diff mbox

[FFmpeg-devel,GSOC] DNN module introduction and SRCNN filter update

Message ID CAAeE=qqtkTgL5zYgRQYGEvFXQby=euFoiZX_PJtfRbWvpugZ-w@mail.gmail.com
State New
Headers show

Commit Message

Sergey Lavrushkin May 28, 2018, 10:52 p.m. UTC
2018-05-28 9:32 GMT+03:00 Guo, Yejun <yejun.guo@intel.com>:

> looks that no tensorflow dependency is introduced, a new model format is
> created together with some CPU implementation for inference.   With this
> idea, Android Neural Network would be a very good reference, see
> https://developer.android.google.cn/ndk/guides/neuralnetworks/. It
> defines how the model is organized, and also provided a CPU optimized
> inference implementation (within the NNAPI runtime, it is open source). It
> is still under development but mature enough to run some popular dnn models
> with proper performance. We can absorb some basic design. Anyway, just a
> reference fyi.  (btw, I'm not sure about any IP issue)

The idea was to first introduce something to use when tensorflow is not
available. Here is another patch, that introduces tensorflow backend.

> For this patch, I have two comments.
> 1. change from "DNNModel* (*load_default_model)(DNNDefaultModel
> model_type);" to " DNNModel* (*load_builtin_model)(DNNBuiltinModel
> model_type);"
> The DNNModule can be invoked by many filters,  default model is a good
> name at the filter level, while built-in model is better within the DNN
> scope.
> 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;
> 2. add a new variable 'number' for DNNData/InputParams
> As a typical DNN concept, the data shape usually is: <number, height,
> width, channel> or <number, channel, height, width>, the last component
> denotes its index changes the fastest in the memory. We can add this
> concept into the API, and decide to support <NHWC> or <NCHW> or both.

I did not add number of elements in batch because I thought, that we would
not feed more than one element at once to a network in a ffmpeg filter.
But it can be easily added if necessary.

So here is the patch that adds tensorflow backend with the previous patch.
I forgot to change include guards from AVUTIL_* to AVFILTER_* in it.


Pedro Arthur May 29, 2018, 1:08 a.m. UTC | #1
2018-05-28 19:52 GMT-03:00 Sergey Lavrushkin <dualfal@gmail.com>:
> 2018-05-28 9:32 GMT+03:00 Guo, Yejun <yejun.guo@intel.com>:
>> looks that no tensorflow dependency is introduced, a new model format is
>> created together with some CPU implementation for inference.   With this
>> idea, Android Neural Network would be a very good reference, see
>> https://developer.android.google.cn/ndk/guides/neuralnetworks/. It
>> defines how the model is organized, and also provided a CPU optimized
>> inference implementation (within the NNAPI runtime, it is open source). It
>> is still under development but mature enough to run some popular dnn models
>> with proper performance. We can absorb some basic design. Anyway, just a
>> reference fyi.  (btw, I'm not sure about any IP issue)
> The idea was to first introduce something to use when tensorflow is not
> available. Here is another patch, that introduces tensorflow backend.
I think it would be better for reviewing if you send the second patch
in a new email.

>> For this patch, I have two comments.
>> 1. change from "DNNModel* (*load_default_model)(DNNDefaultModel
>> model_type);" to " DNNModel* (*load_builtin_model)(DNNBuiltinModel
>> model_type);"
>> The DNNModule can be invoked by many filters,  default model is a good
>> name at the filter level, while built-in model is better within the DNN
>> scope.
>> 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;
>> 2. add a new variable 'number' for DNNData/InputParams
>> As a typical DNN concept, the data shape usually is: <number, height,
>> width, channel> or <number, channel, height, width>, the last component
>> denotes its index changes the fastest in the memory. We can add this
>> concept into the API, and decide to support <NHWC> or <NCHW> or both.
> I did not add number of elements in batch because I thought, that we would
> not feed more than one element at once to a network in a ffmpeg filter.
> But it can be easily added if necessary.
> So here is the patch that adds tensorflow backend with the previous patch.
> I forgot to change include guards from AVUTIL_* to AVFILTER_* in it.
You moved the files from libavutil to libavfilter while it was
proposed to move them to libavformat.

> _______________________________________________
> ffmpeg-devel mailing list
> ffmpeg-devel@ffmpeg.org
> http://ffmpeg.org/mailman/listinfo/ffmpeg-devel
Sergey Lavrushkin May 29, 2018, 8:14 a.m. UTC | #2
2018-05-29 4:08 GMT+03:00 Pedro Arthur <bygrandao@gmail.com>:

> 2018-05-28 19:52 GMT-03:00 Sergey Lavrushkin <dualfal@gmail.com>:
> > 2018-05-28 9:32 GMT+03:00 Guo, Yejun <yejun.guo@intel.com>:
> >
> >> looks that no tensorflow dependency is introduced, a new model format is
> >> created together with some CPU implementation for inference.   With this
> >> idea, Android Neural Network would be a very good reference, see
> >> https://developer.android.google.cn/ndk/guides/neuralnetworks/. It
> >> defines how the model is organized, and also provided a CPU optimized
> >> inference implementation (within the NNAPI runtime, it is open source).
> It
> >> is still under development but mature enough to run some popular dnn
> models
> >> with proper performance. We can absorb some basic design. Anyway, just a
> >> reference fyi.  (btw, I'm not sure about any IP issue)
> >>
> >
> > The idea was to first introduce something to use when tensorflow is not
> > available. Here is another patch, that introduces tensorflow backend.
> I think it would be better for reviewing if you send the second patch
> in a new email.

Then we need to push the first patch, I think.

> >
> >
> >> For this patch, I have two comments.
> >>
> >> 1. change from "DNNModel* (*load_default_model)(DNNDefaultModel
> >> model_type);" to " DNNModel* (*load_builtin_model)(DNNBuiltinModel
> >> model_type);"
> >> The DNNModule can be invoked by many filters,  default model is a good
> >> name at the filter level, while built-in model is better within the DNN
> >> scope.
> >>
> >> 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;
> >>
> >>
> >> 2. add a new variable 'number' for DNNData/InputParams
> >> As a typical DNN concept, the data shape usually is: <number, height,
> >> width, channel> or <number, channel, height, width>, the last component
> >> denotes its index changes the fastest in the memory. We can add this
> >> concept into the API, and decide to support <NHWC> or <NCHW> or both.
> >
> >
> > I did not add number of elements in batch because I thought, that we
> would
> > not feed more than one element at once to a network in a ffmpeg filter.
> > But it can be easily added if necessary.
> >
> > So here is the patch that adds tensorflow backend with the previous
> patch.
> > I forgot to change include guards from AVUTIL_* to AVFILTER_* in it.
> You moved the files from libavutil to libavfilter while it was
> proposed to move them to libavformat.

