How use TAO tool kit trained model in deepstream-python-app?

I have changed paths in pgie configuration file for test 1 of deepstream-python-app.
But the problem is why it cannot deserialize the engine file when i run python module.

i am confused what is the use of TAO tool kit what i known we integrate the tao model with deepstream like i have used deepstream-refrence-app and run through deepstream command but if we want meta data we use python not only [ deepstream -c ] if i am not wrong.
what is advantage of TAO tool kit trained model. becasue if we use TAO models the python give error.

Starting pipeline 

0:00:00.166659798 495600      0x3777060 WARN                 nvinfer gstnvinfer.cpp:643:gst_nvinfer_logger:<primary-inference> NvDsInferContext[UID 1]: Warning from NvDsInferContextImpl::initialize() <nvdsinfer_context_impl.cpp:1170> [UID = 1]: Warning, OpenCV has been deprecated. Using NMS for clustering instead of cv::groupRectangles with topK = 20 and NMS Threshold = 0.5
ERROR: [TRT]: 1: [stdArchiveReader.cpp::StdArchiveReader::40] Error Code 1: Serialization (Serialization assertion stdVersionRead == serializationVersion failed.Version tag does not match. Note: Current Version: 213, Serialized Engine Version: 232)
ERROR: [TRT]: 4: [runtime.cpp::deserializeCudaEngine::50] Error Code 4: Internal Error (Engine deserialization failed.)
ERROR: ../nvdsinfer/nvdsinfer_model_builder.cpp:1528 Deserialize engine failed from file: /home/experts-vision/Desktop/farid/O/Primary_Detector/export_retrain/trt.engine.int8

i used tlt-encoded-model parameter in configuration file instead of model-file the deserialization problem solved but other error came up.

/Desktop/farid/O/Object-Detection-Deepstream$ sudo python3 deepstream_test_1.py  /opt/nvidia/deepstream/deepstream-6.1/samples/streams/camera-1_video_26.mp4 
Creating Pipeline 
 
Creating Source 
 
creating caps_filter 

Creating H264Parser 

Creating Decoder 

Creating EGLSink 

Playing file /opt/nvidia/deepstream/deepstream-6.1/samples/streams/camera-1_video_26.mp4 
Adding elements to Pipeline 

Linking elements in the Pipeline 

Starting pipeline 

0:00:00.159538914 504171      0x37e3120 WARN                 nvinfer gstnvinfer.cpp:643:gst_nvinfer_logger:<primary-inference> NvDsInferContext[UID 1]: Warning from NvDsInferContextImpl::initialize() <nvdsinfer_context_impl.cpp:1170> [UID = 1]: Warning, OpenCV has been deprecated. Using NMS for clustering instead of cv::groupRectangles with topK = 20 and NMS Threshold = 0.5
0:00:00.159641791 504171      0x37e3120 INFO                 nvinfer gstnvinfer.cpp:646:gst_nvinfer_logger:<primary-inference> NvDsInferContext[UID 1]: Info from NvDsInferContextImpl::buildModel() <nvdsinfer_context_impl.cpp:1923> [UID = 1]: Trying to create engine from model files
WARNING: [TRT]: onnx2trt_utils.cpp:369: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32.
WARNING: [TRT]: onnx2trt_utils.cpp:395: One or more weights outside the range of INT32 was clamped
WARNING: [TRT]: onnx2trt_utils.cpp:395: One or more weights outside the range of INT32 was clamped
WARNING: [TRT]: onnx2trt_utils.cpp:395: One or more weights outside the range of INT32 was clamped
WARNING: [TRT]: onnx2trt_utils.cpp:395: One or more weights outside the range of INT32 was clamped
WARNING: [TRT]: onnx2trt_utils.cpp:395: One or more weights outside the range of INT32 was clamped
WARNING: [TRT]: onnx2trt_utils.cpp:395: One or more weights outside the range of INT32 was clamped
WARNING: [TRT]: onnx2trt_utils.cpp:395: One or more weights outside the range of INT32 was clamped
WARNING: [TRT]: onnx2trt_utils.cpp:395: One or more weights outside the range of INT32 was clamped
WARNING: [TRT]: onnx2trt_utils.cpp:395: One or more weights outside the range of INT32 was clamped
WARNING: [TRT]: onnx2trt_utils.cpp:395: One or more weights outside the range of INT32 was clamped
WARNING: [TRT]: onnx2trt_utils.cpp:395: One or more weights outside the range of INT32 was clamped
WARNING: [TRT]: onnx2trt_utils.cpp:395: One or more weights outside the range of INT32 was clamped
WARNING: [TRT]: onnx2trt_utils.cpp:395: One or more weights outside the range of INT32 was clamped
WARNING: [TRT]: onnx2trt_utils.cpp:395: One or more weights outside the range of INT32 was clamped
WARNING: [TRT]: onnx2trt_utils.cpp:395: One or more weights outside the range of INT32 was clamped
WARNING: [TRT]: onnx2trt_utils.cpp:395: One or more weights outside the range of INT32 was clamped
WARNING: [TRT]: onnx2trt_utils.cpp:395: One or more weights outside the range of INT32 was clamped
WARNING: [TRT]: onnx2trt_utils.cpp:395: One or more weights outside the range of INT32 was clamped
WARNING: [TRT]: onnx2trt_utils.cpp:395: One or more weights outside the range of INT32 was clamped
WARNING: [TRT]: onnx2trt_utils.cpp:395: One or more weights outside the range of INT32 was clamped
WARNING: [TRT]: onnx2trt_utils.cpp:395: One or more weights outside the range of INT32 was clamped
WARNING: [TRT]: builtin_op_importers.cpp:4716: Attribute caffeSemantics not found in plugin node! Ensure that the plugin creator has a default value defined or the engine may fail to build.
WARNING: [TRT]: The getMaxBatchSize() function should not be used with an engine built from a network created with NetworkDefinitionCreationFlag::kEXPLICIT_BATCH flag. This function will always return 1.
WARNING: [TRT]: The getMaxBatchSize() function should not be used with an engine built from a network created with NetworkDefinitionCreationFlag::kEXPLICIT_BATCH flag. This function will always return 1.
0:02:05.141006352 504171      0x37e3120 INFO                 nvinfer gstnvinfer.cpp:646:gst_nvinfer_logger:<primary-inference> NvDsInferContext[UID 1]: Info from NvDsInferContextImpl::buildModel() <nvdsinfer_context_impl.cpp:1955> [UID = 1]: serialize cuda engine to file: /home/experts-vision/Desktop/farid/O/Primary_Detector/export_retrain/yolov4_resnet18_epoch_080.etlt_b1_gpu0_fp32.engine successfully
WARNING: [TRT]: The getMaxBatchSize() function should not be used with an engine built from a network created with NetworkDefinitionCreationFlag::kEXPLICIT_BATCH flag. This function will always return 1.
INFO: ../nvdsinfer/nvdsinfer_model_builder.cpp:610 [Implicit Engine Info]: layers num: 5
0   INPUT  kFLOAT Input           3x384x1248      
1   OUTPUT kINT32 BatchedNMS      1               
2   OUTPUT kFLOAT BatchedNMS_1    200x4           
3   OUTPUT kFLOAT BatchedNMS_2    200             
4   OUTPUT kFLOAT BatchedNMS_3    200             

