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PiperOrigin-RevId: 519013105
This commit is contained in:
MediaPipe Team 2023-03-23 18:06:59 -07:00 committed by Copybara-Service
parent 8a55f11952
commit 712ea6f15b
12 changed files with 450 additions and 27 deletions
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2
mediapipe/tasks/cc/core/BUILD
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@ -75,6 +75,8 @@ cc_library(
srcs = ["mediapipe_builtin_op_resolver.cc"],
hdrs = ["mediapipe_builtin_op_resolver.h"],
deps = [
"//mediapipe/tasks/cc/text/language_detector/custom_ops:kmeans_embedding_lookup",
"//mediapipe/tasks/cc/text/language_detector/custom_ops:ngram_hash",
"//mediapipe/util/tflite/operations:landmarks_to_transform_matrix",
"//mediapipe/util/tflite/operations:max_pool_argmax",
"//mediapipe/util/tflite/operations:max_unpooling",

6
mediapipe/tasks/cc/core/mediapipe_builtin_op_resolver.cc
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@ -15,6 +15,8 @@ limitations under the License.
#include "mediapipe/tasks/cc/core/mediapipe_builtin_op_resolver.h"
#include "mediapipe/tasks/cc/text/language_detector/custom_ops/kmeans_embedding_lookup.h"
#include "mediapipe/tasks/cc/text/language_detector/custom_ops/ngram_hash.h"
#include "mediapipe/util/tflite/operations/landmarks_to_transform_matrix.h"
#include "mediapipe/util/tflite/operations/max_pool_argmax.h"
#include "mediapipe/util/tflite/operations/max_unpooling.h"
@ -43,6 +45,10 @@ MediaPipeBuiltinOpResolver::MediaPipeBuiltinOpResolver() {
"Landmarks2TransformMatrix",
mediapipe::tflite_operations::RegisterLandmarksToTransformMatrixV2(),
/*version=*/2);
// For the LanguageDetector model.
AddCustom("NGramHash", mediapipe::tflite_operations::Register_NGRAM_HASH());
AddCustom("KmeansEmbeddingLookup",
mediapipe::tflite_operations::Register_KmeansEmbeddingLookup());
}
} // namespace core
} // namespace tasks

38
mediapipe/tasks/cc/text/language_detector/BUILD Normal file
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@ -0,0 +1,38 @@
# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
#
# 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.
package(default_visibility = ["//mediapipe/tasks:internal"])
licenses(["notice"])
cc_library(
name = "language_detector",
srcs = ["language_detector.cc"],
hdrs = ["language_detector.h"],
visibility = ["//visibility:public"],
deps = [
"//mediapipe/framework/api2:builder",
"//mediapipe/tasks/cc/components/containers:category",
"//mediapipe/tasks/cc/components/containers:classification_result",
"//mediapipe/tasks/cc/components/processors:classifier_options",
"//mediapipe/tasks/cc/core:base_options",
"//mediapipe/tasks/cc/core:base_task_api",
"//mediapipe/tasks/cc/core:task_api_factory",
"//mediapipe/tasks/cc/text/text_classifier:text_classifier_graph",
"//mediapipe/tasks/cc/text/text_classifier/proto:text_classifier_graph_options_cc_proto",
"@com_google_absl//absl/status",
"@com_google_absl//absl/status:statusor",
"@com_google_absl//absl/strings",
],
)

8
mediapipe/tasks/cc/text/language_detector/custom_ops/kmeans_embedding_lookup.cc
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@ -23,7 +23,7 @@ limitations under the License.
#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
#include "tensorflow/lite/kernels/kernel_util.h"
namespace tflite::ops::custom {
namespace mediapipe::tflite_operations {
namespace kmeans_embedding_lookup_op {
namespace {
@ -33,6 +33,10 @@ constexpr int kEncodingTable = 1;
constexpr int kCodebook = 2;
constexpr int kOutputLabel = 0;
using ::tflite::GetInput;
using ::tflite::GetOutput;
using ::tflite::GetTensorData;
} // namespace
TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
@ -142,4 +146,4 @@ TfLiteRegistration* Register_KmeansEmbeddingLookup() {
return &r;
}
} // namespace tflite::ops::custom
} // namespace mediapipe::tflite_operations

