COMPMID-970 : Remove QS8 / QS16 support
Removed fixed point related code.
Change-Id: I487acf138dace3b0450e0d72ca7071eaec254566
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/137678
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
diff --git a/tests/validation/CL/ActivationLayer.cpp b/tests/validation/CL/ActivationLayer.cpp
index 4f97d7b..45b23ed 100644
--- a/tests/validation/CL/ActivationLayer.cpp
+++ b/tests/validation/CL/ActivationLayer.cpp
@@ -61,35 +61,14 @@
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/CL/ArithmeticDivision.cpp b/tests/validation/CL/ArithmeticDivision.cpp
index 42e2d22..5d4fa1f 100644
--- a/tests/validation/CL/ArithmeticDivision.cpp
+++ b/tests/validation/CL/ArithmeticDivision.cpp
@@ -57,19 +57,19 @@
TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::U8), // Window shrink
TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::U8), // Invalid data type combination
TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32), // Mismatching shapes
- TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32, 2),
+ TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32),
}),
framework::dataset::make("Input2Info",{ TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::U8),
TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::U8),
TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::S16),
TensorInfo(TensorShape(48U, 11U, 2U), 1, DataType::F32),
- TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32, 2),
+ TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32),
})),
framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::S16),
TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::U8),
TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::U8),
TensorInfo(TensorShape(48U, 11U, 2U), 1, DataType::F32),
- TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32, 2),
+ TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32),
})),
framework::dataset::make("Expected", { false, false, false, false, true })),
input1_info, input2_info, output_info, expected)
diff --git a/tests/validation/CL/ConvolutionLayer.cpp b/tests/validation/CL/ConvolutionLayer.cpp
index 30dd850..4ea2eb8 100644
--- a/tests/validation/CL/ConvolutionLayer.cpp
+++ b/tests/validation/CL/ConvolutionLayer.cpp
@@ -71,32 +71,32 @@
TEST_SUITE(ConvolutionLayer)
DATA_TEST_CASE(ValidateConvolutionMethod, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(zip(
- framework::dataset::make("InputInfo", { TensorInfo(TensorShape(17U, 31U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(17U, 31U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(23U, 27U, 5U, 4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(23U, 27U, 31U, 4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(3U, 3U, 2U, 1U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(33U, 27U, 7U, 4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(17U, 31U, 32U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(17U, 31U, 2U), 1, DataType::F32, 0)
+ framework::dataset::make("InputInfo", { TensorInfo(TensorShape(17U, 31U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(17U, 31U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(23U, 27U, 5U, 4U), 1, DataType::F32),
+ TensorInfo(TensorShape(23U, 27U, 31U, 4U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 2U, 1U), 1, DataType::F32),
+ TensorInfo(TensorShape(33U, 27U, 7U, 4U), 1, DataType::F32),
+ TensorInfo(TensorShape(17U, 31U, 32U), 1, DataType::F32),
+ TensorInfo(TensorShape(17U, 31U, 2U), 1, DataType::F32)
}),
- framework::dataset::make("WeightsInfo", { TensorInfo(TensorShape(5U, 5U, 2U, 19U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(5U, 5U, 2U, 19U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(3U, 3U, 5U, 21U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(3U, 3U, 31U, 21U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(3U, 3U, 5U, 21U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(5U, 5U, 7U, 16U), 1, DataType::F16, 0),
- TensorInfo(TensorShape(5U, 5U, 32U, 19U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(5U, 5U, 2U, 19U), 1, DataType::F32, 0)
+ framework::dataset::make("WeightsInfo", { TensorInfo(TensorShape(5U, 5U, 2U, 19U), 1, DataType::F32),
+ TensorInfo(TensorShape(5U, 5U, 2U, 19U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 5U, 21U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 31U, 21U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 5U, 21U), 1, DataType::F32),
+ TensorInfo(TensorShape(5U, 5U, 7U, 16U), 1, DataType::F16),
+ TensorInfo(TensorShape(5U, 5U, 32U, 19U), 1, DataType::F32),
+ TensorInfo(TensorShape(5U, 5U, 2U, 19U), 1, DataType::F32)
})),
- framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(15U, 15U, 19U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(15U, 15U, 19U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(21U, 25U, 21U, 4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(21U, 25U, 21U, 4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U, 25U, 21U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U, 12U, 16U, 4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(17U, 31U, 19U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(17U, 31U, 19U), 1, DataType::F32, 0)
+ framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(15U, 15U, 19U), 1, DataType::F32),
+ TensorInfo(TensorShape(15U, 15U, 19U), 1, DataType::F32),
+ TensorInfo(TensorShape(21U, 25U, 21U, 4U), 1, DataType::F32),
+ TensorInfo(TensorShape(21U, 25U, 21U, 4U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U, 25U, 21U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U, 12U, 16U, 4U), 1, DataType::F32),
+ TensorInfo(TensorShape(17U, 31U, 19U), 1, DataType::F32),
+ TensorInfo(TensorShape(17U, 31U, 19U), 1, DataType::F32)
})),
framework::dataset::make("ConvInfo", { PadStrideInfo(1, 2, 1, 1),
PadStrideInfo(1, 2, 1, 1),
diff --git a/tests/validation/CL/DeconvolutionLayer.cpp b/tests/validation/CL/DeconvolutionLayer.cpp
index 269bf15..0fd7ed4 100644
--- a/tests/validation/CL/DeconvolutionLayer.cpp
+++ b/tests/validation/CL/DeconvolutionLayer.cpp
@@ -103,33 +103,33 @@
// *INDENT-OFF*
// clang-format off
DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(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), // Invalid weights shape
- TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::QASYMM8, 4), // Non supported data type
- TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 11), // Invalid bias shape
- TensorInfo(TensorShape(13U, 11U, 4U, 3U), 1, DataType::F32, 0), // Window shrink
- TensorInfo(TensorShape(32U, 16U, 2U), 1, DataType::F32, 0),
+ framework::dataset::make("InputInfo", { TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Mismatching data type
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid weights shape
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::QASYMM8), // Non supported data type
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid bias shape
+ TensorInfo(TensorShape(13U, 11U, 4U, 3U), 1, DataType::F32), // Window shrink
+ TensorInfo(TensorShape(32U, 16U, 2U), 1, DataType::F32),
}),
- framework::dataset::make("WeightsInfo", { TensorInfo(TensorShape(3U, 3U, 2U, 2U), 1, DataType::F16, 0),
- TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(3U, 3U, 2U, 2U), 1, DataType::QASYMM8, 5),
- TensorInfo(TensorShape(3U, 2U, 2U, 2U), 1, DataType::F32, 11),
- TensorInfo(TensorShape(3U, 3U, 4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(1U, 1U, 2U, 4U), 1, DataType::F32, 0),
+ framework::dataset::make("WeightsInfo", { TensorInfo(TensorShape(3U, 3U, 2U, 2U), 1, DataType::F16),
+ TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 2U, 2U), 1, DataType::QASYMM8),
+ TensorInfo(TensorShape(3U, 2U, 2U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 4U), 1, DataType::F32),
+ TensorInfo(TensorShape(1U, 1U, 2U, 4U), 1, DataType::F32),
})),
- framework::dataset::make("BiasInfo", { TensorInfo(TensorShape(1U), 1, DataType::F16, 0),
- TensorInfo(TensorShape(1U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(1U), 1, DataType::F32, 5),
- TensorInfo(TensorShape(25U, 11U), 1, DataType::F32, 11),
- TensorInfo(TensorShape(1U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(4U), 1, DataType::F32, 0),
+ framework::dataset::make("BiasInfo", { TensorInfo(TensorShape(1U), 1, DataType::F16),
+ TensorInfo(TensorShape(1U), 1, DataType::F32),
+ TensorInfo(TensorShape(1U), 1, DataType::F32),
+ TensorInfo(TensorShape(25U, 11U), 1, DataType::F32),
+ TensorInfo(TensorShape(1U), 1, DataType::F32),
+ TensorInfo(TensorShape(4U), 1, DataType::F32),
})),
- framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F16, 0),
- TensorInfo(TensorShape(25U, 10U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32, 5),
- TensorInfo(TensorShape(13U, 13U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U, 9U, 1U, 3U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(32U, 16U, 4U), 1, DataType::F32, 0),
+ framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F16),
+ TensorInfo(TensorShape(25U, 10U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(13U, 13U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U, 9U, 1U, 3U), 1, DataType::F32),
+ TensorInfo(TensorShape(32U, 16U, 4U), 1, DataType::F32),
})),
framework::dataset::make("PadStrideInfo", { PadStrideInfo(1, 1, 0, 0),
PadStrideInfo(1, 1, 0, 0),
diff --git a/tests/validation/CL/DepthwiseConvolutionLayer.cpp b/tests/validation/CL/DepthwiseConvolutionLayer.cpp
index 5b18f59..fad8140 100644
--- a/tests/validation/CL/DepthwiseConvolutionLayer.cpp
+++ b/tests/validation/CL/DepthwiseConvolutionLayer.cpp
@@ -56,57 +56,57 @@
// *INDENT-OFF*
// clang-format off
DATA_TEST_CASE(Validate3x3, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(zip(
- framework::dataset::make("InputInfo", { TensorInfo(TensorShape(32U, 18U, 2U), 1, DataType::F32, 0), // Mismatching data type input/weights
- TensorInfo(TensorShape(32U, 18U, 3U), 1, DataType::F32, 0), // Mismatching input feature maps
- TensorInfo(TensorShape(32U, 18U, 2U), 1, DataType::F32, 0), // Unsupported weights dimensions
- TensorInfo(TensorShape(32U, 18U, 2U), 1, DataType::QASYMM8, 0), // Unsupported activation
- TensorInfo(TensorShape(32U, 18U, 2U), 1, DataType::F32, 0), // Mismatching depth multiplier
- TensorInfo(TensorShape(32U, 18U, 2U), 1, DataType::F32, 0), // Invalid stride
- TensorInfo(TensorShape(32U, 18U, 2U), 1, DataType::F32, 0), // Invalid biases size
- TensorInfo(TensorShape(32U, 18U, 2U), 1, DataType::F32, 0), // Invalid biases dimensions
- TensorInfo(TensorShape(32U, 18U, 2U), 1, DataType::F32, 0), // Invalid output size
- TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Window shrink
- TensorInfo(TensorShape(32U, 18U, 8U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(50U, 32U, 8U), 1, DataType::QASYMM8, 0),
+ framework::dataset::make("InputInfo", { TensorInfo(TensorShape(32U, 18U, 2U), 1, DataType::F32), // Mismatching data type input/weights
+ TensorInfo(TensorShape(32U, 18U, 3U), 1, DataType::F32), // Mismatching input feature maps
+ TensorInfo(TensorShape(32U, 18U, 2U), 1, DataType::F32), // Unsupported weights dimensions
+ TensorInfo(TensorShape(32U, 18U, 2U), 1, DataType::QASYMM8), // Unsupported activation
+ TensorInfo(TensorShape(32U, 18U, 2U), 1, DataType::F32), // Mismatching depth multiplier
+ TensorInfo(TensorShape(32U, 18U, 2U), 1, DataType::F32), // Invalid stride
+ TensorInfo(TensorShape(32U, 18U, 2U), 1, DataType::F32), // Invalid biases size
+ TensorInfo(TensorShape(32U, 18U, 2U), 1, DataType::F32), // Invalid biases dimensions
+ TensorInfo(TensorShape(32U, 18U, 2U), 1, DataType::F32), // Invalid output size
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Window shrink
+ TensorInfo(TensorShape(32U, 18U, 8U), 1, DataType::F32),
+ TensorInfo(TensorShape(50U, 32U, 8U), 1, DataType::QASYMM8),
}),
- framework::dataset::make("WeightsInfo", { TensorInfo(TensorShape(3U, 3U, 2U), 1, DataType::F16, 0),
- TensorInfo(TensorShape(3U, 3U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(5U, 5U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(3U, 3U, 2U), 1, DataType::QASYMM8, 0),
- TensorInfo(TensorShape(3U, 3U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(3U, 3U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(3U, 3U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(3U, 3U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(3U, 3U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(3U, 3U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(3U, 3U, 16U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(3U, 3U, 24U), 1, DataType::QASYMM8, 0),
+ framework::dataset::make("WeightsInfo", { TensorInfo(TensorShape(3U, 3U, 2U), 1, DataType::F16),
+ TensorInfo(TensorShape(3U, 3U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(5U, 5U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 2U), 1, DataType::QASYMM8),
+ TensorInfo(TensorShape(3U, 3U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 16U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 24U), 1, DataType::QASYMM8),
})),
- framework::dataset::make("BiasesInfo", { TensorInfo(TensorShape(2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(2U), 1, DataType::S32, 0),
- TensorInfo(TensorShape(2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(2U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(16U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(24U), 1, DataType::S32, 0),
+ framework::dataset::make("BiasesInfo", { TensorInfo(TensorShape(2U), 1, DataType::F32),
+ TensorInfo(TensorShape(2U), 1, DataType::F32),
+ TensorInfo(TensorShape(2U), 1, DataType::F32),
+ TensorInfo(TensorShape(2U), 1, DataType::S32),
+ TensorInfo(TensorShape(2U), 1, DataType::F32),
+ TensorInfo(TensorShape(2U), 1, DataType::F32),
+ TensorInfo(TensorShape(4U), 1, DataType::F32),
+ TensorInfo(TensorShape(2U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(2U), 1, DataType::F32),
+ TensorInfo(TensorShape(2U), 1, DataType::F32),
+ TensorInfo(TensorShape(16U), 1, DataType::F32),
+ TensorInfo(TensorShape(24U), 1, DataType::S32),
})),
- framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(30U, 16U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(30U, 16U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(30U, 16U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(30U, 16U, 2U), 1, DataType::QASYMM8, 0),
- TensorInfo(TensorShape(30U, 16U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(30U, 16U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(30U, 16U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(30U, 16U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(32U, 18U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(30U, 16U, 16U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(48U, 30U, 24U), 1, DataType::QASYMM8, 0),
+ framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(30U, 16U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(30U, 16U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(30U, 16U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(30U, 16U, 2U), 1, DataType::QASYMM8),
+ TensorInfo(TensorShape(30U, 16U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(30U, 16U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(30U, 16U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(30U, 16U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(32U, 18U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(30U, 16U, 16U), 1, DataType::F32),
+ TensorInfo(TensorShape(48U, 30U, 24U), 1, DataType::QASYMM8),
})),
framework::dataset::make("ConvInfo", { PadStrideInfo(1, 1, 0, 0),
PadStrideInfo(1, 1, 0, 0),
@@ -155,41 +155,41 @@
}
DATA_TEST_CASE(ValidateGeneric, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(
- framework::dataset::make("InputInfo", { TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Mismatching data type input/weights
- TensorInfo(TensorShape(27U, 13U, 3U), 1, DataType::F32, 0), // Mismatching input feature maps
- TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Mismatching depth multiplier
- TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Invalid biases size
- TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Invalid biases dimensions
- TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Invalid output size
- TensorInfo(TensorShape(27U, 13U, 8U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(32U, 13U, 8U), 1, DataType::QASYMM8, 0),
+ framework::dataset::make("InputInfo", { TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Mismatching data type input/weights
+ TensorInfo(TensorShape(27U, 13U, 3U), 1, DataType::F32), // Mismatching input feature maps
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Mismatching depth multiplier
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid biases size
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid biases dimensions
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid output size
+ TensorInfo(TensorShape(27U, 13U, 8U), 1, DataType::F32),
+ TensorInfo(TensorShape(32U, 13U, 8U), 1, DataType::QASYMM8),
}),
- framework::dataset::make("WeightsInfo", { TensorInfo(TensorShape(3U, 3U, 2U), 1, DataType::F16, 0),
- TensorInfo(TensorShape(3U, 3U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(3U, 3U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(3U, 3U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(3U, 3U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(3U, 3U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(3U, 3U, 16U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(3U, 3U, 24U), 1, DataType::QASYMM8, 0),
+ framework::dataset::make("WeightsInfo", { TensorInfo(TensorShape(3U, 3U, 2U), 1, DataType::F16),
+ TensorInfo(TensorShape(3U, 3U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 16U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 24U), 1, DataType::QASYMM8),
})),
- framework::dataset::make("BiasesInfo", { TensorInfo(TensorShape(2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(2U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(16U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(24U), 1, DataType::S32, 0),
+ framework::dataset::make("BiasesInfo", { TensorInfo(TensorShape(2U), 1, DataType::F32),
+ TensorInfo(TensorShape(2U), 1, DataType::F32),
+ TensorInfo(TensorShape(2U), 1, DataType::F32),
+ TensorInfo(TensorShape(4U), 1, DataType::F32),
+ TensorInfo(TensorShape(2U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(2U), 1, DataType::F32),
+ TensorInfo(TensorShape(16U), 1, DataType::F32),
+ TensorInfo(TensorShape(24U), 1, DataType::S32),
})),
- framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(25U, 11U, 16U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(32U, 11U, 24U), 1, DataType::QASYMM8, 0),
+ framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(25U, 11U, 16U), 1, DataType::F32),
+ TensorInfo(TensorShape(32U, 11U, 24U), 1, DataType::QASYMM8),
})),
framework::dataset::make("ConvInfo", { PadStrideInfo(1, 1, 0, 0),
PadStrideInfo(1, 1, 0, 0),
diff --git a/tests/validation/CL/DilatedConvolutionLayer.cpp b/tests/validation/CL/DilatedConvolutionLayer.cpp
index fdd6cc8..f748f90 100644
--- a/tests/validation/CL/DilatedConvolutionLayer.cpp
+++ b/tests/validation/CL/DilatedConvolutionLayer.cpp
@@ -61,23 +61,23 @@
TEST_SUITE(DilatedConvolutionLayer)
DATA_TEST_CASE(ValidateConvolutionMethod, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(
- framework::dataset::make("InputInfo", { TensorInfo(TensorShape(17U, 31U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(17U, 31U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(23U, 27U, 23U, 4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(3U, 3U, 2U, 1U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(33U, 27U, 7U, 4U), 1, DataType::F32, 0)
