From: <m.kaindl0208@gmail.com>
To: <ffmpeg-devel@ffmpeg.org>
Subject: [FFmpeg-devel] [PATCH v2 FFmpeg 16/20] libavfilter/dnn/dnn_backend_torch: CLIP/CLAP Inference handling and support for detection bboxes from dnn_detect filter
Date: Mon, 10 Mar 2025 20:55:26 +0100
Message-ID: <004401db91f6$5e4bf460$1ae3dd20$@gmail.com> (raw)
Signed-off-by: MaximilianKaindl <m.kaindl0208@gmail.com>
---
libavfilter/dnn/dnn_backend_torch.cpp | 411 +++++++++++++++++++-------
1 file changed, 311 insertions(+), 100 deletions(-)
diff --git a/libavfilter/dnn/dnn_backend_torch.cpp b/libavfilter/dnn/dnn_backend_torch.cpp
index 1d2bfb191a..26b57f08f3 100644
--- a/libavfilter/dnn/dnn_backend_torch.cpp
+++ b/libavfilter/dnn/dnn_backend_torch.cpp
@@ -1,22 +1,22 @@
/*
- * 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
- */
+* 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
@@ -31,7 +31,9 @@ extern "C" {
#include "dnn_io_proc.h"
#include "dnn_backend_common.h"
#include "libavutil/opt.h"
+#include "libavutil/avassert.h"
#include "libavutil/avstring.h"
+#include "libavutil/detection_bbox.h"
#include "libavutil/mem.h"
#include "queue.h"
#include "safe_queue.h"
@@ -65,11 +67,11 @@ typedef struct THInferRequest {
typedef struct THRequestItem {
THInferRequest *infer_request;
- LastLevelTaskItem *lltask;
+ LastLevelTaskItem **lltasks;
+ int lltask_count;
DNNAsyncExecModule exec_module;
} THRequestItem;
-
#define OFFSET(x) offsetof(THOptions, x)
#define FLAGS AV_OPT_FLAG_FILTERING_PARAM
static const AVOption dnn_th_options[] = {
@@ -77,24 +79,95 @@ static const AVOption dnn_th_options[] = {
{ NULL }
};
-static int extract_lltask_from_task(TaskItem *task, Queue *lltask_queue)
+static int extract_lltask_from_task(DNNFunctionType func_type, TaskItem *task, Queue *lltask_queue, DNNExecBaseParams *exec_params)
{
THModel *th_model = (THModel *)task->model;
DnnContext *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);
+
+ switch (func_type) {
+ case DFT_PROCESS_FRAME:
+ case DFT_ANALYTICS_CLAP: {
+ 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->bbox_index = 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;
+ }
+ case DFT_ANALYTICS_CLIP: {
+ const AVDetectionBBoxHeader *header;
+ AVFrame *frame = task->in_frame;
+ AVFrameSideData *sd;
+ LastLevelTaskItem *lltask;
+ DNNExecZeroShotClassificationParams *params = (DNNExecZeroShotClassificationParams *)exec_params;
+
+ if (params->target == NULL) {
+ 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->bbox_index = 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;
+ }
+
+ task->inference_todo = 0;
+ task->inference_done = 0;
+
+ if (!ff_dnn_contain_valid_detection_bbox(frame)) {
+ return 0;
+ }
+
+ sd = av_frame_get_side_data(frame, AV_FRAME_DATA_DETECTION_BBOXES);
+ header = (const AVDetectionBBoxHeader *)sd->data;
+
+ for (uint32_t i = 0; i < header->nb_bboxes; i++) {
+ const AVDetectionBBox *bbox = av_get_detection_bbox(header, i);
+ if (bbox->w * bbox->h <= 0) {
+ continue;
+ }
+ if (params->target) {
+ if (av_strncasecmp(bbox->detect_label, params->target, sizeof(bbox->detect_label)) != 0) {
+ continue;
+ }
+ }
+
+ lltask = (LastLevelTaskItem *)av_malloc(sizeof(*lltask));
+ if (!lltask) {
+ return AVERROR(ENOMEM);
+ }
+ task->inference_todo++;
+ lltask->task = task;
+ lltask->bbox_index = i;
+ if (ff_queue_push_back(lltask_queue, lltask) < 0) {
+ av_freep(&lltask);
+ return AVERROR(ENOMEM);
+ }
+ }
+ return 0;
+ }
+ default: {
+ av_assert0(!"should not reach here");
+ return AVERROR(EINVAL);
+ }
}
- return 0;
}
static void th_free_request(THInferRequest *request)
@@ -121,7 +194,7 @@ static inline void destroy_request_item(THRequestItem **arg)
