arm_compute v19.11
diff --git a/src/runtime/CL/functions/CLDepthwiseConvolutionLayer.cpp b/src/runtime/CL/functions/CLDepthwiseConvolutionLayer.cpp
index f01b58a..e717f79 100644
--- a/src/runtime/CL/functions/CLDepthwiseConvolutionLayer.cpp
+++ b/src/runtime/CL/functions/CLDepthwiseConvolutionLayer.cpp
@@ -38,41 +38,386 @@
using namespace arm_compute::misc;
using namespace arm_compute::misc::shape_calculator;
+namespace
+{
+Status validate_arguments_3x3(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
+ unsigned int depth_multiplier, ActivationLayerInfo act_info, GPUTarget gpu_target, const Size2D &dilation)
+{
+ // This function should be removed and incorporated inside CLDepthwiseConvolutionLayerInternal3x3 once CLDepthwiseConvolutionLayer3x3 is properly removed
+ ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON(input->data_layout() == DataLayout::UNKNOWN);
+
+ const bool is_quantized = is_data_type_quantized_asymmetric(input->data_type());
+ const bool is_nhwc = input->data_layout() == DataLayout::NHWC;
+ const bool needs_permute = is_nhwc && (depth_multiplier > 1);
+ const bool needs_weights_reshape = is_nhwc && (depth_multiplier == 1) && is_quantized;
+ const bool is_stride_1 = ((conv_info.stride().first == conv_info.stride().second) && (conv_info.stride().first == 1));
+ const bool is_stride_1_dilation_1 = (is_stride_1 && dilation.x() == 1 && dilation.y() == 1);
+ const bool is_dot8_supported = dot8_supported(CLKernelLibrary::get().get_device());
+ DepthwiseConvolutionReshapeInfo info;
+ info.c0 = 4;
+ info.transpose = is_stride_1_dilation_1 && is_dot8_supported;
+
+ TensorInfo output_multipliers_shifts_info(TensorInfo(TensorShape(1U), 1, DataType::S32));
+ if(is_quantized)
+ {
+ if(is_data_type_quantized_per_channel(weights->data_type()))
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QSYMM8_PER_CHANNEL);
+
+ const size_t idx_c = get_data_layout_dimension_index(weights->data_layout(), DataLayoutDimension::CHANNEL);
+ output_multipliers_shifts_info.set_tensor_shape(TensorShape(weights->dimension(idx_c)));
+ }
+ else
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
+ }
+ }
+
+ if(needs_permute)
+ {
+ TensorShape permuted_input_shape = input->tensor_shape();
+ TensorShape permuted_weights_shape = weights->tensor_shape();
+ TensorShape permuted_output_shape = shape_calculator::compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier, dilation);
+
+ permute(permuted_input_shape, PermutationVector(1U, 2U, 0U));
+ permute(permuted_weights_shape, PermutationVector(1U, 2U, 0U));
+ permute(permuted_output_shape, PermutationVector(1U, 2U, 0U));
+
+ const TensorInfo permuted_input = input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(permuted_input_shape).set_data_layout(DataLayout::NCHW);
+ const TensorInfo permuted_weights = weights->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(permuted_weights_shape).set_data_layout(DataLayout::NCHW);
+ const TensorInfo permuted_output = output->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(permuted_output_shape).set_data_layout(DataLayout::NCHW);
+
+ ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseConvolutionLayer3x3NCHWKernel::validate(&permuted_input, &permuted_weights, biases, &permuted_output,
+ conv_info, depth_multiplier, act_info, gpu_target,
+ dilation, &output_multipliers_shifts_info, &output_multipliers_shifts_info));
+ }
+ else if(is_nhwc)
+ {
+ if(needs_weights_reshape)
+ {
+ auto reshaped_weights_shape = arm_compute::misc::shape_calculator::compute_reshaped_depthwise_weights_shape(*weights, info);
+ ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseConvolutionLayer3x3NHWCKernel::validate(input, &weights->clone()->set_tensor_shape(reshaped_weights_shape), biases,
+ output, conv_info, depth_multiplier, act_info,
+ dilation, &output_multipliers_shifts_info, &output_multipliers_shifts_info));
+ }
+ else
+ {
+ ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseConvolutionLayer3x3NHWCKernel::validate(input, weights, biases, output, conv_info, depth_multiplier, act_info,
+ dilation, &output_multipliers_shifts_info, &output_multipliers_shifts_info));
+ }
+ }
+ else
+ {
+ ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseConvolutionLayer3x3NCHWKernel::validate(input, weights, biases, output, conv_info, depth_multiplier, act_info, gpu_target,
+ dilation, &output_multipliers_shifts_info, &output_multipliers_shifts_info));
+ }
+ return Status{};
+}
+} // namespace
+
CLDepthwiseConvolutionLayer3x3::CLDepthwiseConvolutionLayer3x3(std::shared_ptr<IMemoryManager> memory_manager)
- : _memory_group(std::move(memory_manager)), _kernel(nullptr), _border_handler(), _permute_input_to_nchw(), _permute_weights_to_nchw(), _permute_output_to_nhwc(), _reshape_weights(), _permuted_input(),
- _permuted_weights(), _permuted_output(), _original_weights(nullptr), _needs_permute(false), _needs_weights_reshape(false), _is_prepared(false)
