Search is not available for this dataset
identifier stringlengths 1 155 | parameters stringlengths 2 6.09k | docstring stringlengths 11 63.4k | docstring_summary stringlengths 0 63.4k | function stringlengths 29 99.8k | function_tokens list | start_point list | end_point list | language stringclasses 1
value | docstring_language stringlengths 2 7 | docstring_language_predictions stringlengths 18 23 | is_langid_reliable stringclasses 2
values |
|---|---|---|---|---|---|---|---|---|---|---|---|
WeekMixin.get_previous_week | (self, date) | Get the previous valid week. | Get the previous valid week. | def get_previous_week(self, date):
"""Get the previous valid week."""
return _get_next_prev(self, date, is_previous=True, period='week') | [
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WeekMixin._get_next_week | (self, date) |
Return the start date of the next interval.
The interval is defined by start date <= item date < next start date.
|
Return the start date of the next interval. | def _get_next_week(self, date):
"""
Return the start date of the next interval.
The interval is defined by start date <= item date < next start date.
"""
try:
return date + datetime.timedelta(days=7 - self._get_weekday(date))
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WeekMixin._get_current_week | (self, date) | Return the start date of the current interval. | Return the start date of the current interval. | def _get_current_week(self, date):
"""Return the start date of the current interval."""
return date - datetime.timedelta(self._get_weekday(date)) | [
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WeekMixin._get_weekday | (self, date) |
Return the weekday for a given date.
The first day according to the week format is 0 and the last day is 6.
|
Return the weekday for a given date. | def _get_weekday(self, date):
"""
Return the weekday for a given date.
The first day according to the week format is 0 and the last day is 6.
"""
week_format = self.get_week_format()
if week_format in {'%W', '%V'}: # week starts on Monday
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DateMixin.get_date_field | (self) | Get the name of the date field to be used to filter by. | Get the name of the date field to be used to filter by. | def get_date_field(self):
"""Get the name of the date field to be used to filter by."""
if self.date_field is None:
raise ImproperlyConfigured("%s.date_field is required." % self.__class__.__name__)
return self.date_field | [
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DateMixin.get_allow_future | (self) |
Return `True` if the view should be allowed to display objects from
the future.
|
Return `True` if the view should be allowed to display objects from
the future.
| def get_allow_future(self):
"""
Return `True` if the view should be allowed to display objects from
the future.
"""
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DateMixin.uses_datetime_field | (self) |
Return `True` if the date field is a `DateTimeField` and `False`
if it's a `DateField`.
|
Return `True` if the date field is a `DateTimeField` and `False`
if it's a `DateField`.
| def uses_datetime_field(self):
"""
Return `True` if the date field is a `DateTimeField` and `False`
if it's a `DateField`.
"""
model = self.get_queryset().model if self.model is None else self.model
field = model._meta.get_field(self.get_date_field())
return isins... | [
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DateMixin._make_date_lookup_arg | (self, value) |
Convert a date into a datetime when the date field is a DateTimeField.
When time zone support is enabled, `date` is assumed to be in the
current time zone, so that displayed items are consistent with the URL.
|
Convert a date into a datetime when the date field is a DateTimeField. | def _make_date_lookup_arg(self, value):
"""
Convert a date into a datetime when the date field is a DateTimeField.
When time zone support is enabled, `date` is assumed to be in the
current time zone, so that displayed items are consistent with the URL.
"""
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DateMixin._make_single_date_lookup | (self, date) |
Get the lookup kwargs for filtering on a single date.
If the date field is a DateTimeField, we can't just filter on
date_field=date because that doesn't take the time into account.
|
Get the lookup kwargs for filtering on a single date. | def _make_single_date_lookup(self, date):
"""
Get the lookup kwargs for filtering on a single date.
If the date field is a DateTimeField, we can't just filter on
date_field=date because that doesn't take the time into account.
"""
date_field = self.get_date_field()
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289,
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BaseDateListView.get_dated_items | (self) | Obtain the list of dates and items. | Obtain the list of dates and items. | def get_dated_items(self):
"""Obtain the list of dates and items."""
raise NotImplementedError('A DateView must provide an implementation of get_dated_items()') | [
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BaseDateListView.get_ordering | (self) |
Return the field or fields to use for ordering the queryset; use the
date field by default.
|
Return the field or fields to use for ordering the queryset; use the
date field by default.
| def get_ordering(self):
"""
Return the field or fields to use for ordering the queryset; use the
date field by default.
"""
return '-%s' % self.get_date_field() if self.ordering is None else self.ordering | [
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BaseDateListView.get_dated_queryset | (self, **lookup) |
Get a queryset properly filtered according to `allow_future` and any
extra lookup kwargs.
|
Get a queryset properly filtered according to `allow_future` and any
extra lookup kwargs.
| def get_dated_queryset(self, **lookup):
"""
Get a queryset properly filtered according to `allow_future` and any
extra lookup kwargs.
"""
qs = self.get_queryset().filter(**lookup)
date_field = self.get_date_field()
allow_future = self.get_allow_future()
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BaseDateListView.get_date_list_period | (self) |
Get the aggregation period for the list of dates: 'year', 'month', or
'day'.
|
Get the aggregation period for the list of dates: 'year', 'month', or
'day'.
| def get_date_list_period(self):
"""
Get the aggregation period for the list of dates: 'year', 'month', or
'day'.
"""
return self.date_list_period | [
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BaseDateListView.get_date_list | (self, queryset, date_type=None, ordering='ASC') |
Get a date list by calling `queryset.dates/datetimes()`, checking
along the way for empty lists that aren't allowed.
|
Get a date list by calling `queryset.dates/datetimes()`, checking
along the way for empty lists that aren't allowed.
| def get_date_list(self, queryset, date_type=None, ordering='ASC'):
"""
Get a date list by calling `queryset.dates/datetimes()`, checking
along the way for empty lists that aren't allowed.
