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find_best_pth.py
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from test import test_Net
from eval import qual_eval
from tensorboardX import SummaryWriter
from myconfig import myParser
import shutil
def test_loop(args):
model_dir = args.model_dir
writer = SummaryWriter(args.writer_path)
# select which epoch_xx.pth to test
epoch_num = [28, 29, 30, 31]
# epoch_num = [31]
# select which dataset to test
# dataset_list = ['HRSOD','UHRSD', 'HR10K']
dataset_list = ['HR10K', 'DAVIS-S', 'DUTS', 'DUT-OMRON', 'ECSSD', 'HKU-IS']
# dataset_list = ['DAVIS-S', 'DUTS', 'DUT-OMRON', 'ECSSD', 'HKU-IS']
# dataset_list = ['HR10K']
model_list = []
for tnum in epoch_num:
model_list.append('epoch_'+str(tnum)+'.pth')
img_list = []
label_list = []
prediction_list = []
for dataname in dataset_list:
if dataname == 'HRSOD':
image_dir = 'train_data/HRSOD/HRSOD_test/'
label_dir = 'train_data/HRSOD/HRSOD_test_mask/'
prediction_dir = 'train_data/HRSOD/Results/hrsod_results/'
img_list.append(image_dir)
label_list.append(label_dir)
prediction_list.append(prediction_dir)
# # # #UHRSD
if dataname == 'UHRSD':
image_dir = 'train_data/UHRSD/UHRSD_TE_2K/image/'
label_dir = 'train_data/UHRSD/UHRSD_TE_2K/mask/'
prediction_dir = 'train_data/UHRSD/Results/uhrsd_results/'
img_list.append(image_dir)
label_list.append(label_dir)
prediction_list.append(prediction_dir)
# #HR10K
if dataname == 'HR10K':
image_dir = 'train_data/HR10K/test/img_test_2560max/'
label_dir = 'train_data/HR10K/test/label_test_2560max/'
prediction_dir = 'train_data/HR10K/Results/10k_results/'
img_list.append(image_dir)
label_list.append(label_dir)
prediction_list.append(prediction_dir)
# LR SOD
if dataname == 'DAVIS-S':
image_dir = 'train_data/OTHER/LowRes_SOD/DAVIS-SOD/Imgs/'
label_dir = 'train_data/OTHER/LowRes_SOD/DAVIS-SOD/Mask/'
prediction_dir = 'train_data/OTHER/LowRes_SOD/Results_collect/DAVIS-S/'
img_list.append(image_dir)
label_list.append(label_dir)
prediction_list.append(prediction_dir)
# DUT-S
if dataname == 'DUTS':
image_dir = 'train_data/OTHER/LowRes_SOD/DUTS/DUTS-TE/DUTS-TE-Image/'
label_dir = 'train_data/OTHER/LowRes_SOD/DUTS/DUTS-TE/DUTS-TE-Mask/'
prediction_dir = 'train_data/OTHER/LowRes_SOD/Results_collect/DUTS/'
img_list.append(image_dir)
label_list.append(label_dir)
prediction_list.append(prediction_dir)
# DUT-OMRON
if dataname == 'DUT-OMRON':
image_dir = 'train_data/OTHER/LowRes_SOD/DUT-OMRON/image/'
label_dir = 'train_data/OTHER/LowRes_SOD/DUT-OMRON/mask/'
prediction_dir = 'train_data/OTHER/LowRes_SOD/Results_collect/DUT-OMRON/'
img_list.append(image_dir)
label_list.append(label_dir)
prediction_list.append(prediction_dir)
# ECSSD
if dataname == 'ECSSD':
image_dir = 'train_data/OTHER/LowRes_SOD/ECSSD/img/'
label_dir = 'train_data/OTHER/LowRes_SOD/ECSSD/gt/'
prediction_dir = 'train_data/OTHER/LowRes_SOD/Results_collect/ECSSD/'
img_list.append(image_dir)
label_list.append(label_dir)
prediction_list.append(prediction_dir)
# HKU-IS
if dataname == 'HKU-IS':
image_dir = 'train_data/OTHER/LowRes_SOD/HKU-IS/imgs/'
label_dir = 'train_data/OTHER/LowRes_SOD/HKU-IS/gt/'
prediction_dir = 'train_data/OTHER/LowRes_SOD/Results_collect/HKU-IS/'
img_list.append(image_dir)
label_list.append(label_dir)
prediction_list.append(prediction_dir)
b_epoch, b_MAE, b_maxF, b_meanF, b_mba = 0,0,0,0,0
# start testing ==============================
for i, name in enumerate(dataset_list):
image_dir = img_list[i]
label_dir = label_list[i]
prediction_dir = prediction_list[i]
for j in range(len(model_list)):
model_path = model_dir + model_list[j]
e_num = epoch_num[j]
test_Net(model_path, image_dir, prediction_dir,args)
MAE, maxF, meanF, mba = qual_eval(label_dir,prediction_dir)
writer.add_scalar('Eval' + '_'+ name +'/MAE',MAE, e_num)
writer.add_scalar('Eval' + '_'+ name +'/maxF',maxF, e_num)
writer.add_scalar('Eval' + '_'+ name +'/meanF',meanF, e_num)
writer.add_scalar('Eval' + '_'+ name +'/mba',mba, e_num)
if j == 0 :
best = maxF + meanF - MAE
filename = (args.model_dir + "bestone.pth")
shutil.copyfile(model_path, filename)
b_epoch = e_num
b_MAE = MAE
b_maxF = maxF
b_meanF = meanF
b_mba = mba
else: # save model with best performance
if maxF + meanF - MAE > best:
best = maxF + meanF - MAE
filename = (args.model_dir + "bestone.pth")
shutil.copyfile(model_path, filename)
b_epoch = e_num
b_MAE = MAE
b_maxF = maxF
b_meanF = meanF
b_mba = mba
print('Dataset: ' + name)
print('b_epoch_%d_MAE_%3f_maxF_%3f_meanF_%3f_mba%3f'%(b_epoch,b_MAE,b_maxF,b_meanF,b_mba))
print('all model tested')
if __name__ == '__main__':
args = myParser()
test_loop(args)