| /* |
| * Copyright (C) 2017 The Android Open Source Project |
| * |
| * 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 "common/embedding-feature-extractor.h" |
| |
| #include <stddef.h> |
| |
| #include <vector> |
| |
| #include "common/feature-extractor.h" |
| #include "common/feature-types.h" |
| #include "common/task-context.h" |
| #include "util/base/integral_types.h" |
| #include "util/base/logging.h" |
| #include "util/strings/numbers.h" |
| #include "util/strings/split.h" |
| |
| namespace libtextclassifier { |
| namespace nlp_core { |
| |
| bool GenericEmbeddingFeatureExtractor::Init(TaskContext *context) { |
| // Don't use version to determine how to get feature FML. |
| const std::string features = context->Get(GetParamName("features"), ""); |
| TC_LOG(INFO) << "Features: " << features; |
| |
| const std::string embedding_names = |
| context->Get(GetParamName("embedding_names"), ""); |
| TC_LOG(INFO) << "Embedding names: " << embedding_names; |
| |
| const std::string embedding_dims = |
| context->Get(GetParamName("embedding_dims"), ""); |
| TC_LOG(INFO) << "Embedding dims: " << embedding_dims; |
| |
| embedding_fml_ = strings::Split(features, ';'); |
| embedding_names_ = strings::Split(embedding_names, ';'); |
| for (const std::string &dim : strings::Split(embedding_dims, ';')) { |
| int32 parsed_dim = 0; |
| if (!ParseInt32(dim.c_str(), &parsed_dim)) { |
| TC_LOG(ERROR) << "Unable to parse dim " << dim; |
| return false; |
| } |
| embedding_dims_.push_back(parsed_dim); |
| } |
| if ((embedding_fml_.size() != embedding_names_.size()) || |
| (embedding_fml_.size() != embedding_dims_.size())) { |
| TC_LOG(ERROR) << "Mismatch: #fml specs = " << embedding_fml_.size() |
| << "; #names = " << embedding_names_.size() |
| << "; #dims = " << embedding_dims_.size(); |
| return false; |
| } |
| return true; |
| } |
| |
| } // namespace nlp_core |
| } // namespace libtextclassifier |