deepin-ocr/3rdparty/ncnn/tools/pnnx/tests/test_torch_ones.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

62 lines
1.7 KiB
Python

# Tencent is pleased to support the open source community by making ncnn available.
#
# Copyright (C) 2022 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, y, z):
x = torch.ones(x.size())
y = torch.ones(y.size())
z = torch.ones(z.size())
return x, y, z
def test():
net = Model()
net.eval()
torch.manual_seed(0)
x = torch.rand(1, 3, 16)
y = torch.rand(1, 5, 9, 11)
z = torch.rand(14, 8, 5, 9, 10)
a = net(x, y, z)
# export torchscript
mod = torch.jit.trace(net, (x, y, z))
mod.save("test_torch_ones.pt")
# torchscript to pnnx
import os
os.system("../src/pnnx test_torch_ones.pt inputshape=[1,3,16],[1,5,9,11],[14,8,5,9,10]")
# pnnx inference
import test_torch_ones_pnnx
b = test_torch_ones_pnnx.test_inference()
for a0, b0 in zip(a, b):
if not torch.equal(a0, b0):
return False
return True
if __name__ == "__main__":
if test():
exit(0)
else:
exit(1)