def main():
args = parse_args()
# --------------------------- Step 1. Initialize OpenVINO Runtime Core ------------------------------------------------
log.info('Creating OpenVINO Runtime Core')
core = Core()
# --------------------------- Step 2. Read a model --------------------------------------------------------------------
if args.model:
log.info(f'Reading the model: {args.model}')
# (.xml and .bin files) or (.onnx file)
model = core.read_model(args.model)
# --------------------------- Step 3. Apply preprocessing -------------------------------------------------------------
if args.output_layers:
output_layer_names, output_layer_ports = parse_outputs_from_args(
args)
model.add_outputs(list(zip(output_layer_names,
output_layer_ports)))
if args.layout:
layouts = parse_input_layouts(args, model.inputs)
ppp = PrePostProcessor(model)
for i in range(len(model.inputs)):
ppp.input(i).tensor().set_element_type(Type.f32)
input_name = model.input(i).get_any_name()
if args.layout and input_name in layouts.keys():
ppp.input(i).tensor().set_layout(Layout(layouts[input_name]))
ppp.input(i).model().set_layout(Layout(layouts[input_name]))
for i in range(len(model.outputs)):
ppp.output(i).tensor().set_element_type(Type.f32)
model = ppp.build()
if args.batch_size:
batch_size = args.batch_size if args.context_window_left == args.context_window_right == 0 else 1
if any([not _input.node.layout.empty for _input in model.inputs]):
set_batch(model, batch_size)
else:
log.warning(
'Layout is not set for any input, so custom batch size is not set'
)
# ---------------------------Step 4. Configure plugin ---------------------------------------------------------
devices = args.device.replace('HETERO:', '').split(',')
plugin_config = {}
if 'GNA' in args.device:
gna_device_mode = devices[0] if '_' in devices[0] else 'GNA_AUTO'
devices[0] = 'GNA'
plugin_config['GNA_DEVICE_MODE'] = gna_device_mode
plugin_config['GNA_PRECISION'] = f'I{args.quantization_bits}'
plugin_config['GNA_EXEC_TARGET'] = args.exec_target
plugin_config['GNA_PWL_MAX_ERROR_PERCENT'] = str(args.pwl_me)
# Set a GNA scale factor
if args.import_gna_model:
if args.scale_factor:
log.warning(
f'Custom scale factor will be used for imported GNA model: {args.import_gna_model}'
)
set_scale_factors(plugin_config, parse_scale_factors(args))
else:
log.info(
f'Using scale factor from the imported GNA model: {args.import_gna_model}'
)
else:
if args.scale_factor:
set_scale_factors(plugin_config, parse_scale_factors(args))
else:
scale_factors = []
for file_name in re.split(', |,', args.input):
_, utterances = read_utterance_file(file_name)
scale_factors.append(get_scale_factor(utterances[0]))
log.info(
'Using scale factor(s) calculated from first utterance')
set_scale_factors(plugin_config, scale_factors)
if args.export_embedded_gna_model:
plugin_config[
'GNA_FIRMWARE_MODEL_IMAGE'] = args.export_embedded_gna_model
plugin_config[
'GNA_FIRMWARE_MODEL_IMAGE_GENERATION'] = args.embedded_gna_configuration
if args.performance_counter:
plugin_config['PERF_COUNT'] = 'YES'
device_str = f'HETERO:{",".join(devices)}' if 'HETERO' in args.device else devices[
0]
# --------------------------- Step 5. Loading model to the device -----------------------------------------------------
log.info('Loading the model to the plugin')
if args.model:
compiled_model = core.compile_model(model, device_str, plugin_config)
else:
with open(args.import_gna_model, 'rb') as f:
buf = BytesIO(f.read())
compiled_model = core.import_model(buf, device_str, plugin_config)
# --------------------------- Exporting GNA model using InferenceEngine AOT API ---------------------------------------
if args.export_gna_model:
log.info(f'Writing GNA Model to {args.export_gna_model}')
user_stream = compiled_model.export_model()
with open(args.export_gna_model, 'wb') as f:
f.write(user_stream)
return 0
if args.export_embedded_gna_model:
log.info(
f'Exported GNA embedded model to file {args.export_embedded_gna_model}'
)
log.info(
f'GNA embedded model export done for GNA generation {args.embedded_gna_configuration}'
)
return 0
# --------------------------- Step 6. Set up input --------------------------------------------------------------------
if args.input_layers:
input_layer_names = re.split(', |,', args.input_layers)
else:
input_layer_names = [
_input.any_name for _input in compiled_model.inputs
]
input_file_names = re.split(', |,', args.input)
if len(input_layer_names) != len(input_file_names):
log.error(
f'Number of model inputs ({len(compiled_model.inputs)}) is not equal '
f'to number of ark files ({len(input_file_names)})')
sys.exit(-3)
input_file_data = [
read_utterance_file(file_name) for file_name in input_file_names
]
infer_data = [{
input_layer_names[j]: input_file_data[j].utterances[i]
for j in range(len(input_layer_names))
} for i in range(len(input_file_data[0].utterances))]
if args.output_layers:
output_layer_names, output_layer_ports = parse_outputs_from_args(args)
# If a name of output layer contains a port number then concatenate output_layer_names and output_layer_ports
if ':' in compiled_model.outputs[0].any_name:
output_layer_names = [
f'{output_layer_names[i]}:{output_layer_ports[i]}'
for i in range(len(output_layer_names))
]
else:
output_layer_names = [compiled_model.outputs[0].any_name]
if args.output:
output_file_names = re.split(', |,', args.output)
if len(output_layer_names) != len(output_file_names):
log.error(
'The number of output files is not equal to the number of model outputs.'
