diff options
| -rw-r--r-- | arm_compute/core/CL/CLHelpers.h | 8 | ||||
| -rw-r--r-- | arm_compute/core/CL/kernels/CLDirectConvolutionLayerKernel.h | 5 | ||||
| -rw-r--r-- | arm_compute/runtime/CL/functions/CLDirectConvolutionLayer.h | 6 | ||||
| -rw-r--r-- | src/core/CL/CLHelpers.cpp | 26 | ||||
| -rw-r--r-- | src/core/CL/CLKernelLibrary.cpp | 11 | ||||
| -rw-r--r-- | src/core/CL/cl_kernels/direct_convolution1x1.cl | 190 | ||||
| -rw-r--r-- | src/core/CL/cl_kernels/direct_convolution3x3.cl (renamed from src/core/CL/cl_kernels/direct_convolution.cl) | 0 | ||||
| -rw-r--r-- | src/core/CL/kernels/CLDirectConvolutionLayerKernel.cpp | 29 | ||||
| -rw-r--r-- | tests/validation/CL/DirectConvolutionLayer.cpp | 45 |
9 files changed, 292 insertions, 28 deletions
diff --git a/arm_compute/core/CL/CLHelpers.h b/arm_compute/core/CL/CLHelpers.h index eeb3e7699d..1a4476e304 100644 --- a/arm_compute/core/CL/CLHelpers.h +++ b/arm_compute/core/CL/CLHelpers.h @@ -53,6 +53,14 @@ static constexpr const unsigned int max_cl_vector_width = 16; */ std::string get_cl_type_from_data_type(const DataType &dt); +/** Get the size of a data type in number of bits. + * + * @param[in] dt @ref DataType. + * + * @return Number of bits in the data type specified. + */ +std::string get_data_size_from_data_type(const DataType &dt); + /** Translates fixed point tensor data type to the underlying OpenCL type. * * @param[in] dt @ref DataType to be translated to OpenCL type. diff --git a/arm_compute/core/CL/kernels/CLDirectConvolutionLayerKernel.h b/arm_compute/core/CL/kernels/CLDirectConvolutionLayerKernel.h index 28eecf029a..635ec883bf 100644 --- a/arm_compute/core/CL/kernels/CLDirectConvolutionLayerKernel.h +++ b/arm_compute/core/CL/kernels/CLDirectConvolutionLayerKernel.h @@ -33,7 +33,6 @@ class ICLTensor; /** Interface for the direct convolution kernel. */ -template <unsigned int kernel_size> class CLDirectConvolutionLayerKernel : public ICLKernel { public: @@ -52,7 +51,7 @@ public: /** Set the input, weights, biases and output tensors. * * @param[in] input The input tensor to convolve. 3 lower dimensions represent a single input [width, height, IFM], - * while every optional dimension from 4 and above represent a batch of inputs. Data types supported: F32. + * while every optional dimension from 4 and above represent a batch of inputs. Data types supported: F16, F32. * @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. * The 3rd dimension must be the same as the input's volume 3rd dimension. * Data type supported:Same as @p input. @@ -80,7 +79,5 @@ private: int _conv_stride_x; int _conv_stride_y; }; - -using CLDirectConvolutionLayer3x3Kernel = CLDirectConvolutionLayerKernel<3>; } #endif /*__ARM_COMPUTE_CLDIRECTCONVOLUTIONLAYERKERNEL_H__ */ diff --git a/arm_compute/runtime/CL/functions/CLDirectConvolutionLayer.h b/arm_compute/runtime/CL/functions/CLDirectConvolutionLayer.h index 8b43e18167..1e12ab95c1 100644 --- a/arm_compute/runtime/CL/functions/CLDirectConvolutionLayer.h +++ b/arm_compute/runtime/CL/functions/CLDirectConvolutionLayer.h @@ -45,7 +45,7 @@ public: * * @param[in] input Source tensor. 