. | def shape(tensor):
'''
Get shape of variable.
Return type is tuple.
'''
temp_s = tensor.get_shape()
return tuple([temp_s[i].value for i in range(0, len(temp_s))]) |
Get variable by name. | def get_variable(name, temp_s):
'''
Get variable by name.
'''
return tf.Variable(tf.zeros(temp_s), name=name) |
Dropout except test. | def dropout(tensor, drop_prob, is_training):
'''
Dropout except test.
'''
if not is_training:
return tensor
return tf.nn.dropout(tensor, 1.0 - drop_prob) |
Calculate time span. | def get_elapsed(self, restart=True):
'''
Calculate time span.
'''
end = time.time()
span = end - self.__start
if restart:
self.__start = end
return span |
return 18000x128x128 np array | 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)
... |
Partitioning MNIST | def partition_dataset():
""" Partitioning MNIST """
dataset = datasets.MNIST(
'./data',
train=True,
download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))
]))
size = dist.get_world_size... |
Gradient averaging. | def average_gradients(model):
""" Gradient averaging. """
size = float(dist.get_world_size())
for param in model.parameters():
dist.all_reduce(param.grad.data, op=dist.reduce_op.SUM, group=0)
param.grad.data /= size |
Distributed Synchronous SGD Example | 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... |
Load graph | def graph_loads(graph_json):
'''
Load graph
'''
layers = []
for layer in graph_json['layers']:
layer_info = Layer(layer['type'], layer['input'], layer['output'], layer['size'])
layer_info.is_delete = layer['is_delete']
layers.append(layer_info)
graph = Graph(graph_json['m... |
Set size. | def set_size(self, graph_id, size):
'''
Set size.
'''
if self.graph_type == LayerType.attention.value:
if self.input[0] == graph_id:
self.size = size
if self.graph_type == LayerType.rnn.value:
self.size = size
if self.graph_type == ... |
Clear size | def clear_size(self):
'''
Clear size
'''
if self.graph_type == LayerType.attention.value or \
LayerType.rnn.value or LayerType.self_attention.value:
self.size = None |
valid the topology | def is_topology(self, layers=None):
'''
valid the topology
'''
if layers is None:
layers = self.layers
layers_nodle = []
result = []
for i, layer in enumerate(layers):
if layer.is_delete is False:
layers_nodle.append(i)
... |
Judge whether is legal for layers | def is_legal(self, layers=None):
'''
Judge whether is legal for layers
'''
if layers is None:
layers = self.layers
for layer in layers:
if layer.is_delete is False:
if len(layer.input) != layer.input_size:
return False
... |
Mutation for a graph | def mutation(self, only_add=False):
'''
Mutation for a graph
'''
types = []
if self.layer_num() < self.max_layer_num:
types.append(0)
types.append(1)
if self.layer_num() > 5 and only_add is False:
types.append(2)
types.appen... |
Main function of SMAC for CLI interface Returns ------- instance optimizer | def _main_cli(self):
"""Main function of SMAC for CLI interface
Returns
-------
instance
optimizer
"""
self.logger.info("SMAC call: %s" % (" ".join(sys.argv)))
cmd_reader = CMDReader()
args, _ = cmd_reader.read_cmd()
root_log... |
TODO: this is urgly we put all the initialization work in this method because initialization relies on search space also because update_search_space is called at the beginning. NOTE: updating search space is not supported. | def update_search_space(self, search_space):
"""TODO: this is urgly, we put all the initialization work in this method, because initialization relies
on search space, also because update_search_space is called at the beginning.
NOTE: updating search space is not supported.
Parameters
... |
receive_trial_result Parameters ---------- parameter_id: int parameter id parameters: parameters value: value Raises ------ RuntimeError Received parameter id not in total_data | def receive_trial_result(self, parameter_id, parameters, value):
"""receive_trial_result
Parameters
----------
parameter_id: int
parameter id
parameters:
parameters
value:
value
Raises
------
Run... |
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 ---------- challenger_dict: dict challenger dict | def convert_loguniform_categorical(self, challenger_dict):
"""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 fun... |
generate one instance of hyperparameters Parameters ---------- parameter_id: int parameter id Returns ------- list new generated parameters | def generate_parameters(self, parameter_id):
"""generate one instance of hyperparameters
Parameters
----------
parameter_id: int
parameter id
Returns
-------
list
new generated parameters
"""
if self.first_... |
generate mutiple instances of hyperparameters Parameters ---------- parameter_id_list: list list of parameter id Returns ------- list list of new generated parameters | def generate_multiple_parameters(self, parameter_id_list):
"""generate mutiple instances of hyperparameters
Parameters
----------
parameter_id_list: list
list of parameter id
Returns
-------
list
list of new generated para... |
Computes gradient of the Lovasz extension w. r. t sorted errors See Alg. 1 in paper | def lovasz_grad(gt_sorted):
"""
Computes gradient of the Lovasz extension w.r.t sorted errors
See Alg. 1 in paper
"""
p = len(gt_sorted)
gts = gt_sorted.sum()
intersection = gts - gt_sorted.float().cumsum(0)
union = gts + (1 - gt_sorted).float().cumsum(0)
jaccard = 1. - intersection ... |
IoU for foreground class binary: 1 foreground 0 background | def iou_binary(preds, labels, EMPTY=1., ignore=None, per_image=True):
"""
IoU for foreground class
binary: 1 foreground, 0 background
"""
if not per_image:
preds, labels = (preds,), (labels,)
ious = []
for pred, label in zip(preds, labels):
intersection = ((label == 1) & (pre... |
Array of IoU for each ( non ignored ) class | 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,)
ious = []
for pred, label in zip(preds, labels):
iou = []
for i in range(C):
if i != ... |
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 | 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
... |
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 | 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
"""
if len(labels) == 0:
# only void pixels, the gra... |
Flattens predictions in the batch ( binary case ) Remove labels equal to ignore | 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... |
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 | 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... |
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 of per batch ignore... | 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... |
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 | 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... |
Flattens predictions in the batch | 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)
if ignore is None:
return probas, labels
valid = (lab... |
Cross entropy loss | def xloss(logits, labels, ignore=None):
"""
Cross entropy loss
"""
return F.cross_entropy(logits, Variable(labels), ignore_index=255) |
nanmean compatible with generators. | 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')
... |
main loop logic for trial keeper | 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... |
Channel shuffle: [ N C H W ] - > [ N g C/ g H W ] - > [ N C/ g g H w ] - > [ 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) |
return embedding for a specific file by given file path. | 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... |
Generate json by prediction. | 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 data | 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
... |
Calculate the f1 score. | 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())
... |
Evaluate function. | 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. | 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 +
... |
Evalutate with predictions/ | 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-' +... |
Send command to Training Service. command: CommandType object. data: string payload. | 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... |
Receive a command from Training Service. Returns a tuple of command ( CommandType ) and payload ( str ) | 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
... |
Change json to search space in hyperopt. | 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 parameters. | 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 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 _value: 3 } hidden_size: { _index: 1... | 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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