libavfi/dnn: add LibTorch as one of DNN backend

PyTorch is an open source machine learning framework that accelerates
the path from research prototyping to production deployment. Official
website: https://pytorch.org/. We call the C++ library of PyTorch as
LibTorch, the same below.

To build FFmpeg with LibTorch, please take following steps as
reference:
1. download LibTorch C++ library in
 https://pytorch.org/get-started/locally/,
please select C++/Java for language, and other options as your need.
Please download cxx11 ABI version:
 (libtorch-cxx11-abi-shared-with-deps-*.zip).
2. unzip the file to your own dir, with command
unzip libtorch-shared-with-deps-latest.zip -d your_dir
3. export libtorch_root/libtorch/include and
libtorch_root/libtorch/include/torch/csrc/api/include to $PATH
export libtorch_root/libtorch/lib/ to $LD_LIBRARY_PATH
4. config FFmpeg with ../configure --enable-libtorch \
 --extra-cflag=-I/libtorch_root/libtorch/include \
 --extra-cflag=-I/libtorch_root/libtorch/include/torch/csrc/api/include \
 --extra-ldflags=-L/libtorch_root/libtorch/lib/
5. make

To run FFmpeg DNN inference with LibTorch backend:
./ffmpeg -i input.jpg -vf \
dnn_processing=dnn_backend=torch:model=LibTorch_model.pt -y output.jpg

The LibTorch_model.pt can be generated by Python with torch.jit.script()
api. https://pytorch.org/tutorials/advanced/cpp_export.html. This is
pytorch official guide about how to convert and load torchscript model.
Please note, torch.jit.trace() is not recommanded, since it does
not support ambiguous input size.

Signed-off-by: Ting Fu <ting.fu@intel.com>
Signed-off-by: Wenbin Chen <wenbin.chen@intel.com>
Reviewed-by: Guo Yejun <yejun.guo@intel.com>

configure

@@ -281,6 +281,7 @@ External library support:
   --enable-libtheora       enable Theora encoding via libtheora [no]
   --enable-libtls          enable LibreSSL (via libtls), needed for https support
                            if openssl, gnutls or mbedtls is not used [no]
+  --enable-libtorch        enable Torch as one DNN backend [no]
   --enable-libtwolame      enable MP2 encoding via libtwolame [no]
   --enable-libuavs3d       enable AVS3 decoding via libuavs3d [no]
   --enable-libv4l2         enable libv4l2/v4l-utils [no]
@@ -1905,6 +1906,7 @@ EXTERNAL_LIBRARY_LIST="
     libtensorflow
     libtesseract
     libtheora
+    libtorch
     libtwolame
     libuavs3d
     libv4l2
@@ -2785,7 +2787,7 @@ cbs_vp9_select="cbs"
 deflate_wrapper_deps="zlib"
 dirac_parse_select="golomb"
 dovi_rpu_select="golomb"
-dnn_suggest="libtensorflow libopenvino"
+dnn_suggest="libtensorflow libopenvino libtorch"
 dnn_deps="avformat swscale"
 error_resilience_select="me_cmp"
 evcparse_select="golomb"
@@ -6884,6 +6886,7 @@ enabled libtensorflow     && require libtensorflow tensorflow/c/c_api.h TF_Versi
 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
+enabled libtorch          && check_cxxflags -std=c++17 && require_cpp libtorch torch/torch.h "torch::Tensor" -ltorch -lc10 -ltorch_cpu -lstdc++ -lpthread
 enabled libtwolame        && require libtwolame twolame.h twolame_init -ltwolame &&
                              { check_lib libtwolame twolame.h twolame_encode_buffer_float32_interleaved -ltwolame ||
                                die "ERROR: libtwolame must be installed and version must be >= 0.3.10"; }

libavfilter/dnn/Makefile

@@ -6,5 +6,6 @@ OBJS-$(CONFIG_DNN)                           += dnn/dnn_backend_common.o
 
 DNN-OBJS-$(CONFIG_LIBTENSORFLOW)             += dnn/dnn_backend_tf.o
 DNN-OBJS-$(CONFIG_LIBOPENVINO)               += dnn/dnn_backend_openvino.o
+DNN-OBJS-$(CONFIG_LIBTORCH)                  += dnn/dnn_backend_torch.o
 
