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https://gitlab.freedesktop.org/gstreamer/gstreamer.git
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d3ebaa316d
- Unmap buffer on Ort exception - Avoid retrieving unused videometa Part-of: <https://gitlab.freedesktop.org/gstreamer/gstreamer/-/merge_requests/5761>
638 lines
20 KiB
C++
638 lines
20 KiB
C++
/*
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* GStreamer gstreamer-onnxinference
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* Copyright (C) 2023 Collabora Ltd.
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*
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* gstonnxinference.cpp
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*
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* This library is free software; you can redistribute it and/or
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* modify it under the terms of the GNU Library General Public
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* License as published by the Free Software Foundation; either
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* version 2 of the License, or (at your option) any later version.
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*
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* This library is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
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* Library General Public License for more details.
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*
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* You should have received a copy of the GNU Library General Public
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* License along with this library; if not, write to the
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* Free Software Foundation, Inc., 51 Franklin St, Fifth Floor,
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* Boston, MA 02110-1301, USA.
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*/
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/**
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* SECTION:element-onnxinference
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* @short_description: Run ONNX inference model on video buffers
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*
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* This element can apply an ONNX model to video buffers. It attaches
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* the tensor output to the buffer as a @ref GstTensorMeta.
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*
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* To install ONNX on your system, recursively clone this repository
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* https://github.com/microsoft/onnxruntime.git
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*
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* and build and install with cmake:
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*
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* CPU:
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*
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* cmake -Donnxruntime_BUILD_SHARED_LIB:ON -DBUILD_TESTING:OFF \
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* $SRC_DIR/onnxruntime/cmake && make -j$(nproc) && sudo make install
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*
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*
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* CUDA :
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*
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* cmake -Donnxruntime_BUILD_SHARED_LIB:ON -DBUILD_TESTING:OFF -Donnxruntime_USE_CUDA:ON \
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* -Donnxruntime_CUDA_HOME=$CUDA_PATH -Donnxruntime_CUDNN_HOME=$CUDA_PATH \
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* $SRC_DIR/onnxruntime/cmake && make -j$(nproc) && sudo make install
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*
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*
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* where :
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*
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* 1. $SRC_DIR and $BUILD_DIR are local source and build directories
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* 2. To run with CUDA, both CUDA and cuDNN libraries must be installed.
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* $CUDA_PATH is an environment variable set to the CUDA root path.
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* On Linux, it would be /usr/local/cuda
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*
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*
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* ## Example launch command:
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*
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* GST_DEBUG=onnxinference:5 gst-launch-1.0 multifilesrc location=bus.jpg ! \
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* jpegdec ! videoconvert ! \
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* onnxinference execution-provider=cpu model-file=model.onnx \
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* videoconvert ! autovideosink
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*
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*
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* Note: in order for downstream tensor decoders to correctly parse the tensor
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* data in the GstTensorMeta, meta data must be attached to the ONNX model
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* assigning a unique string id to each output layer. These unique string ids
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* and corresponding GQuark ids are currently stored in the ONNX plugin source
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* in the file 'gsttensorid.h'. For an output layer with name Foo and with context
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* unique string id Gst.Model.Bar, a meta data key/value pair must be added
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* to the ONNX model with "Foo" mapped to "Gst.Model.Bar" in order for a downstream
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* decoder to make use of this model. If the meta data is absent, the pipeline will
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* fail.
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*
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* As a convenience, there is a python script
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* currently stored at
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* https://gitlab.collabora.com/gstreamer/onnx-models/-/blob/master/scripts/modify_onnx_metadata.py
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* to enable users to easily add and remove meta data from json files. It can also dump
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* the names of all output layers, which can then be used to craft the json meta data file.