ERROR: [TRT]: 3: Cannot find binding of given name: conv2d_bbox
0:02:05.145256102 504171      0x37e3120 WARN                 nvinfer gstnvinfer.cpp:643:gst_nvinfer_logger:<primary-inference> NvDsInferContext[UID 1]: Warning from NvDsInferContextImpl::checkBackendParams() <nvdsinfer_context_impl.cpp:1876> [UID = 1]: Could not find output layer 'conv2d_bbox' in engine
ERROR: [TRT]: 3: Cannot find binding of given name: conv2d_cov/Sigmoid
0:02:05.145273353 504171      0x37e3120 WARN                 nvinfer gstnvinfer.cpp:643:gst_nvinfer_logger:<primary-inference> NvDsInferContext[UID 1]: Warning from NvDsInferContextImpl::checkBackendParams() <nvdsinfer_context_impl.cpp:1876> [UID = 1]: Could not find output layer 'conv2d_cov/Sigmoid' in engine
0:02:05.206911633 504171      0x37e3120 INFO                 nvinfer gstnvinfer_impl.cpp:328:notifyLoadModelStatus:<primary-inference> [UID 1]: Load new model:dstest1_pgie_config.txt sucessfully
Error: gst-stream-error-quark: Failed to parse stream (7): gstbaseparse.c(2998): gst_base_parse_check_sync (): /GstPipeline:pipeline0/GstH264Parse:h264-parser

my config file

[property]
gpu-id=0


tlt-encoded-model= ../Primary_Detector/export_retrain/yolov4_resnet18_epoch_080.etlt
tlt-model-key=NGpmbHN0ZTNrZHFkOGRxNnFsbW9rbXNxbnU6Yzc5NWM5MjQtZDE1YS00NTYxLTg3YzgtNTU2MWVhNDg1M2M3
#model-engine-file=../Primary_Detector/export_retrain/trt.engine
labelfile-path=../Primary_Detector/export_retrain/labels.txt
int8-calib-file=../Primary_Detector/export_retrain/cal.bin

net-scale-factor=1.0
offsets=103.939;116.779;123.68
infer-dims=3;384;1248
force-implicit-batch-dim=1
batch-size=1
network-mode=0

maintain-aspect-ratio=0
output-tensor-meta=0
model-color-format=1
num-detected-classes=6
interval=0
gie-unique-id=1



output-blob-names=conv2d_bbox;conv2d_cov/Sigmoid

[class-attrs-all]
pre-cluster-threshold=0.2
eps=0.2

** .py file **

i have also added GStreamer capsfilter plugin after v4l2src in python module.

