diff options
Diffstat (limited to 'src/core/NEON/kernels/NEDepthwiseConvolutionLayer3x3Kernel.cpp')
| -rw-r--r-- | src/core/NEON/kernels/NEDepthwiseConvolutionLayer3x3Kernel.cpp | 106 |
1 files changed, 66 insertions, 40 deletions
diff --git a/src/core/NEON/kernels/NEDepthwiseConvolutionLayer3x3Kernel.cpp b/src/core/NEON/kernels/NEDepthwiseConvolutionLayer3x3Kernel.cpp index ec672e0fc4..fdafc2da90 100644 --- a/src/core/NEON/kernels/NEDepthwiseConvolutionLayer3x3Kernel.cpp +++ b/src/core/NEON/kernels/NEDepthwiseConvolutionLayer3x3Kernel.cpp @@ -49,7 +49,7 @@ class convolver_3x3 { public: static void convolve(const Window &window, unsigned int num_elems_written_per_iteration, - const ITensor *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier) + const ITensor *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier, const Size2D &dilation) { const int input_offset = -input->info()->quantization_info().offset; const int weights_offset = -weights->info()->quantization_info().offset; @@ -57,6 +57,7 @@ public: 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 input_stride_w = input->info()->strides_in_bytes()[3]; const int output_stride_y = output->info()->strides_in_bytes().y(); const int kernel_stride_y = weights->info()->strides_in_bytes().y(); const int kernel_stride_z = weights->info()->strides_in_bytes().z(); @@ -74,9 +75,10 @@ public: // setup input window for the iterator Window window_in = window; - // we just want execute_window_loop to iterate over the dimensions > 2, so we set the first 2 dimensions to 0 + // Iteration of input is taken care of in execute_window_loop window_in.set(Window::DimX, Window::Dimension(0, 0, 0)); window_in.set(Window::DimY, Window::Dimension(0, 0, 0)); + window_in.set(Window::DimZ, Window::Dimension(0, 0, 0)); Window window_k = calculate_max_window(*weights->info(), Steps(1u)); @@ -91,7 +93,7 @@ public: int ih = 0; int oh = 0; - const uint8_t *input_ptr = in.ptr() - conv_pad_x * input_stride_x - conv_pad_y * input_stride_y - (id.z() - id.z() / depth_multiplier) * input_stride_z; + const uint8_t *input_ptr = in.ptr() - conv_pad_x * input_stride_x - conv_pad_y * input_stride_y + (id.z() / depth_multiplier) * input_stride_z + input_stride_w * id[3]; const uint8_t *ptr_weights_base = weights_ptr + id.z() * kernel_stride_z; const auto ptr_weights_r0 = reinterpret_cast<const T1 *>(ptr_weights_base); @@ -104,45 +106,54 @@ public: for(ih = 0, oh = 0; oh < output_h; ++oh, ih += conv_stride_y) { auto in_top = reinterpret_cast<const T1 *>(input_ptr + (ih + 0) * input_stride_y); - auto in_mid = reinterpret_cast<const T1 *>(input_ptr + (ih + 1) * input_stride_y); - auto in_low = reinterpret_cast<const T1 *>(input_ptr + (ih + 2) * input_stride_y); - auto p_out = reinterpret_cast<T2 *>(out.ptr() + oh * output_stride_y); + auto in_mid = reinterpret_cast<const T1 *>(input_ptr + (ih + dilation.y()) * input_stride_y); + auto in_low = reinterpret_cast<const T1 *>(input_ptr + (ih + 2 * dilation.y()) * input_stride_y); //uint8 + auto p_out = reinterpret_cast<T2 *>(out.ptr() + oh * output_stride_y); //int32 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 = detail::convolve_3x3<stridex>(in_top, in_mid, in_low, vw_r0, vw_r1, vw_r2, input_offset); - detail::store_results<stridex>(p_out, vres); + if(dilation == Size2D(1U, 1U)) + { + auto vres = detail::convolve_3x3<stridex>(in_top, in_mid, in_low, vw_r0, vw_r1, vw_r2, input_offset); + detail::store_results<stridex>(p_out, vres); + } + else + { + auto vres = detail::convolve_3x3_dilation<stridex>(in_top, in_mid, in_low, vw_r0, vw_r1, vw_r2, dilation.x(), input_offset); + detail::store_results<stridex>(p_out, vres); + } } } }, - in, out); + out); } }; template <typename T1, typename T2> inline void convolve_3x3(const Window &window, unsigned int num_elems_written_per_iteration, - const ITensor *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier) + const ITensor *input, const ITensor *weights, ITensor *output, + const PadStrideInfo &conv_info, unsigned int depth_multiplier, const Size2D &dilation) { const unsigned int conv_stride_x = std::get<0>(conv_info.stride()); switch(conv_stride_x) { case 