[FFmpeg-devel] [PATCH v1] libavfi/dnn: add Paddle Inference as one of DNN backend

[wongwwz@foxmail.com]

From: "wenzhe.wang" <wongwwz@foxmail.com>

PaddlePaddle (PArallel Distributed Deep LEarning) is a simple, efficient and extensible deep learning framework that accelerates the path from research prototyping to production deployment and provide many industrial AI solutions. Official websit:https://www.paddlepaddle.org.cn/en. We use the Paddle inference library---Paddle Inference to provide high performance reasoning capability. To build FFmpeg with Paddle Inference, please take following steps as reference:

1. download Paddle C library inhttps://www.paddlepaddle.org.cn/inference/v2.4/guides/install/download_lib.html#id1, please select the options as your need.
2. unzip the file to your own dir, with command unzip paddle_inference_c.tgz in your_dir
3. export paddle_root/paddle/include $C_INCLUDE_PATH  and export paddle_root/paddle/lib/ to $LD_LIBRARY_PATH
4. config FFmpeg with ../configure --enable-libpaddle  --extra-cflags="-I/paddle_root/paddle/include/ /paddle_root/third_party/install/paddle2onnx/lib/libpaddle2onnx.so /paddle_root/third_party/install/onnxruntime/lib/libonnxruntime.so /paddle_root/third_party/install/mklml/lib/libiomp5.so /paddle_root/third_party/install/mkldnn/lib/libdnnl.so.2 /paddle_root/paddle/lib/libpaddle_inference_c.so"
 5. make

 To run FFmpeg DNN inference with paddle backend: ./ffmpeg -i /input.jpg -vf dnn_processing=dnn_backend=3:model=paddle_model/model:input=input_name:output=output_name:options="input_layout=NCHW" -y output.jpg
 The Paddle model dir must contain model.pdmodel and model.pdiparams files.
---
 configure                            |   6 +-
 libavfilter/dnn/Makefile             |   1 +
 libavfilter/dnn/dnn_backend_common.c |   2 +-
 libavfilter/dnn/dnn_backend_pd.c     | 840 +++++++++++++++++++++++++++
 libavfilter/dnn/dnn_backend_pd.h     |  38 ++
 libavfilter/dnn/dnn_interface.c      |  15 +-
 libavfilter/dnn/dnn_io_proc.c        |   8 +
 libavfilter/dnn_interface.h          |   2 +-
 libavfilter/vf_dnn_detect.c          |  82 +++
 libavfilter/vf_dnn_processing.c      |   3 +
 10 files changed, 993 insertions(+), 4 deletions(-)
 create mode 100644 libavfilter/dnn/dnn_backend_pd.c
 create mode 100644 libavfilter/dnn/dnn_backend_pd.h

diff --git a/configure b/configure
index 6e363eb470..41cc8f99e2 100755
--- a/configure
+++ b/configure
@@ -276,6 +276,8 @@ External library support:
   --enable-libsvtav1       enable AV1 encoding via SVT [no]
   --enable-libtensorflow   enable TensorFlow as a DNN module backend
                            for DNN based filters like sr [no]
+  --enable-libpaddle       enable Paddlepaddle as a DNN module backend
+                           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
@@ -1855,6 +1857,7 @@ EXTERNAL_LIBRARY_LIST="
     libssh
     libsvtav1
     libtensorflow
+    libpaddle
     libtesseract
     libtheora
     libtwolame
@@ -2717,7 +2720,7 @@ dct_select="rdft"
 deflate_wrapper_deps="zlib"
 dirac_parse_select="golomb"
 dovi_rpu_select="golomb"
-dnn_suggest="libtensorflow libopenvino"
+dnn_suggest="libtensorflow libopenvino libpaddle"
 dnn_deps="avformat swscale"
 error_resilience_select="me_cmp"
 faandct_deps="faan"
@@ -6695,6 +6698,7 @@ enabled libspeex          && require_pkg_config libspeex speex speex/speex.h spe
 enabled libsrt            && require_pkg_config libsrt "srt >= 1.3.0" srt/srt.h srt_socket
 enabled libsvtav1         && require_pkg_config libsvtav1 "SvtAv1Enc >= 0.9.0" EbSvtAv1Enc.h svt_av1_enc_init_handle
 enabled libtensorflow     && require libtensorflow tensorflow/c/c_api.h TF_Version -ltensorflow
+enabled libpaddle         && require libpaddle pd_inference_api.h PD_GetVersion $CFLAGS
 enabled libtesseract      && require_pkg_config libtesseract tesseract tesseract/capi.h TessBaseAPICreate
 enabled libtheora         && require libtheora theora/theoraenc.h th_info_init -ltheoraenc -ltheoradec -logg
 enabled libtls            && require_pkg_config libtls libtls tls.h tls_configure
diff --git a/libavfilter/dnn/Makefile b/libavfilter/dnn/Makefile
index 4cfbce0efc..b9fc8e4a30 100644
--- a/libavfilter/dnn/Makefile
+++ b/libavfilter/dnn/Makefile
@@ -16,5 +16,6 @@ OBJS-$(CONFIG_DNN)                           += dnn/dnn_backend_native_layer_mat
 
 DNN-OBJS-$(CONFIG_LIBTENSORFLOW)             += dnn/dnn_backend_tf.o
 DNN-OBJS-$(CONFIG_LIBOPENVINO)               += dnn/dnn_backend_openvino.o
+DNN-OBJS-$(CONFIG_LIBPADDLE)                 += dnn/dnn_backend_pd.o
 
 OBJS-$(CONFIG_DNN)                           += $(DNN-OBJS-yes)
diff --git a/libavfilter/dnn/dnn_backend_common.c b/libavfilter/dnn/dnn_backend_common.c
index 91a4a3c4bf..5adeb6bb3b 100644
--- a/libavfilter/dnn/dnn_backend_common.c
+++ b/libavfilter/dnn/dnn_backend_common.c
@@ -43,7 +43,7 @@ int ff_check_exec_params(void *ctx, DNNBackendType backend, DNNFunctionType func
         return AVERROR(EINVAL);
     }
 