#!/usr/bin/env python3
import sys
sys.path.append('../')
import gi
gi.require_version('Gst', '1.0')
from gi.repository import GLib, Gst
from common.is_aarch_64 import is_aarch64
from common.bus_call import bus_call

import pyds



PGIE_ClASS_ID_balcony_with_railing = 0
PGIE_ClASS_ID_balcony_without_railing = 1
PGIE_ClASS_ID_helmet = 2
PGIE_ClASS_ID_incomplete_railing = 3
PGIE_ClASS_ID_person = 4
PGIE_ClASS_ID_rail = 5

def osd_sink_pad_buffer_probe(pad,info,u_data):
    frame_number=0
    #Intiallizing object counter with 0.
    obj_counter = {
     
        
        PGIE_ClASS_ID_balcony_with_railing:0,
        PGIE_ClASS_ID_balcony_without_railing:0,
        PGIE_ClASS_ID_helmet:0,
        PGIE_ClASS_ID_incomplete_railing:0,
        PGIE_ClASS_ID_person:0,
        PGIE_ClASS_ID_rail:0
    }
    num_rects=0

    gst_buffer = info.get_buffer()
    if not gst_buffer:
        print("Unable to get GstBuffer ")
        return

    # Retrieve batch metadata from the gst_buffer
    # Note that pyds.gst_buffer_get_nvds_batch_meta() expects the
    # C address of gst_buffer as input, which is obtained with hash(gst_buffer)
    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:
            # Note that l_frame.data needs a cast to pyds.NvDsFrameMeta
            # The casting is done by pyds.glist_get_nvds_frame_meta()
            # The casting also keeps ownership of the underlying memory
            # in the C code, so the Python garbage collector will leave
            # it alone.
            #frame_meta = pyds.glist_get_nvds_frame_meta(l_frame.data)
            frame_meta = pyds.NvDsFrameMeta.cast(l_frame.data)
        except StopIteration:
            break

        frame_number=frame_meta.frame_num
        num_rects = frame_meta.num_obj_meta
        l_obj=frame_meta.obj_meta_list
        while l_obj is not None:
            try:
                # Casting l_obj.data to pyds.NvDsObjectMeta
                #obj_meta=pyds.glist_get_nvds_object_meta(l_obj.data)
                obj_meta=pyds.NvDsObjectMeta.cast(l_obj.data)
            except StopIteration:
                break
            obj_counter[obj_meta.class_id] += 1
            obj_meta.rect_params.border_color.set(0.0, 0.0, 1.0, 0.0)
            try: 
                l_obj=l_obj.next
            except StopIteration:
                break

        # Acquiring a display meta object. The memory ownership remains in
        # the C code so downstream plugins can still access it. Otherwise
        # the garbage collector will claim it when this probe function exits.
        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]
        # Setting display text to be shown on screen
        # Note that the pyds module allocates a buffer for the string, and the
        # memory will not be claimed by the garbage collector.
        # Reading the display_text field here will return the C address of the
        # allocated string. Use pyds.get_string() to get the string content.
        py_nvosd_text_params.display_text = "Frame Number={} Number of Objects={} Helmet_count={} Person_count={}".format(frame_number, num_rects, obj_counter[PGIE_CLASS_ID_helmet], obj_counter[PGIE_CLASS_ID_person])

        # 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)
        try:
            l_frame=l_frame.next
        except StopIteration:
            break
			
    return Gst.PadProbeReturn.OK	


def main(args):
    # Check input arguments
    if len(args) != 2:
        sys.stderr.write("usage: %s <media file or uri>\n" % args[0])
        sys.exit(1)

    # 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()

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

    # Source element for reading from the file
    print("Creating Source \n ")
    source = Gst.ElementFactory.make("filesrc", "file-source")
    if not source:
        sys.stderr.write(" Unable to create Source \n")
    
    print("creating caps_filter \n")
    caps_v4l2src = Gst.ElementFactory.make("capsfilter", "v4l2src_caps")
    if not caps_v4l2src:
        sys.stderr.write(" Unable to create v4l2src capsfilter \n")
    
    # Since the data format in the input file is elementary h264 stream,
    # we need a h264parser
    print("Creating H264Parser \n")
    h264parser = Gst.ElementFactory.make("h264parse", "h264-parser")
    if not h264parser:
        sys.stderr.write(" Unable to create h264 parser \n")

    # Use nvdec_h264 for hardware accelerated decode on GPU
    print("Creating Decoder \n")
    decoder = Gst.ElementFactory.make("nvv4l2decoder", "nvv4l2-decoder")
    if not decoder:
        sys.stderr.write(" Unable to create Nvv4l2 Decoder \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")

    # Use nvinfer to run inferencing on decoder's output,
    # behaviour of inferencing is set through config file
    pgie = Gst.ElementFactory.make("nvinfer", "primary-inference")
    if not pgie:
        sys.stderr.write(" Unable to create pgie \n")

    # Use convertor to convert from NV12 to RGBA as required by nvosd
    nvvidconv = Gst.ElementFactory.make("nvvideoconvert", "convertor")
    if not nvvidconv:
        sys.stderr.write(" Unable to create nvvidconv \n")

    # Create OSD to draw on the converted RGBA buffer
    nvosd = Gst.ElementFactory.make("nvdsosd", "onscreendisplay")

    if not nvosd:
        sys.stderr.write(" Unable to create nvosd \n")

