Em qui, 5 de set de 2019 às 03:05, Guo, Yejun <yejun....@intel.com> escreveu: > > the logic is that one layer in one separated source file to make > the source files simple for maintaining. > > Signed-off-by: Guo, Yejun <yejun....@intel.com> > --- > libavfilter/dnn/Makefile | 1 + > libavfilter/dnn/dnn_backend_native.c | 80 +---------------- > libavfilter/dnn/dnn_backend_native.h | 13 --- > libavfilter/dnn/dnn_backend_native_layer_conv2d.c | 101 > ++++++++++++++++++++++ > libavfilter/dnn/dnn_backend_native_layer_conv2d.h | 39 +++++++++ > libavfilter/dnn/dnn_backend_tf.c | 1 + > 6 files changed, 143 insertions(+), 92 deletions(-) > create mode 100644 libavfilter/dnn/dnn_backend_native_layer_conv2d.c > create mode 100644 libavfilter/dnn/dnn_backend_native_layer_conv2d.h > > diff --git a/libavfilter/dnn/Makefile b/libavfilter/dnn/Makefile > index 83938e5..40b848b 100644 > --- a/libavfilter/dnn/Makefile > +++ b/libavfilter/dnn/Makefile > @@ -1,6 +1,7 @@ > OBJS-$(CONFIG_DNN) += dnn/dnn_interface.o > OBJS-$(CONFIG_DNN) += dnn/dnn_backend_native.o > OBJS-$(CONFIG_DNN) += > dnn/dnn_backend_native_layer_pad.o > +OBJS-$(CONFIG_DNN) += > dnn/dnn_backend_native_layer_conv2d.o > > DNN-OBJS-$(CONFIG_LIBTENSORFLOW) += dnn/dnn_backend_tf.o > > diff --git a/libavfilter/dnn/dnn_backend_native.c > b/libavfilter/dnn/dnn_backend_native.c > index f56cd81..5dabd15 100644 > --- a/libavfilter/dnn/dnn_backend_native.c > +++ b/libavfilter/dnn/dnn_backend_native.c > @@ -26,6 +26,7 @@ > #include "dnn_backend_native.h" > #include "libavutil/avassert.h" > #include "dnn_backend_native_layer_pad.h" > +#include "dnn_backend_native_layer_conv2d.h" > > static DNNReturnType set_input_output_native(void *model, DNNInputData > *input, const char *input_name, const char **output_names, uint32_t nb_output) > { > @@ -281,85 +282,6 @@ DNNModel *ff_dnn_load_model_native(const char > *model_filename) > return model; > } > > -#define CLAMP_TO_EDGE(x, w) ((x) < 0 ? 0 : ((x) >= (w) ? (w - 1) : (x))) > - > -static int convolve(DnnOperand *operands, const int32_t > *input_operand_indexes, int32_t output_operand_index, const > ConvolutionalParams *conv_params) > -{ > - float *output; > - int32_t input_operand_index = input_operand_indexes[0]; > - int number = operands[input_operand_index].dims[0]; > - int height = operands[input_operand_index].dims[1]; > - int width = operands[input_operand_index].dims[2]; > - int channel = operands[input_operand_index].dims[3]; > - const float *input = operands[input_operand_index].data; > - > - int radius = conv_params->kernel_size >> 1; > - int src_linesize = width * conv_params->input_num; > - int filter_linesize = conv_params->kernel_size * conv_params->input_num; > - int filter_size = conv_params->kernel_size * filter_linesize; > - int pad_size = (conv_params->padding_method == VALID) ? > (conv_params->kernel_size - 1) / 2 * conv_params->dilation : 0; > - > - DnnOperand *output_operand = &operands[output_operand_index]; > - output_operand->dims[0] = number; > - output_operand->dims[1] = height - pad_size * 2; > - output_operand->dims[2] = width - pad_size * 2; > - output_operand->dims[3] = conv_params->output_num; > - output_operand->length = calculate_operand_data_length(output_operand); > - output_operand->data = av_realloc(output_operand->data, > output_operand->length); > - if (!output_operand->data) > - return -1; > - output = output_operand->data; > - > - av_assert0(channel == conv_params->input_num); > - > - for (int y = pad_size; y < height - pad_size; ++y) { > - for (int x = pad_size; x < width - pad_size; ++x) { > - for (int n_filter = 0; n_filter < conv_params->output_num; > ++n_filter) { > - output[n_filter] = conv_params->biases[n_filter]; > - > - for (int ch = 0; ch < conv_params->input_num; ++ch) { > - for (int kernel_y = 0; kernel_y < > conv_params->kernel_size; ++kernel_y) { > - for (int kernel_x = 0; kernel_x < > conv_params->kernel_size; ++kernel_x) { > - float input_pel; > - if (conv_params->padding_method == > SAME_CLAMP_TO_EDGE) { > - int y_pos = CLAMP_TO_EDGE(y + (kernel_y - > radius) * conv_params->dilation, height); > - int x_pos = CLAMP_TO_EDGE(x + (kernel_x - > radius) * conv_params->dilation, width); > - input_pel = input[y_pos * src_linesize + > x_pos * conv_params->input_num + ch]; > - } else { > - int y_pos = y + (kernel_y - radius) * > conv_params->dilation; > - int x_pos = x + (kernel_x - radius) * > conv_params->dilation; > - input_pel = (x_pos < 0 || x_pos >= width || > y_pos < 0 || y_pos >= height) ? 0.0 : > - input[y_pos * > src_linesize + x_pos * conv_params->input_num + ch]; > - } > - > - > - output[n_filter] += input_pel * > conv_params->kernel[n_filter * filter_size + kernel_y * filter_linesize + > - > kernel_x * conv_params->input_num + ch]; > - } > - } > - } > - switch (conv_params->activation){ > - case RELU: > - output[n_filter] = FFMAX(output[n_filter], 0.0); > - break; > - case TANH: > - output[n_filter] = 2.0f / (1.0f + exp(-2.0f * > output[n_filter])) - 1.0f; > - break; > - case SIGMOID: > - output[n_filter] = 1.0f / (1.0f + > exp(-output[n_filter])); > - break; > - case NONE: > - break; > - case LEAKY_RELU: > - output[n_filter] = FFMAX(output[n_filter], 0.0) + 0.2 * > FFMIN(output[n_filter], 0.0); > - } > - } > - output += conv_params->output_num; > - } > - } > - return 0; > -} > - > static int depth_to_space(DnnOperand *operands, const int32_t > *input_operand_indexes, int32_t output_operand_index, int block_size) > { > float *output; > diff --git a/libavfilter/dnn/dnn_backend_native.h > b/libavfilter/dnn/dnn_backend_native.h > index 08e7d15..aa52222 100644 > --- a/libavfilter/dnn/dnn_backend_native.h > +++ b/libavfilter/dnn/dnn_backend_native.h > @@ -32,10 +32,6 @@ > > typedef enum {INPUT, CONV, DEPTH_TO_SPACE, MIRROR_PAD} DNNLayerType; > > -typedef enum {RELU, TANH, SIGMOID, NONE, LEAKY_RELU} DNNActivationFunc; > - > -typedef enum {VALID, SAME, SAME_CLAMP_TO_EDGE} DNNConvPaddingParam; > - > typedef enum {DOT_INPUT = 1, DOT_OUTPUT = 2, DOT_INTERMEDIATE = DOT_INPUT | > DOT_INPUT} DNNOperandType; > > typedef struct Layer{ > @@ -90,15 +86,6 @@ typedef struct DnnOperand{ > int32_t usedNumbersLeft; > }DnnOperand; > > -typedef struct ConvolutionalParams{ > - int32_t input_num, output_num, kernel_size; > - DNNActivationFunc activation; > - DNNConvPaddingParam padding_method; > - int32_t dilation; > - float *kernel; > - float *biases; > -} ConvolutionalParams; > - > typedef struct InputParams{ > int height, width, channels; > } InputParams; > diff --git a/libavfilter/dnn/dnn_backend_native_layer_conv2d.c > b/libavfilter/dnn/dnn_backend_native_layer_conv2d.c > new file mode 100644 > index 0000000..b13b431 > --- /dev/null > +++ b/libavfilter/dnn/dnn_backend_native_layer_conv2d.c > @@ -0,0 +1,101 @@ > +/* > + * Copyright (c) 2018 Sergey Lavrushkin > + * > + * This file is part of FFmpeg. > + * > + * FFmpeg is free software; you can redistribute it and/or > + * modify it under the