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scripts.sh
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## Evaluation
# ResNet-18 with MSGC w/o attention (Top-1 = 70.3%, MAC = 883 M)
#CUDA_VISIBLE_DEVICES=0 python -m torch.distributed.launch --master_port=12345 --nproc_per_node=1 main_dist.py --model msgc_resnet18 --data imagenet --datadir /to/imagenet/dataset --savedir ./results --evaluate checkpoints/msgc_resnet18_noatt.pth
# ResNet-18 with MSGC w/ attention (Top-1 = 71.5%, MAC = 885 M)
#CUDA_VISIBLE_DEVICES=0 python -m torch.distributed.launch --master_port=12345 --nproc_per_node=1 main_dist.py --model msgc_resnet18 --attention --data imagenet --datadir /to/imagenet/dataset --savedir ./results --evaluate checkpoints/msgc_resnet18_att.pth
# ResNet-50 with MSGC w/o attention (Top-1 = 77.2%, MAC = 1886 M)
#CUDA_VISIBLE_DEVICES=0 python -m torch.distributed.launch --master_port=12345 --nproc_per_node=1 main_dist.py --model msgc_resnet50 --data imagenet --datadir /to/imagenet/dataset --savedir ./results --evaluate checkpoints/msgc_resnet50_noatt.pth
# ResNet-50 with MSGC w/ attention (Top-1 = 76.8%, MAC = 1892 M)
#CUDA_VISIBLE_DEVICES=0 python -m torch.distributed.launch --master_port=12345 --nproc_per_node=1 main_dist.py --model msgc_resnet50 --attention --data imagenet --datadir /to/imagenet/dataset --savedir ./results --evaluate checkpoints/msgc_resnet50_att.pth
# MobileNetV2 with MSGC w/o attention (Top-1 = 72.1%, MAC = 198 M)
#CUDA_VISIBLE_DEVICES=0 python -m torch.distributed.launch --master_port=12345 --nproc_per_node=1 main_dist.py --model msgc_mobilenetv2 --data imagenet --datadir /to/imagenet/dataset --savedir ./results --evaluate checkpoints/msgc_mobilenetv2_noatt.pth
# MobileNetV2 with MSGC w/ attention (Top-1 = 72.6%, MAC = 197 M)
#CUDA_VISIBLE_DEVICES=0 python -m torch.distributed.launch --master_port=12345 --nproc_per_node=1 main_dist.py --model msgc_mobilenetv2 --attention --data imagenet --datadir /to/imagenet/dataset --savedir ./results --evaluate checkpoints/msgc_mobilenetv2_att.pth
# CondenseNet with MSGC w/o attention (Top-1 = 74.8%, MAC = 523 M)
#CUDA_VISIBLE_DEVICES=0 python -m torch.distributed.launch --master_port=12345 --nproc_per_node=1 main_dist.py --model msgc_condensenet --data imagenet --datadir /to/imagenet/dataset --savedir ./results --evaluate checkpoints/msgc_condensenet.pth
# ResNet-18 with MSGC w/o attention, \tau_{end} = 0.9 (Top-1 = 72.3%, MAC = 1631 M)
#CUDA_VISIBLE_DEVICES=0 python -m torch.distributed.launch --master_port=12345 --nproc_per_node=1 main_dist.py --model msgc_resnet18 --data imagenet --datadir /to/imagenet/dataset --savedir ./results --evaluate checkpoints/msgc_resnet18_noatt_tau0.9.pth
## Training
# ResNet-18 with MSGC w/o attention
#CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --master_port=12345 --nproc_per_node=2 main_dist.py --model msgc_resnet18 -j 16 --data imagenet --datadir /to/imagenet/dataset --savedir ./results --resume --target 0.49
# ResNet-18 with MSGC w/ attention
#CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --master_port=12345 --nproc_per_node=2 main_dist.py --model msgc_resnet18 --attention -j 16 --data imagenet --datadir /to/imagenet/dataset --savedir ./results --resume --target 0.488
# ResNet-50 with MSGC w/o attention
#CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --master_port=12345 --nproc_per_node=2 main_dist.py --model msgc_resnet50 -j 16 --data imagenet --datadir /to/imagenet/dataset --savedir ./results --resume --target 0.457
# ResNet-50 with MSGC w/ attention
#CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --master_port=12345 --nproc_per_node=2 main_dist.py --model msgc_resnet50 --attention -j 16 --data imagenet --datadir /to/imagenet/dataset --savedir ./results --resume --target 0.457
# MobileNetV2 with MSGC w/o attention
#CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --master_port=12345 --nproc_per_node=2 main_dist.py --model msgc_mobilenetv2 -j 16 --data imagenet --datadir /to/imagenet/dataset --savedir ./results --epochs 150 --resume --target 0.65
# MobileNetV2 with MSGC w/ attention
#CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --master_port=12345 --nproc_per_node=2 main_dist.py --model msgc_mobilenetv2 --attention -j 16 --data imagenet --datadir /to/imagenet/dataset --savedir ./results --epochs 150 --resume --target 0.628
# CondenseNet with MSGC w/o attention
#CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --master_port=12345 --nproc_per_node=2 main_dist.py --model msgc_condensenet -j 16 --data imagenet --datadir /to/imagenet/dataset --savedir ./results --resume --target 0.258
# ResNet-18 with MSGC w/o attention, \tau_{end} = 0.9
#CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --master_port=12345 --nproc_per_node=2 main_dist.py --model msgc_resnet18 -j 16 --data imagenet --datadir /to/imagenet/dataset --savedir ./results --resume --target 0.9