input_data = np.array(random_input, dtype=np.uint8)
benchmark_time = timeit.timeit(
lambda: engine.run_inference(input_data),
number=iterations)
# Time consumed for each iteration (milliseconds).
time_per_inference = (benchmark_time / iterations) * 1000
print(time_per_inference, 'ms (iterations = ', iterations, ')')
return time_per_inference
if __name__ == '__main__':
args = test_utils.parse_args()
machine = test_utils.machine_info()
test_utils.check_cpu_scaling_governor_status()
# Read references from csv file.
model_list, reference = test_utils.read_reference(
'basic_engine_reference_%s.csv' % machine)
total_models = len(model_list)
# Put column names in first row.
results = [('MODEL', 'INFERENCE_TIME')]
for cnt, model in enumerate(model_list, start=1):
print('-------------- Model ', cnt, '/', total_models, ' ---------------')
results.append((model, _run_benchmark_for_model(model)))
test_utils.save_as_csv(
'basic_engine_benchmarks_%s_%s.csv' % (
machine, time.strftime('%Y%m%d-%H%M%S')),
results)
test_utils.check_result(reference, results, args.enable_assertion)
print('Image pre-processing time: ', end_time - start_time, 's')
start_time = end_time
for class_id, tensors in enumerate(image_list_by_category.values()):
engine.train(tensors, class_id)
engine.save_model(output_model_path)
training_time = time.monotonic() - start_time
print('Model: ', model)
print('Data set : ', data_set)
print('Training time : ', training_time, 's')
# Remove the model.
subprocess.call(['rm', output_model_path])
return training_time
if __name__ == '__main__':
args = test_utils.parse_args()
machine = test_utils.machine_info()
models, reference = test_utils.read_reference(
'imprinting_reference_%s.csv' % machine)
model_num = len(models)
results = [('MODEL', 'DATA_SET', 'INFERENCE_TIME')]
for cnt, name in enumerate(models, start=1):
# 10 Categories, each has 20 images.
data = 'open_image_v4_subset'
print('---------------- ', cnt, '/', model_num, ' ----------------')
results.append((name, data, _benchmark_for_training(name, data)))
test_utils.save_as_csv(
'imprinting_benchmarks_%s_%s.csv' %
(machine, time.strftime('%Y%m%d-%H%M%S')), results)
test_utils.check_result(reference, results, args.enable_assertion)
print('Benchmark for [', model_name, '] on ', image)
engine = ClassificationEngine(test_utils.test_data_path(model_name))
iterations = 200 if ('edgetpu' in model_name) else 10
with test_utils.test_image(image) as img_obj:
benchmark_time = timeit.timeit(
lambda: engine.classify_with_image(img_obj, threshold=0.4, top_k=10),
number=iterations)
time_per_inference = (benchmark_time / iterations) * 1000
print(time_per_inference, 'ms (iterations = ', iterations, ')')
return time_per_inference
if __name__ == '__main__':
args = test_utils.parse_args()
images_for_tests = ['cat.bmp', 'cat_720p.jpg', 'cat_1080p.jpg']
machine = test_utils.machine_info()
test_utils.check_cpu_scaling_governor_status()
model_list, reference = test_utils.read_reference(
'classification_reference_%s.csv' % machine)
total_models = len(model_list)
results = [('MODEL', 'IMAGE_NAME', 'INFERENCE_TIME')]
for cnt, model in enumerate(model_list, start=1):
print('-------------- Model ', cnt, '/', total_models, ' ---------------')
for img in images_for_tests:
results.append((model, img, _run_benchmark_for_model(model, img)))
test_utils.save_as_csv('classification_benchmarks_%s_%s.csv' %
(machine, time.strftime('%Y%m%d-%H%M%S')), results)
test_utils.check_result(reference, results, args.enable_assertion)
engines.append(engine)
input_data_list.append(input_data)
benchmark_time = timeit.timeit(
lambda: _run_inferences(engines, input_data_list),
number=iterations)
# Time consumed for each iteration (milliseconds).
time_per_inference = (benchmark_time / iterations) * 1000
print(time_per_inference, 'ms (iterations = ', iterations, ')')
return time_per_inference
if __name__ == '__main__':
args = test_utils.parse_args()
machine = test_utils.machine_info()
test_utils.check_cpu_scaling_governor_status()
# Read references from csv file.
modelsets_list, reference = test_utils.read_reference(
'cocompilation_reference_%s.csv' % machine)
total_modelsets = len(modelsets_list)
# Put column names in first row.
results = [('MODELS', 'INFERENCE_TIME')]
for cnt, modelsets in enumerate(modelsets_list, start=1):
print('-------------- Models ', cnt, '/', total_modelsets, ' ---------------')
results.append((modelsets, _run_benchmark_for_cocompiled_models(modelsets.split(','))))
test_utils.save_as_csv(
'cocompilation_benchmarks_%s_%s.csv' % (
machine, time.strftime('%Y%m%d-%H%M%S')),
results)
test_utils.check_result(reference, results, args.enable_assertion)
engine = DetectionEngine(test_utils.test_data_path(model_name))
iterations = 200 if ('edgetpu' in model_name) else 10
with Image.open(test_utils.test_data_path(image)) as img_obj:
benchmark_time = timeit.timeit(
lambda: engine.detect_with_image(img_obj, threshold=0.4, top_k=10),
number=iterations)
time_per_inference = (benchmark_time / iterations) * 1000
return time_per_inference
if __name__ == '__main__':
args = test_utils.parse_args()
images_for_tests = ['cat.bmp', 'cat_720p.jpg', 'cat_1080p.jpg']
machine = test_utils.machine_info()
test_utils.check_cpu_scaling_governor_status()
model_list, reference = test_utils.read_reference(
'detection_reference_%s.csv' % machine)
total_models = len(model_list)
results = [('MODEL', 'IMAGE_NAME', 'INFERENCE_TIME')]
for cnt, model in enumerate(model_list, start=1):
print('-------------- Model ', cnt, '/', total_models,
' ---------------')
for img in images_for_tests:
results.append((model, img, _run_benchmark_for_model(model, img)))
test_utils.save_as_csv(
'detection_benchmarks_%s_%s.csv' %
(machine, time.strftime('%Y%m%d-%H%M%S')), results)
test_utils.check_result(reference, results, args.enable_assertion)