aboutsummaryrefslogtreecommitdiff
path: root/src/core/NEON/kernels/NEDepthwiseConvolutionLayerNativeKernel.cpp
diff options
context:
space:
mode:
Diffstat (limited to 'src/core/NEON/kernels/NEDepthwiseConvolutionLayerNativeKernel.cpp')
-rw-r--r--src/core/NEON/kernels/NEDepthwiseConvolutionLayerNativeKernel.cpp61
1 files changed, 28 insertions, 33 deletions
diff --git a/src/core/NEON/kernels/NEDepthwiseConvolutionLayerNativeKernel.cpp b/src/core/NEON/kernels/NEDepthwiseConvolutionLayerNativeKernel.cpp
index 4639922da7..ef196ab904 100644
--- a/src/core/NEON/kernels/NEDepthwiseConvolutionLayerNativeKernel.cpp
+++ b/src/core/NEON/kernels/NEDepthwiseConvolutionLayerNativeKernel.cpp
@@ -46,9 +46,9 @@ void pad_vectors(std::vector<int> &mult, std::vector<int> &shift, int vec_size)
}
}
-template <typename T, int S, bool has_biases>
+template <typename T, int S>
void depthwise_loop_multiplier1_fp(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info,
- const Size2D &dilation, const Window &window)
+ const Size2D &dilation, const Window &window, bool has_biases)
{
using VectorType = typename wrapper::traits::neon_vector<T, S>::type;
using TagType = typename wrapper::traits::neon_vector<T, S>::tag_type;
@@ -119,9 +119,9 @@ void depthwise_loop_multiplier1_fp(const ITensor *input, const ITensor *weights,
input_it, weights_it, biases_it, output_it);
}
-template <typename T, bool has_biases>
+template <typename T>
void depthwise_loop_generic_fp(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info,
- const Size2D &dilation, unsigned int depth_multiplier, const Window &window)
+ const Size2D &dilation, unsigned int depth_multiplier, const Window &window, bool has_biases)
{
const size_t input_stride_y = input->info()->strides_in_bytes().y();
const size_t input_stride_z = input->info()->strides_in_bytes().z();
@@ -203,9 +203,9 @@ void depthwise_loop_generic_fp(const ITensor *input, const ITensor *weights, con
input_it, weights_it, biases_it, output_it);
}
-template <typename T, typename TW, int S, bool has_biases, bool is_per_channel>
+template <typename T, typename TW, int S>
void depthwise_loop_multiplier1_quantized(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info,
- const Size2D &dilation, std::vector<int> output_multiplier, std::vector<int> output_shift, const Window &window)
+ const Size2D &dilation, std::vector<int> output_multiplier, std::vector<int> output_shift, const Window &window, bool has_biases)
{
using VectorType = typename wrapper::traits::neon_vector<T, S>::type;
using TagType = typename wrapper::traits::neon_vector<T, S>::tag_type;
@@ -308,9 +308,9 @@ void depthwise_loop_multiplier1_quantized(const ITensor *input, const ITensor *w
input_it, weights_it, biases_it, output_it);
}
-template <typename T, typename TW, bool has_biases, bool is_per_channel>
+template <typename T, typename TW>
void depthwise_loop_generic_quantized(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info,
- const Size2D &dilation, unsigned int depth_multiplier, std::vector<int> output_multiplier, std::vector<int> output_shift, const Window &window)
+ const Size2D &dilation, unsigned int depth_multiplier, std::vector<int> output_multiplier, std::vector<int> output_shift, const Window &window, bool has_biases)
{
const size_t input_stride_y = input->info()->strides_in_bytes().y();
const size_t input_stride_z = input->info()->strides_in_bytes().z();
@@ -497,7 +497,7 @@ std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITen
} // namespace
NEDepthwiseConvolutionLayerNativeKernel::NEDepthwiseConvolutionLayerNativeKernel()
- : _func(), _border_size(0), _input(), _weights(), _biases(), _output(), _conv_info(), _depth_multiplier(1), _dilation(), _output_multiplier(), _output_shift()
+ : _func(), _border_size(0), _input(), _weights(), _biases(), _output(), _conv_info(), _depth_multiplier(1), _dilation(), _output_multiplier(), _output_shift(), _has_biases()
{
}
@@ -520,6 +520,7 @@ void NEDepthwiseConvolutionLayerNativeKernel::configure(const ITensor *input, co
_depth_multiplier = depth_multiplier;
_border_size = BorderSize(_conv_info.pad_left(), 0, std::max(std::max(conv_info.pad_right(), conv_info.pad_bottom()), conv_info.pad_top()), 0);
_dilation = dilation;
+ _has_biases = (biases != nullptr);
if(is_data_type_quantized(_input->info()->data_type()))
{
@@ -550,38 +551,32 @@ void NEDepthwiseConvolutionLayerNativeKernel::configure(const ITensor *input, co
switch(_weights->info()->data_type())
{
case DataType::QASYMM8:
- _func = (biases != nullptr) ? &NEDepthwiseConvolutionLayerNativeKernel::run_depthwise<uint8_t, uint8_t, 8, true, false> :
- &NEDepthwiseConvolutionLayerNativeKernel::run_depthwise<uint8_t, uint8_t, 8, false, false>;
+ _func = &NEDepthwiseConvolutionLayerNativeKernel::run_depthwise<uint8_t, uint8_t, 8>;