"""
date_field = self.get_date_field()
allow_empty = self.get_allow_empty()
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BaseArchiveIndexView.get_dated_items | (self) | Return (date_list, items, extra_context) for this request. | Return (date_list, items, extra_context) for this request. | def get_dated_items(self):
"""Return (date_list, items, extra_context) for this request."""
qs = self.get_dated_queryset()
date_list = self.get_date_list(qs, ordering='DESC')
if not date_list:
qs = qs.none()
return (date_list, qs, {}) | [
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BaseYearArchiveView.get_dated_items | (self) | Return (date_list, items, extra_context) for this request. | Return (date_list, items, extra_context) for this request. | def get_dated_items(self):
"""Return (date_list, items, extra_context) for this request."""
year = self.get_year()
date_field = self.get_date_field()
date = _date_from_string(year, self.get_year_format())
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BaseYearArchiveView.get_make_object_list | (self) |
Return `True` if this view should contain the full list of objects in
the given year.
|
Return `True` if this view should contain the full list of objects in
the given year.
| def get_make_object_list(self):
"""
Return `True` if this view should contain the full list of objects in
the given year.
"""
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BaseMonthArchiveView.get_dated_items | (self) | Return (date_list, items, extra_context) for this request. | Return (date_list, items, extra_context) for this request. | def get_dated_items(self):
"""Return (date_list, items, extra_context) for this request."""
year = self.get_year()
month = self.get_month()
date_field = self.get_date_field()
date = _date_from_string(year, self.get_year_format(),
month, self.get_... | [
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BaseWeekArchiveView.get_dated_items | (self) | Return (date_list, items, extra_context) for this request. | Return (date_list, items, extra_context) for this request. | def get_dated_items(self):
"""Return (date_list, items, extra_context) for this request."""
year = self.get_year()
week = self.get_week()
date_field = self.get_date_field()
week_format = self.get_week_format()
week_choices = {'%W': '1', '%U': '0', '%V': '1'}
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BaseDayArchiveView.get_dated_items | (self) | Return (date_list, items, extra_context) for this request. | Return (date_list, items, extra_context) for this request. | def get_dated_items(self):
"""Return (date_list, items, extra_context) for this request."""
year = self.get_year()
month = self.get_month()
day = self.get_day()
date = _date_from_string(year, self.get_year_format(),
month, self.get_month_format()... | [
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BaseDayArchiveView._get_dated_items | (self, date) |
Do the actual heavy lifting of getting the dated items; this accepts a
date object so that TodayArchiveView can be trivial.
|
Do the actual heavy lifting of getting the dated items; this accepts a
date object so that TodayArchiveView can be trivial.
| def _get_dated_items(self, date):
"""
Do the actual heavy lifting of getting the dated items; this accepts a
date object so that TodayArchiveView can be trivial.
"""
lookup_kwargs = self._make_single_date_lookup(date)
qs = self.get_dated_queryset(**lookup_kwargs)
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BaseTodayArchiveView.get_dated_items | (self) | Return (date_list, items, extra_context) for this request. | Return (date_list, items, extra_context) for this request. | def get_dated_items(self):
"""Return (date_list, items, extra_context) for this request."""
return self._get_dated_items(datetime.date.today()) | [
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BaseDateDetailView.get_object | (self, queryset=None) | Get the object this request displays. | Get the object this request displays. | def get_object(self, queryset=None):
"""Get the object this request displays."""
year = self.get_year()
month = self.get_month()
day = self.get_day()
date = _date_from_string(year, self.get_year_format(),
month, self.get_month_format(),
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PipProvider.get_preference | (
self,
identifier: str,
resolutions: Mapping[str, Candidate],
candidates: Mapping[str, Iterator[Candidate]],
information: Mapping[str, Iterator["PreferenceInformation"]],
) | Produce a sort key for given requirement based on preference.
The lower the return value is, the more preferred this group of
arguments is.
Currently pip considers the followings in order:
* Prefer if any of the known requirements is "direct", e.g. points to an
explicit URL.... | Produce a sort key for given requirement based on preference. | def get_preference(
self,
identifier: str,
resolutions: Mapping[str, Candidate],
candidates: Mapping[str, Iterator[Candidate]],
information: Mapping[str, Iterator["PreferenceInformation"]],
) -> "Preference":
"""Produce a sort key for given requirement based on prefer... | [
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] | [
143,
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] | python | en | ['en', 'en', 'en'] | True |
opener_for | (ca_bundle=None) | Get a urlopen() replacement that uses ca_bundle for verification | Get a urlopen() replacement that uses ca_bundle for verification | def opener_for(ca_bundle=None):
"""Get a urlopen() replacement that uses ca_bundle for verification"""
return urllib.request.build_opener(
VerifyingHTTPSHandler(ca_bundle or find_ca_bundle())
).open | [
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208,
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] | python | en | ['en', 'en', 'en'] | True |
find_ca_bundle | () | Return an existing CA bundle path, or None | Return an existing CA bundle path, or None | def find_ca_bundle():
"""Return an existing CA bundle path, or None"""
extant_cert_paths = filter(os.path.isfile, cert_paths)
return (
get_win_certfile()
or next(extant_cert_paths, None)
or _certifi_where()
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client_with_credentials | (app) | This fixture provides a Flask app test client that has a session
pre-configured with use credentials. | This fixture provides a Flask app test client that has a session
pre-configured with use credentials. | def client_with_credentials(app):
"""This fixture provides a Flask app test client that has a session
pre-configured with use credentials."""
credentials = OAuth2Credentials(
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Menu.run | (self) | Display the menu and respond to choices | Display the menu and respond to choices | def run(self):
"Display the menu and respond to choices"
while True:
self.display_menu()
choice = raw_input("> ")
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_load_client_secrets | (filename) | Loads client secrets from the given filename.
Args:
filename: The name of the file containing the JSON secret key.
Returns:
A 2-tuple, the first item containing the client id, and the second
item containing a client secret.
| Loads client secrets from the given filename. | def _load_client_secrets(filename):
"""Loads client secrets from the given filename.
Args:
filename: The name of the file containing the JSON secret key.
Returns:
A 2-tuple, the first item containing the client id, and the second
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_get_oauth2_client_id_and_secret | (settings_instance) | Initializes client id and client secret based on the settings.
Args:
settings_instance: An instance of ``django.conf.settings``.