+ : _func(std::move(memory_manager))
{
}
void CLDepthwiseConvolutionLayer3x3::configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier,
ActivationLayerInfo act_info, const Size2D &dilation)
{
- ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
- ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
- // idx_w and idx_h only used for validation
- const size_t idx_w = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::WIDTH);
- const size_t idx_h = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::HEIGHT);
- ARM_COMPUTE_UNUSED(idx_w);
- ARM_COMPUTE_UNUSED(idx_h);
+ _func.configure(input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation);
+}
- ARM_COMPUTE_ERROR_ON(weights->info()->dimension(idx_w) + (weights->info()->dimension(idx_w) - 1) * (dilation.x() - 1) > input->info()->dimension(idx_w) + conv_info.pad_left() + conv_info.pad_right());
- ARM_COMPUTE_ERROR_ON(weights->info()->dimension(idx_h) + (weights->info()->dimension(idx_h) - 1) * (dilation.y() - 1) > input->info()->dimension(idx_h) + conv_info.pad_top() + conv_info.pad_bottom());
+Status CLDepthwiseConvolutionLayer3x3::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
+ unsigned int depth_multiplier, ActivationLayerInfo act_info, GPUTarget gpu_target, const Size2D &dilation)
+{
+ return validate_arguments_3x3(input, weights, biases, output, conv_info, depth_multiplier, act_info, gpu_target, dilation);
+}
- const bool is_nhwc = input->info()->data_layout() == DataLayout::NHWC;
+void CLDepthwiseConvolutionLayer3x3::run()
+{
+ _func.run();
+}
- _needs_permute = is_nhwc && (depth_multiplier > 1);
- _needs_weights_reshape = is_nhwc && (depth_multiplier == 1)
- && is_data_type_quantized_asymmetric(input->info()->data_type());
+void CLDepthwiseConvolutionLayer3x3::prepare()
+{
+ _func.prepare();
+}
+
+CLDepthwiseConvolutionLayer::CLDepthwiseConvolutionLayerGeneric::CLDepthwiseConvolutionLayerGeneric(std::shared_ptr<IMemoryManager> memory_manager)
+ : _memory_group(std::move(memory_manager)),
+ _dwc_native_kernel(),
+ _permute_input_to_nhwc(),
+ _permute_weights_to_nhwc(),
+ _permute_output_to_nchw(),
+ _permuted_input(),
+ _permuted_weights(),
+ _permuted_output(),
+ _output_multipliers(),
+ _output_shifts(),
+ _original_weights(),
+ _input(),
+ _output(),
+ _needs_permute(false),
+ _is_prepared(false),
+ _is_quantized(false)
+{
+}
+
+void CLDepthwiseConvolutionLayer::CLDepthwiseConvolutionLayerGeneric::configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info,
+ unsigned int depth_multiplier, const ActivationLayerInfo &act_info, const Size2D &dilation)
+{
+ ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
+ ARM_COMPUTE_ERROR_THROW_ON(CLDepthwiseConvolutionLayer::validate(input->info(),
+ weights->info(),
+ biases != nullptr ? biases->info() : nullptr,
+ output->info(),
+ conv_info,
+ depth_multiplier,
+ act_info,
+ dilation));
+
+ _is_quantized = is_data_type_quantized(input->info()->data_type());
_is_prepared = false;
_original_weights = weights;
+ _input = input;
+ _output = output;
+ _needs_permute = input->info()->data_layout() == DataLayout::NCHW;
+
+ ICLTensor *input_to_use = input;
+ const ICLTensor *weights_to_use = weights;
+ ICLTensor *output_to_use = output;
+ if(_needs_permute)
+ {
+ _memory_group.manage(&_permuted_input);
+ _memory_group.manage(&_permuted_output);
+
+ // Configure the function to transform the input tensor from NCHW -> NHWC
+ _permute_input_to_nhwc.configure(input, &_permuted_input, PermutationVector(2U, 0U, 1U));
+ _permuted_input.info()->set_data_layout(DataLayout::NHWC);
+
+ // Configure the function to transform the weights tensor from IHW -> HWI
+ _permute_weights_to_nhwc.configure(weights, &_permuted_weights, PermutationVector(2U, 0U, 1U));
+ _permuted_weights.info()->set_data_layout(DataLayout::NHWC);
+
+ // Set output quantization info before dwc kernel configure
+ _permuted_output.info()->set_quantization_info(output->info()->quantization_info());
+
+ input_to_use = &_permuted_input;
+ weights_to_use = &_permuted_weights;
+ output_to_use = &_permuted_output;
+ }
+
+ CLTensor *output_multipliers_to_use = nullptr;
+ CLTensor *output_shifts_to_use = nullptr;
+ if(_is_quantized)
+ {
+ const size_t idx_c = get_data_layout_dimension_index(weights->info()->data_layout(), DataLayoutDimension::CHANNEL);
+ const size_t num_filters = (is_data_type_quantized_per_channel(weights->info()->data_type())) ? weights->info()->dimension(idx_c) : 1;
+
+ _output_multipliers.allocator()->init(TensorInfo(TensorShape(num_filters), 1, DataType::S32));
+ _output_shifts.allocator()->init(TensorInfo(TensorShape(num_filters), 1, DataType::S32));
+
+ output_multipliers_to_use = &_output_multipliers;
+ output_shifts_to_use = &_output_shifts;
+ }
+
+ DWCWeightsKernelInfo dwc_weights_info;
+ dwc_weights_info.n0 = (depth_multiplier == 1) ? 8 : 1;
+ DWCKernelInfo dwc_info;
+ dwc_info.activation_info = act_info;
