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def __init__(__self__, *, group_id: Optional[pulumi.Input[str]]=None, users: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]=None):
'\n Input properties used for looking up and filtering GroupMemberships resources.\n :param pulumi.Input[str] group_id: ID of a Okta group.\n :param pulumi.Input[Sequence[pulumi.Input[str]]] users: The list of Okta user IDs which the group should have membership managed for.\n '
if (group_id is not None):
pulumi.set(__self__, 'group_id', group_id)
if (users is not None):
pulumi.set(__self__, 'users', users) | -1,124,800,230,950,680,700 | Input properties used for looking up and filtering GroupMemberships resources.
:param pulumi.Input[str] group_id: ID of a Okta group.
:param pulumi.Input[Sequence[pulumi.Input[str]]] users: The list of Okta user IDs which the group should have membership managed for. | sdk/python/pulumi_okta/group_memberships.py | __init__ | pulumi/pulumi-okta | python | def __init__(__self__, *, group_id: Optional[pulumi.Input[str]]=None, users: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]=None):
'\n Input properties used for looking up and filtering GroupMemberships resources.\n :param pulumi.Input[str] group_id: ID of a Okta group.\n :param pulumi.Input[Sequence[pulumi.Input[str]]] users: The list of Okta user IDs which the group should have membership managed for.\n '
if (group_id is not None):
pulumi.set(__self__, 'group_id', group_id)
if (users is not None):
pulumi.set(__self__, 'users', users) |
@property
@pulumi.getter(name='groupId')
def group_id(self) -> Optional[pulumi.Input[str]]:
'\n ID of a Okta group.\n '
return pulumi.get(self, 'group_id') | -3,177,421,181,971,841,500 | ID of a Okta group. | sdk/python/pulumi_okta/group_memberships.py | group_id | pulumi/pulumi-okta | python | @property
@pulumi.getter(name='groupId')
def group_id(self) -> Optional[pulumi.Input[str]]:
'\n \n '
return pulumi.get(self, 'group_id') |
@property
@pulumi.getter
def users(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:
'\n The list of Okta user IDs which the group should have membership managed for.\n '
return pulumi.get(self, 'users') | 99,600,952,663,328,530 | The list of Okta user IDs which the group should have membership managed for. | sdk/python/pulumi_okta/group_memberships.py | users | pulumi/pulumi-okta | python | @property
@pulumi.getter
def users(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:
'\n \n '
return pulumi.get(self, 'users') |
@overload
def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions]=None, group_id: Optional[pulumi.Input[str]]=None, users: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]=None, __props__=None):
'\n Resource to manage a set of memberships for a specific group.\n\n This resource will allow you to bulk manage group membership in Okta for a given group. This offers an interface to pass multiple users into a single resource call, for better API resource usage. Effectively this is the same as using the `group.Membership` resource several times with a single group and many different users. If you need a relationship of a single user to many groups, please use the `UserGroupMemberships` resource.\n\n When using this with a `user.User` resource, you should add a lifecycle ignore for group memberships to avoid conflicts in desired state.\n\n ## Example Usage\n\n ```python\n import pulumi\n import pulumi_okta as okta\n\n test_group = okta.group.Group("testGroup", description="testing, testing")\n test_group_memberships = okta.GroupMemberships("testGroupMemberships",\n group_id=test_group.id,\n users=[\n okta_user["test1"]["id"],\n okta_user["test2"]["id"],\n ])\n ```\n\n :param str resource_name: The name of the resource.\n :param pulumi.ResourceOptions opts: Options for the resource.\n :param pulumi.Input[str] group_id: ID of a Okta group.\n :param pulumi.Input[Sequence[pulumi.Input[str]]] users: The list of Okta user IDs which the group should have membership managed for.\n '
... | -5,755,180,085,078,006,000 | Resource to manage a set of memberships for a specific group.
This resource will allow you to bulk manage group membership in Okta for a given group. This offers an interface to pass multiple users into a single resource call, for better API resource usage. Effectively this is the same as using the `group.Membership` resource several times with a single group and many different users. If you need a relationship of a single user to many groups, please use the `UserGroupMemberships` resource.
When using this with a `user.User` resource, you should add a lifecycle ignore for group memberships to avoid conflicts in desired state.
## Example Usage
```python
import pulumi
import pulumi_okta as okta
test_group = okta.group.Group("testGroup", description="testing, testing")
test_group_memberships = okta.GroupMemberships("testGroupMemberships",
group_id=test_group.id,
users=[
okta_user["test1"]["id"],
okta_user["test2"]["id"],
])
```
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] group_id: ID of a Okta group.
:param pulumi.Input[Sequence[pulumi.Input[str]]] users: The list of Okta user IDs which the group should have membership managed for. | sdk/python/pulumi_okta/group_memberships.py | __init__ | pulumi/pulumi-okta | python | @overload
def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions]=None, group_id: Optional[pulumi.Input[str]]=None, users: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]=None, __props__=None):
'\n Resource to manage a set of memberships for a specific group.\n\n This resource will allow you to bulk manage group membership in Okta for a given group. This offers an interface to pass multiple users into a single resource call, for better API resource usage. Effectively this is the same as using the `group.Membership` resource several times with a single group and many different users. If you need a relationship of a single user to many groups, please use the `UserGroupMemberships` resource.\n\n When using this with a `user.User` resource, you should add a lifecycle ignore for group memberships to avoid conflicts in desired state.\n\n ## Example Usage\n\n ```python\n import pulumi\n import pulumi_okta as okta\n\n test_group = okta.group.Group("testGroup", description="testing, testing")\n test_group_memberships = okta.GroupMemberships("testGroupMemberships",\n group_id=test_group.id,\n users=[\n okta_user["test1"]["id"],\n okta_user["test2"]["id"],\n ])\n ```\n\n :param str resource_name: The name of the resource.\n :param pulumi.ResourceOptions opts: Options for the resource.\n :param pulumi.Input[str] group_id: ID of a Okta group.\n :param pulumi.Input[Sequence[pulumi.Input[str]]] users: The list of Okta user IDs which the group should have membership managed for.\n '
... |
@overload
def __init__(__self__, resource_name: str, args: GroupMembershipsArgs, opts: Optional[pulumi.ResourceOptions]=None):
'\n Resource to manage a set of memberships for a specific group.\n\n This resource will allow you to bulk manage group membership in Okta for a given group. This offers an interface to pass multiple users into a single resource call, for better API resource usage. Effectively this is the same as using the `group.Membership` resource several times with a single group and many different users. If you need a relationship of a single user to many groups, please use the `UserGroupMemberships` resource.\n\n When using this with a `user.User` resource, you should add a lifecycle ignore for group memberships to avoid conflicts in desired state.\n\n ## Example Usage\n\n ```python\n import pulumi\n import pulumi_okta as okta\n\n test_group = okta.group.Group("testGroup", description="testing, testing")\n test_group_memberships = okta.GroupMemberships("testGroupMemberships",\n group_id=test_group.id,\n users=[\n okta_user["test1"]["id"],\n okta_user["test2"]["id"],\n ])\n ```\n\n :param str resource_name: The name of the resource.\n :param GroupMembershipsArgs args: The arguments to use to populate this resource\'s properties.\n :param pulumi.ResourceOptions opts: Options for the resource.\n '
... | -7,360,302,168,923,495,000 | Resource to manage a set of memberships for a specific group.
This resource will allow you to bulk manage group membership in Okta for a given group. This offers an interface to pass multiple users into a single resource call, for better API resource usage. Effectively this is the same as using the `group.Membership` resource several times with a single group and many different users. If you need a relationship of a single user to many groups, please use the `UserGroupMemberships` resource.
When using this with a `user.User` resource, you should add a lifecycle ignore for group memberships to avoid conflicts in desired state.
## Example Usage
```python
import pulumi
import pulumi_okta as okta
test_group = okta.group.Group("testGroup", description="testing, testing")
test_group_memberships = okta.GroupMemberships("testGroupMemberships",
group_id=test_group.id,
users=[
okta_user["test1"]["id"],
okta_user["test2"]["id"],
])
```
:param str resource_name: The name of the resource.
:param GroupMembershipsArgs args: The arguments to use to populate this resource's properties.
:param pulumi.ResourceOptions opts: Options for the resource. | sdk/python/pulumi_okta/group_memberships.py | __init__ | pulumi/pulumi-okta | python | @overload
def __init__(__self__, resource_name: str, args: GroupMembershipsArgs, opts: Optional[pulumi.ResourceOptions]=None):
'\n Resource to manage a set of memberships for a specific group.\n\n This resource will allow you to bulk manage group membership in Okta for a given group. This offers an interface to pass multiple users into a single resource call, for better API resource usage. Effectively this is the same as using the `group.Membership` resource several times with a single group and many different users. If you need a relationship of a single user to many groups, please use the `UserGroupMemberships` resource.\n\n When using this with a `user.User` resource, you should add a lifecycle ignore for group memberships to avoid conflicts in desired state.\n\n ## Example Usage\n\n ```python\n import pulumi\n import pulumi_okta as okta\n\n test_group = okta.group.Group("testGroup", description="testing, testing")\n test_group_memberships = okta.GroupMemberships("testGroupMemberships",\n group_id=test_group.id,\n users=[\n okta_user["test1"]["id"],\n okta_user["test2"]["id"],\n ])\n ```\n\n :param str resource_name: The name of the resource.\n :param GroupMembershipsArgs args: The arguments to use to populate this resource\'s properties.\n :param pulumi.ResourceOptions opts: Options for the resource.\n '
... |
@staticmethod
def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions]=None, group_id: Optional[pulumi.Input[str]]=None, users: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]=None) -> 'GroupMemberships':
"\n Get an existing GroupMemberships resource's state with the given name, id, and optional extra\n properties used to qualify the lookup.\n\n :param str resource_name: The unique name of the resulting resource.\n :param pulumi.Input[str] id: The unique provider ID of the resource to lookup.\n :param pulumi.ResourceOptions opts: Options for the resource.\n :param pulumi.Input[str] group_id: ID of a Okta group.\n :param pulumi.Input[Sequence[pulumi.Input[str]]] users: The list of Okta user IDs which the group should have membership managed for.\n "
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = _GroupMembershipsState.__new__(_GroupMembershipsState)
__props__.__dict__['group_id'] = group_id
__props__.__dict__['users'] = users
return GroupMemberships(resource_name, opts=opts, __props__=__props__) | -1,442,683,605,509,781,800 | Get an existing GroupMemberships resource's state with the given name, id, and optional extra
properties used to qualify the lookup.
:param str resource_name: The unique name of the resulting resource.
:param pulumi.Input[str] id: The unique provider ID of the resource to lookup.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] group_id: ID of a Okta group.
:param pulumi.Input[Sequence[pulumi.Input[str]]] users: The list of Okta user IDs which the group should have membership managed for. | sdk/python/pulumi_okta/group_memberships.py | get | pulumi/pulumi-okta | python | @staticmethod
def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions]=None, group_id: Optional[pulumi.Input[str]]=None, users: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]=None) -> 'GroupMemberships':
"\n Get an existing GroupMemberships resource's state with the given name, id, and optional extra\n properties used to qualify the lookup.\n\n :param str resource_name: The unique name of the resulting resource.\n :param pulumi.Input[str] id: The unique provider ID of the resource to lookup.\n :param pulumi.ResourceOptions opts: Options for the resource.\n :param pulumi.Input[str] group_id: ID of a Okta group.\n :param pulumi.Input[Sequence[pulumi.Input[str]]] users: The list of Okta user IDs which the group should have membership managed for.\n "
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = _GroupMembershipsState.__new__(_GroupMembershipsState)
__props__.__dict__['group_id'] = group_id
__props__.__dict__['users'] = users
return GroupMemberships(resource_name, opts=opts, __props__=__props__) |
@property
@pulumi.getter(name='groupId')
def group_id(self) -> pulumi.Output[str]:
'\n ID of a Okta group.\n '
return pulumi.get(self, 'group_id') | -3,855,980,625,715,913,000 | ID of a Okta group. | sdk/python/pulumi_okta/group_memberships.py | group_id | pulumi/pulumi-okta | python | @property
@pulumi.getter(name='groupId')
def group_id(self) -> pulumi.Output[str]:
'\n \n '
return pulumi.get(self, 'group_id') |
@property
@pulumi.getter
def users(self) -> pulumi.Output[Sequence[str]]:
'\n The list of Okta user IDs which the group should have membership managed for.\n '
return pulumi.get(self, 'users') | -9,122,935,689,198,096,000 | The list of Okta user IDs which the group should have membership managed for. | sdk/python/pulumi_okta/group_memberships.py | users | pulumi/pulumi-okta | python | @property
@pulumi.getter
def users(self) -> pulumi.Output[Sequence[str]]:
'\n \n '
return pulumi.get(self, 'users') |
def build_grid(hyperparameters):
'Build a grid represented as a list of parameter dictionaries.'
parameter_dicts = []
for parameters in product(*hyperparameters.values()):
parameter_tuples = zip(hyperparameters.keys(), parameters)
parameter_dict = dict(parameter_tuples)
parameter_dicts.append(parameter_dict)
return parameter_dicts | 5,178,821,570,779,621,000 | Build a grid represented as a list of parameter dictionaries. | autotabular/metalearning/optimizers/optimizer_base.py | build_grid | Fanxingye/Autotabular | python | def build_grid(hyperparameters):
parameter_dicts = []
for parameters in product(*hyperparameters.values()):
parameter_tuples = zip(hyperparameters.keys(), parameters)
parameter_dict = dict(parameter_tuples)
parameter_dicts.append(parameter_dict)
return parameter_dicts |
def test_invalid_unicode_in_build_file(self):
'Demonstrate that unicode characters causing parse errors raise real parse errors.'
self.add_to_build_file('BUILD', dedent("\n jvm_binary(name = ‘hello’, # Parse error due to smart quotes (non ascii characters)\n source = 'HelloWorld.java'\n main = 'foo.HelloWorld',\n )\n "))
build_file = self.create_buildfile('BUILD')
self.assert_parser_error(build_file, ('invalid character' if PY3 else 'invalid syntax')) | -6,823,476,443,962,574,000 | Demonstrate that unicode characters causing parse errors raise real parse errors. | tests/python/pants_test/build_graph/test_build_file_parser.py | test_invalid_unicode_in_build_file | omerzach/pants | python | def test_invalid_unicode_in_build_file(self):
self.add_to_build_file('BUILD', dedent("\n jvm_binary(name = ‘hello’, # Parse error due to smart quotes (non ascii characters)\n source = 'HelloWorld.java'\n main = 'foo.HelloWorld',\n )\n "))
build_file = self.create_buildfile('BUILD')
self.assert_parser_error(build_file, ('invalid character' if PY3 else 'invalid syntax')) |
def test_unicode_string_in_build_file(self):
'Demonstrates that a string containing unicode should work in a BUILD file.'
self.add_to_build_file('BUILD', dedent("\n java_library(\n name='foo',\n sources=['א.java']\n )\n "))
build_file = self.create_buildfile('BUILD')
self.build_file_parser.parse_build_file(build_file) | -2,613,146,341,876,287,000 | Demonstrates that a string containing unicode should work in a BUILD file. | tests/python/pants_test/build_graph/test_build_file_parser.py | test_unicode_string_in_build_file | omerzach/pants | python | def test_unicode_string_in_build_file(self):
self.add_to_build_file('BUILD', dedent("\n java_library(\n name='foo',\n sources=['א.java']\n )\n "))
build_file = self.create_buildfile('BUILD')
self.build_file_parser.parse_build_file(build_file) |
def test_build_file_parser_error_hierarcy(self):
'Exception handling code depends on the fact that all explicit exceptions from BuildFileParser\n are subclassed from the BuildFileParserError base class.\n '
def assert_build_file_parser_error(e):
self.assertIsInstance(e, BuildFileParser.BuildFileParserError)
assert_build_file_parser_error(BuildFileParser.BuildFileScanError())
assert_build_file_parser_error(BuildFileParser.AddressableConflictException())
assert_build_file_parser_error(BuildFileParser.SiblingConflictException())
assert_build_file_parser_error(BuildFileParser.ParseError())
assert_build_file_parser_error(BuildFileParser.ExecuteError()) | 1,864,469,725,580,416,000 | Exception handling code depends on the fact that all explicit exceptions from BuildFileParser
are subclassed from the BuildFileParserError base class. | tests/python/pants_test/build_graph/test_build_file_parser.py | test_build_file_parser_error_hierarcy | omerzach/pants | python | def test_build_file_parser_error_hierarcy(self):
'Exception handling code depends on the fact that all explicit exceptions from BuildFileParser\n are subclassed from the BuildFileParserError base class.\n '
def assert_build_file_parser_error(e):
self.assertIsInstance(e, BuildFileParser.BuildFileParserError)
assert_build_file_parser_error(BuildFileParser.BuildFileScanError())
assert_build_file_parser_error(BuildFileParser.AddressableConflictException())
assert_build_file_parser_error(BuildFileParser.SiblingConflictException())
assert_build_file_parser_error(BuildFileParser.ParseError())
assert_build_file_parser_error(BuildFileParser.ExecuteError()) |
def gen_attributes(rp_attributes):
"Generate list of attributes for the API request.\n\n Example of input list:\n ['tag_name:tag_value1', 'tag_value2']\n Output of the function for the given input list:\n [{'key': 'tag_name', 'value': 'tag_value1'}, {'value': 'tag_value2'}]\n\n :param rp_attributes: List of attributes(tags)\n :return: Correctly created list of dictionaries\n to be passed to RP\n "
attrs = []
for rp_attr in rp_attributes:
try:
(key, value) = rp_attr.split(':')
attr_dict = {'key': key, 'value': value}
except ValueError as exc:
logger.debug(str(exc))
attr_dict = {'value': rp_attr}
if all(attr_dict.values()):
attrs.append(attr_dict)
continue
logger.debug('Failed to process "{0}" attribute, attribute value should not be empty.'.format(rp_attr))
return attrs | -4,259,652,237,405,757,000 | Generate list of attributes for the API request.
Example of input list:
['tag_name:tag_value1', 'tag_value2']
Output of the function for the given input list:
[{'key': 'tag_name', 'value': 'tag_value1'}, {'value': 'tag_value2'}]
:param rp_attributes: List of attributes(tags)
:return: Correctly created list of dictionaries
to be passed to RP | reportportal_client/helpers.py | gen_attributes | jyejare/client-Python | python | def gen_attributes(rp_attributes):
"Generate list of attributes for the API request.\n\n Example of input list:\n ['tag_name:tag_value1', 'tag_value2']\n Output of the function for the given input list:\n [{'key': 'tag_name', 'value': 'tag_value1'}, {'value': 'tag_value2'}]\n\n :param rp_attributes: List of attributes(tags)\n :return: Correctly created list of dictionaries\n to be passed to RP\n "
attrs = []
for rp_attr in rp_attributes:
try:
(key, value) = rp_attr.split(':')
attr_dict = {'key': key, 'value': value}
except ValueError as exc:
logger.debug(str(exc))
attr_dict = {'value': rp_attr}
if all(attr_dict.values()):
attrs.append(attr_dict)
continue
logger.debug('Failed to process "{0}" attribute, attribute value should not be empty.'.format(rp_attr))
return attrs |
def get_launch_sys_attrs():
"Generate attributes for the launch containing system information.\n\n :return: dict {'os': 'Windows',\n 'cpu': 'AMD',\n 'machine': 'Windows10_pc'}\n "
return {'os': system(), 'cpu': (processor() or 'unknown'), 'machine': machine(), 'system': True} | 8,550,479,848,873,520,000 | Generate attributes for the launch containing system information.
:return: dict {'os': 'Windows',
'cpu': 'AMD',
'machine': 'Windows10_pc'} | reportportal_client/helpers.py | get_launch_sys_attrs | jyejare/client-Python | python | def get_launch_sys_attrs():
"Generate attributes for the launch containing system information.\n\n :return: dict {'os': 'Windows',\n 'cpu': 'AMD',\n 'machine': 'Windows10_pc'}\n "
return {'os': system(), 'cpu': (processor() or 'unknown'), 'machine': machine(), 'system': True} |
def get_package_version(package_name):
'Get version of the given package.\n\n :param package_name: Name of the package\n :return: Version of the package\n '
try:
package_version = get_distribution(package_name).version
except DistributionNotFound:
package_version = 'not found'
return package_version | 1,597,655,982,996,706,800 | Get version of the given package.
:param package_name: Name of the package
:return: Version of the package | reportportal_client/helpers.py | get_package_version | jyejare/client-Python | python | def get_package_version(package_name):
'Get version of the given package.\n\n :param package_name: Name of the package\n :return: Version of the package\n '
try:
package_version = get_distribution(package_name).version
except DistributionNotFound:
package_version = 'not found'
return package_version |
def neighbord_analysis(x_as, column=0):
'\n\tGiven an array xas this function compute the distance between the elements the mean distance and the variance\n\t\n\tAuthor: Michele Monti\n\t\n\tArgs:\n\t\t\tx_as: the name of the list or data set that you want:\n\t\n\tKwargs:\n\t\tcolumn: is the column of the data set that you need to analyze\n\n\tReturns:\n\t\tmean_distance: the mean distance between neighbords, \n\t\tstd_dev: stdeviation of the distances between neighbords.\n\t\tdiff_neighbor: the difference between the first-neighbours in a list \n\t'
x_as = np.array(x_as)
correct_axis = x_as
if (shape(x_as) > 1):
correct_axis = x_as[:, column]
diff_neighbor = [(itm - correct_axis[(idx - 1)]) for (idx, itm) in enumerate(correct_axis)][1:]
mean_distance = np.mean(diff_neighbor)
std_dev = np.std(diff_neighbor)
return (diff_neighbor, mean_distance, std_dev) | 3,318,488,411,711,542,300 | Given an array xas this function compute the distance between the elements the mean distance and the variance
Author: Michele Monti
Args:
x_as: the name of the list or data set that you want:
Kwargs:
column: is the column of the data set that you need to analyze
Returns:
mean_distance: the mean distance between neighbords,
std_dev: stdeviation of the distances between neighbords.
diff_neighbor: the difference between the first-neighbours in a list | amolf/numerical_data_analysis/NeighbourAnalysis.py | neighbord_analysis | Repythory/Libraries | python | def neighbord_analysis(x_as, column=0):
'\n\tGiven an array xas this function compute the distance between the elements the mean distance and the variance\n\t\n\tAuthor: Michele Monti\n\t\n\tArgs:\n\t\t\tx_as: the name of the list or data set that you want:\n\t\n\tKwargs:\n\t\tcolumn: is the column of the data set that you need to analyze\n\n\tReturns:\n\t\tmean_distance: the mean distance between neighbords, \n\t\tstd_dev: stdeviation of the distances between neighbords.\n\t\tdiff_neighbor: the difference between the first-neighbours in a list \n\t'
x_as = np.array(x_as)
correct_axis = x_as
if (shape(x_as) > 1):
correct_axis = x_as[:, column]
diff_neighbor = [(itm - correct_axis[(idx - 1)]) for (idx, itm) in enumerate(correct_axis)][1:]
mean_distance = np.mean(diff_neighbor)
std_dev = np.std(diff_neighbor)
return (diff_neighbor, mean_distance, std_dev) |
def _setup(self):
'Sets up and resets flags before each test.'