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*
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*
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*/
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#ifdef HAVE_CONFIG_H
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#include "config.h"
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#endif
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#include <gst/gst.h>
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#include "gstonnxinference.h"
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#include "gstonnxclient.h"
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/*
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* GstOnnxInference:
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*
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* @model_file model file
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* @optimization_level: ONNX session optimization level
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* @execution_provider: ONNX execution provider
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* @onnx_client opaque pointer to ONNX client
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* @onnx_disabled true if inference is disabled
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* @video_info @ref GstVideoInfo of sink caps
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*/
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struct _GstOnnxInference
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{
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GstBaseTransform basetransform;
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gchar *model_file;
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GstOnnxOptimizationLevel optimization_level;
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GstOnnxExecutionProvider execution_provider;
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gpointer onnx_client;
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gboolean onnx_disabled;
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GstVideoInfo video_info;
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};
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GST_DEBUG_CATEGORY_STATIC (onnx_inference_debug);
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#define GST_CAT_DEFAULT onnx_inference_debug
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#define GST_ONNX_CLIENT_MEMBER( self ) ((GstOnnxNamespace::GstOnnxClient *) (self->onnx_client))
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GST_ELEMENT_REGISTER_DEFINE (onnx_inference, "onnxinference",
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GST_RANK_PRIMARY, GST_TYPE_ONNX_INFERENCE);
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/* GstOnnxInference properties */
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enum
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{
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PROP_0,
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PROP_MODEL_FILE,
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PROP_INPUT_IMAGE_FORMAT,
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PROP_OPTIMIZATION_LEVEL,
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PROP_EXECUTION_PROVIDER,
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PROP_INPUT_IMAGE_DATATYPE,
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PROP_INPUT_OFFSET,
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PROP_INPUT_SCALE
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};
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#define GST_ONNX_INFERENCE_DEFAULT_EXECUTION_PROVIDER GST_ONNX_EXECUTION_PROVIDER_CPU
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#define GST_ONNX_INFERENCE_DEFAULT_OPTIMIZATION_LEVEL GST_ONNX_OPTIMIZATION_LEVEL_ENABLE_EXTENDED
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static GstStaticPadTemplate gst_onnx_inference_src_template =
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GST_STATIC_PAD_TEMPLATE ("src",
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GST_PAD_SRC,
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GST_PAD_ALWAYS,
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GST_STATIC_CAPS (GST_VIDEO_CAPS_MAKE ("{ RGB,RGBA,BGR,BGRA }"))
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);
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static GstStaticPadTemplate gst_onnx_inference_sink_template =
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GST_STATIC_PAD_TEMPLATE ("sink",
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GST_PAD_SINK,
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GST_PAD_ALWAYS,
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GST_STATIC_CAPS (GST_VIDEO_CAPS_MAKE ("{ RGB,RGBA,BGR,BGRA }"))
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);
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static void gst_onnx_inference_set_property (GObject * object,
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guint prop_id, const GValue * value, GParamSpec * pspec);
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static void gst_onnx_inference_get_property (GObject * object,
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guint prop_id, GValue * value, GParamSpec * pspec);
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static void gst_onnx_inference_finalize (GObject * object);
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static GstFlowReturn gst_onnx_inference_transform_ip (GstBaseTransform *
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trans, GstBuffer * buf);
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static gboolean gst_onnx_inference_process (GstBaseTransform * trans,
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GstBuffer * buf);
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static gboolean gst_onnx_inference_create_session (GstBaseTransform * trans);
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static GstCaps *gst_onnx_inference_transform_caps (GstBaseTransform *
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trans, GstPadDirection direction, GstCaps * caps, GstCaps * filter_caps);
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static gboolean
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gst_onnx_inference_set_caps (GstBaseTransform * trans, GstCaps * incaps,
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GstCaps * outcaps);
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G_DEFINE_TYPE (GstOnnxInference, gst_onnx_inference, GST_TYPE_BASE_TRANSFORM);
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GType gst_onnx_optimization_level_get_type (void);
