diff options
author | Vidhya Sudhan Loganathan <vidhyasudhan.loganathan@arm.com> | 2018-04-25 13:00:09 +0100 |
---|---|---|
committer | Anthony Barbier <anthony.barbier@arm.com> | 2018-11-02 16:49:54 +0000 |
commit | 84ce1f9d5d63c7fcfa5ac3f52e4de5bbb9ccb886 (patch) | |
tree | 934c921975432bc10babffefffceeebd43e3f0f2 /src | |
parent | 764b1af5f41749624c3900fc65c9dad3b1adf98c (diff) | |
download | ComputeLibrary-84ce1f9d5d63c7fcfa5ac3f52e4de5bbb9ccb886.tar.gz |
COMPMID-718 : Winograd: add validate method and tests
Changed API's to use winograd_info struct instead of individual params
Modified validation to test Validate API
Change-Id: I796650092165069e2067e02ace3f42a43f545779
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/128991
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Diffstat (limited to 'src')
-rw-r--r-- | src/core/NEON/kernels/NEWinogradLayerKernel.cpp | 69 | ||||
-rw-r--r-- | src/runtime/NEON/functions/NEWinogradLayer.cpp | 33 |
2 files changed, 55 insertions, 47 deletions
diff --git a/src/core/NEON/kernels/NEWinogradLayerKernel.cpp b/src/core/NEON/kernels/NEWinogradLayerKernel.cpp index 026a6f1be2..3cfe2af470 100644 --- a/src/core/NEON/kernels/NEWinogradLayerKernel.cpp +++ b/src/core/NEON/kernels/NEWinogradLayerKernel.cpp @@ -72,7 +72,7 @@ Status validate_arguments_winograd_gemm(const ITensorInfo *a, const ITensorInfo return Status{}; } -Status validate_arguments_winograd_weight_trans(const ITensorInfo *input, const ITensorInfo *output, const Size2D &output_tile) +Status validate_arguments_winograd_weight_trans(const ITensorInfo *input, const ITensorInfo *output, const WinogradInfo &winograd_info) { ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input); ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output); @@ -83,12 +83,13 @@ Status validate_arguments_winograd_weight_trans(const ITensorInfo *input, const ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(idx_width) != 3 && input->dimension(idx_width) != 5); ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(idx_width) != input->dimension(idx_height)); ARM_COMPUTE_RETURN_ERROR_ON(input->num_dimensions() > 4); + const Size2D &output_tile = winograd_info.output_tile_size; ARM_COMPUTE_RETURN_ERROR_ON(output_tile != Size2D(2U, 2U) && output_tile != Size2D(4U, 4U)); // Checks performed when output is configured if(output->total_size() != 0) { - const TensorInfo tensor_info_output = input->clone()->set_tensor_shape(arm_compute::misc::shape_calculator::compute_winograd_filter_transform_shape(*input, output_tile)); + const TensorInfo tensor_info_output = input->clone()->set_tensor_shape(arm_compute::misc::shape_calculator::compute_winograd_filter_transform_shape(*input, winograd_info)); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &tensor_info_output); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); @@ -97,12 +98,11 @@ Status validate_arguments_winograd_weight_trans(const ITensorInfo *input, const return Status{}; } -std::pair<Status, Window> validate_and_configure_window_winograd_weight_trans(ITensorInfo *input, ITensorInfo *output, const Size2D &output_tile, const Size2D &kernel_dims) +std::pair<Status, Window> validate_and_configure_window_winograd_weight_trans(ITensorInfo *input, ITensorInfo *output, const WinogradInfo &winograd_info) { - ARM_COMPUTE_UNUSED(output_tile); - + const Size2D kernel_dims = winograd_info.kernel_size; // Output tensor auto inizialitation if not yet initialized - auto_init_if_empty(*output, input->clone()->set_tensor_shape(arm_compute::misc::shape_calculator::compute_winograd_filter_transform_shape(*input, output_tile))); + auto_init_if_empty(*output, input->clone()->set_tensor_shape(arm_compute::misc::shape_calculator::compute_winograd_filter_transform_shape(*input, winograd_info))); unsigned int num_elems_processed_per_iteration_x = kernel_dims.width; unsigned int num_elems_processed_per_iteration_y = kernel_dims.height; @@ -122,8 +122,10 @@ std::pair<Status, Window> validate_and_configure_window_winograd_weight_trans(IT return std::make_pair(err, win_collapsed); } -Status validate_arguments_winograd_input_trans(const ITensorInfo *input, const ITensorInfo *output, const PadStrideInfo &conv_info, const Size2D &kernel_dims) +Status validate_arguments_winograd_input_trans(const ITensorInfo *input, const ITensorInfo *output, const WinogradInfo &winograd_info) { + const Size2D &kernel_dims = winograd_info.kernel_size; + const PadStrideInfo &conv_info = winograd_info.convolution_info; ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input); ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output); ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32); @@ -134,7 +136,7 @@ Status validate_arguments_winograd_input_trans(const ITensorInfo *input, const I // Validate configured output if(output->total_size() != 0) { - const TensorShape output_shape = misc::shape_calculator::compute_winograd_input_transform_shape(*input, conv_info, kernel_dims); + const TensorShape output_shape = misc::shape_calculator::compute_winograd_input_transform_shape(*input, winograd_info); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); @@ -143,15 +145,17 @@ Status validate_arguments_winograd_input_trans(const ITensorInfo *input, const I return Status{}; } -std::pair<Status, Window> validate_and_configure_window_winograd_input_trans(ITensorInfo *input, ITensorInfo *output, const PadStrideInfo &conv_info, const Size2D &kernel_dims, - const Size2D &tile_dims) +std::pair<Status, Window> validate_and_configure_window_winograd_input_trans(ITensorInfo *input, ITensorInfo *output, const WinogradInfo &winograd_info) { - const TensorShape output_shape = misc::shape_calculator::compute_winograd_input_transform_shape(*input, conv_info, kernel_dims); + const PadStrideInfo conv_info = winograd_info.convolution_info; + const Size2D output_tile_size = winograd_info.output_tile_size; + const Size2D kernel_dims = winograd_info.kernel_size; + const TensorShape output_shape = misc::shape_calculator::compute_winograd_input_transform_shape(*input, winograd_info); // Output auto inizialitation if not yet initialized auto_init_if_empty(*output, input->clone()->set_tensor_shape(output_shape)); - unsigned int num_elems_read_per_iteration_x = (tile_dims.width + kernel_dims.width - 1); - unsigned int num_elems_read_per_iteration_y = (tile_dims.height + kernel_dims.height - 1); + unsigned int num_elems_read_per_iteration_x = (output_tile_size.width + kernel_dims.width - 1); + unsigned int num_elems_read_per_iteration_y = (output_tile_size.height + kernel_dims.height - 1); Window win = calculate_max_window(*input, Steps(1, 1)); @@ -163,12 +167,20 @@ std::pair<Status, Window> validate_and_configure_window_winograd_input_trans(ITe return std::make_pair(err, win); } -Status validate_arguments_winograd_output_trans(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, const Size2D &kernel_dims, const Size2D &output_convolved_dims, - const Size2D &num_tiles) +Status validate_arguments_winograd_output_trans(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, const WinogradInfo &winograd_info) { + const PadStrideInfo &conv_info = winograd_info.convolution_info; + const Size2D kernel_dims = winograd_info.kernel_size; + + // Number of tiles along the X and Y direction + const unsigned int num_tiles_x = std::ceil((winograd_info.input_dimensions.x() - (kernel_dims.width - 1) + conv_info.pad_left() + conv_info.pad_right()) / 2.f); + const unsigned int num_tiles_y = std::ceil((winograd_info.input_dimensions.y() - (kernel_dims.height - 1) + conv_info.pad_top() + conv_info.pad_bottom()) / 2.f); + const Size2D num_tiles = Size2D(num_tiles_x, num_tiles_y); + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input); ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output); ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON(winograd_info.output_data_layout != DataLayout::NCHW); ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(1) != num_tiles.area()); ARM_COMPUTE_RETURN_ERROR_ON_MSG((kernel_dims.width != 3U && kernel_dims.width != 5U), "Winograd output transform only supports 3x3 and 5x5 kernels"); ARM_COMPUTE_RETURN_ERROR_ON_MSG((kernel_dims.width != kernel_dims.height), "Winograd output transform only supports 3x3 and 5x5 kernels"); @@ -184,17 +196,17 @@ Status validate_arguments_winograd_output_trans(const ITensorInfo *input, const // Checks performed when output is configured if(output->total_size() != 0) { - const TensorInfo tensor_info_output = input->clone()->set_tensor_shape(arm_compute::misc::shape_calculator::compute_winograd_output_transform_shape(*input, output_convolved_dims, DataLayout::NCHW)); + const TensorInfo tensor_info_output = input->clone()->set_tensor_shape(arm_compute::misc::shape_calculator::compute_winograd_output_transform_shape(*input, winograd_info)); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &tensor_info_output); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); } return