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.DEBUG)
if (KerasBenchmark.local_flags is None):
for flag_method in self.flag_methods:
flag_method()
flags.FLAGS(['foo'])
for (k, v) in self.default_flags.items():
setattr(FLAGS, k, v)
saved_flag_values = flagsaver.save_flag_values()
KerasBenchmark.local_flags = saved_flag_values
else:
flagsaver.restore_flag_values(KerasBenchmark.local_flags) | 2,129,715,044,460,102,000 | Sets up and resets flags before each test. | official/resnet/keras/keras_benchmark.py | _setup | LinMiaoShuSheng/models | python | def _setup(self):
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.DEBUG)
if (KerasBenchmark.local_flags is None):
for flag_method in self.flag_methods:
flag_method()
flags.FLAGS(['foo'])
for (k, v) in self.default_flags.items():
setattr(FLAGS, k, v)
saved_flag_values = flagsaver.save_flag_values()
KerasBenchmark.local_flags = saved_flag_values
else:
flagsaver.restore_flag_values(KerasBenchmark.local_flags) |
def _report_benchmark(self, stats, wall_time_sec, top_1_max=None, top_1_min=None, log_steps=None, total_batch_size=None, warmup=1):
"Report benchmark results by writing to local protobuf file.\n\n Args:\n stats: dict returned from keras models with known entries.\n wall_time_sec: the during of the benchmark execution in seconds\n top_1_max: highest passing level for top_1 accuracy.\n top_1_min: lowest passing level for top_1 accuracy.\n log_steps: How often the log was created for stats['step_timestamp_log'].\n total_batch_size: Global batch-size.\n warmup: number of entries in stats['step_timestamp_log'] to ignore.\n "
metrics = []
if ('accuracy_top_1' in stats):
metrics.append({'name': 'accuracy_top_1', 'value': stats['accuracy_top_1'], 'min_value': top_1_min, 'max_value': top_1_max})
metrics.append({'name': 'top_1_train_accuracy', 'value': stats['training_accuracy_top_1']})
if (warmup and ('step_timestamp_log' in stats) and (len(stats['step_timestamp_log']) > warmup)):
time_log = stats['step_timestamp_log']
elapsed = (time_log[(- 1)].timestamp - time_log[warmup].timestamp)
num_examples = ((total_batch_size * log_steps) * ((len(time_log) - warmup) - 1))
examples_per_sec = (num_examples / elapsed)
metrics.append({'name': 'exp_per_second', 'value': examples_per_sec})
if ('avg_exp_per_second' in stats):
metrics.append({'name': 'avg_exp_per_second', 'value': stats['avg_exp_per_second']})
self.report_benchmark(iters=(- 1), wall_time=wall_time_sec, metrics=metrics) | 1,068,746,809,112,859,500 | Report benchmark results by writing to local protobuf file.
Args:
stats: dict returned from keras models with known entries.
wall_time_sec: the during of the benchmark execution in seconds
top_1_max: highest passing level for top_1 accuracy.
top_1_min: lowest passing level for top_1 accuracy.
log_steps: How often the log was created for stats['step_timestamp_log'].
total_batch_size: Global batch-size.
warmup: number of entries in stats['step_timestamp_log'] to ignore. | official/resnet/keras/keras_benchmark.py | _report_benchmark | LinMiaoShuSheng/models | python | def _report_benchmark(self, stats, wall_time_sec, top_1_max=None, top_1_min=None, log_steps=None, total_batch_size=None, warmup=1):
"Report benchmark results by writing to local protobuf file.\n\n Args:\n stats: dict returned from keras models with known entries.\n wall_time_sec: the during of the benchmark execution in seconds\n top_1_max: highest passing level for top_1 accuracy.\n top_1_min: lowest passing level for top_1 accuracy.\n log_steps: How often the log was created for stats['step_timestamp_log'].\n total_batch_size: Global batch-size.\n warmup: number of entries in stats['step_timestamp_log'] to ignore.\n "
metrics = []
if ('accuracy_top_1' in stats):
metrics.append({'name': 'accuracy_top_1', 'value': stats['accuracy_top_1'], 'min_value': top_1_min, 'max_value': top_1_max})
metrics.append({'name': 'top_1_train_accuracy', 'value': stats['training_accuracy_top_1']})
if (warmup and ('step_timestamp_log' in stats) and (len(stats['step_timestamp_log']) > warmup)):
time_log = stats['step_timestamp_log']
elapsed = (time_log[(- 1)].timestamp - time_log[warmup].timestamp)
num_examples = ((total_batch_size * log_steps) * ((len(time_log) - warmup) - 1))
examples_per_sec = (num_examples / elapsed)
metrics.append({'name': 'exp_per_second', 'value': examples_per_sec})
if ('avg_exp_per_second' in stats):
metrics.append({'name': 'avg_exp_per_second', 'value': stats['avg_exp_per_second']})
self.report_benchmark(iters=(- 1), wall_time=wall_time_sec, metrics=metrics) |
def setup_package():
'Run on testing package.'
oauth2client.util.positional_parameters_enforcement = 'EXCEPTION' | 7,842,196,517,937,913,000 | Run on testing package. | tests/__init__.py | setup_package | 1ap/google-api-python-client | python | def setup_package():
oauth2client.util.positional_parameters_enforcement = 'EXCEPTION' |
def initialize_flow(self, img):
' Flow is represented as difference between two coordinate grids flow = coords1 - coords0'
(N, C, H, W) = img.shape
coords0 = coords_grid(N, (H // 8), (W // 8)).to(img.device)
coords1 = coords_grid(N, (H // 8), (W // 8)).to(img.device)
return (coords0, coords1) | 5,953,690,645,597,147,000 | Flow is represented as difference between two coordinate grids flow = coords1 - coords0 | nets/raft_core/backraft.py | initialize_flow | aharley/track_check_repeat | python | def initialize_flow(self, img):
' '
(N, C, H, W) = img.shape
coords0 = coords_grid(N, (H // 8), (W // 8)).to(img.device)
coords1 = coords_grid(N, (H // 8), (W // 8)).to(img.device)
return (coords0, coords1) |
def upsample_flow(self, flow, mask):
' Upsample flow field [H/8, W/8, 2] -> [H, W, 2] using convex combination '
(N, _, H, W) = flow.shape
mask = mask.view(N, 1, 9, 8, 8, H, W)
mask = torch.softmax(mask, dim=2)
up_flow = F.unfold((8 * flow), [3, 3], padding=1)
up_flow = up_flow.view(N, 2, 9, 1, 1, H, W)
up_flow = torch.sum((mask * up_flow), dim=2)
up_flow = up_flow.permute(0, 1, 4, 2, 5, 3)
return up_flow.reshape(N, 2, (8 * H), (8 * W)) | 5,758,413,022,218,375,000 | Upsample flow field [H/8, W/8, 2] -> [H, W, 2] using convex combination | nets/raft_core/backraft.py | upsample_flow | aharley/track_check_repeat | python | def upsample_flow(self, flow, mask):
' '
(N, _, H, W) = flow.shape
mask = mask.view(N, 1, 9, 8, 8, H, W)
mask = torch.softmax(mask, dim=2)
up_flow = F.unfold((8 * flow), [3, 3], padding=1)
up_flow = up_flow.view(N, 2, 9, 1, 1, H, W)
up_flow = torch.sum((mask * up_flow), dim=2)
up_flow = up_flow.permute(0, 1, 4, 2, 5, 3)
return up_flow.reshape(N, 2, (8 * H), (8 * W)) |
def forward(self, image1):
' get featmap for one frame '
image1 = ((2 * (image1 / 255.0)) - 1.0)
image1 = image1.contiguous()
hdim = self.hidden_dim
cdim = self.context_dim
with autocast(enabled=self.args.mixed_precision):
fmap1 = self.fnet(image1)
fmap1 = fmap1.float()
return fmap1 | 9,102,215,985,324,929,000 | get featmap for one frame | nets/raft_core/backraft.py | forward | aharley/track_check_repeat | python | def forward(self, image1):
' '
image1 = ((2 * (image1 / 255.0)) - 1.0)
image1 = image1.contiguous()
hdim = self.hidden_dim
cdim = self.context_dim
with autocast(enabled=self.args.mixed_precision):
fmap1 = self.fnet(image1)
fmap1 = fmap1.float()
return fmap1 |
def old_forward(self, image1, image2, iters=12, flow_init=None, upsample=True, test_mode=False):
' Estimate optical flow between pair of frames '
image1 = ((2 * (image1 / 255.0)) - 1.0)
image2 = ((2 * (image2 / 255.0)) - 1.0)
image1 = image1.contiguous()
image2 = image2.contiguous()
hdim = self.hidden_dim
cdim = self.context_dim
with autocast(enabled=self.args.mixed_precision):
(fmap1, fmap2) = self.fnet([image1, image2])
fmap1 = fmap1.float()
fmap2 = fmap2.float()
if self.args.alternate_corr:
corr_fn = AlternateCorrBlock(fmap1, fmap2, radius=self.args.corr_radius)
else:
corr_fn = CorrBlock(fmap1, fmap2, radius=self.args.corr_radius)
with autocast(enabled=self.args.mixed_precision):
cnet = self.cnet(image1)
(net, inp) = torch.split(cnet, [hdim, cdim], dim=1)
net = torch.tanh(net)
inp = torch.relu(inp)
(coords0, coords1) = self.initialize_flow(image1)
if (flow_init is not None):
coords1 = (coords1 + flow_init)
flow_predictions = []
for itr in range(iters):
coords1 = coords1.detach()
corr = corr_fn(coords1)
flow = (coords1 - coords0)
with autocast(enabled=self.args.mixed_precision):
(net, up_mask, delta_flow) = self.update_block(net, inp, corr, flow)
coords1 = (coords1 + delta_flow)
if (up_mask is None):
flow_up = upflow8((coords1 - coords0))
else:
flow_up = self.upsample_flow((coords1 - coords0), up_mask)
flow_predictions.append(flow_up)
if test_mode:
corr = corr_fn(coords1)
feat = inp
return ((coords1 - coords0), flow_up, (feat, fmap1, fmap2))
return flow_predictions | -4,838,898,532,510,094,000 | Estimate optical flow between pair of frames | nets/raft_core/backraft.py | old_forward | aharley/track_check_repeat | python | def old_forward(self, image1, image2, iters=12, flow_init=None, upsample=True, test_mode=False):
' '
image1 = ((2 * (image1 / 255.0)) - 1.0)
image2 = ((2 * (image2 / 255.0)) - 1.0)
image1 = image1.contiguous()
image2 = image2.contiguous()
hdim = self.hidden_dim
cdim = self.context_dim
with autocast(enabled=self.args.mixed_precision):
(fmap1, fmap2) = self.fnet([image1, image2])
fmap1 = fmap1.float()
fmap2 = fmap2.float()
if self.args.alternate_corr:
corr_fn = AlternateCorrBlock(fmap1, fmap2, radius=self.args.corr_radius)
else:
corr_fn = CorrBlock(fmap1, fmap2, radius=self.args.corr_radius)
with autocast(enabled=self.args.mixed_precision):
cnet = self.cnet(image1)
(net, inp) = torch.split(cnet, [hdim, cdim], dim=1)
net = torch.tanh(net)
inp = torch.relu(inp)
(coords0, coords1) = self.initialize_flow(image1)
if (flow_init is not None):
coords1 = (coords1 + flow_init)
flow_predictions = []
for itr in range(iters):
coords1 = coords1.detach()
corr = corr_fn(coords1)
flow = (coords1 - coords0)
with autocast(enabled=self.args.mixed_precision):
(net, up_mask, delta_flow) = self.update_block(net, inp, corr, flow)
coords1 = (coords1 + delta_flow)
if (up_mask is None):
flow_up = upflow8((coords1 - coords0))
else:
flow_up = self.upsample_flow((coords1 - coords0), up_mask)
flow_predictions.append(flow_up)
if test_mode:
corr = corr_fn(coords1)
feat = inp
return ((coords1 - coords0), flow_up, (feat, fmap1, fmap2))
return flow_predictions |
def start(self, initialization_hook: Optional[Callable[([], None)]]=None):
'Starts the worker group.'
self.worker_group = WorkerGroup(self._num_workers, self._num_cpus_per_worker, self._num_gpus_per_worker)
if initialization_hook:
self.worker_group.execute(initialization_hook)
self._backend.on_start(self.worker_group, self._backend_config) | 4,632,527,791,434,311,000 | Starts the worker group. | python/ray/util/sgd/v2/backends/backend.py | start | cuongnvan/ray | python | def start(self, initialization_hook: Optional[Callable[([], None)]]=None):
self.worker_group = WorkerGroup(self._num_workers, self._num_cpus_per_worker, self._num_gpus_per_worker)
if initialization_hook:
self.worker_group.execute(initialization_hook)
self._backend.on_start(self.worker_group, self._backend_config) |
def start_training(self, train_func: Callable[([], T)]) -> None:
'Executes a training function on all workers in a separate thread.\n\n ``finish_training`` should be called after this.\n\n Args:\n train_func (Callable): The training function to run on each worker.\n '
def initialize_session(world_rank, train_func):
try:
init_session(training_func=train_func, world_rank=world_rank)
except ValueError:
raise SGDBackendError('Attempting to start training but a previous training run is still ongoing. You must call `finish_training` before calling `start_training` again.')
futures = []
for world_rank in range(len(self.worker_group)):
futures.append(self.worker_group.execute_single_async(world_rank, initialize_session, world_rank=world_rank, train_func=train_func))
ray.get(futures)
def train_async():
session = get_session()
session.start()
self.worker_group.execute_async(train_async) | -328,435,917,548,396,860 | Executes a training function on all workers in a separate thread.
``finish_training`` should be called after this.
Args:
train_func (Callable): The training function to run on each worker. | python/ray/util/sgd/v2/backends/backend.py | start_training | cuongnvan/ray | python | def start_training(self, train_func: Callable[([], T)]) -> None:
'Executes a training function on all workers in a separate thread.\n\n ``finish_training`` should be called after this.\n\n Args:\n train_func (Callable): The training function to run on each worker.\n '
def initialize_session(world_rank, train_func):
try:
init_session(training_func=train_func, world_rank=world_rank)
except ValueError:
raise SGDBackendError('Attempting to start training but a previous training run is still ongoing. You must call `finish_training` before calling `start_training` again.')
futures = []
for world_rank in range(len(self.worker_group)):
futures.append(self.worker_group.execute_single_async(world_rank, initialize_session, world_rank=world_rank, train_func=train_func))
ray.get(futures)
def train_async():
session = get_session()
session.start()
self.worker_group.execute_async(train_async) |
def fetch_next_result(self) -> Optional[List[Dict]]:
'Fetch next results produced by ``sgd.report()`` from each worker.\n\n Assumes ``start_training`` has already been called.\n\n Returns:\n A list of dictionaries of values passed to ``sgd.report()`` from\n each worker. Each item corresponds to an intermediate result\n a single worker. If there are no more items to fetch,\n returns None.\n '
def get_next():
try:
session = get_session()
except ValueError:
raise SGDBackendError('`fetch_next_result` has been called before `start_training`. Please call `start_training` before `fetch_next_result`.')
try:
result = session.get_next()
except RuntimeError:
raise SGDBackendError('`fetch_next_result` has been called before `start_training`. Please call `start_training` before `fetch_next_result`.')
return result
futures = self.worker_group.execute_async(get_next)
results = self.get_with_failure_handling(futures)
if any(((r is None) for r in results)):
if (not all(((r is None) for r in results))):
raise RuntimeError("Some workers returned results while others didn't. Make sure that `sgd.report()` is called the same number of times on all workers.")
else:
results = None
return results | 9,198,031,502,204,319,000 | Fetch next results produced by ``sgd.report()`` from each worker.
Assumes ``start_training`` has already been called.
Returns:
A list of dictionaries of values passed to ``sgd.report()`` from
each worker. Each item corresponds to an intermediate result
a single worker. If there are no more items to fetch,
returns None. | python/ray/util/sgd/v2/backends/backend.py | fetch_next_result | cuongnvan/ray | python | def fetch_next_result(self) -> Optional[List[Dict]]:
'Fetch next results produced by ``sgd.report()`` from each worker.\n\n Assumes ``start_training`` has already been called.\n\n Returns:\n A list of dictionaries of values passed to ``sgd.report()`` from\n each worker. Each item corresponds to an intermediate result\n a single worker. If there are no more items to fetch,\n returns None.\n '
def get_next():
try:
session = get_session()
except ValueError:
raise SGDBackendError('`fetch_next_result` has been called before `start_training`. Please call `start_training` before `fetch_next_result`.')
try:
result = session.get_next()
except RuntimeError:
raise SGDBackendError('`fetch_next_result` has been called before `start_training`. Please call `start_training` before `fetch_next_result`.')
return result
futures = self.worker_group.execute_async(get_next)
results = self.get_with_failure_handling(futures)
if any(((r is None) for r in results)):
if (not all(((r is None) for r in results))):
raise RuntimeError("Some workers returned results while others didn't. Make sure that `sgd.report()` is called the same number of times on all workers.")
else:
results = None
return results |
def finish_training(self) -> List[T]:
'Finish training and return final results. Propagate any exceptions.\n\n Blocks until training is finished on all workers.\n\n Assumes `start_training` has already been called.\n\n Returns:\n A list of return values from calling ``train_func`` on each worker.\n Each item corresponds to the return value from a single worker.\n '
def end_training():
try:
session = get_session()
except ValueError:
raise SGDBackendError('`finish_training` has been called before `start_training`. Please call `start_training` before `finish_training`.')
try:
output = session.finish()
finally:
shutdown_session()
return output
futures = self.worker_group.execute_async(end_training)
return self.get_with_failure_handling(futures) | -105,494,462,415,802,850 | Finish training and return final results. Propagate any exceptions.
Blocks until training is finished on all workers.
Assumes `start_training` has already been called.
Returns:
A list of return values from calling ``train_func`` on each worker.
Each item corresponds to the return value from a single worker. | python/ray/util/sgd/v2/backends/backend.py | finish_training | cuongnvan/ray | python | def finish_training(self) -> List[T]:
'Finish training and return final results. Propagate any exceptions.\n\n Blocks until training is finished on all workers.\n\n Assumes `start_training` has already been called.\n\n Returns:\n A list of return values from calling ``train_func`` on each worker.\n Each item corresponds to the return value from a single worker.\n '
def end_training():
try:
session = get_session()
except ValueError:
raise SGDBackendError('`finish_training` has been called before `start_training`. Please call `start_training` before `finish_training`.')
try:
output = session.finish()
finally:
shutdown_session()
return output
futures = self.worker_group.execute_async(end_training)
return self.get_with_failure_handling(futures) |
def get_with_failure_handling(self, remote_values):
'Gets the remote values while handling for worker failures.\n\n Args:\n remote_values (list): List of object refs representing functions\n that may fail in the middle of execution. For example, running\n a SGD training loop in multiple parallel actor calls.\n\n Returns:\n The resolved objects represented by the passed in ObjectRefs.\n '
unfinished = remote_values
try:
while (len(unfinished) > 0):
(finished, unfinished) = ray.wait(unfinished)
ray.get(finished)
except RayActorError as exc:
logger.exception(str(exc))
self.handle_failure()
return
return ray.get(remote_values) | -3,861,662,312,918,846,500 | Gets the remote values while handling for worker failures.
Args:
remote_values (list): List of object refs representing functions
that may fail in the middle of execution. For example, running
a SGD training loop in multiple parallel actor calls.
Returns:
The resolved objects represented by the passed in ObjectRefs. | python/ray/util/sgd/v2/backends/backend.py | get_with_failure_handling | cuongnvan/ray | python | def get_with_failure_handling(self, remote_values):
'Gets the remote values while handling for worker failures.\n\n Args:\n remote_values (list): List of object refs representing functions\n that may fail in the middle of execution. For example, running\n a SGD training loop in multiple parallel actor calls.\n\n Returns:\n The resolved objects represented by the passed in ObjectRefs.\n '
unfinished = remote_values
try:
while (len(unfinished) > 0):
(finished, unfinished) = ray.wait(unfinished)
ray.get(finished)
except RayActorError as exc:
logger.exception(str(exc))
self.handle_failure()
return
return ray.get(remote_values) |
def shutdown(self):
'Shuts down the workers in the worker group.'
try:
self._backend.on_shutdown(self.worker_group, self._backend_config)
except RayActorError:
logger.warning('Graceful shutdown of backend failed. This is expected if one of the workers has crashed.')
self.worker_group.shutdown()
self.worker_group = InactiveWorkerGroup() | 7,046,521,673,996,014,000 | Shuts down the workers in the worker group. | python/ray/util/sgd/v2/backends/backend.py | shutdown | cuongnvan/ray | python | def shutdown(self):
try:
self._backend.on_shutdown(self.worker_group, self._backend_config)
except RayActorError:
logger.warning('Graceful shutdown of backend failed. This is expected if one of the workers has crashed.')