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#define GST_TYPE_ONNX_OPTIMIZATION_LEVEL (gst_onnx_optimization_level_get_type ())
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GType gst_onnx_execution_provider_get_type (void);
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#define GST_TYPE_ONNX_EXECUTION_PROVIDER (gst_onnx_execution_provider_get_type ())
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GType gst_ml_model_input_image_format_get_type (void);
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#define GST_TYPE_ML_MODEL_INPUT_IMAGE_FORMAT (gst_ml_model_input_image_format_get_type ())
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GType gst_onnx_model_input_image_datatype_get_type (void);
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#define GST_TYPE_ONNX_MODEL_INPUT_IMAGE_DATATYPE (gst_onnx_model_input_image_datatype_get_type ())
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GType
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gst_onnx_optimization_level_get_type (void)
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{
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static GType onnx_optimization_type = 0;
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if (g_once_init_enter (&onnx_optimization_type)) {
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static GEnumValue optimization_level_types[] = {
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{GST_ONNX_OPTIMIZATION_LEVEL_DISABLE_ALL, "Disable all optimization",
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"disable-all"},
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{GST_ONNX_OPTIMIZATION_LEVEL_ENABLE_BASIC,
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"Enable basic optimizations (redundant node removals))",
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"enable-basic"},
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{GST_ONNX_OPTIMIZATION_LEVEL_ENABLE_EXTENDED,
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"Enable extended optimizations (redundant node removals + node fusions)",
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"enable-extended"},
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{GST_ONNX_OPTIMIZATION_LEVEL_ENABLE_ALL,
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"Enable all possible optimizations", "enable-all"},
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{0, NULL, NULL},
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};
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GType temp = g_enum_register_static ("GstOnnxOptimizationLevel",
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optimization_level_types);
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g_once_init_leave (&onnx_optimization_type, temp);
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}
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return onnx_optimization_type;
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}
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GType
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gst_onnx_execution_provider_get_type (void)
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{
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static GType onnx_execution_type = 0;
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if (g_once_init_enter (&onnx_execution_type)) {
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static GEnumValue execution_provider_types[] = {
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{GST_ONNX_EXECUTION_PROVIDER_CPU, "CPU execution provider",
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"cpu"},
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{GST_ONNX_EXECUTION_PROVIDER_CUDA,
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"CUDA execution provider",
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"cuda"},
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{0, NULL, NULL},
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};
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GType temp = g_enum_register_static ("GstOnnxExecutionProvider",
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execution_provider_types);
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g_once_init_leave (&onnx_execution_type, temp);
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}
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return onnx_execution_type;
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}
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GType
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gst_ml_model_input_image_format_get_type (void)
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{
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static GType ml_model_input_image_format = 0;
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if (g_once_init_enter (&ml_model_input_image_format)) {
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static GEnumValue ml_model_input_image_format_types[] = {
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{GST_ML_INPUT_IMAGE_FORMAT_HWC,
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"Height Width Channel (HWC) a.k.a. interleaved image data format",
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"hwc"},
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{GST_ML_INPUT_IMAGE_FORMAT_CHW,
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"Channel Height Width (CHW) a.k.a. planar image data format",
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"chw"},
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{0, NULL, NULL},
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};
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GType temp = g_enum_register_static ("GstMlInputImageFormat",
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ml_model_input_image_format_types);
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g_once_init_leave (&ml_model_input_image_format, temp);
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}
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return ml_model_input_image_format;
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}
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GType
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gst_onnx_model_input_image_datatype_get_type (void)
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{
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static GType model_input_image_datatype = 0;
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if (g_once_init_enter (&model_input_image_datatype)) {
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static GEnumValue model_input_image_datatype_types[] = {
