wehub-resource-sync
139 lines
4.2 KiB
Python
139 lines
4.2 KiB
Python
# Copyright (c) 2024 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 os
|
|
import shutil
|
|
import tempfile
|
|
import unittest
|
|
|
|
import numpy as np
|
|
|
|
import paddle
|
|
from paddle import nn, static
|
|
from paddle.inference import Config, PrecisionType, create_predictor
|
|
|
|
paddle.enable_static()
|
|
|
|
|
|
class SimpleNet(nn.Layer):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.conv1 = nn.Conv2D(
|
|
in_channels=4,
|
|
out_channels=4,
|
|
kernel_size=3,
|
|
stride=2,
|
|
padding=0,
|
|
)
|
|
self.relu1 = nn.ReLU()
|
|
self.conv2 = nn.Conv2D(
|
|
in_channels=4,
|
|
out_channels=2,
|
|
kernel_size=3,
|
|
stride=2,
|
|
padding=0,
|
|
)
|
|
self.relu2 = nn.ReLU()
|
|
self.conv3 = nn.Conv2D(
|
|
in_channels=2,
|
|
out_channels=1,
|
|
kernel_size=3,
|
|
stride=2,
|
|
padding=0,
|
|
)
|
|
self.relu3 = nn.ReLU()
|
|
self.flatten = nn.Flatten()
|
|
self.fc = nn.Linear(729, 10)
|
|
self.softmax = nn.Softmax()
|
|
|
|
def forward(self, x):
|
|
x = self.conv1(x)
|
|
x = self.relu1(x)
|
|
x = self.conv2(x)
|
|
x = self.relu2(x)
|
|
x = self.conv3(x)
|
|
x = self.relu3(x)
|
|
x = self.flatten(x)
|
|
x = self.fc(x)
|
|
x = self.softmax(x)
|
|
return x
|
|
|
|
|
|
class TestTRTOptimizationLevel(unittest.TestCase):
|
|
def setUp(self):
|
|
self.place = paddle.CUDAPlace(0)
|
|
self.temp_dir = tempfile.TemporaryDirectory()
|
|
self.path = os.path.join(self.temp_dir.name, 'optimization_level', '')
|
|
self.model_prefix = self.path + 'infer_model'
|
|
|
|
def tearDown(self):
|
|
shutil.rmtree(self.path)
|
|
|
|
def build_model(self):
|
|
image = static.data(
|
|
name='img', shape=[None, 4, 224, 224], dtype='float32'
|
|
)
|
|
predict = SimpleNet()(image)
|
|
exe = paddle.static.Executor(self.place)
|
|
exe.run(paddle.static.default_startup_program())
|
|
paddle.static.save_inference_model(
|
|
self.model_prefix, [image], [predict], exe
|
|
)
|
|
|
|
def init_predictor(self):
|
|
config = Config(
|
|
self.model_prefix + '.json', self.model_prefix + '.pdiparams'
|
|
)
|
|
config.enable_use_gpu(256, 0, PrecisionType.Half)
|
|
config.enable_tensorrt_engine(
|
|
workspace_size=1 << 30,
|
|
max_batch_size=1,
|
|
min_subgraph_size=3,
|
|
precision_mode=PrecisionType.Half,
|
|
use_static=False,
|
|
use_calib_mode=False,
|
|
)
|
|
config.enable_memory_optim()
|
|
config.exp_disable_tensorrt_dynamic_shape_ops(True)
|
|
config.disable_glog_info()
|
|
config.set_tensorrt_optimization_level(0)
|
|
self.assertEqual(config.tensorrt_optimization_level(), 0)
|
|
predictor = create_predictor(config)
|
|
return predictor
|
|
|
|
def infer(self, predictor, img):
|
|
input_names = predictor.get_input_names()
|
|
for i, name in enumerate(input_names):
|
|
input_tensor = predictor.get_input_handle(name)
|
|
input_tensor.reshape(img[i].shape)
|
|
input_tensor.copy_from_cpu(img[i].copy())
|
|
predictor.run()
|
|
results = []
|
|
output_names = predictor.get_output_names()
|
|
for i, name in enumerate(output_names):
|
|
output_tensor = predictor.get_output_handle(name)
|
|
output_data = output_tensor.copy_to_cpu()
|
|
results.append(output_data)
|
|
return results
|
|
|
|
def test_optimization_level(self):
|
|
self.build_model()
|
|
predictor = self.init_predictor()
|
|
img = np.ones((1, 4, 224, 224), dtype=np.float32)
|
|
results = self.infer(predictor, img=[img])
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|