def main():
args = build_argparser().parse_args()
assert args.device.split(':')[0] == "HETERO", "This sample supports only Hetero Plugin. " \
"Please specify correct device, e.g. HETERO:FPGA,CPU"
model_xml = args.model
model_bin = os.path.splitext(model_xml)[0] + ".bin"
# Plugin initialization for specified device and load extensions library if specified
plugin = IEPlugin(device=args.device, plugin_dirs=args.plugin_dir)
if args.cpu_extension and 'CPU' in args.device:
plugin.add_cpu_extension(args.cpu_extension)
# Read IR
net = IENetwork.from_ir(model=model_xml, weights=model_bin)
layers = net.get_layers()
net_ops = set([l['type'] for l in layers.values()])
if not any(op in net_ops for op in ("Convolution", "Concat")):
print("Specified IR doesn't contain any Convolution or Concat operations for which affinity going to be set.\n"
"Try to use another topology to make the affinity setting result more visible.")
# Configure the plugin to initialize default affinity for network in set_initial_affinity() function.
plugin.set_config({"TARGET_FALLBACK": args.device.split(':')[1]})
# Enable graph visualization
plugin.set_config({"HETERO_DUMP_GRAPH_DOT": "YES"})
plugin.set_initial_affinity(net)
net.set_affinity(types_affinity_map={"Convolution": "GPU", "Concat": "CPU"})
# Affinity setting example based on layer name.
# layers_affinity_map has higher priority and will overrides affinity set by layer type.
# net.set_affinity(types_affinity_map={"Convolution": "GPU", "Concat": "CPU"},
# layers_affinity_map={"fire4/expand3x3/Conv2D": "CPU"})
assert len(net.inputs.keys()) == 1, "Sample supports only single input topologies"
assert len(net.outputs) == 1, "Sample supports only single output topologies"
input_blob = next(iter(net.inputs))
out_blob = next(iter(net.outputs))
# Read and pre-process input image
n, c, h, w = net.inputs[input_blob]
image = cv2.imread(args.input)
image = cv2.resize(image, (w, h))
image = image.transpose((2, 0, 1)) # Change data layout from HWC to CHW
image = image.reshape((n, c, h, w))
# Load network to the plugin
exec_net = plugin.load(network=net)
del net
# Start sync inference
res = exec_net.infer(inputs={input_blob: image})
top_ind = np.argsort(res[out_blob], axis=1)[0, -args.number_top:][::-1]
for i in top_ind:
print("%f #%d" % (res[out_blob][0, i], i))
del exec_net
del plugin
cwd = os.getcwd()
print(
"Graphs representing default and resulting affinities dumped to {} and {} files respectively"
.format(os.path.join(cwd, 'hetero_affinity.dot'), os.path.join(cwd, 'hetero_subgraphs.dot'))
)
def test_set_initial_affinity():
with warnings.catch_warnings(record=True) as w:
plugin = IEPlugin("HETERO:CPU", None)
net = IENetwork(model=test_net_xml, weights=test_net_bin)
plugin.set_initial_affinity(net)
for l, params in net.layers.items():
assert params.affinity == "CPU", "Incorrect affinity for {}".format(l)
assert len(w) == 1
assert "IEPlugin class is deprecated. " \
"Please use IECore class instead." in str(w[0].message)
def test_set_initial_affinity_wrong_device(device):
with pytest.raises(RuntimeError) as e:
with warnings.catch_warnings(record=True) as w:
plugin = IEPlugin("CPU", None)
net = IENetwork(model=test_net_xml, weights=test_net_bin)
plugin.set_initial_affinity(net)
assert len(w) == 1
assert "IEPlugin class is deprecated. " \
"Please use IECore class instead." in str(w[0].message)
assert "set_initial_affinity method applicable only for HETERO device" in str(e.value)
def prepare_model(log, model, weights, cpu_extension, device_list, plugin_dir,
thread_num, stream_num):
model_xml = model
model_bin = weights
if len(device_list) == 1:
device = device_list[0]
elif len(device_list) == 2:
device = 'HETERO:{},{}'.format(device_list[0], device_list[1])
else:
log.error('Wrong count devices')
sys.exit(1)
log.info('Plugin initialization.');
plugin = IEPlugin(device = device, plugin_dirs = plugin_dir)
if cpu_extension and 'CPU' in device:
plugin.add_cpu_extension(cpu_extension)
log.info('Loading network files:\n\t {0}\n\t {1}'.format(
model_xml, model_bin))
net = IENetwork(model = model_xml, weights = model_bin)
if plugin.device == 'CPU':
supported_layers = plugin.get_supported_layers(net)
not_supported_layers = [ l for l in net.layers.keys() \
if l not in supported_layers ]
if len(not_supported_layers) != 0:
log.error('Following layers are not supported by the plugin \
for specified device {0}:\n {1}'.format(plugin.device,
', '.join(not_supported_layers)))
log.error('Please try to specify cpu extensions library path in \
sample\'s command line parameters using -l or --cpu_extension \
command line argument')
sys.exit(1)
if thread_num is not None:
if 'CPU' in device_list:
plugin.set_config({'CPU_THREADS_NUM': str(thread_num)})
else:
log.error('Parameter : Number of threads is used only for CPU')
sys.exit(1)
if stream_num is not None:
if 'CPU' in device_list:
plugin.set_config({'CPU_THROUGHPUT_STREAMS': str(stream_num)})
else:
log.error('Parameter : Number of streams is used only for CPU')
sys.exit(1)
if len(device_list) == 2:
plugin.set_config({'TARGET_FALLBACK': device})
plugin.set_initial_affinity(net)
return net, plugin
def get_openvino_plugin(openvino_network, inference_platform, library_path, cpu_libpath):
"""
Method used to load IEPlugin according to given target platform
:param openvino_network: IENetwork object
:param inference_platform: Target Device Plugin name (CPU, GPU, HETERO:MYRIAD,GPU,CPU etc.
