deepin-ocr/3rdparty/ncnn/tools/pnnx/tests/test_F_lp_pool1d.py
wangzhengyang 718c41634f feat: 切换后端至PaddleOCR-NCNN,切换工程为CMake
1.项目后端整体迁移至PaddleOCR-NCNN算法,已通过基本的兼容性测试
2.工程改为使用CMake组织,后续为了更好地兼容第三方库,不再提供QMake工程
3.重整权利声明文件,重整代码工程,确保最小化侵权风险

Log: 切换后端至PaddleOCR-NCNN,切换工程为CMake
Change-Id: I4d5d2c5d37505a4a24b389b1a4c5d12f17bfa38c
2022-05-10 10:22:11 +08:00

61 lines
1.9 KiB
Python

# Tencent is pleased to support the open source community by making ncnn available.
#
# Copyright (C) 2021 THL A29 Limited, a Tencent company. All rights reserved.
#
# Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
# in compliance with the License. You may obtain a copy of the License at
#
# https://opensource.org/licenses/BSD-3-Clause
#
# 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 torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
def forward(self, x):
x = F.lp_pool1d(x, norm_type=2, kernel_size=3)
x = F.lp_pool1d(x, norm_type=2, kernel_size=4, stride=2)
x = F.lp_pool1d(x, norm_type=1, kernel_size=3, stride=1, ceil_mode=False)
x = F.lp_pool1d(x, norm_type=1, kernel_size=5, stride=1, ceil_mode=True)
x = F.lp_pool1d(x, norm_type=1.2, kernel_size=3, stride=2, ceil_mode=False)
x = F.lp_pool1d(x, norm_type=0.5, kernel_size=2, stride=1, ceil_mode=True)
x = F.lp_pool1d(x, norm_type=0.1, kernel_size=4, stride=1, ceil_mode=False)
return x
def test():
net = Model()
net.eval()
torch.manual_seed(0)
x = torch.rand(1, 12, 128)
a = net(x)
# export torchscript
mod = torch.jit.trace(net, x)
mod.save("test_F_lp_pool1d.pt")
# torchscript to pnnx
import os
os.system("../src/pnnx test_F_lp_pool1d.pt inputshape=[1,12,128]")
# pnnx inference
import test_F_lp_pool1d_pnnx
b = test_F_lp_pool1d_pnnx.test_inference()
return torch.equal(a, b)
if __name__ == "__main__":
if test():
exit(0)
else:
exit(1)