Adds ESPCN super resolution filter merged with SRCNN filter.
authorSergey Lavrushkin <dualfal@gmail.com>
Wed, 13 Jun 2018 21:37:12 +0000 (00:37 +0300)
committerPedro Arthur <bygrandao@gmail.com>
Mon, 2 Jul 2018 13:47:14 +0000 (10:47 -0300)
Signed-off-by: Pedro Arthur <bygrandao@gmail.com>
configure
libavfilter/Makefile
libavfilter/allfilters.c
libavfilter/dnn_backend_native.c
libavfilter/dnn_backend_tf.c
libavfilter/dnn_espcn.h [new file with mode: 0644]
libavfilter/dnn_interface.h
libavfilter/dnn_srcnn.h
libavfilter/vf_sr.c [new file with mode: 0644]
libavfilter/vf_srcnn.c [deleted file]

index 0e8e8a6..1d1b6dc 100755 (executable)
--- a/configure
+++ b/configure
@@ -260,7 +260,7 @@ External library support:
   --enable-libsrt          enable Haivision SRT protocol via libsrt [no]
   --enable-libssh          enable SFTP protocol via libssh [no]
   --enable-libtensorflow   enable TensorFlow as a DNN module backend
-                           for DNN based filters like srcnn [no]
+                           for DNN based filters like sr [no]
   --enable-libtesseract    enable Tesseract, needed for ocr filter [no]
   --enable-libtheora       enable Theora encoding via libtheora [no]
   --enable-libtls          enable LibreSSL (via libtls), needed for https support
@@ -3402,8 +3402,8 @@ spectrumsynth_filter_deps="avcodec"
 spectrumsynth_filter_select="fft"
 spp_filter_deps="gpl avcodec"
 spp_filter_select="fft idctdsp fdctdsp me_cmp pixblockdsp"
-srcnn_filter_deps="avformat"
-srcnn_filter_select="dnn"
+sr_filter_deps="avformat swscale"
+sr_filter_select="dnn"
 stereo3d_filter_deps="gpl"
 subtitles_filter_deps="avformat avcodec libass"
 super2xsai_filter_deps="gpl"
@@ -6823,7 +6823,7 @@ enabled signature_filter    && prepend avfilter_deps "avcodec avformat"
 enabled smartblur_filter    && prepend avfilter_deps "swscale"
 enabled spectrumsynth_filter && prepend avfilter_deps "avcodec"
 enabled spp_filter          && prepend avfilter_deps "avcodec"
-enabled srcnn_filter        && prepend avfilter_deps "avformat"
+enabled sr_filter           && prepend avfilter_deps "avformat"
 enabled subtitles_filter    && prepend avfilter_deps "avformat avcodec"
 enabled uspp_filter         && prepend avfilter_deps "avcodec"
 enabled zoompan_filter      && prepend avfilter_deps "swscale"
index 34333aa..15dce43 100644 (file)
@@ -340,7 +340,7 @@ OBJS-$(CONFIG_SMARTBLUR_FILTER)              += vf_smartblur.o
 OBJS-$(CONFIG_SOBEL_FILTER)                  += vf_convolution.o
 OBJS-$(CONFIG_SPLIT_FILTER)                  += split.o
 OBJS-$(CONFIG_SPP_FILTER)                    += vf_spp.o
-OBJS-$(CONFIG_SRCNN_FILTER)                  += vf_srcnn.o
+OBJS-$(CONFIG_SR_FILTER)                     += vf_sr.o
 OBJS-$(CONFIG_SSIM_FILTER)                   += vf_ssim.o framesync.o
 OBJS-$(CONFIG_STEREO3D_FILTER)               += vf_stereo3d.o
 OBJS-$(CONFIG_STREAMSELECT_FILTER)           += f_streamselect.o framesync.o
index e07fe67..e0a32f6 100644 (file)
@@ -328,7 +328,7 @@ extern AVFilter ff_vf_smartblur;
 extern AVFilter ff_vf_sobel;
 extern AVFilter ff_vf_split;
 extern AVFilter ff_vf_spp;
-extern AVFilter ff_vf_srcnn;
+extern AVFilter ff_vf_sr;
 extern AVFilter ff_vf_ssim;
 extern AVFilter ff_vf_stereo3d;
 extern AVFilter ff_vf_streamselect;
index 6e80dd3..02b174a 100644 (file)
 
 #include "dnn_backend_native.h"
 #include "dnn_srcnn.h"
+#include "dnn_espcn.h"
 #include "libavformat/avio.h"
 
-typedef enum {INPUT, CONV} LayerType;
+typedef enum {INPUT, CONV, DEPTH_TO_SPACE} LayerType;
+
+typedef enum {RELU, TANH, SIGMOID} ActivationFunc;
 
 typedef struct Layer{
     LayerType type;
@@ -37,6 +40,7 @@ typedef struct Layer{
 
 typedef struct ConvolutionalParams{
     int32_t input_num, output_num, kernel_size;
+    ActivationFunc activation;
     float* kernel;
     float* biases;
 } ConvolutionalParams;
@@ -45,17 +49,22 @@ typedef struct InputParams{
     int height, width, channels;
 } InputParams;
 
+typedef struct DepthToSpaceParams{
+    int block_size;
+} DepthToSpaceParams;
+
 // 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)
+static DNNReturnType set_input_output_native(void* model, DNNData* input, DNNData* output)
 {
     ConvolutionalNetwork* network = (ConvolutionalNetwork*)model;
     InputParams* input_params;
     ConvolutionalParams* conv_params;
+    DepthToSpaceParams* depth_to_space_params;
     int cur_width, cur_height, cur_channels;
     int32_t layer;
 
