libavfilter/dnn: move dnn files from libavfilter to libavfilter/dnn

it is expected that there will be more files to support native mode,
so put all the dnn codes under libavfilter/dnn

The main change of this patch is to move the file location, see below:
modified:   libavfilter/Makefile
new file:   libavfilter/dnn/Makefile
renamed:    libavfilter/dnn_backend_native.c -> libavfilter/dnn/dnn_backend_native.c
renamed:    libavfilter/dnn_backend_native.h -> libavfilter/dnn/dnn_backend_native.h
renamed:    libavfilter/dnn_backend_tf.c -> libavfilter/dnn/dnn_backend_tf.c
renamed:    libavfilter/dnn_backend_tf.h -> libavfilter/dnn/dnn_backend_tf.h
renamed:    libavfilter/dnn_interface.c -> libavfilter/dnn/dnn_interface.c

Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
Signed-off-by: Pedro Arthur <bygrandao@gmail.com>

1 files changed, 389 insertions, 0 deletions

diff --git a/libavfilter/dnn/dnn_backend_native.c b/libavfilter/dnn/dnn_backend_native.c
new file mode 100644
index 0000000..82e900b
--- /dev/null
+++ b/libavfilter/dnn/dnn_backend_native.c
@@ -0,0 +1,389 @@
+/*
+ * 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
+ */
+
+/**
+ * @file
+ * DNN native backend implementation.
+ */
+
+#include "dnn_backend_native.h"
+#include "libavutil/avassert.h"
+
+static DNNReturnType set_input_output_native(void *model, DNNInputData *input, const char *input_name, const char **output_names, uint32_t nb_output)
+{
+    ConvolutionalNetwork *network = (ConvolutionalNetwork *)model;
+    InputParams *input_params;
+    ConvolutionalParams *conv_params;
+    DepthToSpaceParams *depth_to_space_params;
+    int cur_width, cur_height, cur_channels;
+    int32_t layer;
+
+    if (network->layers_num <= 0 || network->layers[0].type != INPUT){
+        return DNN_ERROR;
+    }
+    else{
+        input_params = (InputParams *)network->layers[0].params;
+        input_params->width = cur_width = input->width;
+        input_params->height = cur_height = input->height;
+        input_params->channels = cur_channels = input->channels;
+        if (input->data){
+            av_freep(&input->data);
+        }
+        av_assert0(input->dt == DNN_FLOAT);
+        network->layers[0].output = input->data = av_malloc(cur_height * cur_width * cur_channels * sizeof(float));
+        if (!network->layers[0].output){
+            return DNN_ERROR;
+        }
+    }
+
+    for (layer = 1; layer < network->layers_num; ++layer){
+        switch (network->layers[layer].type){
+        case CONV:
+            conv_params = (ConvolutionalParams *)network->layers[layer].params;
+            if (conv_params->input_num != cur_channels){
+                return DNN_ERROR;
+            }
+            cur_channels = conv_params->output_num;
+
+            if (conv_params->padding_method == VALID) {
+                int pad_size = (conv_params->kernel_size - 1) * conv_params->dilation;
+                cur_height -= pad_size;
+                cur_width -= pad_size;
+            }
+            break;
+        case DEPTH_TO_SPACE:
+            depth_to_space_params = (DepthToSpaceParams *)network->layers[layer].params;
+            if (cur_channels % (depth_to_space_params->block_size * depth_to_space_params->block_size) != 0){
+                return DNN_ERROR;
+            }
+            cur_channels = cur_channels / (depth_to_space_params->block_size * depth_to_space_params->block_size);
+            cur_height *= depth_to_space_params->block_size;
+            cur_width *= depth_to_space_params->block_size;
+            break;
+        default:
+            return DNN_ERROR;
+        }
+        if (network->layers[layer].output){
+            av_freep(&network->layers[layer].output);
+        }
+
+        if (cur_height <= 0 || cur_width <= 0)
+            return DNN_ERROR;
+
+        network->layers[layer].output = av_malloc(cur_height * cur_width * cur_channels * sizeof(float));
+        if (!network->layers[layer].output){
+            return DNN_ERROR;
+        }
+    }
+
+    return DNN_SUCCESS;
+}
+
+// Loads model and its parameters that are stored in a binary file with following structure:
+// layers_num,layer_type,layer_parameterss,layer_type,layer_parameters...
+// For CONV layer: activation_function, input_num, output_num, kernel_size, kernel, biases
+// For DEPTH_TO_SPACE layer: block_size
+DNNModel *ff_dnn_load_model_native(const char *model_filename)
+{
+    DNNModel *model = NULL;
+    ConvolutionalNetwork *network = NULL;
+    AVIOContext *model_file_context;