4
mediapipe/tasks/cc/text/language_detector/custom_ops/kmeans_embedding_lookup.h
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@ -27,10 +27,10 @@ limitations under the License.
#include "tensorflow/lite/kernels/register.h"
namespace tflite::ops::custom {
namespace mediapipe::tflite_operations {
TfLiteRegistration* Register_KmeansEmbeddingLookup();
} // namespace tflite::ops::custom
} // namespace mediapipe::tflite_operations
#endif // MEDIAPIPE_TASKS_CC_TEXT_LANGUAGE_DETECTOR_CUSTOM_OPS_KMEANS_EMBEDDING_LOOKUP_H_

14
mediapipe/tasks/cc/text/language_detector/custom_ops/kmeans_embedding_lookup_test.cc
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@ -12,14 +12,14 @@
#include "tensorflow/lite/interpreter.h"
#include "tensorflow/lite/kernels/test_util.h"
namespace tflite::ops::custom {
namespace mediapipe::tflite_operations {
namespace {
using ::testing::ElementsAreArray;
using ::tflite::ArrayFloatNear;
// Helper class for testing the op.
class KmeansEmbeddingLookupModel : public SingleOpModel {
class KmeansEmbeddingLookupModel : public tflite::SingleOpModel {
public:
explicit KmeansEmbeddingLookupModel(
std::initializer_list<int> input_shape,
@ -27,7 +27,7 @@ class KmeansEmbeddingLookupModel : public SingleOpModel {
std::initializer_list<int> codebook_shape,
std::initializer_list<int> output_shape) {
// Setup the model inputs and the interpreter.
output_ = AddOutput({TensorType_FLOAT32, output_shape});
output_ = AddOutput({tflite::TensorType_FLOAT32, output_shape});
SetCustomOp("KmeansEmbeddingLookup", std::vector<uint8_t>(),
Register_KmeansEmbeddingLookup);
BuildInterpreter({input_shape, encoding_table_shape, codebook_shape});
@ -68,9 +68,9 @@ class KmeansEmbeddingLookupModel : public SingleOpModel {
std::vector<int> GetOutputShape() { return GetTensorShape(output_); }
private:
int input_ = AddInput(TensorType_INT32);
int encoding_table_ = AddInput(TensorType_UINT8);
int codebook_ = AddInput(TensorType_FLOAT32);
int input_ = AddInput(tflite::TensorType_INT32);
int encoding_table_ = AddInput(tflite::TensorType_UINT8);
int codebook_ = AddInput(tflite::TensorType_FLOAT32);
int output_;
};
@ -173,4 +173,4 @@ TEST(KmeansEmbeddingLookupTest, ThrowsErrorWhenGivenInvalidInputBatchSize) {
}
} // namespace
} // namespace tflite::ops::custom
} // namespace mediapipe::tflite_operations

16
mediapipe/tasks/cc/text/language_detector/custom_ops/ngram_hash.cc
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@ -25,7 +25,7 @@ limitations under the License.
#include "tensorflow/lite/kernels/kernel_util.h"
#include "tensorflow/lite/string_util.h"
namespace tflite::ops::custom {
namespace mediapipe::tflite_operations {
namespace ngram_op {
@ -217,21 +217,21 @@ void Free(TfLiteContext* context, void* buffer) {
}
TfLiteStatus Resize(TfLiteContext* context, TfLiteNode* node) {
TfLiteTensor* output = GetOutput(context, node, kOutputLabel);
TfLiteTensor* output = tflite::GetOutput(context, node, kOutputLabel);
TF_LITE_ENSURE(context, output != nullptr);
SetTensorToDynamic(output);
tflite::SetTensorToDynamic(output);
return kTfLiteOk;
}
TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
NGramHashParams* params = reinterpret_cast<NGramHashParams*>(node->user_data);
TF_LITE_ENSURE_OK(
context,
params->PreprocessInput(GetInput(context, node, kInputMessage), context));
context, params->PreprocessInput(
tflite::GetInput(context, node, kInputMessage), context));
TfLiteTensor* output = GetOutput(context, node, kOutputLabel);
TfLiteTensor* output = tflite::GetOutput(context, node, kOutputLabel);
TF_LITE_ENSURE(context, output != nullptr);
if (IsDynamicTensor(output)) {
if (tflite::IsDynamicTensor(output)) {
TfLiteIntArray* output_size = TfLiteIntArrayCreate(3);
output_size->data[0] = 1;
output_size->data[1] = params->GetNumNGrams();
@ -261,4 +261,4 @@ TfLiteRegistration* Register_NGRAM_HASH() {
return &r;
}
} // namespace tflite::ops::custom
} // namespace mediapipe::tflite_operations

