To pretrain:
#!/bin/bash
cd /workspace/src/
CUDA_VISIBLE_DEVICES=0,1,2,3 \
python \
pretrain1.py \
--batch_size 32 \
--classes 512 \
--contrastive_method pcl \
--data_dir /workspace/data/chd/contrastive \
--dataset chd \
--device cuda:0 \
--do_contrast \
--epochs 51 \
--experiment_name contrast_chd_pcl_ \
--initial_filter_size 32 \
--lr 0.002 \
--num_works 48 \
--patch_size 512 512 \
--results_dir ./results/graph_pretrain \
--slice_threshold 0.1 \
--save full_contrast \
--temp 0.1 \
--weight_cnn_contrast 1.0 \
--weight_graph_contrast 1.0 \
--weight_corr 1.0
To finetune:
#!/bin/bash
cd /workspace/src/
sample_size=$1
fold=$2
WEIGHT_CNN=$3
WEIGHT_GRAPH=$4
WEIGHT_CORR=$5
CUDA_VISIBLE_DEVICES=0 \
python \
sup.py \
--batch_size 10 \
--classes 8 \
--data_dir /workspace/data/chd/supervised \
--dataset chd \
--device cuda:0 \
--enable_few_data \
--epochs 101 \
--experiment_name sup_chd_pcl_sample_$sample_size\_ \
--fold $fold \
--initial_filter_size 32 \
--lr 5e-5 \
--min_lr 5e-6 \
--num_works 12 \
--patch_size 512 512 \
--pretrained_model_path model/latest.pth \
--results_dir ./results/finetune \
--runs_dir ./runs/finetune \
--restart \
--sampling_k $sample_size \
--save finetune_c_$WEIGHT_CNN\_g_$WEIGHT_GRAPH\_co_$WEIGHT_CORR
To train from scratch:
#!/bin/bash
cd /workspace/src/
sample_size=$1
fold=$2
CUDA_VISIBLE_DEVICES=0 \
python \
sup.py \
--batch_size 10 \
--classes 8 \
--data_dir /workspace/data/chd/supervised \
--dataset chd \
--device cuda:0 \
--enable_few_data \
--epochs 101 \
--experiment_name sup_chd_pcl_sample_$sample_size\_fold_$fold\_ \
--fold $fold \
--initial_filter_size 32 \
--lr 0.05 \
--lr 5e-5 \
--min_lr 5e-6 \
--num_works 12 \
--patch_size 512 512 \
--results_dir ./results/sup \
--runs_dir ./runs/sup \
--sampling_k $sample_size \
--save all_graph_features