Not only, it was also proposed to move it to libavfilter if it is going to
be used only
in filters. I do not know if this module is useful anywhere else besides
Pedro Arthur May 29, 2018, 12:45 p.m. UTC | #3
2018-05-29 5:14 GMT-03:00 Sergey Lavrushkin <dualfal@gmail.com>:
> 2018-05-29 4:08 GMT+03:00 Pedro Arthur <bygrandao@gmail.com>:
>> 2018-05-28 19:52 GMT-03:00 Sergey Lavrushkin <dualfal@gmail.com>:
>> > 2018-05-28 9:32 GMT+03:00 Guo, Yejun <yejun.guo@intel.com>:
>> >
>> >> looks that no tensorflow dependency is introduced, a new model format
>> >> is
>> >> created together with some CPU implementation for inference.   With
>> >> this
>> >> idea, Android Neural Network would be a very good reference, see
>> >> https://developer.android.google.cn/ndk/guides/neuralnetworks/. It
>> >> defines how the model is organized, and also provided a CPU optimized
>> >> inference implementation (within the NNAPI runtime, it is open source).
>> >> It
>> >> is still under development but mature enough to run some popular dnn
>> >> models
>> >> with proper performance. We can absorb some basic design. Anyway, just
>> >> a
>> >> reference fyi.  (btw, I'm not sure about any IP issue)
>> >>
>> >
>> > The idea was to first introduce something to use when tensorflow is not
>> > available. Here is another patch, that introduces tensorflow backend.
>> I think it would be better for reviewing if you send the second patch
>> in a new email.
> Then we need to push the first patch, I think.
Not necessarily, 'git send-email' may give you a glimpse of how it is done.

>> >
>> >
>> >> For this patch, I have two comments.
>> >>
>> >> 1. change from "DNNModel* (*load_default_model)(DNNDefaultModel
>> >> model_type);" to " DNNModel* (*load_builtin_model)(DNNBuiltinModel
>> >> model_type);"
>> >> The DNNModule can be invoked by many filters,  default model is a good
>> >> name at the filter level, while built-in model is better within the DNN
>> >> scope.
>> >>
>> >> 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
>> >> otherwise.
>> >>     DNNReturnType (*execute_model)(const DNNModel* model);
>> >>     // Frees memory allocated for model.
>> >>     void (*free_model)(DNNModel** model);
>> >> } DNNModule;
>> >>
>> >>
>> >> 2. add a new variable 'number' for DNNData/InputParams
>> >> As a typical DNN concept, the data shape usually is: <number, height,
>> >> width, channel> or <number, channel, height, width>, the last component
>> >> denotes its index changes the fastest in the memory. We can add this
>> >> concept into the API, and decide to support <NHWC> or <NCHW> or both.
>> >
>> >
>> > I did not add number of elements in batch because I thought, that we
>> > would
>> > not feed more than one element at once to a network in a ffmpeg filter.
>> > But it can be easily added if necessary.
>> >
>> > So here is the patch that adds tensorflow backend with the previous
>> > patch.
>> > I forgot to change include guards from AVUTIL_* to AVFILTER_* in it.
>> You moved the files from libavutil to libavfilter while it was
>> proposed to move them to libavformat.
> Not only, it was also proposed to move it to libavfilter if it is going to
> be used only
> in filters. I do not know if this module is useful anywhere else besides
> libavfilter.
Pedro Arthur May 29, 2018, 1:22 p.m. UTC | #4
Patch 0001 pushed.

Guo, Yejun May 30, 2018, 5:35 a.m. UTC | #5
For the case that ffmpeg is built with TENSORFLOW_BACKEND enabled, while there is no TF at runtime, ff_get_dnn_module always returns valid pointer, and no chance for the filter to fall back to native mode. Looks that we need a runtime check in function ff_get_dnn_module.

static av_cold int init(AVFilterContext* context)
    SRCNNContext* srcnn_context = context->priv;

    srcnn_context->dnn_module = ff_get_dnn_module(DNN_TF);
    if (!srcnn_context->dnn_module){
        av_log(context, AV_LOG_INFO, "could not load tensorflow backend, using native backend instead\n");
        srcnn_context->dnn_module = ff_get_dnn_module(DNN_NATIVE);
    srcnn_context->dnn_module = ff_get_dnn_module(DNN_NATIVE);

DNNModule* ff_get_dnn_module(DNNBackendType backend_type)
    case DNN_TF:
        //add a runtime check here, possible?
        dnn_module->load_model = &ff_dnn_load_model_tf;
        dnn_module->load_default_model = &ff_dnn_load_default_model_tf;
        dnn_module->execute_model = &ff_dnn_execute_model_tf;
        dnn_module->free_model = &ff_dnn_free_model_tf;
        return NULL;

-----Original Message-----
From: ffmpeg-devel [mailto:ffmpeg-devel-bounces@ffmpeg.org] On Behalf Of Pedro Arthur

Sent: Tuesday, May 29, 2018 8:45 PM
To: Sergey Lavrushkin <dualfal@gmail.com>
Cc: FFmpeg development discussions and patches <ffmpeg-devel@ffmpeg.org>
Subject: Re: [FFmpeg-devel] [GSOC] [PATCH] DNN module introduction and SRCNN filter update

2018-05-29 5:14 GMT-03:00 Sergey Lavrushkin <dualfal@gmail.com>:
> 2018-05-29 4:08 GMT+03:00 Pedro Arthur <bygrandao@gmail.com>:


>> 2018-05-28 19:52 GMT-03:00 Sergey Lavrushkin <dualfal@gmail.com>:

>> > 2018-05-28 9:32 GMT+03:00 Guo, Yejun <yejun.guo@intel.com>:

>> >

>> >> looks that no tensorflow dependency is introduced, a new model 

>> >> format is

>> >> created together with some CPU implementation for inference.   With

>> >> this

>> >> idea, Android Neural Network would be a very good reference, see 

>> >> https://developer.android.google.cn/ndk/guides/neuralnetworks/. It 

>> >> defines how the model is organized, and also provided a CPU 

>> >> optimized inference implementation (within the NNAPI runtime, it is open source).

>> >> It

>> >> is still under development but mature enough to run some popular 

>> >> dnn models with proper performance. We can absorb some basic 

>> >> design. Anyway, just a reference fyi.  (btw, I'm not sure about 

>> >> any IP issue)

>> >>

>> >

>> > The idea was to first introduce something to use when tensorflow is 

>> > not available. Here is another patch, that introduces tensorflow backend.

>> I think it would be better for reviewing if you send the second patch 

>> in a new email.



> Then we need to push the first patch, I think.

Not necessarily, 'git send-email' may give you a glimpse of how it is done.



>> >

>> >

>> >> For this patch, I have two comments.

>> >>

>> >> 1. change from "DNNModel* (*load_default_model)(DNNDefaultModel

>> >> model_type);" to " DNNModel* (*load_builtin_model)(DNNBuiltinModel

>> >> model_type);"

>> >> The DNNModule can be invoked by many filters,  default model is a 

>> >> good name at the filter level, while built-in model is better 

>> >> within the DNN scope.

>> >>

>> >> 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;

>> >>

>> >>

>> >> 2. add a new variable 'number' for DNNData/InputParams As a 

>> >> typical DNN concept, the data shape usually is: <number, height, 

>> >> width, channel> or <number, channel, height, width>, the last 

>> >> component denotes its index changes the fastest in the memory. We 

>> >> can add this concept into the API, and decide to support <NHWC> or <NCHW> or both.

>> >

>> >

>> > I did not add number of elements in batch because I thought, that 

>> > we would not feed more than one element at once to a network in a 

>> > ffmpeg filter.

>> > But it can be easily added if necessary.

>> >

>> > So here is the patch that adds tensorflow backend with the previous 

>> > patch.

>> > I forgot to change include guards from AVUTIL_* to AVFILTER_* in it.

>> You moved the files from libavutil to libavfilter while it was 

>> proposed to move them to libavformat.