4
mediapipe/tasks/cc/text/language_detector/custom_ops/ngram_hash.h
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@ -18,10 +18,10 @@ limitations under the License.
#include "tensorflow/lite/kernels/register.h"
namespace tflite::ops::custom {
namespace mediapipe::tflite_operations {
TfLiteRegistration* Register_NGRAM_HASH();
} // namespace tflite::ops::custom
} // namespace mediapipe::tflite_operations
#endif // MEDIAPIPE_TASKS_CC_TEXT_LANGUAGE_DETECTOR_CUSTOM_OPS_NGRAM_HASH_H_

12
mediapipe/tasks/cc/text/language_detector/custom_ops/ngram_hash_test.cc
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@ -32,7 +32,7 @@ limitations under the License.
#include "tensorflow/lite/model.h"
#include "tensorflow/lite/string_util.h"
namespace tflite::ops::custom {
namespace mediapipe::tflite_operations {
namespace {
using ::flexbuffers::Builder;
@ -42,7 +42,7 @@ using ::testing::ElementsAreArray;
using ::testing::Message;
// Helper class for testing the op.
class NGramHashModel : public SingleOpModel {
class NGramHashModel : public tflite::SingleOpModel {
public:
explicit NGramHashModel(const uint64_t seed,
const std::vector<int>& ngram_lengths,
@ -71,7 +71,7 @@ class NGramHashModel : public SingleOpModel {
}
fbb.EndMap(start);
fbb.Finish();
output_ = AddOutput({TensorType_INT32, {}});
output_ = AddOutput({tflite::TensorType_INT32, {}});
SetCustomOp("NGramHash", fbb.GetBuffer(), Register_NGRAM_HASH);
BuildInterpreter({GetShape(input_)});
}
@ -100,7 +100,7 @@ class NGramHashModel : public SingleOpModel {
std::vector<int> GetOutputShape() { return GetTensorShape(output_); }
private:
int input_ = AddInput(TensorType_STRING);
int input_ = AddInput(tflite::TensorType_STRING);
int output_;
};
@ -173,7 +173,7 @@ TEST(NGramHashTest, ReturnsExpectedValueWhenInputIsSane) {
NGramHashModel m(kSeed, ngram_lengths, vocab_sizes);
for (int test_idx = 0; test_idx < testcase_inputs.size(); test_idx++) {
const string& testcase_input = testcase_inputs[test_idx];
const std::string& testcase_input = testcase_inputs[test_idx];
m.Invoke(testcase_input);
SCOPED_TRACE(Message() << "Where the testcases' input is: "
<< testcase_input);
@ -310,4 +310,4 @@ TEST(NGramHashTest, MismatchNgramLengthsAndVocabSizes) {
}
} // namespace
} // namespace tflite::ops::custom
} // namespace mediapipe::tflite_operations

