718c41634f
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
111 lines
4.4 KiB
C++
111 lines
4.4 KiB
C++
// 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 "../utils.h"
|
|
|
|
namespace pnnx {
|
|
|
|
class GRU : public FuseModulePass
|
|
{
|
|
public:
|
|
const char* match_type_str() const
|
|
{
|
|
return "__torch__.torch.nn.modules.rnn.GRU";
|
|
}
|
|
|
|
const char* type_str() const
|
|
{
|
|
return "nn.GRU";
|
|
}
|
|
|
|
void write(Operator* op, const std::shared_ptr<torch::jit::Graph>& graph, const torch::jit::Module& mod) const
|
|
{
|
|
// mod.dump(true, true, true);
|
|
|
|
// graph->dump();
|
|
|
|
const torch::jit::Node* gru = find_node_by_kind(graph, "aten::gru");
|
|
|
|
const torch::jit::Node* return_tuple = find_node_by_kind(graph, "prim::TupleConstruct");
|
|
if (return_tuple && return_tuple->inputs().size() == 2 && gru->outputs().size() == 2
|
|
&& return_tuple->inputs()[0] == gru->outputs()[1] && return_tuple->inputs()[1] == gru->outputs()[0])
|
|
{
|
|
// mark the swapped output tuple
|
|
// we would restore the fine order in pass_level3/fuse_rnn_unpack
|
|
fprintf(stderr, "swapped detected !\n");
|
|
op->params["pnnx_rnn_output_swapped"] = 1;
|
|
}
|
|
|
|
// for (auto aa : gru->schema().arguments())
|
|
// {
|
|
// fprintf(stderr, "arg %s\n", aa.name().c_str());
|
|
// }
|
|
|
|
const auto& weight_ih_l0 = mod.attr("weight_ih_l0").toTensor();
|
|
|
|
op->params["input_size"] = weight_ih_l0.size(1);
|
|
op->params["hidden_size"] = weight_ih_l0.size(0) / 3;
|
|
op->params["num_layers"] = gru->namedInput("num_layers");
|
|
op->params["bias"] = gru->namedInput("has_biases");
|
|
op->params["batch_first"] = gru->namedInput("batch_first");
|
|
op->params["bidirectional"] = gru->namedInput("bidirectional");
|
|
|
|
const int num_layers = op->params["num_layers"].i;
|
|
const bool bias = op->params["bias"].b;
|
|
const bool bidirectional = op->params["bidirectional"].b;
|
|
|
|
for (int k = 0; k < num_layers; k++)
|
|
{
|
|
std::string weight_ih_lk_key = std::string("weight_ih_l") + std::to_string(k);
|
|
std::string weight_hh_lk_key = std::string("weight_hh_l") + std::to_string(k);
|
|
|
|
op->attrs[weight_ih_lk_key] = mod.attr(weight_ih_lk_key).toTensor();
|
|
op->attrs[weight_hh_lk_key] = mod.attr(weight_hh_lk_key).toTensor();
|
|
|
|
if (bias)
|
|
{
|
|
std::string bias_ih_lk_key = std::string("bias_ih_l") + std::to_string(k);
|
|
std::string bias_hh_lk_key = std::string("bias_hh_l") + std::to_string(k);
|
|
|
|
op->attrs[bias_ih_lk_key] = mod.attr(bias_ih_lk_key).toTensor();
|
|
op->attrs[bias_hh_lk_key] = mod.attr(bias_hh_lk_key).toTensor();
|
|
}
|
|
|
|
if (bidirectional)
|
|
{
|
|
std::string weight_ih_lk_reverse_key = std::string("weight_ih_l") + std::to_string(k) + "_reverse";
|
|
std::string weight_hh_lk_reverse_key = std::string("weight_hh_l") + std::to_string(k) + "_reverse";
|
|
|
|
op->attrs[weight_ih_lk_reverse_key] = mod.attr(weight_ih_lk_reverse_key).toTensor();
|
|
op->attrs[weight_hh_lk_reverse_key] = mod.attr(weight_hh_lk_reverse_key).toTensor();
|
|
|
|
if (bias)
|
|
{
|
|
std::string bias_ih_lk_reverse_key = std::string("bias_ih_l") + std::to_string(k) + "_reverse";
|
|
std::string bias_hh_lk_reverse_key = std::string("bias_hh_l") + std::to_string(k) + "_reverse";
|
|
|
|
op->attrs[bias_ih_lk_reverse_key] = mod.attr(bias_ih_lk_reverse_key).toTensor();
|
|
op->attrs[bias_hh_lk_reverse_key] = mod.attr(bias_hh_lk_reverse_key).toTensor();
|
|
}
|
|
}
|
|
}
|
|
}
|
|
};
|
|
|
|
REGISTER_GLOBAL_PNNX_FUSE_MODULE_PASS(GRU)
|
|
|
|
} // namespace pnnx
|