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Diffstat (limited to 'tensorflow/core/kernels/colorspace_op_test.cc')
-rw-r--r--tensorflow/core/kernels/colorspace_op_test.cc384
1 files changed, 221 insertions, 163 deletions
diff --git a/tensorflow/core/kernels/colorspace_op_test.cc b/tensorflow/core/kernels/colorspace_op_test.cc
index 5130a5d398..4719a59b63 100644
--- a/tensorflow/core/kernels/colorspace_op_test.cc
+++ b/tensorflow/core/kernels/colorspace_op_test.cc
@@ -29,183 +29,241 @@ limitations under the License.
namespace tensorflow {
+template <typename T>
class RGBToHSVOpTest : public OpsTestBase {
protected:
- RGBToHSVOpTest() {
+ void MakeOp(DataType data_type) {
TF_EXPECT_OK(NodeDefBuilder("rgb_to_hsv_op", "RGBToHSV")
- .Input(FakeInput(DT_FLOAT))
+ .Input(FakeInput(data_type))
.Finalize(node_def()));
TF_EXPECT_OK(InitOp());
}
-};
-TEST_F(RGBToHSVOpTest, CheckBlack) {
- // Black pixel should map to hsv = [0,0,0]
- AddInputFromArray<float>(TensorShape({3}), {0, 0, 0});
- TF_ASSERT_OK(RunOpKernel());
-
- Tensor expected(allocator(), DT_FLOAT, TensorShape({3}));
- test::FillValues<float>(&expected, {0.0, 0.0, 0.0});
- test::ExpectTensorEqual<float>(expected, *GetOutput(0));
-}
-
-TEST_F(RGBToHSVOpTest, CheckGray) {
- // Gray pixel should have hue = saturation = 0.0, value = r/255
- AddInputFromArray<float>(TensorShape({3}), {.5, .5, .5});
- TF_ASSERT_OK(RunOpKernel());
-
- Tensor expected(allocator(), DT_FLOAT, TensorShape({3}));
- test::FillValues<float>(&expected, {0.0, 0.0, .5});
- test::ExpectTensorEqual<float>(expected, *GetOutput(0));
-}
-
-TEST_F(RGBToHSVOpTest, CheckWhite) {
- // Gray pixel should have hue = saturation = 0.0, value = 1.0
- AddInputFromArray<float>(TensorShape({3}), {1, 1, 1});
- TF_ASSERT_OK(RunOpKernel());
-
- Tensor expected(allocator(), DT_FLOAT, TensorShape({3}));
- test::FillValues<float>(&expected, {0.0, 0.0, 1.0});
- test::ExpectTensorEqual<float>(expected, *GetOutput(0));
-}
-
-TEST_F(RGBToHSVOpTest, CheckRedMax) {
- // Test case where red channel dominates
- AddInputFromArray<float>(TensorShape({3}), {.8, .4, .2});
- TF_ASSERT_OK(RunOpKernel());
-
- float expected_h = 1. / 6. * .2 / .6;
- float expected_s = .6 / .8;
- float expected_v = .8 / 1.;
-
- Tensor expected(allocator(), DT_FLOAT, TensorShape({3}));
- test::FillValues<float>(&expected, {expected_h, expected_s, expected_v});
- test::ExpectTensorNear<float>(expected, *GetOutput(0), 1e-6);
-}
-
-TEST_F(RGBToHSVOpTest, CheckGreenMax) {
- // Test case where green channel dominates
- AddInputFromArray<float>(TensorShape({3}), {.2, .8, .4});
- TF_ASSERT_OK(RunOpKernel());
-
- float expected_h = 1. / 6. * (2.0 + (.2 / .6));
- float expected_s = .6 / .8;
- float expected_v = .8 / 1.;
-
