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/*
* Copyright (C) 2018 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 "utils/tflite/blacklist.h"
#include "utils/tflite/blacklist_base.h"
#include "utils/tflite/skipgram_finder.h"
#include "flatbuffers/flexbuffers.h"
namespace tflite {
namespace ops {
namespace custom {
namespace libtextclassifier3 {
namespace blacklist {
// Generates prediction vectors for input strings using a skipgram blacklist.
// This uses the framework in `blacklist_base.h`, with the implementation detail
// that the input is a string tensor of messages and the terms are skipgrams.
class BlacklistOp : public BlacklistOpBase {
public:
explicit BlacklistOp(const flexbuffers::Map& custom_options)
: BlacklistOpBase(custom_options),
skipgram_finder_(custom_options["max_skip_size"].AsInt32()),
input_(nullptr) {
auto blacklist = custom_options["blacklist"].AsTypedVector();
auto blacklist_category =
custom_options["blacklist_category"].AsTypedVector();
for (int i = 0; i < blacklist.size(); i++) {
int category = blacklist_category[i].AsInt32();
flexbuffers::String s = blacklist[i].AsString();
skipgram_finder_.AddSkipgram(std::string(s.c_str(), s.length()),
category);
}
}
TfLiteStatus InitializeInput(TfLiteContext* context,
TfLiteNode* node) override {
input_ = &context->tensors[node->inputs->data[kInputMessage]];
return kTfLiteOk;
}
absl::flat_hash_set<int> GetCategories(int i) const override {
StringRef input = GetString(input_, i);
return skipgram_finder_.FindSkipgrams(std::string(input.str, input.len));
}
void FinalizeInput() override { input_ = nullptr; }
TfLiteIntArray* GetInputShape(TfLiteContext* context,
TfLiteNode* node) override {
return context->tensors[node->inputs->data[kInputMessage]].dims;
}
private:
::libtextclassifier3::SkipgramFinder skipgram_finder_;
TfLiteTensor* input_;
static constexpr int kInputMessage = 0;
};
void* BlacklistOpInit(TfLiteContext* context, const char* buffer,
size_t length) {
const uint8_t* buffer_t = reinterpret_cast<const uint8_t*>(buffer);
return new BlacklistOp(flexbuffers::GetRoot(buffer_t, length).AsMap());
}
} // namespace blacklist
TfLiteRegistration* Register_BLACKLIST() {
static TfLiteRegistration r = {libtextclassifier3::blacklist::BlacklistOpInit,
libtextclassifier3::blacklist::Free,
libtextclassifier3::blacklist::Resize,
libtextclassifier3::blacklist::Eval};
return &r;
}
} // namespace libtextclassifier3
} // namespace custom
} // namespace ops
} // namespace tflite