def test_post_delete_batch_images(channel):
stub = service_pb2_grpc.V2Stub(channel)
post_response = stub.PostInputs(
service_pb2.PostInputsRequest(
inputs=[
resources_pb2.Input(
data=resources_pb2.Data(
image=resources_pb2.Image(url=TRUCK_IMAGE_URL, allow_duplicate_url=True)
)
),
resources_pb2.Input(
data=resources_pb2.Data(
image=resources_pb2.Image(url=TRUCK_IMAGE_URL, allow_duplicate_url=True)
)
),
]
),
metadata=metadata(),
)
raise_on_failure(post_response)
input_id1 = post_response.inputs[0].id
input_id2 = post_response.inputs[1].id
wait_for_inputs_upload(stub, metadata(), [input_id1, input_id2])
delete_response = stub.DeleteInputs(
service_pb2.DeleteInputsRequest(ids=[input_id1, input_id2]), metadata=metadata()
)
raise_on_failure(delete_response)
def test_image_with_bytes(channel):
stub = service_pb2_grpc.V2Stub(channel)
with open(RED_TRUCK_IMAGE_FILE_PATH, "rb") as f:
file_bytes = f.read()
post_response = stub.PostInputs(
service_pb2.PostInputsRequest(
inputs=[
resources_pb2.Input(
data=resources_pb2.Data(image=resources_pb2.Image(base64=file_bytes))
)
]
),
metadata=metadata(),
)
raise_on_failure(post_response)
input_id = post_response.inputs[0].id
wait_for_inputs_upload(stub, metadata(), [input_id])
delete_response = stub.DeleteInputs(
service_pb2.DeleteInputsRequest(ids=[input_id]), metadata=metadata()
)
raise_on_failure(delete_response)
def test_post_list_patch_get_delete_image(channel):
stub = service_pb2_grpc.V2Stub(channel)
post_response = stub.PostInputs(
service_pb2.PostInputsRequest(
inputs=[
resources_pb2.Input(
data=resources_pb2.Data(
image=resources_pb2.Image(url=TRUCK_IMAGE_URL, allow_duplicate_url=True),
concepts=[resources_pb2.Concept(id="some-concept")],
)
)
]
),
metadata=metadata(),
)
raise_on_failure(post_response)
input_id = post_response.inputs[0].id
try:
wait_for_inputs_upload(stub, metadata(), [input_id])
list_response = stub.ListInputs(
service_pb2.ListInputsRequest(per_page=1), metadata=metadata()
)
raise_on_failure(list_response)
assert len(list_response.inputs) == 1
# Most likely we don"t have that many inputs, so this should return 0.
list_response2 = stub.ListInputs(
service_pb2.ListInputsRequest(per_page=500, page=1000), metadata=metadata()
)
raise_on_failure(list_response2)
assert len(list_response2.inputs) == 0
patch_response = stub.PatchInputs(
service_pb2.PatchInputsRequest(
action="overwrite",
inputs=[
resources_pb2.Input(
id=input_id,
data=resources_pb2.Data(
concepts=[resources_pb2.Concept(id="some-new-concept")]
),
)
],
),
metadata=metadata(),
)
raise_on_failure(patch_response)
get_response = stub.GetInput(
service_pb2.GetInputRequest(input_id=input_id), metadata=metadata()
)
raise_on_failure(get_response)
assert get_response.input.data.concepts[0].name == "some-new-concept"
finally:
delete_request = service_pb2.DeleteInputRequest(input_id=input_id)
delete_response = stub.DeleteInput(delete_request, metadata=metadata())
raise_on_failure(delete_response)
def __enter__(self) -> resources_pb2.Input:
my_concept_id = "my-concept-id-" + uuid.uuid4().hex
my_concept_name = "my concept name " + uuid.uuid4().hex
image_metadata = struct_pb2.Struct()
image_metadata.update({
"some-key": "some-value",
"another-key": {
"inner-key": "inner-value"
}
})
post_response = self._stub.PostInputs(
service_pb2.PostInputsRequest(inputs=[
resources_pb2.Input(data=resources_pb2.Data(
image=resources_pb2.Image(url=DOG_IMAGE_URL,
allow_duplicate_url=True),
concepts=[
resources_pb2.Concept(
id=my_concept_id, name=my_concept_name, value=1)
],
metadata=image_metadata,
geo=resources_pb2.Geo(geo_point=resources_pb2.GeoPoint(
longitude=44, latitude=55)),
), )
]),
metadata=metadata(),
)
raise_on_failure(post_response)
self._input = post_response.inputs[0]
wait_for_inputs_upload(self._stub, metadata(), [self._input.id])
return self._input
def __enter__(self) -> resources_pb2.Input:
post_response = self._stub.PostInputs(
service_pb2.PostInputsRequest(inputs=[
resources_pb2.Input(data=resources_pb2.Data(
image=resources_pb2.Image(url=TRAVEL_IMAGE_URL,
allow_duplicate_url=True), ), )
]),
metadata=metadata(),
)
raise_on_failure(post_response)
self._input = post_response.inputs[0]
wait_for_inputs_upload(self._stub, metadata(), [self._input.id])
return self._input
def test_post_patch_get_train_evaluate_delete_model(channel):
stub = service_pb2_grpc.V2Stub(channel)