Returns:
A 2-tuple, the first item is the client id and the second
item is the client secret.
| Initializes client id and client secret based on the settings. | def _get_oauth2_client_id_and_secret(settings_instance):
"""Initializes client id and client secret based on the settings.
Args:
settings_instance: An instance of ``django.conf.settings``.
Returns:
A 2-tuple, the first item is the client id and the second
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_get_storage_model | () | This configures whether the credentials will be stored in the session
or the Django ORM based on the settings. By default, the credentials
will be stored in the session, unless `GOOGLE_OAUTH2_STORAGE_MODEL`
is found in the settings. Usually, the ORM storage is used to integrate
credentials into an exist... | This configures whether the credentials will be stored in the session
or the Django ORM based on the settings. By default, the credentials
will be stored in the session, unless `GOOGLE_OAUTH2_STORAGE_MODEL`
is found in the settings. Usually, the ORM storage is used to integrate
credentials into an exist... | def _get_storage_model():
"""This configures whether the credentials will be stored in the session
or the Django ORM based on the settings. By default, the credentials
will be stored in the session, unless `GOOGLE_OAUTH2_STORAGE_MODEL`
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get_storage | (request) | Gets a Credentials storage object provided by the Django OAuth2 Helper
object.
Args:
request: Reference to the current request object.
Returns:
An :class:`oauth2.client.Storage` object.
| Gets a Credentials storage object provided by the Django OAuth2 Helper
object. | def get_storage(request):
""" Gets a Credentials storage object provided by the Django OAuth2 Helper
object.
Args:
request: Reference to the current request object.
Returns:
An :class:`oauth2.client.Storage` object.
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_redirect_with_params | (url_name, *args, **kwargs) | Helper method to create a redirect response with URL params.
This builds a redirect string that converts kwargs into a
query string.
Args:
url_name: The name of the url to redirect to.
kwargs: the query string param and their values to build.
Returns:
A properly formatted redi... | Helper method to create a redirect response with URL params. | def _redirect_with_params(url_name, *args, **kwargs):
"""Helper method to create a redirect response with URL params.
This builds a redirect string that converts kwargs into a
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Args:
url_name: The name of the url to redirect to.
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_credentials_from_request | (request) | Gets the authorized credentials for this flow, if they exist. | Gets the authorized credentials for this flow, if they exist. | def _credentials_from_request(request):
"""Gets the authorized credentials for this flow, if they exist."""
# ORM storage requires a logged in user
if (oauth2_settings.storage_model is None or
request.user.is_authenticated()):
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UserOAuth2.__init__ | (self, request, scopes=None, return_url=None) | Initialize the Oauth2 Object.
Args:
request: Django request object.
scopes: Scopes desired for this OAuth2 flow.
return_url: The url to return to after the OAuth flow is complete,
defaults to the request's current URL path.
| Initialize the Oauth2 Object. | def __init__(self, request, scopes=None, return_url=None):
"""Initialize the Oauth2 Object.
Args:
request: Django request object.
scopes: Scopes desired for this OAuth2 flow.
return_url: The url to return to after the OAuth flow is complete,
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UserOAuth2.get_authorize_redirect | (self) | Creates a URl to start the OAuth2 authorization flow. | Creates a URl to start the OAuth2 authorization flow. | def get_authorize_redirect(self):
"""Creates a URl to start the OAuth2 authorization flow."""
get_params = {
'return_url': self.return_url,
'scopes': self._get_scopes()
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return _redirect_with_params('google_oauth:authorize', **get_params) | [
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UserOAuth2.has_credentials | (self) | Returns True if there are valid credentials for the current user
and required scopes. | Returns True if there are valid credentials for the current user
and required scopes. | def has_credentials(self):
"""Returns True if there are valid credentials for the current user
and required scopes."""
credentials = _credentials_from_request(self.request)
return (credentials and not credentials.invalid and
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UserOAuth2._get_scopes | (self) | Returns the scopes associated with this object, kept up to
date for incremental auth. | Returns the scopes associated with this object, kept up to
date for incremental auth. | def _get_scopes(self):
"""Returns the scopes associated with this object, kept up to
date for incremental auth."""
if _credentials_from_request(self.request):
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UserOAuth2.scopes | (self) | Returns the scopes associated with this OAuth2 object. | Returns the scopes associated with this OAuth2 object. | def scopes(self):
"""Returns the scopes associated with this OAuth2 object."""
# make sure previously requested custom scopes are maintained
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UserOAuth2.credentials | (self) | Gets the authorized credentials for this flow, if they exist. | Gets the authorized credentials for this flow, if they exist. | def credentials(self):
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UserOAuth2.http | (self) | Helper: create HTTP client authorized with OAuth2 credentials. | Helper: create HTTP client authorized with OAuth2 credentials. | def http(self):
"""Helper: create HTTP client authorized with OAuth2 credentials."""
if self.has_credentials():
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keygen | () | Key generator. | Key generator. | def keygen():
"""Key generator."""
# Parse the CLI options
parser = OptionParser(usage='usage: %prog [options] keysize',
description='Generates a new RSA keypair of "keysize" bits.')
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CryptoOperation.perform_operation | (self, indata, key, cli_args) | Performs the program's operation.
Implement in a subclass.
:returns: the data to write to the output.
| Performs the program's operation. | def perform_operation(self, indata, key, cli_args):
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CryptoOperation.__call__ | (self) | Runs the program. | Runs the program. | def __call__(self):
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(cli, cli_args) = self.parse_cli()
key = self.read_key(cli_args[0], cli.keyform)
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CryptoOperation.parse_cli | (self) | Parse the CLI options
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| Parse the CLI options | def parse_cli(self):
"""Parse the CLI options
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CryptoOperation.read_key | (self, filename, keyform) | Reads a public or private key. | Reads a public or private key. | def read_key(self, filename, keyform):
"""Reads a public or private key."""
