# 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.avg_pool1d(x, kernel_size=3) x = F.avg_pool1d(x, kernel_size=4, stride=2, padding=2) x = F.avg_pool1d(x, kernel_size=3, stride=1, padding=(0), ceil_mode=False, count_include_pad=True) x = F.avg_pool1d(x, kernel_size=5, stride=2, padding=(2), ceil_mode=True, count_include_pad=False) x = F.avg_pool1d(x, kernel_size=3, stride=2, padding=1, ceil_mode=False, count_include_pad=True) x = F.avg_pool1d(x, kernel_size=2, stride=1, padding=0, ceil_mode=True, count_include_pad=True) x = F.avg_pool1d(x, kernel_size=4, stride=1, padding=2, ceil_mode=False, count_include_pad=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_avg_pool1d.pt") # torchscript to pnnx import os os.system("../src/pnnx test_F_avg_pool1d.pt inputshape=[1,12,128]") # pnnx inference import test_F_avg_pool1d_pnnx b = test_F_avg_pool1d_pnnx.test_inference() return torch.equal(a, b) if __name__ == "__main__": if test(): exit(0) else: exit(1)