def init_data_processing():
kvs = GlobalKVS()
train_augs = init_train_augs()
dataset = OAProgressionDataset(dataset=kvs['args'].dataset_root, split=kvs['metadata'], trf=train_augs)
mean_vector, std_vector = init_mean_std(snapshots_dir=kvs['args'].snapshots,
dataset=dataset, batch_size=kvs['args'].bs,
n_threads=kvs['args'].n_threads)
print(colored('====> ', 'red') + 'Mean:', mean_vector)
print(colored('====> ', 'red') + 'Std:', std_vector)
norm_trf = tv_transforms.Normalize(mean_vector.tolist(), std_vector.tolist())
train_trf = tv_transforms.Compose([
train_augs,
partial(apply_by_index, transform=norm_trf, idx=0)
])
val_trf = tv_transforms.Compose([
img_labels2solt,
slc.Stream([
slt.ResizeTransform((310, 310)),
slt.CropTransform(crop_size=(300, 300), crop_mode='c'),
slt.ImageColorTransform(mode='gs2rgb'),
], interpolation='bicubic'),
unpack_solt_data,
partial(apply_by_index, transform=tv_transforms.ToTensor(), idx=0),
partial(apply_by_index, transform=norm_trf, idx=0)
])
kvs.update('train_trf', train_trf)
kvs.update('val_trf', val_trf)
kvs.save_pkl(os.path.join(kvs['args'].snapshots, kvs['snapshot_name'], 'session.pkl'))
def save_checkpoint(model, val_metric_name, comparator='lt'):
if isinstance(model, torch.nn.DataParallel):
model = model.module
kvs = GlobalKVS()
fold_id = kvs['cur_fold']
epoch = kvs['cur_epoch']
val_metric = kvs[f'val_metrics_fold_[{fold_id}]'][-1][0][val_metric_name]
comparator = getattr(operator, comparator)
cur_snapshot_name = os.path.join(kvs['args'].snapshots, kvs['snapshot_name'],
f'fold_{fold_id}_epoch_{epoch + 1}.pth')
if kvs['prev_model'] is None:
print(colored('====> ', 'red') + 'Snapshot was saved to', cur_snapshot_name)
torch.save(model.state_dict(), cur_snapshot_name)
kvs.update('prev_model', cur_snapshot_name)
kvs.update('best_val_metric', val_metric)
else:
if comparator(val_metric, kvs['best_val_metric']):
print(colored('====> ', 'red') + 'Snapshot was saved to', cur_snapshot_name)
os.remove(kvs['prev_model'])
torch.save(model.state_dict(), cur_snapshot_name)
kvs.update('prev_model', cur_snapshot_name)
kvs.update('best_val_metric', val_metric)
kvs.save_pkl(os.path.join(kvs['args'].snapshots, kvs['snapshot_name'], 'session.pkl'))
def init_session(snapshot_name_prefix=None):
if not torch.cuda.is_available():
raise EnvironmentError('The code must be run on GPU.')
kvs = GlobalKVS()
# Getting the arguments
args = parse_args()
kvs.update('args', args)
# Initializing the seeds
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
np.random.seed(args.seed)
# Creating the snapshot
init_snapshot_dir(snapshot_name_prefix)
kvs.update('pytorch_version', torch.__version__)
if torch.cuda.is_available():
kvs.update('cuda', torch.version.cuda)
kvs.update('gpus', torch.cuda.device_count())
else:
kvs.update('cuda', None)
kvs.update('gpus', None)
kvs.save_pkl(os.path.join(args.snapshots, kvs['snapshot_name'], 'session.pkl'))
return args, kvs['snapshot_name']
def log_metrics_prog(boardlogger, train_loss, val_loss, gt_progression, preds_progression, gt_kl, preds_kl):
kvs = GlobalKVS()
res = testtools.calc_metrics(gt_progression, gt_kl, preds_progression, preds_kl)
res['val_loss'] = val_loss,
res['epoch'] = kvs['cur_epoch']
print(colored('====> ', 'green') + f'Train loss: {train_loss:.5f}')
print(colored('====> ', 'green') + f'Validation loss: {val_loss:.5f}')
print(colored('====> ', 'green') + f'Validation AUC [prog]: {res["auc_prog"]:.5f}')
print(colored('====> ', 'green') + f'Validation F1 @ 0.3 [prog]: {res["f1_score_03_prog"]:.5f}')