print('Reading %s key from %s' % (self.keyname, filename), file=sys.stderr)
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CryptoOperation.read_infile | (self, inname) | Read the input file | Read the input file | def read_infile(self, inname):
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CryptoOperation.write_outfile | (self, outdata, outname) | Write the output file | Write the output file | def write_outfile(self, outdata, outname):
"""Write the output file"""
if outname:
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EncryptOperation.perform_operation | (self, indata, pub_key, cli_args=None) | Encrypts files. | Encrypts files. | def perform_operation(self, indata, pub_key, cli_args=None):
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DecryptOperation.perform_operation | (self, indata, priv_key, cli_args=None) | Decrypts files. | Decrypts files. | def perform_operation(self, indata, priv_key, cli_args=None):
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SignOperation.perform_operation | (self, indata, priv_key, cli_args) | Signs files. | Signs files. | def perform_operation(self, indata, priv_key, cli_args):
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hash_method = cli_args[1]
if hash_method not in HASH_METHODS:
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"'Invalid hash method, choose on... | [
243,
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] | [
251,
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VerifyOperation.perform_operation | (self, indata, pub_key, cli_args) | Verifies files. | Verifies files. | def perform_operation(self, indata, pub_key, cli_args):
"""Verifies files."""
signature_file = cli_args[1]
with open(signature_file, 'rb') as sigfile:
signature = sigfile.read()
try:
rsa.verify(indata, signature, pub_key)
except rsa.VerificationError:
... | [
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average_losses | (all_losses) | Average the losses into one dict of losses.
Args:
all_losses: List of dictionary of losses.
Returns:
combined: A dictionary with same keys as individual dicts, with
all losses combined.
| Average the losses into one dict of losses.
Args:
all_losses: List of dictionary of losses.
Returns:
combined: A dictionary with same keys as individual dicts, with
all losses combined.
| def average_losses(all_losses):
"""Average the losses into one dict of losses.
Args:
all_losses: List of dictionary of losses.
Returns:
combined: A dictionary with same keys as individual dicts, with
all losses combined.
"""
if len(all_losses) == 0:
return {}
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combine_obj_pixels | (obj_pix, obj_dim) | Combine obj-split pixels into a single image.
Args:
obj_pix: B, ..., Nobj, ..., C, H, W
obj_dim: The dimension to reduce over -- which corresponds to objs
Returns
B, ..., ..., C, H, W
| Combine obj-split pixels into a single image.
Args:
obj_pix: B, ..., Nobj, ..., C, H, W
obj_dim: The dimension to reduce over -- which corresponds to objs
Returns
B, ..., ..., C, H, W
| def combine_obj_pixels(obj_pix, obj_dim):
"""Combine obj-split pixels into a single image.
Args:
obj_pix: B, ..., Nobj, ..., C, H, W
obj_dim: The dimension to reduce over -- which corresponds to objs
Returns
B, ..., ..., C, H, W
"""
if obj_pix is None:
return None
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1156,
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DynConcat.forward | (self, features, pixels) |
This dyn model does not use pixels, so will just return the last history
frame
Args:
features: (B, T, Nobj, D, H', W')
pixels: (B, T, Nobj, C, H, W)
Returns:
pred: (B, Nobj, D, H', W')
pixels: (B, Nobj, C, H, W)
addl_losses... |
This dyn model does not use pixels, so will just return the last history
frame
Args:
features: (B, T, Nobj, D, H', W')
pixels: (B, T, Nobj, C, H, W)
Returns:
pred: (B, Nobj, D, H', W')
pixels: (B, Nobj, C, H, W)
addl_losses... | def forward(self, features, pixels):
"""
This dyn model does not use pixels, so will just return the last history
frame
Args:
features: (B, T, Nobj, D, H', W')
pixels: (B, T, Nobj, C, H, W)
Returns:
pred: (B, Nobj, D, H', W')
pi... | [
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266,
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285,
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MultiSTN.__init__ | (self,
input_dim,
num_tx,
dof='affine',
inp_type='pix',
affine_tx_mode='bilinear',
kernel_size=3,
stochastic=False) |
Args:
input_dim (int): Dimension of the features used to predict the STN
parameters
num_tx (int): Number of transformations to predict, will apply to
the tensor, split along some dimension
dof (str): Controls how generic of a affine matrix to ... |
Args:
input_dim (int): Dimension of the features used to predict the STN
parameters
num_tx (int): Number of transformations to predict, will apply to
the tensor, split along some dimension
dof (str): Controls how generic of a affine matrix to ... | def __init__(self,
input_dim,
num_tx,
dof='affine',
inp_type='pix',
affine_tx_mode='bilinear',
kernel_size=3,
stochastic=False):
"""
Args:
input_dim (int): Dimension of the ... | [
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291,
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357,
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MultiSTN.transform_pix | (self, feat, theta, mode='bilinear') | Transform the features using theta. | Transform the features using theta. | def transform_pix(self, feat, theta, mode='bilinear'):
"""Transform the features using theta."""
grid = nn.functional.affine_grid(theta,
feat.size(),
align_corners=True)
return nn.functional.grid_sample(feat,
... | [
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359,
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] | [
367,
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] | python | en | ['en', 'en', 'en'] | True |
MultiSTN.transform_pt | (self, feat, theta) | Transform pt-net style feature using theta.
Here, it assumes the first 2 dimensions of the feature are loc.
Args:
feat (B, C, H, W), C >= 2
Returns:
tx feat (B, C, H, W)
| Transform pt-net style feature using theta.
Here, it assumes the first 2 dimensions of the feature are loc.
Args:
feat (B, C, H, W), C >= 2
Returns:
tx feat (B, C, H, W)
| def transform_pt(self, feat, theta):
"""Transform pt-net style feature using theta.
Here, it assumes the first 2 dimensions of the feature are loc.
