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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
valid | Droplet.get_actions | Returns a list of Action objects
This actions can be used to check the droplet's status | digitalocean/Droplet.py | def get_actions(self):
"""
Returns a list of Action objects
This actions can be used to check the droplet's status
"""
answer = self.get_data("droplets/%s/actions/" % self.id, type=GET)
actions = []
for action_dict in answer['actions']:
action = Action(**action_dict)
action.token = self.token
action.droplet_id = self.id
action.load()
actions.append(action)
return actions | def get_actions(self):
"""
Returns a list of Action objects
This actions can be used to check the droplet's status
"""
answer = self.get_data("droplets/%s/actions/" % self.id, type=GET)
actions = []
for action_dict in answer['actions']:
action = Action(**action_dict)
action.token = self.token
action.droplet_id = self.id
action.load()
actions.append(action)
return actions | [
"Returns",
"a",
"list",
"of",
"Action",
"objects",
"This",
"actions",
"can",
"be",
"used",
"to",
"check",
"the",
"droplet",
"s",
"status"
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/Droplet.py#L579-L593 | [
"def",
"get_actions",
"(",
"self",
")",
":",
"answer",
"=",
"self",
".",
"get_data",
"(",
"\"droplets/%s/actions/\"",
"%",
"self",
".",
"id",
",",
"type",
"=",
"GET",
")",
"actions",
"=",
"[",
"]",
"for",
"action_dict",
"in",
"answer",
"[",
"'actions'",
"]",
":",
"action",
"=",
"Action",
"(",
"*",
"*",
"action_dict",
")",
"action",
".",
"token",
"=",
"self",
".",
"token",
"action",
".",
"droplet_id",
"=",
"self",
".",
"id",
"action",
".",
"load",
"(",
")",
"actions",
".",
"append",
"(",
"action",
")",
"return",
"actions"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | Droplet.get_action | Returns a specific Action by its ID.
Args:
action_id (int): id of action | digitalocean/Droplet.py | def get_action(self, action_id):
"""Returns a specific Action by its ID.
Args:
action_id (int): id of action
"""
return Action.get_object(
api_token=self.token,
action_id=action_id
) | def get_action(self, action_id):
"""Returns a specific Action by its ID.
Args:
action_id (int): id of action
"""
return Action.get_object(
api_token=self.token,
action_id=action_id
) | [
"Returns",
"a",
"specific",
"Action",
"by",
"its",
"ID",
"."
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/Droplet.py#L595-L604 | [
"def",
"get_action",
"(",
"self",
",",
"action_id",
")",
":",
"return",
"Action",
".",
"get_object",
"(",
"api_token",
"=",
"self",
".",
"token",
",",
"action_id",
"=",
"action_id",
")"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | Droplet.get_snapshots | This method will return the snapshots/images connected to that
specific droplet. | digitalocean/Droplet.py | def get_snapshots(self):
"""
This method will return the snapshots/images connected to that
specific droplet.
"""
snapshots = list()
for id in self.snapshot_ids:
snapshot = Image()
snapshot.id = id
snapshot.token = self.token
snapshots.append(snapshot)
return snapshots | def get_snapshots(self):
"""
This method will return the snapshots/images connected to that
specific droplet.
"""
snapshots = list()
for id in self.snapshot_ids:
snapshot = Image()
snapshot.id = id
snapshot.token = self.token
snapshots.append(snapshot)
return snapshots | [
"This",
"method",
"will",
"return",
"the",
"snapshots",
"/",
"images",
"connected",
"to",
"that",
"specific",
"droplet",
"."
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/Droplet.py#L606-L617 | [
"def",
"get_snapshots",
"(",
"self",
")",
":",
"snapshots",
"=",
"list",
"(",
")",
"for",
"id",
"in",
"self",
".",
"snapshot_ids",
":",
"snapshot",
"=",
"Image",
"(",
")",
"snapshot",
".",
"id",
"=",
"id",
"snapshot",
".",
"token",
"=",
"self",
".",
"token",
"snapshots",
".",
"append",
"(",
"snapshot",
")",
"return",
"snapshots"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | Droplet.get_kernel_available | Get a list of kernels available | digitalocean/Droplet.py | def get_kernel_available(self):
"""
Get a list of kernels available
"""
kernels = list()
data = self.get_data("droplets/%s/kernels/" % self.id)
while True:
for jsond in data[u'kernels']:
kernel = Kernel(**jsond)
kernel.token = self.token
kernels.append(kernel)
try:
url = data[u'links'][u'pages'].get(u'next')
if not url:
break
data = self.get_data(url)
except KeyError: # No links.
break
return kernels | def get_kernel_available(self):
"""
Get a list of kernels available
"""
kernels = list()
data = self.get_data("droplets/%s/kernels/" % self.id)
while True:
for jsond in data[u'kernels']:
kernel = Kernel(**jsond)
kernel.token = self.token
kernels.append(kernel)
try:
url = data[u'links'][u'pages'].get(u'next')
if not url:
break
data = self.get_data(url)
except KeyError: # No links.
break
return kernels | [
"Get",
"a",
"list",
"of",
"kernels",
"available"
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/Droplet.py#L619-L639 | [
"def",
"get_kernel_available",
"(",
"self",
")",
":",
"kernels",
"=",
"list",
"(",
")",
"data",
"=",
"self",
".",
"get_data",
"(",
"\"droplets/%s/kernels/\"",
"%",
"self",
".",
"id",
")",
"while",
"True",
":",
"for",
"jsond",
"in",
"data",
"[",
"u'kernels'",
"]",
":",
"kernel",
"=",
"Kernel",
"(",
"*",
"*",
"jsond",
")",
"kernel",
".",
"token",
"=",
"self",
".",
"token",
"kernels",
".",
"append",
"(",
"kernel",
")",
"try",
":",
"url",
"=",
"data",
"[",
"u'links'",
"]",
"[",
"u'pages'",
"]",
".",
"get",
"(",
"u'next'",
")",
"if",
"not",
"url",
":",
"break",
"data",
"=",
"self",
".",
"get_data",
"(",
"url",
")",
"except",
"KeyError",
":",
"# No links.",
"break",
"return",
"kernels"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | Domain.get_object | Class method that will return a Domain object by ID. | digitalocean/Domain.py | def get_object(cls, api_token, domain_name):
"""
Class method that will return a Domain object by ID.
"""
domain = cls(token=api_token, name=domain_name)
domain.load()
return domain | def get_object(cls, api_token, domain_name):
"""
Class method that will return a Domain object by ID.
"""
domain = cls(token=api_token, name=domain_name)
domain.load()
return domain | [
"Class",
"method",
"that",
"will",
"return",
"a",
"Domain",
"object",
"by",
"ID",
"."
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/Domain.py#L16-L22 | [
"def",
"get_object",
"(",
"cls",
",",
"api_token",
",",
"domain_name",
")",
":",
"domain",
"=",
"cls",
"(",
"token",
"=",
"api_token",
",",
"name",
"=",
"domain_name",
")",
"domain",
".",
"load",
"(",
")",
"return",
"domain"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | Domain.create_new_domain_record | Create new domain record.
https://developers.digitalocean.com/#create-a-new-domain-record
Args:
type: The record type (A, MX, CNAME, etc).
name: The host name, alias, or service being defined by the record
data: Variable data depending on record type.
Optional Args:
priority: The priority of the host
port: The port that the service is accessible on
weight: The weight of records with the same priority | digitalocean/Domain.py | def create_new_domain_record(self, *args, **kwargs):
"""
Create new domain record.
https://developers.digitalocean.com/#create-a-new-domain-record
Args:
type: The record type (A, MX, CNAME, etc).
name: The host name, alias, or service being defined by the record
data: Variable data depending on record type.
Optional Args:
priority: The priority of the host
port: The port that the service is accessible on
weight: The weight of records with the same priority
"""
data = {
"type": kwargs.get("type", None),
"name": kwargs.get("name", None),
"data": kwargs.get("data", None)
}
# Optional Args
if kwargs.get("priority", None):
data['priority'] = kwargs.get("priority", None)
if kwargs.get("port", None):
data['port'] = kwargs.get("port", None)
if kwargs.get("weight", None):
data['weight'] = kwargs.get("weight", None)
return self.get_data(
"domains/%s/records" % self.name,
type=POST,
params=data
) | def create_new_domain_record(self, *args, **kwargs):
"""
Create new domain record.
https://developers.digitalocean.com/#create-a-new-domain-record
Args:
type: The record type (A, MX, CNAME, etc).
name: The host name, alias, or service being defined by the record
data: Variable data depending on record type.
Optional Args:
priority: The priority of the host
port: The port that the service is accessible on
weight: The weight of records with the same priority
"""
data = {
"type": kwargs.get("type", None),
"name": kwargs.get("name", None),
"data": kwargs.get("data", None)
}
# Optional Args
if kwargs.get("priority", None):
data['priority'] = kwargs.get("priority", None)
if kwargs.get("port", None):
data['port'] = kwargs.get("port", None)
if kwargs.get("weight", None):
data['weight'] = kwargs.get("weight", None)
return self.get_data(
"domains/%s/records" % self.name,
type=POST,
params=data
) | [
"Create",
"new",
"domain",
"record",
".",
"https",
":",
"//",
"developers",
".",
"digitalocean",
".",
"com",
"/",
"#create",
"-",
"a",
"-",
"new",
"-",
"domain",
"-",
"record"
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/Domain.py#L39-L74 | [
"def",
"create_new_domain_record",
"(",
"self",
",",
"*",
"args",
",",
"*",
"*",
"kwargs",
")",
":",
"data",
"=",
"{",
"\"type\"",
":",
"kwargs",
".",
"get",
"(",
"\"type\"",
",",
"None",
")",
",",
"\"name\"",
":",
"kwargs",
".",
"get",
"(",
"\"name\"",
",",
"None",
")",
",",
"\"data\"",
":",
"kwargs",
".",
"get",
"(",
"\"data\"",
",",
"None",
")",
"}",
"# Optional Args",
"if",
"kwargs",
".",
"get",
"(",
"\"priority\"",
",",
"None",
")",
":",
"data",
"[",
"'priority'",
"]",
"=",
"kwargs",
".",
"get",
"(",
"\"priority\"",
",",
"None",
")",
"if",
"kwargs",
".",
"get",
"(",
"\"port\"",
",",
"None",
")",
":",
"data",
"[",
"'port'",
"]",
"=",
"kwargs",
".",
"get",
"(",
"\"port\"",
",",
"None",
")",
"if",
"kwargs",
".",
"get",
"(",
"\"weight\"",
",",
"None",
")",
":",
"data",
"[",
"'weight'",
"]",
"=",
"kwargs",
".",
"get",
"(",
"\"weight\"",
",",
"None",
")",
"return",
"self",
".",
"get_data",
"(",
"\"domains/%s/records\"",
"%",
"self",
".",
"name",
",",
"type",
"=",
"POST",
",",
"params",
"=",
"data",
")"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | Domain.create | Create new doamin | digitalocean/Domain.py | def create(self):
"""
Create new doamin
"""
# URL https://api.digitalocean.com/v2/domains
data = {
"name": self.name,
"ip_address": self.ip_address,
}
domain = self.get_data("domains", type=POST, params=data)
return domain | def create(self):
"""
Create new doamin
"""
# URL https://api.digitalocean.com/v2/domains
data = {
"name": self.name,
"ip_address": self.ip_address,
}
domain = self.get_data("domains", type=POST, params=data)
return domain | [
"Create",
"new",
"doamin"
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/Domain.py#L76-L87 | [
"def",
"create",
"(",
"self",
")",
":",
"# URL https://api.digitalocean.com/v2/domains",
"data",
"=",
"{",
"\"name\"",
":",
"self",
".",
"name",
",",
"\"ip_address\"",
":",
"self",
".",
"ip_address",
",",
"}",
"domain",
"=",
"self",
".",
"get_data",
"(",
"\"domains\"",
",",
"type",
"=",
"POST",
",",
"params",
"=",
"data",
")",
"return",
"domain"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | Domain.get_records | Returns a list of Record objects | digitalocean/Domain.py | def get_records(self, params=None):
"""
Returns a list of Record objects
"""
if params is None:
params = {}
# URL https://api.digitalocean.com/v2/domains/[NAME]/records/
records = []
data = self.get_data("domains/%s/records/" % self.name, type=GET, params=params)
for record_data in data['domain_records']:
record = Record(domain_name=self.name, **record_data)
record.token = self.token
records.append(record)
return records | def get_records(self, params=None):
"""
Returns a list of Record objects
"""
if params is None:
params = {}
# URL https://api.digitalocean.com/v2/domains/[NAME]/records/
records = []
data = self.get_data("domains/%s/records/" % self.name, type=GET, params=params)
for record_data in data['domain_records']:
record = Record(domain_name=self.name, **record_data)
record.token = self.token
records.append(record)
return records | [
"Returns",
"a",
"list",
"of",
"Record",
"objects"
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/Domain.py#L89-L106 | [
"def",
"get_records",
"(",
"self",
",",
"params",
"=",
"None",
")",
":",
"if",
"params",
"is",
"None",
":",
"params",
"=",
"{",
"}",
"# URL https://api.digitalocean.com/v2/domains/[NAME]/records/",
"records",
"=",
"[",
"]",
"data",
"=",
"self",
".",
"get_data",
"(",
"\"domains/%s/records/\"",
"%",
"self",
".",
"name",
",",
"type",
"=",
"GET",
",",
"params",
"=",
"params",
")",
"for",
"record_data",
"in",
"data",
"[",
"'domain_records'",
"]",
":",
"record",
"=",
"Record",
"(",
"domain_name",
"=",
"self",
".",
"name",
",",
"*",
"*",
"record_data",
")",
"record",
".",
"token",
"=",
"self",
".",
"token",
"records",
".",
"append",
"(",
"record",
")",
"return",
"records"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | Account.get_object | Class method that will return an Account object. | digitalocean/Account.py | def get_object(cls, api_token):
"""
Class method that will return an Account object.
"""
acct = cls(token=api_token)
acct.load()
return acct | def get_object(cls, api_token):
"""
Class method that will return an Account object.
"""
acct = cls(token=api_token)
acct.load()
return acct | [
"Class",
"method",
"that",
"will",
"return",
"an",
"Account",
"object",
"."
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/Account.py#L18-L24 | [
"def",
"get_object",
"(",
"cls",
",",
"api_token",
")",
":",
"acct",
"=",
"cls",
"(",
"token",
"=",
"api_token",
")",
"acct",
".",
"load",
"(",
")",
"return",
"acct"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | FloatingIP.get_object | Class method that will return a FloatingIP object by its IP.
Args:
api_token: str - token
ip: str - floating ip address | digitalocean/FloatingIP.py | def get_object(cls, api_token, ip):
"""
Class method that will return a FloatingIP object by its IP.
Args:
api_token: str - token
ip: str - floating ip address
"""
floating_ip = cls(token=api_token, ip=ip)
floating_ip.load()
return floating_ip | def get_object(cls, api_token, ip):
"""
Class method that will return a FloatingIP object by its IP.
Args:
api_token: str - token
ip: str - floating ip address
"""
floating_ip = cls(token=api_token, ip=ip)
floating_ip.load()
return floating_ip | [
"Class",
"method",
"that",
"will",
"return",
"a",
"FloatingIP",
"object",
"by",
"its",
"IP",
"."
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/FloatingIP.py#L14-L24 | [
"def",
"get_object",
"(",
"cls",
",",
"api_token",
",",
"ip",
")",
":",
"floating_ip",
"=",
"cls",
"(",
"token",
"=",
"api_token",
",",
"ip",
"=",
"ip",
")",
"floating_ip",
".",
"load",
"(",
")",
"return",
"floating_ip"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | FloatingIP.load | Load the FloatingIP object from DigitalOcean.
Requires self.ip to be set. | digitalocean/FloatingIP.py | def load(self):
"""
Load the FloatingIP object from DigitalOcean.
Requires self.ip to be set.
"""
data = self.get_data('floating_ips/%s' % self.ip, type=GET)
floating_ip = data['floating_ip']
# Setting the attribute values
for attr in floating_ip.keys():
setattr(self, attr, floating_ip[attr])
return self | def load(self):
"""
Load the FloatingIP object from DigitalOcean.
Requires self.ip to be set.
"""
data = self.get_data('floating_ips/%s' % self.ip, type=GET)
floating_ip = data['floating_ip']
# Setting the attribute values
for attr in floating_ip.keys():
setattr(self, attr, floating_ip[attr])
return self | [
"Load",
"the",
"FloatingIP",
"object",
"from",
"DigitalOcean",
"."
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/FloatingIP.py#L26-L39 | [
"def",
"load",
"(",
"self",
")",
":",
"data",
"=",
"self",
".",
"get_data",
"(",
"'floating_ips/%s'",
"%",
"self",
".",
"ip",
",",
"type",
"=",
"GET",
")",
"floating_ip",
"=",
"data",
"[",
"'floating_ip'",
"]",
"# Setting the attribute values",
"for",
"attr",
"in",
"floating_ip",
".",
"keys",
"(",
")",
":",
"setattr",
"(",
"self",
",",
"attr",
",",
"floating_ip",
"[",
"attr",
"]",
")",
"return",
"self"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | FloatingIP.create | Creates a FloatingIP and assigns it to a Droplet.
Note: Every argument and parameter given to this method will be
assigned to the object.
Args:
droplet_id: int - droplet id | digitalocean/FloatingIP.py | def create(self, *args, **kwargs):
"""
Creates a FloatingIP and assigns it to a Droplet.
Note: Every argument and parameter given to this method will be
assigned to the object.
Args:
droplet_id: int - droplet id
"""
data = self.get_data('floating_ips/',
type=POST,
params={'droplet_id': self.droplet_id})
if data:
self.ip = data['floating_ip']['ip']
self.region = data['floating_ip']['region']
return self | def create(self, *args, **kwargs):
"""
Creates a FloatingIP and assigns it to a Droplet.
Note: Every argument and parameter given to this method will be
assigned to the object.
Args:
droplet_id: int - droplet id
"""
data = self.get_data('floating_ips/',
type=POST,
params={'droplet_id': self.droplet_id})
if data:
self.ip = data['floating_ip']['ip']
self.region = data['floating_ip']['region']
return self | [
"Creates",
"a",
"FloatingIP",
"and",
"assigns",
"it",
"to",
"a",
"Droplet",
"."
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/FloatingIP.py#L41-L59 | [
"def",
"create",
"(",
"self",
",",
"*",
"args",
",",
"*",
"*",
"kwargs",
")",
":",
"data",
"=",
"self",
".",
"get_data",
"(",
"'floating_ips/'",
",",
"type",
"=",
"POST",
",",
"params",
"=",
"{",
"'droplet_id'",
":",
"self",
".",
"droplet_id",
"}",
")",
"if",
"data",
":",
"self",
".",
"ip",
"=",
"data",
"[",
"'floating_ip'",
"]",
"[",
"'ip'",
"]",
"self",
".",
"region",
"=",
"data",
"[",
"'floating_ip'",
"]",
"[",
"'region'",
"]",
"return",
"self"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | FloatingIP.reserve | Creates a FloatingIP in a region without assigning
it to a specific Droplet.
Note: Every argument and parameter given to this method will be
assigned to the object.
Args:
region_slug: str - region's slug (e.g. 'nyc3') | digitalocean/FloatingIP.py | def reserve(self, *args, **kwargs):
"""
Creates a FloatingIP in a region without assigning
it to a specific Droplet.
Note: Every argument and parameter given to this method will be
assigned to the object.
Args:
region_slug: str - region's slug (e.g. 'nyc3')
"""
data = self.get_data('floating_ips/',
type=POST,
params={'region': self.region_slug})
if data:
self.ip = data['floating_ip']['ip']
self.region = data['floating_ip']['region']
return self | def reserve(self, *args, **kwargs):
"""
Creates a FloatingIP in a region without assigning
it to a specific Droplet.
Note: Every argument and parameter given to this method will be
assigned to the object.
Args:
region_slug: str - region's slug (e.g. 'nyc3')
"""
data = self.get_data('floating_ips/',
type=POST,
params={'region': self.region_slug})
if data:
self.ip = data['floating_ip']['ip']
self.region = data['floating_ip']['region']
return self | [
"Creates",
"a",
"FloatingIP",
"in",
"a",
"region",
"without",
"assigning",
"it",
"to",
"a",
"specific",
"Droplet",
"."
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/FloatingIP.py#L61-L80 | [
"def",
"reserve",
"(",
"self",
",",
"*",
"args",
",",
"*",
"*",
"kwargs",
")",
":",
"data",
"=",
"self",
".",
"get_data",
"(",
"'floating_ips/'",
",",
"type",
"=",
"POST",
",",
"params",
"=",
"{",
"'region'",
":",
"self",
".",
"region_slug",
"}",
")",
"if",
"data",
":",
"self",
".",
"ip",
"=",
"data",
"[",
"'floating_ip'",
"]",
"[",
"'ip'",
"]",
"self",
".",
"region",
"=",
"data",
"[",
"'floating_ip'",
"]",
"[",
"'region'",
"]",
"return",
"self"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | FloatingIP.assign | Assign a FloatingIP to a Droplet.
Args:
droplet_id: int - droplet id | digitalocean/FloatingIP.py | def assign(self, droplet_id):
"""
Assign a FloatingIP to a Droplet.
Args:
droplet_id: int - droplet id
"""
return self.get_data(
"floating_ips/%s/actions/" % self.ip,
type=POST,
params={"type": "assign", "droplet_id": droplet_id}
) | def assign(self, droplet_id):
"""
Assign a FloatingIP to a Droplet.
Args:
droplet_id: int - droplet id
"""
return self.get_data(
"floating_ips/%s/actions/" % self.ip,
type=POST,
params={"type": "assign", "droplet_id": droplet_id}
) | [
"Assign",
"a",
"FloatingIP",
"to",
"a",
"Droplet",
"."
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/FloatingIP.py#L88-L99 | [
"def",
"assign",
"(",
"self",
",",
"droplet_id",
")",
":",
"return",
"self",
".",
"get_data",
"(",
"\"floating_ips/%s/actions/\"",
"%",
"self",
".",
"ip",
",",
"type",
"=",
"POST",
",",
"params",
"=",
"{",
"\"type\"",
":",
"\"assign\"",
",",
"\"droplet_id\"",
":",
"droplet_id",
"}",
")"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | Firewall.get_object | Class method that will return a Firewall object by ID. | digitalocean/Firewall.py | def get_object(cls, api_token, firewall_id):
"""
Class method that will return a Firewall object by ID.
"""
firewall = cls(token=api_token, id=firewall_id)
firewall.load()
return firewall | def get_object(cls, api_token, firewall_id):
"""
Class method that will return a Firewall object by ID.
"""
firewall = cls(token=api_token, id=firewall_id)
firewall.load()
return firewall | [
"Class",
"method",
"that",
"will",
"return",
"a",
"Firewall",
"object",
"by",
"ID",
"."
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/Firewall.py#L148-L154 | [
"def",
"get_object",
"(",
"cls",
",",
"api_token",
",",
"firewall_id",
")",
":",
"firewall",
"=",
"cls",
"(",
"token",
"=",
"api_token",
",",
"id",
"=",
"firewall_id",
")",
"firewall",
".",
"load",
"(",
")",
"return",
"firewall"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | Firewall.add_tags | Add tags to this Firewall. | digitalocean/Firewall.py | def add_tags(self, tags):
"""
Add tags to this Firewall.
"""
return self.get_data(
"firewalls/%s/tags" % self.id,
type=POST,
params={"tags": tags}
) | def add_tags(self, tags):
"""
Add tags to this Firewall.
"""
return self.get_data(
"firewalls/%s/tags" % self.id,
type=POST,
params={"tags": tags}
) | [
"Add",
"tags",
"to",
"this",
"Firewall",
"."
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/Firewall.py#L218-L226 | [
"def",
"add_tags",
"(",
"self",
",",
"tags",
")",
":",
"return",
"self",
".",
"get_data",
"(",
"\"firewalls/%s/tags\"",
"%",
"self",
".",
"id",
",",
"type",
"=",
"POST",
",",
"params",
"=",
"{",
"\"tags\"",
":",
"tags",
"}",
")"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | Firewall.remove_tags | Remove tags from this Firewall. | digitalocean/Firewall.py | def remove_tags(self, tags):
"""
Remove tags from this Firewall.
"""
return self.get_data(
"firewalls/%s/tags" % self.id,
type=DELETE,
params={"tags": tags}
) | def remove_tags(self, tags):
"""
Remove tags from this Firewall.
"""
return self.get_data(
"firewalls/%s/tags" % self.id,
type=DELETE,
params={"tags": tags}
) | [
"Remove",
"tags",
"from",
"this",
"Firewall",
"."
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/Firewall.py#L228-L236 | [
"def",
"remove_tags",
"(",
"self",
",",
"tags",
")",
":",
"return",
"self",
".",
"get_data",
"(",
"\"firewalls/%s/tags\"",
"%",
"self",
".",
"id",
",",
"type",
"=",
"DELETE",
",",
"params",
"=",
"{",
"\"tags\"",
":",
"tags",
"}",
")"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | SSHKey.get_object | Class method that will return a SSHKey object by ID. | digitalocean/SSHKey.py | def get_object(cls, api_token, ssh_key_id):
"""
Class method that will return a SSHKey object by ID.
"""
ssh_key = cls(token=api_token, id=ssh_key_id)
ssh_key.load()
return ssh_key | def get_object(cls, api_token, ssh_key_id):
"""
Class method that will return a SSHKey object by ID.
"""
ssh_key = cls(token=api_token, id=ssh_key_id)
ssh_key.load()
return ssh_key | [
"Class",
"method",
"that",
"will",
"return",
"a",
"SSHKey",
"object",
"by",
"ID",
"."
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/SSHKey.py#L15-L21 | [
"def",
"get_object",
"(",
"cls",
",",
"api_token",
",",
"ssh_key_id",
")",
":",
"ssh_key",
"=",
"cls",
"(",
"token",
"=",
"api_token",
",",
"id",
"=",
"ssh_key_id",
")",
"ssh_key",
".",
"load",
"(",
")",
"return",
"ssh_key"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | SSHKey.load | Load the SSHKey object from DigitalOcean.
Requires either self.id or self.fingerprint to be set. | digitalocean/SSHKey.py | def load(self):
"""
Load the SSHKey object from DigitalOcean.
Requires either self.id or self.fingerprint to be set.
"""
identifier = None
if self.id:
identifier = self.id
elif self.fingerprint is not None:
identifier = self.fingerprint
data = self.get_data("account/keys/%s" % identifier, type=GET)
ssh_key = data['ssh_key']
# Setting the attribute values
for attr in ssh_key.keys():
setattr(self, attr, ssh_key[attr])
self.id = ssh_key['id'] | def load(self):
"""
Load the SSHKey object from DigitalOcean.
Requires either self.id or self.fingerprint to be set.
"""
identifier = None
if self.id:
identifier = self.id
elif self.fingerprint is not None:
identifier = self.fingerprint
data = self.get_data("account/keys/%s" % identifier, type=GET)
ssh_key = data['ssh_key']
# Setting the attribute values
for attr in ssh_key.keys():
setattr(self, attr, ssh_key[attr])
self.id = ssh_key['id'] | [
"Load",
"the",
"SSHKey",
"object",
"from",
"DigitalOcean",
"."
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/SSHKey.py#L23-L42 | [
"def",
"load",
"(",
"self",
")",
":",
"identifier",
"=",
"None",
"if",
"self",
".",
"id",
":",
"identifier",
"=",
"self",
".",
"id",
"elif",
"self",
".",
"fingerprint",
"is",
"not",
"None",
":",
"identifier",
"=",
"self",
".",
"fingerprint",
"data",
"=",
"self",
".",
"get_data",
"(",
"\"account/keys/%s\"",
"%",
"identifier",
",",
"type",
"=",
"GET",
")",
"ssh_key",
"=",
"data",
"[",
"'ssh_key'",
"]",
"# Setting the attribute values",
"for",
"attr",
"in",
"ssh_key",
".",
"keys",
"(",
")",
":",
"setattr",
"(",
"self",
",",
"attr",
",",
"ssh_key",
"[",
"attr",
"]",
")",
"self",
".",
"id",
"=",
"ssh_key",
"[",
"'id'",
"]"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | SSHKey.load_by_pub_key | This method will load a SSHKey object from DigitalOcean
from a public_key. This method will avoid problems like
uploading the same public_key twice. | digitalocean/SSHKey.py | def load_by_pub_key(self, public_key):
"""
This method will load a SSHKey object from DigitalOcean
from a public_key. This method will avoid problems like
uploading the same public_key twice.
"""
data = self.get_data("account/keys/")
for jsoned in data['ssh_keys']:
if jsoned.get('public_key', "") == public_key:
self.id = jsoned['id']
self.load()
return self
return None | def load_by_pub_key(self, public_key):
"""
This method will load a SSHKey object from DigitalOcean
from a public_key. This method will avoid problems like
uploading the same public_key twice.
"""
data = self.get_data("account/keys/")
for jsoned in data['ssh_keys']:
if jsoned.get('public_key', "") == public_key:
self.id = jsoned['id']
self.load()
return self
return None | [
"This",
"method",
"will",
"load",
"a",
"SSHKey",
"object",
"from",
"DigitalOcean",
"from",
"a",
"public_key",
".",
"This",
"method",
"will",
"avoid",
"problems",
"like",
"uploading",
"the",
"same",
"public_key",
"twice",
"."
