# 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)