@@ -63,11 +72,17 @@ static DNNReturnType set_input_output_native(void* model, const DNNData* 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;
+        if (input->data){
+            av_freep(&input->data);
+        }
+        network->layers[0].output = input->data = av_malloc(cur_height * cur_width * cur_channels * sizeof(float));
+        if (!network->layers[0].output){
+            return DNN_ERROR;
+        }
     }
 
     for (layer = 1; layer < network->layers_num; ++layer){
@@ -78,32 +93,40 @@ static DNNReturnType set_input_output_native(void* model, const DNNData* input,
                 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;
+        case DEPTH_TO_SPACE:
+            depth_to_space_params = (DepthToSpaceParams*)network->layers[layer].params;
+            if (cur_channels % (depth_to_space_params->block_size * depth_to_space_params->block_size) != 0){
+                return DNN_ERROR;
             }
+            cur_channels = cur_channels / (depth_to_space_params->block_size * depth_to_space_params->block_size);
+            cur_height *= depth_to_space_params->block_size;
+            cur_width *= depth_to_space_params->block_size;
             break;
         default:
             return DNN_ERROR;
         }
+        if (network->layers[layer].output){
+            av_freep(&network->layers[layer].output);
+        }
+        network->layers[layer].output = av_malloc(cur_height * cur_width * cur_channels * sizeof(float));
+        if (!network->layers[layer].output){
+            return DNN_ERROR;
+        }
     }
 
+    output->data = network->layers[network->layers_num - 1].output;
+    output->height = cur_height;
+    output->width = cur_width;
+    output->channels = cur_channels;
+
     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...
+// layers_num,layer_type,layer_parameterss,layer_type,layer_parameters...
+// For CONV layer: activation_function, input_num, output_num, kernel_size, kernel, biases
+// For DEPTH_TO_SPACE layer: block_size
 DNNModel* ff_dnn_load_model_native(const char* model_filename)
 {
     DNNModel* model = NULL;
@@ -111,7 +134,9 @@ DNNModel* ff_dnn_load_model_native(const char* model_filename)
     AVIOContext* model_file_context;
     int file_size, dnn_size, kernel_size, i;
     int32_t layer;
+    LayerType layer_type;
     ConvolutionalParams* conv_params;
+    DepthToSpaceParams* depth_to_space_params;
 
     model = av_malloc(sizeof(DNNModel));
     if (!model){
@@ -156,39 +181,62 @@ DNNModel* ff_dnn_load_model_native(const char* model_filename)
     }
 
     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){
+        layer_type = (int32_t)avio_rl32(model_file_context);
+        dnn_size += 4;
+        switch (layer_type){
+        case CONV:
+            conv_params = av_malloc(sizeof(ConvolutionalParams));
+            if (!conv_params){
+                avio_closep(&model_file_context);
+                ff_dnn_free_model_native(&model);
+                return NULL;
+            }
+            conv_params->activation = (int32_t)avio_rl32(model_file_context);
+            conv_params->input_num = (int32_t)avio_rl32(model_file_context);
+            conv_params->output_num = (int32_t)avio_rl32(model_file_context);
+            conv_params->kernel_size = (int32_t)avio_rl32(model_file_context);
+            kernel_size = conv_params->input_num * conv_params->output_num *
+                          conv_params->kernel_size * conv_params->kernel_size;
+            dnn_size += 16 + (kernel_size + conv_params->output_num << 2);
+            if (dnn_size > file_size || conv_params->input_num <= 0 ||
+                conv_params->output_num <= 0 || conv_params->kernel_size <= 0){
+                avio_closep(&model_file_context);
+                ff_dnn_free_model_native(&model);
+                return NULL;
+            }
+            conv_params->kernel = av_malloc(kernel_size * sizeof(float));
+            conv_params->biases = av_malloc(conv_params->output_num * sizeof(float));
+            if (!conv_params->kernel || !conv_params->biases){
+                avio_closep(&model_file_context);
+                ff_dnn_free_model_native(&model);
+                return NULL;
+            }
+            for (i = 0; i < kernel_size; ++i){
+                conv_params->kernel[i] = av_int2float(avio_rl32(model_file_context));
+            }
+            for (i = 0; i < conv_params->output_num; ++i){
+                conv_params->biases[i] = av_int2float(avio_rl32(model_file_context));
+            }
+            network->layers[layer].type = CONV;
+            network->layers[layer].params = conv_params;
+            break;
+        case DEPTH_TO_SPACE:
+            depth_to_space_params = av_malloc(sizeof(DepthToSpaceParams));
+            if (!depth_to_space_params){
+                avio_closep(&model_file_context);
+                ff_dnn_free_model_native(&model);
+                return NULL;
+            }
+            depth_to_space_params->block_size = (int32_t)avio_rl32(model_file_context);
+            dnn_size += 4;
+            network->layers[layer].type = DEPTH_TO_SPACE;
+            network->layers[layer].params = depth_to_space_params;
+            break;
+        default:
             avio_closep(&model_file_context);
             ff_dnn_free_model_native(&model);
             return NULL;
         }
-        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);
@@ -203,7 +251,8 @@ DNNModel* ff_dnn_load_model_native(const char* model_filename)
     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)
+static int set_up_conv_layer(Layer* layer, const float* kernel, const float* biases, ActivationFunc activation,
+                             int32_t input_num, int32_t output_num, int32_t size)
 {
     ConvolutionalParams* conv_params;
     int kernel_size;
@@ -212,6 +261,7 @@ static int set_up_conv_layer(Layer* layer, const float* kernel, const float* bia
     if (!conv_params){
         return DNN_ERROR;
     }
+    conv_params->activation = activation;
     conv_params->input_num = input_num;
     conv_params->output_num = output_num;
     conv_params->kernel_size = size;
@@ -236,6 +286,7 @@ DNNModel* ff_dnn_load_default_model_native(DNNDefaultModel model_type)
 {
     DNNModel* model = NULL;
     ConvolutionalNetwork* network = NULL;
+    DepthToSpaceParams* depth_to_space_params;
     int32_t layer;
 
     model = av_malloc(sizeof(DNNModel));
@@ -253,45 +304,68 @@ DNNModel* ff_dnn_load_default_model_native(DNNDefaultModel model_type)
     switch (model_type){
     case DNN_SRCNN:
         network->layers_num = 4;
+        break;
+    case DNN_ESPCN:
+        network->layers_num = 5;
+        break;
+    default:
+        av_freep(&network);
+        av_freep(&model);
+        return NULL;
+    }
 