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/SSHKey.py#L44-L57 | [
"def",
"load_by_pub_key",
"(",
"self",
",",
"public_key",
")",
":",
"data",
"=",
"self",
".",
"get_data",
"(",
"\"account/keys/\"",
")",
"for",
"jsoned",
"in",
"data",
"[",
"'ssh_keys'",
"]",
":",
"if",
"jsoned",
".",
"get",
"(",
"'public_key'",
",",
"\"\"",
")",
"==",
"public_key",
":",
"self",
".",
"id",
"=",
"jsoned",
"[",
"'id'",
"]",
"self",
".",
"load",
"(",
")",
"return",
"self",
"return",
"None"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | SSHKey.create | Create the SSH Key | digitalocean/SSHKey.py | def create(self):
"""
Create the SSH Key
"""
input_params = {
"name": self.name,
"public_key": self.public_key,
}
data = self.get_data("account/keys/", type=POST, params=input_params)
if data:
self.id = data['ssh_key']['id'] | def create(self):
"""
Create the SSH Key
"""
input_params = {
"name": self.name,
"public_key": self.public_key,
}
data = self.get_data("account/keys/", type=POST, params=input_params)
if data:
self.id = data['ssh_key']['id'] | [
"Create",
"the",
"SSH",
"Key"
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/SSHKey.py#L59-L71 | [
"def",
"create",
"(",
"self",
")",
":",
"input_params",
"=",
"{",
"\"name\"",
":",
"self",
".",
"name",
",",
"\"public_key\"",
":",
"self",
".",
"public_key",
",",
"}",
"data",
"=",
"self",
".",
"get_data",
"(",
"\"account/keys/\"",
",",
"type",
"=",
"POST",
",",
"params",
"=",
"input_params",
")",
"if",
"data",
":",
"self",
".",
"id",
"=",
"data",
"[",
"'ssh_key'",
"]",
"[",
"'id'",
"]"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | SSHKey.edit | Edit the SSH Key | digitalocean/SSHKey.py | def edit(self):
"""
Edit the SSH Key
"""
input_params = {
"name": self.name,
"public_key": self.public_key,
}
data = self.get_data(
"account/keys/%s" % self.id,
type=PUT,
params=input_params
)
if data:
self.id = data['ssh_key']['id'] | def edit(self):
"""
Edit the SSH Key
"""
input_params = {
"name": self.name,
"public_key": self.public_key,
}
data = self.get_data(
"account/keys/%s" % self.id,
type=PUT,
params=input_params
)
if data:
self.id = data['ssh_key']['id'] | [
"Edit",
"the",
"SSH",
"Key"
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/SSHKey.py#L73-L89 | [
"def",
"edit",
"(",
"self",
")",
":",
"input_params",
"=",
"{",
"\"name\"",
":",
"self",
".",
"name",
",",
"\"public_key\"",
":",
"self",
".",
"public_key",
",",
"}",
"data",
"=",
"self",
".",
"get_data",
"(",
"\"account/keys/%s\"",
"%",
"self",
".",
"id",
",",
"type",
"=",
"PUT",
",",
"params",
"=",
"input_params",
")",
"if",
"data",
":",
"self",
".",
"id",
"=",
"data",
"[",
"'ssh_key'",
"]",
"[",
"'id'",
"]"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | Manager.get_all_regions | This function returns a list of Region object. | digitalocean/Manager.py | def get_all_regions(self):
"""
This function returns a list of Region object.
"""
data = self.get_data("regions/")
regions = list()
for jsoned in data['regions']:
region = Region(**jsoned)
region.token = self.token
regions.append(region)
return regions | def get_all_regions(self):
"""
This function returns a list of Region object.
"""
data = self.get_data("regions/")
regions = list()
for jsoned in data['regions']:
region = Region(**jsoned)
region.token = self.token
regions.append(region)
return regions | [
"This",
"function",
"returns",
"a",
"list",
"of",
"Region",
"object",
"."
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/Manager.py#L36-L46 | [
"def",
"get_all_regions",
"(",
"self",
")",
":",
"data",
"=",
"self",
".",
"get_data",
"(",
"\"regions/\"",
")",
"regions",
"=",
"list",
"(",
")",
"for",
"jsoned",
"in",
"data",
"[",
"'regions'",
"]",
":",
"region",
"=",
"Region",
"(",
"*",
"*",
"jsoned",
")",
"region",
".",
"token",
"=",
"self",
".",
"token",
"regions",
".",
"append",
"(",
"region",
")",
"return",
"regions"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | Manager.get_all_droplets | This function returns a list of Droplet object. | digitalocean/Manager.py | def get_all_droplets(self, tag_name=None):
"""
This function returns a list of Droplet object.
"""
params = dict()
if tag_name:
params["tag_name"] = tag_name
data = self.get_data("droplets/", params=params)
droplets = list()
for jsoned in data['droplets']:
droplet = Droplet(**jsoned)
droplet.token = self.token
for net in droplet.networks['v4']:
if net['type'] == 'private':
droplet.private_ip_address = net['ip_address']
if net['type'] == 'public':
droplet.ip_address = net['ip_address']
if droplet.networks['v6']:
droplet.ip_v6_address = droplet.networks['v6'][0]['ip_address']
if "backups" in droplet.features:
droplet.backups = True
else:
droplet.backups = False
if "ipv6" in droplet.features:
droplet.ipv6 = True
else:
droplet.ipv6 = False
if "private_networking" in droplet.features:
droplet.private_networking = True
else:
droplet.private_networking = False
droplets.append(droplet)
return droplets | def get_all_droplets(self, tag_name=None):
"""
This function returns a list of Droplet object.
"""
params = dict()
if tag_name:
params["tag_name"] = tag_name
data = self.get_data("droplets/", params=params)
droplets = list()
for jsoned in data['droplets']:
droplet = Droplet(**jsoned)
droplet.token = self.token
for net in droplet.networks['v4']:
if net['type'] == 'private':
droplet.private_ip_address = net['ip_address']
if net['type'] == 'public':
droplet.ip_address = net['ip_address']
if droplet.networks['v6']:
droplet.ip_v6_address = droplet.networks['v6'][0]['ip_address']
if "backups" in droplet.features:
droplet.backups = True
else:
droplet.backups = False
if "ipv6" in droplet.features:
droplet.ipv6 = True
else:
droplet.ipv6 = False
if "private_networking" in droplet.features:
droplet.private_networking = True
else:
droplet.private_networking = False
droplets.append(droplet)
return droplets | [
"This",
"function",
"returns",
"a",
"list",
"of",
"Droplet",
"object",
"."
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/Manager.py#L48-L86 | [
"def",
"get_all_droplets",
"(",
"self",
",",
"tag_name",
"=",
"None",
")",
":",
"params",
"=",
"dict",
"(",
")",
"if",
"tag_name",
":",
"params",
"[",
"\"tag_name\"",
"]",
"=",
"tag_name",
"data",
"=",
"self",
".",
"get_data",
"(",
"\"droplets/\"",
",",
"params",
"=",
"params",
")",
"droplets",
"=",
"list",
"(",
")",
"for",
"jsoned",
"in",
"data",
"[",
"'droplets'",
"]",
":",
"droplet",
"=",
"Droplet",
"(",
"*",
"*",
"jsoned",
")",
"droplet",
".",
"token",
"=",
"self",
".",
"token",
"for",
"net",
"in",
"droplet",
".",
"networks",
"[",
"'v4'",
"]",
":",
"if",
"net",
"[",
"'type'",
"]",
"==",
"'private'",
":",
"droplet",
".",
"private_ip_address",
"=",
"net",
"[",
"'ip_address'",
"]",
"if",
"net",
"[",
"'type'",
"]",
"==",
"'public'",
":",
"droplet",
".",
"ip_address",
"=",
"net",
"[",
"'ip_address'",
"]",
"if",
"droplet",
".",
"networks",
"[",
"'v6'",
"]",
":",
"droplet",
".",
"ip_v6_address",
"=",
"droplet",
".",
"networks",
"[",
"'v6'",
"]",
"[",
"0",
"]",
"[",
"'ip_address'",
"]",
"if",
"\"backups\"",
"in",
"droplet",
".",
"features",
":",
"droplet",
".",
"backups",
"=",
"True",
"else",
":",
"droplet",
".",
"backups",
"=",
"False",
"if",
"\"ipv6\"",
"in",
"droplet",
".",
"features",
":",
"droplet",
".",
"ipv6",
"=",
"True",
"else",
":",
"droplet",
".",
"ipv6",
"=",
"False",
"if",
"\"private_networking\"",
"in",
"droplet",
".",
"features",
":",
"droplet",
".",
"private_networking",
"=",
"True",
"else",
":",
"droplet",
".",
"private_networking",
"=",
"False",
"droplets",
".",
"append",
"(",
"droplet",
")",
"return",
"droplets"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | Manager.get_droplet | Return a Droplet by its ID. | digitalocean/Manager.py | def get_droplet(self, droplet_id):
"""
Return a Droplet by its ID.
"""
return Droplet.get_object(api_token=self.token, droplet_id=droplet_id) | def get_droplet(self, droplet_id):
"""
Return a Droplet by its ID.
"""
return Droplet.get_object(api_token=self.token, droplet_id=droplet_id) | [
"Return",
"a",
"Droplet",
"by",
"its",
"ID",
"."
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/Manager.py#L88-L92 | [
"def",
"get_droplet",
"(",
"self",
",",
"droplet_id",
")",
":",
"return",
"Droplet",
".",
"get_object",
"(",
"api_token",
"=",
"self",
".",
"token",
",",
"droplet_id",
"=",
"droplet_id",
")"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | Manager.get_all_sizes | This function returns a list of Size object. | digitalocean/Manager.py | def get_all_sizes(self):
"""
This function returns a list of Size object.
"""
data = self.get_data("sizes/")
sizes = list()
for jsoned in data['sizes']:
size = Size(**jsoned)
size.token = self.token
sizes.append(size)
return sizes | def get_all_sizes(self):
"""
This function returns a list of Size object.
"""
data = self.get_data("sizes/")
sizes = list()
for jsoned in data['sizes']:
size = Size(**jsoned)
size.token = self.token
sizes.append(size)
return sizes | [
"This",
"function",
"returns",
"a",
"list",
"of",
"Size",
"object",
"."
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/Manager.py#L94-L104 | [
"def",
"get_all_sizes",
"(",
"self",
")",
":",
"data",
"=",
"self",
".",
"get_data",
"(",
"\"sizes/\"",
")",
"sizes",
"=",
"list",
"(",
")",
"for",
"jsoned",
"in",
"data",
"[",
"'sizes'",
"]",
":",
"size",
"=",
"Size",
"(",
"*",
"*",
"jsoned",
")",
"size",
".",
"token",
"=",
"self",
".",
"token",
"sizes",
".",
"append",
"(",
"size",
")",
"return",
"sizes"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | Manager.get_images | This function returns a list of Image object. | digitalocean/Manager.py | def get_images(self, private=False, type=None):
"""
This function returns a list of Image object.
"""
params = {}
if private:
params['private'] = 'true'
if type:
params['type'] = type
data = self.get_data("images/", params=params)
images = list()
for jsoned in data['images']:
image = Image(**jsoned)
image.token = self.token
images.append(image)
return images | def get_images(self, private=False, type=None):
"""
This function returns a list of Image object.
"""
params = {}
if private:
params['private'] = 'true'
if type:
params['type'] = type
data = self.get_data("images/", params=params)
images = list()
for jsoned in data['images']:
image = Image(**jsoned)
image.token = self.token
images.append(image)
return images | [
"This",
"function",
"returns",
"a",
"list",
"of",
"Image",
"object",
"."
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/Manager.py#L106-L121 | [
"def",
"get_images",
"(",
"self",
",",
"private",
"=",
"False",
",",
"type",
"=",
"None",
")",
":",
"params",
"=",
"{",
"}",
"if",
"private",
":",
"params",
"[",
"'private'",
"]",
"=",
"'true'",
"if",
"type",
":",
"params",
"[",
"'type'",
"]",
"=",
"type",
"data",
"=",
"self",
".",
"get_data",
"(",
"\"images/\"",
",",
"params",
"=",
"params",
")",
"images",
"=",
"list",
"(",
")",
"for",
"jsoned",
"in",
"data",
"[",
"'images'",
"]",
":",
"image",
"=",
"Image",
"(",
"*",
"*",
"jsoned",
")",
"image",
".",
"token",
"=",
"self",
".",
"token",
"images",
".",
"append",
"(",
"image",
")",
"return",
"images"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | Manager.get_image | Return a Image by its ID/Slug. | digitalocean/Manager.py | def get_image(self, image_id_or_slug):
"""
Return a Image by its ID/Slug.
"""
return Image.get_object(
api_token=self.token,
image_id_or_slug=image_id_or_slug,
) | def get_image(self, image_id_or_slug):
"""
Return a Image by its ID/Slug.
"""
return Image.get_object(
api_token=self.token,
image_id_or_slug=image_id_or_slug,
) | [
"Return",
"a",
"Image",
"by",
"its",
"ID",
"/",
"Slug",
"."
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/Manager.py#L131-L138 | [
"def",
"get_image",
"(",
"self",
",",
"image_id_or_slug",
")",
":",
"return",
"Image",
".",
"get_object",
"(",
"api_token",
"=",
"self",
".",
"token",
",",
"image_id_or_slug",
"=",
"image_id_or_slug",
",",
")"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | Manager.get_global_images | This function returns a list of Image objects representing
public DigitalOcean images (e.g. base distribution images
and 'One-Click' applications). | digitalocean/Manager.py | def get_global_images(self):
"""
This function returns a list of Image objects representing
public DigitalOcean images (e.g. base distribution images
and 'One-Click' applications).
"""
data = self.get_images()
images = list()
for i in data:
if i.public:
i.token = self.token
images.append(i)
return images | def get_global_images(self):
"""
This function returns a list of Image objects representing
public DigitalOcean images (e.g. base distribution images
and 'One-Click' applications).
"""
data = self.get_images()
images = list()
for i in data:
if i.public:
i.token = self.token
images.append(i)
return images | [
"This",
"function",
"returns",
"a",
"list",
"of",
"Image",
"objects",
"representing",
"public",
"DigitalOcean",
"images",
"(",
"e",
".",
"g",
".",
"base",
"distribution",
"images",
"and",
"One",
"-",
"Click",
"applications",
")",
"."
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/Manager.py#L148-L160 | [
"def",
"get_global_images",
"(",
"self",
")",
":",
"data",
"=",
"self",
".",
"get_images",
"(",
")",
"images",
"=",
"list",
"(",
")",
"for",
"i",
"in",
"data",
":",
"if",
"i",
".",
"public",
":",
"i",
".",
"token",
"=",
"self",
".",
"token",
"images",
".",
"append",
"(",
"i",
")",
"return",
"images"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | Manager.get_all_domains | This function returns a list of Domain object. | digitalocean/Manager.py | def get_all_domains(self):
"""
This function returns a list of Domain object.
"""
data = self.get_data("domains/")
domains = list()
for jsoned in data['domains']:
domain = Domain(**jsoned)
domain.token = self.token
domains.append(domain)
return domains | def get_all_domains(self):
"""
This function returns a list of Domain object.
"""
data = self.get_data("domains/")
domains = list()
for jsoned in data['domains']:
domain = Domain(**jsoned)
domain.token = self.token
domains.append(domain)
return domains | [
"This",
"function",
"returns",
"a",
"list",
"of",
"Domain",
"object",
"."
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/Manager.py#L178-L188 | [
"def",
"get_all_domains",
"(",
"self",
")",
":",
"data",
"=",
"self",
".",
"get_data",
"(",
"\"domains/\"",
")",
"domains",
"=",
"list",
"(",
")",
"for",
"jsoned",
"in",
"data",
"[",
"'domains'",
"]",
":",
"domain",
"=",
"Domain",
"(",
"*",
"*",
"jsoned",
")",
"domain",
".",
"token",
"=",
"self",
".",
"token",
"domains",
".",
"append",
"(",
"domain",
")",
"return",
"domains"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | Manager.get_domain | Return a Domain by its domain_name | digitalocean/Manager.py | def get_domain(self, domain_name):
"""
Return a Domain by its domain_name
"""
return Domain.get_object(api_token=self.token, domain_name=domain_name) | def get_domain(self, domain_name):
"""
Return a Domain by its domain_name
"""
return Domain.get_object(api_token=self.token, domain_name=domain_name) | [
"Return",
"a",
"Domain",
"by",
"its",
"domain_name"
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/Manager.py#L190-L194 | [
"def",
"get_domain",
"(",
"self",
",",
"domain_name",
")",
":",
"return",
"Domain",
".",
"get_object",
"(",
"api_token",
"=",
"self",
".",
"token",
",",
"domain_name",
"=",
"domain_name",
")"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | Manager.get_all_sshkeys | This function returns a list of SSHKey object. | digitalocean/Manager.py | def get_all_sshkeys(self):
"""
This function returns a list of SSHKey object.
"""
data = self.get_data("account/keys/")
ssh_keys = list()
for jsoned in data['ssh_keys']:
ssh_key = SSHKey(**jsoned)
ssh_key.token = self.token
ssh_keys.append(ssh_key)
return ssh_keys | def get_all_sshkeys(self):
"""
This function returns a list of SSHKey object.
"""
data = self.get_data("account/keys/")
ssh_keys = list()
for jsoned in data['ssh_keys']:
ssh_key = SSHKey(**jsoned)
ssh_key.token = self.token
ssh_keys.append(ssh_key)
return ssh_keys | [
"This",
"function",
"returns",
"a",
"list",
"of",
"SSHKey",
"object",
"."
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/Manager.py#L196-L206 | [
"def",
"get_all_sshkeys",
"(",
"self",
")",
":",
"data",
"=",
"self",
".",
"get_data",
"(",
"\"account/keys/\"",
")",
"ssh_keys",
"=",
"list",
"(",
")",
"for",
"jsoned",
"in",
"data",
"[",
"'ssh_keys'",
"]",
":",
"ssh_key",
"=",
"SSHKey",
"(",
"*",
"*",
"jsoned",
")",
"ssh_key",
".",
"token",
"=",
"self",
".",
"token",
"ssh_keys",
".",
"append",
"(",
"ssh_key",
")",
"return",
"ssh_keys"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | Manager.get_ssh_key | Return a SSHKey object by its ID. | digitalocean/Manager.py | def get_ssh_key(self, ssh_key_id):
"""
Return a SSHKey object by its ID.
"""
return SSHKey.get_object(api_token=self.token, ssh_key_id=ssh_key_id) | def get_ssh_key(self, ssh_key_id):
"""
Return a SSHKey object by its ID.
"""
return SSHKey.get_object(api_token=self.token, ssh_key_id=ssh_key_id) | [
"Return",
"a",
"SSHKey",
"object",
"by",
"its",
"ID",
"."
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/Manager.py#L208-L212 | [
"def",
"get_ssh_key",
"(",
"self",
",",
"ssh_key_id",
")",
":",
"return",
"SSHKey",
".",
"get_object",
"(",
"api_token",
"=",
"self",
".",
"token",
",",
"ssh_key_id",
"=",
"ssh_key_id",
")"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | Manager.get_all_tags | This method returns a list of all tags. | digitalocean/Manager.py | def get_all_tags(self):
"""
This method returns a list of all tags.
"""
data = self.get_data("tags")
return [
Tag(token=self.token, **tag) for tag in data['tags']
] | def get_all_tags(self):
"""
This method returns a list of all tags.
"""
data = self.get_data("tags")
return [
Tag(token=self.token, **tag) for tag in data['tags']
] | [
"This",
"method",
"returns",
"a",
"list",
"of",
"all",
"tags",
"."
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/Manager.py#L214-L221 | [
"def",
"get_all_tags",
"(",
"self",
")",
":",
"data",
"=",
"self",
".",
"get_data",
"(",
"\"tags\"",
")",
"return",
"[",
"Tag",
"(",
"token",
"=",
"self",
".",
"token",
",",
"*",
"*",
"tag",
")",
"for",
"tag",
"in",
"data",
"[",
"'tags'",
"]",
"]"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | Manager.get_all_floating_ips | This function returns a list of FloatingIP objects. | digitalocean/Manager.py | def get_all_floating_ips(self):
"""
This function returns a list of FloatingIP objects.
"""
data = self.get_data("floating_ips")
floating_ips = list()
for jsoned in data['floating_ips']:
floating_ip = FloatingIP(**jsoned)
floating_ip.token = self.token
floating_ips.append(floating_ip)
return floating_ips | def get_all_floating_ips(self):
"""
This function returns a list of FloatingIP objects.
"""
data = self.get_data("floating_ips")
floating_ips = list()
for jsoned in data['floating_ips']:
floating_ip = FloatingIP(**jsoned)
floating_ip.token = self.token
floating_ips.append(floating_ip)
return floating_ips | [
"This",
"function",
"returns",
"a",
"list",
"of",
"FloatingIP",
"objects",
"."
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/Manager.py#L229-L239 | [
"def",
"get_all_floating_ips",
"(",
"self",
")",
":",
"data",
"=",
"self",
".",
"get_data",
"(",
"\"floating_ips\"",
")",
"floating_ips",
"=",
"list",
"(",
")",
"for",
"jsoned",
"in",
"data",
"[",
"'floating_ips'",
"]",
":",
"floating_ip",
"=",
"FloatingIP",
"(",
"*",
"*",
"jsoned",
")",
"floating_ip",
".",
"token",
"=",
"self",
".",
"token",
"floating_ips",
".",
"append",
"(",
"floating_ip",
")",
"return",
"floating_ips"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | Manager.get_floating_ip | Returns a of FloatingIP object by its IP address. | digitalocean/Manager.py | def get_floating_ip(self, ip):
"""
Returns a of FloatingIP object by its IP address.
"""
return FloatingIP.get_object(api_token=self.token, ip=ip) | def get_floating_ip(self, ip):
"""
Returns a of FloatingIP object by its IP address.
"""
return FloatingIP.get_object(api_token=self.token, ip=ip) | [
"Returns",
"a",
"of",
"FloatingIP",
"object",
"by",
"its",
"IP",
"address",
"."
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/Manager.py#L241-L245 | [
"def",
"get_floating_ip",
"(",
"self",
",",
"ip",
")",
":",
"return",
"FloatingIP",
".",
"get_object",
"(",
"api_token",
"=",
"self",
".",
"token",
",",
"ip",
"=",
"ip",
")"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | Manager.get_all_load_balancers | Returns a list of Load Balancer objects. | digitalocean/Manager.py | def get_all_load_balancers(self):
"""
Returns a list of Load Balancer objects.
"""
data = self.get_data("load_balancers")
load_balancers = list()
for jsoned in data['load_balancers']:
load_balancer = LoadBalancer(**jsoned)
load_balancer.token = self.token
load_balancer.health_check = HealthCheck(**jsoned['health_check'])
load_balancer.sticky_sessions = StickySesions(**jsoned['sticky_sessions'])
forwarding_rules = list()
for rule in jsoned['forwarding_rules']:
forwarding_rules.append(ForwardingRule(**rule))
load_balancer.forwarding_rules = forwarding_rules
load_balancers.append(load_balancer)
return load_balancers | def get_all_load_balancers(self):
"""
Returns a list of Load Balancer objects.
"""
data = self.get_data("load_balancers")
load_balancers = list()
for jsoned in data['load_balancers']:
load_balancer = LoadBalancer(**jsoned)
load_balancer.token = self.token
load_balancer.health_check = HealthCheck(**jsoned['health_check'])
load_balancer.sticky_sessions = StickySesions(**jsoned['sticky_sessions'])
forwarding_rules = list()
for rule in jsoned['forwarding_rules']:
forwarding_rules.append(ForwardingRule(**rule))
load_balancer.forwarding_rules = forwarding_rules
load_balancers.append(load_balancer)
return load_balancers | [
"Returns",
"a",
"list",
"of",
"Load",
"Balancer",
"objects",
"."
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/Manager.py#L247-L264 | [
"def",
"get_all_load_balancers",
"(",
"self",
")",
":",
"data",
"=",
"self",
".",
"get_data",
"(",
"\"load_balancers\"",
")",
"load_balancers",
"=",
"list",
"(",
")",
"for",
"jsoned",
"in",
"data",
"[",
"'load_balancers'",
"]",
":",
"load_balancer",
"=",
"LoadBalancer",
"(",
"*",
"*",
"jsoned",
")",
"load_balancer",
".",
"token",
"=",
"self",
".",
"token",
"load_balancer",
".",
"health_check",
"=",
"HealthCheck",
"(",
"*",
"*",
"jsoned",
"[",
"'health_check'",
"]",
")",
"load_balancer",
".",
"sticky_sessions",
"=",
"StickySesions",
"(",
"*",
"*",
"jsoned",
"[",
"'sticky_sessions'",
"]",
")",
"forwarding_rules",
"=",
"list",
"(",
")",
"for",
"rule",
"in",
"jsoned",
"[",
"'forwarding_rules'",
"]",
":",
"forwarding_rules",
".",
"append",
"(",
"ForwardingRule",
"(",
"*",
"*",
"rule",
")",
")",
"load_balancer",
".",
"forwarding_rules",
"=",
"forwarding_rules",
"load_balancers",
".",
"append",
"(",
"load_balancer",
")",
"return",
"load_balancers"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | Manager.get_load_balancer | Returns a Load Balancer object by its ID.
Args:
id (str): Load Balancer ID | digitalocean/Manager.py | def get_load_balancer(self, id):
"""
Returns a Load Balancer object by its ID.
Args:
id (str): Load Balancer ID
"""
return LoadBalancer.get_object(api_token=self.token, id=id) | def get_load_balancer(self, id):
"""
Returns a Load Balancer object by its ID.
Args:
id (str): Load Balancer ID
"""
return LoadBalancer.get_object(api_token=self.token, id=id) | [
"Returns",
"a",
"Load",
"Balancer",
"object",
"by",
"its",
"ID",
"."
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/Manager.py#L266-L273 | [
"def",
"get_load_balancer",
"(",
"self",
",",
"id",
")",
":",
"return",
"LoadBalancer",
".",
"get_object",
"(",
"api_token",
"=",
"self",
".",
"token",
",",
"id",
"=",
"id",
")"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | Manager.get_certificate | Returns a Certificate object by its ID.
Args:
id (str): Certificate ID | digitalocean/Manager.py | def get_certificate(self, id):
"""
Returns a Certificate object by its ID.
Args:
id (str): Certificate ID
"""
return Certificate.get_object(api_token=self.token, cert_id=id) | def get_certificate(self, id):
"""
Returns a Certificate object by its ID.
Args:
id (str): Certificate ID
"""
return Certificate.get_object(api_token=self.token, cert_id=id) | [
"Returns",
"a",
"Certificate",
"object",
"by",
"its",
"ID",
"."
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/Manager.py#L275-L282 | [
"def",
"get_certificate",
"(",
"self",
",",
"id",
")",
":",
"return",
"Certificate",
".",
"get_object",
"(",
"api_token",
"=",
"self",
".",
"token",
",",
"cert_id",
"=",
"id",
")"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | Manager.get_all_certificates | This function returns a list of Certificate objects. | digitalocean/Manager.py | def get_all_certificates(self):
"""
This function returns a list of Certificate objects.
"""
data = self.get_data("certificates")
certificates = list()
for jsoned in data['certificates']:
cert = Certificate(**jsoned)
cert.token = self.token
certificates.append(cert)
return certificates | def get_all_certificates(self):
"""
This function returns a list of Certificate objects.
"""
data = self.get_data("certificates")
certificates = list()
for jsoned in data['certificates']:
cert = Certificate(**jsoned)
cert.token = self.token
certificates.append(cert)
return certificates | [
"This",
"function",
"returns",
"a",
"list",
"of",
"Certificate",
"objects",
"."
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/Manager.py#L284-L295 | [
"def",
"get_all_certificates",
"(",
"self",
")",
":",
"data",
"=",
"self",
".",
"get_data",
"(",
"\"certificates\"",
")",
"certificates",
"=",
"list",
"(",
")",
"for",
"jsoned",
"in",
"data",
"[",
"'certificates'",
"]",
":",
"cert",
"=",
"Certificate",
"(",
"*",
"*",
"jsoned",
")",
"cert",
".",
"token",
"=",
"self",
".",
"token",
"certificates",
".",
"append",
"(",
"cert",
")",
"return",
"certificates"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | Manager.get_snapshot | Return a Snapshot by its ID. | digitalocean/Manager.py | def get_snapshot(self, snapshot_id):
"""
Return a Snapshot by its ID.
"""
return Snapshot.get_object(
api_token=self.token, snapshot_id=snapshot_id
) | def get_snapshot(self, snapshot_id):
"""
Return a Snapshot by its ID.
"""
return Snapshot.get_object(
api_token=self.token, snapshot_id=snapshot_id
) | [
"Return",
"a",
"Snapshot",
"by",
"its",
"ID",
"."
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/Manager.py#L297-L303 | [
"def",
"get_snapshot",
"(",
"self",
",",
"snapshot_id",
")",
":",
"return",
"Snapshot",
".",
"get_object",
"(",
"api_token",
"=",
"self",
".",
"token",
",",
"snapshot_id",
"=",
"snapshot_id",
")"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | Manager.get_all_snapshots | This method returns a list of all Snapshots. | digitalocean/Manager.py | def get_all_snapshots(self):
"""
This method returns a list of all Snapshots.
"""
data = self.get_data("snapshots/")
return [
Snapshot(token=self.token, **snapshot)
for snapshot in data['snapshots']
] | def get_all_snapshots(self):
"""
This method returns a list of all Snapshots.
"""
data = self.get_data("snapshots/")
return [
Snapshot(token=self.token, **snapshot)
for snapshot in data['snapshots']
] | [
"This",
"method",
"returns",
"a",
"list",
"of",
"all",
"Snapshots",
"."
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/Manager.py#L305-L313 | [
"def",
"get_all_snapshots",
"(",
"self",
")",
":",
"data",
"=",
"self",
".",
"get_data",
"(",
"\"snapshots/\"",
")",
"return",
"[",
"Snapshot",
"(",
"token",
"=",
"self",
".",
"token",
",",
"*",
"*",
"snapshot",
")",
"for",
"snapshot",
"in",
"data",
"[",
"'snapshots'",
"]",
"]"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | Manager.get_droplet_snapshots | This method returns a list of all Snapshots based on Droplets. | digitalocean/Manager.py | def get_droplet_snapshots(self):
"""
This method returns a list of all Snapshots based on Droplets.
"""
data = self.get_data("snapshots?resource_type=droplet")
return [
Snapshot(token=self.token, **snapshot)
for snapshot in data['snapshots']
] | def get_droplet_snapshots(self):
"""
This method returns a list of all Snapshots based on Droplets.
"""
data = self.get_data("snapshots?resource_type=droplet")
return [
Snapshot(token=self.token, **snapshot)
for snapshot in data['snapshots']
] | [
"This",
"method",
"returns",
"a",
"list",
"of",
"all",
"Snapshots",
"based",
"on",
"Droplets",
"."
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/Manager.py#L315-L323 | [
"def",
"get_droplet_snapshots",
"(",
"self",
")",
":",
"data",
"=",
"self",
".",
"get_data",
"(",
"\"snapshots?resource_type=droplet\"",
")",
"return",
"[",
"Snapshot",
"(",
"token",
"=",
"self",
".",
"token",
",",
"*",
"*",
"snapshot",
")",
"for",
"snapshot",
"in",
"data",
"[",
"'snapshots'",
"]",
"]"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | Manager.get_volume_snapshots | This method returns a list of all Snapshots based on volumes. | digitalocean/Manager.py | def get_volume_snapshots(self):
"""
This method returns a list of all Snapshots based on volumes.
"""
data = self.get_data("snapshots?resource_type=volume")
return [
Snapshot(token=self.token, **snapshot)
for snapshot in data['snapshots']
] | def get_volume_snapshots(self):
"""
This method returns a list of all Snapshots based on volumes.
