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
		
			
				
	
	
		
			59 lines
		
	
	
		
			1.7 KiB
		
	
	
	
		
			Python
		
	
	
	
	
	
			
		
		
	
	
			59 lines
		
	
	
		
			1.7 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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| 
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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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| 
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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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| 
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|     def forward(self, x, y):
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|         x = F.affine_grid(x, torch.Size((32, 3, 24, 24)), align_corners=False)
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| 
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|         y = F.affine_grid(y, torch.Size((12, 3, 10, 20, 30)), align_corners=False)
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| 
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|         return x, y
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| 
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| def test():
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|     net = Model()
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|     net.eval()
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| 
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|     torch.manual_seed(0)
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|     x = torch.rand(32, 2, 3)
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|     y = torch.rand(12, 3, 4)
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| 
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|     a0, a1 = net(x, y)
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| 
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|     # export torchscript
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|     mod = torch.jit.trace(net, (x, y))
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|     mod.save("test_F_affine_grid.pt")
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| 
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|     # torchscript to pnnx
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|     import os
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|     os.system("../src/pnnx test_F_affine_grid.pt inputshape=[32,2,3],[12,3,4]")
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| 
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|     # pnnx inference
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|     import test_F_affine_grid_pnnx
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|     b0, b1 = test_F_affine_grid_pnnx.test_inference()
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| 
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|     return torch.equal(a0, b0) and torch.equal(a1, b1)
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| 
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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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