-    if (exec_params->nb_output != 1 && backend != DNN_TF) {
+    if (exec_params->nb_output != 1 && backend != DNN_TF && backend !=DNN_PD) {
         // currently, the filter does not need multiple outputs,
         // so we just pending the support until we really need it.
         avpriv_report_missing_feature(ctx, "multiple outputs");
diff --git a/libavfilter/dnn/dnn_backend_pd.c b/libavfilter/dnn/dnn_backend_pd.c
new file mode 100644
index 0000000000..b397b945b1
--- /dev/null
+++ b/libavfilter/dnn/dnn_backend_pd.c
@@ -0,0 +1,840 @@
+/*
+ * Copyright (c) 2023
+ *
+ * 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 paddle backend implementation.
+ */
+
+#include "dnn_backend_pd.h"
+#include "dnn_backend_native.h"
+#include "libavutil/avassert.h"
+#include "libavutil/avstring.h"
+#include "libavutil/cpu.h"
+#include "../internal.h"
+#include "dnn_io_proc.h"
+#include "dnn_backend_common.h"
+#include "safe_queue.h"
+#include <paddle/pd_inference_api.h>
+
+typedef struct PDOptions {
+    float im_height, im_width;
+    float scale_factorH, scale_factorW;
+    char *input_layout;
+    uint8_t async;
+    uint32_t nireq;
+} PDOptions;
+
+typedef struct PDContext {
+    const AVClass *class;
+    PDOptions options;
+} PDContext;
+
+typedef struct PDModel {
+    PDContext ctx;
+    DNNModel *model;
+    PD_Config *config;
+    PD_Predictor *predictor;
+    PD_Bool status;
+    SafeQueue *request_queue;
+    Queue *lltask_queue;
+    Queue *task_queue;
+} PDModel;
+/**
+ * Stores execution parameters for single
+ * call to the Paddlepaddle C API
+ */
+typedef struct PDInferRequest {
+
+    PD_OneDimArrayCstr *input_names;
+    PD_OneDimArrayCstr *output_names;
+    PD_Tensor **output_tensors;
+    PD_Tensor *input_tensor;
+} PDInferRequest;
+
+typedef struct PDRequestItem {
+    PDInferRequest *infer_request;
+    LastLevelTaskItem *lltask;
+    PD_Bool status;
+    DNNAsyncExecModule exec_module;
+} PDRequestItem;
+
+#define OFFSET(x) offsetof(PDContext, x)
+#define FLAGS AV_OPT_FLAG_FILTERING_PARAM
+static const AVOption dnn_paddle_options[] = {
+        {"im_height", "image shape(H,W)", OFFSET(options.im_height), AV_OPT_TYPE_FLOAT, {.dbl = 320}, 0, 10000,
+         FLAGS},
+        {"im_width", "image shape(H,W)", OFFSET(options.im_width), AV_OPT_TYPE_FLOAT, {.dbl = 320}, 0, 10000,
+         FLAGS},
+        {"scale_factorH", "scalar factor for height", OFFSET(options.scale_factorH), AV_OPT_TYPE_FLOAT, {.dbl = 1.0},
+         0, 10000,
+         FLAGS},
+        {"scale_factorW", "scalar factor for height", OFFSET(options.scale_factorW), AV_OPT_TYPE_FLOAT, {.dbl = 1.0},
+         0, 10000,
+         FLAGS},
+        {"input_layout", "NHWC or NCHW", OFFSET(options.input_layout), AV_OPT_TYPE_STRING, {.str = "NCHW"}, 0, 0,
+         FLAGS},
+        DNN_BACKEND_COMMON_OPTIONS
+        {NULL}
+};
+
+AVFILTER_DEFINE_CLASS(dnn_paddle);
+
+static int execute_model_pd(PDRequestItem *request, Queue *lltask_queue);
+
+static void infer_completion_callback(void *args);
+
+static inline void destroy_request_item(PDRequestItem **arg);
+
+
+/**
+ * Free the contents of Paddle inference request.
+ * It does not free the PDInferRequest instance.
+ *
+ * @param request pointer to PDInferRequest instance.
+ * NULL pointer is allowed.
+ */
+static void pd_free_request(PDInferRequest *request) {
+    if (!request)
+        return;
+    if (request->input_tensor) {
+        PD_TensorDestroy(request->input_tensor);
+        request->input_tensor = NULL;
+    }
+    av_freep(&request->input_names);
+    av_freep(&request->output_names);
+    if (request->output_tensors) {
+        int nb_output = sizeof(*request->output_tensors) / sizeof(request->output_tensors[0]);
+        for (uint32_t i = 0; i < nb_output; ++i) {
+            if (request->output_tensors[i]) {
+                PD_TensorDestroy(request->output_tensors[i]);
+                request->output_tensors[i] = NULL;
+            }
+        }
+        av_freep(&request->output_tensors);
+    }
+}
+
+/**
+ * Free the PaddkeRequestItem completely.
+ *
+ * @param arg Address of the PaddleInferRequest instance.
+ */
+static inline void destroy_request_item(PDRequestItem **arg) {
+    PDRequestItem *request;
+    if (!arg) {
+        return;
+    }
+    request = *arg;
+    pd_free_request(request->infer_request);
+    av_freep(&request->infer_request);
+    av_freep(&request->lltask);
+    ff_dnn_async_module_cleanup(&request->exec_module);
+    av_freep(arg);
+}
+
+/**
+ * Create a Paddle inference request. All properties
+ * are initially unallocated and set as NULL.
+ *
+ * @return pointer to the allocated PDInferRequest instance.
+ */
+static PDInferRequest *pd_create_inference_request(void) {
+    PDInferRequest *infer_request = av_malloc(sizeof(PDInferRequest));
+    if (!infer_request) {
+        return NULL;
+    }
+    infer_request->input_names = NULL;
+    infer_request->output_names = NULL;
+    infer_request->input_tensor = NULL;
+    infer_request->output_tensors = NULL;
+    return infer_request;
+}
+
+static int load_pd_model(PDModel *pd_model, const char *model_filename) {
+
+    PDContext *ctx = &pd_model->ctx;
+    char *model_path = (char *) malloc(strlen(model_filename) + strlen(".pdmodel")+1);
+    char *params_path = (char *) malloc(strlen(model_filename) + strlen(".pdiparams")+1);
+    pd_model->config = PD_ConfigCreate();
+    strcpy(model_path, model_filename);
+    strcat(model_path, ".pdmodel");
+    strcpy(params_path, model_filename);
+    strcat(params_path, ".pdiparams");
+    PD_ConfigSetModel(pd_model->config, model_path, params_path);
+    free(model_path);
+    free(params_path);
+    pd_model->status = PD_ConfigIsValid(pd_model->config);
+    pd_model->predictor = PD_PredictorCreate(pd_model->config);
+    if (!pd_model->status) {
+        av_log(ctx, AV_LOG_ERROR, "Failed to read model \"%s\" graph\n", model_filename);
+        PD_ConfigDestroy(pd_model->config);
+        PD_PredictorDestroy(pd_model->predictor);
+        return DNN_GENERIC_ERROR;
+    }
+    return 0;
+}
+