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{GST_TENSOR_TYPE_INT8, "8 Bits integer", "int8"},
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{GST_TENSOR_TYPE_FLOAT32, "32 Bits floating points", "float"},
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{0, NULL, NULL},
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};
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GType temp = g_enum_register_static ("GstTensorType",
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model_input_image_datatype_types);
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g_once_init_leave (&model_input_image_datatype, temp);
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}
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return model_input_image_datatype;
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}
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static void
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gst_onnx_inference_class_init (GstOnnxInferenceClass * klass)
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{
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GObjectClass *gobject_class = (GObjectClass *) klass;
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GstElementClass *element_class = (GstElementClass *) klass;
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GstBaseTransformClass *basetransform_class = (GstBaseTransformClass *) klass;
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GST_DEBUG_CATEGORY_INIT (onnx_inference_debug, "onnxinference",
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0, "onnx_inference");
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gobject_class->set_property = gst_onnx_inference_set_property;
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gobject_class->get_property = gst_onnx_inference_get_property;
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gobject_class->finalize = gst_onnx_inference_finalize;
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/**
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* GstOnnxInference:model-file
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*
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* ONNX model file
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*
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* Since: 1.24
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*/
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g_object_class_install_property (G_OBJECT_CLASS (klass), PROP_MODEL_FILE,
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g_param_spec_string ("model-file",
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"ONNX model file", "ONNX model file", NULL, (GParamFlags)
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(G_PARAM_READWRITE | G_PARAM_STATIC_STRINGS)));
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/**
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* GstOnnxInference:input-image-format
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*
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* Model input image format
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*
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* Since: 1.24
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*/
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g_object_class_install_property (G_OBJECT_CLASS (klass),
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PROP_INPUT_IMAGE_FORMAT,
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g_param_spec_enum ("input-image-format",
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"Input image format",
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"Input image format",
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GST_TYPE_ML_MODEL_INPUT_IMAGE_FORMAT,
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GST_ML_INPUT_IMAGE_FORMAT_HWC, (GParamFlags)
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(G_PARAM_READWRITE | G_PARAM_STATIC_STRINGS)));
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/**
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* GstOnnxInference:optimization-level
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*
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* ONNX optimization level
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*
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* Since: 1.24
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*/
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g_object_class_install_property (G_OBJECT_CLASS (klass),
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PROP_OPTIMIZATION_LEVEL,
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g_param_spec_enum ("optimization-level",
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"Optimization level",
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"ONNX optimization level",
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GST_TYPE_ONNX_OPTIMIZATION_LEVEL,
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GST_ONNX_OPTIMIZATION_LEVEL_ENABLE_EXTENDED, (GParamFlags)
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(G_PARAM_READWRITE | G_PARAM_STATIC_STRINGS)));
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/**
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* GstOnnxInference:execution-provider
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*
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* ONNX execution provider
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*
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* Since: 1.24
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*/
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g_object_class_install_property (G_OBJECT_CLASS (klass),
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PROP_EXECUTION_PROVIDER,
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g_param_spec_enum ("execution-provider",
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"Execution provider",
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"ONNX execution provider",
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GST_TYPE_ONNX_EXECUTION_PROVIDER,
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GST_ONNX_EXECUTION_PROVIDER_CPU, (GParamFlags)
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(G_PARAM_READWRITE | G_PARAM_STATIC_STRINGS)));
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/**
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* GstOnnxInference:input-image-datatype
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*
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* Temporary hack, this should be discovered from the model and exposed
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* on sinkpad caps based on model contrains.