- Tensor expected(allocator(), DT_FLOAT, TensorShape({3}));
- test::FillValues<float>(&expected, {expected_h, expected_s, expected_v});
- test::ExpectTensorNear<float>(expected, *GetOutput(0), 1e-6);
-}
-
-TEST_F(RGBToHSVOpTest, CheckBlueMax) {
- // Test case where blue channel dominates
- AddInputFromArray<float>(TensorShape({3}), {.4, .2, .8});
- TF_ASSERT_OK(RunOpKernel());
-
- float expected_h = 1. / 6. * (4.0 + (.2 / .6));
- float expected_s = .6 / .8;
- float expected_v = .8 / 1.;
-
- Tensor expected(allocator(), DT_FLOAT, TensorShape({3}));
- test::FillValues<float>(&expected, {expected_h, expected_s, expected_v});
- test::ExpectTensorNear<float>(expected, *GetOutput(0), 1e-6);
-}
-
-TEST_F(RGBToHSVOpTest, CheckNegativeDifference) {
- AddInputFromArray<float>(TensorShape({3}), {0, .1, .2});
- TF_ASSERT_OK(RunOpKernel());
-
- float expected_h = 1. / 6. * (4.0 + (-.1 / .2));
- float expected_s = .2 / .2;
- float expected_v = .2 / 1.;
-
- Tensor expected(allocator(), DT_FLOAT, TensorShape({3}));
- test::FillValues<float>(&expected, {expected_h, expected_s, expected_v});
- test::ExpectTensorNear<float>(expected, *GetOutput(0), 1e-6);
-}
+ void CheckBlack(DataType data_type) {
+ // Black pixel should map to hsv = [0,0,0]
+ AddInputFromArray<T>(TensorShape({3}), {0, 0, 0});
+ TF_ASSERT_OK(RunOpKernel());
+
+ Tensor expected(allocator(), data_type, TensorShape({3}));
+ test::FillValues<T>(&expected, {0.0, 0.0, 0.0});
+ test::ExpectTensorEqual<T>(expected, *GetOutput(0));
+ }
+
+ void CheckGray(DataType data_type) {
+ // Gray pixel should have hue = saturation = 0.0, value = r/255
+ AddInputFromArray<T>(TensorShape({3}), {.5, .5, .5});
+ TF_ASSERT_OK(RunOpKernel());
+
+ Tensor expected(allocator(), data_type, TensorShape({3}));
+ test::FillValues<T>(&expected, {0.0, 0.0, .5});
+ test::ExpectTensorEqual<T>(expected, *GetOutput(0));
+ }
+
+ void CheckWhite(DataType data_type) {
+ // Gray pixel should have hue = saturation = 0.0, value = 1.0
+ AddInputFromArray<T>(TensorShape({3}), {1, 1, 1});
+ TF_ASSERT_OK(RunOpKernel());
+
+ Tensor expected(allocator(), data_type, TensorShape({3}));
+ test::FillValues<T>(&expected, {0.0, 0.0, 1.0});
+ test::ExpectTensorEqual<T>(expected, *GetOutput(0));
+ }
+
+ void CheckRedMax(DataType data_type) {
+ // Test case where red channel dominates
+ AddInputFromArray<T>(TensorShape({3}), {.8, .4, .2});
+ TF_ASSERT_OK(RunOpKernel());
+
+ T expected_h = 1. / 6. * .2 / .6;
+ T expected_s = .6 / .8;
+ T expected_v = .8 / 1.;
+
+ Tensor expected(allocator(), data_type, TensorShape({3}));
+ test::FillValues<T>(&expected, {expected_h, expected_s, expected_v});
+ test::ExpectTensorNear<T>(expected, *GetOutput(0), 1e-6);
+ }
+
+ void CheckGreenMax(DataType data_type) {
