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Diffstat (limited to 'src/cpu/operators/CpuConv2d.cpp')
-rw-r--r--src/cpu/operators/CpuConv2d.cpp140
1 files changed, 92 insertions, 48 deletions
diff --git a/src/cpu/operators/CpuConv2d.cpp b/src/cpu/operators/CpuConv2d.cpp
index 16ac16b3ba..19311733db 100644
--- a/src/cpu/operators/CpuConv2d.cpp
+++ b/src/cpu/operators/CpuConv2d.cpp
@@ -22,8 +22,10 @@
* SOFTWARE.
*/
#include "src/cpu/operators/CpuConv2d.h"
-#include "arm_compute/runtime/NEON/NEScheduler.h"
+
#include "arm_compute/runtime/NEON/functions/NEFFTConvolutionLayer.h"
+#include "arm_compute/runtime/NEON/NEScheduler.h"
+
#include "src/common/utils/Log.h"
#include "src/cpu/operators/CpuDirectConv2d.h"
#include "src/cpu/operators/CpuGemm.h"
@@ -35,26 +37,35 @@ namespace arm_compute
{
namespace cpu
{
-CpuConv2d::CpuConv2d()
- : _function()
+CpuConv2d::CpuConv2d() : _function()
{
}
CpuConv2d::~CpuConv2d() = default;
-void CpuConv2d::configure(ITensorInfo *input, ITensorInfo *weights, const ITensorInfo *biases, ITensorInfo *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info,
- const Size2D &dilation, const ActivationLayerInfo &act_info, bool enable_fast_math, unsigned int num_groups)
+void CpuConv2d::configure(ITensorInfo *input,
+ ITensorInfo *weights,
+ const ITensorInfo *biases,
+ ITensorInfo *output,
+ const PadStrideInfo &conv_info,
+ const WeightsInfo &weights_info,
+ const Size2D &dilation,
+ const ActivationLayerInfo &act_info,
+ bool enable_fast_math,
+ unsigned int num_groups)
{
// Perform validate step
ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
ARM_COMPUTE_UNUSED(num_groups);
- ARM_COMPUTE_ERROR_THROW_ON(CpuConv2d::validate(input, weights, biases, output, conv_info, weights_info, dilation, act_info,
- enable_fast_math, num_groups));
+ ARM_COMPUTE_ERROR_THROW_ON(CpuConv2d::validate(input, weights, biases, output, conv_info, weights_info, dilation,
+ act_info, enable_fast_math, num_groups));
- ARM_COMPUTE_LOG_PARAMS(input, weights, biases, output, conv_info, weights_info, dilation, act_info, enable_fast_math, num_groups);
+ ARM_COMPUTE_LOG_PARAMS(input, weights, biases, output, conv_info, weights_info, dilation, act_info,
+ enable_fast_math, num_groups);
const Conv2dInfo info(conv_info, dilation, act_info, enable_fast_math, num_groups);
- switch(CpuConv2d::get_convolution_method(input, weights, output, conv_info, weights_info, dilation, act_info, enable_fast_math))
+ switch (CpuConv2d::get_convolution_method(input, weights, output, conv_info, weights_info, dilation, act_info,
+ enable_fast_math))
{
case ConvolutionMethod::WINOGRAD:
{
@@ -92,19 +103,30 @@ void CpuConv2d::configure(ITensorInfo *input, ITensorInfo *weights, const ITenso
_aux_mem = _function->workspace();
}
-Status CpuConv2d::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
- const WeightsInfo &weights_info, const Size2D &dilation, const ActivationLayerInfo &act_info, bool enable_fast_math, unsigned int num_groups)
+Status CpuConv2d::validate(const ITensorInfo *input,
+ const ITensorInfo *weights,
+ const ITensorInfo *biases,
+ const ITensorInfo *output,
+ const PadStrideInfo &conv_info,
+ const WeightsInfo &weights_info,
+ const Size2D &dilation,
+ const ActivationLayerInfo &act_info,
+ bool enable_fast_math,
