diff options
Diffstat (limited to 'tests/validation/fixtures/DeconvolutionLayerFixture.h')
| -rw-r--r-- | tests/validation/fixtures/DeconvolutionLayerFixture.h | 79 |
1 files changed, 58 insertions, 21 deletions
diff --git a/tests/validation/fixtures/DeconvolutionLayerFixture.h b/tests/validation/fixtures/DeconvolutionLayerFixture.h index 12ce9cefc7..7741557f48 100644 --- a/tests/validation/fixtures/DeconvolutionLayerFixture.h +++ b/tests/validation/fixtures/DeconvolutionLayerFixture.h @@ -43,39 +43,57 @@ template <typename TensorType, typename AccessorType, typename FunctionType, typ 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 @@ protected: 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 @@ public: 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); } }; |