> Not only, it was also proposed to move it to libavfilter if it is 

> going to be used only in filters. I do not know if this module is 

> useful anywhere else besides libavfilter.

ffmpeg-devel mailing list
Pedro Arthur May 30, 2018, 2:32 p.m. UTC | #6
Please, send patch 0002 in a new email and discuss it  there.
diff mbox


From 74dd5905ce26524051b963de5075ead50cb7d112 Mon Sep 17 00:00:00 2001
From: Sergey Lavrushkin <dualfal@gmail.com>
Date: Tue, 29 May 2018 01:20:52 +0300
Subject: [PATCH 2/2] Adds TensorFlow backend for dnn inference module.

 Changelog                    |    1 +
 configure                    |    5 +-
 libavfilter/Makefile         |    1 +
 libavfilter/dnn_backend_tf.c |  313 +++++
 libavfilter/dnn_backend_tf.h |   44 +
 libavfilter/dnn_interface.c  |   12 +
 libavfilter/dnn_interface.h  |    2 +-
 libavfilter/dnn_srcnn.h      | 2877 ++++++++++++++++++++++++++++++++++++++++++
 libavfilter/vf_srcnn.c       |    9 +-
 9 files changed, 3261 insertions(+), 3 deletions(-)
 create mode 100644 libavfilter/dnn_backend_tf.c
 create mode 100644 libavfilter/dnn_backend_tf.h

