def test_batch_request_for_batching_model(self):
input_size = 16
# graphdef_nobatch_int32_int8_int8 is non batching version.
# The server should return an error if the batch size dimension
# is included in the shape
tensor_shape = (1, input_size)
for protocol in ["http", "grpc"]:
model_name = tu.get_model_name("graphdef", np.int32, np.int8, np.int8)
in0 = np.random.randint(low=0, high=100, size=tensor_shape, dtype=np.int32)
in1 = np.random.randint(low=0, high=100, size=tensor_shape, dtype=np.int32)
inputs = []
outputs = []
if protocol == "http":
triton_client = tritonhttpclient.InferenceServerClient(url='localhost:8000', verbose=True)
inputs.append(tritonhttpclient.InferInput('INPUT0', tensor_shape , "INT32"))
inputs.append(tritonhttpclient.InferInput('INPUT1', tensor_shape, "INT32"))
outputs.append(tritonhttpclient.InferRequestedOutput('OUTPUT0'))
outputs.append(tritonhttpclient.InferRequestedOutput('OUTPUT1'))
else:
triton_client = tritongrpcclient.InferenceServerClient(url='localhost:8001', verbose=True)
inputs.append(tritongrpcclient.InferInput('INPUT0', tensor_shape , "INT32"))
inputs.append(tritongrpcclient.InferInput('INPUT1', tensor_shape, "INT32"))
outputs.append(tritongrpcclient.InferRequestedOutput('OUTPUT0'))
outputs.append(tritongrpcclient.InferRequestedOutput('OUTPUT1'))
# Initialize the data
inputs[0].set_data_from_numpy(in0)
inputs[1].set_data_from_numpy(in1)
results = triton_client.infer(model_name,
inputs,
outputs=outputs)
def _basic_inference(self,
shm_ip0_handle,
shm_ip1_handle,
shm_op0_handle,
shm_op1_handle,
error_msg,
big_shm_name="",
big_shm_size=64):
input0_data = np.arange(start=0, stop=16, dtype=np.int32)
input1_data = np.ones(shape=16, dtype=np.int32)
inputs = []
outputs = []
if _protocol == "http":
triton_client = httpclient.InferenceServerClient(_url,
verbose=True)
inputs.append(httpclient.InferInput("INPUT0", [1, 16], "INT32"))
inputs.append(httpclient.InferInput("INPUT1", [1, 16], "INT32"))
outputs.append(
httpclient.InferRequestedOutput('OUTPUT0', binary_data=False))
outputs.append(
httpclient.InferRequestedOutput('OUTPUT1', binary_data=False))
else:
triton_client = grpcclient.InferenceServerClient(_url,
verbose=True)
inputs.append(grpcclient.InferInput("INPUT0", [1, 16], "INT32"))
inputs.append(grpcclient.InferInput("INPUT1", [1, 16], "INT32"))
outputs.append(grpcclient.InferRequestedOutput('OUTPUT0'))
outputs.append(grpcclient.InferRequestedOutput('OUTPUT1'))
inputs[0].set_shared_memory("input0_data", 64)
if type(shm_ip1_handle) == np.array:
inputs[1].set_data_from_numpy(input0_data, binary_data=False)
elif big_shm_name != "":
inputs[1].set_shared_memory(big_shm_name, big_shm_size)
else:
inputs[1].set_shared_memory("input1_data", 64)
outputs[0].set_shared_memory("output0_data", 64)
outputs[1].set_shared_memory("output1_data", 64)
try:
results = triton_client.infer("simple",
inputs,
model_version="",
outputs=outputs)
output = results.get_output('OUTPUT0')
if _protocol == "http":
output_datatype = output['datatype']
output_shape = output['shape']
else:
output_datatype = output.datatype
output_shape = output.shape
output_dtype = triton_to_np_dtype(output_datatype)
output_data = shm.get_contents_as_numpy(shm_op0_handle,
output_dtype, output_shape)
self.assertTrue(
(output_data[0] == (input0_data + input1_data)).all())
except Exception as ex:
error_msg.append(str(ex))
def requestGenerator(input_name, output_name, c, h, w, format, dtype, FLAGS):
# Preprocess image into input data according to model requirements
image_data = None
with Image.open(FLAGS.image_filename) as img:
image_data = preprocess(img, format, dtype, c, h, w, FLAGS.scaling)
repeated_image_data = [image_data for _ in range(FLAGS.batch_size)]
batched_image_data = np.stack(repeated_image_data, axis=0)
# Set the input data
inputs = []
if FLAGS.protocol.lower() == "grpc":
inputs.append(
tritongrpcclient.InferInput(input_name, batched_image_data.shape, dtype))
inputs[0].set_data_from_numpy(batched_image_data)
else:
inputs.append(
tritonhttpclient.InferInput(input_name, batched_image_data.shape, dtype))
inputs[0].set_data_from_numpy(batched_image_data, binary_data=False)
outputs = []
if FLAGS.protocol.lower() == "grpc":
outputs.append(
tritongrpcclient.InferRequestedOutput(output_name,
class_count=FLAGS.classes))
else:
outputs.append(
tritonhttpclient.InferRequestedOutput(output_name,
binary_data=False,
class_count=FLAGS.classes))
yield inputs, outputs, FLAGS.model_name, FLAGS.model_version
def sync_send(triton_client, result_list, values, batch_size, sequence_id,
model_name, model_version):
count = 1
for value in values:
# Create the tensor for INPUT
value_data = np.full(shape=[batch_size, 1],
fill_value=value,
dtype=np.int32)
inputs = []
inputs.append(
tritongrpcclient.InferInput('INPUT', value_data.shape, "INT32"))
# Initialize the data
inputs[0].set_data_from_numpy(value_data)
outputs = []
outputs.append(tritongrpcclient.InferRequestedOutput('OUTPUT'))
