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dev_traintest_ginipa.py
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dev_traintest_ginipa.py
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# Main script for training and testing
import time
import shutil
import SimpleITK as sitk
import torch
import numpy as np
import os
import dataloaders.niftiio as nio
import pickle as pkl
from models import create_forward
from my_utils.util import AttrDict, worker_init_fn
from torch.utils.data import DataLoader
from pdb import set_trace
from tqdm import tqdm
from configs_exp import ex # configuration files
from tensorboardX import SummaryWriter
def prediction_wrapper(model, test_loader, opt, epoch, label_name, mode = 'base', save_prediction = False):
"""
A wrapper for the ease of evaluation
Args:
model: Module The network to evalute on
test_loader: DataLoader Dataloader for the dataset to test
mode: str Adding a note for the saved testing results
"""
with torch.no_grad():
out_prediction_list = {} # a buffer for saving results
recomp_img_list = []
for idx, batch in tqdm(enumerate(test_loader), total = len(test_loader)):
if batch['is_start']:
slice_idx = 0
scan_id_full = str(batch['scan_id'][0])
out_prediction_list[scan_id_full] = {}
nframe = batch['nframe']
nb, nc, nx, ny = batch['img'].shape
curr_pred = torch.Tensor(np.zeros( [ nframe, nx, ny] )).cuda() # nb/nz, nc, nx, ny
curr_gth = torch.Tensor(np.zeros( [nframe, nx, ny] )).cuda()
curr_img = np.zeros( [nx, ny, nframe] )
assert batch['lb'].shape[0] == 1 # enforce a batchsize of 1
test_input = {
'img': batch['img'],
'lb': batch['lb']
}
model.set_input(test_input)
gth, pred = model.get_segmentation_gpu(raw_logits = False)
curr_pred[slice_idx, ...] = pred[0, ...] # nb (1), nc, nx, ny
curr_gth[slice_idx, ...] = gth[0, ...]
curr_img[:,:,slice_idx] = batch['img'][0, 1,...].numpy()
slice_idx += 1
if batch['is_end']:
out_prediction_list[scan_id_full]['pred'] = curr_pred
out_prediction_list[scan_id_full]['gth'] = curr_gth
if opt.phase == 'test':
recomp_img_list.append(curr_img)
print("Epoch {} test result on mode {} segmentation are shown as follows:".format(epoch, mode))
error_dict, dsc_table, domain_names = eval_list_wrapper(out_prediction_list, len(label_name ), model, label_name)
error_dict["mode"] = mode
if not save_prediction: # to save memory
del out_prediction_list
out_prediction_list = []
torch.cuda.empty_cache()
return out_prediction_list, dsc_table, error_dict, domain_names
def eval_list_wrapper(vol_list, nclass, model, label_name):
"""
Evaluatation and arrange predictions
"""
out_count = len(vol_list)
tables_by_domain = {} # tables by domain
conf_mat_list = [] # confusion matrices
dsc_table = np.ones([ out_count, nclass ] ) # rows and samples, columns are structures
idx = 0
for scan_id, comp in vol_list.items():
domain, pid = scan_id.split("_")
if domain not in tables_by_domain.keys():
tables_by_domain[domain] = {'scores': [],
'scan_ids': []}
pred_ = comp['pred']
gth_ = comp['gth']
dices = model.ScoreDiceEval(torch.unsqueeze(pred_, 1), gth_, dense_input = True).cpu().numpy() # this includes the background class
tables_by_domain[domain]['scores'].append( [_sc for _sc in dices] )
tables_by_domain[domain]['scan_ids'].append( scan_id )
dsc_table[idx, ...] = np.reshape(dices, (-1))
del pred_
del gth_
idx += 1
torch.cuda.empty_cache()
# then output the result
error_dict = {}
for organ in range(nclass):
mean_dc = np.mean( dsc_table[:, organ] )
std_dc = np.std( dsc_table[:, organ] )
print("Organ {} with dice: mean: {:06.5f} \n, std: {:06.5f}".format(label_name[organ], mean_dc, std_dc))
error_dict[label_name[organ]] = mean_dc
print("Overall mean dice by sample {:06.5f}".format( dsc_table[:,1:].mean())) # background is noted as class 0 and therefore not counted
error_dict['overall'] = dsc_table[:,1:].mean()
# then deal with table_by_domain issue
overall_by_domain = []
domain_names = []
for domain_name, domain_dict in tables_by_domain.items():
domain_scores = np.array( tables_by_domain[domain_name]['scores'] )
domain_mean_score = np.mean(domain_scores[:, 1:])
error_dict[f'domain_{domain_name}_overall'] = domain_mean_score
error_dict[f'domain_{domain_name}_table'] = domain_scores
