From 1b1064054c8f3b4ad3b52d14f0c8ee1c4e8200fd Mon Sep 17 00:00:00 2001 From: Ting Fu Date: Thu, 6 May 2021 16:46:08 +0800 Subject: lavfi/dnn_backend_tensorflow: add multiple outputs support Signed-off-by: Ting Fu --- libavfilter/dnn/dnn_backend_tf.c | 49 +++++++++++++++++++------------------ libavfilter/dnn_filter_common.c | 53 +++++++++++++++++++++++++++++++++------- libavfilter/dnn_filter_common.h | 6 +++-- libavfilter/vf_derain.c | 2 +- libavfilter/vf_sr.c | 2 +- 5 files changed, 75 insertions(+), 37 deletions(-) diff --git a/libavfilter/dnn/dnn_backend_tf.c b/libavfilter/dnn/dnn_backend_tf.c index 45da29ae70..b6b1812cd9 100644 --- a/libavfilter/dnn/dnn_backend_tf.c +++ b/libavfilter/dnn/dnn_backend_tf.c @@ -155,7 +155,7 @@ static DNNReturnType get_input_tf(void *model, DNNData *input, const char *input TF_DeleteStatus(status); // currently only NHWC is supported - av_assert0(dims[0] == 1); + av_assert0(dims[0] == 1 || dims[0] == -1); input->height = dims[1]; input->width = dims[2]; input->channels = dims[3]; @@ -707,7 +707,7 @@ static DNNReturnType execute_model_tf(const DNNModel *model, const char *input_n TF_Output *tf_outputs; TFModel *tf_model = model->model; TFContext *ctx = &tf_model->ctx; - DNNData input, output; + DNNData input, *outputs; TF_Tensor **output_tensors; TF_Output tf_input; TF_Tensor *input_tensor; @@ -738,14 +738,6 @@ static DNNReturnType execute_model_tf(const DNNModel *model, const char *input_n } } - if (nb_output != 1) { - // currently, the filter does not need multiple outputs, - // so we just pending the support until we really need it. - TF_DeleteTensor(input_tensor); - avpriv_report_missing_feature(ctx, "multiple outputs"); - return DNN_ERROR; - } - tf_outputs = av_malloc_array(nb_output, sizeof(*tf_outputs)); if (tf_outputs == NULL) { TF_DeleteTensor(input_tensor); @@ -785,23 +777,31 @@ static DNNReturnType execute_model_tf(const DNNModel *model, const char *input_n return DNN_ERROR; } + outputs = av_malloc_array(nb_output, sizeof(*outputs)); + if (!outputs) { + TF_DeleteTensor(input_tensor); + av_freep(&tf_outputs); + av_freep(&output_tensors); + av_log(ctx, AV_LOG_ERROR, "Failed to allocate memory for *outputs\n"); \ + return DNN_ERROR; + } + for (uint32_t i = 0; i < nb_output; ++i) { - output.height = TF_Dim(output_tensors[i], 1); - output.width = TF_Dim(output_tensors[i], 2); - output.channels = TF_Dim(output_tensors[i], 3); - output.data = TF_TensorData(output_tensors[i]); - output.dt = TF_TensorType(output_tensors[i]); - - if (do_ioproc) { - if (tf_model->model->frame_post_proc != NULL) { - tf_model->model->frame_post_proc(out_frame, &output, tf_model->model->filter_ctx); - } else { - ff_proc_from_dnn_to_frame(out_frame, &output, ctx); - } + outputs[i].height = TF_Dim(output_tensors[i], 1); + outputs[i].width = TF_Dim(output_tensors[i], 2); + outputs[i].channels = TF_Dim(output_tensors[i], 3); + outputs[i].data = TF_TensorData(output_tensors[i]); + outputs[i].dt = TF_TensorType(output_tensors[i]); + } + if (do_ioproc) { + if (tf_model->model->frame_post_proc != NULL) { + tf_model->model->frame_post_proc(out_frame, outputs, tf_model->model->filter_ctx); } else { - out_frame->width = output.width; - out_frame->height = output.height; + ff_proc_from_dnn_to_frame(out_frame, outputs, ctx); } + } else { + out_frame->width = outputs[0].width; + out_frame->height = outputs[0].height; } for (uint32_t i = 0; i < nb_output; ++i) { @@ -812,6 +812,7 @@ static DNNReturnType execute_model_tf(const DNNModel *model, const char *input_n TF_DeleteTensor(input_tensor); av_freep(&output_tensors); av_freep(&tf_outputs); + av_freep(&outputs); return DNN_SUCCESS; } diff --git a/libavfilter/dnn_filter_common.c b/libavfilter/dnn_filter_common.c index 52c7a5392a..0ed0ac2e30 100644 --- a/libavfilter/dnn_filter_common.c +++ b/libavfilter/dnn_filter_common.c @@ -17,6 +17,39 @@ */ #include "dnn_filter_common.h" +#include "libavutil/avstring.h" + +#define MAX_SUPPORTED_OUTPUTS_NB 4 + +static char **separate_output_names(const char *expr, const char *val_sep, int *separated_nb) +{ + char *val, **parsed_vals = NULL; + int val_num = 0; + if (!expr || !val_sep || !separated_nb) { + return NULL; + } + + parsed_vals = av_mallocz_array(MAX_SUPPORTED_OUTPUTS_NB, sizeof(*parsed_vals)); + if (!parsed_vals) { + return NULL; + } + + do { + val = av_get_token(&expr, val_sep); + if(val) { + parsed_vals[val_num] = val; + val_num++; + } + if (*expr) { + expr++; + } + } while(*expr); + + parsed_vals[val_num] = NULL; + *separated_nb = val_num; + + return parsed_vals; +} int ff_dnn_init(DnnContext *ctx, DNNFunctionType func_type, AVFilterContext *filter_ctx) { @@ -28,8 +61,10 @@ int ff_dnn_init(DnnContext *ctx, DNNFunctionType func_type, AVFilterContext *fil av_log(filter_ctx, AV_LOG_ERROR, "input name of the model network is not specified\n"); return AVERROR(EINVAL); } - if (!ctx->model_outputname) { - av_log(filter_ctx, AV_LOG_ERROR, "output name of the model network is not specified\n"); + + ctx->model_outputnames = separate_output_names(ctx->model_outputnames_string, "&", &ctx->nb_outputs); + if (!ctx->model_outputnames) { + av_log(filter_ctx, AV_LOG_ERROR, "could not parse model output names\n"); return AVERROR(EINVAL); } @@ -91,15 +126,15 @@ DNNReturnType ff_dnn_get_input(DnnContext *ctx, DNNData *input) DNNReturnType ff_dnn_get_output(DnnContext *ctx, int input_width, int input_height, int *output_width, int *output_height) { return ctx->model->get_output(ctx->model->model, ctx->model_inputname, input_width, input_height, - ctx->model_outputname, output_width, output_height); + (const char *)ctx->model_outputnames[0], output_width, output_height); } DNNReturnType ff_dnn_execute_model(DnnContext *ctx, AVFrame *in_frame, AVFrame *out_frame) { DNNExecBaseParams exec_params = { .input_name = ctx->model_inputname, - .output_names = (const char **)&ctx->model_outputname, - .nb_output = 1, + .output_names = (const char **)ctx->model_outputnames, + .nb_output = ctx->nb_outputs, .in_frame = in_frame, .out_frame = out_frame, }; @@ -110,8 +145,8 @@ DNNReturnType ff_dnn_execute_model_async(DnnContext *ctx, AVFrame *in_frame, AVF { DNNExecBaseParams exec_params = { .input_name = ctx->model_inputname, - .output_names = (const char **)&ctx->model_outputname, - .nb_output = 1, + .output_names = (const char **)ctx->model_outputnames, + .nb_output = ctx->nb_outputs, .in_frame = in_frame, .out_frame = out_frame, }; @@ -123,8 +158,8 @@ DNNReturnType ff_dnn_execute_model_classification(DnnContext *ctx, AVFrame *in_f DNNExecClassificationParams