3 lower dimensions represent a single input [width, height, IFM], * while every optional dimension from 4 and above represent a batch of inputs. - * Data types supported: F32. + * Data types supported: F16, F32. * @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported:Same as @p input. * @param[in] biases Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. Data type supported:Same as @p input. * @param[out] output Destination tensor. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs. @@ -58,8 +58,8 @@ public: void run() override; private: - CLDirectConvolutionLayer3x3Kernel _direct_conv_kernel; - CLFillBorderKernel _input_border_handler; + CLDirectConvolutionLayerKernel _direct_conv_kernel; + CLFillBorderKernel _input_border_handler; }; } #endif /* __ARM_COMPUTE_CLDIRECTCONVOLUTIONLAYER_H__ */ diff --git a/src/core/CL/CLHelpers.cpp b/src/core/CL/CLHelpers.cpp index 1073b39ca7..1c855e4ff0 100644 --- a/src/core/CL/CLHelpers.cpp +++ b/src/core/CL/CLHelpers.cpp @@ -100,6 +100,32 @@ std::string get_cl_type_from_data_type(const DataType &dt) } } +std::string get_data_size_from_data_type(const DataType &dt) +{ + switch(dt) + { + case DataType::U8: + case DataType::QS8: + case DataType::S8: + return "8"; + case DataType::U16: + case DataType::S16: + case DataType::QS16: + case DataType::F16: + return "16"; + case DataType::U32: + case DataType::S32: + case DataType::F32: + return "32"; + case DataType::U64: + case DataType::S64: + return "64"; + default: + ARM_COMPUTE_ERROR("Unsupported input data type."); + return "0"; + } +} + std::string get_underlying_cl_type_from_data_type(const DataType &dt) { switch(dt) diff --git a/src/core/CL/CLKernelLibrary.cpp b/src/core/CL/CLKernelLibrary.cpp index 9c8be36b49..dec269691c 100644 --- a/src/core/CL/CLKernelLibrary.cpp +++ b/src/core/CL/CLKernelLibrary.cpp @@ -145,7 +145,8 @@ const std::map<std::string, std::string> CLKernelLibrary::_kernel_program_map = { "copy_to_keypoint", "fast_corners.cl" }, { "derivative", "derivative.cl" }, { "dilate", "dilate.cl" }, - { "direct_convolution3x3", "direct_convolution.cl" }, + { "direct_convolution1x1", "direct_convolution1x1.cl" }, + { "direct_convolution3x3", "direct_convolution3x3.cl" }, { "erode", "erode.cl" }, { "fast_corners", "fast_corners.cl" }, { "fill_image_borders_constant", "fill_border.cl" }, @@ -350,8 +351,12 @@ const std::map<std::string, std::string> CLKernelLibrary::_program_source_map = #include "./cl_kernels/dilate.clembed" }, { - "direct_convolution.cl", -#include "./cl_kernels/direct_convolution.clembed" + "direct_convolution1x1.cl", +#include "./cl_kernels/direct_convolution1x1.clembed" + }, + { + "direct_convolution3x3.cl", +#include "./cl_kernels/direct_convolution3x3.clembed" }, { "erode.cl", diff --git a/src/core/CL/cl_kernels/direct_convolution1x1.cl b/src/core/CL/cl_kernels/direct_convolution1x1.cl new file mode 100644 index 0000000000..d161f80fea --- /dev/null +++ b/src/core/CL/cl_kernels/direct_convolution1x1.cl @@ -0,0 +1,190 @@ +/* + * Copyright (c) 2016, 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 "helpers.h" + +#if STRIDE_X == 3 +#define INPUT_PIXEL_STR(data_size) extract_input_stride3_##data_size +#define INPUT_PIXEL(data_size) INPUT_PIXEL_STR(data_size) +#elif STRIDE_X == 2 +#define INPUT_PIXEL(data_size) extract_input_stride2 +#elif STRIDE_X == 1 +#define INPUT_PIXEL(data_size) extract_input_stride1 +#else /* STRIDE_X not equals 1, 2 or 3 */ +#error "Only support strides 1, 2 and 3" +#endif /* STRIDE_X == 3 */ + +/** Extracts a 