[IE Python Speech Sample] Add --scale_factor and --performance_counter options (#6663)
* Adds perf counters, and scale factor args * Adding defined choices for arch type for -a/--arch option * changing print to logger, frequencies are now global consts * change to log info formatting * Fix style issues * doc: Update inference-engine/ie_bridges/python/sample/speech_sample/README.md * doc: Update inference-engine/ie_bridges/python/sample/speech_sample/README.md * doc: Update inference-engine/ie_bridges/python/sample/speech_sample/arg_parser.py * doc: Update inference-engine/ie_bridges/python/sample/speech_sample/arg_parser.py Co-authored-by: Koyanagi, Ken <ken.koyanagi@intel.com> Co-authored-by: Kate Generalova <kate.generalova@intel.com>
This commit is contained in:
Dmitry Pigasin
GitHub
@@ -80,7 +80,8 @@ Usage message:
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usage: speech_sample.py [-h] (-m MODEL | -rg IMPORT_GNA_MODEL) -i INPUT
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[-o OUTPUT] [-r REFERENCE] [-d DEVICE]
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[-bs BATCH_SIZE] [-qb QUANTIZATION_BITS]
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[-wg EXPORT_GNA_MODEL] [-iname INPUT_LAYERS]
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[-sf SCALE_FACTOR] [-wg EXPORT_GNA_MODEL] [-pc]
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[-a {CORE,ATOM}] [-iname INPUT_LAYERS]
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[-oname OUTPUT_LAYERS]
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optional arguments:
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@@ -94,9 +95,10 @@ optional arguments:
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Options:
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-h, --help Show this help message and exit.
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-i INPUT, --input INPUT
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Required. Path to an input file (.ark or .npz).
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Required. Path to an input file (.ark or .npz).
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-o OUTPUT, --output OUTPUT
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Optional. Output file name to save inference results (.ark or .npz).
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Optional. Output file name to save inference results
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(.ark or .npz).
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-r REFERENCE, --reference REFERENCE
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Optional. Read reference score file and compare
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scores.
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@@ -113,9 +115,18 @@ Options:
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-qb QUANTIZATION_BITS, --quantization_bits QUANTIZATION_BITS
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Optional. Weight bits for quantization: 8 or 16
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(default 16).
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-sf SCALE_FACTOR, --scale_factor SCALE_FACTOR
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Optional. The user-specified input scale factor for
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quantization.
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-wg EXPORT_GNA_MODEL, --export_gna_model EXPORT_GNA_MODEL
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Optional. Write GNA model to file using path/filename
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provided.
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-pc, --performance_counter
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Optional. Enables performance report (specify -a to
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ensure arch accurate results).
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-a {CORE,ATOM}, --arch {CORE,ATOM}
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Optional. Specify architecture. CORE, ATOM with the
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combination of -pc.
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-iname INPUT_LAYERS, --input_layers INPUT_LAYERS
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Optional. Layer names for input blobs. The names are
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separated with ",". Allows to change the order of
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@@ -28,10 +28,16 @@ def parse_args() -> argparse.Namespace:
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args.add_argument('-bs', '--batch_size', default=1, type=int, help='Optional. Batch size 1-8 (default 1).')
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args.add_argument('-qb', '--quantization_bits', default=16, type=int,
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help='Optional. Weight bits for quantization: 8 or 16 (default 16).')
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args.add_argument('-sf', '--scale_factor', type=float,
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help='Optional. The user-specified input scale factor for quantization.')
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args.add_argument('-wg', '--export_gna_model', type=str,
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help='Optional. Write GNA model to file using path/filename provided.')
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args.add_argument('-we', '--export_embedded_gna_model', type=str, help=argparse.SUPPRESS)
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args.add_argument('-we_gen', '--embedded_gna_configuration', default='GNA1', type=str, help=argparse.SUPPRESS)
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args.add_argument('-pc', '--performance_counter', action='store_true',
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help='Optional. Enables performance report (specify -a to ensure arch accurate results).')