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*/
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g_object_class_install_property (G_OBJECT_CLASS (klass),
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PROP_INPUT_IMAGE_DATATYPE,
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g_param_spec_enum ("input-image-datatype",
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"Inference input image datatype",
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"Datatype that will be used as an input for the inference",
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GST_TYPE_ONNX_MODEL_INPUT_IMAGE_DATATYPE,
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GST_TENSOR_TYPE_INT8,
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(GParamFlags)(G_PARAM_READWRITE | G_PARAM_STATIC_STRINGS)));
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g_object_class_install_property (G_OBJECT_CLASS (klass),
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PROP_INPUT_OFFSET,
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g_param_spec_float ("input-tensor-offset",
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"Input tensor offset",
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"offset each tensor value by this value",
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-G_MAXFLOAT, G_MAXFLOAT, 0.0,
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(GParamFlags)(G_PARAM_READWRITE | G_PARAM_STATIC_STRINGS)));
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g_object_class_install_property (G_OBJECT_CLASS (klass),
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PROP_INPUT_SCALE,
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g_param_spec_float ("input-tensor-scale",
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"Input tensor scale",
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"Divide each tensor value by this value",
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G_MINFLOAT, G_MAXFLOAT, 1.0,
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(GParamFlags)(G_PARAM_READWRITE | G_PARAM_STATIC_STRINGS)));
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gst_element_class_set_static_metadata (element_class, "onnxinference",
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"Filter/Effect/Video",
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"Apply neural network to video frames and create tensor output",
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"Aaron Boxer <aaron.boxer@collabora.com>");
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gst_element_class_add_pad_template (element_class,
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gst_static_pad_template_get (&gst_onnx_inference_sink_template));
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gst_element_class_add_pad_template (element_class,
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gst_static_pad_template_get (&gst_onnx_inference_src_template));
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basetransform_class->transform_ip =
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GST_DEBUG_FUNCPTR (gst_onnx_inference_transform_ip);
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basetransform_class->transform_caps =
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GST_DEBUG_FUNCPTR (gst_onnx_inference_transform_caps);
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basetransform_class->set_caps =
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GST_DEBUG_FUNCPTR (gst_onnx_inference_set_caps);
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}
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static void
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gst_onnx_inference_init (GstOnnxInference * self)
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{
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self->onnx_client = new GstOnnxNamespace::GstOnnxClient ();
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self->onnx_disabled = TRUE;
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}
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static void
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gst_onnx_inference_finalize (GObject * object)
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{
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GstOnnxInference *self = GST_ONNX_INFERENCE (object);
