SGIE Error

• Hardware Platform (Jetson / GPU) GPU
• DeepStream Version 6.4
• TensorRT Version 8.6.1.6-1+cuda12.0
• NVIDIA GPU Driver Version (valid for GPU only) 545.23.08

This is my python code

# python3 test4.py -i file:///opt/nvidia/deepstream/deepstream-6.4/sources/deepstream_python_apps/apps/deepstream-imagedata-multistream-redaction/face.mp4 file:///opt/nvidia/deepstream/deepstream-6.4/sources/deepstream_python_apps/apps/deepstream-imagedata-multistream-redaction/testface.mp4 -c H264
 

import argparse
import sys

sys.path.append('../')
import gi
import configparser

gi.require_version('Gst', '1.0')
gi.require_version('GstRtspServer', '1.0')
from gi.repository import GLib, Gst, GstRtspServer
from ctypes import *
import time
import sys
import math
import platform
from common.is_aarch_64 import is_aarch64
from common.bus_call import bus_call

from common.FPS import PERF_DATA
import numpy as np
import pyds
import cv2
import os
import os.path
from os import path

#🅲🅻🅰🆂🆂 🅸🅳 🅳🅴🅵🅸🅽🅸🅽🅶

PGIE_CLASS_ID_PERSON = 0
PGIE_CLASS_ID_FACE = 1
PGIE_CLASS_ID_BAG = 2

pgie_classes_str = ["Person", "Bag", "Face"]


MAX_DISPLAY_LEN = 64
perf_data = None
MUXER_OUTPUT_WIDTH = 720
MUXER_OUTPUT_HEIGHT = 576
MUXER_BATCH_TIMEOUT_USEC = 33000
TILED_OUTPUT_WIDTH = 720  
TILED_OUTPUT_HEIGHT = 576
GST_CAPS_FEATURES_NVMM = "memory:NVMM"


def tiler_sink_pad_buffer_probe(pad, info, u_data):
    
    frame_number = 0
    num_rects = 0
    gst_buffer = info.get_buffer()

    #🅶🆂🆃 🅱🆄🅵🅵🅴🆁 🅸🅽🅸🆃🅸🅰🅻🅸🆉🅰🆃🅸🅾🅽

    if not gst_buffer:
        print("Unable to get GstBuffer ")
        return

    #Intiallizing object counter with 0

    obj_counter = {
            PGIE_CLASS_ID_PERSON: 0,
            PGIE_CLASS_ID_BAG: 0,
            PGIE_CLASS_ID_FACE: 0
        }

    #🅱🅰🆃🅲🅷 🅼🅴🆃🅰

    batch_meta = pyds.gst_buffer_get_nvds_batch_meta(hash(gst_buffer))
    l_frame = batch_meta.frame_meta_list
    while l_frame is not None:
        try:
            frame_meta = pyds.NvDsFrameMeta.cast(l_frame.data)
        except StopIteration:
            break

        frame_number = frame_meta.frame_num
        l_obj = frame_meta.obj_meta_list
        num_rects = frame_meta.num_obj_meta

        # 🅾🅱🅹🅴🅲🆃 🅼🅴🆃🅰

        while l_obj is not None:
            try:
                obj_meta = pyds.NvDsObjectMeta.cast(l_obj.data)
            except StopIteration:
                break
            obj_counter[obj_meta.class_id] += 1

            if (obj_meta.class_id == PGIE_CLASS_ID_FACE):
                obj_meta.rect_params.border_width = 0
                obj_meta.rect_params.has_bg_color = 0
                obj_meta.rect_params.bg_color.red = 1
                obj_meta.rect_params.bg_color.green = 0.0
                obj_meta.rect_params.bg_color.blue = 0.0
                obj_meta.rect_params.bg_color.alpha = 1.0
            elif (obj_meta.class_id == PGIE_CLASS_ID_PERSON ) :
                obj_meta.rect_params.border_width = 0
                obj_meta.rect_params.has_bg_color = 0
                obj_meta.rect_params.bg_color.red = 0.0
                obj_meta.rect_params.bg_color.green = 1
                obj_meta.rect_params.bg_color.blue = 0.0
                obj_meta.rect_params.bg_color.alpha = 0.5

