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
2022-05-10 09:54:44 +08:00
parent ecdd171c6f
commit 718c41634f
10018 changed files with 3593797 additions and 186748 deletions
--- a/3rdparty/ncnn/tools/pnnx/src/pass_level1/nn_Conv1d.cpp
+++ b/3rdparty/ncnn/tools/pnnx/src/pass_level1/nn_Conv1d.cpp
@ -0,0 +1,121 @@
+// 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 "../pass_level3/fuse_expression.h"
+
+#include "../utils.h"
+
+namespace pnnx {
+
+class Conv1d : public FuseModulePass
+{
+public:
+    const char* match_type_str() const
+    {
+        return "__torch__.torch.nn.modules.conv.Conv1d";
+    }
+
+    const char* type_str() const
+    {
+        return "nn.Conv1d";
+    }
+
+    void write(Operator* op, const std::shared_ptr<torch::jit::Graph>& graph, const torch::jit::Module& mod) const
+    {
+        //         {
+        //             pnnx::Graph pnnx_graph;
+        //
+        //             pnnx_graph.load(mod, graph);
+        //
+        //             pnnx::fuse_expression(pnnx_graph);
+        //
+        //             pnnx_graph.save("tmp.param", "tmp.bin");
+        //         }
+
+        const torch::jit::Node* convolution = find_node_by_kind(graph, "aten::_convolution");
+        const torch::jit::Node* convolution_mode = find_node_by_kind(graph, "aten::_convolution_mode");
+        const torch::jit::Node* reflection_pad1d = find_node_by_kind(graph, "aten::reflection_pad1d");
+        const torch::jit::Node* replication_pad1d = find_node_by_kind(graph, "aten::replication_pad1d");
+
+        if (convolution_mode)
+        {
+            convolution = convolution_mode;
+        }
+
+        const auto& weight = mod.attr("weight").toTensor();
+
+        op->params["groups"] = convolution->namedInput("groups");
+        op->params["in_channels"] = weight.size(1) * op->params["groups"].i;
+        op->params["out_channels"] = weight.size(0);
+        op->params["kernel_size"] = Parameter{weight.size(2)};
+        op->params["stride"] = convolution->namedInput("stride");
+        if (reflection_pad1d)
+        {
+            op->params["padding_mode"] = "reflect";
+            op->params["padding"] = reflection_pad1d->namedInput("padding");
+            std::vector<int>& padding = op->params["padding"].ai;
+            if (padding.size() == 2)
+            {
+                // Conv1d only accepts tuple of one integer
+                if (padding[0] == padding[1])
+                {
+                    padding.resize(1);
+                }
+                else if (padding[0] != padding[1])
+                {
+                    padding.resize(0);
+                    op->params["padding"].s = "same";
+                }
+            }
+        }
+        else if (replication_pad1d)
+        {
+            op->params["padding_mode"] = "replicate";
+            op->params["padding"] = replication_pad1d->namedInput("padding");
+            std::vector<int>& padding = op->params["padding"].ai;
+            if (padding.size() == 2)
+            {
+                // Conv1d only accepts tuple of one integer
+                if (padding[0] == padding[1])
+                {
+                    padding.resize(1);
+                }
+                else if (padding[0] != padding[1])
+                {
+                    padding.resize(0);
+                    op->params["padding"].s = "same";
+                }
+            }
+        }
+        else
+        {
+            op->params["padding_mode"] = "zeros";
+            op->params["padding"] = convolution->namedInput("padding");
+        }
+        op->params["dilation"] = convolution->namedInput("dilation");
+        op->params["bias"] = mod.hasattr("bias");
+
+        op->attrs["weight"] = weight;
+        if (mod.hasattr("bias"))
+        {
+            op->attrs["bias"] = mod.attr("bias").toTensor();
+        }
+    }
+};
+
+REGISTER_GLOBAL_PNNX_FUSE_MODULE_PASS(Conv1d)
+
+} // namespace pnnx