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(httpclient.InferInput('INPUT', value_data.shape,
"INT32"))
# Initialize the data
# FIXME, negative value in binary form can't be handled properly,
# which causes the library to raise decode exception.
inputs[0].set_data_from_numpy(value_data, binary_data=False)
outputs = []
outputs.append(httpclient.InferOutput('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(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(
grpcclient.InferInput(input_name, batched_image_data.shape, dtype))
inputs[0].set_data_from_numpy(batched_image_data)
else:
inputs.append(
httpclient.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(
grpcclient.InferOutput(output_name, class_count=FLAGS.classes))
else:
outputs.append(
httpclient.InferOutput(output_name,
binary_data=False,
class_count=FLAGS.classes))
yield inputs, outputs, FLAGS.model_name, FLAGS.model_version
def test_infer(model_name, input0_data, input1_data):
inputs = []
outputs = []
inputs.append(httpclient.InferInput('INPUT0'))
inputs.append(httpclient.InferInput('INPUT1'))
# Initialize the data
inputs[0].set_data_from_numpy(input0_data)
inputs[1].set_data_from_numpy(input1_data)
outputs.append(httpclient.InferOutput('OUTPUT0'))
outputs.append(httpclient.InferOutput('OUTPUT1'))
query_params = {'test_1': 1, 'test_2': 2}
results = triton_client.infer(model_name=model_name,
inputs=inputs,
outputs=outputs,
query_params=query_params)
return results
def TestIdentityInference(np_array, binary_data):
model_name = "savedmodel_zero_1_object"
inputs = []
outputs = []
inputs.append(httpclient.InferInput('INPUT0', np_array.shape, "BYTES"))
inputs[0].set_data_from_numpy(np_array, binary_data=binary_data)
outputs.append(httpclient.InferOutput('OUTPUT0', binary_data=binary_data))
results = triton_client.infer(model_name=model_name,
inputs=inputs,
outputs=outputs)
if np_array.dtype == np.object:
if not np.array_equal(np_array,
np.char.decode(results.as_numpy('OUTPUT0'))):
print(results.as_numpy('OUTPUT0'))
sys.exit(1)
else:
if not np.array_equal(np_array, results.as_numpy('OUTPUT0')):
print(results.as_numpy('OUTPUT0'))
sys.exit(1)
in0 = np.arange(start=0, stop=16, dtype=np.int32)
in0 = np.expand_dims(in0, axis=0)
in1 = np.ones(shape=(1, 16), dtype=np.int32)
expected_sum = np.add(in0, in1)
expected_diff = np.subtract(in0, in1)
in0n = np.array([str(x) for x in in0.reshape(in0.size)], dtype=object)
input0_data = in0n.reshape(in0.shape)
in1n = np.array([str(x) for x in in1.reshape(in1.size)], dtype=object)
input1_data = in1n.reshape(in1.shape)
# Initialize the data
inputs[0].set_data_from_numpy(input0_data, binary_data=True)
inputs[1].set_data_from_numpy(input1_data, binary_data=False)
outputs.append(httpclient.InferOutput('OUTPUT0', binary_data=True))
outputs.append(httpclient.InferOutput('OUTPUT1', binary_data=False))
results = triton_client.infer(model_name=model_name,
inputs=inputs,
outputs=outputs)
# Get the output arrays from the results
output0_data = results.as_numpy('OUTPUT0')
output1_data = results.as_numpy('OUTPUT1')
for i in range(16):
print(str(input0_data[0][i]) + " + " + str(input1_data[0][i]) + " = " +
str(output0_data[0][i]))
print(str(input0_data[0][i]) + " - " + str(input1_data[0][i]) + " = " +
str(output1_data[0][i]))
inputs = []
outputs = []
inputs.append(httpclient.InferInput('INPUT0'))
inputs.append(httpclient.InferInput('INPUT1'))
# Create the data for the two input tensors. Initialize the first
# to unique integers and the second to all ones.
input0_data = np.arange(start=0, stop=16, dtype=np.int32)
input0_data = np.expand_dims(input0_data, axis=0)
input1_data = np.ones(shape=(1, 16), dtype=np.int32)
# Initialize the data
inputs[0].set_data_from_numpy(input0_data)
inputs[1].set_data_from_numpy(input1_data)
outputs.append(httpclient.InferOutput('OUTPUT0'))
outputs.append(httpclient.InferOutput('OUTPUT1'))
# Define the callback function. Note the last two parameters should be
# result and error. InferenceServerClient would povide the results of an
# inference as tritongrpcclient.core.InferResult in result. For successful
# inference, error will be None, otherwise it will be an object of
# tritongrpcclient.utils.InferenceServerException holding the error details.
def callback(user_data, result, error):
if not error:
user_data.append(result)
else:
user_data.append(error)
# list to hold the results of inference.
user_data = []