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Open-sources a unit test.

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PiperOrigin-RevId: 493184055
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MediaPipe Team 2022-12-05 20:07:29 -08:00 committed by Copybara-Service
parent 3174b20fbe
commit af43687f2e
1 changed files with 42 additions and 9 deletions
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51
mediapipe/tasks/cc/text/text_classifier/text_classifier_test.cc
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@ -38,10 +38,7 @@ limitations under the License.
#include "mediapipe/tasks/cc/text/text_classifier/text_classifier_test_utils.h"
#include "tensorflow/lite/core/shims/cc/shims_test_util.h"
namespace mediapipe {
namespace tasks {
namespace text {
namespace text_classifier {
namespace mediapipe::tasks::text::text_classifier {
namespace {
using ::mediapipe::file::JoinPath;
@ -88,6 +85,8 @@ void ExpectApproximatelyEqual(const TextClassifierResult& actual,
}
}
} // namespace
class TextClassifierTest : public tflite_shims::testing::Test {};
TEST_F(TextClassifierTest, CreateSucceedsWithBertModel) {
@ -217,8 +216,42 @@ TEST_F(TextClassifierTest, TextClassifierWithStringToBool) {
MP_ASSERT_OK(classifier->Close());
}
} // namespace
} // namespace text_classifier
} // namespace text
} // namespace tasks
} // namespace mediapipe
TEST_F(TextClassifierTest, BertLongPositive) {
std::stringstream ss_for_positive_review;
ss_for_positive_review
<< "it's a charming and often affecting journey and this is a long";
for (int i = 0; i < kMaxSeqLen; ++i) {
ss_for_positive_review << " long";
}
ss_for_positive_review << " movie review";
auto options = std::make_unique<TextClassifierOptions>();
options->base_options.model_asset_path = GetFullPath(kTestBertModelPath);
MP_ASSERT_OK_AND_ASSIGN(std::unique_ptr<TextClassifier> classifier,
TextClassifier::Create(std::move(options)));
MP_ASSERT_OK_AND_ASSIGN(TextClassifierResult result,
classifier->Classify(ss_for_positive_review.str()));
TextClassifierResult expected;
std::vector<Category> categories;
// Predicted scores are slightly different on Mac OS.
#ifdef __APPLE__
categories.push_back(
{/*index=*/1, /*score=*/0.974181, /*category_name=*/"positive"});
categories.push_back(
{/*index=*/0, /*score=*/0.025819, /*category_name=*/"negative"});
#else
categories.push_back(
{/*index=*/1, /*score=*/0.985889, /*category_name=*/"positive"});
categories.push_back(
{/*index=*/0, /*score=*/0.014112, /*category_name=*/"negative"});
#endif // __APPLE__
expected.classifications.emplace_back(
Classifications{/*categories=*/categories,
/*head_index=*/0,
/*head_name=*/"probability"});
ExpectApproximatelyEqual(result, expected);
MP_ASSERT_OK(classifier->Close());
}
} // namespace mediapipe::tasks::text::text_classifier