Adds dnn inference module for simple convolutional networks. Reimplements srcnn filter based on it.

Signed-off-by: Pedro Arthur <bygrandao@gmail.com>

Changelog

@@ -9,6 +9,8 @@ version <next>:
 - aderivative and aintegral audio filters
 - pal75bars and pal100bars video filter sources
 - support mbedTLS based TLS
+- DNN inference interface
+- Reimplemented SRCNN filter using DNN inference interface
 
 
 version 4.0:

libavfilter/Makefile

@@ -12,6 +12,8 @@ OBJS = allfilters.o                                                     \
        avfiltergraph.o                                                  \
        buffersink.o                                                     \
        buffersrc.o                                                      \
+       dnn_interface.o                                                  \
+       dnn_backend_native.o                                             \
        drawutils.o                                                      \
        fifo.o                                                           \
        formats.o                                                        \

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);
+    }
+}

libavfilter/dnn_backend_native.h

@@ -0,0 +1,40 @@
+/*
+ * 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 inference functions interface for native backend.
+ */
+
+
+#ifndef AVFILTER_DNN_BACKEND_NATIVE_H
+#define AVFILTER_DNN_BACKEND_NATIVE_H
+
+#include "dnn_interface.h"
+
+DNNModel* ff_dnn_load_model_native(const char* model_filename);
+
+DNNModel* ff_dnn_load_default_model_native(DNNDefaultModel model_type);
+
+DNNReturnType ff_dnn_execute_model_native(const DNNModel* model);
+
+void ff_dnn_free_model_native(DNNModel** model);
+
+#endif

libavfilter/dnn_interface.c

@@ -0,0 +1,48 @@
+/*
+ * 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
+ * Implements DNN module initialization with specified backend.
+ */
+
+#include "dnn_interface.h"
+#include "dnn_backend_native.h"
+#include "libavutil/mem.h"
+
+DNNModule* ff_get_dnn_module(DNNBackendType backend_type)
+{
+    DNNModule* dnn_module;
+
+    dnn_module = av_malloc(sizeof(DNNModule));
+    if(!dnn_module){
+        return NULL;
+    }
+
+    switch(backend_type){
+    case DNN_NATIVE:
+        dnn_module->load_model = &ff_dnn_load_model_native;
+        dnn_module->load_default_model = &ff_dnn_load_default_model_native;
+        dnn_module->execute_model = &ff_dnn_execute_model_native;
+        dnn_module->free_model = &ff_dnn_free_model_native;
+    }
+
+    return dnn_module;
+}

libavfilter/dnn_interface.h

@@ -0,0 +1,64 @@
+/*
+ * 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 inference engine interface.
+ */
+
+#ifndef AVFILTER_DNN_INTERFACE_H
+#define AVFILTER_DNN_INTERFACE_H
+
+typedef enum {DNN_SUCCESS, DNN_ERROR} DNNReturnType;
+
+typedef enum {DNN_NATIVE} DNNBackendType;
+
+typedef enum {DNN_SRCNN} DNNDefaultModel;
+
+typedef struct DNNData{
+    float* data;
+    int width, height, channels;
+} DNNData;
+
+
+typedef struct DNNModel{
+    // Stores model that can be different for different backends.
+    void* model;
+    // Sets model input and output, while allocating additional memory for intermediate calculations.
+    // Should be called at least once before model execution.
+    DNNReturnType (*set_input_output)(void* model, const DNNData* input, const DNNData* output);
+} DNNModel;
+
+// Stores pointers to functions for loading, executing, freeing DNN models for one of the backends.
+typedef struct DNNModule{
+    // Loads model and parameters from given file. Returns NULL if it is not possible.
+    DNNModel* (*load_model)(const char* model_filename);
+    // Loads one of the default models
+    DNNModel* (*load_default_model)(DNNDefaultModel model_type);
+    // Executes model with specified input and output. Returns DNN_ERROR otherwise.
+    DNNReturnType (*execute_model)(const DNNModel* model);
+    // Frees memory allocated for model.
+    void (*free_model)(DNNModel** model);
+} DNNModule;
+
+// Initializes DNNModule depending on chosen backend.
+DNNModule* ff_get_dnn_module(DNNBackendType backend_type);
+
+#endif

libavfilter/vf_srcnn.c

@@ -28,164 +28,47 @@
 #include "formats.h"
 #include "internal.h"
 #include "libavutil/opt.h"
-#include "vf_srcnn.h"
 #include "libavformat/avio.h"
-
-typedef struct Convolution
-{
-    double* kernel;
-    double* biases;
-    int32_t size, input_channels, output_channels;
-} Convolution;
+#include "dnn_interface.h"
 
 typedef struct SRCNNContext {
     const AVClass *class;
 
-    /// SRCNN convolutions
-    struct Convolution conv1, conv2, conv3;
-    /// Path to binary file with kernels specifications
-    char* config_file_path;
-    /// Buffers for network input/output and feature maps
-    double* input_output_buf;
-    double* conv1_buf;
-    double* conv2_buf;
+    char* model_filename;
+    float* input_output_buf;
+    DNNModule* dnn_module;
+    DNNModel* model;
+    DNNData input_output;
 } SRCNNContext;
 
 
 #define OFFSET(x) offsetof(SRCNNContext, x)
 #define FLAGS AV_OPT_FLAG_FILTERING_PARAM | AV_OPT_FLAG_VIDEO_PARAM
 static const AVOption srcnn_options[] = {
-    { "config_file", "path to configuration file with network parameters", OFFSET(config_file_path), AV_OPT_TYPE_STRING, {.str=NULL}, 0, 0, FLAGS },
+    { "model_filename", "path to model file specifying network architecture and its parameters", OFFSET(model_filename), AV_OPT_TYPE_STRING, {.str=NULL}, 0, 0, FLAGS },
     { NULL }
 };
 
 AVFILTER_DEFINE_CLASS(srcnn);
 
-#define CHECK_FILE_SIZE(file_size, srcnn_size, avio_context)    if (srcnn_size > file_size){ \
-                                                                    av_log(context, AV_LOG_ERROR, "error reading configuration file\n");\
-                                                                    avio_closep(&avio_context); \
-                                                                    return AVERROR(EIO); \
-                                                                }
-
-#define CHECK_ALLOCATION(call, end_call)    if (call){ \
-                                                av_log(context, AV_LOG_ERROR, "could not allocate memory for convolutions\n"); \
-                                                end_call; \
-                                                return AVERROR(ENOMEM); \
-                                            }
-
-static int allocate_read_conv_data(Convolution* conv, AVIOContext* config_file_context)
-{
-    int32_t kernel_size = conv->output_channels * conv->size * conv->size * conv->input_channels;
-    int32_t i;
-
-    conv->kernel = av_malloc(kernel_size * sizeof(double));
-    if (!conv->kernel){
-        return AVERROR(ENOMEM);
-    }
-    for (i = 0; i < kernel_size; ++i){
-        conv->kernel[i] = av_int2double(avio_rl64(config_file_context));
-    }
-
-    conv->biases = av_malloc(conv->output_channels * sizeof(double));
-    if (!conv->biases){
-        return AVERROR(ENOMEM);
-    }
-    for (i = 0; i < conv->output_channels; ++i){
-        conv->biases[i] = av_int2double(avio_rl64(config_file_context));
-    }
-
-    return 0;
-}
-
-static int allocate_copy_conv_data(Convolution* conv, const double* kernel, const double* biases)
-{
-    int32_t kernel_size = conv->output_channels * conv->size * conv->size * conv->input_channels;
-
-    conv->kernel = av_malloc(kernel_size * sizeof(double));
-    if (!conv->kernel){
-        return AVERROR(ENOMEM);
-    }
-    memcpy(conv->kernel, kernel, kernel_size * sizeof(double));
-
-    conv->biases = av_malloc(conv->output_channels * sizeof(double));
-    if (!conv->kernel){
-        return AVERROR(ENOMEM);
-    }
-    memcpy(conv->biases, biases, conv->output_channels * sizeof(double));
-
-    return 0;
-}
-
 static av_cold int init(AVFilterContext* context)
 {
-    SRCNNContext *srcnn_context = context->priv;
-    AVIOContext* config_file_context;
-    int64_t file_size, srcnn_size;
+    SRCNNContext* srcnn_context = context->priv;
 