diff --git a/Changelog b/Changelog
index df2024fb59..a667fd045d 100644
--- a/Changelog
+++ b/Changelog
@@ -11,6 +11,7 @@  version <next>:
 - support mbedTLS based TLS
 - DNN inference interface
 - Reimplemented SRCNN filter using DNN inference interface
+- TensorFlow DNN backend
 version 4.0:
diff --git a/configure b/configure
index 09ff0c55e2..47e21fec39 100755
--- a/configure
+++ b/configure
@@ -259,6 +259,7 @@  External library support:
   --enable-libspeex        enable Speex de/encoding via libspeex [no]
   --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 [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
@@ -1713,6 +1714,7 @@  EXTERNAL_LIBRARY_LIST="
+    libtensorflow
@@ -3453,7 +3455,7 @@  avcodec_select="null_bsf"
 avdevice_deps="avformat avcodec avutil"
+avfilter_suggest="libm libtensorflow"
 avformat_deps="avcodec avutil"
 avformat_suggest="libm network zlib"
@@ -6055,6 +6057,7 @@  enabled libsoxr           && require libsoxr soxr.h soxr_create -lsoxr
 enabled libssh            && require_pkg_config libssh libssh libssh/sftp.h sftp_init
 enabled libspeex          && require_pkg_config libspeex speex speex/speex.h speex_decoder_init
 enabled libsrt            && require_pkg_config libsrt "srt >= 1.2.0" srt/srt.h srt_socket
+enabled libtensorflow     && require libtensorflow tensorflow/c/c_api.h TF_Version -ltensorflow && add_cflags -DTENSORFLOW_BACKEND
 enabled libtesseract      && require_pkg_config libtesseract tesseract tesseract/capi.h TessBaseAPICreate
 enabled libtheora         && require libtheora theora/theoraenc.h th_info_init -ltheoraenc -ltheoradec -logg
 enabled libtls            && require_pkg_config libtls libtls tls.h tls_configure
diff --git a/libavfilter/Makefile b/libavfilter/Makefile
index 3201cbeacf..82915e2f75 100644
--- a/libavfilter/Makefile
+++ b/libavfilter/Makefile
@@ -14,6 +14,7 @@  OBJS = allfilters.o                                                     \
        buffersrc.o                                                      \
        dnn_interface.o                                                  \
        dnn_backend_native.o                                             \
+       dnn_backend_tf.o                                                 \
        drawutils.o                                                      \
        fifo.o                                                           \
        formats.o                                                        \
diff --git a/libavfilter/dnn_backend_tf.c b/libavfilter/dnn_backend_tf.c
new file mode 100644
index 0000000000..1930589e41
--- /dev/null
+++ b/libavfilter/dnn_backend_tf.c
@@ -0,0 +1,313 @@ 
+ * 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
+ * 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 tensorflow backend implementation.
+ */
+#include "dnn_backend_tf.h"
+#include "dnn_srcnn.h"
+#include "libavformat/avio.h"
+#include <tensorflow/c/c_api.h>
+typedef struct TFModel{
+    TF_Graph* graph;
+    TF_Session* session;
+    TF_Status* status;
+    TF_Output input, output;
+    TF_Tensor* input_tensor;
+    TF_Tensor* output_tensor;
+    const DNNData* input_data;
+    const DNNData* output_data;
+} TFModel;
+static void free_buffer(void* data, size_t length)
+    av_freep(&data);
+static TF_Buffer* read_graph(const char* model_filename)
+    TF_Buffer* graph_buf;
+    unsigned char* graph_data = NULL;
+    AVIOContext* model_file_context;
+    long size, bytes_read;
+    if (avio_open(&model_file_context, model_filename, AVIO_FLAG_READ) < 0){
+        return NULL;
+    }
+    size = avio_size(model_file_context);
+    graph_data = av_malloc(size);
+    if (!graph_data){
+        avio_closep(&model_file_context);
+        return NULL;
+    }
+    bytes_read = avio_read(model_file_context, graph_data, size);
+    avio_closep(&model_file_context);
+    if (bytes_read != size){
+        av_freep(&graph_data);
+        return NULL;
+    }
+    graph_buf = TF_NewBuffer();
+    graph_buf->data = (void*)graph_data;
+    graph_buf->length = size;
+    graph_buf->data_deallocator = free_buffer;
+    return graph_buf;
+static DNNReturnType set_input_output_tf(void* model, const DNNData* input, const 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");
+    // Input operation should be named 'x'
+    tf_model->input.oper = TF_GraphOperationByName(tf_model->graph, "x");
+    if (!tf_model->input.oper){
+        return DNN_ERROR;
+    }
+    tf_model->input.index = 0;
+    if (tf_model->input_tensor){
+        TF_DeleteTensor(tf_model->input_tensor);
+    }
+    tf_model->input_tensor = TF_AllocateTensor(TF_FLOAT, input_dims, 4,
+                                               input_dims[1] * input_dims[2] * input_dims[3] * sizeof(float));
+    if (!tf_model->input_tensor){
+        return DNN_ERROR;
+    }
+    // Output operation should be named 'y'
+    tf_model->output.oper = TF_GraphOperationByName(tf_model->graph, "y");
+    if (!tf_model->output.oper){
+        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);
+        TF_DeleteSession(tf_model->session, tf_model->status);
+    }
+    sess_opts = TF_NewSessionOptions();
+    tf_model->session = TF_NewSession(tf_model->graph, sess_opts, tf_model->status);