+ _dwc_native_kernel.configure(input_to_use, weights_to_use, biases, output_to_use,
+ dwc_weights_info, dwc_info, conv_info, depth_multiplier, dilation,
+ output_multipliers_to_use, output_shifts_to_use);
+
+ if(_needs_permute)
+ {
+ _permuted_input.allocator()->allocate();
+
+ // Configure the function to transform the convoluted output to NCHW format
+ _permuted_output.info()->set_data_layout(DataLayout::NCHW);
+ _permute_output_to_nchw.configure(&_permuted_output, output, PermutationVector(1U, 2U, 0U));
+ _permuted_output.allocator()->allocate();
+ }
+
+ if(_is_quantized)
+ {
+ _output_multipliers.allocator()->allocate();
+ _output_shifts.allocator()->allocate();
+ }
+}
+
+Status CLDepthwiseConvolutionLayer::CLDepthwiseConvolutionLayerGeneric::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output,
+ const PadStrideInfo &conv_info,
+ unsigned int depth_multiplier, const ActivationLayerInfo &act_info, const Size2D &dilation)
+{
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, output);
+ const size_t idx_w = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::WIDTH);
+ const size_t idx_h = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT);
+
+ ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(idx_w) + (weights->dimension(idx_w) - 1) * (dilation.x() - 1) > input->dimension(idx_w) + conv_info.pad_left() + conv_info.pad_right());
+ ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(idx_h) + (weights->dimension(idx_h) - 1) * (dilation.y() - 1) > input->dimension(idx_h) + conv_info.pad_top() + conv_info.pad_bottom());
+
+ DWCWeightsKernelInfo dwc_weights_info;
+ dwc_weights_info.n0 = (depth_multiplier == 1) ? 8 : 1;
+ DWCKernelInfo dwc_info;
+ dwc_info.activation_info = act_info;
+
+ const bool needs_permute = input->data_layout() == DataLayout::NCHW;
+
+ const bool is_quantized = is_data_type_quantized(input->data_type());
+
+ TensorInfo output_multipliers_shifts_info(TensorInfo(TensorShape(1U), 1, DataType::S32));
+ if(is_quantized)
+ {
+ if(is_data_type_quantized_per_channel(weights->data_type()))
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QSYMM8_PER_CHANNEL);
+
+ const size_t idx_c = get_data_layout_dimension_index(weights->data_layout(), DataLayoutDimension::CHANNEL);
+ output_multipliers_shifts_info.set_tensor_shape(TensorShape(weights->dimension(idx_c)));
+ }
+ else
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
+ }
+ }
+
+ if(needs_permute)
+ {
+ TensorShape permuted_input_shape = input->tensor_shape();
+ TensorShape permuted_weights_shape = weights->tensor_shape();
+ TensorShape permuted_output_shape = shape_calculator::compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier, dilation);
+
+ permute(permuted_input_shape, PermutationVector(2U, 0U, 1U));
+ permute(permuted_weights_shape, PermutationVector(2U, 0U, 1U));
+ permute(permuted_output_shape, PermutationVector(2U, 0U, 1U));
+
+ const TensorInfo permuted_input = input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(permuted_input_shape).set_data_layout(DataLayout::NHWC);
+ const TensorInfo permuted_weights = weights->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(permuted_weights_shape).set_data_layout(DataLayout::NHWC);
+ const TensorInfo permuted_output = output->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(permuted_output_shape).set_data_layout(DataLayout::NHWC);
+
+ ARM_COMPUTE_RETURN_ON_ERROR(CLPermute::validate(input, &permuted_input, PermutationVector(2U, 0U, 1U)));
+ ARM_COMPUTE_RETURN_ON_ERROR(CLPermute::validate(weights, &permuted_weights, PermutationVector(2U, 0U, 1U)));
+ ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseConvolutionLayerNativeKernel::validate(&permuted_input, &permuted_weights, biases, &permuted_output, dwc_weights_info,
+ dwc_info, conv_info, depth_multiplier, dilation,
+ &output_multipliers_shifts_info, &output_multipliers_shifts_info));
+ ARM_COMPUTE_RETURN_ON_ERROR(CLPermute::validate(&permuted_output, output, PermutationVector(1U, 2U, 0U)));
+ }
+ else
+ {
+ ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseConvolutionLayerNativeKernel::validate(input, weights, biases, output, dwc_weights_info, dwc_info, conv_info, depth_multiplier,
+ dilation, &output_multipliers_shifts_info, &output_multipliers_shifts_info));
+ }
+ return Status{};
+}
+
+void CLDepthwiseConvolutionLayer::CLDepthwiseConvolutionLayerGeneric::run()
+{
+ prepare();
+
+ MemoryGroupResourceScope scope_mg(_memory_group);
+
+ if(_needs_permute)
+ {
+ _permute_input_to_nhwc.run();
+ }
+ CLScheduler::get().enqueue(_dwc_native_kernel);
+ if(_needs_permute)
+ {
+ _permute_output_to_nchw.run();
+ }
+}
+
+void CLDepthwiseConvolutionLayer::CLDepthwiseConvolutionLayerGeneric::prepare()
+{
+ if(!_is_prepared)
+ {
+ if(_is_quantized)
+ {
+ _output_multipliers.map();
+ _output_shifts.map();
+ const unsigned int idx_ofms = get_data_layout_dimension_index(_output->info()->data_layout(), DataLayoutDimension::CHANNEL);