-    /// Check specified confguration file name and read network weights from it
-    if (!srcnn_context->config_file_path){
-        av_log(context, AV_LOG_INFO, "configuration file for network was not specified, using default weights for x2 upsampling\n");
-
-        /// Create convolution kernels and copy default weights
-        srcnn_context->conv1.input_channels = 1;
-        srcnn_context->conv1.output_channels = 64;
-        srcnn_context->conv1.size = 9;
-        CHECK_ALLOCATION(allocate_copy_conv_data(&srcnn_context->conv1, conv1_kernel, conv1_biases), )
-
-        srcnn_context->conv2.input_channels = 64;
-        srcnn_context->conv2.output_channels = 32;
-        srcnn_context->conv2.size = 1;
-        CHECK_ALLOCATION(allocate_copy_conv_data(&srcnn_context->conv2, conv2_kernel, conv2_biases), )
-
-        srcnn_context->conv3.input_channels = 32;
-        srcnn_context->conv3.output_channels = 1;
-        srcnn_context->conv3.size = 5;
-        CHECK_ALLOCATION(allocate_copy_conv_data(&srcnn_context->conv3, conv3_kernel, conv3_biases), )
+    srcnn_context->dnn_module = ff_get_dnn_module(DNN_NATIVE);
+    if (!srcnn_context->dnn_module){
+        av_log(context, AV_LOG_ERROR, "could not create dnn module\n");
+        return AVERROR(ENOMEM);
     }
-    else if (avio_check(srcnn_context->config_file_path, AVIO_FLAG_READ) > 0){
-        if (avio_open(&config_file_context, srcnn_context->config_file_path, AVIO_FLAG_READ) < 0){
-            av_log(context, AV_LOG_ERROR, "failed to open configuration file\n");
-            return AVERROR(EIO);
-        }
-
-        file_size = avio_size(config_file_context);
-
-        /// Create convolution kernels and read weights from file
-        srcnn_context->conv1.input_channels = 1;
-        srcnn_context->conv1.size = (int32_t)avio_rl32(config_file_context);
-        srcnn_context->conv1.output_channels = (int32_t)avio_rl32(config_file_context);
-        srcnn_size = 8 + (srcnn_context->conv1.output_channels * srcnn_context->conv1.size *
-                          srcnn_context->conv1.size * srcnn_context->conv1.input_channels +
-                          srcnn_context->conv1.output_channels << 3);
-        CHECK_FILE_SIZE(file_size, srcnn_size, config_file_context)
-        CHECK_ALLOCATION(allocate_read_conv_data(&srcnn_context->conv1, config_file_context), avio_closep(&config_file_context))
-
-        srcnn_context->conv2.input_channels = (int32_t)avio_rl32(config_file_context);
-        srcnn_context->conv2.size = (int32_t)avio_rl32(config_file_context);
-        srcnn_context->conv2.output_channels = (int32_t)avio_rl32(config_file_context);
-        srcnn_size += 12 + (srcnn_context->conv2.output_channels * srcnn_context->conv2.size *
-                            srcnn_context->conv2.size * srcnn_context->conv2.input_channels +
-                            srcnn_context->conv2.output_channels << 3);
-        CHECK_FILE_SIZE(file_size, srcnn_size, config_file_context)
-        CHECK_ALLOCATION(allocate_read_conv_data(&srcnn_context->conv2, config_file_context), avio_closep(&config_file_context))
-
-        srcnn_context->conv3.input_channels = (int32_t)avio_rl32(config_file_context);
-        srcnn_context->conv3.size = (int32_t)avio_rl32(config_file_context);
-        srcnn_context->conv3.output_channels = 1;
-        srcnn_size += 8 + (srcnn_context->conv3.output_channels * srcnn_context->conv3.size *
-                           srcnn_context->conv3.size * srcnn_context->conv3.input_channels
-                           + srcnn_context->conv3.output_channels << 3);
-        if (file_size != srcnn_size){
-            av_log(context, AV_LOG_ERROR, "error reading configuration file\n");
-            avio_closep(&config_file_context);
-            return AVERROR(EIO);
-        }
-        CHECK_ALLOCATION(allocate_read_conv_data(&srcnn_context->conv3, config_file_context), avio_closep(&config_file_context))
-
-        avio_closep(&config_file_context);
+    if (!srcnn_context->model_filename){
+        av_log(context, AV_LOG_INFO, "model file for network was not specified, using default network for x2 upsampling\n");
+        srcnn_context->model = (srcnn_context->dnn_module->load_default_model)(DNN_SRCNN);
     }
     else{
-        av_log(context, AV_LOG_ERROR, "specified configuration file does not exist or not readable\n");
+        srcnn_context->model = (srcnn_context->dnn_module->load_model)(srcnn_context->model_filename);
+    }
+    if (!srcnn_context->model){
+        av_log(context, AV_LOG_ERROR, "could not load dnn model\n");
         return AVERROR(EIO);
     }
 
@@ -197,7 +80,7 @@ static int query_formats(AVFilterContext* context)
     const enum AVPixelFormat pixel_formats[] = {AV_PIX_FMT_YUV420P, AV_PIX_FMT_YUV422P, AV_PIX_FMT_YUV444P,
                                                 AV_PIX_FMT_YUV410P, AV_PIX_FMT_YUV411P, AV_PIX_FMT_GRAY8,
                                                 AV_PIX_FMT_NONE};
-    AVFilterFormats *formats_list;
+    AVFilterFormats* formats_list;
 
     formats_list = ff_make_format_list(pixel_formats);
     if (!formats_list){
@@ -209,28 +92,29 @@ static int query_formats(AVFilterContext* context)
 
 static int config_props(AVFilterLink* inlink)
 {
-    AVFilterContext *context = inlink->dst;
-    SRCNNContext *srcnn_context = context->priv;
-    int min_dim;
+    AVFilterContext* context = inlink->dst;
+    SRCNNContext* srcnn_context = context->priv;
+    DNNReturnType result;
 
-    /// Check if input data width or height is too low
-    min_dim = FFMIN(inlink->w, inlink->h);
-    if (min_dim <= srcnn_context->conv1.size >> 1 || min_dim <= srcnn_context->conv2.size >> 1 || min_dim <= srcnn_context->conv3.size >> 1){
-        av_log(context, AV_LOG_ERROR, "input width or height is too low\n");
-        return AVERROR(EIO);
-    }
-
-    /// Allocate network buffers
-    srcnn_context->input_output_buf = av_malloc(inlink->h * inlink->w * sizeof(double));
-    srcnn_context->conv1_buf = av_malloc(inlink->h * inlink->w * srcnn_context->conv1.output_channels * sizeof(double));
-    srcnn_context->conv2_buf = av_malloc(inlink->h * inlink->w * srcnn_context->conv2.output_channels * sizeof(double));
-
-    if (!srcnn_context->input_output_buf || !srcnn_context->conv1_buf || !srcnn_context->conv2_buf){
-        av_log(context, AV_LOG_ERROR, "could not allocate memory for srcnn buffers\n");
+    srcnn_context->input_output_buf = av_malloc(inlink->h * inlink->w * sizeof(float));
+    if (!srcnn_context->input_output_buf){
+        av_log(context, AV_LOG_ERROR, "could not allocate memory for input/output buffer\n");
         return AVERROR(ENOMEM);
     }
 
+    srcnn_context->input_output.data = srcnn_context->input_output_buf;
+    srcnn_context->input_output.width = inlink->w;
+    srcnn_context->input_output.height = inlink->h;
+    srcnn_context->input_output.channels = 1;
+
+    result = (srcnn_context->model->set_input_output)(srcnn_context->model->model, &srcnn_context->input_output, &srcnn_context->input_output);
+    if (result != DNN_SUCCESS){
+        av_log(context, AV_LOG_ERROR, "could not set input and output for the model\n");
+        return AVERROR(EIO);
+    }
+    else{
         return 0;
+    }
 }
 
 typedef struct ThreadData{
@@ -238,28 +122,19 @@ typedef struct ThreadData{
     int out_linesize, height, width;
 } ThreadData;
 
-typedef struct ConvThreadData
-{
-    const Convolution* conv;
-    const double* input;
-    double* output;
-    int height, width;
-} ConvThreadData;
-
-/// Convert uint8 data to double and scale it to use in network
-static int uint8_to_double(AVFilterContext* context, void* arg, int jobnr, int nb_jobs)
+static int uint8_to_float(AVFilterContext* context, void* arg, int jobnr, int nb_jobs)
 {
     SRCNNContext* srcnn_context = context->priv;
     const ThreadData* td = arg;
     const int slice_start = (td->height *  jobnr     ) / nb_jobs;
     const int slice_end   = (td->height * (jobnr + 1)) / nb_jobs;
     const uint8_t* src = td->out + slice_start * td->out_linesize;
-    double* dst = srcnn_context->input_output_buf + slice_start * td->width;
+    float* dst = srcnn_context->input_output_buf + slice_start * td->width;
     int y, x;
 
     for (y = slice_start; y < slice_end; ++y){
         for (x = 0; x < td->width; ++x){
-            dst[x] = (double)src[x] / 255.0;
+            dst[x] = (float)src[x] / 255.0f;
         }
         src += td->out_linesize;
         dst += td->width;
@@ -268,20 +143,19 @@ static int uint8_to_double(AVFilterContext* context, void* arg, int jobnr, int n
     return 0;
 }
 
-/// Convert double data from network to uint8 and scale it to output as filter result
-static int double_to_uint8(AVFilterContext* context, void* arg, int jobnr, int nb_jobs)
+static int float_to_uint8(AVFilterContext* context, void* arg, int jobnr, int nb_jobs)
 {
     SRCNNContext* srcnn_context = context->priv;
     const ThreadData* td = arg;
     const int slice_start = (td->height *  jobnr     ) / nb_jobs;
     const int slice_end   = (td->height * (jobnr + 1)) / nb_jobs;
-    const double* src = srcnn_context->input_output_buf + slice_start * td->width;
+    const float* src = srcnn_context->input_output_buf + slice_start * td->width;
     uint8_t* dst = td->out + slice_start * td->out_linesize;
     int y, x;
 
     for (y = slice_start; y < slice_end; ++y){
         for (x = 0; x < td->width; ++x){
-            dst[x] = (uint8_t)(255.0 * FFMIN(src[x], 1.0));
+            dst[x] = (uint8_t)(255.0f * FFMIN(src[x], 1.0f));
         }
         src += td->width;
         dst += td->out_linesize;
@@ -290,45 +164,6 @@ static int double_to_uint8(AVFilterContext* context, void* arg, int jobnr, int n
     return 0;
 }
 
