wehub-resource-sync
70 lines
2.2 KiB
Python
70 lines
2.2 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
import unittest
|
|
from functools import partial
|
|
|
|
import hypothesis.strategies as st
|
|
import numpy as np
|
|
from auto_scan_test import OnednnAutoScanTest
|
|
from hypothesis import given
|
|
from program_config import OpConfig, ProgramConfig, TensorConfig
|
|
|
|
|
|
class TestOnednnShapeOp(OnednnAutoScanTest):
|
|
def is_program_valid(self, program_config: ProgramConfig) -> bool:
|
|
return True
|
|
|
|
def sample_program_configs(self, *args, **kwargs):
|
|
def generate_input(*args, **kwargs):
|
|
return np.random.random(kwargs['in_shape']).astype(
|
|
kwargs['in_dtype']
|
|
)
|
|
|
|
shape_op = OpConfig(
|
|
type="shape",
|
|
inputs={"Input": ["input_data"]},
|
|
outputs={"Out": ["output_data"]},
|
|
)
|
|
|
|
program_config = ProgramConfig(
|
|
ops=[shape_op],
|
|
weights={},
|
|
inputs={
|
|
"input_data": TensorConfig(
|
|
data_gen=partial(generate_input, *args, **kwargs)
|
|
),
|
|
},
|
|
outputs=["output_data"],
|
|
)
|
|
|
|
yield program_config
|
|
|
|
def sample_predictor_configs(self, program_config):
|
|
config = self.create_inference_config(use_onednn=True)
|
|
yield config, (1e-5, 1e-5)
|
|
|
|
@given(
|
|
in_shape=st.lists(
|
|
st.integers(min_value=1, max_value=3), min_size=1, max_size=6
|
|
),
|
|
in_dtype=st.sampled_from([np.float32, np.uint16, np.int8, np.uint8]),
|
|
)
|
|
def test(self, *args, **kwargs):
|
|
self.run_test(quant=False, *args, **kwargs)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|