function stringlengths 11 56k | repo_name stringlengths 5 60 | features list |
|---|---|---|
def test_view_person_othername_list_unauthorized(self):
response = self.client.get("/en/persons/8497ba86-7485-42d2-9596-2ab14520f1f4/othernames/")
self.assertEqual(response.status_code, status.HTTP_200_OK) | Sinar/popit_ng | [
21,
4,
21,
91,
1443407491
] |
def test_view_person_othername_details_unauthorized(self):
response = self.client.get(
"/en/persons/8497ba86-7485-42d2-9596-2ab14520f1f4/othernames/cf93e73f-91b6-4fad-bf76-0782c80297a8/"
)
self.assertEqual(response.status_code, status.HTTP_200_OK) | Sinar/popit_ng | [
21,
4,
21,
91,
1443407491
] |
def test_view_person_othername_details_authorized(self):
token = Token.objects.get(user__username="admin")
self.client.credentials(HTTP_AUTHORIZATION='Token ' + token.key)
response = self.client.get(
"/en/persons/8497ba86-7485-42d2-9596-2ab14520f1f4/othernames/cf93e73f-91b6-4fad-bf76-0782c80297a8/"
)
self.assertEqual(response.status_code, status.HTTP_200_OK) | Sinar/popit_ng | [
21,
4,
21,
91,
1443407491
] |
def test_create_person_othername_unauthorized(self):
data = {
"name": "jane",
"family_name": "jambul",
"given_name": "test person",
"start_date": "1950-01-01",
"end_date": "2010-01-01",
}
response = self.client.post(
"/en/persons/8497ba86-7485-42d2-9596-2ab14520f1f4/othernames/", data
)
self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED) | Sinar/popit_ng | [
21,
4,
21,
91,
1443407491
] |
def test_update_person_othername_unauthorized(self):
data = {
"family_name": "jambul",
}
person = Person.objects.language('en').get(id='8497ba86-7485-42d2-9596-2ab14520f1f4')
other_name = person.other_names.language('en').get(id="cf93e73f-91b6-4fad-bf76-0782c80297a8")
response = self.client.put(
"/en/persons/8497ba86-7485-42d2-9596-2ab14520f1f4/othernames/cf93e73f-91b6-4fad-bf76-0782c80297a8/",
data
)
self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED) | Sinar/popit_ng | [
21,
4,
21,
91,
1443407491
] |
def test_update_person_othername_authorized(self):
data = {
"family_name": "jambul",
}
token = Token.objects.get(user__username="admin")
self.client.credentials(HTTP_AUTHORIZATION='Token ' + token.key)
response = self.client.put(
"/en/persons/8497ba86-7485-42d2-9596-2ab14520f1f4/othernames/cf93e73f-91b6-4fad-bf76-0782c80297a8/",
data
)
self.assertEqual(response.status_code, status.HTTP_200_OK)
person = Person.objects.language('en').get(id='8497ba86-7485-42d2-9596-2ab14520f1f4')
other_name = person.other_names.language('en').get(id="cf93e73f-91b6-4fad-bf76-0782c80297a8")
self.assertEqual(other_name.family_name, "jambul") | Sinar/popit_ng | [
21,
4,
21,
91,
1443407491
] |
def test_delete_person_othername_unauthorized(self):
response = self.client.delete(
"/en/persons/8497ba86-7485-42d2-9596-2ab14520f1f4/othernames/cf93e73f-91b6-4fad-bf76-0782c80297a8/"
)
self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED) | Sinar/popit_ng | [
21,
4,
21,
91,
1443407491
] |
def test_delete_person_othername_authorized(self):
token = Token.objects.get(user__username="admin")
self.client.credentials(HTTP_AUTHORIZATION='Token ' + token.key)
response = self.client.delete(
"/en/persons/8497ba86-7485-42d2-9596-2ab14520f1f4/othernames/cf93e73f-91b6-4fad-bf76-0782c80297a8/"
)
self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT) | Sinar/popit_ng | [
21,
4,
21,
91,
1443407491
] |
def test_get_person_identifier_link_list_unauthorized(self):
# identifier af7c01b5-1c4f-4c08-9174-3de5ff270bdb
# link 9c9a2093-c3eb-4b51-b869-0d3b4ab281fd
# person 8497ba86-7485-42d2-9596-2ab14520f1f4
response = self.client.get(
"/en/persons/8497ba86-7485-42d2-9596-2ab14520f1f4/identifiers/af7c01b5-1c4f-4c08-9174-3de5ff270bdb/links/"
)
self.assertEqual(response.status_code, status.HTTP_200_OK)
data = response.data["results"][0]
self.assertEqual(data["url"], "http://github.com/sinarproject/") | Sinar/popit_ng | [
21,
4,
21,
91,
1443407491
] |
def test_get_person_identifier_link_detail_unauthorized(self):
response = self.client.get(
"/en/persons/8497ba86-7485-42d2-9596-2ab14520f1f4/identifiers/af7c01b5-1c4f-4c08-9174-3de5ff270bdb/links/9c9a2093-c3eb-4b51-b869-0d3b4ab281fd/"
)
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertEqual(response.data["results"]["url"], "http://github.com/sinarproject/") | Sinar/popit_ng | [
21,
4,
21,
91,
1443407491
] |
def test_get_person_identifier_link_detail_authorized(self):
token = Token.objects.get(user__username="admin")
self.client.credentials(HTTP_AUTHORIZATION='Token ' + token.key)
response = self.client.get(
"/en/persons/8497ba86-7485-42d2-9596-2ab14520f1f4/identifiers/af7c01b5-1c4f-4c08-9174-3de5ff270bdb/links/9c9a2093-c3eb-4b51-b869-0d3b4ab281fd/"
)
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertEqual(response.data["results"]["url"], "http://github.com/sinarproject/") | Sinar/popit_ng | [
21,
4,
21,
91,
1443407491
] |
def test_create_person_identifier_link_unauthorized(self):
data = {
"url": "http://twitter.com/sinarproject"
}
response = self.client.post(
"/en/persons/8497ba86-7485-42d2-9596-2ab14520f1f4/identifiers/af7c01b5-1c4f-4c08-9174-3de5ff270bdb/links/",
data
)
self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED) | Sinar/popit_ng | [
21,
4,
21,
91,
1443407491
] |
def test_update_person_identifier_link_unauthorized(self):
data = {
"note":"This is a nested link"
}
response = self.client.put(
"/en/persons/8497ba86-7485-42d2-9596-2ab14520f1f4/identifiers/af7c01b5-1c4f-4c08-9174-3de5ff270bdb/links/9c9a2093-c3eb-4b51-b869-0d3b4ab281fd/",
data
)
self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED) | Sinar/popit_ng | [
21,
4,
21,
91,
1443407491
] |
def test_update_person_identifier_link_authorized(self):
data = {
"note":"This is a nested link"
}
token = Token.objects.get(user__username="admin")
self.client.credentials(HTTP_AUTHORIZATION='Token ' + token.key)
response = self.client.put(
"/en/persons/8497ba86-7485-42d2-9596-2ab14520f1f4/identifiers/af7c01b5-1c4f-4c08-9174-3de5ff270bdb/links/9c9a2093-c3eb-4b51-b869-0d3b4ab281fd/",
data
)
self.assertEqual(response.status_code, status.HTTP_200_OK)
# 9c9a2093-c3eb-4b51-b869-0d3b4ab281fd
person = Person.objects.language("en").get(id="8497ba86-7485-42d2-9596-2ab14520f1f4")
identifier = person.identifiers.language("en").get(id="af7c01b5-1c4f-4c08-9174-3de5ff270bdb")
link = identifier.links.language("en").get(id="9c9a2093-c3eb-4b51-b869-0d3b4ab281fd")
self.assertEqual(link.note, "This is a nested link") | Sinar/popit_ng | [
21,
4,
21,
91,
1443407491
] |
def test_delete_person_identifier_link_unauthorized(self):
response = self.client.delete(
"/en/persons/8497ba86-7485-42d2-9596-2ab14520f1f4/identifiers/af7c01b5-1c4f-4c08-9174-3de5ff270bdb/links/9c9a2093-c3eb-4b51-b869-0d3b4ab281fd/"
)
self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED) | Sinar/popit_ng | [
21,
4,
21,
91,
1443407491
] |
def test_delete_person_identifier_link_authorized(self):
token = Token.objects.get(user__username="admin")
self.client.credentials(HTTP_AUTHORIZATION='Token ' + token.key)
response = self.client.delete(
"/en/persons/8497ba86-7485-42d2-9596-2ab14520f1f4/identifiers/af7c01b5-1c4f-4c08-9174-3de5ff270bdb/links/9c9a2093-c3eb-4b51-b869-0d3b4ab281fd/"
)
self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT) | Sinar/popit_ng | [
21,
4,
21,
91,
1443407491
] |