+ // Test case where green channel dominates
+ AddInputFromArray<T>(TensorShape({3}), {.2, .8, .4});
+ TF_ASSERT_OK(RunOpKernel());
+ T expected_h = 1. / 6. * (2.0 + (.2 / .6));
+ T expected_s = .6 / .8;
+ T expected_v = .8 / 1.;
+
+ Tensor expected(allocator(), data_type, TensorShape({3}));
+ test::FillValues<T>(&expected, {expected_h, expected_s, expected_v});
+ test::ExpectTensorNear<T>(expected, *GetOutput(0), 1e-6);
+ }
+
+ void CheckBlueMax(DataType data_type) {
+ // Test case where blue channel dominates
+ AddInputFromArray<T>(TensorShape({3}), {.4, .2, .8});
+ TF_ASSERT_OK(RunOpKernel());
+
+ T expected_h = 1. / 6. * (4.0 + (.2 / .6));
+ T expected_s = .6 / .8;
+ T expected_v = .8 / 1.;
+
+ Tensor expected(allocator(), data_type, TensorShape({3}));
+ test::FillValues<T>(&expected, {expected_h, expected_s, expected_v});
+ test::ExpectTensorNear<T>(expected, *GetOutput(0), 1e-6);
+ }
+
+ void CheckNegativeDifference(DataType data_type) {
+ AddInputFromArray<T>(TensorShape({3}), {0, .1, .2});
+ TF_ASSERT_OK(RunOpKernel());
+
+ T expected_h = 1. / 6. * (4.0 + (-.1 / .2));
+ T expected_s = .2 / .2;
+ T expected_v = .2 / 1.;
+
+ Tensor expected(allocator(), data_type, TensorShape({3}));
+ test::FillValues<T>(&expected, {expected_h, expected_s, expected_v});
+ test::ExpectTensorNear<T>(expected, *GetOutput(0), 1e-6);
+ }
+};
+
+template <typename T>
class HSVToRGBOpTest : public OpsTestBase {
protected:
- HSVToRGBOpTest() {
+ void MakeOp(DataType data_type) {
TF_EXPECT_OK(NodeDefBuilder("hsv_to_rgb_op", "HSVToRGB")
- .Input(FakeInput(DT_FLOAT))
+ .Input(FakeInput(data_type))
.Finalize(node_def()));
TF_EXPECT_OK(InitOp());
}
+
+ void CheckBlack(DataType data_type) {
+ // Black pixel should map to rgb = [0,0,0]
+ AddInputFromArray<T>(TensorShape({3}), {0.0, 0.0, 0.0});
+ TF_ASSERT_OK(RunOpKernel());
+
+ Tensor expected(allocator(), data_type, TensorShape({3}));
+ test::FillValues<T>(&expected, {0, 0, 0});
+ test::ExpectTensorEqual<T>(expected, *GetOutput(0));
+ }
+
+ void CheckGray(DataType data_type) {
+ // Gray pixel should have hue = saturation = 0.0, value = r/255
+ AddInputFromArray<T>(TensorShape({3}), {0.0, 0.0, .5});
+ TF_ASSERT_OK(RunOpKernel());
+
+ Tensor expected(allocator(), data_type, TensorShape({3}));
+ test::FillValues<T>(&expected, {.5, .5, .5});
+ test::ExpectTensorEqual<T>(expected, *GetOutput(0));
+ }
+
+ void CheckWhite(DataType data_type) {
+ // Gray pixel should have hue = saturation = 0.0, value = 1.0
+ AddInputFromArray<T>(TensorShape({3}), {0.0, 0.0, 1.0});
+ TF_ASSERT_OK(RunOpKernel());
+
+ Tensor expected(allocator(), data_type, TensorShape({3}));
+ test::FillValues<T>(&expected, {1, 1, 1});
+ test::ExpectTensorEqual<T>(expected, *GetOutput(0));
+ }
+
+ void CheckRedMax(DataType data_type) {