1: - convolver_3x3<T1, T2, 1>::convolve(window, num_elems_written_per_iteration, input, weights, output, conv_info, depth_multiplier); + convolver_3x3<T1, T2, 1>::convolve(window, num_elems_written_per_iteration, input, weights, output, conv_info, depth_multiplier, dilation); break; case 2: - convolver_3x3<T1, T2, 2>::convolve(window, num_elems_written_per_iteration, input, weights, output, conv_info, depth_multiplier); + convolver_3x3<T1, T2, 2>::convolve(window, num_elems_written_per_iteration, input, weights, output, conv_info, depth_multiplier, dilation); break; case 3: - convolver_3x3<T1, T2, 3>::convolve(window, num_elems_written_per_iteration, input, weights, output, conv_info, depth_multiplier); + convolver_3x3<T1, T2, 3>::convolve(window, num_elems_written_per_iteration, input, weights, output, conv_info, depth_multiplier, dilation); break; default: ARM_COMPUTE_ERROR("Not implemented"); } } -Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier) +Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier, const Size2D &dilation) { ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input); ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32); @@ -157,7 +168,7 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, if(output->total_size() != 0) { - const TensorShape output_shape = misc::shape_calculator::compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier); + const TensorShape output_shape = misc::shape_calculator::compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier, dilation); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape); if(is_data_type_quantized_asymmetric(input->data_type())) @@ -173,13 +184,14 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, return Status{}; } -std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *weights, ITensorInfo *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier) +std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *weights, ITensorInfo *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier, + const Size2D &dilation) { Window win; bool window_changed = false; // Get convolved dimensions - const TensorShape output_shape = misc::shape_calculator::compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier); + const TensorShape output_shape = misc::shape_calculator::compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier, dilation); const DataType output_dt = (input->data_type() == DataType::QASYMM8) ? DataType::S32 : input->data_type(); // Output auto inizialitation if not yet initialized @@ -197,15 +209,16 @@ std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITen switch(input->data_type()) { case DataType::QASYMM8: - num_elems_read_per_iteration = 16; + num_elems_read_per_iteration = 16 + 15 * (dilation.x() - 1); break; #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC case DataType::F16: - num_elems_read_per_iteration = 24; + num_elems_written_per_iteration = 32 >> conv_stride_x; + num_elems_read_per_iteration = 24 + 23 * (dilation.x() - 1); break; #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC case DataType::F32: - num_elems_read_per_iteration = 12; + num_elems_read_per_iteration = 12 + 11 * (dilation.x() - 1); break; default: ARM_COMPUTE_ERROR("Data type not supported."); @@ -214,7 +227,7 @@ std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITen // Configure kernel window win = calculate_max_window(*output, Steps(num_elems_written_per_iteration)); - AccessWindowRectangle input_access(input, -conv_pad_left, -conv_pad_top, num_elems_read_per_iteration, 3, conv_stride_x, conv_stride_y); + AccessWindowRectangle input_access(input, -conv_pad_left, -conv_pad_top, num_elems_read_per_iteration, 3 + 2 * (dilation.y() - 1), conv_stride_x, conv_stride_y); AccessWindowStatic weights_access(weights, 0, 0, 3, 3); AccessWindowHorizontal output_access(output, 0, num_elems_written_per_iteration); @@ -227,7 +240,7 @@ std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITen } // namespace