+    TF_DeleteSessionOptions(sess_opts);
+    if (TF_GetCode(tf_model->status) != TF_OK)
+    {
+        return DNN_ERROR;
+    }
+    // Run initialization operation with name "init" if it is present in graph
+    if (init_op){
+        TF_SessionRun(tf_model->session, NULL,
+                      NULL, NULL, 0,
+                      NULL, NULL, 0,
+                      &init_op, 1, NULL, tf_model->status);
+        if (TF_GetCode(tf_model->status) != TF_OK)
+        {
+            return DNN_ERROR;
+        }
+    }
+    return DNN_SUCCESS;
+DNNModel* ff_dnn_load_model_tf(const char* model_filename)
+    DNNModel* model = NULL;
+    TFModel* tf_model = NULL;
+    TF_Buffer* graph_def;
+    TF_ImportGraphDefOptions* graph_opts;
+    model = av_malloc(sizeof(DNNModel));
+    if (!model){
+        return NULL;
+    }
+    tf_model = av_malloc(sizeof(TFModel));
+    if (!tf_model){
+        av_freep(&model);
+        return NULL;
+    }
+    tf_model->session = NULL;
+    tf_model->input_tensor = NULL;
+    tf_model->output_tensor = NULL;
+    graph_def = read_graph(model_filename);
+    if (!graph_def){
+        av_freep(&tf_model);
+        av_freep(&model);
+        return NULL;
+    }
+    tf_model->graph = TF_NewGraph();
+    tf_model->status = TF_NewStatus();
+    graph_opts = TF_NewImportGraphDefOptions();
+    TF_GraphImportGraphDef(tf_model->graph, graph_def, graph_opts, tf_model->status);
+    TF_DeleteImportGraphDefOptions(graph_opts);
+    TF_DeleteBuffer(graph_def);
+    if (TF_GetCode(tf_model->status) != TF_OK){
+        TF_DeleteGraph(tf_model->graph);
+        TF_DeleteStatus(tf_model->status);
+        av_freep(&tf_model);
+        av_freep(&model);
+        return NULL;
+    }
+    model->model = (void*)tf_model;
+    model->set_input_output = &set_input_output_tf;
+    return model;
+DNNModel* ff_dnn_load_default_model_tf(DNNDefaultModel model_type)
+    DNNModel* model = NULL;
+    TFModel* tf_model = NULL;
+    TF_Buffer* graph_def;
+    unsigned char* graph_data = NULL;
+    TF_ImportGraphDefOptions* graph_opts;
+    graph_def = TF_NewBuffer();
+    switch (model_type){
+    case DNN_SRCNN:
+        graph_data = av_malloc(srcnn_tf_size);
+        if (!graph_data){
+            TF_DeleteBuffer(graph_def);
+            return NULL;
+        }
+        memcpy(graph_data, srcnn_tf_model, srcnn_tf_size);
+        graph_def->data = (void*)graph_data;
+        graph_def->length = srcnn_tf_size;
+        graph_def->data_deallocator = free_buffer;
+        break;
+    default:
+        TF_DeleteBuffer(graph_def);
+        return NULL;
+    }
+    model = av_malloc(sizeof(DNNModel));
+    if (!model){
+        TF_DeleteBuffer(graph_def);
+        return NULL;
+    }
+    tf_model = av_malloc(sizeof(TFModel));
+    if (!tf_model){
+        TF_DeleteBuffer(graph_def);
+        av_freep(&model);
+        return NULL;
+    }
+    tf_model->session = NULL;
+    tf_model->input_tensor = NULL;
+    tf_model->output_tensor = NULL;
+    tf_model->graph = TF_NewGraph();
+    tf_model->status = TF_NewStatus();
+    graph_opts = TF_NewImportGraphDefOptions();
+    TF_GraphImportGraphDef(tf_model->graph, graph_def, graph_opts, tf_model->status);
+    TF_DeleteImportGraphDefOptions(graph_opts);
+    TF_DeleteBuffer(graph_def);
+    if (TF_GetCode(tf_model->status) != TF_OK){
+        TF_DeleteGraph(tf_model->graph);
+        TF_DeleteStatus(tf_model->status);
+        av_freep(&tf_model);
+        av_freep(&model);
+        return NULL;
+    }
+    model->model = (void*)tf_model;
+    model->set_input_output = &set_input_output_tf;
+    return model;
+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_SessionRun(tf_model->session, NULL,
+                  &tf_model->input, &tf_model->input_tensor, 1,
+                  &tf_model->output, &tf_model->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));
+        return DNN_SUCCESS;
+    }
+void ff_dnn_free_model_tf(DNNModel** model)
+    TFModel* tf_model;
+    if (*model){
+        tf_model = (TFModel*)(*model)->model;
+        if (tf_model->graph){
+            TF_DeleteGraph(tf_model->graph);
+        }
+        if (tf_model->session){
+            TF_CloseSession(tf_model->session, tf_model->status);
+            TF_DeleteSession(tf_model->session, tf_model->status);
+        }
+        if (tf_model->status){
+            TF_DeleteStatus(tf_model->status);
+        }
+        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);
+        av_freep(model);
+    }
diff --git a/libavfilter/dnn_backend_tf.h b/libavfilter/dnn_backend_tf.h
new file mode 100644
index 0000000000..11f6470124
--- /dev/null
+++ b/libavfilter/dnn_backend_tf.h
@@ -0,0 +1,44 @@ 
+ * 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
+ * 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 TensorFlow backend.
+ */
+#include "dnn_interface.h"
+DNNModel* ff_dnn_load_model_tf(const char* model_filename);
+DNNModel* ff_dnn_load_default_model_tf(DNNDefaultModel model_type);
+DNNReturnType ff_dnn_execute_model_tf(const DNNModel* model);
+void ff_dnn_free_model_tf(DNNModel** model);
diff --git a/libavfilter/dnn_interface.c b/libavfilter/dnn_interface.c
index e981829b95..c552b938de 100644
--- a/libavfilter/dnn_interface.c