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g_free (self->model_file);
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delete GST_ONNX_CLIENT_MEMBER (self);
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G_OBJECT_CLASS (gst_onnx_inference_parent_class)->finalize (object);
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}
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static void
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gst_onnx_inference_set_property (GObject * object, guint prop_id,
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const GValue * value, GParamSpec * pspec)
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{
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GstOnnxInference *self = GST_ONNX_INFERENCE (object);
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const gchar *filename;
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auto onnxClient = GST_ONNX_CLIENT_MEMBER (self);
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switch (prop_id) {
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case PROP_MODEL_FILE:
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filename = g_value_get_string (value);
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if (filename
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&& g_file_test (filename,
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(GFileTest) (G_FILE_TEST_EXISTS | G_FILE_TEST_IS_REGULAR))) {
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if (self->model_file)
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g_free (self->model_file);
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self->model_file = g_strdup (filename);
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self->onnx_disabled = FALSE;
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} else {
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GST_WARNING_OBJECT (self, "Model file '%s' not found!", filename);
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}
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break;
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case PROP_OPTIMIZATION_LEVEL:
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self->optimization_level =
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(GstOnnxOptimizationLevel) g_value_get_enum (value);
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break;
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case PROP_EXECUTION_PROVIDER:
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self->execution_provider =
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(GstOnnxExecutionProvider) g_value_get_enum (value);
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break;
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case PROP_INPUT_IMAGE_FORMAT:
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onnxClient->setInputImageFormat ((GstMlInputImageFormat)
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g_value_get_enum (value));
|
|
break;
|
|
case PROP_INPUT_IMAGE_DATATYPE:
|
|
onnxClient->setInputImageDatatype ((GstTensorType)
|
|
g_value_get_enum (value));
|
|
break;
|
|
case PROP_INPUT_OFFSET:
|
|
onnxClient->setInputImageOffset (g_value_get_float (value));
|
|
break;
|
|
case PROP_INPUT_SCALE:
|
|
onnxClient->setInputImageScale (g_value_get_float (value));
|
|
break;
|
|
default:
|
|
G_OBJECT_WARN_INVALID_PROPERTY_ID (object, prop_id, pspec);
|
|
break;
|
|
}
|
|
}
|
|
|
|
static void
|
|
gst_onnx_inference_get_property (GObject * object, guint prop_id,
|
|
GValue * value, GParamSpec * pspec)
|
|
{
|
|
GstOnnxInference *self = GST_ONNX_INFERENCE (object);
|
|
auto onnxClient = GST_ONNX_CLIENT_MEMBER (self);
|
|
|
|
switch (prop_id) {
|
|
case PROP_MODEL_FILE:
|
|
g_value_set_string (value, self->model_file);
|
|
break;
|
|
case PROP_OPTIMIZATION_LEVEL:
|
|
g_value_set_enum (value, self->optimization_level);
|
|
break;
|
|
case PROP_EXECUTION_PROVIDER:
|
|
g_value_set_enum (value, self->execution_provider);
|
|
break;
|
|
case PROP_INPUT_IMAGE_FORMAT:
|
|
g_value_set_enum (value, onnxClient->getInputImageFormat ());
|
|
break;
|
|
case PROP_INPUT_IMAGE_DATATYPE:
|
|
g_value_set_enum (value, onnxClient->getInputImageDatatype ());
|
|
break;
|
|
case PROP_INPUT_OFFSET:
|
|