# Issue the synchronous sequence inference.
result = triton_client.infer(model_name=model_name,
inputs=inputs,
outputs=outputs,
sequence_id=sequence_id,
sequence_start=(count == 1),
sequence_end=(count == len(values)))
result_list.append(result.as_numpy('OUTPUT'))
count = count + 1
def requestGenerator(batched_image_data, input_name, output_name, dtype, FLAGS):
# Set the input data
inputs = []
if FLAGS.protocol.lower() == "grpc":
inputs.append(
tritongrpcclient.InferInput(input_name, batched_image_data.shape,
dtype))
inputs[0].set_data_from_numpy(batched_image_data)
else:
inputs.append(
tritonhttpclient.InferInput(input_name, batched_image_data.shape,
dtype))
inputs[0].set_data_from_numpy(batched_image_data, binary_data=True)
outputs = []
if FLAGS.protocol.lower() == "grpc":
outputs.append(
tritongrpcclient.InferRequestedOutput(output_name,
class_count=FLAGS.classes))
else:
outputs.append(
tritonhttpclient.InferRequestedOutput(output_name,
binary_data=True,
class_count=FLAGS.classes))
yield inputs, outputs, FLAGS.model_name, FLAGS.model_version
def detector(self, frames):
infer_inputs = [
triton.InferInput('input_1', (len(frames), 3, *self.resize[::-1]),
"FP32")
]
frames = np.array(frames, dtype=np.float32)
frames = np.transpose(frames, (0, 3, 1, 2))
infer_inputs[0].set_data_from_numpy(frames)
result = self.triton_client.infer('retinanet', infer_inputs)
scores = result.as_numpy('scores').reshape((-1, 100))
boxes = result.as_numpy('boxes').reshape((-1, 100, 4))
classes = result.as_numpy('classes').reshape((-1, 100))
# Calculate embeddings for all the detected subjects
embs = []
scores_filtered = []
boxes_filters = []
for i in range(len(frames)):
mask = (scores[i] > 0.4) & (
classes[i] == 0) # only care about 'person' with score > 0.4
scores_i = scores[i, mask]
boxes_i = boxes[i, mask]
scores_i, boxes_i = self.bbox_filter(scores_i, boxes_i)
img = frames[i].astype(np.uint8) # (3, 800, 1280)
embs_i = []
boxes_i = boxes_i.astype(int)
for j in range(len(boxes_i)):
imp = img[:, boxes_i[j, 1]:boxes_i[j, 3],
boxes_i[j, 0]:boxes_i[j, 2]]
imp = np.transpose(imp, (1, 2, 0))
imp = Image.fromarray(imp)
data = [
np.asarray(transforms.Resize(size=(256, 128))(imp)).astype(
np.float32)
]
inputs = []
inputs.append(
tritongrpcclient.InferInput('image',
[len(data), 256, 128, 3],
"FP32"))
# Initialize the data
inputs[0].set_data_from_numpy(np.asarray(data))
outputs = []
outputs.append(
tritongrpcclient.InferRequestedOutput('features'))
results = self.triton_client.infer('osnet_ensemble',
inputs,
outputs=outputs)
emb = np.squeeze(results.as_numpy('features'))
embs_i.append(emb / np.linalg.norm(emb))
embs.append(embs_i)
scores_filtered.append(scores_i)
boxes_filters.append(boxes_i)
return np.asarray(scores_filtered), np.asarray(
boxes_filters), np.asarray(embs)
def setUp(self):
self.trials_ = [("repeat_int32", None), ("simple_repeat", None),
("sequence_repeat", None),
("repeat_square", self._nested_validate),
("nested_square", self._nested_validate)]
self.model_name_ = "repeat_int32"
self.inputs_ = []
self.inputs_.append(grpcclient.InferInput('IN', [1], "INT32"))
self.inputs_.append(grpcclient.InferInput('DELAY', [1], "UINT32"))
self.inputs_.append(grpcclient.InferInput('WAIT', [1], "UINT32"))
self.outputs_ = []
self.outputs_.append(grpcclient.InferRequestedOutput('OUT'))
self.outputs_.append(grpcclient.InferRequestedOutput('IDX'))
# Some trials only expect a subset of outputs
self.requested_outputs_ = self.outputs_
def _initialize_model(self):
input_cfg = self.model_config['config']['input']
output_cfg = self.model_config['config']['output']
input_names = [i['name'] for i in input_cfg]
output_names = [o['name'] for o in output_cfg]
print('Input layers: ', output_names)
print('Output layers: ', output_names)
input_dims = [[int(dim) for dim in input_cfg[i]['dims']]
for i in range(len(input_cfg))]
output_dims = [[int(dim) for dim in output_cfg[i]['dims']]
for i in range(len(output_cfg))]
self.input_shape = input_dims[0]
self.output_dims = output_dims
if self.triton_cfg['model']['precision'] == "FP32":
mult = 4
elif self.triton_cfg['model']['precision'] == "FP16":
mult = 2 # TODO: Fix this
elif self.triton_cfg['model']['precision'] == "INT8":
mult = 1 # TODO: Fix this
else:
print("unsupported precision in config file: " +
str(self.triton_cfg['model']['precision']))
sys.exit()
input_byte_sizes_list = [
self._prod(dims) * mult for dims in input_dims
]
output_byte_sizes_list = [
self._prod(dims) * mult for dims in output_dims
]
for i in range(len(input_cfg)):
shm_region_name = self.model_name + "_input" + str(i)
self._register_system_shm_regions(shm_region_name,
self.input_handles,
input_byte_sizes_list[i],
input_names[i])
self.input_layers.append(
tritongrpcclient.InferInput(
input_names[i],
[1, input_dims[i][0], input_dims[i][1], input_dims[i][2]],
"FP32"))
self.input_layers[-1].set_shared_memory(shm_region_name,
input_byte_sizes_list[i])
for i in range(len(output_cfg)):
shm_region_name = self.model_name + "_output" + str(i)
self._register_system_shm_regions(shm_region_name,
self.output_handles,
output_byte_sizes_list[i],
output_names[i])
self.output_layers.append(
tritongrpcclient.InferRequestedOutput(output_names[i]))
self.output_layers[-1].set_shared_memory(shm_region_name,
output_byte_sizes_list[i])
def main():
FLAGS = parse_args()
try:
triton_client = tritongrpcclient.InferenceServerClient(url=FLAGS.url, verbose=FLAGS.verbose)
except Exception as e:
print("channel creation failed: " + str(e))
sys.exit(1)
model_name = FLAGS.model_name
model_version = -1
print("Loading images")
image_data, labels = load_images(FLAGS.img_dir if FLAGS.img_dir is not None else FLAGS.img)
image_data = array_from_list(image_data)
print("Images loaded, inferring")
# Infer
outputs = []
input_name = "INPUT"
output_name = "OUTPUT"
input_shape = list(image_data.shape)
outputs.append(tritongrpcclient.InferRequestedOutput(output_name))
img_idx = 0
for batch in batcher(image_data, FLAGS.batch_size):
print("Input mean before backend processing:", np.mean(batch))
