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/src/pass_level1/nn_GRU.cpp

@@ -0,0 +1,110 @@
+// 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.
+
+#include "pass_level1.h"
+
+#include "../utils.h"
+
+namespace pnnx {
+
+class GRU : public FuseModulePass
+{
+public:
+    const char* match_type_str() const
+    {
+        return "__torch__.torch.nn.modules.rnn.GRU";
+    }
+
+    const char* type_str() const
+    {
+        return "nn.GRU";
+    }
+
+    void write(Operator* op, const std::shared_ptr<torch::jit::Graph>& graph, const torch::jit::Module& mod) const
+    {
+        //         mod.dump(true, true, true);
+
+        //         graph->dump();
+
+        const torch::jit::Node* gru = find_node_by_kind(graph, "aten::gru");
+
+        const torch::jit::Node* return_tuple = find_node_by_kind(graph, "prim::TupleConstruct");
+        if (return_tuple && return_tuple->inputs().size() == 2 && gru->outputs().size() == 2
+                && return_tuple->inputs()[0] == gru->outputs()[1] && return_tuple->inputs()[1] == gru->outputs()[0])
+        {
+            // mark the swapped output tuple
+            // we would restore the fine order in pass_level3/fuse_rnn_unpack
+            fprintf(stderr, "swapped detected !\n");
+            op->params["pnnx_rnn_output_swapped"] = 1;
+        }
+
+        //         for (auto aa : gru->schema().arguments())
+        //         {
+        //             fprintf(stderr, "arg %s\n", aa.name().c_str());
+        //         }
+
+        const auto& weight_ih_l0 = mod.attr("weight_ih_l0").toTensor();
+
+        op->params["input_size"] = weight_ih_l0.size(1);
+        op->params["hidden_size"] = weight_ih_l0.size(0) / 3;
+        op->params["num_layers"] = gru->namedInput("num_layers");
+        op->params["bias"] = gru->namedInput("has_biases");
+        op->params["batch_first"] = gru->namedInput("batch_first");
+        op->params["bidirectional"] = gru->namedInput("bidirectional");
+
+        const int num_layers = op->params["num_layers"].i;
+        const bool bias = op->params["bias"].b;
+        const bool bidirectional = op->params["bidirectional"].b;
+
+        for (int k = 0; k < num_layers; k++)
+        {
+            std::string weight_ih_lk_key = std::string("weight_ih_l") + std::to_string(k);
+            std::string weight_hh_lk_key = std::string("weight_hh_l") + std::to_string(k);
+
+            op->attrs[weight_ih_lk_key] = mod.attr(weight_ih_lk_key).toTensor();
+            op->attrs[weight_hh_lk_key] = mod.attr(weight_hh_lk_key).toTensor();
+
+            if (bias)
+            {
+                std::string bias_ih_lk_key = std::string("bias_ih_l") + std::to_string(k);
+                std::string bias_hh_lk_key = std::string("bias_hh_l") + std::to_string(k);
+
+                op->attrs[bias_ih_lk_key] = mod.attr(bias_ih_lk_key).toTensor();
+                op->attrs[bias_hh_lk_key] = mod.attr(bias_hh_lk_key).toTensor();
+            }
+
+            if (bidirectional)
+            {
+                std::string weight_ih_lk_reverse_key = std::string("weight_ih_l") + std::to_string(k) + "_reverse";
+                std::string weight_hh_lk_reverse_key = std::string("weight_hh_l") + std::to_string(k) + "_reverse";
+
+                op->attrs[weight_ih_lk_reverse_key] = mod.attr(weight_ih_lk_reverse_key).toTensor();
+                op->attrs[weight_hh_lk_reverse_key] = mod.attr(weight_hh_lk_reverse_key).toTensor();
+
+                if (bias)
+                {
+                    std::string bias_ih_lk_reverse_key = std::string("bias_ih_l") + std::to_string(k) + "_reverse";
+                    std::string bias_hh_lk_reverse_key = std::string("bias_hh_l") + std::to_string(k) + "_reverse";
+
+                    op->attrs[bias_ih_lk_reverse_key] = mod.attr(bias_ih_lk_reverse_key).toTensor();
+                    op->attrs[bias_hh_lk_reverse_key] = mod.attr(bias_hh_lk_reverse_key).toTensor();
+                }
+            }
+        }
+    }
+};
+
+REGISTER_GLOBAL_PNNX_FUSE_MODULE_PASS(GRU)
+
+} // namespace pnnx