feat: 切换后端至PaddleOCR-NCNN，切换工程为CMake

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

3rdparty/ncnn/tools/pnnx/tests/test_F_conv_transpose3d.py

@@ -0,0 +1,66 @@
+# 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__()
+
+        self.w2 = nn.Parameter(torch.rand(6, 12, 4, 4, 4))
+        self.b2 = nn.Parameter(torch.rand(12))
+        self.w3 = nn.Parameter(torch.rand(12, 2, 3, 3, 3))
+
+    def forward(self, x, w0, w1, b1, y):
+        x = F.conv_transpose3d(x, w0, None, stride=(2,2,2), padding=(1,0,1), output_padding=(1,1,0))
+        x = F.conv_transpose3d(x, w1, b1, stride=(1,1,2), padding=(2,2,1), dilation=(2,2,1), groups=2)
+
+        y = F.conv_transpose3d(y, self.w2, self.b2, stride=(2,2,2), padding=(1,0,1), output_padding=(1,1,0))
+        y = F.conv_transpose3d(y, self.w3, None, stride=(1,1,2), padding=(2,2,1), dilation=(2,2,1), groups=3)
+        return x, y
+
+def test():
+    net = Model()
+    net.eval()
+
+    torch.manual_seed(0)
+    x = torch.rand(1, 12, 10, 12, 14)
+    w0 = torch.rand(12, 16, 3, 2, 3)
+    w1 = torch.rand(16, 8, 5, 4, 5)
+    b1 = torch.rand(16)
+    y = torch.rand(1, 6, 4, 5, 6)
+
+    a0, a1 = net(x, w0, w1, b1, y)
+
+    # export torchscript
+    mod = torch.jit.trace(net, (x, w0, w1, b1, y))
+    mod.save("test_F_conv_transpose3d.pt")
+
+    # torchscript to pnnx
+    import os
+    os.system("../src/pnnx test_F_conv_transpose3d.pt inputshape=[1,12,10,12,14],[12,16,3,2,3],[16,8,5,4,5],[16],[1,6,4,5,6]")
+
+    # pnnx inference
+    import test_F_conv_transpose3d_pnnx
+    b0, b1 = test_F_conv_transpose3d_pnnx.test_inference()
+
+    return torch.equal(a0, b0) and torch.equal(a1, b1)
+
+if __name__ == "__main__":
+    if test():
+        exit(0)
+    else:
+        exit(1)