input_shape[0] = np.shape(batch)[0]
print("Batch size: ", input_shape[0])
inputs = [tritongrpcclient.InferInput(input_name, input_shape, "UINT8")]
# Initialize the data
inputs[0].set_data_from_numpy(batch)
# Test with outputs
results = triton_client.infer(model_name=model_name,
inputs=inputs,
outputs=outputs)
# Get the output arrays from the results
output0_data = results.as_numpy(output_name)
print("Output mean after backend processing:", np.mean(output0_data))
print("Output shape: ", np.shape(output0_data))
maxs = np.argmax(output0_data, axis=1)
for i in range(len(maxs)):
print("Sample ", i, " - label: ", maxs[i], " ~ ", output0_data[i, maxs[i]])
if maxs[i] != labels[img_idx]:
sys.exit(1)
else:
print("pass")
img_idx += 1
statistics = triton_client.get_inference_statistics(model_name=model_name)
if len(statistics.model_stats) != 1:
print("FAILED: Inference Statistics")
sys.exit(1)
def setUp(self):
self.model_name_ = "repeat_int32"
self.inputs_ = []
self.inputs_.append(grpcclient.InferInput('IN', [1, 1], "INT32"))
self.inputs_.append(grpcclient.InferInput('DELAY', [1, 1], "UINT32"))
self.inputs_.append(grpcclient.InferInput('WAIT', [1, 1], "UINT32"))
self.outputs_ = []
self.outputs_.append(grpcclient.InferRequestedOutput('OUT'))
def main():
FLAGS = parse_args()
try:
triton_client = tritongrpcclient.InferenceServerClient(
url=FLAGS.url, verbose=FLAGS.verbose)
except Exception as e:
print("channel creation failed: " + str(e))
sys.exit(1)
model_name = FLAGS.model_name
model_version = -1
input_data = [
randint(0, 255, size=randint(100), dtype='uint8')
for _ in range(randint(100) * FLAGS.batch_size)
]
input_data = array_from_list(input_data)
# Infer
outputs = []
input_name = "DALI_INPUT_0"
output_name = "DALI_OUTPUT_0"
input_shape = list(input_data.shape)
outputs.append(tritongrpcclient.InferRequestedOutput(output_name))
for batch in batcher(input_data, FLAGS.batch_size):
print("Input mean before backend processing:", np.mean(batch))
input_shape[0] = np.shape(batch)[0]
print("Batch size: ", input_shape[0])
inputs = [
tritongrpcclient.InferInput(input_name, input_shape, "UINT8")
]
# Initialize the data
inputs[0].set_data_from_numpy(batch)
# Test with outputs
results = triton_client.infer(model_name=model_name,
inputs=inputs,
outputs=outputs)
# Get the output arrays from the results
output0_data = results.as_numpy(output_name)
print("Output mean after backend processing:", np.mean(output0_data))
print("Output shape: ", np.shape(output0_data))
if not math.isclose(np.mean(output0_data), np.mean(batch)):
print("Pre/post average does not match")
sys.exit(1)
else:
print("pass")
statistics = triton_client.get_inference_statistics(model_name=model_name)
if len(statistics.model_stats) != 1:
print("FAILED: Inference Statistics")
sys.exit(1)
def test_nobatch_request_for_batching_model(self):
input_size = 16
# graphdef_int32_int8_int8 has a batching version with max batch size of 8.
# The server should return an error if the batch size is not included in the
# input shapes.
tensor_shape = (input_size,)
for protocol in ["http", "grpc"]:
model_name = tu.get_model_name("graphdef", np.int32, np.int8, np.int8)
in0 = np.random.randint(low=0, high=100, size=tensor_shape, dtype=np.int32)
in1 = np.random.randint(low=0, high=100, size=tensor_shape, dtype=np.int32)
inputs = []
outputs = []
if protocol == "http":
triton_client = tritonhttpclient.InferenceServerClient(url='localhost:8000', verbose=True)
inputs.append(tritonhttpclient.InferInput('INPUT0', tensor_shape , "INT32"))
inputs.append(tritonhttpclient.InferInput('INPUT1', tensor_shape, "INT32"))
outputs.append(tritonhttpclient.InferRequestedOutput('OUTPUT0'))
outputs.append(tritonhttpclient.InferRequestedOutput('OUTPUT1'))
else:
triton_client = tritongrpcclient.InferenceServerClient(url='localhost:8001', verbose=True)
inputs.append(tritongrpcclient.InferInput('INPUT0', tensor_shape , "INT32"))
inputs.append(tritongrpcclient.InferInput('INPUT1', tensor_shape, "INT32"))
outputs.append(tritongrpcclient.InferRequestedOutput('OUTPUT0'))
outputs.append(tritongrpcclient.InferRequestedOutput('OUTPUT1'))
# Initialize the data
inputs[0].set_data_from_numpy(in0)
inputs[1].set_data_from_numpy(in1)
try:
results = triton_client.infer(model_name,
inputs,
outputs=outputs)
self.assertTrue(False, "expected failure with no batch request for batching model")
except InferenceServerException as ex:
pass
def _prepare_request(self, protocol):
if (protocol == "grpc"):
self.inputs_ = []
self.inputs_.append(grpcclient.InferInput('INPUT0', [1, 1],
"INT32"))
self.outputs_ = []
self.outputs_.append(grpcclient.InferRequestedOutput('OUTPUT0'))
else:
self.inputs_ = []
self.inputs_.append(httpclient.InferInput('INPUT0', [1, 1],
"INT32"))
self.outputs_ = []
self.outputs_.append(httpclient.InferRequestedOutput('OUTPUT0'))
self.inputs_[0].set_data_from_numpy(self.input0_data_)
def request_eval(hit_data,row_splits, triton_client, model_name):
np_rs_type = 'int64'
tr_rs_type = 'INT64'
inputs = []
outputs = []
#print(hit_data.shape)
#print(row_splits.shape)
inputs.append(tritongrpcclient.InferInput('input_1', hit_data.shape, 'FP32'))
inputs.append(tritongrpcclient.InferInput('input_2', row_splits.shape, tr_rs_type)) #INT64
inputs[0].set_data_from_numpy(hit_data)
inputs[1].set_data_from_numpy(row_splits)
outputs.append(tritongrpcclient.InferRequestedOutput('output'))
outputs.append(tritongrpcclient.InferRequestedOutput('output_1'))
#outputs.append(tritongrpcclient.InferRequestedOutput('predicted_final_condensates'))
#outputs.append(tritongrpcclient.InferRequestedOutput('output_row_splits'))
# predicted_final_1 doesn't matter
results = triton_client.infer(
model_name=model_name,
inputs=inputs,
outputs=outputs
)
condensates = results.as_numpy('output')
#condensates = results.as_numpy('predicted_final_condensates')
#rs = results.as_numpy('output_row_splits')
#print('output',condensates,condensates.shape)
return condensates
def main(_):
"""
Ask a question of context on Triton.