overall_by_domain.append(domain_mean_score)
domain_names.append(domain_name)
error_dict['overall_by_domain'] = np.mean(overall_by_domain)
print("Overall mean dice by domain {:06.5f}".format( error_dict['overall_by_domain'] ) )
# for prostate dataset, we use by-domain results to mitigate the differences in number of samples for each target domain
return error_dict, dsc_table, domain_names
@ex.automain
def main(_run, _config, _log):
# configs for sacred
if _run.observers:
os.makedirs(f'{_run.observers[0].dir}/snapshots', exist_ok=True)
os.makedirs(f'{_run.observers[0].dir}/interm_preds', exist_ok=True)
for source_file, _ in _run.experiment_info['sources']:
os.makedirs(os.path.dirname(f'{_run.observers[0].dir}/source/{source_file}'),
exist_ok=True)
_run.observers[0].save_file("../"+source_file, f'source/{source_file}')
shutil.rmtree(f'{_run.observers[0].basedir}/_sources')
_config['run_dir'] = _run.observers[0].dir
_config['snapshot_dir'] = f'{_run.observers[0].dir}/snapshots'
_config['pred_dir'] = f'{_run.observers[0].dir}/interm_preds'
tbfile_dir = os.path.join( _run.observers[0].dir, 'tboard_file' ); os.mkdir(tbfile_dir)
tb_writer = SummaryWriter( tbfile_dir )
opt = AttrDict(_config)
if opt.data_name == 'FETAL':
import dataloaders.FetalDataset as FETA
train_set = FETA.get_training(modality = opt.tr_domain )
val_source_set = FETA.get_validation(modality = opt.tr_domain)
if opt.exclu_domain is not None:
test_set = FETA.get_test_exclu(tr_modality = opt.tr_domain)
else:
test_set = FETA.get_test(modality = opt.te_domain)
test_source_set = FETA.get_test(modality = opt.tr_domain)
label_name = FETA.LABEL_NAME
elif opt.data_name == 'ABDOMINAL':
import dataloaders.AbdominalDataset as ABD
if not isinstance(opt.tr_domain, list):
opt.tr_domain = [opt.tr_domain]
opt.te_domain = [opt.te_domain]
train_set = ABD.get_training(modality = opt.tr_domain )
val_source_set = ABD.get_validation(modality = opt.tr_domain, norm_func = train_set.normalize_op) # not really using it as there is no validation for target
if opt.te_domain[0] == opt.tr_domain[0]:
test_set = ABD.get_test(modality = opt.te_domain, norm_func = train_set.normalize_op) # if same domain, then use the normalize op from the source
test_source_set = test_set
else:
test_set = ABD.get_test_all(modality = opt.te_domain, norm_func = None)
test_source_set = ABD.get_test(modality = opt.tr_domain, norm_func = train_set.normalize_op)
label_name = ABD.LABEL_NAME
elif opt.data_name == 'PROSTATE':
import dataloaders.ProstateDataset as PROS
train_set = PROS.get_training(modality = opt.tr_domain )
val_source_set = PROS.get_validation(modality = opt.tr_domain)
if opt.exclu_domain is not None:
test_set = PROS.get_test_exclu(tr_modality = opt.tr_domain)
else:
test_set = PROS.get_test(modality = opt.te_domain)
test_source_set = PROS.get_test(modality = opt.tr_domain)
label_name = PROS.LABEL_NAME
else:
raise NotImplementedError(opt.data_name)
print(f'Using TR domain {opt.tr_domain}; TE domain {opt.te_domain}')
train_loader = DataLoader(
dataset = train_set, num_workers = opt.nThreads,
batch_size = opt.batchSize, shuffle = True, drop_last = True,
worker_init_fn = worker_init_fn, pin_memory = True
)
"""
val_loader = iter(
DataLoader(
dataset = val_source_set, num_workers = 1, batch_size = 1,
shuffle = True, drop_last = True, pin_memory = True
)
)
test_loader = DataLoader(
dataset = test_set, num_workers = 1, batch_size = 1, shuffle = False,
pin_memory = True
)
test_src_loader = DataLoader(
dataset = test_source_set, num_workers = 1, batch_size = 1,
shuffle = False, pin_memory = True
)"""
if opt.exp_type == 'gin' or opt.exp_type == 'ginipa':
model = create_forward(opt)
elif opt.exp_type == 'erm':
raise NotImplementedError # coming soon
else:
raise NotImplementedError(opt.exp_type)
total_steps = 0
if opt.phase == 'test':
opt.epoch_count = 0
opt.niter = 0
opt.niter_decay = 0
for epoch in range(opt.epoch_count, opt.niter + opt.niter_decay + 1):
epoch_start_time = time.time()
iter_data_time = time.time()
epoch_iter = 0
np.random.seed()
if opt.phase == 'train':
for i, train_batch in tqdm(enumerate(train_loader), total = train_loader.dataset.size // opt.batchSize - 1):
iter_start_time = time.time()
