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01_adaptation.py
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import logging
import time
import os
import numpy as np
import torch
from tqdm import tqdm
from copy import deepcopy
import csv
from robustbench.data import get_dataset,convert_2d
from robustbench.utils import load_model
from robustbench.losses import DiceLoss
from robustbench.tta import setup_norm,setup_tent,setup_cotta,setup_wjh01,setup_sar,setup_upl,setup_meant,setup_source
from utils.evaluate import get_multi_class_evaluation_score
import numpy as np
import SimpleITK as sitk
from conf import cfg, load_cfg_fom_args
logger = logging.getLogger(__name__)
def evaluate(description,adaptation_target = True,infer_test_data = True,save = False):
load_cfg_fom_args(description)
# configure model
base_model = load_model(cfg.MODEL.NETWORK, cfg.MODEL.CKPT_DIR,
cfg.MODEL.DATASET,cfg.MODEL.METHOD,cfg.MODEL.NUMBER_CLASS,).cuda()
if cfg.MODEL.METHOD == "source_test":
logger.info("test-time adaptation: source model")
model = setup_source(base_model)
elif cfg.MODEL.METHOD == "norm":
logger.info("test-time adaptation: NORM")
model = setup_norm(base_model)
elif cfg.MODEL.METHOD == "tent":
logger.info("test-time adaptation: TENT")
config_model, model = setup_tent(base_model)
elif cfg.MODEL.METHOD == "cotta":
logger.info("test-time adaptation: CoTTA")
model = setup_cotta(base_model)
elif cfg.MODEL.METHOD == "sar":
logger.info("test-time adaptation: SAR")
model = setup_sar(base_model)
elif cfg.MODEL.METHOD == "upl":
logger.info("test-time adaptation: upl")
model = setup_upl(base_model)
elif cfg.MODEL.METHOD == "FAR-TTA":
logger.info("test-time adaptation: FAR-TTA")
model = setup_wjh01(base_model)
elif cfg.MODEL.METHOD == "meant":
logger.info("test-time adaptation: Mean Teacher")
model = setup_meant(base_model)
else:
raise "no specific method of {}".format(cfg.MODEL.METHOD)
dice_loss = DiceLoss(cfg.MODEL.NUMBER_CLASS)
dice_loss.cuda()
# metric = ['dice','assd']
metric = ['dice','dice']
save_model_dir = os.path.join('save_model',cfg.MODEL.DATASET+'_'+cfg.MODEL.NETWORK)
if ( not os.path.exists(save_model_dir)):
os.mkdir(save_model_dir)
if adaptation_target:
for epoch_num in tqdm(range(cfg.ADAPTATION.EPOCH), ncols=70):
if epoch_num == 0:
try:
model.reset()
logger.info("resetting model")
except:
logger.warning("not resetting model")
score_all_data_1 = []
name_score_list_1= []
score_all_data_11 = []
name_score_list_11= []
for target_domain in cfg.ADAPTATION.TARGET_DOMAIN:
score_all_data_0 = []
name_score_list_0= []
score_all_data_01 = []
name_score_list_01= []
time1 = time.time()
results = '{}-{}/{}-{}-{}-I-{}-M-{}'.format('results',cfg.MODEL.DATASET,cfg.MODEL.METHOD,cfg.MODEL.DATASET,cfg.MODEL.EXPNAME,target_domain,cfg.SOURCE.SOURCE_DOMAIN)
if(not os.path.exists(results)):
os.mkdir(results)
os.mkdir(os.path.join(results, 'mask'))
db_all,_,_ = get_dataset(dataset=cfg.MODEL.DATASET, domain=target_domain, online = True)
train_loader = torch.utils.data.DataLoader(db_all, batch_size = cfg.ADAPTATION.BATCH_SIZE, shuffle=False, drop_last=False, num_workers= 25)
for i_batch, sampled_batch in enumerate(train_loader):
volume_batch, label_batch, names = sampled_batch['image'].cuda(), sampled_batch['label'].cuda(), sampled_batch['names']
volume_batch, label_batch = convert_2d(volume_batch, label_batch)
with torch.no_grad():
if cfg.MODEL.METHOD == "wjh01" or cfg.MODEL.METHOD == "meant":
output_soft = model(volume_batch,label_batch,names).softmax(1)
else:
output_soft = model(volume_batch).softmax(1)
if epoch_num == (cfg.ADAPTATION.EPOCH - 1):