-        network->layers = av_malloc(network->layers_num * sizeof(Layer));
-        if (!network->layers){
-            av_freep(&network);
-            av_freep(&model);
-            return NULL;
-        }
+    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;
+    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;
+    }
+
+    switch (model_type){
+    case DNN_SRCNN:
+        if (set_up_conv_layer(network->layers + 1, srcnn_conv1_kernel, srcnn_conv1_biases, RELU, 1, 64, 9) != DNN_SUCCESS ||
+            set_up_conv_layer(network->layers + 2, srcnn_conv2_kernel, srcnn_conv2_biases, RELU, 64, 32, 1) != DNN_SUCCESS ||
+            set_up_conv_layer(network->layers + 3, srcnn_conv3_kernel, srcnn_conv3_biases, RELU, 32, 1, 5) != DNN_SUCCESS){
+            ff_dnn_free_model_native(&model);
+            return NULL;
         }
-        network->layers[0].type = INPUT;
-        network->layers[0].params = av_malloc(sizeof(InputParams));
-        if (!network->layers[0].params){
+        break;
+    case DNN_ESPCN:
+        if (set_up_conv_layer(network->layers + 1, espcn_conv1_kernel, espcn_conv1_biases, TANH, 1, 64, 5) != DNN_SUCCESS ||
+            set_up_conv_layer(network->layers + 2, espcn_conv2_kernel, espcn_conv2_biases, TANH, 64, 32, 3) != DNN_SUCCESS ||
+            set_up_conv_layer(network->layers + 3, espcn_conv3_kernel, espcn_conv3_biases, SIGMOID, 32, 4, 3) != DNN_SUCCESS){
             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){
+        network->layers[4].type = DEPTH_TO_SPACE;
+        depth_to_space_params = av_malloc(sizeof(DepthToSpaceParams));
+        if (!depth_to_space_params){
             ff_dnn_free_model_native(&model);
             return NULL;
         }
+        depth_to_space_params->block_size = 2;
+        network->layers[4].params = depth_to_space_params;
+    }
 
-        model->set_input_output = &set_input_output_native;
+    model->set_input_output = &set_input_output_native;
 
-        return model;
-    default:
-        av_freep(&network);
-        av_freep(&model);
-        return NULL;
-    }
+    return model;
 }
 
 #define CLAMP_TO_EDGE(x, w) ((x) < 0 ? 0 : ((x) >= (w) ? (w - 1) : (x)))
 
-static void convolve(const float* input, float* output, const ConvolutionalParams* conv_params, int32_t width, int32_t height)
+static void convolve(const float* input, float* output, const ConvolutionalParams* conv_params, int width, int height)
 {
     int y, x, n_filter, ch, kernel_y, kernel_x;
     int radius = conv_params->kernel_size >> 1;
@@ -313,19 +387,53 @@ static void convolve(const float* input, float* output, const ConvolutionalParam
                         }
                     }
                 }
-                output[n_filter] = FFMAX(output[n_filter], 0.0);
+                switch (conv_params->activation){
+                case RELU:
+                    output[n_filter] = FFMAX(output[n_filter], 0.0);
+                    break;
+                case TANH:
+                    output[n_filter] = 2.0f  / (1.0f + exp(-2.0f * output[n_filter])) - 1.0f;
+                    break;
+                case SIGMOID:
+                    output[n_filter] = 1.0f / (1.0f + exp(-output[n_filter]));
+                }
             }
             output += conv_params->output_num;
         }
     }
 }
 
+static void depth_to_space(const float* input, float* output, int block_size, int width, int height, int channels)
+{
+    int y, x, by, bx, ch;
+    int new_channels = channels / (block_size * block_size);
+    int output_linesize = width * channels;
+    int by_linesize = output_linesize / block_size;
+    int x_linesize = new_channels * block_size;
+
+    for (y = 0; y < height; ++y){
+        for (x = 0; x < width; ++x){
+            for (by = 0; by < block_size; ++by){
+                for (bx = 0; bx < block_size; ++bx){
+                    for (ch = 0; ch < new_channels; ++ch){
+                        output[by * by_linesize + x * x_linesize + bx * new_channels + ch] = input[ch];
+                    }
+                    input += new_channels;
+                }
+            }
+        }
+        output += output_linesize;
+    }
+}
+
 DNNReturnType ff_dnn_execute_model_native(const DNNModel* model)
 {
     ConvolutionalNetwork* network = (ConvolutionalNetwork*)model->model;
-    InputParams* input_params;
-    int cur_width, cur_height;
+    int cur_width, cur_height, cur_channels;
     int32_t layer;
+    InputParams* input_params;
+    ConvolutionalParams* conv_params;
+    DepthToSpaceParams* depth_to_space_params;
 
     if (network->layers_num <= 0 || network->layers[0].type != INPUT || !network->layers[0].output){
         return DNN_ERROR;
@@ -334,6 +442,7 @@ DNNReturnType ff_dnn_execute_model_native(const DNNModel* model)
         input_params = (InputParams*)network->layers[0].params;
         cur_width = input_params->width;
         cur_height = input_params->height;
+        cur_channels = input_params->channels;
     }
 
     for (layer = 1; layer < network->layers_num; ++layer){
@@ -342,7 +451,17 @@ DNNReturnType ff_dnn_execute_model_native(const DNNModel* model)
         }
         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);
+            conv_params = (ConvolutionalParams*)network->layers[layer].params;
+            convolve(network->layers[layer - 1].output, network->layers[layer].output, conv_params, cur_width, cur_height);
+            cur_channels = conv_params->output_num;
+            break;
+        case DEPTH_TO_SPACE:
+            depth_to_space_params = (DepthToSpaceParams*)network->layers[layer].params;
+            depth_to_space(network->layers[layer - 1].output, network->layers[layer].output,
+                           depth_to_space_params->block_size, cur_width, cur_height, cur_channels);
+            cur_height *= depth_to_space_params->block_size;
+            cur_width *= depth_to_space_params->block_size;
+            cur_channels /= depth_to_space_params->block_size * depth_to_space_params->block_size;
             break;
         case INPUT:
             return DNN_ERROR;
@@ -362,19 +481,13 @@ void ff_dnn_free_model_native(DNNModel** 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);
-                }
+            av_freep(&network->layers[layer].output);
+            if (network->layers[layer].type == CONV){
                 conv_params = (ConvolutionalParams*)network->layers[layer].params;
                 av_freep(&conv_params->kernel);
                 av_freep(&conv_params->biases);
-                av_freep(&conv_params);
-                break;
-            case INPUT:
-                av_freep(&network->layers[layer].params);
             }
+            av_freep(&network->layers[layer].params);
         }
         av_freep(network);
         av_freep(model);
index e46b1ad..302ff9e 100644 (file)
@@ -25,6 +25,7 @@
 