    # Finally render the osd output
    if is_aarch64():
        transform = Gst.ElementFactory.make("nvegltransform", "nvegl-transform")

    print("Creating EGLSink \n")
    sink = Gst.ElementFactory.make("nveglglessink", "nvvideo-renderer")
    if not sink:
        sys.stderr.write(" Unable to create egl sink \n")

    print("Playing file %s " %args[1])
    source.set_property('location', args[1])
    streammux.set_property('width', 1920)
    streammux.set_property('height', 1080)
    streammux.set_property('batch-size', 1)
    streammux.set_property('batched-push-timeout', 4000000)
    pgie.set_property('config-file-path', "dstest1_pgie_config.txt")

    print("Adding elements to Pipeline \n")
    pipeline.add(source)
    pipeline.add(caps_v4l2src)
    pipeline.add(h264parser)
    pipeline.add(decoder)
    pipeline.add(streammux)
    pipeline.add(pgie)
    pipeline.add(nvvidconv)
    pipeline.add(nvosd)
    pipeline.add(sink)
    if is_aarch64():
        pipeline.add(transform)

    # we link the elements together
    # file-source -> h264-parser -> nvh264-decoder ->
    # nvinfer -> nvvidconv -> nvosd -> video-renderer
    print("Linking elements in the Pipeline \n")
    source.link(caps_v4l2src)
    caps_v4l2src.link(h264parser)
    h264parser.link(decoder)

    sinkpad = streammux.get_request_pad("sink_0")
    if not sinkpad:
        sys.stderr.write(" Unable to get the sink pad of streammux \n")
    srcpad = decoder.get_static_pad("src")
    if not srcpad:
        sys.stderr.write(" Unable to get source pad of decoder \n")
    srcpad.link(sinkpad)
    streammux.link(pgie)
    pgie.link(nvvidconv)
    nvvidconv.link(nvosd)
    if is_aarch64():
        nvosd.link(transform)
        transform.link(sink)
    else:
        nvosd.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)

    # Lets add probe to get informed of the meta data generated, we add probe to
    # the sink pad of the osd element, since by that time, the buffer would have
    # had got all the metadata.
    osdsinkpad = nvosd.get_static_pad("sink")
    if not osdsinkpad:
        sys.stderr.write(" Unable to get sink pad of nvosd \n")

    osdsinkpad.add_probe(Gst.PadProbeType.BUFFER, osd_sink_pad_buffer_probe, 0)

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

if __name__ == '__main__':
    sys.exit(main(sys.argv))

please kindly help me out i am stuck here

Did you set option “–gen_ds_config” when exporting the model? Not sure if the value for output-blob-names is same the layer name in the model. Normally replace the config from tao exporting command (config file: nvinfer_config.txt) should be enough.

Hi @yingliu thanks for your reply.
yes i have used -gen_ds_config.
there is how to handle this now i cannot see ouput-blob-name.

net-scale-factor=1.0
offsets=103.939;116.779;123.68
infer-dims=3;384;1248
tlt-model-key=NGpmbHN0ZTNrZHFkOGRxNnFsbW9rbXNxbnU6Yzc5NWM5MjQtZDE1YS00NTYxLTg3YzgtNTU2MWVhNDg1M2M3
network-type=0
num-detected-classes=6
model-color-format=1
maintain-aspect-ratio=0
output-tensor-meta=0

commenting all config parameter and used only -gen_ds_config parameter is not working and got me error please solve this problem

now with previous configuration still same error as mentioned above.

hi @yingliu downlaod this shared folder if you want models and other file for extecution

Just found you retrained a yolov4 model, there are more configs to be adapted, for example, output_blob_names, parse-bbox-func-name and corresponding custom-lib-path etc. You can refer to this config file: deepstream_tao_apps/pgie_yolov4_tao_config.txt at master · NVIDIA-AI-IOT/deepstream_tao_apps (github.com)

Please also check the README in the repo GitHub - NVIDIA-AI-IOT/deepstream_tao_apps: Sample apps to demonstrate how to deploy models trained with TAO on DeepStream, it can be a good starter for integrating TAO models.

Hi @yingliu thanks for reply.
i add all configuration parameter as ou have mentioned and output_blob_names resolved
but why output is not showing?
kindly do solve my problem i am stuck from 2 days i am doing this because i want metadata of frames later on will be deploying this into jetson .
the error remain and i check with capsfilter plugin and without but same error remained.

