Add CPU tests for TensorsToSegmentationCalculator
PiperOrigin-RevId: 576208735
This commit is contained in:
Youchuan Hu
Copybara-Service
parent
5b0f1f9ac4
commit
905a18c88c
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@ -62,12 +62,14 @@ struct FormattingTestCase {
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Options::Activation activation;
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int rows;
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int cols;
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int rows_new;
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int cols_new;
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int channels;
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double max_abs_diff;
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};
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using TensorsToSegmentationCalculatorTest = TestWithParam<FormattingTestCase>;
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// Currently only useable for tests with no output resize.
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TEST_P(TensorsToSegmentationCalculatorTest, ParameterizedTests) {
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const FormattingTestCase& test_case = GetParam();
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std::vector<float> inputs = test_case.inputs;
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@ -75,7 +77,10 @@ TEST_P(TensorsToSegmentationCalculatorTest, ParameterizedTests) {
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Options::Activation activation = test_case.activation;
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int rows = test_case.rows;
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int cols = test_case.cols;
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int rows_new = test_case.rows_new;
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int cols_new = test_case.cols_new;
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int channels = test_case.channels;
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double max_abs_diff = test_case.max_abs_diff;
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std::string string_config = absl::Substitute(
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R"pb(
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@ -119,28 +124,31 @@ TEST_P(TensorsToSegmentationCalculatorTest, ParameterizedTests) {
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MP_ASSERT_OK(graph.AddPacketToInputStream(
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"tensors", mediapipe::Adopt(tensors.release()).At(Timestamp(0))));
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}
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// The output size is defined as pair(new_width, new_height).
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MP_ASSERT_OK(graph.AddPacketToInputStream(
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"size",
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mediapipe::Adopt(new std::pair<int, int>(rows, cols)).At(Timestamp(0))));
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"size", mediapipe::Adopt(new std::pair<int, int>(cols_new, rows_new))
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.At(Timestamp(0))));
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MP_ASSERT_OK(graph.WaitUntilIdle());
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ASSERT_THAT(output_packets, SizeIs(1));
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const Image& image_as_mask = output_packets[0].Get<Image>();
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std::shared_ptr<cv::Mat> result_mat = formats::MatView(&image_as_mask);
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EXPECT_EQ(result_mat->rows, rows);
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EXPECT_EQ(result_mat->cols, cols);
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EXPECT_EQ(result_mat->channels(), channels);
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EXPECT_EQ(result_mat->rows, rows_new);
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EXPECT_EQ(result_mat->cols, cols_new);
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EXPECT_EQ(result_mat->channels(), 1);
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// Compare the real result with the expected result.
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cv::Mat expected_result = cv::Mat(
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rows, cols, CV_32FC1, const_cast<float*>(expected_outputs.data()));
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cv::Mat expected_result =
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cv::Mat(rows_new, cols_new, CV_32FC1,
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const_cast<float*>(expected_outputs.data()));
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cv::Mat diff;
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cv::absdiff(*result_mat, expected_result, diff);
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double max_val;
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cv::minMaxLoc(diff, nullptr, &max_val);
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// Expects the maximum absolute pixel-by-pixel difference is less than 1e-5.
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// This delta is for passthorugh accuracy only.
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EXPECT_LE(max_val, 1e-5);
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// The max allowable diff between output and expected output varies between
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// tests.
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EXPECT_LE(max_val, max_abs_diff);
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MP_ASSERT_OK(graph.CloseInputStream("tensors"));
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MP_ASSERT_OK(graph.CloseInputStream("size"));
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@ -149,19 +157,104 @@ TEST_P(TensorsToSegmentationCalculatorTest, ParameterizedTests) {
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INSTANTIATE_TEST_SUITE_P(
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TensorsToSegmentationCalculatorTests, TensorsToSegmentationCalculatorTest,
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testing::ValuesIn<FormattingTestCase>({
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{/*test_name=*/"NoActivationAndNoOutputResize",
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/*inputs=*/
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{1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0,
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14.0, 15.0, 16.0},
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/*expected_outputs=*/
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{1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0,
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14.0, 15.0, 16.0},
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/*activation=*/Options::NONE,
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/*rows=*/4,
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/*cols=*/4,
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/*channels=*/1},
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}),
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testing::ValuesIn<FormattingTestCase>(
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{{/*test_name=*/"NoActivationAndNoOutputResize",
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/*inputs=*/
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{1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0,
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14.0, 15.0, 16.0},
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/*expected_outputs=*/
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{1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0,