:param context: str
:param question: str
:param question_id: int
:return:
"""
os.environ[
"TF_XLA_FLAGS"] = "--tf_xla_enable_lazy_compilation=false" #causes memory fragmentation for bert leading to OOM
tf.compat.v1.logging.info("***** Configuaration *****")
for key in FLAGS.__flags.keys():
tf.compat.v1.logging.info(' {}: {}'.format(key, getattr(FLAGS, key)))
tf.compat.v1.logging.info("**************************")
tokenizer = tokenization.FullTokenizer(vocab_file=FLAGS.vocab_file,
do_lower_case=FLAGS.do_lower_case)
# Get the Data
if FLAGS.question and FLAGS.context:
input_data = [{
"paragraphs": [{
"context": FLAGS.context,
"qas": [{
"id": 0,
"question": FLAGS.question
}]
}]
}]
eval_examples = read_squad_examples(
input_file=None,
is_training=False,
version_2_with_negative=FLAGS.version_2_with_negative,
input_data=input_data)
elif FLAGS.predict_file:
eval_examples = read_squad_examples(
input_file=FLAGS.predict_file,
is_training=False,
version_2_with_negative=FLAGS.version_2_with_negative)
else:
raise ValueError(
"Either predict_file or question+answer need to defined")
# Get Eval Features = Preprocessing
eval_features = []
def append_feature(feature):
eval_features.append(feature)
convert_examples_to_features(examples=eval_examples,
tokenizer=tokenizer,
max_seq_length=FLAGS.max_seq_length,
doc_stride=FLAGS.doc_stride,
max_query_length=FLAGS.max_query_length,
is_training=False,
output_fn=append_feature)
protocol_str = 'grpc' # http or grpc
url = FLAGS.triton_server_url
verbose = False
model_name = FLAGS.triton_model_name
model_version = str(FLAGS.triton_model_version)
batch_size = FLAGS.predict_batch_size
triton_client = tritongrpcclient.InferenceServerClient(url, verbose)
model_metadata = triton_client.get_model_metadata(
model_name=model_name, model_version=model_version)
model_config = triton_client.get_model_config(model_name=model_name,
model_version=model_version)
user_data = UserData()
max_outstanding = 20
# Number of outstanding requests
outstanding = 0
sent_prog = tqdm.tqdm(desc="Send Requests", total=len(eval_features))
recv_prog = tqdm.tqdm(desc="Recv Requests", total=len(eval_features))
def process_outstanding(do_wait, outstanding):
if (outstanding == 0 or do_wait is False):
return outstanding
# Wait for deferred items from callback functions
(result, error, idx, start_time,
inputs) = user_data._completed_requests.get()
if (result is None):
return outstanding
stop = time.time()
if (error is not None):
raise ValueError(
"Context returned null for async id marked as done")
outstanding -= 1
time_list.append(stop - start_time)
batch_count = len(inputs[label_id_key])
if FLAGS.trt_engine:
cls_squad_logits = result.as_numpy("cls_squad_logits")
try: #when batch size > 1
start_logits_results = np.array(
cls_squad_logits.squeeze()[:, :, 0])
end_logits_results = np.array(cls_squad_logits.squeeze()[:, :,
1])
except:
start_logits_results = np.expand_dims(np.array(
cls_squad_logits.squeeze()[:, 0]),
axis=0)
end_logits_results = np.expand_dims(np.array(
cls_squad_logits.squeeze()[:, 1]),
axis=0)
else:
start_logits_results = result.as_numpy("start_logits")
end_logits_results = result.as_numpy("end_logits")
for i in range(batch_count):
unique_id = int(inputs[label_id_key][i][0])
start_logits = [float(x) for x in start_logits_results[i].flat]
end_logits = [float(x) for x in end_logits_results[i].flat]
all_results.append(
RawResult(unique_id=unique_id,
start_logits=start_logits,
end_logits=end_logits))
recv_prog.update(n=batch_count)
return outstanding
all_results = []
time_list = []
print("Starting Sending Requests....\n")
all_results_start = time.time()
idx = 0
for inputs_dict in batch(eval_features, batch_size):
present_batch_size = len(inputs_dict[label_id_key])
if not FLAGS.trt_engine:
label_ids_data = np.stack(inputs_dict[label_id_key])
input_ids_data = np.stack(inputs_dict['input_ids'])
input_mask_data = np.stack(inputs_dict['input_mask'])
segment_ids_data = np.stack(inputs_dict['segment_ids'])
inputs = []
inputs.append(
tritongrpcclient.InferInput('input_ids', input_ids_data.shape,
"INT32"))
inputs[0].set_data_from_numpy(input_ids_data)
inputs.append(
tritongrpcclient.InferInput('input_mask', input_mask_data.shape,
"INT32"))
inputs[1].set_data_from_numpy(input_mask_data)
inputs.append(
tritongrpcclient.InferInput('segment_ids', segment_ids_data.shape,
"INT32"))
inputs[2].set_data_from_numpy(segment_ids_data)
if not FLAGS.trt_engine:
inputs.append(
tritongrpcclient.InferInput(label_id_key, label_ids_data.shape,
"INT32"))
inputs[3].set_data_from_numpy(label_ids_data)
outputs = []
if FLAGS.trt_engine:
outputs.append(
tritongrpcclient.InferRequestedOutput('cls_squad_logits'))
else:
outputs.append(
tritongrpcclient.InferRequestedOutput('start_logits'))
outputs.append(tritongrpcclient.InferRequestedOutput('end_logits'))
start_time = time.time()
triton_client.async_infer(model_name,
inputs,
partial(completion_callback, user_data, idx,
start_time, inputs_dict),
request_id=str(idx),
model_version=model_version,
outputs=outputs)
outstanding += 1
idx += 1
sent_prog.update(n=present_batch_size)
# Try to process at least one response per request
outstanding = process_outstanding(outstanding >= max_outstanding,
outstanding)
tqdm.tqdm.write(
"All Requests Sent! Waiting for responses. Outstanding: {}.\n".format(
outstanding))
# Now process all outstanding requests
while (outstanding > 0):
outstanding = process_outstanding(True, outstanding)
all_results_end = time.time()
all_results_total = (all_results_end - all_results_start) * 1000.0
print("-----------------------------")
print("Total Time: {} ms".format(all_results_total))
print("-----------------------------")
print("-----------------------------")
