File size: 1,666 Bytes
be903e2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 | // 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
|