+ unsigned int num_groups)
{
ARM_COMPUTE_RETURN_ERROR_ON_MSG((num_groups != 1), "Grouping (num_groups != 1) is not supported on Neon");
const Conv2dInfo info(conv_info, dilation, act_info, enable_fast_math, num_groups);
- switch(CpuConv2d::get_convolution_method(input, weights, output, conv_info, weights_info, dilation, act_info, enable_fast_math))
+ switch (CpuConv2d::get_convolution_method(input, weights, output, conv_info, weights_info, dilation, act_info,
+ enable_fast_math))
{
case ConvolutionMethod::WINOGRAD:
- ARM_COMPUTE_RETURN_ON_ERROR(CpuWinogradConv2d::validate(input, weights, biases, output, conv_info, act_info, enable_fast_math));
+ ARM_COMPUTE_RETURN_ON_ERROR(
+ CpuWinogradConv2d::validate(input, weights, biases, output, conv_info, act_info, enable_fast_math));
break;
case ConvolutionMethod::GEMM:
- ARM_COMPUTE_RETURN_ON_ERROR(CpuGemmConv2d::validate(input, weights, biases, output, conv_info, weights_info, dilation, act_info, enable_fast_math));
+ ARM_COMPUTE_RETURN_ON_ERROR(CpuGemmConv2d::validate(input, weights, biases, output, conv_info, weights_info,
+ dilation, act_info, enable_fast_math));
break;
case ConvolutionMethod::GEMM_CONV2D:
ARM_COMPUTE_RETURN_ON_ERROR(CpuGemmDirectConv2d::validate(input, weights, biases, output, info));
@@ -120,9 +142,14 @@ Status CpuConv2d::validate(const ITensorInfo *input, const ITensorInfo *weights,
return Status{};
}
-ConvolutionMethod CpuConv2d::get_convolution_method(const ITensorInfo *input, const ITensorInfo *weights,
- const ITensorInfo *output, const PadStrideInfo &conv_info,
- const WeightsInfo &weights_info, const Size2D &dilation, const ActivationLayerInfo &act_info, bool enable_fast_math)
+ConvolutionMethod CpuConv2d::get_convolution_method(const ITensorInfo *input,
+ const ITensorInfo *weights,
+ const ITensorInfo *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, output, weights);
ARM_COMPUTE_UNUSED(weights_info);
@@ -137,35 +164,46 @@ ConvolutionMethod CpuConv2d::get_convolution_method(const ITensorInfo *input, co
using ConvolutionConfiguration = std::tuple<Size2D, Size2D, Size2D, PadStrideInfo>;
using ConfigurationMethod = std::pair<ConvolutionConfiguration, ConvolutionMethod>;
- const std::vector<ConfigurationMethod> known_configs =
- {
+ const std::vector<ConfigurationMethod> known_configs = {
// Alexnet
- ConfigurationMethod(ConvolutionConfiguration(Size2D(27U, 27U), Size2D(5U, 5U), Size2D(48U, 128U), PadStrideInfo(1U, 1U, 2U, 2U)), ConvolutionMethod::GEMM),
+ ConfigurationMethod(ConvolutionConfiguration(Size2D(27U, 27U), Size2D(5U, 5U), Size2D(48U, 128U),
+ PadStrideInfo(1U, 1U, 2U, 2U)),
+ ConvolutionMethod::GEMM),
// VGG16 / VGG19
- ConfigurationMethod(ConvolutionConfiguration(Size2D(224U, 224U), Size2D(3U, 3U), Size2D(3U, 64U), PadStrideInfo(1U, 1U, 1U, 1U)), ConvolutionMethod::GEMM),
+ ConfigurationMethod(ConvolutionConfiguration(Size2D(224U, 224U), Size2D(3U, 3U), Size2D(3U, 64U),
+ PadStrideInfo(1U, 1U, 1U, 1U)),
+ ConvolutionMethod::GEMM),
// Mobilenet 224
- ConfigurationMethod(ConvolutionConfiguration(Size2D(224U, 224U), Size2D(3U, 3U), Size2D(3U, 32U), PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR)), ConvolutionMethod::GEMM),
+ ConfigurationMethod(
+ ConvolutionConfiguration(Size2D(224U, 224U), Size2D(3U, 3U), Size2D(3U, 32U),
+ PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR)),
+ ConvolutionMethod::GEMM),
// Mobilenet 160
- ConfigurationMethod(ConvolutionConfiguration(Size2D(160U, 160U), Size2D(3U, 3U), Size2D(3U, 24U), PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR)), ConvolutionMethod::GEMM)
- };
+ ConfigurationMethod(
+ ConvolutionConfiguration(Size2D(160U, 160U), Size2D(3U, 3U), Size2D(3U, 24U),
+ PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR)),
+ ConvolutionMethod::GEMM)};
const auto find_config = [&](ConfigurationMethod c)
{
const ConvolutionConfiguration config = c.first;
const PadStrideInfo info = std::get<3>(config);
- return std::get<0>(config) == Size2D(input->dimension(idx_w), input->dimension(idx_h)) && std::get<1>(config) == Size2D(weights->dimension(idx_w), weights->dimension(idx_h))
- && std::get<2>(config) == Size2D(weights->dimension(idx_c), weights->dimension(3)) && info.pad_top() == conv_info.pad_top() && info.pad_right() == conv_info.pad_right()
- && info.pad_bottom() == conv_info.pad_bottom() && info.pad_left() == conv_info.pad_left() && info.stride() == conv_info.stride();
+ return std::get<0>(config) == Size2D(input->dimension(idx_w), input->dimension(idx_h)) &&
+ std::get<1>(config) == Size2D(weights->dimension(idx_w), weights->dimension(idx_h)) &&
+ std::get<2>(config) == Size2D(weights->dimension(idx_c), weights->dimension(3)) &&
+ info.pad_top() == conv_info.pad_top() && info.pad_right() == conv_info.pad_right() &&
+ info.pad_bottom() == conv_info.pad_bottom() && info.pad_left() == conv_info.pad_left() &&
+ info.stride() == conv_info.stride();
};
std::vector<ConfigurationMethod>::const_iterator found;
- if((found = std::find_if(known_configs.begin(), known_configs.end(), find_config)) != known_configs.end())
+ if ((found = std::find_if(known_configs.begin(), known_configs.end(), find_config)) != known_configs.end())
{
return (*found).second;
}
- if(dilation != Size2D(1U, 1U))
+ if (dilation != Size2D(1U, 1U))
{
return ConvolutionMethod::GEMM;
}
@@ -173,43 +211,49 @@ ConvolutionMethod CpuConv2d::get_convolution_method(const ITensorInfo *input, co
{
// SRGAN
// Output might not be initialized when it is an internal tensor of the layer using the convolution
- if(input->total_size() > 1e7 && (weights->dimension(idx_h) > 7)
- && (CpuDirectConv2d::validate(input, weights, nullptr, output, conv_info, act_info)))
+ if (input->total_size() > 1e7 && (weights->dimension(idx_h) > 7) &&
+ (CpuDirectConv2d::validate(input, weights, nullptr, output, conv_info, act_info)))
{
return ConvolutionMethod::DIRECT;
}
- if(input->dimension(idx_c) < 16)
+ if (input->dimension(idx_c) < 16)
{
return ConvolutionMethod::GEMM;
}
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
// This heuristics only applies to F16 data type on A55r1
- if(NEScheduler::get().cpu_info().get_cpu_model() == CPUModel::A55r1 && enable_fast_math && input->data_type() == DataType::F16)
+ if (NEScheduler::get().cpu_info().get_cpu_model() == CPUModel::A55r1 && enable_fast_math &&
+ input->data_type() == DataType::F16)
{
// Exclude known bad winograd configs (and defaults to GEMM)
- const std::vector<ConvolutionConfiguration> known_bad_winograd_f16_with_fastmath_configs =
- {
+ const std::vector<ConvolutionConfiguration> known_bad_winograd_f16_with_fastmath_configs = {