 OBJS-$(CONFIG_DNN)                           += $(DNN-OBJS-yes)

libavfilter/dnn/dnn_backend_torch.cpp

@@ -0,0 +1,597 @@
+/*
+ * Copyright (c) 2024
+ *
+ * This file is part of FFmpeg.
+ *
+ * FFmpeg is free software; you can redistribute it and/or
+ * modify it under the terms of the GNU Lesser General Public
+ * License as published by the Free Software Foundation; either
+ * version 2.1 of the License, or (at your option) any later version.
+ *
+ * FFmpeg is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+ * Lesser General Public License for more details.
+ *
+ * You should have received a copy of the GNU Lesser General Public
+ * License along with FFmpeg; if not, write to the Free Software
+ * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
+ */
+
+/**
+ * @file
+ * DNN Torch backend implementation.
+ */
+
+#include <torch/torch.h>
+#include <torch/script.h>
+
+extern "C" {
+#include "../internal.h"
+#include "dnn_io_proc.h"
+#include "dnn_backend_common.h"
+#include "libavutil/opt.h"
+#include "queue.h"
+#include "safe_queue.h"
+}
+
+typedef struct THOptions{
+    char *device_name;
+    int optimize;
+} THOptions;
+
+typedef struct THContext {
+    const AVClass *c_class;
+    THOptions options;
+} THContext;
+
+typedef struct THModel {
+    THContext ctx;
+    DNNModel *model;
+    torch::jit::Module *jit_model;
+    SafeQueue *request_queue;
+    Queue *task_queue;
+    Queue *lltask_queue;
+} THModel;
+
+typedef struct THInferRequest {
+    torch::Tensor *output;
+    torch::Tensor *input_tensor;
+} THInferRequest;
+
+typedef struct THRequestItem {
+    THInferRequest *infer_request;
+    LastLevelTaskItem *lltask;
+    DNNAsyncExecModule exec_module;
+} THRequestItem;
+
+
+#define OFFSET(x) offsetof(THContext, x)
+#define FLAGS AV_OPT_FLAG_FILTERING_PARAM
+static const AVOption dnn_th_options[] = {
+    { "device", "device to run model", OFFSET(options.device_name), AV_OPT_TYPE_STRING, { .str = "cpu" }, 0, 0, FLAGS },
+    { "optimize", "turn on graph executor optimization", OFFSET(options.optimize), AV_OPT_TYPE_INT, { .i64 = 0 }, 0, 1, FLAGS},
+    { NULL }
+};
+
+AVFILTER_DEFINE_CLASS(dnn_th);
+
+static int extract_lltask_from_task(TaskItem *task, Queue *lltask_queue)
+{
+    THModel *th_model = (THModel *)task->model;
+    THContext *ctx = &th_model->ctx;
+    LastLevelTaskItem *lltask = (LastLevelTaskItem *)av_malloc(sizeof(*lltask));
+    if (!lltask) {
+        av_log(ctx, AV_LOG_ERROR, "Failed to allocate memory for LastLevelTaskItem\n");
+        return AVERROR(ENOMEM);
+    }
+    task->inference_todo = 1;
+    task->inference_done = 0;
+    lltask->task = task;
+    if (ff_queue_push_back(lltask_queue, lltask) < 0) {
+        av_log(ctx, AV_LOG_ERROR, "Failed to push back lltask_queue.\n");
+        av_freep(&lltask);
+        return AVERROR(ENOMEM);
+    }
+    return 0;
+}
+
+static void th_free_request(THInferRequest *request)
+{
+    if (!request)
+        return;
+    if (request->output) {
+        delete(request->output);
+        request->output = NULL;
+    }
+    if (request->input_tensor) {
+        delete(request->input_tensor);
+        request->input_tensor = NULL;
+    }
+    return;
+}
+
+static inline void destroy_request_item(THRequestItem **arg)
+{
+    THRequestItem *item;
+    if (!arg || !*arg) {
+        return;
+    }
+    item = *arg;
+    th_free_request(item->infer_request);
+    av_freep(&item->infer_request);
+    av_freep(&item->lltask);
+    ff_dnn_async_module_cleanup(&item->exec_module);
+    av_freep(arg);
+}
+
+static void dnn_free_model_th(DNNModel **model)
+{
+    THModel *th_model;
+    if (!model || !*model)
+        return;
+
+    th_model = (THModel *) (*model)->model;
