def requestGenerator(input_name, output_name, c, h, w, format, dtype, FLAGS,
input_filenames):
request = grpc_service_v2_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_v2_pb2.ModelInferRequest(
).InferRequestedOutputTensor()
output.name = output_name
output.parameters['classification'].int64_param = FLAGS.classes
request.outputs.extend([output])
input = grpc_service_v2_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.
input_bytes = None
for idx in range(FLAGS.batch_size):
# wrap over if requested batch size exceeds number of provided images
img_idx = idx % len(filenames)
input_filenames.append(filenames[img_idx])
if input_bytes is None:
input_bytes = image_data[img_idx].tobytes()
else:
input_bytes += image_data[img_idx].tobytes()
input_contents = grpc_service_v2_pb2.InferTensorContents()
input_contents.raw_contents = input_bytes
input.contents.CopyFrom(input_contents)
request.inputs.extend([input])
yield request
def requestGenerator(input_name, output_name, c, h, w, format, dtype, FLAGS):
request = grpc_service_v2_pb2.ModelInferRequest()
request.model_name = FLAGS.model_name
request.model_version = FLAGS.model_version
output = grpc_service_v2_pb2.ModelInferRequest(
).InferRequestedOutputTensor()
output.name = output_name
request.outputs.extend([output])
input = grpc_service_v2_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
image_data = None
with Image.open(FLAGS.image_filename) as img:
image_data = preprocess(img, format, dtype, c, h, w, FLAGS.scaling)
# Send requests of FLAGS.batch_size images.
input_bytes = None
for idx in range(FLAGS.batch_size):
if input_bytes is None:
input_bytes = image_data.tobytes()
else:
input_bytes += image_data.tobytes()
input_contents = grpc_service_v2_pb2.InferTensorContents()
input_contents.raw_contents = input_bytes
input.contents.CopyFrom(input_contents)
request.inputs.extend([input])
yield request
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
target_shape = (360, 360)
height, width, _ = img.shape
resized_img = cv2.resize(img, target_shape)
image_np = resized_img / 255.0
image_exp = np.expand_dims(image_np, axis=0)
image_transposed = image_exp.transpose((0, 3, 1, 2))
raw_input = np.float32(image_transposed).flatten().tobytes()
conv2d = grpc_service_v2_pb2.ModelInferRequest().InferInputTensor()
conv2d.name = "conv2d__0"
conv2d.shape.extend([1, 3, 360, 360])
conv2d_contents = grpc_service_v2_pb2.InferTensorContents()
conv2d_contents.raw_contents = raw_input
conv2d.contents.CopyFrom(conv2d_contents)
request.inputs.extend([conv2d])
loc_conv = grpc_service_v2_pb2.ModelInferRequest(
).InferRequestedOutputTensor()
loc_conv.name = "loc_branch_concat__0"
cls_conv = grpc_service_v2_pb2.ModelInferRequest(
).InferRequestedOutputTensor()
cls_conv.name = "cls_branch_concat__1"
request.outputs.extend([loc_conv, cls_conv])
response = grpc_stub.ModelInfer(request)
print("model infer:\n{}".format(response))
def requestGenerator(input_name, output_name, c, h, w, format, dtype, FLAGS,
result_filenames):
request = grpc_service_v2_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_v2_pb2.ModelInferRequest(
).InferRequestedOutputTensor()
output.name = output_name
output.parameters['classification'].int64_param = FLAGS.classes
request.outputs.extend([output])
input = grpc_service_v2_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_v2_pb2.InferTensorContents()
input_contents.raw_contents = input_bytes
input.contents.CopyFrom(input_contents)
request.inputs.extend([input])
result_filenames.append(input_filenames)
yield request
target_shape = (260, 260)
resized_img = cv2.resize(img, target_shape)
image_np = resized_img / 255.0
image_exp = np.expand_dims(image_np, axis=0)
processed_data = np.float32(image_exp)
raw_input = processed_data.flatten().tobytes()
raw_height = np.array([np.int64(height)]).tobytes()
raw_width = np.array([np.int64(width)]).tobytes()
input_data = grpc_service_v2_pb2.ModelInferRequest().InferInputTensor()
input_data.name = "data"
input_data.shape.extend([1, 260, 260, 3])
input_data_contents = grpc_service_v2_pb2.InferTensorContents()
input_data_contents.raw_contents = raw_input
input_data.contents.CopyFrom(input_data_contents)
input_height = grpc_service_v2_pb2.ModelInferRequest().InferInputTensor()
input_height.name = "height"
input_height.shape.extend([1, 1])
input_height_contents = grpc_service_v2_pb2.InferTensorContents()
input_height_contents.raw_contents = raw_height
input_height.contents.CopyFrom(input_height_contents)
input_width = grpc_service_v2_pb2.ModelInferRequest().InferInputTensor()
input_width.name = "width"
input_width.shape.extend([1, 1])