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run_train.sh
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run_train.sh
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#!/bin/bash
uname -a
date
CS_PATH='/data/zzw/segment/data/lip/images_labels'
POSE_ANNO='/data/zzw/segment/data/lip/TrainVal_pose_annotations/LIP_SP_TRAIN_annotations.json'
LR=1e-3
WD=5e-4
BS=4
GPU_IDS=4,5
RESTORE_FROM='/data/zzw/segment/data/pre_trained_models/resnet101-imagenet.pth'
INPUT_SIZE='384,384'
SNAPSHOT_DIR='/data/zzw/segment/snapshots/test_corrpm'
DATA_LIST='_id.txt'
DATASET='train'
NUM_CLASSES=20
NPOINTS=16
EPOCHS=180
START_EPOCH=0
if [[ ! -e ${SNAPSHOT_DIR} ]]; then
mkdir -p ${SNAPSHOT_DIR}
fi
python3 train.py --data-dir ${CS_PATH} \
--pose-anno-file ${POSE_ANNO} \
--random-mirror\
--random-scale\
--restore-from ${RESTORE_FROM}\
--gpu ${GPU_IDS}\
--learning-rate ${LR} \
--weight-decay ${WD}\
--batch-size ${BS} \
--input-size ${INPUT_SIZE}\
--snapshot-dir ${SNAPSHOT_DIR}\
--dataset ${DATASET}\
--dataset-list ${DATA_LIST} \
--num-classes ${NUM_CLASSES} \
--num-points ${NPOINTS} \
--epochs ${EPOCHS} \
--start-epoch ${START_EPOCH}