+/* 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.
+==============================================================================*/
+
+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) {
+
+ TFLITE_DCHECK(IsPackedWithoutStrides(input_dims));
+ TFLITE_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;
+ }
+ }
+ }
+}
+
+inline void Conv(const float* input_data, const Dims<4>& input_dims,
+ const float* filter_data, const Dims<4>& filter_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;
+
+ // NB: static_cast<float>(0x00000000h) == 0.0f
+ const uint8 float_zero_byte = 0x00;
+ 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) {
+ TFLITE_DCHECK(im2col_data);
+ Im2col(input_data, input_dims, stride_width, stride_height, pad_width,
+ pad_height, filter_height, filter_width, float_zero_byte,
+ im2col_data, im2col_dims);
+ gemm_input_data = im2col_data;
+ gemm_input_dims = &im2col_dims;
+ } else {
+ // TODO(aselle): We need to make sure to not send im2col if it is not
+ // needed.
+ TFLITE_DCHECK(!im2col_data);
+ 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);
+}
projects / platform / core / ml / nnfw.git / commitdiff