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train_iPER_Place2.sh
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train_iPER_Place2.sh
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#! /bin/bash
# basic configs
#gpu_ids=0,1 # if using multi-gpus, increasing the batch_size
gpu_ids=1
# dataset configs
dataset_model=iPER_place # use iPER_place dataset
data_dir=/p300/iccv/iPER_examples # need to be replaced!!!!!
place_dir=/p300/iccv/places365_standard # need to be replaced!!!!!
images_folder=images
smpls_folder=smpls
train_ids_file=train.txt
test_ids_file=val.txt
# saving configs
checkpoints_dir=/p300/iccv/ckpts_models # directory to save models, need to be replaced!!!!!
name=exp_iPER_place # the directory is ${checkpoints_dir}/name, which is used to save the checkpoints.
# model configs
model=impersonator_trainer_aug
gen_name=impersonator
image_size=256
# training configs
load_path="None"
batch_size=4
lambda_rec=10.0
lambda_tsf=10.0
lambda_face=5.0
lambda_style=0.0
lambda_mask=1.0
lambda_mask_smooth=1.0
nepochs_no_decay=5 # fixing learning rate when epoch ranges in [0, 5]
nepochs_decay=25 # decreasing the learning rate when epoch ranges in [6, 25+5]
python train.py --gpu_ids ${gpu_ids} \
--data_dir ${data_dir} \
--place_dir ${place_dir} \
--images_folder ${images_folder} \
--smpls_folder ${smpls_folder} \
--checkpoints_dir ${checkpoints_dir} \
--load_path ${load_path} \
--train_ids_file ${train_ids_file} \
--test_ids_file ${test_ids_file} \
--model ${model} \
--gen_name ${gen_name} \
--name ${name} \
--dataset_mode ${dataset_model} \
--image_size ${image_size} \
--batch_size ${batch_size} \
--lambda_face ${lambda_face} \
--lambda_tsf ${lambda_tsf} \
--lambda_style ${lambda_style} \
--lambda_rec ${lambda_rec} \
--lambda_mask ${lambda_mask} \
--lambda_mask_smooth ${lambda_mask_smooth} \
--nepochs_no_decay ${nepochs_no_decay} --nepochs_decay ${nepochs_decay} \
--mask_bce --use_vgg --use_face