+ quantization::compute_quantized_multipliers_and_shifts(_input->info(),
+ _original_weights->info(),
+ _output->info(),
+ idx_ofms,
+ reinterpret_cast<int32_t *>(_output_multipliers.ptr_to_element(Coordinates(0))),
+ reinterpret_cast<int32_t *>(_output_shifts.ptr_to_element(Coordinates(0))));
+ _output_multipliers.unmap();
+ _output_shifts.unmap();
+ }
+
+ if(_needs_permute)
+ {
+ ARM_COMPUTE_ERROR_ON(!_original_weights->is_used());
+
+ _permuted_weights.allocator()->allocate();
+ _permute_weights_to_nhwc.run();
+ _original_weights->mark_as_unused();
+ }
+ _is_prepared = true;
+ }
+}
+
+CLDepthwiseConvolutionLayer::CLDepthwiseConvolutionLayerInternal3x3::CLDepthwiseConvolutionLayerInternal3x3(std::shared_ptr<IMemoryManager> memory_manager)
+ : _memory_group(std::move(memory_manager)),
+ _kernel(nullptr),
+ _border_handler(),
+ _permute_input_to_nchw(),
+ _permute_weights_to_nchw(),
+ _permute_output_to_nhwc(),
+ _reshape_weights(),
+ _permuted_input(),
+ _permuted_weights(),
+ _permuted_output(),
+ _output_multipliers(),
+ _output_shifts(),
+ _original_weights(nullptr),
+ _input(nullptr),
+ _output(nullptr),
+ _needs_permute(false),
+ _needs_weights_reshape(false),
+ _is_prepared(false),
+ _is_quantized(false)
+{
+}
+
+void CLDepthwiseConvolutionLayer::CLDepthwiseConvolutionLayerInternal3x3::configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output,
+ const PadStrideInfo &conv_info, unsigned int depth_multiplier, ActivationLayerInfo act_info, const Size2D &dilation)
+{
+ const GPUTarget gpu_target = CLScheduler::get().target();
+
+ // Perform validation step
+ ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
+ ARM_COMPUTE_ERROR_THROW_ON(CLDepthwiseConvolutionLayer3x3::validate(input->info(),
+ weights->info(),
+ biases != nullptr ? biases->info() : nullptr,
+ output->info(),
+ conv_info,
+ depth_multiplier,
+ act_info,
+ gpu_target,
+ dilation));
+
+ const bool is_nhwc = input->info()->data_layout() == DataLayout::NHWC;
+ _is_quantized = is_data_type_quantized_asymmetric(input->info()->data_type());
+ _needs_permute = is_nhwc && (depth_multiplier > 1);
+ _needs_weights_reshape = is_nhwc && (depth_multiplier == 1) && _is_quantized;
+
+ _is_prepared = false;
+ _original_weights = weights;
+ _input = input;
+ _output = output;
ICLTensor *input_to_use = input;
const ICLTensor *weights_to_use = weights;
ICLTensor *output_to_use = output;
- const bool is_stride_1 = ((conv_info.stride().first == conv_info.stride().second) && (conv_info.stride().first == 1));
- const bool is_dot8_supported = dot8_supported(CLKernelLibrary::get().get_device());
- const bool is_stride_1_dilation_1 = (is_stride_1 && dilation.x() == 1 && dilation.y() == 1);
+ const bool is_quantized_per_channel = is_data_type_quantized_per_channel(weights->info()->data_type());
+ const bool is_stride_1 = ((conv_info.stride().first == conv_info.stride().second) && (conv_info.stride().first == 1));
+ const bool is_dot8_supported = dot8_supported(CLKernelLibrary::get().get_device()) && !is_quantized_per_channel;
+ const bool is_stride_1_dilation_1 = (is_stride_1 && dilation.x() == 1 && dilation.y() == 1);
DepthwiseConvolutionReshapeInfo info;
info.c0 = 4;
@@ -112,9 +457,30 @@
_kernel = arm_compute::support::cpp14::make_unique<CLDepthwiseConvolutionLayer3x3NCHWKernel>();
}
+ CLTensor *output_multipliers_to_use = nullptr;
+ CLTensor *output_shifts_to_use = nullptr;
+ if(_is_quantized)
+ {
+ const size_t idx_c = get_data_layout_dimension_index(weights->info()->data_layout(), DataLayoutDimension::CHANNEL);
+ const size_t num_filters = (is_quantized_per_channel) ? weights->info()->dimension(idx_c) : 1;
+
+ _output_multipliers.allocator()->init(TensorInfo(TensorShape(num_filters), 1, DataType::S32));
+ _output_shifts.allocator()->init(TensorInfo(TensorShape(num_filters), 1, DataType::S32));
+
+ output_multipliers_to_use = &_output_multipliers;
+ output_shifts_to_use = &_output_shifts;
+ }
+
// Configure kernel
- _kernel->set_target(CLScheduler::get().target());
- _kernel->configure(input_to_use, weights_to_use, biases, output_to_use, conv_info, depth_multiplier, act_info, dilation);
+ _kernel->set_target(gpu_target);
+ _kernel->configure(input_to_use, weights_to_use, biases, output_to_use, conv_info, depth_multiplier,
+ act_info, dilation, output_multipliers_to_use, output_shifts_to_use);
+
+ if(_is_quantized)
+ {
+ _output_multipliers.allocator()->allocate();
+ _output_shifts.allocator()->allocate();
+ }
// Permute output if needed
if(_needs_permute)
@@ -136,73 +502,13 @@
_border_handler.configure(input_to_use, _kernel->border_size(), BorderMode::CONSTANT, zero_value);
}
-Status CLDepthwiseConvolutionLayer3x3::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
- unsigned int depth_multiplier, ActivationLayerInfo act_info, GPUTarget gpu_target, const Size2D &dilation)
+Status CLDepthwiseConvolutionLayer::CLDepthwiseConvolutionLayerInternal3x3::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output,