 #include "dnn_backend_tf.h"
 #include "dnn_srcnn.h"
+#include "dnn_espcn.h"
 #include "libavformat/avio.h"
 
 #include <tensorflow/c/c_api.h>
@@ -35,9 +36,7 @@ typedef struct TFModel{
     TF_Status* status;
     TF_Output input, output;
     TF_Tensor* input_tensor;
-    TF_Tensor* output_tensor;
-    const DNNData* input_data;
-    const DNNData* output_data;
+    DNNData* output_data;
 } TFModel;
 
 static void free_buffer(void* data, size_t length)
@@ -78,13 +77,13 @@ static TF_Buffer* read_graph(const char* model_filename)
     return graph_buf;
 }
 
-static DNNReturnType set_input_output_tf(void* model, const DNNData* input, const DNNData* output)
+static DNNReturnType set_input_output_tf(void* model, DNNData* input, DNNData* output)
 {
     TFModel* tf_model = (TFModel*)model;
     int64_t input_dims[] = {1, input->height, input->width, input->channels};
-    int64_t output_dims[] = {1, output->height, output->width, output->channels};
     TF_SessionOptions* sess_opts;
     const TF_Operation* init_op = TF_GraphOperationByName(tf_model->graph, "init");
+    TF_Tensor* output_tensor;
 
     // Input operation should be named 'x'
     tf_model->input.oper = TF_GraphOperationByName(tf_model->graph, "x");
@@ -100,6 +99,7 @@ static DNNReturnType set_input_output_tf(void* model, const DNNData* input, cons
     if (!tf_model->input_tensor){
         return DNN_ERROR;
     }
+    input->data = (float*)TF_TensorData(tf_model->input_tensor);
 
     // Output operation should be named 'y'
     tf_model->output.oper = TF_GraphOperationByName(tf_model->graph, "y");
@@ -107,17 +107,6 @@ static DNNReturnType set_input_output_tf(void* model, const DNNData* input, cons
         return DNN_ERROR;
     }
     tf_model->output.index = 0;
-    if (tf_model->output_tensor){
-        TF_DeleteTensor(tf_model->output_tensor);
-    }
-    tf_model->output_tensor = TF_AllocateTensor(TF_FLOAT, output_dims, 4,
-                                                output_dims[1] * output_dims[2] * output_dims[3] * sizeof(float));
-    if (!tf_model->output_tensor){
-        return DNN_ERROR;
-    }
-
-    tf_model->input_data = input;
-    tf_model->output_data = output;
 
     if (tf_model->session){
         TF_CloseSession(tf_model->session, tf_model->status);
@@ -144,6 +133,26 @@ static DNNReturnType set_input_output_tf(void* model, const DNNData* input, cons
         }
     }
 
+    // Execute network to get output height, width and number of channels
+    TF_SessionRun(tf_model->session, NULL,
+                  &tf_model->input, &tf_model->input_tensor, 1,
+                  &tf_model->output, &output_tensor, 1,
+                  NULL, 0, NULL, tf_model->status);
+    if (TF_GetCode(tf_model->status) != TF_OK){
+        return DNN_ERROR;
+    }
+    else{
+        output->height = TF_Dim(output_tensor, 1);
+        output->width = TF_Dim(output_tensor, 2);
+        output->channels = TF_Dim(output_tensor, 3);
+        output->data = av_malloc(output->height * output->width * output->channels * sizeof(float));
+        if (!output->data){
+            return DNN_ERROR;
+        }
+        tf_model->output_data = output;
+        TF_DeleteTensor(output_tensor);
+    }
+
     return DNN_SUCCESS;
 }
 
@@ -166,7 +175,7 @@ DNNModel* ff_dnn_load_model_tf(const char* model_filename)
     }
     tf_model->session = NULL;
     tf_model->input_tensor = NULL;
-    tf_model->output_tensor = NULL;
+    tf_model->output_data = NULL;
 
     graph_def = read_graph(model_filename);
     if (!graph_def){
@@ -215,6 +224,17 @@ DNNModel* ff_dnn_load_default_model_tf(DNNDefaultModel model_type)
         graph_def->length = srcnn_tf_size;
         graph_def->data_deallocator = free_buffer;
         break;
+    case DNN_ESPCN:
+        graph_data = av_malloc(espcn_tf_size);
+        if (!graph_data){
+            TF_DeleteBuffer(graph_def);
+            return NULL;
+        }
+        memcpy(graph_data, espcn_tf_model, espcn_tf_size);
+        graph_def->data = (void*)graph_data;
+        graph_def->length = espcn_tf_size;
+        graph_def->data_deallocator = free_buffer;
+        break;
     default:
         TF_DeleteBuffer(graph_def);
         return NULL;
@@ -234,7 +254,7 @@ DNNModel* ff_dnn_load_default_model_tf(DNNDefaultModel model_type)
     }
     tf_model->session = NULL;
     tf_model->input_tensor = NULL;
-    tf_model->output_tensor = NULL;
+    tf_model->output_data = NULL;
 
     tf_model->graph = TF_NewGraph();
     tf_model->status = TF_NewStatus();
@@ -259,23 +279,21 @@ DNNModel* ff_dnn_load_default_model_tf(DNNDefaultModel model_type)
 DNNReturnType ff_dnn_execute_model_tf(const DNNModel* model)
 {
     TFModel* tf_model = (TFModel*)model->model;
-
-    memcpy(TF_TensorData(tf_model->input_tensor), tf_model->input_data->data,
-           tf_model->input_data->height * tf_model->input_data->width *
-           tf_model->input_data->channels * sizeof(float));
+    TF_Tensor* output_tensor;
 