+ framework::dataset::make("InputInfo", { TensorInfo(TensorShape(17U, 31U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(17U, 31U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(23U, 27U, 23U, 4U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 2U, 1U), 1, DataType::F32),
+ TensorInfo(TensorShape(33U, 27U, 7U, 4U), 1, DataType::F32)
}),
- framework::dataset::make("WeightsInfo", { TensorInfo(TensorShape(5U, 5U, 2U, 19U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(5U, 5U, 2U, 19U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(3U, 3U, 23U, 21U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(3U, 3U, 5U, 21U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(5U, 5U, 7U, 16U), 1, DataType::F16, 0)
+ framework::dataset::make("WeightsInfo", { TensorInfo(TensorShape(5U, 5U, 2U, 19U), 1, DataType::F32),
+ TensorInfo(TensorShape(5U, 5U, 2U, 19U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 23U, 21U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 5U, 21U), 1, DataType::F32),
+ TensorInfo(TensorShape(5U, 5U, 7U, 16U), 1, DataType::F16)
})),
- framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(15U, 15U, 19U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(15U, 15U, 19U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(21U, 25U, 21U, 4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U, 25U, 21U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U, 12U, 16U, 4U), 1, DataType::F32, 0)
+ framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(15U, 15U, 19U), 1, DataType::F32),
+ TensorInfo(TensorShape(15U, 15U, 19U), 1, DataType::F32),
+ TensorInfo(TensorShape(21U, 25U, 21U, 4U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U, 25U, 21U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U, 12U, 16U, 4U), 1, DataType::F32)
})),
framework::dataset::make("ConvInfo", { PadStrideInfo(1, 2, 1, 1),
PadStrideInfo(1, 2, 1, 1),
diff --git a/tests/validation/CL/DirectConvolutionLayer.cpp b/tests/validation/CL/DirectConvolutionLayer.cpp
index a796b6e..87f9449 100644
--- a/tests/validation/CL/DirectConvolutionLayer.cpp
+++ b/tests/validation/CL/DirectConvolutionLayer.cpp
@@ -61,16 +61,7 @@
combine(framework::dataset::make("PadY", 0, 2),
framework::dataset::make("KernelSize", { 3, 5 })))),
framework::dataset::make("NumKernels", { 1, 4, 8, 16 })))));
-const auto data_fixed_point = combine(datasets::TinyDirectConvolutionShapes(),
- combine(framework::dataset::make("StrideX", 1, 3),
- combine(framework::dataset::make("StrideY", 1, 3),
- combine(concat(combine(framework::dataset::make("PadX", 0),
- combine(framework::dataset::make("PadY", 0),
- framework::dataset::make("KernelSize", 1))),
- combine(framework::dataset::make("PadX", 0, 2),
- combine(framework::dataset::make("PadY", 0, 2),
- framework::dataset::make("KernelSize", { 3 })))),
- framework::dataset::make("NumKernels", { 1, 4, 8, 16 })))));
+
/** Activation function Dataset*/
const auto ActivationFunctionsDataset = framework::dataset::make("ActivationInfo",
{
@@ -89,53 +80,53 @@
// *INDENT-OFF*
// clang-format off
DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(
- framework::dataset::make("InputInfo", { TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Mismatching data type input/weights
- TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Mismatching input feature maps
- TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Unsupported kernel width
- TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Non-rectangular weights dimensions
- TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Invalid weights dimensions
- TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Invalid stride
- TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Invalid biases size
- TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Invalid biases dimensions
- TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Invalid output size
- TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Window shrink
- TensorInfo(TensorShape(32U, 16U, 2U), 1, DataType::F32, 0),
+ framework::dataset::make("InputInfo", { TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Mismatching data type input/weights
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Mismatching input feature maps
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Unsupported kernel width
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Non-rectangular weights dimensions
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid weights dimensions
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid stride
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid biases size
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid biases dimensions
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid output size
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Window shrink
+ TensorInfo(TensorShape(32U, 16U, 2U), 1, DataType::F32),
}),
- framework::dataset::make("WeightsInfo",{ TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F16, 0),
- TensorInfo(TensorShape(3U, 3U, 3U, 4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(9U, 9U, 2U, 4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(5U, 3U, 2U, 4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(3U, 3U, 2U, 4U, 3U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(1U, 1U, 2U, 4U), 1, DataType::F32, 0),
+ framework::dataset::make("WeightsInfo",{ TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F16),
+ TensorInfo(TensorShape(3U, 3U, 3U, 4U), 1, DataType::F32),
+ TensorInfo(TensorShape(9U, 9U, 2U, 4U), 1, DataType::F32),
+ TensorInfo(TensorShape(5U, 3U, 2U, 4U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 2U, 4U, 3U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32),
+ TensorInfo(TensorShape(1U, 1U, 2U, 4U), 1, DataType::F32),
})),
- framework::dataset::make("BiasesInfo",{ TensorInfo(TensorShape(4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(3U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(4U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(4U), 1, DataType::F32, 0),
+ framework::dataset::make("BiasesInfo",{ TensorInfo(TensorShape(4U), 1, DataType::F32),
+ TensorInfo(TensorShape(4U), 1, DataType::F32),
+ TensorInfo(TensorShape(4U), 1, DataType::F32),
+ TensorInfo(TensorShape(4U), 1, DataType::F32),
+ TensorInfo(TensorShape(4U), 1, DataType::F32),
+ TensorInfo(TensorShape(4U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U), 1, DataType::F32),
+ TensorInfo(TensorShape(4U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(4U), 1, DataType::F32),
+ TensorInfo(TensorShape(4U), 1, DataType::F32),
+ TensorInfo(TensorShape(4U), 1, DataType::F32),
})),
- framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(26U, 11U, 4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(32U, 16U, 4U), 1, DataType::F32, 0),
+ framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32),
+ TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32),
+ TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32),
+ TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32),
+ TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32),
+ TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32),
+ TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32),
+ TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32),
+ TensorInfo(TensorShape(26U, 11U, 4U), 1, DataType::F32),
+ TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32),
+ TensorInfo(TensorShape(32U, 16U, 4U), 1, DataType::F32),
})),
framework::dataset::make("ConvInfo", { PadStrideInfo(1, 1, 0, 0),
PadStrideInfo(1, 1, 0, 0),
diff --git a/tests/validation/CL/LSTMLayer.cpp b/tests/validation/CL/LSTMLayer.cpp
index bd43678..e1d4cbe 100644
--- a/tests/validation/CL/LSTMLayer.cpp
+++ b/tests/validation/CL/LSTMLayer.cpp
@@ -49,77 +49,77 @@
// *INDENT-OFF*
// clang-format off
DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(zip(zip(zip(
- framework::dataset::make("InputInfo", { TensorInfo(TensorShape(8U, 2U), 1, DataType::U8, 0), // Wrong data type
- TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Wrong input size
- TensorInfo(TensorShape(8U, 2U), 1, DataType::F32, 0), // Wrong input weights size
- TensorInfo(TensorShape(8U, 2U), 1, DataType::F32, 0), // Wrong recurrent weights size
- TensorInfo(TensorShape(8U, 2U), 1, DataType::F32, 0), // Wrong cell bias size
- TensorInfo(TensorShape(8U, 2U), 1, DataType::F32, 0), // Wrong cell state size
- TensorInfo(TensorShape(8U, 2U), 1, DataType::F32, 0), // Wrong output size
- TensorInfo(TensorShape(8U, 2U), 1, DataType::F32, 0), // Wrong scratch size
+ framework::dataset::make("InputInfo", { TensorInfo(TensorShape(8U, 2U), 1, DataType::U8), // Wrong data type
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Wrong input size
+ TensorInfo(TensorShape(8U, 2U), 1, DataType::F32), // Wrong input weights size
+ TensorInfo(TensorShape(8U, 2U), 1, DataType::F32), // Wrong recurrent weights size
+ TensorInfo(TensorShape(8U, 2U), 1, DataType::F32), // Wrong cell bias size
+ TensorInfo(TensorShape(8U, 2U), 1, DataType::F32), // Wrong cell state size
+ TensorInfo(TensorShape(8U, 2U), 1, DataType::F32), // Wrong output size
+ TensorInfo(TensorShape(8U, 2U), 1, DataType::F32), // Wrong scratch size
}),
- framework::dataset::make("InputWeightsInfo", { TensorInfo(TensorShape(8U, 16U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(8U, 16U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(27U, 11U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(8U, 16U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(8U, 16U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(8U, 16U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(8U, 16U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(8U, 16U), 1, DataType::F32, 0),
+ framework::dataset::make("InputWeightsInfo", { TensorInfo(TensorShape(8U, 16U), 1, DataType::F32),
+ TensorInfo(TensorShape(8U, 16U), 1, DataType::F32),
+ TensorInfo(TensorShape(27U, 11U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(8U, 16U), 1, DataType::F32),
+ TensorInfo(TensorShape(8U, 16U), 1, DataType::F32),
+ TensorInfo(TensorShape(8U, 16U), 1, DataType::F32),
+ TensorInfo(TensorShape(8U, 16U), 1, DataType::F32),
+ TensorInfo(TensorShape(8U, 16U), 1, DataType::F32),
})),
- framework::dataset::make("RecurrentWeightsInfo", { TensorInfo(TensorShape(16U, 16U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(16U, 16U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(16U, 16U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(16U, 16U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(16U, 16U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(16U, 16U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(16U, 16U), 1, DataType::F32, 0),
+ framework::dataset::make("RecurrentWeightsInfo", { TensorInfo(TensorShape(16U, 16U), 1, DataType::F32),
+ TensorInfo(TensorShape(16U, 16U), 1, DataType::F32),
+ TensorInfo(TensorShape(16U, 16U), 1, DataType::F32),
+ TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(16U, 16U), 1, DataType::F32),
+ TensorInfo(TensorShape(16U, 16U), 1, DataType::F32),
+ TensorInfo(TensorShape(16U, 16U), 1, DataType::F32),
+ TensorInfo(TensorShape(16U, 16U), 1, DataType::F32),
})),
- framework::dataset::make("CellBiasInfo", { TensorInfo(TensorShape(16U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(16U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(16U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(16U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(30U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(16U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(16U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(16U), 1, DataType::F32, 0),
+ framework::dataset::make("CellBiasInfo", { TensorInfo(TensorShape(16U), 1, DataType::F32),
+ TensorInfo(TensorShape(16U), 1, DataType::F32),
+ TensorInfo(TensorShape(16U), 1, DataType::F32),
+ TensorInfo(TensorShape(16U), 1, DataType::F32),
+ TensorInfo(TensorShape(30U), 1, DataType::F32),
+ TensorInfo(TensorShape(16U), 1, DataType::F32),
+ TensorInfo(TensorShape(16U), 1, DataType::F32),
+ TensorInfo(TensorShape(16U), 1, DataType::F32),
})),
- framework::dataset::make("ProjectionBiasInfo", { TensorInfo(TensorShape(16U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(16U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(16U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(16U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(16U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(16U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(16U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(16U), 1, DataType::F32, 0),
+ framework::dataset::make("ProjectionBiasInfo", { TensorInfo(TensorShape(16U), 1, DataType::F32),
+ TensorInfo(TensorShape(16U), 1, DataType::F32),
+ TensorInfo(TensorShape(16U), 1, DataType::F32),
+ TensorInfo(TensorShape(16U), 1, DataType::F32),
+ TensorInfo(TensorShape(16U), 1, DataType::F32),
+ TensorInfo(TensorShape(16U), 1, DataType::F32),
+ TensorInfo(TensorShape(16U), 1, DataType::F32),
+ TensorInfo(TensorShape(16U), 1, DataType::F32),
})),
- framework::dataset::make("CellStateInfo", { TensorInfo(TensorShape(16U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(16U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(16U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(16U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(16U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(16U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(16U, 2U), 1, DataType::F32, 0),
+ framework::dataset::make("CellStateInfo", { TensorInfo(TensorShape(16U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(16U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(16U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(16U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(16U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U), 1, DataType::F32),
+ TensorInfo(TensorShape(16U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(16U, 2U), 1, DataType::F32),
})),
- framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(16U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(16U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(16U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(16U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(16U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(16U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U, 13U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(16U, 2U), 1, DataType::F32, 0),
+ framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(16U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(16U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(16U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(16U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(16U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(16U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U, 13U), 1, DataType::F32),
+ TensorInfo(TensorShape(16U, 2U), 1, DataType::F32),
})),
- framework::dataset::make("ScratchInfo", { TensorInfo(TensorShape(64U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(64U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(64U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(64U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(64U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(64U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(64U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(12U, 2U), 1, DataType::F32, 0),
+ framework::dataset::make("ScratchInfo", { TensorInfo(TensorShape(64U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(64U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(64U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(64U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(64U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(64U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(64U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(12U, 2U), 1, DataType::F32),
})),
framework::dataset::make("ActivationInfo", { ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
diff --git a/tests/validation/CL/LocallyConnected.cpp b/tests/validation/CL/LocallyConnected.cpp
index d8f236c..5381072 100644
--- a/tests/validation/CL/LocallyConnected.cpp
+++ b/tests/validation/CL/LocallyConnected.cpp
@@ -52,41 +52,41 @@
// *INDENT-OFF*
// clang-format off
DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(
- framework::dataset::make("InputInfo", { TensorInfo(TensorShape(23U, 27U, 5U), 1, DataType::F32, 0), // Mismatching data type input/weights
- TensorInfo(TensorShape(23U, 27U, 5U), 1, DataType::F32, 0), // Mismatching data type input/bias
- TensorInfo(TensorShape(23U, 27U, 5U), 1, DataType::F32, 0), // Mismatching data type input/output
- TensorInfo(TensorShape(23U, 27U, 5U), 1, DataType::F32, 0), // Mismatching shape input/weights
- TensorInfo(TensorShape(23U, 27U, 5U), 1, DataType::F32, 0), // Mismatching shape input/bias
- TensorInfo(TensorShape(23U, 27U, 5U), 1, DataType::F32, 0), // Mismatching shape input/output
- TensorInfo(TensorShape(23U, 27U, 5U), 1, DataType::F32, 0), // Asymmetric padding
- TensorInfo(TensorShape(23U, 27U, 5U), 1, DataType::F32, 0)
+ framework::dataset::make("InputInfo", { TensorInfo(TensorShape(23U, 27U, 5U), 1, DataType::F32), // Mismatching data type input/weights
+ TensorInfo(TensorShape(23U, 27U, 5U), 1, DataType::F32), // Mismatching data type input/bias
+ TensorInfo(TensorShape(23U, 27U, 5U), 1, DataType::F32), // Mismatching data type input/output
+ TensorInfo(TensorShape(23U, 27U, 5U), 1, DataType::F32), // Mismatching shape input/weights
+ TensorInfo(TensorShape(23U, 27U, 5U), 1, DataType::F32), // Mismatching shape input/bias
+ TensorInfo(TensorShape(23U, 27U, 5U), 1, DataType::F32), // Mismatching shape input/output
+ TensorInfo(TensorShape(23U, 27U, 5U), 1, DataType::F32), // Asymmetric padding
+ TensorInfo(TensorShape(23U, 27U, 5U), 1, DataType::F32)
}),
- framework::dataset::make("WeightsInfo",{ TensorInfo(TensorShape(3U, 3U, 5U, 21U, 275U), 1, DataType::F16, 0),
- TensorInfo(TensorShape(3U, 3U, 5U, 21U, 275U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(3U, 3U, 5U, 21U, 275U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(3U, 3U, 5U, 21U, 274U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(3U, 3U, 5U, 21U, 275U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(3U, 3U, 5U, 21U, 275U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(3U, 3U, 5U, 21U, 275U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(3U, 3U, 5U, 21U, 275U), 1, DataType::F32, 0)
+ framework::dataset::make("WeightsInfo",{ TensorInfo(TensorShape(3U, 3U, 5U, 21U, 275U), 1, DataType::F16),
+ TensorInfo(TensorShape(3U, 3U, 5U, 21U, 275U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 5U, 21U, 275U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 5U, 21U, 274U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 5U, 21U, 275U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 5U, 21U, 275U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 5U, 21U, 275U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 5U, 21U, 275U), 1, DataType::F32)
})),
- framework::dataset::make("BiasInfo", { TensorInfo(TensorShape(21U, 275U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(21U, 275U), 1, DataType::F16, 0),
- TensorInfo(TensorShape(21U, 275U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(21U, 275U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(21U, 274U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(21U, 275U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(21U, 275U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(21U, 275U), 1, DataType::F32, 0)
+ framework::dataset::make("BiasInfo", { TensorInfo(TensorShape(21U, 275U), 1, DataType::F32),
