diff options
Diffstat (limited to 'arm_compute/core/NEON/kernels/convolution/winograd/transforms/input.hpp')
| -rw-r--r-- | arm_compute/core/NEON/kernels/convolution/winograd/transforms/input.hpp | 94 |
1 files changed, 58 insertions, 36 deletions
diff --git a/arm_compute/core/NEON/kernels/convolution/winograd/transforms/input.hpp b/arm_compute/core/NEON/kernels/convolution/winograd/transforms/input.hpp index fc4b255a9c..13218030d2 100644 --- a/arm_compute/core/NEON/kernels/convolution/winograd/transforms/input.hpp +++ b/arm_compute/core/NEON/kernels/convolution/winograd/transforms/input.hpp @@ -33,35 +33,38 @@ namespace winograd int kernel_rows, int kernel_cols> template <typename T> void WinogradGEMM<output_tile_rows, output_tile_cols, kernel_rows, kernel_cols>::InputTransform<T>::execute( - const T *inptr, - const Tensor4DShape& input_shape, - const PaddingType padding_type, + const T* const input, /** Input tensor data */ + const int n_batches, /** Number of batches in input tensor. */ + const int in_batch_stride, /** Stride between batches of the input. */ + const int n_rows, /** Number of rows in input tensor. */ + const int in_row_stride, /** Stride between rows of the input. */ + const int n_cols, /** Number of columns in input tensor. */ + const int in_col_stride, /** Stride between columns of the input. */ + const int n_channels, /** Number of channels in input tensor. */ + const PaddingType padding, /** Padding type. */ const int tile_M, const int tile_N, - T *outptr_base, - const int matrix_stride, - const int matrix_batch_stride, - const int matrix_row_stride + T* const output, /** Base of output matrices. */ + const int matrix_stride, /** Stride between output matrices. */ + const int matrix_batch_stride, /** Stride between batches within the matrix. */ + const int matrix_row_stride /** Stride within matrices. */ ) { // Compute the padding required on each edge of the image - const int pad_top = (padding_type == PADDING_SAME) ? (kernel_rows - 1) / 2 : 0; - const int pad_left = (padding_type == PADDING_SAME) ? (kernel_cols - 1) / 2 : 0; + const int pad_top = (padding == PADDING_SAME) ? (kernel_rows - 1) / 2 : 0; + const int pad_left = (padding == PADDING_SAME) ? (kernel_cols - 1) / 2 : 0; const int tile_overlap = kernel_rows - 1; // Compute striding values (assuming NHWC ordered data) - const int input_col_stride = input_shape.n_channels; - const int input_row_stride = input_shape.n_cols * input_col_stride; - const int input_batch_stride = input_shape.n_rows * input_row_stride; const int output_col_stride = matrix_row_stride; const int output_row_stride = tile_N * output_col_stride; // Loop over batches - for (int batch = 0; batch < input_shape.n_batches; batch++) + for (int batch = 0; batch < n_batches; batch++) { // Pointer to the batch - const T* const input_base_batch = inptr + batch * input_batch_stride; - T* const outptr_base_batch = outptr_base + batch * matrix_batch_stride; + const T* const input_base_batch = input + batch * in_batch_stride; + T* const outptr_base_batch = output + batch * matrix_batch_stride; // Loop over rows of tiles for (int tile_i = 0; tile_i < tile_M; tile_i++) @@ -69,7 +72,7 @@ namespace winograd // Pointer to the row const int row_offset = (tile_i == 0) ? 0 : pad_top; const T* const input_base_row = ( - input_base_batch + ((inner_tile_rows - (kernel_rows - 1))*tile_i - row_offset)*input_row_stride + input_base_batch + ((inner_tile_rows - (kernel_rows - 1))*tile_i - row_offset)*in_row_stride ); T* const outptr_base_row = outptr_base_batch + tile_i*output_row_stride; @@ -77,14 +80,14 @@ namespace winograd const int row_top = tile_i*(inner_tile_rows - tile_overlap) - pad_top; const int row_bottom = row_top + inner_tile_rows; const int row_pad_top = (tile_i == 0) ? pad_top : 0; - const int row_pad_bottom = (row_bottom <= input_shape.n_rows) ? 