+++ b/libavfilter/dnn_interface.c
@@ -25,6 +25,7 @@ 
 #include "dnn_interface.h"
 #include "dnn_backend_native.h"
+#include "dnn_backend_tf.h"
 #include "libavutil/mem.h"
 DNNModule* ff_get_dnn_module(DNNBackendType backend_type)
@@ -42,6 +43,17 @@  DNNModule* ff_get_dnn_module(DNNBackendType backend_type)
         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;
+        break;
+    case DNN_TF:
+        dnn_module->load_model = &ff_dnn_load_model_tf;
+        dnn_module->load_default_model = &ff_dnn_load_default_model_tf;
+        dnn_module->execute_model = &ff_dnn_execute_model_tf;
+        dnn_module->free_model = &ff_dnn_free_model_tf;
+    #else
+        av_freep(dnn_module);
+        return NULL;
+    #endif
     return dnn_module;
diff --git a/libavfilter/dnn_interface.h b/libavfilter/dnn_interface.h
index 52e344cee4..fbe038cc3c 100644
--- a/libavfilter/dnn_interface.h
+++ b/libavfilter/dnn_interface.h
@@ -28,7 +28,7 @@ 
 typedef enum {DNN_SUCCESS, DNN_ERROR} DNNReturnType;
-typedef enum {DNN_NATIVE} DNNBackendType;
+typedef enum {DNN_NATIVE, DNN_TF} DNNBackendType;
 typedef enum {DNN_SRCNN} DNNDefaultModel;
diff --git a/libavfilter/dnn_srcnn.h b/libavfilter/dnn_srcnn.h
index e3105e4281..ec7e586a77 100644
--- a/libavfilter/dnn_srcnn.h
+++ b/libavfilter/dnn_srcnn.h
@@ -2077,4 +2077,2881 @@  static const float conv3_biases[] = {
+static const unsigned long srcnn_tf_size = 34464;
+static const unsigned char srcnn_tf_model[] = {
+    0x0A, 0x4C, 0x0A, 0x01, 0x78, 0x12, 0x0B, 0x50, 0x6C, 0x61, 0x63, 0x65,
+    0x68, 0x6F, 0x6C, 0x64, 0x65, 0x72, 0x2A, 0x0B, 0x0A, 0x05, 0x64, 0x74,
+    0x79, 0x70, 0x65, 0x12, 0x02, 0x30, 0x01, 0x2A, 0x2D, 0x0A, 0x05, 0x73,
+    0x68, 0x61, 0x70, 0x65, 0x12, 0x24, 0x3A, 0x22, 0x12, 0x02, 0x08, 0x01,
+    0x12, 0x0B, 0x08, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
+    0x01, 0x12, 0x0B, 0x08, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
+    0xFF, 0x01, 0x12, 0x02, 0x08, 0x01, 0x0A, 0x56, 0x0A, 0x05, 0x43, 0x6F,
+    0x6E, 0x73, 0x74, 0x12, 0x05, 0x43, 0x6F, 0x6E, 0x73, 0x74, 0x2A, 0x39,
+    0x0A, 0x05, 0x76, 0x61, 0x6C, 0x75, 0x65, 0x12, 0x30, 0x42, 0x2E, 0x08,
+    0x03, 0x12, 0x08, 0x12, 0x02, 0x08, 0x04, 0x12, 0x02, 0x08, 0x02, 0x22,
+    0x20, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00,
+    0x00, 0x04, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2A, 0x0B, 0x0A,
+    0x05, 0x64, 0x74, 0x79, 0x70, 0x65, 0x12, 0x02, 0x30, 0x03, 0x0A, 0x4F,
+    0x0A, 0x09, 0x4D, 0x69, 0x72, 0x72, 0x6F, 0x72, 0x50, 0x61, 0x64, 0x12,
+    0x09, 0x4D, 0x69, 0x72, 0x72, 0x6F, 0x72, 0x50, 0x61, 0x64, 0x1A, 0x01,
+    0x78, 0x1A, 0x05, 0x43, 0x6F, 0x6E, 0x73, 0x74, 0x2A, 0x07, 0x0A, 0x01,
+    0x54, 0x12, 0x02, 0x30, 0x01, 0x2A, 0x0F, 0x0A, 0x09, 0x54, 0x70, 0x61,
+    0x64, 0x64, 0x69, 0x6E, 0x67, 0x73, 0x12, 0x02, 0x30, 0x03, 0x2A, 0x13,
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+    0x07, 0x43, 0x6F, 0x6E, 0x73, 0x74, 0x5F, 0x31, 0x12, 0x05, 0x43, 0x6F,
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+    0xB6, 0x3A, 0x23, 0x7E, 0xB5, 0xBA, 0x6A, 0x93, 0x53, 0xB8, 0xB3, 0x8E,
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+    0x93, 0x3D, 0x6A, 0x1E, 0xEE, 0x3E, 0x5D, 0xF3, 0x56, 0x3D, 0x09, 0xB8,
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+    0x2C, 0x3E, 0xF5, 0x5A, 0xB0, 0xBD, 0xB5, 0xBA, 0x4B, 0x3B, 0x93, 0x5A,
+    0x25, 0xBE, 0x7A, 0x84, 0xEF, 0xBC, 0x78, 0x36, 0xE7, 0xBE, 0xD0, 0x63,
+    0x52, 0x3C, 0xB7, 0x42, 0x23, 0xBE, 0x73, 0x4D, 0x01, 0x3A, 0xAF, 0xB8,
+    0x52, 0xBC, 0x19, 0xDC, 0xA3, 0x3E, 0x17, 0x63, 0x45, 0xBD, 0x4B, 0xF7,
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+    0x98, 0xBA, 0xAE, 0xDA, 0x21, 0x3B, 0x64, 0xB6, 0x2F, 0x3F, 0x8A, 0x4E,
+    0x33, 0xBA, 0x46, 0x6D, 0xA4, 0xBD, 0x8C, 0xE1, 0xD3, 0x3A, 0x7E, 0xF4,
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+    0x75, 0x3C, 0x21, 0xBE, 0xA5, 0x81, 0x1D, 0xBE, 0xC7, 0xAD, 0xFF, 0xBD,
+    0x71, 0x15, 0x4F, 0xBD, 0x88, 0x30, 0x08, 0xBD, 0x93, 0xA7, 0x13, 0xBE,
+    0xB4, 0xDE, 0x33, 0xBE, 0x9F, 0x1E, 0xD7, 0xBD, 0xC6, 0x2C, 0xC1, 0xBD,
+    0xCD, 0x7C, 0x85, 0xBE, 0x14, 0xCC, 0x02, 0xBC, 0xEB, 0xBE, 0xCE, 0xBC,
+    0x68, 0x52, 0x19, 0x3D, 0xAE, 0x87, 0x16, 0xBE, 0x14, 0xD3, 0x29, 0xBE,
+    0xC6, 0xE7, 0x43, 0xBC, 0xE2, 0xCC, 0x26, 0xBE, 0xBC, 0x3D, 0x67, 0xBE,
+    0xE5, 0xFF, 0xBC, 0xBD, 0xF3, 0xD0, 0x95, 0xBD, 0x3B, 0xC3, 0x7B, 0xBE,
+    0x1B, 0x10, 0xA5, 0xBB, 0xA2, 0xF7, 0x6B, 0xBD, 0xEA, 0x54, 0x94, 0x3A,
+    0xF3, 0x6A, 0xBC, 0xBD, 0x43, 0xC9, 0xD1, 0xBD, 0xA6, 0x6E, 0xA6, 0x3C,
+    0x5D, 0x80, 0x93, 0xBE, 0xF1, 0xE9, 0x14, 0xBF, 0x64, 0x94, 0x86, 0x39,
+    0x3C, 0xF0, 0x3B, 0x3A, 0xAD, 0x29, 0xE8, 0xB9, 0x86, 0x0B, 0xD7, 0x3A,
+    0x1B, 0x41, 0x77, 0xBA, 0x2A, 0x97, 0xDD, 0x39, 0x9E, 0x02, 0x96, 0xBA,
+    0x39, 0x3A, 0x56, 0xBA, 0x3E, 0xCD, 0xF9, 0xBA, 0x66, 0xAD, 0x0F, 0xBA,
+    0xFC, 0x2E, 0x8A, 0x3A, 0x70, 0xDB, 0x69, 0x3A, 0x69, 0xF1, 0x20, 0x39,
+    0xD8, 0xE8, 0xF7, 0xB8, 0x40, 0xFE, 0x5E, 0x3A, 0x3D, 0xD2, 0x63, 0xB9,
+    0x1E, 0x66, 0xD2, 0x39, 0x1E, 0x18, 0x9F, 0x3A, 0xE3, 0x47, 0x7D, 0xBA,
+    0x78, 0x69, 0x51, 0xBA, 0x3F, 0x36, 0xB0, 0x39, 0x03, 0x36, 0x3F, 0x3A,
+    0xE1, 0xA6, 0x0F, 0x3A, 0xB5, 0x42, 0xCB, 0x39, 0x9C, 0xB7, 0xE1, 0xB9,