def test_list_person_othername_link(self):
response = self.client.get(
"/en/persons/8497ba86-7485-42d2-9596-2ab14520f1f4/othernames/cf93e73f-91b6-4fad-bf76-0782c80297a8/links/"
)
self.assertEqual(response.status_code, status.HTTP_200_OK) | Sinar/popit_ng | [
21,
4,
21,
91,
1443407491
] |
def test_show_person_othername_link_detail(self):
response = self.client.get(
"/en/persons/8497ba86-7485-42d2-9596-2ab14520f1f4/othernames/cf93e73f-91b6-4fad-bf76-0782c80297a8/links/4d8d71c4-20ea-4ed1-ae38-4b7d7550cdf6/"
)
self.assertEqual(response.status_code, status.HTTP_200_OK) | Sinar/popit_ng | [
21,
4,
21,
91,
1443407491
] |
def test_create_person_othername_link_authorized(self):
data = {
"url": "http://github.com/sinar"
}
token = Token.objects.get(user__username="admin")
self.client.credentials(HTTP_AUTHORIZATION='Token ' + token.key)
response = self.client.post(
"/en/persons/8497ba86-7485-42d2-9596-2ab14520f1f4/othernames/cf93e73f-91b6-4fad-bf76-0782c80297a8/links/",
data
)
self.assertEqual(response.status_code, status.HTTP_201_CREATED) | Sinar/popit_ng | [
21,
4,
21,
91,
1443407491
] |
def test_update_person_othername_link_not_exist_authorized(self):
data = {
"note": "Just a link"
}
token = Token.objects.get(user__username="admin")
self.client.credentials(HTTP_AUTHORIZATION='Token ' + token.key)
response = self.client.put(
"/en/persons/8497ba86-7485-42d2-9596-2ab14520f1f4/othernames/cf93e73f-91b6-4fad-bf76-0782c80297a8/links/not_exist/",
data
)
self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND) | Sinar/popit_ng | [
21,
4,
21,
91,
1443407491
] |
def test_update_person_othername_link_authorized(self):
data = {
"note": "Just a link"
}
token = Token.objects.get(user__username="admin")
self.client.credentials(HTTP_AUTHORIZATION='Token ' + token.key)
response = self.client.put(
"/en/persons/8497ba86-7485-42d2-9596-2ab14520f1f4/othernames/cf93e73f-91b6-4fad-bf76-0782c80297a8/links/4d8d71c4-20ea-4ed1-ae38-4b7d7550cdf6/",
data
)
self.assertEqual(response.status_code, status.HTTP_200_OK) | Sinar/popit_ng | [
21,
4,
21,
91,
1443407491
] |
def test_delete_person_othername_link_not_exist_authorized(self):
token = Token.objects.get(user__username="admin")
self.client.credentials(HTTP_AUTHORIZATION='Token ' + token.key)
response = self.client.delete(
"/en/persons/8497ba86-7485-42d2-9596-2ab14520f1f4/othernames/cf93e73f-91b6-4fad-bf76-0782c80297a8/links/not_exist/"
)
self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND) | Sinar/popit_ng | [
21,
4,
21,
91,
1443407491
] |
def test_delete_person_othername_link_authorized(self):
token = Token.objects.get(user__username="admin")
self.client.credentials(HTTP_AUTHORIZATION='Token ' + token.key)
response = self.client.delete(
"/en/persons/8497ba86-7485-42d2-9596-2ab14520f1f4/othernames/cf93e73f-91b6-4fad-bf76-0782c80297a8/links/4d8d71c4-20ea-4ed1-ae38-4b7d7550cdf6/"
)
self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT) | Sinar/popit_ng | [
21,
4,
21,
91,
1443407491
] |
def test_list_person_contact_link(self):
response = self.client.get(
"/en/persons/8497ba86-7485-42d2-9596-2ab14520f1f4/contact_details/2256ec04-2d1d-4994-b1f1-16d3f5245441/links/"
)
self.assertEqual(response.status_code, status.HTTP_200_OK) | Sinar/popit_ng | [
21,
4,
21,
91,
1443407491
] |
def test_show_person_contact_link(self):
response = self.client.get(
"/en/persons/8497ba86-7485-42d2-9596-2ab14520f1f4/contact_details/2256ec04-2d1d-4994-b1f1-16d3f5245441/links/6d0afb46-67d4-4708-87c4-4d51ce99767e/"
)
self.assertEqual(response.status_code, status.HTTP_200_OK) | Sinar/popit_ng | [
21,
4,
21,
91,
1443407491
] |
def test_create_person_contact_link_authorized(self):
data = {
"url": "http://github.com/sinar"
}
token = Token.objects.get(user__username="admin")
self.client.credentials(HTTP_AUTHORIZATION='Token ' + token.key)
response = self.client.post(
"/en/persons/8497ba86-7485-42d2-9596-2ab14520f1f4/contact_details/2256ec04-2d1d-4994-b1f1-16d3f5245441/links/",
data
)
self.assertEqual(response.status_code, status.HTTP_201_CREATED) | Sinar/popit_ng | [
21,
4,
21,
91,
1443407491
] |
def test_update_person_contact_link_not_exist_authorized(self):
data = {
"note": "Just a link"
}
token = Token.objects.get(user__username="admin")
self.client.credentials(HTTP_AUTHORIZATION='Token ' + token.key)
response = self.client.put(
"/en/persons/8497ba86-7485-42d2-9596-2ab14520f1f4/contact_details/2256ec04-2d1d-4994-b1f1-16d3f5245441/links/not_exist/",
data
)
self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND) | Sinar/popit_ng | [
21,
4,
21,
91,
1443407491
] |
def test_update_person_contact_link_authorized(self):
data = {
"note": "Just a link"
}
token = Token.objects.get(user__username="admin")
self.client.credentials(HTTP_AUTHORIZATION='Token ' + token.key)
response = self.client.put(
"/en/persons/8497ba86-7485-42d2-9596-2ab14520f1f4/contact_details/2256ec04-2d1d-4994-b1f1-16d3f5245441/links/6d0afb46-67d4-4708-87c4-4d51ce99767e/",
data
)
self.assertEqual(response.status_code, status.HTTP_200_OK) | Sinar/popit_ng | [
21,
4,
21,
91,
1443407491
] |
def test_delete_person_contact_link_not_exist_authorized(self):
token = Token.objects.get(user__username="admin")
self.client.credentials(HTTP_AUTHORIZATION='Token ' + token.key)
response = self.client.delete(
"/en/persons/8497ba86-7485-42d2-9596-2ab14520f1f4/contact_details/2256ec04-2d1d-4994-b1f1-16d3f5245441/links/not_exist/"
)
self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND) | Sinar/popit_ng | [
21,
4,
21,
91,
1443407491
] |
def test_fetch_non_empty_field_person_serializer(self):
person = Person.objects.untranslated().get(id='8497ba86-7485-42d2-9596-2ab14520f1f4')
serializer = PersonSerializer(person, language='en')
data = serializer.data
self.assertEqual(data["name"], "John") | Sinar/popit_ng | [
21,
4,
21,
91,
1443407491
] |
def test_fetch_not_empty_relation_person_serializer(self):
person = Person.objects.untranslated().get(id='8497ba86-7485-42d2-9596-2ab14520f1f4')
serializer = PersonSerializer(person, language='en')
data = serializer.data
self.assertTrue(data["other_names"]) | Sinar/popit_ng | [
21,
4,
21,
91,
1443407491
] |
def test_create_person_with_all_field_serializer(self):
person_data = {
"name": "joe",
"family_name": "doe",
"given_name": "joe jambul",
"additional_name": "not john doe",
"gender": "unknown",
"summary": "person unit test api",
"honorific_prefix": "Chief",
"honorific_suffix": "of the fake people league",
"biography": "He does not exists!!!!",
"birth_date": "1950-01-01",
"death_data": "2000-01-01",
"email": "joejambul@sinarproject.org",
"contact_details":[
{
"type":"twitter",
"value": "sinarproject",
}
],
"links":[
{
"url":"http://sinarproject.org",
}
],
"identifiers":[
{
"identifier": "9089098098",
"scheme": "rakyat",
}
],
"other_names":[
{
"name":"Jane",
"family_name":"Jambul",
"start_date": "1950-01-01",
"end_date": "2010-01-01",
}
]
}
person_serial = PersonSerializer(data=person_data, language='en')
person_serial.is_valid()
self.assertEqual(person_serial.errors, {})
person_serial.save()
person = Person.objects.language("en").get(name="joe")
self.assertEqual(person.given_name, "joe jambul") | Sinar/popit_ng | [
21,
4,
21,
91,
1443407491
] |
def test_create_links_person_serializers(self):
person_data = {
"links": [
{
"url": "http://twitter.com/sweemeng",
}
]
}
person = Person.objects.language('en').get(id='ab1a5788e5bae955c048748fa6af0e97')
