def create_network(model_file: str, backends: list):
"""
Creates a network based on the model file and a list of backends.
Args:
model_file: User-specified model file.
backends: List of backends to optimize network.
Returns:
net_id: Unique ID of the network to run.
runtime: Runtime context for executing inference.
input_binding_info: Contains essential information about the model input.
output_binding_info: Used to map output tensor and its memory.
"""
if not os.path.exists(model_file):
raise FileNotFoundError(f'Model file not found for: {model_file}')
# Determine which parser to create based on model file extension
parser = None
_, ext = os.path.splitext(model_file)
if ext == '.tflite':
parser = ann.ITfLiteParser()
elif ext == '.pb':
parser = ann.ITfParser()
elif ext == '.onnx':
parser = ann.IOnnxParser()
assert (parser is not None)
network = parser.CreateNetworkFromBinaryFile(model_file)
# Specify backends to optimize network
preferred_backends = []
for b in backends:
preferred_backends.append(ann.BackendId(b))
# Select appropriate device context and optimize the network for that device
options = ann.CreationOptions()
runtime = ann.IRuntime(options)
opt_network, messages = ann.Optimize(network, preferred_backends, runtime.GetDeviceSpec(),
ann.OptimizerOptions())
print(f'Preferred backends: {backends}\n{runtime.GetDeviceSpec()}\n'
f'Optimization warnings: {messages}')
# Load the optimized network onto the Runtime device
net_id, _ = runtime.LoadNetwork(opt_network)
# Get input and output binding information
graph_id = parser.GetSubgraphCount() - 1
input_names = parser.GetSubgraphInputTensorNames(graph_id)
input_binding_info = parser.GetNetworkInputBindingInfo(graph_id, input_names[0])
output_names = parser.GetSubgraphOutputTensorNames(graph_id)
output_binding_info = []
for output_name in output_names:
outBindInfo = parser.GetNetworkOutputBindingInfo(graph_id, output_name)
output_binding_info.append(outBindInfo)
return net_id, runtime, input_binding_info, output_binding_info
def create_onnx_network(model_file: str, backends: list = ['CpuAcc', 'CpuRef']):
"""Creates a network from an onnx model file.
Args:
model_file (str): Path of the model file.
backends (list): List of backends to use when running inference.
Returns:
int: Network ID.
IOnnxParser: ONNX parser instance.
IRuntime: Runtime object instance.
"""
return __create_network(model_file, backends, ann.IOnnxParser())
def test_onnx_filenotfound_exception(shared_data_folder):
parser = ann.IOnnxParser()
# path to model
path_to_model = os.path.join(shared_data_folder, 'some_unknown_model.onnx')
# parse onnx binary & create network
with pytest.raises(RuntimeError) as err:
parser.CreateNetworkFromBinaryFile(path_to_model)
# Only check for part of the exception since the exception returns
# absolute path which will change on different machines.
assert 'Invalid (null) filename' in str(err.value)
def parser(shared_data_folder):
"""
Parse and setup the test network to be used for the tests below
"""
# create onnx parser
parser = ann.IOnnxParser()
# path to model
path_to_model = os.path.join(shared_data_folder, 'mock_model.onnx')
# parse onnx binary & create network
parser.CreateNetworkFromBinaryFile(path_to_model)
yield parser
def __create_network(model_file: str, backends: list, parser=None):
"""Creates a network based on a file and parser type.
Args:
model_file (str): Path of the model file.
backends (list): List of backends to use when running inference.
parser_type: Parser instance. (pyarmnn.ITFliteParser/pyarmnn.IOnnxParser...)
Returns:
int: Network ID.
int: Graph ID.
IParser: TF Lite parser instance.
IRuntime: Runtime object instance.
"""
args = parse_command_line()
options = ann.CreationOptions()
runtime = ann.IRuntime(options)
if parser is None:
# try to determine what parser to create based on model extension
_, ext = os.path.splitext(model_file)
if ext == ".onnx":
parser = ann.IOnnxParser()
elif ext == ".tflite":
parser = ann.ITfLiteParser()
assert (parser is not None)
network = parser.CreateNetworkFromBinaryFile(model_file)
preferred_backends = []
for b in backends:
preferred_backends.append(ann.BackendId(b))
opt_network, messages = ann.Optimize(network, preferred_backends,
runtime.GetDeviceSpec(),
ann.OptimizerOptions())
if args.verbose:
for m in messages:
warnings.warn(m)
net_id, w = runtime.LoadNetwork(opt_network)
if args.verbose and w:
warnings.warn(w)
return net_id, parser, runtime
def test_onnx_parser_end_to_end(shared_data_folder):
parser = ann.IOnnxParser = ann.IOnnxParser()
network = parser.CreateNetworkFromBinaryFile(
os.path.join(shared_data_folder, 'mock_model.onnx'))
# load test image data stored in input_onnx.npy
input_binding_info = parser.GetNetworkInputBindingInfo("input")
input_tensor_data = np.load(
os.path.join(shared_data_folder,
'onnx_parser/input_onnx.npy')).astype(np.float32)
options = ann.CreationOptions()
runtime = ann.IRuntime(options)
preferred_backends = [ann.BackendId('CpuAcc'), ann.BackendId('CpuRef')]
opt_network, messages = ann.Optimize(network, preferred_backends,
runtime.GetDeviceSpec(),
ann.OptimizerOptions())
assert 0 == len(messages)
net_id, messages = runtime.LoadNetwork(opt_network)
assert "" == messages
input_tensors = ann.make_input_tensors([input_binding_info],
[input_tensor_data])
output_tensors = ann.make_output_tensors(
[parser.GetNetworkOutputBindingInfo("output")])
runtime.EnqueueWorkload(net_id, input_tensors, output_tensors)
output = ann.workload_tensors_to_ndarray(output_tensors)
# Load golden output file for result comparison.
golden_output = np.load(
os.path.join(shared_data_folder, 'onnx_parser/golden_output_onnx.npy'))
# Check that output matches golden output to 4 decimal places (there are slight rounding differences after this)
np.testing.assert_almost_equal(output[0], golden_output, decimal=4)
