• Hardware Platform (GTX 1650 GPU)
• DeepStream Version 6.1
• TensorRT Version 8.5.2.2-1+cuda11.8
• NVIDIA GPU Driver Version 510.108.03
• Issue Type( questions, new requirements, bugs)
• 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)
cfg file for facenet
[property]
gpu-id=0
net-scale-factor=0.0039215697906911373
tlt-model-key=tlt_encode
tlt-encoded-model=…/…/models/tao_pretrained_models/facedetectir/resnet18_facedetectir_pruned.etlt
labelfile-path=labels_facedetectir.txt
int8-calib-file=…/…/models/tao_pretrained_models/facedetectir/facedetectir_int8.txt
model-engine-file=…/…/models/tao_pretrained_models/facedetectir/resnet18_facedetectir_pruned.etlt_b1_gpu0_int8.engine
input-dims=3;240;384;0
uff-input-blob-name=input_1
batch-size=1
process-mode=1
model-color-format=0
0=FP32, 1=INT8, 2=FP16 mode
network-mode=1
num-detected-classes=1
interval=0
gie-unique-id=1
output-blob-names=output_bbox/BiasAdd;output_cov/Sigmoid
cluster-mode=2
#Use the config params below for dbscan clustering mode
#[class-attrs-all]
#detected-min-w=4
#detected-min-h=4
#minBoxes=3
#eps=0.7
#Use the config params below for NMS clustering mode
[class-attrs-all]
topk=20
nms-iou-threshold=0.5
pre-cluster-threshold=0.2
Per class configurations
[class-attrs-0]
topk=20
nms-iou-threshold=0.5
pre-cluster-threshold=0.4
#[class-attrs-1]
#pre-cluster-threshold=0.05
#eps=0.7
#dbscan-min-score=0.5
cfg file for facial_Landmark
[property]
gpu-id=0
int8-calib-file=/opt/nvidia/deepstream/deepstream-6.1/sources/deepstream_python_apps/apps/Model1/int8_calibration.txt
tlt-encoded-model=/opt/nvidia/deepstream/deepstream-6.1/sources/deepstream_python_apps/apps/Model1/FacialLandmarks.etlt
tlt-model-key=nvidia_tlt
model-engine-file=/opt/nvidia/deepstream/deepstream-6.1/sources/deepstream_python_apps/apps/Model1/FacialLandmarks.etlt_b30_gpu0_fp32.engine
#dynamic batch size
batch-size=32
0=FP32, 1=INT8, 2=FP16 mode
network-mode=1
num-detected-classes=1
output-blob-names=softargmax,softargmax:1,conv_keypoints_m80
#0=Detection 1=Classifier 2=Segmentation 100=other
network-type=100
Enable tensor metadata output
output-tensor-meta=1
#1-Primary 2-Secondary
process-mode=2
gie-unique-id=2
operate-on-gie-id=1
net-scale-factor=1.0
offsets=0.0
input-object-min-width=5
input-object-min-height=5
#0=RGB 1=BGR 2=GRAY
model-color-format=2
classifier-async-mode=1
classifier-threshold=0.2
[class-attrs-all]
threshold=0.0
pre-cluster-threshold=0.2
nms-iou-threshold=0.2
dbscan-min-score=0.2
cfg file for EmotionNet
[property]
gpu-id=0
preprocessing parameters: These are the same for all classification models generated by TAO Toolkit.
net-scale-factor=1
model-color-format=1
batch-size=16
Model specific paths. These need to be updated for every classification model.
labelfile-path=/home/jaypear/Desktop/Genel/Görev_Emotion_Analysis/results/output/Labels_emotion.txt
tlt-encoded-model=/home/jaypear/Desktop/Genel/Görev_Emotion_Analysis/results/output/weights/Emotion.etlt
tlt-model-key=nvidia_tao
model-engine-file=/opt/nvidia/deepstream/deepstream-6.1/sources/deepstream_python_apps/apps/Model1/EmotionNet.etlt_b30_gpu0_fp32.engine
infer-dims=3;300;300 # where c = number of channels, h = height of the model input, w = width of model input
uff-input-blob-name=input_1
uff-input-order=0
output-blob-names=predictions/Softmax
0=FP32, 1=INT8, 2=FP16 mode
network-mode=0
process-mode: 2 - inferences on crops from primary detector, 1 - inferences on whole frame
process-mode=2
interval=1
network-type=1 # defines that the model is a classifier.
gie-unique-id=3
operate-on-gie-id=1
classifier-threshold=0.2
num-detected-classes=6
[class-attrs-all]
pre-cluster-threshold=0.2
eps=0.2
group-threshold=1
command line
python3 deepstream_test_2.py /home/jaypear/Downloads/video.h264