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args.add_argument('-a', '--arch', default='CORE', type=str.upper, choices=['CORE', 'ATOM'],
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help='Optional. Specify architecture. CORE, ATOM with the combination of -pc.')
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args.add_argument('-iname', '--input_layers', type=str,
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help='Optional. Layer names for input blobs. The names are separated with ",". '
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'Allows to change the order of input layers for -i flag. Example: Input1,Input2')
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@@ -14,6 +14,10 @@ from arg_parser import parse_args
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from file_options import read_utterance_file, write_utterance_file
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from openvino.inference_engine import ExecutableNetwork, IECore, IENetwork
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# Operating Frequency for GNA HW devices for Core and Atom architecture
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GNA_CORE_FREQUENCY = 400
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GNA_ATOM_FREQUENCY = 200
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def get_scale_factor(matrix: np.ndarray) -> float:
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"""Get scale factor for quantization using utterance matrix"""
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@@ -143,21 +147,26 @@ def main():
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plugin_config['GNA_DEVICE_MODE'] = gna_device_mode
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plugin_config['GNA_PRECISION'] = f'I{args.quantization_bits}'
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# Get a GNA scale factor
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# Set a GNA scale factor
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if args.import_gna_model:
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log.info(f'Using scale factor from the imported GNA model: {args.import_gna_model}')
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elif args.scale_factor:
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log.info(f'Using scale factor of {args.scale_factor:.7f} specified by user.')
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plugin_config['GNA_SCALE_FACTOR'] = str(args.scale_factor)
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else:
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utterances = read_utterance_file(args.input.split(',')[0])
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key = sorted(utterances)[0]
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scale_factor = get_scale_factor(utterances[key])
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log.info(f'Using scale factor of {scale_factor:.7f} calculated from first utterance.')
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plugin_config['GNA_SCALE_FACTOR'] = str(scale_factor)
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if args.export_embedded_gna_model:
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plugin_config['GNA_FIRMWARE_MODEL_IMAGE'] = args.export_embedded_gna_model
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plugin_config['GNA_FIRMWARE_MODEL_IMAGE_GENERATION'] = args.embedded_gna_configuration
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if args.performance_counter:
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plugin_config['PERF_COUNT'] = 'YES'
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device_str = f'HETERO:{",".join(devices)}' if 'HETERO' in args.device else devices[0]
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log.info('Loading the model to the plugin')
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@@ -220,6 +229,7 @@ def main():
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log.info('Starting inference in synchronous mode')
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results = {blob_name: {} for blob_name in output_blobs}
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infer_times = []
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perf_counters = []
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for key in sorted(input_data):
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start_infer_time = default_timer()
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@@ -235,6 +245,7 @@ def main():
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results[blob_name][key] = result[blob_name]
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infer_times.append(default_timer() - start_infer_time)
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perf_counters.append(exec_net.requests[0].get_perf_counts())
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# ---------------------------Step 8. Process output--------------------------------------------------------------------
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for blob_name in output_blobs:
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@@ -247,6 +258,26 @@ def main():
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if args.reference:
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compare_with_reference(results[blob_name][key], references[blob_name][key])
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if args.performance_counter:
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if 'GNA' in args.device:
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pc = perf_counters[i]
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total_cycles = int(pc['1.1 Total scoring time in HW']['real_time'])
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stall_cycles = int(pc['1.2 Stall scoring time in HW']['real_time'])
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active_cycles = total_cycles - stall_cycles
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frequency = 10**6
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if args.arch == 'CORE':
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frequency *= GNA_CORE_FREQUENCY
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else:
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frequency *= GNA_ATOM_FREQUENCY
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total_inference_time = total_cycles / frequency
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active_time = active_cycles / frequency
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stall_time = stall_cycles / frequency
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log.info('')
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log.info('Performance Statistics of GNA Hardware')
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log.info(f' Total Inference Time: {(total_inference_time * 1000):.4f} ms')
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log.info(f' Active Time: {(active_time * 1000):.4f} ms')
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log.info(f' Stall Time: {(stall_time * 1000):.4f} ms')
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log.info('')
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log.info(f'Total sample time: {sum(infer_times) * 1000:.2f}ms')
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