I’m having some problems with TLT 3.0 (not TAO) running inside the Docker, inference of Faster-RCNN with exported etlt doesn’t work correctly if I increase the batch size. With batch size > 1, only the results for the 1st image is correct, all other results from the batch are incorrect.
This happens only after exporting to etlt (and consequently with the created engine file, or with the engine created while exporting), using the model from the tlt files works well.
Inferences with TLT 2.0 from my previous trainings with Faster-RCNN worked with any batch size.
Please provide the following information when requesting support.
• Hardware: Quadro P5000 (fp32) nvidia-smi.txt (1.7 KB)
• Network Type: Faster_rcnn
• TLT Version (Please run “tlt info --verbose” and share “docker_tag” here): TLT 3.0 (docker from nvcr.io/nvidia/tlt-streamanalytics:v3.0-dp-py3)
• Training spec file default_spec_resnet18_retrain_spec.txt (5.8 KB)
• How to reproduce the issue ?
Jupyter Notebook running at Docker TLT 3.0
!faster_rcnn export --gpu_index $GPU_INDEX -m $USER_EXPERIMENT_DIR/2-retrained_pruned/frcnn_pruned_resnet18_retrain.epoch12.tlt
log_tlt_export_command.txt (19.7 KB)
!faster_rcnn inference --gpu_index $GPU_INDEX -e $SPECS_DIR/default_spec_resnet18_retrain_spec.txt