NEDepthwiseConvolutionLayer3x3Kernel::NEDepthwiseConvolutionLayer3x3Kernel() - : _border_size(0), _input(), _output(), _weights(), _conv_info(), _num_elems_written_per_iteration(0), _depth_multiplier(1) + : _border_size(0), _input(), _output(), _weights(), _conv_info(), _num_elems_written_per_iteration(0), _depth_multiplier(1), _dilation() { } @@ -236,28 +249,41 @@ BorderSize NEDepthwiseConvolutionLayer3x3Kernel::border_size() const return _border_size; } -void NEDepthwiseConvolutionLayer3x3Kernel::configure(const ITensor *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier) +void NEDepthwiseConvolutionLayer3x3Kernel::configure(const ITensor *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier, + const Size2D &dilation) { ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output); - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), weights->info(), output->info(), conv_info, depth_multiplier)); - - _input = input; - _output = output; - _weights = weights; - _conv_info = conv_info; - _depth_multiplier = depth_multiplier; - _num_elems_written_per_iteration = 16 >> _conv_info.stride().first; - _border_size = BorderSize(_conv_info.pad_top(), _conv_info.pad_right(), _conv_info.pad_bottom(), _conv_info.pad_left()); - - auto win_config = validate_and_configure_window(_input->info(), _weights->info(), _output->info(), _conv_info, _depth_multiplier); + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), weights->info(), output->info(), conv_info, depth_multiplier, dilation)); + + _input = input; + _output = output; + _weights = weights; + _conv_info = conv_info; + _depth_multiplier = depth_multiplier; + switch(input->info()->data_type()) + { + case DataType::QASYMM8: + case DataType::F32: + _num_elems_written_per_iteration = 16 >> _conv_info.stride().first; + break; + case DataType::F16: + _num_elems_written_per_iteration = 32 >> _conv_info.stride().first; + break; + default: + ARM_COMPUTE_ERROR("Data type not supported."); + } + _border_size = BorderSize(_conv_info.pad_top(), _conv_info.pad_right(), _conv_info.pad_bottom(), _conv_info.pad_left()); + _dilation = dilation; + auto win_config = validate_and_configure_window(_input->info(), _weights->info(), _output->info(), _conv_info, _depth_multiplier, dilation); ARM_COMPUTE_ERROR_THROW_ON(win_config.first); INEKernel::configure(win_config.second); } -Status NEDepthwiseConvolutionLayer3x3Kernel::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier) +Status NEDepthwiseConvolutionLayer3x3Kernel::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier, + const Size2D &dilation) { - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, weights, output, conv_info, depth_multiplier)); - ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), weights->clone().get(), output->clone().get(), conv_info, depth_multiplier).first); + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, weights, output, conv_info, depth_multiplier, dilation)); + ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), weights->clone().get(), output->clone().get(), conv_info, depth_multiplier, dilation).first); return Status{}; } @@ -272,14 +298,14 @@ void NEDepthwiseConvolutionLayer3x3Kernel::run(const Window &window, const Threa { #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC case DataType::F16: - convolve_3x3<float16_t, float16_t>(window, _num_elems_written_per_iteration, _input, _weights, _output, _conv_info, _depth_multiplier); + convolve_3x3<float16_t, float16_t>(window, _num_elems_written_per_iteration, _input, _weights, _output, _conv_info, _depth_multiplier, _dilation); break; #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC case DataType::F32: - convolve_3x3<float, float>(window, _num_elems_written_per_iteration, _input, _weights, _output, _conv_info, _depth_multiplier); + convolve_3x3<float, float>(window, _num_elems_written_per_iteration, _input, _weights, _output, _conv_info, _depth_multiplier, _dilation); break; case DataType::QASYMM8: - convolve_3x3<uint8_t, int32_t>(window, _num_elems_written_per_iteration, _input, _weights, _output, _conv_info, _depth_multiplier); + convolve_3x3<uint8_t, int32_t>(window, _num_elems_written_per_iteration, _input, _weights, _output, _conv_info, _depth_multiplier, _dilation); break; default: ARM_COMPUTE_ERROR("Not implemented"); |