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Diffstat (limited to 'compiler/ann-ref/src/ops/Conv2D.float.cpp')
| -rw-r--r-- | compiler/ann-ref/src/ops/Conv2D.float.cpp | 256 |
1 files changed, 256 insertions, 0 deletions
diff --git a/compiler/ann-ref/src/ops/Conv2D.float.cpp b/compiler/ann-ref/src/ops/Conv2D.float.cpp new file mode 100644 index 000000000..b47fcce27 --- /dev/null +++ b/compiler/ann-ref/src/ops/Conv2D.float.cpp @@ -0,0 +1,256 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (C) 2017 The Android Open Source Project + * Copyright 2017 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Conv2D.float.h" + +#include "internal/Spatial.h" +#include "internal/Array.h" +#include "internal/Matrix.h" +#include "internal/Fused.h" +#include "internal/GEMM.h" +#include "internal/ActivationUtils.h" + +// From optimized_ops.h in TensorFlow Lite +template <typename T> +inline void ExtractPatchIntoBufferColumn(const Dims<4> &input_dims, int w, int h, int b, + int kheight, int kwidth, int stride_width, + int stride_height, int pad_width, int pad_height, + int in_width, int in_height, int in_depth, + int single_buffer_length, int buffer_id, const T *in_data, + T *conv_buffer_data, uint8 byte_zero) +{ + // This chunk of code reshapes all the inputs corresponding to + // output (b, h, w) to a column vector in conv_buffer(:, buffer_id). + const int kwidth_times_indepth = kwidth * in_depth; + const int inwidth_times_indepth = in_width * in_depth; + const int ih_ungated_start = h * stride_height - pad_height; + const int ih_ungated_end = (ih_ungated_start + kheight); + const int ih_end = std::min(ih_ungated_end, in_height); + const int iw_ungated_start = w * stride_width - pad_width; + const int iw_ungated_end = (iw_ungated_start + kwidth); + const int iw_end = std::min(iw_ungated_end, in_width); + // If the patch is off the edge of the input image, skip writing those rows + // and columns from the patch into the output array. + const int h_offset = std::max(0, -ih_ungated_start); + const int w_offset = std::max(0, -iw_ungated_start); + const int ih_start = std::max(0, ih_ungated_start); + const int iw_start = std::max(0, iw_ungated_start); + const int single_row_num = std::min(kwidth - w_offset, in_width - iw_start) * in_depth; + const int output_row_offset = (buffer_id * single_buffer_length); + int out_offset = output_row_offset + (h_offset * kwidth + w_offset) * in_depth; + int in_offset = Offset(input_dims, 0, iw_start, ih_start, b); + + // Express all of the calculations as padding around the input patch. + const int top_padding = h_offset; + const int bottom_padding = (ih_ungated_end - ih_end); + const int left_padding = w_offset; + const int right_padding = (iw_ungated_end - iw_end); + assert(single_row_num == ((kwidth - (left_padding + right_padding)) * in_depth)); + + // Write out zeroes to the elements representing the top rows of the input + // patch that are off the edge of the input image. + if (top_padding > 0) + { + const int top_row_elements = (top_padding * kwidth * in_depth); + memset(conv_buffer_data + output_row_offset, byte_zero, (top_row_elements * sizeof(T))); + } + + // If the patch is on the interior of the input image horizontally, just copy + // over the rows sequentially, otherwise add zero padding at the start or end. + if ((left_padding == 0) && (right_padding == 0)) + { + for (int ih = ih_start; ih < ih_end; ++ih) + { + memcpy(conv_buffer_data + out_offset, in_data + in_offset, single_row_num * sizeof(T)); + out_offset += kwidth_times_indepth; + in_offset += inwidth_times_indepth; + } + } + else + { + for (int ih = ih_start; ih < ih_end; ++ih) + { + if (left_padding > 0) + { + const int left_start = (out_offset - (left_padding * in_depth)); + memset(conv_buffer_data + left_start, byte_zero, (left_padding * in_depth * sizeof(T))); + } + memcpy(conv_buffer_data + out_offset, in_data + in_offset, single_row_num * sizeof(T)); + if (right_padding > 0) + { + const int right_start = (out_offset + single_row_num); + memset(conv_buffer_data + right_start, byte_zero, (right_padding * in_depth * sizeof(T))); + } + out_offset += kwidth_times_indepth; + in_offset += inwidth_times_indepth; + } + } + + // If the bottom of the patch falls off the input image, pad the values + // representing those input rows with zeroes. + if (bottom_padding > 0) + { + const int bottom_row_elements = (bottom_padding * kwidth * in_depth); + const int bottom_start = + output_row_offset + ((top_padding + (ih_end - ih_start)) * kwidth * in_depth); + memset(conv_buffer_data + bottom_start, byte_zero, (bottom_row_elements * sizeof(T))); + } +} + +template <typename T> +void Im2col(const T *input_data, const Dims<4> &input_dims, int stride_width, int stride_height, + int pad_width, int pad_height, int kheight, int kwidth, uint8 byte_zero, T *output_data, + const Dims<4> &output_dims) +{ + DCHECK(IsPackedWithoutStrides(input_dims)); + DCHECK(IsPackedWithoutStrides(output_dims)); + const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); + const