output = output_soft.argmax(1).cpu().numpy()
label = label_batch.cpu().numpy().squeeze(1)
assert output.shape[0] == len(names)
for i in range(len(names)):
name = names[i].split('/')[-1]
predict_dir = os.path.join(results, 'mask',name)
out_lab_obj = sitk.GetImageFromArray(output[i]/1.0)
sitk.WriteImage(out_lab_obj, predict_dir)
score_vector_0 = get_multi_class_evaluation_score(output[i], label[i], cfg.MODEL.NUMBER_CLASS, metric[0] )
score_vector_01 = get_multi_class_evaluation_score(output[i], label[i], cfg.MODEL.NUMBER_CLASS, metric[1] )
if(cfg.MODEL.NUMBER_CLASS > 2):
score_vector_0.append(np.asarray(score_vector_0).mean())
score_vector_01.append(np.asarray(score_vector_01).mean())
score_all_data_0.append(score_vector_0)
name_score_list_0.append([name] + score_vector_0)
score_all_data_1.append(score_vector_0)
name_score_list_1.append([name] + score_vector_0)
score_all_data_01.append(score_vector_01)
name_score_list_01.append([name] + score_vector_01)
score_all_data_11.append(score_vector_01)
name_score_list_11.append([name] + score_vector_01)
score_all_data_0 = np.asarray(score_all_data_0)
score_mean0 = score_all_data_0.mean(axis = 0)
score_std0 = score_all_data_0.std(axis = 0)
score_all_data_01 = np.asarray(score_all_data_01)
score_mean01 = score_all_data_01.mean(axis = 0)
score_std01 = score_all_data_01.std(axis = 0)
name_score_list_0.append(['mean'] + list(score_mean0))
name_score_list_0.append(['std'] + list(score_std0))
name_score_list_01.append(['mean'] + list(score_mean01))
name_score_list_01.append(['std'] + list(score_std01))
score_csv0 = "{0:}/test_{1:}_all.csv".format(results, metric[0])
score_csv01 = "{0:}/test_{1:}_all.csv".format(results, metric[1])
with open(score_csv0, mode='w') as csv_file:
csv_writer = csv.writer(csv_file, delimiter=',',
quotechar='"',quoting=csv.QUOTE_MINIMAL)
head = ['image'] + ["class_{0:}".format(i) for i in range(1,cfg.MODEL.NUMBER_CLASS)]
if(cfg.MODEL.NUMBER_CLASS > 2):
head = head + ["average"]
csv_writer.writerow(head)
for item in name_score_list_0:
csv_writer.writerow(item)
print('**********',target_domain,'**********')
print("Test dice: {0:} mean ".format(metric[0]), score_mean0)
print("Test dice: {0:} std ".format(metric[0]), score_std0)
with open(score_csv01, mode='w') as csv_file:
csv_writer = csv.writer(csv_file, delimiter=',',
quotechar='"',quoting=csv.QUOTE_MINIMAL)
head = ['image'] + ["class_{0:}".format(i) for i in range(1,cfg.MODEL.NUMBER_CLASS)]
if(cfg.MODEL.NUMBER_CLASS > 2):
head = head + ["average"]
csv_writer.writerow(head)
for item in name_score_list_01:
csv_writer.writerow(item)
print('**********',target_domain,'**********')
print("Test dice: {0:} mean ".format(metric[1]), score_mean01)
print("Test dice: {0:} std ".format(metric[1]), score_std01)
torch.save(model.state_dict(),'{}/{}-{}-{}-model-latest.pth'.format(save_model_dir,cfg.MODEL.METHOD,cfg.SOURCE.SOURCE_DOMAIN,cfg.MODEL.EXPNAME))
score_all_data_1 = np.asarray(score_all_data_1)
score_mean1 = score_all_data_1.mean(axis = 0)
score_std1 = score_all_data_1.std(axis = 0)
print('**********','average','**********')
print("Test dice: {0:} mean ".format(metric[0]), score_mean1)
print("Test dice: {0:} std ".format(metric[0]), score_std1)
score_all_data_11 = np.asarray(score_all_data_11)
score_mean11 = score_all_data_11.mean(axis = 0)
score_std11 = score_all_data_11.std(axis = 0)
print('**********','average','**********')
print("Test dice: {0:} mean ".format(metric[1]), score_mean11)
print("Test dice: {0:} std ".format(metric[1]), score_std11)
if __name__ == '__main__':
evaluate('mms train source.',adaptation_target = True, infer_test_data = True,save = True)