+ const PadStrideInfo &conv_info, unsigned int depth_multiplier, ActivationLayerInfo act_info, GPUTarget gpu_target, const Size2D &dilation)
{
- ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output);
- ARM_COMPUTE_RETURN_ERROR_ON(input->data_layout() == DataLayout::UNKNOWN);
-
- const bool is_quantized = is_data_type_quantized_asymmetric(input->data_type());
- const bool is_nhwc = input->data_layout() == DataLayout::NHWC;
- const bool needs_permute = is_nhwc && (depth_multiplier > 1);
- const bool needs_weights_reshape = is_nhwc && (depth_multiplier == 1) && is_quantized;
- const bool is_stride_1 = ((conv_info.stride().first == conv_info.stride().second) && (conv_info.stride().first == 1));
- const bool is_stride_1_dilation_1 = (is_stride_1 && dilation.x() == 1 && dilation.y() == 1);
- const bool is_dot8_supported = dot8_supported(CLKernelLibrary::get().get_device());
- DepthwiseConvolutionReshapeInfo info;
- info.c0 = 4;
- info.transpose = is_stride_1_dilation_1 && is_dot8_supported;
-
- if(is_quantized)
- {
- const UniformQuantizationInfo iq_info = input->quantization_info().uniform();
- const UniformQuantizationInfo wq_info = weights->quantization_info().uniform();
- const UniformQuantizationInfo oq_info = (output->total_size() == 0) ? iq_info : output->quantization_info().uniform();
-
- const float multiplier = iq_info.scale * wq_info.scale / oq_info.scale;
- ARM_COMPUTE_UNUSED(multiplier);
- ARM_COMPUTE_RETURN_ERROR_ON(multiplier > 1.0f);
- }
-
- if(needs_permute)
- {
- TensorShape permuted_input_shape = input->tensor_shape();
- TensorShape permuted_weights_shape = weights->tensor_shape();
- TensorShape permuted_output_shape = shape_calculator::compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier, dilation);
-
- permute(permuted_input_shape, PermutationVector(1U, 2U, 0U));
- permute(permuted_weights_shape, PermutationVector(1U, 2U, 0U));
- permute(permuted_output_shape, PermutationVector(1U, 2U, 0U));
-
- const TensorInfo permuted_input = input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(permuted_input_shape).set_data_layout(DataLayout::NCHW);
- const TensorInfo permuted_weights = weights->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(permuted_weights_shape).set_data_layout(DataLayout::NCHW);
- const TensorInfo permuted_output = output->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(permuted_output_shape).set_data_layout(DataLayout::NCHW);
-
- ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseConvolutionLayer3x3NCHWKernel::validate(&permuted_input, &permuted_weights, biases, &permuted_output, conv_info, depth_multiplier, act_info, gpu_target,
- dilation));
- }
- else if(is_nhwc)
- {
- if(needs_weights_reshape)
- {
- auto reshaped_weights_shape = arm_compute::misc::shape_calculator::compute_reshaped_depthwise_weights_shape(*weights, info);
- ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseConvolutionLayer3x3NHWCKernel::validate(input, &weights->clone()->set_tensor_shape(reshaped_weights_shape), biases, output, conv_info, depth_multiplier,
- act_info, dilation));
- }
- else
- {
- ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseConvolutionLayer3x3NHWCKernel::validate(input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation));
- }
- }
- else
- {
- ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseConvolutionLayer3x3NCHWKernel::validate(input, weights, biases, output, conv_info, depth_multiplier, act_info, gpu_target, dilation));
- }
-
- return Status{};
+ return validate_arguments_3x3(input, weights, biases, output, conv_info, depth_multiplier, act_info, gpu_target, dilation);
}
-void CLDepthwiseConvolutionLayer3x3::run()
+void CLDepthwiseConvolutionLayer::CLDepthwiseConvolutionLayerInternal3x3::run()
{
prepare();
@@ -221,10 +527,25 @@
}
}
-void CLDepthwiseConvolutionLayer3x3::prepare()
+void CLDepthwiseConvolutionLayer::CLDepthwiseConvolutionLayerInternal3x3::prepare()
{
if(!_is_prepared)
{
+ if(_is_quantized)
+ {
+ _output_multipliers.map();
+ _output_shifts.map();
+ const unsigned int idx_ofms = get_data_layout_dimension_index(_output->info()->data_layout(), DataLayoutDimension::CHANNEL);
+ quantization::compute_quantized_multipliers_and_shifts(_input->info(),
+ _original_weights->info(),
+ _output->info(),
+ idx_ofms,
+ reinterpret_cast<int32_t *>(_output_multipliers.ptr_to_element(Coordinates(0))),
+ reinterpret_cast<int32_t *>(_output_shifts.ptr_to_element(Coordinates(0))));
+ _output_multipliers.unmap();
+ _output_shifts.unmap();
+ }
+
if(_needs_permute)
{
ARM_COMPUTE_ERROR_ON(!_original_weights->is_used());
@@ -246,259 +567,92 @@
}
}
-CLDepthwiseConvolutionLayer::CLDepthwiseConvolutionLayer()
- : _im2col_kernel(), _weights_reshape_kernel(), _v2mm_kernel(), _vector_to_tensor_kernel(), _output_stage_kernel(), _activationlayer_function(), _v2mm_input_fill_border(), _v2mm_weights_fill_border(),