1D horizontal vector from the input tensor with stride as 1. + * + * @param[in] input_pixel Pointer to the first pixel. + * + * @return extracted input pixels. + */ +inline VEC_DATA_TYPE(DATA_TYPE, 8) extract_input_stride1(__global const DATA_TYPE *input_pixel) +{ + return vload8(0, input_pixel); +} + +/** Extracts a 1D horizontal vector from the input tensor with stride as 2. + * + * @param[in] input_pixel Pointer to the first pixel. + * + * @return extracted input pixels. + */ +inline VEC_DATA_TYPE(DATA_TYPE, 8) extract_input_stride2(__global const DATA_TYPE *input_pixel) +{ + VEC_DATA_TYPE(DATA_TYPE, 16) + temp = vload16(0, input_pixel); + return temp.s02468ace; +} + +/** Extracts a 1D horizontal vector from the input tensor with stride as 3 and 32-bit data size. + * + * @param[in] input_pixel Pointer to the first pixel. + * + * @return extracted input pixels. + */ +inline VEC_DATA_TYPE(DATA_TYPE, 8) extract_input_stride3_32(__global const DATA_TYPE *input_pixel) +{ + VEC_DATA_TYPE(DATA_TYPE, 4) + temp1 = vload4(0, input_pixel); + VEC_DATA_TYPE(DATA_TYPE, 4) + temp2 = vload4(0, input_pixel + 6); + VEC_DATA_TYPE(DATA_TYPE, 4) + temp3 = vload4(0, input_pixel + 12); + VEC_DATA_TYPE(DATA_TYPE, 4) + temp4 = vload4(0, input_pixel + 18); + return (VEC_DATA_TYPE(DATA_TYPE, 8))(temp1.s03, temp2.s03, temp3.s03, temp4.s03); +} + +/** Extracts a 1D horizontal vector from the input tensor with stride as 3 and 16-bit data size. + * + * @param[in] input_pixel Pointer to the first pixel. + * + * @return extracted input pixels. + */ +inline VEC_DATA_TYPE(DATA_TYPE, 8) extract_input_stride3_16(__global const DATA_TYPE *input_pixel) +{ + VEC_DATA_TYPE(DATA_TYPE, 8) + temp1 = vload8(0, input_pixel); + VEC_DATA_TYPE(DATA_TYPE, 8) + temp2 = vload8(0, input_pixel + 8); + VEC_DATA_TYPE(DATA_TYPE, 8) + temp3 = vload8(0, input_pixel + 16); + return (VEC_DATA_TYPE(DATA_TYPE, 8))(temp1.s036, temp2.s147, temp3.s25); +} + +/** Extracts a 1D horizontal vector from the input tensor with stride as 3 and 8-bit data size. + * + * @param[in] input_pixel Pointer to the first pixel. + * + * @return extracted input pixels. + */ +inline VEC_DATA_TYPE(DATA_TYPE, 8) extract_input_stride3_8(__global const DATA_TYPE *input_pixel) +{ + VEC_DATA_TYPE(DATA_TYPE, 16) + temp1 = vload16(0, input_pixel); + VEC_DATA_TYPE(DATA_TYPE, 16) + temp2 = vload16(0, input_pixel + 12); + return (VEC_DATA_TYPE(DATA_TYPE, 8))(temp1.s0369, temp2.s0369); +} + +/** This kernel performs a direct convolution to convolve the low three dimensions. + * + * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float + * @note The data size must be passed at compile time using -DDATA_SIZE e.g. -DDATA_SIZE=32 + * @note The convolution stride x and stride y must be passed at compile time using -DSTRIDE_X and -DSTRIDE_Y: e.g. -DSTRIDE_X=1, _DSTRIDE_Y=1 + * @note In case biases will be added to the convolution -DHAS_BIAS has to be passed to append the final matrix with 1 in each row. + * + * @param[in] src_ptr Pointer to the source tensor. Supported data types: QS8/F16/F32 + * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) + * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) + * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor + * @param[out] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr + * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes) + * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes) + * @param[in] dst_step_y dst_stride_y * number of elements along Z processed per workitem(in bytes) + * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes) + * @param[in] dst_step_z dst_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor + * @param[out] weights_ptr Pointer to the weights tensor. Supported data types: same as @p weights_ptr + * @param[in] weights_stride_x Stride of the weights tensor in X dimension (in bytes) + * @param[in] weights_step_x weights_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] weights_stride_y Stride of the weights tensor in Y dimension (in bytes) + * @param[in] weights_step_y weights_stride_y * number of elements along y processed per workitem(in bytes) + * @param[in] weights_stride_z Stride of the weights tensor in Z dimension (in bytes) + * @param[in] weights_step_z weights_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] weights_offset_first_element_in_bytes The offset of the first element in the weights tensor + * @param[in] biases_ptr Pointer to the biases tensor. Same as @p src_ptr + * @param[in] biases_stride_x Stride of the biases tensor in X dimension (in bytes) + * @param[in] biases_step_x biases_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] biases_offset_first_element_in_bytes The offset of the first element in the biases tensor + * @param[in] weights_stride_w Stride of the weights tensor in W dimension + * @param[in] filter_depth The depth size of the filter + */ +__kernel void direct_convolution1x1( + TENSOR3D_DECLARATION(src), + TENSOR3D_DECLARATION(dst), + TENSOR3D_DECLARATION(weights), +#ifdef HAS_BIAS + VECTOR_DECLARATION(biases), +#endif /* defined(HAS_BIAS) */ + unsigned int weights_stride_w, + unsigned int filter_depth) +{ + Image src = CONVERT_TO_IMAGE_STRUCT(src); + Tensor3D weights = CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(weights); + Tensor3D dst = CONVERT_TO_TENSOR3D_STRUCT(dst); + +#ifdef HAS_BIAS + Vector biases = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases); +#endif /* defined(HAS_BIAS) */ + + VEC_DATA_TYPE(DATA_TYPE, 8) + pixels = 0; + + const uint z_index = get_global_id(2); + + weights.ptr += z_index * weights_stride_w; + + for(int d = 0; d < filter_depth; ++d) + { + DATA_TYPE weight = *(__global DATA_TYPE *)weights.ptr; + VEC_DATA_TYPE(DATA_TYPE, 8) + input_pixel = INPUT_PIXEL(DATA_SIZE)((__global DATA_TYPE *)src.ptr); + pixels += weight * input_pixel; + src.ptr += src_stride_z; + weights.ptr += weights_stride_z; + } + +#ifdef HAS_BIAS + pixels += (VEC_DATA_TYPE(DATA_TYPE, 8)) * ((__global DATA_TYPE *)(vector_offset(&biases, z_index))); +#endif /* defined(HAS_BIAS) */ + + vstore8(pixels, 0, (__global DATA_TYPE *)dst.ptr); +} diff --git a/src/core/CL/cl_kernels/direct_convolution.cl b/src/core/CL/cl_kernels/direct_convolution3x3.cl index b5524e1d4b..b5524e1d4b 100644 --- a/src/core/CL/cl_kernels/direct_convolution.cl +++ b/src/core/CL/cl_kernels/direct_convolution3x3.cl diff --git a/src/core/CL/kernels/CLDirectConvolutionLayerKernel.cpp b/src/core/CL/kernels/CLDirectConvolutionLayerKernel.cpp index 7f9e9d20e1..1f481de921 100644 --- a/src/core/CL/kernels/CLDirectConvolutionLayerKernel.cpp +++ b/src/core/CL/kernels/CLDirectConvolutionLayerKernel.cpp @@ -37,32 +37,33 @@ using namespace arm_compute; -template <unsigned int kernel_size> -CLDirectConvolutionLayerKernel<kernel_size>::CLDirectConvolutionLayerKernel() +CLDirectConvolutionLayerKernel::CLDirectConvolutionLayerKernel() : _input(nullptr), _biases(nullptr), _weights(nullptr), _output(nullptr), _border_size(0), _conv_pad_x(0), _conv_pad_y(0), _conv_stride_x(0), _conv_stride_y(0) { } -template <unsigned int kernel_size> -BorderSize CLDirectConvolutionLayerKernel<kernel_size>::border_size() const +BorderSize CLDirectConvolutionLayerKernel::border_size() const { return _border_size; } -template <unsigned int kernel_size> -void CLDirectConvolutionLayerKernel<kernel_size>::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info) +void CLDirectConvolutionLayerKernel::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info) { - static_assert(kernel_size == 3, "Currently only 3x3 direct convolution is supported!"); - - ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32); + const unsigned int kernel_size = weights->info()->dimension(0); + ARM_COMPUTE_ERROR_ON_MSG(kernel_size != 1 && kernel_size != 3, + "Kernel sizes other than 1x1 or 3x3 are not supported"); + ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32); ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights, output); ARM_COMPUTE_ERROR_ON(weights->info()->dimension(2) != input->info()->dimension(2)); ARM_COMPUTE_ERROR_ON(weights->info()->dimension(0) != weights->info()->dimension(1)); ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4); + ARM_COMPUTE_ERROR_ON_MSG(weights->info()->dimension(0) == 1 && (std::get<0>(conv_info.pad()) || std::get<1>(conv_info.pad())), + "Pad > 0 not supported for 1x1 weights"); + ARM_COMPUTE_ERROR_ON_MSG(weights->info()->dimension(0) == 3 && (std::get<0>(conv_info.pad()) > 1 || std::get<1>(conv_info.pad()) > 1), + "Pad > 1 not supported for 3x3 weights"); + ARM_COMPUTE_ERROR_ON_MSG(std::get<0>(conv_info.stride()) > 3, "Strides larger than 3 not supported."); ARM_COMPUTE_ERROR_ON_MSG((kernel_size == 3 && std::get<0>(conv_info.stride()) > 2), "Strides larger than 2 not supported in 3x3 direct convolution!"); - ARM_COMPUTE_ERROR_ON(kernel_size != weights->info()->dimension(0)); - if(biases != nullptr) { ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases); @@ -86,6 +87,7 @@ void CLDirectConvolutionLayerKernel<kernel_size>::configure(const ICLTensor *inp kernel_name << "direct_convolution" << kernel_size << "x" << kernel_size; options.insert("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type())); + options.insert("-DDATA_SIZE=" + get_data_size_from_data_type(input->info()->data_type())); options.emplace("-DSTRIDE_X=" + support::cpp11::to_string(_conv_stride_x)); @@ -130,8 +132,7 @@ void CLDirectConvolutionLayerKernel<kernel_size>::configure(const ICLTensor *inp ICLKernel::configure(win); } -template <unsigned int kernel_size> -void CLDirectConvolutionLayerKernel<kernel_size>::run(const Window &window, cl::CommandQueue &queue) +void CLDirectConvolutionLayerKernel::run(const Window &window, cl::CommandQueue &queue) { ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window); @@ -167,5 +168,3 @@ void CLDirectConvolutionLayerKernel<kernel_size>::run(const Window &window, cl:: } while(window.slide_window_slice_3D(slice) && win_in.slide_window_slice_3D(slice_in)); } - -template