     TF_SessionRun(tf_model->session, NULL,
                   &tf_model->input, &tf_model->input_tensor, 1,
-                  &tf_model->output, &tf_model->output_tensor, 1,
+                  &tf_model->output, &output_tensor, 1,
                   NULL, 0, NULL, tf_model->status);
 
     if (TF_GetCode(tf_model->status) != TF_OK){
         return DNN_ERROR;
     }
     else{
-        memcpy(tf_model->output_data->data, TF_TensorData(tf_model->output_tensor),
-           tf_model->output_data->height * tf_model->output_data->width *
-           tf_model->output_data->channels * sizeof(float));
+        memcpy(tf_model->output_data->data, TF_TensorData(output_tensor),
+               tf_model->output_data->height * tf_model->output_data->width *
+               tf_model->output_data->channels * sizeof(float));
+        TF_DeleteTensor(output_tensor);
 
         return DNN_SUCCESS;
     }
@@ -300,9 +318,7 @@ void ff_dnn_free_model_tf(DNNModel** model)
         if (tf_model->input_tensor){
             TF_DeleteTensor(tf_model->input_tensor);
         }
-        if (tf_model->output_tensor){
-            TF_DeleteTensor(tf_model->output_tensor);
-        }
+        av_freep(&tf_model->output_data->data);
         av_freep(&tf_model);
         av_freep(model);
     }
diff --git a/libavfilter/dnn_espcn.h b/libavfilter/dnn_espcn.h
new file mode 100644 (file)
index 0000000..0bd3132
--- /dev/null
@@ -0,0 +1,12637 @@
+/*
+ * 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 espcn model.
+ */
+
+#ifndef AVFILTER_DNN_ESPCN_H
+#define AVFILTER_DNN_ESPCN_H
+
+
+static const float espcn_conv1_kernel[] = {
+    -0.033651225f,    0.08141546f,      -0.028117338f,    0.1370229f,
+    0.21719787f,      0.0018106321f,    -0.21415733f,     0.08370989f,
+    0.04526427f,      0.13084698f,      -0.016193666f,    0.048420854f,
+    -0.043297544f,    -0.010689146f,    -0.15213025f,     0.12779328f,
+    -0.12508103f,     -0.03025096f,     0.050621416f,     0.06129373f,
+    -0.31008086f,     0.090241484f,     0.11289834f,      0.024204707f,
+    -0.15997766f,     -0.4491874f,      1.7992994f,       -2.2776546f,
+    0.92356527f,      -0.22333145f,     -0.3484276f,      -0.30224112f,
+    2.6481814f,       -3.5702555f,      1.6448367f,       0.821054f,
+    -2.194776f,       2.9794505f,       -0.74629736f,     -0.71420956f,
+    -0.19285335f,     0.45863184f,      -1.3390704f,      1.9217515f,
+    -0.9752292f,      0.17233354f,      -0.42186308f,     0.0067120134f,
+    0.15278922f,      0.25349525f,      0.04239441f,      0.32368666f,
+    -0.32882598f,     -0.09148329f,     0.13972661f,      -0.33373186f,
+    1.3149962f,       -1.3097465f,      -1.0351702f,      0.5667829f,
+    0.28915232f,      -0.12594104f,     0.19577143f,      -0.010226973f,
+    0.27975535f,      0.08592676f,      -0.3827023f,      0.28327408f,
+    0.2345602f,       -0.28909206f,     -0.01707916f,     0.02990479f,
+    0.029567802f,     0.16850159f,      0.03417645f,      -0.089996934f,
+    0.059985105f,     -0.15995955f,     -0.17444675f,     -0.059467997f,
+    0.08160676f,      0.26426318f,      1.01275f,         0.8737046f,
+    0.10697361f,      0.37341496f,      0.34371883f,      2.3972206f,
+    0.3797688f,       0.32201457f,      -0.15579607f,     0.004243462f,
+    -0.9925481f,      -0.08061044f,     -0.06980977f,     0.004541201f,
+    0.04380443f,      0.35560706f,      -0.00929085f,     0.09188179f,
+    0.0667816f,       -0.13034518f,     0.14290251f,      -0.104175836f,
+    0.0013874007f,    -0.21087192f,     0.33723673f,      -0.81895316f,
+    0.2068736f,       -0.024198757f,    0.20320402f,      -0.9201378f,
+    -22.43354f,       -0.55837536f,     -0.18251857f,     -0.06481952f,
+    0.13386513f,      -0.92357135f,     0.12469517f,      0.07433132f,
+    -0.019609528f,    0.020879095f,     -0.037496805f,    -0.020767067f,
+    -0.023763046f,    0.014306224f,     -0.21043892f,     0.16374084f,
+    0.060567956f,     -0.13136876f,     -0.24900316f,     0.35642996f,
+    -0.39053982f,     0.006153686f,     0.10833906f,      -0.109700225f,
+    0.16616625f,      0.0967383f,       0.26237366f,      -0.18988574f,
+    0.23701477f,      -0.4337045f,      0.1442159f,       -0.3304001f,
+    0.12143453f,      0.069193594f,     0.1799313f,       -0.05647911f,
+    -0.017935714f,    0.043117225f,     0.06470898f,      -0.004627622f,
+    -0.019676162f,    0.012499647f,     0.016022062f,     0.042290222f,
+    -0.02982633f,     -0.037106734f,    0.039087374f,     0.00029811377f,
+    -0.15740614f,     0.13884911f,      -0.06417996f,     -0.102875255f,
+    0.17561884f,      -0.046311516f,    -0.10099872f,     0.17903659f,
+    -0.055862334f,    0.059019238f,     0.11083021f,      -0.015236638f,
+    -0.06907288f,     0.09691499f,      -0.22299959f,     0.010823794f,
+    0.010635834f,     -0.0023554056f,   -0.012515517f,    0.029083649f,
+    -0.0041044154f,   0.039968647f,     -0.036370832f,    -0.056957614f,
+    0.06255996f,      0.035850722f,     -0.055701878f,    0.06230328f,
+    -0.080863565f,    0.035210107f,     0.015096615f,     -0.04939917f,
+    0.011800731f,     0.07148536f,      -0.047290847f,    -0.06091541f,