"""
data = self.get_data("snapshots?resource_type=volume")
return [
Snapshot(token=self.token, **snapshot)
for snapshot in data['snapshots']
] | [
"This",
"method",
"returns",
"a",
"list",
"of",
"all",
"Snapshots",
"based",
"on",
"volumes",
"."
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/Manager.py#L325-L333 | [
"def",
"get_volume_snapshots",
"(",
"self",
")",
":",
"data",
"=",
"self",
".",
"get_data",
"(",
"\"snapshots?resource_type=volume\"",
")",
"return",
"[",
"Snapshot",
"(",
"token",
"=",
"self",
".",
"token",
",",
"*",
"*",
"snapshot",
")",
"for",
"snapshot",
"in",
"data",
"[",
"'snapshots'",
"]",
"]"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | Manager.get_all_volumes | This function returns a list of Volume objects. | digitalocean/Manager.py | def get_all_volumes(self, region=None):
"""
This function returns a list of Volume objects.
"""
if region:
url = "volumes?region={}".format(region)
else:
url = "volumes"
data = self.get_data(url)
volumes = list()
for jsoned in data['volumes']:
volume = Volume(**jsoned)
volume.token = self.token
volumes.append(volume)
return volumes | def get_all_volumes(self, region=None):
"""
This function returns a list of Volume objects.
"""
if region:
url = "volumes?region={}".format(region)
else:
url = "volumes"
data = self.get_data(url)
volumes = list()
for jsoned in data['volumes']:
volume = Volume(**jsoned)
volume.token = self.token
volumes.append(volume)
return volumes | [
"This",
"function",
"returns",
"a",
"list",
"of",
"Volume",
"objects",
"."
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/Manager.py#L335-L349 | [
"def",
"get_all_volumes",
"(",
"self",
",",
"region",
"=",
"None",
")",
":",
"if",
"region",
":",
"url",
"=",
"\"volumes?region={}\"",
".",
"format",
"(",
"region",
")",
"else",
":",
"url",
"=",
"\"volumes\"",
"data",
"=",
"self",
".",
"get_data",
"(",
"url",
")",
"volumes",
"=",
"list",
"(",
")",
"for",
"jsoned",
"in",
"data",
"[",
"'volumes'",
"]",
":",
"volume",
"=",
"Volume",
"(",
"*",
"*",
"jsoned",
")",
"volume",
".",
"token",
"=",
"self",
".",
"token",
"volumes",
".",
"append",
"(",
"volume",
")",
"return",
"volumes"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | Manager.get_volume | Returns a Volume object by its ID. | digitalocean/Manager.py | def get_volume(self, volume_id):
"""
Returns a Volume object by its ID.
"""
return Volume.get_object(api_token=self.token, volume_id=volume_id) | def get_volume(self, volume_id):
"""
Returns a Volume object by its ID.
"""
return Volume.get_object(api_token=self.token, volume_id=volume_id) | [
"Returns",
"a",
"Volume",
"object",
"by",
"its",
"ID",
"."
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/Manager.py#L351-L355 | [
"def",
"get_volume",
"(",
"self",
",",
"volume_id",
")",
":",
"return",
"Volume",
".",
"get_object",
"(",
"api_token",
"=",
"self",
".",
"token",
",",
"volume_id",
"=",
"volume_id",
")"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | Manager.get_all_firewalls | This function returns a list of Firewall objects. | digitalocean/Manager.py | def get_all_firewalls(self):
"""
This function returns a list of Firewall objects.
"""
data = self.get_data("firewalls")
firewalls = list()
for jsoned in data['firewalls']:
firewall = Firewall(**jsoned)
firewall.token = self.token
in_rules = list()
for rule in jsoned['inbound_rules']:
in_rules.append(InboundRule(**rule))
firewall.inbound_rules = in_rules
out_rules = list()
for rule in jsoned['outbound_rules']:
out_rules.append(OutboundRule(**rule))
firewall.outbound_rules = out_rules
firewalls.append(firewall)
return firewalls | def get_all_firewalls(self):
"""
This function returns a list of Firewall objects.
"""
data = self.get_data("firewalls")
firewalls = list()
for jsoned in data['firewalls']:
firewall = Firewall(**jsoned)
firewall.token = self.token
in_rules = list()
for rule in jsoned['inbound_rules']:
in_rules.append(InboundRule(**rule))
firewall.inbound_rules = in_rules
out_rules = list()
for rule in jsoned['outbound_rules']:
out_rules.append(OutboundRule(**rule))
firewall.outbound_rules = out_rules
firewalls.append(firewall)
return firewalls | [
"This",
"function",
"returns",
"a",
"list",
"of",
"Firewall",
"objects",
"."
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/Manager.py#L357-L375 | [
"def",
"get_all_firewalls",
"(",
"self",
")",
":",
"data",
"=",
"self",
".",
"get_data",
"(",
"\"firewalls\"",
")",
"firewalls",
"=",
"list",
"(",
")",
"for",
"jsoned",
"in",
"data",
"[",
"'firewalls'",
"]",
":",
"firewall",
"=",
"Firewall",
"(",
"*",
"*",
"jsoned",
")",
"firewall",
".",
"token",
"=",
"self",
".",
"token",
"in_rules",
"=",
"list",
"(",
")",
"for",
"rule",
"in",
"jsoned",
"[",
"'inbound_rules'",
"]",
":",
"in_rules",
".",
"append",
"(",
"InboundRule",
"(",
"*",
"*",
"rule",
")",
")",
"firewall",
".",
"inbound_rules",
"=",
"in_rules",
"out_rules",
"=",
"list",
"(",
")",
"for",
"rule",
"in",
"jsoned",
"[",
"'outbound_rules'",
"]",
":",
"out_rules",
".",
"append",
"(",
"OutboundRule",
"(",
"*",
"*",
"rule",
")",
")",
"firewall",
".",
"outbound_rules",
"=",
"out_rules",
"firewalls",
".",
"append",
"(",
"firewall",
")",
"return",
"firewalls"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | Manager.get_firewall | Return a Firewall by its ID. | digitalocean/Manager.py | def get_firewall(self, firewall_id):
"""
Return a Firewall by its ID.
"""
return Firewall.get_object(
api_token=self.token,
firewall_id=firewall_id,
) | def get_firewall(self, firewall_id):
"""
Return a Firewall by its ID.
"""
return Firewall.get_object(
api_token=self.token,
firewall_id=firewall_id,
) | [
"Return",
"a",
"Firewall",
"by",
"its",
"ID",
"."
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/Manager.py#L377-L384 | [
"def",
"get_firewall",
"(",
"self",
",",
"firewall_id",
")",
":",
"return",
"Firewall",
".",
"get_object",
"(",
"api_token",
"=",
"self",
".",
"token",
",",
"firewall_id",
"=",
"firewall_id",
",",
")"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | LoadBalancer.get_object | Class method that will return a LoadBalancer object by its ID.
Args:
api_token (str): DigitalOcean API token
id (str): Load Balancer ID | digitalocean/LoadBalancer.py | def get_object(cls, api_token, id):
"""
Class method that will return a LoadBalancer object by its ID.
Args:
api_token (str): DigitalOcean API token
id (str): Load Balancer ID
"""
load_balancer = cls(token=api_token, id=id)
load_balancer.load()
return load_balancer | def get_object(cls, api_token, id):
"""
Class method that will return a LoadBalancer object by its ID.
Args:
api_token (str): DigitalOcean API token
id (str): Load Balancer ID
"""
load_balancer = cls(token=api_token, id=id)
load_balancer.load()
return load_balancer | [
"Class",
"method",
"that",
"will",
"return",
"a",
"LoadBalancer",
"object",
"by",
"its",
"ID",
"."
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/LoadBalancer.py#L146-L156 | [
"def",
"get_object",
"(",
"cls",
",",
"api_token",
",",
"id",
")",
":",
"load_balancer",
"=",
"cls",
"(",
"token",
"=",
"api_token",
",",
"id",
"=",
"id",
")",
"load_balancer",
".",
"load",
"(",
")",
"return",
"load_balancer"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | LoadBalancer.load | Loads updated attributues for a LoadBalancer object.
Requires self.id to be set. | digitalocean/LoadBalancer.py | def load(self):
"""
Loads updated attributues for a LoadBalancer object.
Requires self.id to be set.
"""
data = self.get_data('load_balancers/%s' % self.id, type=GET)
load_balancer = data['load_balancer']
# Setting the attribute values
for attr in load_balancer.keys():
if attr == 'health_check':
health_check = HealthCheck(**load_balancer['health_check'])
setattr(self, attr, health_check)
elif attr == 'sticky_sessions':
sticky_ses = StickySesions(**load_balancer['sticky_sessions'])
setattr(self, attr, sticky_ses)
elif attr == 'forwarding_rules':
rules = list()
for rule in load_balancer['forwarding_rules']:
rules.append(ForwardingRule(**rule))
setattr(self, attr, rules)
else:
setattr(self, attr, load_balancer[attr])
return self | def load(self):
"""
Loads updated attributues for a LoadBalancer object.
Requires self.id to be set.
"""
data = self.get_data('load_balancers/%s' % self.id, type=GET)
load_balancer = data['load_balancer']
# Setting the attribute values
for attr in load_balancer.keys():
if attr == 'health_check':
health_check = HealthCheck(**load_balancer['health_check'])
setattr(self, attr, health_check)
elif attr == 'sticky_sessions':
sticky_ses = StickySesions(**load_balancer['sticky_sessions'])
setattr(self, attr, sticky_ses)
elif attr == 'forwarding_rules':
rules = list()
for rule in load_balancer['forwarding_rules']:
rules.append(ForwardingRule(**rule))
setattr(self, attr, rules)
else:
setattr(self, attr, load_balancer[attr])
return self | [
"Loads",
"updated",
"attributues",
"for",
"a",
"LoadBalancer",
"object",
"."
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/LoadBalancer.py#L158-L183 | [
"def",
"load",
"(",
"self",
")",
":",
"data",
"=",
"self",
".",
"get_data",
"(",
"'load_balancers/%s'",
"%",
"self",
".",
"id",
",",
"type",
"=",
"GET",
")",
"load_balancer",
"=",
"data",
"[",
"'load_balancer'",
"]",
"# Setting the attribute values",
"for",
"attr",
"in",
"load_balancer",
".",
"keys",
"(",
")",
":",
"if",
"attr",
"==",
"'health_check'",
":",
"health_check",
"=",
"HealthCheck",
"(",
"*",
"*",
"load_balancer",
"[",
"'health_check'",
"]",
")",
"setattr",
"(",
"self",
",",
"attr",
",",
"health_check",
")",
"elif",
"attr",
"==",
"'sticky_sessions'",
":",
"sticky_ses",
"=",
"StickySesions",
"(",
"*",
"*",
"load_balancer",
"[",
"'sticky_sessions'",
"]",
")",
"setattr",
"(",
"self",
",",
"attr",
",",
"sticky_ses",
")",
"elif",
"attr",
"==",
"'forwarding_rules'",
":",
"rules",
"=",
"list",
"(",
")",
"for",
"rule",
"in",
"load_balancer",
"[",
"'forwarding_rules'",
"]",
":",
"rules",
".",
"append",
"(",
"ForwardingRule",
"(",
"*",
"*",
"rule",
")",
")",
"setattr",
"(",
"self",
",",
"attr",
",",
"rules",
")",
"else",
":",
"setattr",
"(",
"self",
",",
"attr",
",",
"load_balancer",
"[",
"attr",
"]",
")",
"return",
"self"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | LoadBalancer.create | Creates a new LoadBalancer.
Note: Every argument and parameter given to this method will be
assigned to the object.
Args:
name (str): The Load Balancer's name
region (str): The slug identifier for a DigitalOcean region
algorithm (str, optional): The load balancing algorithm to be
used. Currently, it must be either "round_robin" or
"least_connections"
forwarding_rules (obj:`list`): A list of `ForwrdingRules` objects
health_check (obj, optional): A `HealthCheck` object
sticky_sessions (obj, optional): A `StickySessions` object
redirect_http_to_https (bool, optional): A boolean indicating
whether HTTP requests to the Load Balancer should be
redirected to HTTPS
droplet_ids (obj:`list` of `int`): A list of IDs representing
Droplets to be added to the Load Balancer (mutually
exclusive with 'tag')
tag (str): A string representing a DigitalOcean Droplet tag
(mutually exclusive with 'droplet_ids') | digitalocean/LoadBalancer.py | def create(self, *args, **kwargs):
"""
Creates a new LoadBalancer.
Note: Every argument and parameter given to this method will be
assigned to the object.
Args:
name (str): The Load Balancer's name
region (str): The slug identifier for a DigitalOcean region
algorithm (str, optional): The load balancing algorithm to be
used. Currently, it must be either "round_robin" or
"least_connections"
forwarding_rules (obj:`list`): A list of `ForwrdingRules` objects
health_check (obj, optional): A `HealthCheck` object
sticky_sessions (obj, optional): A `StickySessions` object
redirect_http_to_https (bool, optional): A boolean indicating
whether HTTP requests to the Load Balancer should be
redirected to HTTPS
droplet_ids (obj:`list` of `int`): A list of IDs representing
Droplets to be added to the Load Balancer (mutually
exclusive with 'tag')
tag (str): A string representing a DigitalOcean Droplet tag
(mutually exclusive with 'droplet_ids')
"""
rules_dict = [rule.__dict__ for rule in self.forwarding_rules]
params = {'name': self.name, 'region': self.region,
'forwarding_rules': rules_dict,
'redirect_http_to_https': self.redirect_http_to_https}
if self.droplet_ids and self.tag:
raise ValueError('droplet_ids and tag are mutually exclusive args')
elif self.tag:
params['tag'] = self.tag
else:
params['droplet_ids'] = self.droplet_ids
if self.algorithm:
params['algorithm'] = self.algorithm
if self.health_check:
params['health_check'] = self.health_check.__dict__
if self.sticky_sessions:
params['sticky_sessions'] = self.sticky_sessions.__dict__
data = self.get_data('load_balancers/', type=POST, params=params)
if data:
self.id = data['load_balancer']['id']
self.ip = data['load_balancer']['ip']
self.algorithm = data['load_balancer']['algorithm']
self.health_check = HealthCheck(
**data['load_balancer']['health_check'])
self.sticky_sessions = StickySesions(
**data['load_balancer']['sticky_sessions'])
self.droplet_ids = data['load_balancer']['droplet_ids']
self.status = data['load_balancer']['status']
self.created_at = data['load_balancer']['created_at']
return self | def create(self, *args, **kwargs):
"""
Creates a new LoadBalancer.
Note: Every argument and parameter given to this method will be
assigned to the object.
Args:
name (str): The Load Balancer's name
region (str): The slug identifier for a DigitalOcean region
algorithm (str, optional): The load balancing algorithm to be
used. Currently, it must be either "round_robin" or
"least_connections"
forwarding_rules (obj:`list`): A list of `ForwrdingRules` objects
health_check (obj, optional): A `HealthCheck` object
sticky_sessions (obj, optional): A `StickySessions` object
redirect_http_to_https (bool, optional): A boolean indicating
whether HTTP requests to the Load Balancer should be
redirected to HTTPS
droplet_ids (obj:`list` of `int`): A list of IDs representing
Droplets to be added to the Load Balancer (mutually
exclusive with 'tag')
tag (str): A string representing a DigitalOcean Droplet tag
(mutually exclusive with 'droplet_ids')
"""
rules_dict = [rule.__dict__ for rule in self.forwarding_rules]
params = {'name': self.name, 'region': self.region,
'forwarding_rules': rules_dict,
'redirect_http_to_https': self.redirect_http_to_https}
if self.droplet_ids and self.tag:
raise ValueError('droplet_ids and tag are mutually exclusive args')
elif self.tag:
params['tag'] = self.tag
else:
params['droplet_ids'] = self.droplet_ids
if self.algorithm:
params['algorithm'] = self.algorithm
if self.health_check:
params['health_check'] = self.health_check.__dict__
if self.sticky_sessions:
params['sticky_sessions'] = self.sticky_sessions.__dict__
data = self.get_data('load_balancers/', type=POST, params=params)
if data:
self.id = data['load_balancer']['id']
self.ip = data['load_balancer']['ip']
self.algorithm = data['load_balancer']['algorithm']
self.health_check = HealthCheck(
**data['load_balancer']['health_check'])
self.sticky_sessions = StickySesions(
**data['load_balancer']['sticky_sessions'])
self.droplet_ids = data['load_balancer']['droplet_ids']
self.status = data['load_balancer']['status']
self.created_at = data['load_balancer']['created_at']
return self | [
"Creates",
"a",
"new",
"LoadBalancer",
"."
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/LoadBalancer.py#L185-L244 | [
"def",
"create",
"(",
"self",
",",
"*",
"args",
",",
"*",
"*",
"kwargs",
")",
":",
"rules_dict",
"=",
"[",
"rule",
".",
"__dict__",
"for",
"rule",
"in",
"self",
".",
"forwarding_rules",
"]",
"params",
"=",
"{",
"'name'",
":",
"self",
".",
"name",
",",
"'region'",
":",
"self",
".",
"region",
",",
"'forwarding_rules'",
":",
"rules_dict",
",",
"'redirect_http_to_https'",
":",
"self",
".",
"redirect_http_to_https",
"}",
"if",
"self",
".",
"droplet_ids",
"and",
"self",
".",
"tag",
":",
"raise",
"ValueError",
"(",
"'droplet_ids and tag are mutually exclusive args'",
")",
"elif",
"self",
".",
"tag",
":",
"params",
"[",
"'tag'",
"]",
"=",
"self",
".",
"tag",
"else",
":",
"params",
"[",
"'droplet_ids'",
"]",
"=",
"self",
".",
"droplet_ids",
"if",
"self",
".",
"algorithm",
":",
"params",
"[",
"'algorithm'",
"]",
"=",
"self",
".",
"algorithm",
"if",
"self",
".",
"health_check",
":",
"params",
"[",
"'health_check'",
"]",
"=",
"self",
".",
"health_check",
".",
"__dict__",
"if",
"self",
".",
"sticky_sessions",
":",
"params",
"[",
"'sticky_sessions'",
"]",
"=",
"self",
".",
"sticky_sessions",
".",
"__dict__",
"data",
"=",
"self",
".",
"get_data",
"(",
"'load_balancers/'",
",",
"type",
"=",
"POST",
",",
"params",
"=",
"params",
")",
"if",
"data",
":",
"self",
".",
"id",
"=",
"data",
"[",
"'load_balancer'",
"]",
"[",
"'id'",
"]",
"self",
".",
"ip",
"=",
"data",
"[",
"'load_balancer'",
"]",
"[",
"'ip'",
"]",
"self",
".",
"algorithm",
"=",
"data",
"[",
"'load_balancer'",
"]",
"[",
"'algorithm'",
"]",
"self",
".",
"health_check",
"=",
"HealthCheck",
"(",
"*",
"*",
"data",
"[",
"'load_balancer'",
"]",
"[",
"'health_check'",
"]",
")",
"self",
".",
"sticky_sessions",
"=",
"StickySesions",
"(",
"*",
"*",
"data",
"[",
"'load_balancer'",
"]",
"[",
"'sticky_sessions'",
"]",
")",
"self",
".",
"droplet_ids",
"=",
"data",
"[",
"'load_balancer'",
"]",
"[",
"'droplet_ids'",
"]",
"self",
".",
"status",
"=",
"data",
"[",
"'load_balancer'",
"]",
"[",
"'status'",
"]",
"self",
".",
"created_at",
"=",
"data",
"[",
"'load_balancer'",
"]",
"[",
"'created_at'",
"]",
"return",
"self"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | LoadBalancer.save | Save the LoadBalancer | digitalocean/LoadBalancer.py | def save(self):
"""
Save the LoadBalancer
"""
forwarding_rules = [rule.__dict__ for rule in self.forwarding_rules]
data = {
'name': self.name,
'region': self.region['slug'],
'forwarding_rules': forwarding_rules,
'redirect_http_to_https': self.redirect_http_to_https
}
if self.tag:
data['tag'] = self.tag
else:
data['droplet_ids'] = self.droplet_ids
if self.algorithm:
data["algorithm"] = self.algorithm
if self.health_check:
data['health_check'] = self.health_check.__dict__
if self.sticky_sessions:
data['sticky_sessions'] = self.sticky_sessions.__dict__
return self.get_data("load_balancers/%s/" % self.id,
type=PUT,
params=data) | def save(self):
"""
Save the LoadBalancer
"""
forwarding_rules = [rule.__dict__ for rule in self.forwarding_rules]
data = {
'name': self.name,
'region': self.region['slug'],
'forwarding_rules': forwarding_rules,
'redirect_http_to_https': self.redirect_http_to_https
}
if self.tag:
data['tag'] = self.tag
else:
data['droplet_ids'] = self.droplet_ids
if self.algorithm:
data["algorithm"] = self.algorithm
if self.health_check:
data['health_check'] = self.health_check.__dict__
if self.sticky_sessions:
data['sticky_sessions'] = self.sticky_sessions.__dict__
return self.get_data("load_balancers/%s/" % self.id,
type=PUT,
params=data) | [
"Save",
"the",
"LoadBalancer"
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/LoadBalancer.py#L246-L273 | [
"def",
"save",
"(",
"self",
")",
":",
"forwarding_rules",
"=",
"[",
"rule",
".",
"__dict__",
"for",
"rule",
"in",
"self",
".",
"forwarding_rules",
"]",
"data",
"=",
"{",
"'name'",
":",
"self",
".",
"name",
",",
"'region'",
":",
"self",
".",
"region",
"[",
"'slug'",
"]",
",",
"'forwarding_rules'",
":",
"forwarding_rules",
",",
"'redirect_http_to_https'",
":",
"self",
".",
"redirect_http_to_https",
"}",
"if",
"self",
".",
"tag",
":",
"data",
"[",
"'tag'",
"]",
"=",
"self",
".",
"tag",
"else",
":",
"data",
"[",
"'droplet_ids'",
"]",
"=",
"self",
".",
"droplet_ids",
"if",
"self",
".",
"algorithm",
":",
"data",
"[",
"\"algorithm\"",
"]",
"=",
"self",
".",
"algorithm",
"if",
"self",
".",
"health_check",
":",
"data",
"[",
"'health_check'",
"]",
"=",
"self",
".",
"health_check",
".",
"__dict__",
"if",
"self",
".",
"sticky_sessions",
":",
"data",
"[",
"'sticky_sessions'",
"]",
"=",
"self",
".",
"sticky_sessions",
".",
"__dict__",
"return",
"self",
".",
"get_data",
"(",
"\"load_balancers/%s/\"",
"%",
"self",
".",
"id",
",",
"type",
"=",
"PUT",
",",
"params",
"=",
"data",
")"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | LoadBalancer.add_droplets | Assign a LoadBalancer to a Droplet.
Args:
droplet_ids (obj:`list` of `int`): A list of Droplet IDs | digitalocean/LoadBalancer.py | def add_droplets(self, droplet_ids):
"""
Assign a LoadBalancer to a Droplet.
Args:
droplet_ids (obj:`list` of `int`): A list of Droplet IDs
"""
return self.get_data(
"load_balancers/%s/droplets/" % self.id,
type=POST,
params={"droplet_ids": droplet_ids}
) | def add_droplets(self, droplet_ids):
"""
Assign a LoadBalancer to a Droplet.
Args:
droplet_ids (obj:`list` of `int`): A list of Droplet IDs
"""
return self.get_data(
"load_balancers/%s/droplets/" % self.id,
type=POST,
params={"droplet_ids": droplet_ids}
) | [
"Assign",
"a",
"LoadBalancer",
"to",
"a",
"Droplet",
"."
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/LoadBalancer.py#L281-L292 | [
"def",
"add_droplets",
"(",
"self",
",",
"droplet_ids",
")",
":",
"return",
"self",
".",
"get_data",
"(",
"\"load_balancers/%s/droplets/\"",
"%",
"self",
".",
"id",
",",
"type",
"=",
"POST",
",",
"params",
"=",
"{",
"\"droplet_ids\"",
":",
"droplet_ids",
"}",
")"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | LoadBalancer.remove_droplets | Unassign a LoadBalancer.
Args:
droplet_ids (obj:`list` of `int`): A list of Droplet IDs | digitalocean/LoadBalancer.py | def remove_droplets(self, droplet_ids):
"""
Unassign a LoadBalancer.
Args:
droplet_ids (obj:`list` of `int`): A list of Droplet IDs
"""
return self.get_data(
"load_balancers/%s/droplets/" % self.id,
type=DELETE,
params={"droplet_ids": droplet_ids}
) | def remove_droplets(self, droplet_ids):
"""
Unassign a LoadBalancer.
Args:
droplet_ids (obj:`list` of `int`): A list of Droplet IDs
"""
return self.get_data(
"load_balancers/%s/droplets/" % self.id,
type=DELETE,
params={"droplet_ids": droplet_ids}
) | [
"Unassign",
"a",
"LoadBalancer",
"."
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/LoadBalancer.py#L294-L305 | [
"def",
"remove_droplets",
"(",
"self",
",",
"droplet_ids",
")",
":",
"return",
"self",
".",
"get_data",
"(",
"\"load_balancers/%s/droplets/\"",
"%",
"self",
".",
"id",
",",
"type",
"=",
"DELETE",
",",
"params",
"=",
"{",
"\"droplet_ids\"",
":",
"droplet_ids",
"}",
")"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | LoadBalancer.add_forwarding_rules | Adds new forwarding rules to a LoadBalancer.
Args:
forwarding_rules (obj:`list`): A list of `ForwrdingRules` objects | digitalocean/LoadBalancer.py | def add_forwarding_rules(self, forwarding_rules):
"""
Adds new forwarding rules to a LoadBalancer.
Args:
forwarding_rules (obj:`list`): A list of `ForwrdingRules` objects
"""
rules_dict = [rule.__dict__ for rule in forwarding_rules]
return self.get_data(
"load_balancers/%s/forwarding_rules/" % self.id,
type=POST,
params={"forwarding_rules": rules_dict}
) | def add_forwarding_rules(self, forwarding_rules):
"""
Adds new forwarding rules to a LoadBalancer.
Args:
forwarding_rules (obj:`list`): A list of `ForwrdingRules` objects
"""
rules_dict = [rule.__dict__ for rule in forwarding_rules]
return self.get_data(
"load_balancers/%s/forwarding_rules/" % self.id,
type=POST,
params={"forwarding_rules": rules_dict}
) | [
"Adds",
"new",
"forwarding",
"rules",
"to",
"a",
"LoadBalancer",
"."
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/LoadBalancer.py#L307-L320 | [
"def",
"add_forwarding_rules",
"(",
"self",
",",
"forwarding_rules",
")",
":",
"rules_dict",
"=",
"[",
"rule",
".",
"__dict__",
"for",
"rule",
"in",
"forwarding_rules",
"]",
"return",
"self",
".",
"get_data",
"(",
"\"load_balancers/%s/forwarding_rules/\"",
"%",
"self",
".",
"id",
",",
"type",
"=",
"POST",
",",
"params",
"=",
"{",
"\"forwarding_rules\"",
":",
"rules_dict",
"}",
")"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | LoadBalancer.remove_forwarding_rules | Removes existing forwarding rules from a LoadBalancer.
Args:
forwarding_rules (obj:`list`): A list of `ForwrdingRules` objects | digitalocean/LoadBalancer.py | def remove_forwarding_rules(self, forwarding_rules):
"""
Removes existing forwarding rules from a LoadBalancer.
Args:
forwarding_rules (obj:`list`): A list of `ForwrdingRules` objects
"""
rules_dict = [rule.__dict__ for rule in forwarding_rules]
return self.get_data(
"load_balancers/%s/forwarding_rules/" % self.id,
type=DELETE,
params={"forwarding_rules": rules_dict}
) | def remove_forwarding_rules(self, forwarding_rules):
"""
Removes existing forwarding rules from a LoadBalancer.
Args:
forwarding_rules (obj:`list`): A list of `ForwrdingRules` objects
"""
rules_dict = [rule.__dict__ for rule in forwarding_rules]
return self.get_data(
"load_balancers/%s/forwarding_rules/" % self.id,
type=DELETE,
params={"forwarding_rules": rules_dict}
) | [
"Removes",
"existing",
"forwarding",
"rules",
"from",
"a",
"LoadBalancer",
"."
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/LoadBalancer.py#L322-L335 | [
"def",
"remove_forwarding_rules",
"(",
"self",
",",
"forwarding_rules",
")",
":",
"rules_dict",
"=",
"[",
"rule",
".",
"__dict__",
"for",
"rule",
"in",
"forwarding_rules",
"]",
"return",
"self",
".",
"get_data",
"(",
"\"load_balancers/%s/forwarding_rules/\"",
"%",
"self",
".",
"id",
",",
"type",
"=",
"DELETE",
",",
"params",
"=",
"{",
"\"forwarding_rules\"",
":",
"rules_dict",
"}",
")"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | Metadata.get_data | Customized version of get_data to directly get the data without
using the authentication method. | digitalocean/Metadata.py | def get_data(self, url, headers=dict(), params=dict(), render_json=True):
"""
Customized version of get_data to directly get the data without
using the authentication method.
"""
url = urljoin(self.end_point, url)
response = requests.get(url, headers=headers, params=params,
timeout=self.get_timeout())
if render_json:
return response.json()
return response.content | def get_data(self, url, headers=dict(), params=dict(), render_json=True):
"""
Customized version of get_data to directly get the data without
using the authentication method.
"""
url = urljoin(self.end_point, url)
response = requests.get(url, headers=headers, params=params,
timeout=self.get_timeout())
if render_json:
return response.json()
return response.content | [
"Customized",
"version",
"of",
"get_data",
"to",
"directly",
"get",
"the",
"data",
"without",
"using",
"the",
"authentication",
"method",
"."
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/Metadata.py#L23-L35 | [
"def",
"get_data",
"(",
"self",
",",
"url",
",",
"headers",
"=",
"dict",
"(",
")",
",",
"params",
"=",
"dict",
"(",
")",
",",
"render_json",
"=",
"True",
")",
":",
"url",
"=",
"urljoin",
"(",
"self",
".",
"end_point",
",",
"url",
")",
"response",
"=",
"requests",
".",
"get",
"(",
"url",
",",
"headers",
"=",
"headers",
",",
"params",
"=",
"params",
",",
"timeout",
"=",
"self",
".",
"get_timeout",
"(",
")",
")",
"if",
"render_json",
":",
"return",
"response",
".",
"json",
"(",
")",
"return",
"response",
".",
"content"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | Record.get_object | Class method that will return a Record object by ID and the domain. | digitalocean/Record.py | def get_object(cls, api_token, domain, record_id):
"""
Class method that will return a Record object by ID and the domain.