Args:
feat (B, C, H, W), C >= 2
Returns:
tx feat (B, C, H, W)
"""
assert feat.shape[1] >= 2
... | [
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387,
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MultiSTN.forward | (self, feat_for_tx, feat_to_tx, split_dim=1) |
Args:
feat_for_tx (B, D, H, W): The features to use to compute the
transformation
feat_to_tx (B, D', H, W): Features to apply the tx onto
split_dim (int): Dimension to split on
|
Args:
feat_for_tx (B, D, H, W): The features to use to compute the
transformation
feat_to_tx (B, D', H, W): Features to apply the tx onto
split_dim (int): Dimension to split on
| def forward(self, feat_for_tx, feat_to_tx, split_dim=1):
"""
Args:
feat_for_tx (B, D, H, W): The features to use to compute the
transformation
feat_to_tx (B, D', H, W): Features to apply the tx onto
split_dim (int): Dimension to split on
"""
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399,
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460,
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DynSTN.forward | (self, features, pixels) |
This dyn model does not use pixels, so will just return the last history
frame
Args:
features: (B, T, Nobj, D, H', W')
pixels: (B, T, Nobj, C, H, W)
Returns:
pred: (B, Nobj, D, H', W')
pix
addl_losses
|
This dyn model does not use pixels, so will just return the last history
frame
Args:
features: (B, T, Nobj, D, H', W')
pixels: (B, T, Nobj, C, H, W)
Returns:
pred: (B, Nobj, D, H', W')
pix
addl_losses
| def forward(self, features, pixels):
"""
This dyn model does not use pixels, so will just return the last history
frame
Args:
features: (B, T, Nobj, D, H', W')
pixels: (B, T, Nobj, C, H, W)
Returns:
pred: (B, Nobj, D, H', W')
pi... | [
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492,
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DynSTNPixels_DEPRECATED.forward | (self, features, pixels) |
Args:
features: (B, T, Nobj, D, H', W')
pixels: (B, T, C, H, W)
Returns:
pred: (B, Nobj, D, H', W')
|
Args:
features: (B, T, Nobj, D, H', W')
pixels: (B, T, C, H, W)
Returns:
pred: (B, Nobj, D, H', W')
| def forward(self, features, pixels):
"""
Args:
features: (B, T, Nobj, D, H', W')
pixels: (B, T, C, H, W)
Returns:
pred: (B, Nobj, D, H', W')
"""
raise NotImplementedError('Deal with objectified pixel input. '
'... | [
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DynSTNPixelChannels_DEPRECATED.forward | (self, features, pixels) |
Args:
features: (B, T, Nobj, D, H', W')
pixels: (B, T, C, H, W)
Returns:
pred: (B, Nobj, D, H', W')
|
Args:
features: (B, T, Nobj, D, H', W')
pixels: (B, T, C, H, W)
Returns:
pred: (B, Nobj, D, H', W')
| def forward(self, features, pixels):
"""
Args:
features: (B, T, Nobj, D, H', W')
pixels: (B, T, C, H, W)
Returns:
pred: (B, Nobj, D, H', W')
"""
raise NotImplementedError('Deal with objectified pixel input. '
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572,
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DynSTNPixelChannelsGenBg_DEPRECATED.forward | (self, features, pixels) |
Args:
features: (B, T, Nobj, D, H', W')
pixels: (B, T, C, H, W)
Returns:
pred: (B, Nobj, D, H', W')
|
Args:
features: (B, T, Nobj, D, H', W')
pixels: (B, T, C, H, W)
Returns:
pred: (B, Nobj, D, H', W')
| def forward(self, features, pixels):
"""
Args:
features: (B, T, Nobj, D, H', W')
pixels: (B, T, C, H, W)
Returns:
pred: (B, Nobj, D, H', W')
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raise NotImplementedError('Deal with objectified pixel input. '
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DynSTNPixelChannelsDetBg.forward | (self, features, pixels) |
Args:
features: (B, T, Nobj, D, H', W')
pixels: (B, T, Nobj, C, H, W)
Returns:
pred: (B, Nobj, D, H', W')
pix
addl_losses
|
Args:
features: (B, T, Nobj, D, H', W')
pixels: (B, T, Nobj, C, H, W)
Returns:
pred: (B, Nobj, D, H', W')
pix
addl_losses
| def forward(self, features, pixels):
"""
Args:
features: (B, T, Nobj, D, H', W')
pixels: (B, T, Nobj, C, H, W)
Returns:
pred: (B, Nobj, D, H', W')
pix
addl_losses
"""
assert pixels.shape[3] >= self.num_tx
cat_fea... | [
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BasicDecoder.forward | (self, features, pixels) |
Args:
features (BxNobjxDxH'xW'): Features to be decoded
pixels (BxNobjxCxHxW): Pixels generated by the dynamics model
Returns:
imgs (BxNobjxD_outxHxW): Output frames (per obj, aggregation is
done later in the Fwd class)
|
Args:
features (BxNobjxDxH'xW'): Features to be decoded
pixels (BxNobjxCxHxW): Pixels generated by the dynamics model
Returns:
imgs (BxNobjxD_outxHxW): Output frames (per obj, aggregation is
done later in the Fwd class)
| def forward(self, features, pixels):
"""
Args:
features (BxNobjxDxH'xW'): Features to be decoded
pixels (BxNobjxCxHxW): Pixels generated by the dynamics model
Returns:
imgs (BxNobjxD_outxHxW): Output frames (per obj, aggregation is
done later i... | [
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... | [
750,
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] | [
775,
18
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TrivialDecoder.forward | (self, features, pixels) |
Args:
features (BxNobjxDxH'xW'): Features to be decoded
pixels (BxNobjxCxHxW): Pixels generated by the dynamics model
Returns:
imgs (BxNobjxCxHxW): Output frames
|
Args:
features (BxNobjxDxH'xW'): Features to be decoded
pixels (BxNobjxCxHxW): Pixels generated by the dynamics model
Returns:
imgs (BxNobjxCxHxW): Output frames
| def forward(self, features, pixels):
"""
Args:
features (BxNobjxDxH'xW'): Features to be decoded
pixels (BxNobjxCxHxW): Pixels generated by the dynamics model
Returns:
imgs (BxNobjxCxHxW): Output frames
"""
del features # assumes the dynamics ... | [
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793,
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BasicObjEncoder.forward | (self, feat) |
Args:
feat: (B, T, Nobj, D, H', W')
|
Args:
feat: (B, T, Nobj, D, H', W')
| def forward(self, feat):
"""
Args:
feat: (B, T, Nobj, D, H', W')
"""
if self.encoder:
feat_flat = torch.flatten(feat, 0, 2)
obj_embed_flat = self.encoder(feat_flat)
obj_embed = torch.reshape(
obj_embed_flat, feat.shape[:3] +... | [
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ContextGatingObjectifier.forward | (self, vid_feat) |
Decompose the video features into object level representation.