+    0.113620274f,     -0.080722176f,    0.056476723f,     -0.066667825f,
+    -0.14979011f,     0.15063208f,      -0.11805492f,     -0.138875f,
+    0.14232866f,      -0.03414186f,     0.47239187f,      0.009407899f,
+    0.01444684f,      -0.10259039f,     -0.07597026f,     0.12913887f,
+    -0.67652786f,     0.49300277f,      -0.15496327f,     0.11434867f,
+    0.0086330585f,    0.0016397188f,    -0.093785115f,    0.12697084f,
+    0.09329895f,      -0.098265454f,    0.015779398f,     0.024828607f,
+    -0.20260698f,     -0.0043203263f,   0.008699304f,     -0.0068465173f,
+    0.002962457f,     0.007240551f,     0.006284914f,     -0.018137073f,
+    -0.009959204f,    0.041471813f,     -0.018904453f,    -0.042574976f,
+    0.024426522f,     0.018850524f,     -0.035666943f,    0.012505667f,
+    -0.014740345f,    0.0039879885f,    0.015514583f,     0.0046489495f,
+    -0.007373316f,    0.039615016f,     -0.04171012f,     -0.0011046156f,
+    -0.013045653f,    0.027342325f,     0.034585696f,     -0.025510559f,
+    -0.0030336082f,   0.000485477f,     -0.007296067f,    -0.027812503f,
+    0.025867466f,     -0.011391817f,    0.018295178f,     -0.00917634f,
+    -0.040564276f,    0.018141888f,     -0.00067481253f,  -0.022856772f,
+    0.02692952f,      0.015732365f,     -0.027582686f,    0.0110019455f,
+    -0.01288451f,     -0.0003018919f,   0.019714799f,     0.01728935f,
+    -0.002913521f,    0.008114081f,     0.0027821003f,    0.4531545f,
+    0.053950854f,     -0.6977125f,      0.2064479f,       -0.109914035f,
+    0.1841823f,       -0.13620034f,     0.27128533f,      -0.0609876f,
+    0.003371579f,     0.1930245f,       2.4499261f,       -4.972192f,
+    1.2972376f,       -0.14015496f,     0.2681309f,       4.5717506f,
+    -4.222891f,       0.28777757f,      -0.47991228f,     0.030688927f,
+    0.24025695f,      -0.010329718f,    0.22207767f,      -0.106166996f,
+    -0.005659934f,    -0.012544079f,    -0.01702093f,     -0.000517936f,
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+    0.0029141766f,    -0.023563737f,    0.22623633f,      -0.26986006f,
+    0.033467237f,     0.3039329f,       0.09788029f,      -0.23093647f,
+    0.04797184f,      -0.5435371f,      0.28225297f,      -0.12393708f,
+    0.06615679f,      0.21791485f,      0.23950522f,      -0.38103038f,
+    0.057797447f,     0.012778321f,     0.18788584f,      -0.23953205f,
+    0.003463926f,     0.1519535f,       -0.025784193f,    0.022954147f,
+    0.021384975f,     0.031615477f,     -0.024224572f,    0.013137536f,
+    -0.015687706f,    0.024815697f,     -0.05859628f,     0.031555064f,
+    -0.0019542053f,   0.0120559605f,    -0.027779348f,    0.04989342f,
+    -0.018062575f,    0.011863047f,     -0.009080397f,    -0.032487895f,
+    0.023245774f,     0.0034800111f,    -0.012821287f,    -0.004105503f,
+    0.0097069405f,    -0.0123393545f,   -0.01738414f,     -0.24763492f,
+    0.22560623f,      -0.33906525f,     0.17742047f,      0.29261616f,
+    0.23450398f,      0.08458583f,      0.31558466f,      -0.34477365f,
+    -0.036315188f,    -0.63012654f,     1.020803f,        -6.822575f,
+    4.672898f,        0.907094f,        0.0921175f,       0.42199847f,
+    -2.4781747f,      2.6292892f,       -0.20802575f,     -0.040282898f,
+    -0.16429797f,     0.158319f,        -0.21606922f,     0.17297447f,
+    0.10854483f,      -0.21564475f,     0.10645375f,      -0.0852769f,
+    -0.123929694f,    -0.070597954f,    0.1399093f,       -0.21883014f,
+    0.25562203f,      0.015535234f,     0.008046734f,     0.20049386f,
+    -0.10580072f,     -0.288465f,       0.12385275f,      -0.13853587f,
+    0.14443761f,      -0.11708488f,     0.16184115f,      0.32197487f,
+    -0.14791423f,     0.16313978f,      0.049589388f,     -0.09173289f,
+    -0.12055817f,     -0.03492332f,     0.0457395f,       -0.027245864f,
+    0.08227852f,      -0.03637111f,     0.019504743f,     -0.05241149f,
+    0.08827175f,      -0.09138424f,     0.049888045f,     0.03992102f,
+    -0.013001513f,    -0.06494804f,     0.10308159f,      -0.058084413f,
+    0.0041539194f,    0.03151993f,      -0.06009176f,     0.010160405f,
+    0.0142107215f,    -0.0069897706f,   -0.03624838f,     0.08260462f,
+    -0.07326148f,     -0.0082091065f,   0.05924571f,      -0.07805568f,
+    0.0003147011f,    -0.024013342f,    0.003571202f,     -0.0687727f,
+    0.085256286f,     -0.028858652f,    0.008514979f,     -0.01370254f,
+    -0.02649602f,     -0.049568724f,    0.072079524f,     -0.019674798f,
+    0.01081535f,      0.02266942f,      -0.006238329f,    -0.017271083f,
+    -0.016328055f,    0.005945148f,     0.010414125f,     0.0649988f,
+    -0.011186836f,    0.0031077736f,    0.01488379f,      0.10748715f,
+    -0.06637495f,     -0.040573314f,    -0.0037701947f,   0.08946786f,
+    0.020519216f,     0.18580866f,      -0.11058605f,     -0.10992421f,
+    0.009827032f,     0.0054510324f,    0.048547767f,     0.16061442f,
+    -0.05525848f,     -0.13149387f,     0.011624945f,     -0.11132381f,
+    -0.029880086f,    0.14393318f,      0.035286944f,     -0.098463364f,
+    0.038993724f,     -0.06352473f,     -0.06792619f,     0.098394215f,