"""
record = cls(token=api_token, domain=domain, id=record_id)
record.load()
return record | def get_object(cls, api_token, domain, record_id):
"""
Class method that will return a Record object by ID and the domain.
"""
record = cls(token=api_token, domain=domain, id=record_id)
record.load()
return record | [
"Class",
"method",
"that",
"will",
"return",
"a",
"Record",
"object",
"by",
"ID",
"and",
"the",
"domain",
"."
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/Record.py#L39-L45 | [
"def",
"get_object",
"(",
"cls",
",",
"api_token",
",",
"domain",
",",
"record_id",
")",
":",
"record",
"=",
"cls",
"(",
"token",
"=",
"api_token",
",",
"domain",
"=",
"domain",
",",
"id",
"=",
"record_id",
")",
"record",
".",
"load",
"(",
")",
"return",
"record"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | Record.create | Creates a new record for a domain.
Args:
type (str): The type of the DNS record (e.g. A, CNAME, TXT).
name (str): The host name, alias, or service being defined by the
record.
data (int): Variable data depending on record type.
priority (int): The priority for SRV and MX records.
port (int): The port for SRV records.
ttl (int): The time to live for the record, in seconds.
weight (int): The weight for SRV records.
flags (int): An unsigned integer between 0-255 used for CAA records.
tags (string): The parameter tag for CAA records. Valid values are
"issue", "wildissue", or "iodef" | digitalocean/Record.py | def create(self):
"""
Creates a new record for a domain.
Args:
type (str): The type of the DNS record (e.g. A, CNAME, TXT).
name (str): The host name, alias, or service being defined by the
record.
data (int): Variable data depending on record type.
priority (int): The priority for SRV and MX records.
port (int): The port for SRV records.
ttl (int): The time to live for the record, in seconds.
weight (int): The weight for SRV records.
flags (int): An unsigned integer between 0-255 used for CAA records.
tags (string): The parameter tag for CAA records. Valid values are
"issue", "wildissue", or "iodef"
"""
input_params = {
"type": self.type,
"data": self.data,
"name": self.name,
"priority": self.priority,
"port": self.port,
"ttl": self.ttl,
"weight": self.weight,
"flags": self.flags,
"tags": self.tags
}
data = self.get_data(
"domains/%s/records" % (self.domain),
type=POST,
params=input_params,
)
if data:
self.id = data['domain_record']['id'] | def create(self):
"""
Creates a new record for a domain.
Args:
type (str): The type of the DNS record (e.g. A, CNAME, TXT).
name (str): The host name, alias, or service being defined by the
record.
data (int): Variable data depending on record type.
priority (int): The priority for SRV and MX records.
port (int): The port for SRV records.
ttl (int): The time to live for the record, in seconds.
weight (int): The weight for SRV records.
flags (int): An unsigned integer between 0-255 used for CAA records.
tags (string): The parameter tag for CAA records. Valid values are
"issue", "wildissue", or "iodef"
"""
input_params = {
"type": self.type,
"data": self.data,
"name": self.name,
"priority": self.priority,
"port": self.port,
"ttl": self.ttl,
"weight": self.weight,
"flags": self.flags,
"tags": self.tags
}
data = self.get_data(
"domains/%s/records" % (self.domain),
type=POST,
params=input_params,
)
if data:
self.id = data['domain_record']['id'] | [
"Creates",
"a",
"new",
"record",
"for",
"a",
"domain",
"."
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/Record.py#L47-L83 | [
"def",
"create",
"(",
"self",
")",
":",
"input_params",
"=",
"{",
"\"type\"",
":",
"self",
".",
"type",
",",
"\"data\"",
":",
"self",
".",
"data",
",",
"\"name\"",
":",
"self",
".",
"name",
",",
"\"priority\"",
":",
"self",
".",
"priority",
",",
"\"port\"",
":",
"self",
".",
"port",
",",
"\"ttl\"",
":",
"self",
".",
"ttl",
",",
"\"weight\"",
":",
"self",
".",
"weight",
",",
"\"flags\"",
":",
"self",
".",
"flags",
",",
"\"tags\"",
":",
"self",
".",
"tags",
"}",
"data",
"=",
"self",
".",
"get_data",
"(",
"\"domains/%s/records\"",
"%",
"(",
"self",
".",
"domain",
")",
",",
"type",
"=",
"POST",
",",
"params",
"=",
"input_params",
",",
")",
"if",
"data",
":",
"self",
".",
"id",
"=",
"data",
"[",
"'domain_record'",
"]",
"[",
"'id'",
"]"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | Record.destroy | Destroy the record | digitalocean/Record.py | def destroy(self):
"""
Destroy the record
"""
return self.get_data(
"domains/%s/records/%s" % (self.domain, self.id),
type=DELETE,
) | def destroy(self):
"""
Destroy the record
"""
return self.get_data(
"domains/%s/records/%s" % (self.domain, self.id),
type=DELETE,
) | [
"Destroy",
"the",
"record"
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/Record.py#L85-L92 | [
"def",
"destroy",
"(",
"self",
")",
":",
"return",
"self",
".",
"get_data",
"(",
"\"domains/%s/records/%s\"",
"%",
"(",
"self",
".",
"domain",
",",
"self",
".",
"id",
")",
",",
"type",
"=",
"DELETE",
",",
")"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | Record.save | Save existing record | digitalocean/Record.py | def save(self):
"""
Save existing record
"""
data = {
"type": self.type,
"data": self.data,
"name": self.name,
"priority": self.priority,
"port": self.port,
"ttl": self.ttl,
"weight": self.weight,
"flags": self.flags,
"tags": self.tags
}
return self.get_data(
"domains/%s/records/%s" % (self.domain, self.id),
type=PUT,
params=data
) | def save(self):
"""
Save existing record
"""
data = {
"type": self.type,
"data": self.data,
"name": self.name,
"priority": self.priority,
"port": self.port,
"ttl": self.ttl,
"weight": self.weight,
"flags": self.flags,
"tags": self.tags
}
return self.get_data(
"domains/%s/records/%s" % (self.domain, self.id),
type=PUT,
params=data
) | [
"Save",
"existing",
"record"
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/Record.py#L94-L113 | [
"def",
"save",
"(",
"self",
")",
":",
"data",
"=",
"{",
"\"type\"",
":",
"self",
".",
"type",
",",
"\"data\"",
":",
"self",
".",
"data",
",",
"\"name\"",
":",
"self",
".",
"name",
",",
"\"priority\"",
":",
"self",
".",
"priority",
",",
"\"port\"",
":",
"self",
".",
"port",
",",
"\"ttl\"",
":",
"self",
".",
"ttl",
",",
"\"weight\"",
":",
"self",
".",
"weight",
",",
"\"flags\"",
":",
"self",
".",
"flags",
",",
"\"tags\"",
":",
"self",
".",
"tags",
"}",
"return",
"self",
".",
"get_data",
"(",
"\"domains/%s/records/%s\"",
"%",
"(",
"self",
".",
"domain",
",",
"self",
".",
"id",
")",
",",
"type",
"=",
"PUT",
",",
"params",
"=",
"data",
")"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | BaseAPI.__perform_request | This method will perform the real request,
in this way we can customize only the "output" of the API call by
using self.__call_api method.
This method will return the request object. | digitalocean/baseapi.py | def __perform_request(self, url, type=GET, params=None):
"""
This method will perform the real request,
in this way we can customize only the "output" of the API call by
using self.__call_api method.
This method will return the request object.
"""
if params is None:
params = {}
if not self.token:
raise TokenError("No token provided. Please use a valid token")
url = urlparse.urljoin(self.end_point, url)
# lookup table to find out the appropriate requests method,
# headers and payload type (json or query parameters)
identity = lambda x: x
json_dumps = lambda x: json.dumps(x)
lookup = {
GET: (self._session.get, {}, 'params', identity),
POST: (self._session.post, {'Content-type': 'application/json'}, 'data',
json_dumps),
PUT: (self._session.put, {'Content-type': 'application/json'}, 'data',
json_dumps),
DELETE: (self._session.delete,
{'content-type': 'application/json'},
'data', json_dumps),
}
requests_method, headers, payload, transform = lookup[type]
agent = "{0}/{1} {2}/{3}".format('python-digitalocean',
__version__,
requests.__name__,
requests.__version__)
headers.update({'Authorization': 'Bearer ' + self.token,
'User-Agent': agent})
kwargs = {'headers': headers, payload: transform(params)}
timeout = self.get_timeout()
if timeout:
kwargs['timeout'] = timeout
# remove token from log
headers_str = str(headers).replace(self.token.strip(), 'TOKEN')
self._log.debug('%s %s %s:%s %s %s' %
(type, url, payload, params, headers_str, timeout))
return requests_method(url, **kwargs) | def __perform_request(self, url, type=GET, params=None):
"""
This method will perform the real request,
in this way we can customize only the "output" of the API call by
using self.__call_api method.
This method will return the request object.
"""
if params is None:
params = {}
if not self.token:
raise TokenError("No token provided. Please use a valid token")
url = urlparse.urljoin(self.end_point, url)
# lookup table to find out the appropriate requests method,
# headers and payload type (json or query parameters)
identity = lambda x: x
json_dumps = lambda x: json.dumps(x)
lookup = {
GET: (self._session.get, {}, 'params', identity),
POST: (self._session.post, {'Content-type': 'application/json'}, 'data',
json_dumps),
PUT: (self._session.put, {'Content-type': 'application/json'}, 'data',
json_dumps),
DELETE: (self._session.delete,
{'content-type': 'application/json'},
'data', json_dumps),
}
requests_method, headers, payload, transform = lookup[type]
agent = "{0}/{1} {2}/{3}".format('python-digitalocean',
__version__,
requests.__name__,
requests.__version__)
headers.update({'Authorization': 'Bearer ' + self.token,
'User-Agent': agent})
kwargs = {'headers': headers, payload: transform(params)}
timeout = self.get_timeout()
if timeout:
kwargs['timeout'] = timeout
# remove token from log
headers_str = str(headers).replace(self.token.strip(), 'TOKEN')
self._log.debug('%s %s %s:%s %s %s' %
(type, url, payload, params, headers_str, timeout))
return requests_method(url, **kwargs) | [
"This",
"method",
"will",
"perform",
"the",
"real",
"request",
"in",
"this",
"way",
"we",
"can",
"customize",
"only",
"the",
"output",
"of",
"the",
"API",
"call",
"by",
"using",
"self",
".",
"__call_api",
"method",
".",
"This",
"method",
"will",
"return",
"the",
"request",
"object",
"."
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/baseapi.py#L68-L116 | [
"def",
"__perform_request",
"(",
"self",
",",
"url",
",",
"type",
"=",
"GET",
",",
"params",
"=",
"None",
")",
":",
"if",
"params",
"is",
"None",
":",
"params",
"=",
"{",
"}",
"if",
"not",
"self",
".",
"token",
":",
"raise",
"TokenError",
"(",
"\"No token provided. Please use a valid token\"",
")",
"url",
"=",
"urlparse",
".",
"urljoin",
"(",
"self",
".",
"end_point",
",",
"url",
")",
"# lookup table to find out the appropriate requests method,",
"# headers and payload type (json or query parameters)",
"identity",
"=",
"lambda",
"x",
":",
"x",
"json_dumps",
"=",
"lambda",
"x",
":",
"json",
".",
"dumps",
"(",
"x",
")",
"lookup",
"=",
"{",
"GET",
":",
"(",
"self",
".",
"_session",
".",
"get",
",",
"{",
"}",
",",
"'params'",
",",
"identity",
")",
",",
"POST",
":",
"(",
"self",
".",
"_session",
".",
"post",
",",
"{",
"'Content-type'",
":",
"'application/json'",
"}",
",",
"'data'",
",",
"json_dumps",
")",
",",
"PUT",
":",
"(",
"self",
".",
"_session",
".",
"put",
",",
"{",
"'Content-type'",
":",
"'application/json'",
"}",
",",
"'data'",
",",
"json_dumps",
")",
",",
"DELETE",
":",
"(",
"self",
".",
"_session",
".",
"delete",
",",
"{",
"'content-type'",
":",
"'application/json'",
"}",
",",
"'data'",
",",
"json_dumps",
")",
",",
"}",
"requests_method",
",",
"headers",
",",
"payload",
",",
"transform",
"=",
"lookup",
"[",
"type",
"]",
"agent",
"=",
"\"{0}/{1} {2}/{3}\"",
".",
"format",
"(",
"'python-digitalocean'",
",",
"__version__",
",",
"requests",
".",
"__name__",
",",
"requests",
".",
"__version__",
")",
"headers",
".",
"update",
"(",
"{",
"'Authorization'",
":",
"'Bearer '",
"+",
"self",
".",
"token",
",",
"'User-Agent'",
":",
"agent",
"}",
")",
"kwargs",
"=",
"{",
"'headers'",
":",
"headers",
",",
"payload",
":",
"transform",
"(",
"params",
")",
"}",
"timeout",
"=",
"self",
".",
"get_timeout",
"(",
")",
"if",
"timeout",
":",
"kwargs",
"[",
"'timeout'",
"]",
"=",
"timeout",
"# remove token from log",
"headers_str",
"=",
"str",
"(",
"headers",
")",
".",
"replace",
"(",
"self",
".",
"token",
".",
"strip",
"(",
")",
",",
"'TOKEN'",
")",
"self",
".",
"_log",
".",
"debug",
"(",
"'%s %s %s:%s %s %s'",
"%",
"(",
"type",
",",
"url",
",",
"payload",
",",
"params",
",",
"headers_str",
",",
"timeout",
")",
")",
"return",
"requests_method",
"(",
"url",
",",
"*",
"*",
"kwargs",
")"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | BaseAPI.__deal_with_pagination | Perform multiple calls in order to have a full list of elements
when the API are "paginated". (content list is divided in more
than one page) | digitalocean/baseapi.py | def __deal_with_pagination(self, url, method, params, data):
"""
Perform multiple calls in order to have a full list of elements
when the API are "paginated". (content list is divided in more
than one page)
"""
all_data = data
while data.get("links", {}).get("pages", {}).get("next"):
url, query = data["links"]["pages"]["next"].split("?", 1)
# Merge the query parameters
for key, value in urlparse.parse_qs(query).items():
params[key] = value
data = self.__perform_request(url, method, params).json()
# Merge the dictionaries
for key, value in data.items():
if isinstance(value, list) and key in all_data:
all_data[key] += value
else:
all_data[key] = value
return all_data | def __deal_with_pagination(self, url, method, params, data):
"""
Perform multiple calls in order to have a full list of elements
when the API are "paginated". (content list is divided in more
than one page)
"""
all_data = data
while data.get("links", {}).get("pages", {}).get("next"):
url, query = data["links"]["pages"]["next"].split("?", 1)
# Merge the query parameters
for key, value in urlparse.parse_qs(query).items():
params[key] = value
data = self.__perform_request(url, method, params).json()
# Merge the dictionaries
for key, value in data.items():
if isinstance(value, list) and key in all_data:
all_data[key] += value
else:
all_data[key] = value
return all_data | [
"Perform",
"multiple",
"calls",
"in",
"order",
"to",
"have",
"a",
"full",
"list",
"of",
"elements",
"when",
"the",
"API",
"are",
"paginated",
".",
"(",
"content",
"list",
"is",
"divided",
"in",
"more",
"than",
"one",
"page",
")"
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/baseapi.py#L118-L141 | [
"def",
"__deal_with_pagination",
"(",
"self",
",",
"url",
",",
"method",
",",
"params",
",",
"data",
")",
":",
"all_data",
"=",
"data",
"while",
"data",
".",
"get",
"(",
"\"links\"",
",",
"{",
"}",
")",
".",
"get",
"(",
"\"pages\"",
",",
"{",
"}",
")",
".",
"get",
"(",
"\"next\"",
")",
":",
"url",
",",
"query",
"=",
"data",
"[",
"\"links\"",
"]",
"[",
"\"pages\"",
"]",
"[",
"\"next\"",
"]",
".",
"split",
"(",
"\"?\"",
",",
"1",
")",
"# Merge the query parameters",
"for",
"key",
",",
"value",
"in",
"urlparse",
".",
"parse_qs",
"(",
"query",
")",
".",
"items",
"(",
")",
":",
"params",
"[",
"key",
"]",
"=",
"value",
"data",
"=",
"self",
".",
"__perform_request",
"(",
"url",
",",
"method",
",",
"params",
")",
".",
"json",
"(",
")",
"# Merge the dictionaries",
"for",
"key",
",",
"value",
"in",
"data",
".",
"items",
"(",
")",
":",
"if",
"isinstance",
"(",
"value",
",",
"list",
")",
"and",
"key",
"in",
"all_data",
":",
"all_data",
"[",
"key",
"]",
"+=",
"value",
"else",
":",
"all_data",
"[",
"key",
"]",
"=",
"value",
"return",
"all_data"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | BaseAPI.get_timeout | Checks if any timeout for the requests to DigitalOcean is required.
To set a timeout, use the REQUEST_TIMEOUT_ENV_VAR environment
variable. | digitalocean/baseapi.py | def get_timeout(self):
"""
Checks if any timeout for the requests to DigitalOcean is required.
To set a timeout, use the REQUEST_TIMEOUT_ENV_VAR environment
variable.
"""
timeout_str = os.environ.get(REQUEST_TIMEOUT_ENV_VAR)
if timeout_str:
try:
return float(timeout_str)
except:
self._log.error('Failed parsing the request read timeout of '
'"%s". Please use a valid float number!' %
timeout_str)
return None | def get_timeout(self):
"""
Checks if any timeout for the requests to DigitalOcean is required.
To set a timeout, use the REQUEST_TIMEOUT_ENV_VAR environment
variable.
"""
timeout_str = os.environ.get(REQUEST_TIMEOUT_ENV_VAR)
if timeout_str:
try:
return float(timeout_str)
except:
self._log.error('Failed parsing the request read timeout of '
'"%s". Please use a valid float number!' %
timeout_str)
return None | [
"Checks",
"if",
"any",
"timeout",
"for",
"the",
"requests",
"to",
"DigitalOcean",
"is",
"required",
".",
"To",
"set",
"a",
"timeout",
"use",
"the",
"REQUEST_TIMEOUT_ENV_VAR",
"environment",
"variable",
"."
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/baseapi.py#L151-L165 | [
"def",
"get_timeout",
"(",
"self",
")",
":",
"timeout_str",
"=",
"os",
".",
"environ",
".",
"get",
"(",
"REQUEST_TIMEOUT_ENV_VAR",
")",
"if",
"timeout_str",
":",
"try",
":",
"return",
"float",
"(",
"timeout_str",
")",
"except",
":",
"self",
".",
"_log",
".",
"error",
"(",
"'Failed parsing the request read timeout of '",
"'\"%s\". Please use a valid float number!'",
"%",
"timeout_str",
")",
"return",
"None"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | BaseAPI.get_data | This method is a basic implementation of __call_api that checks
errors too. In case of success the method will return True or the
content of the response to the request.
Pagination is automatically detected and handled accordingly | digitalocean/baseapi.py | def get_data(self, url, type=GET, params=None):
"""
This method is a basic implementation of __call_api that checks
errors too. In case of success the method will return True or the
content of the response to the request.
Pagination is automatically detected and handled accordingly
"""
if params is None:
params = dict()
# If per_page is not set, make sure it has a sane default
if type is GET:
params.setdefault("per_page", 200)
req = self.__perform_request(url, type, params)
if req.status_code == 204:
return True
if req.status_code == 404:
raise NotFoundError()
try:
data = req.json()
except ValueError as e:
raise JSONReadError(
'Read failed from DigitalOcean: %s' % str(e)
)
if not req.ok:
msg = [data[m] for m in ("id", "message") if m in data][1]
raise DataReadError(msg)
# init request limits
self.__init_ratelimit(req.headers)
# If there are more elements available (total) than the elements per
# page, try to deal with pagination. Note: Breaking the logic on
# multiple pages,
pages = data.get("links", {}).get("pages", {})
if pages.get("next") and "page" not in params:
return self.__deal_with_pagination(url, type, params, data)
else:
return data | def get_data(self, url, type=GET, params=None):
"""
This method is a basic implementation of __call_api that checks
errors too. In case of success the method will return True or the
content of the response to the request.
Pagination is automatically detected and handled accordingly
"""
if params is None:
params = dict()
# If per_page is not set, make sure it has a sane default
if type is GET:
params.setdefault("per_page", 200)
req = self.__perform_request(url, type, params)
if req.status_code == 204:
return True
if req.status_code == 404:
raise NotFoundError()
try:
data = req.json()
except ValueError as e:
raise JSONReadError(
'Read failed from DigitalOcean: %s' % str(e)
)
if not req.ok:
msg = [data[m] for m in ("id", "message") if m in data][1]
raise DataReadError(msg)
# init request limits
self.__init_ratelimit(req.headers)
# If there are more elements available (total) than the elements per
# page, try to deal with pagination. Note: Breaking the logic on
# multiple pages,
pages = data.get("links", {}).get("pages", {})
if pages.get("next") and "page" not in params:
return self.__deal_with_pagination(url, type, params, data)
else:
return data | [
"This",
"method",
"is",
"a",
"basic",
"implementation",
"of",
"__call_api",
"that",
"checks",
"errors",
"too",
".",
"In",
"case",
"of",
"success",
"the",
"method",
"will",
"return",
"True",
"or",
"the",
"content",
"of",
"the",
"response",
"to",
"the",
"request",
"."
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/baseapi.py#L167-L210 | [
"def",
"get_data",
"(",
"self",
",",
"url",
",",
"type",
"=",
"GET",
",",
"params",
"=",
"None",
")",
":",
"if",
"params",
"is",
"None",
":",
"params",
"=",
"dict",
"(",
")",
"# If per_page is not set, make sure it has a sane default",
"if",
"type",
"is",
"GET",
":",
"params",
".",
"setdefault",
"(",
"\"per_page\"",
",",
"200",
")",
"req",
"=",
"self",
".",
"__perform_request",
"(",
"url",
",",
"type",
",",
"params",
")",
"if",
"req",
".",
"status_code",
"==",
"204",
":",
"return",
"True",
"if",
"req",
".",
"status_code",
"==",
"404",
":",
"raise",
"NotFoundError",
"(",
")",
"try",
":",
"data",
"=",
"req",
".",
"json",
"(",
")",
"except",
"ValueError",
"as",
"e",
":",
"raise",
"JSONReadError",
"(",
"'Read failed from DigitalOcean: %s'",
"%",
"str",
"(",
"e",
")",
")",
"if",
"not",
"req",
".",
"ok",
":",
"msg",
"=",
"[",
"data",
"[",
"m",
"]",
"for",
"m",
"in",
"(",
"\"id\"",
",",
"\"message\"",
")",
"if",
"m",
"in",
"data",
"]",
"[",
"1",
"]",
"raise",
"DataReadError",
"(",
"msg",
")",
"# init request limits",
"self",
".",
"__init_ratelimit",
"(",
"req",
".",
"headers",
")",
"# If there are more elements available (total) than the elements per",
"# page, try to deal with pagination. Note: Breaking the logic on",
"# multiple pages,",
"pages",
"=",
"data",
".",
"get",
"(",
"\"links\"",
",",
"{",
"}",
")",
".",
"get",
"(",
"\"pages\"",
",",
"{",
"}",
")",
"if",
"pages",
".",
"get",
"(",
"\"next\"",
")",
"and",
"\"page\"",
"not",
"in",
"params",
":",
"return",
"self",
".",
"__deal_with_pagination",
"(",
"url",
",",
"type",
",",
"params",
",",
"data",
")",
"else",
":",
"return",
"data"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | Volume.get_object | Class method that will return an Volume object by ID. | digitalocean/Volume.py | def get_object(cls, api_token, volume_id):
"""
Class method that will return an Volume object by ID.
"""
volume = cls(token=api_token, id=volume_id)
volume.load()
return volume | def get_object(cls, api_token, volume_id):
"""
Class method that will return an Volume object by ID.
"""
volume = cls(token=api_token, id=volume_id)
volume.load()
return volume | [
"Class",
"method",
"that",
"will",
"return",
"an",
"Volume",
"object",
"by",
"ID",
"."
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/Volume.py#L21-L27 | [
"def",
"get_object",
"(",
"cls",
",",
"api_token",
",",
"volume_id",
")",
":",
"volume",
"=",
"cls",
"(",
"token",
"=",
"api_token",
",",
"id",
"=",
"volume_id",
")",
"volume",
".",
"load",
"(",
")",
"return",
"volume"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | Volume.create_from_snapshot | Creates a Block Storage volume
Note: Every argument and parameter given to this method will be
assigned to the object.
Args:
name: string - a name for the volume
snapshot_id: string - unique identifier for the volume snapshot
size_gigabytes: int - size of the Block Storage volume in GiB
filesystem_type: string, optional - name of the filesystem type the
volume will be formated with ('ext4' or 'xfs')
filesystem_label: string, optional - the label to be applied to the
filesystem, only used in conjunction with filesystem_type
Optional Args:
description: string - text field to describe a volume | digitalocean/Volume.py | def create_from_snapshot(self, *args, **kwargs):
"""
Creates a Block Storage volume
Note: Every argument and parameter given to this method will be
assigned to the object.
Args:
name: string - a name for the volume
snapshot_id: string - unique identifier for the volume snapshot
size_gigabytes: int - size of the Block Storage volume in GiB
filesystem_type: string, optional - name of the filesystem type the
volume will be formated with ('ext4' or 'xfs')
filesystem_label: string, optional - the label to be applied to the
filesystem, only used in conjunction with filesystem_type
Optional Args:
description: string - text field to describe a volume
"""
data = self.get_data('volumes/',
type=POST,
params={'name': self.name,
'snapshot_id': self.snapshot_id,
'region': self.region,
'size_gigabytes': self.size_gigabytes,
'description': self.description,
'filesystem_type': self.filesystem_type,
'filesystem_label': self.filesystem_label
})
if data:
self.id = data['volume']['id']
self.created_at = data['volume']['created_at']
return self | def create_from_snapshot(self, *args, **kwargs):
"""
Creates a Block Storage volume
Note: Every argument and parameter given to this method will be
assigned to the object.
Args:
name: string - a name for the volume
snapshot_id: string - unique identifier for the volume snapshot
size_gigabytes: int - size of the Block Storage volume in GiB
filesystem_type: string, optional - name of the filesystem type the
volume will be formated with ('ext4' or 'xfs')
filesystem_label: string, optional - the label to be applied to the
filesystem, only used in conjunction with filesystem_type
Optional Args:
description: string - text field to describe a volume
"""
data = self.get_data('volumes/',
type=POST,
params={'name': self.name,
'snapshot_id': self.snapshot_id,
'region': self.region,
'size_gigabytes': self.size_gigabytes,
'description': self.description,
'filesystem_type': self.filesystem_type,
'filesystem_label': self.filesystem_label
})
if data:
self.id = data['volume']['id']
self.created_at = data['volume']['created_at']
return self | [
"Creates",
"a",
"Block",
"Storage",
"volume"
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/Volume.py#L74-L108 | [
"def",
"create_from_snapshot",
"(",
"self",
",",
"*",
"args",
",",
"*",
"*",
"kwargs",
")",
":",
"data",
"=",
"self",
".",
"get_data",
"(",
"'volumes/'",
",",
"type",
"=",
"POST",
",",
"params",
"=",
"{",
"'name'",
":",
"self",
".",
"name",
",",
"'snapshot_id'",
":",
"self",
".",
"snapshot_id",
",",
"'region'",
":",
"self",
".",
"region",
",",
"'size_gigabytes'",
":",
"self",
".",
"size_gigabytes",
",",
"'description'",
":",
"self",
".",
"description",
",",
"'filesystem_type'",
":",
"self",
".",
"filesystem_type",
",",
"'filesystem_label'",
":",
"self",
".",
"filesystem_label",
"}",
")",
"if",
"data",
":",
"self",
".",
"id",
"=",
"data",
"[",
"'volume'",
"]",
"[",
"'id'",
"]",
"self",
".",
"created_at",
"=",
"data",
"[",
"'volume'",
"]",
"[",
"'created_at'",
"]",
"return",
"self"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | Volume.attach | Attach a Volume to a Droplet.
Args:
droplet_id: int - droplet id
region: string - slug identifier for the region | digitalocean/Volume.py | def attach(self, droplet_id, region):
"""
Attach a Volume to a Droplet.
Args:
droplet_id: int - droplet id
region: string - slug identifier for the region
"""
return self.get_data(
"volumes/%s/actions/" % self.id,
type=POST,
params={"type": "attach",
"droplet_id": droplet_id,
"region": region}
) | def attach(self, droplet_id, region):
"""
Attach a Volume to a Droplet.
Args:
droplet_id: int - droplet id
region: string - slug identifier for the region
"""
return self.get_data(
"volumes/%s/actions/" % self.id,
type=POST,
params={"type": "attach",
"droplet_id": droplet_id,
"region": region}
) | [
"Attach",
"a",
"Volume",
"to",
"a",
"Droplet",
"."
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/Volume.py#L116-L130 | [
"def",
"attach",
"(",
"self",
",",
"droplet_id",
",",
"region",
")",
":",
"return",
"self",
".",
"get_data",
"(",
"\"volumes/%s/actions/\"",
"%",
"self",
".",
"id",
",",
"type",
"=",
"POST",
",",
"params",
"=",
"{",
"\"type\"",
":",
"\"attach\"",
",",
"\"droplet_id\"",
":",
"droplet_id",
",",
"\"region\"",
":",
"region",
"}",
")"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | Volume.resize | Detach a Volume to a Droplet.
Args:
size_gigabytes: int - size of the Block Storage volume in GiB
region: string - slug identifier for the region | digitalocean/Volume.py | def resize(self, size_gigabytes, region):
"""
Detach a Volume to a Droplet.
Args:
size_gigabytes: int - size of the Block Storage volume in GiB
region: string - slug identifier for the region
"""
return self.get_data(
"volumes/%s/actions/" % self.id,
type=POST,
params={"type": "resize",
"size_gigabytes": size_gigabytes,
"region": region}
) | def resize(self, size_gigabytes, region):
"""
Detach a Volume to a Droplet.
Args:
size_gigabytes: int - size of the Block Storage volume in GiB
region: string - slug identifier for the region
"""
return self.get_data(
"volumes/%s/actions/" % self.id,
type=POST,
params={"type": "resize",
"size_gigabytes": size_gigabytes,
"region": region}
) | [
"Detach",
"a",
"Volume",
"to",
"a",
"Droplet",
"."