)
sys.exit(-6)
if args.reference:
reference_file_names = re.split(', |,', args.reference)
if len(output_layer_names) != len(reference_file_names):
log.error(
'The number of reference files is not equal to the number of model outputs.'
)
sys.exit(-5)
reference_file_data = [
read_utterance_file(file_name)
for file_name in reference_file_names
]
references = [{
output_layer_names[j]: reference_file_data[j].utterances[i]
for j in range(len(output_layer_names))
} for i in range(len(input_file_data[0].utterances))]
# --------------------------- Step 7. Create infer request ------------------------------------------------------------
infer_request = compiled_model.create_infer_request()
# --------------------------- Step 8. Do inference --------------------------------------------------------------------
log.info('Starting inference in synchronous mode')
results = []
total_infer_time = 0
for i in range(len(infer_data)):
start_infer_time = default_timer()
# Reset states between utterance inferences to remove a memory impact
for state in infer_request.query_state():
state.reset()
results.append(
do_inference(
infer_data[i],
infer_request,
args.context_window_left,
args.context_window_right,
))
infer_time = default_timer() - start_infer_time
total_infer_time += infer_time
num_of_frames = infer_data[i][input_layer_names[0]].shape[0]
avg_infer_time_per_frame = infer_time / num_of_frames
# --------------------------- Step 9. Process output ------------------------------------------------------------------
log.info('')
log.info(f'Utterance {i}:')
log.info(f'Total time in Infer (HW and SW): {infer_time * 1000:.2f}ms')
log.info(f'Frames in utterance: {num_of_frames}')
log.info(
f'Average Infer time per frame: {avg_infer_time_per_frame * 1000:.2f}ms'
)
for name in output_layer_names:
log.info('')
log.info(f'Output blob name: {name}')
log.info(f'Number scores per frame: {results[i][name].shape[1]}')
if args.reference:
log.info('')
compare_with_reference(results[i][name], references[i][name])
if args.performance_counter:
if 'GNA' in args.device:
total_cycles = infer_request.profiling_info[
0].real_time.total_seconds()
stall_cycles = infer_request.profiling_info[
1].real_time.total_seconds()
active_cycles = total_cycles - stall_cycles
frequency = 10**6
if args.arch == 'CORE':
frequency *= GNA_CORE_FREQUENCY
else:
frequency *= GNA_ATOM_FREQUENCY
total_inference_time = total_cycles / frequency
active_time = active_cycles / frequency
stall_time = stall_cycles / frequency
log.info('')
log.info('Performance Statistics of GNA Hardware')
log.info(
f' Total Inference Time: {(total_inference_time * 1000):.4f} ms'
)
log.info(f' Active Time: {(active_time * 1000):.4f} ms')
log.info(f' Stall Time: {(stall_time * 1000):.4f} ms')
log.info('')
log.info(f'Total sample time: {total_infer_time * 1000:.2f}ms')
if args.output:
for i, name in enumerate(output_layer_names):
data = [
results[i][name]
for i in range(len(input_file_data[0].utterances))
]
write_utterance_file(output_file_names[i], input_file_data[0].keys,
data)
log.info(f'File {output_file_names[i]} was created!')