+    0x34, 0xDE, 0x69, 0xBA, 0xE8, 0xA7, 0x50, 0xBA, 0xDD, 0xBD, 0xC0, 0x38,
+    0x9F, 0x29, 0x88, 0x38, 0xC4, 0xE1, 0x9C, 0xBA, 0x0F, 0x79, 0x20, 0xBB,
+    0x84, 0x4B, 0x2E, 0xBA, 0x43, 0x6E, 0x37, 0x3E, 0xDD, 0xA1, 0x1F, 0xBE,
+    0x02, 0xF2, 0x7D, 0xBD, 0x59, 0xDB, 0xD2, 0x3D, 0x8D, 0xED, 0x8B, 0x3D,
+    0x68, 0x64, 0xBE, 0x3D, 0xE4, 0xF3, 0x17, 0xBB, 0x3F, 0x49, 0x67, 0x3E,
+    0x71, 0xE0, 0x13, 0x3D, 0x11, 0x44, 0x17, 0x3E, 0x47, 0x24, 0xD6, 0xBA,
+    0x7A, 0x25, 0xC9, 0x3D, 0x08, 0x16, 0x80, 0x3E, 0xB1, 0x30, 0x18, 0x3E,
+    0x0B, 0x9C, 0x68, 0x3D, 0xA7, 0xA3, 0xA9, 0x3E, 0xE8, 0x0B, 0x23, 0x3E,
+    0xE1, 0x4E, 0x9B, 0x3D, 0x42, 0x22, 0x68, 0x3C, 0x26, 0xCA, 0x96, 0x3D,
+    0x5B, 0xBE, 0x27, 0xBD, 0xF5, 0xD6, 0x0B, 0x3F, 0xF6, 0x0C, 0xE0, 0xBC,
+    0x70, 0x53, 0x68, 0xBD, 0x7D, 0xC2, 0x9F, 0x3E, 0x81, 0x0B, 0xA2, 0xBC,
+    0xF3, 0x39, 0x85, 0x3A, 0x0E, 0xEF, 0xE7, 0x3D, 0xE2, 0x71, 0x14, 0x3E,
+    0x6E, 0x9D, 0x47, 0xBD, 0x49, 0xB5, 0x37, 0x3E, 0x6C, 0x2D, 0xE4, 0x3E,
+    0x3A, 0x55, 0x18, 0xBF, 0x54, 0xE4, 0x06, 0x3E, 0x0C, 0x10, 0xB3, 0xBC,
+    0x80, 0xBD, 0x66, 0xBD, 0x57, 0x0E, 0x99, 0xBD, 0x5F, 0xCA, 0xC6, 0xBE,
+    0xEB, 0xF7, 0xF3, 0xBD, 0x52, 0xAC, 0x63, 0xBE, 0x24, 0x2C, 0x3A, 0xBD,
+    0x56, 0xBA, 0x3B, 0xBD, 0x0C, 0x31, 0xCC, 0xBE, 0x66, 0x24, 0xC0, 0xBD,
+    0x7A, 0xAF, 0x83, 0xBE, 0xC3, 0x98, 0xAF, 0xBC, 0xB6, 0xBD, 0x04, 0xBF,
+    0xAB, 0xE9, 0x9C, 0xBE, 0x53, 0x04, 0x97, 0xBD, 0x22, 0x7A, 0xA5, 0xBD,
+    0xA6, 0x6C, 0x07, 0xBE, 0x1F, 0x4E, 0x6D, 0xBD, 0x76, 0xCD, 0xA1, 0xBE,
+    0x18, 0xE5, 0x10, 0x3E, 0x4E, 0x7D, 0xAD, 0xBD, 0x1D, 0xAD, 0x41, 0x3B,
+    0xCD, 0xF9, 0x63, 0x3E, 0xB8, 0x16, 0x33, 0xBF, 0xA5, 0xBC, 0x87, 0xB8,
+    0x21, 0xCF, 0x79, 0xBE, 0x57, 0x84, 0x05, 0xBF, 0xD7, 0x50, 0xC3, 0x3C,
+    0xE9, 0x6F, 0x4D, 0xBE, 0x9E, 0x1D, 0x03, 0xBF, 0x55, 0xE7, 0xA6, 0x3E,
+    0xA2, 0x38, 0x30, 0x3D, 0x20, 0xC1, 0x06, 0x3E, 0x68, 0x55, 0xF9, 0x3D,
+    0xCE, 0xA5, 0x49, 0x3E, 0xC0, 0x51, 0x99, 0x3E, 0xF8, 0xCB, 0xB7, 0xBC,
+    0x17, 0xA2, 0x5F, 0xBD, 0x74, 0xBC, 0x0E, 0x3E, 0x73, 0x5D, 0x2D, 0x3E,
+    0x8A, 0xB2, 0x09, 0x3E, 0x74, 0xDF, 0x20, 0x3D, 0x1A, 0x4C, 0xAB, 0x3E,
+    0x7B, 0x71, 0xE2, 0x3B, 0xBF, 0x71, 0xDA, 0x3D, 0x01, 0xA9, 0x8A, 0x3E,
+    0x34, 0xA3, 0x89, 0x3D, 0x65, 0xD7, 0x80, 0x3D, 0x4D, 0x41, 0xD1, 0xBC,
+    0x15, 0x5C, 0x14, 0x3E, 0xA0, 0x70, 0xEF, 0x3D, 0x6F, 0xDE, 0x31, 0xBC,
+    0x75, 0x4A, 0x55, 0x3D, 0x72, 0x79, 0x2D, 0x3E, 0x2A, 0xFC, 0x49, 0x3D,
+    0x0B, 0xF5, 0xFC, 0x3D, 0x28, 0xE2, 0xDA, 0xBA, 0x00, 0x5A, 0x51, 0x3E,
+    0x4A, 0x26, 0x45, 0x3E, 0xDA, 0xDB, 0xC0, 0xBC, 0x9A, 0x61, 0xA2, 0x3E,
+    0xA4, 0x49, 0x7C, 0x3E, 0x08, 0xC8, 0xB4, 0x3A, 0x71, 0x7B, 0x0C, 0x3A,
+    0x86, 0x45, 0x96, 0x3A, 0xE6, 0x1E, 0x9D, 0xB9, 0x4D, 0x2D, 0x73, 0xB9,
+    0xF7, 0xDE, 0x4B, 0x3A, 0x84, 0x85, 0x17, 0x39, 0x3E, 0x1F, 0xCE, 0xB8,
+    0x7C, 0xD6, 0x5A, 0x39, 0xEA, 0x7F, 0x12, 0xBB, 0x9A, 0x11, 0x1F, 0x3A,
+    0x76, 0xD0, 0x03, 0x3A, 0x69, 0x66, 0x64, 0xB9, 0x93, 0xEC, 0x49, 0x3A,
+    0x46, 0x72, 0x74, 0xB9, 0xB4, 0x5B, 0xCB, 0x38, 0x13, 0xC2, 0xD8, 0x38,
+    0x08, 0xD7, 0x15, 0xBA, 0x47, 0xAC, 0x48, 0x3A, 0x5D, 0xDA, 0x1B, 0xB9,
+    0xF8, 0x05, 0xAB, 0x3A, 0xE8, 0x66, 0xA1, 0xB9, 0xE3, 0xD9, 0x50, 0x3A,
+    0x9D, 0x13, 0x10, 0x3A, 0xAB, 0xAC, 0xAF, 0x3A, 0x84, 0xF6, 0x02, 0x3B,
+    0xC1, 0x70, 0x44, 0xBA, 0x86, 0xA0, 0xB0, 0xBA, 0x32, 0xAF, 0x16, 0x3A,
+    0x46, 0xA2, 0xDF, 0x3A, 0x39, 0x08, 0x5B, 0x3A, 0xCA, 0x18, 0x8F, 0x3A,
+    0xE2, 0xEB, 0xB6, 0xBD, 0xED, 0x5C, 0x14, 0xBE, 0x21, 0xA7, 0xF8, 0xBD,
+    0x99, 0xA0, 0x3D, 0xBD, 0x9C, 0x3F, 0x93, 0xBD, 0x8C, 0x99, 0x90, 0xBE,
+    0xC9, 0x8A, 0x0C, 0xBE, 0xA4, 0xD2, 0x18, 0xBE, 0x00, 0xF3, 0xC2, 0xBD,
+    0x69, 0x9D, 0x96, 0xBD, 0x87, 0x87, 0x07, 0xBD, 0xF2, 0x60, 0x06, 0xBE,
+    0xFC, 0x24, 0xE9, 0xBD, 0x9D, 0xEC, 0x01, 0x3D, 0x32, 0x03, 0xEF, 0xBB,
+    0x6C, 0x98, 0x9D, 0xBE, 0xCA, 0x55, 0xD9, 0xBD, 0x17, 0xCC, 0x1F, 0xBE,
+    0x8D, 0x7F, 0x37, 0xBE, 0x4E, 0xCC, 0x53, 0xBD, 0x4A, 0xD3, 0xA9, 0xBD,
+    0xCA, 0x45, 0x57, 0xBE, 0xEC, 0x5A, 0x03, 0xBE, 0x8E, 0xDD, 0x41, 0xBE,
+    0xC2, 0xA0, 0x21, 0x3D, 0x70, 0x58, 0x5F, 0xBD, 0xA7, 0x2E, 0x32, 0xBA,
+    0x4B, 0xE7, 0x8D, 0x3B, 0x32, 0x8D, 0xB1, 0xBC, 0x83, 0xF2, 0xB3, 0x3C,
+    0xB6, 0xB8, 0x90, 0xBD, 0x25, 0xAB, 0x73, 0xBE, 0xCC, 0xCD, 0x05, 0xBE,
+    0x67, 0x7F, 0x66, 0xBD, 0x8F, 0x7C, 0x80, 0xBD, 0xF1, 0x23, 0x55, 0xBD,
+    0xE5, 0x0D, 0xA4, 0xBD, 0x54, 0x21, 0xC8, 0xBD, 0x9C, 0x20, 0xC6, 0xBC,
+    0x64, 0xF2, 0x1F, 0xBD, 0xFC, 0xDA, 0x86, 0xBD, 0x4C, 0x9C, 0xA6, 0xBD,
+    0x44, 0xC6, 0x38, 0xBD, 0xA2, 0x1F, 0x4B, 0xBD, 0xF7, 0x19, 0x07, 0xBE,
+    0x8B, 0xB9, 0x4D, 0xBD, 0xCC, 0x07, 0x07, 0xBE, 0x0C, 0x07, 0x06, 0xBE,
+    0x9D, 0x68, 0x61, 0xBD, 0xAD, 0x6C, 0xA2, 0xBD, 0xFF, 0x62, 0x11, 0xBD,
+    0x12, 0xC6, 0x85, 0xBD, 0xF2, 0x35, 0xE9, 0xBD, 0xB1, 0x41, 0xF2, 0xBC,
+    0xB8, 0x97, 0x33, 0xBD, 0x8A, 0x8D, 0xD6, 0xBD, 0xA7, 0x0F, 0x14, 0xBE,
+    0x3A, 0xE6, 0x83, 0xBD, 0xF9, 0xC7, 0x7C, 0xBA, 0xD5, 0x8B, 0x84, 0xBC,
+    0x19, 0xFE, 0xE2, 0xBD, 0xE7, 0x93, 0xB0, 0x39, 0xE5, 0x85, 0x0C, 0xBE,
+    0x39, 0x72, 0x6E, 0xBE, 0xB2, 0x26, 0x58, 0x3E, 0x1A, 0x53, 0x26, 0x3D,
+    0x65, 0xBF, 0x82, 0x3E, 0x5F, 0x75, 0x37, 0x3E, 0x06, 0x3B, 0x67, 0x3E,