+    int file_size, dnn_size, kernel_size, i;
+    int32_t layer;
+    DNNLayerType layer_type;
+    ConvolutionalParams *conv_params;
+    DepthToSpaceParams *depth_to_space_params;
+
+    model = av_malloc(sizeof(DNNModel));
+    if (!model){
+        return NULL;
+    }
+
+    if (avio_open(&model_file_context, model_filename, AVIO_FLAG_READ) < 0){
+        av_freep(&model);
+        return NULL;
+    }
+    file_size = avio_size(model_file_context);
+
+    network = av_malloc(sizeof(ConvolutionalNetwork));
+    if (!network){
+        avio_closep(&model_file_context);
+        av_freep(&model);
+        return NULL;
+    }
+    model->model = (void *)network;
+
+    network->layers_num = 1 + (int32_t)avio_rl32(model_file_context);
+    dnn_size = 4;
+
+    network->layers = av_malloc(network->layers_num * sizeof(Layer));
+    if (!network->layers){
+        av_freep(&network);
+        avio_closep(&model_file_context);
+        av_freep(&model);
+        return NULL;
+    }
+
+    for (layer = 0; layer < network->layers_num; ++layer){
+        network->layers[layer].output = NULL;
+        network->layers[layer].params = NULL;
+    }
+    network->layers[0].type = INPUT;
+    network->layers[0].params = av_malloc(sizeof(InputParams));
+    if (!network->layers[0].params){
+        avio_closep(&model_file_context);
+        ff_dnn_free_model_native(&model);
+        return NULL;
+    }
+
+    for (layer = 1; layer < network->layers_num; ++layer){
+        layer_type = (int32_t)avio_rl32(model_file_context);
+        dnn_size += 4;
+        switch (layer_type){
+        case CONV:
+            conv_params = av_malloc(sizeof(ConvolutionalParams));
+            if (!conv_params){
+                avio_closep(&model_file_context);
+                ff_dnn_free_model_native(&model);
+                return NULL;
+            }
+            conv_params->dilation = (int32_t)avio_rl32(model_file_context);
+            conv_params->padding_method = (int32_t)avio_rl32(model_file_context);
+            conv_params->activation = (int32_t)avio_rl32(model_file_context);
+            conv_params->input_num = (int32_t)avio_rl32(model_file_context);
+            conv_params->output_num = (int32_t)avio_rl32(model_file_context);
+            conv_params->kernel_size = (int32_t)avio_rl32(model_file_context);
+            kernel_size = conv_params->input_num * conv_params->output_num *
+                          conv_params->kernel_size * conv_params->kernel_size;
+            dnn_size += 24 + (kernel_size + conv_params->output_num << 2);
+            if (dnn_size > file_size || conv_params->input_num <= 0 ||
+                conv_params->output_num <= 0 || conv_params->kernel_size <= 0){
+                avio_closep(&model_file_context);
+                ff_dnn_free_model_native(&model);
+                return NULL;
+            }
+            conv_params->kernel = av_malloc(kernel_size * sizeof(float));
+            conv_params->biases = av_malloc(conv_params->output_num * sizeof(float));
+            if (!conv_params->kernel || !conv_params->biases){
+                avio_closep(&model_file_context);
+                ff_dnn_free_model_native(&model);
+                return NULL;
+            }
+            for (i = 0; i < kernel_size; ++i){
+                conv_params->kernel[i] = av_int2float(avio_rl32(model_file_context));
+            }
+            for (i = 0; i < conv_params->output_num; ++i){
+                conv_params->biases[i] = av_int2float(avio_rl32(model_file_context));
+            }
+            network->layers[layer].type = CONV;
+            network->layers[layer].params = conv_params;
+            break;
+        case DEPTH_TO_SPACE:
+            depth_to_space_params = av_malloc(sizeof(DepthToSpaceParams));
+            if (!depth_to_space_params){
+                avio_closep(&model_file_context);
+                ff_dnn_free_model_native(&model);
+                return NULL;
+            }
+            depth_to_space_params->block_size = (int32_t)avio_rl32(model_file_context);
+            dnn_size += 4;
+            network->layers[layer].type = DEPTH_TO_SPACE;
+            network->layers[layer].params = depth_to_space_params;
+            break;
+        default:
+            avio_closep(&model_file_context);
+            ff_dnn_free_model_native(&model);
+            return NULL;
+        }
+    }
+
+    avio_closep(&model_file_context);
+
+    if (dnn_size != file_size){
+        ff_dnn_free_model_native(&model);
