def test_search_by_geo_box_and_annotated_name_and_predicted_name(channel):
stub = service_pb2_grpc.V2Stub(channel)
with SetupImage(stub) as input_:
my_concept_name = input_.data.concepts[0].name
response = stub.PostSearches(
service_pb2.PostSearchesRequest(
query=resources_pb2.Query(ands=[
resources_pb2.And(input=resources_pb2.Input(
data=resources_pb2.Data(geo=resources_pb2.Geo(geo_box=[
resources_pb2.GeoBoxedPoint(
geo_point=resources_pb2.GeoPoint(longitude=43,
latitude=54)),
resources_pb2.GeoBoxedPoint(
geo_point=resources_pb2.GeoPoint(longitude=45,
latitude=56)),
])))),
resources_pb2.And(input=resources_pb2.Input(
data=resources_pb2.Data(concepts=[
resources_pb2.Concept(name=my_concept_name,
value=1)
]))),
resources_pb2.And(output=resources_pb2.Output(
data=resources_pb2.Data(concepts=[
resources_pb2.Concept(name="dog", value=1)
]))),
]),
pagination=service_pb2.Pagination(page=1, per_page=1000),
),
metadata=metadata(),
)
raise_on_failure(response)
assert len(response.hits) > 0
assert input_.id in [hit.input.id for hit in response.hits]
def test_search_by_image_url_and_geo_box(channel):
stub = service_pb2_grpc.V2Stub(channel)
with SetupImage(stub) as input_:
response = stub.PostSearches(
service_pb2.PostSearchesRequest(
query=resources_pb2.Query(ands=[
resources_pb2.And(output=resources_pb2.Output(
input=resources_pb2.Input(data=resources_pb2.Data(
image=resources_pb2.Image(url=DOG_IMAGE_URL))))),
resources_pb2.And(input=resources_pb2.Input(
data=resources_pb2.Data(geo=resources_pb2.Geo(geo_box=[
resources_pb2.GeoBoxedPoint(
geo_point=resources_pb2.GeoPoint(longitude=43,
latitude=54)),
resources_pb2.GeoBoxedPoint(
geo_point=resources_pb2.GeoPoint(longitude=45,
latitude=56)),
])))),
]),
pagination=service_pb2.Pagination(page=1, per_page=1000),
),
metadata=metadata(),
)
raise_on_failure(response)
assert len(response.hits) > 0
assert input_.id in [hit.input.id for hit in response.hits]
def test_save_and_execute_search_by_id(channel):
stub = service_pb2_grpc.V2Stub(channel)
search_id = "my-search-id-" + uuid.uuid4().hex[:15]
with SetupImage(stub) as input_:
my_concept_id = input_.data.concepts[0].id
# This saves the search under an ID, but does not execute it / return any results.
save_search_response = stub.PostSearches(
service_pb2.PostSearchesRequest(searches=[
resources_pb2.Search(
id=search_id,
save=True,
query=resources_pb2.Query(ands=[
resources_pb2.And(input=resources_pb2.Input(
data=resources_pb2.Data(concepts=[
resources_pb2.Concept(id=my_concept_id,
value=1)
])))
]),
)
]),
metadata=metadata(),
)
raise_on_failure(save_search_response)
# Executing the search returns results.
post_search_by_id_response = stub.PostSearchesByID(
service_pb2.PostSearchesByIDRequest(id=search_id),
metadata=metadata(),
)
raise_on_failure(post_search_by_id_response)
assert len(post_search_by_id_response.hits) == 1
assert post_search_by_id_response.hits[0].input.id == input_.id
def test_search_by_annotated_concept_name(channel):
stub = service_pb2_grpc.V2Stub(channel)
with SetupImage(stub) as input_:
my_concept_name = input_.data.concepts[0].name
response = stub.PostSearches(
service_pb2.PostSearchesRequest(query=resources_pb2.Query(ands=[
resources_pb2.And(input=resources_pb2.Input(
data=resources_pb2.Data(concepts=[
resources_pb2.Concept(name=my_concept_name, value=1)
])))
])),
metadata=metadata(),
)
raise_on_failure(response)
assert len(response.hits) == 1
assert response.hits[0].input.id == input_.id
def test_search_by_image_url(channel):
stub = service_pb2_grpc.V2Stub(channel)
with SetupImage(stub) as input_:
response = stub.PostSearches(
service_pb2.PostSearchesRequest(
query=resources_pb2.Query(ands=[
resources_pb2.And(output=resources_pb2.Output(