            # get embedding
            #print("obj Meta :",dir(obj_meta))
            #obj Meta : ['__class__', '__delattr__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', 'base_meta', 'cast', 'class_id', 'classifier_meta_list', 'confidence', 'detector_bbox_info', 'mask_params', 'misc_obj_info', 'obj_label', 'obj_user_meta_list', 'object_id', 'parent', 'rect_params', 'reserved', 'text_params', 'tracker_bbox_info', 'tracker_confidence', 'unique_component_id']
            # print("Tracker bbox info : " , obj_meta.tracker_bbox_info)
            # print("Tracker Confidence : ", obj_meta.tracker_confidence)
            # print("User Meta list : ", obj_meta.obj_user_meta_list)
            # l_user = obj_meta.obj_user_meta_list
            # print("L_user",l_user)
            while l_user is not None:
                try:
                    user_meta = pyds.NvDsUserMeta.cast(l_user.data)
                except StopIteration:
                    break
                
                if (user_meta.base_meta.meta_type == pyds.NvDsMetaType.NVDSINFER_TENSOR_OUTPUT_META):
                    tensor_meta = pyds.NvDsInferTensorMeta.cast(user_meta.user_meta_data)
                    embeddings = tensor_meta.out_buf_ptrs_host[0]
                    # embedding_dims = tensor_meta.output_layers_info[0].inferDims
                    print("emb: ", embeddings)
                
                try:
                    l_user = l_user.next
                except StopIteration:
                    break


            try:
                l_obj = l_obj.next
            except StopIteration:
                break

            display_meta=pyds.nvds_acquire_display_meta_from_pool(batch_meta)
            display_meta.num_labels = 1
            py_nvosd_text_params = display_meta.text_params[0]
            py_nvosd_text_params.display_text = "Frame Number={} Number of Objects={} Number of Persons={} Number of Face ={}, Number of Bags ={}".format(frame_number, num_rects, obj_counter[PGIE_CLASS_ID_PERSON], obj_counter[PGIE_CLASS_ID_FACE], obj_counter[PGIE_CLASS_ID_BAG]) 

            # Now set the offsets where the string should appear
            py_nvosd_text_params.x_offset = 10
            py_nvosd_text_params.y_offset = 12

            # Font , font-color and font-size
            py_nvosd_text_params.font_params.font_name = "Serif"
            py_nvosd_text_params.font_params.font_size = 10
            # set(red, green, blue, alpha); set to White
            py_nvosd_text_params.font_params.font_color.set(1.0, 1.0, 1.0, 1.0)

            # Text background color
            py_nvosd_text_params.set_bg_clr = 1
            # set(red, green, blue, alpha); set to Black
            py_nvosd_text_params.text_bg_clr.set(0.0, 0.0, 0.0, 1.0)
            # Using pyds.get_string() to get display_text as string
            print(pyds.get_string(py_nvosd_text_params.display_text))
            pyds.nvds_add_display_meta_to_frame(frame_meta, display_meta)

        print("Frame Number=", frame_number, "Number of Objects=", num_rects, "Face_count=",
              obj_counter[PGIE_CLASS_ID_FACE], "Person_count=", obj_counter[PGIE_CLASS_ID_PERSON])


        # Update frame rate through this probe
        stream_index = "stream{0}".format(frame_meta.pad_index)
        global perf_data
        perf_data.update_fps(stream_index)

        try:
            l_frame = l_frame.next
        except StopIteration:
            break
    

    return Gst.PadProbeReturn.OK




def cb_newpad(decodebin, decoder_src_pad, data):
    print("In cb_newpad\n")
    caps = decoder_src_pad.get_current_caps()
    gststruct = caps.get_structure(0)
    gstname = gststruct.get_name()
    source_bin = data
    features = caps.get_features(0)

    if (gstname.find("video") != -1):
        if features.contains("memory:NVMM"):
            bin_ghost_pad = source_bin.get_static_pad("src")
            if not bin_ghost_pad.set_target(decoder_src_pad):
                sys.stderr.write("Failed to link decoder src pad to source bin ghost pad\n")
        else:
            sys.stderr.write(" Error: Decodebin did not pick nvidia decoder plugin.\n")


def decodebin_child_added(child_proxy, Object, name, user_data):
    print("Decodebin child added:", name, "\n")
    if name.find("decodebin") != -1:
        Object.connect("child-added", decodebin_child_added, user_data)
    if not is_aarch64() and name.find("nvv4l2decoder") != -1:
        Object.set_property("cudadec-memtype", 2)



