def run_model(baseline_model, all_inputs, use_gpu, use_openmp,
graph_optimization_level):
session = create_session(baseline_model,
use_gpu,
use_openmp,
graph_optimization_level,
num_threads=psutil.cpu_count(logical=True),
wait_policy='ACTIVE')
output_names = [output.name for output in session.get_outputs()]
results, latency_list = onnxruntime_inference(session, all_inputs,
output_names)
return results, latency_list, output_names
def run_model(model_path, all_inputs, use_gpu, disable_optimization):
import onnxruntime
graph_optimization_level = None
if disable_optimization:
graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_DISABLE_ALL
intra_op_num_threads = psutil.cpu_count(logical=False)
session = create_session(model_path, use_gpu, intra_op_num_threads,
graph_optimization_level)
output_names = [output.name for output in session.get_outputs()]
results, latency_list = onnxruntime_inference(session, all_inputs,
output_names)
return results, latency_list, output_names
def run_model(model_path, all_inputs, use_gpu, use_openmp,
disable_optimization):
# Import onnxruntime shall be after OpenMP environment variable setting.
# So we put import here to delay importing.
import onnxruntime
graph_optimization_level = None
if disable_optimization:
graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_DISABLE_ALL
intra_op_num_threads = 1 if use_openmp else psutil.cpu_count(logical=False)
session = create_session(model_path, use_gpu, intra_op_num_threads,
graph_optimization_level)
output_names = [output.name for output in session.get_outputs()]
results, latency_list = onnxruntime_inference(session, all_inputs,
output_names)
return results, latency_list, output_names