+static float *transposeNHWC2NCHW(float *data, const int32_t shape[4]) {
+    // the shape layout is NCHW
+    int N = shape[0];
+    int H = shape[2];
+    int W = shape[3];
+    int C = shape[1];
+    float *transposed = calloc(shape[0] * shape[1] * shape[2] * shape[3], sizeof(float));
+    // [N,H,W,C] -> [N,C,H,W]
+    for (int n = 0; n < N; ++n) {
+        for (int c = 0; c < C; ++c) {
+            for (int h = 0; h < H; ++h) {
+                for (int w = 0; w < W; ++w) {
+                    int old_index = n * H * W * C + h * W * C + w * C + c;
+                    int new_index = n * C * H * W + c * H * W + h * W + w;
+                    transposed[new_index] = data[old_index];
+                }
+            }
+        }
+    }
+    memcpy(data, transposed, shape[0] * shape[1] * shape[2] * shape[3] * sizeof(float));
+    free(transposed);
+    return data;
+}
+
+static float *transposeNCHW2NHWC(float *data, const int32_t shape[4]) {
+    // the shape layout is NCHW
+    int N = shape[0];
+    int C = shape[1];
+    int H = shape[2];
+    int W = shape[3];
+    float *transposed = calloc(shape[0] * shape[1] * shape[2] * shape[3], sizeof(float));
+    // [N,C,H,W] -> [N,H,W,C]
+    for (int n = 0; n < N; ++n) {
+        for (int h = 0; h < H; ++h) {
+            for (int w = 0; w < W; ++w) {
+                for (int c = 0; c < C; ++c) {
+                    int old_index = n * C * H * W + c * H * W + h * W + w;
+                    int new_index = n * H * W * C + h * W * C + w * C + c;
+                    transposed[new_index] = data[old_index];
+                }
+            }
+        }
+    }
+    memcpy(data, transposed, shape[0] * shape[1] * shape[2] * shape[3] * sizeof(float));
+    free(transposed);
+    return data;
+}
+
+static int get_name_index(PDModel *pd_model, TaskItem *task) {
+    int name_index = -1;
+    PD_OneDimArrayCstr *pd_input_names = PD_PredictorGetInputNames(pd_model->predictor);
+    for (int i = 0; i < pd_input_names->size; ++i) {
+        if (strcmp(pd_input_names->data[i], task->input_name) == 0) {
+            name_index = i;
+        }
+    }
+    PD_OneDimArrayCstrDestroy(pd_input_names);
+    if (name_index == -1) {
+        av_log(&pd_model->ctx, AV_LOG_ERROR, "Could not find \"%s\" in model\n", task->input_name);
+        return AVERROR(EINVAL);
+    }
+    return name_index;
+}
+
+static int pd_start_inference(void *args) {
+    DNNData input;
+    PDRequestItem *request = args;
+    PDInferRequest *infer_request = request->infer_request;
+    LastLevelTaskItem *lltask = request->lltask;
+    TaskItem *task = lltask->task;
+    PDModel *pd_model = task->model;
+    // get input data nhwc
+    PD_Tensor *input_tensor = infer_request->input_tensor;
+    int32_t input_shape[4] = {1, -1, -1, -1};
+
+    for (int i = 0; i < infer_request->input_names->size; ++i) {
+
+        if (strcmp(infer_request->input_names->data[i], "im_shape") == 0) {
+            PD_Tensor *im_shape_tensor = PD_PredictorGetInputHandle(pd_model->predictor,
+                                                                    infer_request->input_names->data[i]);
+            int32_t im_shape_shape[2] = {1, 2};
+            float im_shape_data[2] = {pd_model->ctx.options.im_height, pd_model->ctx.options.im_height};
+            PD_TensorReshape(im_shape_tensor, 2, im_shape_shape);
+            PD_TensorCopyFromCpuFloat(im_shape_tensor, im_shape_data);
+        } else if (strcmp(infer_request->input_names->data[i], "scale_factor") == 0) {
+            PD_Tensor *scale_factor_tensor = PD_PredictorGetInputHandle(pd_model->predictor,
+                                                                        infer_request->input_names->data[i]);
+            int32_t scale_factor_shape[2] = {1, 2};
+            float scale_factor_data[2] = {pd_model->ctx.options.scale_factorH, pd_model->ctx.options.scale_factorW};
+            PD_TensorReshape(scale_factor_tensor, 2, scale_factor_shape);
+            PD_TensorCopyFromCpuFloat(scale_factor_tensor, scale_factor_data);
+        }
+    }
+
+    if (strcmp(pd_model->ctx.options.input_layout, "NCHW") == 0) {
+        input_shape[1] = 3;
+        input_shape[2] = task->in_frame->height;
+        input_shape[3] = task->in_frame->width;
+    } else if (strcmp(pd_model->ctx.options.input_layout, "NHWC") == 0) {
+        input_shape[1] = task->in_frame->height;
+        input_shape[2] = task->in_frame->width;
+        input_shape[3] = 3;
+    } else {
+        av_log(&pd_model->ctx, AV_LOG_ERROR, "The input layout should be NCHW or NHWC\n");
+    }
+    float *in_data = (float *) calloc(1 * input_shape[1] * input_shape[2] * input_shape[3], sizeof(float));
+    PD_TensorCopyToCpuFloat(input_tensor, in_data);
+    if (strcmp(pd_model->ctx.options.input_layout, "NCHW") == 0) {
+        in_data = transposeNHWC2NCHW(in_data, input_shape);
+    }
+
+    PD_TensorReshape(input_tensor, 4, input_shape);
+    PD_TensorCopyFromCpuFloat(input_tensor, in_data);
+    free(in_data);
+
+    request->status = PD_PredictorRun(pd_model->predictor);
+
+    if (!request->status) {
+        av_log(&pd_model->ctx, AV_LOG_ERROR, "%s", "paddlepaddle predictor run fail!");
+        pd_free_request(infer_request);
+        if (ff_safe_queue_push_back(pd_model->request_queue, request) < 0) {
+            destroy_request_item(&request);
+        }
+        return DNN_GENERIC_ERROR;
+    }
+    return 0;
+}
+
+static void infer_completion_callback(void *args) {
+    PDRequestItem *request = args;
+    LastLevelTaskItem *lltask = request->lltask;
+    TaskItem *task = lltask->task;
+    DNNData *outputs;
+    PDInferRequest *infer_request = request->infer_request;
+    PDModel *pd_model = task->model;
+    PDContext *ctx = &pd_model->ctx;
+
+    outputs = av_malloc_array(task->nb_output, sizeof(*outputs));
+    if (!outputs) {
+        av_log(ctx, AV_LOG_ERROR, "Failed to allocate memory for *outputs\n");
+        goto err;
+    }
+
+    for (uint32_t i = 0; i < task->nb_output; ++i) {
+        const size_t shape_size = PD_TensorGetShape(infer_request->output_tensors[i])->size;
+        int32_t length = 1;