def create_source_bin(index, uri):
    print("Creating source bin")

    bin_name = "source-bin-%02d" % index
    print(bin_name)
    nbin = Gst.Bin.new(bin_name)
    if not nbin:
        sys.stderr.write(" Unable to create source bin \n")

    uri_decode_bin = Gst.ElementFactory.make("uridecodebin", "uri-decode-bin")
    if not uri_decode_bin:
        sys.stderr.write(" Unable to create uri decode bin \n")

    uri_decode_bin.set_property("uri", uri)

    uri_decode_bin.connect("pad-added", cb_newpad, nbin)
    uri_decode_bin.connect("child-added", decodebin_child_added, nbin)

    Gst.Bin.add(nbin, uri_decode_bin)
    bin_pad = nbin.add_pad(Gst.GhostPad.new_no_target("src", Gst.PadDirection.SRC))
    if not bin_pad:
        sys.stderr.write(" Failed to add ghost pad in source bin \n")
        return None
    return nbin

def main(uri_inputs,codec,bitrate ):
    
    # Check input arguments
    number_sources = len(uri_inputs)

    global perf_data
    perf_data = PERF_DATA(number_sources)
    
    # Standard GStreamer initialization
    Gst.init(None)

    # Create gstreamer elements */
    # Create Pipeline element that will form a connection of other elements

    # 🅲🆁🅴🅰🆃🅸🅽🅶 🅿🅸🅿🅴🅻🅸🅽🅴
    
    print("Creating Pipeline \n ")
    pipeline = Gst.Pipeline()
    is_live = False

    if not pipeline:
        sys.stderr.write(" Unable to create Pipeline \n")
    print("Creating streamux \n ")

    # 🅲🆁🅴🅰🆃🅸🅽🅶 🆂🆃🆁🅴🅰🅼🆄🆇

    # Create nvstreammux instance to form batches from one or more sources.
    streammux = Gst.ElementFactory.make("nvstreammux", "Stream-muxer")
    if not streammux:
        sys.stderr.write(" Unable to create NvStreamMux \n")

    # Streammux pipeline is added here
    pipeline.add(streammux)
    
    for i in range(number_sources):
        print("Creating source_bin ", i, " \n ")
        uri_name = uri_inputs[i]
        if uri_name.find("rtsp://") == 0:
            is_live = True
        source_bin = create_source_bin(i, uri_name)
        if not source_bin:
            sys.stderr.write("Unable to create source bin \n")
        pipeline.add(source_bin)
        padname = "sink_%u" % i
        sinkpad = streammux.get_request_pad(padname)
        if not sinkpad:
            sys.stderr.write("Unable to create sink pad bin \n")
        srcpad = source_bin.get_static_pad("src")
        if not srcpad:
            sys.stderr.write("Unable to create src pad bin \n")
        srcpad.link(sinkpad)

    # # 🅲🆁🅴🅰🆃🅸🅽🅶 🆀🆄🅴🆄🅴
        
    # queue1=Gst.ElementFactory.make("queue","queue1")
    # queue2=Gst.ElementFactory.make("queui didnt add any sgie in the python code
    # queue3=Gst.ElementFactory.make("queue","queue3")
    # queue4=Gst.ElementFactory.make("queue","queue4")
    # queue5=Gst.ElementFactory.make("queue","queue5")
    # queue6=Gst.ElementFactory.make("queue","queue6")
    # queue7=Gst.ElementFactory.make("queue","queue7")
    
    # # Adding pipeline
        
    # pipeline.add(queue1)
    # pipeline.add(queue2)
    # pipeline.add(queue3)
    # pipeline.add(queue4)
    # pipeline.add(queue5)
    # pipeline.add(queue6)
    # pipeline.add(queue7)

    # 🅲🆁🅴🅰🆃🅸🅽🅶 🅿🅶🅸🅴
    
    print("Creating Pgie \n ")
    pgie = Gst.ElementFactory.make("nvinfer", "primary-inference")
    if not pgie:
        sys.stderr.write(" Unable to create pgie \n")

    # 🅲🆁🅴🅰🆃🅸🅽🅶 🆃🆁🅰🅲🅺🅴🆁

    tracker = Gst.ElementFactory.make("nvtracker", "tracker")
    if not tracker:
        sys.stderr.write(" Unable to create tracker \n")

