diff options
Diffstat (limited to 'src/core/NEON/kernels/NEDirectConvolutionLayerKernel.cpp')
| -rw-r--r-- | src/core/NEON/kernels/NEDirectConvolutionLayerKernel.cpp | 310 |
1 files changed, 69 insertions, 241 deletions
diff --git a/src/core/NEON/kernels/NEDirectConvolutionLayerKernel.cpp b/src/core/NEON/kernels/NEDirectConvolutionLayerKernel.cpp index 5eafdf0363..54a046846a 100644 --- a/src/core/NEON/kernels/NEDirectConvolutionLayerKernel.cpp +++ b/src/core/NEON/kernels/NEDirectConvolutionLayerKernel.cpp @@ -43,34 +43,6 @@ using namespace arm_compute::detail; namespace { -template <unsigned int stridex> -qint16x8_t internal_vld1q(const qint16_t *in); - -template <> -qint16x8_t internal_vld1q<1>(const qint16_t *in) -{ - return vld1q_qs16(in); -} - -template <> -qint16x8_t internal_vld1q<2>(const qint16_t *in) -{ - const int16x8x2_t tmp = vld2q_s16(in); - return tmp.val[0]; -} - -template <> -qint16x8_t internal_vld1q<3>(const qint16_t *in) -{ - const int16x8x3_t tmp = vld3q_s16(in); - return tmp.val[0]; -} - -inline qint16x8_t internal_vdupq_n(qint16_t v) -{ - return vdupq_n_qs16(v); -} - #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC template <unsigned int stridex> float16x8_t internal_vld1q(const float16_t *in); @@ -105,15 +77,13 @@ inline void internal_vst1q(float16_t *p, const float16x8_t &v) vst1q_f16(p, v); } -float16x8_t internal_vmull(const float16x8_t &x, const float16x8_t &y, int fixed_point_position) +float16x8_t internal_vmull(const float16x8_t &x, const float16x8_t &y) { - ARM_COMPUTE_UNUSED(fixed_point_position); return vmulq_f16(x, y); } -inline float16x8_t internal_vmlal(const float16x8_t &x, const float16x8_t &y, const float16x8_t &z, int fixed_point_position) +inline float16x8_t internal_vmlal(const float16x8_t &x, const float16x8_t &y, const float16x8_t &z) { - ARM_COMPUTE_UNUSED(fixed_point_position); return vaddq_f16(x, vmulq_f16(y, z)); } #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ @@ -151,107 +121,16 @@ inline void internal_vst1q(float *p, const float32x4_t &v) vst1q_f32(p, v); } -float32x4_t internal_vmull(const float32x4_t &x, const float32x4_t &y, int fixed_point_position) +float32x4_t internal_vmull(const float32x4_t &x, const float32x4_t &y) { - ARM_COMPUTE_UNUSED(fixed_point_position); return vmulq_f32(x, y); } -inline float32x4_t internal_vmlal(const float32x4_t &x, const float32x4_t &y, const float32x4_t &z, int fixed_point_position) +inline float32x4_t internal_vmlal(const float32x4_t &x, const float32x4_t &y, const float32x4_t &z) { - ARM_COMPUTE_UNUSED(fixed_point_position); return vmlaq_f32(x, y, z); } -template <unsigned int stridex> -qint8x8_t internal_vld1q(const qint8_t *in); - -template <> -qint8x8_t internal_vld1q<1>(const qint8_t *in) -{ - return vld1_qs8(in); -} - -template <> -qint8x8_t internal_vld1q<2>(const qint8_t *in) -{ - const qint8x8x2_t tmp = vld2_s8(in); - return tmp.val[0]; -} - -template <> -qint8x8_t internal_vld1q<3>(const qint8_t *in) -{ - const qint8x8x3_t tmp = vld3_s8(in); - return tmp.val[0]; -} - -inline qint8x8_t internal_vdupq_n(qint8_t v) -{ - return vdup_n_qs8(v); -} - -inline qint16x8_t internal_vmull(const qint8x8_t &x, const qint8x8_t &y, int fixed_point_position) -{ - return vmull_qs8(x, y, fixed_point_position); -} - -inline qint16x8_t internal_vmlal(const qint16x8_t &x, const qint8x8_t &y, const qint8x8_t &z, int fixed_point_position) -{ - return vqmlal_qs8(x, y, z, fixed_point_position); -} - -inline void internal_vst1q(qint16_t *p, const qint16x8_t &v) -{ - vst1q_qs16(p, v); -} - -inline void internal_vst1q(int32_t *p, const qint32x4x2_t &v) -{ - vst1q_s32(p, v.val[0]); - vst1q_s32(p + 4, v.val[1]); -} - -template <unsigned int stridex> -qint32x4x2_t internal_vld1q(const qint32_t *in); - -template <> -qint32x4x2_t