+        return NULL;
+    }
+
+    model->set_input_output = &set_input_output_native;
+
+    return model;
+}
+
+#define CLAMP_TO_EDGE(x, w) ((x) < 0 ? 0 : ((x) >= (w) ? (w - 1) : (x)))
+
+static void convolve(const float *input, float *output, const ConvolutionalParams *conv_params, int width, int height)
+{
+    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;
+
+    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;
+        }
+    }
+}
+
+static void depth_to_space(const float *input, float *output, int block_size, int width, int height, int channels)
+{
+    int y, x, by, bx, ch;
+    int new_channels = channels / (block_size * block_size);
+    int output_linesize = width * channels;
+    int by_linesize = output_linesize / block_size;
+    int x_linesize = new_channels * block_size;
+
+    for (y = 0; y < height; ++y){
+        for (x = 0; x < width; ++x){
+            for (by = 0; by < block_size; ++by){
+                for (bx = 0; bx < block_size; ++bx){
+                    for (ch = 0; ch < new_channels; ++ch){
+                        output[by * by_linesize + x * x_linesize + bx * new_channels + ch] = input[ch];
+                    }
+                    input += new_channels;
+                }
+            }
+        }
+        output += output_linesize;
+    }
+}
+
+DNNReturnType ff_dnn_execute_model_native(const DNNModel *model, DNNData *outputs, uint32_t nb_output)
+{
+    ConvolutionalNetwork *network = (ConvolutionalNetwork *)model->model;
+    int cur_width, cur_height, cur_channels;
+    int32_t layer;
+    InputParams *input_params;
+    ConvolutionalParams *conv_params;
+    DepthToSpaceParams *depth_to_space_params;
+
+    if (network->layers_num <= 0 || network->layers[0].type != INPUT || !network->layers[0].output){
+        return DNN_ERROR;
+    }
+    else{
+        input_params = (InputParams *)network->layers[0].params;
+        cur_width = input_params->width;
+        cur_height = input_params->height;
+        cur_channels = input_params->channels;
+    }
+
+    for (layer = 1; layer < network->layers_num; ++layer){
+        if (!network->layers[layer].output){
+            return DNN_ERROR;
+        }
+        switch (network->layers[layer].type){
+        case CONV:
+            conv_params = (ConvolutionalParams *)network->layers[layer].params;
+            convolve(network->layers[layer - 1].output, network->layers[layer].output, conv_params, cur_width, cur_height);
+            cur_channels = conv_params->output_num;
+            if (conv_params->padding_method == VALID) {
+                int pad_size = (conv_params->kernel_size - 1) * conv_params->dilation;
+                cur_height -= pad_size;
+                cur_width -= pad_size;
+            }
+            break;
+        case DEPTH_TO_SPACE:
+            depth_to_space_params = (DepthToSpaceParams *)network->layers[layer].params;
+            depth_to_space(network->layers[layer - 1].output, network->layers[layer].output,
+                           depth_to_space_params->block_size, cur_width, cur_height, cur_channels);
+            cur_height *= depth_to_space_params->block_size;
+            cur_width *= depth_to_space_params->block_size;
+            cur_channels /= depth_to_space_params->block_size * depth_to_space_params->block_size;
+            break;
+        case INPUT:
+            return DNN_ERROR;
+        }
+    }
+
+    // native mode does not support multiple outputs yet
+    if (nb_output > 1)
+        return DNN_ERROR;
+    outputs[0].data = network->layers[network->layers_num - 1].output;
+    outputs[0].height = cur_height;
+    outputs[0].width = cur_width;
+    outputs[0].channels = cur_channels;
+
+    return DNN_SUCCESS;
+}
+
+void ff_dnn_free_model_native(DNNModel **model)
+{
+    ConvolutionalNetwork *network;
+    ConvolutionalParams *conv_params;
+    int32_t layer;
+
+    if (*model)
+    {
+        network = (ConvolutionalNetwork *)(*model)->model;
+        for (layer = 0; layer < network->layers_num; ++layer){
+            av_freep(&network->layers[layer].output);
+            if (network->layers[layer].type == CONV){
+                conv_params = (ConvolutionalParams *)network->layers[layer].params;
+                av_freep(&conv_params->kernel);
+                av_freep(&conv_params->biases);
+            }
+            av_freep(&network->layers[layer].params);
+        }
+        av_freep(&network->layers);
+        av_freep(&network);
+        av_freep(model);
+    }
+}