Status{}; } -std::pair<Status, Window> validate_and_configure_window_winograd_output_trans(ITensorInfo *input, ITensorInfo *bias, ITensorInfo *output, const Size2D &output_convolved_dims) +std::pair<Status, Window> validate_and_configure_window_winograd_output_trans(ITensorInfo *input, ITensorInfo *bias, ITensorInfo *output, const WinogradInfo &winograd_info) { // Output tensor auto initialization if not yet initialized - auto_init_if_empty(*output, input->clone()->set_tensor_shape(arm_compute::misc::shape_calculator::compute_winograd_output_transform_shape(*input, output_convolved_dims, DataLayout::NCHW))); + auto_init_if_empty(*output, input->clone()->set_tensor_shape(arm_compute::misc::shape_calculator::compute_winograd_output_transform_shape(*input, winograd_info))); constexpr unsigned int num_elems_processed_per_iteration = 1; @@ -348,10 +360,11 @@ bool NEWinogradLayerTransformWeightsKernel<T, OutputTileRows, OutputTileCols, Ke } template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -Status NEWinogradLayerTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::validate(const ITensorInfo *input, const ITensorInfo *output, const Size2D &output_tile) +Status NEWinogradLayerTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::validate(const ITensorInfo *input, const ITensorInfo *output, + const WinogradInfo &winograd_info) { - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_winograd_weight_trans(input, output, output_tile)); - ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window_winograd_weight_trans(input->clone().get(), output->clone().get(), output_tile, Size2D(KernelRows, KernelCols)).first); + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_winograd_weight_trans(input, output, winograd_info)); + ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window_winograd_weight_trans(input->clone().get(), output->clone().get(), winograd_info).first); return Status{}; } @@ -426,11 +439,10 @@ bool NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCols, Kern } template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -Status NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::validate(const ITensorInfo *input, const ITensorInfo *output, const PadStrideInfo &conv_info, - const Size2D &kernel_dims) +Status NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::validate(const ITensorInfo *input, const ITensorInfo *output, const WinogradInfo &winograd_info) { - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_winograd_input_trans(input, output, conv_info, kernel_dims)); - ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window_winograd_input_trans(input->clone().get(), output->clone().get(), conv_info, kernel_dims, Size2D(OutputTileRows, OutputTileCols)).first); + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_winograd_input_trans(input, output, winograd_info)); + ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window_winograd_input_trans(input->clone().get(), output->clone().get(), winograd_info).first); return Status{}; } @@ -533,12 +545,11 @@ bool NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, Ker template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> Status NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, - const Size2D &kernel_dims, const Size2D &output_convolved_dims, - const Size2D &num_tiles) + const WinogradInfo &winograd_info) { - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_winograd_output_trans(input, (bias != nullptr ? bias->clone().get() : nullptr), output, kernel_dims, output_convolved_dims, num_tiles)); + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_winograd_output_trans(input, (bias != nullptr ? bias->clone().get() : nullptr), output, winograd_info)); ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window_winograd_output_trans(input->clone().get(), (bias != nullptr ? bias->clone().get() : nullptr), output->clone().get(), - output_convolved_dims) + winograd_info) .first); return Status{}; diff --git a/src/runtime/NEON/functions/NEWinogradLayer.cpp b/src/runtime/NEON/functions/NEWinogradLayer.cpp index 7f4761020c..264b97f7c1 100644 --- a/src/runtime/NEON/functions/NEWinogradLayer.cpp +++ b/src/runtime/NEON/functions/NEWinogradLayer.cpp @@ -270,31 +270,32 @@ Status NEWinogradLayer::validate(const ITensorInfo *input, const ITensorInfo *we // Get indices for the width and height const size_t idx_width = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::WIDTH); const