terms of the GNU Lesser General Public > + * License as published by the Free Software Foundation; either > + * version 2.1 of the License, or (at your option) any later version. > + * > + * FFmpeg is distributed in the hope that it will be useful, > + * but WITHOUT ANY WARRANTY; without even the implied warranty of > + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU > + * Lesser General Public License for more details. > + * > + * You should have received a copy of the GNU Lesser General Public > + * License along with FFmpeg; if not, write to the Free Software > + * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 > USA > + */ > + > +#include "libavutil/avassert.h" > +#include "dnn_backend_native_layer_conv2d.h" > + > +#define CLAMP_TO_EDGE(x, w) ((x) < 0 ? 0 : ((x) >= (w) ? (w - 1) : (x))) > + > +int convolve(DnnOperand *operands, const int32_t *input_operand_indexes, > int32_t output_operand_index, const ConvolutionalParams *conv_params) > +{ > + float *output; > + int32_t input_operand_index = input_operand_indexes[0]; > + int number = operands[input_operand_index].dims[0]; > + int height = operands[input_operand_index].dims[1]; > + int width = operands[input_operand_index].dims[2]; > + int channel = operands[input_operand_index].dims[3]; > + const float *input = operands[input_operand_index].data; > + > + int radius = conv_params->kernel_size >> 1; > + int src_linesize = width * conv_params->input_num; > + int filter_linesize = conv_params->kernel_size * conv_params->input_num; > + int filter_size = conv_params->kernel_size * filter_linesize; > + int pad_size = (conv_params->padding_method == VALID) ? > (conv_params->kernel_size - 1) / 2 * conv_params->dilation : 0; > + > + DnnOperand *output_operand = &operands[output_operand_index]; > + output_operand->dims[0] = number; > + output_operand->dims[1] = height - pad_size * 2; > + output_operand->dims[2] = width - pad_size * 2; > + output_operand->dims[3] = conv_params->output_num; > + output_operand->length = calculate_operand_data_length(output_operand); > + output_operand->data = av_realloc(output_operand->data, > output_operand->length); > + if (!output_operand->data) > + return -1; > + output = output_operand->data; > + > + av_assert0(channel == conv_params->input_num); > + > + for (int y = pad_size; y < height - pad_size; ++y) { > + for (int x = pad_size; x < width - pad_size; ++x) { > + for (int n_filter = 0; n_filter < conv_params->output_num; > ++n_filter) { > + output[n_filter] = conv_params->biases[n_filter]; > + > + for (int ch = 0; ch < conv_params->input_num; ++ch) { > + for (int kernel_y = 0; kernel_y < > conv_params->kernel_size; ++kernel_y) { > + for (int kernel_x = 0; kernel_x < > conv_params->kernel_size; ++kernel_x) { > + float input_pel; > + if (conv_params->padding_method == > SAME_CLAMP_TO_EDGE) { > + int y_pos = CLAMP_TO_EDGE(y + (kernel_y - > radius) * conv_params->dilation, height); > + int x_pos = CLAMP_TO_EDGE(x + (kernel_x - > radius) * conv_params->dilation, width); > + input_pel = input[y_pos * src_linesize + > x_pos * conv_params->input_num + ch]; > + } else { > + int y_pos = y + (kernel_y - radius) * > conv_params->dilation; > + int x_pos = x + (kernel_x - radius) * > conv_params->dilation; > + input_pel = (x_pos < 0 || x_pos >= width || > y_pos < 0 || y_pos >= height) ? 