adding scipts

scripts/imagenet100_resnet50_gap.sh

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+#!/bin/bash
+#SBATCH --job-name=m_v_mpn
+#SBATCH --time=12:00:00
+#SBATCH --ntasks-per-node=12
+#SBATCH --gres=gpu:4
+#SBATCH --mem=20g
+
+eval "$(command conda 'shell.bash' 'hook' 2> /dev/null)"
+#module load pytorch/1.9.0-py39-cuda112 torchvision cvxpylayers
+conda activate convnext
+cd /scratch2/rah025
+
+set -e
+:<<!
+*****************Instruction*****************
+Here you can easily creat a model by selecting
+an arbitray backbone model and global method.
+You can fine-tune it on your own datasets by
+using a pre-trained model.
+Modify the following settings as you wish !
+*********************************************
+!
+
+#***************Backbone model****************
+#Our code provides some mainstream architectures:
+#alexnet
+#vgg family:vgg11, vgg11_bn, vgg13, vgg13_bn,
+#           vgg16, vgg16_bn, vgg19_bn, vgg19
+#resnet family: resnet18, resnet34, resnet50,
+#               resnet101, resnet152
+#mpncovresnet: mpncovresnet50, mpncovresnet101
+#inceptionv3
+#You can also add your own network in src/network
+arch=resnet50
+#*********************************************
+
+#***************global method****************
+#Our code provides some global method at the end
+#of network:
+#GAvP (global average pooling),
+#MPNCOV (matrix power normalized cov pooling),
+#BCNN (bilinear pooling)
+#CBP (compact bilinear pooling)
+#...
+#You can also add your own method in src/representation
+image_representation=GAvP_CONVNEXT
+# short description of method
+description=reproduce
+#*********************************************
+
+#*******************Dataset*******************
+#Choose the dataset folder
+benchmark=imagenet100
+datadir=dataset
+dataset=$datadir/$benchmark
+num_classes=100
+#*********************************************
+
+#****************Hyper-parameters*************
+
+# Freeze the layers before a certain layer.
+freeze_layer=0
+# Batch size
+batchsize=50
+# The number of total epochs for training
+epoch=100
+# The inital learning rate
+# dcreased by step method
+#lr=1.2e-6
+lr=1.2e-4
+lr_method=step
+lr_params=15\ 30
+# log method
+# description: lr = logspace(params1, params2, #epoch)
+
+#lr_method=log
+#lr_params=-1.1\ -5.0
+weight_decay=1e-4
+classifier_factor=5
+#*********************************************
+echo "Start finetuning!"
+modeldir=Results/Finetune-$benchmark-$arch-$image_representation-$description-lr$lr-bs$batchsize-448
+if [ ! -d  "Results" ]; then
+
+mkdir Results
+
+fi
+if [ ! -e $modeldir/*.pth.tar ]; then
+
+if [ ! -d  "$modeldir" ]; then
+
+mkdir $modeldir
+
+fi
+#cp finetune_mpncovvggd16.sh $modeldir
+
+python main.py $dataset\
+               --benchmark $benchmark\
+               --pretrained\
+               -a $arch\
+               -p 10\
+               --epochs $epoch\
+               --lr $lr\
+               --lr-method $lr_method\
+               --lr-params $lr_params\
+              --weight-decay $weight_decay\
+               -j 8\
+               -b $batchsize\
+               --num-classes $num_classes\
+               --representation $image_representation\
+               --freezed-layer $freeze_layer\
+               --classifier-factor $classifier_factor\
+               --benchmark $benchmark\
+               --modeldir $modeldir
+
+else
+checkpointfile=$(ls -rt $modeldir/*.pth.tar | tail -1)
+
+python main.py $dataset\
+               --benchmark $benchmark\
+               --pretrained\
+               -a $arch\
+               -p 10\
+               --epochs $epoch\
+               --lr $lr\
+               --lr-method $lr_method\
+               --lr-params $lr_params\
+               --weight-decay $weight_decay\
+               -j 8\
+               -b $batchsize\
+               --num-classes $num_classes\
+               --representation $image_representation\
+               --freezed-layer $freeze_layer\
+               --modeldir $modeldir\
+               --classifier-factor $classifier_factor\
+               --benchmark $benchmark\
+               --resume $checkpointfile
+
+fi
+echo "Done!"