-#define CLAMP_TO_EDGE(x, w) ((x) < 0 ? 0 : ((x) >= (w) ? (w - 1) : (x)))
-
-static int convolve(AVFilterContext* context, void* arg, int jobnr, int nb_jobs)
-{
-    const ConvThreadData* td = arg;
-    const int slice_start = (td->height *  jobnr     ) / nb_jobs;
-    const int slice_end   = (td->height * (jobnr + 1)) / nb_jobs;
-    const double* src = td->input;
-    double* dst = td->output + slice_start * td->width * td->conv->output_channels;
-    int y, x;
-    int32_t n_filter, ch, kernel_y, kernel_x;
-    int32_t radius = td->conv->size >> 1;
-    int src_linesize = td->width * td->conv->input_channels;
-    int filter_linesize = td->conv->size * td->conv->input_channels;
-    int filter_size = td->conv->size * filter_linesize;
-
-    for (y = slice_start; y < slice_end; ++y){
-        for (x = 0; x < td->width; ++x){
-            for (n_filter = 0; n_filter < td->conv->output_channels; ++n_filter){
-                dst[n_filter] = td->conv->biases[n_filter];
-                for (ch = 0; ch < td->conv->input_channels; ++ch){
-                    for (kernel_y = 0; kernel_y < td->conv->size; ++kernel_y){
-                        for (kernel_x = 0; kernel_x < td->conv->size; ++kernel_x){
-                            dst[n_filter] += src[CLAMP_TO_EDGE(y + kernel_y - radius, td->height) * src_linesize +
-                                                 CLAMP_TO_EDGE(x + kernel_x - radius, td->width) * td->conv->input_channels + ch] *
-                                             td->conv->kernel[n_filter * filter_size + kernel_y * filter_linesize +
-                                                              kernel_x * td->conv->input_channels + ch];
-                        }
-                    }
-                }
-                dst[n_filter] = FFMAX(dst[n_filter], 0.0);
-            }
-            dst += td->conv->output_channels;
-        }
-    }
-
-    return 0;
-}
-
 static int filter_frame(AVFilterLink* inlink, AVFrame* in)
 {
     AVFilterContext* context = inlink->dst;
@@ -336,8 +171,8 @@ static int filter_frame(AVFilterLink* inlink, AVFrame* in)
     AVFilterLink* outlink = context->outputs[0];
     AVFrame* out = ff_get_video_buffer(outlink, outlink->w, outlink->h);
     ThreadData td;
-    ConvThreadData ctd;
     int nb_threads;
+    DNNReturnType dnn_result;
 
     if (!out){
         av_log(context, AV_LOG_ERROR, "could not allocate memory for output frame\n");
@@ -349,24 +184,19 @@ static int filter_frame(AVFilterLink* inlink, AVFrame* in)
     av_frame_free(&in);
     td.out = out->data[0];
     td.out_linesize = out->linesize[0];
-    td.height = ctd.height = out->height;
-    td.width = ctd.width = out->width;
+    td.height = out->height;
+    td.width = out->width;
 
     nb_threads = ff_filter_get_nb_threads(context);
-    context->internal->execute(context, uint8_to_double, &td, NULL, FFMIN(td.height, nb_threads));
-    ctd.conv = &srcnn_context->conv1;
-    ctd.input = srcnn_context->input_output_buf;
-    ctd.output = srcnn_context->conv1_buf;
-    context->internal->execute(context, convolve, &ctd, NULL, FFMIN(ctd.height, nb_threads));
-    ctd.conv = &srcnn_context->conv2;
-    ctd.input = srcnn_context->conv1_buf;
-    ctd.output = srcnn_context->conv2_buf;
-    context->internal->execute(context, convolve, &ctd, NULL, FFMIN(ctd.height, nb_threads));
-    ctd.conv = &srcnn_context->conv3;
-    ctd.input = srcnn_context->conv2_buf;
-    ctd.output = srcnn_context->input_output_buf;
-    context->internal->execute(context, convolve, &ctd, NULL, FFMIN(ctd.height, nb_threads));
-    context->internal->execute(context, double_to_uint8, &td, NULL, FFMIN(td.height, nb_threads));
+    context->internal->execute(context, uint8_to_float, &td, NULL, FFMIN(td.height, nb_threads));
+
+    dnn_result = (srcnn_context->dnn_module->execute_model)(srcnn_context->model);
+    if (dnn_result != DNN_SUCCESS){
+        av_log(context, AV_LOG_ERROR, "failed to execute loaded model\n");
+        return AVERROR(EIO);
+    }
+
+    context->internal->execute(context, float_to_uint8, &td, NULL, FFMIN(td.height, nb_threads));
 
     return ff_filter_frame(outlink, out);
 }
@@ -375,18 +205,11 @@ static av_cold void uninit(AVFilterContext* context)
 {
     SRCNNContext* srcnn_context = context->priv;
 
-    /// Free convolution data
-    av_freep(&srcnn_context->conv1.kernel);
-    av_freep(&srcnn_context->conv1.biases);
-    av_freep(&srcnn_context->conv2.kernel);
-    av_freep(&srcnn_context->conv2.biases);
-    av_freep(&srcnn_context->conv3.kernel);
-    av_freep(&srcnn_context->conv3.kernel);
-
-    /// Free network buffers
+    if (srcnn_context->dnn_module){
+        (srcnn_context->dnn_module->free_model)(&srcnn_context->model);
+        av_freep(&srcnn_context->dnn_module);
+    }
     av_freep(&srcnn_context->input_output_buf);
-    av_freep(&srcnn_context->conv1_buf);
-    av_freep(&srcnn_context->conv2_buf);
 }
 
 static const AVFilterPad srcnn_inputs[] = {
@@ -419,3 +242,4 @@ AVFilter ff_vf_srcnn = {
     .priv_class    = &srcnn_class,
     .flags         = AVFILTER_FLAG_SUPPORT_TIMELINE_GENERIC | AVFILTER_FLAG_SLICE_THREADS,
 };
+

