Make TextEmbedder and TextClassifier tests pass on Windows
PiperOrigin-RevId: 506421383
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@ -135,28 +135,46 @@ TEST_F(TextClassifierTest, TextClassifierWithBert) {
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options->base_options.model_asset_path = GetFullPath(kTestBertModelPath);
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MP_ASSERT_OK_AND_ASSIGN(std::unique_ptr<TextClassifier> classifier,
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TextClassifier::Create(std::move(options)));
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MP_ASSERT_OK_AND_ASSIGN(
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TextClassifierResult negative_result,
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classifier->Classify("unflinchingly bleak and desperate"));
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TextClassifierResult negative_expected;
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TextClassifierResult positive_expected;
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#ifdef _WIN32
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negative_expected.classifications.emplace_back(Classifications{
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/*categories=*/{
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{/*index=*/0, /*score=*/0.956124, /*category_name=*/"negative"},
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{/*index=*/1, /*score=*/0.043875, /*category_name=*/"positive"}},
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/*head_index=*/0,
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/*head_name=*/"probability"});
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positive_expected.classifications.emplace_back(Classifications{
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/*categories=*/{
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{/*index=*/1, /*score=*/0.999951, /*category_name=*/"positive"},
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{/*index=*/0, /*score=*/0.000048, /*category_name=*/"negative"}},
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/*head_index=*/0,
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/*head_name=*/"probability"});
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#else
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negative_expected.classifications.emplace_back(Classifications{
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/*categories=*/{
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{/*index=*/0, /*score=*/0.956317, /*category_name=*/"negative"},
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{/*index=*/1, /*score=*/0.043683, /*category_name=*/"positive"}},
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/*head_index=*/0,
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/*head_name=*/"probability"});
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ExpectApproximatelyEqual(negative_result, negative_expected);
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MP_ASSERT_OK_AND_ASSIGN(
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TextClassifierResult positive_result,
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classifier->Classify("it's a charming and often affecting journey"));
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TextClassifierResult positive_expected;
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positive_expected.classifications.emplace_back(Classifications{
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/*categories=*/{
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{/*index=*/1, /*score=*/0.999945, /*category_name=*/"positive"},
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{/*index=*/0, /*score=*/0.000056, /*category_name=*/"negative"}},
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/*head_index=*/0,
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/*head_name=*/"probability"});
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#endif // _WIN32
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MP_ASSERT_OK_AND_ASSIGN(
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TextClassifierResult negative_result,
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classifier->Classify("unflinchingly bleak and desperate"));
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ExpectApproximatelyEqual(negative_result, negative_expected);
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MP_ASSERT_OK_AND_ASSIGN(
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TextClassifierResult positive_result,
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classifier->Classify("it's a charming and often affecting journey"));
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ExpectApproximatelyEqual(positive_result, positive_expected);
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MP_ASSERT_OK(classifier->Close());
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@ -233,12 +251,17 @@ TEST_F(TextClassifierTest, BertLongPositive) {
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TextClassifierResult expected;
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std::vector<Category> categories;
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// Predicted scores are slightly different on Mac OS.
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// Predicted scores are slightly different across platforms.
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#ifdef __APPLE__
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categories.push_back(
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{/*index=*/1, /*score=*/0.974181, /*category_name=*/"positive"});
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categories.push_back(
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{/*index=*/0, /*score=*/0.025819, /*category_name=*/"negative"});
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#elif defined _WIN32
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categories.push_back(
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{/*index=*/1, /*score=*/0.976686, /*category_name=*/"positive"});
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categories.push_back(
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{/*index=*/0, /*score=*/0.023313, /*category_name=*/"negative"});
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#else
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categories.push_back(
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{/*index=*/1, /*score=*/0.985889, /*category_name=*/"positive"});
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@ -75,7 +75,11 @@ TEST_F(EmbedderTest, SucceedsWithMobileBert) {
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text_embedder->Embed("it's a charming and often affecting journey"));
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ASSERT_EQ(result0.embeddings.size(), 1);
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ASSERT_EQ(result0.embeddings[0].float_embedding.size(), 512);
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#ifdef _WIN32
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ASSERT_NEAR(result0.embeddings[0].float_embedding[0], 21.2148f, kEpsilon);
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#else
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ASSERT_NEAR(result0.embeddings[0].float_embedding[0], 19.9016f, kEpsilon);
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#endif // _WIN32
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MP_ASSERT_OK_AND_ASSIGN(
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auto result1, text_embedder->Embed("what a great and fantastic trip"));
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@ -87,7 +91,11 @@ TEST_F(EmbedderTest, SucceedsWithMobileBert) {
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MP_ASSERT_OK_AND_ASSIGN(
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double similarity, TextEmbedder::CosineSimilarity(result0.embeddings[0],
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result1.embeddings[0]));
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#ifdef _WIN32
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EXPECT_NEAR(similarity, 0.971417, kSimilarityTolerancy);
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#else
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EXPECT_NEAR(similarity, 0.969514, kSimilarityTolerancy);
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#endif // _WIN32
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MP_ASSERT_OK(text_embedder->Close());
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}
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@ -160,8 +168,12 @@ TEST_F(EmbedderTest, SucceedsWithMobileBertAndDifferentThemes) {
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MP_ASSERT_OK_AND_ASSIGN(
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double similarity, TextEmbedder::CosineSimilarity(result0.embeddings[0],
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result1.embeddings[0]));
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// TODO: The similarity should likely be lower
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// TODO: These similarity should likely be lower
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#ifdef _WIN32
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EXPECT_NEAR(similarity, 0.98152, kSimilarityTolerancy);
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#else
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EXPECT_NEAR(similarity, 0.98088, kSimilarityTolerancy);
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#endif // _WIN32
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MP_ASSERT_OK(text_embedder->Close());
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}
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