Args:
vid_feat: (BxTxDxH'xW')
nobj (int): Max number of objects in the scene. The hope is that the
extra channels will just have some degenerate information
Returns:
BxTxNobjxDxH''... |
Decompose the video features into object level representation.
Args:
vid_feat: (BxTxDxH'xW')
nobj (int): Max number of objects in the scene. The hope is that the
extra channels will just have some degenerate information
Returns:
BxTxNobjxDxH''... | def forward(self, vid_feat):
"""
Decompose the video features into object level representation.
Args:
vid_feat: (BxTxDxH'xW')
nobj (int): Max number of objects in the scene. The hope is that the
extra channels will just have some degenerate information
... | [
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912,
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ChannelSplitObjectifier.forward | (self, vid_feat) |
Decompose the video features into object level representation.
Args:
vid_feat: (BxTxNobjxDxH'xW')
Returns:
BxTxNobjx(D/Nobj)xH'xW'
|
Decompose the video features into object level representation.
Args:
vid_feat: (BxTxNobjxDxH'xW')
Returns:
BxTxNobjx(D/Nobj)xH'xW'
| def forward(self, vid_feat):
"""
Decompose the video features into object level representation.
Args:
vid_feat: (BxTxNobjxDxH'xW')
Returns:
BxTxNobjx(D/Nobj)xH'xW'
"""
assert vid_feat.shape[2] == 1, (
'Channel split can not deal with pr... | [
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SimpleBaseEncoder.__init__ | (self, in_dim, width_scale_factor) | Simple encoder weights.
For a 256x256 input, it'll give a 4x4 output. | Simple encoder weights.
For a 256x256 input, it'll give a 4x4 output. | def __init__(self, in_dim, width_scale_factor):
"""Simple encoder weights.
For a 256x256 input, it'll give a 4x4 output."""
super().__init__()
self.width_scale_factor = width_scale_factor
_s = self._scale_int
self.stem = nn.Sequential(
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SimpleBaseEncoder._scale_int | (self, n) | Scale the number by a factor. To control width of this network. | Scale the number by a factor. To control width of this network. | def _scale_int(self, n):
"""Scale the number by a factor. To control width of this network."""
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BasicEncoder.__init__ | (self, in_dim, nobj, feat_ext, objectifier, obj_encoder,
spatial_mean, feat_ext_eval_mode, process_objs_together) |
Args:
obj_before_enc: If true, do the objectify in the input (pixel) space
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spatial_mean: Avg pool the features to 1x1
feat_ext_eval_mode: Set the feature extractor to eval mode for BN,
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BasicEncoder._forward_vid | (self, batch_vid_obs, l2_norm_feats=False) |
Convert a video into images to run the forward model.
Args:
batch_vid_obs: BxTxCxHxW or BxTxNobjxCxHxW
Returns:
features: BxTxDxH'xW' or BxTxNobjxDxH'xW'
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Convert a video into images to run the forward model.
Args:
batch_vid_obs: BxTxCxHxW or BxTxNobjxCxHxW
Returns:
features: BxTxDxH'xW' or BxTxNobjxDxH'xW'
| def _forward_vid(self, batch_vid_obs, l2_norm_feats=False):
"""
Convert a video into images to run the forward model.
Args:
batch_vid_obs: BxTxCxHxW or BxTxNobjxCxHxW
Returns:
features: BxTxDxH'xW' or BxTxNobjxDxH'xW'
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BasicEncoder.forward | (self, vid) |
Args:
vid (B, T, Nobj, C, H, W): Input video, in preprocessed form; i.e.
one-hot
Returns:
obj_feat (B, T, Nobj', D, H', W'): Features with objects, if needed
|
Args:
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Returns:
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MLPClassifier.forward | (self, preds, pixs, process_all_frames=False) |
Run the classifier on the predictions.
Args:
preds: (BxTx1xDxH'xW')
pixs: (BxTx1xDxHxW)
Retuns:
solved: (BxT)
process_all_frames: Set true when used by other classifiers for
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Run the classifier on the predictions.
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Retuns:
solved: (BxT)
process_all_frames: Set true when used by other classifiers for
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Run the classifier on the predictions.
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preds: (BxTx1xDxH'xW')
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Retuns:
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ConvNetClassifier.forward | (self, preds, pixs, process_all_frames=False) |
Run the classifier on the predictions.
Args:
preds: (BxTx1xDxH'xW')
pixs: (BxTx1xDxHxW)
process_all_frames: Set true when used by other classifiers for
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Retuns:... | def forward(self, preds, pixs, process_all_frames=False):
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Run the classifier on the predictions.
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TxClassifier.forward | (self, preds, pixs) |
Run the classifier on the predictions.
Args:
preds: (BxTx1xDxH'xW')
pixs: (BxTx1xDxHxW)
Retuns:
solved: (Bx1)
|
Run the classifier on the predictions.
Args:
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| def forward(self, preds, pixs):
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Run the classifier on the predictions.
Args:
preds: (BxTx1xDxH'xW')
pixs: (BxTx1xDxHxW)
Retuns:
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ConvTxClassifier.forward | (self, preds, pixs) |
Run the classifier on the predictions.
Args:
preds: (BxTx1xDxH'xW')
pixs: (BxTx1xDxHxW)
Retuns:
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Run the classifier on the predictions.
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| def forward(self, preds, pixs):
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Run the classifier on the predictions.
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Retuns:
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Conv3dClassifier.forward | (self, preds, pixs) |
Run the classifier on the predictions.
Args:
preds: (BxTx1xDxH'xW')
pixs: (BxTx1xDxHxW)
Retuns:
solved: (Bx1)
|
Run the classifier on the predictions.
Args:
preds: (BxTx1xDxH'xW')
pixs: (BxTx1xDxHxW)
Retuns:
solved: (Bx1)
| def forward(self, preds, pixs):
"""
Run the classifier on the predictions.
Args:
preds: (BxTx1xDxH'xW')
pixs: (BxTx1xDxHxW)
Retuns:
solved: (Bx1)
"""
del pixs
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ConvConv3dClassifier.forward | (self, preds, pixs) |
Run the classifier on the predictions.
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pixs: (BxTx1xDxHxW)
Retuns:
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|
Run the classifier on the predictions.
