| /** |
| * Licensed to the Apache Software Foundation (ASF) under one |
| * or more contributor license agreements. See the NOTICE file |
| * distributed with this work for additional information |
| * regarding copyright ownership. The ASF licenses this file |
| * to you 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. |
| */ |
| |
| #ifndef SINGA_MODEL_METRIC_H_ |
| #define SINGA_MODEL_METRIC_H_ |
| #include "singa/core/tensor.h" |
| #include "singa/proto/model.pb.h" |
| namespace singa { |
| |
| /// The base metric class, which declares the APIs for computing the performance |
| /// evaluation metrics given the prediction of the model and the ground truth, |
| /// i.e., the target. |
| /// The target type is a template argument. For data samples with a single |
| /// label, T could be 1-d tenor (or vector<int>); If each data sample has |
| /// multiple labels, T could be vector<vector<int>>, one vector per sample. |
| // template <typename T = Tensor> |
| class Metric { |
| public: |
| // TODO(wangwei) call Setup using a default MetricConf. |
| Metric() = default; |
| virtual ~Metric() {} |
| virtual void ToDevice(std::shared_ptr<Device> device) {} |
| void Setup(const string& conf) { |
| MetricConf metric; |
| metric.ParseFromString(conf); |
| Setup(metric); |
| } |
| |
| /// Set meta fields from user configurations. |
| virtual void Setup(const MetricConf& conf) {} |
| |
| /// Compute the metric for each data sample |
| virtual Tensor Forward(const Tensor& prediction, const Tensor& target) = 0; |
| |
| /// Comptue the metric value averaged over all samples (in a batch) |
| float Evaluate(const Tensor& prediction, const Tensor& target) { |
| const Tensor metric = Forward(prediction, target); |
| return Sum<float>(metric) / (1.0f * metric.Size()); |
| } |
| }; |
| /// Compute the accuray of the prediction, which is matched against the |
| /// ground truth labels. |
| /// TODO(wangwei) consider multi-label cases. |
| class Accuracy : public Metric { |
| public: |
| /// Set meta fields from user configurations. |
| void Setup(const MetricConf& conf) override { top_k_ = conf.top_k(); } |
| |
| /// Check the prediction against the target (ground truth) for each data |
| /// sample. The returned Tensor has a float value for each sample, 0 for wrong |
| /// and 1 for correct. Users can call Sum(const Tensor&) / Tensor::Size() to |
| /// get the accuracy. |
| Tensor Forward(const Tensor& prediction, const Tensor& target); |
| |
| private: |
| /// \copydoc Match(const Tensor&, const Tensor&); |
| Tensor Match(const Tensor& prediction, const vector<int>& target); |
| /// If the ground truth label is in the top k predicted labels, then the |
| /// prediction is correct. |
| size_t top_k_ = 1; |
| }; |
| |
| |
| } // namespace singa |
| |
| #endif // SINGA_MODEL_METRIC_H_ |