COMPMID-1586: Add support for NHWC CLDeconvolutionLayer
COMPMID-1651: Fix QASYMM8 CLDeconvolutionLayer
This patch also extends the range of values used for testing Convolution and
Deconvolution to cover quantized [-1.0f, 1.0f].
Change-Id: I8b280669db67bb3ec25bf5d411c8f5954f5b0dab
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/149869
Reviewed-by: Michalis Spyrou <michalis.spyrou@arm.com>
Tested-by: bsgcomp <bsgcomp@arm.com>
diff --git a/tests/validation/fixtures/DeconvolutionLayerFixture.h b/tests/validation/fixtures/DeconvolutionLayerFixture.h
index d3a7be7..85c7ed5 100644
--- a/tests/validation/fixtures/DeconvolutionLayerFixture.h
+++ b/tests/validation/fixtures/DeconvolutionLayerFixture.h
@@ -23,6 +23,7 @@
*/
#include "arm_compute/core/TensorShape.h"
#include "arm_compute/core/Types.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
#include "tests/AssetsLibrary.h"
#include "tests/Globals.h"
#include "tests/IAccessor.h"
@@ -39,6 +40,8 @@
{
namespace validation
{
+using namespace arm_compute::misc::shape_calculator;
+
template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
class DeconvolutionLayerFixtureBase : public framework::Fixture
{
@@ -48,12 +51,15 @@
public:
template <typename...>
void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info,
- const std::pair<unsigned int, unsigned int> &inner_border, DataType data_type, QuantizationInfo quantization_info)
+ const std::pair<unsigned int, unsigned int> &inner_border, DataType data_type, DataLayout data_layout, QuantizationInfo quantization_info)
{
- _data_type = data_type;
+ _data_type = data_type;
+ _bias_data_type = is_data_type_quantized_asymmetric(data_type) ? DataType::S32 : data_type;
+ _data_layout = data_layout;
+ _quantization_info = quantization_info;
- _target = compute_target(input_shape, weights_shape, bias_shape, output_shape, info, inner_border, data_type, quantization_info);
- _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, info, inner_border, data_type, quantization_info);
+ _target = compute_target(input_shape, weights_shape, bias_shape, output_shape, info, inner_border);
+ _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, info, inner_border);
}
protected:
@@ -64,7 +70,8 @@
{
case DataType::QASYMM8:
{
- std::uniform_int_distribution<uint8_t> distribution(0, 3);
+ std::pair<int, int> bounds = get_quantized_bounds(tensor.quantization_info(), -1.0f, 1.0f);
+ std::uniform_int_distribution<uint8_t> distribution(bounds.first, bounds.second);
library->fill(tensor, distribution, i);
break;
}
@@ -86,14 +93,21 @@
}
}
- TensorType compute_target(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape,
- const PadStrideInfo &info, const std::pair<unsigned int, unsigned int> &inner_border, DataType data_type, QuantizationInfo quantization_info)
+ TensorType compute_target(TensorShape input_shape, TensorShape weights_shape, const TensorShape bias_shape, TensorShape output_shape,
+ const PadStrideInfo &info, const std::pair<unsigned int, unsigned int> &inner_border)
{
+ if(_data_layout == DataLayout::NHWC)
+ {
+ permute(input_shape, PermutationVector(2U, 0U, 1U));
+ permute(weights_shape, PermutationVector(2U, 0U, 1U));
+ permute(output_shape, PermutationVector(2U, 0U, 1U));
+ }
+
// Create tensors
- TensorType src = create_tensor<TensorType>(input_shape, data_type, 1, quantization_info);
- TensorType weights = create_tensor<TensorType>(weights_shape, data_type, 1, quantization_info);
- TensorType bias = create_tensor<TensorType>(bias_shape, is_data_type_quantized_asymmetric(data_type) ? DataType::S32 : data_type, 1, quantization_info);
- TensorType dst = create_tensor<TensorType>(output_shape, data_type, 1, quantization_info);
+ TensorType src = create_tensor<TensorType>(input_shape, _data_type, 1, _quantization_info, _data_layout);
+ TensorType weights = create_tensor<TensorType>(weights_shape, _data_type, 1, _quantization_info, _data_layout);
+ TensorType bias = create_tensor<TensorType>(bias_shape, _bias_data_type, 1, _quantization_info, _data_layout);
+ TensorType dst = create_tensor<TensorType>(output_shape, _data_type, 1, _quantization_info, _data_layout);
// Create and configure function
FunctionType conv;
@@ -127,12 +141,12 @@
}
SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape,
- const PadStrideInfo &info, const std::pair<unsigned int, unsigned int> inner_border, DataType data_type, QuantizationInfo quantization_info)
+ const PadStrideInfo &info, const std::pair<unsigned int, unsigned int> inner_border)
