53d7b8f807
* [runtime/onnxruntime] add onnxruntime support * add cpplint and clang-format * fix lint
102 lines
3.7 KiB
C++
102 lines
3.7 KiB
C++
// Copyright (c) 2022 Binbin Zhang (binbzha@qq.com)
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include "kws/keyword_spotting.h"
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#include <iostream>
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#include <memory>
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#include <string>
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#include <utility>
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#include <vector>
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namespace wekws {
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Ort::Env KeywordSpotting::env_ = Ort::Env(ORT_LOGGING_LEVEL_WARNING, "");
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Ort::SessionOptions KeywordSpotting::session_options_ = Ort::SessionOptions();
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KeywordSpotting::KeywordSpotting(const std::string& model_path) {
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// 1. Load sessions
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session_ = std::make_shared<Ort::Session>(env_, model_path.c_str(),
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session_options_);
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// 2. Model info
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in_names_ = {"input", "cache"};
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out_names_ = {"output", "r_cache"};
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auto metadata = session_->GetModelMetadata();
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Ort::AllocatorWithDefaultOptions allocator;
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cache_dim_ = std::stoi(metadata.LookupCustomMetadataMap("cache_dim",
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allocator));
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cache_len_ = std::stoi(metadata.LookupCustomMetadataMap("cache_len",
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allocator));
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std::cout << "Kws Model Info:" << std::endl
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<< "\tcache_dim: " << cache_dim_ << std::endl
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<< "\tcache_len: " << cache_len_ << std::endl;
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Reset();
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}
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void KeywordSpotting::Reset() {
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Ort::MemoryInfo memory_info =
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Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeCPU);
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cache_.resize(cache_dim_ * cache_len_, 0.0);
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const int64_t cache_shape[] = {1, cache_dim_, cache_len_};
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cache_ort_ = Ort::Value::CreateTensor<float>(
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memory_info, cache_.data(), cache_.size(), cache_shape, 3);
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}
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void KeywordSpotting::Forward(
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const std::vector<std::vector<float>>& feats,
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std::vector<std::vector<float>>* prob) {
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prob->clear();
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if (feats.size() == 0) return;
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Ort::MemoryInfo memory_info =
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Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeCPU);
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// 1. Prepare input
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int num_frames = feats.size();
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int feature_dim = feats[0].size();
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std::vector<float> slice_feats;
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for (int i = 0; i < feats.size(); i++) {
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slice_feats.insert(slice_feats.end(), feats[i].begin(), feats[i].end());
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}
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const int64_t feats_shape[3] = {1, num_frames, feature_dim};
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Ort::Value feats_ort = Ort::Value::CreateTensor<float>(
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memory_info, slice_feats.data(), slice_feats.size(), feats_shape, 3);
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// 2. Ort forward
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std::vector<Ort::Value> inputs;
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inputs.emplace_back(std::move(feats_ort));
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inputs.emplace_back(std::move(cache_ort_));
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// ort_outputs.size() == 2
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std::vector<Ort::Value> ort_outputs = session_->Run(
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Ort::RunOptions{nullptr}, in_names_.data(), inputs.data(),
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inputs.size(), out_names_.data(), out_names_.size());
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// 3. Update cache
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cache_ort_ = std::move(ort_outputs[1]);
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// 4. Get keyword prob
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float* data = ort_outputs[0].GetTensorMutableData<float>();
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auto type_info = ort_outputs[0].GetTensorTypeAndShapeInfo();
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int num_outputs = type_info.GetShape()[1];
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int output_dim = type_info.GetShape()[2];
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prob->resize(num_outputs);
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for (int i = 0; i < num_outputs; i++) {
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(*prob)[i].resize(output_dim);
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memcpy((*prob)[i].data(), data + i * output_dim,
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sizeof(float) * output_dim);
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}
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}
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} // namespace wekws
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