126
mediapipe/tasks/cc/text/language_detector/language_detector.cc Normal file
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@ -0,0 +1,126 @@
/* Copyright 2023 The MediaPipe Authors. All Rights Reserved.
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 "mediapipe/tasks/cc/text/language_detector/language_detector.h"
#include <memory>
#include <utility>
#include "absl/status/status.h"
#include "absl/status/statusor.h"
#include "mediapipe/framework/api2/builder.h"
#include "mediapipe/tasks/cc/components/containers/category.h"
#include "mediapipe/tasks/cc/components/containers/classification_result.h"
#include "mediapipe/tasks/cc/core/task_api_factory.h"
#include "mediapipe/tasks/cc/text/text_classifier/proto/text_classifier_graph_options.pb.h"
namespace mediapipe::tasks::text::language_detector {
namespace {
using ::mediapipe::tasks::components::containers::Category;
using ::mediapipe::tasks::components::containers::ClassificationResult;
using ::mediapipe::tasks::components::containers::Classifications;
using ::mediapipe::tasks::components::containers::ConvertToClassificationResult;
using ClassificationResultProto =
::mediapipe::tasks::components::containers::proto::ClassificationResult;
using ::mediapipe::tasks::text::text_classifier::proto::
TextClassifierGraphOptions;
constexpr char kTextStreamName[] = "text_in";
constexpr char kTextTag[] = "TEXT";
constexpr char kClassificationsStreamName[] = "classifications_out";
constexpr char kClassificationsTag[] = "CLASSIFICATIONS";
constexpr char kSubgraphTypeName[] =
"mediapipe.tasks.text.text_classifier.TextClassifierGraph";
// Creates a MediaPipe graph config that only contains a single subgraph node of
// type "TextClassifierGraph".
CalculatorGraphConfig CreateGraphConfig(
std::unique_ptr<TextClassifierGraphOptions> options) {
api2::builder::Graph graph;
auto& subgraph = graph.AddNode(kSubgraphTypeName);
subgraph.GetOptions<TextClassifierGraphOptions>().Swap(options.get());
graph.In(kTextTag).SetName(kTextStreamName) >> subgraph.In(kTextTag);
subgraph.Out(kClassificationsTag).SetName(kClassificationsStreamName) >>
graph.Out(kClassificationsTag);
return graph.GetConfig();
}
// Converts the user-facing LanguageDetectorOptions struct to the internal
// TextClassifierGraphOptions proto.
std::unique_ptr<TextClassifierGraphOptions>
ConvertLanguageDetectorOptionsToProto(LanguageDetectorOptions* options) {
auto options_proto = std::make_unique<TextClassifierGraphOptions>();
auto base_options_proto = std::make_unique<tasks::core::proto::BaseOptions>(
tasks::core::ConvertBaseOptionsToProto(&(options->base_options)));
options_proto->mutable_base_options()->Swap(base_options_proto.get());
auto classifier_options_proto =
std::make_unique<tasks::components::processors::proto::ClassifierOptions>(
components::processors::ConvertClassifierOptionsToProto(
&(options->classifier_options)));
options_proto->mutable_classifier_options()->Swap(
classifier_options_proto.get());
return options_proto;
}
absl::StatusOr<LanguageDetectorResult>
ExtractLanguageDetectorResultFromClassificationResult(
const ClassificationResult& classification_result) {
if (classification_result.classifications.size() != 1) {
return absl::InvalidArgumentError(
"The LanguageDetector TextClassifierGraph should have exactly one "
"classification head.");
}
const Classifications& languages_and_scores =
classification_result.classifications[0];
LanguageDetectorResult language_detector_result;
for (const Category& category : languages_and_scores.categories) {
if (!category.category_name.has_value()) {
return absl::InvalidArgumentError(
"LanguageDetector ClassificationResult has a missing language code.");
}
language_detector_result.push_back(
{.language_code = *category.category_name,
.probability = category.score});
}
return language_detector_result;
}
} // namespace
absl::StatusOr<std::unique_ptr<LanguageDetector>> LanguageDetector::Create(
std::unique_ptr<LanguageDetectorOptions> options) {
auto options_proto = ConvertLanguageDetectorOptionsToProto(options.get());
return core::TaskApiFactory::Create<LanguageDetector,
TextClassifierGraphOptions>(
CreateGraphConfig(std::move(options_proto)),
std::move(options->base_options.op_resolver));
}
absl::StatusOr<LanguageDetectorResult> LanguageDetector::Detect(
absl::string_view text) {
ASSIGN_OR_RETURN(
auto output_packets,
runner_->Process(
{{kTextStreamName, MakePacket<std::string>(std::string(text))}}));
ClassificationResult classification_result =
ConvertToClassificationResult(output_packets[kClassificationsStreamName]
.Get<ClassificationResultProto>());
return ExtractLanguageDetectorResultFromClassificationResult(
classification_result);
}
} // namespace mediapipe::tasks::text::language_detector