/opt/nvidia/deepstream/deepstream-6.1/sources/deepstream_python_apps/apps/deepstream-test1$ sudo python3 deepstream_test_1.py  /opt/nvidia/deepstream/deepstream-6.1/samples/streams/camera-1_video_26.mp4 
Creating Pipeline 
 
Creating Source 
 
Creating H264Parser 

Creating Decoder 

Creating EGLSink 

Playing file /opt/nvidia/deepstream/deepstream-6.1/samples/streams/camera-1_video_26.mp4 
Adding elements to Pipeline 

Linking elements in the Pipeline 

Starting pipeline 

ERROR: [TRT]: 1: [stdArchiveReader.cpp::StdArchiveReader::40] Error Code 1: Serialization (Serialization assertion stdVersionRead == serializationVersion failed.Version tag does not match. Note: Current Version: 213, Serialized Engine Version: 232)
ERROR: [TRT]: 4: [runtime.cpp::deserializeCudaEngine::50] Error Code 4: Internal Error (Engine deserialization failed.)
ERROR: ../nvdsinfer/nvdsinfer_model_builder.cpp:1528 Deserialize engine failed from file: /home/experts-vision/Desktop/farid/O/Primary_Detector/export_retrain/trt.engine
0:00:01.028342340 626145      0x433ee60 WARN                 nvinfer gstnvinfer.cpp:643:gst_nvinfer_logger:<primary-inference> NvDsInferContext[UID 1]: Warning from NvDsInferContextImpl::deserializeEngineAndBackend() <nvdsinfer_context_impl.cpp:1897> [UID = 1]: deserialize engine from file :/home/experts-vision/Desktop/farid/O/Primary_Detector/export_retrain/trt.engine failed
0:00:01.124874800 626145      0x433ee60 WARN                 nvinfer gstnvinfer.cpp:643:gst_nvinfer_logger:<primary-inference> NvDsInferContext[UID 1]: Warning from NvDsInferContextImpl::generateBackendContext() <nvdsinfer_context_impl.cpp:2002> [UID = 1]: deserialize backend context from engine from file :/home/experts-vision/Desktop/farid/O/Primary_Detector/export_retrain/trt.engine failed, try rebuild
0:00:01.124894775 626145      0x433ee60 INFO                 nvinfer gstnvinfer.cpp:646:gst_nvinfer_logger:<primary-inference> NvDsInferContext[UID 1]: Info from NvDsInferContextImpl::buildModel() <nvdsinfer_context_impl.cpp:1923> [UID = 1]: Trying to create engine from model files
ERROR: ../nvdsinfer/nvdsinfer_func_utils.cpp:410 Invalid deviceType string bg_leaky_c>. Using default kGPU deviceType
ERROR: ../nvdsinfer/nvdsinfer_func_utils.cpp:398 Invalid precisionType string bg_leaky_c>. Using default kFLOAT(fp32) precisonType
WARNING: [TRT]: onnx2trt_utils.cpp:369: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32.
WARNING: [TRT]: onnx2trt_utils.cpp:395: One or more weights outside the range of INT32 was clamped
WARNING: [TRT]: onnx2trt_utils.cpp:395: One or more weights outside the range of INT32 was clamped
WARNING: [TRT]: onnx2trt_utils.cpp:395: One or more weights outside the range of INT32 was clamped
WARNING: [TRT]: onnx2trt_utils.cpp:395: One or more weights outside the range of INT32 was clamped
WARNING: [TRT]: onnx2trt_utils.cpp:395: One or more weights outside the range of INT32 was clamped
WARNING: [TRT]: onnx2trt_utils.cpp:395: One or more weights outside the range of INT32 was clamped
WARNING: [TRT]: onnx2trt_utils.cpp:395: One or more weights outside the range of INT32 was clamped
WARNING: [TRT]: onnx2trt_utils.cpp:395: One or more weights outside the range of INT32 was clamped
WARNING: [TRT]: onnx2trt_utils.cpp:395: One or more weights outside the range of INT32 was clamped
WARNING: [TRT]: onnx2trt_utils.cpp:395: One or more weights outside the range of INT32 was clamped
WARNING: [TRT]: onnx2trt_utils.cpp:395: One or more weights outside the range of INT32 was clamped
WARNING: [TRT]: onnx2trt_utils.cpp:395: One or more weights outside the range of INT32 was clamped
WARNING: [TRT]: onnx2trt_utils.cpp:395: One or more weights outside the range of INT32 was clamped
WARNING: [TRT]: onnx2trt_utils.cpp:395: One or more weights outside the range of INT32 was clamped
WARNING: [TRT]: onnx2trt_utils.cpp:395: One or more weights outside the range of INT32 was clamped
WARNING: [TRT]: onnx2trt_utils.cpp:395: One or more weights outside the range of INT32 was clamped
WARNING: [TRT]: onnx2trt_utils.cpp:395: One or more weights outside the range of INT32 was clamped
WARNING: [TRT]: onnx2trt_utils.cpp:395: One or more weights outside the range of INT32 was clamped
WARNING: [TRT]: onnx2trt_utils.cpp:395: One or more weights outside the range of INT32 was clamped
WARNING: [TRT]: onnx2trt_utils.cpp:395: One or more weights outside the range of INT32 was clamped
WARNING: [TRT]: onnx2trt_utils.cpp:395: One or more weights outside the range of INT32 was clamped
WARNING: [TRT]: builtin_op_importers.cpp:4716: Attribute caffeSemantics not found in plugin node! Ensure that the plugin creator has a default value defined or the engine may fail to build.
WARNING: [TRT]: Missing scale and zero-point for tensor (Unnamed Layer* 199) [Constant]_output, expect fall back to non-int8 implementation for any layer consuming or producing given tensor
WARNING: [TRT]: Missing scale and zero-point for tensor (Unnamed Layer* 203) [Constant]_output, expect fall back to non-int8 implementation for any layer consuming or producing given tensor
WARNING: [TRT]: Missing scale and zero-point for tensor (Unnamed Layer* 209) [Constant]_output, expect fall back to non-int8 implementation for any layer consuming or producing given tensor
WARNING: [TRT]: Missing scale and zero-point for tensor (Unnamed Layer* 314) [Constant]_output, expect fall back to non-int8 implementation for any layer consuming or producing given tensor
WARNING: [TRT]: Missing scale and zero-point for tensor (Unnamed Layer* 318) [Constant]_output, expect fall back to non-int8 implementation for any layer consuming or producing given tensor
WARNING: [TRT]: Missing scale and zero-point for tensor (Unnamed Layer* 323) [Constant]_output, expect fall back to non-int8 implementation for any layer consuming or producing given tensor
WARNING: [TRT]: Missing scale and zero-point for tensor (Unnamed Layer* 417) [Constant]_output, expect fall back to non-int8 implementation for any layer consuming or producing given tensor
WARNING: [TRT]: Missing scale and zero-point for tensor (Unnamed Layer* 420) [Constant]_output, expect fall back to non-int8 implementation for any layer consuming or producing given tensor
WARNING: [TRT]: Missing scale and zero-point for tensor (Unnamed Layer* 424) [Constant]_output, expect fall back to non-int8 implementation for any layer consuming or producing given tensor
WARNING: [TRT]: Missing scale and zero-point for tensor (Unnamed Layer* 681) [Constant]_output, expect fall back to non-int8 implementation for any layer consuming or producing given tensor
WARNING: [TRT]: Missing scale and zero-point for tensor (Unnamed Layer* 685) [Constant]_output, expect fall back to non-int8 implementation for any layer consuming or producing given tensor
WARNING: [TRT]: Missing scale and zero-point for tensor BatchedNMS, expect fall back to non-int8 implementation for any layer consuming or producing given tensor
WARNING: [TRT]: The getMaxBatchSize() function should not be used with an engine built from a network created with NetworkDefinitionCreationFlag::kEXPLICIT_BATCH flag. This function will always return 1.
WARNING: [TRT]: The getMaxBatchSize() function should not be used with an engine built from a network created with NetworkDefinitionCreationFlag::kEXPLICIT_BATCH flag. This function will always return 1.
0:05:16.309487810 626145      0x433ee60 INFO                 nvinfer gstnvinfer.cpp:646:gst_nvinfer_logger:<primary-inference> NvDsInferContext[UID 1]: Info from NvDsInferContextImpl::buildModel() <nvdsinfer_context_impl.cpp:1955> [UID = 1]: serialize cuda engine to file: /home/experts-vision/Desktop/farid/O/Primary_Detector/export_retrain/yolov4_resnet18_epoch_080.etlt_b1_gpu0_int8.engine successfully
WARNING: [TRT]: The getMaxBatchSize() function should not be used with an engine built from a network created with NetworkDefinitionCreationFlag::kEXPLICIT_BATCH flag. This function will always return 1.
INFO: ../nvdsinfer/nvdsinfer_model_builder.cpp:610 [Implicit Engine Info]: layers num: 5
0   INPUT  kFLOAT Input           3x384x1248      
1   OUTPUT kINT32 BatchedNMS      1               
2   OUTPUT kFLOAT BatchedNMS_1    200x4           
3   OUTPUT kFLOAT BatchedNMS_2    200             
4   OUTPUT kFLOAT BatchedNMS_3    200             

