def test_predict_image_url_with_selected_concepts(channel):
stub = service_pb2_grpc.V2Stub(channel)
request = service_pb2.PostModelOutputsRequest(
model_id=GENERAL_MODEL_ID,
inputs=[
resources_pb2.Input(data=resources_pb2.Data(
image=resources_pb2.Image(url=DOG_IMAGE_URL, ), ), )
],
model=resources_pb2.Model(output_info=resources_pb2.OutputInfo(
output_config=resources_pb2.OutputConfig(select_concepts=[
resources_pb2.Concept(name="dog"),
resources_pb2.Concept(name="cat"),
]))),
)
response = post_model_outputs_and_maybe_allow_retries(stub,
request,
metadata=metadata())
raise_on_failure(response)
concepts = response.outputs[0].data.concepts
assert len(concepts) == 2
dog_concept = [c for c in concepts if c.name == "dog"][0]
cat_concept = [c for c in concepts if c.name == "cat"][0]
assert dog_concept.value > cat_concept.value
def test_post_model_with_hyper_params(channel):
stub = service_pb2_grpc.V2Stub(channel)
model_id = uuid.uuid4().hex[:30]
hyper_params = struct_pb2.Struct()
hyper_params.update({
"MAX_NITEMS": 1000000,
"MIN_NITEMS": 1000,
"N_EPOCHS": 5,
"custom_training_cfg": "custom_training_1layer",
"custom_training_cfg_args": {},
})
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")
], ),
output_config=resources_pb2.OutputConfig(
hyper_params=hyper_params),
),
)
]),
metadata=metadata(),
)
raise_on_failure(post_response)
assert (post_response.model.output_info.output_config.
hyper_params["custom_training_cfg"] == "custom_training_1layer")
delete_response = stub.DeleteModel(
service_pb2.DeleteModelRequest(model_id=model_id), metadata=metadata())
raise_on_failure(delete_response)
def test_predict_video_url_with_custom_sample_ms(channel):
stub = service_pb2_grpc.V2Stub(channel)
request = service_pb2.PostModelOutputsRequest(
model_id=GENERAL_MODEL_ID,
inputs=[
resources_pb2.Input(data=resources_pb2.Data(
video=resources_pb2.Video(url=BEER_VIDEO_URL)))
],
model=resources_pb2.Model(output_info=resources_pb2.OutputInfo(
output_config=resources_pb2.OutputConfig(sample_ms=2000))),
)
response = post_model_outputs_and_maybe_allow_retries(stub,
request,
metadata=metadata())
raise_on_failure(response)
# The expected time per frame is the middle between the start and the end of the frame
# (in milliseconds).
expected_time = 1000
assert len(response.outputs[0].data.frames) > 0
for frame in response.outputs[0].data.frames:
assert frame.frame_info.time == expected_time
expected_time += 2000
def request_call_integration(user_url, user_lan):
request = service_pb2.PostModelOutputsRequest(
model_id='aaa03c23b3724a16a56b629203edc62c',
inputs=[
resources_pb2.Input(data=resources_pb2.Data(image=resources_pb2.Image(url=user_url)))
],
model=resources_pb2.Model(
output_info=resources_pb2.OutputInfo(
output_config=resources_pb2.OutputConfig(
language=user_lan
)
)
))
response = stub.PostModelOutputs(request, metadata=metadata)
if response.status.code != status_code_pb2.SUCCESS:
raise Exception("Request failed, status code: " + str(response.status.code))
request_data=[]
for concept in response.outputs[0].data.concepts:
request_data.append(concept.name)
return request_data
def test_predict_image_url_with_max_concepts(channel):
stub = service_pb2_grpc.V2Stub(channel)
request = service_pb2.PostModelOutputsRequest(
model_id=GENERAL_MODEL_ID,
inputs=[
resources_pb2.Input(data=resources_pb2.Data(
image=resources_pb2.Image(url=DOG_IMAGE_URL, ), ), )