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/Volume.py#L148-L162 | [
"def",
"resize",
"(",
"self",
",",
"size_gigabytes",
",",
"region",
")",
":",
"return",
"self",
".",
"get_data",
"(",
"\"volumes/%s/actions/\"",
"%",
"self",
".",
"id",
",",
"type",
"=",
"POST",
",",
"params",
"=",
"{",
"\"type\"",
":",
"\"resize\"",
",",
"\"size_gigabytes\"",
":",
"size_gigabytes",
",",
"\"region\"",
":",
"region",
"}",
")"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | Volume.snapshot | Create a snapshot of the volume.
Args:
name: string - a human-readable name for the snapshot | digitalocean/Volume.py | def snapshot(self, name):
"""
Create a snapshot of the volume.
Args:
name: string - a human-readable name for the snapshot
"""
return self.get_data(
"volumes/%s/snapshots/" % self.id,
type=POST,
params={"name": name}
) | def snapshot(self, name):
"""
Create a snapshot of the volume.
Args:
name: string - a human-readable name for the snapshot
"""
return self.get_data(
"volumes/%s/snapshots/" % self.id,
type=POST,
params={"name": name}
) | [
"Create",
"a",
"snapshot",
"of",
"the",
"volume",
"."
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/Volume.py#L164-L175 | [
"def",
"snapshot",
"(",
"self",
",",
"name",
")",
":",
"return",
"self",
".",
"get_data",
"(",
"\"volumes/%s/snapshots/\"",
"%",
"self",
".",
"id",
",",
"type",
"=",
"POST",
",",
"params",
"=",
"{",
"\"name\"",
":",
"name",
"}",
")"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | Volume.get_snapshots | Retrieve the list of snapshots that have been created from a volume.
Args: | digitalocean/Volume.py | def get_snapshots(self):
"""
Retrieve the list of snapshots that have been created from a volume.
Args:
"""
data = self.get_data("volumes/%s/snapshots/" % self.id)
snapshots = list()
for jsond in data[u'snapshots']:
snapshot = Snapshot(**jsond)
snapshot.token = self.token
snapshots.append(snapshot)
return snapshots | def get_snapshots(self):
"""
Retrieve the list of snapshots that have been created from a volume.
Args:
"""
data = self.get_data("volumes/%s/snapshots/" % self.id)
snapshots = list()
for jsond in data[u'snapshots']:
snapshot = Snapshot(**jsond)
snapshot.token = self.token
snapshots.append(snapshot)
return snapshots | [
"Retrieve",
"the",
"list",
"of",
"snapshots",
"that",
"have",
"been",
"created",
"from",
"a",
"volume",
"."
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/Volume.py#L177-L190 | [
"def",
"get_snapshots",
"(",
"self",
")",
":",
"data",
"=",
"self",
".",
"get_data",
"(",
"\"volumes/%s/snapshots/\"",
"%",
"self",
".",
"id",
")",
"snapshots",
"=",
"list",
"(",
")",
"for",
"jsond",
"in",
"data",
"[",
"u'snapshots'",
"]",
":",
"snapshot",
"=",
"Snapshot",
"(",
"*",
"*",
"jsond",
")",
"snapshot",
".",
"token",
"=",
"self",
".",
"token",
"snapshots",
".",
"append",
"(",
"snapshot",
")",
"return",
"snapshots"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | Certificate.get_object | Class method that will return a Certificate object by its ID. | digitalocean/Certificate.py | def get_object(cls, api_token, cert_id):
"""
Class method that will return a Certificate object by its ID.
"""
certificate = cls(token=api_token, id=cert_id)
certificate.load()
return certificate | def get_object(cls, api_token, cert_id):
"""
Class method that will return a Certificate object by its ID.
"""
certificate = cls(token=api_token, id=cert_id)
certificate.load()
return certificate | [
"Class",
"method",
"that",
"will",
"return",
"a",
"Certificate",
"object",
"by",
"its",
"ID",
"."
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/Certificate.py#L61-L67 | [
"def",
"get_object",
"(",
"cls",
",",
"api_token",
",",
"cert_id",
")",
":",
"certificate",
"=",
"cls",
"(",
"token",
"=",
"api_token",
",",
"id",
"=",
"cert_id",
")",
"certificate",
".",
"load",
"(",
")",
"return",
"certificate"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | Certificate.load | Load the Certificate object from DigitalOcean.
Requires self.id to be set. | digitalocean/Certificate.py | def load(self):
"""
Load the Certificate object from DigitalOcean.
Requires self.id to be set.
"""
data = self.get_data("certificates/%s" % self.id)
certificate = data["certificate"]
for attr in certificate.keys():
setattr(self, attr, certificate[attr])
return self | def load(self):
"""
Load the Certificate object from DigitalOcean.
Requires self.id to be set.
"""
data = self.get_data("certificates/%s" % self.id)
certificate = data["certificate"]
for attr in certificate.keys():
setattr(self, attr, certificate[attr])
return self | [
"Load",
"the",
"Certificate",
"object",
"from",
"DigitalOcean",
"."
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/Certificate.py#L69-L81 | [
"def",
"load",
"(",
"self",
")",
":",
"data",
"=",
"self",
".",
"get_data",
"(",
"\"certificates/%s\"",
"%",
"self",
".",
"id",
")",
"certificate",
"=",
"data",
"[",
"\"certificate\"",
"]",
"for",
"attr",
"in",
"certificate",
".",
"keys",
"(",
")",
":",
"setattr",
"(",
"self",
",",
"attr",
",",
"certificate",
"[",
"attr",
"]",
")",
"return",
"self"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | Certificate.create | Create the Certificate | digitalocean/Certificate.py | def create(self):
"""
Create the Certificate
"""
params = {
"name": self.name,
"type": self.type,
"dns_names": self.dns_names,
"private_key": self.private_key,
"leaf_certificate": self.leaf_certificate,
"certificate_chain": self.certificate_chain
}
data = self.get_data("certificates/", type=POST, params=params)
if data:
self.id = data['certificate']['id']
self.not_after = data['certificate']['not_after']
self.sha1_fingerprint = data['certificate']['sha1_fingerprint']
self.created_at = data['certificate']['created_at']
self.type = data['certificate']['type']
self.dns_names = data['certificate']['dns_names']
self.state = data['certificate']['state']
return self | def create(self):
"""
Create the Certificate
"""
params = {
"name": self.name,
"type": self.type,
"dns_names": self.dns_names,
"private_key": self.private_key,
"leaf_certificate": self.leaf_certificate,
"certificate_chain": self.certificate_chain
}
data = self.get_data("certificates/", type=POST, params=params)
if data:
self.id = data['certificate']['id']
self.not_after = data['certificate']['not_after']
self.sha1_fingerprint = data['certificate']['sha1_fingerprint']
self.created_at = data['certificate']['created_at']
self.type = data['certificate']['type']
self.dns_names = data['certificate']['dns_names']
self.state = data['certificate']['state']
return self | [
"Create",
"the",
"Certificate"
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/Certificate.py#L83-L107 | [
"def",
"create",
"(",
"self",
")",
":",
"params",
"=",
"{",
"\"name\"",
":",
"self",
".",
"name",
",",
"\"type\"",
":",
"self",
".",
"type",
",",
"\"dns_names\"",
":",
"self",
".",
"dns_names",
",",
"\"private_key\"",
":",
"self",
".",
"private_key",
",",
"\"leaf_certificate\"",
":",
"self",
".",
"leaf_certificate",
",",
"\"certificate_chain\"",
":",
"self",
".",
"certificate_chain",
"}",
"data",
"=",
"self",
".",
"get_data",
"(",
"\"certificates/\"",
",",
"type",
"=",
"POST",
",",
"params",
"=",
"params",
")",
"if",
"data",
":",
"self",
".",
"id",
"=",
"data",
"[",
"'certificate'",
"]",
"[",
"'id'",
"]",
"self",
".",
"not_after",
"=",
"data",
"[",
"'certificate'",
"]",
"[",
"'not_after'",
"]",
"self",
".",
"sha1_fingerprint",
"=",
"data",
"[",
"'certificate'",
"]",
"[",
"'sha1_fingerprint'",
"]",
"self",
".",
"created_at",
"=",
"data",
"[",
"'certificate'",
"]",
"[",
"'created_at'",
"]",
"self",
".",
"type",
"=",
"data",
"[",
"'certificate'",
"]",
"[",
"'type'",
"]",
"self",
".",
"dns_names",
"=",
"data",
"[",
"'certificate'",
"]",
"[",
"'dns_names'",
"]",
"self",
".",
"state",
"=",
"data",
"[",
"'certificate'",
"]",
"[",
"'state'",
"]",
"return",
"self"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | Image.get_object | Class method that will return an Image object by ID or slug.
This method is used to validate the type of the image. If it is a
number, it will be considered as an Image ID, instead if it is a
string, it will considered as slug. | digitalocean/Image.py | def get_object(cls, api_token, image_id_or_slug):
"""
Class method that will return an Image object by ID or slug.
This method is used to validate the type of the image. If it is a
number, it will be considered as an Image ID, instead if it is a
string, it will considered as slug.
"""
if cls._is_string(image_id_or_slug):
image = cls(token=api_token, slug=image_id_or_slug)
image.load(use_slug=True)
else:
image = cls(token=api_token, id=image_id_or_slug)
image.load()
return image | def get_object(cls, api_token, image_id_or_slug):
"""
Class method that will return an Image object by ID or slug.
This method is used to validate the type of the image. If it is a
number, it will be considered as an Image ID, instead if it is a
string, it will considered as slug.
"""
if cls._is_string(image_id_or_slug):
image = cls(token=api_token, slug=image_id_or_slug)
image.load(use_slug=True)
else:
image = cls(token=api_token, id=image_id_or_slug)
image.load()
return image | [
"Class",
"method",
"that",
"will",
"return",
"an",
"Image",
"object",
"by",
"ID",
"or",
"slug",
"."
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/Image.py#L64-L78 | [
"def",
"get_object",
"(",
"cls",
",",
"api_token",
",",
"image_id_or_slug",
")",
":",
"if",
"cls",
".",
"_is_string",
"(",
"image_id_or_slug",
")",
":",
"image",
"=",
"cls",
"(",
"token",
"=",
"api_token",
",",
"slug",
"=",
"image_id_or_slug",
")",
"image",
".",
"load",
"(",
"use_slug",
"=",
"True",
")",
"else",
":",
"image",
"=",
"cls",
"(",
"token",
"=",
"api_token",
",",
"id",
"=",
"image_id_or_slug",
")",
"image",
".",
"load",
"(",
")",
"return",
"image"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | Image._is_string | Checks if the value provided is a string (True) or not integer
(False) or something else (None). | digitalocean/Image.py | def _is_string(value):
"""
Checks if the value provided is a string (True) or not integer
(False) or something else (None).
"""
if type(value) in [type(u''), type('')]:
return True
elif type(value) in [int, type(2 ** 64)]:
return False
else:
return None | def _is_string(value):
"""
Checks if the value provided is a string (True) or not integer
(False) or something else (None).
"""
if type(value) in [type(u''), type('')]:
return True
elif type(value) in [int, type(2 ** 64)]:
return False
else:
return None | [
"Checks",
"if",
"the",
"value",
"provided",
"is",
"a",
"string",
"(",
"True",
")",
"or",
"not",
"integer",
"(",
"False",
")",
"or",
"something",
"else",
"(",
"None",
")",
"."
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/Image.py#L81-L91 | [
"def",
"_is_string",
"(",
"value",
")",
":",
"if",
"type",
"(",
"value",
")",
"in",
"[",
"type",
"(",
"u''",
")",
",",
"type",
"(",
"''",
")",
"]",
":",
"return",
"True",
"elif",
"type",
"(",
"value",
")",
"in",
"[",
"int",
",",
"type",
"(",
"2",
"**",
"64",
")",
"]",
":",
"return",
"False",
"else",
":",
"return",
"None"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | Image.create | Creates a new custom DigitalOcean Image from the Linux virtual machine
image located at the provided `url`. | digitalocean/Image.py | def create(self):
"""
Creates a new custom DigitalOcean Image from the Linux virtual machine
image located at the provided `url`.
"""
params = {'name': self.name,
'region': self.region,
'url': self.url,
'distribution': self.distribution,
'description': self.description,
'tags': self.tags}
data = self.get_data('images', type=POST, params=params)
if data:
for attr in data['image'].keys():
setattr(self, attr, data['image'][attr])
return self | def create(self):
"""
Creates a new custom DigitalOcean Image from the Linux virtual machine
image located at the provided `url`.
"""
params = {'name': self.name,
'region': self.region,
'url': self.url,
'distribution': self.distribution,
'description': self.description,
'tags': self.tags}
data = self.get_data('images', type=POST, params=params)
if data:
for attr in data['image'].keys():
setattr(self, attr, data['image'][attr])
return self | [
"Creates",
"a",
"new",
"custom",
"DigitalOcean",
"Image",
"from",
"the",
"Linux",
"virtual",
"machine",
"image",
"located",
"at",
"the",
"provided",
"url",
"."
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/Image.py#L93-L111 | [
"def",
"create",
"(",
"self",
")",
":",
"params",
"=",
"{",
"'name'",
":",
"self",
".",
"name",
",",
"'region'",
":",
"self",
".",
"region",
",",
"'url'",
":",
"self",
".",
"url",
",",
"'distribution'",
":",
"self",
".",
"distribution",
",",
"'description'",
":",
"self",
".",
"description",
",",
"'tags'",
":",
"self",
".",
"tags",
"}",
"data",
"=",
"self",
".",
"get_data",
"(",
"'images'",
",",
"type",
"=",
"POST",
",",
"params",
"=",
"params",
")",
"if",
"data",
":",
"for",
"attr",
"in",
"data",
"[",
"'image'",
"]",
".",
"keys",
"(",
")",
":",
"setattr",
"(",
"self",
",",
"attr",
",",
"data",
"[",
"'image'",
"]",
"[",
"attr",
"]",
")",
"return",
"self"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | Image.load | Load slug.
Loads by id, or by slug if id is not present or use slug is True. | digitalocean/Image.py | def load(self, use_slug=False):
"""
Load slug.
Loads by id, or by slug if id is not present or use slug is True.
"""
identifier = None
if use_slug or not self.id:
identifier = self.slug
else:
identifier = self.id
if not identifier:
raise NotFoundError("One of self.id or self.slug must be set.")
data = self.get_data("images/%s" % identifier)
image_dict = data['image']
# Setting the attribute values
for attr in image_dict.keys():
setattr(self, attr, image_dict[attr])
return self | def load(self, use_slug=False):
"""
Load slug.
Loads by id, or by slug if id is not present or use slug is True.
"""
identifier = None
if use_slug or not self.id:
identifier = self.slug
else:
identifier = self.id
if not identifier:
raise NotFoundError("One of self.id or self.slug must be set.")
data = self.get_data("images/%s" % identifier)
image_dict = data['image']
# Setting the attribute values
for attr in image_dict.keys():
setattr(self, attr, image_dict[attr])
return self | [
"Load",
"slug",
"."
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/Image.py#L113-L133 | [
"def",
"load",
"(",
"self",
",",
"use_slug",
"=",
"False",
")",
":",
"identifier",
"=",
"None",
"if",
"use_slug",
"or",
"not",
"self",
".",
"id",
":",
"identifier",
"=",
"self",
".",
"slug",
"else",
":",
"identifier",
"=",
"self",
".",
"id",
"if",
"not",
"identifier",
":",
"raise",
"NotFoundError",
"(",
"\"One of self.id or self.slug must be set.\"",
")",
"data",
"=",
"self",
".",
"get_data",
"(",
"\"images/%s\"",
"%",
"identifier",
")",
"image_dict",
"=",
"data",
"[",
"'image'",
"]",
"# Setting the attribute values",
"for",
"attr",
"in",
"image_dict",
".",
"keys",
"(",
")",
":",
"setattr",
"(",
"self",
",",
"attr",
",",
"image_dict",
"[",
"attr",
"]",
")",
"return",
"self"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | Image.transfer | Transfer the image | digitalocean/Image.py | def transfer(self, new_region_slug):
"""
Transfer the image
"""
return self.get_data(
"images/%s/actions/" % self.id,
type=POST,
params={"type": "transfer", "region": new_region_slug}
) | def transfer(self, new_region_slug):
"""
Transfer the image
"""
return self.get_data(
"images/%s/actions/" % self.id,
type=POST,
params={"type": "transfer", "region": new_region_slug}
) | [
"Transfer",
"the",
"image"
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/Image.py#L141-L149 | [
"def",
"transfer",
"(",
"self",
",",
"new_region_slug",
")",
":",
"return",
"self",
".",
"get_data",
"(",
"\"images/%s/actions/\"",
"%",
"self",
".",
"id",
",",
"type",
"=",
"POST",
",",
"params",
"=",
"{",
"\"type\"",
":",
"\"transfer\"",
",",
"\"region\"",
":",
"new_region_slug",
"}",
")"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | Image.rename | Rename an image | digitalocean/Image.py | def rename(self, new_name):
"""
Rename an image
"""
return self.get_data(
"images/%s" % self.id,
type=PUT,
params={"name": new_name}
) | def rename(self, new_name):
"""
Rename an image
"""
return self.get_data(
"images/%s" % self.id,
type=PUT,
params={"name": new_name}
) | [
"Rename",
"an",
"image"
] | koalalorenzo/python-digitalocean | python | https://github.com/koalalorenzo/python-digitalocean/blob/d0221b57856fb1e131cafecf99d826f7b07a947c/digitalocean/Image.py#L151-L159 | [
"def",
"rename",
"(",
"self",
",",
"new_name",
")",
":",
"return",
"self",
".",
"get_data",
"(",
"\"images/%s\"",
"%",
"self",
".",
"id",
",",
"type",
"=",
"PUT",
",",
"params",
"=",
"{",
"\"name\"",
":",
"new_name",
"}",
")"
] | d0221b57856fb1e131cafecf99d826f7b07a947c |
valid | convert_conv | Convert convolution layer.
Args:
params: dictionary with layer parameters
w_name: name prefix in state_dict
scope_name: pytorch scope name
inputs: pytorch node inputs
layers: dictionary with keras tensors
weights: pytorch state_dict
names: use short names for keras layers | pytorch2keras/convolution_layers.py | def convert_conv(params, w_name, scope_name, inputs, layers, weights, names):
"""
Convert convolution layer.
Args:
params: dictionary with layer parameters
w_name: name prefix in state_dict
scope_name: pytorch scope name
inputs: pytorch node inputs
layers: dictionary with keras tensors
weights: pytorch state_dict
names: use short names for keras layers
"""
print('Converting convolution ...')
if names == 'short':
tf_name = 'C' + random_string(7)
elif names == 'keep':
tf_name = w_name
else:
tf_name = w_name + str(random.random())
bias_name = '{0}.bias'.format(w_name)
weights_name = '{0}.weight'.format(w_name)
input_name = inputs[0]
if len(weights[weights_name].numpy().shape) == 5: # 3D conv
W = weights[weights_name].numpy().transpose(2, 3, 4, 1, 0)
height, width, channels, n_layers, n_filters = W.shape
if bias_name in weights:
biases = weights[bias_name].numpy()
has_bias = True
else:
biases = None
has_bias = False
if params['pads'][0] > 0 or params['pads'][1] > 0:
padding_name = tf_name + '_pad'
padding_layer = keras.layers.ZeroPadding3D(
padding=(params['pads'][0],
params['pads'][1],
params['pads'][2]),
name=padding_name
)
layers[padding_name] = padding_layer(layers[input_name])
input_name = padding_name
if has_bias:
weights = [W, biases]
else:
weights = [W]
conv = keras.layers.Conv3D(
filters=n_filters,
kernel_size=(channels, height, width),
strides=(params['strides'][0],
params['strides'][1],
params['strides'][2]),
padding='valid',
weights=weights,
use_bias=has_bias,
activation=None,
dilation_rate=params['dilations'][0],
bias_initializer='zeros', kernel_initializer='zeros',
name=tf_name
)
layers[scope_name] = conv(layers[input_name])
elif len(weights[weights_name].numpy().shape) == 4: # 2D conv
if params['pads'][0] > 0 or params['pads'][1] > 0:
padding_name = tf_name + '_pad'
padding_layer = keras.layers.ZeroPadding2D(
padding=(params['pads'][0], params['pads'][1]),
name=padding_name
)
layers[padding_name] = padding_layer(layers[input_name])
input_name = padding_name
W = weights[weights_name].numpy().transpose(2, 3, 1, 0)
height, width, channels_per_group, out_channels = W.shape
n_groups = params['group']
in_channels = channels_per_group * n_groups
if n_groups == in_channels and n_groups != 1:
if bias_name in weights:
biases = weights[bias_name].numpy()
has_bias = True
else:
biases = None
has_bias = False
W = W.transpose(0, 1, 3, 2)
if has_bias:
weights = [W, biases]
else:
weights = [W]
conv = keras.layers.DepthwiseConv2D(
kernel_size=(height, width),
strides=(params['strides'][0], params['strides'][1]),
padding='valid',
use_bias=has_bias,
activation=None,
depth_multiplier=1,
weights = weights,
dilation_rate=params['dilations'][0],
bias_initializer='zeros', kernel_initializer='zeros'
)
layers[scope_name] = conv(layers[input_name])
elif n_groups != 1:
# Example from https://kratzert.github.io/2017/02/24/finetuning-alexnet-with-tensorflow.html
# # Split input and weights and convolve them separately
# input_groups = tf.split(axis=3, num_or_size_splits=groups, value=x)
# weight_groups = tf.split(axis=3, num_or_size_splits=groups, value=weights)
# output_groups = [convolve(i, k) for i, k in zip(input_groups, weight_groups)]
# # Concat the convolved output together again
# conv = tf.concat(axis=3, values=output_groups)
def target_layer(x, groups=params['group'], stride_y=params['strides'][0], stride_x=params['strides'][1]):
x = tf.transpose(x, [0, 2, 3, 1])
def convolve_lambda(i, k):
return tf.nn.conv2d(i, k, strides=[1, stride_y, stride_x, 1], padding='VALID')
input_groups = tf.split(axis=3, num_or_size_splits=groups, value=x)
weight_groups = tf.split(axis=3, num_or_size_splits=groups, value=W.transpose(0, 1, 2, 3))
output_groups = [convolve_lambda(i, k) for i, k in zip(input_groups, weight_groups)]
layer = tf.concat(axis=3, values=output_groups)
layer = tf.transpose(layer, [0, 3, 1, 2])
return layer
lambda_layer = keras.layers.Lambda(target_layer)
layers[scope_name] = lambda_layer(layers[input_name])
else:
if bias_name in weights:
biases = weights[bias_name].numpy()
has_bias = True
else:
biases = None
has_bias = False
if has_bias:
weights = [W, biases]
else:
weights = [W]
conv = keras.layers.Conv2D(
filters=out_channels,
kernel_size=(height, width),
strides=(params['strides'][0], params['strides'][1]),
padding='valid',
weights=weights,
use_bias=has_bias,
activation=None,
dilation_rate=params['dilations'][0],
bias_initializer='zeros', kernel_initializer='zeros',
name=tf_name
)
layers[scope_name] = conv(layers[input_name])
else: # 1D conv
W = weights[weights_name].numpy().transpose(2, 1, 0)
width, channels, n_filters = W.shape
n_groups = params['group']
if n_groups > 1:
raise AssertionError('Cannot convert conv1d with groups != 1')
if bias_name in weights:
biases = weights[bias_name].numpy()
has_bias = True
else:
biases = None
has_bias = False
padding_name = tf_name + '_pad'
padding_layer = keras.layers.ZeroPadding1D(
padding=params['pads'][0],
name=padding_name
)
layers[padding_name] = padding_layer(layers[inputs[0]])
input_name = padding_name
if has_bias:
weights = [W, biases]
else:
weights = [W]
conv = keras.layers.Conv1D(
filters=channels,
kernel_size=width,
strides=params['strides'],
padding='valid',
weights=weights,
use_bias=has_bias,
activation=None,
data_format='channels_first',
dilation_rate=params['dilations'],
bias_initializer='zeros', kernel_initializer='zeros',
name=tf_name
)
layers[scope_name] = conv(layers[input_name]) | def convert_conv(params, w_name, scope_name, inputs, layers, weights, names):
"""
Convert convolution layer.
Args:
params: dictionary with layer parameters
w_name: name prefix in state_dict
scope_name: pytorch scope name
inputs: pytorch node inputs
layers: dictionary with keras tensors
weights: pytorch state_dict
names: use short names for keras layers
"""
print('Converting convolution ...')
if names == 'short':
tf_name = 'C' + random_string(7)
elif names == 'keep':
tf_name = w_name
else:
tf_name = w_name + str(random.random())
bias_name = '{0}.bias'.format(w_name)
weights_name = '{0}.weight'.format(w_name)
input_name = inputs[0]
if len(weights[weights_name].numpy().shape) == 5: # 3D conv
W = weights[weights_name].numpy().transpose(2, 3, 4, 1, 0)
height, width, channels, n_layers, n_filters = W.shape
if bias_name in weights:
biases = weights[bias_name].numpy()
has_bias = True
else:
biases = None
has_bias = False
if params['pads'][0] > 0 or params['pads'][1] > 0:
padding_name = tf_name + '_pad'
padding_layer = keras.layers.ZeroPadding3D(
padding=(params['pads'][0],
params['pads'][1],
params['pads'][2]),
name=padding_name
)
layers[padding_name] = padding_layer(layers[input_name])
input_name = padding_name
if has_bias:
weights = [W, biases]
else:
weights = [W]
conv = keras.layers.Conv3D(
filters=n_filters,
kernel_size=(channels, height, width),
strides=(params['strides'][0],
params['strides'][1],
params['strides'][2]),
padding='valid',
weights=weights,
use_bias=has_bias,
activation=None,
dilation_rate=params['dilations'][0],
bias_initializer='zeros', kernel_initializer='zeros',
name=tf_name
)
layers[scope_name] = conv(layers[input_name])
elif len(weights[weights_name].numpy().shape) == 4: # 2D conv
if params['pads'][0] > 0 or params['pads'][1] > 0:
padding_name = tf_name + '_pad'
padding_layer = keras.layers.ZeroPadding2D(
padding=(params['pads'][0], params['pads'][1]),
name=padding_name
)
layers[padding_name] = padding_layer(layers[input_name])
input_name = padding_name
W = weights[weights_name].numpy().transpose(2, 3, 1, 0)
height, width, channels_per_group, out_channels = W.shape
n_groups = params['group']
in_channels = channels_per_group * n_groups
if n_groups == in_channels and n_groups != 1:
if bias_name in weights:
biases = weights[bias_name].numpy()
has_bias = True
else:
biases = None
has_bias = False
W = W.transpose(0, 1, 3, 2)
if has_bias:
weights = [W, biases]
else:
weights = [W]
conv = keras.layers.DepthwiseConv2D(
kernel_size=(height, width),
strides=(params['strides'][0], params['strides'][1]),
padding='valid',
use_bias=has_bias,
activation=None,
depth_multiplier=1,
weights = weights,
dilation_rate=params['dilations'][0],
bias_initializer='zeros', kernel_initializer='zeros'
)
layers[scope_name] = conv(layers[input_name])
elif n_groups != 1:
# Example from https://kratzert.github.io/2017/02/24/finetuning-alexnet-with-tensorflow.html
# # Split input and weights and convolve them separately
# input_groups = tf.split(axis=3, num_or_size_splits=groups, value=x)
# weight_groups = tf.split(axis=3, num_or_size_splits=groups, value=weights)
# output_groups = [convolve(i, k) for i, k in zip(input_groups, weight_groups)]
# # Concat the convolved output together again
# conv = tf.concat(axis=3, values=output_groups)
def target_layer(x, groups=params['group'], stride_y=params['strides'][0], stride_x=params['strides'][1]):
x = tf.transpose(x, [0, 2, 3, 1])
def convolve_lambda(i, k):
return tf.nn.conv2d(i, k, strides=[1, stride_y, stride_x, 1], padding='VALID')
input_groups = tf.split(axis=3, num_or_size_splits=groups, value=x)
weight_groups = tf.split(axis=3, num_or_size_splits=groups, value=W.transpose(0, 1, 2, 3))
output_groups = [convolve_lambda(i, k) for i, k in zip(input_groups, weight_groups)]
layer = tf.concat(axis=3, values=output_groups)
layer = tf.transpose(layer, [0, 3, 1, 2])
return layer
lambda_layer = keras.layers.Lambda(target_layer)
layers[scope_name] = lambda_layer(layers[input_name])
else:
if bias_name in weights:
biases = weights[bias_name].numpy()
has_bias = True
else:
biases = None
has_bias = False
if has_bias:
weights = [W, biases]
else:
weights = [W]
conv = keras.layers.Conv2D(
filters=out_channels,
kernel_size=(height, width),
strides=(params['strides'][0], params['strides'][1]),
padding='valid',
weights=weights,
use_bias=has_bias,
activation=None,
dilation_rate=params['dilations'][0],
bias_initializer='zeros', kernel_initializer='zeros',
name=tf_name
)
layers[scope_name] = conv(layers[input_name])
else: # 1D conv
W = weights[weights_name].numpy().transpose(2, 1, 0)
width, channels, n_filters = W.shape
n_groups = params['group']
if n_groups > 1:
raise AssertionError('Cannot convert conv1d with groups != 1')
if bias_name in weights:
biases = weights[bias_name].numpy()
has_bias = True
else:
biases = None
has_bias = False
padding_name = tf_name + '_pad'
padding_layer = keras.layers.ZeroPadding1D(
padding=params['pads'][0],
name=padding_name
)
layers[padding_name] = padding_layer(layers[inputs[0]])
input_name = padding_name
if has_bias:
weights = [W, biases]
else:
weights = [W]
conv = keras.layers.Conv1D(
filters=channels,
kernel_size=width,
strides=params['strides'],
padding='valid',
weights=weights,
use_bias=has_bias,
activation=None,
data_format='channels_first',
dilation_rate=params['dilations'],
bias_initializer='zeros', kernel_initializer='zeros',
name=tf_name
)
layers[scope_name] = conv(layers[input_name]) | [
"Convert",
"convolution",
"layer",
"."