person_serializer = PersonSerializer(person, data=person_data, partial=True, language='en')
person_serializer.is_valid()
self.assertEqual(person_serializer.errors, {})
person_serializer.save()
person_ = Person.objects.language('en').get(id='ab1a5788e5bae955c048748fa6af0e97')
url = person_.links.language("en").get(url="http://twitter.com/sweemeng")
self.assertEqual(url.url, "http://twitter.com/sweemeng") | Sinar/popit_ng | [
21,
4,
21,
91,
1443407491
] |
def test_update_create_nested_links_persons_serializer(self):
person_data = {
"id":"ab1a5788e5bae955c048748fa6af0e97",
"contact_details":[
{
"id": "a66cb422-eec3-4861-bae1-a64ae5dbde61",
"links": [{
"url": "http://facebook.com",
}]
}
],
}
person = Person.objects.language('en').get(id='ab1a5788e5bae955c048748fa6af0e97')
person_serializer = PersonSerializer(person, data=person_data, partial=True, language='en')
person_serializer.is_valid()
self.assertEqual(person_serializer.errors, {})
person_serializer.save()
person_ = Person.objects.language('en').get(id='ab1a5788e5bae955c048748fa6af0e97')
# There should be only 1 links in that contact
contact = person_.contact_details.language('en').get(id='a66cb422-eec3-4861-bae1-a64ae5dbde61')
links = contact.links.language('en').filter(url="http://sinarproject.org")
self.assertEqual(links[0].url, "http://sinarproject.org") | Sinar/popit_ng | [
21,
4,
21,
91,
1443407491
] |
def test_create_identifier_person_serializer(self):
person_data = {
"identifiers": [
{
"scheme": "IC",
"identifier": "129031309",
}
]
}
person = Person.objects.language('en').get(id='ab1a5788e5bae955c048748fa6af0e97')
person_serializer = PersonSerializer(person, data=person_data, partial=True, language='en')
person_serializer.is_valid()
self.assertEqual(person_serializer.errors, {})
person_serializer.save()
person_ = Person.objects.language('en').get(id='ab1a5788e5bae955c048748fa6af0e97')
identifier = person_.identifiers.language('en').get(identifier="129031309")
self.assertEqual(identifier.scheme, "IC") | Sinar/popit_ng | [
21,
4,
21,
91,
1443407491
] |
def test_create_contact_person_serializer(self):
person_data = {
"contact_details": [
{
"type":"twitter",
"value": "sinarproject",
}
]
}
person = Person.objects.language('en').get(id='8497ba86-7485-42d2-9596-2ab14520f1f4')
person_serializer = PersonSerializer(person, data=person_data, partial=True, language='en')
person_serializer.is_valid()
self.assertEqual(person_serializer.errors, {})
person_serializer.save()
person_ = Person.objects.language('en').get(id='8497ba86-7485-42d2-9596-2ab14520f1f4')
contact = person_.contact_details.language('en').get(type="twitter")
self.assertEqual(contact.value, "sinarproject") | Sinar/popit_ng | [
21,
4,
21,
91,
1443407491
] |
def test_create_other_name_person_serializer(self):
person_data = {
"other_names": [
{
"name": "jane",
"family_name": "jambul",
"given_name": "test person",
"start_date": "1950-01-01",
"end_date": "2010-01-01",
}
]
}
person = Person.objects.language('en').get(id='ab1a5788e5bae955c048748fa6af0e97')
person_serializer = PersonSerializer(person, data=person_data, partial=True, language='en')
person_serializer.is_valid()
self.assertEqual(person_serializer.errors, {})
person_serializer.save()
person_ = Person.objects.language('en').get(id='ab1a5788e5bae955c048748fa6af0e97')
other_name = person_.other_names.language('en').get(name="jane")
self.assertEqual(other_name.given_name, "test person") | Sinar/popit_ng | [
21,
4,
21,
91,
1443407491
] |
def test_create_person_invalid_date_serializer(self):
person_data = {
"name": "joe",
"family_name": "doe",
"given_name": "joe jambul",
"additional_name": "not john doe",
"gender": "unknown",
"summary": "person unit test api",
"honorific_prefix": "Chief",
"honorific_suffix": "of the fake people league",
"biography": "He does not exists!!!!",
"birth_date": "invalid date",
"death_data": "invalid date",
"email": "joejambul@sinarproject.org",
}
person_serial = PersonSerializer(data=person_data, language='en')
person_serial.is_valid()
self.assertNotEqual(person_serial.errors, {}) | Sinar/popit_ng | [
21,
4,
21,
91,
1443407491
] |
def test_create_person_translated_serializer(self):
person_data = {
"name": "joe",
"family_name": "doe",
"given_name": "joe jambul",
"additional_name": "bukan john doe",
"gender": "tak tahu",
"summary": "orang ujian",
"honorific_prefix": "Datuk Seri",
"biography": "Dia Tak wujud!!!!",
"email": "joejambul@sinarproject.org",
}
person_serial = PersonSerializer(data=person_data, language='ms')
person_serial.is_valid()
self.assertEqual(person_serial.errors, {})
person_serial.save()
person = Person.objects.language("ms").get(name="joe")
self.assertEqual(person.given_name, "joe jambul") | Sinar/popit_ng | [
21,
4,
21,
91,
1443407491
] |
def test_load_translated_person_membership_organization(self):
person = Person.objects.untranslated().get(id="078541c9-9081-4082-b28f-29cbb64440cb")
person_serializer = PersonSerializer(person, language="ms")
data = person_serializer.data
for membership in data["memberships"]:
if membership["organization"]:
self.assertEqual(membership["organization"]["language_code"], "ms") | Sinar/popit_ng | [
21,
4,
21,
91,
1443407491
] |
def test_fetch_person_minimized_serializer(self):
person = Person.objects.untranslated().get(id='8497ba86-7485-42d2-9596-2ab14520f1f4')
person_serializer = MinPersonSerializer(person)
membership_count = person.memberships.count()
self.assertTrue(len(person_serializer.data["memberships"]), membership_count) | Sinar/popit_ng | [
21,
4,
21,
91,
1443407491
] |
def test_view_person_list(self):
response = self.client.get("/en/persons/")
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertTrue("page" in response.data)
self.assertEqual(response.data["per_page"], settings.REST_FRAMEWORK["PAGE_SIZE"])
self.assertEqual(response.data["num_pages"], 1) | Sinar/popit_ng | [
21,
4,
21,
91,
1443407491
] |
def test_view_person_detail_not_exist(self):
response = self.client.get("/en/persons/not_exist/")
self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND) | Sinar/popit_ng | [
21,
4,
21,
91,
1443407491
] |
def test_create_person_authorized(self):
person_data = {
"name": "joe",
"family_name": "doe",
"given_name": "joe jambul",
"additional_name": "not john doe",
"gender": "unknown",
"summary": "person unit test api",
"honorific_prefix": "Chief",
"honorific_suffix": "of the fake people league",
"biography": "He does not exists!!!!",
"birth_date": "1950-01-01",
"death_data": "2000-01-01",
"email": "joejambul@sinarproject.org",
"contact_details":[
{
"type":"twitter",
"value": "sinarproject",
}
],
"links":[
{
"url":"http://sinarproject.org",
}
],
"identifiers":[
{
"identifier": "9089098098",
"scheme": "rakyat",
}
],
"other_names":[
{
"name":"Jane",
"family_name":"Jambul",
"start_date": "1950-01-01",
"end_date": "2010-01-01",
}
]
}
token = Token.objects.get(user__username="admin")
self.client.credentials(HTTP_AUTHORIZATION='Token ' + token.key)
response = self.client.post("/en/persons/", person_data)
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
person = Person.objects.language("en").get(name="joe")
self.assertEqual(person.name, "joe") | Sinar/popit_ng | [
21,
4,
21,
91,
1443407491
] |
def test_update_person_not_exist_unauthorized(self):
person_data = {
"given_name": "jerry jambul",
