// 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_level2.h" namespace pnnx { class F_conv1d : public GraphRewriterPass { public: const char* match_pattern_graph() const { return R"PNNXIR(7767517 16 15 pnnx.Input input_0 0 1 input pnnx.Input input_1 0 1 weight pnnx.Input input_2 0 1 bias pnnx.Input input_3 0 1 stride pnnx.Input input_4 0 1 padding pnnx.Input input_5 0 1 dilation pnnx.Input input_6 0 1 groups prim::Constant op_0 0 1 transposed value=False prim::Constant op_1 0 1 output_padding_w value=0 prim::ListConstruct op_2 1 1 output_padding_w output_padding prim::Constant op_3 0 1 benchmark value=* prim::Constant op_4 0 1 deterministic value=* prim::Constant op_5 0 1 cudnn_enabled value=* prim::Constant op_6 0 1 allow_tf32 value=* aten::_convolution op_7 13 1 input weight bias stride padding dilation transposed output_padding groups benchmark deterministic cudnn_enabled allow_tf32 out pnnx.Output output 1 0 out )PNNXIR"; } const char* type_str() const { return "F.conv1d"; } }; REGISTER_GLOBAL_PNNX_GRAPH_REWRITER_PASS(F_conv1d, 10) class F_convmode : public GraphRewriterPass { public: const char* match_pattern_graph() const { return R"PNNXIR(7767517 9 8 pnnx.Input input_0 0 1 input pnnx.Input input_1 0 1 weight pnnx.Input input_2 0 1 bias pnnx.Input input_3 0 1 stride pnnx.Input input_4 0 1 padding pnnx.Input input_5 0 1 dilation pnnx.Input input_6 0 1 groups aten::_convolution_mode op_0 7 1 input weight bias stride padding dilation groups out pnnx.Output output 1 0 out )PNNXIR"; } const char* type_str() const { return "F.convmode"; } }; REGISTER_GLOBAL_PNNX_GRAPH_REWRITER_PASS(F_convmode, 10) class F_conv1d_onnx : public GraphRewriterPass { public: const char* match_pattern_graph() const { return R"PNNXIR(7767517 5 4 pnnx.Input input_0 0 1 input pnnx.Input input_1 0 1 weight pnnx.Input input_2 0 1 bias Conv op_0 3 1 input weight bias out kernel_shape=%kernel_shape strides=%strides pads=%pads dilations=%dilations group=%group pnnx.Output output 1 0 out )PNNXIR"; } const char* type_str() const { return "F.conv1d"; } bool match(const std::map& captured_params) const { if (captured_params.at("kernel_shape").type != 5) return false; if (captured_params.at("kernel_shape").ai.size() != 1) return false; if (captured_params.at("strides").type != 5) return false; if (captured_params.at("strides").ai.size() != 1) return false; if (captured_params.at("dilations").type != 5) return false; if (captured_params.at("dilations").ai.size() != 1) return false; if (captured_params.at("group").type != 2) return false; if (captured_params.at("pads").type != 5) return false; const std::vector& pads = captured_params.at("pads").ai; if (pads.size() != 2 || pads[0] != pads[1]) return false; return true; } void write(Operator* op, const std::map& captured_params) const { const std::vector& pads = captured_params.at("pads").ai; op->params["stride"] = captured_params.at("strides"); op->params["dilation"] = captured_params.at("dilations"); op->params["groups"] = captured_params.at("group"); op->params["padding"] = {pads[0]}; } }; REGISTER_GLOBAL_PNNX_GRAPH_REWRITER_PASS(F_conv1d_onnx, 10) class F_conv1d_onnx_0 : public F_conv1d_onnx { public: const char* match_pattern_graph() const { return R"PNNXIR(7767517 4 3 pnnx.Input input_0 0 1 input pnnx.Input input_1 0 1 weight Conv op_0 2 1 input weight out kernel_shape=%kernel_shape strides=%strides pads=%pads dilations=%dilations group=%group pnnx.Output output 1 0 out )PNNXIR"; } const char* replace_pattern_graph() const { return R"PNNXIR(7767517 4 3 pnnx.Input input_0 0 1 input pnnx.Input input_1 0 1 weight F.conv1d conv 2 1 input weight out bias=None pnnx.Output output 1 0 out )PNNXIR"; } }; REGISTER_GLOBAL_PNNX_GRAPH_REWRITER_PASS(F_conv1d_onnx_0, 10) class F_conv1d_onnx_1 : public GraphRewriterPass { public: const char* match_pattern_graph() const { return R"PNNXIR(7767517 5 4 pnnx.Input input_0 0 1 input pnnx.Input input_1 0 1 weight pnnx.Input input_2 0 1 bias Conv op_0 3 1 input weight bias out strides=%strides pads=%pads dilations=%dilations group=%group auto_pad=NOTSET pnnx.Output output 1 0 out )PNNXIR"; } const char* type_str() const { return "F.conv1d"; } bool match(const std::map& captured_params) const { if (captured_params.at("strides").type != 5) return false; if (captured_params.at("strides").ai.size() != 1) return false; if (captured_params.at("dilations").type != 5) return false; if (captured_params.at("dilations").ai.size() != 1) return false; if (captured_params.at("group").type != 2) return false; if (captured_params.at("pads").type != 5) return false; const std::vector& pads = captured_params.at("pads").ai; if (pads.size() != 2 || pads[0] != pads[1]) return false; return true; } void write(Operator* op, const std::map& captured_params) const { const std::vector& pads = captured_params.at("pads").ai; op->params["stride"] = captured_params.at("strides"); op->params["dilation"] = captured_params.at("dilations"); op->params["groups"] = captured_params.at("group"); op->params["padding"] = {pads[0]}; } }; REGISTER_GLOBAL_PNNX_GRAPH_REWRITER_PASS(F_conv1d_onnx_1, 10) } // namespace pnnx