+ TensorInfo(TensorShape(21U, 275U), 1, DataType::F16),
+ TensorInfo(TensorShape(21U, 275U), 1, DataType::F32),
+ TensorInfo(TensorShape(21U, 275U), 1, DataType::F32),
+ TensorInfo(TensorShape(21U, 274U), 1, DataType::F32),
+ TensorInfo(TensorShape(21U, 275U), 1, DataType::F32),
+ TensorInfo(TensorShape(21U, 275U), 1, DataType::F32),
+ TensorInfo(TensorShape(21U, 275U), 1, DataType::F32)
})),
- framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(11U, 25U, 21U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U, 25U, 21U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U, 25U, 21U), 1, DataType::F16, 0),
- TensorInfo(TensorShape(11U, 25U, 21U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U, 25U, 21U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U, 25U, 22U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U, 25U, 21U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U, 25U, 21U), 1, DataType::F32, 0)
+ framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(11U, 25U, 21U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U, 25U, 21U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U, 25U, 21U), 1, DataType::F16),
+ TensorInfo(TensorShape(11U, 25U, 21U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U, 25U, 21U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U, 25U, 22U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U, 25U, 21U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U, 25U, 21U), 1, DataType::F32)
})),
framework::dataset::make("PadStride", { PadStrideInfo(2, 1, 0, 0),
PadStrideInfo(2, 1, 0, 0),
diff --git a/tests/validation/CL/NormalizationLayer.cpp b/tests/validation/CL/NormalizationLayer.cpp
index a2dbaff..e640e01 100644
--- a/tests/validation/CL/NormalizationLayer.cpp
+++ b/tests/validation/CL/NormalizationLayer.cpp
@@ -71,19 +71,19 @@
// *INDENT-OFF*
// clang-format off
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 input/output
- TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Mismatching shapes
- TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Even normalization
- TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Non implemented IN_MAP_2D
- TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Window shrink
- TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32, 0),
+ framework::dataset::make("InputInfo", { TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Mismatching data type input/output
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Mismatching shapes
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Even normalization
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Non implemented IN_MAP_2D
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Window shrink
+ TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32),
}),
- framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F16, 0),
- TensorInfo(TensorShape(27U, 11U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32, 0),
+ framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F16),
+ TensorInfo(TensorShape(27U, 11U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32),
})),
framework::dataset::make("NormInfo", { NormalizationLayerInfo(NormType::IN_MAP_1D, 5),
NormalizationLayerInfo(NormType::IN_MAP_1D, 5),
diff --git a/tests/validation/CL/PoolingLayer.cpp b/tests/validation/CL/PoolingLayer.cpp
index 0b8a11f..1331522 100644
--- a/tests/validation/CL/PoolingLayer.cpp
+++ b/tests/validation/CL/PoolingLayer.cpp
@@ -65,23 +65,23 @@
// *INDENT-OFF*
// clang-format off
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(27U, 13U, 2U), 1, DataType::QASYMM8, 0), // Invalid parameters
- 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),
+ framework::dataset::make("InputInfo", { 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(27U, 13U, 2U), 1, DataType::QASYMM8), // Invalid parameters
+ 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(27U, 13U, 2U), 1, DataType::QASYMM8, 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),
+ framework::dataset::make("OutputInfo",{ 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(27U, 13U, 2U), 1, DataType::QASYMM8),
+ 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", { PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 0, 0)),
PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 0, 0)),
diff --git a/tests/validation/CL/RNNLayer.cpp b/tests/validation/CL/RNNLayer.cpp
index 0af6f8e..9179c09 100644
--- a/tests/validation/CL/RNNLayer.cpp
+++ b/tests/validation/CL/RNNLayer.cpp
@@ -49,53 +49,53 @@
// *INDENT-OFF*
// clang-format off
DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(zip(
- framework::dataset::make("InputInfo", { TensorInfo(TensorShape(27U, 13U), 1, DataType::U8, 0), // Wrong data type
- TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Wrong input size
- TensorInfo(TensorShape(27U, 13U), 1, DataType::F32, 0), // Wrong weights size
- TensorInfo(TensorShape(27U, 13U), 1, DataType::F32, 0), // Wrong recurrent weights size
- TensorInfo(TensorShape(27U, 13U), 1, DataType::F32, 0), // Wrong bias size
- TensorInfo(TensorShape(27U, 13U), 1, DataType::F32, 0), // Wrong output size
- TensorInfo(TensorShape(27U, 13U), 1, DataType::F32, 0), // Wrong hidden output size
+ framework::dataset::make("InputInfo", { TensorInfo(TensorShape(27U, 13U), 1, DataType::U8), // Wrong data type
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Wrong input size
+ TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), // Wrong weights size
+ TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), // Wrong recurrent weights size
+ TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), // Wrong bias size
+ TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), // Wrong output size
+ TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), // Wrong hidden output size
}),
- framework::dataset::make("WeightsInfo", { TensorInfo(TensorShape(27U, 11U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(27U, 11U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(27U, 11U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(27U, 11U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(27U, 11U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(27U, 11U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(27U, 11U), 1, DataType::F32, 0),
+ framework::dataset::make("WeightsInfo", { TensorInfo(TensorShape(27U, 11U), 1, DataType::F32),
+ TensorInfo(TensorShape(27U, 11U), 1, DataType::F32),
+ TensorInfo(TensorShape(27U, 11U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(27U, 11U), 1, DataType::F32),
+ TensorInfo(TensorShape(27U, 11U), 1, DataType::F32),
+ TensorInfo(TensorShape(27U, 11U), 1, DataType::F32),
+ TensorInfo(TensorShape(27U, 11U), 1, DataType::F32),
})),
- framework::dataset::make("RecurrentWeightsInfo", { TensorInfo(TensorShape(11U, 11U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U, 11U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U, 11U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U, 11U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U, 11U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U, 11U), 1, DataType::F32, 0),
+ framework::dataset::make("RecurrentWeightsInfo", { TensorInfo(TensorShape(11U, 11U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U, 11U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U, 11U), 1, DataType::F32),
+ TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U, 11U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U, 11U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U, 11U), 1, DataType::F32),
})),
- framework::dataset::make("BiasInfo", { TensorInfo(TensorShape(11U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(30U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U), 1, DataType::F32, 0),
+ framework::dataset::make("BiasInfo", { TensorInfo(TensorShape(11U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U), 1, DataType::F32),
+ TensorInfo(TensorShape(30U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U), 1, DataType::F32),
})),
- framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(11U, 13U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U, 13U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U, 13U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U, 13U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U, 13U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U, 13U), 1, DataType::F32, 0),
+ framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(11U, 13U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U, 13U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U, 13U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U, 13U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U, 13U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U, 13U), 1, DataType::F32),
})),
- framework::dataset::make("HiddenStateInfo", { TensorInfo(TensorShape(11U, 13U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U, 13U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U, 13U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U, 13U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U, 13U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U, 13U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U, 13U, 2U), 1, DataType::F32, 0),
+ framework::dataset::make("HiddenStateInfo", { TensorInfo(TensorShape(11U, 13U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U, 13U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U, 13U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U, 13U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U, 13U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U, 13U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U, 13U, 2U), 1, DataType::F32),
})),
framework::dataset::make("ActivationInfo", { ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
diff --git a/tests/validation/CL/WidthConcatenateLayer.cpp b/tests/validation/CL/WidthConcatenateLayer.cpp
index 36a5e6f..6af3c64 100644
--- a/tests/validation/CL/WidthConcatenateLayer.cpp
+++ b/tests/validation/CL/WidthConcatenateLayer.cpp
@@ -44,20 +44,20 @@
// *INDENT-OFF*
// clang-format off
DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(
- framework::dataset::make("InputInfo1", { TensorInfo(TensorShape(23U, 27U, 5U), 1, DataType::F32, 0), // Mismatching data type input/output
- TensorInfo(TensorShape(23U, 27U, 5U), 1, DataType::F32, 0), // Mismatching y dimension
- TensorInfo(TensorShape(23U, 27U, 5U), 1, DataType::F32, 0), // Mismatching total width
- TensorInfo(TensorShape(16U, 27U, 5U), 1, DataType::F32, 0)
+ framework::dataset::make("InputInfo1", { TensorInfo(TensorShape(23U, 27U, 5U), 1, DataType::F32), // Mismatching data type input/output
+ TensorInfo(TensorShape(23U, 27U, 5U), 1, DataType::F32), // Mismatching y dimension
+ TensorInfo(TensorShape(23U, 27U, 5U), 1, DataType::F32), // Mismatching total width
+ TensorInfo(TensorShape(16U, 27U, 5U), 1, DataType::F32)
}),
- framework::dataset::make("InputInfo2", { TensorInfo(TensorShape(24U, 27U, 4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(52U, 27U, 5U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(52U, 27U, 5U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(16U, 27U, 5U), 1, DataType::F32, 0)
+ framework::dataset::make("InputInfo2", { TensorInfo(TensorShape(24U, 27U, 4U), 1, DataType::F32),
+ TensorInfo(TensorShape(52U, 27U, 5U), 1, DataType::F32),
+ TensorInfo(TensorShape(52U, 27U, 5U), 1, DataType::F32),
+ TensorInfo(TensorShape(16U, 27U, 5U), 1, DataType::F32)
})),
- framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(47U, 27U, 5U), 1, DataType::F16, 0),
- TensorInfo(TensorShape(75U, 12U, 5U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U, 27U, 5U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(32U, 27U, 5U), 1, DataType::F32, 0)
+ framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(47U, 27U, 5U), 1, DataType::F16),
+ TensorInfo(TensorShape(75U, 12U, 5U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U, 27U, 5U), 1, DataType::F32),
+ TensorInfo(TensorShape(32U, 27U, 5U), 1, DataType::F32)
})),
framework::dataset::make("Expected", { false, false, false, true })),
input_info1, input_info2, output_info,expected)
diff --git a/tests/validation/FixedPoint.h b/tests/validation/FixedPoint.h
deleted file mode 100644
index 81c4f53..0000000
--- a/tests/validation/FixedPoint.h
+++ /dev/null
@@ -1,997 +0,0 @@
-/*
- * Copyright (c) 2017-2018 ARM Limited.
- *
- * SPDX-License-Identifier: MIT
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to
- * deal in the Software without restriction, including without limitation the
- * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
- * sell copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in all
- * copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- */
-#ifndef __ARM_COMPUTE_TEST_VALIDATION_FIXEDPOINT_H__
-#define __ARM_COMPUTE_TEST_VALIDATION_FIXEDPOINT_H__
-
-#include "support/ToolchainSupport.h"
-#include "tests/Utils.h"
-
-#include <cassert>
-#include <cstdint>
-#include <cstdlib>
-#include <limits>
-#include <string>
-#include <type_traits>
-
-namespace arm_compute
-{
-namespace test
-{
-namespace fixed_point_arithmetic
-{
-namespace detail
-{
-// Forward declare structs
-struct functions;
-template <typename T>
-struct constant_expr;
-}
-
-/** Fixed point traits */
-namespace traits
-{
-// Promote types
-// *INDENT-OFF*
-// clang-format off
-/** Promote a type */
-template <typename T> struct promote { };
-/** Promote uint8_t to uint16_t */
-template <> struct promote<uint8_t> { using type = uint16_t; /**< Promoted type */ };
-/** Promote int8_t to int16_t */
-template <> struct promote<int8_t> { using type = int16_t; /**< Promoted type */ };
-/** Promote uint16_t to uint32_t */
-template <> struct promote<uint16_t> { using type = uint32_t; /**< Promoted type */ };
-/** Promote int16_t to int32_t */
-template <> struct promote<int16_t> { using type = int32_t; /**< Promoted type */ };
-/** Promote uint32_t to uint64_t */
-template <> struct promote<uint32_t> { using type = uint64_t; /**< Promoted type */ };
-/** Promote int32_t to int64_t */
-template <> struct promote<int32_t> { using type = int64_t; /**< Promoted type */ };
-/** Promote float to float */
-template <> struct promote<float> { using type = float; /**< Promoted type */ };
-/** Promote half to half */
-template <> struct promote<half> { using type = half; /**< Promoted type */ };
-
-/** Get promoted type */
-template <typename T>
-using promote_t = typename promote<T>::type;
-// clang-format on
-// *INDENT-ON*
-}
-
-/** Strongly typed enum class representing the overflow policy */
-enum class OverflowPolicy
-{
- WRAP, /**< Wrap policy */
- SATURATE /**< Saturate policy */
-};
-/** Strongly typed enum class representing the rounding policy */
-enum class RoundingPolicy
-{
- TO_ZERO, /**< Round to zero policy */
- TO_NEAREST_EVEN /**< Round to nearest even policy */
-};
-
-/** Arbitrary fixed-point arithmetic class */
-template <typename T>
-class fixed_point
-{
-public:
- // Static Checks
- static_assert(std::is_integral<T>::value, "Type is not an integer");
-
- /** Constructor (from different fixed point type)
- *
- * @param[in] val Fixed point
- * @param[in] p Fixed point precision
- */
- template <typename U>
- fixed_point(fixed_point<U> val, uint8_t p)
- : _value(0), _fixed_point_position(p)
- {
- assert(p > 0 && p < std::numeric_limits<T>::digits);
- T v = 0;
-
- if(std::numeric_limits<T>::digits < std::numeric_limits<U>::digits)
- {
- val.rescale(p);
- v = detail::constant_expr<T>::saturate_cast(val.raw());
- }
- else
- {
- auto v_cast = static_cast<fixed_point<T>>(val);
- v_cast.rescale(p);
- v = v_cast.raw();
- }
- _value = static_cast<T>(v);
- }
- /** Constructor (from integer)
- *
- * @param[in] val Integer value to be represented as fixed point
- * @param[in] p Fixed point precision
- * @param[in] is_raw If true val is a raw fixed point value else an integer
- */
- template <typename U, typename = typename std::enable_if<std::is_integral<U>::value>::type>
- fixed_point(U val, uint8_t p, bool is_raw = false)
- : _value(val << p), _fixed_point_position(p)
- {
- if(is_raw)
- {
- _value = val;
- }
- }
- /** Constructor (from float)
- *
- * @param[in] val Float value to be represented as fixed point
- * @param[in] p Fixed point precision
- */
- fixed_point(float val, uint8_t p)
- : _value(detail::constant_expr<T>::to_fixed(val, p)), _fixed_point_position(p)
- {
- assert(p > 0 && p < std::numeric_limits<T>::digits);
- }
- /** Constructor (from float string)
- *
- * @param[in] str Float string to be represented as fixed point
- * @param[in] p Fixed point precision
- */
- fixed_point(std::string str, uint8_t p)
- : _value(detail::constant_expr<T>::to_fixed(support::cpp11::stof(str), p)), _fixed_point_position(p)
- {
- assert(p > 0 && p < std::numeric_limits<T>::digits);
- }
- /** Default copy constructor */
- fixed_point &operator=(const fixed_point &) = default;
- /** Default move constructor */
- fixed_point &operator=(fixed_point &&) = default;
- /** Default copy assignment operator */
- fixed_point(const fixed_point &) = default;
- /** Default move assignment operator */
- fixed_point(fixed_point &&) = default;
-
- /** Float conversion operator
- *
- * @return Float representation of fixed point
- */
- operator float() const
- {
- return detail::constant_expr<T>::to_float(_value, _fixed_point_position);
- }
- /** Integer conversion operator
- *
- * @return Integer representation of fixed point
- */
- template <typename U, typename = typename std::enable_if<std::is_integral<T>::value>::type>
- operator U() const
- {
- return detail::constant_expr<T>::to_int(_value, _fixed_point_position);
- }
- /** Convert to different fixed point of different type but same precision
- *
- * @note Down-conversion might fail.
- */
- template <typename U>
- operator fixed_point<U>()
- {
- U val = static_cast<U>(_value);
- if(std::numeric_limits<U>::digits < std::numeric_limits<T>::digits)
- {
- val = detail::constant_expr<U>::saturate_cast(_value);
- }
- return fixed_point<U>(val, _fixed_point_position, true);
- }
-
- /** Arithmetic += assignment operator
- *
- * @param[in] rhs Fixed point operand
- *
- * @return Reference to this fixed point
- */
- template <typename U>
- fixed_point<T> &operator+=(const fixed_point<U> &rhs)
- {
- fixed_point<T> val(rhs, _fixed_point_position);
- _value += val.raw();
- return *this;
- }
- /** Arithmetic -= assignment operator
- *
- * @param[in] rhs Fixed point operand
- *
- * @return Reference to this fixed point
- */
- template <typename U>
- fixed_point<T> &operator-=(const fixed_point<U> &rhs)
- {
- fixed_point<T> val(rhs, _fixed_point_position);
- _value -= val.raw();
- return *this;
- }
-
- /** Raw value accessor
- *
- * @return Raw fixed point value
- */
- T raw() const
- {
- return _value;
- }
- /** Precision accessor
- *
- * @return Precision of fixed point
- */
- uint8_t precision() const
- {
- return _fixed_point_position;
- }
- /** Rescale a fixed point to a new precision
- *
- * @param[in] p New fixed point precision
- */
- void rescale(uint8_t p)
- {
- assert(p > 0 && p < std::numeric_limits<T>::digits);
-
- using promoted_T = typename traits::promote<T>::type;
- promoted_T val = _value;
- if(p > _fixed_point_position)
- {
- val <<= (p - _fixed_point_position);
- }
- else if(p < _fixed_point_position)
- {
- uint8_t pbar = _fixed_point_position - p;
- val += (pbar != 0) ? (1 << (pbar - 1)) : 0;
- val >>= pbar;
- }
-
- _value = detail::constant_expr<T>::saturate_cast(val);
- _fixed_point_position = p;
- }
-
-private:
- T _value; /**< Fixed point raw value */
- uint8_t _fixed_point_position; /**< Fixed point precision */
-};
-
-namespace detail
-{
-/** Count the number of leading zero bits in the given value.
- *
- * @param[in] value Input value.