}
response = self.client.put("/en/persons/not_exist/", person_data)
self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED) | Sinar/popit_ng | [
21,
4,
21,
91,
1443407491
] |
def test_update_person_not_exist_authorized(self):
person_data = {
"given_name": "jerry jambul",
}
token = Token.objects.get(user__username="admin")
self.client.credentials(HTTP_AUTHORIZATION='Token ' + token.key)
response = self.client.put("/en/persons/not_exist/", person_data)
self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND) | Sinar/popit_ng | [
21,
4,
21,
91,
1443407491
] |
def test_create_person_links_authorized(self):
person_data = {
"links": [
{
"url": "http://twitter.com/sweemeng",
}
]
}
token = Token.objects.get(user__username="admin")
self.client.credentials(HTTP_AUTHORIZATION='Token ' + token.key)
response = self.client.put("/en/persons/ab1a5788e5bae955c048748fa6af0e97/", person_data)
self.assertEqual(response.status_code, status.HTTP_200_OK)
person_ = Person.objects.language('en').get(id='ab1a5788e5bae955c048748fa6af0e97')
url = person_.links.language("en").get(url="http://twitter.com/sweemeng")
self.assertEqual(url.url, "http://twitter.com/sweemeng") | Sinar/popit_ng | [
21,
4,
21,
91,
1443407491
] |
def test_update_person_links_authorized(self):
person_data = {
"id":"ab1a5788e5bae955c048748fa6af0e97",
"links":[
{
"id": "a4ffa24a9ef3cbcb8cfaa178c9329367",
"note": "just a random repo"
}
]
}
token = Token.objects.get(user__username="admin")
self.client.credentials(HTTP_AUTHORIZATION='Token ' + token.key)
response = self.client.put("/en/persons/ab1a5788e5bae955c048748fa6af0e97/", person_data)
self.assertEqual(response.status_code, status.HTTP_200_OK)
person_ = Person.objects.language('en').get(id='ab1a5788e5bae955c048748fa6af0e97')
url = person_.links.language("en").get(id="a4ffa24a9ef3cbcb8cfaa178c9329367")
self.assertEqual(url.note, "just a random repo") | Sinar/popit_ng | [
21,
4,
21,
91,
1443407491
] |
def test_create_nested_person_links_authorized(self):
person_data = {
"id":"ab1a5788e5bae955c048748fa6af0e97",
"contact_details":[
{
"id": "a66cb422-eec3-4861-bae1-a64ae5dbde61",
"links": [{
"url": "http://facebook.com",
}]
}
],
}
token = Token.objects.get(user__username="admin")
self.client.credentials(HTTP_AUTHORIZATION='Token ' + token.key)
response = self.client.put("/en/persons/ab1a5788e5bae955c048748fa6af0e97/", person_data)
self.assertEqual(response.status_code, status.HTTP_200_OK)
person_ = Person.objects.language('en').get(id='ab1a5788e5bae955c048748fa6af0e97')
# There should be only 1 links in that contact
contact = person_.contact_details.language('en').get(id='a66cb422-eec3-4861-bae1-a64ae5dbde61')
links = contact.links.language('en').all()
check = False
for i in links:
if i.url == "http://sinarproject.org":
check = True
self.assertTrue(check, "http://sinarproject.org does not exist") | Sinar/popit_ng | [
21,
4,
21,
91,
1443407491
] |
def test_update_nested_person_links_authorized(self):
person_data = {
"id":"8497ba86-7485-42d2-9596-2ab14520f1f4",
"identifiers":[
{
"id": "af7c01b5-1c4f-4c08-9174-3de5ff270bdb",
"links": [{
"id": "9c9a2093-c3eb-4b51-b869-0d3b4ab281fd",
"note": "this is just a test note",
}]
}
],
}
token = Token.objects.get(user__username="admin")
self.client.credentials(HTTP_AUTHORIZATION='Token ' + token.key)
response = self.client.put("/en/persons/8497ba86-7485-42d2-9596-2ab14520f1f4/", person_data)
self.assertEqual(response.status_code, status.HTTP_200_OK)
person_ = Person.objects.language('en').get(id='8497ba86-7485-42d2-9596-2ab14520f1f4')
identifier = person_.identifiers.language('en').get(id="af7c01b5-1c4f-4c08-9174-3de5ff270bdb")
link = identifier.links.language('en').get(id="9c9a2093-c3eb-4b51-b869-0d3b4ab281fd")
self.assertEqual(link.note, "this is just a test note") | Sinar/popit_ng | [
21,
4,
21,
91,
1443407491
] |
def test_create_other_names_authorized(self):
person_data = {
"other_names": [
{
"name": "jane",
"family_name": "jambul",
"given_name": "test person",
"start_date": "1950-01-01",
"end_date": "2010-01-01",
}
]
}
token = Token.objects.get(user__username="admin")
self.client.credentials(HTTP_AUTHORIZATION='Token ' + token.key)
response = self.client.put("/en/persons/ab1a5788e5bae955c048748fa6af0e97/", person_data)
self.assertEqual(response.status_code, status.HTTP_200_OK)
person_ = Person.objects.language('en').get(id='ab1a5788e5bae955c048748fa6af0e97')
other_name = person_.other_names.language('en').get(name="jane")
self.assertEqual(other_name.given_name, "test person") | Sinar/popit_ng | [
21,
4,
21,
91,
1443407491
] |
def test_update_other_names_authorized(self):
person_data = {
"other_names": [
{
"id": "cf93e73f-91b6-4fad-bf76-0782c80297a8",
"family_name": "jambul",
}
]
}
token = Token.objects.get(user__username="admin")
self.client.credentials(HTTP_AUTHORIZATION='Token ' + token.key)
response = self.client.put("/en/persons/8497ba86-7485-42d2-9596-2ab14520f1f4/", person_data)
self.assertEqual(response.status_code, status.HTTP_200_OK)
person_ = Person.objects.language('en').get(id='8497ba86-7485-42d2-9596-2ab14520f1f4')
other_name = person_.other_names.language('en').get(id="cf93e73f-91b6-4fad-bf76-0782c80297a8")
self.assertEqual(other_name.family_name, "jambul") | Sinar/popit_ng | [
21,
4,
21,
91,
1443407491
] |
def test_delete_person_not_exist_unauthorized(self):
response = self.client.delete("/en/persons/not_exist/")
self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED) | Sinar/popit_ng | [
21,
4,
21,
91,
1443407491
] |
def test_delete_person_not_exist_authorized(self):
token = Token.objects.get(user__username="admin")
self.client.credentials(HTTP_AUTHORIZATION='Token ' + token.key)
response = self.client.delete("/en/persons/not_exist/")
self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND) | Sinar/popit_ng | [
21,
4,
21,
91,
1443407491
] |
def test_person_api_invalid_date(self):
person_data = {
"name": "joe",
"family_name": "doe",
"given_name": "joe jambul",
"additional_name": "not john doe",
"gender": "unknown",
"summary": "person unit test api",
"honorific_prefix": "Chief",
"honorific_suffix": "of the fake people league",
"biography": "He does not exists!!!!",
"birth_date": "invalid date",
"death_date": "invalid date",
"email": "joejambul@sinarproject.org",
}
token = Token.objects.get(user__username="admin")
self.client.credentials(HTTP_AUTHORIZATION='Token ' + token.key)
response = self.client.post("/en/persons/", person_data)
self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)
self.assertTrue("errors" in response.data) | Sinar/popit_ng | [
21,
4,
21,
91,
1443407491
] |
def test_create_person_authorized_translated(self):
person_data = {
"name": "joe",
"family_name": "doe",
"given_name": "joe jambul",
"additional_name": "bukan john doe",
"gender": "tak tahu",
"summary": "orang ujian",
"honorific_prefix": "Datuk Seri",
"biography": "Dia Tak wujud!!!!",
"email": "joejambul@sinarproject.org",
}
token = Token.objects.get(user__username="admin")
self.client.credentials(HTTP_AUTHORIZATION='Token ' + token.key)
response = self.client.post("/ms/persons/", person_data)
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
person = Person.objects.language("ms").get(name="joe")
self.assertEqual(person.name, "joe") | Sinar/popit_ng | [
21,
4,
21,
91,
1443407491
] |
def test_create_person_identifier_blank_id_authorized(self):
