718c41634f
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
69 lines
2.2 KiB
Python
69 lines
2.2 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__()
|
|
|
|
self.pool_0 = nn.LPPool2d(norm_type=2, kernel_size=3)
|
|
self.pool_1 = nn.LPPool2d(norm_type=2, kernel_size=4, stride=2)
|
|
self.pool_2 = nn.LPPool2d(norm_type=1, kernel_size=(1,3), stride=1, ceil_mode=False)
|
|
self.pool_3 = nn.LPPool2d(norm_type=1, kernel_size=(4,5), stride=(1,2), ceil_mode=True)
|
|
self.pool_4 = nn.LPPool2d(norm_type=1.2, kernel_size=(5,3), stride=(2,1), ceil_mode=False)
|
|
self.pool_5 = nn.LPPool2d(norm_type=0.5, kernel_size=2, stride=1, ceil_mode=True)
|
|
self.pool_6 = nn.LPPool2d(norm_type=0.1, kernel_size=(5,4), stride=1, ceil_mode=False)
|
|
|
|
def forward(self, x):
|
|
x = self.pool_0(x)
|
|
x = self.pool_1(x)
|
|
x = self.pool_2(x)
|
|
x = self.pool_3(x)
|
|
x = self.pool_4(x)
|
|
x = self.pool_5(x)
|
|
x = self.pool_6(x)
|
|
return x
|
|
|
|
def test():
|
|
net = Model()
|
|
net.eval()
|
|
|
|
torch.manual_seed(0)
|
|
x = torch.rand(1, 12, 128, 128)
|
|
|
|
a = net(x)
|
|
|
|
# export torchscript
|
|
mod = torch.jit.trace(net, x)
|
|
mod.save("test_nn_LPPool2d.pt")
|
|
|
|
# torchscript to pnnx
|
|
import os
|
|
os.system("../src/pnnx test_nn_LPPool2d.pt inputshape=[1,12,128,128]")
|
|
|
|
# pnnx inference
|
|
import test_nn_LPPool2d_pnnx
|
|
b = test_nn_LPPool2d_pnnx.test_inference()
|
|
|
|
return torch.equal(a, b)
|
|
|
|
if __name__ == "__main__":
|
|
if test():
|
|
exit(0)
|
|
else:
|
|
exit(1)
|