    # 🅲🆁🅴🅰🆃🅸🅽🅶 🆂🅶🅸🅴 1

    sgie1 = Gst.ElementFactory.make("nvinfer", "secondary1-nvinference-engine")
    if not sgie1:
        sys.stderr.write(" Unable to make sgie1 \n")

    # 🅲🆁🅴🅰🆃🅸🅽🅶 🅽🆅🆅🅸🅳🅲🅾🅽🆅 1    
    
    print("Creating nvvidconv1 \n ")
    nvvidconv1 = Gst.ElementFactory.make("nvvideoconvert", "convertor1")
    if not nvvidconv1:
        sys.stderr.write(" Unable to create nvvidconv1 \n")

    # Creating filter 1
    
    print("Creating filter1 \n ")
    caps1 = Gst.Caps.from_string("video/x-raw(memory:NVMM), format=RGBA")
    filter1 = Gst.ElementFactory.make("capsfilter", "filter1")
    if not filter1:
        sys.stderr.write(" Unable to get the caps filter1 \n")
    filter1.set_property("caps", caps1)

    #🅲🆁🅴🅰🆃🅸🅽🅶 🆃🅸🅻🅴🆁
    
    print("Creating tiler \n ")
    tiler = Gst.ElementFactory.make("nvmultistreamtiler", "nvtiler")
    if not tiler:
        sys.stderr.write(" Unable to create tiler \n")

    # 🅲🆁🅴🅰🆃🅸🅽🅶 🅽🆅🆅🅸🅳🅲🅾🅽🆅    
    
    print("Creating nvvidconv \n ")
    nvvidconv = Gst.ElementFactory.make("nvvideoconvert", "convertor")
    if not nvvidconv:
        sys.stderr.write(" Unable to create nvvidconv \n")

    # 🅲🆁🅴🅰🆃🅸🅽🅶 🅽🆅🅾🆂🅳
    
    print("Creating nvosd \n ")
    nvosd = Gst.ElementFactory.make("nvdsosd", "onscreendisplay")
    if not nvosd:
        sys.stderr.write(" Unable to create nvosd \n")
    nvvidconv_postosd = Gst.ElementFactory.make("nvvideoconvert", "convertor_postosd")
    if not nvvidconv_postosd:
        sys.stderr.write(" Unable to create nvvidconv_postosd \n")

    # Create Caps Filter 
    
    # Create a caps filter
    caps = Gst.ElementFactory.make("capsfilter", "filter")
    caps.set_property("caps", Gst.Caps.from_string("video/x-raw(memory:NVMM), format=I420"))

    # Create a Encoder
    
    # Make the encoder
    if codec == "H264":
        encoder = Gst.ElementFactory.make("nvv4l2h264enc", "encoder")
        print("Creating H264 Encoder")
    elif codec == "H265":
        encoder = Gst.ElementFactory.make("nvv4l2h265enc", "encoder")
        print("Creating H265 Encoder")
    if not encoder:
        sys.stderr.write(" Unable to create encoder")
    encoder.set_property('bitrate', bitrate)
    if is_aarch64():
        encoder.set_property('preset-level', 1)
        encoder.set_property('insert-sps-pps', 1)
        #encoder.set_property('bufapi-version', 1)
    
    # Make the payload-encode video into RTP packets
    if codec == "H264":
        rtppay = Gst.ElementFactory.make("rtph264pay", "rtppay")
        print("Creating H264 rtppay")
    elif codec == "H265":
        rtppay = Gst.ElementFactory.make("rtph265pay", "rtppay")
        print("Creating H265 rtppay")
    if not rtppay:
        sys.stderr.write(" Unable to create rtppay")
    
    # Make the UDP sink
    updsink_port_num = 5400
    sink = Gst.ElementFactory.make("udpsink", "udpsink")
    if not sink:
        sys.stderr.write(" Unable to create udpsink")
    
    sink.set_property('host', '224.224.255.255')
    sink.set_property('port', updsink_port_num)
    sink.set_property('async', False)
    sink.set_property('sync', 1)
    
    print("Playing file {} ".format(uri_inputs))
    
    streammux.set_property('width', 1920)
    streammux.set_property('height', 1080)
    streammux.set_property('batch-size', number_sources)
    streammux.set_property('batched-push-timeout', MUXER_BATCH_TIMEOUT_USEC)\