],
model=resources_pb2.Model(output_info=resources_pb2.OutputInfo(
output_config=resources_pb2.OutputConfig(max_concepts=3))),
)
response = stub.PostModelOutputs(request, metadata=metadata())
raise_on_failure(response)
assert len(response.outputs[0].data.concepts) == 3
def test_workflow_predict_image_url(channel):
stub = service_pb2_grpc.V2Stub(channel)
post_workflows_response = stub.PostWorkflowResults(
service_pb2.PostWorkflowResultsRequest(
workflow_id="General",
inputs=[
resources_pb2.Input(data=resources_pb2.Data(
image=resources_pb2.Image(url=DOG_IMAGE_URL)))
],
output_config=resources_pb2.OutputConfig(max_concepts=3),
),
metadata=metadata(),
)
raise_on_failure(post_workflows_response)
assert len(
post_workflows_response.results[0].outputs[0].data.concepts) == 3
def test_predict_video_url_with_max_concepts(channel):
stub = service_pb2_grpc.V2Stub(channel)
request = service_pb2.PostModelOutputsRequest(
model_id=GENERAL_MODEL_ID,
inputs=[
resources_pb2.Input(data=resources_pb2.Data(
video=resources_pb2.Video(url=CONAN_GIF_VIDEO_URL)))
],
model=resources_pb2.Model(output_info=resources_pb2.OutputInfo(
output_config=resources_pb2.OutputConfig(max_concepts=3))),
)
response = stub.PostModelOutputs(request, metadata=metadata())
raise_on_failure(response)
assert len(response.outputs[0].data.frames) > 0
for frame in response.outputs[0].data.frames:
assert len(frame.data.concepts) == 3
def test_predict_image_url_with_min_value(channel):
stub = service_pb2_grpc.V2Stub(channel)
request = service_pb2.PostModelOutputsRequest(
model_id=GENERAL_MODEL_ID,
inputs=[
resources_pb2.Input(data=resources_pb2.Data(
image=resources_pb2.Image(url=DOG_IMAGE_URL, ), ), )
],
model=resources_pb2.Model(output_info=resources_pb2.OutputInfo(
output_config=resources_pb2.OutputConfig(min_value=0.98))),
)
response = post_model_outputs_and_maybe_allow_retries(stub,
request,
metadata=metadata())
raise_on_failure(response)
assert len(response.outputs[0].data.concepts) > 0
for c in response.outputs[0].data.concepts:
assert c.value >= 0.98
def test_workflow_predict_image_bytes(channel):
stub = service_pb2_grpc.V2Stub(channel)
with open(RED_TRUCK_IMAGE_FILE_PATH, "rb") as f:
file_bytes = f.read()
post_workflows_response = stub.PostWorkflowResults(
service_pb2.PostWorkflowResultsRequest(
workflow_id="General",
inputs=[
resources_pb2.Input(data=resources_pb2.Data(
image=resources_pb2.Image(base64=file_bytes)))
],
output_config=resources_pb2.OutputConfig(max_concepts=3),
),
metadata=metadata(),
)
raise_on_failure(post_workflows_response)
assert len(
post_workflows_response.results[0].outputs[0].data.concepts) == 3
def test_predict_video_url_with_min_value(channel):
stub = service_pb2_grpc.V2Stub(channel)
request = service_pb2.PostModelOutputsRequest(
model_id=GENERAL_MODEL_ID,
inputs=[
resources_pb2.Input(data=resources_pb2.Data(
video=resources_pb2.Video(url=CONAN_GIF_VIDEO_URL)))
],
model=resources_pb2.Model(output_info=resources_pb2.OutputInfo(
output_config=resources_pb2.OutputConfig(min_value=0.95))),
)
response = post_model_outputs_and_maybe_allow_retries(stub,
request,
metadata=metadata())
raise_on_failure(response)
assert len(response.outputs[0].data.frames) > 0
for frame in response.outputs[0].data.frames:
assert len(frame.data.concepts) > 0
for concept in frame.data.concepts:
assert concept.value >= 0.95