+ // Test case where red channel dominates
+ T expected_h = 1. / 6. * .2 / .6;
+ T expected_s = .6 / .8;
+ T expected_v = .8 / 1.;
+
+ AddInputFromArray<T>(TensorShape({3}),
+ {expected_h, expected_s, expected_v});
+ TF_ASSERT_OK(RunOpKernel());
+
+ Tensor expected(allocator(), data_type, TensorShape({3}));
+ test::FillValues<T>(&expected, {.8, .4, .2});
+ test::ExpectTensorNear<T>(expected, *GetOutput(0), 1e-6);
+ }
+
+ void CheckGreenMax(DataType data_type) {
+ // Test case where green channel dominates
+ T expected_h = 1. / 6. * (2.0 + (.2 / .6));
+ T expected_s = .6 / .8;
+ T expected_v = .8 / 1.;
+
+ AddInputFromArray<T>(TensorShape({3}),
+ {expected_h, expected_s, expected_v});
+ TF_ASSERT_OK(RunOpKernel());
+
+ Tensor expected(allocator(), data_type, TensorShape({3}));
+ test::FillValues<T>(&expected, {.2, .8, .4});
+ test::ExpectTensorNear<T>(expected, *GetOutput(0), 1e-6);
+ }
+
+ void CheckBlueMax(DataType data_type) {
+ // Test case where blue channel dominates
+ T expected_h = 1. / 6. * (4.0 + (.2 / .6));
+ T expected_s = .6 / .8;
+ T expected_v = .8 / 1.0;
+
+ AddInputFromArray<T>(TensorShape({3}),
+ {expected_h, expected_s, expected_v});
+ TF_ASSERT_OK(RunOpKernel());
+
+ Tensor expected(allocator(), data_type, TensorShape({3}));
+ test::FillValues<T>(&expected, {.4, .2, .8});
+ test::ExpectTensorNear<T>(expected, *GetOutput(0), 1e-6);
+ }
+
+ void CheckNegativeDifference(DataType data_type) {
+ T expected_h = 1. / 6. * (4.0 + (-.1 / .2));
+ T expected_s = .2 / .2;
+ T expected_v = .2 / 1.;
+
+ AddInputFromArray<T>(TensorShape({3}),
+ {expected_h, expected_s, expected_v});
+ TF_ASSERT_OK(RunOpKernel());
+
+ Tensor expected(allocator(), data_type, TensorShape({3}));
+ test::FillValues<T>(&expected, {0, .1, .2});
+ test::ExpectTensorNear<T>(expected, *GetOutput(0), 1e-6);
+ }
};
-TEST_F(HSVToRGBOpTest, CheckBlack) {
- // Black pixel should map to rgb = [0,0,0]
- AddInputFromArray<float>(TensorShape({3}), {0.0, 0.0, 0.0});
- TF_ASSERT_OK(RunOpKernel());
-
- Tensor expected(allocator(), DT_FLOAT, TensorShape({3}));
- test::FillValues<float>(&expected, {0, 0, 0});
- test::ExpectTensorEqual<float>(expected, *GetOutput(0));
-}
-
-TEST_F(HSVToRGBOpTest, CheckGray) {
- // Gray pixel should have hue = saturation = 0.0, value = r/255
- AddInputFromArray<float>(TensorShape({3}), {0.0, 0.0, .5});
- TF_ASSERT_OK(RunOpKernel());
-
- Tensor expected(allocator(), DT_FLOAT, TensorShape({3}));
- test::FillValues<float>(&expected, {.5, .5, .5});
- test::ExpectTensorEqual<float>(expected, *GetOutput(0));
-}
-
-TEST_F(HSVToRGBOpTest, CheckWhite) {
- // Gray pixel should have hue = saturation = 0.0, value = 1.0
- AddInputFromArray<float>(TensorShape({3}), {0.0, 0.0, 1.0});
