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_nn_GRU.py

@@ -0,0 +1,76 @@
+# 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.gru_0_0 = nn.GRU(input_size=32, hidden_size=16)
+        self.gru_0_1 = nn.GRU(input_size=16, hidden_size=16, num_layers=3, bias=False)
+        self.gru_0_2 = nn.GRU(input_size=16, hidden_size=16, num_layers=4, bias=True, bidirectional=True)
+        self.gru_0_3 = nn.GRU(input_size=16, hidden_size=16, num_layers=4, bias=True, bidirectional=True)
+
+        self.gru_1_0 = nn.GRU(input_size=25, hidden_size=16, batch_first=True)
+        self.gru_1_1 = nn.GRU(input_size=16, hidden_size=16, num_layers=3, bias=False, batch_first=True)
+        self.gru_1_2 = nn.GRU(input_size=16, hidden_size=16, num_layers=4, bias=True, batch_first=True, bidirectional=True)
+        self.gru_1_3 = nn.GRU(input_size=16, hidden_size=16, num_layers=4, bias=True, batch_first=True, bidirectional=True)
+
+    def forward(self, x, y):
+        x0, h0 = self.gru_0_0(x)
+        x1, h1 = self.gru_0_1(x0)
+        x2, h2 = self.gru_0_2(x1)
+        x3, h3 = self.gru_0_3(x1, h2)
+
+        y0, h4 = self.gru_1_0(y)
+        y1, h5 = self.gru_1_1(y0)
+        y2, h6 = self.gru_1_2(y1)
+        y3, h7 = self.gru_1_3(y1, h6)
+        return x2, x3, h0, h1, h2, h3, y2, y3, h4, h5, h6, h7
+
+def test():
+    net = Model()
+    net.eval()
+
+    torch.manual_seed(0)
+    x = torch.rand(10, 1, 32)
+    y = torch.rand(1, 12, 25)
+
+    a = net(x, y)
+
+    # export torchscript
+    mod = torch.jit.trace(net, (x, y))
+    mod.save("test_nn_GRU.pt")
+
+    # torchscript to pnnx
+    import os
+    os.system("../src/pnnx test_nn_GRU.pt inputshape=[10,1,32],[1,12,25]")
+
+    # pnnx inference
+    import test_nn_GRU_pnnx
+    b = test_nn_GRU_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)