diff options
Diffstat (limited to 'tests/validation/GLES_COMPUTE')
| -rw-r--r-- | tests/validation/GLES_COMPUTE/ActivationLayer.cpp | 27 | ||||
| -rw-r--r-- | tests/validation/GLES_COMPUTE/PoolingLayer.cpp | 16 |
2 files changed, 11 insertions, 32 deletions
diff --git a/tests/validation/GLES_COMPUTE/ActivationLayer.cpp b/tests/validation/GLES_COMPUTE/ActivationLayer.cpp index a8c7253b8f..7676b858f6 100644 --- a/tests/validation/GLES_COMPUTE/ActivationLayer.cpp +++ b/tests/validation/GLES_COMPUTE/ActivationLayer.cpp @@ -61,35 +61,14 @@ AbsoluteTolerance<float> tolerance(ActivationLayerInfo::ActivationFunction activ case ActivationLayerInfo::ActivationFunction::SQUARE: return AbsoluteTolerance<float>(data_type == DataType::F16 ? 0.1f : epsilon); case ActivationLayerInfo::ActivationFunction::LOGISTIC: - if(is_data_type_fixed_point(data_type)) - { - return AbsoluteTolerance<float>(5.f); - } - else - { - return AbsoluteTolerance<float>(data_type == DataType::F16 ? 0.001f : epsilon); - } + return AbsoluteTolerance<float>(data_type == DataType::F16 ? 0.001f : epsilon); case ActivationLayerInfo::ActivationFunction::LEAKY_RELU: return AbsoluteTolerance<float>(data_type == DataType::F16 ? 0.00001f : epsilon); case ActivationLayerInfo::ActivationFunction::SOFT_RELU: case ActivationLayerInfo::ActivationFunction::SQRT: - if(is_data_type_fixed_point(data_type)) - { - return AbsoluteTolerance<float>(5.f); - } - else - { - return AbsoluteTolerance<float>(data_type == DataType::F16 ? 0.01f : 0.00001f); - } + return AbsoluteTolerance<float>(data_type == DataType::F16 ? 0.01f : 0.00001f); case ActivationLayerInfo::ActivationFunction::TANH: - if(is_data_type_fixed_point(data_type)) - { - return AbsoluteTolerance<float>(5.f); - } - else - { - return AbsoluteTolerance<float>(data_type == DataType::F16 ? 0.001f : 0.00001f); - } + return AbsoluteTolerance<float>(data_type == DataType::F16 ? 0.001f : 0.00001f); default: return AbsoluteTolerance<float>(epsilon); } diff --git a/tests/validation/GLES_COMPUTE/PoolingLayer.cpp b/tests/validation/GLES_COMPUTE/PoolingLayer.cpp index ac1bd724ac..7679007a82 100644 --- a/tests/validation/GLES_COMPUTE/PoolingLayer.cpp +++ b/tests/validation/GLES_COMPUTE/PoolingLayer.cpp @@ -59,17 +59,17 @@ TEST_SUITE(PoolingLayer) DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip( framework::dataset::make("InputInfo", { - TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Mismatching data type - TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Window shrink - TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Invalid pad/size combination - TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Invalid pad/size combination - TensorInfo(TensorShape(15U, 13U, 5U), 1, DataType::F32, 0), // Non-rectangular Global Pooling - TensorInfo(TensorShape(13U, 13U, 5U), 1, DataType::F32, 0), // Invalid output Global Pooling - TensorInfo(TensorShape(13U, 13U, 5U), 1, DataType::F32, 0), + TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Mismatching data type + TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Window shrink + TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid pad/size combination + TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid pad/size combination + TensorInfo(TensorShape(15U, 13U, 5U), 1, DataType::F32), // Non-rectangular Global Pooling + TensorInfo(TensorShape(13U, 13U, 5U), 1, DataType::F32), // Invalid output Global Pooling + TensorInfo(TensorShape(13U, 13U, 5U), 1, DataType::F32), }), framework::dataset::make("OutputInfo", { - TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F16, 0), TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32, 0), TensorInfo(TensorShape(30U, 11U, 2U), 1, DataType::F32, 0), TensorInfo(TensorShape(25U, 16U, 2U), 1, DataType::F32, 0), TensorInfo(TensorShape(1U, 1U, 5U), 1, DataType::F32, 0), TensorInfo(TensorShape(2U, 2U, 5U), 1, DataType::F32, 0), TensorInfo(TensorShape(1U, 1U, 5U), 1, DataType::F32, 0), + TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F16), TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32), TensorInfo(TensorShape(30U, 11U, 2U), 1, DataType::F32), TensorInfo(TensorShape(25U, 16U, 2U), 1, DataType::F32), TensorInfo(TensorShape(1U, 1U, 5U), 1, DataType::F32), TensorInfo(TensorShape(2U, 2U, 5U), 1, DataType::F32), TensorInfo(TensorShape(1U, 1U, 5U), 1, DataType::F32), })), framework::dataset::make("PoolInfo", { |