def run_test_tsv(times=1):
cases = parse_tsv_key(tsv_path)
srv = InferenceServer(const.app_name, const.cfg)
run_test_empty_image(srv)
run_test_bad_image(srv)
# run_test_bad_arg(srv)
for _ in range(times):
shuffle(cases)
for index, case in enumerate(cases):
start = time.time()
log.info('run case {}/{} image:{}'.format(index + 1, len(cases),
case[0]))
request = pb.InferenceRequest(data=pb.InferenceRequest.RequestData(
body=read_test_image_with_cache(tsv_prefix + case[0])),
params=json.dumps({'limit': 3}))
response = srv.net_inference_wrap(request)
assert isinstance(response, pb.InferenceResponse)
assert response.result
assert response.code == 200
result = json.loads(response.result)
confidences = result['confidences']
confidences.sort(key=lambda x: x['index'])
actual = json.loads(json.dumps(confidences))
expected = json.loads(case[1])
assert DeepDiff(expected, actual, significant_digits=3) == {}
log.info('use time {}'.format(time.time() - start))
def run_test_bad_image(srv):
request = pb.InferenceRequest(
data=pb.InferenceRequest.RequestData(body=b'xxxx'),
params=json.dumps({'limit': 3}))
response = srv.net_inference_wrap(request)
assert response.code == 400
assert response.message == 'cv2 load image from body failed'
def serve(self):
network_in_context = zmq.Context()
network_in = network_in_context.socket(zmq.REP)
network_in.connect(const.INFERENCE_ZMQ_IN)
self.monitor_push.send(
pb.MonitorMetric(
kind="inference_started_success",
pid=str(self.pid),
).SerializeToString())
while True:
buf = network_in.recv()
request = pb.InferenceRequest()
request.ParseFromString(buf)
response = self.net_inference_wrap(request)
network_in.send(response.SerializeToString())
def run_test_empty_image(srv):
request = pb.InferenceRequest(
data=pb.InferenceRequest.RequestData(body=b''))
response = srv.net_inference_wrap(request)
assert response.code == 400
assert response.message == 'cv2 load image from body failed'