arm_compute v18.05
diff --git a/src/runtime/CL/functions/CLConvolutionLayer.cpp b/src/runtime/CL/functions/CLConvolutionLayer.cpp
index 1a486ce..47a8d5f 100644
--- a/src/runtime/CL/functions/CLConvolutionLayer.cpp
+++ b/src/runtime/CL/functions/CLConvolutionLayer.cpp
@@ -42,25 +42,34 @@
{
}
-void CLConvolutionLayer::configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info)
+void CLConvolutionLayer::configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info,
+ const Size2D &dilation, const ActivationLayerInfo &act_info, bool enable_fast_math)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
- ARM_COMPUTE_ERROR_THROW_ON(CLConvolutionLayer::validate(input->info(), weights->info(), ((biases != nullptr) ? biases->info() : nullptr), output->info(), conv_info, weights_info));
+ ARM_COMPUTE_ERROR_THROW_ON(CLConvolutionLayer::validate(input->info(), weights->info(), ((biases != nullptr) ? biases->info() : nullptr), output->info(), conv_info, weights_info, dilation, act_info,
+ enable_fast_math));
- switch(CLConvolutionLayer::get_convolution_method(input->info(), weights->info(), ((biases != nullptr) ? biases->info() : nullptr), output->info(), conv_info,
- weights_info, CLScheduler::get().target()))
+ switch(CLConvolutionLayer::get_convolution_method(input->info(), weights->info(), output->info(), conv_info,
+ weights_info, act_info, CLScheduler::get().target(), dilation, enable_fast_math))
{
+ case ConvolutionMethod::WINOGRAD:
+ {
+ auto f = arm_compute::support::cpp14::make_unique<CLWinogradConvolutionLayer>(_memory_manager);
+ f->configure(input, weights, biases, output, conv_info, act_info, enable_fast_math);
+ _function = std::move(f);
+ break;
+ }
case ConvolutionMethod::DIRECT:
{
auto f = arm_compute::support::cpp14::make_unique<CLDirectConvolutionLayer>();
- f->configure(input, weights, biases, output, conv_info);
+ f->configure(input, weights, biases, output, conv_info, act_info);
_function = std::move(f);
break;
}
case ConvolutionMethod::GEMM:
{
auto f = arm_compute::support::cpp14::make_unique<CLGEMMConvolutionLayer>(_memory_manager);
- f->configure(input, weights, biases, output, conv_info, weights_info);
+ f->configure(input, weights, biases, output, conv_info, weights_info, dilation, act_info);
_function = std::move(f);
break;
}
@@ -71,25 +80,30 @@
}
Status CLConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
- const WeightsInfo &weights_info)
+ const WeightsInfo &weights_info, const Size2D &dilation, const ActivationLayerInfo &act_info, bool enable_fast_math)
{
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output);
- //Configure if the parameters match the direct convolution or the gemm-based
const GPUTarget gpu_target = CLScheduler::get().target();
- switch(CLConvolutionLayer::get_convolution_method(input, weights, biases, output, conv_info, weights_info, gpu_target))
+ switch(CLConvolutionLayer::get_convolution_method(input, weights, output, conv_info, weights_info, act_info, gpu_target, dilation, enable_fast_math))
{
+ case ConvolutionMethod::WINOGRAD:
+ {
+ //Validate Winograd
+ ARM_COMPUTE_RETURN_ON_ERROR(CLWinogradConvolutionLayer::validate(input, weights, biases, output, conv_info, act_info, enable_fast_math));
+ break;
+ }
case ConvolutionMethod::DIRECT:
{
// Validate direct convolution layer
- CLDirectConvolutionLayer::validate(input, weights, biases, output, conv_info);
+ ARM_COMPUTE_RETURN_ON_ERROR(CLDirectConvolutionLayer::validate(input, weights, biases, output, conv_info, act_info));
break;
}
case ConvolutionMethod::GEMM:
{
// Validate gemm-based convolution layer
- CLGEMMConvolutionLayer::validate(input, weights, biases, output, conv_info, weights_info);
+ ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMConvolutionLayer::validate(input, weights, biases, output, conv_info, weights_info, dilation, act_info));
break;
}
default:
@@ -100,21 +114,34 @@
return Status{};
}
-ConvolutionMethod CLConvolutionLayer::get_convolution_method(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
- const WeightsInfo &weights_info, const GPUTarget gpu_target)
+ConvolutionMethod CLConvolutionLayer::get_convolution_method(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output, const PadStrideInfo &conv_info,
+ const WeightsInfo &weights_info, const ActivationLayerInfo &act_info, const GPUTarget gpu_target, const Size2D &dilation, bool enable_fast_math)
{
- ARM_COMPUTE_UNUSED(input);
- ARM_COMPUTE_UNUSED(weights);
- ARM_COMPUTE_UNUSED(biases);
- ARM_COMPUTE_UNUSED(output);
- ARM_COMPUTE_UNUSED(conv_info);
+ ARM_COMPUTE_ERROR_ON_NULLPTR(input);
+ ARM_COMPUTE_ERROR_ON_NULLPTR(output);
+ ARM_COMPUTE_ERROR_ON_NULLPTR(weights);
ARM_COMPUTE_UNUSED(weights_info);
ARM_COMPUTE_UNUSED(gpu_target);
- return ConvolutionMethod::GEMM;
+ const size_t idx_c = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::CHANNEL);
+
+ if(dilation != Size2D(1U, 1U) || (input->dimension(idx_c) < 16))
+ {
+ return ConvolutionMethod::GEMM;
+ }
+ else
+ {
+ return bool(CLWinogradConvolutionLayer::validate(input, weights, nullptr, output, conv_info, act_info, enable_fast_math)) ? ConvolutionMethod::WINOGRAD : ConvolutionMethod::GEMM;
+ }
}
void CLConvolutionLayer::run()
{
+ prepare();
_function->run();
}
+
+void CLConvolutionLayer::prepare()
+{
+ _function->prepare();
+}