pad_vectors(_output_multiplier, _output_shift, 8);
break;
case DataType::QASYMM8_SIGNED:
- _func = (biases != nullptr) ? &NEDepthwiseConvolutionLayerNativeKernel::run_depthwise<int8_t, int8_t, 8, true, false> :
- &NEDepthwiseConvolutionLayerNativeKernel::run_depthwise<int8_t, int8_t, 8, false, false>;
+ _func = &NEDepthwiseConvolutionLayerNativeKernel::run_depthwise<int8_t, int8_t, 8>;
pad_vectors(_output_multiplier, _output_shift, 8);
break;
case DataType::QSYMM8_PER_CHANNEL:
if(_input->info()->data_type() == DataType::QASYMM8)
{
- _func = (biases != nullptr) ? &NEDepthwiseConvolutionLayerNativeKernel::run_depthwise<uint8_t, int8_t, 8, true, true> :
- &NEDepthwiseConvolutionLayerNativeKernel::run_depthwise<uint8_t, int8_t, 8, false, true>;
+ _func = &NEDepthwiseConvolutionLayerNativeKernel::run_depthwise<uint8_t, int8_t, 8>;
}
else
{
- _func = (biases != nullptr) ? &NEDepthwiseConvolutionLayerNativeKernel::run_depthwise<int8_t, int8_t, 8, true, true> :
- &NEDepthwiseConvolutionLayerNativeKernel::run_depthwise<int8_t, int8_t, 8, false, true>;
+ _func = &NEDepthwiseConvolutionLayerNativeKernel::run_depthwise<int8_t, int8_t, 8>;
}
pad_vectors(_output_multiplier, _output_shift, 8);
break;
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
case DataType::F16:
- _func = (biases != nullptr) ? &NEDepthwiseConvolutionLayerNativeKernel::run_depthwise<float16_t, float16_t, 4, true, false> :
- &NEDepthwiseConvolutionLayerNativeKernel::run_depthwise<float16_t, float16_t, 4, false, false>;
+ _func = &NEDepthwiseConvolutionLayerNativeKernel::run_depthwise<float16_t, float16_t, 4>;
pad_vectors(_output_multiplier, _output_shift, 4);
break;
#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
case DataType::F32:
- _func = (biases != nullptr) ? &NEDepthwiseConvolutionLayerNativeKernel::run_depthwise<float, float, 2, true, false> :
- &NEDepthwiseConvolutionLayerNativeKernel::run_depthwise<float, float, 2, false, false>;
+ _func = &NEDepthwiseConvolutionLayerNativeKernel::run_depthwise<float, float, 2>;
pad_vectors(_output_multiplier, _output_shift, 2);
break;
default:
@@ -611,43 +606,43 @@ void NEDepthwiseConvolutionLayerNativeKernel::run(const Window &window, const Th
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
- (this->*_func)(window);
+ (this->*_func)(window, _has_biases);
}
-template < typename T, typename TW, int S, bool has_biases, bool is_per_channel, typename std::enable_if < std::is_same<T, float>::value
+template < typename T, typename TW, int S, typename std::enable_if < std::is_same<T, float>::value
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
- || std::is_same<T, float16_t>::value
+ || std::is_same<T, float16_t>::value
#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
- ,
- int >::type >
-void NEDepthwiseConvolutionLayerNativeKernel::run_depthwise(const Window &window)
+ ,
+ int >::type >
+void NEDepthwiseConvolutionLayerNativeKernel::run_depthwise(const Window &window, bool has_biases)
{
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
if(_depth_multiplier == 1)
{
- depthwise_loop_multiplier1_fp<T, S, has_biases>(_input, _weights, _biases, _output, _conv_info, _dilation, window);
+ depthwise_loop_multiplier1_fp<T, S>(_input, _weights, _biases, _output, _conv_info, _dilation, window, has_biases);
}
else
{
- depthwise_loop_generic_fp<T, has_biases>(_input, _weights, _biases, _output, _conv_info, _dilation, _depth_multiplier, window);
+ depthwise_loop_generic_fp<T>(_input, _weights, _biases, _output, _conv_info, _dilation, _depth_multiplier, window, has_biases);
}
}
-template <typename T, typename TW, int S, bool has_biases, bool is_per_channel, typename>
-void NEDepthwiseConvolutionLayerNativeKernel::run_depthwise(const Window &window)
+template <typename T, typename TW, int S, typename>
+void NEDepthwiseConvolutionLayerNativeKernel::run_depthwise(const Window &window, bool has_biases)
{
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
if(_depth_multiplier == 1)
{
- depthwise_loop_multiplier1_quantized<T, TW, S, has_biases, is_per_channel>(_input, _weights, _biases, _output, _conv_info, _dilation, _output_multiplier, _output_shift, window);
+ depthwise_loop_multiplier1_quantized<T, TW, S>(_input, _weights, _biases, _output, _conv_info, _dilation, _output_multiplier, _output_shift, window, has_biases);
}
else
{
- depthwise_loop_generic_quantized<T, TW, has_biases, is_per_channel>(_input, _weights, _biases, _output, _conv_info, _dilation, _depth_multiplier, _output_multiplier, _output_shift, window);
+ depthwise_loop_generic_quantized<T, TW>(_input, _weights, _biases, _output, _conv_info, _dilation, _depth_multiplier, _output_multiplier, _output_shift, window, has_biases);
}
}
} // namespace arm_compute
generated by cgit v1.2.1 (git 2.23.0) at 2024-05-04 22:07:32 +0000