def test_proto_bin_data():
user_object = UserObject()
app = SeldonModelGRPC(user_object)
bdata = b"123"
bdata_base64 = base64.b64encode(bdata)
request = prediction_pb2.SeldonMessage(binData=bdata_base64)
resp = app.Predict(request, None)
assert resp.binData == bdata_base64
def test_proto_bin_data_nparray():
user_object = UserObject(ret_nparray=True)
app = SeldonModelGRPC(user_object)
binData = b"\0\1"
request = prediction_pb2.SeldonMessage(binData=binData)
resp = app.Predict(request, None)
jStr = json_format.MessageToJson(resp)
j = json.loads(jStr)
print(j)
assert j["data"]["tensor"]["values"] == list(user_object.nparray.flatten())
def test_proto_feedback_custom():
user_object = UserObjectLowLevel()
app = SeldonModelGRPC(user_object)
arr = np.array([1, 2])
datadef = prediction_pb2.DefaultData(
tensor=prediction_pb2.Tensor(
shape=(2, 1),
values=arr
)
)
request = prediction_pb2.SeldonMessage(data=datadef)
feedback = prediction_pb2.Feedback(request=request,reward=1.0)
resp = app.SendFeedback(feedback, None)
def test_proto_lowlevel():
user_object = UserObjectLowLevel()
app = SeldonModelGRPC(user_object)
arr = np.array([1, 2])
datadef = prediction_pb2.DefaultData(
tensor=prediction_pb2.Tensor(
shape=(2, 1),
values=arr
)
)
request = prediction_pb2.SeldonMessage(data=datadef)
resp = app.Predict(request, None)
jStr = json_format.MessageToJson(resp)
j = json.loads(jStr)
print(j)
assert j["data"]["tensor"]["shape"] == [2, 1]
assert j["data"]["tensor"]["values"] == [9, 9]
def test_proto_tftensor_ok():
user_object = UserObject()
app = SeldonModelGRPC(user_object)
arr = np.array([1, 2])
datadef = prediction_pb2.DefaultData(
tftensor=tf.make_tensor_proto(arr)
)
request = prediction_pb2.SeldonMessage(data=datadef)
resp = app.Predict(request, None)
jStr = json_format.MessageToJson(resp)
j = json.loads(jStr)
print(j)
assert j["meta"]["tags"] == {"mytag": 1}
# add default type
j["meta"]["metrics"][0]["type"] = "COUNTER"
assert j["meta"]["metrics"] == user_object.metrics()
arr2 = tf.make_ndarray(resp.data.tftensor)
assert np.array_equal(arr, arr2)
def test_proto_ok():
user_object = UserObject()
app = SeldonModelGRPC(user_object)
arr = np.array([1, 2])
datadef = prediction_pb2.DefaultData(
tensor=prediction_pb2.Tensor(
shape=(2, 1),
values=arr
)
)
request = prediction_pb2.SeldonMessage(data=datadef)
resp = app.Predict(request, None)
jStr = json_format.MessageToJson(resp)
j = json.loads(jStr)
print(j)
assert j["meta"]["tags"] == {"mytag": 1}
# add default type
j["meta"]["metrics"][0]["type"] = "COUNTER"
assert j["meta"]["metrics"] == user_object.metrics()
assert j["data"]["tensor"]["shape"] == [2, 1]
assert j["data"]["tensor"]["values"] == [1, 2]
def test_proto_gets_meta():
user_object = UserObject(ret_meta=True)
app = SeldonModelGRPC(user_object)
arr = np.array([1, 2])
datadef = prediction_pb2.DefaultData(
tensor=prediction_pb2.Tensor(
shape=(2, 1),
values=arr
)
)
meta = prediction_pb2.Meta()
metaJson = {"puid":"abc"}
json_format.ParseDict(metaJson, meta)
request = prediction_pb2.SeldonMessage(data=datadef, meta=meta)
resp = app.Predict(request, None)
jStr = json_format.MessageToJson(resp)
j = json.loads(jStr)
print(j)
assert j["meta"]["tags"] == {"inc_meta":{"puid":"abc"}}
# add default type
j["meta"]["metrics"][0]["type"] = "COUNTER"
assert j["meta"]["metrics"] == user_object.metrics()
assert j["data"]["tensor"]["shape"] == [2, 1]
assert j["data"]["tensor"]["values"] == [1, 2]