+    -0.06928554f,     0.058781754f,     -0.059582964f,    -0.017070632f,
+    -0.0157639f,      0.1672481f,       -0.20033248f,     0.08431539f,
+    0.18242975f,      0.14837542f,      -0.17650276f,     -0.046665296f,
+    -0.1051493f,      -0.50624675f,     -0.10856225f,     -0.16793527f,
+    -0.07197735f,     0.28330138f,      -0.39458862f,     0.023908805f,
+    0.053147294f,     -0.061758015f,    -0.27711603f,     0.04967058f,
+    0.068010144f,     -0.29855344f,     0.07553203f,      0.00070996344f,
+    0.0021598968f,    -0.34284872f,     0.3667866f,       -1.7976327f,
+    -3.8050454f,      -0.27751672f,     0.06710288f,      -0.11527652f,
+    2.0488567f,       3.8449504f,       -0.019417979f,    0.25272283f,
+    -0.7124749f,      0.6854457f,       1.2635205f,       -0.6528006f,
+    0.20464845f,      0.11308838f,      -0.2125412f,      -0.4034266f,
+    -0.008234763f,    -0.02797744f,     0.041370988f,     -0.09540984f,
+    -0.001699344f,    -0.019013003f,    0.0010383653f,    -0.016509417f,
+    0.28561717f,      -0.024256004f,    0.24378292f,      -0.02724916f,
+    -0.020548128f,    -0.1074765f,      2.074155f,        -0.3262673f,
+    0.024195021f,     0.3360093f,       -0.068875425f,    -9.405202f,
+    1.627166f,        -0.37471554f,     -0.14033297f,     0.011253595f,
+    0.33248034f,      -0.101982914f,    0.038908735f,     -0.08949174f,
+    0.020867223f,     0.04687899f,      -0.060135193f,    0.05768169f,
+    -0.015182229f,    0.0089645665f,    -0.0532681f,      0.0694109f,
+    -0.029092036f,    0.052889585f,     -0.043014023f,    0.08994548f,
+    -0.074426785f,    -0.018725326f,    -0.021360457f,    0.01875993f,
+    0.0013528034f,    0.034160204f,     -0.038930826f,    0.009579829f,
+    0.009250521f,     -0.09578013f,     0.04048143f,      0.078915566f,
+    -0.13183434f,     0.40188128f,      -0.03411511f,     0.07047681f,
+    -0.10000601f,     -0.0060591656f,   0.65625215f,      0.31422958f,
+    0.18896863f,      0.17151128f,      0.34385997f,      0.030188305f,
+    -0.16014273f,     -0.42167395f,     -0.018569563f,    -0.28614002f,
+    0.029776182f,     0.049304806f,     -0.22978953f,     0.09140162f,
+    0.022890963f,     0.11712325f,      0.17694704f,      -0.21161927f,
+    0.07646972f,      -0.01295703f,     0.009571042f,     -0.106058836f,
+    0.15918256f,      -0.06587445f,     0.08662464f,      -0.19923525f,
+    0.34388235f,      -0.9267652f,      -0.09304384f,     -0.30417427f,
+    0.7931898f,       -1.7338952f,      9.8180275f,       0.55701333f,
+    0.064932264f,     -0.17937589f,     0.115376815f,     -0.09499582f,
+    -0.2485727f,      -0.0046541383f,   0.028371822f,     0.03606203f,
+    0.0067365323f,    0.11616945f,      0.043863643f,     -0.14730981f,
+    0.27774972f,      0.060969193f,     -0.11612988f,     -0.28526798f,
+    0.471017f,        -0.6997008f,      0.12838572f,      -0.055129312f,
+    0.2856449f,       -1.4332232f,      1.3067651f,       -0.24192092f,
+    0.2081171f,       -0.53965205f,     -6.1775684f,      -0.98148304f,
+    0.17084767f,      -0.17830072f,     -0.04059373f,     0.3728461f,
+    0.20867456f,      0.1925686f,       -0.057526354f,    0.14585286f,
+    -0.853003f,       0.3680343f,       -0.14916426f,     0.13524614f,
+    -0.11169913f,     0.86978984f,      0.13307375f,      0.16287537f,
+    -0.006425193f,    0.14676112f,      -2.3489091f,      0.8756857f,
+    -0.20259936f,     0.16589902f,      -0.5012086f,      5.3331738f,
+    2.7984626f,       -0.9493607f,      0.033240914f,     0.4609847f,
+    -1.6910734f,      -5.0659533f,      0.72995746f,      -0.51399076f,
+    -0.065757565f,    -0.05140033f,     -0.11702281f,     0.074511275f,
+    0.071474716f,     0.10055807f,      -0.0506172f,      0.044049654f,
+    0.0014753543f,    -0.060616218f,    -0.03317927f,     0.17350663f,
+    -0.041833654f,    -0.09485293f,     0.07951459f,      0.14834303f,
+    -0.14340387f,     0.101668894f,     0.122225344f,     -0.2774673f,
+    -0.08121955f,     0.14949931f,      -0.11217966f,     -0.1307467f,
+    0.20548299f,      0.013154022f,     -0.013632002f,    0.011080129f,
+    -0.023512319f,    0.0034349402f,    -0.024646418f,    0.047568355f,
+    -0.025344204f,    -0.0053444174f,   0.0009639232f,    0.016193729f,
+    -0.027206594f,    0.018514376f,     -0.016472122f,    0.011601346f,
+    0.024698649f,     -0.029349223f,    0.0025446075f,    0.002941529f,
+    0.004151751f,     -0.04827001f,     0.054990288f,     -0.024588568f,
+    0.03452298f,      -0.009801387f,    -0.026963536f,    0.024885468f,
+    0.0077325925f,    -0.12784983f,     -0.0004727017f,   0.0524453f,
+    -0.0024653298f,   -0.16912785f,     0.39179403f,      0.068885565f,
+    -0.11209827f,     1.1931618f,       0.04712435f,      -2.251133f,
+    0.04459844f,      -0.20346907f,     1.195541f,        -0.2519252f,
+    -1.2471386f,      -0.1414118f,      0.07511332f,      -0.23009096f,
+    0.2610551f,       0.29286104f,      0.11769671f,      -0.023729652f,
+    0.019181583f,     -0.028368853f,    -0.011860269f,    0.032248575f,
+    0.05148718f,      -0.047919888f,    0.013449175f,     0.01674022f,
+    -0.038879845f,    -0.049415056f,    0.045062315f,     -0.0224045f,