- *
- * @return Number of leading zero bits.
- */
-template <typename T>
-constexpr int clz(T value)
-{
- using unsigned_T = typename std::make_unsigned<T>::type;
- // __builtin_clz is available for int. Need to correct reported number to
- // match the original type.
- return __builtin_clz(value) - (32 - std::numeric_limits<unsigned_T>::digits);
-}
-
-/** Constant expressions */
-template <typename T>
-struct constant_expr
-{
- /** Calculate representation of 1 in fixed point given a fixed point precision
- *
- * @param[in] p Fixed point precision
- *
- * @return Representation of value 1 in fixed point.
- */
- static constexpr T fixed_one(uint8_t p)
- {
- return (1 << p);
- }
- /** Calculate fixed point precision step given a fixed point precision
- *
- * @param[in] p Fixed point precision
- *
- * @return Fixed point precision step
- */
- static constexpr float fixed_step(uint8_t p)
- {
- return (1.0f / static_cast<float>(1 << p));
- }
-
- /** Convert a fixed point value to float given its precision.
- *
- * @param[in] val Fixed point value
- * @param[in] p Fixed point precision
- *
- * @return Float representation of the fixed point number
- */
- static constexpr float to_float(T val, uint8_t p)
- {
- return static_cast<float>(val * fixed_step(p));
- }
- /** Convert a fixed point value to integer given its precision.
- *
- * @param[in] val Fixed point value
- * @param[in] p Fixed point precision
- *
- * @return Integer of the fixed point number
- */
- static constexpr T to_int(T val, uint8_t p)
- {
- return val >> p;
- }
- /** Convert a single precision floating point value to a fixed point representation given its precision.
- *
- * @param[in] val Floating point value
- * @param[in] p Fixed point precision
- *
- * @return The raw fixed point representation
- */
- static constexpr T to_fixed(float val, uint8_t p)
- {
- return static_cast<T>(saturate_cast<float>(val * fixed_one(p) + ((val >= 0) ? 0.5 : -0.5)));
- }
- /** Clamp value between two ranges
- *
- * @param[in] val Value to clamp
- * @param[in] min Minimum value to clamp to
- * @param[in] max Maximum value to clamp to
- *
- * @return clamped value
- */
- static constexpr T clamp(T val, T min, T max)
- {
- return std::min(std::max(val, min), max);
- }
- /** Saturate given number
- *
- * @param[in] val Value to saturate
- *
- * @return Saturated value
- */
- template <typename U>
- static constexpr T saturate_cast(U val)
- {
- return static_cast<T>(std::min<U>(std::max<U>(val, static_cast<U>(std::numeric_limits<T>::min())), static_cast<U>(std::numeric_limits<T>::max())));
- }
-};
-/** Functions */
-struct functions
-{
- /** Output stream operator
- *
- * @param[in] s Output stream
- * @param[in] x Fixed point value
- *
- * @return Reference output to updated stream
- */
- template <typename T, typename U, typename traits>
- static std::basic_ostream<T, traits> &write(std::basic_ostream<T, traits> &s, fixed_point<U> &x)
- {
- return s << static_cast<float>(x);
- }
- /** Signbit of a fixed point number.
- *
- * @param[in] x Fixed point number
- *
- * @return True if negative else false.
- */
- template <typename T>
- static bool signbit(fixed_point<T> x)
- {
- return ((x.raw() >> std::numeric_limits<T>::digits) != 0);
- }
- /** Checks if two fixed point numbers are equal
- *
- * @param[in] x First fixed point operand
- * @param[in] y Second fixed point operand
- *
- * @return True if fixed points are equal else false
- */
- template <typename T>
- static bool isequal(fixed_point<T> x, fixed_point<T> y)
- {
- uint8_t p = std::min(x.precision(), y.precision());
- x.rescale(p);
- y.rescale(p);
- return (x.raw() == y.raw());
- }
- /** Checks if two fixed point number are not equal
- *
- * @param[in] x First fixed point operand
- * @param[in] y Second fixed point operand
- *
- * @return True if fixed points are not equal else false
- */
- template <typename T>
- static bool isnotequal(fixed_point<T> x, fixed_point<T> y)
- {
- return !isequal(x, y);
- }
- /** Checks if one fixed point is greater than the other
- *
- * @param[in] x First fixed point operand
- * @param[in] y Second fixed point operand
- *
- * @return True if fixed point is greater than other
- */
- template <typename T>
- static bool isgreater(fixed_point<T> x, fixed_point<T> y)
- {
- uint8_t p = std::min(x.precision(), y.precision());
- x.rescale(p);
- y.rescale(p);
- return (x.raw() > y.raw());
- }
- /** Checks if one fixed point is greater or equal than the other
- *
- * @param[in] x First fixed point operand
- * @param[in] y Second fixed point operand
- *
- * @return True if fixed point is greater or equal than other
- */
- template <typename T>
- static bool isgreaterequal(fixed_point<T> x, fixed_point<T> y)
- {
- uint8_t p = std::min(x.precision(), y.precision());
- x.rescale(p);
- y.rescale(p);
- return (x.raw() >= y.raw());
- }
- /** Checks if one fixed point is less than the other
- *
- * @param[in] x First fixed point operand
- * @param[in] y Second fixed point operand
- *
- * @return True if fixed point is less than other
- */
- template <typename T>
- static bool isless(fixed_point<T> x, fixed_point<T> y)
- {
- uint8_t p = std::min(x.precision(), y.precision());
- x.rescale(p);
- y.rescale(p);
- return (x.raw() < y.raw());
- }
- /** Checks if one fixed point is less or equal than the other
- *
- * @param[in] x First fixed point operand
- * @param[in] y Second fixed point operand
- *
- * @return True if fixed point is less or equal than other
- */
- template <typename T>
- static bool islessequal(fixed_point<T> x, fixed_point<T> y)
- {
- uint8_t p = std::min(x.precision(), y.precision());
- x.rescale(p);
- y.rescale(p);
- return (x.raw() <= y.raw());
- }
- /** Checks if one fixed point is less or greater than the other
- *
- * @param[in] x First fixed point operand
- * @param[in] y Second fixed point operand
- *
- * @return True if fixed point is less or greater than other
- */
- template <typename T>
- static bool islessgreater(fixed_point<T> x, fixed_point<T> y)
- {
- return isnotequal(x, y);
- }
- /** Clamp fixed point to specific range.
- *
- * @param[in] x Fixed point operand
- * @param[in] min Minimum value to clamp to
- * @param[in] max Maximum value to clamp to
- *
- * @return Clamped result
- */
- template <typename T>
- static fixed_point<T> clamp(fixed_point<T> x, T min, T max)
- {
- return fixed_point<T>(constant_expr<T>::clamp(x.raw(), min, max), x.precision(), true);
- }
- /** Negate number
- *
- * @param[in] x Fixed point operand
- *
- * @return Negated fixed point result
- */
- template <OverflowPolicy OP = OverflowPolicy::SATURATE, typename T>
- static fixed_point<T> negate(fixed_point<T> x)
- {
- using promoted_T = typename traits::promote<T>::type;
- promoted_T val = -x.raw();
- if(OP == OverflowPolicy::SATURATE)
- {
- val = constant_expr<T>::saturate_cast(val);
- }
- return fixed_point<T>(static_cast<T>(val), x.precision(), true);
- }
- /** Perform addition among two fixed point numbers
- *
- * @param[in] x First fixed point operand
- * @param[in] y Second fixed point operand
- *
- * @return Result fixed point with precision equal to minimum precision of both operands
- */
- template <OverflowPolicy OP = OverflowPolicy::SATURATE, typename T>
- static fixed_point<T> add(fixed_point<T> x, fixed_point<T> y)
- {
- uint8_t p = std::min(x.precision(), y.precision());
- x.rescale(p);
- y.rescale(p);
- if(OP == OverflowPolicy::SATURATE)
- {
- using type = typename traits::promote<T>::type;
- type val = static_cast<type>(x.raw()) + static_cast<type>(y.raw());
- val = constant_expr<T>::saturate_cast(val);
- return fixed_point<T>(static_cast<T>(val), p, true);
- }
- else
- {
- return fixed_point<T>(x.raw() + y.raw(), p, true);
- }
- }
- /** Perform subtraction among two fixed point numbers
- *
- * @param[in] x First fixed point operand
- * @param[in] y Second fixed point operand
- *
- * @return Result fixed point with precision equal to minimum precision of both operands
- */
- template <OverflowPolicy OP = OverflowPolicy::SATURATE, typename T>
- static fixed_point<T> sub(fixed_point<T> x, fixed_point<T> y)
- {
- uint8_t p = std::min(x.precision(), y.precision());
- x.rescale(p);
- y.rescale(p);
- if(OP == OverflowPolicy::SATURATE)
- {
- using type = typename traits::promote<T>::type;
- type val = static_cast<type>(x.raw()) - static_cast<type>(y.raw());
- val = constant_expr<T>::saturate_cast(val);
- return fixed_point<T>(static_cast<T>(val), p, true);
- }
- else
- {
- return fixed_point<T>(x.raw() - y.raw(), p, true);
- }
- }
- /** Perform multiplication among two fixed point numbers
- *
- * @param[in] x First fixed point operand
- * @param[in] y Second fixed point operand
- *
- * @return Result fixed point with precision equal to minimum precision of both operands
- */
- template <OverflowPolicy OP = OverflowPolicy::SATURATE, typename T>
- static fixed_point<T> mul(fixed_point<T> x, fixed_point<T> y)
- {
- using promoted_T = typename traits::promote<T>::type;
- uint8_t p_min = std::min(x.precision(), y.precision());
- uint8_t p_max = std::max(x.precision(), y.precision());
- promoted_T round_factor = (1 << (p_max - 1));
- promoted_T val = ((static_cast<promoted_T>(x.raw()) * static_cast<promoted_T>(y.raw())) + round_factor) >> p_max;
- if(OP == OverflowPolicy::SATURATE)
- {
- val = constant_expr<T>::saturate_cast(val);
- }
- return fixed_point<T>(static_cast<T>(val), p_min, true);
- }
- /** Perform division among two fixed point numbers
- *
- * @param[in] x First fixed point operand
- * @param[in] y Second fixed point operand
- *
- * @return Result fixed point with precision equal to minimum precision of both operands
- */
- template <OverflowPolicy OP = OverflowPolicy::SATURATE, typename T>
- static fixed_point<T> div(fixed_point<T> x, fixed_point<T> y)
- {
- using promoted_T = typename traits::promote<T>::type;
- uint8_t p = std::min(x.precision(), y.precision());
- promoted_T denom = static_cast<promoted_T>(y.raw());
- if(denom != 0)
- {
- promoted_T val = (static_cast<promoted_T>(x.raw()) << std::max(x.precision(), y.precision())) / denom;
- if(OP == OverflowPolicy::SATURATE)
- {
- val = constant_expr<T>::saturate_cast(val);
- }
- return fixed_point<T>(static_cast<T>(val), p, true);
- }
- else
- {
- T val = (x.raw() < 0) ? std::numeric_limits<T>::min() : std::numeric_limits<T>::max();
- return fixed_point<T>(val, p, true);
- }
- }
- /** Shift left
- *
- * @param[in] x Fixed point operand
- * @param[in] shift Shift value
- *
- * @return Shifted value
- */
- template <OverflowPolicy OP = OverflowPolicy::SATURATE, typename T>
- static fixed_point<T> shift_left(fixed_point<T> x, size_t shift)
- {
- using promoted_T = typename traits::promote<T>::type;
- promoted_T val = static_cast<promoted_T>(x.raw()) << shift;
- if(OP == OverflowPolicy::SATURATE)
- {
- val = constant_expr<T>::saturate_cast(val);
- }
- return fixed_point<T>(static_cast<T>(val), x.precision(), true);
- }
- /** Shift right
- *
- * @param[in] x Fixed point operand
- * @param[in] shift Shift value
- *
- * @return Shifted value
- */
- template <typename T>
- static fixed_point<T> shift_right(fixed_point<T> x, size_t shift)
- {
- return fixed_point<T>(x.raw() >> shift, x.precision(), true);
- }
- /** Calculate absolute value
- *
- * @param[in] x Fixed point operand
- *
- * @return Absolute value of operand
- */
- template <typename T>
- static fixed_point<T> abs(fixed_point<T> x)
- {
- using promoted_T = typename traits::promote<T>::type;
- T val = (x.raw() < 0) ? constant_expr<T>::saturate_cast(-static_cast<promoted_T>(x.raw())) : x.raw();
- return fixed_point<T>(val, x.precision(), true);
- }
- /** Calculate the logarithm of a fixed point number
- *
- * @param[in] x Fixed point operand
- *
- * @return Logarithm value of operand
- */
- template <typename T>
- static fixed_point<T> log(fixed_point<T> x)
- {
- uint8_t p = x.precision();
- auto const_one = fixed_point<T>(static_cast<T>(1), p);
-
- // Logarithm of 1 is zero and logarithm of negative values is not defined in R, so return 0.
- // Also, log(x) == -log(1/x) for 0 < x < 1.
- if(isequal(x, const_one) || islessequal(x, fixed_point<T>(static_cast<T>(0), p)))
- {
- return fixed_point<T>(static_cast<T>(0), p, true);
- }
- else if(isless(x, const_one))
- {
- return mul(log(div(const_one, x)), fixed_point<T>(-1, p));
- }
-
- // Remove even powers of 2
- T shift_val = 31 - __builtin_clz(x.raw() >> p);
- x = shift_right(x, shift_val);
- x = sub(x, const_one);
-
- // Constants
- auto ln2 = fixed_point<T>(0.6931471, p);
- auto A = fixed_point<T>(1.4384189, p);
- auto B = fixed_point<T>(-0.67719, p);
- auto C = fixed_point<T>(0.3218538, p);
- auto D = fixed_point<T>(-0.0832229, p);
-
- // Polynomial expansion
- auto sum = add(mul(x, D), C);
- sum = add(mul(x, sum), B);
- sum = add(mul(x, sum), A);
- sum = mul(x, sum);
-
- return mul(add(sum, fixed_point<T>(static_cast<T>(shift_val), p)), ln2);
- }
- /** Calculate the exponential of a fixed point number.
- *
- * exp(x) = exp(floor(x)) * exp(x - floor(x))
- * = pow(2, floor(x) / ln(2)) * exp(x - floor(x))
- * = exp(x - floor(x)) << (floor(x) / ln(2))
- *
- * @param[in] x Fixed point operand
- *
- * @return Exponential value of operand
- */
- template <typename T>
- static fixed_point<T> exp(fixed_point<T> x)
- {
- uint8_t p = x.precision();
- // Constants
- auto const_one = fixed_point<T>(1, p);
- auto ln2 = fixed_point<T>(0.6931471, p);
- auto inv_ln2 = fixed_point<T>(1.442695, p);
- auto A = fixed_point<T>(0.9978546, p);
- auto B = fixed_point<T>(0.4994721, p);
- auto C = fixed_point<T>(0.1763723, p);
- auto D = fixed_point<T>(0.0435108, p);
-
- T scaled_int_part = detail::constant_expr<T>::to_int(mul(x, inv_ln2).raw(), p);
-
- // Polynomial expansion
- auto frac_part = sub(x, mul(ln2, fixed_point<T>(scaled_int_part, p)));
- auto taylor = add(mul(frac_part, D), C);
- taylor = add(mul(frac_part, taylor), B);
- taylor = add(mul(frac_part, taylor), A);
- taylor = mul(frac_part, taylor);
- taylor = add(taylor, const_one);
-
- // Saturate value
- if(static_cast<T>(clz(taylor.raw())) <= scaled_int_part)
- {
- return fixed_point<T>(std::numeric_limits<T>::max(), p, true);
- }
-
- return (scaled_int_part < 0) ? shift_right(taylor, -scaled_int_part) : shift_left(taylor, scaled_int_part);
- }
- /** Calculate the inverse square root of a fixed point number
- *
- * @param[in] x Fixed point operand
- *
- * @return Inverse square root value of operand
- */
- template <typename T>
- static fixed_point<T> inv_sqrt(fixed_point<T> x)
- {
- const uint8_t p = x.precision();
- int8_t shift = std::numeric_limits<T>::digits - (p + detail::clz(x.raw()));
-
- shift += std::numeric_limits<T>::is_signed ? 1 : 0;
-
- // Use volatile to restrict compiler optimizations on shift as compiler reports maybe-uninitialized error on Android
- volatile int8_t *shift_ptr = &shift;
-
- auto const_three = fixed_point<T>(3, p);
- auto a = (*shift_ptr < 0) ? shift_left(x, -(shift)) : shift_right(x, shift);
- fixed_point<T> x2 = a;
-
- // We need three iterations to find the result for QS8 and five for QS16
- constexpr int num_iterations = std::is_same<T, int8_t>::value ? 3 : 5;
- for(int i = 0; i < num_iterations; ++i)
- {
- fixed_point<T> three_minus_dx = sub(const_three, mul(a, mul(x2, x2)));
- x2 = shift_right(mul(x2, three_minus_dx), 1);
- }
-
- return (shift < 0) ? shift_left(x2, (-shift) >> 1) : shift_right(x2, shift >> 1);
- }
- /** Calculate the hyperbolic tangent of a fixed point number
- *
- * @param[in] x Fixed point operand
- *
- * @return Hyperbolic tangent of the operand
- */
- template <typename T>
- static fixed_point<T> tanh(fixed_point<T> x)
- {
- uint8_t p = x.precision();
- // Constants
- auto const_one = fixed_point<T>(1, p);
- auto const_two = fixed_point<T>(2, p);
-
- auto exp2x = exp(const_two * x);
- auto num = exp2x - const_one;
- auto den = exp2x + const_one;
- auto tanh = num / den;
-
- return tanh;
- }
- /** Calculate the a-th power of a fixed point number.