{
// Create reference
- SimpleTensor<T> src{ input_shape, data_type, 1, quantization_info };
- SimpleTensor<T> weights{ weights_shape, data_type, 1, quantization_info };
- SimpleTensor<TBias> bias{ bias_shape, data_type, 1, quantization_info };
+ SimpleTensor<T> src{ input_shape, _data_type, 1, _quantization_info };
+ SimpleTensor<T> weights{ weights_shape, _data_type, 1, _quantization_info };
+ SimpleTensor<TBias> bias{ bias_shape, _bias_data_type, 1, _quantization_info };
// Fill reference
fill(src, 0);
@@ -142,9 +156,12 @@
return reference::deconvolution_layer<T>(src, weights, bias, output_shape, info, inner_border);
}
- TensorType _target{};
- SimpleTensor<T> _reference{};
- DataType _data_type{};
+ TensorType _target{};
+ SimpleTensor<T> _reference{};
+ DataType _data_type{};
+ DataType _bias_data_type{};
+ DataLayout _data_layout{};
+ QuantizationInfo _quantization_info{};
};
template <typename TensorType, typename AccessorType, typename FunctionType, typename T, unsigned int kernel_size_x, unsigned int kernel_size_y>
@@ -153,16 +170,18 @@
public:
template <typename...>
void setup(TensorShape input_shape, unsigned int sx, unsigned int sy, unsigned int padx, unsigned int pady,
- unsigned int inner_border_right, unsigned int inner_border_top, unsigned int num_kernels, DataType data_type)
+ unsigned int inner_border_right, unsigned int inner_border_top, unsigned int num_kernels, DataType data_type, DataLayout data_layout)
{
ARM_COMPUTE_ERROR_ON_MSG(kernel_size_x != kernel_size_y, "Only square kernels supported");
const TensorShape weights_shape(kernel_size_x, kernel_size_y, input_shape.z(), num_kernels);
const TensorShape bias_shape(num_kernels);
const PadStrideInfo info(sx, sy, padx, pady, DimensionRoundingType::CEIL);
const std::pair<unsigned int, unsigned int> inner_border(inner_border_right, inner_border_top);
- auto out_dim = deconvolution_output_dimensions(input_shape.x(), input_shape.y(), kernel_size_x, kernel_size_y, padx, pady, sx, sy);
- TensorShape output_shape = deconvolution_output_shape(out_dim, input_shape, weights_shape);
- DeconvolutionLayerFixtureBase<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, inner_border, data_type, QuantizationInfo());
+ auto out_dim = deconvolution_output_dimensions(input_shape.x(), input_shape.y(), kernel_size_x, kernel_size_y, padx, pady, sx, sy);
+ TensorInfo input_info(input_shape, 1, data_type);
+ TensorInfo weights_info(weights_shape, 1, data_type);
+ TensorShape output_shape = compute_deconvolution_output_shape(out_dim, input_info, weights_info);
+ DeconvolutionLayerFixtureBase<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, inner_border, data_type, data_layout, QuantizationInfo());
}
};
@@ -172,16 +191,18 @@
public:
template <typename...>
void setup(TensorShape input_shape, unsigned int sx, unsigned int sy, unsigned int padx, unsigned int pady,
- unsigned int inner_border_right, unsigned int inner_border_top, unsigned int num_kernels, DataType data_type, QuantizationInfo quantization_info)
+ unsigned int inner_border_right, unsigned int inner_border_top, unsigned int num_kernels, DataType data_type, DataLayout data_layout, QuantizationInfo quantization_info)
{
ARM_COMPUTE_ERROR_ON_MSG(kernel_size_x != kernel_size_y, "Only square kernels supported");
const TensorShape weights_shape(kernel_size_x, kernel_size_y, input_shape.z(), num_kernels);
const TensorShape bias_shape(num_kernels);
const PadStrideInfo info(sx, sy, padx, pady, DimensionRoundingType::CEIL);
const std::pair<unsigned int, unsigned int> inner_border(inner_border_right, inner_border_top);
- auto out_dim = deconvolution_output_dimensions(input_shape.x(), input_shape.y(), kernel_size_x, kernel_size_y, padx, pady, sx, sy);
- TensorShape output_shape = deconvolution_output_shape(out_dim, input_shape, weights_shape);
- DeconvolutionLayerFixtureBase<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, inner_border, data_type, quantization_info);
+ auto out_dim = deconvolution_output_dimensions(input_shape.x(), input_shape.y(), kernel_size_x, kernel_size_y, padx, pady, sx, sy);
+ TensorInfo input_info(input_shape, 1, data_type, quantization_info);
+ TensorInfo weights_info(weights_shape, 1, data_type, quantization_info);
+ TensorShape output_shape = compute_deconvolution_output_shape(out_dim, input_info, weights_info);
+ DeconvolutionLayerFixtureBase<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, inner_border, data_type, data_layout, quantization_info);
}
};