+        PD_DataType out_dt = PD_TensorGetDataType(infer_request->output_tensors[i]);
+        size_t size;
+        float *out_data;
+
+        if (strcmp(pd_model->ctx.options.input_layout, "NCHW") == 0) {
+            outputs[i].height = PD_TensorGetShape(infer_request->output_tensors[i])->data[2];
+            outputs[i].width = PD_TensorGetShape(infer_request->output_tensors[i])->data[3];
+            outputs[i].channels = PD_TensorGetShape(infer_request->output_tensors[i])->data[1];
+        } else {
+            outputs[i].height = PD_TensorGetShape(infer_request->output_tensors[i])->data[1];
+            outputs[i].width = PD_TensorGetShape(infer_request->output_tensors[i])->data[2];
+            outputs[i].channels = PD_TensorGetShape(infer_request->output_tensors[i])->data[3];
+        }
+
+        for (int j = 0; j < shape_size; ++j) {
+            length *= PD_TensorGetShape(infer_request->output_tensors[i])->data[j];
+        }
+
+        if (out_dt != PD_DATA_FLOAT32){
+            av_log(&pd_model->ctx, AV_LOG_ERROR, "The model output datatype has to be float.\n");
+        } else {
+            outputs[i].dt = DNN_FLOAT;
+            size = sizeof(float);
+            out_data = (float *) malloc(length * size);
+            PD_TensorCopyToCpuFloat(infer_request->output_tensors[i], out_data);
+        }
+
+        if (shape_size == 4 && (strcmp(pd_model->ctx.options.input_layout, "NCHW") == 0)) {
+            int32_t output_shape[4] = {PD_TensorGetShape(infer_request->output_tensors[i])->data[0],
+                                       PD_TensorGetShape(infer_request->output_tensors[i])->data[1],
+                                       PD_TensorGetShape(infer_request->output_tensors[i])->data[2],
+                                       PD_TensorGetShape(infer_request->output_tensors[i])->data[3]};
+            out_data = transposeNCHW2NHWC(out_data, output_shape);
+        }
+
+        outputs[i].order = DCO_BGR;
+        outputs[i].data = out_data;
+    }
+    switch (pd_model->model->func_type) {
+        case DFT_PROCESS_FRAME:
+            //it only support 1 output if it's frame in & frame out
+            if (task->do_ioproc) {
+                if (pd_model->model->frame_post_proc != NULL) {
+                    pd_model->model->frame_post_proc(task->out_frame, outputs, pd_model->model->filter_ctx);
+                } else {
+                    ff_proc_from_dnn_to_frame(task->out_frame, outputs, ctx);
+                }
+            } else {
+                task->out_frame->width = outputs[0].width;
+                task->out_frame->height = outputs[0].height;
+            }
+            break;
+        case DFT_ANALYTICS_DETECT:
+            if (!pd_model->model->detect_post_proc) {
+                av_log(ctx, AV_LOG_ERROR, "Detect filter needs provide post proc\n");
+                return;
+            }
+            pd_model->model->detect_post_proc(task->in_frame, outputs, task->nb_output, pd_model->model->filter_ctx);
+            break;
+        default:
+            av_log(ctx, AV_LOG_ERROR, "Paddle Inference backend does not support this kind of dnn filter now\n");
+            goto err;
+    }
+    task->inference_done++;
+    err:
+    pd_free_request(infer_request);
+    av_freep(&outputs);
+    if (ff_safe_queue_push_back(pd_model->request_queue, request) < 0) {
+        destroy_request_item(&request);
+        av_log(ctx, AV_LOG_ERROR, "Failed to push back request_queue.\n");
+    }
+}
+
+static int extract_lltask_from_task(TaskItem *task, Queue *lltask_queue) {
+    PDModel *pd_model = task->model;
+    PDContext *ctx = &pd_model->ctx;
+    LastLevelTaskItem *lltask = av_malloc(sizeof(*lltask));
+    if (!lltask) {
+        av_log(ctx, AV_LOG_ERROR, "Unable to allocate space 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 int get_input_pd(void *model, DNNData *input, const char *input_name) {
+    PDModel *pd_model = model;
+    PDContext *ctx = &pd_model->ctx;
+    int has_name = -1;
+    PD_OneDimArrayCstr *pd_input_names = PD_PredictorGetInputNames(pd_model->predictor);
+    for (int i = 0; i < pd_input_names->size; ++i) {
+        if (strcmp(pd_input_names->data[i], input_name) == 0) {
+            has_name = i;
+            break;
+        }
+    }
+    PD_OneDimArrayCstrDestroy(pd_input_names);
+    if (has_name == -1) {
+        av_log(ctx, AV_LOG_ERROR, "Could not find \"%s\" in model\n", input_name);
+        return AVERROR(EINVAL);
+    }
+    input->dt = DNN_FLOAT;
+    input->order = DCO_RGB;
+    input->height = -1;
+    input->width = -1;
+    input->channels = 3;
+    return 0;
+}
+
+static int get_output_pd(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;
+    PDModel *pd_model = model;
+    PDContext *ctx = &pd_model->ctx;
+    TaskItem task;
+    PDRequestItem *request;
+    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, pd_model, input_height, input_width, ctx);
+    if (ret != 0) {
+        goto err;
+    }
+
+    ret = extract_lltask_from_task(&task, pd_model->lltask_queue);
+    if (ret != 0) {
+        av_log(ctx, AV_LOG_ERROR, "unable to extract inference from task.\n");
+        goto err;
+    }
+
+    request = ff_safe_queue_pop_front(pd_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_pd(request, pd_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;
+}
+
+DNNModel *ff_dnn_load_model_pd(const char *model_filename, DNNFunctionType func_type, const char *options,
+                               AVFilterContext *filter_ctx) {
+    DNNModel *model = NULL;
+    PDModel *pd_model = NULL;
+    PDRequestItem *item = NULL;
+    PDContext *ctx = NULL;
+
+    model = av_mallocz(sizeof(DNNModel));
+    if (!model) {
+        return NULL;
+    }
+
+    pd_model = av_mallocz(sizeof(PDModel));
+    if (!pd_model) {
+        av_freep(&model);
+        return NULL;
+    }
+    pd_model->model = model;
+    ctx = &pd_model->ctx;
+    ctx->class = &dnn_paddle_class;
+
+    //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);
+        goto err;
+    }
+
+    if (load_pd_model(pd_model, model_filename) != 0) {
