def run_onnxruntime(self,
name,
model_proto,
inputs,
external_tensor_storage=None):
"""Run test against onnxruntime backend."""
import onnxruntime as rt
model_path = utils.save_onnx_model(
TEMP_DIR,
name,
inputs,
model_proto,
include_test_data=True,
as_text=utils.is_debug_mode(),
external_tensor_storage=external_tensor_storage)
logger.info("Model saved to %s", model_path)
if self.use_custom_ops:
from ortcustomops import get_library_path
opt = rt.SessionOptions()
opt.register_custom_ops_library(get_library_path())
m = rt.InferenceSession(model_path, opt)
else:
m = rt.InferenceSession(model_path)
results = m.run(self.output_names, inputs)
if self.perf:
start = time.time()
for _ in range(PERFITER):
_ = m.run(self.output_names, inputs)
self.onnx_runtime = time.time() - start
return results
def run_onnxruntime(self, model_path, inputs, output_names):
"""Run test against onnxruntime backend."""
from ortcustomops import get_library_path
import onnxruntime as rt
opt = rt.SessionOptions()
opt.register_custom_ops_library(get_library_path())
m = rt.InferenceSession(model_path, opt)
results = m.run(output_names, inputs)
return results
def run_onnxruntime(self,
name,
model_proto,
inputs,
outputs,
external_tensor_storage=None):
"""Run test against onnxruntime backend."""
import onnxruntime as rt
model_path = utils.save_onnx_model(
TEMP_DIR,
name,
inputs,
model_proto,
include_test_data=True,
as_text=utils.is_debug_mode(),
external_tensor_storage=external_tensor_storage)
logger.info("Model saved to %s", model_path)
opt = rt.SessionOptions()
if self.use_custom_ops:
from ortcustomops import get_library_path
opt.register_custom_ops_library(get_library_path())
m = rt.InferenceSession(model_path, opt)
if self.ort_profile is not None:
opt.enable_profiling = True
m = rt.InferenceSession(model_path, opt)
results = m.run(outputs, inputs)
if self.perf:
n = 0
start = time.time()
stop = start + PERF_TIME
while time.time() < stop:
for _ in range(PERF_STEP):
_ = m.run(outputs, inputs)
n += PERF_STEP
self.onnx_runtime = 1000 * (time.time() - start) / n
logger.info("ORT perf {:.2f}ms/inference, n={}".format(
self.onnx_runtime, n))
if self.ort_profile is not None:
tmp_path = m.end_profiling()
shutil.move(tmp_path, self.ort_profile)
return results
def setUpClass(cls):
try:
from ortcustomops import (onnx_op, PyCustomOpDef, get_library_path)
except ImportError:
return
@onnx_op(op_type="SolveFloat",
inputs=[PyCustomOpDef.dt_float, PyCustomOpDef.dt_float],
outputs=[PyCustomOpDef.dt_float])
def solveopf(a, b):
# The user custom op implementation here.
return scipy.linalg.solve(a, b).astype(np.float32)
@onnx_op(op_type="SolveDouble",
inputs=[PyCustomOpDef.dt_double, PyCustomOpDef.dt_double],
outputs=[PyCustomOpDef.dt_double])
def solveopd(a, b):
# The user custom op implementation here.
return scipy.linalg.solve(a, b).astype(np.float64)
cls.path = get_library_path()