libavfilter/vf_srcnn.h

@@ -1,855 +0,0 @@
-/*
- * 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
- * Default cnn weights for x2 upsampling with srcnn filter.
- */
-#ifndef AVFILTER_SRCNN_H
-#define AVFILTER_SRCNN_H
-
-/// First convolution kernel
-static const double conv1_kernel[] = {
--0.08866337686777115, 0.055409565567970276, 0.037196505814790726, -0.11961404234170914, -0.12341991066932678, 0.2996342182159424, -0.09118170291185379, -0.00013613555347546935, -0.049023594707250595,
-0.038421183824539185, -0.07726779580116272, 0.027273094281554222, 0.45762088894844055, 0.023581644520163536, -0.20363852381706238, -0.07738441973924637, 0.07288403809070587, 0.06679897010326385,
--0.07315085083246231, 0.2665306031703949, 0.07836370915174484, -0.2898528277873993, -0.506912887096405, 0.0446651317179203, -0.06246858835220337, -0.024424826726317406, -0.029072929173707962,
--0.16884787380695343, 0.20686236023902893, -0.2536700665950775, -0.38499701023101807, -0.12601901590824127, 0.7257882356643677, 0.09588377922773361, -0.04487278312444687, -0.011251595802605152,
--0.02955852635204792, 0.2211706042289734, -0.3845316469669342, -0.053509537130594254, 0.10955915600061417, 0.3725738823413849, -0.28968045115470886, -0.10883618891239166, 0.19628790020942688,
-0.09746792912483215, -0.005647096782922745, -0.13035884499549866, 0.6254252195358276, 0.19104234874248505, -0.011692332103848457, -0.27067890763282776, -0.030754156410694122, 0.029875505715608597,
-0.016808750107884407, -0.1831790804862976, -0.16016070544719696, 0.3598291277885437, -0.1889011114835739, -0.22343340516090393, -0.11111768335103989, 0.3221142590045929, -0.18514074385166168,
--0.06647012382745743, 0.1800554096698761, -0.0356597937643528, -0.25352174043655396, -0.18370479345321655, 0.2839542329311371, 0.0030237233731895685, 0.24072711169719696, -0.1435547173023224,
-0.0474187433719635, -0.03221237286925316, -0.08801338076591492, 0.2959175109863281, 0.04108741134405136, -0.103544220328331, -0.11681117117404938, -0.010267493315041065, 0.023703521117568016,
-
--0.043954458087682724, 0.0953388437628746, 0.12233797460794449, -0.0521148182451725, 0.068812295794487, 0.0576486811041832, 0.08686650544404984, -0.12001238018274307, 0.02353638783097267,
--0.022270673885941505, -0.06377912312746048, 0.010644146241247654, -0.27824145555496216, -0.23719865083694458, -0.21418367326259613, -0.09665020555257797, 0.10458534955978394, 0.06599418818950653,
--0.031174281612038612, -0.03465353325009346, 0.022143816575407982, -0.24322354793548584, -0.08923598378896713, -0.02724810689687729, -0.09314896911382675, -0.03740973770618439, 0.07819665223360062,
-0.12521375715732574, 0.11189836263656616, 0.34981977939605713, 0.24303556978702545, 0.21350803971290588, 0.3712073266506195, 0.12192775309085846, -0.15492133796215057, 0.01884031854569912,
--0.08351074159145355, -0.305499792098999, -0.11462165415287018, -0.11833594739437103, -0.35027629137039185, -0.007749142590910196, -0.05184945464134216, -0.14423955976963043, -0.04047594219446182,
-0.18357796967029572, 0.16708122193813324, 0.0748625323176384, 0.17892862856388092, 0.12331069260835648, 0.16825254261493683, -0.040668703615665436, 0.12498869001865387, 0.12445469200611115,
--0.08529794961214066, -0.23087382316589355, -0.2426449954509735, -0.003364269156008959, -0.019013511016964912, 0.007406715769320726, -0.09762480109930038, 0.09727375954389572, -0.14308449625968933,
-0.20924511551856995, 0.047315631061792374, 0.17839451134204865, 0.20142769813537598, -0.2138431817293167, -0.16858182847499847, 0.07769212126731873, 0.11478294432163239, 0.002249526558443904,
--0.09524660557508469, -0.11904185265302658, -0.1190405860543251, 0.1193549856543541, 0.040786921977996826, -0.07873278856277466, 0.19328512251377106, -0.06008544936776161, -0.03619224950671196,
-
--0.03508936986327171, -0.02129603736102581, 0.04467615857720375, 0.0351480171084404, 0.047732554376125336, -0.036008983850479126, -0.09819480776786804, -0.07464613020420074, 0.16331301629543304,
-0.05766148120164871, -0.06068359687924385, 0.023710887879133224, 0.04077879339456558, 0.0195282194763422, -0.04709528759121895, -0.0026468061842024326, 0.01308137271553278, -0.04843336343765259,
--0.05547124147415161, -0.007511544041335583, -0.0018498365534469485, 0.006324829533696175, 0.08334493637084961, -0.03534594178199768, -0.05873340740799904, -0.11714734137058258, -0.049685098230838776,
--0.01065769698470831, 0.08840381354093552, 0.06828710436820984, 0.020970111712813377, 0.2620500922203064, 0.11938150972127914, 0.04214100167155266, -0.0640568658709526, -0.04790668934583664,
-0.05482594668865204, 0.07402177155017853, 0.21512433886528015, 0.25780582427978516, 0.42716339230537415, 0.18216213583946228, 0.12531153857707977, 0.10390171408653259, -0.010482890531420708,
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-0.21901138126850128, -0.035849299281835556, -0.08650030195713043, -0.018982043489813805, 0.0695781335234642, 0.4145001173019409, -0.027209550142288208, -0.004038578364998102, 0.06541971862316132,
-0.12227083742618561, -0.1073189526796341, -0.0841955840587616, -0.15702803432941437, -0.7429218888282776, -0.04395433887839317, -0.011260250583291054, 0.10814431309700012, -0.28268298506736755,
--0.12526658177375793, -0.026223644614219666, 0.08096180856227875, 0.6802259683609009, 0.13561013340950012, 0.08618369698524475, -0.0501398928463459, 0.35771113634109497, -0.041319262236356735,
-0.27181851863861084, -0.23890241980552673, -0.5387949347496033, 0.21392858028411865, 0.07953313738107681, 0.019276024773716927, -0.5713126063346863, -0.037591978907585144, 0.1565358191728592,
-0.15565475821495056, 0.00460081035271287, -0.0456286296248436, 0.10213345289230347, 0.17657290399074554, 0.6814069747924805, -0.13053438067436218, -0.06998789310455322, 0.003884716657921672,
--0.44260460138320923, 0.28802698850631714, 0.2503567337989807, -0.3661724030971527, -0.6118166446685791, 0.052883099764585495, -0.009912409819662571, -0.022988222539424896, 0.010322963818907738,
--0.021636782214045525, 0.05146769434213638, 0.11953992396593094, 0.3151896297931671, -0.035797424614429474, -0.17633333802223206, 0.014319990761578083, -0.09696750342845917, 0.19342084228992462,
-0.08160614222288132, -0.08066563308238983, -0.1464836746454239, -0.13353408873081207, 0.15164321660995483, 0.05195532366633415, 0.0950581356883049, -0.02673575095832348, -0.08893080055713654,
-
-0.08918213844299316, 0.08657902479171753, 0.005215317010879517, -0.006473506335169077, 0.04260111600160599, 0.016859546303749084, -0.013135830871760845, -0.05732234939932823, -0.06841449439525604,
--0.08908746391534805, -0.015611836686730385, -0.061792485415935516, -0.07461176067590714, -0.03767390176653862, -0.06813358515501022, 0.008849180303514004, 0.034659937024116516, -0.11153175681829453,
--0.018928145989775658, 0.12819842994213104, 0.03857557848095894, 0.01105338055640459, 0.08118841797113419, -0.020579038187861443, -0.016684940084815025, -0.010985679924488068, 0.035327933728694916,
-0.06822128593921661, 0.11152952909469604, 0.013529916293919086, -0.016081996262073517, 0.18805059790611267, 0.0926079973578453, 0.032322220504283905, -0.07684376835823059, -0.0065249307081103325,
-0.018553031608462334, 0.022786445915699005, 0.026319757103919983, 0.05849913880228996, 0.206848606467247, 0.08509713411331177, 0.09237775206565857, 0.03649907559156418, 0.0662459060549736,
--0.08157840371131897, 0.028796279802918434, -0.022867100313305855, -0.04078856483101845, 0.027245769277215004, -0.1352597326040268, 0.003301734570413828, 0.036037247627973557, -0.001803244580514729,
--0.021063758060336113, 0.09868303686380386, -0.004615786485373974, 0.06155001372098923, 0.1704898625612259, -0.009244422428309917, 0.04022917151451111, 0.00843940768390894, 0.017657065764069557,
-1.8606752973937546e-06, 0.022133613005280495, -0.02020549401640892, 0.07613987475633621, 0.07413829118013382, -0.038554999977350235, 0.006358420941978693, -0.03960535675287247, 0.005950190592557192,
--0.08430261164903641, 0.0006188381812535226, -0.002441024174913764, 0.08848552405834198, 0.06897841393947601, -0.03736359626054764, 0.0491594560444355, 0.08249298483133316, 0.0369696244597435,
-
--0.002494501881301403, -0.07609808444976807, 0.1119963526725769, -0.10137500613927841, 0.12085500359535217, 0.020787041634321213, -0.08020990341901779, -0.07437735795974731, -0.016652707010507584,
-0.1702006757259369, -0.0884813442826271, 0.04040154069662094, 0.04366397485136986, -0.04981914907693863, -0.10109864920377731, 0.18095622956752777, 0.07635517418384552, 0.16501443088054657,
-0.04764076694846153, -0.29989609122276306, -0.10478614270687103, -0.1188390702009201, 0.02361522614955902, -0.15416087210178375, 0.04873557388782501, 0.04563599079847336, -0.35214775800704956,
-0.24584366381168365, -0.10939999669790268, 0.23839618265628815, 0.12754511833190918, 0.20347249507904053, -0.0935775637626648, 0.0144122913479805, 0.2616703510284424, 0.05943511798977852,
-0.0567740872502327, -0.03670964017510414, 0.07175207138061523, -0.1313132643699646, -0.1811451017856598, -0.24757787585258484, 0.05534401535987854, -0.11349410563707352, -0.1484820395708084,
--0.18046405911445618, 0.12988997995853424, 0.10476439446210861, -0.1005360409617424, 0.012329879216849804, 0.251142293214798, 0.3151717782020569, -0.15847274661064148, 0.3126358389854431,
--0.2669127285480499, -0.010338036343455315, 0.1929193139076233, -0.09310273081064224, -0.03839711472392082, 0.058026671409606934, -0.1498262733221054, -0.320158451795578, 0.24430842697620392,
-0.24325047433376312, 0.055703017860651016, 0.04047559201717377, -0.11337616294622421, 0.03028351254761219, -0.1718759685754776, -0.0747850239276886, -0.058253467082977295, 0.045693881809711456,
--0.12651756405830383, 0.0029598521068692207, -0.0746246948838234, 0.04430631548166275, 0.15831680595874786, -0.06299256533384323, 0.20761297643184662, -0.18382751941680908, 0.04795093461871147,