+        goto err;
+    }
+
+    if (ctx->options.nireq <= 0) {
+        ctx->options.nireq = av_cpu_count() / 2 + 1;
+    }
+
+#if !HAVE_PTHREAD_CANCEL
+    if (ctx->options.async) {
+        ctx->options.async = 0;
+        av_log(filter_ctx, AV_LOG_WARNING, "pthread is not supported, roll back to sync.\n");
+    }
+#endif
+
+    pd_model->request_queue = ff_safe_queue_create();
+    if (!pd_model->request_queue) {
+        goto err;
+    }
+
+    for (int i = 0; i < ctx->options.nireq; i++) {
+        PDRequestItem *item = av_mallocz(sizeof(*item));
+        if (!item) {
+            goto err;
+        }
+        item->lltask = NULL;
+        item->infer_request = pd_create_inference_request();
+        if (!item->infer_request) {
+            av_log(ctx, AV_LOG_ERROR, "Failed to allocate memory for Paddle inference request\n");
+            av_freep(&item);
+            goto err;
+        }
+        item->exec_module.start_inference = &pd_start_inference;
+        item->exec_module.callback = &infer_completion_callback;
+        item->exec_module.args = item;
+
+        if (ff_safe_queue_push_back(pd_model->request_queue, item) < 0) {
+            destroy_request_item(&item);
+            goto err;
+        }
+    }
+
+    pd_model->lltask_queue = ff_queue_create();
+    if (!pd_model->lltask_queue) {
+        goto err;
+    }
+
+    pd_model->task_queue = ff_queue_create();
+    if (!pd_model->task_queue) {
+        goto err;
+    }
+
+    model->model = pd_model;
+    model->get_input = &get_input_pd;
+    model->get_output = &get_output_pd;
+    model->options = options;
+    model->filter_ctx = filter_ctx;
+    model->func_type = func_type;
+
+    return model;
+    err:
+    ff_dnn_free_model_pd(&model);
+    return NULL;
+}
+
+static int fill_model_input_pd(PDModel *pd_model, PDRequestItem *request) {
+    DNNData input;
+    LastLevelTaskItem *lltask;
+    TaskItem *task;
+    PDInferRequest *infer_request;
+    PDContext *ctx = &pd_model->ctx;
+    int ret = 0;
+    int32_t input_shape[4] = {1, -1, -1, -1};
+
+    lltask = ff_queue_pop_front(pd_model->lltask_queue);
+    av_assert0(lltask);
+    task = lltask->task;
+    request->lltask = lltask;
+
+    ret = get_input_pd(pd_model, &input, task->input_name);
+    if (ret != 0) {
+        goto err;
+    }
+
+    infer_request = request->infer_request;
+    input.height = task->in_frame->height;
+    input.width = task->in_frame->width;
+
+    infer_request->input_names = av_malloc(sizeof(PD_OneDimArrayCstr));
+    if (!infer_request->input_names) {
+        av_log(ctx, AV_LOG_ERROR, "Failed to allocate memory for input tensor\n");
+        ret = AVERROR(ENOMEM);
+        goto err;
+    }
+
+    int name_index = get_name_index(pd_model, task);
+    infer_request->input_names = PD_PredictorGetInputNames(pd_model->predictor);
+    infer_request->input_tensor = PD_PredictorGetInputHandle(pd_model->predictor,
+                                                             infer_request->input_names->data[name_index]);
+    if (!infer_request->input_tensor) {
+        av_log(ctx, AV_LOG_ERROR, "Failed to allocate memory for input tensor\n");
+        ret = AVERROR(ENOMEM);
+        goto err;
+    }
+
+    if (strcmp(pd_model->ctx.options.input_layout, "NCHW") == 0) {
+        input_shape[1] = input.channels;
+        input_shape[2] = input.height;
+        input_shape[3] = input.width;
+    } else if (strcmp(pd_model->ctx.options.input_layout, "NHWC") == 0) {
+        input_shape[1] = input.height;
+        input_shape[2] = input.width;
+        input_shape[3] = input.channels;
+    } else {
+        av_log(ctx, AV_LOG_ERROR, "The input layout should be NCHW or NHWC\n");
+    }
+    float *in_data = (float *) calloc(1 * input_shape[1] * input_shape[2] * input_shape[3], sizeof(float));
+    PD_TensorReshape(infer_request->input_tensor, 4, input_shape);
+    input.data = in_data;
+    PD_TensorCopyFromCpuFloat(infer_request->input_tensor, input.data);
+
+    switch (pd_model->model->func_type) {
+        case DFT_PROCESS_FRAME:
+            if (task->do_ioproc) {
+                if (pd_model->model->frame_pre_proc != NULL) {
+                    pd_model->model->frame_pre_proc(task->in_frame, &input, pd_model->model->filter_ctx);
+                } else {
+                    ff_proc_from_frame_to_dnn(task->in_frame, &input, ctx);
+                }
+                PD_TensorCopyFromCpuFloat(infer_request->input_tensor, input.data);
+            }
+            break;
+        case DFT_ANALYTICS_DETECT:
+            ff_proc_from_frame_to_dnn(task->in_frame, &input, ctx);
+            PD_TensorCopyFromCpuFloat(infer_request->input_tensor, input.data);
+            break;
+        default:
+            avpriv_report_missing_feature(ctx, "model function type %d", pd_model->model->func_type);
+            break;
+    }
+
+    infer_request->output_names = PD_PredictorGetOutputNames(pd_model->predictor);;
+    if (infer_request->output_names == NULL) {
+        av_log(ctx, AV_LOG_ERROR, "Failed to allocate memory for *pd_outputs\n");
+        ret = AVERROR(ENOMEM);
+        goto err;
+    }
+
+    infer_request->output_tensors = av_calloc(task->nb_output, sizeof(*infer_request->output_tensors));
+    if (!infer_request->output_tensors) {
+        av_log(ctx, AV_LOG_ERROR, "Failed to allocate memory for output tensor\n");
+        ret = AVERROR(ENOMEM);
+        goto err;
+    }
+
+
+    for (int i = 0; i < task->nb_output; ++i) {
+        infer_request->output_tensors[i] = PD_PredictorGetOutputHandle(pd_model->predictor,
+                                                                       infer_request->output_names->data[i]);
+        if (strcmp(infer_request->output_names->data[i], task->output_names[i]) != 0) {
+            av_log(ctx, AV_LOG_ERROR, "Could not find output \"%s\" in model\n", task->output_names[i]);
+            ret = DNN_GENERIC_ERROR;
+            goto err;
+        }
+    }
+    return 0;
+    err:
+    pd_free_request(infer_request);
+    return ret;
+}
+
+static int execute_model_pd(PDRequestItem *request, Queue *lltask_queue) {