+    while (ff_safe_queue_size(th_model->request_queue) != 0) {
+        THRequestItem *item = (THRequestItem *)ff_safe_queue_pop_front(th_model->request_queue);
+        destroy_request_item(&item);
+    }
+    ff_safe_queue_destroy(th_model->request_queue);
+
+    while (ff_queue_size(th_model->lltask_queue) != 0) {
+        LastLevelTaskItem *item = (LastLevelTaskItem *)ff_queue_pop_front(th_model->lltask_queue);
+        av_freep(&item);
+    }
+    ff_queue_destroy(th_model->lltask_queue);
+
+    while (ff_queue_size(th_model->task_queue) != 0) {
+        TaskItem *item = (TaskItem *)ff_queue_pop_front(th_model->task_queue);
+        av_frame_free(&item->in_frame);
+        av_frame_free(&item->out_frame);
+        av_freep(&item);
+    }
+    ff_queue_destroy(th_model->task_queue);
+    delete th_model->jit_model;
+    av_opt_free(&th_model->ctx);
+    av_freep(&th_model);
+    av_freep(model);
+}
+
+static int get_input_th(void *model, DNNData *input, const char *input_name)
+{
+    input->dt = DNN_FLOAT;
+    input->order = DCO_RGB;
+    input->layout = DL_NCHW;
+    input->dims[0] = 1;
+    input->dims[1] = 3;
+    input->dims[2] = -1;
+    input->dims[3] = -1;
+    return 0;
+}
+
+static void deleter(void *arg)
+{
+    av_freep(&arg);
+}
+
+static int fill_model_input_th(THModel *th_model, THRequestItem *request)
+{
+    LastLevelTaskItem *lltask = NULL;
+    TaskItem *task = NULL;
+    THInferRequest *infer_request = NULL;
+    DNNData input = { 0 };
+    THContext *ctx = &th_model->ctx;
+    int ret, width_idx, height_idx, channel_idx;
+
+    lltask = (LastLevelTaskItem *)ff_queue_pop_front(th_model->lltask_queue);
+    if (!lltask) {
+        ret = AVERROR(EINVAL);
+        goto err;
+    }
+    request->lltask = lltask;
+    task = lltask->task;
+    infer_request = request->infer_request;
+
+    ret = get_input_th(th_model, &input, NULL);
+    if ( ret != 0) {
+        goto err;
+    }
+    width_idx = dnn_get_width_idx_by_layout(input.layout);
+    height_idx = dnn_get_height_idx_by_layout(input.layout);
+    channel_idx = dnn_get_channel_idx_by_layout(input.layout);
+    input.dims[height_idx] = task->in_frame->height;
+    input.dims[width_idx] = task->in_frame->width;
+    input.data = av_malloc(input.dims[height_idx] * input.dims[width_idx] *
+                           input.dims[channel_idx] * sizeof(float));
+    if (!input.data)
+        return AVERROR(ENOMEM);
+    infer_request->input_tensor = new torch::Tensor();
+    infer_request->output = new torch::Tensor();
+
+    switch (th_model->model->func_type) {
+    case DFT_PROCESS_FRAME:
+        input.scale = 255;
+        if (task->do_ioproc) {
+            if (th_model->model->frame_pre_proc != NULL) {
+                th_model->model->frame_pre_proc(task->in_frame, &input, th_model->model->filter_ctx);
+            } else {
+                ff_proc_from_frame_to_dnn(task->in_frame, &input, ctx);
+            }
+        }
+        break;
+    default:
+        avpriv_report_missing_feature(NULL, "model function type %d", th_model->model->func_type);
+        break;
+    }
+    *infer_request->input_tensor = torch::from_blob(input.data,
+        {1, input.dims[channel_idx], input.dims[height_idx], input.dims[width_idx]},
+        deleter, torch::kFloat32);
+    return 0;
+
+err:
+    th_free_request(infer_request);
+    return ret;
+}
+
+static int th_start_inference(void *args)
+{
+    THRequestItem *request = (THRequestItem *)args;
+    THInferRequest *infer_request = NULL;
+    LastLevelTaskItem *lltask = NULL;
+    TaskItem *task = NULL;
+    THModel *th_model = NULL;
+    THContext *ctx = NULL;
+    std::vector<torch::jit::IValue> inputs;
+    torch::NoGradGuard no_grad;
+
+    if (!request) {