-
-0.03453255072236061, 0.08143798261880875, -0.07827461510896683, -0.008095886558294296, 0.09633331745862961, -0.032731134444475174, 0.03896036744117737, 0.07796624302864075, -0.09800060838460922,
--0.17390114068984985, 0.013836466707289219, -0.0955653116106987, -0.06489729881286621, -0.06499230861663818, -0.12440793216228485, 0.07461445778608322, 0.16298134624958038, -0.14313311874866486,
--0.02622191607952118, 0.17264506220817566, 0.01862080581486225, 0.041154250502586365, 0.057074639946222305, -0.06986492872238159, -0.011844002641737461, 0.03608855977654457, 0.004727501422166824,
-0.054055534303188324, 0.06472594290971756, -0.07746946066617966, -0.04556572064757347, 0.1383574903011322, 0.006478244438767433, -0.03284274414181709, -0.10053438693284988, -0.030013108626008034,
-0.08107316493988037, 0.05337893217802048, 0.016869626939296722, 0.12709975242614746, 0.2540443539619446, 0.09076768904924393, 0.07549874484539032, -0.013449955731630325, 0.05161766707897186,
--0.049904003739356995, 0.11237815767526627, -0.004943950101733208, 0.03591571003198624, 0.05699837952852249, -0.14426246285438538, -0.014709394425153732, -0.04324657842516899, -0.09033934026956558,
--0.006868680473417044, 0.17643220722675323, -0.01222564373165369, 0.07162804156541824, 0.1575816571712494, -0.031030887737870216, 0.05114923045039177, 0.04688235744833946, 0.004272202495485544,
-0.045018624514341354, 0.09796737879514694, -0.05488642305135727, -0.004248837940394878, 0.025244703516364098, -0.06603638082742691, 0.026735050603747368, 0.06231154873967171, 0.045271750539541245,
--0.031672630459070206, 0.13647104799747467, -0.0009657645132392645, 0.018488561734557152, 0.041390109807252884, -0.10096877068281174, -0.009352818131446838, 0.0745505690574646, 0.02882414497435093,
-
--0.133609339594841, -0.03819651901721954, 0.001151051837950945, 0.019290350377559662, 0.047142498195171356, 0.031506381928920746, 0.06336957216262817, 0.032302893698215485, 0.006281350739300251,
--0.03740052878856659, 0.050428763031959534, 0.08101901412010193, 0.07232176512479782, 0.01183471642434597, -0.04791678488254547, -0.019763002172112465, -0.006771337240934372, 0.027429305016994476,
--0.005318525247275829, 0.03478771448135376, 0.007166128605604172, -0.008718438446521759, -0.012047640979290009, -0.06000327318906784, -0.10047216713428497, -0.09047075361013412, 0.006131005007773638,
--0.018347835168242455, 0.03032604046165943, -0.009940189309418201, -0.026144159957766533, 0.0716538354754448, 0.03843644633889198, -0.03604808822274208, -0.02366860769689083, -0.015906482934951782,
-0.03831234574317932, 0.07807328552007675, 0.08553636074066162, 0.09884383529424667, 0.12573787569999695, 0.04602298140525818, 0.004538014996796846, 0.07166740298271179, 0.01014549657702446,
-0.05794878304004669, 0.02658366598188877, 0.036963190883398056, 0.07439740002155304, 0.07202591001987457, -0.0015225837705656886, 0.017082151025533676, 0.08737485110759735, -0.005794626194983721,
-0.026332100853323936, -0.055387429893016815, -0.10465177893638611, -0.0627688392996788, 0.027715779840946198, 0.017957618460059166, -0.0010449641849845648, 0.013926751911640167, -0.0266911368817091,
--0.022419702261686325, -0.045605115592479706, -0.09064694494009018, -0.06312739849090576, 0.036068208515644073, 0.06525775790214539, 0.019389934837818146, 0.006056784652173519, 0.0026107390876859426,
--0.07533064484596252, -0.004264597315341234, -0.05940620228648186, -0.04531720280647278, 0.01771579310297966, 0.01073805894702673, 0.02580205351114273, 0.061774905771017075, 0.049093276262283325,
-
-0.06254594027996063, -0.08440928906202316, -0.05960290879011154, -0.21179302036762238, -0.03749246150255203, 0.1320458948612213, 0.02225368283689022, -0.20560918748378754, 0.09833671897649765,
--0.010864592157304287, 0.1312112659215927, 0.2729310095310211, 0.2852783203125, 0.036214154213666916, -0.17419104278087616, 0.10605451464653015, 0.11146654188632965, -0.05312469229102135,
--0.034706000238657, -0.40110114216804504, -0.17413057386875153, -0.002180312993004918, -0.023289639502763748, -0.014259926974773407, -0.04321160912513733, -0.1015881896018982, -0.0019524767994880676,
-0.25281256437301636, 0.1888352781534195, 0.2182304710149765, -0.07753387838602066, -0.1263069063425064, 0.20391452312469482, 0.17332755029201508, -0.05573255568742752, 0.08542720973491669,
--0.27624428272247314, -0.0939950942993164, -0.34804075956344604, -0.18735791742801666, -0.3288903534412384, -0.24964070320129395, 0.023052500560879707, -0.0032602131832391024, -0.09734013676643372,
-0.24850279092788696, 0.4498615860939026, 0.2392505258321762, 0.7578790783882141, 0.4650757908821106, -0.09640157222747803, 0.030361128970980644, 0.13835501670837402, 0.03452449291944504,
--0.491862952709198, -0.3327092230319977, -0.115897998213768, 0.08189888298511505, 0.14717335999011993, 0.09036838263273239, -0.1914467215538025, -0.1619413048028946, 0.07546339184045792,
-0.4778578579425812, 0.03341726213693619, -0.2200295627117157, -0.6558915376663208, -0.49498724937438965, 0.2652202248573303, 0.30143415927886963, -0.004251368809491396, -0.011420469731092453,
--0.1778731644153595, 0.03058483451604843, 0.15715107321739197, 0.23354242742061615, 0.017105156555771828, -0.19644701480865479, 0.014484168030321598, -0.17201600968837738, 0.07568902522325516,
-
-0.028962446376681328, -0.06878253817558289, 0.05019852891564369, 0.05476095899939537, -0.04770945757627487, -0.1367853879928589, -0.16251084208488464, -0.056205764412879944, 0.09791768342256546,
-0.16366638243198395, -0.12979918718338013, 0.002729258267208934, 0.03414570540189743, 0.06860813498497009, 0.06334712356328964, 0.04867345467209816, 0.07738835364580154, -0.003124517621472478,
--0.07452563941478729, -0.20245549082756042, 0.027065815404057503, -0.0021526585333049297, 0.051124200224876404, 0.1550229787826538, 0.14651484787464142, 0.0758848711848259, -0.10572951287031174,
--0.06748813390731812, -0.1996242254972458, 0.047433897852897644, -0.011952214874327183, -0.02098195068538189, 0.1696663796901703, 0.14325915277004242, 0.026020854711532593, -0.0003333611530251801,
-0.06684102863073349, -0.1179869994521141, 0.08975568413734436, 0.027904972434043884, 0.07524381577968597, 0.2602478861808777, 0.16163015365600586, 0.005560749676078558, 0.018227512016892433,
-0.029731912538409233, -0.07488886266946793, 0.1038120687007904, 0.0014797549229115248, 0.052478183060884476, 0.1951654702425003, 0.0512106716632843, -0.07954861223697662, -0.05595332756638527,
--0.10098253190517426, -0.04282565042376518, 0.12107565999031067, 0.015420041978359222, -0.02609303407371044, 0.09977199882268906, 0.0654386356472969, -0.05860965698957443, -0.04621657729148865,
--0.024240786209702492, 0.003574902657419443, 0.09980470687150955, 0.041678234934806824, -0.047122664749622345, 0.01244963426142931, 0.02494264580309391, -0.03977813571691513, 0.08772413432598114,
-0.12657013535499573, -0.076152004301548, -0.0581919401884079, -0.09207551181316376, -0.05024847760796547, 0.08484440296888351, 0.011602652259171009, -0.06636865437030792, 0.07597231864929199,
-
-0.03471222519874573, 0.11924102157354355, -0.09907552599906921, 0.0718424990773201, -0.11197686940431595, 0.07318370789289474, 0.11031656712293625, -0.468844473361969, 0.09414266049861908,
--0.1416342854499817, 0.15163519978523254, -0.13412059843540192, 0.11831796914339066, 0.0874205157160759, 0.015127725899219513, 0.02830408699810505, 0.2779788076877594, 0.04213378205895424,
--0.11278911679983139, -0.019549937918782234, -0.1138615533709526, -0.0007093751919455826, -0.13948887586593628, 0.17623040080070496, 0.276247501373291, -0.2456439584493637, -0.16273942589759827,
-0.1998843401670456, 0.029753731563687325, 0.11769873648881912, -0.0947619378566742, -0.3595242202281952, 0.08280224353075027, 0.14039701223373413, -0.5054993033409119, 0.32306021451950073,
--0.09488756209611893, 0.18297874927520752, 0.021063625812530518, -0.10248441249132156, 0.31057217717170715, 0.49288955330848694, -0.06318224221467972, -0.5325169563293457, 0.016925616189837456,
--0.23778969049453735, 0.22520707547664642, -0.28116437792778015, -0.25012317299842834, 0.045585665851831436, 0.21047309041023254, 0.13688251376152039, -0.01049390621483326, 0.18622957170009613,
--0.06745558977127075, 0.05942220240831375, 0.07465878874063492, 0.17600689828395844, -0.24391280114650726, -0.1395273208618164, -0.14145755767822266, -0.22604353725910187, 0.0404188446700573,
-0.2511400878429413, -0.04146436229348183, 0.06866158545017242, 0.0936383605003357, -0.17196893692016602, 0.35007256269454956, 0.1466590315103531, -0.11697538942098618, -0.08733940124511719,
--0.1187233105301857, 0.06795468926429749, -0.19596466422080994, 0.09043551236391068, -0.07781103998422623, 0.06362947076559067, -0.04456653818488121, -0.13828079402446747, 0.1451389342546463,
-
-0.002784173935651779, 0.01319826114922762, 0.03299565985798836, -0.009061452932655811, -0.03782084956765175, 0.05905524268746376, 0.03665812313556671, -0.03293836489319801, 0.007655598223209381,
--0.02654659003019333, -0.051362812519073486, -0.019685762003064156, -0.04363385960459709, -0.033689528703689575, -0.04015159234404564, -0.030751371756196022, -0.02246842533349991, 0.018601812422275543,
-0.012158050201833248, 0.015349633991718292, 0.03493561968207359, -0.01330295018851757, 0.03794550150632858, -0.003826811444014311, -0.044383760541677475, -0.07613663375377655, 0.014979931525886059,
-0.0436096116900444, 0.06885120272636414, 0.09881002455949783, 0.031302209943532944, 0.12420085072517395, 0.15065008401870728, 0.07978155463933945, -0.027734771370887756, -0.05383369326591492,
--0.0678027868270874, 0.002182385651394725, 0.13103488087654114, 0.05609790235757828, 0.05619135499000549, 0.08134319633245468, 0.10918274521827698, 0.10301676392555237, -0.02375829964876175,