+    PDModel *pd_model;
+    PDContext *ctx;
+    LastLevelTaskItem *lltask;
+    TaskItem *task;
+    int ret;
+
+    if (ff_queue_size(lltask_queue) == 0) {
+        destroy_request_item(&request);
+        return 0;
+    }
+
+    lltask = ff_queue_peek_front(lltask_queue);
+    task = lltask->task;
+    pd_model = task->model;
+    ctx = &pd_model->ctx;
+
+    ret = fill_model_input_pd(pd_model, request);
+    if (ret != 0) {
+        goto err;
+    }
+
+    ret = pd_start_inference(request);
+    if (ret != 0) {
+        goto err;
+    }
+    infer_completion_callback(request);
+    return (task->inference_done == task->inference_todo) ? 0 : DNN_GENERIC_ERROR;
+
+    err:
+    pd_free_request(request->infer_request);
+    if (ff_safe_queue_push_back(pd_model->request_queue, request) < 0) {
+        destroy_request_item(&request);
+    }
+    return ret;
+}
+
+int ff_dnn_execute_model_pd(const DNNModel *model, DNNExecBaseParams *exec_params) {
+    PDModel *pd_model = model->model;
+    PDContext *ctx = &pd_model->ctx;
+    TaskItem *task;
+    PDRequestItem *request;
+    int ret = 0;
+
+    ret = ff_check_exec_params(ctx, DNN_PD, model->func_type, exec_params);
+    if (ret != 0) {
+        return ret;
+    }
+
+    task = av_malloc(sizeof(*task));
+    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, pd_model, ctx->options.async, 1);
+    if (ret != 0) {
+        av_freep(&task);
+        return ret;
+    }
+
+    if (ff_queue_push_back(pd_model->task_queue, task) < 0) {
+        av_freep(&task);
+        av_log(ctx, AV_LOG_ERROR, "unable to push back task_queue.\n");
+        return AVERROR(ENOMEM);
+    }
+
+    ret = extract_lltask_from_task(task, pd_model->lltask_queue);
+    if (ret != 0) {
+        av_log(ctx, AV_LOG_ERROR, "unable to extract last level task from task.\n");
+        return ret;
+    }
+
+    request = ff_safe_queue_pop_front(pd_model->request_queue);
+    if (!request) {
+        av_log(ctx, AV_LOG_ERROR, "unable to get infer request.\n");
+        return AVERROR(EINVAL);
+    }
+    return execute_model_pd(request, pd_model->lltask_queue);
+}
+
+int ff_dnn_flush_pd(const DNNModel *model) {
+    PDModel *pd_model = model->model;
+    PDContext *ctx = &pd_model->ctx;
+    PDRequestItem *request;
+    int ret;
+
+    if (ff_queue_size(pd_model->lltask_queue) == 0) {
+        // no pending task need to flush
+        return 0;
+    }
+
+    request = ff_safe_queue_pop_front(pd_model->request_queue);
+    if (!request) {
+        av_log(ctx, AV_LOG_ERROR, "unable to get infer request.\n");
+        return AVERROR(EINVAL);
+    }
+
+    ret = fill_model_input_pd(pd_model, request);
+    if (ret != 0) {
+        av_log(ctx, AV_LOG_ERROR, "Failed to fill model input.\n");
+        if (ff_safe_queue_push_back(pd_model->request_queue, request) < 0) {
+            destroy_request_item(&request);
+        }
+        return ret;
+    }
+    return execute_model_pd(request, pd_model->lltask_queue);
+}
+
+void ff_dnn_free_model_pd(DNNModel **model) {
+    PDModel *pd_model;
+
+    if (*model) {
+        pd_model = (*model)->model;
+        while (ff_safe_queue_size(pd_model->request_queue) != 0) {
+            PDRequestItem *item = ff_safe_queue_pop_front(pd_model->request_queue);
+            destroy_request_item(&item);
+        }
+        ff_safe_queue_destroy(pd_model->request_queue);
+
+        while (ff_queue_size(pd_model->lltask_queue) != 0) {
+            LastLevelTaskItem *item = (LastLevelTaskItem *)ff_queue_pop_front(pd_model->lltask_queue);
+            av_freep(&item);
+        }
+        ff_queue_destroy(pd_model->lltask_queue);
+
+        while (ff_queue_size(pd_model->task_queue) != 0) {
+            TaskItem *item = ff_queue_pop_front(pd_model->task_queue);
+            av_frame_free(&item->in_frame);
+            av_frame_free(&item->out_frame);
+            av_freep(&item);
+        }
+        ff_queue_destroy(pd_model->task_queue);
+        av_freep(&pd_model);
+        av_freep(model);
+    }
+}
+
+DNNAsyncStatusType ff_dnn_get_result_pd(const DNNModel *model, AVFrame **in, AVFrame **out) {
+    PDModel *pd_model = model->model;
+    return ff_dnn_get_result_common(pd_model->task_queue, in, out);
+}
diff --git a/libavfilter/dnn/dnn_backend_pd.h b/libavfilter/dnn/dnn_backend_pd.h
new file mode 100644
index 0000000000..67dd3c986f
--- /dev/null
+++ b/libavfilter/dnn/dnn_backend_pd.h
@@ -0,0 +1,38 @@
+/*
+ * Copyright (c) 2023
+ *
+ * This file is part of FFmpeg.
+ *
+ * FFmpeg is free software; you can redistribute it and/or
+ * modify it under the terms of the GNU Lesser General Public
+ * License as published by the Free Software Foundation; either
+ * version 2.1 of the License, or (at your option) any later version.
+ *
+ * FFmpeg is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+ * Lesser General Public License for more details.
+ *
+ * You should have received a copy of the GNU Lesser General Public
+ * License along with FFmpeg; if not, write to the Free Software
+ * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
+ */
+
+/**
+ * @file
+ * DNN inference functions interface for paddle backend.
+ */
+
+#ifndef FFMPEG_DNN_BACKEND_PD_H
+#define FFMPEG_DNN_BACKEND_PD_H
+#include "../dnn_interface.h"
+
+DNNModel *ff_dnn_load_model_pd(const char *model_filename, DNNFunctionType func_type, const char *options, AVFilterContext *filter_ctx);
+
+int ff_dnn_execute_model_pd(const DNNModel *model, DNNExecBaseParams *exec_params);
+DNNAsyncStatusType ff_dnn_get_result_pd(const DNNModel *model, AVFrame **in, AVFrame **out);
+int ff_dnn_flush_pd(const DNNModel *model);
+
+void ff_dnn_free_model_pd(DNNModel **model);
+
+#endif //FFMPEG_DNN_BACKEND_PD_H
diff --git a/libavfilter/dnn/dnn_interface.c b/libavfilter/dnn/dnn_interface.c
index 554a36b0dc..e8d86bbc3a 100644
--- a/libavfilter/dnn/dnn_interface.c
+++ b/libavfilter/dnn/dnn_interface.c
@@ -27,6 +27,7 @@
 #include "dnn_backend_native.h"
 #include "dnn_backend_tf.h"
 #include "dnn_backend_openvino.h"
+#include "dnn_backend_pd.h"
 #include "libavutil/mem.h"
 