self.worker_group.shutdown()
self.worker_group = InactiveWorkerGroup() |
def test_create_user(self):
'\n Test User model can create a user successfully\n '
self.assertIsInstance(User.objects.create_user(username='username', email='[email protected]', password='password'), User) | -8,015,587,724,313,469,000 | Test User model can create a user successfully | authors/apps/authentication/tests/test_create_user.py | test_create_user | andela/ah-bird-box | python | def test_create_user(self):
'\n \n '
self.assertIsInstance(User.objects.create_user(username='username', email='[email protected]', password='password'), User) |
def test_random_color_py(degrees=(0.1, 1.9), plot=False):
'\n Test Python RandomColor\n '
logger.info('Test RandomColor')
data = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
transforms_original = mindspore.dataset.transforms.py_transforms.Compose([F.Decode(), F.Resize((224, 224)), F.ToTensor()])
ds_original = data.map(operations=transforms_original, input_columns='image')
ds_original = ds_original.batch(512)
for (idx, (image, _)) in enumerate(ds_original):
if (idx == 0):
images_original = np.transpose(image.asnumpy(), (0, 2, 3, 1))
else:
images_original = np.append(images_original, np.transpose(image.asnumpy(), (0, 2, 3, 1)), axis=0)
data = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
transforms_random_color = mindspore.dataset.transforms.py_transforms.Compose([F.Decode(), F.Resize((224, 224)), F.RandomColor(degrees=degrees), F.ToTensor()])
ds_random_color = data.map(operations=transforms_random_color, input_columns='image')
ds_random_color = ds_random_color.batch(512)
for (idx, (image, _)) in enumerate(ds_random_color):
if (idx == 0):
images_random_color = np.transpose(image.asnumpy(), (0, 2, 3, 1))
else:
images_random_color = np.append(images_random_color, np.transpose(image.asnumpy(), (0, 2, 3, 1)), axis=0)
num_samples = images_original.shape[0]
mse = np.zeros(num_samples)
for i in range(num_samples):
mse[i] = diff_mse(images_random_color[i], images_original[i])
logger.info('MSE= {}'.format(str(np.mean(mse))))
if plot:
visualize_list(images_original, images_random_color) | -2,311,255,942,487,829,500 | Test Python RandomColor | tests/ut/python/dataset/test_random_color.py | test_random_color_py | king4arabs/mindspore | python | def test_random_color_py(degrees=(0.1, 1.9), plot=False):
'\n \n '
logger.info('Test RandomColor')
data = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
transforms_original = mindspore.dataset.transforms.py_transforms.Compose([F.Decode(), F.Resize((224, 224)), F.ToTensor()])
ds_original = data.map(operations=transforms_original, input_columns='image')
ds_original = ds_original.batch(512)
for (idx, (image, _)) in enumerate(ds_original):
if (idx == 0):
images_original = np.transpose(image.asnumpy(), (0, 2, 3, 1))
else:
images_original = np.append(images_original, np.transpose(image.asnumpy(), (0, 2, 3, 1)), axis=0)
data = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
transforms_random_color = mindspore.dataset.transforms.py_transforms.Compose([F.Decode(), F.Resize((224, 224)), F.RandomColor(degrees=degrees), F.ToTensor()])
ds_random_color = data.map(operations=transforms_random_color, input_columns='image')
ds_random_color = ds_random_color.batch(512)
for (idx, (image, _)) in enumerate(ds_random_color):
if (idx == 0):
images_random_color = np.transpose(image.asnumpy(), (0, 2, 3, 1))
else:
images_random_color = np.append(images_random_color, np.transpose(image.asnumpy(), (0, 2, 3, 1)), axis=0)
num_samples = images_original.shape[0]
mse = np.zeros(num_samples)
for i in range(num_samples):
mse[i] = diff_mse(images_random_color[i], images_original[i])
logger.info('MSE= {}'.format(str(np.mean(mse))))
if plot:
visualize_list(images_original, images_random_color) |
def test_random_color_c(degrees=(0.1, 1.9), plot=False, run_golden=True):
'\n Test Cpp RandomColor\n '
logger.info('test_random_color_op')
original_seed = config_get_set_seed(10)
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
data1 = ds.TFRecordDataset(C_DATA_DIR, C_SCHEMA_DIR, columns_list=['image'], shuffle=False)
data2 = ds.TFRecordDataset(C_DATA_DIR, C_SCHEMA_DIR, columns_list=['image'], shuffle=False)
if (degrees is None):
c_op = vision.RandomColor()
else:
c_op = vision.RandomColor(degrees)
data1 = data1.map(operations=[vision.Decode()], input_columns=['image'])
data2 = data2.map(operations=[vision.Decode(), c_op], input_columns=['image'])
image_random_color_op = []
image = []
for (item1, item2) in zip(data1.create_dict_iterator(num_epochs=1, output_numpy=True), data2.create_dict_iterator(num_epochs=1, output_numpy=True)):
actual = item1['image']
expected = item2['image']
image.append(actual)
image_random_color_op.append(expected)
if run_golden:
filename = 'random_color_op_02_result.npz'
save_and_check_md5(data2, filename, generate_golden=GENERATE_GOLDEN)
if plot:
visualize_list(image, image_random_color_op)
ds.config.set_seed(original_seed)
ds.config.set_num_parallel_workers(original_num_parallel_workers) | 8,684,165,931,818,608,000 | Test Cpp RandomColor | tests/ut/python/dataset/test_random_color.py | test_random_color_c | king4arabs/mindspore | python | def test_random_color_c(degrees=(0.1, 1.9), plot=False, run_golden=True):
'\n \n '
logger.info('test_random_color_op')
original_seed = config_get_set_seed(10)
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
data1 = ds.TFRecordDataset(C_DATA_DIR, C_SCHEMA_DIR, columns_list=['image'], shuffle=False)
data2 = ds.TFRecordDataset(C_DATA_DIR, C_SCHEMA_DIR, columns_list=['image'], shuffle=False)
if (degrees is None):
c_op = vision.RandomColor()
else:
c_op = vision.RandomColor(degrees)
data1 = data1.map(operations=[vision.Decode()], input_columns=['image'])
data2 = data2.map(operations=[vision.Decode(), c_op], input_columns=['image'])
image_random_color_op = []
image = []
for (item1, item2) in zip(data1.create_dict_iterator(num_epochs=1, output_numpy=True), data2.create_dict_iterator(num_epochs=1, output_numpy=True)):
actual = item1['image']
expected = item2['image']
image.append(actual)
image_random_color_op.append(expected)
if run_golden:
filename = 'random_color_op_02_result.npz'
save_and_check_md5(data2, filename, generate_golden=GENERATE_GOLDEN)
if plot:
visualize_list(image, image_random_color_op)
ds.config.set_seed(original_seed)
ds.config.set_num_parallel_workers(original_num_parallel_workers) |
def test_random_color_py_md5():
'\n Test Python RandomColor with md5 check\n '
logger.info('Test RandomColor with md5 check')
original_seed = config_get_set_seed(10)
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
data = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
transforms = mindspore.dataset.transforms.py_transforms.Compose([F.Decode(), F.RandomColor((2.0, 2.5)), F.ToTensor()])
data = data.map(operations=transforms, input_columns='image')
filename = 'random_color_01_result.npz'
save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
ds.config.set_seed(original_seed)
ds.config.set_num_parallel_workers(original_num_parallel_workers) | 8,487,769,337,350,655,000 | Test Python RandomColor with md5 check | tests/ut/python/dataset/test_random_color.py | test_random_color_py_md5 | king4arabs/mindspore | python | def test_random_color_py_md5():
'\n \n '
logger.info('Test RandomColor with md5 check')
original_seed = config_get_set_seed(10)
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
data = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
transforms = mindspore.dataset.transforms.py_transforms.Compose([F.Decode(), F.RandomColor((2.0, 2.5)), F.ToTensor()])
data = data.map(operations=transforms, input_columns='image')
filename = 'random_color_01_result.npz'
save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
ds.config.set_seed(original_seed)
ds.config.set_num_parallel_workers(original_num_parallel_workers) |
def test_compare_random_color_op(degrees=None, plot=False):
'\n Compare Random Color op in Python and Cpp\n '
logger.info('test_random_color_op')
original_seed = config_get_set_seed(5)
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
data1 = ds.TFRecordDataset(C_DATA_DIR, C_SCHEMA_DIR, columns_list=['image'], shuffle=False)
data2 = ds.TFRecordDataset(C_DATA_DIR, C_SCHEMA_DIR, columns_list=['image'], shuffle=False)
if (degrees is None):
c_op = vision.RandomColor()
p_op = F.RandomColor()
else:
c_op = vision.RandomColor(degrees)
p_op = F.RandomColor(degrees)
transforms_random_color_py = mindspore.dataset.transforms.py_transforms.Compose([(lambda img: img.astype(np.uint8)), F.ToPIL(), p_op, np.array])
data1 = data1.map(operations=[vision.Decode(), c_op], input_columns=['image'])
data2 = data2.map(operations=[vision.Decode()], input_columns=['image'])
data2 = data2.map(operations=transforms_random_color_py, input_columns=['image'])
image_random_color_op = []
image = []
for (item1, item2) in zip(data1.create_dict_iterator(num_epochs=1, output_numpy=True), data2.create_dict_iterator(num_epochs=1, output_numpy=True)):
actual = item1['image']
expected = item2['image']
image_random_color_op.append(actual)
image.append(expected)
assert (actual.shape == expected.shape)
mse = diff_mse(actual, expected)
logger.info('MSE= {}'.format(str(np.mean(mse))))
ds.config.set_seed(original_seed)
ds.config.set_num_parallel_workers(original_num_parallel_workers)
if plot:
visualize_list(image, image_random_color_op) | -6,266,641,206,013,360,000 | Compare Random Color op in Python and Cpp | tests/ut/python/dataset/test_random_color.py | test_compare_random_color_op | king4arabs/mindspore | python | def test_compare_random_color_op(degrees=None, plot=False):
'\n \n '
logger.info('test_random_color_op')
original_seed = config_get_set_seed(5)
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
data1 = ds.TFRecordDataset(C_DATA_DIR, C_SCHEMA_DIR, columns_list=['image'], shuffle=False)
data2 = ds.TFRecordDataset(C_DATA_DIR, C_SCHEMA_DIR, columns_list=['image'], shuffle=False)
if (degrees is None):
c_op = vision.RandomColor()
p_op = F.RandomColor()
else:
c_op = vision.RandomColor(degrees)
p_op = F.RandomColor(degrees)
transforms_random_color_py = mindspore.dataset.transforms.py_transforms.Compose([(lambda img: img.astype(np.uint8)), F.ToPIL(), p_op, np.array])
data1 = data1.map(operations=[vision.Decode(), c_op], input_columns=['image'])
data2 = data2.map(operations=[vision.Decode()], input_columns=['image'])
data2 = data2.map(operations=transforms_random_color_py, input_columns=['image'])
image_random_color_op = []
image = []
for (item1, item2) in zip(data1.create_dict_iterator(num_epochs=1, output_numpy=True), data2.create_dict_iterator(num_epochs=1, output_numpy=True)):
actual = item1['image']
expected = item2['image']
image_random_color_op.append(actual)
image.append(expected)
assert (actual.shape == expected.shape)
mse = diff_mse(actual, expected)
logger.info('MSE= {}'.format(str(np.mean(mse))))
ds.config.set_seed(original_seed)
ds.config.set_num_parallel_workers(original_num_parallel_workers)
if plot:
visualize_list(image, image_random_color_op) |
def test_random_color_c_errors():
'\n Test that Cpp RandomColor errors with bad input\n '
with pytest.raises(TypeError) as error_info:
vision.RandomColor(12)
assert ('degrees must be either a tuple or a list.' in str(error_info.value))
with pytest.raises(TypeError) as error_info:
vision.RandomColor(('col', 3))
assert ("Argument degrees[0] with value col is not of type (<class 'int'>, <class 'float'>)." in str(error_info.value))
with pytest.raises(ValueError) as error_info:
vision.RandomColor((0.9, 0.1))
assert ('degrees should be in (min,max) format. Got (max,min).' in str(error_info.value))
with pytest.raises(ValueError) as error_info:
vision.RandomColor((0.9,))
assert ('degrees must be a sequence with length 2.' in str(error_info.value))
mnist_ds = ds.MnistDataset(dataset_dir=MNIST_DATA_DIR, num_samples=2, shuffle=False)
mnist_ds = mnist_ds.map(operations=vision.RandomColor(), input_columns='image')
with pytest.raises(RuntimeError) as error_info:
for _ in enumerate(mnist_ds):
pass
assert ('image shape is not <H,W,C> or channel is not 3' in str(error_info.value)) | -8,695,921,805,919,751,000 | Test that Cpp RandomColor errors with bad input | tests/ut/python/dataset/test_random_color.py | test_random_color_c_errors | king4arabs/mindspore | python | def test_random_color_c_errors():
'\n \n '
with pytest.raises(TypeError) as error_info:
vision.RandomColor(12)
assert ('degrees must be either a tuple or a list.' in str(error_info.value))
with pytest.raises(TypeError) as error_info:
vision.RandomColor(('col', 3))
assert ("Argument degrees[0] with value col is not of type (<class 'int'>, <class 'float'>)." in str(error_info.value))
with pytest.raises(ValueError) as error_info:
vision.RandomColor((0.9, 0.1))
assert ('degrees should be in (min,max) format. Got (max,min).' in str(error_info.value))
with pytest.raises(ValueError) as error_info:
vision.RandomColor((0.9,))
assert ('degrees must be a sequence with length 2.' in str(error_info.value))
mnist_ds = ds.MnistDataset(dataset_dir=MNIST_DATA_DIR, num_samples=2, shuffle=False)
mnist_ds = mnist_ds.map(operations=vision.RandomColor(), input_columns='image')
with pytest.raises(RuntimeError) as error_info:
for _ in enumerate(mnist_ds):
pass
assert ('image shape is not <H,W,C> or channel is not 3' in str(error_info.value)) |
def __init__(self, resource_handle, create_op, name):
'Creates a _TreeEnsembleSavable object.\n\n Args:\n resource_handle: handle to the decision tree ensemble variable.\n create_op: the op to initialize the variable.\n name: the name to save the tree ensemble variable under.\n '
(stamp_token, serialized) = gen_boosted_trees_ops.boosted_trees_serialize_ensemble(resource_handle)
slice_spec = ''
specs = [saver.BaseSaverBuilder.SaveSpec(stamp_token, slice_spec, (name + '_stamp')), saver.BaseSaverBuilder.SaveSpec(serialized, slice_spec, (name + '_serialized'))]
super(_TreeEnsembleSavable, self).__init__(resource_handle, specs, name)
self._resource_handle = resource_handle
self._create_op = create_op | -7,466,408,169,068,972,000 | Creates a _TreeEnsembleSavable object.
Args:
resource_handle: handle to the decision tree ensemble variable.
create_op: the op to initialize the variable.
name: the name to save the tree ensemble variable under. | tensorflow/python/ops/boosted_trees_ops.py | __init__ | AnyaTracy/tensorflow | python | def __init__(self, resource_handle, create_op, name):
'Creates a _TreeEnsembleSavable object.\n\n Args:\n resource_handle: handle to the decision tree ensemble variable.\n create_op: the op to initialize the variable.\n name: the name to save the tree ensemble variable under.\n '
(stamp_token, serialized) = gen_boosted_trees_ops.boosted_trees_serialize_ensemble(resource_handle)
slice_spec =
specs = [saver.BaseSaverBuilder.SaveSpec(stamp_token, slice_spec, (name + '_stamp')), saver.BaseSaverBuilder.SaveSpec(serialized, slice_spec, (name + '_serialized'))]
super(_TreeEnsembleSavable, self).__init__(resource_handle, specs, name)
self._resource_handle = resource_handle
self._create_op = create_op |
def restore(self, restored_tensors, unused_restored_shapes):
"Restores the associated tree ensemble from 'restored_tensors'.\n\n Args:\n restored_tensors: the tensors that were loaded from a checkpoint.\n unused_restored_shapes: the shapes this object should conform to after\n restore. Not meaningful for trees.\n\n Returns:\n The operation that restores the state of the tree ensemble variable.\n "
with ops.control_dependencies([self._create_op]):
return gen_boosted_trees_ops.boosted_trees_deserialize_ensemble(self._resource_handle, stamp_token=restored_tensors[0], tree_ensemble_serialized=restored_tensors[1]) | 4,755,641,117,312,512,000 | Restores the associated tree ensemble from 'restored_tensors'.
Args:
restored_tensors: the tensors that were loaded from a checkpoint.
unused_restored_shapes: the shapes this object should conform to after
restore. Not meaningful for trees.
Returns:
The operation that restores the state of the tree ensemble variable. | tensorflow/python/ops/boosted_trees_ops.py | restore | AnyaTracy/tensorflow | python | def restore(self, restored_tensors, unused_restored_shapes):
"Restores the associated tree ensemble from 'restored_tensors'.\n\n Args:\n restored_tensors: the tensors that were loaded from a checkpoint.\n unused_restored_shapes: the shapes this object should conform to after\n restore. Not meaningful for trees.\n\n Returns:\n The operation that restores the state of the tree ensemble variable.\n "
with ops.control_dependencies([self._create_op]):
return gen_boosted_trees_ops.boosted_trees_deserialize_ensemble(self._resource_handle, stamp_token=restored_tensors[0], tree_ensemble_serialized=restored_tensors[1]) |
def get_stamp_token(self):
'Returns the current stamp token of the resource.'
(stamp_token, _, _, _, _) = gen_boosted_trees_ops.boosted_trees_get_ensemble_states(self.resource_handle)
return stamp_token | -9,195,282,269,080,987,000 | Returns the current stamp token of the resource. | tensorflow/python/ops/boosted_trees_ops.py | get_stamp_token | AnyaTracy/tensorflow | python | def get_stamp_token(self):
(stamp_token, _, _, _, _) = gen_boosted_trees_ops.boosted_trees_get_ensemble_states(self.resource_handle)
return stamp_token |
def get_states(self):
'Returns states of the tree ensemble.\n\n Returns:\n stamp_token, num_trees, num_finalized_trees, num_attempted_layers and\n range of the nodes in the latest layer.\n '
(stamp_token, num_trees, num_finalized_trees, num_attempted_layers, nodes_range) = gen_boosted_trees_ops.boosted_trees_get_ensemble_states(self.resource_handle)
return (array_ops.identity(stamp_token, name='stamp_token'), array_ops.identity(num_trees, name='num_trees'), array_ops.identity(num_finalized_trees, name='num_finalized_trees'), array_ops.identity(num_attempted_layers, name='num_attempted_layers'), array_ops.identity(nodes_range, name='last_layer_nodes_range')) | 4,252,967,200,812,865,500 | Returns states of the tree ensemble.
Returns:
stamp_token, num_trees, num_finalized_trees, num_attempted_layers and
range of the nodes in the latest layer. | tensorflow/python/ops/boosted_trees_ops.py | get_states | AnyaTracy/tensorflow | python | def get_states(self):
'Returns states of the tree ensemble.\n\n Returns:\n stamp_token, num_trees, num_finalized_trees, num_attempted_layers and\n range of the nodes in the latest layer.\n '
(stamp_token, num_trees, num_finalized_trees, num_attempted_layers, nodes_range) = gen_boosted_trees_ops.boosted_trees_get_ensemble_states(self.resource_handle)
return (array_ops.identity(stamp_token, name='stamp_token'), array_ops.identity(num_trees, name='num_trees'), array_ops.identity(num_finalized_trees, name='num_finalized_trees'), array_ops.identity(num_attempted_layers, name='num_attempted_layers'), array_ops.identity(nodes_range, name='last_layer_nodes_range')) |
def serialize(self):
'Serializes the ensemble into proto and returns the serialized proto.\n\n Returns:\n stamp_token: int64 scalar Tensor to denote the stamp of the resource.\n serialized_proto: string scalar Tensor of the serialized proto.\n '
return gen_boosted_trees_ops.boosted_trees_serialize_ensemble(self.resource_handle) | 1,912,311,882,728,369 | Serializes the ensemble into proto and returns the serialized proto.
Returns:
stamp_token: int64 scalar Tensor to denote the stamp of the resource.
serialized_proto: string scalar Tensor of the serialized proto. | tensorflow/python/ops/boosted_trees_ops.py | serialize | AnyaTracy/tensorflow | python | def serialize(self):
'Serializes the ensemble into proto and returns the serialized proto.\n\n Returns:\n stamp_token: int64 scalar Tensor to denote the stamp of the resource.\n serialized_proto: string scalar Tensor of the serialized proto.\n '
return gen_boosted_trees_ops.boosted_trees_serialize_ensemble(self.resource_handle) |
def deserialize(self, stamp_token, serialized_proto):
'Deserialize the input proto and resets the ensemble from it.\n\n Args:\n stamp_token: int64 scalar Tensor to denote the stamp of the resource.\n serialized_proto: string scalar Tensor of the serialized proto.\n\n Returns:\n Operation (for dependencies).\n '
return gen_boosted_trees_ops.boosted_trees_deserialize_ensemble(self.resource_handle, stamp_token, serialized_proto) | 660,778,015,599,044,900 | Deserialize the input proto and resets the ensemble from it.
Args:
stamp_token: int64 scalar Tensor to denote the stamp of the resource.
serialized_proto: string scalar Tensor of the serialized proto.
Returns:
Operation (for dependencies). | tensorflow/python/ops/boosted_trees_ops.py | deserialize | AnyaTracy/tensorflow | python | def deserialize(self, stamp_token, serialized_proto):
'Deserialize the input proto and resets the ensemble from it.\n\n Args:\n stamp_token: int64 scalar Tensor to denote the stamp of the resource.\n serialized_proto: string scalar Tensor of the serialized proto.\n\n Returns:\n Operation (for dependencies).\n '
return gen_boosted_trees_ops.boosted_trees_deserialize_ensemble(self.resource_handle, stamp_token, serialized_proto) |
def ZZZ(self):
'hardcoded/mock instance of the class'
return CookieContainer() | 7,398,721,178,004,803,000 | hardcoded/mock instance of the class | release/stubs.min/System/Net/__init___parts/CookieContainer.py | ZZZ | tranconbv/ironpython-stubs | python | def ZZZ(self):
return CookieContainer() |
def Add(self, *__args):
'\n Add(self: CookieContainer,cookie: Cookie)\n\n Adds a System.Net.Cookie to a System.Net.CookieContainer. This method uses the domain from the System.Net.Cookie to determine which domain collection to associate the \n\n System.Net.Cookie with.\n\n \n\n \n\n cookie: The System.Net.Cookie to be added to the System.Net.CookieContainer.\n\n Add(self: CookieContainer,cookies: CookieCollection)\n\n Adds the contents of a System.Net.CookieCollection to the System.Net.CookieContainer.\n\n \n\n cookies: The System.Net.CookieCollection to be added to the System.Net.CookieContainer.\n\n Add(self: CookieContainer,uri: Uri,cookie: Cookie)\n\n Adds a System.Net.Cookie to the System.Net.CookieContainer for a particular URI.\n\n \n\n uri: The URI of the System.Net.Cookie to be added to the System.Net.CookieContainer.\n\n cookie: The System.Net.Cookie to be added to the System.Net.CookieContainer.\n\n Add(self: CookieContainer,uri: Uri,cookies: CookieCollection)\n\n Adds the contents of a System.Net.CookieCollection to the System.Net.CookieContainer for a particular URI.\n\n \n\n uri: The URI of the System.Net.CookieCollection to be added to the System.Net.CookieContainer.\n\n cookies: The System.Net.CookieCollection to be added to the System.Net.CookieContainer.\n '
pass | -8,225,875,563,953,717,000 | Add(self: CookieContainer,cookie: Cookie)
Adds a System.Net.Cookie to a System.Net.CookieContainer. This method uses the domain from the System.Net.Cookie to determine which domain collection to associate the
System.Net.Cookie with.
cookie: The System.Net.Cookie to be added to the System.Net.CookieContainer.
Add(self: CookieContainer,cookies: CookieCollection)
Adds the contents of a System.Net.CookieCollection to the System.Net.CookieContainer.
cookies: The System.Net.CookieCollection to be added to the System.Net.CookieContainer.
Add(self: CookieContainer,uri: Uri,cookie: Cookie)
Adds a System.Net.Cookie to the System.Net.CookieContainer for a particular URI.
uri: The URI of the System.Net.Cookie to be added to the System.Net.CookieContainer.
cookie: The System.Net.Cookie to be added to the System.Net.CookieContainer.
Add(self: CookieContainer,uri: Uri,cookies: CookieCollection)
Adds the contents of a System.Net.CookieCollection to the System.Net.CookieContainer for a particular URI.
uri: The URI of the System.Net.CookieCollection to be added to the System.Net.CookieContainer.
cookies: The System.Net.CookieCollection to be added to the System.Net.CookieContainer. | release/stubs.min/System/Net/__init___parts/CookieContainer.py | Add | tranconbv/ironpython-stubs | python | def Add(self, *__args):
'\n Add(self: CookieContainer,cookie: Cookie)\n\n Adds a System.Net.Cookie to a System.Net.CookieContainer. This method uses the domain from the System.Net.Cookie to determine which domain collection to associate the \n\n System.Net.Cookie with.\n\n \n\n \n\n cookie: The System.Net.Cookie to be added to the System.Net.CookieContainer.\n\n Add(self: CookieContainer,cookies: CookieCollection)\n\n Adds the contents of a System.Net.CookieCollection to the System.Net.CookieContainer.\n\n \n\n cookies: The System.Net.CookieCollection to be added to the System.Net.CookieContainer.\n\n Add(self: CookieContainer,uri: Uri,cookie: Cookie)\n\n Adds a System.Net.Cookie to the System.Net.CookieContainer for a particular URI.\n\n \n\n uri: The URI of the System.Net.Cookie to be added to the System.Net.CookieContainer.\n\n cookie: The System.Net.Cookie to be added to the System.Net.CookieContainer.\n\n Add(self: CookieContainer,uri: Uri,cookies: CookieCollection)\n\n Adds the contents of a System.Net.CookieCollection to the System.Net.CookieContainer for a particular URI.\n\n \n\n uri: The URI of the System.Net.CookieCollection to be added to the System.Net.CookieContainer.\n\n cookies: The System.Net.CookieCollection to be added to the System.Net.CookieContainer.\n '
pass |
def GetCookieHeader(self, uri):
'\n GetCookieHeader(self: CookieContainer,uri: Uri) -> str\n\n \n\n Gets the HTTP cookie header that contains the HTTP cookies that represent the System.Net.Cookie instances that are associated with a specific URI.\n\n \n\n uri: The URI of the System.Net.Cookie instances desired.\n\n Returns: An HTTP cookie header,with strings representing System.Net.Cookie instances delimited by semicolons.\n '
pass | 5,028,364,629,411,337,000 | GetCookieHeader(self: CookieContainer,uri: Uri) -> str
Gets the HTTP cookie header that contains the HTTP cookies that represent the System.Net.Cookie instances that are associated with a specific URI.
uri: The URI of the System.Net.Cookie instances desired.