- _input_reshaped(), _weights_reshaped(), _v2mm_output(), _output_reshaped(), _is_prepared(false), _is_quantized(false), _is_activationlayer_enabled(false), _original_weights(nullptr),
- _optimised_function(nullptr)
+CLDepthwiseConvolutionLayer::CLDepthwiseConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager)
+ : _memory_manager(std::move(memory_manager)), _depth_conv_func(DepthwiseConvolutionFunction::GENERIC), _func_3x3(), _func_generic()
{
}
-void CLDepthwiseConvolutionLayer::configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info,
- unsigned int depth_multiplier, const ActivationLayerInfo &act_info, const Size2D &dilation)
+void CLDepthwiseConvolutionLayer::configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier,
+ ActivationLayerInfo act_info, const Size2D &dilation)
{
- ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
- ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
- ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, output);
-
- const size_t idx_w = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::WIDTH);
- const size_t idx_h = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::HEIGHT);
-
- ARM_COMPUTE_ERROR_ON(weights->info()->dimension(idx_w) + (weights->info()->dimension(idx_w) - 1) * (dilation.x() - 1) > input->info()->dimension(idx_w) + conv_info.pad_left() + conv_info.pad_right());
- ARM_COMPUTE_ERROR_ON(weights->info()->dimension(idx_h) + (weights->info()->dimension(idx_h) - 1) * (dilation.y() - 1) > input->info()->dimension(idx_h) + conv_info.pad_top() + conv_info.pad_bottom());
-
- const bool can_run_optimised_3x3_kernel = (weights->info()->dimension(idx_w) == 3) && (weights->info()->dimension(idx_h) == 3);
-
- if(bool(can_run_optimised_3x3_kernel))
+ const GPUTarget gpu_target = CLScheduler::get().target();
+ _depth_conv_func = get_depthwiseconvolution_function(input->info(), weights->info(), (biases != nullptr) ? biases->info() : nullptr, output->info(), conv_info, depth_multiplier, act_info,
+ dilation, gpu_target);
+ switch(_depth_conv_func)
{
- auto f = arm_compute::support::cpp14::make_unique<CLDepthwiseConvolutionLayer3x3>();
- f->configure(input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation);
- _optimised_function = std::move(f);
- }
- else
- {
- const size_t idx_c = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::CHANNEL);
-
- const size_t weights_w = weights->info()->dimension(idx_w);
- const size_t weights_h = weights->info()->dimension(idx_h);
- const size_t weights_z = weights->info()->dimension(idx_c);
-
- _is_prepared = false;
- _original_weights = weights;
- _is_quantized = is_data_type_quantized_asymmetric(input->info()->data_type());
-
- bool append_bias = (biases != nullptr) && !_is_quantized;
- const GPUTarget gpu_target = CLScheduler::get().target();
-
- // Calculate output shape
- TensorShape output_shape = shape_calculator::compute_depthwise_convolution_shape(*input->info(), *weights->info(), conv_info, depth_multiplier, dilation);
-
- // Output auto inizialitation if not yet initialized
- auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(output_shape));
- ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(output->info()->tensor_shape(), output_shape);
-
- // Output width and height
- const unsigned int conv_w = output_shape[idx_w];
- const unsigned int conv_h = output_shape[idx_h];
-
- // Set up intermediate tensors
- const size_t patch_size = weights_w * weights_h + ((append_bias) ? 1 : 0);
- const size_t conv_size = conv_w * conv_h;
-
- const UniformQuantizationInfo iq_info = input->info()->quantization_info().uniform();
- const UniformQuantizationInfo wq_info = weights->info()->quantization_info().uniform();
- const UniformQuantizationInfo oq_info = output->info()->quantization_info().uniform();
-
- // Im2Col configuration
- TensorShape shape_im2col = input->info()->tensor_shape();
- shape_im2col.set(0, patch_size);
- shape_im2col.set(1, conv_size);
- shape_im2col.set(2, weights_z);
- _input_reshaped.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_im2col));
- _im2col_kernel.set_target(gpu_target);
- _im2col_kernel.configure(input, &_input_reshaped, Size2D(weights_w, weights_h), conv_info, append_bias, depth_multiplier, dilation);
- CLScheduler::get().tune_kernel_static(_im2col_kernel);
-
- // Weights reshape configuration
- const TensorShape shape_weights_reshape(patch_size, weights_z);
- _weights_reshaped.allocator()->init(weights->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_weights_reshape));
- _weights_reshape_kernel.configure(weights, &_weights_reshaped, append_bias ? biases : nullptr);
-
- // GEMV configuration
- DataType v2mm_dt = (input->info()->data_type() == DataType::QASYMM8) ? DataType::S32 : input->info()->data_type();
- TensorShape shape_v2mm_out = input->info()->tensor_shape();
- shape_v2mm_out.set(0, conv_size * weights_z);
- shape_v2mm_out.set(1, 1);
- shape_v2mm_out.set(2, 1);
