wehub-resource-sync
94 lines
3.0 KiB
Python
Executable File
94 lines
3.0 KiB
Python
Executable File
#!/usr/bin/env python3
|
|
|
|
# Copyright (c) 2021 CINN 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 sys
|
|
import unittest
|
|
|
|
import numpy as np
|
|
|
|
from paddle import base
|
|
from paddle.cinn.common import DefaultHostTarget, DefaultNVGPUTarget
|
|
from paddle.cinn.frontend import Interpreter
|
|
|
|
enable_gpu = sys.argv.pop()
|
|
model_dir = sys.argv.pop()
|
|
|
|
|
|
class TestLoadResnetModel(unittest.TestCase):
|
|
def setUp(self):
|
|
if enable_gpu == "ON":
|
|
self.target = DefaultNVGPUTarget()
|
|
else:
|
|
self.target = DefaultHostTarget()
|
|
|
|
self.model_dir = model_dir
|
|
|
|
self.x_shape = [1, 160, 7, 7]
|
|
|
|
def get_paddle_inference_result(self, data):
|
|
config = base.core.AnalysisConfig(
|
|
self.model_dir + ".pdmodel", self.model_dir + ".pdiparams"
|
|
)
|
|
config.disable_gpu()
|
|
config.switch_ir_optim(False)
|
|
self.paddle_predictor = base.core.create_paddle_predictor(config)
|
|
data = base.core.PaddleTensor(data)
|
|
results = self.paddle_predictor.run([data])
|
|
return results[0].as_ndarray()
|
|
|
|
def apply_test(self):
|
|
np.random.seed(0)
|
|
x_data = np.random.random(self.x_shape).astype("float32")
|
|
self.executor = Interpreter(["resnet_input"], [self.x_shape])
|
|
self.executor.load_paddle_model(self.model_dir, self.target, True)
|
|
a_t = self.executor.get_tensor("resnet_input")
|
|
a_t.from_numpy(x_data, self.target)
|
|
|
|
out = self.executor.get_tensor("save_infer_model/scale_0.tmp_0")
|
|
out.from_numpy(np.zeros(out.shape(), dtype='float32'), self.target)
|
|
|
|
self.executor.run()
|
|
|
|
out = out.numpy(self.target)
|
|
target_result = self.get_paddle_inference_result(x_data)
|
|
|
|
print("result in test_model: \n")
|
|
out = out.reshape(-1)
|
|
target_result = target_result.reshape(-1)
|
|
# out.shape[0]
|
|
for i in range(0, min(out.shape[0], 200)):
|
|
if np.abs(out[i] - target_result[i]) > 1e-3:
|
|
print(
|
|
"Error! ",
|
|
i,
|
|
"-th data has diff with target data:\n",
|
|
out[i],
|
|
" vs: ",
|
|
target_result[i],
|
|
". Diff is: ",
|
|
out[i] - target_result[i],
|
|
)
|
|
np.testing.assert_allclose(out, target_result, atol=1e-3)
|
|
|
|
def test_model(self):
|
|
self.apply_test()
|
|
# self.target.arch = Target.NVGPUArch()
|
|
# self.apply_test()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|