- *
- * The power is computed as x^a = e^(log(x) * a)
- *
- * @param[in] x Fixed point operand
- * @param[in] a Fixed point exponent
- *
- * @return a-th power of the operand
- */
- template <typename T>
- static fixed_point<T> pow(fixed_point<T> x, fixed_point<T> a)
- {
- return exp(log(x) * a);
- }
-};
-
-template <typename T>
-bool operator==(const fixed_point<T> &lhs, const fixed_point<T> &rhs)
-{
- return functions::isequal(lhs, rhs);
-}
-template <typename T>
-bool operator!=(const fixed_point<T> &lhs, const fixed_point<T> &rhs)
-{
- return !operator==(lhs, rhs);
-}
-template <typename T>
-bool operator<(const fixed_point<T> &lhs, const fixed_point<T> &rhs)
-{
- return functions::isless(lhs, rhs);
-}
-template <typename T>
-bool operator>(const fixed_point<T> &lhs, const fixed_point<T> &rhs)
-{
- return operator<(rhs, lhs);
-}
-template <typename T>
-bool operator<=(const fixed_point<T> &lhs, const fixed_point<T> &rhs)
-{
- return !operator>(lhs, rhs);
-}
-template <typename T>
-bool operator>=(const fixed_point<T> &lhs, const fixed_point<T> &rhs)
-{
- return !operator<(lhs, rhs);
-}
-template <typename T>
-fixed_point<T> operator+(const fixed_point<T> &lhs, const fixed_point<T> &rhs)
-{
- return functions::add(lhs, rhs);
-}
-template <typename T>
-fixed_point<T> operator-(const fixed_point<T> &lhs, const fixed_point<T> &rhs)
-{
- return functions::sub(lhs, rhs);
-}
-template <typename T>
-fixed_point<T> operator-(const fixed_point<T> &rhs)
-{
- return functions::negate(rhs);
-}
-template <typename T>
-fixed_point<T> operator*(fixed_point<T> x, fixed_point<T> y)
-{
- return functions::mul(x, y);
-}
-template <typename T>
-fixed_point<T> operator/(fixed_point<T> x, fixed_point<T> y)
-{
- return functions::div(x, y);
-}
-template <typename T>
-fixed_point<T> operator>>(fixed_point<T> x, size_t shift)
-{
- return functions::shift_right(x, shift);
-}
-template <typename T>
-fixed_point<T> operator<<(fixed_point<T> x, size_t shift)
-{
- return functions::shift_left(x, shift);
-}
-template <typename T, typename U, typename traits>
-std::basic_ostream<T, traits> &operator<<(std::basic_ostream<T, traits> &s, fixed_point<U> x)
-{
- return functions::write(s, x);
-}
-template <typename T>
-inline fixed_point<T> min(fixed_point<T> x, fixed_point<T> y)
-{
- return x > y ? y : x;
-}
-template <typename T>
-inline fixed_point<T> max(fixed_point<T> x, fixed_point<T> y)
-{
- return x > y ? x : y;
-}
-template <OverflowPolicy OP = OverflowPolicy::SATURATE, typename T>
-inline fixed_point<T> add(fixed_point<T> x, fixed_point<T> y)
-{
- return functions::add<OP>(x, y);
-}
-template <OverflowPolicy OP = OverflowPolicy::SATURATE, typename T>
-inline fixed_point<T> sub(fixed_point<T> x, fixed_point<T> y)
-{
- return functions::sub<OP>(x, y);
-}
-template <OverflowPolicy OP = OverflowPolicy::SATURATE, typename T>
-inline fixed_point<T> mul(fixed_point<T> x, fixed_point<T> y)
-{
- return functions::mul<OP>(x, y);
-}
-template <typename T>
-inline fixed_point<T> div(fixed_point<T> x, fixed_point<T> y)
-{
- return functions::div(x, y);
-}
-template <typename T>
-inline fixed_point<T> abs(fixed_point<T> x)
-{
- return functions::abs(x);
-}
-template <typename T>
-inline fixed_point<T> clamp(fixed_point<T> x, T min, T max)
-{
- return functions::clamp(x, min, max);
-}
-template <typename T>
-inline fixed_point<T> exp(fixed_point<T> x)
-{
- return functions::exp(x);
-}
-template <typename T>
-inline fixed_point<T> log(fixed_point<T> x)
-{
- return functions::log(x);
-}
-template <typename T>
-inline fixed_point<T> inv_sqrt(fixed_point<T> x)
-{
- return functions::inv_sqrt(x);
-}
-template <typename T>
-inline fixed_point<T> tanh(fixed_point<T> x)
-{
- return functions::tanh(x);
-}
-template <typename T>
-inline fixed_point<T> pow(fixed_point<T> x, fixed_point<T> a)
-{
- return functions::pow(x, a);
-}
-} // namespace detail
-
-// Expose operators
-using detail::operator==;
-using detail::operator!=;
-using detail::operator<;
-using detail::operator>;
-using detail::operator<=;
-using detail::operator>=;
-using detail::operator+;
-using detail::operator-;
-using detail::operator*;
-using detail::operator/;
-using detail::operator>>;
-using detail::operator<<;
-
-// Expose additional functions
-using detail::min;
-using detail::max;
-using detail::add;
-using detail::sub;
-using detail::mul;
-using detail::div;
-using detail::abs;
-using detail::clamp;
-using detail::exp;
-using detail::log;
-using detail::inv_sqrt;
-using detail::tanh;
-using detail::pow;
-// TODO: floor
-// TODO: ceil
-// TODO: sqrt
-} // namespace fixed_point_arithmetic
-} // namespace test
-} // namespace arm_compute
-#endif /*__ARM_COMPUTE_TEST_VALIDATION_FIXEDPOINT_H__ */
diff --git a/tests/validation/GLES_COMPUTE/ActivationLayer.cpp b/tests/validation/GLES_COMPUTE/ActivationLayer.cpp
index a8c7253..7676b85 100644
--- a/tests/validation/GLES_COMPUTE/ActivationLayer.cpp
+++ b/tests/validation/GLES_COMPUTE/ActivationLayer.cpp
@@ -61,35 +61,14 @@
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 ac1bd72..7679007 100644
--- a/tests/validation/GLES_COMPUTE/PoolingLayer.cpp
+++ b/tests/validation/GLES_COMPUTE/PoolingLayer.cpp
@@ -59,17 +59,17 @@
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",
{
diff --git a/tests/validation/Helpers.h b/tests/validation/Helpers.h
index 2b4d277..814d1f5 100644
--- a/tests/validation/Helpers.h
+++ b/tests/validation/Helpers.h
@@ -177,12 +177,10 @@
/** Helper function to get the testing range for batch normalization layer.
*
- * @param[in] fixed_point_position (Optional) Number of bits for the fractional part. Defaults to 0.
- *
* @return A pair containing the lower upper testing bounds.
*/
template <typename T>
-std::pair<T, T> get_batchnormalization_layer_test_bounds(int fixed_point_position = 0)
+std::pair<T, T> get_batchnormalization_layer_test_bounds()
{
const bool is_float = std::is_floating_point<T>::value;
std::pair<T, T> bounds;
@@ -194,7 +192,7 @@
}
else
{
- bounds = std::make_pair(1, 1 << (fixed_point_position));
+ bounds = std::make_pair(1, 1);
}
return bounds;
diff --git a/tests/validation/NEON/ConvolutionLayer.cpp b/tests/validation/NEON/ConvolutionLayer.cpp
index 591d142..1d82ff0 100644
--- a/tests/validation/NEON/ConvolutionLayer.cpp
+++ b/tests/validation/NEON/ConvolutionLayer.cpp
@@ -75,20 +75,20 @@
TEST_SUITE(ConvolutionLayer)
DATA_TEST_CASE(ValidateConvolutionMethod, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(
- framework::dataset::make("InputInfo", { TensorInfo(TensorShape(18U, 18U, 32U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(23U, 27U, 32U, 4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(3U, 3U, 2U, 1U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(33U, 27U, 7U, 4U), 1, DataType::F32, 0)
+ framework::dataset::make("InputInfo", { TensorInfo(TensorShape(18U, 18U, 32U), 1, DataType::F32),
+ TensorInfo(TensorShape(23U, 27U, 32U, 4U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 2U, 1U), 1, DataType::F32),
+ TensorInfo(TensorShape(33U, 27U, 7U, 4U), 1, DataType::F32)
}),
- framework::dataset::make("WeightsInfo", { TensorInfo(TensorShape(3U, 3U, 32U, 21U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(5U, 5U, 32U, 21U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(3U, 3U, 5U, 21U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(5U, 5U, 7U, 16U), 1, DataType::F16, 0)
+ framework::dataset::make("WeightsInfo", { TensorInfo(TensorShape(3U, 3U, 32U, 21U), 1, DataType::F32),
+ TensorInfo(TensorShape(5U, 5U, 32U, 21U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 5U, 21U), 1, DataType::F32),
+ TensorInfo(TensorShape(5U, 5U, 7U, 16U), 1, DataType::F16)
})),
- framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(16U, 16U, 21U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(19U, 23U, 21U, 4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U, 25U, 21U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U, 12U, 16U, 4U), 1, DataType::F32, 0)
+ framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(16U, 16U, 21U), 1, DataType::F32),
+ TensorInfo(TensorShape(19U, 23U, 21U, 4U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U, 25U, 21U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U, 12U, 16U, 4U), 1, DataType::F32)
})),
framework::dataset::make("ConvInfo", { PadStrideInfo(1, 1, 0, 0),
PadStrideInfo(1, 1, 0, 0),
diff --git a/tests/validation/NEON/DeconvolutionLayer.cpp b/tests/validation/NEON/DeconvolutionLayer.cpp
index 87d413f..277953b 100644
--- a/tests/validation/NEON/DeconvolutionLayer.cpp
+++ b/tests/validation/NEON/DeconvolutionLayer.cpp
@@ -100,33 +100,33 @@
// *INDENT-OFF*
// clang-format off
DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(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), // Invalid weights shape
- TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F16, 4), // Non supported data type
- TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 11), // Invalid bias shape
- TensorInfo(TensorShape(13U, 11U, 4U, 3U), 1, DataType::F32, 0), // Window shrink
- TensorInfo(TensorShape(32U, 16U, 2U), 1, DataType::F32, 0),
+ framework::dataset::make("InputInfo", { TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Mismatching data type
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid weights shape
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F16), // Non supported data type
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid bias shape
+ TensorInfo(TensorShape(13U, 11U, 4U, 3U), 1, DataType::F32), // Window shrink
+ TensorInfo(TensorShape(32U, 16U, 2U), 1, DataType::F32),
}),
- framework::dataset::make("WeightsInfo", { TensorInfo(TensorShape(3U, 3U, 2U, 2U), 1, DataType::F16, 0),
- TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(3U, 3U, 2U, 2U), 1, DataType::F16, 5),
- TensorInfo(TensorShape(3U, 2U, 2U, 2U), 1, DataType::F32, 11),
- TensorInfo(TensorShape(3U, 3U, 4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(1U, 1U, 2U, 4U), 1, DataType::F32, 0),
+ framework::dataset::make("WeightsInfo", { TensorInfo(TensorShape(3U, 3U, 2U, 2U), 1, DataType::F16),
+ TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 2U, 2U), 1, DataType::F16),
+ TensorInfo(TensorShape(3U, 2U, 2U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 4U), 1, DataType::F32),
+ TensorInfo(TensorShape(1U, 1U, 2U, 4U), 1, DataType::F32),
})),
- framework::dataset::make("BiasInfo", { TensorInfo(TensorShape(1U), 1, DataType::F16, 0),
- TensorInfo(TensorShape(1U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(1U), 1, DataType::F32, 5),
- TensorInfo(TensorShape(25U, 11U), 1, DataType::F32, 11),
- TensorInfo(TensorShape(1U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(4U), 1, DataType::F32, 0),
+ framework::dataset::make("BiasInfo", { TensorInfo(TensorShape(1U), 1, DataType::F16),
+ TensorInfo(TensorShape(1U), 1, DataType::F32),
+ TensorInfo(TensorShape(1U), 1, DataType::F32),
+ TensorInfo(TensorShape(25U, 11U), 1, DataType::F32),
+ TensorInfo(TensorShape(1U), 1, DataType::F32),
+ TensorInfo(TensorShape(4U), 1, DataType::F32),
})),
- framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F16, 0),
- TensorInfo(TensorShape(25U, 10U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32, 5),
- TensorInfo(TensorShape(13U, 13U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U, 9U, 1U, 3U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(32U, 16U, 4U), 1, DataType::F32, 0),
+ framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F16),
+ TensorInfo(TensorShape(25U, 10U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(13U, 13U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U, 9U, 1U, 3U), 1, DataType::F32),
+ TensorInfo(TensorShape(32U, 16U, 4U), 1, DataType::F32),
})),
framework::dataset::make("PadStrideInfo", { PadStrideInfo(1, 1, 0, 0),
PadStrideInfo(1, 1, 0, 0),
diff --git a/tests/validation/NEON/DilatedConvolutionLayer.cpp b/tests/validation/NEON/DilatedConvolutionLayer.cpp
index 7cfffc0..25b357e 100644
--- a/tests/validation/NEON/DilatedConvolutionLayer.cpp
+++ b/tests/validation/NEON/DilatedConvolutionLayer.cpp
@@ -64,20 +64,20 @@
TEST_SUITE(DilatedConvolutionLayer)
DATA_TEST_CASE(ValidateConvolutionMethod, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(
- framework::dataset::make("InputInfo", { TensorInfo(TensorShape(8U, 8U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(23U, 27U, 5U, 4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(3U, 3U, 2U, 1U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(33U, 27U, 7U, 4U), 1, DataType::F32, 0)
+ framework::dataset::make("InputInfo", { TensorInfo(TensorShape(8U, 8U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(23U, 27U, 5U, 4U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 2U, 1U), 1, DataType::F32),
+ TensorInfo(TensorShape(33U, 27U, 7U, 4U), 1, DataType::F32)
}),
- framework::dataset::make("WeightsInfo", { TensorInfo(TensorShape(3U, 3U, 5U, 21U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(3U, 3U, 5U, 21U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(3U, 3U, 5U, 21U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(5U, 5U, 7U, 16U), 1, DataType::F16, 0)
+ framework::dataset::make("WeightsInfo", { TensorInfo(TensorShape(3U, 3U, 5U, 21U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 5U, 21U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 5U, 21U), 1, DataType::F32),
+ TensorInfo(TensorShape(5U, 5U, 7U, 16U), 1, DataType::F16)
})),
- framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(6U, 6U, 1U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(21U, 25U, 21U, 4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U, 25U, 21U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U, 12U, 16U, 4U), 1, DataType::F32, 0)
+ framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(6U, 6U, 1U), 1, DataType::F32),
+ TensorInfo(TensorShape(21U, 25U, 21U, 4U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U, 25U, 21U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U, 12U, 16U, 4U), 1, DataType::F32)
})),
framework::dataset::make("ConvInfo", { PadStrideInfo(1, 1, 0, 0),
PadStrideInfo(1, 1, 0, 0),
diff --git a/tests/validation/NEON/DirectConvolutionLayer.cpp b/tests/validation/NEON/DirectConvolutionLayer.cpp
index bf5b33c..acd0e5d 100644
--- a/tests/validation/NEON/DirectConvolutionLayer.cpp
+++ b/tests/validation/NEON/DirectConvolutionLayer.cpp
@@ -80,45 +80,45 @@
// *INDENT-OFF*
// clang-format off
DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(
- framework::dataset::make("InputInfo", { TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Mismatching data type input/weights
- TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Mismatching input feature maps
- TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Unsupported kernel width
- TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Non-rectangular weights dimensions
- TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Invalid weights dimensions
- TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Invalid stride
- TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Invalid biases size
- TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Invalid biases dimensions
- TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Invalid output size
+ framework::dataset::make("InputInfo", { TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Mismatching data type input/weights
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Mismatching input feature maps
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Unsupported kernel width
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Non-rectangular weights dimensions
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid weights dimensions
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid stride
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid biases size
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid biases dimensions
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid output size
}),
- framework::dataset::make("WeightsInfo",{ TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F16, 0),
- TensorInfo(TensorShape(3U, 3U, 3U, 4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(9U, 9U, 2U, 4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(5U, 3U, 2U, 4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(3U, 3U, 2U, 4U, 3U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32, 0),
+ framework::dataset::make("WeightsInfo",{ TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F16),
+ TensorInfo(TensorShape(3U, 3U, 3U, 4U), 1, DataType::F32),
+ TensorInfo(TensorShape(9U, 9U, 2U, 4U), 1, DataType::F32),
+ TensorInfo(TensorShape(5U, 3U, 2U, 4U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 2U, 4U, 3U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32),
})),