item = *arg;
th_free_request(item->infer_request);
av_freep(&item->infer_request);
- av_freep(&item->lltask);
+ av_freep(&item->lltasks);
ff_dnn_async_module_cleanup(&item->exec_module);
av_freep(arg);
}
@@ -187,9 +260,9 @@ static int get_input_th(DNNModel *model, DNNData *input, const char *input_name)
return 0;
}
-static void deleter(void *arg)
-{
- av_freep(&arg);
+static void deleter(void *arg)
+{
+ av_freep(&arg);
}
#if (CONFIG_LIBTOKENIZERS == 1)
@@ -590,19 +663,11 @@ static int fill_model_input_th(THModel *th_model, THRequestItem *request)
{
LastLevelTaskItem *lltask = NULL;
TaskItem *task = NULL;
- THInferRequest *infer_request = NULL;
+ THInferRequest *infer_request = request->infer_request;
DNNData input = { 0 };
DnnContext *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;
+ std::vector<torch::Tensor> batch_tensors;
ret = get_input_th(&th_model->model, &input, NULL);
if ( ret != 0) {
@@ -611,36 +676,92 @@ static int fill_model_input_th(THModel *th_model, THRequestItem *request)
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);
+ // Handle audio input
+ if (th_model->model.func_type == DFT_ANALYTICS_CLAP) {
+ lltask = (LastLevelTaskItem *)ff_queue_pop_front(th_model->lltask_queue);
+ if (!lltask) {
+ return -1;
+ }
+ request->lltasks[request->lltask_count++] = lltask;
+ task = lltask->task;
+ return prepare_audio_tensor(th_model, request);
+ }
+
+ while (ff_queue_size(th_model->lltask_queue) != 0) {
+ lltask = (LastLevelTaskItem *)ff_queue_pop_front(th_model->lltask_queue);
+ if (!lltask) {
+ break;
+ }
+ request->lltasks[request->lltask_count++] = lltask;
+ task = lltask->task;
+
+ 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) {
+ ret = AVERROR(ENOMEM);
+ goto err;
+ }
+ switch (th_model->model.func_type) {
+ case DFT_PROCESS_FRAME:
+ case DFT_ANALYTICS_CLIP:
+ 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);
+ ret = AVERROR(EINVAL);
+ goto err;
+ }
+
+ try {
+ auto tensor = torch::from_blob(input.data,
+ {1, input.dims[channel_idx], input.dims[height_idx], input.dims[width_idx]},
+ deleter, torch::kFloat32).clone();
+ if (th_model->model.func_type == DFT_ANALYTICS_CLIP) {
+ preprocess_image_tensor(th_model, &tensor, torch::kCPU);
}
+ batch_tensors.push_back(tensor);
+ input.data = NULL;
+ } catch (const c10::Error &e) {
+ av_log(ctx, AV_LOG_ERROR, "Error creating tensor: %s\n", e.what());
+ ret = AVERROR(EINVAL);
+ goto err;
}
- break;
- default:
- avpriv_report_missing_feature(NULL, "model function type %d", th_model->model.func_type);
- break;
+
+ av_freep(&input.data);
+ }
+
+ // Stack tensors into batch
+ try {
+ if (!batch_tensors.empty()) {
+ *infer_request->input_tensor = torch::cat(batch_tensors, 0);
+ } else {
+ av_log(ctx, AV_LOG_ERROR, "No tensors to process\n");
+ ret = AVERROR(EINVAL);
+ goto err;
+ }
+ } catch (const c10::Error &e) {
+ av_log(ctx, AV_LOG_ERROR, "Error creating batch tensor: %s\n", e.what());
+ ret = AVERROR(EINVAL);
+ goto err;
}
- *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:
+ if (input.data) {
+ av_freep(&input.data);
+ }
th_free_request(infer_request);
return ret;
}
@@ -661,7 +782,7 @@ static int th_start_inference(void *args)
return AVERROR(EINVAL);
}
infer_request = request->infer_request;
- lltask = request->lltask;
+ lltask = request->lltasks[0];
task = lltask->task;
th_model = (THModel *)task->model;
ctx = th_model->ctx;
@@ -681,16 +802,54 @@ static int th_start_inference(void *args)
*infer_request->input_tensor = infer_request->input_tensor->to(device);
inputs.push_back(*infer_request->input_tensor);
- *infer_request->output = th_model->jit_model->forward(inputs).toTensor();