    # Creating PGIE Property    
    
    pgie.set_property('config-file-path', "config_infer_primary_gie.txt")
    pgie_batch_size = pgie.get_property("batch-size")
    if (pgie_batch_size != number_sources):
        print("WARNING: Overriding infer-config batch-size", pgie_batch_size, " with number of sources ",
              number_sources, " \n")
        pgie.set_property("batch-size", number_sources)

    # SGIE1 set property

    sgie1.set_property('config-file-path', "sgie1_config.txt")
    
    tiler_rows = int(math.sqrt(number_sources))
    tiler_columns = int(math.ceil((1.0 * number_sources) / tiler_rows))
    tiler.set_property("rows", tiler_rows)
    tiler.set_property("columns", tiler_columns)
    tiler.set_property("width", TILED_OUTPUT_WIDTH)
    tiler.set_property("height", TILED_OUTPUT_HEIGHT)

    if not is_aarch64():
        # Use CUDA unified memory in the pipeline so frames
        # can be easily accessed on CPU in Python.
        mem_type = int(pyds.NVBUF_MEM_CUDA_UNIFIED)
        streammux.set_property("nvbuf-memory-type", mem_type)
        nvvidconv.set_property("nvbuf-memory-type", mem_type)
        nvvidconv1.set_property("nvbuf-memory-type", mem_type)
        tiler.set_property("nvbuf-memory-type", mem_type)
        nvvidconv_postosd.set_property("nvbuf-memory-type", mem_type)

    #Set properties of tracker
    
    config = configparser.ConfigParser()
    config.read('tracker_config.txt')
    config.sections()

    for key in config['tracker']:
        if key == 'tracker-width' :
            tracker_width = config.getint('tracker', key)
            tracker.set_property('tracker-width', tracker_width)
        if key == 'tracker-height' :
            tracker_height = config.getint('tracker', key)
            tracker.set_property('tracker-height', tracker_height)
        if key == 'gpu-id' :
            tracker_gpu_id = config.getint('tracker', key)
            tracker.set_property('gpu_id', tracker_gpu_id)
        if key == 'll-lib-file' :
            tracker_ll_lib_file = config.get('tracker', key)
            tracker.set_property('ll-lib-file', tracker_ll_lib_file)
        if key == 'll-config-file' :
            tracker_ll_config_file = config.get('tracker', key)
            tracker.set_property('ll-config-file', tracker_ll_config_file)

    print("Adding elements to Pipeline \n")
    pipeline.add(pgie)
    pipeline.add(tracker)
    pipeline.add(sgie1)
    pipeline.add(tiler)
    pipeline.add(nvvidconv)
    pipeline.add(filter1)
    pipeline.add(nvvidconv1)
    pipeline.add(nvosd)
    pipeline.add(nvvidconv_postosd)
    pipeline.add(caps)
    pipeline.add(encoder)
    pipeline.add(rtppay)
    pipeline.add(sink)

    print("Linking elements in the Pipeline \n")
    streammux.link(pgie)
    pgie.link(tracker)
    tracker.link(sgie1) 
    sgie1.link(nvvidconv1)
    nvvidconv1.link(filter1)
    filter1.link(tiler)
    tiler.link(nvvidconv)
    nvvidconv.link(nvosd)
    nvosd.link(nvvidconv_postosd)
    nvvidconv_postosd.link(caps)
    caps.link(encoder)
    encoder.link(rtppay)
    rtppay.link(sink)
    
    # create an event loop and feed gstreamer bus mesages to it
    loop = GLib.MainLoop()
    bus = pipeline.get_bus()
    bus.add_signal_watch()
    bus.connect("message", bus_call, loop)
    
    # Start streaming
    rtsp_port_num = 8554
    
    server = GstRtspServer.RTSPServer.new()
    server.props.service = "%d" % rtsp_port_num
    server.attach(None)
    
    factory = GstRtspServer.RTSPMediaFactory.new()
    factory.set_launch( "( udpsrc name=pay0 port=%d buffer-size=524288 caps=\"application/x-rtp, media=video, clock-rate=90000, encoding-name=(string)%s, payload=96 \" )" % (updsink_port_num, codec))
    factory.set_shared(True)
    server.get_mount_points().add_factory("/ds-test", factory)
    