0.0 : > + input[y_pos * > src_linesize + x_pos * conv_params->input_num + ch]; > + } > + > + > + output[n_filter] += input_pel * > conv_params->kernel[n_filter * filter_size + kernel_y * filter_linesize + > + > kernel_x * conv_params->input_num + ch]; > + } > + } > + } > + switch (conv_params->activation){ > + case RELU: > + output[n_filter] = FFMAX(output[n_filter], 0.0); > + break; > + case TANH: > + output[n_filter] = 2.0f / (1.0f + exp(-2.0f * > output[n_filter])) - 1.0f; > + break; > + case SIGMOID: > + output[n_filter] = 1.0f / (1.0f + > exp(-output[n_filter])); > + break; > + case NONE: > + break; > + case LEAKY_RELU: > + output[n_filter] = FFMAX(output[n_filter], 0.0) + 0.2 * > FFMIN(output[n_filter], 0.0); > + } > + } > + output += conv_params->output_num; > + } > + } > + return 0; > +} > diff --git a/libavfilter/dnn/dnn_backend_native_layer_conv2d.h > b/libavfilter/dnn/dnn_backend_native_layer_conv2d.h > new file mode 100644 > index 0000000..7ddfff3 > --- /dev/null > +++ b/libavfilter/dnn/dnn_backend_native_layer_conv2d.h > @@ -0,0 +1,39 @@ > +/* > + * Copyright (c) 2018 Sergey Lavrushkin > + * > + * This file is part of FFmpeg. > + * > + * FFmpeg is free software; you can redistribute it and/or > + * modify it under the terms of the GNU Lesser General Public > + * License as published by the Free Software Foundation; either > + * version 2.1 of the License, or (at your option) any later version. > + * > + * FFmpeg is distributed in the hope that it will be useful, > + * but WITHOUT ANY WARRANTY; without even the implied warranty of > + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU > + * Lesser General Public License for more details. > + * > + * You should have received a copy of the GNU Lesser General Public > + * License along with FFmpeg; if not, write to the Free Software > + * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 > USA > + */ > + > +#ifndef AVFILTER_DNN_DNN_BACKEND_NATIVE_LAYER_CONV2D_H > +#define AVFILTER_DNN_DNN_BACKEND_NATIVE_LAYER_CONV2D_H > + > +#include "dnn_backend_native.h" > + > +typedef enum {RELU, TANH, SIGMOID, NONE, LEAKY_RELU} DNNActivationFunc; > +typedef enum {VALID, SAME, SAME_CLAMP_TO_EDGE} DNNConvPaddingParam; > + > +typedef struct ConvolutionalParams{ > + int32_t input_num, output_num, kernel_size; > + DNNActivationFunc activation; > + DNNConvPaddingParam padding_method; > + int32_t dilation; > + float *kernel; > + float *biases; > +} ConvolutionalParams; > + > +int convolve(DnnOperand *operands, const int32_t *input_operand_indexes, > int32_t output_operand_index, const ConvolutionalParams *conv_params); > +#endif > diff --git a/libavfilter/dnn/dnn_backend_tf.c > b/libavfilter/dnn/dnn_backend_tf.c > index 626fba9..46dfa00 100644 > --- a/libavfilter/dnn/dnn_backend_tf.c > +++ b/libavfilter/dnn/dnn_backend_tf.c > @@ -25,6 +25,7 @@ > > #include "dnn_backend_tf.h" > #include "dnn_backend_native.h" > +#include "dnn_backend_native_layer_conv2d.h" > #include "libavformat/avio.h" > #include "libavutil/avassert.h" > #include "dnn_backend_native_layer_pad.h" > -- > 2.7.4 >
LGTM Pushed, thanks! > _______________________________________________ > ffmpeg-devel mailing list > ffmpeg-devel@ffmpeg.org > https://ffmpeg.org/mailman/listinfo/ffmpeg-devel > > To unsubscribe, visit link above, or email > ffmpeg-devel-requ...@ffmpeg.org with subject "unsubscribe". _______________________________________________ ffmpeg-devel mailing list ffmpeg-devel@ffmpeg.org https://ffmpeg.org/mailman/listinfo/ffmpeg-devel To unsubscribe, visit link above, or email ffmpeg-devel-requ...@ffmpeg.org with subject "unsubscribe".