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
74 lines
2.2 KiB
Python
74 lines
2.2 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) 2021 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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self.w4 = nn.Parameter(torch.rand(16))
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self.w5 = nn.Parameter(torch.rand(2))
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self.w6 = nn.Parameter(torch.rand(3))
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self.w7 = nn.Parameter(torch.rand(1))
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def forward(self, x, y, z, w, w0, w1, w2, w3):
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x = F.prelu(x, w0)
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x = F.prelu(x, self.w4)
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y = F.prelu(y, w1)
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y = F.prelu(y, self.w5)
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z = F.prelu(z, w2)
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z = F.prelu(z, self.w6)
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w = F.prelu(w, w3)
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w = F.prelu(w, self.w7)
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return x, y, z, w
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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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x = torch.rand(1, 16)
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y = torch.rand(12, 2, 16)
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z = torch.rand(1, 3, 12, 16)
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w = torch.rand(1, 5, 7, 9, 11)
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w0 = torch.rand(16)
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w1 = torch.rand(2)
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w2 = torch.rand(3)
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w3 = torch.rand(1)
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a0, a1, a2, a3 = net(x, y, z, w, w0, w1, w2, w3)
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# export torchscript
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mod = torch.jit.trace(net, (x, y, z, w, w0, w1, w2, w3))
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mod.save("test_F_prelu.pt")
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# torchscript to pnnx
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import os
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os.system("../src/pnnx test_F_prelu.pt inputshape=[1,16],[12,2,16],[1,3,12,16],[1,5,7,9,11],[16],[2],[3],[1]")
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# pnnx inference
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import test_F_prelu_pnnx
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b0, b1, b2, b3 = test_F_prelu_pnnx.test_inference()
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return torch.equal(a0, b0) and torch.equal(a1, b1) and torch.equal(a2, b2) and torch.equal(a3, b3)
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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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