onnx_inference.sh

export HIP_PRINTF_DEBUG_FOR_FP64=0
export HSA_FORCE_FINE_GRAIN_PCIE=1
# /datasets/cifar100/cifar-100-python/test
export HIP_VISIBLE_DEVICES=0

numactl -N 0 -m 0 python resnet50_migraphx.py --model ./resnet50.onnx --dataset /datasets/cifar100/test --batch_size 24 --fp16 True

# nohup numactl -N 0 -m 0 python resnet50_migraphx.py --model ./resnet50.onnx --dataset ./datasets/cifar100/test --batch_size 24 --fp16 True 2>&1 | tee result_0.log &

# export HIP_VISIBLE_DEVICES=1
# nohup numactl -N 1 -m 1 python resnet50_migraphx.py --model ./resnet50.onnx --dataset ./datasets/cifar100/test --batch_size 24 --fp16 True 2>&1 | tee result_1.log &

# export HIP_VISIBLE_DEVICES=2
# nohup numactl -N 2 -m 2 python resnet50_migraphx.py --model ./resnet50.onnx --dataset ./datasets/cifar100/test --batch_size 24 --fp16 True 2>&1 | tee result_2.log &

# export HIP_VISIBLE_DEVICES=3
# nohup numactl -N 3 -m 3 python resnet50_migraphx.py --model ./resnet50.onnx --dataset ./datasets/cifar100/test --batch_size 24 --fp16 True 2>&1 | tee result_3.log &

# export HIP_VISIBLE_DEVICES=4
# nohup python resnet50_migraphx.py --model /workspace/mmpretrain-main/resnet50.onnx --dataset /datasets/cifar100/ --batch_size 24 --fp16 True 2>&1 | tee result_4.log &

# export HIP_VISIBLE_DEVICES=5
# nohup python resnet50_migraphx.py --model /workspace/mmpretrain-main/resnet50.onnx --dataset /datasets/cifar100/ --batch_size 24 --fp16 True 2>&1 | tee result_5.log &

# export HIP_VISIBLE_DEVICES=6
# nohup python resnet50_migraphx.py --model /workspace/mmpretrain-main/resnet50.onnx --dataset /datasets/cifar100/ --batch_size 24 --fp16 True 2>&1 | tee result_6.log &

# export HIP_VISIBLE_DEVICES=7
# nohup python resnet50_migraphx.py --model /workspace/mmpretrain-main/resnet50.onnx --dataset /datasets/cifar100/ --batch_size 24 --fp16 True 2>&1 | tee result_7.log &