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Retuns:
solved: (Bx1)
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ConcatClassifier.forward | (self, preds, pixs) |
Run the classifier on the predictions.
Args:
preds: (BxTx1xDxH'xW')
pixs: (BxTx1xDxHxW)
Retuns:
solved: (Bx1)
|
Run the classifier on the predictions.
Args:
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pixs: (BxTx1xDxHxW)
Retuns:
solved: (Bx1)
| def forward(self, preds, pixs):
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Run the classifier on the predictions.
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preds: (BxTx1xDxH'xW')
pixs: (BxTx1xDxHxW)
Retuns:
solved: (Bx1)
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ConvConcatClassifier.forward | (self, preds, pixs) |
Run the classifier on the predictions.
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pixs: (BxTx1xDxHxW)
Retuns:
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Run the classifier on the predictions.
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TrivialInteractor.forward | (cls, feat) |
Args:
feat: (B, T, Nobj, C, H', W')
Returns:
feat as is
|
Args:
feat: (B, T, Nobj, C, H', W')
Returns:
feat as is
| def forward(cls, feat):
"""
Args:
feat: (B, T, Nobj, C, H', W')
Returns:
feat as is
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TxEncoder.__init__ | (self, in_dim, nheads, nlayers, maintain_dim=False) |
Args:
maintain_dim (bool): If true, it maps the final output to the same
dimensionality as the input
|
Args:
maintain_dim (bool): If true, it maps the final output to the same
dimensionality as the input
| def __init__(self, in_dim, nheads, nlayers, maintain_dim=False):
"""
Args:
maintain_dim (bool): If true, it maps the final output to the same
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"""
super().__init__()
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TxEncoder.forward | (self, feat) |
Args:
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Returns:
Same shape as input
|
Args:
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| def forward(self, feat):
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TxInteractor.forward | (self, feat) |
Args:
feat: (B, T, Nobj, C, H', W')
Returns:
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|
Args:
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| def forward(self, feat):
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TxSpatialAttention.forward | (self, feat) |
Args:
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|
Args:
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| def forward(self, feat):
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Args:
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Fwd.__init__ | (self, agent_cfg) |
Args:
dyn_type: The type of dynamics model to use.
dyn_n: Number of previous features used for prediction.
|
Args:
dyn_type: The type of dynamics model to use.
dyn_n: Number of previous features used for prediction.
| def __init__(self, agent_cfg):
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dyn_type: The type of dynamics model to use.
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Fwd._forward_dyn | (self, feats, vids, n_fwd_times, need_intermediate=False) |
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"all_pixs",
"=",
"[",
"]",
"all_addl_losses",
"=",
"[",
"]",
"if",
"n_fwd_times",
"==",
"0",
... | [
1590,
4
] | [
1637,
51
] | python | en | ['en', 'error', 'th'] | False |
Fwd._slice_for_dyn | (self, features_batched, n_hist_frames, nslices=-1) |
Args:
features_batched: BxTx.... can deal with any following
dimensions, typically it is (BxTxNobjxDxH'xW')
n_hist_frames (int): Number of frames to use as history
nslices (int): If -1, make as many slices of the training data
as possible. If ... |
Args:
features_batched: BxTx.... can deal with any following
dimensions, typically it is (BxTxNobjxDxH'xW')
n_hist_frames (int): Number of frames to use as history
nslices (int): If -1, make as many slices of the training data
as possible. If ... | def _slice_for_dyn(self, features_batched, n_hist_frames, nslices=-1):
"""
Args:
features_batched: BxTx.... can deal with any following
dimensions, typically it is (BxTxNobjxDxH'xW')
n_hist_frames (int): Number of frames to use as history
nslices (int)... | [
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... | [
1639,
4
] | [
1660,
24
] | python | en | ['en', 'error', 'th'] | False |
Fwd._forward_dec | (self, feats, pixels) |
Args:
feats: List of features (BxD) from the dynamics prediction stage,
one for each time step predicted.
pixels: List of corresponding pixels from the dynamics model. The
dyn model may or may not actually generate new pixels.
|
Args:
feats: List of features (BxD) from the dynamics prediction stage,
one for each time step predicted.
pixels: List of corresponding pixels from the dynamics model. The
dyn model may or may not actually generate new pixels.
| def _forward_dec(self, feats, pixels):
"""
Args:
feats: List of features (BxD) from the dynamics prediction stage,
one for each time step predicted.
pixels: List of corresponding pixels from the dynamics model. The
dyn model may or may not actually... | [
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")",
"]"
] | [
1662,
4
] | [
1670,
72
] | python | en | ['en', 'error', 'th'] | False |
Fwd.cswm_loss | (self, pred, gt, hinge=1.0) |
The energy based contrastive loss.
Args:
pred (BxNobjxDxH'xW')
gt (BxNobjxDxH'xW')
From https://github.com/tkipf/c-swm/blob/master/modules.py#L94
|
The energy based contrastive loss.
Args:
pred (BxNobjxDxH'xW')
gt (BxNobjxDxH'xW')
From https://github.com/tkipf/c-swm/blob/master/modules.py#L94
| def cswm_loss(self, pred, gt, hinge=1.0):
"""
The energy based contrastive loss.
Args:
pred (BxNobjxDxH'xW')
gt (BxNobjxDxH'xW')
From https://github.com/tkipf/c-swm/blob/master/modules.py#L94
"""
pred = pred.view(pred.shape[:2] + (-1, ))
... | [
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... | [
1673,
4
] | [
1701,
34
] | python | en | ['en', 'error', 'th'] | False |
Fwd.autoencoder_loss | (self, pix, latent, autoenc_loss_ratio) |
Runs a random portion of the actual frames through decoder to incur a
loss to encourage the intermediate representation to learn a good
autoencoder as well. Random fraction only for compute reasons.
Ideally would run every frame (ratio = 1)
Args:
pix (B, T, H, W): Ac... |
Runs a random portion of the actual frames through decoder to incur a
loss to encourage the intermediate representation to learn a good
autoencoder as well. Random fraction only for compute reasons.
