#!/usr/bin/env bash
:<<'END'
This is sample bash script for CUB-200-2011 dataset
support model:
vgg, resnet, se_resnet
vgg-16GAP(for localization task)
possible ADL positions:
choose among possible positions
vgg16
'11 12 13 1M 21 22 23 2M 31 32 33 3M 41 42 43 4M 51 52 53 6'
resnet50
'10 11 12 20 21 22 23 30 31 32 33 34 35 40 41 42 '
END

gpu=0
name=resnet
epoch=200
decay=60
model=resnet50
server=tcp://127.0.0.1:12344
batch=128
wd=1e-4
lr=0.01
ADL_pos="30 40 42"
data_root="../CUB_200_2011"

CUDA_VISIBLE_DEVICES=${gpu} python train.py -a ${model} --dist-url ${server} \
    --multiprocessing-distributed --world-size 1 --pretrained \
    --data ${data_root} --dataset CUB \
    --train-list datalist/CUB/train.txt \
    --test-list datalist/CUB/test.txt \
    --data-list datalist/CUB/ \
    --task wsol \
    --batch-size ${batch} --epochs ${epoch} --LR-decay ${decay} \
    --wd ${wd} --lr ${lr} --nest --name ${name} \
    --max-cam \
    --conv-cam \
    --evaluate \
    --resume '/media/zhenyue-qin/New Volume/Science/Science-Data-Hub/Research-Sayaka/Experiments-Results/ADL-with-val-test/CUB-Resnet-CAM-2019-11-01T23-14-33/model_best.pth.tar'