--0.08856120705604553, -0.03078402765095234, 0.08737516403198242, 0.013652728870511055, 0.01163883414119482, -0.00604214845225215, 0.056429434567689896, 0.10691829025745392, 0.041128937155008316,
--0.04340420290827751, -0.002507368801161647, 0.012957162223756313, 0.012340170331299305, 0.11861597746610641, 0.09244049340486526, 0.034744489938020706, -0.024895809590816498, 0.035870663821697235,
--0.040758680552244186, 0.013552304357290268, -0.009587780572474003, 0.01752828061580658, 0.07450903207063675, 0.0692441463470459, 0.0011354148155078292, -0.06966304779052734, 0.008092116564512253,
-0.02915072999894619, 0.04350604489445686, -0.03975748270750046, 0.014310178346931934, 0.0008435412310063839, -0.013759381137788296, 0.02635800465941429, 0.08823874592781067, 0.0821344256401062
-};
-
-/// First convolution biases
-static const double conv1_biases[] = {
--0.01660689152777195,
--0.011107334867119789,
--0.00483096856623888,
--0.048673778772354126,
--0.030040957033634186,
--0.07297247648239136,
--0.01945866458117962,
--0.009738028049468994,
-0.6951230764389038,
--0.07369442284107208,
--0.01354204025119543,
-0.010336088016629219,
--0.02956176921725273,
-0.018691286444664,
--0.02167515829205513,
-0.004088825546205044,
--0.08033090084791183,
--0.02080247923731804,
--0.00025044166250154376,
-0.015665635466575623,
--0.024439847096800804,
--0.00014609392383135855,
--5.004818376619369e-05,
--0.0011854040203616023,
--8.353819430340081e-05,
-0.0,
--0.00037626884295605123,
-0.02971580997109413,
--9.940328891389072e-05,
--0.020136557519435883,
--0.00029498152434825897,
-0.019285818561911583,
-0.005577716510742903,
--0.0700315311551094,
--0.024398552253842354,
--1.7726930309436284e-05,
-0.0050997124053537846,
--0.011211924254894257,
-0.004536128137260675,
--0.024612177163362503,
--0.0225357823073864,
--0.00016232267080340534,
--0.00017362962535116822,
--1.552602043375373e-05,
--0.06559696048498154,
--0.0065806107595562935,
-6.683926585537847e-06,
-0.0252289529889822,
--0.0013730665668845177,
-0.030351819470524788,
-0.0,
-0.003285621991381049,
-0.0055278814397752285,
-0.020766519010066986,
--0.00849062204360962,
-0.026332268491387367,
--0.04678987339138985,
-0.010329566895961761,
--0.023649320006370544,
-0.027851572260260582,
-0.05440746247768402,
--0.08068252354860306,
--0.009446502663195133,
--0.04663233831524849
-};
-
-/// Second convolution kernel
-static const double conv2_kernel[] = {
--0.24004751443862915, 0.10371380299329758, 0.11173403263092041, 0.04352091997861862, -0.2372848093509674, 0.1215374693274498, -0.23676058650016785, -0.28548064827919006, -0.6127380132675171, -0.12218937277793884, -0.06005159020423889, 0.18506519496440887, 0.16014674305915833, 0.13922938704490662, 0.2135942578315735, 0.10787945240736008, -0.26999104022979736, -0.14241325855255127, -0.0013871012488380075, -0.004951587878167629, 0.3402169644832611, 0.0005581695586442947, 4.7351106331916526e-05, 0.0037513396237045527, 0.0011569701600819826, 0.001654176157899201, -0.0011876021744683385, 0.049887992441654205, -7.201619882835075e-05, -0.17735491693019867, 0.0011244657216593623, -0.22381487488746643, 0.0035836962051689625, -0.3210694193840027, -0.16167263686656952, -0.0007922835648059845, 0.05056063085794449, 0.21074672043323517, 0.07716237753629684, -0.3315621614456177, 0.06265880912542343, 0.0002542850561439991, -0.0012989661190658808, 0.0004486947145778686, -0.41594547033309937, -0.019950004294514656, 0.0002566903131082654, 0.17913155257701874, -0.5950504541397095, 0.3259836733341217, 0.0013792524114251137, -0.08931709825992584, -0.13066786527633667, 0.21108511090278625, -0.05854453891515732, -0.11937838047742844, 0.08559372276067734, -0.11413642764091492, 0.28384047746658325, -0.011619674041867256, 0.32102328538894653, -0.20085231959819794, -0.34134379029273987, -0.029155191034078598,
-
--0.15286806225776672, -0.45100340247154236, -0.07077684253454208, 0.14408659934997559, 0.05953073874115944, -0.03657171502709389, 0.3870140314102173, -0.12762507796287537, 0.059950776398181915, 0.00540902791544795, 0.16427578032016754, -0.042501527816057205, 0.032962359488010406, -0.19972456991672516, -0.2558594048023224, -0.05479712411761284, -0.1335245817899704, 0.051931269466876984, -0.0005906695732846856, 0.14123854041099548, -0.06595169752836227, 0.0002593870449345559, 0.0004935731994919479, 0.0018757024081423879, -0.0004157331713940948, 0.0006128620007075369, -0.0008253702544607222, 0.00875023566186428, 0.000315613579005003, -0.031592708081007004, -0.0011055622017011046, 0.5397407412528992, 0.16837793588638306, 0.10016077756881714, -0.04458073899149895, -0.0008208212675526738, 0.14558495581150055, 0.05370355769991875, 0.3998671770095825, 0.3651335835456848, -6.220484647201374e-05, -0.0015507896896451712, 0.00019759316637646407, 0.001477571902796626, 0.17199119925498962, 0.20428548753261566, 0.0007169281598180532, -0.15589089691638947, 0.13173037767410278, 0.0430227592587471, 0.0005358970374800265, -0.14488573372364044, -0.056273844093084335, 0.04060659557580948, 0.12327120453119278, -0.0687263160943985, 0.1168481707572937, 0.20592212677001953, -0.07642778009176254, -0.10591034591197968, -0.2585831880569458, -0.234173983335495, 0.1204083263874054, -0.09154298901557922,
-
-0.1729278564453125, 0.12093151360750198, 0.12173106521368027, -0.17765463888645172, -0.03842559829354286, 0.1379096508026123, -0.018014583736658096, -0.00353326671756804, 0.052375391125679016, -0.013713608495891094, -0.023778876289725304, 0.08754374086856842, 0.009544276632368565, 0.002218334935605526, 0.005476833321154118, 0.14335989952087402, -0.30521509051322937, -0.027671312913298607, 0.0002961017016787082, -0.09087705612182617, 0.08001929521560669, 0.000289166287984699, 8.447665459243581e-05, 0.0032733778934925795, -0.0009773650672286749, 0.000969415414147079, 0.0023891173768788576, 0.1590563803911209, 0.00069234031252563, -0.23975391685962677, 0.0003552400739863515, -0.15277449786663055, 0.09689000993967056, -0.13403186202049255, -0.10830442607402802, -0.0011496500810608268, -0.0791272521018982, -0.04548552632331848, 0.07143749296665192, -0.11953869462013245, 0.06519463658332825, 0.0004770815430674702, -0.0012068727519363165, 0.00023571185010951012, -0.02185177616775036, -0.10972553491592407, -0.0004428153915796429, -0.06199837476015091, -0.02185823768377304, 0.13159608840942383, 0.0011464811395853758, -0.12141252309083939, -0.06273757666349411, 0.2553664743900299, 0.1002097874879837, 0.13120368123054504, 0.16516226530075073, -0.14927655458450317, 0.14413973689079285, 0.039363794028759, 0.19443942606449127, -0.01614764891564846, -0.0876186192035675, 0.08571985363960266,
-
--0.034732215106487274, 0.08880914747714996, 0.115323506295681, -0.2509199380874634, -0.18177348375320435, 0.11170994490385056, -0.06447608023881912, -0.04730312153697014, -0.13159127533435822, 0.01896033063530922, -0.2969268262386322, 0.31513679027557373, 0.016690406948328018, 0.0855814516544342, 0.01695072464644909, 0.1360795795917511, -0.008597755804657936, -0.15839214622974396, -0.0006356500089168549, 0.14604529738426208, 0.029391299933195114, -0.0021237179171293974, -0.0021662425715476274, 0.0011421579401940107, 0.00031981870415620506, -0.00031888089142739773, -0.0013892676215618849, 0.4037177860736847, 0.0010367054492235184, -0.08923333138227463, 0.001726223505102098, 0.04937814548611641, 0.03052854910492897, -0.10994968563318253, 0.041772034019231796, -0.0003509650705382228, -0.09172547608613968, -0.1121240183711052, 0.018248941749334335, -0.1032114252448082, 0.03656036779284477, -0.0008534444496035576, 0.0005108112818561494, 0.001955348066985607, -0.17280977964401245, -0.15745718777179718, 0.0016406632494181395, 0.10295743495225906, -0.056333065032958984, 0.12174493074417114, -0.00029968394665047526, -0.046295735985040665, -0.05203622952103615, 0.17915867269039154, 0.17138905823230743, 0.20149874687194824, 0.14662422239780426, -0.06499027460813522, 0.0864318460226059, 0.026575079187750816, -0.26568517088890076, -0.10079117864370346, -0.20195038616657257, 0.07289135456085205,
-
-0.20596843957901, 0.1484273374080658, 0.11689981073141098, -0.292439341545105, 0.02092818170785904, 0.14047744870185852, -0.032774657011032104, 0.013994990848004818, 0.04893071949481964, -0.0856017917394638, 0.07281561940908432, 0.0699431449174881, -0.006538144778460264, 0.08474163711071014, 0.013837413862347603, 0.10437792539596558, -0.32318544387817383, -0.033461928367614746, -0.0006234075990505517, -0.25232645869255066, 0.08134997636079788, -0.0012573037529364228, -0.00025874844868667424, 0.002164048608392477, 0.000704825681168586, 0.0008626107592135668, -0.0004994627670384943, 0.1802779585123062, 0.0005355331813916564, -0.3850158154964447, -0.000660755904391408, -0.15524353086948395, 0.0770384892821312, -0.1990944892168045, -0.04059717804193497, 0.00019839264859911054, -0.08954143524169922, -0.1745176762342453, 0.19342899322509766, -0.039949242025613785, -0.05351455509662628, 0.0010188281303271651, 0.0012863714946433902, -4.699235432781279e-05, 0.04647690802812576, -0.1538148671388626, -0.0009432003716938198, 0.06832418590784073, -0.07473438233137131, 0.19692155718803406, -0.00023191161744762212, -0.07189866900444031, -0.08010462671518326, 0.22581109404563904, 0.11592476069927216, 0.17914976179599762, 0.13195598125457764, -0.1110662892460823, 0.12462127208709717, 0.006350248120725155, 0.09321404248476028, 0.032797329127788544, 0.0406208336353302, 0.07625015079975128,
-
