def requestGenerator(self, model_name, output_name):
request = grpc_service_pb2.ModelInferRequest()
request.model_name = model_name
request.id = "output name validation"
input = grpc_service_pb2.ModelInferRequest().InferInputTensor()
input.name = "INPUT0"
input.datatype = "FP32"
input.shape.extend([1])
input_contents = grpc_service_pb2.InferTensorContents()
input_contents.raw_contents = bytes(4 * 'a', 'utf-8')
input.contents.CopyFrom(input_contents)
request.inputs.extend([input])
output = grpc_service_pb2.ModelInferRequest(
).InferRequestedOutputTensor()
output.name = output_name
request.outputs.extend([output])
return request
def requestGenerator(input_name, output_name, c, h, w, format, dtype, FLAGS,
result_filenames):
request = grpc_service_pb2.ModelInferRequest()
request.model_name = FLAGS.model_name
request.model_version = FLAGS.model_version
filenames = []
if os.path.isdir(FLAGS.image_filename):
filenames = [
os.path.join(FLAGS.image_filename, f)
for f in os.listdir(FLAGS.image_filename)
if os.path.isfile(os.path.join(FLAGS.image_filename, f))
]
else:
filenames = [
FLAGS.image_filename,
]
filenames.sort()
output = grpc_service_pb2.ModelInferRequest().InferRequestedOutputTensor()
output.name = output_name
output.parameters['classification'].int64_param = FLAGS.classes
request.outputs.extend([output])
input = grpc_service_pb2.ModelInferRequest().InferInputTensor()
input.name = input_name
input.datatype = dtype
if format == mc.ModelInput.FORMAT_NHWC:
input.shape.extend([FLAGS.batch_size, h, w, c])
else:
input.shape.extend([FLAGS.batch_size, c, h, w])
# Preprocess image into input data according to model requirements
# Preprocess the images into input data according to model
# requirements
image_data = []
for filename in filenames:
img = Image.open(filename)
image_data.append(
preprocess(img, format, dtype, c, h, w, FLAGS.scaling))
# Send requests of FLAGS.batch_size images. If the number of
# images isn't an exact multiple of FLAGS.batch_size then just
# start over with the first images until the batch is filled.
image_idx = 0
last_request = False
while not last_request:
input_bytes = None
input_filenames = []
request.ClearField("inputs")
for idx in range(FLAGS.batch_size):
input_filenames.append(filenames[image_idx])
if input_bytes is None:
input_bytes = image_data[image_idx].tobytes()
else:
input_bytes += image_data[image_idx].tobytes()
image_idx = (image_idx + 1) % len(image_data)
if image_idx == 0:
last_request = True
input_contents = grpc_service_pb2.InferTensorContents()
input_contents.raw_contents = input_bytes
input.contents.CopyFrom(input_contents)
request.inputs.extend([input])
result_filenames.append(input_filenames)
yield request
request = grpc_service_pb2.ModelConfigRequest(name="resnet_v1_50_graphdef",
version=model_version)
response = grpc_stub.ModelConfig(request)
print("model config:\n{}".format(response))
# Infer
request = grpc_service_pb2.ModelInferRequest()
request.model_name = "resnet_v1_50_graphdef"
request.model_version = model_version
request.id = "my request id"
input = grpc_service_pb2.ModelInferRequest().InferInputTensor()
input.name = "input"
input.datatype = "FP32"
input.shape.extend([1, 224, 224, 3])
input_contents = grpc_service_pb2.InferTensorContents()
input_contents.raw_contents = bytes(602112 * 'a', 'utf-8')
input.contents.CopyFrom(input_contents)
request.inputs.extend([input])
output = grpc_service_pb2.ModelInferRequest().InferRequestedOutputTensor()
output.name = "resnet_v1_50/predictions/Softmax"
request.outputs.extend([output])
response = grpc_stub.ModelInfer(request)
print("model infer:\n{}".format(response))
print("PASS")