 DNNModule *ff_get_dnn_module(DNNBackendType backend_type)
@@ -70,8 +71,20 @@ DNNModule *ff_get_dnn_module(DNNBackendType backend_type)
         return NULL;
     #endif
         break;
+    case DNN_PD:
+    #if (CONFIG_LIBPADDLE == 1)
+        dnn_module->load_model = &ff_dnn_load_model_pd;
+        dnn_module->execute_model = &ff_dnn_execute_model_pd;
+        dnn_module->get_result = &ff_dnn_get_result_pd;
+        dnn_module->flush = &ff_dnn_flush_pd;
+        dnn_module->free_model = &ff_dnn_free_model_pd;
+    #else
+        av_freep(&dnn_module);
+        return NULL;
+    #endif
+        break;
     default:
-        av_log(NULL, AV_LOG_ERROR, "Module backend_type is not native or tensorflow\n");
+        av_log(NULL, AV_LOG_ERROR, "Module backend_type is not native or tensorflow or paddlepaddle\n");
         av_freep(&dnn_module);
         return NULL;
     }
diff --git a/libavfilter/dnn/dnn_io_proc.c b/libavfilter/dnn/dnn_io_proc.c
index 7961bf6b95..d7a8904e9c 100644
--- a/libavfilter/dnn/dnn_io_proc.c
+++ b/libavfilter/dnn/dnn_io_proc.c
@@ -184,6 +184,14 @@ static enum AVPixelFormat get_pixel_format(DNNData *data)
             av_assert0(!"unsupported data pixel format.\n");
             return AV_PIX_FMT_BGR24;
         }
+    } else if (data->dt == DNN_FLOAT) {
+        switch (data->order) {
+            case DCO_RGB:
+                return AV_PIX_FMT_BGR24;
+            default:
+                av_assert0(!"unsupported data pixel format.\n");
+                return AV_PIX_FMT_BGR24;
+        }
     }
 
