File size: 6,550 Bytes
8207382 | 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 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 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 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 | // Copyright (c) 2025 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 "static_infer.h"
#include <fstream>
#include "src/utils/ilogger.h"
#include "src/utils/mkldnn_blocklist.h"
#include "src/utils/utility.h"
PaddleInfer::PaddleInfer(const std::string &model_name,
const std::string &model_dir,
const std::string &model_file_prefix,
const PaddlePredictorOption &option)
: model_name_(model_name), model_dir_(model_dir),
model_file_prefix_(model_file_prefix), option_(option) {
auto result = Create();
if (!result.ok()) {
INFOE("Create predictor failed: %s", result.status().ToString().c_str());
exit(-1);
}
predictor_ = std::move(result.value());
auto input_names = predictor_->GetInputNames();
for (const auto &name : input_names) {
auto handle = predictor_->GetInputHandle(name);
input_handles_.emplace_back(std::move(handle));
}
auto output_names = predictor_->GetOutputNames();
for (const auto &name : output_names) {
auto handle = predictor_->GetOutputHandle(name);
output_handles_.emplace_back(std::move(handle));
}
}
absl::StatusOr<std::shared_ptr<paddle_infer::Predictor>> PaddleInfer::Create() {
auto model_paths = Utility::GetModelPaths(model_dir_, model_file_prefix_);
if (!model_paths.ok()) {
return model_paths.status();
}
if (model_paths->find("paddle") == model_paths->end()) {
return absl::NotFoundError("No valid PaddlePaddle model found");
}
auto result_check = CheckRunMode();
if (!result_check.ok()) {
return result_check;
}
auto model_files = model_paths.value()["paddle"];
std::string model_file = model_files.first;
std::string params_file = model_files.second;
if (option_.DeviceType() == "cpu" && option_.DeviceId() >= 0) {
auto result_set = option_.SetDeviceId(0);
if (!result_set.ok()) {
return result_set;
}
INFO("`device_id` has been set to nullptr");
}
if (option_.DeviceType() == "gpu" && option_.DeviceId() < 0) {
auto result_device_id = option_.SetDeviceId(0);
if (!result_device_id.ok()) {
return result_device_id;
}
INFO("`device_id` has been set to 0");
}
paddle_infer::Config config;
config.SetModel(model_file, params_file);
if (option_.DeviceType() == "gpu") {
std::unordered_set<std::string> mixed_op_set = {"feed", "fetch"};
config.Exp_DisableMixedPrecisionOps(mixed_op_set);
paddle_infer::PrecisionType precision =
paddle_infer::PrecisionType::kFloat32;
if (option_.RunMode() == "paddle_fp16") {
precision = paddle_infer::PrecisionType::kHalf;
}
config.DisableMKLDNN();
config.EnableUseGpu(100, option_.DeviceId(), precision);
config.EnableNewIR(option_.EnableNewIR());
if (option_.EnableNewIR() && option_.EnableCinn()) {
config.EnableCINN();
}
config.EnableNewExecutor();
config.SetOptimizationLevel(3);
} else if (option_.DeviceType() == "cpu") {
config.DisableGpu();
if (option_.RunMode().find("mkldnn") != std::string::npos) {
config.EnableMKLDNN();
if (option_.RunMode().find("bf16") != std::string::npos) {
config.EnableMkldnnBfloat16();
}
config.SetMkldnnCacheCapacity(option_.MkldnnCacheCapacity());
} else {
config.DisableMKLDNN();
}
config.SetCpuMathLibraryNumThreads(option_.CpuThreads());
config.EnableNewIR(option_.EnableNewIR());
config.EnableNewExecutor();
config.SetOptimizationLevel(3);
} else {
return absl::InvalidArgumentError("Not supported device type: " +
option_.DeviceType());
}
config.EnableMemoryOptim();
for (const auto &del_p : option_.DeletePass()) {
config.DeletePass(del_p);
}
config.DisableGlogInfo();
auto predictor_shared = paddle_infer::CreatePredictor(config);
return predictor_shared;
};
absl::StatusOr<std::vector<cv::Mat>>
PaddleInfer::Apply(const std::vector<cv::Mat> &x) {
for (size_t i = 0; i < x.size(); ++i) {
auto &input_handle = input_handles_[i];
std::vector<int> input_shape(x[0].dims);
for (int i = 0; i < x[0].dims; i++) {
input_shape[i] = x[0].size[i];
}
input_handle->Reshape(input_shape);
input_handle->CopyFromCpu<float>((float *)x[i].data);
}
try {
predictor_->Run();
} catch (const std::exception &e) {
INFOE("static Infer fail: %s", e.what());
exit(-1);
}
std::vector<std::vector<float>> outputs;
std::vector<int> output_shape = {};
for (auto &output_handle : output_handles_) {
output_shape = output_handle->shape();
size_t numel = 1;
for (auto dim : output_shape)
numel *= dim;
std::vector<float> out_data(numel);
output_handle->CopyToCpu(out_data.data());
outputs.push_back(std::move(out_data));
}
auto size_v = outputs[0].size();
cv::Mat pred(output_shape.size(), output_shape.data(), CV_32F);
memcpy(pred.ptr<float>(), outputs[0].data(),
outputs[0].size() * sizeof(float));
std::vector<cv::Mat> pred_outputs = {pred};
return pred_outputs;
};
absl::Status PaddleInfer::CheckRunMode() {
if (option_.RunMode().rfind("mkldnn", 0) == 0 &&
Mkldnn::MKLDNN_BLOCKLIST.count(model_name_) > 0 &&
option_.DeviceType() == "cpu") {
INFOW("The model %s is not supported to run in MKLDNN mode! Using `paddle` "
"instead!",
model_name_.c_str());
auto result = option_.SetRunMode("paddle");
if (!result.ok()) {
return result;
}
}
if (model_name_ == "LaTeX_OCR_rec" && option_.DeviceType() == "cpu") {
if (Utility::IsMkldnnAvailable() && option_.RunMode() != "mkldnn") {
INFOE("Now, the `LaTeX_OCR_rec` model only support `mkldnn` mode when "
"running on Intel CPU devices. So using `mkldnn` instead.");
exit(-1);
auto result = option_.SetRunMode("mkldnn");
if (!result.ok()) {
return result;
}
}
}
return absl::OkStatus();
};
|