:param library_path: Lib path to Shared Libraries /opt/intel/openvino/deployment_tools/inference_engine/lib/ubuntu..
:return: IEPlugin object
"""
openvino_plugin = None
# If OpenVINO Selected, Check for Hardware (GPU, MYRIAD or CPU) is supported for this example
# Load corresponding device library from the indicated paths, this application requires the environment
# variables are already set correctly
# source /opt/intel/openvino/bin/setupvars.sh
if inference_platform == 'GPU':
print('Trying to Load OpenVINO GPU Plugin')
openvino_plugin = IEPlugin(device=inference_platform, plugin_dirs=library_path)
elif inference_platform == 'MYRIAD':
print('Trying to Load OpenVINO Myriad Plugin')
openvino_plugin = IEPlugin(device=inference_platform, plugin_dirs=library_path)
openvino_plugin.set_config({"VPU_FORCE_RESET": "NO"})
elif inference_platform == 'HETERO:CPU,GPU' or inference_platform == 'HETERO:GPU,CPU':
openvino_plugin = IEPlugin(device=inference_platform, plugin_dirs=library_path)
openvino_plugin.add_cpu_extension(cpu_libpath)
openvino_plugin.set_config({"TARGET_FALLBACK": inference_platform.split(':')[1]})
# Enable graph visualization
# openvino_plugin.set_config({"HETERO_DUMP_GRAPH_DOT": "YES"})
openvino_plugin.set_initial_affinity(openvino_network)
supported_layers = openvino_plugin.get_supported_layers(openvino_network)
print('Supported Layers')
# [print(layer) for layer in supported_layers]
not_supported_layers = [l for l in openvino_network.layers.keys() if l not in supported_layers]
print('UnSupported Layers')
# [print(layer) for layer in not_supported_layers]
elif inference_platform == 'HETERO:MYRIAD,GPU' or inference_platform == 'HETERO:GPU,MYRIAD':
openvino_plugin = IEPlugin(device=inference_platform, plugin_dirs=library_path)
openvino_plugin.set_config({"TARGET_FALLBACK": inference_platform.split(':')[1]})
# Enable graph visualization
# openvino_plugin.set_config({"HETERO_DUMP_GRAPH_DOT": "YES"})
openvino_plugin.set_initial_affinity(openvino_network)
supported_layers = openvino_plugin.get_supported_layers(openvino_network)
print('Supported Layers')
# [print(layer) for layer in supported_layers]
not_supported_layers = [l for l in openvino_network.layers.keys() if l not in supported_layers]
print('UnSupported Layers')
# [print(layer) for layer in not_supported_layers]
elif inference_platform == 'HETERO:MYRIAD,CPU' or inference_platform == 'HETERO:CPU,MYRIAD':
openvino_plugin = IEPlugin(device=inference_platform, plugin_dirs=library_path)
openvino_plugin.add_cpu_extension(cpu_libpath)
openvino_plugin.set_config({"TARGET_FALLBACK": inference_platform.split(':')[1]})
# Enable graph visualization
# openvino_plugin.set_config({"HETERO_DUMP_GRAPH_DOT": "YES"})
openvino_plugin.set_initial_affinity(openvino_network)
supported_layers = openvino_plugin.get_supported_layers(openvino_network)
print('Supported Layers')
# [print(layer) for layer in supported_layers]
not_supported_layers = [l for l in openvino_network.layers.keys() if l not in supported_layers]
print('UnSupported Layers')
# [print(layer) for layer in not_supported_layers]
elif inference_platform == "CPU":
# By default try to load CPU library
print('Trying to Load OpenVINO CPU Plugin')
openvino_plugin = IEPlugin(device=inference_platform, plugin_dirs=library_path)
openvino_plugin.add_cpu_extension(cpu_libpath)
else:
print('Undefined Target Platform for OpenVINO: {}'.format(inference_platform))
help_menu()
exit(-2)
return openvino_plugin
from openvino.inference_engine import IENetwork, IEPlugin
model_xml = '/opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_optimizer/10_lrmodels/UNet/FP16/semanticsegmentation_frozen_person_32.xml'
model_bin = '/opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_optimizer/10_lrmodels/UNet/FP16/semanticsegmentation_frozen_person_32.bin'
