Please provide complete information as applicable to your setup.
• Hardware Platform (Jetson / GPU) GPU
• DeepStream Version 6.3 and 7.0(container)
• NVIDIA GPU Driver Version (valid for GPU only) 555
• Issue Type( questions, new requirements, bugs) bug
• How to reproduce the issue ? (This is for bugs. Including which sample app is using, the configuration files content, the command line used and other details for reproducing)
Run the deepstream 3d action recognition app with default values and 3d models
I am running the deepstream 3d action recognition app but getting no labels (only fps on osd),both 2d and 3d models with onnx as well as with etlt file gives same result, i.e. no labels. I am using pretrained models from ngc.
there are warning comings-
WARNING from element primary-nvinference-engine: nvinfer could not find input layer with name = input_rgb
Warning: nvinfer could not find input layer with name = input_rgb
0:00:32.819271434 3655 0x5577cfc6a6a0 WARN nvinfer gstnvinfer.cpp:1993:gst_nvinfer_process_tensor_input: warning: nvinfer could not find input layer with name = input_rgb
config_infer_primary_2d_action.txt
[property]
gpu-id=0
tlt-encoded-model=/media/sameer/Extras/deepstream-6.3/sources/apps/sample_apps/deepstream-3d-action-recognition_new/resnet18_2d_of_hmdb5_32_a100.etlt
tlt-model-key=nvidia_tao
model-engine-file=./resnet18_2d_rgb_hmdb5_32.etlt_b4_gpu0_fp16.engine
labelfile-path=labels.txt
batch-size=4
process-mode=1
requires preprocess metadata input
input-tensor-from-meta=1
0=FP32, 1=INT8, 2=FP16 mode
network-mode=2
gie-unique-id=1
1: classifier, 100: custom
network-type=1
Let application to parse the inference tensor output
output-tensor-meta=1
tensor-meta-pool-size=8
config_preprocess_2d_custom.txt
[property]
enable=1
target-unique-ids=1
# 0=process on objects 1=process on frames
process-on-frame=1
# network-input-shape: batch, channel x sequence, height, width
2D sequence of 64 images
network-input-shape= 4;192;224;224
2D sequence of 32 images
network-input-shape= 4;96;224;224
# 0=RGB, 1=BGR, 2=GRAY
network-color-format=0
# 0=NCHW, 1=NHWC, 2=CUSTOM
network-input-order=2
# 0=FP32, 1=UINT8, 2=INT8, 3=UINT32, 4=INT32, 5=FP16
tensor-data-type=0
tensor-name=input_rgb
processing-width=224
processing-height=224
# 0=NVBUF_MEM_DEFAULT 1=NVBUF_MEM_CUDA_PINNED 2=NVBUF_MEM_CUDA_DEVICE
# 3=NVBUF_MEM_CUDA_UNIFIED 4=NVBUF_MEM_SURFACE_ARRAY(Jetson)
scaling-pool-memory-type=0
# 0=NvBufSurfTransformCompute_Default 1=NvBufSurfTransformCompute_GPU
# 2=NvBufSurfTransformCompute_VIC(Jetson)
scaling-pool-compute-hw=0
# Scaling Interpolation method
# 0=NvBufSurfTransformInter_Nearest 1=NvBufSurfTransformInter_Bilinear 2=NvBufSurfTransformInter_Algo1
# 3=NvBufSurfTransformInter_Algo2 4=NvBufSurfTransformInter_Algo3 5=NvBufSurfTransformInter_Algo4
# 6=NvBufSurfTransformInter_Default
scaling-filter=0
# model input tensor pool size
tensor-buf-pool-size=8
custom-lib-path=/opt/nvidia/deepstream/deepstream/lib/libnvds_custom_sequence_preprocess.so
#custom-lib-path=./custom_sequence_preprocess/libnvds_custom_sequence_preprocess.so
custom-tensor-preparation-function=CustomSequenceTensorPreparation
2D conv custom params
[user-configs]
channel-scale-factors=0.007843137;0.007843137;0.007843137
channel-mean-offsets=127.5;127.5;127.5
stride=1
subsample=0
[group-0]
src-ids=0;1;2;3
process-on-roi=1
roi-params-src-0=0;0;1280;720
roi-params-src-1=0;0;1280;720
roi-params-src-2=0;0;1280;720
roi-params-src-3=0;0;1280;720