print("Total Inference Time = %0.2f for"
"Sentences processed = %d" % (sum(time_list), len(eval_features)))
print("Throughput Average (sentences/sec) = %0.2f" %
(len(eval_features) / all_results_total * 1000.0))
print("-----------------------------")
if FLAGS.output_dir and FLAGS.predict_file:
# When inferencing on a dataset, get inference statistics and write results to json file
time_list.sort()
avg = np.mean(time_list)
cf_95 = max(time_list[:int(len(time_list) * 0.95)])
cf_99 = max(time_list[:int(len(time_list) * 0.99)])
cf_100 = max(time_list[:int(len(time_list) * 1)])
print("-----------------------------")
print("Summary Statistics")
print("Batch size =", FLAGS.predict_batch_size)
print("Sequence Length =", FLAGS.max_seq_length)
print("Latency Confidence Level 95 (ms) =", cf_95 * 1000)
print("Latency Confidence Level 99 (ms) =", cf_99 * 1000)
print("Latency Confidence Level 100 (ms) =", cf_100 * 1000)
print("Latency Average (ms) =", avg * 1000)
print("-----------------------------")
output_prediction_file = os.path.join(FLAGS.output_dir,
"predictions.json")
output_nbest_file = os.path.join(FLAGS.output_dir,
"nbest_predictions.json")
output_null_log_odds_file = os.path.join(FLAGS.output_dir,
"null_odds.json")
write_predictions(eval_examples, eval_features, all_results,
FLAGS.n_best_size, FLAGS.max_answer_length,
FLAGS.do_lower_case, output_prediction_file,
output_nbest_file, output_null_log_odds_file,
FLAGS.version_2_with_negative, FLAGS.verbose_logging)
else:
# When inferencing on a single example, write best answer to stdout
all_predictions, all_nbest_json, scores_diff_json = get_predictions(
eval_examples, eval_features, all_results, FLAGS.n_best_size,
FLAGS.max_answer_length, FLAGS.do_lower_case,
FLAGS.version_2_with_negative, FLAGS.verbose_logging)
print(
"Context is: %s \n\nQuestion is: %s \n\nPredicted Answer is: %s" %
(FLAGS.context, FLAGS.question, all_predictions[0]))
# Set the input data
inputs = []
if FLAGS.protocol.lower() == "grpc":
inputs.append(
tritongrpcclient.InferInput(input_name, batched_image_data.shape,
"BYTES"))
inputs[0].set_data_from_numpy(batched_image_data)
else:
inputs.append(
tritonhttpclient.InferInput(input_name, batched_image_data.shape,
"BYTES"))
inputs[0].set_data_from_numpy(batched_image_data, binary_data=True)
outputs = []
if FLAGS.protocol.lower() == "grpc":
outputs.append(
tritongrpcclient.InferRequestedOutput(output_name,
class_count=FLAGS.classes))
else:
outputs.append(
tritonhttpclient.InferRequestedOutput(output_name,
binary_data=True,
class_count=FLAGS.classes))
# Send request
result = triton_client.infer(model_name, inputs, outputs=outputs)
postprocess(result, output_name, input_filenames, batch_size)
print("PASS")
"input0_data", cudashm.get_raw_handle(shm_ip0_handle), 0,
input_byte_size)
triton_client.register_cuda_shared_memory(
"input1_data", cudashm.get_raw_handle(shm_ip1_handle), 0,
input_byte_size)
# Set the parameters to use data from shared memory
inputs = []
inputs.append(tritongrpcclient.InferInput('INPUT0', [1, 16], "INT32"))
inputs[-1].set_shared_memory("input0_data", input_byte_size)
inputs.append(tritongrpcclient.InferInput('INPUT1', [1, 16], "INT32"))
inputs[-1].set_shared_memory("input1_data", input_byte_size)
outputs = []
outputs.append(tritongrpcclient.InferRequestedOutput('OUTPUT0'))
outputs[-1].set_shared_memory("output0_data", output_byte_size)
outputs.append(tritongrpcclient.InferRequestedOutput('OUTPUT1'))
outputs[-1].set_shared_memory("output1_data", output_byte_size)
results = triton_client.infer(model_name=model_name,
inputs=inputs,
outputs=outputs)
# Read results from the shared memory.
output0 = results.get_output("OUTPUT0")
if output0 is not None:
output0_data = cudashm.get_contents_as_numpy(
shm_op0_handle, utils.triton_to_np_dtype(output0.datatype),
output0.shape)
# Infer
inputs = []
outputs = []
# the built engine with input NCHW
inputs.append(tritongrpcclient.InferInput("data", [1, 3, 608, 608],
"FP32"))
# Initialize the data
image_obj = Image("image_id", raw_image_path=FLAGS.img)
ori_w, ori_h = image_obj.pil_image_obj.size
image_frame, scale_ratio = preprocess(image_obj.pil_image_obj,
input_image_shape=(608, 608))
inputs[0].set_data_from_numpy(image_frame)
outputs.append(tritongrpcclient.InferRequestedOutput("prob"))
# Test with outputs
results = triton_client.infer(model_name=model_name,
inputs=inputs,
outputs=outputs,
headers={"test": "1"})
statistics = triton_client.get_inference_statistics(model_name=model_name)
print(statistics)
# Get the output arrays from the results
output0_data = results.as_numpy("prob")
n_bbox = int(output0_data[0, 0, 0, 0])
bbox_matrix = output0_data[0, 1:(n_bbox * 7 + 1), 0, 0].reshape(-1, 7)
def infer_exact(tester, pf, tensor_shape, batch_size,
input_dtype, output0_dtype, output1_dtype,
output0_raw=True, output1_raw=True,
model_version=None, swap=False,
outputs=("OUTPUT0", "OUTPUT1"), use_http=True, use_grpc=True,
use_http_json_tensors=True, skip_request_id_check=False, use_streaming=True,
correlation_id=0, shm_region_names=None, precreated_shm_regions=None,
use_system_shared_memory=False, use_cuda_shared_memory=False,
priority=0, timeout_us=0):
tester.assertTrue(
use_http or use_http_json_tensors or use_grpc or use_streaming)
configs = []
if use_http:
configs.append(("localhost:8000", "http", False, True))
if output0_raw == output1_raw:
# Float16 not supported for Input and Output via JSON
if use_http_json_tensors and (input_dtype != np.float16) and \
(output0_dtype != np.float16) and (output1_dtype != np.float16):
configs.append(("localhost:8000", "http", False, False))
if use_grpc:
configs.append(("localhost:8001", "grpc", False, False))
if use_streaming:
configs.append(("localhost:8001", "grpc", True, False))