scripts/imagenet100_resnet50_gap_scratch.sh

@@ -0,0 +1,138 @@
+#!/bin/bash
+#SBATCH --job-name=gap
+#SBATCH --time=24:00:00
+#SBATCH --ntasks-per-node=12
+#SBATCH --gres=gpu:4
+#SBATCH --mem=20g
+
+eval "$(command conda 'shell.bash' 'hook' 2> /dev/null)"
+#module load pytorch/1.9.0-py39-cuda112 torchvision cvxpylayers
+conda activate convnext
+cd /scratch2/rah025
+
+set -e
+:<<!
+*****************Instruction*****************
+Here you can easily creat a model by selecting
+an arbitray backbone model and global method.
+You can fine-tune it on your own datasets by
+using a pre-trained model.
+Modify the following settings as you wish !
+*********************************************
+!
+
+#***************Backbone model****************
+#Our code provides some mainstream architectures:
+#alexnet
+#vgg family:vgg11, vgg11_bn, vgg13, vgg13_bn,
+#           vgg16, vgg16_bn, vgg19_bn, vgg19
+#resnet family: resnet18, resnet34, resnet50,
+#               resnet101, resnet152
+#mpncovresnet: mpncovresnet50, mpncovresnet101
+#inceptionv3
+#You can also add your own network in src/network
+arch=resnet50
+#*********************************************
+
+#***************global method****************
+#Our code provides some global method at the end
+#of network:
+#GAvP (global average pooling),
+#MPNCOV (matrix power normalized cov pooling),
+#BCNN (bilinear pooling)
+#CBP (compact bilinear pooling)
+#...
+#You can also add your own method in src/representation
+image_representation=GAvP_CONVNEXT
+# short description of method
+description=reproduce
+#*********************************************
+
+#*******************Dataset*******************
+#Choose the dataset folder
+benchmark=imagenet100
+datadir=dataset
+dataset=$datadir/$benchmark
+num_classes=100
+#*********************************************
+
+#****************Hyper-parameters*************
+
+# Freeze the layers before a certain layer.
+freeze_layer=0
+# Batch size
+batchsize=50
+# The number of total epochs for training
+epoch=100
+# The inital learning rate
+# dcreased by step method
+#lr=1.2e-6
+lr=0.01
+lr_method=step
+lr_params=15\ 30\ 45
+# log method
+# description: lr = logspace(params1, params2, #epoch)
+
+#lr_method=log
+#lr_params=-1.1\ -5.0
+weight_decay=1e-4
+classifier_factor=1
+#*********************************************
+echo "Start finetuning!"
+modeldir=Results/Finetune-$benchmark-$arch-$image_representation-$description-lr$lr-bs$batchsize-scratch
+if [ ! -d  "Results" ]; then
+
+mkdir Results
+
+fi
+if [ ! -e $modeldir/*.pth.tar ]; then
+
+if [ ! -d  "$modeldir" ]; then
+
+mkdir $modeldir
+
+fi
+#cp finetune_mpncovvggd16.sh $modeldir
+
+python main.py $dataset\
+               --benchmark $benchmark\
+               -a $arch\
+               -p 10\
+               --epochs $epoch\
+               --lr $lr\
+               --lr-method $lr_method\
+               --lr-params $lr_params\
+              --weight-decay $weight_decay\
+               -j 8\
+               -b $batchsize\
+               --num-classes $num_classes\
+               --representation $image_representation\
+               --freezed-layer $freeze_layer\
+               --classifier-factor $classifier_factor\
+               --benchmark $benchmark\
+               --modeldir $modeldir
+
+else
+checkpointfile=$(ls -rt $modeldir/*.pth.tar | tail -1)
+
+python main.py $dataset\
+               --benchmark $benchmark\
+               -a $arch\
+               -p 10\
+               --epochs $epoch\
+               --lr $lr\
+               --lr-method $lr_method\
+               --lr-params $lr_params\
+               --weight-decay $weight_decay\
+               -j 8\
+               -b $batchsize\
+               --num-classes $num_classes\
+               --representation $image_representation\
+               --freezed-layer $freeze_layer\
+               --modeldir $modeldir\
+               --classifier-factor $classifier_factor\
+               --benchmark $benchmark\
+               --resume $checkpointfile
+
+fi
+echo "Done!"