] | nerox8664/pytorch2keras | python | https://github.com/nerox8664/pytorch2keras/blob/750eaf747323580e6732d0c5ba9f2f39cb096764/pytorch2keras/convolution_layers.py#L9-L214 | [
"def",
"convert_conv",
"(",
"params",
",",
"w_name",
",",
"scope_name",
",",
"inputs",
",",
"layers",
",",
"weights",
",",
"names",
")",
":",
"print",
"(",
"'Converting convolution ...'",
")",
"if",
"names",
"==",
"'short'",
":",
"tf_name",
"=",
"'C'",
"+",
"random_string",
"(",
"7",
")",
"elif",
"names",
"==",
"'keep'",
":",
"tf_name",
"=",
"w_name",
"else",
":",
"tf_name",
"=",
"w_name",
"+",
"str",
"(",
"random",
".",
"random",
"(",
")",
")",
"bias_name",
"=",
"'{0}.bias'",
".",
"format",
"(",
"w_name",
")",
"weights_name",
"=",
"'{0}.weight'",
".",
"format",
"(",
"w_name",
")",
"input_name",
"=",
"inputs",
"[",
"0",
"]",
"if",
"len",
"(",
"weights",
"[",
"weights_name",
"]",
".",
"numpy",
"(",
")",
".",
"shape",
")",
"==",
"5",
":",
"# 3D conv",
"W",
"=",
"weights",
"[",
"weights_name",
"]",
".",
"numpy",
"(",
")",
".",
"transpose",
"(",
"2",
",",
"3",
",",
"4",
",",
"1",
",",
"0",
")",
"height",
",",
"width",
",",
"channels",
",",
"n_layers",
",",
"n_filters",
"=",
"W",
".",
"shape",
"if",
"bias_name",
"in",
"weights",
":",
"biases",
"=",
"weights",
"[",
"bias_name",
"]",
".",
"numpy",
"(",
")",
"has_bias",
"=",
"True",
"else",
":",
"biases",
"=",
"None",
"has_bias",
"=",
"False",
"if",
"params",
"[",
"'pads'",
"]",
"[",
"0",
"]",
">",
"0",
"or",
"params",
"[",
"'pads'",
"]",
"[",
"1",
"]",
">",
"0",
":",
"padding_name",
"=",
"tf_name",
"+",
"'_pad'",
"padding_layer",
"=",
"keras",
".",
"layers",
".",
"ZeroPadding3D",
"(",
"padding",
"=",
"(",
"params",
"[",
"'pads'",
"]",
"[",
"0",
"]",
",",
"params",
"[",
"'pads'",
"]",
"[",
"1",
"]",
",",
"params",
"[",
"'pads'",
"]",
"[",
"2",
"]",
")",
",",
"name",
"=",
"padding_name",
")",
"layers",
"[",
"padding_name",
"]",
"=",
"padding_layer",
"(",
"layers",
"[",
"input_name",
"]",
")",
"input_name",
"=",
"padding_name",
"if",
"has_bias",
":",
"weights",
"=",
"[",
"W",
",",
"biases",
"]",
"else",
":",
"weights",
"=",
"[",
"W",
"]",
"conv",
"=",
"keras",
".",
"layers",
".",
"Conv3D",
"(",
"filters",
"=",
"n_filters",
",",
"kernel_size",
"=",
"(",
"channels",
",",
"height",
",",
"width",
")",
",",
"strides",
"=",
"(",
"params",
"[",
"'strides'",
"]",
"[",
"0",
"]",
",",
"params",
"[",
"'strides'",
"]",
"[",
"1",
"]",
",",
"params",
"[",
"'strides'",
"]",
"[",
"2",
"]",
")",
",",
"padding",
"=",
"'valid'",
",",
"weights",
"=",
"weights",
",",
"use_bias",
"=",
"has_bias",
",",
"activation",
"=",
"None",
",",
"dilation_rate",
"=",
"params",
"[",
"'dilations'",
"]",
"[",
"0",
"]",
",",
"bias_initializer",
"=",
"'zeros'",
",",
"kernel_initializer",
"=",
"'zeros'",
",",
"name",
"=",
"tf_name",
")",
"layers",
"[",
"scope_name",
"]",
"=",
"conv",
"(",
"layers",
"[",
"input_name",
"]",
")",
"elif",
"len",
"(",
"weights",
"[",
"weights_name",
"]",
".",
"numpy",
"(",
")",
".",
"shape",
")",
"==",
"4",
":",
"# 2D conv",
"if",
"params",
"[",
"'pads'",
"]",
"[",
"0",
"]",
">",
"0",
"or",
"params",
"[",
"'pads'",
"]",
"[",
"1",
"]",
">",
"0",
":",
"padding_name",
"=",
"tf_name",
"+",
"'_pad'",
"padding_layer",
"=",
"keras",
".",
"layers",
".",
"ZeroPadding2D",
"(",
"padding",
"=",
"(",
"params",
"[",
"'pads'",
"]",
"[",
"0",
"]",
",",
"params",
"[",
"'pads'",
"]",
"[",
"1",
"]",
")",
",",
"name",
"=",
"padding_name",
")",
"layers",
"[",
"padding_name",
"]",
"=",
"padding_layer",
"(",
"layers",
"[",
"input_name",
"]",
")",
"input_name",
"=",
"padding_name",
"W",
"=",
"weights",
"[",
"weights_name",
"]",
".",
"numpy",
"(",
")",
".",
"transpose",
"(",
"2",
",",
"3",
",",
"1",
",",
"0",
")",
"height",
",",
"width",
",",
"channels_per_group",
",",
"out_channels",
"=",
"W",
".",
"shape",
"n_groups",
"=",
"params",
"[",
"'group'",
"]",
"in_channels",
"=",
"channels_per_group",
"*",
"n_groups",
"if",
"n_groups",
"==",
"in_channels",
"and",
"n_groups",
"!=",
"1",
":",
"if",
"bias_name",
"in",
"weights",
":",
"biases",
"=",
"weights",
"[",
"bias_name",
"]",
".",
"numpy",
"(",
")",
"has_bias",
"=",
"True",
"else",
":",
"biases",
"=",
"None",
"has_bias",
"=",
"False",
"W",
"=",
"W",
".",
"transpose",
"(",
"0",
",",
"1",
",",
"3",
",",
"2",
")",
"if",
"has_bias",
":",
"weights",
"=",
"[",
"W",
",",
"biases",
"]",
"else",
":",
"weights",
"=",
"[",
"W",
"]",
"conv",
"=",
"keras",
".",
"layers",
".",
"DepthwiseConv2D",
"(",
"kernel_size",
"=",
"(",
"height",
",",
"width",
")",
",",
"strides",
"=",
"(",
"params",
"[",
"'strides'",
"]",
"[",
"0",
"]",
",",
"params",
"[",
"'strides'",
"]",
"[",
"1",
"]",
")",
",",
"padding",
"=",
"'valid'",
",",
"use_bias",
"=",
"has_bias",
",",
"activation",
"=",
"None",
",",
"depth_multiplier",
"=",
"1",
",",
"weights",
"=",
"weights",
",",
"dilation_rate",
"=",
"params",
"[",
"'dilations'",
"]",
"[",
"0",
"]",
",",
"bias_initializer",
"=",
"'zeros'",
",",
"kernel_initializer",
"=",
"'zeros'",
")",
"layers",
"[",
"scope_name",
"]",
"=",
"conv",
"(",
"layers",
"[",
"input_name",
"]",
")",
"elif",
"n_groups",
"!=",
"1",
":",
"# Example from https://kratzert.github.io/2017/02/24/finetuning-alexnet-with-tensorflow.html",
"# # Split input and weights and convolve them separately",
"# input_groups = tf.split(axis=3, num_or_size_splits=groups, value=x)",
"# weight_groups = tf.split(axis=3, num_or_size_splits=groups, value=weights)",
"# output_groups = [convolve(i, k) for i, k in zip(input_groups, weight_groups)]",
"# # Concat the convolved output together again",
"# conv = tf.concat(axis=3, values=output_groups)",
"def",
"target_layer",
"(",
"x",
",",
"groups",
"=",
"params",
"[",
"'group'",
"]",
",",
"stride_y",
"=",
"params",
"[",
"'strides'",
"]",
"[",
"0",
"]",
",",
"stride_x",
"=",
"params",
"[",
"'strides'",
"]",
"[",
"1",
"]",
")",
":",
"x",
"=",
"tf",
".",
"transpose",
"(",
"x",
",",
"[",
"0",
",",
"2",
",",
"3",
",",
"1",
"]",
")",
"def",
"convolve_lambda",
"(",
"i",
",",
"k",
")",
":",
"return",
"tf",
".",
"nn",
".",
"conv2d",
"(",
"i",
",",
"k",
",",
"strides",
"=",
"[",
"1",
",",
"stride_y",
",",
"stride_x",
",",
"1",
"]",
",",
"padding",
"=",
"'VALID'",
")",
"input_groups",
"=",
"tf",
".",
"split",
"(",
"axis",
"=",
"3",
",",
"num_or_size_splits",
"=",
"groups",
",",
"value",
"=",
"x",
")",
"weight_groups",
"=",
"tf",
".",
"split",
"(",
"axis",
"=",
"3",
",",
"num_or_size_splits",
"=",
"groups",
",",
"value",
"=",
"W",
".",
"transpose",
"(",
"0",
",",
"1",
",",
"2",
",",
"3",
")",
")",
"output_groups",
"=",
"[",
"convolve_lambda",
"(",
"i",
",",
"k",
")",
"for",
"i",
",",
"k",
"in",
"zip",
"(",
"input_groups",
",",
"weight_groups",
")",
"]",
"layer",
"=",
"tf",
".",
"concat",
"(",
"axis",
"=",
"3",
",",
"values",
"=",
"output_groups",
")",
"layer",
"=",
"tf",
".",
"transpose",
"(",
"layer",
",",
"[",
"0",
",",
"3",
",",
"1",
",",
"2",
"]",
")",
"return",
"layer",
"lambda_layer",
"=",
"keras",
".",
"layers",
".",
"Lambda",
"(",
"target_layer",
")",
"layers",
"[",
"scope_name",
"]",
"=",
"lambda_layer",
"(",
"layers",
"[",
"input_name",
"]",
")",
"else",
":",
"if",
"bias_name",
"in",
"weights",
":",
"biases",
"=",
"weights",
"[",
"bias_name",
"]",
".",
"numpy",
"(",
")",
"has_bias",
"=",
"True",
"else",
":",
"biases",
"=",
"None",
"has_bias",
"=",
"False",
"if",
"has_bias",
":",
"weights",
"=",
"[",
"W",
",",
"biases",
"]",
"else",
":",
"weights",
"=",
"[",
"W",
"]",
"conv",
"=",
"keras",
".",
"layers",
".",
"Conv2D",
"(",
"filters",
"=",
"out_channels",
",",
"kernel_size",
"=",
"(",
"height",
",",
"width",
")",
",",
"strides",
"=",
"(",
"params",
"[",
"'strides'",
"]",
"[",
"0",
"]",
",",
"params",
"[",
"'strides'",
"]",
"[",
"1",
"]",
")",
",",
"padding",
"=",
"'valid'",
",",
"weights",
"=",
"weights",
",",
"use_bias",
"=",
"has_bias",
",",
"activation",
"=",
"None",
",",
"dilation_rate",
"=",
"params",
"[",
"'dilations'",
"]",
"[",
"0",
"]",
",",
"bias_initializer",
"=",
"'zeros'",
",",
"kernel_initializer",
"=",
"'zeros'",
",",
"name",
"=",
"tf_name",
")",
"layers",
"[",
"scope_name",
"]",
"=",
"conv",
"(",
"layers",
"[",
"input_name",
"]",
")",
"else",
":",
"# 1D conv",
"W",
"=",
"weights",
"[",
"weights_name",
"]",
".",
"numpy",
"(",
")",
".",
"transpose",
"(",
"2",
",",
"1",
",",
"0",
")",
"width",
",",
"channels",
",",
"n_filters",
"=",
"W",
".",
"shape",
"n_groups",
"=",
"params",
"[",
"'group'",
"]",
"if",
"n_groups",
">",
"1",
":",
"raise",
"AssertionError",
"(",
"'Cannot convert conv1d with groups != 1'",
")",
"if",
"bias_name",
"in",
"weights",
":",
"biases",
"=",
"weights",
"[",
"bias_name",
"]",
".",
"numpy",
"(",
")",
"has_bias",
"=",
"True",
"else",
":",
"biases",
"=",
"None",
"has_bias",
"=",
"False",
"padding_name",
"=",
"tf_name",
"+",
"'_pad'",
"padding_layer",
"=",
"keras",
".",
"layers",
".",
"ZeroPadding1D",
"(",
"padding",
"=",
"params",
"[",
"'pads'",
"]",
"[",
"0",
"]",
",",
"name",
"=",
"padding_name",
")",
"layers",
"[",
"padding_name",
"]",
"=",
"padding_layer",
"(",
"layers",
"[",
"inputs",
"[",
"0",
"]",
"]",
")",
"input_name",
"=",
"padding_name",
"if",
"has_bias",
":",
"weights",
"=",
"[",
"W",
",",
"biases",
"]",
"else",
":",
"weights",
"=",
"[",
"W",
"]",
"conv",
"=",
"keras",
".",
"layers",
".",
"Conv1D",
"(",
"filters",
"=",
"channels",
",",
"kernel_size",
"=",
"width",
",",
"strides",
"=",
"params",
"[",
"'strides'",
"]",
",",
"padding",
"=",
"'valid'",
",",
"weights",
"=",
"weights",
",",
"use_bias",
"=",
"has_bias",
",",
"activation",
"=",
"None",
",",
"data_format",
"=",
"'channels_first'",
",",
"dilation_rate",
"=",
"params",
"[",
"'dilations'",
"]",
",",
"bias_initializer",
"=",
"'zeros'",
",",
"kernel_initializer",
"=",
"'zeros'",
",",
"name",
"=",
"tf_name",
")",
"layers",
"[",
"scope_name",
"]",
"=",
"conv",
"(",
"layers",
"[",
"input_name",
"]",
")"
] | 750eaf747323580e6732d0c5ba9f2f39cb096764 |
valid | convert_convtranspose | Convert transposed convolution layer.
Args:
params: dictionary with layer parameters
w_name: name prefix in state_dict
scope_name: pytorch scope name
inputs: pytorch node inputs
layers: dictionary with keras tensors
weights: pytorch state_dict
names: use short names for keras layers | pytorch2keras/convolution_layers.py | def convert_convtranspose(params, w_name, scope_name, inputs, layers, weights, names):
"""
Convert transposed convolution layer.
Args:
params: dictionary with layer parameters
w_name: name prefix in state_dict
scope_name: pytorch scope name
inputs: pytorch node inputs
layers: dictionary with keras tensors
weights: pytorch state_dict
names: use short names for keras layers
"""
print('Converting transposed convolution ...')
if names == 'short':
tf_name = 'C' + random_string(7)
elif names == 'keep':
tf_name = w_name
else:
tf_name = w_name + str(random.random())
bias_name = '{0}.bias'.format(w_name)
weights_name = '{0}.weight'.format(w_name)
if len(weights[weights_name].numpy().shape) == 4:
W = weights[weights_name].numpy().transpose(2, 3, 1, 0)
height, width, n_filters, channels = W.shape
n_groups = params['group']
if n_groups > 1:
raise AssertionError('Cannot convert conv1d with groups != 1')
if params['dilations'][0] > 1:
raise AssertionError('Cannot convert conv1d with dilation_rate != 1')
if bias_name in weights:
biases = weights[bias_name].numpy()
has_bias = True
else:
biases = None
has_bias = False
input_name = inputs[0]
if has_bias:
weights = [W, biases]
else:
weights = [W]
conv = keras.layers.Conv2DTranspose(
filters=n_filters,
kernel_size=(height, width),
strides=(params['strides'][0], params['strides'][1]),
padding='valid',
output_padding=0,
weights=weights,
use_bias=has_bias,
activation=None,
dilation_rate=params['dilations'][0],
bias_initializer='zeros', kernel_initializer='zeros',
name=tf_name
)
layers[scope_name] = conv(layers[input_name])
# Magic ad-hoc.
# See the Keras issue: https://github.com/keras-team/keras/issues/6777
layers[scope_name].set_shape(layers[scope_name]._keras_shape)
pads = params['pads']
if pads[0] > 0:
assert(len(pads) == 2 or (pads[2] == pads[0] and pads[3] == pads[1]))
crop = keras.layers.Cropping2D(
pads[:2],
name=tf_name + '_crop'
)
layers[scope_name] = crop(layers[scope_name])
else:
raise AssertionError('Layer is not supported for now') | def convert_convtranspose(params, w_name, scope_name, inputs, layers, weights, names):
"""
Convert transposed convolution layer.
Args:
params: dictionary with layer parameters
w_name: name prefix in state_dict
scope_name: pytorch scope name
inputs: pytorch node inputs
layers: dictionary with keras tensors
weights: pytorch state_dict
names: use short names for keras layers
"""
print('Converting transposed convolution ...')
if names == 'short':
tf_name = 'C' + random_string(7)
elif names == 'keep':
tf_name = w_name
else:
tf_name = w_name + str(random.random())
bias_name = '{0}.bias'.format(w_name)
weights_name = '{0}.weight'.format(w_name)
if len(weights[weights_name].numpy().shape) == 4:
W = weights[weights_name].numpy().transpose(2, 3, 1, 0)
height, width, n_filters, channels = W.shape
n_groups = params['group']
if n_groups > 1:
raise AssertionError('Cannot convert conv1d with groups != 1')
if params['dilations'][0] > 1:
raise AssertionError('Cannot convert conv1d with dilation_rate != 1')
if bias_name in weights:
biases = weights[bias_name].numpy()
has_bias = True
else:
biases = None
has_bias = False
input_name = inputs[0]
if has_bias:
weights = [W, biases]
else:
weights = [W]
conv = keras.layers.Conv2DTranspose(
filters=n_filters,
kernel_size=(height, width),
strides=(params['strides'][0], params['strides'][1]),
padding='valid',
output_padding=0,
weights=weights,
use_bias=has_bias,
activation=None,
dilation_rate=params['dilations'][0],
bias_initializer='zeros', kernel_initializer='zeros',
name=tf_name
)
layers[scope_name] = conv(layers[input_name])
# Magic ad-hoc.
# See the Keras issue: https://github.com/keras-team/keras/issues/6777
layers[scope_name].set_shape(layers[scope_name]._keras_shape)
pads = params['pads']
if pads[0] > 0:
assert(len(pads) == 2 or (pads[2] == pads[0] and pads[3] == pads[1]))
crop = keras.layers.Cropping2D(
pads[:2],
name=tf_name + '_crop'
)
layers[scope_name] = crop(layers[scope_name])
else:
raise AssertionError('Layer is not supported for now') | [
"Convert",
"transposed",
"convolution",
"layer",
"."
] | nerox8664/pytorch2keras | python | https://github.com/nerox8664/pytorch2keras/blob/750eaf747323580e6732d0c5ba9f2f39cb096764/pytorch2keras/convolution_layers.py#L217-L297 | [
"def",
"convert_convtranspose",
"(",
"params",
",",
"w_name",
",",
"scope_name",
",",
"inputs",
",",
"layers",
",",
"weights",
",",
"names",
")",
":",
"print",
"(",
"'Converting transposed convolution ...'",
")",
"if",
"names",
"==",
"'short'",
":",
"tf_name",
"=",
"'C'",
"+",
"random_string",
"(",
"7",
")",
"elif",
"names",
"==",
"'keep'",
":",
"tf_name",
"=",
"w_name",
"else",
":",
"tf_name",
"=",
"w_name",
"+",
"str",
"(",
"random",
".",
"random",
"(",
")",
")",
"bias_name",
"=",
"'{0}.bias'",
".",
"format",
"(",
"w_name",
")",
"weights_name",
"=",
"'{0}.weight'",
".",
"format",
"(",
"w_name",
")",
"if",
"len",
"(",
"weights",
"[",
"weights_name",
"]",
".",
"numpy",
"(",
")",
".",
"shape",
")",
"==",
"4",
":",
"W",
"=",
"weights",
"[",
"weights_name",
"]",
".",
"numpy",
"(",
")",
".",
"transpose",
"(",
"2",
",",
"3",
",",
"1",
",",
"0",
")",
"height",
",",
"width",
",",
"n_filters",
",",
"channels",
"=",
"W",
".",
"shape",
"n_groups",
"=",
"params",
"[",
"'group'",
"]",
"if",
"n_groups",
">",
"1",
":",
"raise",
"AssertionError",
"(",
"'Cannot convert conv1d with groups != 1'",
")",
"if",
"params",
"[",
"'dilations'",
"]",
"[",
"0",
"]",
">",
"1",
":",
"raise",
"AssertionError",
"(",
"'Cannot convert conv1d with dilation_rate != 1'",
")",
"if",
"bias_name",
"in",
"weights",
":",
"biases",
"=",
"weights",
"[",
"bias_name",
"]",
".",
"numpy",
"(",
")",
"has_bias",
"=",
"True",
"else",
":",
"biases",
"=",
"None",
"has_bias",
"=",
"False",
"input_name",
"=",
"inputs",
"[",
"0",
"]",
"if",
"has_bias",
":",
"weights",
"=",
"[",
"W",
",",
"biases",
"]",
"else",
":",
"weights",
"=",
"[",
"W",
"]",
"conv",
"=",
"keras",
".",
"layers",
".",
"Conv2DTranspose",
"(",
"filters",
"=",
"n_filters",
",",
"kernel_size",
"=",
"(",
"height",
",",
"width",
")",
",",
"strides",
"=",
"(",
"params",
"[",
"'strides'",
"]",
"[",
"0",
"]",
",",
"params",
"[",
"'strides'",
"]",
"[",
"1",
"]",
")",
",",
"padding",
"=",
"'valid'",
",",
"output_padding",
"=",
"0",
",",
"weights",
"=",
"weights",
",",
"use_bias",
"=",
"has_bias",
",",
"activation",
"=",
"None",
",",
"dilation_rate",
"=",
"params",
"[",
"'dilations'",
"]",
"[",
"0",
"]",
",",
"bias_initializer",
"=",
"'zeros'",
",",
"kernel_initializer",
"=",
"'zeros'",
",",
"name",
"=",
"tf_name",
")",
"layers",
"[",
"scope_name",
"]",
"=",
"conv",
"(",
"layers",
"[",
"input_name",
"]",
")",
"# Magic ad-hoc.",
"# See the Keras issue: https://github.com/keras-team/keras/issues/6777",
"layers",
"[",
"scope_name",
"]",
".",
"set_shape",
"(",
"layers",
"[",
"scope_name",
"]",
".",
"_keras_shape",
")",
"pads",
"=",
"params",
"[",
"'pads'",
"]",
"if",
"pads",
"[",
"0",
"]",
">",
"0",
":",
"assert",
"(",
"len",
"(",
"pads",
")",
"==",
"2",
"or",
"(",
"pads",
"[",
"2",
"]",
"==",
"pads",
"[",
"0",
"]",
"and",
"pads",
"[",
"3",
"]",
"==",
"pads",
"[",
"1",
"]",
")",
")",
"crop",
"=",
"keras",
".",
"layers",
".",
"Cropping2D",
"(",
"pads",
"[",
":",
"2",
"]",
",",
"name",
"=",
"tf_name",
"+",
"'_crop'",
")",
"layers",
"[",
"scope_name",
"]",
"=",
"crop",
"(",
"layers",
"[",
"scope_name",
"]",
")",
"else",
":",
"raise",
"AssertionError",
"(",
"'Layer is not supported for now'",
")"
] | 750eaf747323580e6732d0c5ba9f2f39cb096764 |
valid | convert_sum | Convert sum.
Args:
params: dictionary with layer parameters
w_name: name prefix in state_dict
scope_name: pytorch scope name
inputs: pytorch node inputs
layers: dictionary with keras tensors
weights: pytorch state_dict
names: use short names for keras layers | pytorch2keras/operation_layers.py | def convert_sum(
params, w_name, scope_name, inputs, layers, weights, names
):
"""
Convert sum.
Args:
params: dictionary with layer parameters
w_name: name prefix in state_dict
scope_name: pytorch scope name
inputs: pytorch node inputs
layers: dictionary with keras tensors
weights: pytorch state_dict
names: use short names for keras layers
"""
print('Converting Sum ...')
def target_layer(x):
import keras.backend as K
return K.sum(x)
lambda_layer = keras.layers.Lambda(target_layer)
layers[scope_name] = lambda_layer(layers[inputs[0]]) | def convert_sum(
params, w_name, scope_name, inputs, layers, weights, names
):
"""
Convert sum.
Args:
params: dictionary with layer parameters
w_name: name prefix in state_dict
scope_name: pytorch scope name
inputs: pytorch node inputs
layers: dictionary with keras tensors
weights: pytorch state_dict
names: use short names for keras layers
"""
print('Converting Sum ...')
def target_layer(x):
import keras.backend as K
return K.sum(x)
lambda_layer = keras.layers.Lambda(target_layer)
layers[scope_name] = lambda_layer(layers[inputs[0]]) | [
"Convert",
"sum",
"."
] | nerox8664/pytorch2keras | python | https://github.com/nerox8664/pytorch2keras/blob/750eaf747323580e6732d0c5ba9f2f39cb096764/pytorch2keras/operation_layers.py#L10-L32 | [
"def",
"convert_sum",
"(",
"params",
",",
"w_name",
",",
"scope_name",
",",
"inputs",
",",
"layers",
",",
"weights",
",",
"names",
")",
":",
"print",
"(",
"'Converting Sum ...'",
")",
"def",
"target_layer",
"(",
"x",
")",
":",
"import",
"keras",
".",
"backend",
"as",
"K",
"return",
"K",
".",
"sum",
"(",
"x",
")",
"lambda_layer",
"=",
"keras",
".",
"layers",
".",
"Lambda",
"(",
"target_layer",
")",
"layers",
"[",
"scope_name",
"]",
"=",
"lambda_layer",
"(",
"layers",
"[",
"inputs",
"[",
"0",
"]",
"]",
")"
] | 750eaf747323580e6732d0c5ba9f2f39cb096764 |
valid | convert_reduce_sum | Convert reduce_sum layer.
Args:
params: dictionary with layer parameters
w_name: name prefix in state_dict
scope_name: pytorch scope name
inputs: pytorch node inputs
layers: dictionary with keras tensors
weights: pytorch state_dict
names: use short names for keras layers | pytorch2keras/operation_layers.py | def convert_reduce_sum(params, w_name, scope_name, inputs, layers, weights, names):
"""
Convert reduce_sum layer.
Args:
params: dictionary with layer parameters
w_name: name prefix in state_dict
scope_name: pytorch scope name
inputs: pytorch node inputs
layers: dictionary with keras tensors
weights: pytorch state_dict
names: use short names for keras layers
"""
print('Converting reduce_sum ...')
keepdims = params['keepdims'] > 0
axis = params['axes']
def target_layer(x, keepdims=keepdims, axis=axis):
import keras.backend as K
return K.sum(x, keepdims=keepdims, axis=axis)
lambda_layer = keras.layers.Lambda(target_layer)
layers[scope_name] = lambda_layer(layers[inputs[0]]) | def convert_reduce_sum(params, w_name, scope_name, inputs, layers, weights, names):
"""
Convert reduce_sum layer.
Args:
params: dictionary with layer parameters
w_name: name prefix in state_dict
scope_name: pytorch scope name
inputs: pytorch node inputs
layers: dictionary with keras tensors
weights: pytorch state_dict
names: use short names for keras layers
"""
print('Converting reduce_sum ...')
keepdims = params['keepdims'] > 0
axis = params['axes']
def target_layer(x, keepdims=keepdims, axis=axis):
import keras.backend as K
return K.sum(x, keepdims=keepdims, axis=axis)
lambda_layer = keras.layers.Lambda(target_layer)
layers[scope_name] = lambda_layer(layers[inputs[0]]) | [
"Convert",
"reduce_sum",
"layer",
"."
] | nerox8664/pytorch2keras | python | https://github.com/nerox8664/pytorch2keras/blob/750eaf747323580e6732d0c5ba9f2f39cb096764/pytorch2keras/operation_layers.py#L35-L58 | [
"def",
"convert_reduce_sum",
"(",
"params",
",",
"w_name",
",",
"scope_name",
",",
"inputs",
",",
"layers",
",",
"weights",
",",
"names",
")",
":",
"print",
"(",
"'Converting reduce_sum ...'",
")",
"keepdims",
"=",
"params",
"[",
"'keepdims'",
"]",
">",
"0",
"axis",
"=",
"params",
"[",
"'axes'",
"]",
"def",
"target_layer",
"(",
"x",
",",
"keepdims",
"=",
"keepdims",
",",
"axis",
"=",
"axis",
")",
":",
"import",
"keras",
".",
"backend",
"as",
"K",
"return",
"K",
".",
"sum",
"(",
"x",
",",
"keepdims",
"=",
"keepdims",
",",
"axis",
"=",
"axis",
")",
"lambda_layer",
"=",
"keras",
".",
"layers",
".",
"Lambda",
"(",
"target_layer",
")",
"layers",
"[",
"scope_name",
"]",
"=",
"lambda_layer",
"(",
"layers",
"[",
"inputs",
"[",
"0",
"]",
"]",
")"
] | 750eaf747323580e6732d0c5ba9f2f39cb096764 |
valid | convert_concat | Convert concatenation.
Args:
params: dictionary with layer parameters
w_name: name prefix in state_dict
scope_name: pytorch scope name
inputs: pytorch node inputs
layers: dictionary with keras tensors
weights: pytorch state_dict
names: use short names for keras layers | pytorch2keras/operation_layers.py | def convert_concat(params, w_name, scope_name, inputs, layers, weights, names):
"""
Convert concatenation.
Args:
params: dictionary with layer parameters
w_name: name prefix in state_dict
scope_name: pytorch scope name
inputs: pytorch node inputs
layers: dictionary with keras tensors
weights: pytorch state_dict
names: use short names for keras layers
"""
print('Converting concat ...')
concat_nodes = [layers[i] for i in inputs]
if len(concat_nodes) == 1:
# no-op
layers[scope_name] = concat_nodes[0]
return
if names == 'short':
tf_name = 'CAT' + random_string(5)
elif names == 'keep':
tf_name = w_name
else:
tf_name = w_name + str(random.random())
cat = keras.layers.Concatenate(name=tf_name, axis=params['axis'])
layers[scope_name] = cat(concat_nodes) | def convert_concat(params, w_name, scope_name, inputs, layers, weights, names):
"""
Convert concatenation.
Args:
params: dictionary with layer parameters
w_name: name prefix in state_dict
scope_name: pytorch scope name
inputs: pytorch node inputs
layers: dictionary with keras tensors
weights: pytorch state_dict
names: use short names for keras layers
"""
print('Converting concat ...')
concat_nodes = [layers[i] for i in inputs]
if len(concat_nodes) == 1:
# no-op
layers[scope_name] = concat_nodes[0]
return
if names == 'short':
tf_name = 'CAT' + random_string(5)
elif names == 'keep':
tf_name = w_name
else:
tf_name = w_name + str(random.random())
cat = keras.layers.Concatenate(name=tf_name, axis=params['axis'])
layers[scope_name] = cat(concat_nodes) | [
"Convert",
"concatenation",
"."
