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

69 lines
2.0 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__()
self.c0 = nn.Parameter(torch.rand(12))
self.c2 = nn.Parameter(torch.rand(48, 12))
def forward(self, a0, a1, a2, b0, b1, b2, c1):
a = torch.addmm(a0, a1, a2)
b = torch.addmm(b0, b1, b2, beta=1.4, alpha=0.7)
c = torch.addmm(self.c0, c1, self.c2, beta=1, alpha=1)
return a, b, c
def test():
net = Model()
net.eval()
torch.manual_seed(0)
a0 = torch.rand(13, 1)
a1 = torch.rand(13, 16)
a2 = torch.rand(16, 23)
b0 = torch.rand(7, 33)
b1 = torch.rand(7, 26)
b2 = torch.rand(26, 33)
c1 = torch.rand(16, 48)
a = net(a0, a1, a2, b0, b1, b2, c1)
# export torchscript
mod = torch.jit.trace(net, (a0, a1, a2, b0, b1, b2, c1))
mod.save("test_torch_addmm.pt")
# torchscript to pnnx
import os
os.system("../src/pnnx test_torch_addmm.pt inputshape=[13,1],[13,16],[16,23],[7,33],[7,26],[26,33],[16,48]")
# pnnx inference
import test_torch_addmm_pnnx
b = test_torch_addmm_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)