0:05:16.391430155 626145      0x433ee60 INFO                 nvinfer gstnvinfer_impl.cpp:328:notifyLoadModelStatus:<primary-inference> [UID 1]: Load new model:dstest.txt sucessfully
Error: gst-stream-error-quark: Failed to parse stream (7): gstbaseparse.c(2998): gst_base_parse_check_sync (): /GstPipeline:pipeline0/GstH264Parse:h264-parser

i also put configuration file in default location and models and engine files on Desktop

/opt/nvidia/deepstream/deepstream-6.1/sources/deepstream_python_apps/apps/deepstream-test1$ ls
deepstream_test_1.py  dstest1_pgie_config.txt  dstest.txt  nvinfer_config.txt  README

i used dstest.txt in py module.

[property]
gpu-id=0

model-color-format=1
model-color-format=1
tlt-model-key=NGpmbHN0ZTNrZHFkOGRxNnFsbW9rbXNxbnU6Yzc5NWM5MjQtZDE1YS00NTYxLTg3YzgtNTU2MWVhNDg1M2M3
tlt-encoded-model =/home/experts-vision/Desktop/farid/O//Primary_Detector/export_retrain/yolov4_resnet18_epoch_080.etlt
model-engine-file=/home/experts-vision/Desktop/farid/O/Primary_Detector/export_retrain/trt.engine
labelfile-path=/home/experts-vision/Desktop/farid/O/Primary_Detector/export_retrain/labels.txt
int8-calib-file=/home/experts-vision/Desktop/farid/O/Primary_Detector/export_retrain/cal.bin
net-scale-factor=1.0
offsets=103.939;116.779;123.68
infer-dims=3;384;1248
force-implicit-batch-dim=1