Ideally would run every frame (ratio = 1)
Args:
pix (B, T, H, W): Ac... | def autoencoder_loss(self, pix, latent, autoenc_loss_ratio):
"""
Runs a random portion of the actual frames through decoder to incur a
loss to encourage the intermediate representation to learn a good
autoencoder as well. Random fraction only for compute reasons.
Ideally would ru... | [
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"latent",
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"pix_flat",
"=",
"torch",
".",
"flatten",
"(",
"pix",
",",
"0",
",",
"1",
")",
"latent_flat",
"=... | [
1708,
4
] | [
1737,
36
] | python | en | ['en', 'error', 'th'] | False |
Fwd.solved_or_not_loss | (self, clip_preds_solved, vid_is_solved) |
Repeat the is_solved to as many times the batch was repeated to get
the class label at each forward prediction
Args:
clip_preds_solved (B',)
vid_is_solved (B,)
B and B' might be different but B' must be a multiple of B, since
it happens when n... |
Repeat the is_solved to as many times the batch was repeated to get
the class label at each forward prediction
Args:
clip_preds_solved (B',)
vid_is_solved (B,)
B and B' might be different but B' must be a multiple of B, since
it happens when n... | def solved_or_not_loss(self, clip_preds_solved, vid_is_solved):
"""
Repeat the is_solved to as many times the batch was repeated to get
the class label at each forward prediction
Args:
clip_preds_solved (B',)
vid_is_solved (B,)
B and B' might be differ... | [
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"==",
"0",
"return",
"{",
"'ce'",
":",
"self"... | [
1739,
4
] | [
1758,
9
] | python | en | ['en', 'error', 'th'] | False |
Fwd._compute_losses | (self, clip_pred, clip_pred_pix, vid_feat, vid,
n_hist_frames, n_fwd_times) |
Compute all losses possible.
|
Compute all losses possible.
| def _compute_losses(self, clip_pred, clip_pred_pix, vid_feat, vid,
n_hist_frames, n_fwd_times):
"""
Compute all losses possible.
"""
dummy_loss = torch.Tensor([-1]).to(clip_pred.device)
losses = {}
# NCE and pixel loss
# find the GT for eac... | [
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"-",
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".",
"to",
"(",
"cl... | [
1762,
4
] | [
1814,
21
] | python | en | ['en', 'error', 'th'] | False |
Fwd._cls | (self, feat_hist, pix_hist, feat_preds, pix_preds) |
Wrapper around the classifier, collates all the input frames/features
and predicted future frames/features.
The images, features are already summed over the objects
Args:
feat_hist: (B, T, C, H', W')
pix_hist: (B, T, 7, H, W)
feat_preds [list ... |
Wrapper around the classifier, collates all the input frames/features
and predicted future frames/features.
The images, features are already summed over the objects
Args:
feat_hist: (B, T, C, H', W')
pix_hist: (B, T, 7, H, W)
feat_preds [list ... | def _cls(self, feat_hist, pix_hist, feat_preds, pix_preds):
"""
Wrapper around the classifier, collates all the input frames/features
and predicted future frames/features.
The images, features are already summed over the objects
Args:
feat_hist: (B, T, C, H', ... | [
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"(",
"feat_preds",
")",
">",
"0",
":",
"feats_combined"... | [
1816,
4
] | [
1854,
60
] | python | en | ['en', 'error', 'th'] | False |
Fwd.forward | (self,
vid,
vid_is_solved,
n_hist_frames=3,
n_fwd_times=1,
n_fwd_times_incur_loss=999999,
run_decode=False,
compute_losses=False,
need_intermediate=False,
autoenc_loss_ratio=0.... |
Args:
vid: (BxTxNobjxHxW) The input video
vid_is_solved: (Bx1) Whether the video is solved in the end of not.
Could be None at test time.
n_hist_frames: (int) Number of frames to use as history for
prediction
n_fwd_times: (int) How... |
Args:
vid: (BxTxNobjxHxW) The input video
vid_is_solved: (Bx1) Whether the video is solved in the end of not.
Could be None at test time.
n_hist_frames: (int) Number of frames to use as history for
prediction
n_fwd_times: (int) How... | def forward(self,
vid,
vid_is_solved,
n_hist_frames=3,
n_fwd_times=1,
n_fwd_times_incur_loss=999999,
run_decode=False,
compute_losses=False,
need_intermediate=False,
autoenc_lo... | [
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"False",
",",
"compute_losses",
"=",
"False",
",",
"need_inte... | [
1856,
4
] | [
1959,
36
] | python | en | ['en', 'error', 'th'] | False |
block_output_tokens | (blocks, true_tokens) |
blocks = the output from blockify
true_tokens = a list of true tokens
|
blocks = the output from blockify
true_tokens = a list of true tokens
| def block_output_tokens(blocks, true_tokens):
"""
blocks = the output from blockify
true_tokens = a list of true tokens
"""
assert len(blocks) == len(true_tokens)
for k in range_(len(blocks)):
block_tokens = re.split(r"\s+", blocks[k].text.strip())
assert block_tokens == true_tok... | [
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... | [
21,
0
] | [
29,
45
] | python | en | ['en', 'error', 'th'] | False |
TestBlockifier.test_lxml_error | (self) | tests the case where lxml raises an error during parsing
also handles case where lxml returns None for the tree | tests the case where lxml raises an error during parsing | def test_lxml_error(self):
"""tests the case where lxml raises an error during parsing
also handles case where lxml returns None for the tree"""
# this raises an error in parsing
with pytest.raises(BlockifyError):
Blockifier.blockify("")
# this returns None in lxml
... | [
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"assert",
"etree",
".",
"fromstring... | [
48,
4
] | [
58,
39
] | python | en | ['en', 'en', 'en'] | True |
TestBlockifier.test_very_simple | (self) | test_very_simple | test_very_simple | def test_very_simple(self):
"""test_very_simple"""
s = """<div>some text
<script> skip this </script>
more text here
</div>"""
blocks = Blockifier.blockify(s)
block_output_tokens(blocks, [['some', 'text', 'more', 'text', 'here']]) | [
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"(",
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"(",
"s",
")",
"block_output_tokens",... | [
60,
4
] | [
67,
79
] | python | en | ['en', 'en', 'en'] | False |
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