COMPMID-1376: Add support for QASYMM8 in CLDeconvolutionLayer
Change-Id: I13ec79b6668e2b9559d3fa789ae0b51ab6975289
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/139126
Reviewed-by: Michalis Spyrou <michalis.spyrou@arm.com>
Tested-by: Jenkins <bsgcomp@arm.com>
diff --git a/tests/validation/fixtures/DeconvolutionLayerFixture.h b/tests/validation/fixtures/DeconvolutionLayerFixture.h
index 12ce9ce..7741557 100644
--- a/tests/validation/fixtures/DeconvolutionLayerFixture.h
+++ b/tests/validation/fixtures/DeconvolutionLayerFixture.h
@@ -43,39 +43,57 @@
class DeconvolutionLayerFixtureBase : public framework::Fixture
{
public:
+ using TBias = typename std::conditional<std::is_same<typename std::decay<T>::type, uint8_t>::value, int32_t, T>::type;
+
+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)
+ const std::pair<unsigned int, unsigned int> &inner_border, DataType data_type, QuantizationInfo quantization_info)
{
_data_type = data_type;
- _target = compute_target(input_shape, weights_shape, bias_shape, output_shape, info, inner_border, data_type);
- _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, info, inner_border, data_type);
+ _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);
}
protected:
template <typename U>
void fill(U &&tensor, int i)
{
- if(is_data_type_float(tensor.data_type()))
+ switch(tensor.data_type())
{
- std::uniform_real_distribution<> distribution(-1.0f, 1.0f);
- library->fill(tensor, distribution, i);
- }
- else
- {
- library->fill_tensor_uniform(tensor, i);
+ case DataType::QASYMM8:
+ {
+ std::uniform_int_distribution<uint8_t> distribution(0, 255);
+ library->fill(tensor, distribution, i);
+ break;
+ }
+ case DataType::S32:
+ {
+ std::uniform_int_distribution<int32_t> distribution(-100, 100);
+ library->fill(tensor, distribution, i);
+ break;
+ }
+ case DataType::F16:
+ case DataType::F32:
+ {
+ std::uniform_real_distribution<> distribution(-1.0f, 1.0f);
+ library->fill(tensor, distribution, i);
+ break;
+ }
+ default:
+ library->fill_tensor_uniform(tensor, i);
}
}
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)
+ const PadStrideInfo &info, const std::pair<unsigned int, unsigned int> &inner_border, DataType data_type, QuantizationInfo quantization_info)
{
// Create tensors
- TensorType src = create_tensor<TensorType>(input_shape, data_type, 1);
- TensorType weights = create_tensor<TensorType>(weights_shape, data_type, 1);
- TensorType bias = create_tensor<TensorType>(bias_shape, data_type, 1);
- TensorType dst = create_tensor<TensorType>(output_shape, data_type, 1);
+ 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);
// Create and configure function
FunctionType conv;
@@ -102,19 +120,19 @@
fill(AccessorType(weights), 1);
fill(AccessorType(bias), 2);
- // Compute NEConvolutionLayer function
+ // Compute DeconvolutionLayer function
conv.run();
return dst;
}
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)
+ const PadStrideInfo &info, const std::pair<unsigned int, unsigned int> inner_border, DataType data_type, QuantizationInfo quantization_info)
{
// Create reference
- SimpleTensor<T> src{ input_shape, data_type, 1 };
- SimpleTensor<T> weights{ weights_shape, data_type, 1 };
- SimpleTensor<T> bias{ bias_shape, data_type, 1 };
+ 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 };
// Fill reference
fill(src, 0);
@@ -144,7 +162,26 @@
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, inner_border.first, inner_border.second, 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);
+ DeconvolutionLayerFixtureBase<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, inner_border, data_type, QuantizationInfo());
+ }
+};
+
+template <typename TensorType, typename AccessorType, typename FunctionType, typename T, unsigned int kernel_size_x, unsigned int kernel_size_y>
+class DeconvolutionValidationQuantizedFixture : public DeconvolutionLayerFixtureBase<TensorType, AccessorType, FunctionType, T>
+{
+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)
+ {
+ 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, inner_border.first, inner_border.second, 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);
}
};