g_value_set_float (value, onnxClient->getInputImageOffset ());
|
|
break;
|
|
case PROP_INPUT_SCALE:
|
|
g_value_set_float (value, onnxClient->getInputImageScale ());
|
|
break;
|
|
default:
|
|
G_OBJECT_WARN_INVALID_PROPERTY_ID (object, prop_id, pspec);
|
|
break;
|
|
}
|
|
}
|
|
|
|
static gboolean
|
|
gst_onnx_inference_create_session (GstBaseTransform * trans)
|
|
{
|
|
GstOnnxInference *self = GST_ONNX_INFERENCE (trans);
|
|
auto onnxClient = GST_ONNX_CLIENT_MEMBER (self);
|
|
|
|
GST_OBJECT_LOCK (self);
|
|
if (self->onnx_disabled) {
|
|
GST_OBJECT_UNLOCK (self);
|
|
|
|
return FALSE;
|
|
}
|
|
if (onnxClient->hasSession ()) {
|
|
GST_OBJECT_UNLOCK (self);
|
|
|
|
return TRUE;
|
|
}
|
|
if (self->model_file) {
|
|
gboolean ret =
|
|
GST_ONNX_CLIENT_MEMBER (self)->createSession (self->model_file,
|
|
self->optimization_level,
|
|
self->execution_provider);
|
|
if (!ret) {
|
|
GST_ERROR_OBJECT (self,
|
|
"Unable to create ONNX session. Model is disabled.");
|
|
self->onnx_disabled = TRUE;
|
|
}
|
|
} else {
|
|
self->onnx_disabled = TRUE;
|
|
GST_ELEMENT_ERROR (self, STREAM, FAILED, (NULL), ("Model file not found"));
|
|
}
|
|
GST_OBJECT_UNLOCK (self);
|
|
if (self->onnx_disabled) {
|
|
gst_base_transform_set_passthrough (GST_BASE_TRANSFORM (self), TRUE);
|
|
}
|
|
|
|
return TRUE;
|
|
}
|
|
|
|
static GstCaps *
|
|
gst_onnx_inference_transform_caps (GstBaseTransform *
|
|
trans, GstPadDirection direction, GstCaps * caps, GstCaps * filter_caps)
|
|
{
|
|
GstOnnxInference *self = GST_ONNX_INFERENCE (trans);
|
|
auto onnxClient = GST_ONNX_CLIENT_MEMBER (self);
|
|
GstCaps *other_caps;
|
|
guint i;
|
|
|
|
if (!gst_onnx_inference_create_session (trans))
|
|
return NULL;
|
|
GST_LOG_OBJECT (self, "transforming caps %" GST_PTR_FORMAT, caps);
|
|
|
|
if (gst_base_transform_is_passthrough (trans)
|
|
|| (!onnxClient->isFixedInputImageSize ()))
|
|
return gst_caps_ref (caps);
|
|
|
|
other_caps = gst_caps_new_empty ();
|
|
for (i = 0; i < gst_caps_get_size (caps); ++i) {
|
|
GstStructure *structure, *new_structure;
|
|
|
|
structure = gst_caps_get_structure (caps, i);
|
|
new_structure = gst_structure_copy (structure);
|
|
gst_structure_set (new_structure, "width", G_TYPE_INT,
|
|
onnxClient->getWidth (), "height", G_TYPE_INT,
|
|
onnxClient->getHeight (), NULL);
|
|
GST_LOG_OBJECT (self,
|
|
"transformed structure %2d: %" GST_PTR_FORMAT " => %"
|
|
GST_PTR_FORMAT, i, structure, new_structure);
|
|
gst_caps_append_structure (other_caps, new_structure);
|
|
}
|
|
|
|
if (!gst_caps_is_empty (other_caps) && filter_caps) {
|
|
GstCaps *tmp = gst_caps_intersect_full (other_caps, filter_caps,
|
|
GST_CAPS_INTERSECT_FIRST);
|
|
gst_caps_replace (&other_caps, tmp);
|
|
gst_caps_unref (tmp);
|
|
}
|
|
|
|
return other_caps;
|
|
}
|
|
|
|
static gboolean
|
|
gst_onnx_inference_set_caps (GstBaseTransform * trans, GstCaps * incaps,
|
|
GstCaps * outcaps)
|
|
{
|
|
GstOnnxInference *self = GST_ONNX_INFERENCE (trans);
|
|
auto onnxClient = GST_ONNX_CLIENT_MEMBER (self);
|
|
|
|
if (!gst_video_info_from_caps (&self->video_info, incaps)) {
|
|
GST_ERROR_OBJECT (self, "Failed to parse caps");
|
|
return FALSE;
|
|
}
|
|
|
|
onnxClient->parseDimensions (self->video_info);
|
|
return TRUE;
|
|
}
|
|
|
|
static GstFlowReturn
|
|
gst_onnx_inference_transform_ip (GstBaseTransform * trans, GstBuffer * buf)
|
|
{
|
|
if (!gst_base_transform_is_passthrough (trans)
|
|
&& !gst_onnx_inference_process (trans, buf)) {
|
|
GST_ELEMENT_ERROR (trans, STREAM, FAILED,
|
|
(NULL), ("ONNX inference failed"));
|
|
return GST_FLOW_ERROR;
|
|
}
|
|
|
|
return GST_FLOW_OK;
|
|
}
|
|
|
|
static gboolean
|
|
gst_onnx_inference_process (GstBaseTransform * trans, GstBuffer * buf)
|
|
{
|
|
GstMapInfo info;
|
|
if (gst_buffer_map (buf, &info, GST_MAP_READ)) {
|
|
GstOnnxInference *self = GST_ONNX_INFERENCE (trans);
|
|
try {
|
|
auto client = GST_ONNX_CLIENT_MEMBER (self);
|
|
auto outputs = client->run (info.data, self->video_info);
|
|
auto meta = client->copy_tensors_to_meta (outputs, buf);
|
|
if (!meta)
|
|
return FALSE;
|
|
GST_TRACE_OBJECT (trans, "Num tensors:%d", meta->num_tensors);
|
|
meta->batch_size = 1;
|
|
}
|
|
catch (Ort::Exception & ortex) {
|
|
GST_ERROR_OBJECT (self, "%s", ortex.what ());
|
|
gst_buffer_unmap (buf, &info);
|
|
return FALSE;
|
|
}
|
|
|
|
gst_buffer_unmap (buf, &info);
|
|
}
|
|
|
|
return TRUE;
|
|
}
|