- _v2mm_output.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_data_type(v2mm_dt).set_tensor_shape(shape_v2mm_out));
- _v2mm_kernel.set_target(gpu_target);
- _v2mm_kernel.configure(&_input_reshaped, &_weights_reshaped, &_v2mm_output);
- CLScheduler::get().tune_kernel_static(_v2mm_kernel);
- _output_reshaped.allocator()->init(_v2mm_output.info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(output_shape));
- _vector_to_tensor_kernel.configure(&_v2mm_output, (_is_quantized) ? &_output_reshaped : output, conv_w, conv_h);
-
- // Output staged configuration
- if(_is_quantized)
+ case DepthwiseConvolutionFunction::OPTIMIZED:
+ _func_3x3.set_memory_group(_memory_manager);
+ _func_3x3.configure(input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation);
+ break;
+ case DepthwiseConvolutionFunction::GENERIC:
{
- const UniformQuantizationInfo output_quant_info = (output->info()->total_size() == 0) ? iq_info : oq_info;
-
- int output_multiplier = 0;
- int output_shift = 0;
- const float multiplier = iq_info.scale * wq_info.scale / output_quant_info.scale;
- quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift);
- _output_stage_kernel.configure(&_output_reshaped, biases, output, output_multiplier, output_shift, output_quant_info.offset);
- _output_reshaped.allocator()->allocate();
+ _func_generic.set_memory_group(_memory_manager);
+ _func_generic.configure(input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation);
}
-
- // Fill borders on inputs
- PixelValue zero_in(static_cast<int32_t>(0));
- PixelValue zero_w(static_cast<int32_t>(0));
- if(_is_quantized)
- {
- zero_in = PixelValue(static_cast<int32_t>(iq_info.offset));
- zero_w = PixelValue(static_cast<int32_t>(wq_info.offset));
- }
- BorderSize border_size = _v2mm_kernel.border_size();
- _v2mm_input_fill_border.configure(&_input_reshaped, border_size, BorderMode::CONSTANT, zero_in);
-
- border_size.bottom = 0;
- _v2mm_weights_fill_border.configure(&_weights_reshaped, border_size, BorderMode::CONSTANT, zero_w);
-
- // Allocate intermediate tensors
- _input_reshaped.allocator()->allocate();
- _v2mm_output.allocator()->allocate();
-
- //Configure Activation Layer
- _is_activationlayer_enabled = act_info.enabled();
-
- if(_is_activationlayer_enabled)
- {
- _activationlayer_function.configure(output, nullptr, act_info);
- }
+ break;
+ default:
+ ARM_COMPUTE_ERROR("Unsupported DepthwiseConvolutionFunction");
}
}
Status CLDepthwiseConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
- unsigned int depth_multiplier, const ActivationLayerInfo &act_info, const Size2D &dilation)
+ unsigned int depth_multiplier, ActivationLayerInfo act_info, const Size2D &dilation)
{
- const size_t idx_w = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::WIDTH);
- const size_t idx_h = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT);
-
- ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(idx_w) + (weights->dimension(idx_w) - 1) * (dilation.x() - 1) > input->dimension(idx_w) + conv_info.pad_left() + conv_info.pad_right());
- ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(idx_h) + (weights->dimension(idx_h) - 1) * (dilation.y() - 1) > input->dimension(idx_h) + conv_info.pad_top() + conv_info.pad_bottom());
-
- const bool can_run_optimised_3x3_kernel = (weights->dimension(idx_w) == 3) && (weights->dimension(idx_h) == 3);
-
- if(!can_run_optimised_3x3_kernel)
+ const GPUTarget gpu_target = CLScheduler::get().target();
+ DepthwiseConvolutionFunction depth_conv_func = get_depthwiseconvolution_function(input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation, gpu_target);
+ switch(depth_conv_func)
{
- const size_t idx_c = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::CHANNEL);
+ case DepthwiseConvolutionFunction::OPTIMIZED:
+ return CLDepthwiseConvolutionLayerInternal3x3::validate(input, weights, biases, output, conv_info, depth_multiplier, act_info, gpu_target, dilation);
+ case DepthwiseConvolutionFunction::GENERIC:
+ return CLDepthwiseConvolutionLayerGeneric::validate(input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation);
+ default:
+ ARM_COMPUTE_ERROR("Unsupported DepthwiseConvolutionFunction");
+ }
+}
- ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output);
- ARM_COMPUTE_RETURN_ERROR_ON((input->dimension(idx_c) * depth_multiplier) != weights->dimension(idx_c));
-
- const bool is_quantized = is_data_type_quantized_asymmetric(input->data_type());
- const bool append_bias = (biases != nullptr) && !is_quantized;
- const TensorShape output_shape = shape_calculator::compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier, dilation);
- const size_t weights_w = weights->dimension(idx_w);
- const size_t weights_h = weights->dimension(idx_h);
- const size_t weights_z = weights->dimension(idx_c);
- const unsigned int conv_w = output_shape[idx_w];
- const unsigned int conv_h = output_shape[idx_h];
- const size_t patch_size = weights_w * weights_h + ((append_bias) ? 1 : 0);
