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[IE Python Speech Sample] Refactor code to output result after each utterance infer #7450

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Sep 16, 2021
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[IE Python Speech Sample] Refactor code to output result after each utterance infer #7450

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71 changes: 38 additions & 33 deletions inference-engine/ie_bridges/python/sample/speech_sample/speech_sample.py
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Original file line number Diff line number Diff line change
Expand Up @@ -228,10 +228,9 @@ def main():
# ---------------------------Step 7. Do inference----------------------------------------------------------------------
log.info('Starting inference in synchronous mode')
results = {blob_name: {} for blob_name in output_blobs}
infer_times = []
perf_counters = []
total_infer_time = 0

for key in sorted(input_data):
for i, key in enumerate(sorted(input_data)):
start_infer_time = default_timer()

# Reset states between utterance inferences to remove a memory impact
Expand All @@ -244,43 +243,49 @@ def main():
for blob_name in result.keys():
results[blob_name][key] = result[blob_name]

infer_times.append(default_timer() - start_infer_time)
perf_counters.append(exec_net.requests[0].get_perf_counts())
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--------------------------------------------------------------------
for blob_name in output_blobs:
for i, key in enumerate(sorted(results[blob_name])):
log.info(f'Utterance {i} ({key})')
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'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')
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 = perf_counters[i]
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: {sum(infer_times) * 1000:.2f}ms')
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):
Expand Down