+#if (CONFIG_LIBTOKENIZERS == 1)
+ if (th_model->model.func_type == DFT_ANALYTICS_CLIP) {
+ inputs.push_back(*th_model->clxp_ctx->tokenized_text);
+ } else if (th_model->model.func_type == DFT_ANALYTICS_CLAP) {
+ inputs.push_back(*th_model->clxp_ctx->tokenized_text);
+ inputs.push_back(*th_model->clxp_ctx->attention_mask);
+ }
+#endif
+
+ auto result = th_model->jit_model->forward(inputs);
+
+ if (th_model->model.func_type == DFT_PROCESS_FRAME) {
+ *infer_request->output = result.toTensor();
+ } else if (th_model->model.func_type == DFT_ANALYTICS_CLIP || th_model->model.func_type == DFT_ANALYTICS_CLAP) {
+ if (result.isTuple()) {
+ auto result_tuple = result.toTuple();
+ torch::Tensor media_embeddings;
+ torch::Tensor text_embeddings;
+ float logit_scale = th_model->ctx->torch_option.logit_scale;
+ if (th_model->ctx->torch_option.forward_order == 1) {
+ media_embeddings = result_tuple->elements()[1].toTensor();
+ text_embeddings = result_tuple->elements()[0].toTensor();
+ *infer_request->output = calculate_similarity(media_embeddings, text_embeddings,
+ th_model->ctx->torch_option.normalize, logit_scale, ctx);
+ } else {
+ media_embeddings = result_tuple->elements()[0].toTensor();
+ text_embeddings = result_tuple->elements()[1].toTensor();
+ *infer_request->output = calculate_similarity(media_embeddings, text_embeddings,
+ th_model->ctx->torch_option.normalize, logit_scale, ctx);
+ }
+ *infer_request->output =
+ apply_softmax(*infer_request->output, th_model->ctx->torch_option.temperature,
+ th_model->clxp_ctx->softmax_units, th_model->clxp_ctx->softmax_units_count, ctx);
+ }
+ } else {
+ avpriv_report_missing_feature(ctx, "model function type %d", th_model->model.func_type);
+ return AVERROR(EINVAL);
+ }
return 0;
}
-static void infer_completion_callback(void *args) {
- THRequestItem *request = (THRequestItem*)args;
- LastLevelTaskItem *lltask = request->lltask;
+static void infer_completion_callback(void *args)
+{
+ THRequestItem *request = (THRequestItem *)args;
+ LastLevelTaskItem *lltask = request->lltasks[0];
TaskItem *task = lltask->task;
- DNNData outputs = { 0 };
+ DNNData outputs = {0};
THInferRequest *infer_request = request->infer_request;
THModel *th_model = (THModel *)task->model;
torch::Tensor *output = infer_request->output;
@@ -699,7 +858,17 @@ static void infer_completion_callback(void *args) {
outputs.order = DCO_RGB;
outputs.layout = DL_NCHW;
outputs.dt = DNN_FLOAT;
- if (sizes.size() == 4) {
+ if (th_model->model.func_type == DFT_ANALYTICS_CLIP || th_model->model.func_type == DFT_ANALYTICS_CLAP) {
+ // CLIP outputs are similarity scores [batch_size, num_labels]
+ if (sizes.size() != 2) {
+ av_log(th_model->ctx, AV_LOG_ERROR, "Invalid CLIP output dimensions\n");
+ goto err;
+ }
+ outputs.dims[0] = sizes[0]; // batch_size
+ outputs.dims[1] = sizes[1]; // number of labels
+ outputs.order = th_model->model.func_type == DFT_ANALYTICS_CLIP ? DCO_RGB : DCO_NONE;
+ outputs.dt = DNN_FLOAT;
+ } else if (sizes.size() == 4 && th_model->model.func_type == DFT_PROCESS_FRAME) {
// 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
@@ -711,31 +880,62 @@ static void infer_completion_callback(void *args) {
goto err;
}
- switch (th_model->model.func_type) {
- case DFT_PROCESS_FRAME:
- if (task->do_ioproc) {
+ // Process all Tasks
+ for (int i = 0; i < request->lltask_count; i++) {
+ LastLevelTaskItem *lltask = request->lltasks[i];
+ TaskItem *task = lltask->task;
+
+ // Extract single output
+ torch::Tensor single_output;
+ try {
+ single_output = output->select(0, i);
+
// Post process can only deal with CPU memory.