input=resources_pb2.Input(data=resources_pb2.Data(
image=resources_pb2.Image(url=DOG_IMAGE_URL)))))
]),
pagination=service_pb2.Pagination(page=1, per_page=1000),
),
metadata=metadata(),
)
raise_on_failure(response)
assert len(response.hits) > 0
assert input_.id in [hit.input.id for hit in response.hits]
def test_search_by_predicted_concept_name(channel):
stub = service_pb2_grpc.V2Stub(channel)
with SetupImage(stub) as input_:
response = stub.PostSearches(
service_pb2.PostSearchesRequest(
query=resources_pb2.Query(ands=[
resources_pb2.And(output=resources_pb2.Output(
data=resources_pb2.Data(concepts=[
resources_pb2.Concept(name="dog", value=1)
])))
]),
pagination=service_pb2.Pagination(page=1, per_page=1000),
),
metadata=metadata(),
)
raise_on_failure(response)
assert len(response.hits) > 0
assert input_.id in [hit.input.id for hit in response.hits]
def test_search_by_metadata(channel):
stub = service_pb2_grpc.V2Stub(channel)
with SetupImage(stub) as input_:
search_metadata = struct_pb2.Struct()
search_metadata.update({"another-key": {"inner-key": "inner-value"}})
response = stub.PostSearches(
service_pb2.PostSearchesRequest(
query=resources_pb2.Query(ands=[
resources_pb2.And(input=resources_pb2.Input(
data=resources_pb2.Data(metadata=search_metadata)))
]),
pagination=service_pb2.Pagination(page=1, per_page=1000),
),
metadata=metadata(),
)
raise_on_failure(response)
assert len(response.hits) > 0
assert input_.id in [hit.input.id for hit in response.hits]
def test_search_by_geo_point_and_limit(channel):
stub = service_pb2_grpc.V2Stub(channel)
with SetupImage(stub) as input_:
response = stub.PostSearches(
service_pb2.PostSearchesRequest(
query=resources_pb2.Query(ands=[
resources_pb2.And(input=resources_pb2.Input(
data=resources_pb2.Data(geo=resources_pb2.Geo(
geo_point=resources_pb2.GeoPoint(longitude=43,
latitude=56),
geo_limit=resources_pb2.GeoLimit(
value=1000, type="withinKilometers"),
))))
]),
pagination=service_pb2.Pagination(page=1, per_page=1000),
),
metadata=metadata(),
)
raise_on_failure(response)
assert len(response.hits) > 0
assert input_.id in [hit.input.id for hit in response.hits]
def test_save_and_execute_annotations_search_by_id(channel):
stub = service_pb2_grpc.V2Stub(channel)
my_search_id = "my-search-id-" + uuid.uuid4().hex[:15]
my_concept_id = "my-anno-conc-" + uuid.uuid4().hex[:15]
with SetupImage(stub) as input1, SetupImage(stub) as input2:
list_annotations_response = stub.ListAnnotations(
service_pb2.ListAnnotationsRequest(
input_ids=[input1.id, input2.id]),
metadata=metadata(),
)
raise_on_failure(list_annotations_response)
input_id_to_annotation_id = {
an.input_id: an.id
for an in list_annotations_response.annotations
}
patch_annotations_response = stub.PatchAnnotations(
service_pb2.PatchAnnotationsRequest(
action="merge",
annotations=[
resources_pb2.Annotation(
id=input_id_to_annotation_id[input1.id],
input_id=input1.id,
data=resources_pb2.Data(concepts=[
resources_pb2.Concept(id=my_concept_id, value=1)
]),
),
resources_pb2.Annotation(
id=input_id_to_annotation_id[input2.id],
input_id=input2.id,
data=resources_pb2.Data(concepts=[
resources_pb2.Concept(id=my_concept_id, value=1)
]),
),
],
),
metadata=metadata(),
)
raise_on_failure(patch_annotations_response)
as_of = timestamp_pb2.Timestamp()
as_of.FromSeconds(int(time.time() + 5))
save_search_response = stub.PostSearches(
service_pb2.PostSearchesRequest(searches=[
resources_pb2.Search(
id=my_search_id,
save=True,
as_of=as_of,
query=resources_pb2.Query(ands=[
resources_pb2.And(input=resources_pb2.Input(
data=resources_pb2.Data(concepts=[
resources_pb2.Concept(id=my_concept_id,
value=1)
])))
]),
)
]),
metadata=metadata(),
)
raise_on_failure(save_search_response)