+    0x3A, 0x7B, 0x9C, 0x3E, 0x41, 0x8E, 0x78, 0x3E, 0xCA, 0xEA, 0x53, 0x3E,
+    0x68, 0x02, 0x66, 0x3E, 0xB4, 0x96, 0x38, 0x3E, 0xEA, 0xD7, 0xF0, 0x3D,
+    0x9F, 0xDD, 0x67, 0x3E, 0x79, 0x12, 0x8E, 0x3D, 0x72, 0xD4, 0x93, 0x3D,
+    0x2F, 0xA7, 0x71, 0x3E, 0xF0, 0xA1, 0xBD, 0x3E, 0xAF, 0x11, 0x64, 0x3E,
+    0x5F, 0xFB, 0x6A, 0x3E, 0xB6, 0xB1, 0x86, 0x3E, 0x6F, 0x80, 0x34, 0x3E,
+    0x13, 0xD5, 0xA6, 0xBC, 0x0C, 0x4C, 0x7D, 0xBE, 0x85, 0x5A, 0x73, 0x3E,
+    0xF5, 0x53, 0xA7, 0x3E, 0x99, 0xB4, 0xFB, 0xBD, 0x99, 0xBC, 0x55, 0x3E,
+    0xCF, 0x85, 0x31, 0xBB, 0x14, 0x6C, 0x00, 0x3F, 0xB5, 0xFC, 0x5A, 0x3E,
+    0x03, 0x91, 0x43, 0xBE, 0x7B, 0xAC, 0xE0, 0xBD, 0x6E, 0xCD, 0x9C, 0xBD,
+    0x66, 0xCC, 0x6F, 0xBD, 0x9D, 0x75, 0xFC, 0x3D, 0xCA, 0x3A, 0xCD, 0x3D,
+    0x9D, 0x80, 0x2F, 0x3E, 0xF6, 0x69, 0xED, 0x3D, 0x6C, 0x99, 0x58, 0x3E,
+    0xCB, 0x1A, 0x0E, 0x3E, 0x48, 0xCC, 0x9D, 0x3D, 0x7D, 0x0C, 0x03, 0x3E,
+    0xEB, 0xDF, 0xE6, 0x3D, 0x51, 0xFD, 0x22, 0x3E, 0x4D, 0xE2, 0xE9, 0x3D,
+    0x66, 0x74, 0x60, 0x3E, 0xBE, 0x81, 0xB9, 0xBD, 0x91, 0x0B, 0x50, 0x3E,
+    0xAF, 0x0C, 0xD8, 0x3E, 0xC7, 0xEF, 0x12, 0x3E, 0x08, 0xCB, 0x00, 0x3E,
+    0x9D, 0x2D, 0x8B, 0x3D, 0x8D, 0x74, 0x07, 0x3E, 0xFF, 0x82, 0x6A, 0x3E,
+    0x42, 0x7D, 0xCE, 0x3E, 0x40, 0x53, 0xDA, 0x3D, 0x92, 0x5D, 0xAC, 0x3D,
+    0x9A, 0x77, 0x44, 0x3E, 0x59, 0x64, 0x12, 0xBE, 0x9C, 0x40, 0x1D, 0xBA,
+    0x82, 0xB2, 0x05, 0x3F, 0x26, 0x4C, 0xFF, 0x3C, 0x8B, 0x44, 0x85, 0x3D,
+    0x5B, 0x92, 0x70, 0x3D, 0x7A, 0x1D, 0xA9, 0x3E, 0xA7, 0x7C, 0xF4, 0xBD,
+    0x62, 0xC0, 0x8C, 0xBD, 0x42, 0x5A, 0x06, 0x3E, 0xB0, 0x55, 0x4E, 0x3E,
+    0x09, 0x73, 0x37, 0x3E, 0xBF, 0x88, 0x8F, 0x3D, 0x0A, 0x92, 0x0B, 0x3E,
+    0xB0, 0x17, 0x2A, 0x3E, 0x1B, 0xB6, 0x34, 0x3E, 0x8B, 0x86, 0x51, 0x3E,
+    0x3B, 0xE5, 0x3B, 0x3E, 0x76, 0xBA, 0x45, 0x3E, 0xE6, 0xF2, 0x59, 0x3E,
+    0x54, 0xF5, 0x96, 0x3D, 0xEC, 0x1C, 0x11, 0x3F, 0x5E, 0xBF, 0xC4, 0x3E,
+    0x3A, 0xF4, 0x41, 0x3E, 0x26, 0x71, 0xE0, 0x3D, 0xEB, 0x59, 0x26, 0x3E,
+    0xB8, 0x25, 0x3E, 0x3E, 0x5C, 0x25, 0x40, 0x3E, 0x10, 0xD9, 0x73, 0x3F,
+    0x13, 0xF3, 0x17, 0x3E, 0xB3, 0x2C, 0x14, 0x3E, 0x87, 0x00, 0xFA, 0x3E,
+    0x8D, 0xBA, 0xAA, 0xBE, 0x8E, 0x4B, 0xDD, 0x38, 0x46, 0x53, 0xF4, 0x3D,
+    0x04, 0xA6, 0x3A, 0xBE, 0x95, 0x3B, 0xCE, 0xBC, 0x2D, 0x1E, 0x76, 0x3E,
+    0x8F, 0xDF, 0x8B, 0x3E, 0xC3, 0x4B, 0xAF, 0x3D, 0x18, 0x4E, 0xEF, 0x3D,
+    0x4C, 0x20, 0x29, 0x3E, 0xA9, 0x24, 0x16, 0x3E, 0x78, 0x1F, 0x07, 0x3E,
+    0x91, 0xAB, 0xA8, 0x3D, 0xC9, 0x07, 0x21, 0x3E, 0xD0, 0x99, 0x13, 0x3E,
+    0xCF, 0x7B, 0x1B, 0x3E, 0xD4, 0x7D, 0x0F, 0x3E, 0x65, 0xDF, 0x8A, 0x3C,
+    0xF7, 0xC8, 0x21, 0x3E, 0x6D, 0xA8, 0x80, 0x3D, 0x1D, 0x46, 0x5C, 0xBD,
+    0xFE, 0xE3, 0x99, 0xBD, 0x8A, 0x54, 0x3F, 0x3D, 0xA7, 0xFC, 0x17, 0x3E,
+    0x90, 0x8B, 0xDF, 0x3D, 0x83, 0xDC, 0x36, 0x3E, 0xEF, 0x89, 0x08, 0x3E,
+    0x73, 0x58, 0x48, 0x3E, 0x93, 0xB0, 0xC1, 0x3D, 0x97, 0xE1, 0x24, 0x3E,
+    0xEE, 0x47, 0x5C, 0x3D, 0x0D, 0x6E, 0x6B, 0xBD, 0xFB, 0x7A, 0x95, 0x3D,
+    0x94, 0x6E, 0x6F, 0xBA, 0x69, 0x6F, 0x9C, 0x3D, 0x56, 0x98, 0x02, 0x3D,
+    0xE8, 0xF6, 0x3A, 0xBD, 0x12, 0xE4, 0x3A, 0x3E, 0xCA, 0x04, 0xC1, 0x3D,
+    0x5C, 0xC0, 0xE9, 0xBD, 0x40, 0xDD, 0x52, 0x3E, 0xF4, 0xDB, 0x18, 0xBE,
+    0x9F, 0x19, 0x85, 0xBD, 0xB9, 0x76, 0xE3, 0xBD, 0x06, 0x03, 0x1B, 0xBC,
+    0xBB, 0xC6, 0x21, 0xBE, 0x43, 0xCA, 0x73, 0xBE, 0xB0, 0x21, 0xF7, 0xBD,
+    0x7E, 0xB1, 0xFD, 0xBD, 0xA4, 0x2E, 0x01, 0xBE, 0xD4, 0xC0, 0x20, 0xBE,
+    0x17, 0x39, 0x27, 0x3C, 0x7C, 0x5F, 0x88, 0x3D, 0xD7, 0xBE, 0x54, 0xBE,
+    0x06, 0x95, 0x77, 0xBD, 0x3A, 0x36, 0x9C, 0xBD, 0xFA, 0x4C, 0x7C, 0xBE,
+    0x7E, 0xB4, 0x7E, 0xBE, 0xAC, 0xFF, 0xEF, 0xBD, 0xF5, 0x75, 0x86, 0xBE,
+    0x1F, 0x24, 0x41, 0xBF, 0xE7, 0x93, 0x38, 0xBE, 0xD4, 0xE1, 0xD1, 0xBD,
+    0x17, 0x25, 0x72, 0xBE, 0x55, 0xFB, 0xCA, 0x3D, 0xDB, 0x25, 0xF6, 0xBA,
+    0x42, 0x40, 0xFF, 0xBD, 0x6B, 0x8E, 0x34, 0xBC, 0xF9, 0xA2, 0xF9, 0x3C,
+    0x69, 0x98, 0xE1, 0x3D, 0x02, 0xE5, 0x2D, 0xBE, 0x8A, 0x53, 0x91, 0x3E,
+    0x2B, 0x86, 0x9C, 0xBD, 0x5E, 0x99, 0x13, 0x3E, 0x2E, 0x03, 0xB1, 0x3D,
+    0x70, 0x39, 0xFF, 0x3D, 0x44, 0x8B, 0x19, 0x3E, 0xF4, 0xF7, 0xDE, 0x3D,
+    0xFD, 0xCC, 0xD1, 0x3D, 0x30, 0x53, 0xF7, 0x3D, 0x0F, 0x64, 0xD7, 0x3D,
+    0x15, 0x5A, 0x04, 0x3D, 0xB9, 0x0C, 0xE8, 0x3D, 0x59, 0xD8, 0x46, 0xBD,
+    0xEB, 0x98, 0x59, 0xBE, 0xF1, 0x4D, 0xF4, 0xBD, 0x15, 0x98, 0x56, 0x3E,
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diff --git a/libavfilter/vf_srcnn.c b/libavfilter/vf_srcnn.c
index d6efe9b478..d5b6fa0c3d 100644
--- a/libavfilter/vf_srcnn.c
+++ b/libavfilter/vf_srcnn.c
@@ -41,7 +41,6 @@  typedef struct SRCNNContext {
     DNNData input_output;
 } SRCNNContext;
 #define OFFSET(x) offsetof(SRCNNContext, x)
 static const AVOption srcnn_options[] = {
@@ -55,7 +54,15 @@  static av_cold int init(AVFilterContext* context)
     SRCNNContext* srcnn_context = context->priv;
+    srcnn_context->dnn_module = ff_get_dnn_module(DNN_TF);
+    if (!srcnn_context->dnn_module){
+        av_log(context, AV_LOG_INFO, "could not load tensorflow backend, using native backend instead\n");
+        srcnn_context->dnn_module = ff_get_dnn_module(DNN_NATIVE);
+    }
     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);