batch-size=1
network-mode=1

num-detected-classes=6
interval=0
gie-unique-id=1
is-classifier=0
#network-type=0
cluster-mode=3
output-blob-names=BatchedNMS
parse-bbox-func-name=NvDsInferParseCustomBatchedNMSTLT
custom-lib-path=/opt/nvidia/deepstream/deepstream/lib/libnvds_infercustomparser.so
layer-device-precision=cls/mul:fp32:gpu;box/mul_6:fp32:gpu;box/add:fp32:gpu;box/mul_4:fp32:gpu;box/add_1:fp32:gpu;cls/Reshape_reshape:fp32:gpu;box/Reshape_reshape:fp32:gpu;encoded_detections:fp32:gpu;bg_leaky_c>


[class-attrs-all]
pre-cluster-threshold=0.3
roi-top-offset=0
roi-bottom-offset=0
detected-min-w=0
detected-min-h=0
detected-max-w=0
detected-max-h=0

#scaling-filter=0
#scaling-compute-hw=0

#[class-attrs-all]
#pre-cluster-threshold=0.2
#eps=0.2
#group-threshold=1

my python module

import sys
sys.path.append('../')
import gi
gi.require_version('Gst', '1.0')
from gi.repository import GLib, Gst
from common.is_aarch_64 import is_aarch64
from common.bus_call import bus_call

import pyds

#PGIE_CLASS_ID_VEHICLE = 0
#PGIE_CLASS_ID_BICYCLE = 1
#PGIE_CLASS_ID_PERSON = 2
#PGIE_CLASS_ID_ROADSIGN = 3

PGIE_ClASS_ID_balcony_with_railing = 0
PGIE_ClASS_ID_balcony_without_railing = 1
PGIE_ClASS_ID_helmet = 2
PGIE_ClASS_ID_incomplete_railing = 3
PGIE_ClASS_ID_person = 4
PGIE_ClASS_ID_rail = 5

def osd_sink_pad_buffer_probe(pad,info,u_data):
    frame_number=0
    #Intiallizing object counter with 0.
    obj_counter = {
        #PGIE_CLASS_ID_VEHICLE:0,
        #PGIE_CLASS_ID_PERSON:0,
        #PGIE_CLASS_ID_BICYCLE:0,
        #PGIE_CLASS_ID_ROADSIGN:0
        
        PGIE_ClASS_ID_balcony_with_railing:0,
        PGIE_ClASS_ID_balcony_without_railing:0,
        PGIE_ClASS_ID_helmet:0,
        PGIE_ClASS_ID_incomplete_railing:0,
        PGIE_ClASS_ID_person:0,
        PGIE_ClASS_ID_rail:0
    }
    num_rects=0

    gst_buffer = info.get_buffer()
    if not gst_buffer:
        print("Unable to get GstBuffer ")
        return

    # Retrieve batch metadata from the gst_buffer
    # Note that pyds.gst_buffer_get_nvds_batch_meta() expects the
    # C address of gst_buffer as input, which is obtained with hash(gst_buffer)
    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:
            # Note that l_frame.data needs a cast to pyds.NvDsFrameMeta
            # The casting is done by pyds.glist_get_nvds_frame_meta()
            # The casting also keeps ownership of the underlying memory
            # in the C code, so the Python garbage collector will leave
            # it alone.
            #frame_meta = pyds.glist_get_nvds_frame_meta(l_frame.data)
            frame_meta = pyds.NvDsFrameMeta.cast(l_frame.data)
        except StopIteration:
            break

        frame_number=frame_meta.frame_num
        num_rects = frame_meta.num_obj_meta
        l_obj=frame_meta.obj_meta_list
        while l_obj is not None:
            try:
                # Casting l_obj.data to pyds.NvDsObjectMeta
                #obj_meta=pyds.glist_get_nvds_object_meta(l_obj.data)
                obj_meta=pyds.NvDsObjectMeta.cast(l_obj.data)
            except StopIteration:
                break
            obj_counter[obj_meta.class_id] += 1
            obj_meta.rect_params.border_color.set(0.0, 0.0, 1.0, 0.0)
            try: 
                l_obj=l_obj.next
            except StopIteration:
                break

        # Acquiring a display meta object. The memory ownership remains in
        # the C code so downstream plugins can still access it. Otherwise
        # the garbage collector will claim it when this probe function exits.
        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]
        # Setting display text to be shown on screen
        # Note that the pyds module allocates a buffer for the string, and the
        # memory will not be claimed by the garbage collector.
        # Reading the display_text field here will return the C address of the
        # allocated string. Use pyds.get_string() to get the string content.
        py_nvosd_text_params.display_text = "Frame Number={} Number of Objects={} Helmet_count={} Person_count={}".format(frame_number, num_rects, obj_counter[PGIE_CLASS_ID_helmet], obj_counter[PGIE_CLASS_ID_person])