- const size_t conv_size = conv_w * conv_h;
-
- TensorShape shape_im2col = input->tensor_shape();
- shape_im2col.set(0, patch_size);
- shape_im2col.set(1, conv_size);
- shape_im2col.set(2, weights_z);
- TensorInfo input_reshaped(input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_im2col));
- ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseIm2ColKernel::validate(input, &input_reshaped, Size2D(weights_w, weights_h), conv_info, append_bias, depth_multiplier, dilation));
-
- const TensorShape shape_weights_reshape(patch_size, weights_z);
- TensorInfo weights_reshaped(weights->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_weights_reshape));
- ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseConvolutionLayerReshapeWeightsGenericKernel::validate(weights, &weights_reshaped, append_bias ? biases : nullptr));
-
- DataType v2mm_dt = (input->data_type() == DataType::QASYMM8) ? DataType::S32 : input->data_type();
- TensorShape shape_v2mm_out = input->tensor_shape();
- shape_v2mm_out.set(0, conv_size * weights_z);
- shape_v2mm_out.set(1, 1);
- shape_v2mm_out.set(2, 1);
- TensorInfo v2mm_output(input->clone()->set_is_resizable(true).reset_padding().set_data_type(v2mm_dt).set_tensor_shape(shape_v2mm_out));
- ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMMatrixVectorMultiplyKernel::validate(&input_reshaped, &weights_reshaped, &v2mm_output));
-
- TensorInfo output_reshaped(v2mm_output.clone()->set_is_resizable(true).reset_padding().set_tensor_shape(output_shape));
- ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseVectorToTensorKernel::validate(&v2mm_output, (is_quantized) ? &output_reshaped : output, conv_w, conv_h));
-
- if(is_quantized)
- {
- const UniformQuantizationInfo iq_info = input->quantization_info().uniform();
- const UniformQuantizationInfo wq_info = weights->quantization_info().uniform();
- const UniformQuantizationInfo oq_info = (output->total_size() == 0) ? iq_info : output->quantization_info().uniform();
-
- const float multiplier = iq_info.scale * wq_info.scale / oq_info.scale;
- ARM_COMPUTE_UNUSED(multiplier);
- ARM_COMPUTE_RETURN_ERROR_ON(multiplier > 1.0f);
- ARM_COMPUTE_RETURN_ON_ERROR(CLDirectConvolutionLayerOutputStageKernel::validate(&output_reshaped, biases, output));
- }
-
- // Validate Activation Layer
- if(act_info.enabled())
- {
- ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayer::validate(output, nullptr, act_info));
- }
+DepthwiseConvolutionFunction CLDepthwiseConvolutionLayer::get_depthwiseconvolution_function(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output,
+ const PadStrideInfo &conv_info,
+ unsigned int depth_multiplier, ActivationLayerInfo act_info, const Size2D &dilation, GPUTarget gpu_target)
+{
+ if(bool(CLDepthwiseConvolutionLayerInternal3x3::validate(input, weights, biases, output, conv_info, depth_multiplier, act_info, gpu_target, dilation)) && (is_data_type_float(input->data_type())
+ || get_arch_from_target(gpu_target) == GPUTarget::MIDGARD))
+ {
+ return DepthwiseConvolutionFunction::OPTIMIZED;
}
else
{
- ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseConvolutionLayer3x3::validate(input, weights, biases, output, conv_info, depth_multiplier, act_info, GPUTarget::MIDGARD, dilation));
+ return DepthwiseConvolutionFunction::GENERIC;
}
- return Status{};
}
void CLDepthwiseConvolutionLayer::run()
{
- prepare();
-
- if(_optimised_function != nullptr)
+ switch(_depth_conv_func)
{
- _optimised_function->run();
- }
- else
- {
- CLScheduler::get().enqueue(_im2col_kernel);
- CLScheduler::get().enqueue(_v2mm_input_fill_border);
- CLScheduler::get().enqueue(_v2mm_kernel);
- CLScheduler::get().enqueue(_vector_to_tensor_kernel);
- if(_is_quantized)
- {
- CLScheduler::get().enqueue(_output_stage_kernel);
- }
- if(_is_activationlayer_enabled)
- {
- _activationlayer_function.run();
- }
+ case DepthwiseConvolutionFunction::OPTIMIZED:
+ _func_3x3.run();
+ break;
+ case DepthwiseConvolutionFunction::GENERIC:
+ _func_generic.run();
+ break;
+ default:
+ ARM_COMPUTE_ERROR("DepthwiseConvolutionFunction not properly configured");
}
}
void CLDepthwiseConvolutionLayer::prepare()
{
- if(_optimised_function != nullptr)
+ switch(_depth_conv_func)
{
- _optimised_function->prepare();
- }
- else
- {
- if(!_is_prepared)
- {
- ARM_COMPUTE_ERROR_ON(!_original_weights->is_used());
-
- // Run weights reshaping and mark original weights tensor as unused
- _weights_reshaped.allocator()->allocate();
- CLScheduler::get().enqueue(_weights_reshape_kernel);
- CLScheduler::get().enqueue(_v2mm_weights_fill_border);
- _original_weights->mark_as_unused();
-
- CLScheduler::get().queue().finish();
- _is_prepared = true;
- }
+ case DepthwiseConvolutionFunction::OPTIMIZED:
+ _func_3x3.prepare();
+ break;
+ case DepthwiseConvolutionFunction::GENERIC:
+ _func_generic.prepare();
+ break;
+ default:
+ ARM_COMPUTE_ERROR("DepthwiseConvolutionFunction not properly configured");
}
}
} // namespace arm_compute