Returns: An HTTP cookie header,with strings representing System.Net.Cookie instances delimited by semicolons. | release/stubs.min/System/Net/__init___parts/CookieContainer.py | GetCookieHeader | tranconbv/ironpython-stubs | python | def GetCookieHeader(self, uri):
'\n GetCookieHeader(self: CookieContainer,uri: Uri) -> str\n\n \n\n Gets the HTTP cookie header that contains the HTTP cookies that represent the System.Net.Cookie instances that are associated with a specific URI.\n\n \n\n uri: The URI of the System.Net.Cookie instances desired.\n\n Returns: An HTTP cookie header,with strings representing System.Net.Cookie instances delimited by semicolons.\n '
pass |
def GetCookies(self, uri):
'\n GetCookies(self: CookieContainer,uri: Uri) -> CookieCollection\n\n \n\n Gets a System.Net.CookieCollection that contains the System.Net.Cookie instances that are associated with a specific URI.\n\n \n\n uri: The URI of the System.Net.Cookie instances desired.\n\n Returns: A System.Net.CookieCollection that contains the System.Net.Cookie instances that are associated with a specific URI.\n '
pass | -6,849,928,071,652,846,000 | GetCookies(self: CookieContainer,uri: Uri) -> CookieCollection
Gets a System.Net.CookieCollection that contains the System.Net.Cookie instances that are associated with a specific URI.
uri: The URI of the System.Net.Cookie instances desired.
Returns: A System.Net.CookieCollection that contains the System.Net.Cookie instances that are associated with a specific URI. | release/stubs.min/System/Net/__init___parts/CookieContainer.py | GetCookies | tranconbv/ironpython-stubs | python | def GetCookies(self, uri):
'\n GetCookies(self: CookieContainer,uri: Uri) -> CookieCollection\n\n \n\n Gets a System.Net.CookieCollection that contains the System.Net.Cookie instances that are associated with a specific URI.\n\n \n\n uri: The URI of the System.Net.Cookie instances desired.\n\n Returns: A System.Net.CookieCollection that contains the System.Net.Cookie instances that are associated with a specific URI.\n '
pass |
def SetCookies(self, uri, cookieHeader):
'\n SetCookies(self: CookieContainer,uri: Uri,cookieHeader: str)\n\n Adds System.Net.Cookie instances for one or more cookies from an HTTP cookie header to the System.Net.CookieContainer for a specific URI.\n\n \n\n uri: The URI of the System.Net.CookieCollection.\n\n cookieHeader: The contents of an HTTP set-cookie header as returned by a HTTP server,with System.Net.Cookie instances delimited by commas.\n '
pass | 767,938,559,518,995,000 | SetCookies(self: CookieContainer,uri: Uri,cookieHeader: str)
Adds System.Net.Cookie instances for one or more cookies from an HTTP cookie header to the System.Net.CookieContainer for a specific URI.
uri: The URI of the System.Net.CookieCollection.
cookieHeader: The contents of an HTTP set-cookie header as returned by a HTTP server,with System.Net.Cookie instances delimited by commas. | release/stubs.min/System/Net/__init___parts/CookieContainer.py | SetCookies | tranconbv/ironpython-stubs | python | def SetCookies(self, uri, cookieHeader):
'\n SetCookies(self: CookieContainer,uri: Uri,cookieHeader: str)\n\n Adds System.Net.Cookie instances for one or more cookies from an HTTP cookie header to the System.Net.CookieContainer for a specific URI.\n\n \n\n uri: The URI of the System.Net.CookieCollection.\n\n cookieHeader: The contents of an HTTP set-cookie header as returned by a HTTP server,with System.Net.Cookie instances delimited by commas.\n '
pass |
def __add__(self, *args):
' x.__add__(y) <==> x+yx.__add__(y) <==> x+yx.__add__(y) <==> x+yx.__add__(y) <==> x+y '
pass | -6,471,137,132,733,238,000 | x.__add__(y) <==> x+yx.__add__(y) <==> x+yx.__add__(y) <==> x+yx.__add__(y) <==> x+y | release/stubs.min/System/Net/__init___parts/CookieContainer.py | __add__ | tranconbv/ironpython-stubs | python | def __add__(self, *args):
' '
pass |
@staticmethod
def __new__(self, capacity=None, perDomainCapacity=None, maxCookieSize=None):
'\n __new__(cls: type)\n\n __new__(cls: type,capacity: int)\n\n __new__(cls: type,capacity: int,perDomainCapacity: int,maxCookieSize: int)\n '
pass | 2,610,757,587,463,797,000 | __new__(cls: type)
__new__(cls: type,capacity: int)
__new__(cls: type,capacity: int,perDomainCapacity: int,maxCookieSize: int) | release/stubs.min/System/Net/__init___parts/CookieContainer.py | __new__ | tranconbv/ironpython-stubs | python | @staticmethod
def __new__(self, capacity=None, perDomainCapacity=None, maxCookieSize=None):
'\n __new__(cls: type)\n\n __new__(cls: type,capacity: int)\n\n __new__(cls: type,capacity: int,perDomainCapacity: int,maxCookieSize: int)\n '
pass |
@cache_json('cbsa_lookup.json')
def cbsa_lookup():
'\n Construct a County->CBSA Lookup table from NBER data\n Returns: dict\n each key is a (State Code, County FIPS code) tuple\n each value is a (CBSA FIPS code, CBSA Name) tuple\n '
logging.info('Beginning CBSA lookup')
cbsa_lookup = defaultdict(dict)
download = requests.get(URL)
decoded_content = download.content.decode('latin-1').encode('utf-8')
reader = csv.reader(decoded_content.splitlines(), delimiter=',')
next(reader)
for row in reader:
state_code = row[1]
fipscounty = row[3][(- 3):]
cbsa = row[4]
cbsaname = row[5]
cbsa_lookup[state_code][fipscounty] = (cbsa, cbsaname)
return cbsa_lookup | -8,489,966,963,965,884,000 | Construct a County->CBSA Lookup table from NBER data
Returns: dict
each key is a (State Code, County FIPS code) tuple
each value is a (CBSA FIPS code, CBSA Name) tuple | datasets/nber_county_cbsa.py | cbsa_lookup | squatter1/skills-ml | python | @cache_json('cbsa_lookup.json')
def cbsa_lookup():
'\n Construct a County->CBSA Lookup table from NBER data\n Returns: dict\n each key is a (State Code, County FIPS code) tuple\n each value is a (CBSA FIPS code, CBSA Name) tuple\n '
logging.info('Beginning CBSA lookup')
cbsa_lookup = defaultdict(dict)
download = requests.get(URL)
decoded_content = download.content.decode('latin-1').encode('utf-8')
reader = csv.reader(decoded_content.splitlines(), delimiter=',')
next(reader)
for row in reader:
state_code = row[1]
fipscounty = row[3][(- 3):]
cbsa = row[4]
cbsaname = row[5]
cbsa_lookup[state_code][fipscounty] = (cbsa, cbsaname)
return cbsa_lookup |
def gelu(x):
"Implementation of the gelu activation function.\n For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):\n 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))\n Also see https://arxiv.org/abs/1606.08415\n "
return ((x * 0.5) * (1.0 + torch.erf((x / math.sqrt(2.0))))) | 1,420,372,343,885,535,700 | Implementation of the gelu activation function.
For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
Also see https://arxiv.org/abs/1606.08415 | models/SketchTransformer/models/networks.py | gelu | avalonstrel/SketchBERT | python | def gelu(x):
"Implementation of the gelu activation function.\n For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):\n 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))\n Also see https://arxiv.org/abs/1606.08415\n "
return ((x * 0.5) * (1.0 + torch.erf((x / math.sqrt(2.0))))) |
def __init__(self, hidden_size, eps=1e-12):
'\n Construct a layernorm module in the TF style (epsilon inside the square root).\n '
super(SketchLayerNorm, self).__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.bias = nn.Parameter(torch.zeros(hidden_size))
self.variance_epsilon = eps | -8,357,680,159,207,132,000 | Construct a layernorm module in the TF style (epsilon inside the square root). | models/SketchTransformer/models/networks.py | __init__ | avalonstrel/SketchBERT | python | def __init__(self, hidden_size, eps=1e-12):
'\n \n '
super(SketchLayerNorm, self).__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.bias = nn.Parameter(torch.zeros(hidden_size))
self.variance_epsilon = eps |
def transpose_(self, x):
'\n Transpose Function for simplicity.\n '
new_x_shape = (x.size()[:(- 1)] + (self.num_heads, self.head_dim))
x = x.view(*new_x_shape)
return x.permute(0, 2, 1, 3) | -6,336,780,184,453,579,000 | Transpose Function for simplicity. | models/SketchTransformer/models/networks.py | transpose_ | avalonstrel/SketchBERT | python | def transpose_(self, x):
'\n \n '
new_x_shape = (x.size()[:(- 1)] + (self.num_heads, self.head_dim))
x = x.view(*new_x_shape)
return x.permute(0, 2, 1, 3) |
def forward(self, hidden_states, attention_mask, head_mask=None, output_attentions=False, keep_multihead_output=False):
'\n Input:\n hidden_states[batch, seq_len, hidden_dim]\n attention_mask[batch, 1, 1, seq_len]\n Output:\n context_states[batch, seq_len, hidden_dim]\n attention_probs[seq_len, hidden_dim]\n '
query = self.query(hidden_states)
key = self.key(hidden_states)
value = self.value(hidden_states)
multi_query = self.transpose_(query)
multi_key = self.transpose_(key)
multi_value = self.transpose_(value)
attention_scores = torch.matmul(multi_query, multi_key.transpose((- 1), (- 2)))
attention_scores = (attention_scores / self.scale_factor)
attention_scores = (attention_scores + attention_mask)
attention_probs = F.softmax(attention_scores, dim=(- 1))
attention_probs = self.dropout(attention_probs)
if (head_mask is not None):
attention_probs = (attention_probs * head_mask)
context_states = torch.matmul(attention_probs, multi_value)
if keep_multihead_output:
self.multihead_output = context_states
self.multihead_output.retain_grad()
context_states = context_states.permute(0, 2, 1, 3)
context_states = context_states.contiguous().view((context_states.size()[:(- 2)] + ((- 1),)))
if output_attentions:
return (context_states, attention_probs)
return context_states | -3,967,524,633,107,120,000 | Input:
hidden_states[batch, seq_len, hidden_dim]
attention_mask[batch, 1, 1, seq_len]
Output:
context_states[batch, seq_len, hidden_dim]
attention_probs[seq_len, hidden_dim] | models/SketchTransformer/models/networks.py | forward | avalonstrel/SketchBERT | python | def forward(self, hidden_states, attention_mask, head_mask=None, output_attentions=False, keep_multihead_output=False):
'\n Input:\n hidden_states[batch, seq_len, hidden_dim]\n attention_mask[batch, 1, 1, seq_len]\n Output:\n context_states[batch, seq_len, hidden_dim]\n attention_probs[seq_len, hidden_dim]\n '
query = self.query(hidden_states)
key = self.key(hidden_states)
value = self.value(hidden_states)
multi_query = self.transpose_(query)
multi_key = self.transpose_(key)
multi_value = self.transpose_(value)
attention_scores = torch.matmul(multi_query, multi_key.transpose((- 1), (- 2)))
attention_scores = (attention_scores / self.scale_factor)
attention_scores = (attention_scores + attention_mask)
attention_probs = F.softmax(attention_scores, dim=(- 1))
attention_probs = self.dropout(attention_probs)
if (head_mask is not None):
attention_probs = (attention_probs * head_mask)
context_states = torch.matmul(attention_probs, multi_value)
if keep_multihead_output:
self.multihead_output = context_states
self.multihead_output.retain_grad()
context_states = context_states.permute(0, 2, 1, 3)
context_states = context_states.contiguous().view((context_states.size()[:(- 2)] + ((- 1),)))
if output_attentions:
return (context_states, attention_probs)
return context_states |
def get_seg_states(self, hidden_states, segment_index):
'\n Input:\n hidden_states[batch, seq_len, hidden_dim]\n segment_index[batch, seq_len]\n '
seg_states = torch.zeros(hidden_states.size(0), self.max_segment, hidden_states.size(2)).to(hidden_states.device)
length = (segment_index == 0).sum(dim=1)
length_mask = length_to_mask(length, max_len=self.max_segment, dtype=torch.float)
seg_states[(length_mask == 1), :] = hidden_states[(segment_index == 0), :]
return (seg_states, length_mask) | -7,235,732,249,509,121,000 | Input:
hidden_states[batch, seq_len, hidden_dim]
segment_index[batch, seq_len] | models/SketchTransformer/models/networks.py | get_seg_states | avalonstrel/SketchBERT | python | def get_seg_states(self, hidden_states, segment_index):
'\n Input:\n hidden_states[batch, seq_len, hidden_dim]\n segment_index[batch, seq_len]\n '
seg_states = torch.zeros(hidden_states.size(0), self.max_segment, hidden_states.size(2)).to(hidden_states.device)
length = (segment_index == 0).sum(dim=1)
length_mask = length_to_mask(length, max_len=self.max_segment, dtype=torch.float)
seg_states[(length_mask == 1), :] = hidden_states[(segment_index == 0), :]
return (seg_states, length_mask) |
def forward(self, hidden_states, attention_mask, segments, segment_index, head_mask=None, output_attentions=False):
'\n Input:\n hidden_states[batch, seg_len, hidden_dim]:\n attention_mask[batch, seg_len](segment-based)\n segments[batch, seg_len]:\n segment_index[batch, seq_len]\n\n '
local_states = self.local_attention(hidden_states, attention_mask, head_mask)
if output_attentions:
(local_states, attention_probs) = local_states
input_prefix = (hidden_states.size(1) - segment_index.size(1))
(seg_states, seg_atten_mask) = self.get_seg_states(local_states[:, input_prefix:, :], segment_index)
if (self.segment_atten_type == 'multi'):
seg_states = self.segment_attention(seg_states, seg_atten_mask.unsqueeze(1).unsqueeze(2), head_mask)
if output_attentions:
(seg_states, attention_probs) = seg_states
local_inter_states = self.local_inter_layer(local_states)
seg_inter_states = self.seg_inter_layer(seg_states)
aug_seg_inter_states = torch.gather(seg_inter_states, 1, (segments[:, input_prefix:] - 2).view(segments.size(0), (- 1), 1).repeat(1, 1, seg_inter_states.size(2)))
inter_states = torch.zeros(local_inter_states.size(0), local_inter_states.size(1), self.inter_dim).to(local_inter_states.device)
inter_states[:, :, :(self.inter_dim // 2)] = local_inter_states
inter_states[:, input_prefix:, (self.inter_dim // 2):] = aug_seg_inter_states
inter_states[:, :input_prefix, (self.inter_dim // 2):] = seg_inter_states.sum(dim=1, keepdim=True)
output_states = self.output(inter_states, hidden_states)
if output_attentions:
return (output_states, attention_probs)
return output_states | 6,916,960,397,091,090,000 | Input:
hidden_states[batch, seg_len, hidden_dim]:
attention_mask[batch, seg_len](segment-based)
segments[batch, seg_len]:
segment_index[batch, seq_len] | models/SketchTransformer/models/networks.py | forward | avalonstrel/SketchBERT | python | def forward(self, hidden_states, attention_mask, segments, segment_index, head_mask=None, output_attentions=False):
'\n Input:\n hidden_states[batch, seg_len, hidden_dim]:\n attention_mask[batch, seg_len](segment-based)\n segments[batch, seg_len]:\n segment_index[batch, seq_len]\n\n '
local_states = self.local_attention(hidden_states, attention_mask, head_mask)
if output_attentions:
(local_states, attention_probs) = local_states
input_prefix = (hidden_states.size(1) - segment_index.size(1))
(seg_states, seg_atten_mask) = self.get_seg_states(local_states[:, input_prefix:, :], segment_index)
if (self.segment_atten_type == 'multi'):
seg_states = self.segment_attention(seg_states, seg_atten_mask.unsqueeze(1).unsqueeze(2), head_mask)
if output_attentions:
(seg_states, attention_probs) = seg_states
local_inter_states = self.local_inter_layer(local_states)
seg_inter_states = self.seg_inter_layer(seg_states)
aug_seg_inter_states = torch.gather(seg_inter_states, 1, (segments[:, input_prefix:] - 2).view(segments.size(0), (- 1), 1).repeat(1, 1, seg_inter_states.size(2)))
inter_states = torch.zeros(local_inter_states.size(0), local_inter_states.size(1), self.inter_dim).to(local_inter_states.device)
inter_states[:, :, :(self.inter_dim // 2)] = local_inter_states
inter_states[:, input_prefix:, (self.inter_dim // 2):] = aug_seg_inter_states
inter_states[:, :input_prefix, (self.inter_dim // 2):] = seg_inter_states.sum(dim=1, keepdim=True)
output_states = self.output(inter_states, hidden_states)
if output_attentions:
return (output_states, attention_probs)
return output_states |
def forward(self, input_states, attention_mask, targets=None, segments=None, head_mask=None, output_all_states=False, output_attentions=False, keep_multihead_output=False):
'\n Input:\n input_states[batch, seq_len, 5],\n zs[batch, latent_dim]\n '
if (attention_mask is None):
attention_mask = torch.ones(input_states.size(0), input_states.size(1))
if (len(attention_mask.size()) == 3):
extended_attention_mask = attention_mask.unsqueeze(1)
elif (len(attention_mask.size()) == 2):
extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
extended_attention_mask = extended_attention_mask.to(dtype=next(self.parameters()).dtype, device=input_states.device)
extended_attention_mask = ((1.0 - extended_attention_mask) * (- 10000.0))
attention_mask = extended_attention_mask
if (head_mask is not None):
if (head_mask.dim() == 1):
head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze((- 1)).unsqueeze((- 1))
head_mask = head_mask.expand_as(self.num_hidden_layers, (- 1), (- 1), (- 1), (- 1))
elif (head_mask.dim() == 2):
head_mask = head_mask.unsqueeze(1).unsqueeze((- 1)).unsqueeze((- 1))
head_mask = head_mask.to(dtype=next(self.parameters()).dtype, device=input_states.device)
else:
head_mask = None
input_states = self.embedding(input_states)
if (self.pos_embedding is not None):
pos_states = self.pos_embedding(self.get_pos_states(input_states))
input_states = (input_states + pos_states)
if ((self.segment_embedding is not None) and (segments is not None)):
segment_states = self.segment_embedding(segments)
input_states = (input_states + segment_states)
if ((self.cls_embedding is not None) and (targets is not None)):
cls_states = self.cls_embedding(targets)
cls_states = cls_states.unsqueeze(1).repeat(1, input_states.size(1), 1)
input_states = (input_states + cls_states)
input_states = self.embed_refine_net(input_states)
output_states = self.encoder(input_states, attention_mask, head_mask, output_all_states, output_attentions, keep_multihead_output)
if output_attentions:
(output_states, attention_probs) = output_states
return (output_states[(- 1)], attention_probs)
return output_states[(- 1)] | -1,672,195,904,779,026,700 | Input:
input_states[batch, seq_len, 5],
zs[batch, latent_dim] | models/SketchTransformer/models/networks.py | forward | avalonstrel/SketchBERT | python | def forward(self, input_states, attention_mask, targets=None, segments=None, head_mask=None, output_all_states=False, output_attentions=False, keep_multihead_output=False):
'\n Input:\n input_states[batch, seq_len, 5],\n zs[batch, latent_dim]\n '
if (attention_mask is None):
attention_mask = torch.ones(input_states.size(0), input_states.size(1))
if (len(attention_mask.size()) == 3):
extended_attention_mask = attention_mask.unsqueeze(1)
elif (len(attention_mask.size()) == 2):
extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
extended_attention_mask = extended_attention_mask.to(dtype=next(self.parameters()).dtype, device=input_states.device)
extended_attention_mask = ((1.0 - extended_attention_mask) * (- 10000.0))
attention_mask = extended_attention_mask
if (head_mask is not None):
if (head_mask.dim() == 1):
head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze((- 1)).unsqueeze((- 1))
head_mask = head_mask.expand_as(self.num_hidden_layers, (- 1), (- 1), (- 1), (- 1))
elif (head_mask.dim() == 2):
head_mask = head_mask.unsqueeze(1).unsqueeze((- 1)).unsqueeze((- 1))
head_mask = head_mask.to(dtype=next(self.parameters()).dtype, device=input_states.device)
else:
head_mask = None
input_states = self.embedding(input_states)
if (self.pos_embedding is not None):
pos_states = self.pos_embedding(self.get_pos_states(input_states))
input_states = (input_states + pos_states)
if ((self.segment_embedding is not None) and (segments is not None)):
segment_states = self.segment_embedding(segments)
input_states = (input_states + segment_states)
if ((self.cls_embedding is not None) and (targets is not None)):
cls_states = self.cls_embedding(targets)
cls_states = cls_states.unsqueeze(1).repeat(1, input_states.size(1), 1)
input_states = (input_states + cls_states)
input_states = self.embed_refine_net(input_states)
output_states = self.encoder(input_states, attention_mask, head_mask, output_all_states, output_attentions, keep_multihead_output)
if output_attentions:
(output_states, attention_probs) = output_states
return (output_states[(- 1)], attention_probs)
return output_states[(- 1)] |
def forward(self, input_states, zs, attention_mask, targets=None, segments=None, head_mask=None, output_all_states=False, output_attentions=False, keep_multihead_output=False):
'\n Input:\n input_states[batch, seq_len, 5],\n zs[batch, latent_dim]\n '
if (attention_mask is None):
attention_mask = torch.ones(input_states.size(0), input_states.size(1))
if (len(attention_mask.size()) == 3):
extended_attention_mask = attention_mask.unsqueeze(1)
elif (len(attention_mask.size()) == 2):
extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
extended_attention_mask = extended_attention_mask.to(dtype=next(self.parameters()).dtype, device=input_states.device)
extended_attention_mask = ((1.0 - extended_attention_mask) * (- 10000.0))
attention_mask = extended_attention_mask
if (head_mask is not None):
if (head_mask.dim() == 1):
head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze((- 1)).unsqueeze((- 1))
head_mask = head_mask.expand_as(self.num_hidden_layers, (- 1), (- 1), (- 1), (- 1))
elif (head_mask.dim() == 2):
head_mask = head_mask.unsqueeze(1).unsqueeze((- 1)).unsqueeze((- 1))
head_mask = head_mask.to(dtype=next(self.parameters()).dtype, device=input_states.device)
else:
head_mask = None
input_states = self.embedding(input_states)
if (self.pos_embedding is not None):
pos_states = self.pos_embedding(self.get_pos_states(input_states))
input_states = (input_states + pos_states)
if ((self.segment_embedding is not None) and (segments is not None)):
segment_states = self.segment_embedding(segments)
input_states = (input_states + segment_states)
if ((self.cls_embedding is not None) and (targets is not None)):
cls_states = self.cls_embedding(targets)
cls_states = cls_states.unsqueeze(1).repeat(1, input_states.size(1), 1)
input_states = (input_states + cls_states)
input_states = self.embed_refine_net(input_states)
input_states = torch.cat([input_states, zs.unsqueeze(1).repeat(1, input_states.size(1), 1)], dim=2)
input_states = self.latent_fusion(input_states)