     av_assert0(!"unsupported data type.\n");
diff --git a/libavfilter/dnn_interface.h b/libavfilter/dnn_interface.h
index ef8d7ae66f..22fe721e4c 100644
--- a/libavfilter/dnn_interface.h
+++ b/libavfilter/dnn_interface.h
@@ -32,7 +32,7 @@
 
 #define DNN_GENERIC_ERROR FFERRTAG('D','N','N','!')
 
-typedef enum {DNN_NATIVE, DNN_TF, DNN_OV} DNNBackendType;
+typedef enum {DNN_NATIVE, DNN_TF, DNN_OV, DNN_PD} DNNBackendType;
 
 typedef enum {DNN_FLOAT = 1, DNN_UINT8 = 4} DNNDataType;
 
diff --git a/libavfilter/vf_dnn_detect.c b/libavfilter/vf_dnn_detect.c
index 7e133f6af5..6658dfbfc7 100644
--- a/libavfilter/vf_dnn_detect.c
+++ b/libavfilter/vf_dnn_detect.c
@@ -210,6 +210,79 @@ static int dnn_detect_post_proc_tf(AVFrame *frame, DNNData *output, AVFilterCont
     return 0;
 }
 
+static int dnn_detect_post_proc_pd(AVFrame *frame, DNNData *output, AVFilterContext *filter_ctx)
+{
+    DnnDetectContext *ctx = filter_ctx->priv;
+    int proposal_count;
+    float conf_threshold = ctx->confidence;
+    float *box_info;
+    float x0, y0, x1, y1;
+    int nb_bboxes = 0;
+    AVFrameSideData *sd;
+    AVDetectionBBox *bbox;
+    AVDetectionBBoxHeader *header;
+
+    proposal_count = *(int *) (output[1].data);
+    box_info = output[0].data;
+
+    sd = av_frame_get_side_data(frame, AV_FRAME_DATA_DETECTION_BBOXES);
+    if (sd) {
+        av_log(filter_ctx, AV_LOG_ERROR, "already have dnn bounding boxes in side data.\n");
+        return -1;
+    }
+
+    for (int i = 0; i < proposal_count; ++i) {
+        if (box_info[i * 6 + 1] < conf_threshold)
+            continue;
+        nb_bboxes++;
+    }
+
+    if (nb_bboxes == 0) {
+        av_log(filter_ctx, AV_LOG_VERBOSE, "nothing detected in this frame.\n");
+        return 0;
+    }
+
+    header = av_detection_bbox_create_side_data(frame, nb_bboxes);
+    if (!header) {
+        av_log(filter_ctx, AV_LOG_ERROR, "failed to create side data with %d bounding boxes\n", nb_bboxes);
+        return -1;
+    }
+
+    av_strlcpy(header->source, ctx->dnnctx.model_filename, sizeof(header->source));
+
+    for (int i = 0; i < proposal_count; ++i) {
+        if (box_info[i * 6 + 1] < conf_threshold) {
+            continue;
+        }
+        x0 = box_info[i * 6 + 2];
+        y0 = box_info[i * 6 + 3];
+        x1 = box_info[i * 6 + 4];
+        y1 = box_info[i * 6 + 5];
+
+        bbox = av_get_detection_bbox(header, nb_bboxes - 1);
+
+        bbox->x = (int) (x0 );
+        bbox->w = (int) (x1 ) - bbox->x;
+        bbox->y = (int) (y0 );
+        bbox->h = (int) (y1 ) - bbox->y;
+
+        bbox->detect_confidence = av_make_q((int) (box_info[i * 6 + 1] * 10000), 10000);
+        bbox->classify_count = 0;
+
+        if (ctx->labels && box_info[i * 6] < ctx->label_count) {
+            av_strlcpy(bbox->detect_label, ctx->labels[(int) box_info[i * 6]], sizeof(bbox->detect_label));
+        } else {
+            snprintf(bbox->detect_label, sizeof(bbox->detect_label), "%d", (int) box_info[i * 6]);
+        }
+
+        nb_bboxes--;
+        if (nb_bboxes == 0) {
+            break;
+        }
+    }
+    return 0;
+}
+
 static int dnn_detect_post_proc(AVFrame *frame, DNNData *output, uint32_t nb, AVFilterContext *filter_ctx)
 {
     DnnDetectContext *ctx = filter_ctx->priv;
@@ -219,6 +292,8 @@ static int dnn_detect_post_proc(AVFrame *frame, DNNData *output, uint32_t nb, AV
         return dnn_detect_post_proc_ov(frame, output, filter_ctx);
     case DNN_TF:
         return dnn_detect_post_proc_tf(frame, output, filter_ctx);
+    case DNN_PD:
+        return dnn_detect_post_proc_pd(frame, output, filter_ctx);
     default:
         avpriv_report_missing_feature(filter_ctx, "Current dnn backend does not support detect filter\n");
         return AVERROR(EINVAL);
@@ -309,6 +384,13 @@ static int check_output_nb(DnnDetectContext *ctx, DNNBackendType backend_type, i
             return AVERROR(EINVAL);
         }
         return 0;
+    case DNN_PD:
+        if (output_nb != 2) {
+            av_log(ctx, AV_LOG_ERROR, "Dnn detect filter with paddle backend needs 2 output only, \
+                       but get %d instead\n", output_nb);
+            return AVERROR(EINVAL);
+        }
+        return 0;
     default:
         avpriv_report_missing_feature(ctx, "Dnn detect filter does not support current backend\n");
         return AVERROR(EINVAL);
diff --git a/libavfilter/vf_dnn_processing.c b/libavfilter/vf_dnn_processing.c
index 4462915073..260e62c59b 100644
--- a/libavfilter/vf_dnn_processing.c
+++ b/libavfilter/vf_dnn_processing.c
@@ -52,6 +52,9 @@ static const AVOption dnn_processing_options[] = {
 #endif
 #if (CONFIG_LIBOPENVINO == 1)
     { "openvino",    "openvino backend flag",      0,                        AV_OPT_TYPE_CONST,     { .i64 = 2 },    0, 0, FLAGS, "backend" },
+#endif
+#if (CONFIG_LIBPADDLE == 1)
+        { "paddle",   "paddle backend flag",       0,                        AV_OPT_TYPE_CONST,     { .i64 = 3 },    0, 0, FLAGS, "backend" },
 #endif
     DNN_COMMON_OPTIONS
     { NULL }
-- 
2.25.1

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