1 /** 2 * Copyright 2019 Huawei Technologies Co., Ltd 3 * 4 * Licensed under the Apache License, Version 2.0 (the "License"); 5 * you may not use this file except in compliance with the License. 6 * You may obtain a copy of the License at 7 * 8 * http://www.apache.org/licenses/LICENSE-2.0 9 * 10 * Unless required by applicable law or agreed to in writing, software 11 * distributed under the License is distributed on an "AS IS" BASIS, 12 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 13 * See the License for the specific language governing permissions and 14 * limitations under the License. 15 */ 16 #ifndef MINDSPORE_CCSRC_MINDDATA_DATASET_ENGINE_DATASETOPS_SOURCE_SAMPLER_WEIGHTED_RANDOM_SAMPLER_H_ 17 #define MINDSPORE_CCSRC_MINDDATA_DATASET_ENGINE_DATASETOPS_SOURCE_SAMPLER_WEIGHTED_RANDOM_SAMPLER_H_ 18 19 #include <deque> 20 #include <limits> 21 #include <memory> 22 #include <vector> 23 24 #include "minddata/dataset/engine/datasetops/source/sampler/sampler.h" 25 26 namespace mindspore { 27 namespace dataset { 28 // Samples elements from id `0, 1, ..., weights.size()-1` with given probabilities (weights). 29 class WeightedRandomSamplerRT : public SamplerRT { 30 public: 31 // Constructor. 32 // @param weights A lift of sample weights. 33 // @param num_samples Number of samples to be drawn. 34 // @param replacement Determine if samples are drawn with/without replacement. 35 // @param samples_per_tensor The number of ids we draw on each call to GetNextSample(). 36 // When samples_per_tensor=0, GetNextSample() will draw all the sample ids and return them at once. 37 WeightedRandomSamplerRT(const std::vector<double> &weights, int64_t num_samples, bool replacement, 38 int64_t samples_per_tensor = std::numeric_limits<int64_t>::max()); 39 40 // Destructor. 41 ~WeightedRandomSamplerRT() = default; 42 43 // Initialize the sampler. 44 // @param op (Not used in this sampler) 45 // @return Status 46 Status InitSampler() override; 47 48 // Reset the internal variable to the initial state and reshuffle the indices. 49 Status ResetSampler() override; 50 51 // Get the sample ids. 52 // @param[out] TensorRow where the sample ids will be placed. 53 // @note the sample ids (int64_t) will be placed in one Tensor 54 Status GetNextSample(TensorRow *out) override; 55 56 // Printer for debugging purposes. 57 // @param out - output stream to write to 58 // @param show_all - bool to show detailed vs summary 59 void SamplerPrint(std::ostream &out, bool show_all) const override; 60 61 /// \brief Get the arguments of node 62 /// \param[out] out_json JSON string of all attributes 63 /// \return Status of the function 64 Status to_json(nlohmann::json *out_json) override; 65 66 private: 67 // A list of weights for each sample. 68 std::vector<double> weights_; 69 70 // A flag indicating if samples are drawn with/without replacement. 71 bool replacement_; 72 73 // Current sample id. 74 int64_t sample_id_; 75 76 // Random engine and device 77 std::mt19937 rand_gen_; 78 79 // Discrete distribution for generating weighted random numbers with replacement. 80 std::unique_ptr<std::discrete_distribution<int64_t>> discrete_dist_; 81 82 // Exponential distribution for generating weighted random numbers without replacement. 83 // based on "Accelerating weighted random sampling without replacement" by Kirill Muller. 84 std::unique_ptr<std::exponential_distribution<>> exp_dist_; 85 86 // Initialized the computation for generating weighted random numbers without replacement 87 // using onepass method. 88 void InitOnePassSampling(); 89 90 // Store the random weighted ids generated by onepass method in `InitOnePassSampling` 91 std::deque<int64_t> onepass_ids_; 92 }; 93 } // namespace dataset 94 } // namespace mindspore 95 96 #endif 97