# Executing the search returns results.
post_search_by_id_response = stub.PostSearchesByID(
service_pb2.PostSearchesByIDRequest(id=my_search_id),
metadata=metadata(),
)
raise_on_failure(post_search_by_id_response)
hits = post_search_by_id_response.hits
assert len(hits) == 2
assert input1.id in [hit.input.id for hit in hits]
assert input2.id in [hit.input.id for hit in hits]
assert all(hit.score == 1 for hit in hits)
def test_deep_classification_training_with_queries():
stub = service_pb2_grpc.V2Stub(ClarifaiChannel.get_grpc_channel())
app_id = "my-app-" + uuid.uuid4().hex[:20]
post_apps_response = stub.PostApps(
service_pb2.PostAppsRequest(
apps=[
resources_pb2.App(
id=app_id,
default_workflow_id="General",
)
]
),
metadata=pat_key_metadata(),
)
raise_on_failure(post_apps_response)
post_keys_response = stub.PostKeys(
service_pb2.PostKeysRequest(
keys=[
resources_pb2.Key(
description="All scopes",
scopes=["All"],
apps=[resources_pb2.App(id=app_id, user_id="me")],
)
],
),
metadata=pat_key_metadata(),
)
raise_on_failure(post_keys_response)
api_key = post_keys_response.keys[0].id
template_name = "classification_cifar10_v1"
model_id = "my-deep-classification-" + uuid.uuid4().hex
model_type = _get_model_type_for_template(stub, api_key, template_name)
train_info_params = struct_pb2.Struct()
train_info_params.update(
{
"template": template_name,
"num_epochs": 2,
"num_gpus": 0,
}
)
post_models_response = stub.PostModels(
service_pb2.PostModelsRequest(
models=[
resources_pb2.Model(
id=model_id,
model_type_id=model_type.id,
train_info=resources_pb2.TrainInfo(params=train_info_params),
output_info=resources_pb2.OutputInfo(
data=resources_pb2.Data(
concepts=[
resources_pb2.Concept(id="train-concept"),
resources_pb2.Concept(id="test-only-concept"),
]
),
),
)
]
),
metadata=api_key_metadata(api_key),
)
raise_on_failure(post_models_response)
train_and_test = ["train", "test"]
inputs = []
annotations = []
for i, url in enumerate(URLS):
input_id = str(i)
inputs.append(
resources_pb2.Input(
id=input_id, data=resources_pb2.Data(image=resources_pb2.Image(url=url))
)
)
train_annotation_info = struct_pb2.Struct()
train_annotation_info.update({"split": train_and_test[i % 2]})
ann = resources_pb2.Annotation(
input_id=input_id,
annotation_info=train_annotation_info,
data=resources_pb2.Data(concepts=[resources_pb2.Concept(id="train-concept", value=1)]),
)
# Add an extra concept to the test set which show should up in evals, but have a bad score since there is
# no instance of it in the train set.
if i % 2 == 1:
ann.data.concepts.append(resources_pb2.Concept(id="test-only-concept", value=1))
annotations.append(ann)
post_inputs_response = stub.PostInputs(
service_pb2.PostInputsRequest(inputs=inputs),
metadata=api_key_metadata(api_key),
)
raise_on_failure(post_inputs_response)
wait_for_inputs_upload(stub, api_key_metadata(api_key), [str(i) for i in range(len(URLS))])
post_annotations_response = stub.PostAnnotations(
service_pb2.PostAnnotationsRequest(annotations=annotations),
metadata=api_key_metadata(api_key),
)
raise_on_failure(post_annotations_response)
train_annotation_info = struct_pb2.Struct()
train_annotation_info.update({"split": "train"})
train_query = resources_pb2.Query(
ands=[
resources_pb2.And(
annotation=resources_pb2.Annotation(annotation_info=train_annotation_info)
),
]
)
test_annotation_info = struct_pb2.Struct()
test_annotation_info.update({"split": "train"})
test_query = resources_pb2.Query(
ands=[
resources_pb2.And(
negate=True,
annotation=resources_pb2.Annotation(annotation_info=test_annotation_info),
),
]
)
post_model_versions_response = stub.PostModelVersions(
service_pb2.PostModelVersionsRequest(
model_id=model_id,
train_search=resources_pb2.Search(query=train_query),
test_search=resources_pb2.Search(query=test_query),
),
metadata=api_key_metadata(api_key),
)
raise_on_failure(post_model_versions_response)
model_version_id = post_model_versions_response.model.model_version.id
wait_for_model_trained(stub, api_key_metadata(api_key), model_id, model_version_id)
post_model_outputs_response = stub.PostModelOutputs(
service_pb2.PostModelOutputsRequest(
model_id=model_id,
version_id=model_version_id,
inputs=[
resources_pb2.Input(
data=resources_pb2.Data(image=resources_pb2.Image(url=URLS[0]))
)
],
),
metadata=api_key_metadata(api_key),
)
raise_on_failure(post_model_outputs_response)
concepts = post_model_outputs_response.outputs[0].data.concepts
assert len(concepts) == 2
assert concepts[0].id == "train-concept"
assert concepts[1].id == "test-only-concept"
assert concepts[1].value <= 0.0001
delete_app_response = stub.DeleteApp(
service_pb2.DeleteAppRequest(
user_app_id=resources_pb2.UserAppIDSet(user_id="me", app_id=app_id)
),
metadata=pat_key_metadata(),
)
raise_on_failure(delete_app_response)