- framework::dataset::make("BiasesInfo",{ TensorInfo(TensorShape(4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(3U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(4U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(4U), 1, DataType::F32, 0),
+ framework::dataset::make("BiasesInfo",{ TensorInfo(TensorShape(4U), 1, DataType::F32),
+ TensorInfo(TensorShape(4U), 1, DataType::F32),
+ TensorInfo(TensorShape(4U), 1, DataType::F32),
+ TensorInfo(TensorShape(4U), 1, DataType::F32),
+ TensorInfo(TensorShape(4U), 1, DataType::F32),
+ TensorInfo(TensorShape(4U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U), 1, DataType::F32),
+ TensorInfo(TensorShape(4U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(4U), 1, DataType::F32),
})),
- framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(26U, 11U, 4U), 1, DataType::F32, 0),
+ framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32),
+ TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32),
+ TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32),
+ TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32),
+ TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32),
+ TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32),
+ TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32),
+ TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32),
+ TensorInfo(TensorShape(26U, 11U, 4U), 1, DataType::F32),
})),
framework::dataset::make("ConvInfo", { PadStrideInfo(1, 1, 0, 0),
PadStrideInfo(1, 1, 0, 0),
diff --git a/tests/validation/NEON/GEMMLowp.cpp b/tests/validation/NEON/GEMMLowp.cpp
index eb350e1..9eba3c8 100644
--- a/tests/validation/NEON/GEMMLowp.cpp
+++ b/tests/validation/NEON/GEMMLowp.cpp
@@ -102,7 +102,7 @@
// clang-format off
DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(
framework::dataset::make("InputAInfo", { TensorInfo(TensorShape(21U, 13U), 1, DataType::QASYMM8, QuantizationInfo(1.f/255, 10)), // Input not a multiple of 4
- TensorInfo(TensorShape(21U, 13U), 1, DataType::S32, 2), // Mismatching data type
+ TensorInfo(TensorShape(21U, 13U), 1, DataType::S32), // Mismatching data type
TensorInfo(TensorShape(20U, 13U), 1, DataType::QASYMM8, QuantizationInfo(1.f/255, 10)), // Invalid dimensions
TensorInfo(TensorShape(21U, 13U), 1, DataType::QASYMM8, QuantizationInfo(1.f/255, 10)), // Invalid dimensions
TensorInfo(TensorShape(16U, 32U), 1, DataType::QASYMM8, QuantizationInfo(1.f/255, 10)),
diff --git a/tests/validation/NEON/LocallyConnected.cpp b/tests/validation/NEON/LocallyConnected.cpp
index 0c36ff6..bd0999d 100644
--- a/tests/validation/NEON/LocallyConnected.cpp
+++ b/tests/validation/NEON/LocallyConnected.cpp
@@ -51,41 +51,41 @@
// *INDENT-OFF*
// clang-format off
DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(
- framework::dataset::make("InputInfo", { TensorInfo(TensorShape(23U, 27U, 5U), 1, DataType::F32, 0), // Mismatching data type input/weights
- TensorInfo(TensorShape(23U, 27U, 5U), 1, DataType::F32, 0), // Mismatching data type input/bias
- TensorInfo(TensorShape(23U, 27U, 5U), 1, DataType::F32, 0), // Mismatching data type input/output
- TensorInfo(TensorShape(23U, 27U, 5U), 1, DataType::F32, 0), // Mismatching shape input/weights
- TensorInfo(TensorShape(23U, 27U, 5U), 1, DataType::F32, 0), // Mismatching shape input/bias
- TensorInfo(TensorShape(23U, 27U, 5U), 1, DataType::F32, 0), // Mismatching shape input/output
- TensorInfo(TensorShape(23U, 27U, 5U), 1, DataType::F32, 0), // Asymmetric padding
- TensorInfo(TensorShape(23U, 27U, 5U), 1, DataType::F32, 0)
+ framework::dataset::make("InputInfo", { TensorInfo(TensorShape(23U, 27U, 5U), 1, DataType::F32), // Mismatching data type input/weights
+ TensorInfo(TensorShape(23U, 27U, 5U), 1, DataType::F32), // Mismatching data type input/bias
+ TensorInfo(TensorShape(23U, 27U, 5U), 1, DataType::F32), // Mismatching data type input/output
+ TensorInfo(TensorShape(23U, 27U, 5U), 1, DataType::F32), // Mismatching shape input/weights
+ TensorInfo(TensorShape(23U, 27U, 5U), 1, DataType::F32), // Mismatching shape input/bias
+ TensorInfo(TensorShape(23U, 27U, 5U), 1, DataType::F32), // Mismatching shape input/output
+ TensorInfo(TensorShape(23U, 27U, 5U), 1, DataType::F32), // Asymmetric padding
+ TensorInfo(TensorShape(23U, 27U, 5U), 1, DataType::F32)
}),
- framework::dataset::make("WeightsInfo",{ TensorInfo(TensorShape(3U, 3U, 5U, 21U, 275U), 1, DataType::F16, 0),
- TensorInfo(TensorShape(3U, 3U, 5U, 21U, 275U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(3U, 3U, 5U, 21U, 275U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(3U, 3U, 5U, 21U, 274U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(3U, 3U, 5U, 21U, 275U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(3U, 3U, 5U, 21U, 275U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(3U, 3U, 5U, 21U, 275U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(3U, 3U, 5U, 21U, 275U), 1, DataType::F32, 0)
+ framework::dataset::make("WeightsInfo",{ TensorInfo(TensorShape(3U, 3U, 5U, 21U, 275U), 1, DataType::F16),
+ TensorInfo(TensorShape(3U, 3U, 5U, 21U, 275U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 5U, 21U, 275U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 5U, 21U, 274U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 5U, 21U, 275U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 5U, 21U, 275U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 5U, 21U, 275U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 5U, 21U, 275U), 1, DataType::F32)
})),
- framework::dataset::make("BiasInfo", { TensorInfo(TensorShape(21U, 275U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(21U, 275U), 1, DataType::F16, 0),
- TensorInfo(TensorShape(21U, 275U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(21U, 275U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(21U, 274U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(21U, 275U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(21U, 275U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(21U, 275U), 1, DataType::F32, 0)
+ framework::dataset::make("BiasInfo", { TensorInfo(TensorShape(21U, 275U), 1, DataType::F32),
+ TensorInfo(TensorShape(21U, 275U), 1, DataType::F16),
+ TensorInfo(TensorShape(21U, 275U), 1, DataType::F32),
+ TensorInfo(TensorShape(21U, 275U), 1, DataType::F32),
+ TensorInfo(TensorShape(21U, 274U), 1, DataType::F32),
+ TensorInfo(TensorShape(21U, 275U), 1, DataType::F32),
+ TensorInfo(TensorShape(21U, 275U), 1, DataType::F32),
+ TensorInfo(TensorShape(21U, 275U), 1, DataType::F32)
})),
- framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(11U, 25U, 21U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U, 25U, 21U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U, 25U, 21U), 1, DataType::F16, 0),
- TensorInfo(TensorShape(11U, 25U, 21U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U, 25U, 21U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U, 25U, 22U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U, 25U, 21U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U, 25U, 21U), 1, DataType::F32, 0)
+ framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(11U, 25U, 21U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U, 25U, 21U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U, 25U, 21U), 1, DataType::F16),
+ TensorInfo(TensorShape(11U, 25U, 21U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U, 25U, 21U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U, 25U, 22U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U, 25U, 21U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U, 25U, 21U), 1, DataType::F32)
})),
framework::dataset::make("PadStride", { PadStrideInfo(2, 1, 0, 0),
PadStrideInfo(2, 1, 0, 0),
diff --git a/tests/validation/NEON/NormalizationLayer.cpp b/tests/validation/NEON/NormalizationLayer.cpp
index 02cca0b..a432100 100644
--- a/tests/validation/NEON/NormalizationLayer.cpp
+++ b/tests/validation/NEON/NormalizationLayer.cpp
@@ -66,19 +66,19 @@
// *INDENT-OFF*
// clang-format off
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 input/output
- TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Mismatching shapes
- TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Even normalization
- TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Non implemented IN_MAP_2D
- TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Window shrink
- TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32, 0),
+ framework::dataset::make("InputInfo", { TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Mismatching data type input/output
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Mismatching shapes
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Even normalization
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Non implemented IN_MAP_2D
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Window shrink
+ TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32),
}),
- framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F16, 0),
- TensorInfo(TensorShape(27U, 11U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32, 0),
+ framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F16),
+ TensorInfo(TensorShape(27U, 11U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32),
})),
framework::dataset::make("NormInfo", { NormalizationLayerInfo(NormType::IN_MAP_1D, 5),
NormalizationLayerInfo(NormType::IN_MAP_1D, 5),
diff --git a/tests/validation/NEON/PoolingLayer.cpp b/tests/validation/NEON/PoolingLayer.cpp
index bbfca46..336c066 100644
--- a/tests/validation/NEON/PoolingLayer.cpp
+++ b/tests/validation/NEON/PoolingLayer.cpp
@@ -71,21 +71,21 @@
// *INDENT-OFF*
// clang-format off
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),
+ framework::dataset::make("InputInfo", { 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, 10U, 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(25U, 11U, 2U), 1, DataType::F32, 0),
+ framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F16),
+ TensorInfo(TensorShape(25U, 10U, 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(25U, 11U, 2U), 1, DataType::F32),
})),
framework::dataset::make("PoolInfo", { PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 0, 0)),
PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 0, 0)),
diff --git a/tests/validation/NEON/RNNLayer.cpp b/tests/validation/NEON/RNNLayer.cpp
index 7aa3bef..a5f8499 100644
--- a/tests/validation/NEON/RNNLayer.cpp
+++ b/tests/validation/NEON/RNNLayer.cpp
@@ -49,59 +49,59 @@
// *INDENT-OFF*
// clang-format off
DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(zip(
- framework::dataset::make("InputInfo", { TensorInfo(TensorShape(27U, 13U), 1, DataType::U8, 0), // Wrong data type
- TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Wrong input size
- TensorInfo(TensorShape(27U, 13U), 1, DataType::F32, 0), // Wrong weights size
- TensorInfo(TensorShape(27U, 13U), 1, DataType::F32, 0), // Wrong recurrent weights size
- TensorInfo(TensorShape(27U, 13U), 1, DataType::F32, 0), // Wrong bias size
- TensorInfo(TensorShape(27U, 13U), 1, DataType::F32, 0), // Wrong output size
- TensorInfo(TensorShape(27U, 13U), 1, DataType::F32, 0), // Wrong hidden output size
- TensorInfo(TensorShape(32U, 32U), 1, DataType::F32, 0),
+ framework::dataset::make("InputInfo", { TensorInfo(TensorShape(27U, 13U), 1, DataType::U8), // Wrong data type
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Wrong input size
+ TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), // Wrong weights size
+ TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), // Wrong recurrent weights size
+ TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), // Wrong bias size
+ TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), // Wrong output size
+ TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), // Wrong hidden output size
+ TensorInfo(TensorShape(32U, 32U), 1, DataType::F32),
}),
- framework::dataset::make("WeightsInfo", { TensorInfo(TensorShape(27U, 11U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(27U, 11U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(27U, 11U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(27U, 11U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(27U, 11U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(27U, 11U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(27U, 11U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(32U, 32U), 1, DataType::F32, 0),
+ framework::dataset::make("WeightsInfo", { TensorInfo(TensorShape(27U, 11U), 1, DataType::F32),
+ TensorInfo(TensorShape(27U, 11U), 1, DataType::F32),
+ TensorInfo(TensorShape(27U, 11U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(27U, 11U), 1, DataType::F32),
+ TensorInfo(TensorShape(27U, 11U), 1, DataType::F32),
+ TensorInfo(TensorShape(27U, 11U), 1, DataType::F32),
+ TensorInfo(TensorShape(27U, 11U), 1, DataType::F32),
+ TensorInfo(TensorShape(32U, 32U), 1, DataType::F32),
})),
- framework::dataset::make("RecurrentWeightsInfo", { TensorInfo(TensorShape(11U, 11U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U, 11U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U, 11U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U, 11U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U, 11U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U, 11U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(32U, 32U), 1, DataType::F32, 0),
+ framework::dataset::make("RecurrentWeightsInfo", { TensorInfo(TensorShape(11U, 11U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U, 11U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U, 11U), 1, DataType::F32),
+ TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U, 11U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U, 11U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U, 11U), 1, DataType::F32),
+ TensorInfo(TensorShape(32U, 32U), 1, DataType::F32),
})),
- framework::dataset::make("BiasInfo", { TensorInfo(TensorShape(11U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(30U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(32U), 1, DataType::F32, 0),
+ framework::dataset::make("BiasInfo", { TensorInfo(TensorShape(11U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U), 1, DataType::F32),
+ TensorInfo(TensorShape(30U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U), 1, DataType::F32),
+ TensorInfo(TensorShape(32U), 1, DataType::F32),
})),
- framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(11U, 13U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U, 13U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U, 13U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U, 13U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U, 13U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U, 13U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(32U, 32U), 1, DataType::F32, 0),
+ framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(11U, 13U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U, 13U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U, 13U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U, 13U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U, 13U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U, 13U), 1, DataType::F32),
+ TensorInfo(TensorShape(32U, 32U), 1, DataType::F32),
})),
- framework::dataset::make("HiddenStateInfo", { TensorInfo(TensorShape(11U, 13U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U, 13U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U, 13U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U, 13U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U, 13U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U, 13U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(11U, 13U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(32U, 32U), 1, DataType::F32, 0),
+ framework::dataset::make("HiddenStateInfo", { TensorInfo(TensorShape(11U, 13U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U, 13U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U, 13U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U, 13U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U, 13U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U, 13U), 1, DataType::F32),
+ TensorInfo(TensorShape(11U, 13U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(32U, 32U), 1, DataType::F32),
})),
framework::dataset::make("ActivationInfo", { ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
diff --git a/tests/validation/NEON/Scale.cpp b/tests/validation/NEON/Scale.cpp
index 5f5cfdd..0d4a86e 100644
--- a/tests/validation/NEON/Scale.cpp
+++ b/tests/validation/NEON/Scale.cpp
@@ -77,17 +77,17 @@
// *INDENT-OFF*
// clang-format off
DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(
- framework::dataset::make("InputInfo", { TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::U8, 0), // Mismatching data type
- TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Unsupported sampling point
- TensorInfo(TensorShape(4U, 27U, 13U), 1, DataType::F32, 0), // Invalid policy
- TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Insufficient padding
- TensorInfo(TensorShape(4U, 27U, 13U), 1, DataType::F32, 0),
+ framework::dataset::make("InputInfo", { TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::U8), // Mismatching data type
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Unsupported sampling point
+ TensorInfo(TensorShape(4U, 27U, 13U), 1, DataType::F32), // Invalid policy
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Insufficient padding
+ TensorInfo(TensorShape(4U, 27U, 13U), 1, DataType::F32),
}),
- framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(132U, 25U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(132U, 25U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(4U, 132U, 25U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(132U, 25U, 2U), 1, DataType::F32, 0),
- TensorInfo(TensorShape(4U, 132U, 25U), 1, DataType::F32, 0),
+ framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(132U, 25U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(132U, 25U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(4U, 132U, 25U), 1, DataType::F32),
+ TensorInfo(TensorShape(132U, 25U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(4U, 132U, 25U), 1, DataType::F32),
})),
framework::dataset::make("InterpolationPolicy", { InterpolationPolicy::NEAREST_NEIGHBOR,
InterpolationPolicy::NEAREST_NEIGHBOR,
diff --git a/tests/validation/Validation.h b/tests/validation/Validation.h
index 0c96052..9ce597b 100644
--- a/tests/validation/Validation.h
+++ b/tests/validation/Validation.h
@@ -24,7 +24,6 @@
#ifndef __ARM_COMPUTE_TEST_VALIDATION_H__
#define __ARM_COMPUTE_TEST_VALIDATION_H__
-#include "arm_compute/core/FixedPoint.h"
#include "arm_compute/core/IArray.h"
#include "arm_compute/core/Types.h"
#include "support/ToolchainSupport.h"
diff --git a/tests/validation/fixtures/PoolingLayerFixture.h b/tests/validation/fixtures/PoolingLayerFixture.h
index 2453954..499628c 100644
--- a/tests/validation/fixtures/PoolingLayerFixture.h
+++ b/tests/validation/fixtures/PoolingLayerFixture.h
@@ -65,16 +65,10 @@
std::uniform_real_distribution<> distribution(-1.f, 1.f);
library->fill(tensor, distribution, 0);
}
- else if(is_data_type_quantized_asymmetric(tensor.data_type()))
+ else // data type is quantized_asymmetric
{
library->fill_tensor_uniform(tensor, 0);
}
- else
- {
- const int one_fixed = 1;
- std::uniform_int_distribution<> distribution(-one_fixed, one_fixed);
- library->fill(tensor, distribution, 0);
- }
}
TensorType compute_target(TensorShape shape, PoolingLayerInfo info,
diff --git a/tests/validation/fixtures/SoftmaxLayerFixture.h b/tests/validation/fixtures/SoftmaxLayerFixture.h
index 59ce519..99c0710 100644
--- a/tests/validation/fixtures/SoftmaxLayerFixture.h
+++ b/tests/validation/fixtures/SoftmaxLayerFixture.h
@@ -64,17 +64,11 @@
std::uniform_real_distribution<> distribution(-1000.f, 1000.f);
library->fill(tensor, distribution, 0);
}
- else if(is_data_type_quantized_asymmetric(tensor.data_type()))
+ else // data type is quantized_asymmetric
{
std::uniform_int_distribution<> distribution(0, 100);
library->fill(tensor, distribution, 0);
}
- else
- {
- const int one_fixed = 1;
- std::uniform_int_distribution<> distribution(-one_fixed, one_fixed);
- library->fill(tensor, distribution, 0);
- }
}
TensorType compute_target(const TensorShape &shape, DataType data_type,
@@ -139,20 +133,6 @@
};
template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
-class SoftmaxValidationFixedPointFixture : public SoftmaxValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
-{
-public:
- template <typename...>
- void setup(TensorShape shape, DataType data_type)
- {
- SoftmaxValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(shape,
- data_type,
- QuantizationInfo(),
- 1.0f);
- }
-};
-
-template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
class SoftmaxValidationQuantizedFixture : public SoftmaxValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
{
public:
diff --git a/tests/validation/reference/ArithmeticSubtraction.cpp b/tests/validation/reference/ArithmeticSubtraction.cpp
index bed2d37..f39d01f 100644
--- a/tests/validation/reference/ArithmeticSubtraction.cpp
+++ b/tests/validation/reference/ArithmeticSubtraction.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -23,7 +23,6 @@
*/
#include "ArithmeticSubtraction.h"
-#include "tests/validation/FixedPoint.h"
#include "tests/validation/Helpers.h"
namespace arm_compute
diff --git a/tests/validation/reference/BatchNormalizationLayer.cpp b/tests/validation/reference/BatchNormalizationLayer.cpp
index 3d1a6ed..4ea3769 100644
--- a/tests/validation/reference/BatchNormalizationLayer.cpp
+++ b/tests/validation/reference/BatchNormalizationLayer.cpp
@@ -25,7 +25,6 @@
#include "ActivationLayer.h"
-#include "tests/validation/FixedPoint.h"
#include "tests/validation/Helpers.h"
namespace arm_compute
diff --git a/tests/validation/reference/ChannelCombine.cpp b/tests/validation/reference/ChannelCombine.cpp
index c1ec3ec..b76dcac 100644
--- a/tests/validation/reference/ChannelCombine.cpp
+++ b/tests/validation/reference/ChannelCombine.cpp
@@ -24,7 +24,6 @@
#include "ChannelCombine.h"
#include "arm_compute/core/Types.h"
-#include "tests/validation/FixedPoint.h"
#include "tests/validation/Helpers.h"
namespace arm_compute
diff --git a/tests/validation/reference/ChannelExtract.cpp b/tests/validation/reference/ChannelExtract.cpp
index 595bb13..6f17fc0 100644
--- a/tests/validation/reference/ChannelExtract.cpp
+++ b/tests/validation/reference/ChannelExtract.cpp
@@ -24,7 +24,6 @@
#include "ChannelExtract.h"
#include "arm_compute/core/Types.h"
-#include "tests/validation/FixedPoint.h"
#include "tests/validation/Helpers.h"
namespace arm_compute
diff --git a/tests/validation/reference/ColorConvert.cpp b/tests/validation/reference/ColorConvert.cpp
index a8a5304..6aa2ffa 100644
--- a/tests/validation/reference/ColorConvert.cpp
+++ b/tests/validation/reference/ColorConvert.cpp
@@ -24,7 +24,6 @@
#include "ColorConvert.h"
#include "arm_compute/core/Types.h"
-#include "tests/validation/FixedPoint.h"
#include "tests/validation/Helpers.h"
#include "tests/validation/reference/ColorConvertHelper.h"
diff --git a/tests/validation/reference/Convolution3d.h b/tests/validation/reference/Convolution3d.h
index 7001758..2e5fefd 100644
--- a/tests/validation/reference/Convolution3d.h
+++ b/tests/validation/reference/Convolution3d.h
@@ -25,7 +25,6 @@
#define __ARM_COMPUTE_TEST_VALIDATION_CONVOLUTION_H__
#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
-#include "tests/validation/FixedPoint.h"
#include "tests/validation/Helpers.h"
#include "tests/validation/reference/UtilsQuantizedAsymm.h"
@@ -91,74 +90,16 @@
*out_ptr = acc + (*b_ptr);
}
-// 3D convolution for fixed point type
-template < typename T, typename TB, typename std::enable_if < std::is_integral<T>::value &&std::is_integral<TB>::value, int >::type = 0 >
+// 3D convolution for QASYMM8 type
+template < typename T, typename TB, typename std::enable_if < std::is_same<T, uint8_t>::value &&std::is_same<TB, int32_t>::value, int >::type = 0 >
inline void convolution3d(const SimpleTensor<T> &in, const SimpleTensor<T> &weights, const SimpleTensor<TB> &bias, SimpleTensor<T> &out,
int i_offset, int w_offset, int b_offset, int o_offset,
int xi, int yi, int width_in, int height_in, int depth_in, int width_weights, int height_weights, int dilation_x = 1, int dilation_y = 1)
{
- const T *in_ptr = in.data() + i_offset;
- const T *w_ptr = weights.data() + w_offset;
- const T *b_ptr = bias.data() + b_offset;
- T *out_ptr = out.data() + o_offset;
- int fixed_point_position = in.fixed_point_position();
-
- const int half_width_weights_start = width_weights / 2;
- const int half_width_weights_end = ((width_weights % 2) == 0) ? (half_width_weights_start - 1) : half_width_weights_start;
- const int half_height_weights_start = height_weights / 2;
- const int half_height_weights_end = ((height_weights % 2) == 0) ? (half_height_weights_start - 1) : half_height_weights_start;
-
- using namespace fixed_point_arithmetic;
- using promoted_type = fixed_point_arithmetic::traits::promote_t<T>;
-
- // Reset accumulator
- fixed_point<promoted_type> acc(0, fixed_point_position);
-
- // Compute a 2D convolution for each IFM and accumulate the result
- for(int ifm = 0; ifm < depth_in; ++ifm)
- {
- // Compute the offset for the input slice
- const int offset_slice_in = xi + yi * width_in + ifm * width_in * height_in;
-
- // Compute 2D convolution
- for(int yk = -half_height_weights_start; yk <= half_height_weights_end; ++yk)
- {
- for(int xk = -half_width_weights_start; xk <= half_width_weights_end; ++xk)
- {
- // Check if the pixel is out-of-bound
- if(is_valid_pixel(xi + xk * dilation_x, 0, width_in) && is_valid_pixel(yi + yk * dilation_y, 0, height_in))
- {
- const int idx = xk + half_width_weights_start;
- const int idy = yk + half_height_weights_start;
-
- const fixed_point<promoted_type> i_value(in_ptr[offset_slice_in + xk * dilation_x + yk * dilation_y * width_in], fixed_point_position, true);
- const fixed_point<promoted_type> w_value(w_ptr[idx + idy * width_weights + ifm * width_weights * height_weights], fixed_point_position, true);
- const fixed_point<promoted_type> iw = i_value * w_value;
- acc = iw + acc;
- }
- }
- }
- }
-
- // Get the bias
- const fixed_point<promoted_type> b(*b_ptr, fixed_point_position, true);
-
- // Accumulate the bias and covert back
- acc = acc + b;
- fixed_point<T> res(acc);
- *out_ptr = res.raw();
-}
-
-// 3D convolution for QASYMM8 type
-template <>
-inline void convolution3d(const SimpleTensor<uint8_t> &in, const SimpleTensor<uint8_t> &weights, const SimpleTensor<int32_t> &bias, SimpleTensor<uint8_t> &out,
- int i_offset, int w_offset, int b_offset, int o_offset,
- int xi, int yi, int width_in, int height_in, int depth_in, int width_weights, int height_weights, int dilation_x, int dilation_y)
-{
- const uint8_t *in_ptr = in.data() + i_offset;
- const uint8_t *w_ptr = weights.data() + w_offset;
- const int32_t *b_ptr = bias.data() + b_offset;
- uint8_t *out_ptr = out.data() + o_offset;
+ const T *in_ptr = in.data() + i_offset;
+ const T *w_ptr = weights.data() + w_offset;
+ const TB *b_ptr = bias.data() + b_offset;
+ T *out_ptr = out.data() + o_offset;
const int input_offset = -in.quantization_info().offset;
const float input_scale = in.quantization_info().scale;
diff --git a/tests/validation/reference/ConvolutionLayer.cpp b/tests/validation/reference/ConvolutionLayer.cpp
index 00c839d..e212e27 100644
--- a/tests/validation/reference/ConvolutionLayer.cpp
+++ b/tests/validation/reference/ConvolutionLayer.cpp
@@ -23,7 +23,6 @@
*/
#include "ConvolutionLayer.h"
-#include "tests/validation/FixedPoint.h"
#include "tests/validation/Helpers.h"
#include "tests/validation/reference/Convolution3d.h"
#include "tests/validation/reference/Permute.h"
diff --git a/tests/validation/reference/DeconvolutionLayer.cpp b/tests/validation/reference/DeconvolutionLayer.cpp
index d073bbf..e73023e 100644
--- a/tests/validation/reference/DeconvolutionLayer.cpp
+++ b/tests/validation/reference/DeconvolutionLayer.cpp
@@ -23,7 +23,6 @@
*/
#include "ConvolutionLayer.h"
-#include "tests/validation/FixedPoint.h"
#include "tests/validation/Helpers.h"
namespace arm_compute
diff --git a/tests/validation/reference/DepthConcatenateLayer.cpp b/tests/validation/reference/DepthConcatenateLayer.cpp
index c9a2352..dbcd575 100644
--- a/tests/validation/reference/DepthConcatenateLayer.cpp
+++ b/tests/validation/reference/DepthConcatenateLayer.cpp
@@ -23,7 +23,6 @@
*/
#include "DepthConcatenateLayer.h"
-#include "tests/validation/FixedPoint.h"
#include "tests/validation/Helpers.h"
namespace arm_compute
diff --git a/tests/validation/reference/DepthConvertLayer.cpp b/tests/validation/reference/DepthConvertLayer.cpp
index 0220077..6f90963 100644
--- a/tests/validation/reference/DepthConvertLayer.cpp
+++ b/tests/validation/reference/DepthConvertLayer.cpp
@@ -23,7 +23,6 @@
*/
#include "DepthConvertLayer.h"
-#include "tests/validation/FixedPoint.h"
#include "tests/validation/Helpers.h"
#include "tests/Types.h"
@@ -61,33 +60,6 @@
return result;
}
-template < typename T1, typename T2, typename std::enable_if < std::is_integral<T1>::value &&std::is_integral<T2>::value &&std::is_same<T1, T2>::value, int >::type >
-SimpleTensor<T2> depth_convert(const SimpleTensor<T1> &src, DataType dt_out, ConvertPolicy policy, uint32_t shift)
-{
- ARM_COMPUTE_UNUSED(policy);
-
- using namespace fixed_point_arithmetic;
-
- SimpleTensor<T2> result(src.shape(), dt_out);
-
- bool is_in_place = (&src == &result);
-
- const int fixed_point_position_in = src.fixed_point_position();
- const int fixed_point_position_out = (is_in_place) ? static_cast<int>(shift) : result.fixed_point_position();
-
- if(!is_in_place || (fixed_point_position_in != fixed_point_position_out))
- {
- for(int i = 0; i < src.num_elements(); ++i)
- {
- auto x = fixed_point<T2>(src[i], fixed_point_position_in, true);
- x.resacle(fixed_point_position_out);
- result[i] = x.raw();
- }
- }
-
- return result;
-}
-
template SimpleTensor<uint16_t> depth_convert(const SimpleTensor<uint8_t> &src, DataType dt_out, ConvertPolicy policy, uint32_t shift);
template SimpleTensor<int16_t> depth_convert(const SimpleTensor<uint8_t> &src, DataType dt_out, ConvertPolicy policy, uint32_t shift);
template SimpleTensor<int32_t> depth_convert(const SimpleTensor<uint8_t> &src, DataType dt_out, ConvertPolicy policy, uint32_t shift);
diff --git a/tests/validation/reference/DepthwiseConvolutionLayer.cpp b/tests/validation/reference/DepthwiseConvolutionLayer.cpp
index d8f3cba..39429e2 100644
--- a/tests/validation/reference/DepthwiseConvolutionLayer.cpp
+++ b/tests/validation/reference/DepthwiseConvolutionLayer.cpp
@@ -26,7 +26,6 @@
#include "ConvolutionLayer.h"
#include "Utils.h"
-#include "tests/validation/FixedPoint.h"
#include "tests/validation/Helpers.h"
#include "tests/validation/reference/Utils.h"
#include "tests/validation/reference/UtilsQuantizedAsymm.h"
diff --git a/tests/validation/reference/FixedPoint.cpp b/tests/validation/reference/FixedPoint.cpp
deleted file mode 100644
index a016093..0000000
--- a/tests/validation/reference/FixedPoint.cpp
+++ /dev/null
@@ -1,83 +0,0 @@
-/*
- * Copyright (c) 2017 ARM Limited.
- *
- * SPDX-License-Identifier: MIT
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to
- * deal in the Software without restriction, including without limitation the
- * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
- * sell copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in all
- * copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- */
-#include "FixedPoint.h"
-
-#include "arm_compute/core/Types.h"
-#include "tests/validation/FixedPoint.h"
-#include "tests/validation/Helpers.h"
-
-namespace arm_compute
-{
-namespace test
-{
-namespace validation
-{
-namespace reference
-{
-template <typename T>
-SimpleTensor<T> fixed_point_operation(const SimpleTensor<T> &src, FixedPointOp op)
-{
- SimpleTensor<T> result(src.shape(), src.data_type());
-
- const int p = src.fixed_point_position();
- switch(op)
- {
- case FixedPointOp::EXP:
- for(int i = 0; i < src.num_elements(); ++i)
- {
- result[i] = fixed_point_arithmetic::exp(fixed_point_arithmetic::fixed_point<T>(src[i], p, true)).raw();
- }
- break;
- case FixedPointOp::LOG:
- for(int i = 0; i < src.num_elements(); ++i)
- {
- result[i] = fixed_point_arithmetic::log(fixed_point_arithmetic::fixed_point<T>(src[i], p, true)).raw();
- }
- break;
- case FixedPointOp::INV_SQRT:
- for(int i = 0; i < src.num_elements(); ++i)
- {
- result[i] = fixed_point_arithmetic::inv_sqrt(fixed_point_arithmetic::fixed_point<T>(src[i], p, true)).raw();
- }
- break;
- case FixedPointOp::RECIPROCAL:
- for(int i = 0; i < src.num_elements(); ++i)
- {
- result[i] = fixed_point_arithmetic::div(fixed_point_arithmetic::fixed_point<T>(1, p), fixed_point_arithmetic::fixed_point<T>(src[i], p, true)).raw();
- }
- break;
- default:
- ARM_COMPUTE_ERROR("Fixed point operation not supported");
- break;
- }
-
- return result;
-}
-
-template SimpleTensor<int8_t> fixed_point_operation(const SimpleTensor<int8_t> &src, FixedPointOp op);
-template SimpleTensor<int16_t> fixed_point_operation(const SimpleTensor<int16_t> &src, FixedPointOp op);
-} // namespace reference
-} // namespace validation
-} // namespace test
-} // namespace arm_compute
diff --git a/tests/validation/reference/FixedPoint.h b/tests/validation/reference/FixedPoint.h
deleted file mode 100644
index f0117f9..0000000
--- a/tests/validation/reference/FixedPoint.h
+++ /dev/null
@@ -1,44 +0,0 @@
-/*
- * Copyright (c) 2017 ARM Limited.