84
mediapipe/tasks/cc/text/language_detector/language_detector.h Normal file
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@ -0,0 +1,84 @@
/* Copyright 2023 The MediaPipe Authors. All Rights Reserved.
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.
==============================================================================*/
#ifndef MEDIAPIPE_TASKS_CC_TEXT_LANGUAGE_DETECTOR_LANGUAGE_DETECTOR_H_
#define MEDIAPIPE_TASKS_CC_TEXT_LANGUAGE_DETECTOR_LANGUAGE_DETECTOR_H_
#include <memory>
#include <string>
#include "absl/status/status.h"
#include "absl/status/statusor.h"
#include "absl/strings/string_view.h"
#include "mediapipe/tasks/cc/components/processors/classifier_options.h"
#include "mediapipe/tasks/cc/core/base_options.h"
#include "mediapipe/tasks/cc/core/base_task_api.h"
namespace mediapipe::tasks::text::language_detector {
// A language code and its probability.
struct LanguageDetectorPrediction {
// An i18n language / locale code, e.g. "en" for English, "uz" for Uzbek,
// "ja"-Latn for Japanese (romaji).
std::string language_code;
float probability;
};
// Task output.
using LanguageDetectorResult = std::vector<LanguageDetectorPrediction>;
// The options for configuring a MediaPipe LanguageDetector task.
struct LanguageDetectorOptions {
// Base options for configuring MediaPipe Tasks, such as specifying the model
// file with metadata, accelerator options, op resolver, etc.
tasks::core::BaseOptions base_options;
// Options for configuring the classifier behavior, such as score threshold,
// number of results, etc.
components::processors::ClassifierOptions classifier_options;
};
// Predicts the language of an input text.
//
// This API expects a TFLite model with TFLite Model Metadata that
// contains the mandatory (described below) input tensors, output tensor,
// and the language codes in an AssociatedFile.
//
// Input tensors:
// (kTfLiteString)
// - 1 input tensor that is scalar or has shape [1] containing the input
// string.
// Output tensor:
// (kTfLiteFloat32)
// - 1 output tensor of shape`[1 x N]` where `N` is the number of languages.
class LanguageDetector : core::BaseTaskApi {
public:
using BaseTaskApi::BaseTaskApi;
// Creates a LanguageDetector instance from the provided `options`.
static absl::StatusOr<std::unique_ptr<LanguageDetector>> Create(
std::unique_ptr<LanguageDetectorOptions> options);
// Predicts the language of the input `text`.
absl::StatusOr<LanguageDetectorResult> Detect(absl::string_view text);
// Shuts down the LanguageDetector instance when all the work is done.
absl::Status Close() { return runner_->Close(); }
};
} // namespace mediapipe::tasks::text::language_detector
#endif // MEDIAPIPE_TASKS_CC_TEXT_LANGUAGE_DETECTOR_LANGUAGE_DETECTOR_H_