# outputs are sum and difference of inputs so set max input
# values so that they will not overflow the output. This
# allows us to do an exact match. For float types use 8, 16,
# 32 int range for fp 16, 32, 64 respectively. When getting
# class outputs the result value/probability is returned as a
# float so must use fp32 range in that case.
rinput_dtype = _range_repr_dtype(input_dtype)
routput0_dtype = _range_repr_dtype(
output0_dtype if output0_raw else np.float32)
routput1_dtype = _range_repr_dtype(
output1_dtype if output1_raw else np.float32)
val_min = max(np.iinfo(rinput_dtype).min,
np.iinfo(routput0_dtype).min,
np.iinfo(routput1_dtype).min) / 2
val_max = min(np.iinfo(rinput_dtype).max,
np.iinfo(routput0_dtype).max,
np.iinfo(routput1_dtype).max) / 2
num_classes = 3
input0_array = np.random.randint(low=val_min, high=val_max,
size=tensor_shape, dtype=rinput_dtype)
input1_array = np.random.randint(low=val_min, high=val_max,
size=tensor_shape, dtype=rinput_dtype)
if input_dtype != np.object:
input0_array = input0_array.astype(input_dtype)
input1_array = input1_array.astype(input_dtype)
if not swap:
output0_array = input0_array + input1_array
output1_array = input0_array - input1_array
else:
output0_array = input0_array - input1_array
output1_array = input0_array + input1_array
if output0_dtype == np.object:
output0_array = np.array([unicode(str(x), encoding='utf-8')
for x in (output0_array.flatten())], dtype=object).reshape(output0_array.shape)
else:
output0_array = output0_array.astype(output0_dtype)
if output1_dtype == np.object:
output1_array = np.array([unicode(str(x), encoding='utf-8')
for x in (output1_array.flatten())], dtype=object).reshape(output1_array.shape)
else:
output1_array = output1_array.astype(output1_dtype)
if input_dtype == np.object:
in0n = np.array([str(x)
for x in input0_array.reshape(input0_array.size)], dtype=object)
input0_array = in0n.reshape(input0_array.shape)
in1n = np.array([str(x)
for x in input1_array.reshape(input1_array.size)], dtype=object)
input1_array = in1n.reshape(input1_array.shape)
# prepend size of string to output string data
if output0_dtype == np.object:
if batch_size == 1:
output0_array_tmp = serialize_byte_tensor_list([output0_array])
else:
output0_array_tmp = serialize_byte_tensor_list(output0_array)
else:
output0_array_tmp = output0_array
if output1_dtype == np.object:
if batch_size == 1:
output1_array_tmp = serialize_byte_tensor_list([output1_array])
else:
output1_array_tmp = serialize_byte_tensor_list(output1_array)
else:
output1_array_tmp = output1_array
OUTPUT0 = "OUTPUT0"
OUTPUT1 = "OUTPUT1"
INPUT0 = "INPUT0"
INPUT1 = "INPUT1"
if pf == "libtorch" or pf == "libtorch_nobatch":
OUTPUT0 = "OUTPUT__0"
OUTPUT1 = "OUTPUT__1"
INPUT0 = "INPUT__0"
INPUT1 = "INPUT__1"
output0_byte_size = sum([o0.nbytes for o0 in output0_array_tmp])
output1_byte_size = sum([o1.nbytes for o1 in output1_array_tmp])
if batch_size == 1:
input0_list = [input0_array]
input1_list = [input1_array]
else:
input0_list = [x for x in input0_array]
input1_list = [x for x in input1_array]
# Serialization of string tensors in the case of shared memory must be done manually
if input_dtype == np.object:
input0_list_tmp = serialize_byte_tensor_list(input0_list)
input1_list_tmp = serialize_byte_tensor_list(input1_list)
else:
input0_list_tmp = input0_list
input1_list_tmp = input1_list
input0_byte_size = sum([i0.nbytes for i0 in input0_list_tmp])
input1_byte_size = sum([i1.nbytes for i1 in input1_list_tmp])
# Create system/cuda shared memory regions if needed
shm_regions, shm_handles = su.create_set_shm_regions(input0_list_tmp, input1_list_tmp, output0_byte_size,
output1_byte_size, outputs, shm_region_names, precreated_shm_regions,
use_system_shared_memory, use_cuda_shared_memory)
if model_version is not None:
model_version = str(model_version)
else:
model_version = ""
# Run inference and check results for each config
for config in configs:
model_name = tu.get_model_name(
pf, input_dtype, output0_dtype, output1_dtype)
if config[1] == "http":
triton_client = httpclient.InferenceServerClient(
config[0], verbose=True)
else:
triton_client = grpcclient.InferenceServerClient(
config[0], verbose=True)
inputs = []
if config[1] == "http":
inputs.append(httpclient.InferInput(
INPUT0, tensor_shape, np_to_triton_dtype(input_dtype)))
inputs.append(httpclient.InferInput(
INPUT1, tensor_shape, np_to_triton_dtype(input_dtype)))
else:
inputs.append(grpcclient.InferInput(
INPUT0, tensor_shape, np_to_triton_dtype(input_dtype)))
inputs.append(grpcclient.InferInput(
INPUT1, tensor_shape, np_to_triton_dtype(input_dtype)))
if not (use_cuda_shared_memory or use_system_shared_memory):
if config[1] == "http":
inputs[0].set_data_from_numpy(
input0_array, binary_data=config[3])
inputs[1].set_data_from_numpy(
input1_array, binary_data=config[3])
else:
inputs[0].set_data_from_numpy(input0_array)
inputs[1].set_data_from_numpy(input1_array)
else:
# Register necessary shared memory regions/handles
su.register_add_shm_regions(inputs, outputs, shm_regions, precreated_shm_regions, shm_handles,
input0_byte_size, input1_byte_size, output0_byte_size, output1_byte_size,
use_system_shared_memory, use_cuda_shared_memory, triton_client)
if batch_size == 1:
expected0_sort_idx = [np.flip(np.argsort(x.flatten()), 0)
for x in output0_array.reshape((1,) + tensor_shape)]
expected1_sort_idx = [np.flip(np.argsort(x.flatten()), 0)
for x in output1_array.reshape((1,) + tensor_shape)]
else:
expected0_sort_idx = [np.flip(np.argsort(x.flatten()), 0)
for x in output0_array.reshape(tensor_shape)]
expected1_sort_idx = [np.flip(np.argsort(x.flatten()), 0)
for x in output1_array.reshape(tensor_shape)]
# Force binary_data = False for shared memory and class
output_req = []
i = 0
if "OUTPUT0" in outputs:
if len(shm_regions) != 0:
if config[1] == "http":
output_req.append(httpclient.InferRequestedOutput(
OUTPUT0, binary_data=config[3]))
else:
output_req.append(grpcclient.InferRequestedOutput(OUTPUT0))
output_req[-1].set_shared_memory(
shm_regions[2]+'_data', output0_byte_size)
else:
if output0_raw:
if config[1] == "http":
output_req.append(httpclient.InferRequestedOutput(
OUTPUT0, binary_data=config[3]))
else:
output_req.append(
grpcclient.InferRequestedOutput(OUTPUT0))
else:
if config[1] == "http":
output_req.append(httpclient.InferRequestedOutput(
OUTPUT0, binary_data=config[3], class_count=num_classes))
else:
output_req.append(grpcclient.InferRequestedOutput(
OUTPUT0, class_count=num_classes))
i += 1
if "OUTPUT1" in outputs:
if len(shm_regions) != 0:
if config[1] == "http":
output_req.append(httpclient.InferRequestedOutput(
OUTPUT1, binary_data=config[3]))
else:
output_req.append(grpcclient.InferRequestedOutput(OUTPUT1))
output_req[-1].set_shared_memory(
shm_regions[2+i]+'_data', output1_byte_size)
else:
if output1_raw:
if config[1] == "http":
output_req.append(httpclient.InferRequestedOutput(
OUTPUT1, binary_data=config[3]))
else:
output_req.append(
grpcclient.InferRequestedOutput(OUTPUT1))
else:
if config[1] == "http":