    print("\n *** DeepStream: Launched RTSP Streaming at rtsp://localhost:%d/ds-test ***\n\n" % rtsp_port_num)
    
    tiler_sink_pad = tiler.get_static_pad("sink")
    if not tiler_sink_pad:
        sys.stderr.write(" Unable to get sink pad \n")
    else:
        tiler_sink_pad.add_probe(Gst.PadProbeType.BUFFER, tiler_sink_pad_buffer_probe, 0)
        # perf callback function to print fps every 5 sec
        GLib.timeout_add(5000, perf_data.perf_print_callback)


    print("Starting pipeline \n")
    # start play back and listed to events		
    pipeline.set_state(Gst.State.PLAYING)
    try:
        loop.run()
    except:
        pass
    # cleanup
    print("Exiting app\n")
    pipeline.set_state(Gst.State.NULL)

def parse_args():
    parser = argparse.ArgumentParser(description='RTSP Output Sample Application Help ')
				  
    parser.add_argument("-i","--uri_inputs", metavar='N', type=str, nargs='+',
                    help='Path to inputs URI e.g. rtsp:// ...  or file:// seperated by space')
					
    parser.add_argument("-c", "--codec", default="H264",
                  help="RTSP Streaming Codec H264/H265 , default=H264", choices=['H264','H265'])
    parser.add_argument("-b", "--bitrate", default=4000000,
                  help="Set the encoding bitrate ", type=int)
    # Check input arguments
    if len(sys.argv)==1:
        parser.print_help(sys.stderr)
        sys.exit(1)
    args = parser.parse_args()
        
    print("URI Inputs: " + str(args.uri_inputs ))
    
    return args.uri_inputs , args.codec, args.bitrate

if __name__ == '__main__':
    uri_inputs , out_codec, out_bitrate = parse_args()
    sys.exit(main(uri_inputs, out_codec, out_bitrate))

This is my sgie1

[property]
gpu-id=0
gie-unique-id=2
operate-on-gie-id=1
num-detected-classes=2
net-scale-factor=0.01735207357279195
offsets=123.675;116.28;103.53
model-color-format=0
onnx-file=/opt/nvidia/deepstream/deepstream-6.4/samples/configs/test_by_varun/model/resnet50_market1501_aicity156.onnx
model-engine-file=/opt/nvidia/deepstream/deepstream-6.4/samples/configs/test_by_varun/model/resnet50_market1501_aicity156.onnx_b16_gpu0_fp16.engine
infer-dims=3;256;128
batch-size=30
# 0=FP32, 1=INT8, 2=FP16 mode
network-mode=2
network-type=1
interval=1
# Infer Processing Mode 1=Primary Mode 2=Secondary Mode
process-mode=2
# ## Clustering algorithm=0=GroupRectangle 1=DBSCAN 2=NMS 3=Hybrid 4=NoClustering
# cluster-mode=3
classifier-threshold=0
output-tensor-meta=1
maintain-aspect-ratio=0
# Extract embedding on "people" class only
operate-on-class-ids=0

ERROR: …/nvdsinfer/nvdsinfer_model_builder.cpp:307 Cannot access ONNX file ‘/opt/nvidia/deepstream/deepstream-6.4/samples/configs/test_by_varun/model/resnet50_market1501_aicity156.onnx’
ERROR: …/nvdsinfer/nvdsinfer_model_builder.cpp:971 failed to build network since parsing model errors.
ERROR: …/nvdsinfer/nvdsinfer_model_builder.cpp:804 failed to build network.
0:00:11.386077495 3036 0x55bb79324150 ERROR nvinfer gstnvinfer.cpp:676:gst_nvinfer_logger: NvDsInferContext[UID 2]: Error in NvDsInferContextImpl::buildModel() <nvdsinfer_context_impl.cpp:2126> [UID = 2]: build engine file failed
0:00:11.620323457 3036 0x55bb79324150 ERROR nvinfer gstnvinfer.cpp:676:gst_nvinfer_logger: NvDsInferContext[UID 2]: Error in NvDsInferContextImpl::generateBackendContext() <nvdsinfer_context_impl.cpp:2212> [UID = 2]: build backend context failed
0:00:11.621803589 3036 0x55bb79324150 ERROR nvinfer gstnvinfer.cpp:676:gst_nvinfer_logger: NvDsInferContext[UID 2]: Error in NvDsInferContextImpl::initialize() <nvdsinfer_context_impl.cpp:1351> [UID = 2]: generate backend failed, check config file settings
Error: gst-resource-error-quark: Failed to create NvDsInferContext instance (1): gstnvinfer.cpp(898): gst_nvinfer_start (): /GstPipeline:pipeline0/GstNvInfer:secondary1-nvinference-engine:
Config file path: sgie1_config.txt, NvDsInfer Error: NVDSINFER_CONFIG_FAILED
Exiting app

