[FFmpeg-devel] [PATCH v2] libavfi/dnn: add LibTorch as one of DNN backend
wenbin.chen at intel.com
wenbin.chen at intel.com
Fri Feb 2 09:26:29 EET 2024
From: Wenbin Chen <wenbin.chen at intel.com>
PyTorch is an open source machine learning framework that accelerates
the path from research prototyping to production deployment. Official
websit: 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.
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. Please note, torch.jit.trace() is not recommanded, since it does not support ambiguous input size.
Signed-off-by: Ting Fu <ting.fu at intel.com>
Signed-off-by: Wenbin Chen <wenbin.chen at intel.com>
---
configure | 5 +-
libavfilter/dnn/Makefile | 1 +
libavfilter/dnn/dnn_backend_torch.cpp | 587 ++++++++++++++++++++++++++
libavfilter/dnn/dnn_interface.c | 5 +
libavfilter/dnn_filter_common.c | 15 +-
libavfilter/dnn_interface.h | 2 +-
libavfilter/vf_dnn_processing.c | 3 +
7 files changed, 614 insertions(+), 4 deletions(-)
create mode 100644 libavfilter/dnn/dnn_backend_torch.cpp
diff --git a/configure b/configure
index 68f675a4bc..bc11172fe4 100755
--- a/configure
+++ b/configure
@@ -279,6 +279,7 @@ 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]
@@ -1901,6 +1902,7 @@ EXTERNAL_LIBRARY_LIST="
libtensorflow
libtesseract
libtheora
+ libtorch
libtwolame
libuavs3d
libv4l2
@@ -2776,7 +2778,7 @@ 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"
@@ -6873,6 +6875,7 @@ 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++14 && 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"; }
diff --git a/libavfilter/dnn/Makefile b/libavfilter/dnn/Makefile
index 5d5697ea42..3d09927c98 100644
--- a/libavfilter/dnn/Makefile
+++ b/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)
diff --git a/libavfilter/dnn/dnn_backend_torch.cpp b/libavfilter/dnn/dnn_backend_torch.cpp
new file mode 100644
index 0000000000..b905c55175
--- /dev/null
+++ b/libavfilter/dnn/dnn_backend_torch.cpp
@@ -0,0 +1,587 @@
+/*
+ * 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[2] = -1;
+ input->dims[3] = -1;
+ input->dims[1] = 3;
+ 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;
+
+ 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);
+ 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] * 3 * 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, 1, 3, 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();
+ assert(sizes.size == 5);
+ outputs.order = DCO_RGB;
+ outputs.layout = DL_NCHW;
+ outputs.dims[2] = sizes.at(3);
+ outputs.dims[3] = sizes.at(4);
+ outputs.dt = DNN_FLOAT;
+ outputs.dims[1] = 3;
+
+ 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,
+};
diff --git a/libavfilter/dnn/dnn_interface.c b/libavfilter/dnn/dnn_interface.c
index e843826aa6..b9f71aea53 100644
--- a/libavfilter/dnn/dnn_interface.c
+++ b/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",
diff --git a/libavfilter/dnn_filter_common.c b/libavfilter/dnn_filter_common.c
index f012d450a2..7d194c9ade 100644
--- a/libavfilter/dnn_filter_common.c
+++ b/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);
}
diff --git a/libavfilter/dnn_interface.h b/libavfilter/dnn_interface.h
index 852d88baa8..63f492e690 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_TF = 1, DNN_OV} DNNBackendType;
+typedef enum {DNN_TF = 1, DNN_OV, DNN_TH} DNNBackendType;
typedef enum {DNN_FLOAT = 1, DNN_UINT8 = 4} DNNDataType;
diff --git a/libavfilter/vf_dnn_processing.c b/libavfilter/vf_dnn_processing.c
index 0b70c8e024..beaba44694 100644
--- a/libavfilter/vf_dnn_processing.c
+++ b/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, "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 }
--
2.34.1
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