+        av_log(NULL, AV_LOG_ERROR, "THRequestItem is NULL\n");
+        return AVERROR(EINVAL);
+    }
+    infer_request = request->infer_request;
+    lltask = request->lltask;
+    task = lltask->task;
+    th_model = (THModel *)task->model;
+    ctx = &th_model->ctx;
+
+    if (ctx->options.optimize)
+        torch::jit::setGraphExecutorOptimize(true);
+    else
+        torch::jit::setGraphExecutorOptimize(false);
+
+    if (!infer_request->input_tensor || !infer_request->output) {
+        av_log(ctx, AV_LOG_ERROR, "input or output tensor is NULL\n");
+        return DNN_GENERIC_ERROR;
+    }
+    inputs.push_back(*infer_request->input_tensor);
+
+    *infer_request->output = th_model->jit_model->forward(inputs).toTensor();
+
+    return 0;
+}
+
+static void infer_completion_callback(void *args) {
+    THRequestItem *request = (THRequestItem*)args;
+    LastLevelTaskItem *lltask = request->lltask;
+    TaskItem *task = lltask->task;
+    DNNData outputs = { 0 };
+    THInferRequest *infer_request = request->infer_request;
+    THModel *th_model = (THModel *)task->model;
+    torch::Tensor *output = infer_request->output;
+
+    c10::IntArrayRef sizes = output->sizes();
+    outputs.order = DCO_RGB;
+    outputs.layout = DL_NCHW;
+    outputs.dt = DNN_FLOAT;
+    if (sizes.size() == 4) {
+        // 4 dimensions: [batch_size, channel, height, width]
+        // this format of data is normally used for video frame SR
+        outputs.dims[0] = sizes.at(0); // N
+        outputs.dims[1] = sizes.at(1); // C
+        outputs.dims[2] = sizes.at(2); // H
+        outputs.dims[3] = sizes.at(3); // W
+    } else {
+        avpriv_report_missing_feature(&th_model->ctx, "Support of this kind of model");
+        goto err;
+    }
+
+    switch (th_model->model->func_type) {
+    case DFT_PROCESS_FRAME:
+        if (task->do_ioproc) {
+            outputs.scale = 255;
+            outputs.data = output->data_ptr();
+            if (th_model->model->frame_post_proc != NULL) {
+                th_model->model->frame_post_proc(task->out_frame, &outputs, th_model->model->filter_ctx);
+            } else {
+                ff_proc_from_dnn_to_frame(task->out_frame, &outputs, &th_model->ctx);
+            }
+        } else {
+            task->out_frame->width = outputs.dims[dnn_get_width_idx_by_layout(outputs.layout)];
+            task->out_frame->height = outputs.dims[dnn_get_height_idx_by_layout(outputs.layout)];
+        }
+        break;
+    default:
+        avpriv_report_missing_feature(&th_model->ctx, "model function type %d", th_model->model->func_type);
+        goto err;
+    }
+    task->inference_done++;
+    av_freep(&request->lltask);
+err:
+    th_free_request(infer_request);
+
+    if (ff_safe_queue_push_back(th_model->request_queue, request) < 0) {
+        destroy_request_item(&request);
+        av_log(&th_model->ctx, AV_LOG_ERROR, "Unable to push back request_queue when failed to start inference.\n");
+    }
+}
+
+static int execute_model_th(THRequestItem *request, Queue *lltask_queue)
+{
+    THModel *th_model = NULL;
+    LastLevelTaskItem *lltask;
+    TaskItem *task = NULL;
+    int ret = 0;
+
+    if (ff_queue_size(lltask_queue) == 0) {
+        destroy_request_item(&request);
+        return 0;
+    }
+
+    lltask = (LastLevelTaskItem *)ff_queue_peek_front(lltask_queue);
+    if (lltask == NULL) {
+        av_log(NULL, AV_LOG_ERROR, "Failed to get LastLevelTaskItem\n");
+        ret = AVERROR(EINVAL);
+        goto err;
+    }
+    task = lltask->task;
+    th_model = (THModel *)task->model;
+
+    ret = fill_model_input_th(th_model, request);
+    if ( ret != 0) {
+        goto err;
+    }
+    if (task->async) {
+        avpriv_report_missing_feature(&th_model->ctx, "LibTorch async");