-0.26654642820358276, 0.006987582892179489, 0.056940462440252304, -0.21681314706802368, 0.07780187577009201, 0.0550677552819252, -0.07760556042194366, 0.03872932121157646, -0.7930355668067932, -0.1105983555316925, 0.11750592291355133, 0.13262803852558136, -0.06422358006238937, 0.02154429443180561, 0.10654937475919724, 0.05221962183713913, -0.48878803849220276, -0.22840070724487305, 0.0011379553470760584, 0.15314219892024994, 0.07926332950592041, 0.0003804231819231063, 0.0017872434109449387, 0.0007042611250653863, 0.0002458576636854559, -0.001502114930190146, 0.00033512181835249066, 0.1798011213541031, -0.00030350624001584947, -0.09183648973703384, -0.00031797270639799535, -0.17676876485347748, 0.05354951694607735, -0.30156299471855164, 0.019367441534996033, -0.00010185350402025506, -0.03712425008416176, -0.07796591520309448, -0.1688132882118225, -0.5796422958374023, 0.15654867887496948, -0.0013217078521847725, -0.001091445330530405, 0.0011883607367053628, 0.10660554468631744, -0.12484317272901535, 0.0004226503078825772, 0.09296494722366333, -0.38826271891593933, 0.2994518280029297, 0.0007777059799991548, -0.2824214696884155, -0.09771981835365295, 0.30562764406204224, 0.21152275800704956, 0.07008504122495651, 0.08235848695039749, -0.009461170062422752, 0.14994531869888306, 0.025700503960251808, 0.2208600640296936, 0.016543129459023476, -0.04067225754261017, 0.012883936055004597,
-
-0.00617796229198575, -0.013291561044752598, 0.1916685402393341, -0.017105795443058014, -0.07506846636533737, 0.18045775592327118, -0.30787673592567444, 0.04832560196518898, 0.05962129309773445, 0.1261044591665268, -0.2459549754858017, 0.12365790456533432, 0.028636902570724487, -0.006177236791700125, -0.04891420155763626, 0.16809219121932983, -0.10486529022455215, -0.10494883358478546, -0.0004520704969763756, 0.23579229414463043, 0.05076323449611664, -0.001182716921903193, -0.0001052700899890624, 0.0018256279872730374, 0.0008991276263259351, -0.001528103486634791, -0.0005336360190995038, 0.16323579847812653, 0.0020393324084579945, 0.015845002606511116, 0.0016316768014803529, -0.06386037170886993, 0.0540936253964901, -0.14962895214557648, -0.014346976764500141, 0.0012925119372084737, -0.12354017049074173, 0.016353895887732506, -0.2519243061542511, -0.042811259627342224, 0.19090451300144196, -0.00022991615696810186, 0.0018943509785458446, -0.0005430503515526652, -0.11097119003534317, -0.05055755749344826, -0.001144487177953124, -0.002318614162504673, -0.11912520974874496, -0.022436127066612244, 0.00014450220623984933, -0.1372481733560562, -0.02418547123670578, 0.24273015558719635, 0.1387740820646286, 0.1362992823123932, 0.15725626051425934, -0.157984659075737, 0.10887137055397034, 0.07719193398952484, 0.09739567339420319, -0.09165967255830765, -0.2062297910451889, 0.11893536150455475,
-
--0.09508167952299118, -0.07543941587209702, 0.2706283628940582, 0.08787128329277039, 0.10140055418014526, 0.26852965354919434, -0.5909307599067688, 0.19821986556053162, 0.08937512338161469, 0.05567803606390953, -0.11613370478153229, 0.03911638632416725, 0.06315726786851883, 0.10782184451818466, -0.01980738714337349, 0.1451241374015808, -0.060943953692913055, -0.07014007121324539, 0.00016978045459836721, 0.17468222975730896, 0.06063257157802582, -7.286974141607061e-05, 0.0005939672118984163, 0.0018404772272333503, 0.001129048760049045, 0.00019233254715800285, 0.0019600382074713707, 0.07629802823066711, 0.0003428335185162723, -0.0005339051713235676, -0.00011325302330078557, -0.03201869875192642, 0.04179591313004494, -0.3185126781463623, 0.08271040767431259, 0.0021725951228290796, -0.16681934893131256, -0.031819552183151245, -0.3288743495941162, 0.0464513823390007, 0.17611919343471527, 0.000690086861141026, 7.961438677739352e-05, -0.00019473303109407425, -0.0969485193490982, -0.03324940800666809, -0.0008172128000296652, 0.22586534917354584, -0.2223370373249054, -0.05459794029593468, -9.828664769884199e-05, -0.14924103021621704, -0.03904952108860016, 0.2069503366947174, 0.07704979181289673, 0.1661059856414795, 0.14414143562316895, -0.23807625472545624, 0.10244176536798477, 0.09765428304672241, 0.08840622752904892, -0.024117084220051765, 0.0006691144080832601, 0.1786615550518036,
-
-0.11667780578136444, 0.10861541330814362, 0.11338424682617188, -0.23225265741348267, -0.054111186414957047, 0.12930895388126373, -0.0307441595941782, -0.02359997294843197, -0.023976676166057587, -0.022300291806459427, -0.10048258304595947, 0.16049188375473022, -0.0047802492044866085, 0.040883637964725494, 0.0047388202510774136, 0.1291704773902893, -0.213030144572258, -0.07274895906448364, 0.000262087385635823, -0.03659924119710922, 0.05907992646098137, 0.0006437263218685985, -0.00039178295992314816, 0.0010649684118106961, 0.0009931239765137434, -0.001187134999781847, -0.00032986601581797004, 0.2431180775165558, 0.0009192879660986364, -0.2085084170103073, 0.0003986647061537951, -0.07371655851602554, 0.06816515326499939, -0.1429450958967209, -0.029881343245506287, -0.000824992370326072, -0.09339576214551926, -0.11395471543073654, 0.07046190649271011, -0.11395901441574097, 0.027631817385554314, -0.0014277909649536014, -0.0007467216928489506, 0.0006686572451144457, -0.053210023790597916, -0.14419393241405487, -0.0019058359321206808, 0.036102715879678726, -0.04545225203037262, 0.139390766620636, 0.00020870001753792167, -0.09519004821777344, -0.06584736704826355, 0.2246185541152954, 0.12797732651233673, 0.17647592723369598, 0.151839479804039, -0.12066972255706787, 0.12076413631439209, 0.023683056235313416, 0.007647241000086069, -0.04053515940904617, -0.10980767756700516, 0.07816174626350403,
-
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-
-0.5722816586494446, 0.44931724667549133, 0.033467985689640045, -0.05215294659137726, -0.1651076078414917, 0.09514902532100677, -0.2535097599029541, -0.31727728247642517, 0.020857004448771477, 0.01630846969783306, -0.09314350038766861, 0.1957118958234787, -0.39591383934020996, 0.26922011375427246, 0.3317633271217346, 0.11175347119569778, -0.2671031951904297, -0.056412164121866226, -0.0015118089504539967, 0.2937302589416504, 0.1793442815542221, -0.001025194302201271, 0.0010095476172864437, 0.0035850228741765022, 0.0006587953539565206, 0.000939257733989507, 0.0003379232657607645, 0.3479364216327667, -0.0004258739354554564, -0.0808158591389656, 0.00038307232898660004, -0.011362356133759022, -0.0033058011904358864, -0.08819703757762909, -0.32478445768356323, -0.0011537037789821625, -0.273912638425827, -0.11553899198770523, 0.17199908196926117, -0.0041303266771137714, 0.41343948245048523, 0.0003570480039343238, 0.0014987858012318611, 0.0013575225602835417, -0.55711829662323, -0.28808870911598206, -0.002448621904477477, 0.17940248548984528, -0.20062221586704254, 0.31715089082717896, 0.0008355412282980978, -0.07066480815410614, -0.13722951710224152, -0.10970398038625717, 0.05873332545161247, 0.24034948647022247, 0.182510644197464, 0.11015398055315018, 0.24339579045772552, -0.04150482639670372, 0.19960984587669373, -0.15185965597629547, -0.27342841029167175, 0.13767993450164795,
-
-0.22348885238170624, -0.008877726271748543, -0.171895369887352, -0.43331223726272583, 0.20232950150966644, 0.043170031160116196, -0.36004438996315, -0.006740920711308718, 0.18264558911323547, -0.11805032193660736, -0.07412461191415787, 0.032417088747024536, 0.051556725054979324, 0.08561589568853378, 0.4091699719429016, -0.0786430686712265, -0.22887086868286133, -0.2439272403717041, 0.00014124371227808297, 0.6468839049339294, -0.036077044904232025, -0.0014531002379953861, -0.0011161002330482006, 0.003305896883830428, 0.0017938324017450213, -3.7506528315134346e-05, -0.0016759017016738653, 0.40134409070014954, -0.0007333348621614277, -0.2809791564941406, -0.001019181334413588, 0.019602011889219284, 0.09314858913421631, -0.5306268334388733, 0.17580009996891022, -1.5525150956818834e-05, -0.2091640830039978, -0.6634456515312195, -0.14848995208740234, -0.06595541536808014, -0.034880805760622025, -1.1966582178501994e-06, 0.00036120228469371796, -0.0012568037491291761, -0.10609765350818634, -0.5816946625709534, -0.0006648825947195292, 0.4456590414047241, -0.512170672416687, 0.24637466669082642, 0.0010917421896010637, -0.23795755207538605, -0.2328575998544693, -0.07656370103359222, 0.33030301332473755, 0.2731899917125702, 0.09424741566181183, -0.1698189079761505, 0.01702292636036873, -0.10659436136484146, 0.11089695990085602, -0.08941251039505005, -0.35293179750442505, 0.06545879691839218
-};
-
-/// Second convolution biases
-static const double conv2_biases[] = {
-0.12326373159885406,
-0.13270756602287292,
-0.07082673907279968,
-0.051456157118082047,
-0.05844561755657196,
-0.1315319687128067,
-0.08097290247678757,
-0.10153213143348694,
-0.055915363132953644,
-0.052281659096479416,
--0.11212895810604095,
-0.07462140917778015,
--0.10300768911838531,
-0.108175128698349,
-0.15686865150928497,
-0.18225829303264618,
-0.06780990958213806,
--0.027867194265127182,
-0.10957542806863785,
-0.03425288572907448,
-0.05988423153758049,
--0.04818560183048248,
-0.07478125393390656,
--0.07338137924671173,
--0.022100606933236122,
--0.04947621747851372,
-0.00016185229469556361,
-0.16787314414978027,
--0.08640440553426743,
-0.060469429939985275,
--0.17337509989738464,
-0.26549988985061646
-};
-
-/// Third convolution kernel
-static const double conv3_kernel[] = {