internal_vld1q<1>(const qint32_t *in) -{ - const qint32x4x2_t r = - { - { - vld1q_s32(in), - vld1q_s32(in + 4) - } - }; - return r; -} - -inline qint32x4x2_t internal_vmull(const qint16x8_t &x, const qint16x8_t &y, int fixed_point_position) -{ - const qint32x4x2_t r = - { - { - vmull_qs16(vget_low_s16(x), vget_low_s16(y), fixed_point_position), - vmull_qs16(vget_high_s16(x), vget_high_s16(y), fixed_point_position), - } - }; - return r; -} - -inline qint32x4x2_t internal_vmlal(const qint32x4x2_t &x, const qint16x8_t &y, const qint16x8_t &z, int fixed_point_position) -{ - const qint32x4x2_t r = - { - { - vqmlal_qs16(x.val[0], vget_low_s16(y), vget_low_s16(z), fixed_point_position), - vqmlal_qs16(x.val[1], vget_high_s16(y), vget_high_s16(z), fixed_point_position) - } - }; - return r; -} - constexpr int small_tensor_size_optim = 8; inline bool run_optim_small_tensor_info(const ITensorInfo *t) { @@ -355,21 +234,20 @@ public: static void convolve(const Window &window, unsigned int num_elems_read_per_iteration, unsigned int num_elems_written_per_iteration, const ITensor *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info) { - const int input_stride_x = input->info()->strides_in_bytes().x(); - const int input_stride_y = input->info()->strides_in_bytes().y(); - const int input_stride_z = input->info()->strides_in_bytes().z(); - const int output_stride_y = output->info()->strides_in_bytes().y(); - const int output_stride_z = output->info()->strides_in_bytes().z(); - const int kernel_stride_z = weights->info()->strides_in_bytes().z(); - const int kernel_stride_w = weights->info()->strides_in_bytes()[3]; - const int output_w = output->info()->dimension(0); - const int output_h = output->info()->dimension(1); - const int range_z = window.z().end() - window.z().start(); - const int kernel_depth = weights->info()->dimension(Window::DimZ); - const unsigned int conv_stride_y = std::get<1>(conv_info.stride()); - const unsigned int conv_pad_left = conv_info.pad_left(); - const unsigned int conv_pad_top = conv_info.pad_top(); - const int fixed_point_position = input->info()->fixed_point_position(); + const int input_stride_x = input->info()->strides_in_bytes().x(); + const int input_stride_y = input->info()->strides_in_bytes().y(); + const int input_stride_z = input->info()->strides_in_bytes().z(); + const int output_stride_y = output->info()->strides_in_bytes().y(); + const int output_stride_z = output->info()->strides_in_bytes().z(); + const int kernel_stride_z = weights->info()->strides_in_bytes().z(); + const int kernel_stride_w = weights->info()->strides_in_bytes()[3]; + const int output_w = output->info()->dimension(0); + const int output_h = output->info()->dimension(1); + const int range_z = window.z().end() - window.z().start(); + const int kernel_depth = weights->info()->dimension(Window::DimZ); + const unsigned int conv_stride_y = std::get<1>(conv_info.stride()); + const unsigned int conv_pad_left = conv_info.pad_left(); + const unsigned int conv_pad_top = conv_info.pad_top(); // setup output window for the iterator Window window_out = window; @@ -414,7 +292,7 @@ public: auto p_out = reinterpret_cast<T2 *>(p_out_base + oh * output_stride_y); for(int ow = 0; ow < output_w; ow += num_elems_written_per_iteration, in_val += num_elems_read_per_iteration, p_out += num_elems_written_per_iteration) { - internal_vst1q(p_out, internal_vmull(vk, internal_vld1q<stridex>(in_val), fixed_point_position)); + internal_vst1q(p_out, internal_vmull(vk, internal_vld1q<stridex>(in_val))); } } } @@ -431,7 +309,7 @@ public: auto p_out = reinterpret_cast<T2 *>(p_out_base + oh * output_stride_y); for(int ow = 0; ow < output_w; ow += num_elems_written_per_iteration, in_val += num_elems_read_per_iteration, p_out += num_elems_written_per_iteration) { - internal_vst1q(p_out, internal_vmlal(internal_vld1q<1>(p_out), vk, internal_vld1q<stridex>(in_val), fixed_point_position)); + internal_vst1q(p_out, internal_vmlal(internal_vld1q<1>(p_out), vk, internal_vld1q<stridex>(in_val))); } } } @@ -469,7 +347,7 @@ void accumulate_results<3>(float16_t *buffer, const float16x8x2_t &values) template <unsigned int stridex> float32x4x2_t convolve_5x5(const float *in_0, const float *in_1, const float *in_2, const float *in_3, const float *in_4, - const float *m0, const float *m1, const float *m2, const float *m3, const float *m4, int fixed_point_position); + const float *m0, const float *m1, const float *m2, const float *m3, const float *m4); inline float32x4x3_t load_matrix_hi(const float *const m0, const float *const m1, const float *const m2) { @@ -511,9 +389,8 @@ inline float32x4x3_t load_input(const float *const in) template <> inline float32x4x2_t convolve_5x5<1>(const float *in_0, const float *in_1, const float *in_2, const float *in_3, const float *in_4, - const float *m0, const float *m1, const float *m2, const float *m3, const float *m4, int fixed_point_position) + const float *m0, const float *m1, const float *m2, const float *m3, const float *m4) { - ARM_COMPUTE_UNUSED(fixed_point_position); const float32x4x3_t vin0 = load_input(in_0); const float32x4x3_t vin1 = load_input(in_1); const float32x4x3_t vin2 = load_input(in_2); @@ -601,10 +478,9 @@ inline float32x4x2_t convolve_5x5<1>(const float *in_0, const float *in_1, const template <> inline float32x4x2_t convolve_5x5<2>(const float *in_0, const float *in_1, const float *in_2, const float *in_3, const float *in_4, - const float *m0, const float *m1, const float *m2, const float *m3, const float *m4, int fixed_point_position) + const float *m0, const float *m1, const float *m2, const float *m3, const float *m4) { - ARM_COMPUTE_UNUSED(fixed_point_position); - float32x4x2_t out = convolve_5x5<1>(in_0, in_1, in_2, in_3, in_4, m0, m1, m2, m3, m4, fixed_point_position); + float32x4x2_t out = convolve_5x5<1>(in_0, in_1, in_2, in_3, in_4, m0, m1, m2, m3, m4); out.val[0] = vsetq_lane_f32(vgetq_lane_f32(out.val[0], 2), out.val[0], 1); out.val[0] = vsetq_lane_f32(vgetq_lane_f32(out.val[1], 0), out.val[0], 2); out.val[0] = vsetq_lane_f32(vgetq_lane_f32(out.val[1], 2), out.val[0], 3); @@ -613,9 +489,9 @@ inline float32x4x2_t convolve_5x5<2>(const float *in_0, const float *in_1, const template <> inline float32x4x2_t convolve_5x5<3>(const float *in_0, const float *in_1, const float *in_2, const float *in_3, const float *in_4, - const float *m0, const float *m1, const float *m2, const float *m3, const float *m4, int fixed_point_position) + const float *m0, const float *m1, const float *m2, const float *m3, const float *m4) { - float32x4x2_t out = convolve_5x5<1>(in_0, in_1, in_2, in_3, in_4, m0, m1, m2, m3, m4, fixed_point_position); + float32x4x2_t out = convolve_5x5<1>(in_0, in_1, in_2, in_3, in_4, m0, m1, m2, m3, m4); out.val[0] = vsetq_lane_f32(vgetq_lane_f32(out.val[0], 3), out.val[0], 1); return out; } @@ -642,28 +518,6 @@ void accumulate_results<3>(float *buffer, const float32x4x2_t &values) vst1_f32(buffer, vadd_f32(vld1_f32(buffer), vget_low_f32(values.val[0]))); } -template <unsigned int stridex> -void accumulate_results(qint16_t *buffer, const qint16x8x2_t &values); - -template <> -void accumulate_results<1>(qint16_t *buffer, const qint16x8x2_t &values) -{ - vst1q_qs16(buffer, vqaddq_qs16(vld1q_qs16(buffer), values.val[0])); - vst1q_qs16(buffer + 8, vqaddq_qs16(vld1q_qs16(buffer + 8), values.val[1])); -} - -template <> -void accumulate_results<2>(qint16_t *buffer, const qint16x8x2_t &values) -{ - vst1q_qs16(buffer, vqaddq_qs16(vld1q_qs16(buffer), values.val[0])); -} - -template <> -void accumulate_results<3>(qint16_t *buffer, const qint16x8x2_t &values) -{ - vst1_qs16(buffer, vqadd_qs16(vld1_qs16(buffer), vget_low_s16(values.val[0]))); -} - template <typename T1> class convolver_nhwc { @@ -745,7 +599,7 @@ public: const auto we_addr = reinterpret_cast<const T1 *>(we_addr_base1 + x * kernel_stride_x); const auto we_values = internal_vld1q<1>(we_addr); - out_values = internal_vmlal(out_values, in_values, we_values, 0); + out_values = internal_vmlal(out_values, in_values, we_values); } out_val += out_values[0]; @@ -784,24 +638,23 @@ public: const ITensor *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info) { ARM_COMPUTE_UNUSED(num_elems_read_per_iteration); - const int input_stride_x = input->info()->strides_in_bytes().x(); - const int input_stride_y = input->info()->strides_in_bytes().y(); - const int input_stride_z = input->info()->strides_in_bytes().z(); - const int output_stride_y = output->info()->strides_in_bytes().y(); - const int output_stride_z = output->info()->strides_in_bytes().z(); - const int kernel_stride_x = weights->info()->strides_in_bytes().x(); - const int kernel_stride_y = weights->info()->strides_in_bytes().y(); - const int kernel_stride_z = weights->info()->strides_in_bytes().z(); - const int kernel_stride_w = weights->info()->strides_in_bytes()[3]; - const int output_w = output->info()->dimension(0); - const int output_h = output->info()->dimension(1); - const int num_planes_z = window.z().end() - window.z().start(); - const int delta_input = get_input_num_elems_processed<stridex>(num_elems_written_per_iteration); - const int kernel_depth = weights->info()->dimension(Window::DimZ); - const unsigned int conv_stride_y = std::get<1>(conv_info.stride()); - const unsigned int conv_pad_left = conv_info.pad_left(); - const unsigned int conv_pad_top = conv_info.pad_top(); - const int fixed_point_position = input->info()->fixed_point_position(); + const int input_stride_x = input->info()->strides_in_bytes().x(); + const int input_stride_y = input->info()->strides_in_bytes().y(); + const int input_stride_z = input->info()->strides_in_bytes().z(); + const int output_stride_y = output->info()->strides_in_bytes().y(); + const int output_stride_z = output->info()->strides_in_bytes().z(); + const int kernel_stride_x = weights->info()->strides_in_bytes().x(); + const int kernel_stride_y = weights->info()->strides_in_bytes().y(); + const int kernel_stride_z = weights->info()->strides_in_bytes().z(); + const int kernel_stride_w = weights->info()->strides_in_bytes()[3]; + const int output_w = output->info()->dimension(0); + const int output_h = output->info()->dimension(1); + const int num_planes_z = window.z().end() - window.z().start(); + const int delta_input = get_input_num_elems_processed<stridex>(num_elems_written_per_iteration); + const int kernel_depth = weights->info()->dimension(Window::DimZ); + const unsigned int conv_stride_y = std::get<1>(conv_info.stride()); + const unsigned int conv_pad_left = conv_info.pad_left(); + const unsigned int conv_pad_top = conv_info.pad_top(); // setup output window for the iterator Window window_out = window; @@ -864,7 +717,7 @@ public: for(int ow = 0; ow < output_w; ow += num_elems_written_per_iteration, in_top += delta_input, in_mid += delta_input, in_low += delta_input, p_out += num_elems_written_per_iteration) { - auto vres = convolve_3x3<stridex>(in_top, in_mid, in_low, vk_r0, vk_r1, vk_r2, fixed_point_position); + auto vres = convolve_3x3<stridex>(in_top, in_mid, in_low, vk_r0, vk_r1, vk_r2); store_results<stridex>(p_out, vres); } } @@ -889,7 +742,7 @@ public: for(int ow = 0; ow < output_w; ow += num_elems_written_per_iteration, in_top += delta_input, in_mid += delta_input, in_low += delta_input, p_out += num_elems_written_per_iteration) { - auto vres = convolve_3x3<stridex>(in_top, in_mid, in_low, vk_r0, vk_r1, vk_r2, fixed_point_position); + auto vres = convolve_3x3<stridex>(in_top, in_mid, in_low, vk_r0, vk_r1, vk_r2); accumulate_results<stridex>(p_out, vres); } } @@ -908,24 +761,23 @@ public: const ITensor *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info) { ARM_COMPUTE_UNUSED(num_elems_read_per_iteration); - const int input_stride_x = input->info()->strides_in_bytes().x(); - const int input_stride_y = input->info()->strides_in_bytes().y(); - const int input_stride_z = input->info()->strides_in_bytes().z(); - const int output_stride_y = output->info()->strides_in_bytes().y(); - const int output_stride_z = output->info()->strides_in_bytes().z(); - const int kernel_stride_x = weights->info()->strides_in_bytes().x(); - const int kernel_stride_y = weights->info()->strides_in_bytes().y(); - const int kernel_stride_z = weights->info()->strides_in_bytes().z(); - const int kernel_stride_w = weights->info()->strides_in_bytes()[3]; - const int output_w = output->info()->dimension(0); - const int output_h = output->info()->dimension(1); - const int num_planes_z = window.z().end() - window.z().start(); - const int delta_input = get_input_num_elems_processed<stridex>(num_elems_written_per_iteration); - const int kernel_depth = weights->info()->dimension(Window::DimZ); - const unsigned int conv_stride_y = std::get<1>(conv_info.stride()); - const unsigned int conv_pad_left = conv_info.pad_left(); - const unsigned int conv_pad_top = conv_info.pad_top(); - const int fixed_point_position = input->info()->fixed_point_position(); + const int input_stride_x = input->info()->strides_in_bytes().x(); + const int input_stride_y = input->info()->strides_in_bytes().y(); + const int input_stride_z = input->info()->strides_in_bytes().z(); + const int output_stride_y = output->info()->strides_in_bytes().y(); + const int output_stride_z = output->info()->strides_in_bytes().z(); + const int kernel_stride_x = weights->info()->strides_in_bytes().x(); + const int kernel_stride_y = weights->info()->strides_in_bytes().y(); + const int kernel_stride_z = weights->info()->strides_in_bytes().z(); + const int kernel_stride_w = weights->info()->strides_in_bytes()[3]; + const int output_w = output->info()->dimension(0); + const int output_h = output->info()->dimension(1); + const int num_planes_z = window.z().end() - window.z().start(); + const int delta_input = get_input_num_elems_processed<stridex>(num_elems_written_per_iteration); + const int kernel_depth = weights->info()->dimension(Window::DimZ); + const unsigned int conv_stride_y = std::get<1>(conv_info.stride()); + const unsigned int conv_pad_left = conv_info.pad_left(); + const unsigned int conv_pad_top = conv_info.pad_top(); // setup output window for the iterator Window window_out = window; @@ -976,7 +828,7 @@ public: for(int ow = 0; ow < output_w; ow += num_elems_written_per_iteration, in_0 += delta_input, in_1 += delta_input, in_2 += delta_input, in_3 += delta_input, in_4 += delta_input, p_out += num_elems_written_per_iteration) { - auto vres = convolve_5x5<stridex>(in_0, in_1, in_2, in_3, in_4, ptr_k_r0, ptr_k_r1, ptr_k_r2, ptr_k_r3, ptr_k_r4, fixed_point_position); + auto vres = convolve_5x5<stridex>(in_0, in_1, in_2, in_3, in_4, ptr_k_r0, ptr_k_r1, ptr_k_r2, ptr_k_r3, ptr_k_r4); store_results<stridex>(p_out, vres); } } @@ -1001,7 +853,7 @@ public: for(int ow = 0; ow < output_w; ow += num_elems_written_per_iteration, in_0 += delta_input, in_1 += delta_input, in_2 += delta_input, in_3 += delta_input, in_4 += delta_input, p_out += num_elems_written_per_iteration) { - auto vres = convolve_5x5<stridex>(in_0, in_1, in_2, in_3, in_4, ptr_k_r0, ptr_k_r1, ptr_k_r2, ptr_k_r3, ptr_k_r4, fixed_point_position); + auto vres = convolve_5x5<stridex>(in_0, in_1, in_2, in_3, in_4, ptr_k_r0, ptr_k_r1, ptr_k_r2, ptr_k_r3, ptr_k_r4); accumulate_results<stridex>(p_out, vres); } } @@ -1120,7 +972,7 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, { ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output); ARM_COMPUTE_RETURN_ERROR_ON(input->data_layout() == DataLayout::UNKNOWN); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights); const DataLayout data_layout = input->data_layout(); @@ -1140,11 +992,6 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, TensorShape output_shape = misc::shape_calculator::compute_deep_convolution_shape(*input, *weights, conv_info); DataType data_type = input->data_type(); - if(is_data_type_fixed_point(data_type)) - { - // Promote data type in case of fixed point - data_type = ((data_type == DataType::QS8) ? DataType::QS16 : DataType::QS32); - } ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape); ARM_COMPUTE_RETURN_ERROR_ON(output->data_type() != data_type); @@ -1180,11 +1027,9 @@ std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITen { #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC case DataType::F16: -#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ - case DataType::QS8: - case DataType::QS16: num_elems_written_per_iteration = 8; break; +#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ case DataType::F32: if(run_optim_small_tensor_info(input)) { @@ -1215,13 +1060,11 @@ std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITen break; #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC case DataType::F16: -#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ - case DataType::QS8: - case DataType::QS16: num_weight_elems_read_per_row = 8 + kernel_size - 1; num_elems_read_per_iteration = 24; num_elems_written_per_iteration = 32 >> conv_stride_x; break; +#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ default: ARM_COMPUTE_ERROR("Data type not supported."); break; @@ -1315,14 +1158,8 @@ void NEDirectConvolutionLayerKernel::configure(const ITensor *input, const ITens DataType data_type = input->info()->data_type(); - if(is_data_type_fixed_point(data_type)) - { - // Promote data type in case of fixed point - data_type = ((data_type == DataType::QS8) ? DataType::QS16 : DataType::QS32); - } - // Output auto inizialitation if not yet initialized - auto_init_if_empty(*output->info(), output_shape, 1, data_type, input->info()->fixed_point_position()); + auto_init_if_empty(*output->info(), output_shape, 1, data_type); // Perform validation step ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), weights->info(), output->info(), conv_info)); @@ -1371,12 +1208,6 @@ void NEDirectConvolutionLayerKernel::run(const Window &window, const ThreadInfo { switch(_input->info()->data_type()) { - case DataType::QS8: - convolve_1x1<qint8_t, qint16_t>(window, _num_elems_read_per_iteration, _num_elems_written_per_iteration, _input, _weights, _output, _conv_info); - break; - case DataType::QS16: - convolve_1x1<qint16_t, qint32_t>(window, _num_elems_read_per_iteration, _num_elems_written_per_iteration, _input, _weights, _output, _conv_info); - break; case DataType::F32: convolve_1x1<float, float>(window, _num_elems_read_per_iteration, _num_elems_written_per_iteration, _input, _weights, _output, _conv_info); break; @@ -1395,9 +1226,6 @@ void NEDirectConvolutionLayerKernel::run(const Window &window, const ThreadInfo { switch(_input->info()->data_type()) { - case DataType::QS8: - convolve_3x3<qint8_t, qint16_t>(window, _num_elems_read_per_iteration, _num_elems_written_per_iteration, _input, _weights, _output, _conv_info); - break; case DataType::F32: convolve_3x3<float, float>(window, _num_elems_read_per_iteration, _num_elems_written_per_iteration, _input, _weights, _output, _conv_info); break; |