+    } else {
+        ret = th_start_inference((void *)(request));
+        if (ret != 0) {
+            goto err;
+        }
+        infer_completion_callback(request);
+        return (task->inference_done == task->inference_todo) ? 0 : DNN_GENERIC_ERROR;
+    }
+
+err:
+    th_free_request(request->infer_request);
+    if (ff_safe_queue_push_back(th_model->request_queue, request) < 0) {
+        destroy_request_item(&request);
+    }
+    return ret;
+}
+
+static int get_output_th(void *model, const char *input_name, int input_width, int input_height,
+                                   const char *output_name, int *output_width, int *output_height)
+{
+    int ret = 0;
+    THModel *th_model = (THModel*) model;
+    THContext *ctx = &th_model->ctx;
+    TaskItem task = { 0 };
+    THRequestItem *request = NULL;
+    DNNExecBaseParams exec_params = {
+        .input_name     = input_name,
+        .output_names   = &output_name,
+        .nb_output      = 1,
+        .in_frame       = NULL,
+        .out_frame      = NULL,
+    };
+    ret = ff_dnn_fill_gettingoutput_task(&task, &exec_params, th_model, input_height, input_width, ctx);
+    if ( ret != 0) {
+        goto err;
+    }
+
+    ret = extract_lltask_from_task(&task, th_model->lltask_queue);
+    if ( ret != 0) {
+        av_log(ctx, AV_LOG_ERROR, "unable to extract last level task from task.\n");
+        goto err;
+    }
+
+    request = (THRequestItem*) ff_safe_queue_pop_front(th_model->request_queue);
+    if (!request) {
+        av_log(ctx, AV_LOG_ERROR, "unable to get infer request.\n");
+        ret = AVERROR(EINVAL);
+        goto err;
+    }
+
+    ret = execute_model_th(request, th_model->lltask_queue);
+    *output_width = task.out_frame->width;
+    *output_height = task.out_frame->height;
+
+err:
+    av_frame_free(&task.out_frame);
+    av_frame_free(&task.in_frame);
+    return ret;
+}
+
+static THInferRequest *th_create_inference_request(void)
+{
+    THInferRequest *request = (THInferRequest *)av_malloc(sizeof(THInferRequest));
+    if (!request) {
+        return NULL;
+    }
+    request->input_tensor = NULL;
+    request->output = NULL;
+    return request;
+}
+
+static DNNModel *dnn_load_model_th(const char *model_filename, DNNFunctionType func_type, const char *options, AVFilterContext *filter_ctx)
+{
+    DNNModel *model = NULL;
+    THModel *th_model = NULL;
+    THRequestItem *item = NULL;
+    THContext *ctx;
+
+    model = (DNNModel *)av_mallocz(sizeof(DNNModel));
+    if (!model) {
+        return NULL;
+    }
+
+    th_model = (THModel *)av_mallocz(sizeof(THModel));
+    if (!th_model) {
+        av_freep(&model);
+        return NULL;
+    }
+    th_model->model = model;
+    model->model = th_model;
+    th_model->ctx.c_class = &dnn_th_class;
+    ctx = &th_model->ctx;
+    //parse options
+    av_opt_set_defaults(ctx);
+    if (av_opt_set_from_string(ctx, options, NULL, "=", "&") < 0) {
+        av_log(ctx, AV_LOG_ERROR, "Failed to parse options \"%s\"\n", options);
+        return NULL;
+    }
+
+    c10::Device device = c10::Device(ctx->options.device_name);
+    if (!device.is_cpu()) {
+        av_log(ctx, AV_LOG_ERROR, "Not supported device:\"%s\"\n", ctx->options.device_name);
+        goto fail;
+    }
+
+    try {
+        th_model->jit_model = new torch::jit::Module;
+        (*th_model->jit_model) = torch::jit::load(model_filename);
+    } catch (const c10::Error& e) {
+        av_log(ctx, AV_LOG_ERROR, "Failed to load torch model\n");
+        goto fail;
+    }
+
+    th_model->request_queue = ff_safe_queue_create();
+    if (!th_model->request_queue) {
+        goto fail;
+    }
+
+    item = (THRequestItem *)av_mallocz(sizeof(THRequestItem));