This is my error please help me with this

Does this file exist? or if the current user has the correct access privilege?

i am using deepstream in docker and the path is also correct and I am using Re-Identification model

And i made few changes in the file

[property]
gpu-id=0
gie-unique-id=1
net-scale-factor=1
#tlt-model-key=tlt_encode
#tlt-encoded-model=./models/reid/resnet50_market1501_aicity156.etlt
#model-engine-file=./models/resnet50_market1501_aicity156.etlt_b16_gpu0_int8.engine
onnx-file=/opt/nvidia/deepstream/deepstream-6.4/sources/deepstream_python_apps/apps/Varun/model/Reid/resnet50_market1501_aicity156.onnx
model-engine-file=/opt/nvidia/deepstream/deepstream-6.4/sources/deepstream_python_apps/apps/Varun/model/Reid/resnet50_market1501_aicity156.onnx_b16_gpu0_fp16.engine
force-implicit-batch-dim=1
batch-size=30
model-color-format=1
## 0=FP32, 1=INT8, 2=FP16 mode
network-mode=2
process-mode=2
uff-input-blob-name=input_1
output-blob-names=predictions/Softmax
classifier-async-mode=1
classifier-threshold=0.51
input-object-min-width=128
input-object-min-height=128
operate-on-gie-id=1
operate-on-class-ids=1
classifier-type=reid
network-type=100 
output-tensor-meta=1
#scaling-filter=0
#scaling-compute-hw=0
infer-dims=3;224;224

ERROR: …/nvdsinfer/nvdsinfer_model_builder.cpp:307 Cannot access ONNX file ‘/opt/nvidia/deepstream/deepstream-6.4/sources/deepstream_python_apps/apps/Varun/model’
ERROR: …/nvdsinfer/nvdsinfer_model_builder.cpp:971 failed to build network since parsing model errors.
ERROR: …/nvdsinfer/nvdsinfer_model_builder.cpp:804 failed to build network.
0:00:11.385663808 9228 0x5637b4e706a0 ERROR nvinfer gstnvinfer.cpp:676:gst_nvinfer_logger: NvDsInferContext[UID 1]: Error in NvDsInferContextImpl::buildModel() <nvdsinfer_context_impl.cpp:2126> [UID = 1]: build engine file failed
0:00:11.604774264 9228 0x5637b4e706a0 ERROR nvinfer gstnvinfer.cpp:676:gst_nvinfer_logger: NvDsInferContext[UID 1]: Error in NvDsInferContextImpl::generateBackendContext() <nvdsinfer_context_impl.cpp:2212> [UID = 1]: build backend context failed
0:00:11.606507980 9228 0x5637b4e706a0 ERROR nvinfer gstnvinfer.cpp:676:gst_nvinfer_logger: NvDsInferContext[UID 1]: Error in NvDsInferContextImpl::initialize() <nvdsinfer_context_impl.cpp:1351> [UID = 1]: generate backend failed, check config file settings
0:00:11.606567198 9228 0x5637b4e706a0 WARN nvinfer gstnvinfer.cpp:898:gst_nvinfer_start: error: Failed to create NvDsInferContext instance
0:00:11.606599337 9228 0x5637b4e706a0 WARN nvinfer gstnvinfer.cpp:898:gst_nvinfer_start: error: Config file path: sgie1_config.txt, NvDsInfer Error: NVDSINFER_CONFIG_FAILED

**PERF: {‘stream0’: 0.0, ‘stream1’: 0.0}

Error: gst-resource-error-quark: Failed to create NvDsInferContext instance (1): gstnvinfer.cpp(898): gst_nvinfer_start (): /GstPipeline:pipeline0/GstNvInfer:secondary1-nvinference-engine:
Config file path: sgie1_config.txt, NvDsInfer Error: NVDSINFER_CONFIG_FAILED
Exiting app

This is the error i get

This still seems to be a model path issue