class_params = { { .input_name = ctx->model_inputname, - .output_names = (const char **)&ctx->model_outputname, - .nb_output = 1, + .output_names = (const char **)ctx->model_outputnames, + .nb_output = ctx->nb_outputs, .in_frame = in_frame, .out_frame = out_frame, }, diff --git a/libavfilter/dnn_filter_common.h b/libavfilter/dnn_filter_common.h index e7736d2bac..09ddd8a5ca 100644 --- a/libavfilter/dnn_filter_common.h +++ b/libavfilter/dnn_filter_common.h @@ -30,10 +30,12 @@ typedef struct DnnContext { char *model_filename; DNNBackendType backend_type; char *model_inputname; - char *model_outputname; + char *model_outputnames_string; char *backend_options; int async; + char **model_outputnames; + uint32_t nb_outputs; DNNModule *dnn_module; DNNModel *model; } DnnContext; @@ -41,7 +43,7 @@ typedef struct DnnContext { #define DNN_COMMON_OPTIONS \ { "model", "path to model file", OFFSET(model_filename), AV_OPT_TYPE_STRING, { .str = NULL }, 0, 0, FLAGS },\ { "input", "input name of the model", OFFSET(model_inputname), AV_OPT_TYPE_STRING, { .str = NULL }, 0, 0, FLAGS },\ - { "output", "output name of the model", OFFSET(model_outputname), AV_OPT_TYPE_STRING, { .str = NULL }, 0, 0, FLAGS },\ + { "output", "output name of the model", OFFSET(model_outputnames_string), AV_OPT_TYPE_STRING, { .str = NULL }, 0, 0, FLAGS },\ { "backend_configs", "backend configs", OFFSET(backend_options), AV_OPT_TYPE_STRING, { .str = NULL }, 0, 0, FLAGS },\ { "options", "backend configs", OFFSET(backend_options), AV_OPT_TYPE_STRING, { .str = NULL }, 0, 0, FLAGS },\ { "async", "use DNN async inference", OFFSET(async), AV_OPT_TYPE_BOOL, { .i64 = 1}, 0, 1, FLAGS}, diff --git a/libavfilter/vf_derain.c b/libavfilter/vf_derain.c index 76c4ef414f..5037f3a5f7 100644 --- a/libavfilter/vf_derain.c +++ b/libavfilter/vf_derain.c @@ -50,7 +50,7 @@ static const AVOption derain_options[] = { #endif { "model", "path to model file", OFFSET(dnnctx.model_filename), AV_OPT_TYPE_STRING, { .str = NULL }, 0, 0, FLAGS }, { "input", "input name of the model", OFFSET(dnnctx.model_inputname), AV_OPT_TYPE_STRING, { .str = "x" }, 0, 0, FLAGS }, - { "output", "output name of the model", OFFSET(dnnctx.model_outputname), AV_OPT_TYPE_STRING, { .str = "y" }, 0, 0, FLAGS }, + { "output", "output name of the model", OFFSET(dnnctx.model_outputnames_string), AV_OPT_TYPE_STRING, { .str = "y" }, 0, 0, FLAGS }, { NULL } }; diff --git a/libavfilter/vf_sr.c b/libavfilter/vf_sr.c index 4360439ca6..f930b38748 100644 --- a/libavfilter/vf_sr.c +++ b/libavfilter/vf_sr.c @@ -54,7 +54,7 @@ static const AVOption sr_options[] = { { "scale_factor", "scale factor for SRCNN model", OFFSET(scale_factor), AV_OPT_TYPE_INT, { .i64 = 2 }, 2, 4, FLAGS }, { "model", "path to model file specifying network architecture and its parameters", OFFSET(dnnctx.model_filename), AV_OPT_TYPE_STRING, {.str=NULL}, 0, 0, FLAGS }, { "input", "input name of the model", OFFSET(dnnctx.model_inputname), AV_OPT_TYPE_STRING, { .str = "x" }, 0, 0, FLAGS }, - { "output", "output name of the model", OFFSET(dnnctx.model_outputname), AV_OPT_TYPE_STRING, { .str = "y" }, 0, 0, FLAGS }, + { "output", "output name of the model", OFFSET(dnnctx.model_outputnames_string), AV_OPT_TYPE_STRING, { .str = "y" }, 0, 0, FLAGS }, { NULL } }; -- cgit v1.2.3