--0.01733648031949997, 0.014926089905202389, 0.019393086433410645, -0.00444532185792923, 0.02693970873951912, 0.0003883102326653898, 0.004221527837216854, 0.0050745452754199505, 0.012986100278794765, 0.008007168769836426, 0.008950762450695038, 0.005279690958559513, 0.01519874669611454, -0.010862989351153374, 0.011825689114630222, -0.019965801388025284, 0.007991938851773739, -0.004620695952326059, 0.011911056004464626, 0.0043943473137915134, -0.01527450606226921, 0.007058924064040184, 0.010290278121829033, 0.013065044768154621, -0.010343007743358612, 0.017746344208717346, -4.6092932279862e-06, -0.0030044177547097206, 0.004238004796206951, 0.011623515747487545, 0.004292194731533527, 0.003601675620302558, 0.02504684589803219, -0.012476416304707527, -0.001889116014353931, -0.014844696037471294, 0.0037114194128662348, 0.0015292391180992126, -0.01960979402065277, -0.023836420848965645, -0.004658454097807407, -0.005420898552983999, -0.011578674428164959, -0.012782600708305836, -0.013413185253739357, -0.0026978880632668734, -0.008449232205748558, -0.001977386884391308, -0.009994491934776306, -0.019018622115254402, -0.01420928630977869, -0.010814856737852097, -0.012635751627385616, -0.02571050450205803, -0.011209324933588505, 0.006185243371874094, -0.028722697868943214, -0.015991590917110443, -7.262170129251899e-06, 0.0059218574315309525, -0.012753800489008427, 0.004631821997463703, -0.012866009026765823, -0.015347815118730068, 0.015386374667286873, 0.023364262655377388, -0.008774848654866219, 0.0003316070360597223, -0.013394287787377834, 0.01839779131114483, -0.0010600975947454572, 0.0013282642466947436, -0.0046541206538677216, -0.003481027204543352, -0.03873523697257042, 0.00432006549090147, -0.012688318267464638, 0.005344531964510679, -0.029875176027417183, 0.009807406924664974, 0.013965515419840813, -0.003954270388931036, -0.002595012541860342, -0.01060534082353115, -0.022159360349178314, -0.04049196094274521, -0.00826672650873661, -0.019309749826788902, -0.005398318637162447, -0.02717643231153488, -7.683406693104189e-06, 0.005884414538741112, -0.0025554620660841465, 0.003152097575366497, 0.007737628649920225, -0.0016007261583581567, 0.009788384661078453, -0.00019503672956489027, -0.016453150659799576, -0.009283055551350117, -0.023557187989354134, 0.0199913140386343, -0.0016918116016313434, 0.006789462175220251, -0.015027258545160294, -0.013967354781925678, -0.025483066216111183, -0.0011143273441120982, -0.024852922186255455, -0.0025222464464604855, -0.006810196675360203, -0.00522286631166935, 0.003403729060664773, -0.00575729925185442, 3.009885585925076e-05, -0.01669410802423954, -0.02657824568450451, -0.0558585524559021, -0.010408259928226471, -0.02449175715446472, -0.01373971812427044, -0.0016934958985075355, -6.971432412683498e-06, 0.016726776957511902, 0.012803487479686737, 0.0015065578045323491, -0.009287698194384575, -0.026466000825166702, 0.011461018584668636, 0.01290250662714243, 0.011383261531591415, -0.014642436988651752, 0.0025061562191694975, -0.005041358061134815, 0.020811449736356735, 0.036239612847566605, 4.4364631321514025e-05, -0.0060895150527358055, -0.0045987810008227825, 0.013829104602336884, -0.009625028818845749, -0.0024851823691278696, 0.011963721364736557, 0.005744087975472212, 0.004409838002175093, -0.002875274745747447, 0.03727300092577934, -0.00797036848962307, -0.023841897025704384, -0.020694682374596596, 0.01830098032951355, -0.0013982733944430947, -0.029326260089874268, 0.03960532322525978, -4.999998509447323e-06, 0.002121264347806573, 0.017022226005792618, 0.00307935057207942, 0.004140383563935757, -0.024156462401151657,
--0.006691846530884504, 0.014618276618421078, 0.0088097108528018, -0.003097307402640581, 0.0045377472415566444, 0.022115124389529228, 0.0010284727904945612, -0.011907028965651989, 0.003773882519453764, -0.003491810755804181, -0.001970492536202073, -0.0022535324096679688, -0.0008858706569299102, -0.009839004836976528, -0.011421483010053635, 0.02180883288383484, -0.0011487964075058699, -0.004101413302123547, -0.00041377657908014953, -0.0025395506527274847, -0.017455890774726868, -0.01648567058146, 0.0025676502846181393, 0.007391395978629589, -0.049815498292446136, -0.008180997334420681, -5.978379249427235e-06, 0.01758566126227379, -0.011831379495561123, 0.011075240559875965, -0.01834580861032009, -0.02892209403216839, -0.008543230593204498, -0.02222561091184616, 0.00198202021420002, -0.0173275638371706, -0.002889616647735238, -0.016437765210866928, -0.0019898039754480124, -0.0046544563956558704, -0.005625379737466574, -0.012663842178881168, -0.0021303114481270313, -0.005864009726792574, -0.02050960063934326, -0.0041898381896317005, -0.001599422306753695, -0.014543072320520878, -0.012980197556316853, -0.012110203504562378, 0.005697437096387148, -0.01134384237229824, -0.006273128557950258, -0.020269395783543587, 0.00206595566123724, -0.006627210881561041, -0.036355212330818176, -0.009883278980851173, -6.210894298419589e-06, -0.026439880952239037, -0.028891965746879578, -0.0013150871964171529, -0.02360166423022747, -0.010725725442171097, -0.02183191105723381, 0.039243895560503006, 0.024056749418377876, 0.016758441925048828, 0.022510681301355362, -0.01064172014594078, 0.01852872408926487, 0.018505385145545006, 0.02285805717110634, 0.019767427816987038, 0.009467927739024162, 0.021334582939743996, 0.006701075471937656, 0.004998492076992989, 0.013553347438573837, -0.01105016004294157, 0.021290680393576622, 0.017781538888812065, 0.023486388847231865, 0.016260700300335884, 0.024738412350416183, 0.06549705564975739, 0.020338254049420357, 0.017187029123306274, -0.005591356195509434, -0.030282070860266685, -3.964171810366679e-06, -0.04010002315044403, -0.02367369644343853, 0.003125580260530114, 0.0036818883381783962, 0.0356549471616745, -0.04779026657342911, -0.011736183427274227, -0.006996487732976675, -0.0037266379222273827, -0.004441506695002317, -0.013957538641989231, -0.01441804226487875, -0.015041263774037361, -0.004064843058586121, -0.001813759095966816, -0.019498180598020554, -0.009017589502036572, -0.022034406661987305, -0.0017824879614636302, -0.02208147756755352, -0.023862911388278008, -0.011861376464366913, -0.009369430132210255, -0.011684310622513294, -0.002783849136903882, 0.0015959303127601743, 0.013797302730381489, -0.009703001007437706, -0.016293305903673172, -0.002277540508657694, -0.058384086936712265, -5.658412646880606e-06, -0.010031881742179394, -0.0515611469745636, -0.0016375213162973523, -0.03382877632975578, -0.014860027469694614, 0.010203640908002853, -0.01677648164331913, -0.008142654784023762, -0.015476839616894722, -0.009880786761641502, -0.009551423601806164, 0.0052429791539907455, 0.025618556886911392, -0.011334288865327835, -0.01023068092763424, -0.007289142347872257, 0.001425967551767826, -0.0038161007687449455, -9.109660459216684e-05, -0.010165611281991005, 0.012489739805459976, -0.00568031333386898, -0.010443033650517464, 0.016427909955382347, -0.009935266338288784, -0.022226503118872643, 0.015649763867259026, 0.0014033179031684995, -0.026624053716659546, -0.0072030117735266685, 0.02539876475930214, -6.032416877133073e-06, 0.02203439362347126, 0.02422020398080349, -0.002492350060492754, -0.0025713376235216856, -0.024256963282823563,
-0.028391066938638687, -0.008681230247020721, -0.0075459606014192104, -0.006617182400077581, -0.021044472232460976, -0.014440059661865234, -0.006936822552233934, -0.029412558302283287, -0.011166023090481758, -0.01849893480539322, -0.0005780624924227595, -0.014950568787753582, -0.005253868643194437, -0.005220833234488964, -0.028963478282094002, 0.021637193858623505, -0.019347840920090675, -0.011709429323673248, -0.014264274388551712, -0.011820941232144833, 0.00298744416795671, -0.04346560686826706, -0.008389514870941639, -0.020286206156015396, 0.018381241708993912, 0.009402075782418251, -6.492645752587123e-06, 0.023731611669063568, -0.012512442655861378, 0.0038244938477873802, -0.009620792232453823, -0.07217538356781006, -0.013373111374676228, -0.034088119864463806, 0.018013516440987587, 0.03110465221107006, 0.00410833302885294, -0.03156536445021629, 0.022587930783629417, 0.0012497875140979886, 0.018505631014704704, 0.015937509015202522, 0.03397117182612419, 0.018751027062535286, 0.028309335932135582, 0.0013284453889355063, -0.003424218390136957, -0.049040183424949646, 0.017518097534775734, 0.02935495600104332, 0.012375129386782646, 0.021071018651127815, 0.043245572596788406, -0.0017072117188945413, 0.019170910120010376, 0.02005029283463955, 0.0443248376250267, 0.0003568085958249867, -3.7825570871063974e-06, -0.05497012287378311, 0.00025986708351410925, -0.0030980752781033516, 0.04201911762356758, -0.014718047343194485, 0.09136182069778442, 0.050251998007297516, 0.047536175698041916, 0.055385034531354904, 0.04384322464466095, 0.09153757244348526, 0.05585036426782608, 0.057650815695524216, 0.04947146400809288, 0.049772344529628754, 0.06513509154319763, 0.05598533898591995, 0.07879935204982758, 0.04116233438253403, 0.11658136546611786, 0.11621594429016113, 0.055703893303871155, 0.061708055436611176, 0.05569135770201683, 0.05010691657662392, 0.053597111254930496, 0.11819006502628326, 0.05187384411692619, 0.05305469036102295, 0.09488381445407867, 0.059720080345869064, 0.03125, 0.0926225483417511, 0.07122859358787537, 0.03559500724077225, 0.10132692754268646, 0.033046673983335495, 0.008198931813240051, 0.0003483742184471339, 0.01791943609714508, 0.001972371246665716, 0.028092924505472183, -0.03384608402848244, -0.002994880545884371, 0.007743468973785639, 0.014146284200251102, 0.018825070932507515, 0.014400491490960121, 0.0019946275278925896, 0.02691826969385147, 0.0009523414191789925, 0.02702765353024006, -0.052713021636009216, -0.008234074339270592, 0.018862949684262276, 0.009218212217092514, 0.018653536215424538, 0.018532507121562958, 0.01908375509083271, 0.011865130625665188, 0.020338265225291252, 0.04508337005972862, 0.023039977997541428, -2.3969303128978936e-06, -0.03341738134622574, 0.0004288993077352643, -0.0008280515321530402, 0.010723913088440895, 0.0077158669009804726, 0.006444958969950676, -0.019155774265527725, -5.477798913489096e-05, -0.02078878879547119, 0.008315970189869404, 0.010842232033610344, -0.031902797520160675, -0.026402516290545464, -0.006352603901177645, -0.0014378208434209228, 0.009403265081346035, -0.0235301461070776, 0.030138930305838585, 0.0009560910402797163, 0.0034558959305286407, 0.013105751015245914, -0.03245479613542557, 0.00014958641258999705, -0.01846451871097088, -0.004078384954482317, 0.0037151798605918884, -0.034191444516181946, -0.01241487730294466, 0.020487261936068535, 0.004675493575632572, -0.001711744931526482, -5.699709618056659e-06, -0.016188936308026314, 0.0009379374678246677, -0.004474718123674393, 0.015641530975699425, -0.031059851869940758,
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-};
-
-/// Third convolution biases
-static const double conv3_biases[] = {
-0.05037664
-};
-#endif