output_req.append(httpclient.InferRequestedOutput(
OUTPUT1, binary_data=config[3], class_count=num_classes))
else:
output_req.append(grpcclient.InferRequestedOutput(
OUTPUT1, class_count=num_classes))
if config[2]:
user_data = UserData()
triton_client.start_stream(partial(completion_callback, user_data))
try:
results = triton_client.async_stream_infer(model_name,
inputs,
model_version=model_version,
outputs=output_req,
request_id=str(_unique_request_id()))
except Exception as e:
triton_client.stop_stream()
raise e
triton_client.stop_stream()
(results, error) = user_data._completed_requests.get()
if error is not None:
raise error
else:
results = triton_client.infer(model_name,
inputs,
model_version=model_version,
outputs=output_req,
request_id=str(_unique_request_id()))
last_response = results.get_response()
if not skip_request_id_check:
global _seen_request_ids
if config[1] == "http":
request_id = int(last_response["id"])
else:
request_id = int(last_response.id)
tester.assertFalse(request_id in _seen_request_ids,
"request_id: {}".format(request_id))
_seen_request_ids.add(request_id)
if config[1] == "http":
response_model_name = last_response["model_name"]
if model_version != "":
response_model_version = last_response["model_version"]
response_outputs = last_response["outputs"]
else:
response_model_name = last_response.model_name
if model_version != "":
response_model_version = last_response.model_version
response_outputs = last_response.outputs
tester.assertEqual(response_model_name, model_name)
if model_version != "":
tester.assertEqual(str(response_model_version), model_version)
tester.assertEqual(len(response_outputs), len(outputs))
for result in response_outputs:
if config[1] == "http":
result_name = result["name"]
else:
result_name = result.name
if ((result_name == OUTPUT0 and output0_raw) or
(result_name == OUTPUT1 and output1_raw)):
if use_system_shared_memory or use_cuda_shared_memory:
if result_name == OUTPUT0:
shm_handle = shm_handles[2]
else:
shm_handle = shm_handles[3]
output = results.get_output(result_name)
if config[1] == "http":
output_datatype = output['datatype']
output_shape = output['shape']
else:
output_datatype = output.datatype
output_shape = output.shape
output_dtype = triton_to_np_dtype(output_datatype)
if use_system_shared_memory:
output_data = shm.get_contents_as_numpy(
shm_handle, output_dtype, output_shape)
elif use_cuda_shared_memory:
output_data = cudashm.get_contents_as_numpy(
shm_handle, output_dtype, output_shape)
else:
output_data = results.as_numpy(result_name)
if (output_data.dtype == np.object) and (config[3] == False):
output_data = output_data.astype(np.bytes_)
if result_name == OUTPUT0:
tester.assertTrue(np.array_equal(output_data, output0_array),
"{}, {} expected: {}, got {}".format(
model_name, OUTPUT0, output0_array, output_data))
elif result_name == OUTPUT1:
tester.assertTrue(np.array_equal(output_data, output1_array),
"{}, {} expected: {}, got {}".format(
model_name, OUTPUT1, output1_array, output_data))
else:
tester.assertTrue(
False, "unexpected raw result {}".format(result_name))
else:
for b in range(batch_size):
# num_classes values must be returned and must
# match expected top values
if "nobatch" in pf:
class_list = results.as_numpy(result_name)
else:
class_list = results.as_numpy(result_name)[b]
tester.assertEqual(len(class_list), num_classes)
if batch_size == 1:
expected0_flatten = output0_array.flatten()
expected1_flatten = output1_array.flatten()
else:
expected0_flatten = output0_array[b].flatten()
expected1_flatten = output1_array[b].flatten()
for idx, class_label in enumerate(class_list):
# can't compare indices since could have different
# indices with the same value/prob, so check that
# the value of each index equals the expected value.
# Only compare labels when the indices are equal.
if type(class_label) == str:
ctuple = class_label.split(':')
else:
ctuple = "".join(chr(x)
for x in class_label).split(':')
cval = float(ctuple[0])
cidx = int(ctuple[1])
if result_name == OUTPUT0:
tester.assertEqual(cval, expected0_flatten[cidx])
tester.assertEqual(
cval, expected0_flatten[expected0_sort_idx[b][idx]])
if cidx == expected0_sort_idx[b][idx]:
tester.assertEqual(ctuple[2], 'label{}'.format(
expected0_sort_idx[b][idx]))
elif result_name == OUTPUT1:
tester.assertEqual(cval, expected1_flatten[cidx])
tester.assertEqual(
cval, expected1_flatten[expected1_sort_idx[b][idx]])
else:
tester.assertTrue(
False, "unexpected class result {}".format(result_name))
# Unregister system/cuda shared memory regions if they exist
su.unregister_cleanup_shm_regions(shm_regions, shm_handles, precreated_shm_regions, outputs,
use_system_shared_memory, use_cuda_shared_memory)
return results
def infer_zero(tester, pf, batch_size, tensor_dtype, input_shapes, output_shapes,
model_version=None, use_http=True, use_grpc=True,
use_http_json_tensors=True, use_streaming=True, shm_region_name_prefix=None,
use_system_shared_memory=False, use_cuda_shared_memory=False,
priority=0, timeout_us=0):
tester.assertTrue(
use_http or use_grpc or use_http_json_tensors or use_streaming)
configs = []
if use_http:
configs.append(("localhost:8000", "http", False, True))
if use_http_json_tensors and (tensor_dtype != np.float16):
configs.append(("localhost:8000", "http", False, False))
if use_grpc:
configs.append(("localhost:8001", "grpc", False, False))
if use_streaming:
configs.append(("localhost:8001", "grpc", True, False))
tester.assertEqual(len(input_shapes), len(output_shapes))
io_cnt = len(input_shapes)
if shm_region_name_prefix is None:
shm_region_name_prefix = ["input", "output"]
input_dict = {}
expected_dict = {}
shm_ip_handles = list()
shm_op_handles = list()
for io_num in range(io_cnt):
if pf == "libtorch" or pf == "libtorch_nobatch":
input_name = "INPUT__{}".format(io_num)
output_name = "OUTPUT__{}".format(io_num)
else:
input_name = "INPUT{}".format(io_num)
output_name = "OUTPUT{}".format(io_num)
input_shape = input_shapes[io_num]
output_shape = output_shapes[io_num]
rtensor_dtype = _range_repr_dtype(tensor_dtype)
if (rtensor_dtype != np.bool):
input_array = np.random.randint(low=np.iinfo(rtensor_dtype).min,
high=np.iinfo(rtensor_dtype).max,
size=input_shape, dtype=rtensor_dtype)
else:
input_array = np.random.choice(a=[False, True], size=input_shape)
if tensor_dtype != np.object:
input_array = input_array.astype(tensor_dtype)
expected_array = np.ndarray.copy(input_array)
else:
expected_array = np.array([unicode(str(x), encoding='utf-8')
for x in input_array.flatten()], dtype=object)
input_array = np.array([str(x) for x in input_array.flatten()],
dtype=object).reshape(input_array.shape)
expected_array = expected_array.reshape(output_shape)
expected_dict[output_name] = expected_array
output_byte_size = expected_array.nbytes
if batch_size == 1:
input_list = [input_array]
else:
input_list = [x for x in input_array]
# Serialization of string tensors in the case of shared memory must be done manually
if tensor_dtype == np.object:
input_list_tmp = serialize_byte_tensor_list(input_list)
else:
input_list_tmp = input_list
input_byte_size = sum([ip.nbytes for ip in input_list_tmp])
# create and register shared memory region for inputs and outputs
shm_io_handles = su.create_set_either_shm_region([shm_region_name_prefix[0]+str(io_num),
shm_region_name_prefix[1]+str(io_num)],
input_list_tmp, input_byte_size, output_byte_size,
use_system_shared_memory, use_cuda_shared_memory)
if len(shm_io_handles) != 0:
shm_ip_handles.append(shm_io_handles[0])
shm_op_handles.append(shm_io_handles[1])
input_dict[input_name] = input_array
if model_version is not None:
model_version = str(model_version)
else:
model_version = ""
# Run inference and check results for each config
for config in configs:
model_name = tu.get_zero_model_name(pf, io_cnt, tensor_dtype)
if config[1] == "http":
triton_client = httpclient.InferenceServerClient(
config[0], verbose=True)
else:
triton_client = grpcclient.InferenceServerClient(
config[0], verbose=True)
inputs = []
output_req = []
for io_num, (input_name, output_name) in enumerate(zip(input_dict.keys(), expected_dict.keys())):
input_data = input_dict[input_name]
input_byte_size = input_data.nbytes
output_byte_size = expected_dict[output_name].nbytes
if config[1] == "http":
inputs.append(httpclient.InferInput(
input_name, input_data.shape, np_to_triton_dtype(tensor_dtype)))
output_req.append(httpclient.InferRequestedOutput(
output_name, binary_data=config[3]))
else:
inputs.append(grpcclient.InferInput(
input_name, input_data.shape, np_to_triton_dtype(tensor_dtype)))
output_req.append(
grpcclient.InferRequestedOutput(output_name))
if not (use_cuda_shared_memory or use_system_shared_memory):
if config[1] == "http":
inputs[-1].set_data_from_numpy(input_data, binary_data=config[3])
else:
inputs[-1].set_data_from_numpy(input_data)
else:
# Register necessary shared memory regions/handles
su.register_add_either_shm_regions(inputs, output_req, shm_region_name_prefix,
(shm_ip_handles, shm_op_handles), io_num, input_byte_size, output_byte_size,
use_system_shared_memory, use_cuda_shared_memory, triton_client)
if config[2]:
user_data = UserData()
triton_client.start_stream(partial(completion_callback, user_data))
try:
results = triton_client.async_stream_infer(model_name,
inputs,
model_version=model_version,
outputs=output_req,
request_id=str(_unique_request_id()),
priority=priority, timeout=timeout_us)
except Exception as e:
triton_client.stop_stream()
raise e
triton_client.stop_stream()
(results, error) = user_data._completed_requests.get()
if error is not None:
raise error
else:
results = triton_client.infer(model_name,
inputs,
model_version=model_version,
outputs=output_req,
request_id=str(_unique_request_id()),
priority=priority, timeout=timeout_us)
last_response = results.get_response()
if config[1] == "http":
response_model_name = last_response["model_name"]
if model_version != "":
response_model_version = last_response["model_version"]
response_outputs = last_response["outputs"]
else:
response_model_name = last_response.model_name
if model_version != "":
response_model_version = last_response.model_version
response_outputs = last_response.outputs
tester.assertEqual(response_model_name, model_name)
if model_version != "":
tester.assertEqual(response_model_version, model_version)
tester.assertEqual(len(response_outputs), io_cnt)
for result in response_outputs:
if config[1] == "http":
result_name = result["name"]
else:
result_name = result.name
tester.assertTrue(result_name in expected_dict)
if use_system_shared_memory or use_cuda_shared_memory:
if pf == "libtorch" or pf == "libtorch_nobatch":
io_num = int(result_name.split("OUTPUT__")[1])
else:
io_num = int(result_name.split("OUTPUT")[1])
shm_handle = shm_op_handles[io_num]
output = results.get_output(result_name)
if config[1] == "http":
output_datatype = output['datatype']
output_shape = output['shape']
else:
output_datatype = output.datatype
output_shape = output.shape
output_dtype = triton_to_np_dtype(output_datatype)
if use_system_shared_memory:
output_data = shm.get_contents_as_numpy(
shm_handle, output_dtype, output_shape)
elif use_cuda_shared_memory:
output_data = cudashm.get_contents_as_numpy(
shm_handle, output_dtype, output_shape)
else:
output_data = results.as_numpy(result_name)
if (output_data.dtype == np.object) and (config[3] == False):
output_data = output_data.astype(np.bytes_)
expected = expected_dict[result_name]
tester.assertEqual(output_data.shape, expected.shape)
tester.assertTrue(np.array_equal(output_data, expected),
"{}, {}, expected: {}, got {}".format(
model_name, result_name, expected, output_data))
if len(shm_ip_handles) != 0:
for io_num in range(io_cnt):
if use_cuda_shared_memory:
triton_client.unregister_cuda_shared_memory(
shm_region_name_prefix[0]+str(io_num)+'_data')
triton_client.unregister_cuda_shared_memory(
shm_region_name_prefix[0]+str(io_num)+'_data')
cudashm.destroy_shared_memory_region(shm_ip_handles[io_num])
cudashm.destroy_shared_memory_region(shm_op_handles[io_num])
else:
triton_client.unregister_system_shared_memory(
shm_region_name_prefix[1]+str(io_num)+'_data')
triton_client.unregister_system_shared_memory(
shm_region_name_prefix[1]+str(io_num)+'_data')
shm.destroy_shared_memory_region(shm_ip_handles[io_num])
shm.destroy_shared_memory_region(shm_op_handles[io_num])
return results
print("channel creation failed: " + str(e))
sys.exit(1)
with open(args.label_file) as f:
labels_dict = {idx: line.strip() for idx, line in enumerate(f)}
inputs = []
outputs = []
input_name = "INPUT"
output_name = "OUTPUT"
image_data = load_image(args.image)
image_data = np.expand_dims(image_data, axis=0)
inputs.append(
tritongrpcclient.InferInput(input_name, image_data.shape, "UINT8"))
outputs.append(tritongrpcclient.InferRequestedOutput(output_name))
inputs[0].set_data_from_numpy(image_data)
start_time = time.time()
# Test with outputs
results = triton_client.infer(model_name=args.model_name,
inputs=inputs,
outputs=outputs)
latency = time.time() - start_time
output0_data = results.as_numpy(output_name)
maxs = np.argmax(output0_data, axis=1)
print("{}ms class: {}".format(latency, labels_dict[maxs[0]]))
edge_index = build_edge_index(x.shape[0], data['Ri_rows'],
data['Ri_cols'], data['Ro_rows'],
data['Ro_cols'])
print(x.shape, edge_index.shape)
nnodes = x.shape[0]
nedges = edge_index.shape[1]
inputs.append(tritongrpcclient.InferInput('x__0', [nnodes, 5], 'FP32'))
inputs.append(
tritongrpcclient.InferInput('edge_index__1', [2, nedges], "INT64"))
inputs[0].set_data_from_numpy(x)
inputs[1].set_data_from_numpy(edge_index)
outputs.append(tritongrpcclient.InferRequestedOutput('output__0'))
results = triton_client.infer(model_name=model_name,
inputs=inputs,
outputs=outputs)
output0_data = results.as_numpy('output__0')
print(output0_data)
del output0_data
statistics = triton_client.get_inference_statistics(model_name=model_name)
print(statistics)
if len(statistics.model_stats) != 1:
print("FAILED: Inference Statistics")
sys.exit(1)
print('PASS: infer')
