def optimize(onnx_model_path: Path) -> Path:
"""
Load the model at the specified path and let onnxruntime look at transformations on the graph
to enable all the optimizations possible
Args:
onnx_model_path: filepath where the model binary description is stored
Returns: Path where the optimized model binary description has been saved
"""
from onnxruntime import InferenceSession, SessionOptions
# Generate model name with suffix "optimized"
opt_model_path = generate_identified_filename(onnx_model_path,
"-optimized")
sess_option = SessionOptions()
sess_option.optimized_model_filepath = opt_model_path.as_posix()
_ = InferenceSession(onnx_model_path.as_posix(), sess_option)
print(
f"Optimized model has been written at {opt_model_path}: \N{heavy check mark}"
)
print(
"/!\\ Optimized model contains hardware specific operators which might not be portable. /!\\"
)
return opt_model_path
def optimize_model(model_path: Path, opt_model_path: Path):
"""
Generate model that applies graph optimization (constant folding, etc.)
parameter model_path: path to the original onnx model
parameter opt_model_path: path to the optimized onnx model
:return: optimized onnx model
"""
sess_option = SessionOptions()
sess_option.optimized_model_filepath = opt_model_path.as_posix()
sess_option.graph_optimization_level = GraphOptimizationLevel.ORT_ENABLE_BASIC
_ = InferenceSession(model_path.as_posix(), sess_option, providers=["CPUExecutionProvider"])
def optimize_model(model_path: Path):
'''
Generate model that applies graph optimization (constant folding,etc.)
parameter model_path: path to the original onnx model
return: optimized onnx model
'''
opt_model_path = generate_identified_filename(model_path, "-opt")
sess_option = SessionOptions()
sess_option.optimized_model_filepath = opt_model_path.as_posix()
sess_option.graph_optimization_level = GraphOptimizationLevel.ORT_ENABLE_BASIC
_ = InferenceSession(model_path.as_posix(), sess_option)
optimized_model = onnx.load(opt_model_path.as_posix())
return optimized_model
provider = 'CPUExecutionProvider'
print(f"provider = {provider!r}")
####################################
# We load the graph.
with open(filename, 'rb') as f:
onx = onnx.load(f)
###############################
# Create of the session.
so = SessionOptions()
so.enable_profiling = True
so.optimized_model_filepath = os.path.split(filename)[-1] + ".optimized.onnx"
sess = InferenceSession(onx.SerializeToString(), so, providers=[provider])
bind = SessionIOBinding(sess._sess)
print("graph_optimization_level:", so.graph_optimization_level)
#####################################
# Creates random data
feed = random_feed(sess, batch)
#####################################
# moving the data on CPU or GPU
feed_ort_value = OrderedDict(
(name, (C_OrtValue.ortvalue_from_numpy(v, ort_device), v.dtype))
for name, v in feed.items())
outputs = [o.name for o in sess.get_outputs()]
###############################
# Create of the session.
data = []
files = []
legend = []
for graph_opt, name_opt in tqdm([
(GraphOptimizationLevel.ORT_DISABLE_ALL, "ORT_DISABLE_ALL"),
(GraphOptimizationLevel.ORT_ENABLE_BASIC, "ORT_ENABLE_BASIC"),
(GraphOptimizationLevel.ORT_ENABLE_EXTENDED, "ORT_ENABLE_EXTENDED"),
(GraphOptimizationLevel.ORT_ENABLE_ALL, "ORT_ENABLE_ALL")
]):
so = SessionOptions()
so.graph_optimization_level = graph_opt
so.optimized_model_filepath = (os.path.split(filename)[-1] +
f".optimized.{name_opt}.onnx")
files.append(so.optimized_model_filepath)
legend.append(name_opt)
sess = InferenceSession(onx.SerializeToString(), so, providers=[provider])
bind = SessionIOBinding(sess._sess)
#####################################
# Creates random data
feed = random_feed(sess, batch)
#####################################
# moving the data on CPU or GPU
feed_ort_value = OrderedDict(
(name, (C_OrtValue.ortvalue_from_numpy(v, ort_device), v.dtype))
for name, v in feed.items())
outputs = [o.name for o in sess.get_outputs()]
