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run_isd.sh
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#!/usr/bin/env bash
#SBATCH --account=pi_hpirsiav
#SBATCH --time=9-00:00:00
#SBATCH --job-name=isd_5_cos_lr_0x01_m_0x99_aug_ws_mlp_resnet18
#SBATCH --output=logs/isd_5_cos_lr_0x01_m_0x99_aug_ws_mlp_resnet18.txt
#SBATCH --error=logs/isd_5_cos_lr_0x01_m_0x99_aug_ws_mlp_resnet18.txt
#SBATCH --partition=gpu
#SBATCH --gres=gpu:4
#SBATCH --qos=normal
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --cpus-per-task=24
#SBATCH --mem=100G
set -x
set -e
# CUDA_VISIBLE_DEVICES=$1 python train_isd_r18.py \
# --save_freq 5 \
# --num_workers 16 \
# --epochs 200 \
# --learning_rate 0.01 \
# --lr_decay_epochs '140,180' \
# --momentum 0.999 \
# --arch resnet18 \
# --augmentation 'weak/strong' \
# --checkpoint_path output/isd_1_step_lr_0x01_140_180_m_0x999_aug_ws_resnet18 \
# /nfs/ada/hpirsiav/datasets/imagenet
# CUDA_VISIBLE_DEVICES=$1 python train_isd_r18.py \
# --save_freq 5 \
# --num_workers 16 \
# --epochs 200 \
# --learning_rate 0.01 \
# --cos \
# --momentum 0.999 \
# --arch resnet18 \
# --augmentation 'strong/strong' \
# --checkpoint_path output/isd_2_cos_lr_0x01_m_0x999_aug_ss_resnet18 \
# /nfs/ada/hpirsiav/datasets/imagenet
# CUDA_VISIBLE_DEVICES=$1 python train_isd_r18.py \
# --save_freq 5 \
# --num_workers 16 \
# --epochs 200 \
# --learning_rate 0.01 \
# --lr_decay_epochs '140,180' \
# --momentum 0.99 \
# --arch resnet18 \
# --augmentation 'strong/strong' \
# --checkpoint_path output/isd_3_step_lr_0x01_140_180_m_0x99_aug_ss_resnet18 \
# /nfs/ada/hpirsiav/datasets/imagenet
# CUDA_VISIBLE_DEVICES=$1 python train_isd_r18.py \
# --save_freq 5 \
# --num_workers 16 \
# --epochs 200 \
# --learning_rate 0.01 \
# --cos \
# --momentum 0.99 \
# --arch resnet18 \
# --augmentation 'weak/strong' \
# --checkpoint_path output/isd_4_cos_lr_0x01_m_0x99_aug_ws_resnet18 \
# /nfs/ada/hpirsiav/datasets/imagenet
# CUDA_VISIBLE_DEVICES=$1 python train_isd.py \
# --save_freq 5 \
# --num_workers 16 \
# --print_freq 100 \
# --queue_size 128000 \
# --temp 0.020 \
# --epochs 200 \
# --lr_decay_epochs '140,180' \
# --arch resnet18 \
# --checkpoint_path output/isd_1_t_020_augv2_lr_decay_140_180_epochs_200_resnet18 \
# /datasets/imagenet
# CUDA_VISIBLE_DEVICES=$1 python train_isd.py \
# --save_freq 1 \
# --num_workers 16 \
# --print_freq 100 \
# --queue_size 1280 \
# --learning_rate 0.005 \
# --temp 0.020 \
# --epochs 10 \
# --lr_decay_epochs '7,9' \
# --arch resnet50 \
# --weights weights/byol_resnet50_everything.pth.tar \
# --checkpoint_path output/isd_2_init_byol_everything_lr_005_step_7_9_epochs_10_k12h_resnet50 \
# /datasets/imagenet