// Squeezenet_V1_1 fire2 and fire3
- ConvolutionConfiguration(Size2D(56U, 56U), Size2D(3U, 3U), Size2D(16U, 64U), PadStrideInfo(1U, 1U, 1U, 1U)),
+ ConvolutionConfiguration(Size2D(56U, 56U), Size2D(3U, 3U), Size2D(16U, 64U),
+ PadStrideInfo(1U, 1U, 1U, 1U)),
// Squeezenet_V1_1 fire6 and fire7
- ConvolutionConfiguration(Size2D(14U, 14U), Size2D(3U, 3U), Size2D(48U, 192U), PadStrideInfo(1U, 1U, 1U, 1U)),
+ ConvolutionConfiguration(Size2D(14U, 14U), Size2D(3U, 3U), Size2D(48U, 192U),
+ PadStrideInfo(1U, 1U, 1U, 1U)),
// Squeezenet_V1_1 fire8 and fire9
- ConvolutionConfiguration(Size2D(14U, 14U), Size2D(3U, 3U), Size2D(64U, 256U), PadStrideInfo(1U, 1U, 1U, 1U)),
+ ConvolutionConfiguration(Size2D(14U, 14U), Size2D(3U, 3U), Size2D(64U, 256U),
+ PadStrideInfo(1U, 1U, 1U, 1U)),
};
const auto find_conv_config = [&](ConvolutionConfiguration c)
{
const PadStrideInfo info = std::get<3>(c);
- return std::get<0>(c) == Size2D(input->dimension(idx_w), input->dimension(idx_h)) && std::get<1>(c) == Size2D(weights->dimension(idx_w), weights->dimension(idx_h))
- && std::get<2>(c) == Size2D(weights->dimension(idx_c), weights->dimension(3)) && info.pad_top() == conv_info.pad_top() && info.pad_right() == conv_info.pad_right()
- && info.pad_bottom() == conv_info.pad_bottom() && info.pad_left() == conv_info.pad_left() && info.stride() == conv_info.stride();
+ return std::get<0>(c) == Size2D(input->dimension(idx_w), input->dimension(idx_h)) &&
+ std::get<1>(c) == Size2D(weights->dimension(idx_w), weights->dimension(idx_h)) &&
+ std::get<2>(c) == Size2D(weights->dimension(idx_c), weights->dimension(3)) &&
+ info.pad_top() == conv_info.pad_top() && info.pad_right() == conv_info.pad_right() &&
+ info.pad_bottom() == conv_info.pad_bottom() && info.pad_left() == conv_info.pad_left() &&
+ info.stride() == conv_info.stride();
};
- bool found_bad = std::find_if(known_bad_winograd_f16_with_fastmath_configs.begin(), known_bad_winograd_f16_with_fastmath_configs.end(),
- find_conv_config)
- != known_bad_winograd_f16_with_fastmath_configs.end();
- if(found_bad)
+ bool found_bad = std::find_if(known_bad_winograd_f16_with_fastmath_configs.begin(),
+ known_bad_winograd_f16_with_fastmath_configs.end(),
+ find_conv_config) != known_bad_winograd_f16_with_fastmath_configs.end();
+ if (found_bad)
{
return ConvolutionMethod::GEMM;
}
@@ -217,16 +261,16 @@ ConvolutionMethod CpuConv2d::get_convolution_method(const ITensorInfo *input, co
#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
// For 1x1 convolutions run the default GEMM
- if(weights->dimension(idx_w) == 1 && weights->dimension(idx_h) == 1)
+ if (weights->dimension(idx_w) == 1 && weights->dimension(idx_h) == 1)
{
return ConvolutionMethod::GEMM;
}
- if(bool(CpuWinogradConv2d::validate(input, weights, nullptr, output, conv_info, act_info, enable_fast_math)))
+ if (bool(CpuWinogradConv2d::validate(input, weights, nullptr, output, conv_info, act_info, enable_fast_math)))
{
return ConvolutionMethod::WINOGRAD;
}
- if(bool(CpuGemmDirectConv2d::validate(input, weights, nullptr, output, info)))
+ if (bool(CpuGemmDirectConv2d::validate(input, weights, nullptr, output, info)))
{
return ConvolutionMethod::GEMM_CONV2D;
}
generated by cgit v1.2.1 (git 2.23.0) at 2024-05-20 01:25:48 +0000