net = IENetwork.from_ir(model=model_xml, weights=model_bin)
seg_image = Image.open("data/input/009649.png")
palette = seg_image.getpalette() # Get a color palette
index_void = 2 # Define index_void Back Ground
camera_width = 320
camera_height = 240
fps = ""
elapsedTime = 0
plugin = IEPlugin(device="HETERO:MYRIAD,CPU")
plugin.set_config({"TARGET_FALLBACK": "HETERO:MYRIAD,CPU"})
plugin.set_initial_affinity(net)
#plugin = IEPlugin(device="CPU")
exec_net = plugin.load(network=net)
input_blob = next(iter(net.inputs)) #input_blob = 'input'
out_blob = next(iter(net.outputs)) #out_blob = 'output/BiasAdd'
n, c, h, w = net.inputs[input_blob].shape #n, c, h, w = 1, 3, 256, 256
del net
cap = cv2.VideoCapture(0)
cap.set(cv2.CAP_PROP_FPS, 30)
cap.set(cv2.CAP_PROP_FRAME_WIDTH, camera_width)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, camera_height)
time.sleep(1)
def main():
log.basicConfig(format="[ %(levelname)s ] %(message)s", level=log.INFO, stream=sys.stdout)
args = build_argparser().parse_args()
assert args.device.split(':')[0] == "HETERO", "This demo supports only Hetero Plugin. " \
"Please specify correct device, e.g. HETERO:FPGA,CPU"
model_xml = args.model
model_bin = os.path.splitext(model_xml)[0] + ".bin"
# Plugin initialization for specified device and load extensions library if specified
plugin = IEPlugin(device=args.device, plugin_dirs=args.plugin_dir)
if args.cpu_extension and 'CPU' in args.device:
plugin.add_cpu_extension(args.cpu_extension)
# Read IR
net = IENetwork(model=model_xml, weights=model_bin)
if plugin.device == "CPU":
supported_layers = plugin.get_supported_layers(net)
not_supported_layers = [l for l in net.layers.keys() if l not in supported_layers]
if len(not_supported_layers) != 0:
log.error("Following layers are not supported by the plugin for specified device {}:\n {}".
format(plugin.device, ', '.join(not_supported_layers)))
log.error("Please try to specify cpu extensions library path in demo's command line parameters using -l "
"or --cpu_extension command line argument")
sys.exit(1)
net_ops = set([l.type for l in net.layers.values()])
if not any([op == "Convolution" for op in net_ops]):
log.warning("Specified IR doesn't contain any Convolution operations for which affinity going to be set.\n"
"Try to use another topology to make the affinity setting result more visible.")
# Configure the plugin to initialize default affinity for network in set_initial_affinity() function.
plugin.set_config({"TARGET_FALLBACK": args.device.split(':')[1]})
# Enable graph visualization
plugin.set_config({"HETERO_DUMP_GRAPH_DOT": "YES"})
plugin.set_initial_affinity(net)
for l in net.layers.values():
if l.type == "Convolution":
l.affinity = "GPU"
assert len(net.inputs.keys()) == 1, "Demo supports only single input topologies"
assert len(net.outputs) == 1, "Demo supports only single output topologies"
input_blob = next(iter(net.inputs))
out_blob = next(iter(net.outputs))
# Read and pre-process input image
n, c, h, w = net.inputs[input_blob].shape
image = cv2.imread(args.input)
image = cv2.resize(image, (w, h))
image = image.transpose((2, 0, 1)) # Change data layout from HWC to CHW
image = image.reshape((n, c, h, w))
# Load network to the plugin
exec_net = plugin.load(network=net)
del net
# Start sync inference
res = exec_net.infer(inputs={input_blob: image})
top_ind = np.argsort(res[out_blob], axis=1)[0, -args.number_top:][::-1]
for i in top_ind:
log.info("%f #%d" % (res[out_blob][0, i], i))
del exec_net
del plugin
cwd = os.getcwd()
log.info(
"Graphs representing default and resulting affinities dumped to {} and {} files respectively"
.format(os.path.join(cwd, 'hetero_affinity.dot'), os.path.join(cwd, 'hetero_subgraphs.dot'))
)