# ----------------------------------------------------------------------------------------------------------------------
log.info(
'This sample is an API example, '
'for any performance measurements please use the dedicated benchmark_app tool\n'
)
return 0
def main():
log.basicConfig(format='[ %(levelname)s ] %(message)s', level=log.INFO, stream=sys.stdout)
args = parse_args()
# ---------------------------Step 1. Initialize inference engine core--------------------------------------------------
log.info('Creating Inference Engine')
ie = IECore()
# ---------------------------Step 2. Read a model in OpenVINO Intermediate Representation---------------
if args.model:
log.info(f'Reading the network: {args.model}')
# .xml and .bin files
net = ie.read_network(model=args.model)
# ---------------------------Step 3. Configure input & output----------------------------------------------------------
log.info('Configuring input and output blobs')
# Mark layers from args.output_layers as outputs
if args.output_layers:
net.add_outputs(get_output_layer_list(net, args, with_ports=True))
# Get names of input and output blobs
input_blobs = get_input_layer_list(net, args)
output_blobs = get_output_layer_list(net, args, with_ports=False)
# Set input and output precision manually
for blob_name in input_blobs:
net.input_info[blob_name].precision = 'FP32'
for blob_name in output_blobs:
net.outputs[blob_name].precision = 'FP32'
net.batch_size = args.batch_size
# ---------------------------Step 4. Loading model to the device-------------------------------------------------------
devices = args.device.replace('HETERO:', '').split(',')
plugin_config = {}
if 'GNA' in args.device:
gna_device_mode = devices[0] if '_' in devices[0] else 'GNA_AUTO'
devices[0] = 'GNA'
plugin_config['GNA_DEVICE_MODE'] = gna_device_mode
plugin_config['GNA_PRECISION'] = f'I{args.quantization_bits}'
# Set a GNA scale factor
if args.import_gna_model:
if args.scale_factor:
log.warning(f'Custom scale factor will be used for imported GNA model: {args.import_gna_model}')
set_scale_factors(plugin_config, parse_scale_factors(args))
else:
log.info(f'Using scale factor from the imported GNA model: {args.import_gna_model}')
else:
if args.scale_factor:
set_scale_factors(plugin_config, parse_scale_factors(args))
else:
scale_factors = []
for file_name in re.split(', |,', args.input):
first_utterance = next(iter(read_utterance_file(file_name).values()))
scale_factors.append(get_scale_factor(first_utterance))
log.info('Using scale factor(s) calculated from first utterance')
set_scale_factors(plugin_config, scale_factors)
if args.export_embedded_gna_model:
plugin_config['GNA_FIRMWARE_MODEL_IMAGE'] = args.export_embedded_gna_model
plugin_config['GNA_FIRMWARE_MODEL_IMAGE_GENERATION'] = args.embedded_gna_configuration
if args.performance_counter:
plugin_config['PERF_COUNT'] = 'YES'
device_str = f'HETERO:{",".join(devices)}' if 'HETERO' in args.device else devices[0]
log.info('Loading the model to the plugin')
if args.model:
exec_net = ie.load_network(net, device_str, plugin_config)
else:
exec_net = ie.import_network(args.import_gna_model, device_str, plugin_config)
input_blobs = get_input_layer_list(exec_net, args)
output_blobs = get_output_layer_list(exec_net, args, with_ports=False)
if args.input:
input_files = re.split(', |,', args.input)
if len(input_blobs) != len(input_files):
log.error(f'Number of network inputs ({len(input_blobs)}) is not equal '
f'to number of ark files ({len(input_files)})')
sys.exit(-3)
if args.reference:
reference_files = re.split(', |,', args.reference)
if len(output_blobs) != len(reference_files):
log.error('The number of reference files is not equal to the number of network outputs.')
sys.exit(-5)
if args.output:
output_files = re.split(', |,', args.output)
if len(output_blobs) != len(output_files):
log.error('The number of output files is not equal to the number of network outputs.')
sys.exit(-6)
if args.export_gna_model:
log.info(f'Writing GNA Model to {args.export_gna_model}')
exec_net.export(args.export_gna_model)
return 0
if args.export_embedded_gna_model:
log.info(f'Exported GNA embedded model to file {args.export_embedded_gna_model}')
log.info(f'GNA embedded model export done for GNA generation {args.embedded_gna_configuration}')