# Add some inputs with the concepts that we'll need in the model.
post_inputs_response = stub.PostInputs(
service_pb2.PostInputsRequest(
inputs=[
resources_pb2.Input(
data=resources_pb2.Data(
image=resources_pb2.Image(url=TRUCK_IMAGE_URL, allow_duplicate_url=True),
concepts=[resources_pb2.Concept(id="some-initial-concept")],
)
),
resources_pb2.Input(
data=resources_pb2.Data(
image=resources_pb2.Image(url=DOG_IMAGE_URL, allow_duplicate_url=True),
concepts=[resources_pb2.Concept(id="some-new-concept")],
)
),
]
),
metadata=metadata(),
)
raise_on_failure(post_inputs_response)
input_id_1 = post_inputs_response.inputs[0].id
input_id_2 = post_inputs_response.inputs[1].id
wait_for_inputs_upload(stub, metadata(), [input_id_1, input_id_2])
model_id = u"我的新模型-" + uuid.uuid4().hex
post_response = stub.PostModels(
service_pb2.PostModelsRequest(
models=[
resources_pb2.Model(
id=model_id,
output_info=resources_pb2.OutputInfo(
data=resources_pb2.Data(
concepts=[resources_pb2.Concept(id="some-initial-concept")],
),
),
)
]
),
metadata=metadata(),
)
raise_on_failure(post_response)
try:
patch_response = stub.PatchModels(
service_pb2.PatchModelsRequest(
action="overwrite",
models=[
resources_pb2.Model(
id=model_id,
name="some new name",
output_info=resources_pb2.OutputInfo(
data=resources_pb2.Data(
concepts=[resources_pb2.Concept(id="some-new-concept", value=1)]
),
),
)
],
),
metadata=metadata(),
)
raise_on_failure(patch_response)
get_response = stub.GetModelOutputInfo(
service_pb2.GetModelRequest(model_id=model_id), metadata=metadata()
)
raise_on_failure(get_response)
assert get_response.model.id == model_id
assert get_response.model.name == "some new name"
assert len(get_response.model.output_info.data.concepts) == 1
assert get_response.model.output_info.data.concepts[0].id == "some-new-concept"
post_model_versions_response = stub.PostModelVersions(
service_pb2.PostModelVersionsRequest(model_id=model_id), metadata=metadata()
)
raise_on_failure(post_model_versions_response)
model_version_id = post_model_versions_response.model.model_version.id
wait_for_model_trained(stub, metadata(), model_id, model_version_id)
post_model_version_metrics_response = stub.PostModelVersionMetrics(
service_pb2.PostModelVersionMetricsRequest(
model_id=model_id, version_id=model_version_id
),
metadata=metadata(),
)
raise_on_failure(post_model_version_metrics_response)
wait_for_model_evaluated(stub, metadata(), model_id, model_version_id)
finally:
delete_response = stub.DeleteModel(
service_pb2.DeleteModelRequest(model_id=model_id), metadata=metadata()
)
raise_on_failure(delete_response)
delete_inputs_response = stub.DeleteInputs(
service_pb2.DeleteInputsRequest(ids=[input_id_1, input_id_2]), metadata=metadata()
)
raise_on_failure(delete_inputs_response)
def test_model_creation_training_and_evaluation(channel):
model_id = str(uuid.uuid4())
stub = service_pb2_grpc.V2Stub(channel)
raise_on_failure(
stub.PostModels(
service_pb2.PostModelsRequest(
models=[
resources_pb2.Model(
id=model_id,
output_info=resources_pb2.OutputInfo(
data=resources_pb2.Data(
concepts=[
resources_pb2.Concept(id="dog"),
resources_pb2.Concept(id="toddler"),
]
)
),
)
]
),
metadata=metadata(),
)
)
post_inputs_response = stub.PostInputs(
service_pb2.PostInputsRequest(
inputs=[
resources_pb2.Input(
data=resources_pb2.Data(
image=resources_pb2.Image(
url="https://samples.clarifai.com/dog2.jpeg",
allow_duplicate_url=True,
),
concepts=[resources_pb2.Concept(id="dog")],
)
),
resources_pb2.Input(
data=resources_pb2.Data(
image=resources_pb2.Image(
url="https://samples.clarifai.com/toddler-flowers.jpeg",
allow_duplicate_url=True,
),
concepts=[resources_pb2.Concept(id="toddler")],
)
),
]
),
metadata=metadata(),