output_states = self.encoder(input_states, attention_mask, head_mask, output_all_states, output_attentions, keep_multihead_output)
if output_attentions:
(output_states, attention_probs) = output_states
output_states = output_states[(- 1)]
for re_fc in self.re_fcs:
output_states = re_fc(output_states)
return (output_states, attention_probs)
output_states = output_states[(- 1)]
for re_fc in self.re_fcs:
output_states = re_fc(output_states)
return output_states | -4,880,509,655,281,427,000 | Input:
input_states[batch, seq_len, 5],
zs[batch, latent_dim] | models/SketchTransformer/models/networks.py | forward | avalonstrel/SketchBERT | python | def forward(self, input_states, zs, attention_mask, targets=None, segments=None, head_mask=None, output_all_states=False, output_attentions=False, keep_multihead_output=False):
'\n Input:\n input_states[batch, seq_len, 5],\n zs[batch, latent_dim]\n '
if (attention_mask is None):
attention_mask = torch.ones(input_states.size(0), input_states.size(1))
if (len(attention_mask.size()) == 3):
extended_attention_mask = attention_mask.unsqueeze(1)
elif (len(attention_mask.size()) == 2):
extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
extended_attention_mask = extended_attention_mask.to(dtype=next(self.parameters()).dtype, device=input_states.device)
extended_attention_mask = ((1.0 - extended_attention_mask) * (- 10000.0))
attention_mask = extended_attention_mask
if (head_mask is not None):
if (head_mask.dim() == 1):
head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze((- 1)).unsqueeze((- 1))
head_mask = head_mask.expand_as(self.num_hidden_layers, (- 1), (- 1), (- 1), (- 1))
elif (head_mask.dim() == 2):
head_mask = head_mask.unsqueeze(1).unsqueeze((- 1)).unsqueeze((- 1))
head_mask = head_mask.to(dtype=next(self.parameters()).dtype, device=input_states.device)
else:
head_mask = None
input_states = self.embedding(input_states)
if (self.pos_embedding is not None):
pos_states = self.pos_embedding(self.get_pos_states(input_states))
input_states = (input_states + pos_states)
if ((self.segment_embedding is not None) and (segments is not None)):
segment_states = self.segment_embedding(segments)
input_states = (input_states + segment_states)
if ((self.cls_embedding is not None) and (targets is not None)):
cls_states = self.cls_embedding(targets)
cls_states = cls_states.unsqueeze(1).repeat(1, input_states.size(1), 1)
input_states = (input_states + cls_states)
input_states = self.embed_refine_net(input_states)
input_states = torch.cat([input_states, zs.unsqueeze(1).repeat(1, input_states.size(1), 1)], dim=2)
input_states = self.latent_fusion(input_states)
output_states = self.encoder(input_states, attention_mask, head_mask, output_all_states, output_attentions, keep_multihead_output)
if output_attentions:
(output_states, attention_probs) = output_states
output_states = output_states[(- 1)]
for re_fc in self.re_fcs:
output_states = re_fc(output_states)
return (output_states, attention_probs)
output_states = output_states[(- 1)]
for re_fc in self.re_fcs:
output_states = re_fc(output_states)
return output_states |
def forward(self, hidden_states):
'\n Input:\n hidden_states[batch, seq_len+cls_dim, hidden_dim](0 dim is cls)\n Output:\n x_pred[batch, seq_len+cls_dim, 2*max_size[0]+1]\n y_pred[batch, seq_len+cls_dim, 2*max_size[1]+1]\n type_pred[batch, seq_len+cls_dim, type_size]\n '
hidden_states = hidden_states[:, (self.cls_in_input + self.rel_in_input):, :]
x_pred = self.x_pooling(hidden_states)
y_pred = self.y_pooling(hidden_states)
type_pred = self.type_pooling(hidden_states)
return (x_pred, y_pred, type_pred) | 5,573,380,538,648,980,000 | Input:
hidden_states[batch, seq_len+cls_dim, hidden_dim](0 dim is cls)
Output:
x_pred[batch, seq_len+cls_dim, 2*max_size[0]+1]
y_pred[batch, seq_len+cls_dim, 2*max_size[1]+1]
type_pred[batch, seq_len+cls_dim, type_size] | models/SketchTransformer/models/networks.py | forward | avalonstrel/SketchBERT | python | def forward(self, hidden_states):
'\n Input:\n hidden_states[batch, seq_len+cls_dim, hidden_dim](0 dim is cls)\n Output:\n x_pred[batch, seq_len+cls_dim, 2*max_size[0]+1]\n y_pred[batch, seq_len+cls_dim, 2*max_size[1]+1]\n type_pred[batch, seq_len+cls_dim, type_size]\n '
hidden_states = hidden_states[:, (self.cls_in_input + self.rel_in_input):, :]
x_pred = self.x_pooling(hidden_states)
y_pred = self.y_pooling(hidden_states)
type_pred = self.type_pooling(hidden_states)
return (x_pred, y_pred, type_pred) |
def forward(self, hidden_states, segment_index):
'\n Input:\n hidden_states[batch, seg_len, hidden_dim]\n segment_index[batch, seq_len]\n '
seg_states = hidden_states[:, (self.cls_in_input + self.rel_in_input):, :][(segment_index == 0), :]
return self.sg_fc(seg_states) | 1,118,064,636,496,338,400 | Input:
hidden_states[batch, seg_len, hidden_dim]
segment_index[batch, seq_len] | models/SketchTransformer/models/networks.py | forward | avalonstrel/SketchBERT | python | def forward(self, hidden_states, segment_index):
'\n Input:\n hidden_states[batch, seg_len, hidden_dim]\n segment_index[batch, seq_len]\n '
seg_states = hidden_states[:, (self.cls_in_input + self.rel_in_input):, :][(segment_index == 0), :]
return self.sg_fc(seg_states) |
@property
def function_object(self):
'get the generated function object'
return self._function | -4,883,460,870,703,305,000 | get the generated function object | mlrun/runtimes/function_reference.py | function_object | AlonMaor14/mlrun | python | @property
def function_object(self):
return self._function |
def to_function(self, default_kind=None):
'generate a function object from the ref definitions'
if (self.url and ('://' not in self.url)):
if (not os.path.isfile(self.url)):
raise OSError(f'{self.url} not found')
kind = (self.kind or default_kind)
if self.url:
if (self.url.endswith('.yaml') or self.url.startswith('db://') or self.url.startswith('hub://')):
func = mlrun.import_function(self.url)
if self.image:
func.spec.image = self.image
elif self.url.endswith('.ipynb'):
func = mlrun.code_to_function(self.name, filename=self.url, image=self.image, kind=kind)
elif self.url.endswith('.py'):
if (not self.image):
raise ValueError('image must be provided with py code files, use function object for more control/settings')
func = mlrun.code_to_function(self.name, filename=self.url, image=self.image, kind=kind)
else:
raise ValueError(f'unsupported function url {self.url} or no spec')
if self.spec:
func = enrich_function_from_dict(func, self.spec)
elif (self.code is not None):
code = self.code
if (kind == mlrun.runtimes.RuntimeKinds.serving):
code = (code + mlrun_footer.format(mlrun.runtimes.serving.serving_subkind))
func = mlrun.new_function(self.name, kind=kind, image=self.image)
data = b64encode(code.encode('utf-8')).decode('utf-8')
func.spec.build.functionSourceCode = data
if (kind not in mlrun.runtimes.RuntimeKinds.nuclio_runtimes()):
func.spec.default_handler = 'handler'
if self.spec:
func = enrich_function_from_dict(func, self.spec)
elif self.spec:
func = mlrun.new_function(self.name, runtime=self.spec)
else:
raise ValueError('url or spec or code must be specified')
if self.requirements:
func.with_requirements(self.requirements)
self._function = func
return func | -1,610,808,312,545,578,200 | generate a function object from the ref definitions | mlrun/runtimes/function_reference.py | to_function | AlonMaor14/mlrun | python | def to_function(self, default_kind=None):
if (self.url and ('://' not in self.url)):
if (not os.path.isfile(self.url)):
raise OSError(f'{self.url} not found')
kind = (self.kind or default_kind)
if self.url:
if (self.url.endswith('.yaml') or self.url.startswith('db://') or self.url.startswith('hub://')):
func = mlrun.import_function(self.url)
if self.image:
func.spec.image = self.image
elif self.url.endswith('.ipynb'):
func = mlrun.code_to_function(self.name, filename=self.url, image=self.image, kind=kind)
elif self.url.endswith('.py'):
if (not self.image):
raise ValueError('image must be provided with py code files, use function object for more control/settings')
func = mlrun.code_to_function(self.name, filename=self.url, image=self.image, kind=kind)
else:
raise ValueError(f'unsupported function url {self.url} or no spec')
if self.spec:
func = enrich_function_from_dict(func, self.spec)
elif (self.code is not None):
code = self.code
if (kind == mlrun.runtimes.RuntimeKinds.serving):
code = (code + mlrun_footer.format(mlrun.runtimes.serving.serving_subkind))
func = mlrun.new_function(self.name, kind=kind, image=self.image)
data = b64encode(code.encode('utf-8')).decode('utf-8')
func.spec.build.functionSourceCode = data
if (kind not in mlrun.runtimes.RuntimeKinds.nuclio_runtimes()):
func.spec.default_handler = 'handler'
if self.spec:
func = enrich_function_from_dict(func, self.spec)
elif self.spec:
func = mlrun.new_function(self.name, runtime=self.spec)
else:
raise ValueError('url or spec or code must be specified')
if self.requirements:
func.with_requirements(self.requirements)
self._function = func
return func |
def deploy(self, **kwargs):
'deploy the function'
self._address = self._function.deploy(**kwargs)
return self._address | 3,107,861,624,235,756,500 | deploy the function | mlrun/runtimes/function_reference.py | deploy | AlonMaor14/mlrun | python | def deploy(self, **kwargs):
self._address = self._function.deploy(**kwargs)
return self._address |
def __init__(self, allow_crash_consistent_snapshot=None):
'Constructor for the HypervBackupEnvParams class'
self.allow_crash_consistent_snapshot = allow_crash_consistent_snapshot | 2,498,918,717,880,499,700 | Constructor for the HypervBackupEnvParams class | cohesity_management_sdk/models/hyperv_backup_env_params.py | __init__ | anoopbhat/management-sdk-python | python | def __init__(self, allow_crash_consistent_snapshot=None):
self.allow_crash_consistent_snapshot = allow_crash_consistent_snapshot |
@classmethod
def from_dictionary(cls, dictionary):
"Creates an instance of this model from a dictionary\n\n Args:\n dictionary (dictionary): A dictionary representation of the object as\n obtained from the deserialization of the server's response. The keys\n MUST match property names in the API description.\n\n Returns:\n object: An instance of this structure class.\n\n "
if (dictionary is None):
return None
allow_crash_consistent_snapshot = dictionary.get('allowCrashConsistentSnapshot')
return cls(allow_crash_consistent_snapshot) | -3,474,178,223,966,733,300 | Creates an instance of this model from a dictionary
Args:
dictionary (dictionary): A dictionary representation of the object as
obtained from the deserialization of the server's response. The keys
MUST match property names in the API description.
Returns:
object: An instance of this structure class. | cohesity_management_sdk/models/hyperv_backup_env_params.py | from_dictionary | anoopbhat/management-sdk-python | python | @classmethod
def from_dictionary(cls, dictionary):
"Creates an instance of this model from a dictionary\n\n Args:\n dictionary (dictionary): A dictionary representation of the object as\n obtained from the deserialization of the server's response. The keys\n MUST match property names in the API description.\n\n Returns:\n object: An instance of this structure class.\n\n "
if (dictionary is None):
return None
allow_crash_consistent_snapshot = dictionary.get('allowCrashConsistentSnapshot')
return cls(allow_crash_consistent_snapshot) |
@staticmethod
def promote_ase_atoms(obj, symmetry=None):
' Convert ASE Atoms object to the one usable by SPRKKR.\n For the case of the usability it is a bit ugly hack: The __class__ attribute\n is replaced so the extra methods and properties of the objects will\n be available.\n\n Parameters\n ----------\n obj: ase.Atoms\n The atoms object to be promoted to be used for SPRKKR calculations\n\n symmetry: boolean or None\n The sites property of the resulting object will consider the symmetry of the structure.\n I.e., the by-symmetry-equal atomic sites will share the same sites object.\n Default None is the same as True, however it does not change the symmetry\n of the already promoted obj passed into the routine.\n '
if (obj and (not isinstance(obj, SPRKKRAtoms))):
if (obj.__class__ is Atoms):
obj.__class__ = SPRKKRAtoms
else:
if (not isinstance(obj, Atoms)):
raise f'Can not promote class {obj} of class {obj.__class__} to {SPRKKRAtoms}'
class SprKKrAtomsEx(obj.__class__, SPRKKRAtoms):
pass
obj.__class__ = SprKKrAtomsEx
obj._init((True if (symmetry is None) else symmetry))
elif (symmetry is not None):
obj.symmetry = symmetry
return obj | -8,266,258,733,905,201,000 | Convert ASE Atoms object to the one usable by SPRKKR.
For the case of the usability it is a bit ugly hack: The __class__ attribute
is replaced so the extra methods and properties of the objects will
be available.
Parameters
----------
obj: ase.Atoms
The atoms object to be promoted to be used for SPRKKR calculations
symmetry: boolean or None
The sites property of the resulting object will consider the symmetry of the structure.
I.e., the by-symmetry-equal atomic sites will share the same sites object.
Default None is the same as True, however it does not change the symmetry
of the already promoted obj passed into the routine. | src/ase2sprkkr/sprkkr/sprkkr_atoms.py | promote_ase_atoms | ase2sprkkr/ase2sprkkr | python | @staticmethod
def promote_ase_atoms(obj, symmetry=None):
' Convert ASE Atoms object to the one usable by SPRKKR.\n For the case of the usability it is a bit ugly hack: The __class__ attribute\n is replaced so the extra methods and properties of the objects will\n be available.\n\n Parameters\n ----------\n obj: ase.Atoms\n The atoms object to be promoted to be used for SPRKKR calculations\n\n symmetry: boolean or None\n The sites property of the resulting object will consider the symmetry of the structure.\n I.e., the by-symmetry-equal atomic sites will share the same sites object.\n Default None is the same as True, however it does not change the symmetry\n of the already promoted obj passed into the routine.\n '
if (obj and (not isinstance(obj, SPRKKRAtoms))):
if (obj.__class__ is Atoms):
obj.__class__ = SPRKKRAtoms
else:
if (not isinstance(obj, Atoms)):
raise f'Can not promote class {obj} of class {obj.__class__} to {SPRKKRAtoms}'
class SprKKrAtomsEx(obj.__class__, SPRKKRAtoms):
pass
obj.__class__ = SprKKrAtomsEx
obj._init((True if (symmetry is None) else symmetry))
elif (symmetry is not None):
obj.symmetry = symmetry
return obj |
def __init__(self, *args, symmetry=True, potential=None, **kwargs):
'\n Creates SPRKKRAtoms atoms\n\n Parameters\n ----------\n *args: list\n The positionals arguments of ase.Atoms.__init__\n symmetry: boolean\n The symmetry will be computed when the sites property will be initialized.\n I.e., the by-symmetry-equal atomic sites will share the same sites object.\n **kwargs: dict\n The named arguments of ase.Atoms.__init__\n '
self._init(symmetry, potential)
super().__init__(*args, **kwargs) | 5,208,751,560,967,905,000 | Creates SPRKKRAtoms atoms
Parameters
----------
*args: list
The positionals arguments of ase.Atoms.__init__
symmetry: boolean
The symmetry will be computed when the sites property will be initialized.
I.e., the by-symmetry-equal atomic sites will share the same sites object.
**kwargs: dict
The named arguments of ase.Atoms.__init__ | src/ase2sprkkr/sprkkr/sprkkr_atoms.py | __init__ | ase2sprkkr/ase2sprkkr | python | def __init__(self, *args, symmetry=True, potential=None, **kwargs):
'\n Creates SPRKKRAtoms atoms\n\n Parameters\n ----------\n *args: list\n The positionals arguments of ase.Atoms.__init__\n symmetry: boolean\n The symmetry will be computed when the sites property will be initialized.\n I.e., the by-symmetry-equal atomic sites will share the same sites object.\n **kwargs: dict\n The named arguments of ase.Atoms.__init__\n '
self._init(symmetry, potential)
super().__init__(*args, **kwargs) |
def _init(self, symmetry=True, potential=None):
' The initialization of the additional (not-in-ASE) properties. To be used\n by constructor and by promote_ase_atoms'
self._unique_sites = None
self._potential = potential
self._symmetry = symmetry | 7,673,537,083,973,724,000 | The initialization of the additional (not-in-ASE) properties. To be used
by constructor and by promote_ase_atoms | src/ase2sprkkr/sprkkr/sprkkr_atoms.py | _init | ase2sprkkr/ase2sprkkr | python | def _init(self, symmetry=True, potential=None):
' The initialization of the additional (not-in-ASE) properties. To be used\n by constructor and by promote_ase_atoms'
self._unique_sites = None
self._potential = potential
self._symmetry = symmetry |
@property
def symmetry(self):
'\n Whether the sites property is/will be generated using symmetry, i.e.\n whether the Sites objects in the sites property will be shared among\n symmetric atomic sites.\n '
return self._symmetry | -7,475,728,776,709,522,000 | Whether the sites property is/will be generated using symmetry, i.e.
whether the Sites objects in the sites property will be shared among
symmetric atomic sites. | src/ase2sprkkr/sprkkr/sprkkr_atoms.py | symmetry | ase2sprkkr/ase2sprkkr | python | @property
def symmetry(self):
'\n Whether the sites property is/will be generated using symmetry, i.e.\n whether the Sites objects in the sites property will be shared among\n symmetric atomic sites.\n '
return self._symmetry |
@symmetry.setter
def symmetry(self, value):
'\n Recomputes the sites with enabled/disabled symmetry if the value of the property\n has changed.\n '
if (self._symmetry == value):
return
self._symmetry = value
if (self._unique_sites is not None):
if value:
self._compute_sites_symmetry()
else:
self._cancel_sites_symmetry() | 4,876,355,944,345,147,000 | Recomputes the sites with enabled/disabled symmetry if the value of the property
has changed. | src/ase2sprkkr/sprkkr/sprkkr_atoms.py | symmetry | ase2sprkkr/ase2sprkkr | python | @symmetry.setter
def symmetry(self, value):
'\n Recomputes the sites with enabled/disabled symmetry if the value of the property\n has changed.\n '
if (self._symmetry == value):
return
self._symmetry = value
if (self._unique_sites is not None):
if value:
self._compute_sites_symmetry()
else:
self._cancel_sites_symmetry() |
def compute_spacegroup_for_atomic_numbers(self, atomic_numbers=None, symprec=1e-05):
" Return spacegroup that suits to the atoms' cell structure and to the given\n atomic_numbers (not necessary the real ones, they can be just ''labels'').\n "
atomic_numbers = (atomic_numbers if (atomic_numbers is not None) else self.get_atomic_numbers())
sg = spglib.get_spacegroup((self.get_cell(), self.get_scaled_positions(), atomic_numbers), symprec=symprec)
if (sg is None):
return None
sg_no = int(sg[(sg.find('(') + 1):sg.find(')')])
spacegroup = Spacegroup(sg_no)
return spacegroup | 254,699,370,758,858,100 | Return spacegroup that suits to the atoms' cell structure and to the given
atomic_numbers (not necessary the real ones, they can be just ''labels''). | src/ase2sprkkr/sprkkr/sprkkr_atoms.py | compute_spacegroup_for_atomic_numbers | ase2sprkkr/ase2sprkkr | python | def compute_spacegroup_for_atomic_numbers(self, atomic_numbers=None, symprec=1e-05):
" Return spacegroup that suits to the atoms' cell structure and to the given\n atomic_numbers (not necessary the real ones, they can be just labels).\n "
atomic_numbers = (atomic_numbers if (atomic_numbers is not None) else self.get_atomic_numbers())
sg = spglib.get_spacegroup((self.get_cell(), self.get_scaled_positions(), atomic_numbers), symprec=symprec)
if (sg is None):
return None
sg_no = int(sg[(sg.find('(') + 1):sg.find(')')])
spacegroup = Spacegroup(sg_no)
return spacegroup |
def compute_sites_symmetry(self, spacegroup=None, atomic_numbers=None, consider_old=False, symprec=1e-05):
' SPRKKR has some properties shared by all by-symmetry-equal sites.\n This method initializes _sites property, that holds these properties:\n makes identical all the atoms on the "symmetry identical positions" with\n the same atomic number.\n\n The method is called automatically when the sites property is firstly accessed.\n The effect of the method is the nearly same as setting the symmetry property.\n However, setting the symmetry property on an \'already symmetrized\' object has\n no effect, while this methods always recompute the sites property.\n\n Parameters\n ----------\n spacegroup: Spacegroup\n If not None, the given spacegroup is used for determining the symmetry,\n instead of the one determined by cell geometry.\n\n atomic_numbers: [ int ]\n Atomic numbers used to determine the spacegroup (if it is not given) to compute\n the symmetry. The atomic numbers can be \'\'virtual\'\', just to denote the equivalence\n of the sites.\n The array should have the same length as the number of atoms in the unit cell.\n If None, self.symbols are used.\n\n consider_old: bool\n If True, and _unique_sites is not None, the non-symmetry-equivalent sites won\'t\n be equivalent in the newly computed symmetry.\n\n symprec: float\n A threshold for spatial error for the symmetry computing. See spglib.get_spacegroup\n\n '
self._symmetry = True
SPRKKRAtoms._compute_sites_symmetry(**locals()) | 8,060,052,139,602,305,000 | SPRKKR has some properties shared by all by-symmetry-equal sites.
This method initializes _sites property, that holds these properties:
makes identical all the atoms on the "symmetry identical positions" with
the same atomic number.
The method is called automatically when the sites property is firstly accessed.
The effect of the method is the nearly same as setting the symmetry property.
However, setting the symmetry property on an 'already symmetrized' object has
no effect, while this methods always recompute the sites property.
Parameters
----------
spacegroup: Spacegroup
If not None, the given spacegroup is used for determining the symmetry,
instead of the one determined by cell geometry.
atomic_numbers: [ int ]
Atomic numbers used to determine the spacegroup (if it is not given) to compute
the symmetry. The atomic numbers can be ''virtual'', just to denote the equivalence
of the sites.
The array should have the same length as the number of atoms in the unit cell.
