718c41634f
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
104 lines
3.5 KiB
Python
104 lines
3.5 KiB
Python
# Tencent is pleased to support the open source community by making ncnn available.
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#
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# Copyright (C) 2022 THL A29 Limited, a Tencent company. All rights reserved.
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#
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# Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
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# in compliance with the License. You may obtain a copy of the License at
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#
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# https://opensource.org/licenses/BSD-3-Clause
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#
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# Unless required by applicable law or agreed to in writing, software distributed
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# under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
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# CONDITIONS OF ANY KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations under the License.
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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class Model(nn.Module):
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def __init__(self):
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super(Model, self).__init__()
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def forward(self, a0, a1, b0, b1, c0, c1, d0, d1, e0, e1, f0, f1, g0, g1, h0, h1, i0, i1, j0, j1, k0, k1, l0, l1, m0, m1, n0, n1, o0, o1, p0, p1):
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a = torch.matmul(a0, a1)
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b = torch.matmul(b0, b1)
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c = torch.matmul(c0, c1)
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d = torch.matmul(d0, d1)
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e = torch.matmul(e0, e1)
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f = torch.matmul(f0, f1)
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g = torch.matmul(g0, g1)
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h = torch.matmul(h0, h1)
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i = torch.matmul(i0, i1)
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j = torch.matmul(j0, j1)
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k = torch.matmul(k0, k1)
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l = torch.matmul(l0, l1)
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m = torch.matmul(m0, m1)
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n = torch.matmul(n0, n1)
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o = torch.matmul(o0, o1)
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p = torch.matmul(p0, p1)
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return a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p
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def test():
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net = Model()
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net.eval()
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torch.manual_seed(0)
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a0 = torch.rand(13)
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a1 = torch.rand(13)
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b0 = torch.rand(14)
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b1 = torch.rand(14, 6)
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c0 = torch.rand(13)
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c1 = torch.rand(7, 13, 4)
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d0 = torch.rand(15)
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d1 = torch.rand(5, 7, 15, 9)
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e0 = torch.rand(5, 12)
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e1 = torch.rand(12)
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f0 = torch.rand(10, 3, 4)
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f1 = torch.rand(4)
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g0 = torch.rand(6, 3, 7, 14)
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g1 = torch.rand(14)
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h0 = torch.rand(23, 14)
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h1 = torch.rand(14, 25)
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i0 = torch.rand(4, 5)
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i1 = torch.rand(10, 5, 40)
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j0 = torch.rand(14, 6)
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j1 = torch.rand(2, 4, 6, 20)
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k0 = torch.rand(10, 23, 14)
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k1 = torch.rand(14, 5)
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l0 = torch.rand(7, 8, 13, 14)
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l1 = torch.rand(14, 35)
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m0 = torch.rand(10, 23, 14)
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m1 = torch.rand(10, 14, 5)
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n0 = torch.rand(10, 13, 18)
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n1 = torch.rand(3, 1, 18, 8)
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o0 = torch.rand(1, 5, 23, 11)
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o1 = torch.rand(8, 1, 11, 9)
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p0 = torch.rand(6, 9, 13, 14)
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p1 = torch.rand(6, 9, 14, 15)
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a = net(a0, a1, b0, b1, c0, c1, d0, d1, e0, e1, f0, f1, g0, g1, h0, h1, i0, i1, j0, j1, k0, k1, l0, l1, m0, m1, n0, n1, o0, o1, p0, p1)
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# export torchscript
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mod = torch.jit.trace(net, (a0, a1, b0, b1, c0, c1, d0, d1, e0, e1, f0, f1, g0, g1, h0, h1, i0, i1, j0, j1, k0, k1, l0, l1, m0, m1, n0, n1, o0, o1, p0, p1))
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mod.save("test_torch_matmul.pt")
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# torchscript to pnnx
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import os
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os.system("../src/pnnx test_torch_matmul.pt inputshape=[13],[13],[14],[14,6],[13],[7,13,4],[15],[5,7,15,9],[5,12],[12],[10,3,4],[4],[6,3,7,14],[14],[23,14],[14,25],[4,5],[10,5,40],[14,6],[2,4,6,20],[10,23,14],[14,5],[7,8,13,14],[14,35],[10,23,14],[10,14,5],[10,13,18],[3,1,18,8],[1,5,23,11],[8,1,11,9],[6,9,13,14],[6,9,14,15]")
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# pnnx inference
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import test_torch_matmul_pnnx
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b = test_torch_matmul_pnnx.test_inference()
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for a0, b0 in zip(a, b):
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if not torch.equal(a0, b0):
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return False
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return True
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if __name__ == "__main__":
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if test():
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exit(0)
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else:
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exit(1)
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