size_t idx_height = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT); + // Input shape + const TensorShape input_shape = input->tensor_shape(); // Kernel size const unsigned int kernel_w = weights->tensor_shape()[idx_width]; const unsigned int kernel_h = weights->tensor_shape()[idx_height]; - // Number of tiles along the X and Y direction - const unsigned int num_tiles_x = std::ceil((input->tensor_shape().x() - (kernel_w - 1) + conv_info.pad_left() + conv_info.pad_right()) / 2.f); - const unsigned int num_tiles_y = std::ceil((input->tensor_shape().y() - (kernel_h - 1) + conv_info.pad_top() + conv_info.pad_bottom()) / 2.f); - - // Compute output shape - const TensorShape output_convolved_shape = misc::shape_calculator::compute_deep_convolution_shape(*input, *weights, conv_info); + const WinogradInfo winograd_info = WinogradInfo(Size2D(2, 2), + Size2D(kernel_w, kernel_h), + Size2D(input_shape[idx_width], input_shape[idx_height]), + conv_info, + input->data_layout()); // Validate input transform - const TensorShape input0_shape = misc::shape_calculator::compute_winograd_input_transform_shape(*input, conv_info, Size2D(kernel_w, kernel_h)); + const TensorShape input0_shape = misc::shape_calculator::compute_winograd_input_transform_shape(*input, winograd_info); const TensorInfo input0 = input->clone()->set_tensor_shape(input0_shape); switch(weights->dimension(0)) { case 3: { - ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 2, 2, 3, 3>::validate(input, &input0, conv_info, Size2D(kernel_w, kernel_h)))); + ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 2, 2, 3, 3>::validate(input, &input0, winograd_info))); break; } case 5: { - ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 2, 2, 5, 5>::validate(input, &input0, conv_info, Size2D(kernel_w, kernel_h)))); + ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 2, 2, 5, 5>::validate(input, &input0, winograd_info))); break; } default: @@ -304,19 +305,19 @@ Status NEWinogradLayer::validate(const ITensorInfo *input, const ITensorInfo *we } } // Validate filter transform - const TensorShape input1_shape = misc::shape_calculator::compute_winograd_filter_transform_shape(*weights, Size2D(2U, 2U)); + const TensorShape input1_shape = misc::shape_calculator::compute_winograd_filter_transform_shape(*weights, winograd_info); const TensorInfo input1 = weights->clone()->set_tensor_shape(input1_shape); switch(weights->dimension(0)) { case 3: { - ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 2, 2, 3, 3>::validate(weights, &input1, Size2D(2U, 2U)))); + ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 2, 2, 3, 3>::validate(weights, &input1, winograd_info))); break; } case 5: { - ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 2, 2, 5, 5>::validate(weights, &input1, Size2D(2U, 2U)))); + ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 2, 2, 5, 5>::validate(weights, &input1, winograd_info))); break; } default: @@ -336,9 +337,7 @@ Status NEWinogradLayer::validate(const ITensorInfo *input, const ITensorInfo *we ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerBatchedGEMMKernel<float, float, 2, 2, 3, 3>::validate(&input0, &input1, nullptr, &batched_mm_output, 1.0f, 0.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run*/)))); // Validate output transform - ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 2, 2, 3, 3>::validate(&batched_mm_output, biases, output, Size2D(kernel_w, kernel_h), Size2D(output_convolved_shape[idx_width], - output_convolved_shape[idx_height]), - Size2D(num_tiles_x, num_tiles_y)))); + ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 2, 2, 3, 3>::validate(&batched_mm_output, biases, output, winograd_info))); break; } case 5: @@ -346,9 +345,7 @@ Status NEWinogradLayer::validate(const ITensorInfo *input, const ITensorInfo *we ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerBatchedGEMMKernel<float, float, 2, 2, 5, 5>::validate(&input0, &input1, nullptr, &batched_mm_output, 1.0f, 0.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run*/)))); // Validate output transform - ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 2, 2, 5, 5>::validate(&batched_mm_output, biases, output, Size2D(kernel_w, kernel_h), Size2D(output_convolved_shape[idx_width], - output_convolved_shape[idx_height]), - Size2D(num_tiles_x, num_tiles_y)))); + ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 2, 2, 5, 5>::validate(&batched_mm_output, biases, output, winograd_info))); break; } default: |