] | nerox8664/pytorch2keras | python | https://github.com/nerox8664/pytorch2keras/blob/750eaf747323580e6732d0c5ba9f2f39cb096764/pytorch2keras/operation_layers.py#L60-L89 | [
"def",
"convert_concat",
"(",
"params",
",",
"w_name",
",",
"scope_name",
",",
"inputs",
",",
"layers",
",",
"weights",
",",
"names",
")",
":",
"print",
"(",
"'Converting concat ...'",
")",
"concat_nodes",
"=",
"[",
"layers",
"[",
"i",
"]",
"for",
"i",
"in",
"inputs",
"]",
"if",
"len",
"(",
"concat_nodes",
")",
"==",
"1",
":",
"# no-op",
"layers",
"[",
"scope_name",
"]",
"=",
"concat_nodes",
"[",
"0",
"]",
"return",
"if",
"names",
"==",
"'short'",
":",
"tf_name",
"=",
"'CAT'",
"+",
"random_string",
"(",
"5",
")",
"elif",
"names",
"==",
"'keep'",
":",
"tf_name",
"=",
"w_name",
"else",
":",
"tf_name",
"=",
"w_name",
"+",
"str",
"(",
"random",
".",
"random",
"(",
")",
")",
"cat",
"=",
"keras",
".",
"layers",
".",
"Concatenate",
"(",
"name",
"=",
"tf_name",
",",
"axis",
"=",
"params",
"[",
"'axis'",
"]",
")",
"layers",
"[",
"scope_name",
"]",
"=",
"cat",
"(",
"concat_nodes",
")"
] | 750eaf747323580e6732d0c5ba9f2f39cb096764 |
valid | convert_slice | Convert slice operation.
Args:
params: dictionary with layer parameters
w_name: name prefix in state_dict
scope_name: pytorch scope name
inputs: pytorch node inputs
layers: dictionary with keras tensors
weights: pytorch state_dict
names: use short names for keras layers | pytorch2keras/operation_layers.py | def convert_slice(params, w_name, scope_name, inputs, layers, weights, names):
"""
Convert slice operation.
Args:
params: dictionary with layer parameters
w_name: name prefix in state_dict
scope_name: pytorch scope name
inputs: pytorch node inputs
layers: dictionary with keras tensors
weights: pytorch state_dict
names: use short names for keras layers
"""
print('Converting slice ...')
if len(params['axes']) > 1:
raise AssertionError('Cannot convert slice by multiple dimensions')
if params['axes'][0] not in [0, 1, 2, 3]:
raise AssertionError('Slice by dimension more than 3 or less than 0 is not supported')
def target_layer(x, axis=int(params['axes'][0]), start=int(params['starts'][0]), end=int(params['ends'][0])):
if axis == 0:
return x[start:end]
elif axis == 1:
return x[:, start:end]
elif axis == 2:
return x[:, :, start:end]
elif axis == 3:
return x[:, :, :, start:end]
lambda_layer = keras.layers.Lambda(target_layer)
layers[scope_name] = lambda_layer(layers[inputs[0]]) | def convert_slice(params, w_name, scope_name, inputs, layers, weights, names):
"""
Convert slice operation.
Args:
params: dictionary with layer parameters
w_name: name prefix in state_dict
scope_name: pytorch scope name
inputs: pytorch node inputs
layers: dictionary with keras tensors
weights: pytorch state_dict
names: use short names for keras layers
"""
print('Converting slice ...')
if len(params['axes']) > 1:
raise AssertionError('Cannot convert slice by multiple dimensions')
if params['axes'][0] not in [0, 1, 2, 3]:
raise AssertionError('Slice by dimension more than 3 or less than 0 is not supported')
def target_layer(x, axis=int(params['axes'][0]), start=int(params['starts'][0]), end=int(params['ends'][0])):
if axis == 0:
return x[start:end]
elif axis == 1:
return x[:, start:end]
elif axis == 2:
return x[:, :, start:end]
elif axis == 3:
return x[:, :, :, start:end]
lambda_layer = keras.layers.Lambda(target_layer)
layers[scope_name] = lambda_layer(layers[inputs[0]]) | [
"Convert",
"slice",
"operation",
"."
] | nerox8664/pytorch2keras | python | https://github.com/nerox8664/pytorch2keras/blob/750eaf747323580e6732d0c5ba9f2f39cb096764/pytorch2keras/operation_layers.py#L92-L124 | [
"def",
"convert_slice",
"(",
"params",
",",
"w_name",
",",
"scope_name",
",",
"inputs",
",",
"layers",
",",
"weights",
",",
"names",
")",
":",
"print",
"(",
"'Converting slice ...'",
")",
"if",
"len",
"(",
"params",
"[",
"'axes'",
"]",
")",
">",
"1",
":",
"raise",
"AssertionError",
"(",
"'Cannot convert slice by multiple dimensions'",
")",
"if",
"params",
"[",
"'axes'",
"]",
"[",
"0",
"]",
"not",
"in",
"[",
"0",
",",
"1",
",",
"2",
",",
"3",
"]",
":",
"raise",
"AssertionError",
"(",
"'Slice by dimension more than 3 or less than 0 is not supported'",
")",
"def",
"target_layer",
"(",
"x",
",",
"axis",
"=",
"int",
"(",
"params",
"[",
"'axes'",
"]",
"[",
"0",
"]",
")",
",",
"start",
"=",
"int",
"(",
"params",
"[",
"'starts'",
"]",
"[",
"0",
"]",
")",
",",
"end",
"=",
"int",
"(",
"params",
"[",
"'ends'",
"]",
"[",
"0",
"]",
")",
")",
":",
"if",
"axis",
"==",
"0",
":",
"return",
"x",
"[",
"start",
":",
"end",
"]",
"elif",
"axis",
"==",
"1",
":",
"return",
"x",
"[",
":",
",",
"start",
":",
"end",
"]",
"elif",
"axis",
"==",
"2",
":",
"return",
"x",
"[",
":",
",",
":",
",",
"start",
":",
"end",
"]",
"elif",
"axis",
"==",
"3",
":",
"return",
"x",
"[",
":",
",",
":",
",",
":",
",",
"start",
":",
"end",
"]",
"lambda_layer",
"=",
"keras",
".",
"layers",
".",
"Lambda",
"(",
"target_layer",
")",
"layers",
"[",
"scope_name",
"]",
"=",
"lambda_layer",
"(",
"layers",
"[",
"inputs",
"[",
"0",
"]",
"]",
")"
] | 750eaf747323580e6732d0c5ba9f2f39cb096764 |
valid | convert_clip | Convert clip operation.
Args:
params: dictionary with layer parameters
w_name: name prefix in state_dict
scope_name: pytorch scope name
inputs: pytorch node inputs
layers: dictionary with keras tensors
weights: pytorch state_dict
names: use short names for keras layers | pytorch2keras/operation_layers.py | def convert_clip(params, w_name, scope_name, inputs, layers, weights, names):
"""
Convert clip operation.
Args:
params: dictionary with layer parameters
w_name: name prefix in state_dict
scope_name: pytorch scope name
inputs: pytorch node inputs
layers: dictionary with keras tensors
weights: pytorch state_dict
names: use short names for keras layers
"""
print('Converting clip ...')
if params['min'] == 0:
print("using ReLU({0})".format(params['max']))
layer = keras.layers.ReLU(max_value=params['max'])
else:
def target_layer(x, vmin=params['min'], vmax=params['max']):
import tensorflow as tf
return tf.clip_by_value(x, vmin, vmax)
layer = keras.layers.Lambda(target_layer)
layers[scope_name] = layer(layers[inputs[0]]) | def convert_clip(params, w_name, scope_name, inputs, layers, weights, names):
"""
Convert clip operation.
Args:
params: dictionary with layer parameters
w_name: name prefix in state_dict
scope_name: pytorch scope name
inputs: pytorch node inputs
layers: dictionary with keras tensors
weights: pytorch state_dict
names: use short names for keras layers
"""
print('Converting clip ...')
if params['min'] == 0:
print("using ReLU({0})".format(params['max']))
layer = keras.layers.ReLU(max_value=params['max'])
else:
def target_layer(x, vmin=params['min'], vmax=params['max']):
import tensorflow as tf
return tf.clip_by_value(x, vmin, vmax)
layer = keras.layers.Lambda(target_layer)
layers[scope_name] = layer(layers[inputs[0]]) | [
"Convert",
"clip",
"operation",
"."
] | nerox8664/pytorch2keras | python | https://github.com/nerox8664/pytorch2keras/blob/750eaf747323580e6732d0c5ba9f2f39cb096764/pytorch2keras/operation_layers.py#L127-L151 | [
"def",
"convert_clip",
"(",
"params",
",",
"w_name",
",",
"scope_name",
",",
"inputs",
",",
"layers",
",",
"weights",
",",
"names",
")",
":",
"print",
"(",
"'Converting clip ...'",
")",
"if",
"params",
"[",
"'min'",
"]",
"==",
"0",
":",
"print",
"(",
"\"using ReLU({0})\"",
".",
"format",
"(",
"params",
"[",
"'max'",
"]",
")",
")",
"layer",
"=",
"keras",
".",
"layers",
".",
"ReLU",
"(",
"max_value",
"=",
"params",
"[",
"'max'",
"]",
")",
"else",
":",
"def",
"target_layer",
"(",
"x",
",",
"vmin",
"=",
"params",
"[",
"'min'",
"]",
",",
"vmax",
"=",
"params",
"[",
"'max'",
"]",
")",
":",
"import",
"tensorflow",
"as",
"tf",
"return",
"tf",
".",
"clip_by_value",
"(",
"x",
",",
"vmin",
",",
"vmax",
")",
"layer",
"=",
"keras",
".",
"layers",
".",
"Lambda",
"(",
"target_layer",
")",
"layers",
"[",
"scope_name",
"]",
"=",
"layer",
"(",
"layers",
"[",
"inputs",
"[",
"0",
"]",
"]",
")"
] | 750eaf747323580e6732d0c5ba9f2f39cb096764 |
valid | convert_elementwise_add | Convert elementwise addition.
Args:
params: dictionary with layer parameters
w_name: name prefix in state_dict
scope_name: pytorch scope name
inputs: pytorch node inputs
layers: dictionary with keras tensors
weights: pytorch state_dict
names: use short names for keras layers | pytorch2keras/elementwise_layers.py | def convert_elementwise_add(
params, w_name, scope_name, inputs, layers, weights, names
):
"""
Convert elementwise addition.
Args:
params: dictionary with layer parameters
w_name: name prefix in state_dict
scope_name: pytorch scope name
inputs: pytorch node inputs
layers: dictionary with keras tensors
weights: pytorch state_dict
names: use short names for keras layers
"""
print('Converting elementwise_add ...')
if 'broadcast' in params:
model0 = layers[inputs[0]]
model1 = layers[inputs[1]]
if names == 'short':
tf_name = 'A' + random_string(7)
elif names == 'keep':
tf_name = w_name
else:
tf_name = w_name + str(random.random())
def target_layer(x):
layer = tf.add(x[0], x[1])
return layer
lambda_layer = keras.layers.Lambda(target_layer, name=tf_name)
layers[scope_name] = lambda_layer([layers[inputs[0]], layers[inputs[1]]])
else:
model0 = layers[inputs[0]]
model1 = layers[inputs[1]]
if names == 'short':
tf_name = 'A' + random_string(7)
elif names == 'keep':
tf_name = w_name
else:
tf_name = w_name + str(random.random())
add = keras.layers.Add(name=tf_name)
layers[scope_name] = add([model0, model1]) | def convert_elementwise_add(
params, w_name, scope_name, inputs, layers, weights, names
):
"""
Convert elementwise addition.
Args:
params: dictionary with layer parameters
w_name: name prefix in state_dict
scope_name: pytorch scope name
inputs: pytorch node inputs
layers: dictionary with keras tensors
weights: pytorch state_dict
names: use short names for keras layers
"""
print('Converting elementwise_add ...')
if 'broadcast' in params:
model0 = layers[inputs[0]]
model1 = layers[inputs[1]]
if names == 'short':
tf_name = 'A' + random_string(7)
elif names == 'keep':
tf_name = w_name
else:
tf_name = w_name + str(random.random())
def target_layer(x):
layer = tf.add(x[0], x[1])
return layer
lambda_layer = keras.layers.Lambda(target_layer, name=tf_name)
layers[scope_name] = lambda_layer([layers[inputs[0]], layers[inputs[1]]])
else:
model0 = layers[inputs[0]]
model1 = layers[inputs[1]]
if names == 'short':
tf_name = 'A' + random_string(7)
elif names == 'keep':
tf_name = w_name
else:
tf_name = w_name + str(random.random())
add = keras.layers.Add(name=tf_name)
layers[scope_name] = add([model0, model1]) | [
"Convert",
"elementwise",
"addition",
"."
] | nerox8664/pytorch2keras | python | https://github.com/nerox8664/pytorch2keras/blob/750eaf747323580e6732d0c5ba9f2f39cb096764/pytorch2keras/elementwise_layers.py#L9-L54 | [
"def",
"convert_elementwise_add",
"(",
"params",
",",
"w_name",
",",
"scope_name",
",",
"inputs",
",",
"layers",
",",
"weights",
",",
"names",
")",
":",
"print",
"(",
"'Converting elementwise_add ...'",
")",
"if",
"'broadcast'",
"in",
"params",
":",
"model0",
"=",
"layers",
"[",
"inputs",
"[",
"0",
"]",
"]",
"model1",
"=",
"layers",
"[",
"inputs",
"[",
"1",
"]",
"]",
"if",
"names",
"==",
"'short'",
":",
"tf_name",
"=",
"'A'",
"+",
"random_string",
"(",
"7",
")",
"elif",
"names",
"==",
"'keep'",
":",
"tf_name",
"=",
"w_name",
"else",
":",
"tf_name",
"=",
"w_name",
"+",
"str",
"(",
"random",
".",
"random",
"(",
")",
")",
"def",
"target_layer",
"(",
"x",
")",
":",
"layer",
"=",
"tf",
".",
"add",
"(",
"x",
"[",
"0",
"]",
",",
"x",
"[",
"1",
"]",
")",
"return",
"layer",
"lambda_layer",
"=",
"keras",
".",
"layers",
".",
"Lambda",
"(",
"target_layer",
",",
"name",
"=",
"tf_name",
")",
"layers",
"[",
"scope_name",
"]",
"=",
"lambda_layer",
"(",
"[",
"layers",
"[",
"inputs",
"[",
"0",
"]",
"]",
",",
"layers",
"[",
"inputs",
"[",
"1",
"]",
"]",
"]",
")",
"else",
":",
"model0",
"=",
"layers",
"[",
"inputs",
"[",
"0",
"]",
"]",
"model1",
"=",
"layers",
"[",
"inputs",
"[",
"1",
"]",
"]",
"if",
"names",
"==",
"'short'",
":",
"tf_name",
"=",
"'A'",
"+",
"random_string",
"(",
"7",
")",
"elif",
"names",
"==",
"'keep'",
":",
"tf_name",
"=",
"w_name",
"else",
":",
"tf_name",
"=",
"w_name",
"+",
"str",
"(",
"random",
".",
"random",
"(",
")",
")",
"add",
"=",
"keras",
".",
"layers",
".",
"Add",
"(",
"name",
"=",
"tf_name",
")",
"layers",
"[",
"scope_name",
"]",
"=",
"add",
"(",
"[",
"model0",
",",
"model1",
"]",
")"
] | 750eaf747323580e6732d0c5ba9f2f39cb096764 |
valid | convert_elementwise_mul | Convert elementwise multiplication.
Args:
params: dictionary with layer parameters
w_name: name prefix in state_dict
scope_name: pytorch scope name
inputs: pytorch node inputs
layers: dictionary with keras tensors
weights: pytorch state_dict
names: use short names for keras layers | pytorch2keras/elementwise_layers.py | def convert_elementwise_mul(
params, w_name, scope_name, inputs, layers, weights, names
):
"""
Convert elementwise multiplication.
Args:
params: dictionary with layer parameters
w_name: name prefix in state_dict
scope_name: pytorch scope name
inputs: pytorch node inputs
layers: dictionary with keras tensors
weights: pytorch state_dict
names: use short names for keras layers
"""
print('Converting elementwise_mul ...')
model0 = layers[inputs[0]]
model1 = layers[inputs[1]]
if names == 'short':
tf_name = 'M' + random_string(7)
elif names == 'keep':
tf_name = w_name
else:
tf_name = w_name + str(random.random())
def target_layer(x):
layer = tf.multiply(
x[0],
x[1]
)
return layer
lambda_layer = keras.layers.Lambda(target_layer, name=tf_name)
layers[scope_name] = lambda_layer([layers[inputs[0]], layers[inputs[1]]]) | def convert_elementwise_mul(
params, w_name, scope_name, inputs, layers, weights, names
):
"""
Convert elementwise multiplication.
Args:
params: dictionary with layer parameters
w_name: name prefix in state_dict
scope_name: pytorch scope name
inputs: pytorch node inputs
layers: dictionary with keras tensors
weights: pytorch state_dict
names: use short names for keras layers
"""
print('Converting elementwise_mul ...')
model0 = layers[inputs[0]]
model1 = layers[inputs[1]]
if names == 'short':
tf_name = 'M' + random_string(7)
elif names == 'keep':
tf_name = w_name
else:
tf_name = w_name + str(random.random())
def target_layer(x):
layer = tf.multiply(
x[0],
x[1]
)
return layer
lambda_layer = keras.layers.Lambda(target_layer, name=tf_name)
layers[scope_name] = lambda_layer([layers[inputs[0]], layers[inputs[1]]]) | [
"Convert",
"elementwise",
"multiplication",
"."
] | nerox8664/pytorch2keras | python | https://github.com/nerox8664/pytorch2keras/blob/750eaf747323580e6732d0c5ba9f2f39cb096764/pytorch2keras/elementwise_layers.py#L57-L91 | [
"def",
"convert_elementwise_mul",
"(",
"params",
",",
"w_name",
",",
"scope_name",
",",
"inputs",
",",
"layers",
",",
"weights",
",",
"names",
")",
":",
"print",
"(",
"'Converting elementwise_mul ...'",
")",
"model0",
"=",
"layers",
"[",
"inputs",
"[",
"0",
"]",
"]",
"model1",
"=",
"layers",
"[",
"inputs",
"[",
"1",
"]",
"]",
"if",
"names",
"==",
"'short'",
":",
"tf_name",
"=",
"'M'",
"+",
"random_string",
"(",
"7",
")",
"elif",
"names",
"==",
"'keep'",
":",
"tf_name",
"=",
"w_name",
"else",
":",
"tf_name",
"=",
"w_name",
"+",
"str",
"(",
"random",
".",
"random",
"(",
")",
")",
"def",
"target_layer",
"(",
"x",
")",
":",
"layer",
"=",
"tf",
".",
"multiply",
"(",
"x",
"[",
"0",
"]",
",",
"x",
"[",
"1",
"]",
")",
"return",
"layer",
"lambda_layer",
"=",
"keras",
".",
"layers",
".",
"Lambda",
"(",
"target_layer",
",",
"name",
"=",
"tf_name",
")",
"layers",
"[",
"scope_name",
"]",
"=",
"lambda_layer",
"(",
"[",
"layers",
"[",
"inputs",
"[",
"0",
"]",
"]",
",",
"layers",
"[",
"inputs",
"[",
"1",
"]",
"]",
"]",
")"
] | 750eaf747323580e6732d0c5ba9f2f39cb096764 |
valid | convert_elementwise_div | Convert elementwise multiplication.
Args:
params: dictionary with layer parameters
w_name: name prefix in state_dict
scope_name: pytorch scope name
inputs: pytorch node inputs
layers: dictionary with keras tensors
weights: pytorch state_dict
names: use short names for keras layers | pytorch2keras/elementwise_layers.py | def convert_elementwise_div(
params, w_name, scope_name, inputs, layers, weights, names
):
"""
Convert elementwise multiplication.
Args:
params: dictionary with layer parameters
w_name: name prefix in state_dict
scope_name: pytorch scope name
inputs: pytorch node inputs
layers: dictionary with keras tensors
weights: pytorch state_dict
names: use short names for keras layers
"""
print('Converting elementwise_div ...')
if names == 'short':
tf_name = 'D' + random_string(7)
elif names == 'keep':
tf_name = w_name
else:
tf_name = w_name + str(random.random())
def target_layer(x):
layer = tf.div(
x[0],
x[1]
)
return layer
lambda_layer = keras.layers.Lambda(target_layer, name=tf_name)
layers[scope_name] = lambda_layer([layers[inputs[0]], layers[inputs[1]]]) | def convert_elementwise_div(
params, w_name, scope_name, inputs, layers, weights, names
):
"""
Convert elementwise multiplication.
Args:
params: dictionary with layer parameters
w_name: name prefix in state_dict
scope_name: pytorch scope name
inputs: pytorch node inputs
layers: dictionary with keras tensors
weights: pytorch state_dict
names: use short names for keras layers
"""
print('Converting elementwise_div ...')
if names == 'short':
tf_name = 'D' + random_string(7)
elif names == 'keep':
tf_name = w_name
else:
tf_name = w_name + str(random.random())
def target_layer(x):
layer = tf.div(
x[0],
x[1]
)
return layer
lambda_layer = keras.layers.Lambda(target_layer, name=tf_name)
layers[scope_name] = lambda_layer([layers[inputs[0]], layers[inputs[1]]]) | [
"Convert",
"elementwise",
"multiplication",
"."
] | nerox8664/pytorch2keras | python | https://github.com/nerox8664/pytorch2keras/blob/750eaf747323580e6732d0c5ba9f2f39cb096764/pytorch2keras/elementwise_layers.py#L94-L126 | [
"def",
"convert_elementwise_div",
"(",
"params",
",",
"w_name",
",",
"scope_name",
",",
"inputs",
",",
"layers",
",",
"weights",
",",
"names",
")",
":",
"print",
"(",
"'Converting elementwise_div ...'",
")",
"if",
"names",
"==",
"'short'",
":",
"tf_name",
"=",
"'D'",
"+",
"random_string",
"(",
"7",
")",
"elif",
"names",
"==",
"'keep'",
":",
"tf_name",
"=",
"w_name",
"else",
":",
"tf_name",
"=",
"w_name",
"+",
"str",
"(",
"random",
".",
"random",
"(",
")",
")",
"def",
"target_layer",
"(",
"x",
")",
":",
"layer",
"=",
"tf",
".",
"div",
"(",
"x",
"[",
"0",
"]",
",",
"x",
"[",
"1",
"]",
")",
"return",
"layer",
"lambda_layer",
"=",
"keras",
".",
"layers",
".",
"Lambda",
"(",
"target_layer",
",",
"name",
"=",
"tf_name",
")",
"layers",
"[",
"scope_name",
"]",
"=",
"lambda_layer",
"(",
"[",
"layers",
"[",
"inputs",
"[",
"0",
"]",
"]",
",",
"layers",
"[",
"inputs",
"[",
"1",
"]",
"]",
"]",
")"
] | 750eaf747323580e6732d0c5ba9f2f39cb096764 |
valid | convert_elementwise_sub | Convert elementwise subtraction.
Args:
params: dictionary with layer parameters
w_name: name prefix in state_dict
scope_name: pytorch scope name
inputs: pytorch node inputs
layers: dictionary with keras tensors
weights: pytorch state_dict
names: use short names for keras layers | pytorch2keras/elementwise_layers.py | def convert_elementwise_sub(
params, w_name, scope_name, inputs, layers, weights, names
):
"""
Convert elementwise subtraction.
Args:
params: dictionary with layer parameters
w_name: name prefix in state_dict
scope_name: pytorch scope name
inputs: pytorch node inputs
layers: dictionary with keras tensors
weights: pytorch state_dict
names: use short names for keras layers
"""
print('Converting elementwise_sub ...')
model0 = layers[inputs[0]]
model1 = layers[inputs[1]]
if names == 'short':
tf_name = 'S' + random_string(7)
elif names == 'keep':
tf_name = w_name
else:
tf_name = w_name + str(random.random())
sub = keras.layers.Subtract(name=tf_name)
layers[scope_name] = sub([model0, model1]) | def convert_elementwise_sub(
params, w_name, scope_name, inputs, layers, weights, names
):
"""
Convert elementwise subtraction.
Args:
params: dictionary with layer parameters
w_name: name prefix in state_dict
scope_name: pytorch scope name
inputs: pytorch node inputs
layers: dictionary with keras tensors
weights: pytorch state_dict
names: use short names for keras layers
"""
print('Converting elementwise_sub ...')
model0 = layers[inputs[0]]
model1 = layers[inputs[1]]
if names == 'short':
tf_name = 'S' + random_string(7)
elif names == 'keep':
tf_name = w_name
else:
tf_name = w_name + str(random.random())
sub = keras.layers.Subtract(name=tf_name)
layers[scope_name] = sub([model0, model1]) | [
"Convert",
"elementwise",
"subtraction",
"."
] | nerox8664/pytorch2keras | python | https://github.com/nerox8664/pytorch2keras/blob/750eaf747323580e6732d0c5ba9f2f39cb096764/pytorch2keras/elementwise_layers.py#L129-L156 | [
"def",
"convert_elementwise_sub",
"(",
"params",
",",
"w_name",
",",
"scope_name",
",",
"inputs",
",",
"layers",
",",
"weights",
",",
"names",
")",
":",
"print",
"(",
"'Converting elementwise_sub ...'",
")",
"model0",
"=",
"layers",
"[",
"inputs",
"[",
"0",
"]",
"]",
"model1",
"=",
"layers",
"[",
"inputs",
"[",
"1",
"]",
"]",
"if",
"names",
"==",
"'short'",
":",
"tf_name",
"=",
"'S'",
"+",
"random_string",
"(",
"7",
")",
"elif",
"names",
"==",
"'keep'",
":",
"tf_name",
"=",
"w_name",
"else",
":",
"tf_name",
"=",
"w_name",
"+",
"str",
"(",
"random",
".",
"random",
"(",
")",
")",
"sub",
"=",
"keras",
".",
"layers",
".",
"Subtract",
"(",
"name",
"=",
"tf_name",
")",
"layers",
"[",
"scope_name",
"]",
"=",
"sub",
"(",
"[",
"model0",
",",
"model1",
"]",
")"
] | 750eaf747323580e6732d0c5ba9f2f39cb096764 |
valid | convert_gemm | Convert Linear.
Args:
params: dictionary with layer parameters
w_name: name prefix in state_dict
scope_name: pytorch scope name
inputs: pytorch node inputs
layers: dictionary with keras tensors
weights: pytorch state_dict
names: use short names for keras layers | pytorch2keras/linear_layers.py | def convert_gemm(params, w_name, scope_name, inputs, layers, weights, names):
"""
Convert Linear.
Args:
params: dictionary with layer parameters
w_name: name prefix in state_dict
scope_name: pytorch scope name
inputs: pytorch node inputs
layers: dictionary with keras tensors
weights: pytorch state_dict
names: use short names for keras layers
"""
print('Converting Linear ...')
if names == 'short':
tf_name = 'FC' + random_string(6)
elif names == 'keep':
tf_name = w_name
else:
tf_name = w_name + str(random.random())
bias_name = '{0}.bias'.format(w_name)
weights_name = '{0}.weight'.format(w_name)
W = weights[weights_name].numpy().transpose()
input_channels, output_channels = W.shape
keras_weights = [W]
has_bias = False
if bias_name in weights:
bias = weights[bias_name].numpy()
keras_weights = [W, bias]
has_bias = True
dense = keras.layers.Dense(
output_channels,
weights=keras_weights, use_bias=has_bias, name=tf_name, bias_initializer='zeros', kernel_initializer='zeros',
)
layers[scope_name] = dense(layers[inputs[0]]) | def convert_gemm(params, w_name, scope_name, inputs, layers, weights, names):
"""
Convert Linear.
Args:
params: dictionary with layer parameters
w_name: name prefix in state_dict
scope_name: pytorch scope name
inputs: pytorch node inputs
layers: dictionary with keras tensors
weights: pytorch state_dict
names: use short names for keras layers
"""
print('Converting Linear ...')
if names == 'short':
tf_name = 'FC' + random_string(6)
elif names == 'keep':
tf_name = w_name
else:
tf_name = w_name + str(random.random())
bias_name = '{0}.bias'.format(w_name)
weights_name = '{0}.weight'.format(w_name)
W = weights[weights_name].numpy().transpose()
input_channels, output_channels = W.shape
keras_weights = [W]
has_bias = False
if bias_name in weights:
bias = weights[bias_name].numpy()
keras_weights = [W, bias]
has_bias = True
dense = keras.layers.Dense(
output_channels,
weights=keras_weights, use_bias=has_bias, name=tf_name, bias_initializer='zeros', kernel_initializer='zeros',
)
layers[scope_name] = dense(layers[inputs[0]]) | [
"Convert",
"Linear",
"."
] | nerox8664/pytorch2keras | python | https://github.com/nerox8664/pytorch2keras/blob/750eaf747323580e6732d0c5ba9f2f39cb096764/pytorch2keras/linear_layers.py#L9-L49 | [
"def",
"convert_gemm",
"(",
"params",
",",
"w_name",
",",
"scope_name",
",",
"inputs",
",",
"layers",
",",
"weights",
",",
"names",
")",
":",
"print",
"(",
"'Converting Linear ...'",
")",
"if",
"names",
"==",
"'short'",
":",
"tf_name",
"=",
"'FC'",
"+",
"random_string",
"(",
"6",
")",
"elif",
"names",
"==",
"'keep'",
":",
"tf_name",
"=",
"w_name",
"else",
":",
"tf_name",
"=",
"w_name",
"+",
"str",
"(",
"random",
".",
"random",
"(",
")",
")",
"bias_name",
"=",
"'{0}.bias'",
".",
"format",
"(",
"w_name",
")",
"weights_name",
"=",
"'{0}.weight'",
".",
"format",
"(",
"w_name",
")",
"W",
"=",
"weights",
"[",
"weights_name",
"]",
".",
"numpy",
"(",
")",
".",
"transpose",
"(",
")",
"input_channels",
",",
"output_channels",
"=",
"W",
".",
"shape",
"keras_weights",
"=",
"[",
"W",
"]",
"has_bias",
"=",
"False",
"if",
"bias_name",
"in",
"weights",
":",
"bias",
"=",
"weights",
"[",
"bias_name",
"]",
".",
"numpy",
"(",
")",
"keras_weights",
"=",
"[",
"W",
",",
"bias",
"]",
"has_bias",
"=",
"True",
"dense",
"=",
"keras",
".",
"layers",
".",
"Dense",
"(",
"output_channels",
",",
"weights",
"=",
"keras_weights",
",",
"use_bias",
"=",
"has_bias",
",",
"name",
"=",
"tf_name",
",",
"bias_initializer",
"=",
"'zeros'",
",",
"kernel_initializer",
"=",
"'zeros'",
",",
")",
"layers",
"[",
"scope_name",
"]",
"=",
"dense",
"(",
"layers",
"[",
"inputs",
"[",
"0",
"]",
"]",
")"
] | 750eaf747323580e6732d0c5ba9f2f39cb096764 |
valid | convert_matmul | Convert matmul layer.
