def test_concat():
lst = [
[1],
[1, 2],
[],
[1, 2, 3],
]
datas, slices = concat_list(lst)
for s, origin_data in zip(slices, lst):
assert origin_data == datas[s]
def handle_batch_request(
self, requests: Iterable[SimpleRequest], func
) -> Iterable[SimpleResponse]:
"""
TODO(hrmthw):
1. check content type
1. specify batch dim
1. output str fromat
"""
import tensorflow as tf
bad_resp = SimpleResponse(b"Bad Input", None, 400)
instances_list = [None] * len(requests)
responses = [bad_resp] * len(requests)
for i, request in enumerate(requests):
try:
raw_str = request[0] # .decode("utf-8")
parsed_json = json.loads(raw_str)
if parsed_json.get("instances") is not None:
instances = parsed_json.get("instances")
if instances is None:
continue
instances = decode_b64_if_needed(instances)
if not isinstance(instances, (list, tuple)):
instances = [instances]
instances_list[i] = instances
elif parsed_json.get("inputs"):
responses[i] = SimpleResponse(
"Column format 'inputs' not implemented", None, 501,
)
except (json.exceptions.JSONDecodeError, UnicodeDecodeError):
import traceback
traceback.print_exc()
merged_instances, slices = concat_list(instances_list)
parsed_tensor = tf.constant(merged_instances)
merged_result = func(parsed_tensor)
merged_result = decode_tf_if_needed(merged_result)
assert isinstance(merged_result, (list, tuple))
results = [merged_result[s] for s in slices]
for i, result in enumerate(results):
result_str = api_func_result_to_json(result)
responses[i] = SimpleResponse(result_str, dict(), 200)
return responses
def handle_batch_request(
self, requests: Iterable[SimpleRequest], func
) -> Iterable[SimpleResponse]:
bad_resp = SimpleResponse(400, None, "Bad Input")
instances_list = [None] * len(requests)
responses = [bad_resp] * len(requests)
batch_flags = [None] * len(requests)
for i, request in enumerate(requests):
batch_flags[i] = (
request.formated_headers.get(
self._BATCH_REQUEST_HEADER.lower(),
"true" if self.config.get('is_batch_input') else "false",
)
== "true"
)
try:
raw_str = request.data
parsed_json = json.loads(raw_str)
if not batch_flags[i]:
parsed_json = (parsed_json,)
instances_list[i] = parsed_json
except (json.JSONDecodeError, UnicodeDecodeError):
responses[i] = SimpleResponse(400, None, "Not a valid json")
except Exception: # pylint: disable=broad-except
import traceback
err = traceback.format_exc()
responses[i] = SimpleResponse(
500, None, f"Internal Server Error: {err}"
)
merged_instances, slices = concat_list(instances_list)
merged_result = func(merged_instances)
if not isinstance(merged_result, (list, tuple)) or len(merged_result) != len(
merged_instances
):
raise ValueError(
"The return value with JsonHandler must be list of jsonable objects, "
"and have same length as the inputs."
)
for i, s in enumerate(slices):
if s is None:
continue
result = merged_result[s]
if not batch_flags[i]:
result = result[0]
result_str = api_func_result_to_json(result)
responses[i] = SimpleResponse(200, dict(), result_str)
return responses
def handle_batch_request(self, requests: Iterable[SimpleRequest],
func) -> Iterable[SimpleResponse]:
bad_resp = SimpleResponse(400, None, "Bad Input")
instances_list = [None] * len(requests)
responses = [bad_resp] * len(requests)
for i, request in enumerate(requests):
try:
raw_str = request.data
parsed_json = json.loads(raw_str)
instances_list[i] = parsed_json
except (json.JSONDecodeError, UnicodeDecodeError):
responses[i] = SimpleResponse(400, None,
"not a valid json input")
except Exception: # pylint: disable=broad-except
responses[i] = SimpleResponse(500, None,
"internal server error")
import traceback
traceback.print_exc()
merged_instances, slices = concat_list(instances_list)
merged_result = func(merged_instances)
if not isinstance(
merged_result,
(list, tuple)) or len(merged_result) != len(merged_instances):
raise ValueError(
"The return value with JsonHandler must be list of jsonable objects, "
"and have same length as the inputs.")
for i, s in enumerate(slices):
if s is None:
continue
result_str = api_func_result_to_json(merged_result[s])
responses[i] = SimpleResponse(200, dict(), result_str)
return responses
def handle_batch_request(
self, requests: Iterable[SimpleRequest], func
) -> Iterable[SimpleResponse]:
"""
TODO(hrmthw):
1. specify batch dim
1. output str fromat
"""
import tensorflow as tf
bad_resp = SimpleResponse(400, None, "input format error")
instances_list = [None] * len(requests)
responses = [bad_resp] * len(requests)
batch_flags = [None] * len(requests)
for i, request in enumerate(requests):
try:
raw_str = request.data
batch_flags[i] = (
request.formated_headers.get(
self._BATCH_REQUEST_HEADER.lower(),
"true" if self.config.get("is_batch_input") else "false",
)
== "true"
)
parsed_json = json.loads(raw_str)
if parsed_json.get("instances") is not None:
instances = parsed_json.get("instances")
if instances is None:
continue
instances = decode_b64_if_needed(instances)
if not batch_flags[i]:
instances = (instances,)
instances_list[i] = instances
elif parsed_json.get("inputs"):
responses[i] = SimpleResponse(
501, None, "Column format 'inputs' not implemented"
)
except (json.JSONDecodeError, UnicodeDecodeError):
pass
except Exception: # pylint: disable=broad-except
import traceback
err = traceback.format_exc()
responses[i] = SimpleResponse(
500, None, f"Internal Server Error: {err}"
)
merged_instances, slices = concat_list(instances_list)
parsed_tensor = tf.constant(merged_instances)
merged_result = func(parsed_tensor)
merged_result = decode_tf_if_needed(merged_result)
assert isinstance(merged_result, (list, tuple))
for i, s in enumerate(slices):
if s is None:
continue
result = merged_result[s]
if not batch_flags[i]:
result = result[0]
result_str = api_func_result_to_json(result)
responses[i] = SimpleResponse(200, dict(), result_str)
return responses