1 files changed, 382 insertions, 0 deletions
diff --git a/libavfilter/dnn_backend_native.c b/libavfilter/dnn_backend_native.c
new file mode 100644
index 0000000000..6e80dd3663
--- /dev/null
+++ b/libavfilter/dnn_backend_native.c
@@ -0,0 +1,382 @@
+/*
+ * 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 "dnn_srcnn.h"
+#include "libavformat/avio.h"
+
+typedef enum {INPUT, CONV} LayerType;
+
+typedef struct Layer{
+    LayerType type;
+    float* output;
+    void* params;
+} Layer;
+
+typedef struct ConvolutionalParams{
+    int32_t input_num, output_num, kernel_size;
+    float* kernel;
+    float* biases;
+} ConvolutionalParams;
+
+typedef struct InputParams{
+    int height, width, channels;
+} InputParams;
+
+// Represents simple feed-forward convolutional network.
+typedef struct ConvolutionalNetwork{
+    Layer* layers;
+    int32_t layers_num;
+} ConvolutionalNetwork;
+
+static DNNReturnType set_input_output_native(void* model, const DNNData* input, const DNNData* output)
+{
+    ConvolutionalNetwork* network = (ConvolutionalNetwork*)model;
+    InputParams* input_params;
+    ConvolutionalParams* conv_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{
+        network->layers[0].output = input->data;
+        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;
+    }
+
+    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 (layer < network->layers_num - 1){
+                if (!network->layers[layer].output){
+                    av_freep(&network->layers[layer].output);
+                }
+                network->layers[layer].output = av_malloc(cur_height * cur_width * cur_channels * sizeof(float));
+                if (!network->layers[layer].output){
+                    return DNN_ERROR;
+                }
+            }
+            else{
+                network->layers[layer].output = output->data;
+                if (output->width != cur_width || output->height != cur_height || output->channels != cur_channels){
+                    return DNN_ERROR;
+                }
+            }
+            break;
+        default:
+            return DNN_ERROR;
+        }
+    }
+
+    return DNN_SUCCESS;
+}
+
+// Loads model and its parameters that are stored in a binary file with following structure:
+// layers_num,conv_input_num,conv_output_num,conv_kernel_size,conv_kernel,conv_biases,conv_input_num...
+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;
+    ConvolutionalParams* conv_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){
+        conv_params = av_malloc(sizeof(ConvolutionalParams));
+        if (!conv_params){
+            avio_closep(&model_file_context);
+            ff_dnn_free_model_native(&model);
+            return NULL;
+        }
+        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 += 12 + (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;
+    }
+
+    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;
+}
+
+static int set_up_conv_layer(Layer* layer, const float* kernel, const float* biases, int32_t input_num, int32_t output_num, int32_t size)
+{
+    ConvolutionalParams* conv_params;
+    int kernel_size;
+
+    conv_params = av_malloc(sizeof(ConvolutionalParams));
+    if (!conv_params){
+        return DNN_ERROR;
+    }
+    conv_params->input_num = input_num;
+    conv_params->output_num = output_num;
+    conv_params->kernel_size = size;
+    kernel_size = input_num * output_num * size * size;
+    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){
+        av_freep(&conv_params->kernel);
+        av_freep(&conv_params->biases);
+        av_freep(&conv_params);
+        return DNN_ERROR;
+    }
+    memcpy(conv_params->kernel, kernel, kernel_size * sizeof(float));
+    memcpy(conv_params->biases, biases, output_num * sizeof(float));
+    layer->type = CONV;
+    layer->params = conv_params;
+
+    return DNN_SUCCESS;
+}
+
+DNNModel* ff_dnn_load_default_model_native(DNNDefaultModel model_type)
+{
+    DNNModel* model = NULL;
+    ConvolutionalNetwork* network = NULL;
+    int32_t layer;
+
+    model = av_malloc(sizeof(DNNModel));
+    if (!model){
+        return NULL;
+    }
+
+    network = av_malloc(sizeof(ConvolutionalNetwork));
+    if (!network){
+        av_freep(&model);
+        return NULL;
+    }
+    model->model = (void*)network;
+
+    switch (model_type){
+    case DNN_SRCNN:
+        network->layers_num = 4;
+
+        network->layers = av_malloc(network->layers_num * sizeof(Layer));
+        if (!network->layers){
+            av_freep(&network);
+            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){
+            ff_dnn_free_model_native(&model);
+            return NULL;
+        }
+
+        if (set_up_conv_layer(network->layers + 1, conv1_kernel, conv1_biases, 1, 64, 9) != DNN_SUCCESS ||
+            set_up_conv_layer(network->layers + 2, conv2_kernel, conv2_biases, 64, 32, 1) != DNN_SUCCESS ||
+            set_up_conv_layer(network->layers + 3, conv3_kernel, conv3_biases, 32, 1, 5) != DNN_SUCCESS){
+            ff_dnn_free_model_native(&model);
+            return NULL;
+        }
+
+        model->set_input_output = &set_input_output_native;
+
+        return model;
+    default:
+        av_freep(&network);
+        av_freep(&model);
+        return NULL;
+    }
+}
+
+#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, int32_t width, int32_t height)
+{
+    int y, x, n_filter, ch, kernel_y, kernel_x;
+    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;
+
+    for (y = 0; y < height; ++y){
+        for (x = 0; x < width; ++x){
+            for (n_filter = 0; n_filter < conv_params->output_num; ++n_filter){
+                output[n_filter] = conv_params->biases[n_filter];
+                for (ch = 0; ch < conv_params->input_num; ++ch){
+                    for (kernel_y = 0; kernel_y < conv_params->kernel_size; ++kernel_y){
+                        for (kernel_x = 0; kernel_x < conv_params->kernel_size; ++kernel_x){
+                            output[n_filter] += input[CLAMP_TO_EDGE(y + kernel_y - radius, height) * src_linesize +
+                                                      CLAMP_TO_EDGE(x + kernel_x - radius, width) * conv_params->input_num + ch] *
+                                                conv_params->kernel[n_filter * filter_size + kernel_y * filter_linesize +
+                                                                    kernel_x * conv_params->input_num + ch];
+                        }
+                    }
+                }
+                output[n_filter] = FFMAX(output[n_filter], 0.0);
+            }
+            output += conv_params->output_num;
+        }
+    }
+}
+
+DNNReturnType ff_dnn_execute_model_native(const DNNModel* model)
+{
+    ConvolutionalNetwork* network = (ConvolutionalNetwork*)model->model;
+    InputParams* input_params;
+    int cur_width, cur_height;
+    int32_t layer;
+
+    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;
+    }
+
+    for (layer = 1; layer < network->layers_num; ++layer){
+        if (!network->layers[layer].output){
+            return DNN_ERROR;
+        }
+        switch (network->layers[layer].type){
+        case CONV:
+            convolve(network->layers[layer - 1].output, network->layers[layer].output, (ConvolutionalParams*)network->layers[layer].params, cur_width, cur_height);
+            break;
+        case INPUT:
+            return DNN_ERROR;
+        }
+    }
+
+    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){
+            switch (network->layers[layer].type){
+            case CONV:
+                if (layer < network->layers_num - 1){
+                    av_freep(&network->layers[layer].output);
+                }
+                conv_params = (ConvolutionalParams*)network->layers[layer].params;
+                av_freep(&conv_params->kernel);
+                av_freep(&conv_params->biases);
+                av_freep(&conv_params);
+                break;
+            case INPUT:
+                av_freep(&network->layers[layer].params);
+            }
+        }
+        av_freep(network);
+        av_freep(model);
+    }
+}