tools/pnnx/src/pass_level0.cpp

// 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_level0.h"

#include "pass_level0/constant_unpooling.h"
#include "pass_level0/flatten_input.h"
#include "pass_level0/inline_block.h"
#include "pass_level0/reset_device.h"
#include "pass_level0/shape_inference.h"

namespace pnnx {

void pass_level0(const torch::jit::Module& mod, std::shared_ptr<torch::jit::Graph>& g, const std::vector<at::Tensor>& input_tensors, const std::vector<at::Tensor>& input_tensors2, const std::vector<std::string>& module_operators, const std::string& ptpath, const std::string& device, std::set<std::string>& foldable_constants, const std::string& foldable_constants_zippath)
{
    inline_block(g, module_operators);

    reset_device(g, device);

    flatten_input(g);

    constant_unpooling(g);

    if (!input_tensors.empty())
    {
        shape_inference(mod, g, input_tensors, input_tensors2, module_operators, ptpath, device, foldable_constants, foldable_constants_zippath);
    }
}

} // namespace pnnx