+    if (!item) {
+        goto fail;
+    }
+    item->lltask = NULL;
+    item->infer_request = th_create_inference_request();
+    if (!item->infer_request) {
+        av_log(NULL, AV_LOG_ERROR, "Failed to allocate memory for Torch inference request\n");
+        goto fail;
+    }
+    item->exec_module.start_inference = &th_start_inference;
+    item->exec_module.callback = &infer_completion_callback;
+    item->exec_module.args = item;
+
+    if (ff_safe_queue_push_back(th_model->request_queue, item) < 0) {
+        goto fail;
+    }
+    item = NULL;
+
+    th_model->task_queue = ff_queue_create();
+    if (!th_model->task_queue) {
+        goto fail;
+    }
+
+    th_model->lltask_queue = ff_queue_create();
+    if (!th_model->lltask_queue) {
+        goto fail;
+    }
+
+    model->get_input = &get_input_th;
+    model->get_output = &get_output_th;
+    model->options = NULL;
+    model->filter_ctx = filter_ctx;
+    model->func_type = func_type;
+    return model;
+
+fail:
+    if (item) {
+        destroy_request_item(&item);
+        av_freep(&item);
+    }
+    dnn_free_model_th(&model);
+    return NULL;
+}
+
+static int dnn_execute_model_th(const DNNModel *model, DNNExecBaseParams *exec_params)
+{
+    THModel *th_model = (THModel *)model->model;
+    THContext *ctx = &th_model->ctx;
+    TaskItem *task;
+    THRequestItem *request;
+    int ret = 0;
+
+    ret = ff_check_exec_params(ctx, DNN_TH, model->func_type, exec_params);
+    if (ret != 0) {
+        av_log(ctx, AV_LOG_ERROR, "exec parameter checking fail.\n");
+        return ret;
+    }
+
+    task = (TaskItem *)av_malloc(sizeof(TaskItem));
+    if (!task) {
+        av_log(ctx, AV_LOG_ERROR, "unable to alloc memory for task item.\n");
+        return AVERROR(ENOMEM);
+    }
+
+    ret = ff_dnn_fill_task(task, exec_params, th_model, 0, 1);
+    if (ret != 0) {
+        av_freep(&task);
+        av_log(ctx, AV_LOG_ERROR, "unable to fill task.\n");
+        return ret;
+    }
+
+    ret = ff_queue_push_back(th_model->task_queue, task);
+    if (ret < 0) {
+        av_freep(&task);
+        av_log(ctx, AV_LOG_ERROR, "unable to push back task_queue.\n");
+        return ret;
+    }
+
+    ret = extract_lltask_from_task(task, th_model->lltask_queue);
+    if (ret != 0) {
+        av_log(ctx, AV_LOG_ERROR, "unable to extract last level task from task.\n");
+        return ret;
+    }
+
+    request = (THRequestItem *)ff_safe_queue_pop_front(th_model->request_queue);
+    if (!request) {
+        av_log(ctx, AV_LOG_ERROR, "unable to get infer request.\n");
+        return AVERROR(EINVAL);
+    }
+
+    return execute_model_th(request, th_model->lltask_queue);
+}
+
+static DNNAsyncStatusType dnn_get_result_th(const DNNModel *model, AVFrame **in, AVFrame **out)
+{
+    THModel *th_model = (THModel *)model->model;
+    return ff_dnn_get_result_common(th_model->task_queue, in, out);
+}
+
+static int dnn_flush_th(const DNNModel *model)
+{
+    THModel *th_model = (THModel *)model->model;
+    THRequestItem *request;
+
+    if (ff_queue_size(th_model->lltask_queue) == 0)
+        // no pending task need to flush
+        return 0;
+
+    request = (THRequestItem *)ff_safe_queue_pop_front(th_model->request_queue);
+    if (!request) {
+        av_log(&th_model->ctx, AV_LOG_ERROR, "unable to get infer request.\n");
+        return AVERROR(EINVAL);
+    }
+
+    return execute_model_th(request, th_model->lltask_queue);
+}
+
+extern const DNNModule ff_dnn_backend_torch = {
+    .load_model     = dnn_load_model_th,
+    .execute_model  = dnn_execute_model_th,
+    .get_result     = dnn_get_result_th,
+    .flush          = dnn_flush_th,
+    .free_model     = dnn_free_model_th,
+};