return 0
# ---------------------------Step 5. Create infer request--------------------------------------------------------------
# load_network() method of the IECore class with a specified number of requests (default 1) returns an ExecutableNetwork
# instance which stores infer requests. So you already created Infer requests in the previous step.
# ---------------------------Step 6. Prepare input---------------------------------------------------------------------
file_data = [read_utterance_file(file_name) for file_name in input_files]
input_data = {
utterance_name: {
input_blobs[i]: file_data[i][utterance_name] for i in range(len(input_blobs))
}
for utterance_name in file_data[0].keys()
}
if args.reference:
references = {output_blobs[i]: read_utterance_file(reference_files[i]) for i in range(len(output_blobs))}
# ---------------------------Step 7. Do inference----------------------------------------------------------------------
log.info('Starting inference in synchronous mode')
results = {blob_name: {} for blob_name in output_blobs}
total_infer_time = 0
for i, key in enumerate(sorted(input_data)):
start_infer_time = default_timer()
# Reset states between utterance inferences to remove a memory impact
for request in exec_net.requests:
for state in request.query_state():
state.reset()
result = infer_data(input_data[key], exec_net, input_blobs, output_blobs)
for blob_name in result.keys():
results[blob_name][key] = result[blob_name]
infer_time = default_timer() - start_infer_time
total_infer_time += infer_time
num_of_frames = file_data[0][key].shape[0]
avg_infer_time_per_frame = infer_time / num_of_frames
# ---------------------------Step 8. Process output--------------------------------------------------------------------
log.info('')
log.info(f'Utterance {i} ({key}):')
log.info(f'Total time in Infer (HW and SW): {infer_time * 1000:.2f}ms')
log.info(f'Frames in utterance: {num_of_frames}')
log.info(f'Average Infer time per frame: {avg_infer_time_per_frame * 1000:.2f}ms')
for blob_name in output_blobs:
log.info('')
log.info(f'Output blob name: {blob_name}')
log.info(f'Number scores per frame: {results[blob_name][key].shape[1]}')
if args.reference:
log.info('')
compare_with_reference(results[blob_name][key], references[blob_name][key])
if args.performance_counter:
if 'GNA' in args.device:
pc = exec_net.requests[0].get_perf_counts()
total_cycles = int(pc['1.1 Total scoring time in HW']['real_time'])
stall_cycles = int(pc['1.2 Stall scoring time in HW']['real_time'])
active_cycles = total_cycles - stall_cycles
frequency = 10**6
if args.arch == 'CORE':
frequency *= GNA_CORE_FREQUENCY
else:
frequency *= GNA_ATOM_FREQUENCY
total_inference_time = total_cycles / frequency
active_time = active_cycles / frequency
stall_time = stall_cycles / frequency
log.info('')
log.info('Performance Statistics of GNA Hardware')
log.info(f' Total Inference Time: {(total_inference_time * 1000):.4f} ms')
log.info(f' Active Time: {(active_time * 1000):.4f} ms')
log.info(f' Stall Time: {(stall_time * 1000):.4f} ms')
log.info('')
log.info(f'Total sample time: {total_infer_time * 1000:.2f}ms')
if args.output:
for i, blob_name in enumerate(results):
write_utterance_file(output_files[i], results[blob_name])
log.info(f'File {output_files[i]} was created!')
# ----------------------------------------------------------------------------------------------------------------------
log.info('This sample is an API example, '
'for any performance measurements please use the dedicated benchmark_app tool\n')
return 0
def main():
log.basicConfig(format='[ %(levelname)s ] %(message)s',
level=log.INFO,
stream=sys.stdout)
args = parse_args()
# ---------------------------Step 1. Initialize inference engine core--------------------------------------------------
log.info('Creating Inference Engine')
ie = IECore()
# ---------------------------Step 2. Read a model in OpenVINO Intermediate Representation---------------
if args.model:
log.info(f'Reading the network: {args.model}')
# .xml and .bin files
net = ie.read_network(model=args.model)
# ---------------------------Step 3. Configure input & output----------------------------------------------------------
log.info('Configuring input and output blobs')
# Get names of input and output blobs
if args.input_layers:
input_blobs = re.split(', |,', args.input_layers)
else:
input_blobs = [next(iter(net.input_info))]
if args.output_layers:
output_name_port = [
output.split(':')
for output in re.split(', |,', args.output_layers)
]
try:
output_name_port = [(blob_name, int(port))
for blob_name, port in output_name_port]
except ValueError:
log.error('Output Parameter does not have a port.')
sys.exit(-4)
net.add_outputs(output_name_port)
output_blobs = [blob_name for blob_name, port in output_name_port]
else:
output_blobs = [list(net.outputs.keys())[-1]]
# Set input and output precision manually
for blob_name in input_blobs:
net.input_info[blob_name].precision = 'FP32'
for blob_name in output_blobs:
net.outputs[blob_name].precision = 'FP32'
net.batch_size = args.batch_size
# ---------------------------Step 4. Loading model to the device-------------------------------------------------------
devices = args.device.replace('HETERO:', '').split(',')
plugin_config = {}
if 'GNA' in args.device:
gna_device_mode = devices[0] if '_' in devices[0] else 'GNA_AUTO'
devices[0] = 'GNA'
plugin_config['GNA_DEVICE_MODE'] = gna_device_mode
plugin_config['GNA_PRECISION'] = f'I{args.quantization_bits}'
# Get a GNA scale factor
if args.import_gna_model:
log.info(
f'Using scale factor from the imported GNA model: {args.import_gna_model}'
)
else:
utterances = read_utterance_file(args.input.split(',')[0])
key = sorted(utterances)[0]
scale_factor = get_scale_factor(utterances[key])
log.info(
f'Using scale factor of {scale_factor:.7f} calculated from first utterance.'