- *
- * SPDX-License-Identifier: MIT
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to
- * deal in the Software without restriction, including without limitation the
- * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
- * sell copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in all
- * copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- */
-#ifndef __ARM_COMPUTE_TEST_FIXED_POINT_OPERATION_H__
-#define __ARM_COMPUTE_TEST_FIXED_POINT_OPERATION_H__
-
-#include "tests/SimpleTensor.h"
-#include "tests/Types.h"
-
-namespace arm_compute
-{
-namespace test
-{
-namespace validation
-{
-namespace reference
-{
-template <typename T>
-SimpleTensor<T> fixed_point_operation(const SimpleTensor<T> &src, FixedPointOp op);
-} // namespace reference
-} // namespace validation
-} // namespace test
-} // namespace arm_compute
-#endif /* __ARM_COMPUTE_TEST_FIXED_POINT_OPERATION_H__ */
diff --git a/tests/validation/reference/FlattenLayer.cpp b/tests/validation/reference/FlattenLayer.cpp
index e140d75..381ce37 100644
--- a/tests/validation/reference/FlattenLayer.cpp
+++ b/tests/validation/reference/FlattenLayer.cpp
@@ -23,8 +23,6 @@
*/
#include "FlattenLayer.h"
-#include "tests/validation/FixedPoint.h"
-
namespace arm_compute
{
namespace test
diff --git a/tests/validation/reference/FullyConnectedLayer.cpp b/tests/validation/reference/FullyConnectedLayer.cpp
index 3ef10ea..d65d0ca 100644
--- a/tests/validation/reference/FullyConnectedLayer.cpp
+++ b/tests/validation/reference/FullyConnectedLayer.cpp
@@ -24,7 +24,6 @@
#include "FullyConnectedLayer.h"
#include "arm_compute/core/Types.h"
-#include "tests/validation/FixedPoint.h"
#include "tests/validation/reference/UtilsQuantizedAsymm.h"
#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
diff --git a/tests/validation/reference/GEMM.cpp b/tests/validation/reference/GEMM.cpp
index 7378ada..2feab89 100644
--- a/tests/validation/reference/GEMM.cpp
+++ b/tests/validation/reference/GEMM.cpp
@@ -24,7 +24,6 @@
#include "GEMM.h"
#include "arm_compute/core/Types.h"
-#include "tests/validation/FixedPoint.h"
namespace arm_compute
{
@@ -85,75 +84,6 @@
return dst;
}
-template <typename T, typename std::enable_if<std::is_integral<T>::value, int>::type>
-SimpleTensor<T> gemm(const SimpleTensor<T> &a, const SimpleTensor<T> &b, const SimpleTensor<T> &c, float alpha, float beta)
-{
- using namespace fixed_point_arithmetic;
-
- // Create reference
- SimpleTensor<T> dst{ c.shape(), c.data_type(), 1 };
-
- // Compute reference
- using promoted_type = fixed_point_arithmetic::traits::promote_t<T>;
-
- const int M = dst.shape().y();
- const int N = dst.shape().x();
- const int K = a.shape().x();
- const int D = a.shape().z(); // Number of matrices in a batch
- const int W = a.shape()[3]; // Number of batched-gemm (Winograd case)
-
- const int a_stride_z = K * M;
- const int a_stride_w = K * M * D;
-
- const int b_stride_z = b.shape().num_dimensions() > 2 ? N * K : 0; // Do not slide the matrix B along the 3th dimension in case matrix B has less than 3 dimensions
- const int b_stride_w = b.shape().num_dimensions() > 3 ? K * N * D : 0; // Do not slide the matrix B along the 4th dimension in case matrix B has less than 4 dimensions
-
- const int c_stride_z = N * M;
- const int c_stride_w = N * M * D;
-
- const int fixed_point_position = a.fixed_point_position();
- const fixed_point<T> alpha_q(alpha, fixed_point_position);
- const fixed_point<T> beta_q(beta, fixed_point_position);
-
- for(int w = 0; w < W; ++w)
- {
- for(int depth = 0; depth < D; ++depth)
- {
- const int base_addr_a = depth * a_stride_z + w * a_stride_w;
- const int base_addr_b = depth * b_stride_z + w * b_stride_w;
- const int base_addr_c = depth * c_stride_z + w * c_stride_w;
-
- for(int row = 0; row < M; ++row)
- {
- for(int col = 0; col < N; ++col)
- {
- fixed_point<promoted_type> acc_q(0, fixed_point_position);
-
- for(int k = 0; k < K; ++k)
- {
- const fixed_point<promoted_type> a0_q(a[base_addr_a + row * K + k], fixed_point_position, true);
- const fixed_point<promoted_type> b0_q(b[base_addr_b + k * N + col], fixed_point_position, true);
-
- acc_q = acc_q + (a0_q * b0_q);
- }
-
- // Finalize the result: alpha * A * B + beta * C
- const fixed_point<T> c0_q(c[base_addr_c + col + row * N], fixed_point_position, true);
-
- fixed_point<T> res_q(acc_q);
- res_q = alpha_q * res_q;
- res_q = res_q + (beta_q * c0_q);
-
- // Store the result
- dst[base_addr_c + col + row * N] = res_q.raw();
- }
- }
- }
- }
-
- return dst;
-}
-
template SimpleTensor<float> gemm(const SimpleTensor<float> &a, const SimpleTensor<float> &b, const SimpleTensor<float> &c, float alpha, float beta);
template SimpleTensor<half> gemm(const SimpleTensor<half> &a, const SimpleTensor<half> &b, const SimpleTensor<half> &c, float alpha, float beta);
} // namespace reference
diff --git a/tests/validation/reference/GEMM.h b/tests/validation/reference/GEMM.h
index cda792b..39007c6 100644
--- a/tests/validation/reference/GEMM.h
+++ b/tests/validation/reference/GEMM.h
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -38,8 +38,6 @@
template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type = 0>
SimpleTensor<T> gemm(const SimpleTensor<T> &a, const SimpleTensor<T> &b, const SimpleTensor<T> &c, float alpha, float beta);
-template <typename T, typename std::enable_if<std::is_integral<T>::value, int>::type = 0>
-SimpleTensor<T> gemm(const SimpleTensor<T> &a, const SimpleTensor<T> &b, const SimpleTensor<T> &c, float alpha, float beta);
} // namespace reference
} // namespace validation
} // namespace test
diff --git a/tests/validation/reference/GEMMInterleave4x4.h b/tests/validation/reference/GEMMInterleave4x4.h
index e6b09af..e3d72d9 100644
--- a/tests/validation/reference/GEMMInterleave4x4.h
+++ b/tests/validation/reference/GEMMInterleave4x4.h
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -24,7 +24,6 @@
#include "GEMM.h"
#include "arm_compute/core/Types.h"
-#include "tests/validation/FixedPoint.h"
namespace arm_compute
{
diff --git a/tests/validation/reference/GEMMInterleaveBlocked.h b/tests/validation/reference/GEMMInterleaveBlocked.h
index ff5a0d6..d649a51 100644
--- a/tests/validation/reference/GEMMInterleaveBlocked.h
+++ b/tests/validation/reference/GEMMInterleaveBlocked.h
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -24,7 +24,6 @@
#include "GEMM.h"
#include "arm_compute/core/Types.h"
-#include "tests/validation/FixedPoint.h"
namespace arm_compute
{
diff --git a/tests/validation/reference/GEMMTranspose1xW.h b/tests/validation/reference/GEMMTranspose1xW.h
index d6a2e89..6ec70b1 100644
--- a/tests/validation/reference/GEMMTranspose1xW.h
+++ b/tests/validation/reference/GEMMTranspose1xW.h
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017, 2018 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -24,7 +24,6 @@
#include "GEMM.h"
#include "arm_compute/core/Types.h"
-#include "tests/validation/FixedPoint.h"
namespace arm_compute
{
diff --git a/tests/validation/reference/NormalizationLayer.cpp b/tests/validation/reference/NormalizationLayer.cpp
index 85872c8..2ae68c6 100644
--- a/tests/validation/reference/NormalizationLayer.cpp
+++ b/tests/validation/reference/NormalizationLayer.cpp
@@ -24,7 +24,6 @@
#include "NormalizationLayer.h"
#include "arm_compute/core/Types.h"
-#include "tests/validation/FixedPoint.h"
namespace arm_compute
{
@@ -146,125 +145,6 @@
return dst;
}
-template <typename T, typename std::enable_if<std::is_integral<T>::value, int>::type>
-SimpleTensor<T> normalization_layer(const SimpleTensor<T> &src, NormalizationLayerInfo info)
-{
- using namespace fixed_point_arithmetic;
-
- // Create reference
- SimpleTensor<T> dst{ src.shape(), src.data_type(), 1 };
-
- // Compute reference
- const int fixed_point_position = src.fixed_point_position();
-
- const uint32_t norm_size = info.norm_size();
- NormType type = info.type();
- fixed_point<T> beta(info.beta(), fixed_point_position);
- fixed_point<T> kappa(info.kappa(), fixed_point_position);
-
- const int cols = src.shape()[0];
- const int rows = src.shape()[1];
- const int depth = src.shape()[2];
- int upper_dims = src.shape().total_size() / (cols * rows);
-
- fixed_point<T> coeff(info.scale_coeff(), fixed_point_position);
- int radius_cols = norm_size / 2;
-
- // IN_MAP_1D and CROSS_MAP normalize over a single axis only
- int radius_rows = (NormType::IN_MAP_2D == type) ? norm_size / 2 : 0;
-
- if(type == NormType::CROSS_MAP)
- {
- // Remove also depth from upper dimensions since it is the dimension we
- // want to use for normalization
- upper_dims /= depth;
-
- for(int r = 0; r < upper_dims; ++r)
- {
- for(int i = 0; i < rows; ++i)
- {
- for(int k = 0; k < cols; ++k)
- {
- for(int l = 0; l < depth; ++l)
- {
- fixed_point<T> accumulated_scale(0.f, fixed_point_position);
-
- for(int j = -radius_cols; j <= radius_cols; ++j)
- {
- const int z = l + j;
-
- if(z >= 0 && z < depth)
- {
- const T value = src[k + i * cols + z * rows * cols + r * cols * rows * depth];
- const fixed_point<T> fp_value(value, fixed_point_position, true);
- accumulated_scale = add(accumulated_scale, mul(fp_value, fp_value));
- }
- }
-
- accumulated_scale = add(kappa, mul(accumulated_scale, coeff));
- dst[k + i * cols + l * rows * cols + r * cols * rows * depth] = accumulated_scale.raw();
- }
- }
- }
- }
- }
- else
- {
- for(int r = 0; r < upper_dims; ++r)
- {
- for(int i = 0; i < rows; ++i)
- {
- for(int k = 0; k < cols; ++k)
- {
- fixed_point<T> accumulated_scale(0.f, fixed_point_position);
-
- for(int j = -radius_rows; j <= radius_rows; ++j)
- {
- const int y = i + j;
-
- for(int l = -radius_cols; l <= radius_cols; ++l)
- {
- const int x = k + l;
-
- if((x >= 0 && y >= 0) && (x < cols && y < rows))
- {
- const T value = src[x + y * cols + r * cols * rows];
- const fixed_point<T> fp_value(value, fixed_point_position, true);
- accumulated_scale = add(accumulated_scale, mul(fp_value, fp_value));
- }
- }
- }
-
- accumulated_scale = add(kappa, mul(accumulated_scale, coeff));
- dst[k + i * cols + r * cols * rows] = accumulated_scale.raw();
- }
- }
- }
- }
-
- if(info.beta() == 1.f)
- {
- for(int i = 0; i < dst.num_elements(); ++i)
- {
- fixed_point<T> res = div(fixed_point<T>(src[i], fixed_point_position, true), fixed_point<T>(dst[i], fixed_point_position, true));
- dst[i] = res.raw();
- }
- }
- else
- {
- const fixed_point<T> beta(info.beta(), fixed_point_position);
-
- for(int i = 0; i < dst.num_elements(); ++i)
- {
- fixed_point<T> res = pow(fixed_point<T>(dst[i], fixed_point_position, true), beta);
- res = div(fixed_point<T>(src[i], fixed_point_position, true), res);
- dst[i] = res.raw();
- }
- }
-
- return dst;
-}
-
template SimpleTensor<float> normalization_layer(const SimpleTensor<float> &src, NormalizationLayerInfo info);
template SimpleTensor<half> normalization_layer(const SimpleTensor<half> &src, NormalizationLayerInfo info);
} // namespace reference
diff --git a/tests/validation/reference/NormalizationLayer.h b/tests/validation/reference/NormalizationLayer.h
index 3f624ff..3448baf 100644
--- a/tests/validation/reference/NormalizationLayer.h
+++ b/tests/validation/reference/NormalizationLayer.h
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -38,8 +38,6 @@
template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type = 0>
SimpleTensor<T> normalization_layer(const SimpleTensor<T> &src, NormalizationLayerInfo info);
-template <typename T, typename std::enable_if<std::is_integral<T>::value, int>::type = 0>
-SimpleTensor<T> normalization_layer(const SimpleTensor<T> &src, NormalizationLayerInfo info);
} // namespace reference
} // namespace validation
} // namespace test
diff --git a/tests/validation/reference/PixelWiseMultiplication.cpp b/tests/validation/reference/PixelWiseMultiplication.cpp
index 7304fb0..859da5c 100644
--- a/tests/validation/reference/PixelWiseMultiplication.cpp
+++ b/tests/validation/reference/PixelWiseMultiplication.cpp
@@ -23,8 +23,6 @@
*/
#include "PixelWiseMultiplication.h"
-#include "tests/validation/FixedPoint.h"
-
namespace arm_compute
{
namespace test
diff --git a/tests/validation/reference/PoolingLayer.cpp b/tests/validation/reference/PoolingLayer.cpp
index e9054b9..02c430a 100644
--- a/tests/validation/reference/PoolingLayer.cpp
+++ b/tests/validation/reference/PoolingLayer.cpp
@@ -25,7 +25,6 @@
#include "arm_compute/core/Types.h"
#include "arm_compute/core/utils/misc/ShapeCalculator.h"
-#include "tests/validation/FixedPoint.h"
#include "tests/validation/Helpers.h"
namespace arm_compute
@@ -44,7 +43,7 @@
ARM_COMPUTE_ERROR_ON(info.is_global_pooling() && (src.shape().x() != src.shape().y()));
// Create reference
- SimpleTensor<T> dst{ compute_pool_shape(TensorInfo(src.shape(), 1, src.data_type(), src.fixed_point_position()), info), src.data_type(), 1 };
+ SimpleTensor<T> dst{ compute_pool_shape(TensorInfo(src.shape(), 1, src.data_type()), info), src.data_type(), 1 };
const int pool_size_x = info.is_global_pooling() ? src.shape().x() : info.pool_size().width;
const int pool_size_y = info.is_global_pooling() ? src.shape().y() : info.pool_size().height;
diff --git a/tests/validation/reference/SoftmaxLayer.cpp b/tests/validation/reference/SoftmaxLayer.cpp
index ae4bcd8..aa640ad 100644
--- a/tests/validation/reference/SoftmaxLayer.cpp
+++ b/tests/validation/reference/SoftmaxLayer.cpp
@@ -24,7 +24,6 @@
#include "SoftmaxLayer.h"
#include "arm_compute/core/Types.h"
-#include "tests/validation/FixedPoint.h"
namespace arm_compute
{
@@ -71,63 +70,21 @@
return dst;
}
-template <typename T, typename std::enable_if<std::is_integral<T>::value, int>::type>
+template <typename T, typename std::enable_if<std::is_same<T, uint8_t>::value, int>::type>
SimpleTensor<T> softmax_layer(const SimpleTensor<T> &src, float beta)
{
- ARM_COMPUTE_UNUSED(beta);
-
- using namespace fixed_point_arithmetic;
-
- // Create reference
- SimpleTensor<T> dst{ src.shape(), src.data_type(), 1 };
-
- // Compute reference
- const int cols = src.shape()[0];
- const int upper_dims = src.num_elements() / cols;
-
- for(int r = 0; r < upper_dims; ++r)
- {
- const T *src_row_ptr = src.data() + r * cols;
- T *dst_row_ptr = dst.data() + r * cols;
-
- // Find max
- const fixed_point<T> max(*std::max_element(src_row_ptr, src_row_ptr + cols), src.fixed_point_position(), true);
-
- // Regularize
- using promoted_type = fixed_point_arithmetic::traits::promote_t<T>;
- fixed_point<promoted_type> sum(0, src.fixed_point_position(), true);
- std::transform(src_row_ptr, src_row_ptr + cols, dst_row_ptr, [&](T val)
- {
- const fixed_point<T> res = exp(fixed_point<T>(val, src.fixed_point_position(), true) - max);
- sum = add(sum, fixed_point<promoted_type>(res.raw(), src.fixed_point_position(), true));
- return res.raw();
- });
-
- // Normalize
- fixed_point<T> saturated_sum(sum);
- std::transform(dst_row_ptr, dst_row_ptr + cols, dst_row_ptr, [&](T val)
- {
- return div(fixed_point<T>(val, src.fixed_point_position(), true), saturated_sum).raw();
- });
- }
-
- return dst;
-}
-
-template <>
-SimpleTensor<uint8_t> softmax_layer<uint8_t>(const SimpleTensor<uint8_t> &src, float beta)
-{
// Note: Output quantization info should always have scale = 1/256 and offset = 0
const QuantizationInfo output_quantization_info = QuantizationInfo(1.f / 256, 0);
- SimpleTensor<float> src_tmp = convert_from_asymmetric(src);
- SimpleTensor<float> dst_tmp = softmax_layer<float>(src_tmp, beta);
- SimpleTensor<uint8_t> dst = convert_to_asymmetric(dst_tmp, output_quantization_info);
+ SimpleTensor<float> src_tmp = convert_from_asymmetric(src);
+ SimpleTensor<float> dst_tmp = softmax_layer<float>(src_tmp, beta);
+ SimpleTensor<T> dst = convert_to_asymmetric(dst_tmp, output_quantization_info);
return dst;
}
template SimpleTensor<float> softmax_layer(const SimpleTensor<float> &src, float beta);
template SimpleTensor<half> softmax_layer(const SimpleTensor<half> &src, float beta);
+template SimpleTensor<uint8_t> softmax_layer(const SimpleTensor<uint8_t> &src, float beta);
} // namespace reference
} // namespace validation
} // namespace test
diff --git a/tests/validation/reference/SoftmaxLayer.h b/tests/validation/reference/SoftmaxLayer.h
index a6d4c3b..21dca1e 100644
--- a/tests/validation/reference/SoftmaxLayer.h
+++ b/tests/validation/reference/SoftmaxLayer.h
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -38,7 +38,7 @@
template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type = 0>
SimpleTensor<T> softmax_layer(const SimpleTensor<T> &src, float beta);
-template <typename T, typename std::enable_if<std::is_integral<T>::value, int>::type = 0>
+template <typename T, typename std::enable_if<std::is_same<T, uint8_t>::value, int>::type = 0>
SimpleTensor<T> softmax_layer(const SimpleTensor<T> &src, float beta);
} // namespace reference
} // namespace validation
diff --git a/tests/validation/reference/Transpose.cpp b/tests/validation/reference/Transpose.cpp
index 736f37e..348c703 100644
--- a/tests/validation/reference/Transpose.cpp
+++ b/tests/validation/reference/Transpose.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -24,7 +24,6 @@
#include "Transpose.h"
#include "arm_compute/core/Types.h"
-#include "tests/validation/FixedPoint.h"
#include "tests/validation/Helpers.h"
namespace arm_compute
diff --git a/tests/validation/reference/WidthConcatenateLayer.cpp b/tests/validation/reference/WidthConcatenateLayer.cpp
index 5b89934..7a5ece8 100644
--- a/tests/validation/reference/WidthConcatenateLayer.cpp
+++ b/tests/validation/reference/WidthConcatenateLayer.cpp
@@ -23,7 +23,6 @@
*/
#include "WidthConcatenateLayer.h"
-#include "tests/validation/FixedPoint.h"
#include "tests/validation/Helpers.h"
namespace arm_compute