Args:
params: dictionary with layer parameters
w_name: name prefix in state_dict
scope_name: pytorch scope name
inputs: pytorch node inputs
layers: dictionary with keras tensors
weights: pytorch state_dict
names: use short names for keras layers | pytorch2keras/linear_layers.py | def convert_matmul(params, w_name, scope_name, inputs, layers, weights, names):
"""
Convert matmul layer.
Args:
params: dictionary with layer parameters
w_name: name prefix in state_dict
scope_name: pytorch scope name
inputs: pytorch node inputs
layers: dictionary with keras tensors
weights: pytorch state_dict
names: use short names for keras layers
"""
print('Converting matmul ...')
if names == 'short':
tf_name = 'MMUL' + random_string(4)
elif names == 'keep':
tf_name = w_name
else:
tf_name = w_name + str(random.random())
if len(inputs) == 1:
weights_name = '{0}.weight'.format(w_name)
W = weights[weights_name].numpy().transpose()
input_channels, output_channels = W.shape
keras_weights = [W]
dense = keras.layers.Dense(
output_channels,
weights=keras_weights, use_bias=False, name=tf_name, bias_initializer='zeros', kernel_initializer='zeros',
)
layers[scope_name] = dense(layers[inputs[0]])
elif len(inputs) == 2:
weights_name = '{0}.weight'.format(w_name)
W = weights[weights_name].numpy().transpose()
input_channels, output_channels = W.shape
keras_weights = [W]
dense = keras.layers.Dense(
output_channels,
weights=keras_weights, use_bias=False, name=tf_name, bias_initializer='zeros', kernel_initializer='zeros',
)
layers[scope_name] = dense(layers[inputs[0]])
else:
raise AssertionError('Cannot convert matmul layer') | def convert_matmul(params, w_name, scope_name, inputs, layers, weights, names):
"""
Convert matmul layer.
Args:
params: dictionary with layer parameters
w_name: name prefix in state_dict
scope_name: pytorch scope name
inputs: pytorch node inputs
layers: dictionary with keras tensors
weights: pytorch state_dict
names: use short names for keras layers
"""
print('Converting matmul ...')
if names == 'short':
tf_name = 'MMUL' + random_string(4)
elif names == 'keep':
tf_name = w_name
else:
tf_name = w_name + str(random.random())
if len(inputs) == 1:
weights_name = '{0}.weight'.format(w_name)
W = weights[weights_name].numpy().transpose()
input_channels, output_channels = W.shape
keras_weights = [W]
dense = keras.layers.Dense(
output_channels,
weights=keras_weights, use_bias=False, name=tf_name, bias_initializer='zeros', kernel_initializer='zeros',
)
layers[scope_name] = dense(layers[inputs[0]])
elif len(inputs) == 2:
weights_name = '{0}.weight'.format(w_name)
W = weights[weights_name].numpy().transpose()
input_channels, output_channels = W.shape
keras_weights = [W]
dense = keras.layers.Dense(
output_channels,
weights=keras_weights, use_bias=False, name=tf_name, bias_initializer='zeros', kernel_initializer='zeros',
)
layers[scope_name] = dense(layers[inputs[0]])
else:
raise AssertionError('Cannot convert matmul layer') | [
"Convert",
"matmul",
"layer",
"."
] | nerox8664/pytorch2keras | python | https://github.com/nerox8664/pytorch2keras/blob/750eaf747323580e6732d0c5ba9f2f39cb096764/pytorch2keras/linear_layers.py#L52-L101 | [
"def",
"convert_matmul",
"(",
"params",
",",
"w_name",
",",
"scope_name",
",",
"inputs",
",",
"layers",
",",
"weights",
",",
"names",
")",
":",
"print",
"(",
"'Converting matmul ...'",
")",
"if",
"names",
"==",
"'short'",
":",
"tf_name",
"=",
"'MMUL'",
"+",
"random_string",
"(",
"4",
")",
"elif",
"names",
"==",
"'keep'",
":",
"tf_name",
"=",
"w_name",
"else",
":",
"tf_name",
"=",
"w_name",
"+",
"str",
"(",
"random",
".",
"random",
"(",
")",
")",
"if",
"len",
"(",
"inputs",
")",
"==",
"1",
":",
"weights_name",
"=",
"'{0}.weight'",
".",
"format",
"(",
"w_name",
")",
"W",
"=",
"weights",
"[",
"weights_name",
"]",
".",
"numpy",
"(",
")",
".",
"transpose",
"(",
")",
"input_channels",
",",
"output_channels",
"=",
"W",
".",
"shape",
"keras_weights",
"=",
"[",
"W",
"]",
"dense",
"=",
"keras",
".",
"layers",
".",
"Dense",
"(",
"output_channels",
",",
"weights",
"=",
"keras_weights",
",",
"use_bias",
"=",
"False",
",",
"name",
"=",
"tf_name",
",",
"bias_initializer",
"=",
"'zeros'",
",",
"kernel_initializer",
"=",
"'zeros'",
",",
")",
"layers",
"[",
"scope_name",
"]",
"=",
"dense",
"(",
"layers",
"[",
"inputs",
"[",
"0",
"]",
"]",
")",
"elif",
"len",
"(",
"inputs",
")",
"==",
"2",
":",
"weights_name",
"=",
"'{0}.weight'",
".",
"format",
"(",
"w_name",
")",
"W",
"=",
"weights",
"[",
"weights_name",
"]",
".",
"numpy",
"(",
")",
".",
"transpose",
"(",
")",
"input_channels",
",",
"output_channels",
"=",
"W",
".",
"shape",
"keras_weights",
"=",
"[",
"W",
"]",
"dense",
"=",
"keras",
".",
"layers",
".",
"Dense",
"(",
"output_channels",
",",
"weights",
"=",
"keras_weights",
",",
"use_bias",
"=",
"False",
",",
"name",
"=",
"tf_name",
",",
"bias_initializer",
"=",
"'zeros'",
",",
"kernel_initializer",
"=",
"'zeros'",
",",
")",
"layers",
"[",
"scope_name",
"]",
"=",
"dense",
"(",
"layers",
"[",
"inputs",
"[",
"0",
"]",
"]",
")",
"else",
":",
"raise",
"AssertionError",
"(",
"'Cannot convert matmul layer'",
")"
] | 750eaf747323580e6732d0c5ba9f2f39cb096764 |
valid | convert_constant | Convert constant layer.
Args:
params: dictionary with layer parameters
w_name: name prefix in state_dict
scope_name: pytorch scope name
inputs: pytorch node inputs
layers: dictionary with keras tensors
weights: pytorch state_dict
names: use short names for keras layers | pytorch2keras/constant_layers.py | def convert_constant(params, w_name, scope_name, inputs, layers, weights, names):
"""
Convert constant layer.
Args:
params: dictionary with layer parameters
w_name: name prefix in state_dict
scope_name: pytorch scope name
inputs: pytorch node inputs
layers: dictionary with keras tensors
weights: pytorch state_dict
names: use short names for keras layers
"""
print('Converting constant ...')
params_list = params['value'].numpy()
def target_layer(x, value=params_list):
return tf.constant(value.tolist(), shape=value.shape)
lambda_layer = keras.layers.Lambda(target_layer)
layers[scope_name + '_np'] = params_list # ad-hoc
layers[scope_name] = lambda_layer(layers[list(layers.keys())[0]]) | def convert_constant(params, w_name, scope_name, inputs, layers, weights, names):
"""
Convert constant layer.
Args:
params: dictionary with layer parameters
w_name: name prefix in state_dict
scope_name: pytorch scope name
inputs: pytorch node inputs
layers: dictionary with keras tensors
weights: pytorch state_dict
names: use short names for keras layers
"""
print('Converting constant ...')
params_list = params['value'].numpy()
def target_layer(x, value=params_list):
return tf.constant(value.tolist(), shape=value.shape)
lambda_layer = keras.layers.Lambda(target_layer)
layers[scope_name + '_np'] = params_list # ad-hoc
layers[scope_name] = lambda_layer(layers[list(layers.keys())[0]]) | [
"Convert",
"constant",
"layer",
"."
] | nerox8664/pytorch2keras | python | https://github.com/nerox8664/pytorch2keras/blob/750eaf747323580e6732d0c5ba9f2f39cb096764/pytorch2keras/constant_layers.py#L9-L31 | [
"def",
"convert_constant",
"(",
"params",
",",
"w_name",
",",
"scope_name",
",",
"inputs",
",",
"layers",
",",
"weights",
",",
"names",
")",
":",
"print",
"(",
"'Converting constant ...'",
")",
"params_list",
"=",
"params",
"[",
"'value'",
"]",
".",
"numpy",
"(",
")",
"def",
"target_layer",
"(",
"x",
",",
"value",
"=",
"params_list",
")",
":",
"return",
"tf",
".",
"constant",
"(",
"value",
".",
"tolist",
"(",
")",
",",
"shape",
"=",
"value",
".",
"shape",
")",
"lambda_layer",
"=",
"keras",
".",
"layers",
".",
"Lambda",
"(",
"target_layer",
")",
"layers",
"[",
"scope_name",
"+",
"'_np'",
"]",
"=",
"params_list",
"# ad-hoc",
"layers",
"[",
"scope_name",
"]",
"=",
"lambda_layer",
"(",
"layers",
"[",
"list",
"(",
"layers",
".",
"keys",
"(",
")",
")",
"[",
"0",
"]",
"]",
")"
] | 750eaf747323580e6732d0c5ba9f2f39cb096764 |
valid | convert_flatten | Convert reshape(view).
Args:
params: dictionary with layer parameters
w_name: name prefix in state_dict
scope_name: pytorch scope name
inputs: pytorch node inputs
layers: dictionary with keras tensors
weights: pytorch state_dict
names: use short names for keras layers | pytorch2keras/reshape_layers.py | def convert_flatten(params, w_name, scope_name, inputs, layers, weights, names):
"""
Convert reshape(view).
Args:
params: dictionary with layer parameters
w_name: name prefix in state_dict
scope_name: pytorch scope name
inputs: pytorch node inputs
layers: dictionary with keras tensors
weights: pytorch state_dict
names: use short names for keras layers
"""
print('Converting flatten ...')
if names == 'short':
tf_name = 'R' + random_string(7)
elif names == 'keep':
tf_name = w_name
else:
tf_name = w_name + str(random.random())
reshape = keras.layers.Reshape([-1], name=tf_name)
layers[scope_name] = reshape(layers[inputs[0]]) | def convert_flatten(params, w_name, scope_name, inputs, layers, weights, names):
"""
Convert reshape(view).
Args:
params: dictionary with layer parameters
w_name: name prefix in state_dict
scope_name: pytorch scope name
inputs: pytorch node inputs
layers: dictionary with keras tensors
weights: pytorch state_dict
names: use short names for keras layers
"""
print('Converting flatten ...')
if names == 'short':
tf_name = 'R' + random_string(7)
elif names == 'keep':
tf_name = w_name
else:
tf_name = w_name + str(random.random())
reshape = keras.layers.Reshape([-1], name=tf_name)
layers[scope_name] = reshape(layers[inputs[0]]) | [
"Convert",
"reshape",
"(",
"view",
")",
"."
] | nerox8664/pytorch2keras | python | https://github.com/nerox8664/pytorch2keras/blob/750eaf747323580e6732d0c5ba9f2f39cb096764/pytorch2keras/reshape_layers.py#L9-L32 | [
"def",
"convert_flatten",
"(",
"params",
",",
"w_name",
",",
"scope_name",
",",
"inputs",
",",
"layers",
",",
"weights",
",",
"names",
")",
":",
"print",
"(",
"'Converting flatten ...'",
")",
"if",
"names",
"==",
"'short'",
":",
"tf_name",
"=",
"'R'",
"+",
"random_string",
"(",
"7",
")",
"elif",
"names",
"==",
"'keep'",
":",
"tf_name",
"=",
"w_name",
"else",
":",
"tf_name",
"=",
"w_name",
"+",
"str",
"(",
"random",
".",
"random",
"(",
")",
")",
"reshape",
"=",
"keras",
".",
"layers",
".",
"Reshape",
"(",
"[",
"-",
"1",
"]",
",",
"name",
"=",
"tf_name",
")",
"layers",
"[",
"scope_name",
"]",
"=",
"reshape",
"(",
"layers",
"[",
"inputs",
"[",
"0",
"]",
"]",
")"
] | 750eaf747323580e6732d0c5ba9f2f39cb096764 |
valid | convert_transpose | Convert transpose layer.
Args:
params: dictionary with layer parameters
w_name: name prefix in state_dict
scope_name: pytorch scope name
inputs: pytorch node inputs
layers: dictionary with keras tensors
weights: pytorch state_dict
names: use short names for keras layers | pytorch2keras/reshape_layers.py | def convert_transpose(params, w_name, scope_name, inputs, layers, weights, names):
"""
Convert transpose layer.
Args:
params: dictionary with layer parameters
w_name: name prefix in state_dict
scope_name: pytorch scope name
inputs: pytorch node inputs
layers: dictionary with keras tensors
weights: pytorch state_dict
names: use short names for keras layers
"""
print('Converting transpose ...')
if params['perm'][0] != 0:
if inputs[0] in layers:
print('!!! Cannot permute batch dimension. Result may be wrong !!!')
layers[scope_name] = layers[inputs[0]]
else:
print('Skip weight matrix transpose, result may be wrong.')
else:
if names:
tf_name = 'PERM' + random_string(4)
else:
tf_name = w_name + str(random.random())
permute = keras.layers.Permute(params['perm'][1:], name=tf_name)
layers[scope_name] = permute(layers[inputs[0]]) | def convert_transpose(params, w_name, scope_name, inputs, layers, weights, names):
"""
Convert transpose layer.
Args:
params: dictionary with layer parameters
w_name: name prefix in state_dict
scope_name: pytorch scope name
inputs: pytorch node inputs
layers: dictionary with keras tensors
weights: pytorch state_dict
names: use short names for keras layers
"""
print('Converting transpose ...')
if params['perm'][0] != 0:
if inputs[0] in layers:
print('!!! Cannot permute batch dimension. Result may be wrong !!!')
layers[scope_name] = layers[inputs[0]]
else:
print('Skip weight matrix transpose, result may be wrong.')
else:
if names:
tf_name = 'PERM' + random_string(4)
else:
tf_name = w_name + str(random.random())
permute = keras.layers.Permute(params['perm'][1:], name=tf_name)
layers[scope_name] = permute(layers[inputs[0]]) | [
"Convert",
"transpose",
"layer",
"."
] | nerox8664/pytorch2keras | python | https://github.com/nerox8664/pytorch2keras/blob/750eaf747323580e6732d0c5ba9f2f39cb096764/pytorch2keras/reshape_layers.py#L35-L61 | [
"def",
"convert_transpose",
"(",
"params",
",",
"w_name",
",",
"scope_name",
",",
"inputs",
",",
"layers",
",",
"weights",
",",
"names",
")",
":",
"print",
"(",
"'Converting transpose ...'",
")",
"if",
"params",
"[",
"'perm'",
"]",
"[",
"0",
"]",
"!=",
"0",
":",
"if",
"inputs",
"[",
"0",
"]",
"in",
"layers",
":",
"print",
"(",
"'!!! Cannot permute batch dimension. Result may be wrong !!!'",
")",
"layers",
"[",
"scope_name",
"]",
"=",
"layers",
"[",
"inputs",
"[",
"0",
"]",
"]",
"else",
":",
"print",
"(",
"'Skip weight matrix transpose, result may be wrong.'",
")",
"else",
":",
"if",
"names",
":",
"tf_name",
"=",
"'PERM'",
"+",
"random_string",
"(",
"4",
")",
"else",
":",
"tf_name",
"=",
"w_name",
"+",
"str",
"(",
"random",
".",
"random",
"(",
")",
")",
"permute",
"=",
"keras",
".",
"layers",
".",
"Permute",
"(",
"params",
"[",
"'perm'",
"]",
"[",
"1",
":",
"]",
",",
"name",
"=",
"tf_name",
")",
"layers",
"[",
"scope_name",
"]",
"=",
"permute",
"(",
"layers",
"[",
"inputs",
"[",
"0",
"]",
"]",
")"
] | 750eaf747323580e6732d0c5ba9f2f39cb096764 |
valid | convert_reshape | Convert reshape layer.
Args:
params: dictionary with layer parameters
w_name: name prefix in state_dict
scope_name: pytorch scope name
inputs: pytorch node inputs
layers: dictionary with keras tensors
weights: pytorch state_dict
names: use short names for keras layers | pytorch2keras/reshape_layers.py | def convert_reshape(params, w_name, scope_name, inputs, layers, weights, names):
"""
Convert reshape layer.
Args:
params: dictionary with layer parameters
w_name: name prefix in state_dict
scope_name: pytorch scope name
inputs: pytorch node inputs
layers: dictionary with keras tensors
weights: pytorch state_dict
names: use short names for keras layers
"""
print('Converting reshape ...')
if names == 'short':
tf_name = 'RESH' + random_string(4)
elif names == 'keep':
tf_name = w_name
else:
tf_name = w_name + str(random.random())
if len(inputs) > 1:
if layers[inputs[1]][0] == -1:
print('Cannot deduct batch size! It will be omitted, but result may be wrong.')
reshape = keras.layers.Reshape(layers[inputs[1] + '_np'], name=tf_name)
layers[scope_name] = reshape(layers[inputs[0]])
else:
if inputs[0] in layers:
reshape = keras.layers.Reshape(params['shape'][1:], name=tf_name)
layers[scope_name] = reshape(layers[inputs[0]])
else:
print('Skip weight matrix transpose, but result may be wrong.') | def convert_reshape(params, w_name, scope_name, inputs, layers, weights, names):
"""
Convert reshape layer.
Args:
params: dictionary with layer parameters
w_name: name prefix in state_dict
scope_name: pytorch scope name
inputs: pytorch node inputs
layers: dictionary with keras tensors
weights: pytorch state_dict
names: use short names for keras layers
"""
print('Converting reshape ...')
if names == 'short':
tf_name = 'RESH' + random_string(4)
elif names == 'keep':
tf_name = w_name
else:
tf_name = w_name + str(random.random())
if len(inputs) > 1:
if layers[inputs[1]][0] == -1:
print('Cannot deduct batch size! It will be omitted, but result may be wrong.')
reshape = keras.layers.Reshape(layers[inputs[1] + '_np'], name=tf_name)
layers[scope_name] = reshape(layers[inputs[0]])
else:
if inputs[0] in layers:
reshape = keras.layers.Reshape(params['shape'][1:], name=tf_name)
layers[scope_name] = reshape(layers[inputs[0]])
else:
print('Skip weight matrix transpose, but result may be wrong.') | [
"Convert",
"reshape",
"layer",
"."
] | nerox8664/pytorch2keras | python | https://github.com/nerox8664/pytorch2keras/blob/750eaf747323580e6732d0c5ba9f2f39cb096764/pytorch2keras/reshape_layers.py#L64-L96 | [
"def",
"convert_reshape",
"(",
"params",
",",
"w_name",
",",
"scope_name",
",",
"inputs",
",",
"layers",
",",
"weights",
",",
"names",
")",
":",
"print",
"(",
"'Converting reshape ...'",
")",
"if",
"names",
"==",
"'short'",
":",
"tf_name",
"=",
"'RESH'",
"+",
"random_string",
"(",
"4",
")",
"elif",
"names",
"==",
"'keep'",
":",
"tf_name",
"=",
"w_name",
"else",
":",
"tf_name",
"=",
"w_name",
"+",
"str",
"(",
"random",
".",
"random",
"(",
")",
")",
"if",
"len",
"(",
"inputs",
")",
">",
"1",
":",
"if",
"layers",
"[",
"inputs",
"[",
"1",
"]",
"]",
"[",
"0",
"]",
"==",
"-",
"1",
":",
"print",
"(",
"'Cannot deduct batch size! It will be omitted, but result may be wrong.'",
")",
"reshape",
"=",
"keras",
".",
"layers",
".",
"Reshape",
"(",
"layers",
"[",
"inputs",
"[",
"1",
"]",
"+",
"'_np'",
"]",
",",
"name",
"=",
"tf_name",
")",
"layers",
"[",
"scope_name",
"]",
"=",
"reshape",
"(",
"layers",
"[",
"inputs",
"[",
"0",
"]",
"]",
")",
"else",
":",
"if",
"inputs",
"[",
"0",
"]",
"in",
"layers",
":",
"reshape",
"=",
"keras",
".",
"layers",
".",
"Reshape",
"(",
"params",
"[",
"'shape'",
"]",
"[",
"1",
":",
"]",
",",
"name",
"=",
"tf_name",
")",
"layers",
"[",
"scope_name",
"]",
"=",
"reshape",
"(",
"layers",
"[",
"inputs",
"[",
"0",
"]",
"]",
")",
"else",
":",
"print",
"(",
"'Skip weight matrix transpose, but result may be wrong.'",
")"
] | 750eaf747323580e6732d0c5ba9f2f39cb096764 |
valid | convert_squeeze | Convert squeeze operation.
Args:
params: dictionary with layer parameters
w_name: name prefix in state_dict
scope_name: pytorch scope name
inputs: pytorch node inputs
layers: dictionary with keras tensors
weights: pytorch state_dict
names: use short names for keras layers | pytorch2keras/reshape_layers.py | def convert_squeeze(params, w_name, scope_name, inputs, layers, weights, names):
"""
Convert squeeze operation.
Args:
params: dictionary with layer parameters
w_name: name prefix in state_dict
scope_name: pytorch scope name
inputs: pytorch node inputs
layers: dictionary with keras tensors
weights: pytorch state_dict
names: use short names for keras layers
"""
print('Converting squeeze ...')
if len(params['axes']) > 1:
raise AssertionError('Cannot convert squeeze by multiple dimensions')
def target_layer(x, axis=int(params['axes'][0])):
import tensorflow as tf
return tf.squeeze(x, axis=axis)
lambda_layer = keras.layers.Lambda(target_layer)
layers[scope_name] = lambda_layer(layers[inputs[0]]) | def convert_squeeze(params, w_name, scope_name, inputs, layers, weights, names):
"""
Convert squeeze operation.
Args:
params: dictionary with layer parameters
w_name: name prefix in state_dict
scope_name: pytorch scope name
inputs: pytorch node inputs
layers: dictionary with keras tensors
weights: pytorch state_dict
names: use short names for keras layers
"""
print('Converting squeeze ...')
if len(params['axes']) > 1:
raise AssertionError('Cannot convert squeeze by multiple dimensions')
def target_layer(x, axis=int(params['axes'][0])):
import tensorflow as tf
return tf.squeeze(x, axis=axis)
lambda_layer = keras.layers.Lambda(target_layer)
layers[scope_name] = lambda_layer(layers[inputs[0]]) | [
"Convert",
"squeeze",
"operation",
"."
] | nerox8664/pytorch2keras | python | https://github.com/nerox8664/pytorch2keras/blob/750eaf747323580e6732d0c5ba9f2f39cb096764/pytorch2keras/reshape_layers.py#L98-L121 | [
"def",
"convert_squeeze",
"(",
"params",
",",
"w_name",
",",
"scope_name",
",",
"inputs",
",",
"layers",
",",
"weights",
",",
"names",
")",
":",
"print",
"(",
"'Converting squeeze ...'",
")",
"if",
"len",
"(",
"params",
"[",
"'axes'",
"]",
")",
">",
"1",
":",
"raise",
"AssertionError",
"(",
"'Cannot convert squeeze by multiple dimensions'",
")",
"def",
"target_layer",
"(",
"x",
",",
"axis",
"=",
"int",
"(",
"params",
"[",
"'axes'",
"]",
"[",
"0",
"]",
")",
")",
":",
"import",
"tensorflow",
"as",
"tf",
"return",
"tf",
".",
"squeeze",
"(",
"x",
",",
"axis",
"=",
"axis",
")",
"lambda_layer",
"=",
"keras",
".",
"layers",
".",
"Lambda",
"(",
"target_layer",
")",
"layers",
"[",
"scope_name",
"]",
"=",
"lambda_layer",
"(",
"layers",
"[",
"inputs",
"[",
"0",
"]",
"]",
")"
] | 750eaf747323580e6732d0c5ba9f2f39cb096764 |
valid | convert_unsqueeze | Convert unsqueeze operation.
Args:
params: dictionary with layer parameters
w_name: name prefix in state_dict
scope_name: pytorch scope name
inputs: pytorch node inputs
layers: dictionary with keras tensors
weights: pytorch state_dict
names: use short names for keras layers | pytorch2keras/reshape_layers.py | def convert_unsqueeze(params, w_name, scope_name, inputs, layers, weights, names):
"""
Convert unsqueeze operation.
Args:
params: dictionary with layer parameters
w_name: name prefix in state_dict
scope_name: pytorch scope name
inputs: pytorch node inputs
layers: dictionary with keras tensors
weights: pytorch state_dict
names: use short names for keras layers
"""
print('Converting unsqueeze ...')
if names == 'short':
tf_name = 'UNSQ' + random_string(4)
elif names == 'keep':
tf_name = w_name
else:
tf_name = w_name + str(random.random())
def target_layer(x):
import keras
return keras.backend.expand_dims(x)
lambda_layer = keras.layers.Lambda(target_layer, name=tf_name + 'E')
layers[scope_name] = lambda_layer(layers[inputs[0]]) | def convert_unsqueeze(params, w_name, scope_name, inputs, layers, weights, names):
"""
Convert unsqueeze operation.
Args:
params: dictionary with layer parameters
w_name: name prefix in state_dict
scope_name: pytorch scope name
inputs: pytorch node inputs
layers: dictionary with keras tensors
weights: pytorch state_dict
names: use short names for keras layers
"""
print('Converting unsqueeze ...')
if names == 'short':
tf_name = 'UNSQ' + random_string(4)
elif names == 'keep':
tf_name = w_name
else:
tf_name = w_name + str(random.random())
def target_layer(x):
import keras
return keras.backend.expand_dims(x)
lambda_layer = keras.layers.Lambda(target_layer, name=tf_name + 'E')
layers[scope_name] = lambda_layer(layers[inputs[0]]) | [
"Convert",
"unsqueeze",
"operation",
"."
] | nerox8664/pytorch2keras | python | https://github.com/nerox8664/pytorch2keras/blob/750eaf747323580e6732d0c5ba9f2f39cb096764/pytorch2keras/reshape_layers.py#L124-L151 | [
"def",
"convert_unsqueeze",
"(",
"params",
",",
"w_name",
",",
"scope_name",
",",
"inputs",
",",
"layers",
",",
"weights",
",",
"names",
")",
":",
"print",
"(",
"'Converting unsqueeze ...'",
")",
"if",
"names",
"==",
"'short'",
":",
"tf_name",
"=",
"'UNSQ'",
"+",
"random_string",
"(",
"4",
")",
"elif",
"names",
"==",
"'keep'",
":",
"tf_name",
"=",
"w_name",
"else",
":",
"tf_name",
"=",
"w_name",
"+",
"str",
"(",
"random",
".",
"random",
"(",
")",
")",
"def",
"target_layer",
"(",
"x",
")",
":",
"import",
"keras",
"return",
"keras",
".",
"backend",
".",
"expand_dims",
"(",
"x",
")",
"lambda_layer",
"=",
"keras",
".",
"layers",
".",
"Lambda",
"(",
"target_layer",
",",
"name",
"=",
"tf_name",
"+",
"'E'",
")",
"layers",
"[",
"scope_name",
"]",
"=",
"lambda_layer",
"(",
"layers",
"[",
"inputs",
"[",
"0",
"]",
"]",
")"
] | 750eaf747323580e6732d0c5ba9f2f39cb096764 |
valid | convert_shape | Convert shape operation.
Args:
params: dictionary with layer parameters
w_name: name prefix in state_dict
scope_name: pytorch scope name
inputs: pytorch node inputs
layers: dictionary with keras tensors
weights: pytorch state_dict
names: use short names for keras layers | pytorch2keras/reshape_layers.py | def convert_shape(params, w_name, scope_name, inputs, layers, weights, names):
"""
Convert shape operation.
Args:
params: dictionary with layer parameters
w_name: name prefix in state_dict
scope_name: pytorch scope name
inputs: pytorch node inputs
layers: dictionary with keras tensors
weights: pytorch state_dict
names: use short names for keras layers
"""
print('Converting shape ...')
def target_layer(x):
import tensorflow as tf
return tf.shape(x)
lambda_layer = keras.layers.Lambda(target_layer)
layers[scope_name] = lambda_layer(layers[inputs[0]]) | def convert_shape(params, w_name, scope_name, inputs, layers, weights, names):
"""
Convert shape operation.
Args:
params: dictionary with layer parameters
w_name: name prefix in state_dict
scope_name: pytorch scope name
inputs: pytorch node inputs
layers: dictionary with keras tensors
weights: pytorch state_dict
names: use short names for keras layers
"""
print('Converting shape ...')
def target_layer(x):
import tensorflow as tf
return tf.shape(x)
lambda_layer = keras.layers.Lambda(target_layer)
layers[scope_name] = lambda_layer(layers[inputs[0]]) | [
"Convert",
"shape",
"operation",
"."
] | nerox8664/pytorch2keras | python | https://github.com/nerox8664/pytorch2keras/blob/750eaf747323580e6732d0c5ba9f2f39cb096764/pytorch2keras/reshape_layers.py#L154-L174 | [
"def",
"convert_shape",
"(",
"params",
",",
"w_name",
",",
"scope_name",
",",
"inputs",
",",
"layers",
",",
"weights",
",",
"names",
")",
":",
"print",
"(",
"'Converting shape ...'",
")",
"def",
"target_layer",
"(",
"x",
")",
":",
"import",
"tensorflow",
"as",
"tf",
"return",
"tf",
".",
"shape",
"(",
"x",
")",
"lambda_layer",
"=",
"keras",
".",
"layers",
".",
"Lambda",
"(",
"target_layer",
")",
"layers",
"[",
"scope_name",
"]",
"=",
"lambda_layer",
"(",
"layers",
"[",
"inputs",
"[",
"0",
"]",
"]",
")"
] | 750eaf747323580e6732d0c5ba9f2f39cb096764 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.