COMPMID-1013 - Create WinogradInfo data structure
COMPMID-1014 - Refactoring Winograd's dataset
Change-Id: I6abdcbf9a90d663f4db666cd410afece9f1d034d
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/125899
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
diff --git a/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp b/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp
index 7af36bf..0aa7f8d 100644
--- a/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp
+++ b/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp
@@ -39,21 +39,22 @@
void CLWinogradConvolutionLayer::configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info)
{
- // TODO(COMPMID-1013): This part will be removed
- // Get indeces for the width and height
+ // Get indices for the width and height
const size_t idx_width = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::WIDTH);
const size_t idx_height = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::HEIGHT);
+ // Input shape
+ const TensorShape input_shape = input->info()->tensor_shape();
+
// Kernel size
const unsigned int kernel_w = weights->info()->tensor_shape()[idx_width];
const unsigned int kernel_h = weights->info()->tensor_shape()[idx_height];
- // Number of tiles along the X and Y direction
- const unsigned int num_tiles_x = std::ceil((input->info()->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->info()->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->info(), *weights->info(), 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->info()->data_layout());
// Manage intermediate tensors
_memory_group.manage(&_input0);
@@ -62,17 +63,16 @@
// Do not manage _input1 as it contains the weights
// Configure input transform
- _input_transform.configure(input, &_input0, conv_info, Size2D(kernel_w, kernel_h));
+ _input_transform.configure(input, &_input0, winograd_info);
// Configure filter transform
- _filter_transform.configure(weights, &_input1, Size2D(2U, 2U));
+ _filter_transform.configure(weights, &_input1, winograd_info);
// Configure batched matrix multiply
_batched_mm.configure(&_input0, &_input1, nullptr, &_batched_mm_output, 1.0f, 0.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run*/));
// Configure output transform
- _output_transform.configure(&_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));
+ _output_transform.configure(&_batched_mm_output, biases, output, winograd_info);
// Configure activation layer
_is_activationlayer_enabled = act_info.enabled();
@@ -90,31 +90,32 @@
Status CLWinogradConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
const ActivationLayerInfo &act_info)
{
- // TODO(COMPMID-1013): This part will be removed
// Get indeces 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);
- ARM_COMPUTE_RETURN_ON_ERROR(CLWinogradInputTransform::validate(input, &input0, conv_info, Size2D(kernel_w, kernel_h)));
+ ARM_COMPUTE_RETURN_ON_ERROR(CLWinogradInputTransform::validate(input, &input0, winograd_info));
// 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);
- ARM_COMPUTE_RETURN_ON_ERROR(CLWinogradFilterTransformKernel::validate(weights, &input1, Size2D(2U, 2U)));
+ ARM_COMPUTE_RETURN_ON_ERROR(CLWinogradFilterTransformKernel::validate(weights, &input1, winograd_info));
// Validate batched matrix multiply
TensorShape batched_mm_output_shape = input0.tensor_shape();
@@ -122,10 +123,8 @@
const TensorInfo batched_mm_output = input0.clone()->set_tensor_shape(batched_mm_output_shape);
ARM_COMPUTE_RETURN_ON_ERROR(CLGEMM::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(CLWinogradOutputTransformKernel::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)));
+ // Configure output transform
+ ARM_COMPUTE_RETURN_ON_ERROR(CLWinogradOutputTransformKernel::validate(&batched_mm_output, biases, output, winograd_info));
// Validate Activation Layer
if(act_info.enabled())