person_data = {
"name": "joe",
"family_name": "doe",
"given_name": "joe jambul",
"additional_name": "not john doe",
"gender": "unknown",
"summary": "person unit test api",
"honorific_prefix": "Chief",
"honorific_suffix": "of the fake people league",
"biography": "He does not exists!!!!",
"birth_date": "1950-01-01",
"death_data": "2000-01-01",
"email": "joejambul@sinarproject.org",
"identifiers":[
{
"id": "",
"identifier": "9089098098",
"scheme": "rakyat",
}
],
}
token = Token.objects.get(user__username="admin")
self.client.credentials(HTTP_AUTHORIZATION='Token ' + token.key)
response = self.client.post("/en/persons/", person_data)
logging.warn(response.data["result"]["other_names"])
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
identifiers = response.data["result"]["identifiers"][0]
self.assertNotEqual(identifiers["id"], "") | Sinar/popit_ng | [
21,
4,
21,
91,
1443407491
] |
def test_create_person_links_blank_id_authorized(self):
person_data = {
"name": "joe",
"family_name": "doe",
"given_name": "joe jambul",
"additional_name": "not john doe",
"gender": "unknown",
"summary": "person unit test api",
"honorific_prefix": "Chief",
"honorific_suffix": "of the fake people league",
"biography": "He does not exists!!!!",
"birth_date": "1950-01-01",
"death_data": "2000-01-01",
"email": "joejambul@sinarproject.org",
"links":[
{
"id": "",
"url":"http://sinarproject.org",
}
],
}
token = Token.objects.get(user__username="admin")
self.client.credentials(HTTP_AUTHORIZATION='Token ' + token.key)
response = self.client.post("/en/persons/", person_data)
logging.warn(response.data["result"]["other_names"])
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
links = response.data["result"]["links"][0]
self.assertNotEqual(links["id"], "") | Sinar/popit_ng | [
21,
4,
21,
91,
1443407491
] |
def test_create_person_with_all_field_birthdate_deathdate_blank_serializer(self):
person_data = {
"name": "joe",
"family_name": "doe",
"given_name": "joe jambul",
"additional_name": "not john doe",
"gender": "unknown",
"summary": "person unit test api",
"honorific_prefix": "Chief",
"honorific_suffix": "of the fake people league",
"biography": "He does not exists!!!!",
"birth_date": "",
"death_data": "",
"email": "joejambul@sinarproject.org",
"contact_details":[
{
"type":"twitter",
"value": "sinarproject",
}
],
"links":[
{
"url":"http://sinarproject.org",
}
],
"identifiers":[
{
"identifier": "9089098098",
"scheme": "rakyat",
}
],
"other_names":[
{
"name":"Jane",
"family_name":"Jambul",
"start_date": "1950-01-01",
"end_date": "2010-01-01",
}
]
}
person_serial = PersonSerializer(data=person_data, language='en')
person_serial.is_valid()
self.assertEqual(person_serial.errors, {})
person_serial.save()
person = Person.objects.language("en").get(name="joe")
self.assertEqual(person.given_name, "joe jambul") | Sinar/popit_ng | [
21,
4,
21,
91,
1443407491
] |
def world_to_image_projection(p_world, intrinsics, pose_w2c):
"""Project points in the world frame to the image plane.
Args:
p_world: [HEIGHT, WIDTH, 3] points in the world's coordinate frame.
intrinsics: [3, 3] camera's intrinsic matrix.
pose_w2c: [3, 4] camera pose matrix (world to camera).
Returns:
[HEIGHT, WIDTH, 2] points in the image coordinate.
[HEIGHT, WIDTH, 1] the z depth.
"""
shape = p_world.shape.as_list()
height, width = shape[0], shape[1]
p_world_homogeneous = tf.concat([p_world, tf.ones([height, width, 1])], -1)
p_camera = tf.squeeze(
tf.matmul(pose_w2c[tf.newaxis, tf.newaxis, :],
tf.expand_dims(p_world_homogeneous, -1)), -1)
p_camera = p_camera*tf.constant([1., 1., -1.], shape=[1, 1, 3])
p_image = tf.squeeze(tf.matmul(intrinsics[tf.newaxis, tf.newaxis, :],
tf.expand_dims(p_camera, -1)), -1)
z = p_image[:, :, -1:]
return tf.math.divide_no_nan(p_image[:, :, :2], z), z | google-research/google-research | [
27788,
6881,
27788,
944,
1538678568
] |
def overlap_mask(depth1,
pose1_c2w,
depth2,
pose2_c2w,
intrinsics):
"""Compute the overlap masks of two views using triangulation.
The masks have the same shape of the input images. A pixel value is true if it
can be seen by both cameras.
Args:
depth1: [HEIGHT, WIDTH, 1] the depth map of the first view.
pose1_c2w: [3, 4] camera pose matrix (camera to world) of the first view.
pose1_c2w[:, :3] is the rotation and pose1_c2w[:, -1] is the translation.
depth2: [HEIGHT, WIDTH, 1] the depth map of the second view.
pose2_c2w: [3, 4] camera pose matrix (camera to world) of the second view.
pose1_c2w[:, :3] is the rotation and pose1_c2w[:, -1] is the translation.
intrinsics: [3, 3] camera's intrinsic matrix.
Returns:
[HEIGHT, WIDTH] two overlap masks of the two inputs respectively.
"""
pose1_w2c = tf.matrix_inverse(
tf.concat([pose1_c2w, tf.constant([[0., 0., 0., 1.]])], 0))[:3]
pose2_w2c = tf.matrix_inverse(
tf.concat([pose2_c2w, tf.constant([[0., 0., 0., 1.]])], 0))[:3]
p_world1 = image_to_world_projection(depth1, intrinsics, pose1_c2w)
p_image1_in_2, z1_c2 = world_to_image_projection(
p_world1, intrinsics, pose2_w2c)
p_world2 = image_to_world_projection(depth2, intrinsics, pose2_c2w)
p_image2_in_1, z2_c1 = world_to_image_projection(
p_world2, intrinsics, pose1_w2c)
shape = depth1.shape.as_list()
height, width = shape[0], shape[1]
height = tf.cast(height, tf.float32)
width = tf.cast(width, tf.float32)
# Error tolerance.
eps = 1e-4
# check the object seen by camera 2 is also projected to camera 1's image
# plane and in front of the camera 1.
mask_h2_in_1 = tf.logical_and(
tf.less_equal(p_image2_in_1[:, :, 1], height+eps),
tf.greater_equal(p_image2_in_1[:, :, 1], 0.-eps))
mask_w2_in_1 = tf.logical_and(
tf.less_equal(p_image2_in_1[:, :, 0], width+eps),
tf.greater_equal(p_image2_in_1[:, :, 0], 0.-eps))
# check the projected points are within the image boundaries and in front of
# the camera.
mask2_in_1 = tf.logical_and(
tf.logical_and(mask_h2_in_1, mask_w2_in_1), tf.squeeze(z2_c1, -1) > 0)
# check the object seen by camera 1 is also projected to camera 2's image
# plane and in front of the camera 2.
mask_h1_in_2 = tf.logical_and(
tf.less_equal(p_image1_in_2[:, :, 1], height+eps),
tf.greater_equal(p_image1_in_2[:, :, 1], 0.-eps))
mask_w1_in_2 = tf.logical_and(
tf.less_equal(p_image1_in_2[:, :, 0], width+eps),
tf.greater_equal(p_image1_in_2[:, :, 0], 0.-eps))
# check the projected points are within the image boundaries and in front of
# the camera.
mask1_in_2 = tf.logical_and(
tf.logical_and(mask_h1_in_2, mask_w1_in_2), tf.squeeze(z1_c2, -1) > 0)
return mask1_in_2, mask2_in_1 | google-research/google-research | [
27788,
6881,
27788,
944,
1538678568
] |
def generate_from_meta(meta_data_path,
pano_data_dir,
pano_height=1024,
pano_width=2048,
output_height=512,
output_width=512):
"""Generate the stereo image dataset from Matterport3D using the meta data.
Example call:
ds = generate_from_meta(
meta_data_path='matterport3d/saved_meta/R90_fov90/test_meta/',
pano_data_dir='matterport3d/pano/')
Args:
meta_data_path: (string) the path to the meta data files.
pano_data_dir: (string) the path to the panorama images of the Matterport3D.
pano_height: (int) the height dimension of the panorama images.
pano_width: (int) the width dimension of the panorama images.
output_height: (int) the height dimension of the output perspective images.
output_width: (int) the width dimension of the output perspective images.
