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MeanStddevNormalization tests: have input values that are representable in FP16.

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100.01 rounds to 100, so the test can "fail" in precision even before calling the tested function.

PiperOrigin-RevId: 327647552
Change-Id: I31b5a19c584f710d7ce646689dabb497da81232d
This commit is contained in:
Robert David 2020-08-20 10:07:52 -07:00 committed by TensorFlower Gardener
parent f847090e28
commit b954e55670
1 changed files with 16 additions and 13 deletions
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29
tensorflow/lite/delegates/gpu/cl/kernels/mean_stddev_normalization_test.cc
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@ -54,7 +54,8 @@ TEST_P(MeanStddevNormalizationTest, SeparateBatches) {
op_def.src_tensors.push_back({data_type, storage, Layout::BHWC});
op_def.dst_tensors.push_back({data_type, storage, Layout::BHWC});
TensorFloat32 dst_tensor;
auto operation = CreateMeanStdDevNormalization(op_def);
auto operation =
CreateMeanStdDevNormalization(op_def, env_.GetDevicePtr()->info_);
ASSERT_OK(ExecuteGPUOperation({src_tensor}, creation_context_, &operation,
BHWC(1, 1, 1, 4), &dst_tensor));
@ -72,6 +73,7 @@ TEST_P(MeanStddevNormalizationTest, SeparateBatches) {
}
}
// note: 100.01 is not representable in FP16 (is in FP32), so use 101.0 instead.
INSTANTIATE_TEST_SUITE_P(
uKernels, MeanStddevNormalizationTest,
testing::Values(
@ -80,9 +82,9 @@ INSTANTIATE_TEST_SUITE_P(
std::make_tuple(0.0f, 100.0f, 1.20e-7f), // zero mean, large variance
std::make_tuple(0.01f, 0.0f, 0.0f), // small mean, zero variance
std::make_tuple(0.01f, 0.01f, 2.53e-5f), // small mean, small variance
std::make_tuple(0.01f, 100.0f, 1.20e-7f), // small mean, large variance
std::make_tuple(1.0f, 100.0f, 1.20e-7f), // small mean, large variance
std::make_tuple(100.0f, 0.0f, 0.0f), // large mean, zero variance
std::make_tuple(100.0f, 0.01f, 1.81e-4f), // large mean, small variance
std::make_tuple(100.0f, 1.0f, 1.81e-4f), // large mean, small variance
std::make_tuple(100.0f, 100.0f, 1.20e-7f) // large mean, large variance
));
@ -92,15 +94,15 @@ TEST_F(OpenCLOperationTest, MeanStddevNormalizationAllBatches) {
TensorFloat32 src_tensor;
src_tensor.shape = BHWC(9, 1, 1, 4);
src_tensor.data = {
0.0f, 0.0f, 0.0f, 0.0f, // zero mean, zero variance
-0.02f, -0.01f, 0.01f, 0.02f, // zero mean, small variance
-200.0f, -100.0f, 100.0f, 200.0f, // zero mean, large variance
0.01f, 0.01f, 0.01f, 0.01f, // small mean, zero variance
-0.01f, 0.0f, 0.02f, 0.03f, // small mean, small variance
-199.99f, -99.99f, 100.01f, 200.01f, // small mean, large variance
100.0f, 100.0f, 100.0f, 100.0f, // large mean, zero variance
99.98f, 99.99f, 100.01f, 100.02f, // large mean, small variance
-100.0f, 0.0f, 200.0f, 300.0f, // large mean, large variance
0.0f, 0.0f, 0.0f, 0.0f, // zero mean, zero variance
-0.02f, -0.01f, 0.01f, 0.02f, // zero mean, small variance
-200.0f, -100.0f, 100.0f, 200.0f, // zero mean, large variance
0.01f, 0.01f, 0.01f, 0.01f, // small mean, zero variance
-0.01f, 0.0f, 0.02f, 0.03f, // small mean, small variance
-199.0f, -99.0f, 101.0f, 201.0f, // small mean, large variance
100.0f, 100.0f, 100.0f, 100.0f, // large mean, zero variance
98.0f, 99.0f, 101.0f, 102.0f, // large mean, small variance
-100.0f, 0.0f, 200.0f, 300.0f, // large mean, large variance
};
for (auto storage : env_.GetSupportedStorages()) {
for (auto precision : env_.GetSupportedPrecisions()) {
@ -110,7 +112,8 @@ TEST_F(OpenCLOperationTest, MeanStddevNormalizationAllBatches) {
op_def.src_tensors.push_back({data_type, storage, Layout::BHWC});
op_def.dst_tensors.push_back({data_type, storage, Layout::BHWC});
TensorFloat32 dst_tensor;
auto operation = CreateMeanStdDevNormalization(op_def);
auto operation =
CreateMeanStdDevNormalization(op_def, env_.GetDevicePtr()->info_);
ASSERT_OK(ExecuteGPUOperation({src_tensor}, creation_context_, &operation,
BHWC(9, 1, 1, 4), &dst_tensor));