paddle_mobile.cpp 4.7 KB
Newer Older
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

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 "io/paddle_mobile.h"

namespace paddle_mobile {

19 20 21 22 23 24 25 26
template <typename Dtype, Precision P>
void PaddleMobile<Dtype, P>::SetThreadNum(int num) {
#ifdef _OPENMP
  //  omp_set_dynamic(0);
  omp_set_num_threads(num);
#endif
};

27
template <typename Dtype, Precision P>
W
wangliu 已提交
28
bool PaddleMobile<Dtype, P>::Load(const std::string &dirname, bool optimize,
xiebaiyuan's avatar
xiebaiyuan 已提交
29 30
                                  bool quantification, int batch_size,
                                  bool loddable) {
31 32 33 34 35 36 37 38
  if (loader_.get() == nullptr) {
    loader_ = std::make_shared<Loader<Dtype, P>>();
  } else {
    LOG(kLOG_INFO) << "loader inited";
  }

  if (executor_.get() == nullptr) {
    executor_ = std::make_shared<Executor<Dtype, P>>(
xiebaiyuan's avatar
xiebaiyuan 已提交
39 40
        loader_->Load(dirname, optimize, quantification), batch_size, optimize,
        loddable);
41 42 43 44 45 46 47 48 49
  } else {
    LOG(kLOG_INFO) << "executor inited";
  }

  return true;
}

template <typename Dtype, Precision P>
bool PaddleMobile<Dtype, P>::Load(const std::string &model_path,
W
wangliu 已提交
50
                                  const std::string &para_path, bool optimize,
xiebaiyuan's avatar
xiebaiyuan 已提交
51 52
                                  bool quantification, int batch_size,
                                  bool loddable) {
53 54 55 56 57 58 59 60
  if (loader_.get() == nullptr) {
    loader_ = std::make_shared<Loader<Dtype, P>>();
  } else {
    LOG(kLOG_INFO) << "loader inited";
  }

  if (executor_.get() == nullptr) {
    executor_ = std::make_shared<Executor<Dtype, P>>(
W
wangliu 已提交
61
        loader_->Load(model_path, para_path, optimize, quantification),
xiebaiyuan's avatar
xiebaiyuan 已提交
62
        batch_size, optimize, loddable);
63 64 65 66 67 68 69
  } else {
    LOG(kLOG_INFO) << "executor inited";
  }

  return true;
}

70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95
template <typename Dtype, Precision P>
bool PaddleMobile<Dtype, P>::LoadCombinedMemory(
    size_t model_len, const uint8_t *model_buf, size_t combined_params_len,
    const uint8_t *combined_params_buf) {
  int batch_size = 1;
  bool optimise = true;
  bool quantification = false;

  if (loader_.get() == nullptr) {
    loader_ = std::make_shared<Loader<Dtype, P>>();
  } else {
    LOG(kLOG_INFO) << "loader inited";
  }

  if (executor_.get() == nullptr) {
    executor_ = std::make_shared<Executor<Dtype, P>>(
        loader_->LoadCombinedMemory(model_len, model_buf, combined_params_len,
                                    combined_params_buf, optimise,
                                    quantification),
        batch_size, optimise);
  } else {
    LOG(kLOG_INFO) << "executor inited";
  }

  return true;
}
96 97 98 99 100 101
template <typename Dtype, Precision P>
std::shared_ptr<framework::Tensor> PaddleMobile<Dtype, P>::Predict(
    const framework::Tensor &t) {
  return executor_->Predict(t);
}

xiebaiyuan's avatar
xiebaiyuan 已提交
102 103 104 105 106 107
template <typename Dtype, Precision P>
std::shared_ptr<framework::Tensor> PaddleMobile<Dtype, P>::PredictLod(
    const framework::LoDTensor &t) {
  return executor_->PredictLod(t);
}

108 109 110 111 112 113 114 115 116 117 118 119 120
template <typename Dtype, Precision P>
std::vector<typename PaddleMobile<Dtype, P>::Ptype>
PaddleMobile<Dtype, P>::Predict(const std::vector<Ptype> &input,
                                const std::vector<int64_t> &dims) {
  return executor_->Predict(input, dims);
}

template <typename Dtype, Precision P>
void PaddleMobile<Dtype, P>::Clear() {
  executor_ = nullptr;
  loader_ = nullptr;
}

121
template <typename Dtype, Precision P>
L
liuruilong 已提交
122
PaddleMobile<Dtype, P>::~PaddleMobile() {
123 124 125 126
  executor_ = nullptr;
  loader_ = nullptr;
}

127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153
#ifdef PADDLE_MOBILE_FPGA
template <typename Dtype, Precision P>
void PaddleMobile<Dtype, P>::FeedData(const framework::Tensor &t) {
  return executor_->FeedData(t);
};

template <typename Dtype, Precision P>
std::shared_ptr<framework::Tensor> PaddleMobile<Dtype, P>::FetchResult() {
  return executor_->FetchResult();
};

template <typename Dtype, Precision P>
void PaddleMobile<Dtype, P>::Predict_From_To(int start, int end) {
  executor_->Predict_From_To(start, end);
};

template <typename Dtype, Precision P>
void PaddleMobile<Dtype, P>::Predict_From(int start) {
  executor_->Predict_From(start);
};

template <typename Dtype, Precision P>
void PaddleMobile<Dtype, P>::Predict_To(int end) {
  executor_->Predict_To(end);
};
#endif

154
template class PaddleMobile<CPU, Precision::FP32>;
155

156
template class PaddleMobile<FPGA, Precision::FP32>;
157

158 159 160
template class PaddleMobile<GPU_MALI, Precision::FP32>;

}  // namespace paddle_mobile