Returns:
Tensorflow Dataset.
"""
def load_text(file_path, n_lines=200):
"""Load text data from a file."""
return tf.data.Dataset.from_tensor_slices(
tf.data.experimental.get_single_element(
tf.data.TextLineDataset(file_path).batch(n_lines)))
def load_single_image(filename):
"""Load a single image given the filename."""
image = tf.image.decode_jpeg(tf.read_file(filename), 3)
image = tf.image.convert_image_dtype(image, tf.float32)
image.set_shape([pano_height, pano_width, 3])
return image
def string_to_matrix(s, shape):
"""Decode strings to matrices tensor."""
m = tf.reshape(
tf.stack([tf.decode_csv(s, [0.0] * np.prod(shape))], 0), shape)
m.set_shape(shape)
return m
def decode_line(line):
"""Decode text lines."""
DataPair = collections.namedtuple(
'DataPair', ['src_img', 'trt_img', 'fov', 'rotation', 'translation'])
splitted = tf.decode_csv(line, ['']*10, field_delim=' ')
img1 = load_single_image(pano_data_dir+splitted[0]+'/'+splitted[1]+'.jpeg')
img2 = load_single_image(pano_data_dir+splitted[0]+'/'+splitted[2]+'.jpeg')
fov = string_to_matrix(splitted[3], [1])
r1 = string_to_matrix(splitted[4], [3, 3])
t1 = string_to_matrix(splitted[5], [3])
r2 = string_to_matrix(splitted[6], [3, 3])
t2 = string_to_matrix(splitted[7], [3])
sampled_r1 = string_to_matrix(splitted[8], [3, 3])
sampled_r2 = string_to_matrix(splitted[9], [3, 3])
r_c2_to_c1 = tf.matmul(sampled_r1, sampled_r2, transpose_a=True)
t_c1 = tf.squeeze(tf.matmul(sampled_r1,
tf.expand_dims(tf.nn.l2_normalize(t2-t1), -1),
transpose_a=True))
sampled_rotation = tf.matmul(tf.stack([sampled_r1, sampled_r2], 0),
tf.stack([r1, r2], 0), transpose_a=True)
sampled_views = transformation.rectilinear_projection(
tf.stack([img1, img2], 0),
[output_height, output_width],
fov,
tf.matrix_transpose(sampled_rotation))
src_img, trt_img = sampled_views[0], sampled_views[1]
return DataPair(src_img, trt_img, fov, r_c2_to_c1, t_c1)
# meta_data_path has slash '/' at the end.
ds = tf.data.Dataset.list_files(meta_data_path+'*')
ds = ds.flat_map(load_text)
ds = ds.map(decode_line)
return ds | google-research/google-research | [
27788,
6881,
27788,
944,
1538678568
] |
def __init__(self,
reader_class,
common_queue,
num_readers=4,
reader_kwargs=None):
"""ParallelReader creates num_readers instances of the reader_class.
Each instance is created by calling the `reader_class` function passing
the arguments specified in `reader_kwargs` as in:
reader_class(**read_kwargs)
When you read from a ParallelReader, with its `read()` method,
you just dequeue examples from the `common_queue`.
The readers will read different files in parallel, asynchronously enqueueing
their output into `common_queue`. The `common_queue.dtypes` must be
[tf.string, tf.string]
Because each reader can read from a different file, the examples in the
`common_queue` could be from different files. Due to the asynchronous
reading there is no guarantee that all the readers will read the same
number of examples.
If the `common_queue` is a shuffling queue, then the examples are shuffled.
Usage:
common_queue = tf.queue.RandomShuffleQueue(
capacity=256,
min_after_dequeue=128,
dtypes=[tf.string, tf.string])
p_reader = ParallelReader(tf.compat.v1.TFRecordReader, common_queue)
common_queue = tf.queue.FIFOQueue(
capacity=256,
dtypes=[tf.string, tf.string])
p_reader = ParallelReader(readers, common_queue, num_readers=2)
Args:
reader_class: one of the io_ops.ReaderBase subclasses ex: TFRecordReader
common_queue: a Queue to hold (key, value pairs) with `dtypes` equal to
[tf.string, tf.string]. Must be one of the data_flow_ops.Queues
instances, ex. `tf.queue.FIFOQueue()`, `tf.queue.RandomShuffleQueue()`,
...
num_readers: a integer, number of instances of reader_class to create.
reader_kwargs: an optional dict of kwargs to create the readers.
Raises:
TypeError: if `common_queue.dtypes` is not [tf.string, tf.string].
"""
if len(common_queue.dtypes) != 2:
raise TypeError('common_queue.dtypes must be [tf.string, tf.string]')
for dtype in common_queue.dtypes:
if not dtype.is_compatible_with(tf_dtypes.string):
raise TypeError('common_queue.dtypes must be [tf.string, tf.string]')
reader_kwargs = reader_kwargs or {}
self._readers = [reader_class(**reader_kwargs) for _ in range(num_readers)]
self._common_queue = common_queue | google-research/tf-slim | [
334,
98,
334,
11,
1561681288
] |
def num_readers(self):
return len(self._readers) | google-research/tf-slim | [
334,
98,
334,
11,
1561681288
] |
def common_queue(self):
return self._common_queue | google-research/tf-slim | [
334,
98,
334,
11,
1561681288
] |
def read_up_to(self, queue, num_records, name=None):
"""Returns up to num_records (key, value pairs) produced by a reader.
Will dequeue a work unit from queue if necessary (e.g., when the
Reader needs to start reading from a new file since it has
finished with the previous file).
It may return less than num_records even before the last batch.
**Note** This operation is not supported by all types of `common_queue`s.
If a `common_queue` does not support `dequeue_up_to()`, then a
`tf.errors.UnimplementedError` is raised.
Args:
queue: A Queue or a mutable string Tensor representing a handle to a
Queue, with string work items.
num_records: Number of records to read.
name: A name for the operation (optional).
Returns:
A tuple of Tensors (keys, values) from common_queue.
keys: A 1-D string Tensor.
values: A 1-D string Tensor.
"""
self._configure_readers_by(queue)
return self._common_queue.dequeue_up_to(num_records, name) | google-research/tf-slim | [
334,
98,
334,
11,
1561681288
] |
def num_records_produced(self, name=None):
"""Returns the number of records this reader has produced.
Args:
name: A name for the operation (optional).
Returns:
An int64 Tensor.
"""
num_records = [r.num_records_produced() for r in self._readers]
return math_ops.add_n(num_records, name=name) | google-research/tf-slim | [
334,
98,
334,
11,
1561681288
] |
def parallel_read(data_sources,
reader_class,
num_epochs=None,
num_readers=4,
reader_kwargs=None,
shuffle=True,
dtypes=None,
capacity=256,
min_after_dequeue=128,
seed=None,
scope=None):
"""Reads multiple records in parallel from data_sources using n readers.
It uses a ParallelReader to read from multiple files in parallel using
multiple readers created using `reader_class` with `reader_kwargs'.
If shuffle is True the common_queue would be a RandomShuffleQueue otherwise
it would be a FIFOQueue.
Usage:
data_sources = ['path_to/train*']
key, value = parallel_read(data_sources, tf.CSVReader, num_readers=4)
Args:
data_sources: a list/tuple of files or the location of the data, i.e.
/path/to/train@128, /path/to/train* or /tmp/.../train*
reader_class: one of the io_ops.ReaderBase subclasses ex: TFRecordReader
num_epochs: The number of times each data source is read. If left as None,
the data will be cycled through indefinitely.
num_readers: a integer, number of Readers to create.
reader_kwargs: an optional dict, of kwargs for the reader.
shuffle: boolean, whether should shuffle the files and the records by using
RandomShuffleQueue as common_queue.
dtypes: A list of types. The length of dtypes must equal the number of
elements in each record. If it is None it will default to [tf.string,
tf.string] for (key, value).
capacity: integer, capacity of the common_queue.
min_after_dequeue: integer, minimum number of records in the common_queue
after dequeue. Needed for a good shuffle.
seed: A seed for RandomShuffleQueue.
scope: Optional name scope for the ops.
Returns:
key, value: a tuple of keys and values from the data_source.