163
mediapipe/tasks/cc/text/language_detector/language_detector_test.cc Normal file
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@ -0,0 +1,163 @@
/* Copyright 2022 The MediaPipe Authors. All Rights Reserved.
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 "mediapipe/tasks/cc/text/language_detector/language_detector.h"
#include <cmath>
#include <cstdlib>
#include <memory>
#include <string>
#include <utility>
#include "absl/flags/flag.h"
#include "absl/status/status.h"
#include "absl/strings/cord.h"
#include "absl/strings/str_cat.h"
#include "absl/strings/string_view.h"
#include "absl/strings/substitute.h"
#include "mediapipe/framework/deps/file_path.h"
#include "mediapipe/framework/port/gmock.h"
#include "mediapipe/framework/port/gtest.h"
#include "mediapipe/framework/port/status_matchers.h"
#include "mediapipe/tasks/cc/common.h"
#include "tensorflow/lite/core/shims/cc/shims_test_util.h"
namespace mediapipe::tasks::text::language_detector {
namespace {
using ::mediapipe::file::JoinPath;
using ::testing::HasSubstr;
using ::testing::Optional;
constexpr char kTestDataDirectory[] = "/mediapipe/tasks/testdata/text/";
constexpr char kInvalidModelPath[] = "i/do/not/exist.tflite";
constexpr char kLanguageDetector[] = "language_detector.tflite";
constexpr float kTolerance = 0.000001;
std::string GetFullPath(absl::string_view file_name) {
return JoinPath("./", kTestDataDirectory, file_name);
}
absl::Status MatchesLanguageDetectorResult(
const LanguageDetectorResult& expected,
const LanguageDetectorResult& actual, float tolerance) {
if (expected.size() != actual.size()) {
return absl::FailedPreconditionError(absl::Substitute(
"Expected $0 predictions, but got $1", expected.size(), actual.size()));
}
for (int i = 0; i < expected.size(); ++i) {
if (expected[i].language_code != actual[i].language_code) {
return absl::FailedPreconditionError(absl::Substitute(
"Expected prediction $0 to have language_code $1, but got $2", i,
expected[i].language_code, actual[i].language_code));
}
if (std::abs(expected[i].probability - actual[i].probability) > tolerance) {
return absl::FailedPreconditionError(absl::Substitute(
"Expected prediction $0 to have probability $1, but got $2", i,
expected[i].probability, actual[i].probability));
}
}
return absl::OkStatus();
}
} // namespace
class LanguageDetectorTest : public tflite_shims::testing::Test {};
TEST_F(LanguageDetectorTest, CreateFailsWithMissingModel) {
auto options = std::make_unique<LanguageDetectorOptions>();
options->base_options.model_asset_path = GetFullPath(kInvalidModelPath);
absl::StatusOr<std::unique_ptr<LanguageDetector>> language_detector =
LanguageDetector::Create(std::move(options));
EXPECT_EQ(language_detector.status().code(), absl::StatusCode::kNotFound);
EXPECT_THAT(language_detector.status().message(),
HasSubstr("Unable to open file at"));
EXPECT_THAT(language_detector.status().GetPayload(kMediaPipeTasksPayload),
Optional(absl::Cord(absl::StrCat(
MediaPipeTasksStatus::kRunnerInitializationError))));
}
TEST_F(LanguageDetectorTest, TestL2CModel) {
auto options = std::make_unique<LanguageDetectorOptions>();
options->base_options.model_asset_path = GetFullPath(kLanguageDetector);
options->classifier_options.score_threshold = 0.3;
MP_ASSERT_OK_AND_ASSIGN(std::unique_ptr<LanguageDetector> language_detector,
LanguageDetector::Create(std::move(options)));
MP_ASSERT_OK_AND_ASSIGN(
LanguageDetectorResult result_en,
language_detector->Detect("To be, or not to be, that is the question"));
MP_EXPECT_OK(MatchesLanguageDetectorResult(
{{.language_code = "en", .probability = 0.999856}}, result_en,
kTolerance));
MP_ASSERT_OK_AND_ASSIGN(
LanguageDetectorResult result_fr,
language_detector->Detect(
"Il y a beaucoup de bouches qui parlent et fort peu "
"de têtes qui pensent."));
MP_EXPECT_OK(MatchesLanguageDetectorResult(
{{.language_code = "fr", .probability = 0.999781}}, result_fr,
kTolerance));
MP_ASSERT_OK_AND_ASSIGN(
LanguageDetectorResult result_ru,
language_detector->Detect("это какой-то английский язык"));
MP_EXPECT_OK(MatchesLanguageDetectorResult(
{{.language_code = "ru", .probability = 0.993362}}, result_ru,
kTolerance));
}
TEST_F(LanguageDetectorTest, TestMultiplePredictions) {
auto options = std::make_unique<LanguageDetectorOptions>();
options->base_options.model_asset_path = GetFullPath(kLanguageDetector);
options->classifier_options.score_threshold = 0.3;
MP_ASSERT_OK_AND_ASSIGN(std::unique_ptr<LanguageDetector> language_detector,
LanguageDetector::Create(std::move(options)));
MP_ASSERT_OK_AND_ASSIGN(LanguageDetectorResult result_mixed,
language_detector->Detect("分久必合合久必分"));
MP_EXPECT_OK(MatchesLanguageDetectorResult(
{{.language_code = "zh", .probability = 0.505424},
{.language_code = "ja", .probability = 0.481617}},
result_mixed, kTolerance));
}
TEST_F(LanguageDetectorTest, TestAllowList) {
auto options = std::make_unique<LanguageDetectorOptions>();
options->base_options.model_asset_path = GetFullPath(kLanguageDetector);
options->classifier_options.category_allowlist = {"ja"};
MP_ASSERT_OK_AND_ASSIGN(std::unique_ptr<LanguageDetector> language_detector,
LanguageDetector::Create(std::move(options)));
MP_ASSERT_OK_AND_ASSIGN(LanguageDetectorResult result_ja,
language_detector->Detect("分久必合合久必分"));
MP_EXPECT_OK(MatchesLanguageDetectorResult(
{{.language_code = "ja", .probability = 0.481617}}, result_ja,
kTolerance));
}
TEST_F(LanguageDetectorTest, TestDenyList) {
auto options = std::make_unique<LanguageDetectorOptions>();
options->base_options.model_asset_path = GetFullPath(kLanguageDetector);
options->classifier_options.score_threshold = 0.3;
options->classifier_options.category_denylist = {"ja"};
MP_ASSERT_OK_AND_ASSIGN(std::unique_ptr<LanguageDetector> language_detector,
LanguageDetector::Create(std::move(options)));
MP_ASSERT_OK_AND_ASSIGN(LanguageDetectorResult result_zh,
language_detector->Detect("分久必合合久必分"));
MP_EXPECT_OK(MatchesLanguageDetectorResult(
{{.language_code = "zh", .probability = 0.505424}}, result_zh,
kTolerance));
}
} // namespace mediapipe::tasks::text::language_detector