onnx_inference.sh

export PYTHONPATH=/opt/dtk/lib:$PYTHONPATH
export HIP_PRINTF_DEBUG_FOR_FP64=0
export HIP_VISIBLE_DEVICES=0

python ./tools/migraphx_eval.py --img 640 --batch-size 24 --data coco.yaml --weight /models/yolov5m_fp16.mxr --device 0

# nohup numactl -N 0 -m 0 python migraphx_eval.py --img 640 --batch-size 24 --data coco.yaml --weight ./yolov5m.onnx --device 0 2>&1 | tee result_0.log &
# export HIP_VISIBLE_DEVICES=1
# nohup numactl -N 1 -m 1 python migraphx_eval.py --img 640 --batch-size 24 --data coco.yaml --weight ./yolov5m.onnx --device 1 2>&1 | tee result_1.log &
# export HIP_VISIBLE_DEVICES=2
# nohup numactl -N 2 -m 2 python migraphx_eval.py --img 640 --batch-size 24 --data coco.yaml --weight ./yolov5m.onnx --device 2 2>&1 | tee result_2.log &
# export HIP_VISIBLE_DEVICES=3
# nohup numactl -N 3 -m 3 python migraphx_eval.py --img 640 --batch-size 24 --data coco.yaml --weight ./yolov5m.onnx --device 3 2>&1 | tee result_3.log &

# nohup python migraphx_eval.py --img 640 --batch-size 24 --data coco.yaml --weight ./yolov5m.onnx --device 4 2>&1 | tee result_4.log &

# nohup python migraphx_eval.py --img 640 --batch-size 24 --data coco.yaml --weight ./yolov5m.onnx --device 5 2>&1 | tee result_5.log &

# nohup python migraphx_eval.py --img 640 --batch-size 24 --data coco.yaml --weight ./yolov5m.onnx --device 6 2>&1 | tee result_6.log &

# nohup python migraphx_eval.py --img 640 --batch-size 24 --data coco.yaml --weight ./yolov5m.onnx --device 7 2>&1 | tee result_7.log &