"""
data_files = get_data_files(data_sources)
with ops.name_scope(scope, 'parallel_read'):
filename_queue = tf_input.string_input_producer(
data_files,
num_epochs=num_epochs,
shuffle=shuffle,
seed=seed,
name='filenames')
dtypes = dtypes or [tf_dtypes.string, tf_dtypes.string]
if shuffle:
common_queue = data_flow_ops.RandomShuffleQueue(
capacity=capacity,
min_after_dequeue=min_after_dequeue,
dtypes=dtypes,
seed=seed,
name='common_queue')
else:
common_queue = data_flow_ops.FIFOQueue(
capacity=capacity, dtypes=dtypes, name='common_queue')
summary.scalar(
'fraction_of_%d_full' % capacity,
math_ops.cast(common_queue.size(), tf_dtypes.float32) * (1. / capacity))
return ParallelReader(
reader_class,
common_queue,
num_readers=num_readers,
reader_kwargs=reader_kwargs).read(filename_queue) | google-research/tf-slim | [
334,
98,
334,
11,
1561681288
] |
def device_broadcast(x, num_devices):
"""Broadcast a value to all devices."""
return jax.pmap(lambda _: x)(jnp.arange(num_devices)) | google-research/google-research | [
27788,
6881,
27788,
944,
1538678568
] |
def batched_loss_fn(model):
"""Apply loss function across a batch of examples."""
loss, metrics = jax.vmap(loss_fn, (None, 0, None))(model, batched_examples,
static_batch_metadata)
return jnp.mean(loss), metrics | google-research/google-research | [
27788,
6881,
27788,
944,
1538678568
] |
def _build_parallel_train_step():
"""Builds an accelerated version of the train step function."""
# We need to wrap and unwrap so that the final function can be called with
# keyword arguments, but we still maintain the proper axes.
@functools.partial(
jax.pmap,
axis_name="devices",
in_axes=(0, 0, None, None, None, None),
static_broadcasted_argnums=(2, 3))
def wrapped(optimizer, batched_examples, static_batch_metadata, loss_fn,
max_global_norm, optimizer_hyper_params):
return _parallel_train_step(optimizer, batched_examples,
static_batch_metadata, loss_fn, max_global_norm,
**optimizer_hyper_params)
@functools.wraps(_parallel_train_step)
def wrapper(optimizer, batched_examples, static_batch_metadata, loss_fn,
max_global_norm, **optimizer_hyper_params):
return wrapped(optimizer, batched_examples, static_batch_metadata, loss_fn,
max_global_norm, optimizer_hyper_params)
return wrapper | google-research/google-research | [
27788,
6881,
27788,
944,
1538678568
] |
def warmup_train_step(
optimizer,
batched_example,
static_batch_metadata,
loss_fn,
optimizer_is_replicated = False,
profile = False,
runner=None, | google-research/google-research | [
27788,
6881,
27788,
944,
1538678568
] |
def go():
# Note that value for learning_rate is arbitrary, but we pass it here to
# warm up the jit cache (since we are passing a learning rate at training
# time).
res = parallel_train_step(
replicated_optimizer,
batched_example,
static_batch_metadata,
loss_fn,
max_global_norm=max_global_norm,
learning_rate=0.0)
jax.tree_map(lambda x: x.block_until_ready(), res) | google-research/google-research | [
27788,
6881,
27788,
944,
1538678568
] |
def build_averaging_validator(
loss_fn,
valid_iterator_factory,
objective_metric_name = None,
include_total_counts = False,
prefetch = True, | google-research/google-research | [
27788,
6881,
27788,
944,
1538678568
] |
def parallel_metrics_batch(model, batched_examples, batch_mask,
static_metadata):
loss, metrics = jax.vmap(loss_fn, (None, 0, None))(model, batched_examples,
static_metadata)
metrics["loss"] = loss
metrics = jax.tree_map(
lambda x: jnp.where(batch_mask, x, jnp.zeros_like(x)), metrics)
metrics = jax.tree_map(lambda x: jax.lax.psum(jnp.sum(x), "devices"),
metrics)
return metrics | google-research/google-research | [
27788,
6881,
27788,
944,
1538678568
] |
def __init__(self, spec):
pass | google-research/google-research | [
27788,
6881,
27788,
944,
1538678568
] |
def default_matrix(self):
"""Returns default matrix for initialization.
Size is taken from spec.
"""
raise NotImplementedError() | google-research/google-research | [
27788,
6881,
27788,
944,
1538678568
] |
def __init__(self, spec):
"""Initializer.
Args:
spec: hparams object with default value given by
self.get_default_hparams().
"""
super(LowRankDecompMatrixCompressor, self).__init__(spec)
self._spec = spec
self.uncompressed_size = 0
self.compressed_size = 0 | google-research/google-research | [
27788,
6881,
27788,
944,
1538678568
] |
def get_default_hparams():
"""Get a tf.HParams object with the default values for the hyperparameters.
name: string
name of the low-rank matrix decompressor specification.
rank: integer
rank of the low-rank decomposition that is performed.
compressor_option: integer
indicates what type of factorization (if any) is used.
is_b_matrix_trainable: bool
indicates whether the b_matrix matrix in the factorization is to be
trained.
is_c_matrix_trainable: bool
indicates whether the c_matrix matrix in the factorization is to be
trained.
Returns:
tf.HParams object initialized to default values.
"""
return HParams(
name='model_compression',
rank=100,
num_rows=10,
num_cols=10,
use_tpu=False,
compressor_option=0,
is_b_matrix_trainable=True,
is_c_matrix_trainable=True,
is_c_matrix_present=True,
block_size=1,
pruning_fraction=0.0,
use_lsh=False) | google-research/google-research | [
27788,
6881,
27788,
944,
1538678568
] |
def __init__(self, scope='default_scope', spec=None, global_step=None):
pass | google-research/google-research | [
27788,
6881,
27788,
944,
1538678568
] |
def get_update_op(self):
"""Update operator.
Returns:
TF operator that implements the update steps that may need to
be applied periodically.
"""
raise NotImplementedError() | google-research/google-research | [
27788,
6881,
27788,
944,
1538678568
] |
def __init__(self,
scope='default_scope',
spec=None,
global_step=None,
layer=None):
"""Initializer.
Args:
scope: TF scope used for creating new TF variables.
spec: compression hyper parameters default value given by
self.get_default_hparams().
global_step: tf variable that has the global step.
layer: Layer to compress.
"""
super(CompressionOp, self).__init__(scope, spec, global_step)
# Compression specification
self._spec = spec
# Sanity check for compression hparams
self._validate_spec()
self._global_step = global_step
# public member variables to track the compressor, the variables and
# other tf nodes corresponding to this OP.
self.matrix_compressor = None
self.a_matrix_tfvar = None
self.b_matrix_tfvar = None
self.c_matrix_tfvar = None
self.alpha = None
self.layer = layer
self.last_alpha_update_step = None
self.uncompressed_size = 0
self.compressed_size = 0 | google-research/google-research | [
27788,
6881,
27788,
944,
1538678568
] |
def get_default_hparams():
"""Get a tf.HParams object with the default values for the hyperparameters.
name: string
name of the compression specification. Used for adding summaries and ops
under a common tensorflow name_scope.
alpha_decrement_value: float
a positive real number by which alpha is decremented at each update.
begin_compression_step: integer
the global step at which to begin compression.
end_compression_step: integer
the global step at which to terminate compression. Defaults to -1
implying that compression continues till the training stops.
use_tpu: False
indicates whether to use TPU.
compression_option: integer
indicates what type of factorization (if any) is used.
rank: integer
indicates what type of factorization (if any) is used.
update_option: integer
indicates how the update logic is being run. More specifically:
0 - run the update logic in TF; needed when using GPU/TPU.
1 - run the update logic in regular python as opposed to TF.
2 - run the update logic in TF and in regular python.
Returns:
tf.HParams object initialized to default values.