)
raise_on_failure(post_inputs_response)
input_ids = [i.id for i in post_inputs_response.inputs]
wait_for_inputs_upload(stub, metadata, input_ids)
response = stub.PostModelVersions(
service_pb2.PostModelVersionsRequest(model_id=model_id), metadata=metadata()
)
raise_on_failure(response)
model_version_id = response.model.model_version.id
wait_for_model_trained(stub, metadata, model_id, model_version_id)
raise_on_failure(
stub.PostModelVersionMetrics(
service_pb2.PostModelVersionMetricsRequest(
model_id=model_id,
version_id=model_version_id,
),
metadata=metadata(),
)
)
wait_for_model_evaluated(stub, metadata, model_id, model_version_id)
response = stub.GetModelVersionMetrics(
service_pb2.GetModelVersionMetricsRequest(
model_id=model_id,
version_id=model_version_id,
fields=resources_pb2.FieldsValue(
confusion_matrix=True,
cooccurrence_matrix=True,
label_counts=True,
binary_metrics=True,
test_set=True,
),
),
metadata=metadata(),
)
raise_on_failure(response)
raise_on_failure(
stub.DeleteModel(service_pb2.DeleteModelRequest(model_id=model_id), metadata=metadata())
)
raise_on_failure(
stub.DeleteInputs(service_pb2.DeleteInputsRequest(ids=input_ids), metadata=metadata())
)
def test_post_patch_get_image_with_id_concepts_geo_and_metadata(channel):
stub = service_pb2_grpc.V2Stub(channel)
input_id = uuid.uuid4().hex
input_metadata = Struct()
input_metadata.update({
"key1": 123,
"key2": {
"inner-key1": "inner-val1",
"inner-key2": "inner-val2",
},
})
post_response = stub.PostInputs(
service_pb2.PostInputsRequest(inputs=[
resources_pb2.Input(
id=input_id,
data=resources_pb2.Data(
image=resources_pb2.Image(url=TRUCK_IMAGE_URL,
allow_duplicate_url=True),
concepts=[
resources_pb2.Concept(id="some-positive-concept",
value=1),
resources_pb2.Concept(id="some-negative-concept",
value=0),
],
geo=resources_pb2.Geo(geo_point=resources_pb2.GeoPoint(
longitude=55.0, latitude=66), ),
metadata=input_metadata,
),
)
]),
metadata=metadata(),
)
raise_on_failure(post_response)
wait_for_inputs_upload(stub, metadata(), [input_id])
get_response = stub.GetInput(
service_pb2.GetInputRequest(input_id=input_id), metadata=metadata())
raise_on_failure(get_response)
inp = get_response.input
assert inp.id == input_id
assert inp.data.concepts[0].id == "some-positive-concept"
assert inp.data.concepts[0].value == 1.0
assert inp.data.concepts[1].id == "some-negative-concept"
assert inp.data.concepts[1].value == 0.0
assert inp.data.metadata["key1"] == 123
assert inp.data.metadata["key2"]["inner-key1"] == "inner-val1"
assert inp.data.metadata["key2"]["inner-key2"] == "inner-val2"
assert inp.data.geo.geo_point.longitude == 55.0
assert inp.data.geo.geo_point.latitude == 66.0
new_metadata = Struct()
new_metadata.update({"new-key": "new-value"})
patch_response = stub.PatchInputs(
service_pb2.PatchInputsRequest(
action="merge",
inputs=[
resources_pb2.Input(
id=input_id,
data=resources_pb2.Data(
concepts=[
resources_pb2.Concept(
id="another-positive-concept", value=1)
],
geo=resources_pb2.Geo(geo_point=resources_pb2.GeoPoint(
longitude=77.0, latitude=88.0)),
metadata=new_metadata,
),
)
],
),
metadata=metadata(),
)
raise_on_failure(patch_response)
inp = patch_response.inputs[0]
assert inp.data.concepts[2].id == "another-positive-concept"
assert inp.data.geo.geo_point.longitude == 77.0
assert inp.data.geo.geo_point.latitude == 88.0
assert inp.data.metadata["new-key"] == "new-value"
assert (inp.data.metadata["key1"] == 123
) # Since we use the merge action, the old values should remain
delete_response = stub.DeleteInputs(
service_pb2.DeleteInputsRequest(ids=[input_id]), metadata=metadata())
raise_on_failure(delete_response)
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)
