Description
After exporting and converting the trained model to an engine in fp16 I’m getting bad results when I try to run inference. If I run inference .tlt then the results are perfect, but if I run inference .engine then the results are very bad.
Environment
I’m using this container: nvcr.io/nvidia/tlt-streamanalytics:v3.0-dp-py3.
FasterRCNN/efficientnet_b1.
Pretrained model: efficientnet_b1_relu.hdf5;
Command:
faster_rcnn export -m $USER_EXPERIMENT_DIR/data/faster_rcnn/frcnn_kitti_efficientnet_b1.epoch41.tlt
-o $USER_EXPERIMENT_DIR/data/faster_rcnn/frcnn_kitti_efficientnet_b1_fp16.etlt
-e $SPECS_DIR/default_spec_efficientnet_b1.txt
-k $KEY
–data_type fp16
–batch_size 4
–max_workspace_size 2000000000
CUDA_VISIBLE_DEVICES=0 tlt-converter -k $KEY
-d 3,512,512
-o NMS
-e $USER_EXPERIMENT_DIR/data/faster_rcnn/trt.fp16.engine
-m 4
-w 2000000000
-t fp16
-i nchw
$USER_EXPERIMENT_DIR/data/faster_rcnn/frcnn_kitti_efficientnet_b1_fp16.etlt
!faster_rcnn inference --gpu_index 0 -e $SPECS_DIR/default_spec_efficientnet_b1.txt!
default_spec_efficientnet_b1.txt (5.1 KB)