"""
return HParams(
name='model_compression',
alpha_decrement_value=0.01,
begin_compression_step=0,
end_compression_step=-1,
compression_frequency=10,
use_tpu=False,
compression_option=0,
rank=100,
update_option=0,
run_update_interval_check=1,
block_size=1,
pruning_fraction=0.0,
begin_pruning_step=0,
end_pruning_step=-1,
weight_sparsity_map=[''],
block_dims_map=[''],
threshold_decay=0.0,
pruning_frequency=10,
nbins=256,
block_height=1,
block_width=1,
block_pooling_function='AVG',
initial_sparsity=0.0,
target_sparsity=0.5,
sparsity_function_begin_step=0,
sparsity_function_end_step=100,
sparsity_function_exponent=3.0,
gradient_decay_rate=0.99,
prune_option='weight') | google-research/google-research | [
27788,
6881,
27788,
944,
1538678568
] |
def compressed_matmul_keras(self, inputs, training=False):
"""Matmul with a convex combination of original and compressed weights."""
if training:
compressed_mat = self.alpha * self.a_matrix_tfvar + (
1 - self.alpha) * tf.matmul(self.b_matrix_tfvar, self.c_matrix_tfvar)
return tf.matmul(inputs, compressed_mat)
else:
# This prevents the TFLite converter from constant-folding the product of
# B & C matrices.
intermediate = tf.matmul(inputs, self.b_matrix_tfvar)
return tf.matmul(intermediate, self.c_matrix_tfvar) | google-research/google-research | [
27788,
6881,
27788,
944,
1538678568
] |
def maybe_update_alpha():
"""Maybe update the alpha param.
Checks if global_step is between begin_compression_step and
end_compression_step, and if the current training step is a
compression step.
Returns:
Boolean tensor whether the training step is a compression step.
"""
is_step_within_compression_range = tf.logical_and(
tf.greater_equal(
tf.cast(self._global_step, tf.int32),
self._spec.begin_compression_step),
tf.logical_or(
tf.less_equal(
tf.cast(self._global_step, tf.int32),
self._spec.end_compression_step),
tf.less(self._spec.end_compression_step, 0)))
is_compression_step = tf.less_equal(
tf.add(self.last_alpha_update_step, self._spec.compression_frequency),
tf.cast(self._global_step, tf.int32))
return tf.logical_and(is_step_within_compression_range,
is_compression_step) | google-research/google-research | [
27788,
6881,
27788,
944,
1538678568
] |
def compressor_and_alpha_update_op_fn():
return self._compressor_and_alpha_update_op() | google-research/google-research | [
27788,
6881,
27788,
944,
1538678568
] |
def _compressor_op(self, matrix_compressor, a_matrix_tfvar):
"""Creates compressor op based on matrix_compressor.
Meant to create the factors once at begin_compression_step.
Args:
matrix_compressor: specifies the matrix compressor object.
a_matrix_tfvar: the tf tensor to be compressed.
"""
[b_matrix_out, c_matrix_out
] = tf.compat.v1.py_function(matrix_compressor.static_matrix_compressor,
[a_matrix_tfvar], [tf.float32, tf.float32])
self.b_matrix_tfvar.assign(b_matrix_out)
self.c_matrix_tfvar.assign(c_matrix_out)
return | google-research/google-research | [
27788,
6881,
27788,
944,
1538678568
] |
def _compressor_and_alpha_update_op(self):
"""Applies compressor and also updates alpha."""
self._compressor_op(self.matrix_compressor, self.a_matrix_tfvar)
self._update_alpha_op()
self.last_alpha_update_step.assign(tf.cast(self._global_step, tf.int32)) | google-research/google-research | [
27788,
6881,
27788,
944,
1538678568
] |
def __init__(self, scope, compression_spec, compressor, global_step=None):
"""Initializer.
Args:
scope: TF scope used for creating new TF variables.
compression_spec: compression hyper parameters.
compressor: matrix compressor object of class MatrixCompressorInferface.
global_step: tf variable that has the global step.
"""
logging.info('Entering ApplyCompression constructor')
self._compression_op_spec = compression_spec
self._scope = scope
self._global_step = global_step
self._matrix_compressor = compressor
self._compression_ops = []
self._update_ops = []
self._all_update_op = None
self.uncompressed_size = 0
self.compressed_size = 0 | google-research/google-research | [
27788,
6881,
27788,
944,
1538678568
] |
def get_operator_hparam(self, hparam):
"""Returns the value of queried hparam of the compression operator."""
return self._compression_op_spec.get(hparam) | google-research/google-research | [
27788,
6881,
27788,
944,
1538678568
] |
def random_part_lens(max_n_parts, max_part_size):
return tuple(random.randint(1, max_part_size) for _ in range(random.randint(1, max_n_parts))) | quantumlib/Cirq | [
3678,
836,
3678,
314,
1513294909
] |
def test_shift_swap_network_gate_acquaintance_opps(left_part_lens, right_part_lens):
gate = cca.ShiftSwapNetworkGate(left_part_lens, right_part_lens)
n_qubits = gate.qubit_count()
qubits = cirq.LineQubit.range(n_qubits)
strategy = cirq.Circuit(gate(*qubits))
# actual_opps
initial_mapping = {q: i for i, q in enumerate(qubits)}
actual_opps = cca.get_logical_acquaintance_opportunities(strategy, initial_mapping)
# expected opps
i = 0
sides = ('left', 'right')
parts = {side: [] for side in sides}
for side, part_lens in zip(sides, (left_part_lens, right_part_lens)):
for part_len in part_lens:
parts[side].append(set(range(i, i + part_len)))
i += part_len
expected_opps = set(
frozenset(left_part | right_part)
for left_part, right_part in itertools.product(parts['left'], parts['right'])
)
assert actual_opps == expected_opps | quantumlib/Cirq | [
3678,
836,
3678,
314,
1513294909
] |
def test_shift_swap_network_gate_diagrams(left_part_lens, right_part_lens):
gate = cca.ShiftSwapNetworkGate(left_part_lens, right_part_lens)
n_qubits = gate.qubit_count()
qubits = cirq.LineQubit.range(n_qubits)
circuit = cirq.Circuit(gate(*qubits))
diagram = circuit_diagrams['undecomposed', left_part_lens, right_part_lens]
cirq.testing.assert_has_diagram(circuit, diagram)
cca.expose_acquaintance_gates(circuit)
diagram = circuit_diagrams['decomposed', left_part_lens, right_part_lens]
cirq.testing.assert_has_diagram(circuit, diagram) | quantumlib/Cirq | [
3678,
836,
3678,
314,
1513294909
] |
def test_shift_swap_network_gate_repr(left_part_lens, right_part_lens):
gate = cca.ShiftSwapNetworkGate(left_part_lens, right_part_lens)
cirq.testing.assert_equivalent_repr(gate)
gate = cca.ShiftSwapNetworkGate(left_part_lens, right_part_lens, cirq.ZZ)
cirq.testing.assert_equivalent_repr(gate) | quantumlib/Cirq | [
3678,
836,
3678,
314,
1513294909
] |
def _read_proto_file(filename, proto):
filename = filename # OSS: removed internal filename loading.
with tf.io.gfile.GFile(filename, 'r') as proto_file:
return text_format.ParseLines(proto_file, proto) | google-research/deeplab2 | [
878,
146,
878,
24,
1620859177
] |
def test_resnet50_encoder_creation(self):
backbone_options = config_pb2.ModelOptions.BackboneOptions(
name='resnet50', output_stride=32)
encoder = builder.create_encoder(
backbone_options,
tf.keras.layers.experimental.SyncBatchNormalization)
self.assertIsInstance(encoder, axial_resnet_instances.ResNet50) | google-research/deeplab2 | [
878,
146,
878,
24,
1620859177
] |
def test_mobilenet_encoder_creation(self, model_name):
backbone_options = config_pb2.ModelOptions.BackboneOptions(
name=model_name, use_squeeze_and_excite=True, output_stride=32)
encoder = builder.create_encoder(
backbone_options,
tf.keras.layers.experimental.SyncBatchNormalization)
self.assertIsInstance(encoder, mobilenet.MobileNet) | google-research/deeplab2 | [
878,
146,
878,
24,
1620859177
] |
def test_decoder_creation(self):
proto_filename = os.path.join(
_CONFIG_PATH, 'example_kitti-step_motion_deeplab.textproto')
model_options = _read_proto_file(proto_filename, config_pb2.ModelOptions())
motion_decoder = builder.create_decoder(
model_options, tf.keras.layers.experimental.SyncBatchNormalization,
ignore_label=255)
self.assertIsInstance(motion_decoder,
motion_deeplab_decoder.MotionDeepLabDecoder) | google-research/deeplab2 | [
878,
146,
878,
24,
1620859177
] |
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
'crop_pct': .96, 'interpolation': 'bicubic',
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD, 'classifier': 'head',
'first_conv': 'stem.0',
**kwargs
} | rwightman/pytorch-image-models | [
23978,
3956,
23978,
96,
1549086672
] |
def __init__(self, fn):
super().__init__()
self.fn = fn | rwightman/pytorch-image-models | [
23978,
3956,
23978,
96,
1549086672
] |
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