If None, self.symbols are used.
consider_old: bool
If True, and _unique_sites is not None, the non-symmetry-equivalent sites won't
be equivalent in the newly computed symmetry.
symprec: float
A threshold for spatial error for the symmetry computing. See spglib.get_spacegroup | src/ase2sprkkr/sprkkr/sprkkr_atoms.py | compute_sites_symmetry | ase2sprkkr/ase2sprkkr | python | def compute_sites_symmetry(self, spacegroup=None, atomic_numbers=None, consider_old=False, symprec=1e-05):
' SPRKKR has some properties shared by all by-symmetry-equal sites.\n This method initializes _sites property, that holds these properties:\n makes identical all the atoms on the "symmetry identical positions" with\n the same atomic number.\n\n The method is called automatically when the sites property is firstly accessed.\n The effect of the method is the nearly same as setting the symmetry property.\n However, setting the symmetry property on an \'already symmetrized\' object has\n no effect, while this methods always recompute the sites property.\n\n Parameters\n ----------\n spacegroup: Spacegroup\n If not None, the given spacegroup is used for determining the symmetry,\n instead of the one determined by cell geometry.\n\n atomic_numbers: [ int ]\n Atomic numbers used to determine the spacegroup (if it is not given) to compute\n the symmetry. The atomic numbers can be \'\'virtual\'\', just to denote the equivalence\n of the sites.\n The array should have the same length as the number of atoms in the unit cell.\n If None, self.symbols are used.\n\n consider_old: bool\n If True, and _unique_sites is not None, the non-symmetry-equivalent sites won\'t\n be equivalent in the newly computed symmetry.\n\n symprec: float\n A threshold for spatial error for the symmetry computing. See spglib.get_spacegroup\n\n '
self._symmetry = True
SPRKKRAtoms._compute_sites_symmetry(**locals()) |
def _compute_sites_symmetry(self, spacegroup=None, atomic_numbers=None, consider_old=False, symprec=1e-05):
' See compute_sites_symmetry - this metod does just the same, but it does not set the symmetry property.'
occupation = self.info.get('occupancy', {})
if ((not spacegroup) and self._symmetry):
if atomic_numbers:
mapping = UniqueValuesMapping(atomic_numbers)
else:
mapping = UniqueValuesMapping(self.get_atomic_numbers())
if (consider_old and self._unique_sites):
mapping = mapping.merge(self._unique_sites)
if occupation:
def gen_occ():
for i in range(len(mapping)):
val = occupation.get(i, None)
if (val is None):
(yield val)
else:
(yield tuple(((k, val[k]) for k in val)))
mapping = mapping.merge(gen_occ())
spacegroup = self.compute_spacegroup_for_atomic_numbers(mapping.mapping, symprec=symprec)
self.info['spacegroup'] = spacegroup
if (not spacegroup):
return self.cancel_sites_symmetry()
tags = spacegroup.tag_sites(self.get_scaled_positions())
mapping = mapping.merge(tags)
tags = mapping.mapping
sites = np.empty(len(tags), dtype=object)
(uniq, umap) = np.unique(tags, return_inverse=True)
used = set()
for i in range(len(uniq)):
index = (umap == i)
if (self._unique_sites is not None):
possible = (i for i in self._unique_sites[index])
site = next(filter(None, possible), None)
if (site in used):
site = site.copy()
else:
used.add(site)
else:
site = None
if (not site):
symbol = self.symbols[numpy_index(umap, i)]
for ai in np.where(index)[0]:
if ((ai in occupation) and occupation[ai]):
symbol = occupation[ai]
site = Site(self, symbol)
sites[index] = site
self.sites = sites | -7,394,917,638,340,702,000 | See compute_sites_symmetry - this metod does just the same, but it does not set the symmetry property. | src/ase2sprkkr/sprkkr/sprkkr_atoms.py | _compute_sites_symmetry | ase2sprkkr/ase2sprkkr | python | def _compute_sites_symmetry(self, spacegroup=None, atomic_numbers=None, consider_old=False, symprec=1e-05):
' '
occupation = self.info.get('occupancy', {})
if ((not spacegroup) and self._symmetry):
if atomic_numbers:
mapping = UniqueValuesMapping(atomic_numbers)
else:
mapping = UniqueValuesMapping(self.get_atomic_numbers())
if (consider_old and self._unique_sites):
mapping = mapping.merge(self._unique_sites)
if occupation:
def gen_occ():
for i in range(len(mapping)):
val = occupation.get(i, None)
if (val is None):
(yield val)
else:
(yield tuple(((k, val[k]) for k in val)))
mapping = mapping.merge(gen_occ())
spacegroup = self.compute_spacegroup_for_atomic_numbers(mapping.mapping, symprec=symprec)
self.info['spacegroup'] = spacegroup
if (not spacegroup):
return self.cancel_sites_symmetry()
tags = spacegroup.tag_sites(self.get_scaled_positions())
mapping = mapping.merge(tags)
tags = mapping.mapping
sites = np.empty(len(tags), dtype=object)
(uniq, umap) = np.unique(tags, return_inverse=True)
used = set()
for i in range(len(uniq)):
index = (umap == i)
if (self._unique_sites is not None):
possible = (i for i in self._unique_sites[index])
site = next(filter(None, possible), None)
if (site in used):
site = site.copy()
else:
used.add(site)
else:
site = None
if (not site):
symbol = self.symbols[numpy_index(umap, i)]
for ai in np.where(index)[0]:
if ((ai in occupation) and occupation[ai]):
symbol = occupation[ai]
site = Site(self, symbol)
sites[index] = site
self.sites = sites |
def cancel_sites_symmetry(self):
' Cancel the use of symmetry in the structure, i.e., makes the Site object\n uniqe (not shared) for each atomic site.\n\n Calling this method is nearly equivalent to the setting the symmetry property\n to False, however, this method always recompute the sites object, while\n setting symmetry=False recomputes the sites property only if it was previously\n set to False.\n '
self._symmetry = False
self._cancel_sites_symmetry() | -1,303,938,581,556,577,000 | Cancel the use of symmetry in the structure, i.e., makes the Site object
uniqe (not shared) for each atomic site.
Calling this method is nearly equivalent to the setting the symmetry property
to False, however, this method always recompute the sites object, while
setting symmetry=False recomputes the sites property only if it was previously
set to False. | src/ase2sprkkr/sprkkr/sprkkr_atoms.py | cancel_sites_symmetry | ase2sprkkr/ase2sprkkr | python | def cancel_sites_symmetry(self):
' Cancel the use of symmetry in the structure, i.e., makes the Site object\n uniqe (not shared) for each atomic site.\n\n Calling this method is nearly equivalent to the setting the symmetry property\n to False, however, this method always recompute the sites object, while\n setting symmetry=False recomputes the sites property only if it was previously\n set to False.\n '
self._symmetry = False
self._cancel_sites_symmetry() |
def _cancel_sites_symmetry(self):
' See cancel_sites_symmetry - this metod does just the same, but it does not set the symmetry property.'
sites = np.empty(len(self), dtype=object)
used = set()
occupation = self.info.get('occupancy', {})
for i in range(len(self)):
if (self._unique_sites is not None):
site = self._unique_sites[i]
if (site in used):
site = site.copy()
else:
used.add(site)
else:
symbol = (occupation[i] if ((i in occupation) and occupation[i]) else self.symbols[i])
site = Site(self, symbol)
sites[i] = site
self.sites = sites | 7,062,645,137,594,079,000 | See cancel_sites_symmetry - this metod does just the same, but it does not set the symmetry property. | src/ase2sprkkr/sprkkr/sprkkr_atoms.py | _cancel_sites_symmetry | ase2sprkkr/ase2sprkkr | python | def _cancel_sites_symmetry(self):
' '
sites = np.empty(len(self), dtype=object)
used = set()
occupation = self.info.get('occupancy', {})
for i in range(len(self)):
if (self._unique_sites is not None):
site = self._unique_sites[i]
if (site in used):
site = site.copy()
else:
used.add(site)
else:
symbol = (occupation[i] if ((i in occupation) and occupation[i]) else self.symbols[i])
site = Site(self, symbol)
sites[i] = site
self.sites = sites |
@property
def sites(self):
' The sites property holds all the information for the SPR-KKR package:\n atomic types (including number of semicore and valence electrons),\n occupancy, symmetries, meshes...\n Some of the properties are stored in the ASE atoms properties\n (e.g. occupancy, atomic symbol), however, ASE is not able to hold them\n all and/or to describe fully the SPR-KKR options; thus, these properties\n are hold in this array.\n\n The changes made on this array are reflected (as is possible)\n to the ASE properties, but the opposite does not hold - to reflect the changes\n in these properties please create a new Atoms object with given properties.\n '
if (self._unique_sites is None):
self._compute_sites_symmetry()
return self._unique_sites | -5,191,896,333,846,247,000 | The sites property holds all the information for the SPR-KKR package:
atomic types (including number of semicore and valence electrons),
occupancy, symmetries, meshes...
Some of the properties are stored in the ASE atoms properties
(e.g. occupancy, atomic symbol), however, ASE is not able to hold them
all and/or to describe fully the SPR-KKR options; thus, these properties
are hold in this array.
The changes made on this array are reflected (as is possible)
to the ASE properties, but the opposite does not hold - to reflect the changes
in these properties please create a new Atoms object with given properties. | src/ase2sprkkr/sprkkr/sprkkr_atoms.py | sites | ase2sprkkr/ase2sprkkr | python | @property
def sites(self):
' The sites property holds all the information for the SPR-KKR package:\n atomic types (including number of semicore and valence electrons),\n occupancy, symmetries, meshes...\n Some of the properties are stored in the ASE atoms properties\n (e.g. occupancy, atomic symbol), however, ASE is not able to hold them\n all and/or to describe fully the SPR-KKR options; thus, these properties\n are hold in this array.\n\n The changes made on this array are reflected (as is possible)\n to the ASE properties, but the opposite does not hold - to reflect the changes\n in these properties please create a new Atoms object with given properties.\n '
if (self._unique_sites is None):
self._compute_sites_symmetry()
return self._unique_sites |
@sites.setter
def sites(self, v):
' Set the sites property and update all other dependent\n properties (symbols, occupancy) according to the sites '
an = np.zeros(len(v), dtype=int)
occ = {}
for (i, j) in enumerate(v):
occ[i] = j.occupation.as_dict
an[i] = j.occupation.primary_atomic_number
self.set_atomic_numbers(an)
self.info['occupancy'] = occ
self._unique_sites = v | 2,869,206,069,894,840,000 | Set the sites property and update all other dependent
properties (symbols, occupancy) according to the sites | src/ase2sprkkr/sprkkr/sprkkr_atoms.py | sites | ase2sprkkr/ase2sprkkr | python | @sites.setter
def sites(self, v):
' Set the sites property and update all other dependent\n properties (symbols, occupancy) according to the sites '
an = np.zeros(len(v), dtype=int)
occ = {}
for (i, j) in enumerate(v):
occ[i] = j.occupation.as_dict
an[i] = j.occupation.primary_atomic_number
self.set_atomic_numbers(an)
self.info['occupancy'] = occ
self._unique_sites = v |
def upload_file_to_shock(self, file_path, token):
'\n Use HTTP multi-part POST to save a file to a SHOCK instance.\n '
if (token is None):
raise Exception('Authentication token required!')
header = {'Authorization': 'Oauth {0}'.format(token)}
if (file_path is None):
raise Exception('No file given for upload to SHOCK!')
with open(os.path.abspath(file_path), 'rb') as data_file:
files = {'upload': data_file}
response = requests.post((self.shockURL + '/node'), headers=header, files=files, stream=True, allow_redirects=True)
self.check_shock_response(response, 'Error trying to upload contig FASTA file {} to Shock: '.format(file_path))
return response.json()['data'] | -7,824,875,898,772,669,000 | Use HTTP multi-part POST to save a file to a SHOCK instance. | lib/kb_SPAdes/kb_SPAdesImpl.py | upload_file_to_shock | mclark58/kb_SPAdes | python | def upload_file_to_shock(self, file_path, token):
'\n \n '
if (token is None):
raise Exception('Authentication token required!')
header = {'Authorization': 'Oauth {0}'.format(token)}
if (file_path is None):
raise Exception('No file given for upload to SHOCK!')
with open(os.path.abspath(file_path), 'rb') as data_file:
files = {'upload': data_file}
response = requests.post((self.shockURL + '/node'), headers=header, files=files, stream=True, allow_redirects=True)
self.check_shock_response(response, 'Error trying to upload contig FASTA file {} to Shock: '.format(file_path))
return response.json()['data'] |
def run_SPAdes(self, ctx, params):
'\n Run SPAdes on paired end libraries\n :param params: instance of type "SPAdesParams" (Input parameters for\n running SPAdes. workspace_name - the name of the workspace from\n which to take input and store output. output_contigset_name - the\n name of the output contigset read_libraries - a list of Illumina\n PairedEndLibrary files in FASTQ or BAM format. dna_source -\n (optional) the source of the DNA used for sequencing\n \'single_cell\': DNA amplified from a single cell via MDA anything\n else: Standard DNA sample from multiple cells. Default value is\n None. min_contig_length - (optional) integer to filter out contigs\n with length < min_contig_length from the SPAdes output. Default\n value is 0 implying no filter. kmer_sizes - (optional) K-mer\n sizes, Default values: 33, 55, 77, 99, 127 (all values must be\n odd, less than 128 and listed in ascending order) In the absence\n of these values, K values are automatically selected.\n skip_error_correction - (optional) Assembly only (No error\n correction). By default this is disabled.) -> structure: parameter\n "workspace_name" of String, parameter "output_contigset_name" of\n String, parameter "read_libraries" of list of type\n "paired_end_lib" (The workspace object name of a PairedEndLibrary\n file, whether of the KBaseAssembly or KBaseFile type.), parameter\n "dna_source" of String, parameter "min_contig_length" of Long,\n parameter "kmer_sizes" of list of Long, parameter\n "skip_error_correction" of type "bool" (A boolean. 0 = false,\n anything else = true.)\n :returns: instance of type "SPAdesOutput" (Output parameters for\n SPAdes run. report_name - the name of the KBaseReport.Report\n workspace object. report_ref - the workspace reference of the\n report.) -> structure: parameter "report_name" of String,\n parameter "report_ref" of String\n '
self.log(('Running run_SPAdes with params:\n' + pformat(params)))
token = ctx['token']
self.process_params(params)
wsname = params[self.PARAM_IN_WS]
obj_ids = []
for r in params[self.PARAM_IN_LIB]:
obj_ids.append({'ref': (r if ('/' in r) else ((wsname + '/') + r))})
ws = Workspace(self.workspaceURL, token=token)
ws_info = ws.get_object_info_new({'objects': obj_ids})
reads_params = []
reftoname = {}
for (wsi, oid) in zip(ws_info, obj_ids):
ref = oid['ref']
reads_params.append(ref)
obj_name = wsi[1]
reftoname[ref] = ((wsi[7] + '/') + obj_name)
readcli = ReadsUtils(self.callbackURL, token=ctx['token'])
typeerr = ((('Supported types: KBaseFile.SingleEndLibrary ' + 'KBaseFile.PairedEndLibrary ') + 'KBaseAssembly.SingleEndLibrary ') + 'KBaseAssembly.PairedEndLibrary')
try:
reads = readcli.download_reads({'read_libraries': reads_params, 'interleaved': 'false', 'gzipped': None})['files']
except ServerError as se:
self.log('logging stacktrace from dynamic client error')
self.log(se.data)
if (typeerr in se.message):
prefix = se.message.split('.')[0]
raise ValueError((((prefix + '. Only the types ') + 'KBaseAssembly.PairedEndLibrary ') + 'and KBaseFile.PairedEndLibrary are supported'))
else:
raise
self.log(('Got reads data from converter:\n' + pformat(reads)))
phred_type = self.check_reads(params, reads, reftoname)
reads_data = []
for ref in reads:
reads_name = reftoname[ref]
f = reads[ref]['files']
seq_tech = reads[ref]['sequencing_tech']
if (f['type'] == 'interleaved'):
reads_data.append({'fwd_file': f['fwd'], 'type': 'paired', 'seq_tech': seq_tech})
elif (f['type'] == 'paired'):
reads_data.append({'fwd_file': f['fwd'], 'rev_file': f['rev'], 'type': 'paired', 'seq_tech': seq_tech})
elif (f['type'] == 'single'):
reads_data.append({'fwd_file': f['fwd'], 'type': 'single', 'seq_tech': seq_tech})
else:
raise ValueError(('Something is very wrong with read lib' + reads_name))
kmer_sizes = None
if ((self.PARAM_IN_KMER_SIZES in params) and (params[self.PARAM_IN_KMER_SIZES] is not None)):
if (len(params[self.PARAM_IN_KMER_SIZES]) > 0):
kmer_sizes = ','.join((str(num) for num in params[self.PARAM_IN_KMER_SIZES]))
skip_error_correction = 0
if ((self.PARAM_IN_SKIP_ERR_CORRECT in params) and (params[self.PARAM_IN_SKIP_ERR_CORRECT] is not None)):
if (params[self.PARAM_IN_SKIP_ERR_CORRECT] == 1):
skip_error_correction = 1
spades_out = self.exec_spades(params[self.PARAM_IN_DNA_SOURCE], reads_data, phred_type, kmer_sizes, skip_error_correction)
self.log(('SPAdes output dir: ' + spades_out))
output_contigs = os.path.join(spades_out, 'scaffolds.fasta')
self.log('Uploading FASTA file to Assembly')
assemblyUtil = AssemblyUtil(self.callbackURL, token=ctx['token'], service_ver='release')
if (params.get('min_contig_length', 0) > 0):
assemblyUtil.save_assembly_from_fasta({'file': {'path': output_contigs}, 'workspace_name': wsname, 'assembly_name': params[self.PARAM_IN_CS_NAME], 'min_contig_length': params['min_contig_length']})
(report_name, report_ref) = self.load_report((output_contigs + '.filtered.fa'), params, wsname)
else:
assemblyUtil.save_assembly_from_fasta({'file': {'path': output_contigs}, 'workspace_name': wsname, 'assembly_name': params[self.PARAM_IN_CS_NAME]})
(report_name, report_ref) = self.load_report(output_contigs, params, wsname)
output = {'report_name': report_name, 'report_ref': report_ref}
if (not isinstance(output, dict)):
raise ValueError(('Method run_SPAdes return value ' + 'output is not type dict as required.'))
return [output] | -7,648,011,864,929,356,000 | Run SPAdes on paired end libraries
:param params: instance of type "SPAdesParams" (Input parameters for
running SPAdes. workspace_name - the name of the workspace from
which to take input and store output. output_contigset_name - the
name of the output contigset read_libraries - a list of Illumina
PairedEndLibrary files in FASTQ or BAM format. dna_source -
(optional) the source of the DNA used for sequencing
'single_cell': DNA amplified from a single cell via MDA anything
else: Standard DNA sample from multiple cells. Default value is
None. min_contig_length - (optional) integer to filter out contigs
with length < min_contig_length from the SPAdes output. Default
value is 0 implying no filter. kmer_sizes - (optional) K-mer
sizes, Default values: 33, 55, 77, 99, 127 (all values must be
odd, less than 128 and listed in ascending order) In the absence
of these values, K values are automatically selected.
skip_error_correction - (optional) Assembly only (No error
correction). By default this is disabled.) -> structure: parameter
"workspace_name" of String, parameter "output_contigset_name" of
String, parameter "read_libraries" of list of type
"paired_end_lib" (The workspace object name of a PairedEndLibrary
file, whether of the KBaseAssembly or KBaseFile type.), parameter
"dna_source" of String, parameter "min_contig_length" of Long,
parameter "kmer_sizes" of list of Long, parameter
"skip_error_correction" of type "bool" (A boolean. 0 = false,
anything else = true.)
:returns: instance of type "SPAdesOutput" (Output parameters for
SPAdes run. report_name - the name of the KBaseReport.Report
workspace object. report_ref - the workspace reference of the
report.) -> structure: parameter "report_name" of String,
parameter "report_ref" of String | lib/kb_SPAdes/kb_SPAdesImpl.py | run_SPAdes | mclark58/kb_SPAdes | python | def run_SPAdes(self, ctx, params):
'\n Run SPAdes on paired end libraries\n :param params: instance of type "SPAdesParams" (Input parameters for\n running SPAdes. workspace_name - the name of the workspace from\n which to take input and store output. output_contigset_name - the\n name of the output contigset read_libraries - a list of Illumina\n PairedEndLibrary files in FASTQ or BAM format. dna_source -\n (optional) the source of the DNA used for sequencing\n \'single_cell\': DNA amplified from a single cell via MDA anything\n else: Standard DNA sample from multiple cells. Default value is\n None. min_contig_length - (optional) integer to filter out contigs\n with length < min_contig_length from the SPAdes output. Default\n value is 0 implying no filter. kmer_sizes - (optional) K-mer\n sizes, Default values: 33, 55, 77, 99, 127 (all values must be\n odd, less than 128 and listed in ascending order) In the absence\n of these values, K values are automatically selected.\n skip_error_correction - (optional) Assembly only (No error\n correction). By default this is disabled.) -> structure: parameter\n "workspace_name" of String, parameter "output_contigset_name" of\n String, parameter "read_libraries" of list of type\n "paired_end_lib" (The workspace object name of a PairedEndLibrary\n file, whether of the KBaseAssembly or KBaseFile type.), parameter\n "dna_source" of String, parameter "min_contig_length" of Long,\n parameter "kmer_sizes" of list of Long, parameter\n "skip_error_correction" of type "bool" (A boolean. 0 = false,\n anything else = true.)\n :returns: instance of type "SPAdesOutput" (Output parameters for\n SPAdes run. report_name - the name of the KBaseReport.Report\n workspace object. report_ref - the workspace reference of the\n report.) -> structure: parameter "report_name" of String,\n parameter "report_ref" of String\n '
self.log(('Running run_SPAdes with params:\n' + pformat(params)))
token = ctx['token']
self.process_params(params)
wsname = params[self.PARAM_IN_WS]
obj_ids = []
for r in params[self.PARAM_IN_LIB]:
obj_ids.append({'ref': (r if ('/' in r) else ((wsname + '/') + r))})
ws = Workspace(self.workspaceURL, token=token)
ws_info = ws.get_object_info_new({'objects': obj_ids})
reads_params = []
reftoname = {}
for (wsi, oid) in zip(ws_info, obj_ids):
ref = oid['ref']
reads_params.append(ref)
obj_name = wsi[1]
reftoname[ref] = ((wsi[7] + '/') + obj_name)
readcli = ReadsUtils(self.callbackURL, token=ctx['token'])
typeerr = ((('Supported types: KBaseFile.SingleEndLibrary ' + 'KBaseFile.PairedEndLibrary ') + 'KBaseAssembly.SingleEndLibrary ') + 'KBaseAssembly.PairedEndLibrary')
try:
reads = readcli.download_reads({'read_libraries': reads_params, 'interleaved': 'false', 'gzipped': None})['files']
except ServerError as se:
self.log('logging stacktrace from dynamic client error')
self.log(se.data)
if (typeerr in se.message):
prefix = se.message.split('.')[0]
raise ValueError((((prefix + '. Only the types ') + 'KBaseAssembly.PairedEndLibrary ') + 'and KBaseFile.PairedEndLibrary are supported'))
else:
raise
self.log(('Got reads data from converter:\n' + pformat(reads)))
phred_type = self.check_reads(params, reads, reftoname)
reads_data = []
for ref in reads:
reads_name = reftoname[ref]
f = reads[ref]['files']
seq_tech = reads[ref]['sequencing_tech']
if (f['type'] == 'interleaved'):
reads_data.append({'fwd_file': f['fwd'], 'type': 'paired', 'seq_tech': seq_tech})
elif (f['type'] == 'paired'):
reads_data.append({'fwd_file': f['fwd'], 'rev_file': f['rev'], 'type': 'paired', 'seq_tech': seq_tech})
elif (f['type'] == 'single'):
reads_data.append({'fwd_file': f['fwd'], 'type': 'single', 'seq_tech': seq_tech})
else:
raise ValueError(('Something is very wrong with read lib' + reads_name))
kmer_sizes = None
if ((self.PARAM_IN_KMER_SIZES in params) and (params[self.PARAM_IN_KMER_SIZES] is not None)):
if (len(params[self.PARAM_IN_KMER_SIZES]) > 0):
kmer_sizes = ','.join((str(num) for num in params[self.PARAM_IN_KMER_SIZES]))
skip_error_correction = 0
if ((self.PARAM_IN_SKIP_ERR_CORRECT in params) and (params[self.PARAM_IN_SKIP_ERR_CORRECT] is not None)):
if (params[self.PARAM_IN_SKIP_ERR_CORRECT] == 1):
skip_error_correction = 1
spades_out = self.exec_spades(params[self.PARAM_IN_DNA_SOURCE], reads_data, phred_type, kmer_sizes, skip_error_correction)
self.log(('SPAdes output dir: ' + spades_out))
output_contigs = os.path.join(spades_out, 'scaffolds.fasta')
self.log('Uploading FASTA file to Assembly')
assemblyUtil = AssemblyUtil(self.callbackURL, token=ctx['token'], service_ver='release')
if (params.get('min_contig_length', 0) > 0):
assemblyUtil.save_assembly_from_fasta({'file': {'path': output_contigs}, 'workspace_name': wsname, 'assembly_name': params[self.PARAM_IN_CS_NAME], 'min_contig_length': params['min_contig_length']})
(report_name, report_ref) = self.load_report((output_contigs + '.filtered.fa'), params, wsname)
else:
assemblyUtil.save_assembly_from_fasta({'file': {'path': output_contigs}, 'workspace_name': wsname, 'assembly_name': params[self.PARAM_IN_CS_NAME]})
(report_name, report_ref) = self.load_report(output_contigs, params, wsname)
output = {'report_name': report_name, 'report_ref': report_ref}
if (not isinstance(output, dict)):
raise ValueError(('Method run_SPAdes return value ' + 'output is not type dict as required.'))