0 : row_bottom - input_shape.n_rows; + const int row_pad_bottom = (row_bottom <= n_rows) ? 0 : row_bottom - n_rows; // Process the row process_tile_row( - tile_N, input_shape.n_channels, - input_base_row, input_row_stride, input_col_stride, + tile_N, n_channels, + input_base_row, in_row_stride, in_col_stride, outptr_base_row, matrix_stride, matrix_row_stride, - row_pad_top, pad_left, row_pad_bottom, input_shape.n_cols + row_pad_top, pad_left, row_pad_bottom, n_cols ); } } @@ -152,7 +155,10 @@ namespace winograd const PaddingType padding, /** Padding type. */ T* const output, /** Base of output matrices. */ const int matrix_stride, /** Stride between output matrices. */ - const int matrix_row_stride /** Stride within matrices. */ + const int matrix_row_stride, /** Stride within matrices. */ + const int in_batch_stride, /** Stride between input batches. */ + const int in_row_stride, /** Stride between input rows. */ + const int in_col_stride /** Stride between input columns. */ ) : _inptr(input), _outptr(output), _n_batches(n_batches), _n_rows(n_rows), _n_cols(n_cols), _n_channels(n_channels), _matrix_stride(matrix_stride), _matrix_row_stride(matrix_row_stride), @@ -160,6 +166,9 @@ namespace winograd output_tile_rows)), _tiles_N(iceildiv((padding == PADDING_SAME) ? n_cols : n_cols - kc + 1, output_tile_cols)), + _in_col_stride(in_col_stride ? in_col_stride : n_channels), + _in_row_stride(in_row_stride ? in_row_stride : n_cols * _in_col_stride), + _in_batch_stride(in_batch_stride ? in_batch_stride : n_rows * _in_row_stride), _padding_type(padding) { } @@ -168,10 +177,9 @@ namespace winograd template <typename T> unsigned int WinogradGEMM<otr, otc, kr, kc>::InputTransform<T>::get_window() const { - // TODO When the input transform supports multithreading, return the total - // number of tile rows (allowing for multiple batches). For now we return 1 - // to indicate that the activations must be transformed as a single block. - return 1; // TODO _tiles_M * _n_batches; + // The final window includes the tail, all other windows will be a multiple + // of the window block in size. + return iceildiv(_n_channels, WINDOW_BLOCK); } template <int otr, int otc, int kr, int kc> @@ -180,18 +188,32 @@ namespace winograd const unsigned int start, const unsigned int stop ) { - // TODO When the input transform supports multithreading call execute for a - // portion of the tile rows. - (void) start; - (void) stop; - - // For now, just do all of the work. - const Tensor4DShape input_shape = { - _n_batches, _n_rows, _n_cols, _n_channels, NHWC - }; + if (start >= get_window()) + { + return; + } + + // Determine the window of work to perform + const unsigned int start_channel = start * WINDOW_BLOCK; + const unsigned int stop_channel = std::min<const unsigned int>( + stop * WINDOW_BLOCK, _n_channels + ); + const unsigned int n_channels = stop_channel - start_channel; + + // Perform the work execute( - _inptr, input_shape, _padding_type, _tiles_M, _tiles_N, _outptr, - _matrix_stride, _matrix_row_stride * _tiles_M * _tiles_N, _matrix_row_stride + _inptr + start_channel, + _n_batches, _in_batch_stride, + _n_rows, _in_row_stride, + _n_cols, _in_col_stride, + n_channels, + _padding_type, + _tiles_M, + _tiles_N, + _outptr + start_channel, + _matrix_stride, + _matrix_row_stride * _tiles_M * _tiles_N, + _matrix_row_stride ); } } |