)
plugin_config['GNA_SCALE_FACTOR'] = str(scale_factor)
if args.export_embedded_gna_model:
plugin_config[
'GNA_FIRMWARE_MODEL_IMAGE'] = args.export_embedded_gna_model
plugin_config[
'GNA_FIRMWARE_MODEL_IMAGE_GENERATION'] = args.embedded_gna_configuration
device_str = f'HETERO:{",".join(devices)}' if 'HETERO' in args.device else devices[
0]
log.info('Loading the model to the plugin')
if args.model:
exec_net = ie.load_network(net, device_str, plugin_config)
else:
exec_net = ie.import_network(args.import_gna_model, device_str,
plugin_config)
input_blobs = [next(iter(exec_net.input_info))]
output_blobs = [list(exec_net.outputs.keys())[-1]]
if args.input:
input_files = re.split(', |,', args.input)
if len(input_blobs) != len(input_files):
log.error(
f'Number of network inputs ({len(input_blobs)}) is not equal '
f'to number of ark files ({len(input_files)})')
sys.exit(-3)
if args.reference:
reference_files = re.split(', |,', args.reference)
if len(output_blobs) != len(reference_files):
log.error(
'The number of reference files is not equal to the number of network outputs.'
)
sys.exit(-5)
if args.output:
output_files = re.split(', |,', args.output)
if len(output_blobs) != len(output_files):
log.error(
'The number of output files is not equal to the number of network outputs.'
)
sys.exit(-6)
if args.export_gna_model:
log.info(f'Writing GNA Model to {args.export_gna_model}')
exec_net.export(args.export_gna_model)
return 0
if args.export_embedded_gna_model:
log.info(
f'Exported GNA embedded model to file {args.export_embedded_gna_model}'
)
log.info(
f'GNA embedded model export done for GNA generation {args.embedded_gna_configuration}'
)
return 0
# ---------------------------Step 5. Create infer request--------------------------------------------------------------
# load_network() method of the IECore class with a specified number of requests (default 1) returns an ExecutableNetwork
# instance which stores infer requests. So you already created Infer requests in the previous step.
# ---------------------------Step 6. Prepare input---------------------------------------------------------------------
file_data = [read_utterance_file(file_name) for file_name in input_files]
input_data = {
utterance_name: {
input_blobs[i]: file_data[i][utterance_name]
for i in range(len(input_blobs))
}
for utterance_name in file_data[0].keys()
}
if args.reference:
references = {
output_blobs[i]: read_utterance_file(reference_files[i])
for i in range(len(output_blobs))
}
# ---------------------------Step 7. Do inference----------------------------------------------------------------------
log.info('Starting inference in synchronous mode')
results = {blob_name: {} for blob_name in output_blobs}
infer_times = []
for key in sorted(input_data):
start_infer_time = default_timer()
# Reset states between utterance inferences to remove a memory impact
for request in exec_net.requests:
for state in request.query_state():
state.reset()
result = infer_data(input_data[key], exec_net, input_blobs,
output_blobs)
for blob_name in result.keys():
results[blob_name][key] = result[blob_name]
infer_times.append(default_timer() - start_infer_time)
# ---------------------------Step 8. Process output--------------------------------------------------------------------
for blob_name in output_blobs:
for i, key in enumerate(sorted(results[blob_name])):
log.info(f'Utterance {i} ({key})')
log.info(f'Output blob name: {blob_name}')
log.info(
f'Frames in utterance: {results[blob_name][key].shape[0]}')
log.info(
f'Total time in Infer (HW and SW): {infer_times[i] * 1000:.2f}ms'
)
if args.reference:
compare_with_reference(results[blob_name][key],
references[blob_name][key])
log.info('')
log.info(f'Total sample time: {sum(infer_times) * 1000:.2f}ms')
if args.output:
for i, blob_name in enumerate(results):
write_utterance_file(output_files[i], results[blob_name])
log.info(f'File {output_files[i]} was created!')
# ----------------------------------------------------------------------------------------------------------------------
log.info(
'This sample is an API example, '
'for any performance measurements please use the dedicated benchmark_app tool\n'
)
return 0
