def _execute_pb_data(
cls,
model_dir: str,
prepared_model: BackendRep,
result_rtol: float,
result_atol: float,
) -> int:
executed_tests = 0
for test_data_dir in model_dir.glob("test_data_set*"):
inputs = []
inputs_num = len(list(test_data_dir.glob("input_*.pb")))
for i in range(inputs_num):
input_file = Path(test_data_dir) / "input_{}.pb".format(i)
tensor = onnx.TensorProto()
with open(input_file, "rb") as f:
tensor.ParseFromString(f.read())
inputs.append(numpy_helper.to_array(tensor))
ref_outputs = []
ref_outputs_num = len(list(test_data_dir.glob("output_*.pb")))
for i in range(ref_outputs_num):
output_file = Path(test_data_dir) / "output_{}.pb".format(i)
tensor = onnx.TensorProto()
with open(output_file, "rb") as f:
tensor.ParseFromString(f.read())
ref_outputs.append(numpy_helper.to_array(tensor))
if (len(inputs) == 0):
continue
outputs = list(prepared_model.run(inputs))
cls.assert_similar_outputs(ref_outputs, outputs, result_rtol,
result_atol)
executed_tests = executed_tests + 1
return executed_tests
def _execute_npz_data(
cls, model_dir: str, prepared_model: BackendRep, result_rtol: float, result_atol: float,
) -> None:
for test_data_npz in glob.glob(os.path.join(model_dir, "test_data_*.npz")):
test_data = np.load(test_data_npz, encoding="bytes")
inputs = list(test_data["inputs"])
outputs = list(prepared_model.run(inputs))
ref_outputs = test_data["outputs"]
cls.assert_similar_outputs(ref_outputs, outputs, result_rtol, result_atol)
def _execute_npz_data(
cls,
model_dir: str,
prepared_model: BackendRep,
result_rtol: float,
result_atol: float,
) -> int:
executed_tests = 0
for test_data_npz in model_dir.glob("test_data_*.npz"):
test_data = np.load(test_data_npz, encoding="bytes")
inputs = list(test_data["inputs"])
outputs = list(prepared_model.run(inputs))
ref_outputs = test_data["outputs"]
cls.assert_similar_outputs(ref_outputs, outputs, result_rtol,
result_atol)
executed_tests = executed_tests + 1
return executed_tests
def _execute_npz_data(
cls,
model_dir: str,
prepared_model: BackendRep,
result_rtol: float,
result_atol: float,
post_processing: Callable[[Sequence[Any]],
Sequence[Any]] = None) -> int:
executed_tests = 0
for test_data_npz in model_dir.glob("test_data_*.npz"):
test_data = np.load(test_data_npz, encoding="bytes")
inputs = list(test_data["inputs"])
outputs = list(prepared_model.run(inputs))
ref_outputs = test_data["outputs"]
if post_processing is not None:
outputs = post_processing(outputs)
cls.assert_similar_outputs(ref_outputs, outputs, result_rtol,
result_atol)
executed_tests = executed_tests + 1
return executed_tests
def _execute_pb_data(
cls, model_dir: str, prepared_model: BackendRep, result_rtol: float, result_atol: float,
) -> None:
for test_data_dir in glob.glob(os.path.join(model_dir, "test_data_set*")):
inputs = []
inputs_num = len(glob.glob(os.path.join(test_data_dir, "input_*.pb")))
for i in range(inputs_num):
input_file = os.path.join(test_data_dir, "input_{}.pb".format(i))
tensor = onnx.TensorProto()
with open(input_file, "rb") as f:
tensor.ParseFromString(f.read())
inputs.append(numpy_helper.to_array(tensor))
ref_outputs = []
ref_outputs_num = len(glob.glob(os.path.join(test_data_dir, "output_*.pb")))
for i in range(ref_outputs_num):
output_file = os.path.join(test_data_dir, "output_{}.pb".format(i))
tensor = onnx.TensorProto()
with open(output_file, "rb") as f:
tensor.ParseFromString(f.read())
ref_outputs.append(numpy_helper.to_array(tensor))
outputs = list(prepared_model.run(inputs))
cls.assert_similar_outputs(ref_outputs, outputs, result_rtol, result_atol)