+    0.0051458273f,    0.026753867f,     -0.020444315f,    -0.009118943f,
+    0.037824873f,     -0.021686908f,    0.0063562766f,    -0.0038951882f,
+    0.008729107f,     0.0036430436f,    0.026570326f,     -0.019414574f,
+    0.047252927f,     0.24848075f,      0.049518216f,     -0.2768678f,
+    0.009328891f,     -0.19252661f,     -0.3930677f,      -0.007572138f,
+    0.11327027f,      0.09987962f,      0.18214096f,      2.3384292f,
+    -0.545856f,       -0.8609355f,      -0.08785435f,     0.19772369f,
+    1.0370896f,       -0.34821796f,     -1.0101713f,      -0.164299f,
+    -0.06544462f,     -0.24169508f,     0.18837783f,      0.20615467f,
+    0.046311665f,     0.21245573f,      -0.16856518f,     0.15108292f,
+    -0.27393705f,     0.015365598f,     -0.3854066f,      1.1974015f,
+    -0.8171824f,      0.38396147f,      0.057925183f,     -0.18901315f,
+    -0.8586682f,      2.095654f,        -0.84733313f,     0.029171998f,
+    0.25229037f,      -0.7818646f,      -1.3056161f,      1.2940466f,
+    -0.134979f,       0.1756112f,       0.059034463f,     0.0018894798f,
+    -0.4029903f,      0.1728906f,       -0.0034107924f,   -0.35439357f,
+    0.19148839f,      0.03786663f,      -0.00047731365f,  -0.019054921f,
+    0.6592522f,       0.2706359f,       0.09047983f,      -0.026447074f,
+    0.049455334f,     0.09238018f,      -0.0045684935f,   -0.368926f,
+    -0.06521345f,     0.06581056f,      -0.22142394f,     -0.54163706f,
+    0.26768303f,      -0.0740894f,      -0.03496943f,     -0.052972406f,
+    0.10855165f,      -0.11435824f,     0.041386828f,     -0.102326035f,
+    0.0018735762f,    0.07658435f,      -0.07127314f,     0.028028022f,
+    0.116911955f,     -0.033302654f,    0.04568431f,      0.082488224f,
+    -0.21965177f,     -0.07413175f,     -0.13298523f,     -0.04259496f,
+    0.10826996f,      0.17827633f,      0.14145942f,      0.111332774f,
+    -0.13057046f,     -0.006932171f,    0.027994003f,     -0.068751104f,
+    0.0040345634f,    -0.0039512743f,   -0.08156678f,     -0.023819147f
+};
+
+static const float espcn_conv1_biases[] = {
+    -0.018524863f,    -0.02344122f,     -0.059815478f,    -0.13828179f,
+    -0.5407016f,      0.06077186f,      0.011967307f,     1.6737873e-05f,
+    0.044080056f,     7.559907e-05f,    -0.0023202975f,   0.036452256f,
+    -0.0011971687f,   0.037468974f,     0.5938281f,       -0.0019778858f,
+    -0.0029633807f,   0.032908075f,     -0.082847565f,    0.5534438f,
+    0.0010720869f,    0.008382872f,     -0.0005540301f,   0.007542251f,
+    -0.00054567686f,  0.002956358f,     0.046740323f,     0.00097470934f,
+    -0.0008472085f,   -0.44804764f,     0.38280413f,      0.030789707f,
+    -0.722147f,       0.46606433f,      -0.03505152f,     -0.0014596627f,
+    0.0030858433f,    0.0030317134f,    0.0025776294f,    0.0231461f,
+    0.01756983f,      -0.07762163f,     0.0032662358f,    0.046537727f,
+    -0.044725697f,    -0.002968841f,    0.0005652894f,    -0.0059821554f,
+    0.6851272f,       -0.092141666f,    -0.29181793f,     0.00046312233f,
+    -0.54941076f,     0.034235522f,     0.11732721f,      0.027588358f,
+    0.0060634925f,    0.004350643f,     0.46479496f,      0.0037383304f,
+    -0.2703324f,      0.026774203f,     0.017801644f,     0.04200875f
+};
+
+static const float espcn_conv2_kernel[] = {
+    -0.046674103f,    0.26428235f,      -0.46103826f,     0.52712137f,
+    -0.33590984f,     -0.17109194f,     -0.0686052f,      -0.07092914f,
+    -0.33207268f,     -0.022449905f,    -0.064986974f,    -0.32484075f,
+    -0.0554371f,      -0.59975314f,     0.11422833f,      0.068143204f,
+    -0.0018858669f,   0.3688631f,       0.041308302f,     -0.037440863f,
+    -0.11112171f,     0.18021569f,      0.07213223f,      0.059388958f,
+    0.09809465f,      -0.07924326f,     0.036813457f,     -0.25266457f,
+    0.049585357f,     -0.325183f,       0.659209f,        0.08045369f,
+    0.7181868f,       -0.25441548f,     0.05403617f,      -0.033545442f,
+    -0.07890143f,     0.06283723f,      -0.0036670712f,   0.060813967f,
+    0.49117467f,      0.08548931f,      0.1397057f,       0.23137523f,
+    0.23626906f,      0.15435082f,      -0.0881616f,      -0.08931251f,
+    0.6407793f,       -0.711827f,       -0.09148442f,     -0.010182393f,
+    -0.22424959f,     0.22931574f,      -0.11653139f,     -0.035417557f,
+    0.09172038f,      -0.06217888f,     -0.13925147f,     0.027134137f,
+    -0.71670324f,     0.35419685f,      -0.12383344f,     0.16469592f,
+    0.5541678f,       -0.34323946f,     -0.060989752f,    0.46205896f,
+    -0.26838145f,     -1.1341122f,      -0.019517051f,    0.20830911f,
+    0.4519294f,       -0.008591234f,    0.0035951894f,    -0.0696347f,
+    0.04464795f,      0.16277248f,      -0.7174859f,      0.07877335f,
+    -0.014362606f,    -0.24790181f,     -0.28197056f,     -0.7512798f,
+    0.38359404f,      -0.18325178f,     -0.020694658f,    -0.24196818f,
+    -0.13549626f,     0.11022808f,      -0.057731092f,    0.2660348f,
+    -0.08914909f,     0.97424257f,      1.0067362f,       0.23840818f,
+    1.3499018f,       -0.2948939f,      -0.18948932f,     0.084086366f,
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