return [output] |
def run_HybridSPAdes(self, ctx, params):
'\n Run HybridSPAdes on paired end libraries with PacBio CLR and Oxford Nanopore reads\n :param params: instance of type "HybridSPAdesParams" (------To run\n HybridSPAdes 3.13.0 you need at least one library of the following\n types:------ 1) Illumina paired-end/high-quality\n mate-pairs/unpaired reads 2) IonTorrent paired-end/high-quality\n mate-pairs/unpaired reads 3) PacBio CCS reads Version 3.13.0 of\n SPAdes supports paired-end reads, mate-pairs and unpaired reads.\n SPAdes can take as input several paired-end and mate-pair\n libraries simultaneously. workspace_name - the name of the\n workspace from which to take input and store output.\n output_contigset_name - the name of the output contigset\n read_libraries - a list of Illumina or IonTorrent\n paired-end/high-quality mate-pairs/unpaired reads\n long_reads_libraries - a list of PacBio, Oxford Nanopore Sanger\n reads and/or additional contigs dna_source - the source of the DNA\n used for sequencing \'single_cell\': DNA amplified from a single\n cell via MDA anything else: Standard DNA sample from multiple\n cells. Default value is None. pipeline_options - a list of string\n specifying how the SPAdes pipeline should be run kmer_sizes -\n (optional) K-mer sizes, Default values: 21, 33, 55, 77, 99, 127\n (all values must be odd, less than 128 and listed in ascending\n order) In the absence of these values, K values are automatically\n selected. min_contig_length - integer to filter out contigs with\n length < min_contig_length from the HybridSPAdes output. Default\n value is 0 implying no filter. @optional dna_source @optional\n pipeline_options @optional kmer_sizes @optional min_contig_length)\n -> structure: parameter "workspace_name" of String, parameter\n "output_contigset_name" of String, parameter "reads_libraries" of\n list of type "ReadsParams" (parameter groups--define attributes\n for specifying inputs with YAML data set file (advanced) The\n following attributes are available: - orientation ("fr", "rf",\n "ff") - type ("paired-end", "mate-pairs", "hq-mate-pairs",\n "single", "pacbio", "nanopore", "sanger", "trusted-contigs",\n "untrusted-contigs") - interlaced reads (comma-separated list of\n files with interlaced reads) - left reads (comma-separated list of\n files with left reads) - right reads (comma-separated list of\n files with right reads) - single reads (comma-separated list of\n files with single reads or unpaired reads from paired library) -\n merged reads (comma-separated list of files with merged reads)) ->\n structure: parameter "lib_ref" of type "obj_ref" (An X/Y/Z style\n KBase object reference), parameter "orientation" of String,\n parameter "lib_type" of String, parameter "long_reads_libraries"\n of list of type "LongReadsParams" -> structure: parameter\n "long_reads_ref" of type "obj_ref" (An X/Y/Z style KBase object\n reference), parameter "long_reads_type" of String, parameter\n "dna_source" of String, parameter "pipeline_options" of list of\n String, parameter "kmer_sizes" of list of Long, parameter\n "min_contig_length" of Long, parameter "create_report" of type\n "bool" (A boolean. 0 = false, anything else = true.)\n :returns: instance of type "SPAdesOutput" (Output parameters for\n SPAdes run. report_name - the name of the KBaseReport.Report\n workspace object. report_ref - the workspace reference of the\n report.) -> structure: parameter "report_name" of String,\n parameter "report_ref" of String\n '
self.log('Running run_HybridSPAdes with params:\n{}'.format(json.dumps(params, indent=1)))
spades_assembler = SPAdesAssembler(self.cfg, ctx.provenance())
output = spades_assembler.run_hybrid_spades(params)
if (not isinstance(output, dict)):
raise ValueError(('Method run_HybridSPAdes return value ' + 'output is not type dict as required.'))
return [output] | -3,089,188,004,854,902,000 | Run HybridSPAdes on paired end libraries with PacBio CLR and Oxford Nanopore reads
:param params: instance of type "HybridSPAdesParams" (------To run
HybridSPAdes 3.13.0 you need at least one library of the following
types:------ 1) Illumina paired-end/high-quality
mate-pairs/unpaired reads 2) IonTorrent paired-end/high-quality
mate-pairs/unpaired reads 3) PacBio CCS reads Version 3.13.0 of
SPAdes supports paired-end reads, mate-pairs and unpaired reads.
SPAdes can take as input several paired-end and mate-pair
libraries simultaneously. workspace_name - the name of the
workspace from which to take input and store output.
output_contigset_name - the name of the output contigset
read_libraries - a list of Illumina or IonTorrent
paired-end/high-quality mate-pairs/unpaired reads
long_reads_libraries - a list of PacBio, Oxford Nanopore Sanger
reads and/or additional contigs dna_source - the source of the DNA
used for sequencing 'single_cell': DNA amplified from a single
cell via MDA anything else: Standard DNA sample from multiple
cells. Default value is None. pipeline_options - a list of string
specifying how the SPAdes pipeline should be run kmer_sizes -
(optional) K-mer sizes, Default values: 21, 33, 55, 77, 99, 127
(all values must be odd, less than 128 and listed in ascending
order) In the absence of these values, K values are automatically
selected. min_contig_length - integer to filter out contigs with
length < min_contig_length from the HybridSPAdes output. Default
value is 0 implying no filter. @optional dna_source @optional
pipeline_options @optional kmer_sizes @optional min_contig_length)
-> structure: parameter "workspace_name" of String, parameter
"output_contigset_name" of String, parameter "reads_libraries" of
list of type "ReadsParams" (parameter groups--define attributes
for specifying inputs with YAML data set file (advanced) The
following attributes are available: - orientation ("fr", "rf",
"ff") - type ("paired-end", "mate-pairs", "hq-mate-pairs",
"single", "pacbio", "nanopore", "sanger", "trusted-contigs",
"untrusted-contigs") - interlaced reads (comma-separated list of
files with interlaced reads) - left reads (comma-separated list of
files with left reads) - right reads (comma-separated list of
files with right reads) - single reads (comma-separated list of
files with single reads or unpaired reads from paired library) -
merged reads (comma-separated list of files with merged reads)) ->
structure: parameter "lib_ref" of type "obj_ref" (An X/Y/Z style
KBase object reference), parameter "orientation" of String,
parameter "lib_type" of String, parameter "long_reads_libraries"
of list of type "LongReadsParams" -> structure: parameter
"long_reads_ref" of type "obj_ref" (An X/Y/Z style KBase object
reference), parameter "long_reads_type" of String, parameter
"dna_source" of String, parameter "pipeline_options" of list of
String, parameter "kmer_sizes" of list of Long, parameter
"min_contig_length" of Long, parameter "create_report" of type
"bool" (A boolean. 0 = false, anything else = true.)
:returns: instance of type "SPAdesOutput" (Output parameters for
SPAdes run. report_name - the name of the KBaseReport.Report
workspace object. report_ref - the workspace reference of the
report.) -> structure: parameter "report_name" of String,
parameter "report_ref" of String | lib/kb_SPAdes/kb_SPAdesImpl.py | run_HybridSPAdes | mclark58/kb_SPAdes | python | def run_HybridSPAdes(self, ctx, params):
'\n Run HybridSPAdes on paired end libraries with PacBio CLR and Oxford Nanopore reads\n :param params: instance of type "HybridSPAdesParams" (------To run\n HybridSPAdes 3.13.0 you need at least one library of the following\n types:------ 1) Illumina paired-end/high-quality\n mate-pairs/unpaired reads 2) IonTorrent paired-end/high-quality\n mate-pairs/unpaired reads 3) PacBio CCS reads Version 3.13.0 of\n SPAdes supports paired-end reads, mate-pairs and unpaired reads.\n SPAdes can take as input several paired-end and mate-pair\n libraries simultaneously. workspace_name - the name of the\n workspace from which to take input and store output.\n output_contigset_name - the name of the output contigset\n read_libraries - a list of Illumina or IonTorrent\n paired-end/high-quality mate-pairs/unpaired reads\n long_reads_libraries - a list of PacBio, Oxford Nanopore Sanger\n reads and/or additional contigs dna_source - the source of the DNA\n used for sequencing \'single_cell\': DNA amplified from a single\n cell via MDA anything else: Standard DNA sample from multiple\n cells. Default value is None. pipeline_options - a list of string\n specifying how the SPAdes pipeline should be run kmer_sizes -\n (optional) K-mer sizes, Default values: 21, 33, 55, 77, 99, 127\n (all values must be odd, less than 128 and listed in ascending\n order) In the absence of these values, K values are automatically\n selected. min_contig_length - integer to filter out contigs with\n length < min_contig_length from the HybridSPAdes output. Default\n value is 0 implying no filter. @optional dna_source @optional\n pipeline_options @optional kmer_sizes @optional min_contig_length)\n -> structure: parameter "workspace_name" of String, parameter\n "output_contigset_name" of String, parameter "reads_libraries" of\n list of type "ReadsParams" (parameter groups--define attributes\n for specifying inputs with YAML data set file (advanced) The\n following attributes are available: - orientation ("fr", "rf",\n "ff") - type ("paired-end", "mate-pairs", "hq-mate-pairs",\n "single", "pacbio", "nanopore", "sanger", "trusted-contigs",\n "untrusted-contigs") - interlaced reads (comma-separated list of\n files with interlaced reads) - left reads (comma-separated list of\n files with left reads) - right reads (comma-separated list of\n files with right reads) - single reads (comma-separated list of\n files with single reads or unpaired reads from paired library) -\n merged reads (comma-separated list of files with merged reads)) ->\n structure: parameter "lib_ref" of type "obj_ref" (An X/Y/Z style\n KBase object reference), parameter "orientation" of String,\n parameter "lib_type" of String, parameter "long_reads_libraries"\n of list of type "LongReadsParams" -> structure: parameter\n "long_reads_ref" of type "obj_ref" (An X/Y/Z style KBase object\n reference), parameter "long_reads_type" of String, parameter\n "dna_source" of String, parameter "pipeline_options" of list of\n String, parameter "kmer_sizes" of list of Long, parameter\n "min_contig_length" of Long, parameter "create_report" of type\n "bool" (A boolean. 0 = false, anything else = true.)\n :returns: instance of type "SPAdesOutput" (Output parameters for\n SPAdes run. report_name - the name of the KBaseReport.Report\n workspace object. report_ref - the workspace reference of the\n report.) -> structure: parameter "report_name" of String,\n parameter "report_ref" of String\n '
self.log('Running run_HybridSPAdes with params:\n{}'.format(json.dumps(params, indent=1)))
spades_assembler = SPAdesAssembler(self.cfg, ctx.provenance())
output = spades_assembler.run_hybrid_spades(params)
if (not isinstance(output, dict)):
raise ValueError(('Method run_HybridSPAdes return value ' + 'output is not type dict as required.'))
return [output] |
def run_metaSPAdes(self, ctx, params):
'\n Run SPAdes on paired end libraries for metagenomes\n :param params: instance of type "SPAdesParams" (Input parameters for\n running SPAdes. workspace_name - the name of the workspace from\n which to take input and store output. output_contigset_name - the\n name of the output contigset read_libraries - a list of Illumina\n PairedEndLibrary files in FASTQ or BAM format. dna_source -\n (optional) the source of the DNA used for sequencing\n \'single_cell\': DNA amplified from a single cell via MDA anything\n else: Standard DNA sample from multiple cells. Default value is\n None. min_contig_length - (optional) integer to filter out contigs\n with length < min_contig_length from the SPAdes output. Default\n value is 0 implying no filter. kmer_sizes - (optional) K-mer\n sizes, Default values: 33, 55, 77, 99, 127 (all values must be\n odd, less than 128 and listed in ascending order) In the absence\n of these values, K values are automatically selected.\n skip_error_correction - (optional) Assembly only (No error\n correction). By default this is disabled.) -> structure: parameter\n "workspace_name" of String, parameter "output_contigset_name" of\n String, parameter "read_libraries" of list of type\n "paired_end_lib" (The workspace object name of a PairedEndLibrary\n file, whether of the KBaseAssembly or KBaseFile type.), parameter\n "dna_source" of String, parameter "min_contig_length" of Long,\n parameter "kmer_sizes" of list of Long, parameter\n "skip_error_correction" of type "bool" (A boolean. 0 = false,\n anything else = true.)\n :returns: instance of type "SPAdesOutput" (Output parameters for\n SPAdes run. report_name - the name of the KBaseReport.Report\n workspace object. report_ref - the workspace reference of the\n report.) -> structure: parameter "report_name" of String,\n parameter "report_ref" of String\n '
output = self.run_SPAdes(ctx, params)[0]
if (not isinstance(output, dict)):
raise ValueError(('Method run_metaSPAdes return value ' + 'output is not type dict as required.'))
return [output] | 1,842,031,156,406,198,000 | Run SPAdes on paired end libraries for metagenomes
:param params: instance of type "SPAdesParams" (Input parameters for
running SPAdes. workspace_name - the name of the workspace from
which to take input and store output. output_contigset_name - the
name of the output contigset read_libraries - a list of Illumina
PairedEndLibrary files in FASTQ or BAM format. dna_source -
(optional) the source of the DNA used for sequencing
'single_cell': DNA amplified from a single cell via MDA anything
else: Standard DNA sample from multiple cells. Default value is
None. min_contig_length - (optional) integer to filter out contigs
with length < min_contig_length from the SPAdes output. Default
value is 0 implying no filter. kmer_sizes - (optional) K-mer
sizes, Default values: 33, 55, 77, 99, 127 (all values must be
odd, less than 128 and listed in ascending order) In the absence
of these values, K values are automatically selected.
skip_error_correction - (optional) Assembly only (No error
correction). By default this is disabled.) -> structure: parameter
"workspace_name" of String, parameter "output_contigset_name" of
String, parameter "read_libraries" of list of type
"paired_end_lib" (The workspace object name of a PairedEndLibrary
file, whether of the KBaseAssembly or KBaseFile type.), parameter
"dna_source" of String, parameter "min_contig_length" of Long,
parameter "kmer_sizes" of list of Long, parameter
"skip_error_correction" of type "bool" (A boolean. 0 = false,
anything else = true.)
:returns: instance of type "SPAdesOutput" (Output parameters for
SPAdes run. report_name - the name of the KBaseReport.Report
workspace object. report_ref - the workspace reference of the
report.) -> structure: parameter "report_name" of String,
parameter "report_ref" of String | lib/kb_SPAdes/kb_SPAdesImpl.py | run_metaSPAdes | mclark58/kb_SPAdes | python | def run_metaSPAdes(self, ctx, params):
'\n Run SPAdes on paired end libraries for metagenomes\n :param params: instance of type "SPAdesParams" (Input parameters for\n running SPAdes. workspace_name - the name of the workspace from\n which to take input and store output. output_contigset_name - the\n name of the output contigset read_libraries - a list of Illumina\n PairedEndLibrary files in FASTQ or BAM format. dna_source -\n (optional) the source of the DNA used for sequencing\n \'single_cell\': DNA amplified from a single cell via MDA anything\n else: Standard DNA sample from multiple cells. Default value is\n None. min_contig_length - (optional) integer to filter out contigs\n with length < min_contig_length from the SPAdes output. Default\n value is 0 implying no filter. kmer_sizes - (optional) K-mer\n sizes, Default values: 33, 55, 77, 99, 127 (all values must be\n odd, less than 128 and listed in ascending order) In the absence\n of these values, K values are automatically selected.\n skip_error_correction - (optional) Assembly only (No error\n correction). By default this is disabled.) -> structure: parameter\n "workspace_name" of String, parameter "output_contigset_name" of\n String, parameter "read_libraries" of list of type\n "paired_end_lib" (The workspace object name of a PairedEndLibrary\n file, whether of the KBaseAssembly or KBaseFile type.), parameter\n "dna_source" of String, parameter "min_contig_length" of Long,\n parameter "kmer_sizes" of list of Long, parameter\n "skip_error_correction" of type "bool" (A boolean. 0 = false,\n anything else = true.)\n :returns: instance of type "SPAdesOutput" (Output parameters for\n SPAdes run. report_name - the name of the KBaseReport.Report\n workspace object. report_ref - the workspace reference of the\n report.) -> structure: parameter "report_name" of String,\n parameter "report_ref" of String\n '
output = self.run_SPAdes(ctx, params)[0]
if (not isinstance(output, dict)):
raise ValueError(('Method run_metaSPAdes return value ' + 'output is not type dict as required.'))
return [output] |
def init(self, var_list=None, ckpt_dir=None, ckpt_file=None, optimistic=False):
'\n :param var_list: vars for restore\n :param ckpt_dir: prefix of model files.\n :param ckpt_file: exact name of model file, priority is higher than `ckpt_dir`\n :param optimistic: only restore weights of same names with model.\n :return:\n '
assert ((var_list is None) or (len(var_list) > 0)), 'invalid var_list: {}'.format(var_list)
assert ((ckpt_dir is not None) or (ckpt_file is not None)), 'ckpt_dir and ckpt_file are both None'
self._var_list = var_list
self._restore_optimistic = optimistic
if (ckpt_file is None):
assert os.path.exists(ckpt_dir), ('invalid checkpoint dir: %s' % ckpt_dir)
self.restore_ckpt_file = tf.train.latest_checkpoint(os.path.dirname((ckpt_dir + os.sep)))
else:
self.restore_ckpt_file = ckpt_file
self._inited = True
return self | 7,315,703,408,418,164,000 | :param var_list: vars for restore
:param ckpt_dir: prefix of model files.
:param ckpt_file: exact name of model file, priority is higher than `ckpt_dir`
:param optimistic: only restore weights of same names with model.
:return: | tensorkit/restore.py | init | nonu116/HDR-GAN | python | def init(self, var_list=None, ckpt_dir=None, ckpt_file=None, optimistic=False):
'\n :param var_list: vars for restore\n :param ckpt_dir: prefix of model files.\n :param ckpt_file: exact name of model file, priority is higher than `ckpt_dir`\n :param optimistic: only restore weights of same names with model.\n :return:\n '
assert ((var_list is None) or (len(var_list) > 0)), 'invalid var_list: {}'.format(var_list)
assert ((ckpt_dir is not None) or (ckpt_file is not None)), 'ckpt_dir and ckpt_file are both None'
self._var_list = var_list
self._restore_optimistic = optimistic
if (ckpt_file is None):
assert os.path.exists(ckpt_dir), ('invalid checkpoint dir: %s' % ckpt_dir)
self.restore_ckpt_file = tf.train.latest_checkpoint(os.path.dirname((ckpt_dir + os.sep)))
else:
self.restore_ckpt_file = ckpt_file
self._inited = True
return self |
def _restore_vars(self, sess):
'\n :param sess:\n :return: boolean for successful or not\n '
if (not self._restore_optimistic):
if (self.restore_ckpt_file is None):
logger.warn(Color.yellow('No checkpoint file for restore vars, checkpoint file is None', bold=True))
return False
self._restore_saver = tf.train.Saver(self._var_list, name='tk_restore')
self._restore_saver.restore(sess, self.restore_ckpt_file)
return True
else:
return self._optimistic_restore_model(sess) | -6,708,701,434,879,647,000 | :param sess:
:return: boolean for successful or not | tensorkit/restore.py | _restore_vars | nonu116/HDR-GAN | python | def _restore_vars(self, sess):
'\n :param sess:\n :return: boolean for successful or not\n '
if (not self._restore_optimistic):
if (self.restore_ckpt_file is None):
logger.warn(Color.yellow('No checkpoint file for restore vars, checkpoint file is None', bold=True))
return False
self._restore_saver = tf.train.Saver(self._var_list, name='tk_restore')
self._restore_saver.restore(sess, self.restore_ckpt_file)
return True
else:
return self._optimistic_restore_model(sess) |
def _optimistic_restore_model(self, sess):
'\n restore weights of same names with model.\n :param sess:\n :return:\n '
if (self.restore_ckpt_file is None):
logger.warn(Color.yellow('No ckpt file for restore vars, ckpt file is None'))
return False
reader = tf.train.NewCheckpointReader(self.restore_ckpt_file)
saved_shapes = reader.get_variable_to_shape_map()
if (self._var_list is None):
restore_key2vars = {var.name.split(':')[0]: var for var in tf.global_variables()}
elif isinstance(self._var_list, list):
restore_key2vars = {var.name.split(':')[0]: var for var in self._var_list}
elif isinstance(self._var_list, dict):
restore_key2vars = self._var_list
else:
raise RuntimeError('type error {}'.format(self._var_list))
assert (len(restore_key2vars) > 0)
restore_key2vars = sorted([(k, v) for (k, v) in restore_key2vars.items() if (k in saved_shapes)])
msg = []
var_list = dict()
with tf.variable_scope('', reuse=True):
for (key, var) in restore_key2vars:
var_shape = var.get_shape().as_list()
if (var_shape == saved_shapes[key]):
var_list[key] = var
var_name = var.name[:var.name.index(':')]
msg.append(('- restoring variable: {}'.format(var_name) if (var_name == key) else '- restoring variable {} from {}'.format(var_name, key)))
else:
msg.append(Color.yellow('- variable({}) with inconsistent shape: {}(graph) != {}(ckpt)'.format(key, var_shape, saved_shapes[key])))
if (len(var_list) != 0):
msg += ['- total variable count: {}'.format(len(var_list))]
logger.info('\n'.join(msg))
saver = tf.train.Saver(var_list, name='tk_restore')
saver.restore(sess, self.restore_ckpt_file)
return True
else:
logger.warn(Color.yellow('No vars need to restore from file: {}'.format(self.restore_ckpt_file)))
return False | -3,370,335,051,060,885,500 | restore weights of same names with model.
