| // Tencent is pleased to support the open source community by making ncnn available. | |
| // | |
| // Copyright (C) 2023 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. | |
| namespace pnnx { | |
| void flatten_input(std::shared_ptr<torch::jit::Graph>& graph) | |
| { | |
| while (1) | |
| { | |
| bool matched = false; | |
| for (torch::jit::Node* n : graph->nodes()) | |
| { | |
| if (n->kind() != c10::prim::TupleUnpack && n->kind() != c10::prim::ListUnpack) | |
| continue; | |
| for (size_t i = 1; i < graph->inputs().size(); i++) | |
| { | |
| if (n->input(0) == graph->inputs()[i]) | |
| { | |
| matched = true; | |
| for (size_t j = 0; j < n->outputs().size(); j++) | |
| { | |
| torch::jit::Value* v2 = graph->insertInput(i + 1 + j); | |
| n->output(j)->replaceAllUsesWith(v2); | |
| } | |
| n->destroy(); | |
| graph->eraseInput(i); | |
| break; | |
| } | |
| } | |
| if (matched) | |
| break; | |
| } | |
| if (!matched) | |
| break; | |
| } | |
| } | |
| } // namespace pnnx | |