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14.0, 15.0, 16.0},
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/*activation=*/Options::NONE,
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/*rows=*/4,
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/*cols=*/4,
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/*rows_new=*/4,
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/*cols_new=*/4,
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/*channels=*/1,
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/*max_abs_diff=*/1e-7},
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{/*test_name=*/"OutputResizeOnly",
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/*inputs=*/
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{1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0,
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14.0, 15.0, 16.0},
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/*expected_outputs=*/
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{1, 1.5, 2.166667, 2.833333, 3.5, 4,
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3.8, 4.3, 4.966667, 5.633333, 6.3, 6.8,
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7, 7.5, 8.166667, 8.833333, 9.5, 10,
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10.2, 10.7, 11.366667, 12.033333, 12.7, 13.2,
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13, 13.5, 14.166667, 14.833333, 15.5, 16},
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/*activation=*/Options::NONE,
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/*rows=*/4,
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/*cols=*/4,
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/*rows_new=*/5,
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/*cols_new=*/6,
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/*channels=*/1,
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/*max_abs_diff=*/1e-6},
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{/*test_name=*/"SigmoidActivationWithNoOutputResize",
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/*inputs=*/
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{1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0,
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14.0, 15.0, 16.0},
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/*expected_outputs=*/
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{0.731059, 0.880797, 0.952574, 0.982014, 0.993307, 0.997527, 0.999089,
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0.999665, 0.999877, 0.999955, 0.999983, 0.999994, 0.999998, 0.999999,
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1.0, 1.0},
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/*activation=*/Options::SIGMOID,
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/*rows=*/4,
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/*cols=*/4,
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/*rows_new=*/4,
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/*cols_new=*/4,
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/*channels=*/1,
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/*max_abs_diff=*/1e-6},
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{/*test_name=*/"SigmoidActivationWithOutputResize",
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/*inputs=*/
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{1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0,
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14.0, 15.0, 16.0},
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/*expected_outputs=*/
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{0.731059, 0.805928, 0.89276, 0.940611, 0.967294, 0.982014,
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0.914633, 0.93857, 0.966279, 0.981363, 0.989752, 0.994369,
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0.996592, 0.997666, 0.998873, 0.999404, 0.999683, 0.999829,
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0.999913, 0.99994, 0.999971, 0.999985, 0.999992, 0.999996,
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0.999998, 0.999998, 0.999999, 1.0, 1.0, 1.0},
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/*activation=*/Options::SIGMOID,
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/*rows=*/4,
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/*cols=*/4,
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/*rows_new=*/5,
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/*cols_new=*/6,
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/*channels=*/1,
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/*max_abs_diff=*/1e-6},
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{/*test_name=*/"SoftmaxActivationWithNoOutputResize",
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/*inputs=*/
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{1.0, 2.0, 4.0, 2.0, 3.0, 5.0, 6.0, 1.5, 7.0, 10.0, 11.0,
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4.0, 12.0, 15.0, 16.0, 18.5, 19.0, 20.0, 22.0, 23.0, 24.5, 23.4,
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25.6, 28.3, 29.2, 30.0, 24.6, 29.2, 30.0, 24.9, 31.2, 30.3},
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/*expected_outputs=*/
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{0.731059, 0.119203, 0.880797, 0.0109869, 0.952574, 0.000911051,
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0.952574, 0.924142, 0.731059, 0.731059, 0.24974, 0.937027, 0.689974,
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0.990048, 0.0060598, 0.28905},
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/*activation=*/Options::SOFTMAX,
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/*rows=*/4,
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/*cols=*/4,
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/*rows_new=*/4,
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/*cols_new=*/4,
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/*channels=*/2,
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/*max_abs_diff=*/1e-6},
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{/*test_name=*/"SoftmaxActivationWithOutputResize",
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/*inputs=*/
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{1.0, 2.0, 4.0, 2.0, 3.0, 5.0, 6.0, 1.5, 7.0, 10.0, 11.0,
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4.0, 12.0, 15.0, 16.0, 18.5, 19.0, 20.0, 22.0, 23.0, 24.5, 23.4,
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25.6, 28.3, 29.2, 30.0, 24.6, 29.2, 30.0, 24.9, 31.2, 30.3},
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/*expected_outputs=*/
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{0.731059, 0.425131, 0.246135, 0.753865, 0.445892, 0.0109869,
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0.886119, 0.461259, 0.185506, 0.781934, 0.790618, 0.650195,
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0.841816, 0.603901, 0.40518, 0.561962, 0.765871, 0.930584,
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0.718733, 0.763744, 0.703402, 0.281989, 0.459635, 0.742634,
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0.689974, 0.840011, 0.82605, 0.170058, 0.147555, 0.28905},
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/*activation=*/Options::SOFTMAX,
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/*rows=*/4,
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/*cols=*/4,
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/*rows_new=*/5,
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/*cols_new=*/6,
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/*channels=*/2,
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/*max_abs_diff=*/1e-6}}),
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[](const testing::TestParamInfo<
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TensorsToSegmentationCalculatorTest::ParamType>& info) {
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return info.param.test_name;
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