- TF_ASSERT_OK(RunOpKernel());
-
- Tensor expected(allocator(), DT_FLOAT, TensorShape({3}));
- test::FillValues<float>(&expected, {1, 1, 1});
- test::ExpectTensorEqual<float>(expected, *GetOutput(0));
-}
-
-TEST_F(HSVToRGBOpTest, CheckRedMax) {
- // Test case where red channel dominates
- float expected_h = 1. / 6. * .2 / .6;
- float expected_s = .6 / .8;
- float expected_v = .8 / 1.;
-
- AddInputFromArray<float>(TensorShape({3}),
- {expected_h, expected_s, expected_v});
- TF_ASSERT_OK(RunOpKernel());
-
- Tensor expected(allocator(), DT_FLOAT, TensorShape({3}));
- test::FillValues<float>(&expected, {.8, .4, .2});
- test::ExpectTensorNear<float>(expected, *GetOutput(0), 1e-6);
-}
-
-TEST_F(HSVToRGBOpTest, CheckGreenMax) {
- // Test case where green channel dominates
- float expected_h = 1. / 6. * (2.0 + (.2 / .6));
- float expected_s = .6 / .8;
- float expected_v = .8 / 1.;
-
- AddInputFromArray<float>(TensorShape({3}),
- {expected_h, expected_s, expected_v});
- TF_ASSERT_OK(RunOpKernel());
-
- Tensor expected(allocator(), DT_FLOAT, TensorShape({3}));
- test::FillValues<float>(&expected, {.2, .8, .4});
- test::ExpectTensorNear<float>(expected, *GetOutput(0), 1e-6);
-}
-
-TEST_F(HSVToRGBOpTest, CheckBlueMax) {
- // Test case where blue channel dominates
- float expected_h = 1. / 6. * (4.0 + (.2 / .6));
- float expected_s = .6 / .8;
- float expected_v = .8 / 1.0;
-
- AddInputFromArray<float>(TensorShape({3}),
- {expected_h, expected_s, expected_v});
- TF_ASSERT_OK(RunOpKernel());
-
- Tensor expected(allocator(), DT_FLOAT, TensorShape({3}));
- test::FillValues<float>(&expected, {.4, .2, .8});
- test::ExpectTensorNear<float>(expected, *GetOutput(0), 1e-6);
-}
+#define TEST_COLORSPACE(test, dt) \
+ TEST_F(test, CheckBlack) { \
+ MakeOp(dt); \
+ CheckBlack(dt); \
+ } \
+ TEST_F(test, CheckGray) { \
+ MakeOp(dt); \
+ CheckGray(dt); \
+ } \
+ TEST_F(test, CheckWhite) { \
+ MakeOp(dt); \
+ CheckWhite(dt); \
+ } \
+ TEST_F(test, CheckRedMax) { \
+ MakeOp(dt); \
+ CheckRedMax(dt); \
+ } \
+ TEST_F(test, CheckGreenMax) { \
+ MakeOp(dt); \
+ CheckGreenMax(dt); \
+ } \
+ TEST_F(test, CheckBlueMax) { \
+ MakeOp(dt); \
+ CheckBlueMax(dt); \
+ } \
+ TEST_F(test, CheckNegativeDifference) { \
+ MakeOp(dt); \
+ CheckNegativeDifference(dt); \
+ }
+
+typedef RGBToHSVOpTest<float> rgb_to_hsv_float;
+typedef RGBToHSVOpTest<double> rgb_to_hsv_double;
+
+TEST_COLORSPACE(rgb_to_hsv_float, DT_FLOAT);
+TEST_COLORSPACE(rgb_to_hsv_double, DT_DOUBLE);
+
+typedef HSVToRGBOpTest<float> hsv_to_rgb_float;
+typedef HSVToRGBOpTest<double> hsv_to_rgb_double;
+
+TEST_COLORSPACE(hsv_to_rgb_float, DT_FLOAT);
+TEST_COLORSPACE(hsv_to_rgb_double, DT_DOUBLE);
} // namespace tensorflow
generated by cgit v1.2.3 (git 2.39.1) at 2024-06-08 02:32:10 +0000