deepin-ocr/3rdparty/ncnn/tools/pnnx/tests/test_nn_UpsamplingBilinear2d.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

67 lines
2.0 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.up_0 = nn.UpsamplingBilinear2d(size=16)
self.up_1 = nn.UpsamplingBilinear2d(scale_factor=2)
self.up_2 = nn.UpsamplingBilinear2d(size=(20,20))
self.up_3 = nn.UpsamplingBilinear2d(scale_factor=(4,4))
self.up_4 = nn.UpsamplingBilinear2d(size=(16,24))
self.up_5 = nn.UpsamplingBilinear2d(scale_factor=(2,3))
def forward(self, x):
x = self.up_0(x)
x = self.up_1(x)
x = self.up_2(x)
x = self.up_3(x)
x = self.up_4(x)
x = self.up_5(x)
return x
def test():
net = Model()
net.eval()
torch.manual_seed(0)
x = torch.rand(1, 3, 32, 32)
a = net(x)
# export torchscript
mod = torch.jit.trace(net, x)
mod.save("test_nn_UpsamplingBilinear2d.pt")
# torchscript to pnnx
import os
os.system("../src/pnnx test_nn_UpsamplingBilinear2d.pt inputshape=[1,3,32,32]")
# pnnx inference
import test_nn_UpsamplingBilinear2d_pnnx
b = test_nn_UpsamplingBilinear2d_pnnx.test_inference()
return torch.equal(a, b)
if __name__ == "__main__":
if test():
exit(0)
else:
exit(1)