if total_steps % opt.print_freq == 0:
t_data = iter_start_time - iter_data_time
total_steps += opt.batchSize
epoch_iter += opt.batchSize
# avoid batchsize issues caused by fetching last training batch
if train_batch["img"].shape[0] != opt.batchSize:
continue
"""
train_input = {'img': train_batch["img"],
'lb': train_batch["lb"]}"""
folder = f"/home/schiarella/Causality-Medical-Image-Domain-Generalization/processed/{opt.tr_domain}"
image_file = os.path.join(folder, f'image_{i}.nii.gz') # sorted(glob.glob(
label_file = os.path.join(folder, f'label_{i}.nii.gz')
img, _info = nio.read_nii_bysitk(image_file, peel_info=True)
lb = nio.read_nii_bysitk(label_file)
img = np.float32(img)
lb = np.float32(lb)
img = np.transpose(img, (1, 2, 0))
lb = np.transpose(lb, (1, 2, 0))
img = torch.from_numpy(img).cuda()
img = img.unsqueeze(0).unsqueeze(0)
lb = torch.from_numpy(lb).cuda()
lb = lb.unsqueeze(0).unsqueeze(0)
img = torch.cat((img, img, img), dim = 1)
lb = torch.cat((lb, lb, lb), dim = 1)
print('shape:', img.shape)
## run a training step
model.set_input_aug_sup(img, lb, i, train_set)
#model.optimize_parameters()
## display training losses
if total_steps % opt.display_freq == 0:
tr_viz = model.get_current_visuals_tr()
model.plot_image_in_tb(tb_writer, tr_viz)
if total_steps % opt.print_freq == 0:
tr_error = model.get_current_errors_tr()
t = (time.time() - iter_start_time) / opt.batchSize
model.track_scalar_in_tb(tb_writer, tr_error, total_steps)
"""
## run and display validation losses
if total_steps % opt.validation_freq == 0:
with torch.no_grad():
try:
val_batch = next(val_loader) # FIXME: use a nicer way
except:
val_loader = iter(DataLoader(dataset = val_source_set, num_workers = opt.nThreads,\
batch_size = 1, drop_last = True, shuffle = True))
val_batch = next(val_loader)
val_input = {
'img': val_batch["img"],
'lb': val_batch["lb"]
}
model.set_input(val_input)
model.validate()
val_errors = model.get_current_errors_val()
if total_steps % opt.display_freq == 0:
val_viz = model.get_current_visuals_val()
model.plot_image_in_tb(tb_writer, val_viz)
val_errors = model.get_current_errors_val()
model.track_scalar_in_tb(tb_writer, val_errors, total_steps)
iter_data_time = time.time()
## test
if (epoch % opt.infer_epoch_freq == 0):
t0 = time.time()
print('infering the model at the end of epoch %d, iters %d' %
(epoch, total_steps))
with torch.no_grad():
print(f'Starting inferring ... ')
preds, dsc_table, error_dict, domain_list = prediction_wrapper(model, test_loader, opt, epoch, label_name, save_prediction = _config["save_prediction"])
_run.log_scalar('rawDiceTarget', dsc_table.tolist())
_run.log_scalar('meanDiceTarget', error_dict['overall'] )
_run.log_scalar('meanDiceAvgTargetDomains', error_dict['overall_by_domain'] ) # for prostate dataset
for _dm in domain_list:
_run.log_scalar(f'meanDice_{_dm}', error_dict[f'domain_{_dm}_overall'])
_run.log_scalar(f'rawDice_{_dm}', error_dict[f'domain_{_dm}_table'].tolist())
print('test for source domain as a reference')
_, dsc_table, error_dict, _ = prediction_wrapper(model, test_src_loader, opt, epoch, label_name, save_prediction = _config["save_prediction"])
_run.log_scalar('source_rawDice', dsc_table.tolist())
_run.log_scalar('source_meanDice', error_dict['overall'] )
if _config["save_prediction"]:
for scan_id, comp in preds.items():
_pred = comp['pred']
itk_pred = sitk.GetImageFromArray(_pred.cpu().numpy())
itk_pred.SetSpacing( test_set.info_by_scan[scan_id]["spacing"] )
itk_pred.SetOrigin( test_set.info_by_scan[scan_id]["origin"] )
itk_pred.SetDirection(test_set.info_by_scan[scan_id]["direction"] )
fid = os.path.join(model.pred_dir, f'pred_{scan_id}_epoch_{epoch}.nii.gz')
sitk.WriteImage(itk_pred, fid, True)
_log.info(f'# {fid} has been saved #')
t1 = time.time()
print("End of model inference, which takes {} seconds".format(t1 - t0))
if opt.phase == 'test':
return"""
if epoch % opt.save_epoch_freq == 0:
print('saving the model at the end of epoch %d, iters %d' %
(epoch, total_steps))
model.save('latest')
model.save(epoch)
if epoch == opt.early_stop_epoch:
return
print('End of epoch %d / %d \t Time Taken: %d sec' %
(epoch, opt.niter + opt.niter_decay, time.time() - epoch_start_time))
model.update_learning_rate()