:param sess:
:return: | tensorkit/restore.py | _optimistic_restore_model | nonu116/HDR-GAN | python | def _optimistic_restore_model(self, sess):
'\n restore weights of same names with model.\n :param sess:\n :return:\n '
if (self.restore_ckpt_file is None):
logger.warn(Color.yellow('No ckpt file for restore vars, ckpt file is None'))
return False
reader = tf.train.NewCheckpointReader(self.restore_ckpt_file)
saved_shapes = reader.get_variable_to_shape_map()
if (self._var_list is None):
restore_key2vars = {var.name.split(':')[0]: var for var in tf.global_variables()}
elif isinstance(self._var_list, list):
restore_key2vars = {var.name.split(':')[0]: var for var in self._var_list}
elif isinstance(self._var_list, dict):
restore_key2vars = self._var_list
else:
raise RuntimeError('type error {}'.format(self._var_list))
assert (len(restore_key2vars) > 0)
restore_key2vars = sorted([(k, v) for (k, v) in restore_key2vars.items() if (k in saved_shapes)])
msg = []
var_list = dict()
with tf.variable_scope(, reuse=True):
for (key, var) in restore_key2vars:
var_shape = var.get_shape().as_list()
if (var_shape == saved_shapes[key]):
var_list[key] = var
var_name = var.name[:var.name.index(':')]
msg.append(('- restoring variable: {}'.format(var_name) if (var_name == key) else '- restoring variable {} from {}'.format(var_name, key)))
else:
msg.append(Color.yellow('- variable({}) with inconsistent shape: {}(graph) != {}(ckpt)'.format(key, var_shape, saved_shapes[key])))
if (len(var_list) != 0):
msg += ['- total variable count: {}'.format(len(var_list))]
logger.info('\n'.join(msg))
saver = tf.train.Saver(var_list, name='tk_restore')
saver.restore(sess, self.restore_ckpt_file)
return True
else:
logger.warn(Color.yellow('No vars need to restore from file: {}'.format(self.restore_ckpt_file)))
return False |
def _type_repr(obj):
'Return the repr() of an object, special-casing types (internal helper).\n If obj is a type, we return a shorter version than the default\n type.__repr__, based on the module and qualified name, which is\n typically enough to uniquely identify a type. For everything\n else, we fall back on repr(obj).\n '
if (isinstance(obj, type) and (obj.__module__ != 'typing')):
if (obj.__module__ == 'builtins'):
return obj.__qualname__
return f'{obj.__module__}.{obj.__qualname__}'
if (obj is ...):
return '...'
if isinstance(obj, types.FunctionType):
return obj.__name__
return repr(obj) | 7,618,330,322,038,824,000 | Return the repr() of an object, special-casing types (internal helper).
If obj is a type, we return a shorter version than the default
type.__repr__, based on the module and qualified name, which is
typically enough to uniquely identify a type. For everything
else, we fall back on repr(obj). | venv/Lib/site-packages/torch/fx/node.py | _type_repr | Westlanderz/AI-Plat1 | python | def _type_repr(obj):
'Return the repr() of an object, special-casing types (internal helper).\n If obj is a type, we return a shorter version than the default\n type.__repr__, based on the module and qualified name, which is\n typically enough to uniquely identify a type. For everything\n else, we fall back on repr(obj).\n '
if (isinstance(obj, type) and (obj.__module__ != 'typing')):
if (obj.__module__ == 'builtins'):
return obj.__qualname__
return f'{obj.__module__}.{obj.__qualname__}'
if (obj is ...):
return '...'
if isinstance(obj, types.FunctionType):
return obj.__name__
return repr(obj) |
@compatibility(is_backward_compatible=True)
def map_arg(a: Argument, fn: Callable[([Node], Argument)]) -> Argument:
'\n Apply fn to each Node appearing arg. arg may be a list, tuple, slice, or dict with string keys.\n '
assert callable(fn), 'torch.fx.map_arg(a, fn): fn must be a callable'
return map_aggregate(a, (lambda x: (fn(x) if isinstance(x, Node) else x))) | -5,129,645,273,626,015,000 | Apply fn to each Node appearing arg. arg may be a list, tuple, slice, or dict with string keys. | venv/Lib/site-packages/torch/fx/node.py | map_arg | Westlanderz/AI-Plat1 | python | @compatibility(is_backward_compatible=True)
def map_arg(a: Argument, fn: Callable[([Node], Argument)]) -> Argument:
'\n \n '
assert callable(fn), 'torch.fx.map_arg(a, fn): fn must be a callable'
return map_aggregate(a, (lambda x: (fn(x) if isinstance(x, Node) else x))) |
@compatibility(is_backward_compatible=True)
def map_aggregate(a: Argument, fn: Callable[([Argument], Argument)]) -> Argument:
'\n Apply fn to each Node appearing arg. arg may be a list, tuple, slice, or dict with string keys.\n '
if isinstance(a, tuple):
return tuple((map_aggregate(elem, fn) for elem in a))
elif isinstance(a, list):
return immutable_list((map_aggregate(elem, fn) for elem in a))
elif isinstance(a, dict):
return immutable_dict(((k, map_aggregate(v, fn)) for (k, v) in a.items()))
elif isinstance(a, slice):
return slice(map_aggregate(a.start, fn), map_aggregate(a.stop, fn), map_aggregate(a.step, fn))
else:
return fn(a) | -8,735,130,783,526,883,000 | Apply fn to each Node appearing arg. arg may be a list, tuple, slice, or dict with string keys. | venv/Lib/site-packages/torch/fx/node.py | map_aggregate | Westlanderz/AI-Plat1 | python | @compatibility(is_backward_compatible=True)
def map_aggregate(a: Argument, fn: Callable[([Argument], Argument)]) -> Argument:
'\n \n '
if isinstance(a, tuple):
return tuple((map_aggregate(elem, fn) for elem in a))
elif isinstance(a, list):
return immutable_list((map_aggregate(elem, fn) for elem in a))
elif isinstance(a, dict):
return immutable_dict(((k, map_aggregate(v, fn)) for (k, v) in a.items()))
elif isinstance(a, slice):
return slice(map_aggregate(a.start, fn), map_aggregate(a.stop, fn), map_aggregate(a.step, fn))
else:
return fn(a) |
@compatibility(is_backward_compatible=True)
def __init__(self, graph: 'Graph', name: str, op: str, target: 'Target', args: Tuple[('Argument', ...)], kwargs: Dict[(str, 'Argument')], return_type: Optional[Any]=None) -> None:
"\n Instantiate an instance of ``Node``. Note: most often, you want to use the\n Graph APIs, i.e. ``Graph.call_module``, ``Graph.call_method``, etc. rather\n than instantiating a ``Node`` directly.\n\n Args:\n graph (Graph): The ``Graph`` to which this ``Node`` should belong.\n\n name (str): The name to which the output of this ``Node`` should be assigned\n\n op (str): The opcode for this ``Node``. Can be one of 'placeholder',\n 'call_method', 'call_module', 'call_function', 'get_attr',\n 'output'\n\n target ('Target'): The target this op should call. See the broader\n ``Node`` docstring for more details.\n\n args (Tuple['Argument']): The args to be passed to ``target``\n\n kwargs (Dict[str, 'Argument']): The kwargs to be passed to ``target``\n\n return_type (Optional[Any]): The python type expression representing the\n type of the output of this node. This field can be used for\n annotation of values in the generated code or for other types\n of analyses.\n "
self.graph = graph
self.name = name
assert (op in ['placeholder', 'call_method', 'call_module', 'call_function', 'get_attr', 'output', 'root'])
self.op = op
if (op == 'call_function'):
if (not callable(target)):
raise ValueError(f"Node [graph = {graph}, name = '{name}'] target {target} has type {torch.typename(target)} but a Callable is expected")
elif (not isinstance(target, str)):
raise ValueError(f"Node [graph = {graph}, name = '{name}'] target {target} has type {torch.typename(target)} but a str is expected")
self.target = target
self._input_nodes: Dict[(Node, None)] = {}
self.__update_args_kwargs(map_arg(args, (lambda x: x)), map_arg(kwargs, (lambda x: x)))
self.users: Dict[('Node', None)] = {}
self.type: Optional[Any] = return_type
self._prev = self
self._next = self
self._erased = False
self._repr_fn: Optional[Callable[([Node], str)]] = None
self._stack_trace: Optional[str] = None
self.meta: Dict[(str, Any)] = {} | -269,260,086,734,934,900 | Instantiate an instance of ``Node``. Note: most often, you want to use the
Graph APIs, i.e. ``Graph.call_module``, ``Graph.call_method``, etc. rather
than instantiating a ``Node`` directly.
Args:
graph (Graph): The ``Graph`` to which this ``Node`` should belong.
name (str): The name to which the output of this ``Node`` should be assigned
op (str): The opcode for this ``Node``. Can be one of 'placeholder',
'call_method', 'call_module', 'call_function', 'get_attr',
'output'
target ('Target'): The target this op should call. See the broader
``Node`` docstring for more details.
args (Tuple['Argument']): The args to be passed to ``target``
kwargs (Dict[str, 'Argument']): The kwargs to be passed to ``target``
return_type (Optional[Any]): The python type expression representing the
type of the output of this node. This field can be used for
annotation of values in the generated code or for other types
of analyses. | venv/Lib/site-packages/torch/fx/node.py | __init__ | Westlanderz/AI-Plat1 | python | @compatibility(is_backward_compatible=True)
def __init__(self, graph: 'Graph', name: str, op: str, target: 'Target', args: Tuple[('Argument', ...)], kwargs: Dict[(str, 'Argument')], return_type: Optional[Any]=None) -> None:
"\n Instantiate an instance of ``Node``. Note: most often, you want to use the\n Graph APIs, i.e. ``Graph.call_module``, ``Graph.call_method``, etc. rather\n than instantiating a ``Node`` directly.\n\n Args:\n graph (Graph): The ``Graph`` to which this ``Node`` should belong.\n\n name (str): The name to which the output of this ``Node`` should be assigned\n\n op (str): The opcode for this ``Node``. Can be one of 'placeholder',\n 'call_method', 'call_module', 'call_function', 'get_attr',\n 'output'\n\n target ('Target'): The target this op should call. See the broader\n ``Node`` docstring for more details.\n\n args (Tuple['Argument']): The args to be passed to ``target``\n\n kwargs (Dict[str, 'Argument']): The kwargs to be passed to ``target``\n\n return_type (Optional[Any]): The python type expression representing the\n type of the output of this node. This field can be used for\n annotation of values in the generated code or for other types\n of analyses.\n "
self.graph = graph
self.name = name
assert (op in ['placeholder', 'call_method', 'call_module', 'call_function', 'get_attr', 'output', 'root'])
self.op = op
if (op == 'call_function'):
if (not callable(target)):
raise ValueError(f"Node [graph = {graph}, name = '{name}'] target {target} has type {torch.typename(target)} but a Callable is expected")
elif (not isinstance(target, str)):
raise ValueError(f"Node [graph = {graph}, name = '{name}'] target {target} has type {torch.typename(target)} but a str is expected")
self.target = target
self._input_nodes: Dict[(Node, None)] = {}
self.__update_args_kwargs(map_arg(args, (lambda x: x)), map_arg(kwargs, (lambda x: x)))
self.users: Dict[('Node', None)] = {}
self.type: Optional[Any] = return_type
self._prev = self
self._next = self
self._erased = False
self._repr_fn: Optional[Callable[([Node], str)]] = None
self._stack_trace: Optional[str] = None
self.meta: Dict[(str, Any)] = {} |
@property
def next(self) -> 'Node':
'\n Returns the next ``Node`` in the linked list of Nodes.\n\n Returns:\n\n The next ``Node`` in the linked list of Nodes.\n '
return self._next | -112,273,230,731,126,510 | Returns the next ``Node`` in the linked list of Nodes.
Returns:
The next ``Node`` in the linked list of Nodes. | venv/Lib/site-packages/torch/fx/node.py | next | Westlanderz/AI-Plat1 | python | @property
def next(self) -> 'Node':
'\n Returns the next ``Node`` in the linked list of Nodes.\n\n Returns:\n\n The next ``Node`` in the linked list of Nodes.\n '
return self._next |
@property
def prev(self) -> 'Node':
'\n Returns the previous ``Node`` in the linked list of Nodes.\n\n Returns:\n\n The previous ``Node`` in the linked list of Nodes.\n '
return self._prev | -7,637,238,228,281,718,000 | Returns the previous ``Node`` in the linked list of Nodes.
Returns:
The previous ``Node`` in the linked list of Nodes. | venv/Lib/site-packages/torch/fx/node.py | prev | Westlanderz/AI-Plat1 | python | @property
def prev(self) -> 'Node':
'\n Returns the previous ``Node`` in the linked list of Nodes.\n\n Returns:\n\n The previous ``Node`` in the linked list of Nodes.\n '
return self._prev |
@compatibility(is_backward_compatible=True)
def prepend(self, x: 'Node') -> None:
'\n Insert x before this node in the list of nodes in the graph. Example::\n\n Before: p -> self\n bx -> x -> ax\n After: p -> x -> self\n bx -> ax\n\n Args:\n x (Node): The node to put before this node. Must be a member of the same graph.\n '
assert (self.graph == x.graph), 'Attempting to move a Node into a different Graph'
x._remove_from_list()
p = self._prev
(p._next, x._prev) = (x, p)
(x._next, self._prev) = (self, x) | -7,424,264,496,378,813,000 | Insert x before this node in the list of nodes in the graph. Example::
Before: p -> self
bx -> x -> ax
After: p -> x -> self
bx -> ax
Args:
x (Node): The node to put before this node. Must be a member of the same graph. | venv/Lib/site-packages/torch/fx/node.py | prepend | Westlanderz/AI-Plat1 | python | @compatibility(is_backward_compatible=True)
def prepend(self, x: 'Node') -> None:
'\n Insert x before this node in the list of nodes in the graph. Example::\n\n Before: p -> self\n bx -> x -> ax\n After: p -> x -> self\n bx -> ax\n\n Args:\n x (Node): The node to put before this node. Must be a member of the same graph.\n '
assert (self.graph == x.graph), 'Attempting to move a Node into a different Graph'
x._remove_from_list()
p = self._prev
(p._next, x._prev) = (x, p)
(x._next, self._prev) = (self, x) |
@compatibility(is_backward_compatible=True)
def append(self, x: 'Node') -> None:
'\n Insert x after this node in the list of nodes in the graph.\n Equvalent to ``self.next.prepend(x)``\n\n Args:\n x (Node): The node to put after this node. Must be a member of the same graph.\n '
self._next.prepend(x) | -1,150,084,651,612,744,200 | Insert x after this node in the list of nodes in the graph.
Equvalent to ``self.next.prepend(x)``
Args:
x (Node): The node to put after this node. Must be a member of the same graph. | venv/Lib/site-packages/torch/fx/node.py | append | Westlanderz/AI-Plat1 | python | @compatibility(is_backward_compatible=True)
def append(self, x: 'Node') -> None:
'\n Insert x after this node in the list of nodes in the graph.\n Equvalent to ``self.next.prepend(x)``\n\n Args:\n x (Node): The node to put after this node. Must be a member of the same graph.\n '
self._next.prepend(x) |
@property
def args(self) -> Tuple[(Argument, ...)]:
"\n The tuple of arguments to this ``Node``. The interpretation of arguments\n depends on the node's opcode. See the :class:`Node` docstring for more\n information.\n\n Assignment to this property is allowed. All accounting of uses and users\n is updated automatically on assignment.\n "
return self._args | 3,899,425,412,167,228,400 | The tuple of arguments to this ``Node``. The interpretation of arguments
depends on the node's opcode. See the :class:`Node` docstring for more
information.
Assignment to this property is allowed. All accounting of uses and users
is updated automatically on assignment. | venv/Lib/site-packages/torch/fx/node.py | args | Westlanderz/AI-Plat1 | python | @property
def args(self) -> Tuple[(Argument, ...)]:
"\n The tuple of arguments to this ``Node``. The interpretation of arguments\n depends on the node's opcode. See the :class:`Node` docstring for more\n information.\n\n Assignment to this property is allowed. All accounting of uses and users\n is updated automatically on assignment.\n "
return self._args |
@args.setter
def args(self, a: Tuple[(Argument, ...)]):
"\n Set the tuple of arguments to this Node. The interpretation of arguments\n depends on the node's opcode. See the ``fx.Graph`` docstring for more\n information.\n "
self.__update_args_kwargs(map_arg(a, (lambda x: x)), self._kwargs) | 6,250,060,837,152,039,000 | Set the tuple of arguments to this Node. The interpretation of arguments
depends on the node's opcode. See the ``fx.Graph`` docstring for more
information. | venv/Lib/site-packages/torch/fx/node.py | args | Westlanderz/AI-Plat1 | python | @args.setter
def args(self, a: Tuple[(Argument, ...)]):
"\n Set the tuple of arguments to this Node. The interpretation of arguments\n depends on the node's opcode. See the ``fx.Graph`` docstring for more\n information.\n "
self.__update_args_kwargs(map_arg(a, (lambda x: x)), self._kwargs) |
@property
def kwargs(self) -> Dict[(str, Argument)]:
"\n The dict of keyword arguments to this ``Node``. The interpretation of arguments\n depends on the node's opcode. See the :class:`Node` docstring for more\n information.\n\n Assignment to this property is allowed. All accounting of uses and users\n is updated automatically on assignment.\n "
return self._kwargs | -5,066,895,608,468,566,000 | The dict of keyword arguments to this ``Node``. The interpretation of arguments
depends on the node's opcode. See the :class:`Node` docstring for more
information.
Assignment to this property is allowed. All accounting of uses and users
is updated automatically on assignment. | venv/Lib/site-packages/torch/fx/node.py | kwargs | Westlanderz/AI-Plat1 | python | @property
def kwargs(self) -> Dict[(str, Argument)]:
"\n The dict of keyword arguments to this ``Node``. The interpretation of arguments\n depends on the node's opcode. See the :class:`Node` docstring for more\n information.\n\n Assignment to this property is allowed. All accounting of uses and users\n is updated automatically on assignment.\n "
return self._kwargs |
@kwargs.setter
def kwargs(self, k: Dict[(str, Argument)]):
"\n Set the dict of kwargs to this Node. The interpretation of arguments\n depends on the node's opcode. See the ``fx.Graph`` docstring for more\n information.\n "
self.__update_args_kwargs(self._args, map_arg(k, (lambda x: x))) | -835,094,360,972,673,800 | Set the dict of kwargs to this Node. The interpretation of arguments
depends on the node's opcode. See the ``fx.Graph`` docstring for more
information. | venv/Lib/site-packages/torch/fx/node.py | kwargs | Westlanderz/AI-Plat1 | python | @kwargs.setter
def kwargs(self, k: Dict[(str, Argument)]):
"\n Set the dict of kwargs to this Node. The interpretation of arguments\n depends on the node's opcode. See the ``fx.Graph`` docstring for more\n information.\n "
self.__update_args_kwargs(self._args, map_arg(k, (lambda x: x))) |
@property
def all_input_nodes(self) -> List['Node']:
'\n Return all Nodes that are inputs to this Node. This is equivalent to\n iterating over ``args`` and ``kwargs`` and only collecting the values that\n are Nodes.\n\n Returns:\n\n List of ``Nodes`` that appear in the ``args`` and ``kwargs`` of this\n ``Node``, in that order.\n '
return list(self._input_nodes.keys()) | -7,689,755,375,074,671,000 | Return all Nodes that are inputs to this Node. This is equivalent to
iterating over ``args`` and ``kwargs`` and only collecting the values that
are Nodes.
Returns:
List of ``Nodes`` that appear in the ``args`` and ``kwargs`` of this
``Node``, in that order. | venv/Lib/site-packages/torch/fx/node.py | all_input_nodes | Westlanderz/AI-Plat1 | python | @property
def all_input_nodes(self) -> List['Node']:
'\n Return all Nodes that are inputs to this Node. This is equivalent to\n iterating over ``args`` and ``kwargs`` and only collecting the values that\n are Nodes.\n\n Returns:\n\n List of ``Nodes`` that appear in the ``args`` and ``kwargs`` of this\n ``Node``, in that order.\n '
return list(self._input_nodes.keys()) |
@compatibility(is_backward_compatible=True)
def update_arg(self, idx: int, arg: Argument) -> None:
'\n Update an existing positional argument to contain the new value\n ``arg``. After calling, ``self.args[idx] == arg``.\n\n Args:\n\n idx (int): The index into ``self.args`` of the element to update\n arg (Argument): The new argument value to write into ``args``\n '
args = list(self.args)
args[idx] = arg
self.args = tuple(args) | -6,276,467,114,808,523,000 | Update an existing positional argument to contain the new value
``arg``. After calling, ``self.args[idx] == arg``.
Args:
idx (int): The index into ``self.args`` of the element to update
arg (Argument): The new argument value to write into ``args`` | venv/Lib/site-packages/torch/fx/node.py | update_arg | Westlanderz/AI-Plat1 | python | @compatibility(is_backward_compatible=True)
def update_arg(self, idx: int, arg: Argument) -> None:
'\n Update an existing positional argument to contain the new value\n ``arg``. After calling, ``self.args[idx] == arg``.\n\n Args:\n\n idx (int): The index into ``self.args`` of the element to update\n arg (Argument): The new argument value to write into ``args``\n '
args = list(self.args)
args[idx] = arg
self.args = tuple(args) |
@compatibility(is_backward_compatible=True)
def update_kwarg(self, key: str, arg: Argument) -> None:
'\n Update an existing keyword argument to contain the new value\n ``arg``. After calling, ``self.kwargs[key] == arg``.\n\n Args:\n\n key (str): The key in ``self.kwargs`` of the element to update\n arg (Argument): The new argument value to write into ``kwargs``\n '
kwargs = dict(self.kwargs)
kwargs[key] = arg
self.kwargs = kwargs | 265,324,208,968,271,550 | Update an existing keyword argument to contain the new value
``arg``. After calling, ``self.kwargs[key] == arg``.
Args:
key (str): The key in ``self.kwargs`` of the element to update
arg (Argument): The new argument value to write into ``kwargs`` | venv/Lib/site-packages/torch/fx/node.py | update_kwarg | Westlanderz/AI-Plat1 | python | @compatibility(is_backward_compatible=True)
def update_kwarg(self, key: str, arg: Argument) -> None:
'\n Update an existing keyword argument to contain the new value\n ``arg``. After calling, ``self.kwargs[key] == arg``.\n\n Args:\n\n key (str): The key in ``self.kwargs`` of the element to update\n arg (Argument): The new argument value to write into ``kwargs``\n '
kwargs = dict(self.kwargs)
kwargs[key] = arg
self.kwargs = kwargs |
@property
def stack_trace(self) -> Optional[str]:
'\n Return the Python stack trace that was recorded during tracing, if any.\n This property is usually populated by `Tracer.create_proxy`. To record\n stack traces during tracing for debug purposes, set\n `record_stack_traces = True` on the `Tracer` instance.\n '
return self._stack_trace | -4,988,679,728,696,897,000 | Return the Python stack trace that was recorded during tracing, if any.
This property is usually populated by `Tracer.create_proxy`. To record
stack traces during tracing for debug purposes, set
`record_stack_traces = True` on the `Tracer` instance. | venv/Lib/site-packages/torch/fx/node.py | stack_trace | Westlanderz/AI-Plat1 | python | @property
def stack_trace(self) -> Optional[str]:
'\n Return the Python stack trace that was recorded during tracing, if any.\n This property is usually populated by `Tracer.create_proxy`. To record\n stack traces during tracing for debug purposes, set\n `record_stack_traces = True` on the `Tracer` instance.\n '
return self._stack_trace |
def __update_args_kwargs(self, new_args: Tuple[('Argument', ...)], new_kwargs: Dict[(str, 'Argument')]):
'\n This API is internal. Do *not* call it directly.\n '
self._args = new_args
self._kwargs = new_kwargs
for old_use in self._input_nodes.keys():
old_use.users.pop(self)
self._input_nodes = {}
map_arg(self._args, (lambda n: self._input_nodes.setdefault(n)))
map_arg(self._kwargs, (lambda n: self._input_nodes.setdefault(n)))
for new_use in self._input_nodes.keys():
new_use.users.setdefault(self) | -4,245,011,480,387,905,500 | This API is internal. Do *not* call it directly. | venv/Lib/site-packages/torch/fx/node.py | __update_args_kwargs | Westlanderz/AI-Plat1 | python | def __update_args_kwargs(self, new_args: Tuple[('Argument', ...)], new_kwargs: Dict[(str, 'Argument')]):
'\n \n '
self._args = new_args
self._kwargs = new_kwargs
for old_use in self._input_nodes.keys():
old_use.users.pop(self)
self._input_nodes = {}
map_arg(self._args, (lambda n: self._input_nodes.setdefault(n)))
map_arg(self._kwargs, (lambda n: self._input_nodes.setdefault(n)))
for new_use in self._input_nodes.keys():
new_use.users.setdefault(self) |
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