- if (output->device() != torch::kCPU)
- *output = output->to(torch::kCPU);
- 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);
+ if (single_output.device() != torch::kCPU) {
+ single_output = single_output.to(torch::kCPU);
}
- } 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)];
+
+ outputs.data = single_output.data_ptr();
+ } catch (const c10::Error &e) {
+ av_log(th_model->ctx, AV_LOG_ERROR, "Error processing output tensor: %s\n", e.what());
+ goto err;
}
- break;
- default:
- avpriv_report_missing_feature(th_model->ctx, "model function type %d", th_model->model.func_type);
- goto err;
+
+ switch (th_model->model.func_type) {
+ case DFT_PROCESS_FRAME:
+ if (task->do_ioproc) {
+ outputs.scale = 255;
+ 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;
+ case DFT_ANALYTICS_CLIP:
+ case DFT_ANALYTICS_CLAP:
+ if (task->do_ioproc) {
+ if (!th_model->model.classify_post_proc) {
+ av_log(th_model->ctx, AV_LOG_ERROR, "CLIP/CLAP filter needs to provide post proc\n");
+ goto err;
+ }
+ th_model->model.classify_post_proc(task->in_frame, &outputs, lltask->bbox_index,
+ th_model->model.filter_ctx);
+ }
+ 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->lltasks[i]);
}
- task->inference_done++;
- av_freep(&request->lltask);
err:
+ av_freep(&request->lltasks);
+ request->lltask_count = 0;
th_free_request(infer_request);
if (ff_safe_queue_push_back(th_model->request_queue, request) < 0) {
@@ -789,7 +989,7 @@ err:
}
static int get_output_th(DNNModel *model, const char *input_name, int input_width, int input_height,
- const char *output_name, int *output_width, int *output_height)
+ const char *output_name, int *output_width, int *output_height)
{
int ret = 0;
THModel *th_model = (THModel*) model;
@@ -808,7 +1008,7 @@ static int get_output_th(DNNModel *model, const char *input_name, int input_widt
goto err;
}
- ret = extract_lltask_from_task(&task, th_model->lltask_queue);
+ ret = extract_lltask_from_task(th_model->model.func_type, &task, th_model->lltask_queue, NULL);
if ( ret != 0) {
av_log(ctx, AV_LOG_ERROR, "unable to extract last level task from task.\n");
goto err;
@@ -918,7 +1118,7 @@ static THModel *init_model_th(DnnContext *ctx, DNNFunctionType func_type, AVFilt
if (!item) {
goto fail;
}
- item->lltask = NULL;
+ item->lltasks = 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");
@@ -1078,18 +1278,29 @@ static int dnn_execute_model_th(const DNNModel *model, DNNExecBaseParams *exec_p
return ret;
}
- ret = extract_lltask_from_task(task, th_model->lltask_queue);
+ ret = extract_lltask_from_task(model->func_type, task, th_model->lltask_queue, exec_params);
if (ret != 0) {
av_log(ctx, AV_LOG_ERROR, "unable to extract last level task from task.\n");
return ret;
}
+ if (task->inference_todo == 0) {
+ return 0;
+ }
+
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);
}
+ request->lltasks = (LastLevelTaskItem **)av_malloc_array(task->inference_todo, sizeof(*request->lltasks));
+ if (!request->lltasks) {
+ av_log(ctx, AV_LOG_ERROR, "unable to create lltasks.\n");
+ return AVERROR(EINVAL);
+ }
+ request->lltask_count = 0;
+
return execute_model_th(request, th_model->lltask_queue);
}
--
2.34.1
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Git Inbox Mirror of the ffmpeg-devel mailing list - see https://ffmpeg.org/mailman/listinfo/ffmpeg-devel
This inbox may be cloned and mirrored by anyone:
git clone --mirror https://master.gitmailbox.com/ffmpegdev/0 ffmpegdev/git/0.git
# If you have public-inbox 1.1+ installed, you may
# initialize and index your mirror using the following commands:
public-inbox-init -V2 ffmpegdev ffmpegdev/ https://master.gitmailbox.com/ffmpegdev \
ffmpegdev@gitmailbox.com
public-inbox-index ffmpegdev
Example config snippet for mirrors.
AGPL code for this site: git clone https://public-inbox.org/public-inbox.git