class arm_compute::CLDirectConvolutionLayerKernel<3>; diff --git a/tests/validation/CL/DirectConvolutionLayer.cpp b/tests/validation/CL/DirectConvolutionLayer.cpp index 5b00a019ba..d9dd34b9ec 100644 --- a/tests/validation/CL/DirectConvolutionLayer.cpp +++ b/tests/validation/CL/DirectConvolutionLayer.cpp @@ -48,7 +48,24 @@ using namespace arm_compute::test::validation; namespace { -const float tolerance_fp = 1e-3f; /**< Tolerance for floating point tests */ +/** Define tolerance of the direct convolution layer + * + * @param[in] dt DataType of the tensor. + * + * @return Tolerance depending on the data type. + */ +float direct_convolution_layer_tolerance(DataType dt) +{ + switch(dt) + { + case DataType::F16: + return 0.1f; + case DataType::F32: + return 1e-3f; + default: + return 0.f; + } +} /** Compute CL direct convolution layer function. * @@ -90,6 +107,7 @@ CLTensor compute_convolution_layer(const TensorShape &src_shape, const TensorSha // Fill tensors switch(dt) { + case DataType::F16: case DataType::F32: { std::uniform_real_distribution<> distribution(-1.f, 1.f); @@ -133,8 +151,29 @@ BOOST_AUTO_TEST_SUITE(DirectConvolutionLayer) BOOST_AUTO_TEST_SUITE(Float) BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit")) -BOOST_DATA_TEST_CASE(W3x3, DirectConvolutionShapes() * CNNFloatDataTypes() * boost::unit_test::data::xrange(1, 3, 1) * boost::unit_test::data::xrange(1, 3, 1) * boost::unit_test::data::xrange(0, 2, +BOOST_DATA_TEST_CASE(W1x1, DirectConvolutionShapes() * boost::unit_test::data::make({ DataType::F16, DataType::F32 }) * boost::unit_test::data::xrange(1, 4, 1) * boost::unit_test::data::xrange(1, 4, + 1) + * boost::unit_test::data::make({ 1, 4, 8, 16 }), + input_shape, dt, sx, sy, num_kernels) +{ + const unsigned int kernel_size = 1; + const PadStrideInfo conv_info(sx, sy, 0, 0, DimensionRoundingType::FLOOR); + const TensorShape w_shape(kernel_size, kernel_size, input_shape.z(), static_cast<unsigned int>(num_kernels)); + const TensorShape b_shape(static_cast<unsigned int>(num_kernels)); + const TensorShape d_shape(get_output_shape(input_shape, w_shape, conv_info)); + + CLTensor dst = compute_convolution_layer(input_shape, w_shape, b_shape, d_shape, dt, conv_info); + + RawTensor ref = Reference::compute_reference_convolution_layer(input_shape, w_shape, b_shape, d_shape, dt, conv_info, 0); + + // Validate output + validate(CLAccessor(dst), ref, direct_convolution_layer_tolerance(dt)); +} + +BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit")) +BOOST_DATA_TEST_CASE(W3x3, DirectConvolutionShapes() * boost::unit_test::data::make({ DataType::F16, DataType::F32 }) * boost::unit_test::data::xrange(1, 3, 1) * boost::unit_test::data::xrange(1, 3, 1) + * boost::unit_test::data::xrange(0, 2, 1) * boost::unit_test::data::xrange(0, 2, 1) * boost::unit_test::data::make({ 1, 4, 8, 16 }), input_shape, dt, sx, sy, px, py, num_kernels) { @@ -149,7 +188,7 @@ BOOST_DATA_TEST_CASE(W3x3, DirectConvolutionShapes() * CNNFloatDataTypes() * boo RawTensor ref = Reference::compute_reference_convolution_layer(input_shape, w_shape, b_shape, d_shape, dt, conv_info, 0); // Validate output - validate(CLAccessor(dst), ref, tolerance_fp); + validate(CLAccessor(dst), ref, direct_convolution_layer_tolerance(dt)); } BOOST_AUTO_TEST_SUITE_END() |