int input_depth = ArraySize(input_dims, 0); + const int input_width = ArraySize(input_dims, 1); + const int input_height = ArraySize(input_dims, 2); + const int output_depth = ArraySize(output_dims, 0); + const int output_width = ArraySize(output_dims, 1); + const int output_height = ArraySize(output_dims, 2); + + int buffer_id = 0; + // Loop over the output nodes. + for (int b = 0; b < batches; ++b) + { + for (int h = 0; h < output_height; ++h) + { + for (int w = 0; w < output_width; ++w) + { + ExtractPatchIntoBufferColumn(input_dims, w, h, b, kheight, kwidth, stride_width, + stride_height, pad_width, pad_height, input_width, + input_height, input_depth, output_depth, buffer_id, input_data, + output_data, byte_zero); + ++buffer_id; + } + } + } +} + +// From optimized_ops.h in TensorFlow Lite +template <FusedActivationFunctionType Ac> +void Conv(const float *input_data, const Dims<4> &input_dims, const float *filter_data, + const Dims<4> &filter_dims, const float *bias_data, const Dims<4> &bias_dims, + int stride_width, int stride_height, int pad_width, int pad_height, float *output_data, + const Dims<4> &output_dims, float *im2col_data, const Dims<4> &im2col_dims) +{ + (void)im2col_data; + (void)im2col_dims; + + const float *gemm_input_data = nullptr; + const Dims<4> *gemm_input_dims = nullptr; + const int filter_width = ArraySize(filter_dims, 1); + const int filter_height = ArraySize(filter_dims, 2); + const bool need_im2col = + stride_width != 1 || stride_height != 1 || filter_width != 1 || filter_height != 1; + if (need_im2col) + { + DCHECK(im2col_data); + Im2col(input_data, input_dims, stride_width, stride_height, pad_width, pad_height, + filter_height, filter_width, 0, im2col_data, im2col_dims); + gemm_input_data = im2col_data; + gemm_input_dims = &im2col_dims; + } + else + { +#if 0 // TODO-NNRT : Check if it needs, 'im2col_data' seems to be always not null. + DCHECK(!im2col_data); +#endif + gemm_input_data = input_data; + gemm_input_dims = &input_dims; + } + + const auto im2col_matrix_map = MapAsMatrixWithFirstDimAsRows(gemm_input_data, *gemm_input_dims); + const auto filter_matrix_map = MapAsMatrixWithLastDimAsCols(filter_data, filter_dims); + auto output_matrix_map = MapAsMatrixWithFirstDimAsRows(output_data, output_dims); + + Gemm(filter_matrix_map.transpose(), im2col_matrix_map, &output_matrix_map); + + AddBiasAndEvalActivationFunction<Ac>(bias_data, bias_dims, output_data, output_dims); +} + +// If possible we will use this static buffer for the tensor. +static constexpr int kStaticBufferSize = 1605632; +static char static_scratch_buffer[kStaticBufferSize]; + +#define ANDROID_NN_CONV_PARAMETERS(Type) \ + uint32_t height = getSizeOfDimension(inputShape, 1); \ + uint32_t width = getSizeOfDimension(inputShape, 2); \ + uint32_t filterHeight = getSizeOfDimension(filterShape, 1); \ + uint32_t filterWidth = getSizeOfDimension(filterShape, 2); \ + uint32_t outHeight = getSizeOfDimension(outputShape, 1); \ + uint32_t outWidth = getSizeOfDimension(outputShape, 2); \ + uint32_t inDepth = getSizeOfDimension(inputShape, 3); \ + \ + uint32_t paddingHeight = (uint32_t)padding_top; \ + uint32_t paddingWidth = (uint32_t)padding_left; \ + \ + Dims<4> im2colDim; \ + im2colDim.sizes[3] = (int)getSizeOfDimension(outputShape, 0); \ + im2colDim.sizes[2] = (int)getSizeOfDimension(outputShape, 1); \ + im2colDim.sizes[1] = (int)getSizeOfDimension(outputShape, 2); \ + im2colDim.sizes[0] = (int)inDepth * filterHeight * filterWidth; \ + \ + im2colDim.strides[0] = 1; \ + for (int i = 1; i < 4; i++) \ + { \ + im2colDim.strides[i] = im2colDim.strides[i - 1] * im2colDim.sizes[i - 1]; \ + } \ + \ + Type *im2colData = nullptr; \ + int im2colByteSize = sizeof(Type); \ + for (int i = 0; i < 4; i++) \ + { \ + im2colByteSize *= im2colDim.sizes[i]; \ + } \ + if (im2colByteSize <= kStaticBufferSize) \ + { \ + im2colData = reinterpret_cast<Type *>(static_scratch_buffer); \ + } \ + else \ + { \ + im2colData = new (std::nothrow) Type[im2colByteSize / sizeof(Type)]; \ + } + +bool convFloat32(const float *inputData, const Shape &inputShape, const float *filterData, + const Shape &filterShape, const float *biasData, const Shape &biasShape, + int32_t padding_left, int32_t padding_right, int32_t padding_top, + int32_t padding_bottom, int32_t stride_width, int32_t stride_height, + int32_t activation, float *outputData, const Shape &outputShape) +{ + + ANDROID_NN_CONV_PARAMETERS(float) + +#define ANDROID_NN_CONV(activation) \ + Conv<FusedActivationFunctionType::activation>( \ + inputData, convertShapeToDims(inputShape), filterData, convertShapeToDims(filterShape), \ + biasData, convertShapeToDims(biasShape), stride_width, stride_height, paddingWidth, \ + paddingHeight, outputData, convertShapeToDims(outputShape), im2colData, im2colDim) + + ANDROID_NN_MACRO_DISPATCH_WITH_DELETE(ANDROID_NN_CONV) +#undef ANDROID_NN_CONV + + if (im2colByteSize > kStaticBufferSize) + { + delete[] im2colData; + } + return true; +} |