libavfilter/dnn/dnn_interface.c

@@ -28,6 +28,7 @@
 
 extern const DNNModule ff_dnn_backend_openvino;
 extern const DNNModule ff_dnn_backend_tf;
+extern const DNNModule ff_dnn_backend_torch;
 
 const DNNModule *ff_get_dnn_module(DNNBackendType backend_type, void *log_ctx)
 {
@@ -40,6 +41,10 @@ const DNNModule *ff_get_dnn_module(DNNBackendType backend_type, void *log_ctx)
     case DNN_OV:
         return &ff_dnn_backend_openvino;
     #endif
+    #if (CONFIG_LIBTORCH == 1)
+    case DNN_TH:
+        return &ff_dnn_backend_torch;
+    #endif
     default:
         av_log(log_ctx, AV_LOG_ERROR,
                 "Module backend_type %d is not supported or enabled.\n",

libavfilter/dnn_filter_common.c

@@ -53,12 +53,22 @@ static char **separate_output_names(const char *expr, const char *val_sep, int *
 
 int ff_dnn_init(DnnContext *ctx, DNNFunctionType func_type, AVFilterContext *filter_ctx)
 {
+    DNNBackendType backend = ctx->backend_type;
+
     if (!ctx->model_filename) {
         av_log(filter_ctx, AV_LOG_ERROR, "model file for network is not specified\n");
         return AVERROR(EINVAL);
     }
 
-    if (ctx->backend_type == DNN_TF) {
+    if (backend == DNN_TH) {
+        if (ctx->model_inputname)
+            av_log(filter_ctx, AV_LOG_WARNING, "LibTorch backend do not require inputname, "\
+                                               "inputname will be ignored.\n");
+        if (ctx->model_outputnames)
+            av_log(filter_ctx, AV_LOG_WARNING, "LibTorch backend do not require outputname(s), "\
+                                               "all outputname(s) will be ignored.\n");
+        ctx->nb_outputs = 1;
+    } else if (backend == DNN_TF) {
         if (!ctx->model_inputname) {
             av_log(filter_ctx, AV_LOG_ERROR, "input name of the model network is not specified\n");
             return AVERROR(EINVAL);
@@ -115,7 +125,8 @@ int ff_dnn_get_input(DnnContext *ctx, DNNData *input)
 
 int ff_dnn_get_output(DnnContext *ctx, int input_width, int input_height, int *output_width, int *output_height)
 {
-    char * output_name = ctx->model_outputnames ? ctx->model_outputnames[0] : NULL;
+    char * output_name = ctx->model_outputnames && ctx->backend_type != DNN_TH ?
+                         ctx->model_outputnames[0] : NULL;
     return ctx->model->get_output(ctx->model->model, ctx->model_inputname, input_width, input_height,
                                     (const char *)output_name, output_width, output_height);
 }

libavfilter/dnn_interface.h

@@ -32,7 +32,7 @@
 
 #define DNN_GENERIC_ERROR FFERRTAG('D','N','N','!')
 
-typedef enum {DNN_TF = 1, DNN_OV} DNNBackendType;
+typedef enum {DNN_TF = 1, DNN_OV, DNN_TH} DNNBackendType;
 
 typedef enum {DNN_FLOAT = 1, DNN_UINT8 = 4} DNNDataType;
 

libavfilter/vf_dnn_processing.c

@@ -50,6 +50,9 @@ static const AVOption dnn_processing_options[] = {
 #endif
 #if (CONFIG_LIBOPENVINO == 1)
     { "openvino",    "openvino backend flag",      0,                        AV_OPT_TYPE_CONST,     { .i64 = DNN_OV },    0, 0, FLAGS, .unit = "backend" },
+#endif
+#if (CONFIG_LIBTORCH == 1)
+    { "torch",       "torch backend flag",         0,                        AV_OPT_TYPE_CONST,     { .i64 = DNN_TH },    0, 0, FLAGS, "backend" },
 #endif
     DNN_COMMON_OPTIONS
     { NULL }