        # 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)
        try:
            l_frame=l_frame.next
        except StopIteration:
            break
			
    return Gst.PadProbeReturn.OK	


def main(args):
    # Check input arguments
    if len(args) != 2:
        sys.stderr.write("usage: %s <media file or uri>\n" % args[0])
        sys.exit(1)

    # 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()

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

    # Source element for reading from the file
    print("Creating Source \n ")
    source = Gst.ElementFactory.make("filesrc", "file-source")
    if not source:
        sys.stderr.write(" Unable to create Source \n")
    
    #print("creating caps_filter \n")
    caps_v4l2src = Gst.ElementFactory.make("capsfilter", "v4l2src_caps")
    if not caps_v4l2src:
        sys.stderr.write(" Unable to create v4l2src capsfilter \n")
    
    # Since the data format in the input file is elementary h264 stream,
    # we need a h264parser
    print("Creating H264Parser \n")
    h264parser = Gst.ElementFactory.make("h264parse", "h264-parser")
    if not h264parser:
        sys.stderr.write(" Unable to create h264 parser \n")

    # Use nvdec_h264 for hardware accelerated decode on GPU
    print("Creating Decoder \n")
    decoder = Gst.ElementFactory.make("nvv4l2decoder", "nvv4l2-decoder")
    if not decoder:
        sys.stderr.write(" Unable to create Nvv4l2 Decoder \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")

    # Use nvinfer to run inferencing on decoder's output,
    # behaviour of inferencing is set through config file
    pgie = Gst.ElementFactory.make("nvinfer", "primary-inference")
    if not pgie:
        sys.stderr.write(" Unable to create pgie \n")

    # Use convertor to convert from NV12 to RGBA as required by nvosd
    nvvidconv = Gst.ElementFactory.make("nvvideoconvert", "convertor")
    if not nvvidconv:
        sys.stderr.write(" Unable to create nvvidconv \n")

    # Create OSD to draw on the converted RGBA buffer
    nvosd = Gst.ElementFactory.make("nvdsosd", "onscreendisplay")

    if not nvosd:
        sys.stderr.write(" Unable to create nvosd \n")

    # Finally render the osd output
    if is_aarch64():
        transform = Gst.ElementFactory.make("nvegltransform", "nvegl-transform")

    print("Creating EGLSink \n")
    sink = Gst.ElementFactory.make("nveglglessink", "nvvideo-renderer")
    if not sink:
        sys.stderr.write(" Unable to create egl sink \n")

    print("Playing file %s " %args[1])
    source.set_property('location', args[1])
    streammux.set_property('width', 1920)
    streammux.set_property('height', 1080)
    streammux.set_property('batch-size', 1)
    streammux.set_property('batched-push-timeout', 4000000)
    pgie.set_property('config-file-path', "dstest.txt")                           # here i set dstest file mentioned above

    print("Adding elements to Pipeline \n")
    pipeline.add(source)
    pipeline.add(caps_v4l2src)
    pipeline.add(h264parser)
    pipeline.add(decoder)
    pipeline.add(streammux)
    pipeline.add(pgie)
    pipeline.add(nvvidconv)
    pipeline.add(nvosd)
    pipeline.add(sink)
    if is_aarch64():
        pipeline.add(transform)

    # we link the elements together
    # file-source -> h264-parser -> nvh264-decoder ->
    # nvinfer -> nvvidconv -> nvosd -> video-renderer
    print("Linking elements in the Pipeline \n")
    #source.link(caps_v4l2src)
    caps_v4l2src.link(h264parser)
    #source.link(h264parser)
    h264parser.link(decoder)

    sinkpad = streammux.get_request_pad("sink_0")
    if not sinkpad:
        sys.stderr.write(" Unable to get the sink pad of streammux \n")
    srcpad = decoder.get_static_pad("src")
    if not srcpad:
        sys.stderr.write(" Unable to get source pad of decoder \n")
    srcpad.link(sinkpad)
    streammux.link(pgie)
    pgie.link(nvvidconv)
    nvvidconv.link(nvosd)
    if is_aarch64():
        nvosd.link(transform)
        transform.link(sink)
    else:
        nvosd.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)

    # Lets add probe to get informed of the meta data generated, we add probe to
    # the sink pad of the osd element, since by that time, the buffer would have
    # had got all the metadata.
    osdsinkpad = nvosd.get_static_pad("sink")
    if not osdsinkpad:
        sys.stderr.write(" Unable to get sink pad of nvosd \n")

    osdsinkpad.add_probe(Gst.PadProbeType.BUFFER, osd_sink_pad_buffer_probe, 0)

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

if __name__ == '__main__':
    sys.exit(main(sys.argv))

run command

sudo python3 deepstream_test_1.py  /opt/nvidia/deepstream/deepstream-6.1/samples/streams/camera-1_video_26.mp4

By default deepstream-test1 uses filesrc and follows h264parse, which can only accept h264 file. You can check the sample streams in /opt/nvidia/deepstream/deepstream/samples/streams with postfix as h264.
You can check deepstream-test3 on accepting mp4 file as source file.

Thank you very much @yingliu it a relief.
i checked with h264. file and it showed output now i am sure that model is working .

===> Now looking for element that can parse mp4 in test3 and will replace with h264parser.