print(colored('====> ', 'green') + f'Validation F1 @ 0.4 [prog]: {res["f1_score_04_prog"]:.5f}')
print(colored('====> ', 'green') + f'Validation F1 @ 0.5 [prog]: {res["f1_score_05_prog"]:.5f}')
print(colored('====> ', 'green') + f'Validation AP [prog]: {res["ap_prog"]:.5f}')
print(colored('====> ', 'green') + f'Validation AUC [oa]: {res["auc_oa"]:.5f}')
print(colored('====> ', 'green') + f'Kappa [oa]: {res["kappa_kl"]:.5f}')
boardlogger.add_scalars('Losses', {'train': train_loss, 'val': val_loss}, kvs['cur_epoch'])
boardlogger.add_scalars('AUC progression', {'val': res['auc_prog']}, kvs['cur_epoch'])
boardlogger.add_scalars('F1-score @ 0.3 progression', {'val': res['f1_score_03_prog']}, kvs['cur_epoch'])
boardlogger.add_scalars('F1-score @ 0.4 progression', {'val': res['f1_score_04_prog']}, kvs['cur_epoch'])
boardlogger.add_scalars('F1-score @ 0.5 progression', {'val': res['f1_score_05_prog']}, kvs['cur_epoch'])
boardlogger.add_scalars('Average Precision progression', {'val': res['ap_prog']}, kvs['cur_epoch'])
kvs.update(f'losses_fold_[{kvs["cur_fold"]}]', {'epoch': kvs['cur_epoch'],
'train_loss': train_loss,
'val_loss': val_loss})
kvs.update(f'val_metrics_fold_[{kvs["cur_fold"]}]', res)
kvs.save_pkl(os.path.join(kvs['args'].snapshots, kvs['snapshot_name'], 'session.pkl'))
def init_age_sex_bmi_metadata():
kvs = GlobalKVS()
oai_meta = pd.read_csv(
os.path.join(kvs['args'].metadata_root, 'OAI_progression.csv'))
clinical_data_oai = pd.read_csv(
os.path.join(kvs['args'].metadata_root, 'OAI_participants.csv'))
oai_meta = pd.merge(oai_meta, clinical_data_oai, on=('ID', 'Side'))
oai_meta = oai_meta[~oai_meta.BMI.isna() & ~oai_meta.AGE.isna()
& ~oai_meta.SEX.isna()]
clinical_data_most = pd.read_csv(
os.path.join(kvs['args'].metadata_root, 'MOST_participants.csv'))
metadata_test = pd.read_csv(
os.path.join(kvs['args'].metadata_root, 'MOST_progression.csv'))
metadata_test = pd.merge(metadata_test,
clinical_data_most,
on=('ID', 'Side'))
kvs.update('metadata', oai_meta)
kvs.update('metadata_test', metadata_test)
gkf = GroupKFold(n_splits=5)
cv_split = [
x for x in gkf.split(kvs['metadata'], kvs['metadata'][
kvs['args'].target_var], kvs['metadata']['ID'].astype(str))
]
kvs.update('cv_split_all_folds', cv_split)
kvs.save_pkl(
os.path.join(kvs['args'].snapshots, kvs['snapshot_name'],
'session.pkl'))
def init_folds(project='OA_progression'):
kvs = GlobalKVS()
writers = {}
cv_split_train = {}
for fold_id, split in enumerate(kvs['cv_split_all_folds']):
if kvs['args'].fold != -1 and fold_id != kvs['args'].fold:
continue
kvs.update(f'losses_fold_[{fold_id}]', None, list)
kvs.update(f'val_metrics_fold_[{fold_id}]', None, list)
cv_split_train[fold_id] = split
writers[fold_id] = SummaryWriter(os.path.join(kvs['args'].logs,
project,
'fold_{}'.format(fold_id), kvs['snapshot_name']))
kvs.update('cv_split_train', cv_split_train)
kvs.save_pkl(os.path.join(kvs['args'].snapshots, kvs['snapshot_name'], 'session.pkl'))
return writers
def init_progression_metadata():
# We get should rid of non-progressors from MOST because we can check
# non-progressors only up to 84 months
kvs = GlobalKVS()
most_meta = pd.read_csv(os.path.join(kvs['args'].metadata_root, 'MOST_progression.csv'))
oai_meta = pd.read_csv(os.path.join(kvs['args'].metadata_root, 'OAI_progression.csv'))
if kvs['args'].subsample_train != -1:
n_train = oai_meta.shape[0]
prevalence = (oai_meta.Progressor > 0).sum() / n_train
sample_pos = int(kvs['args'].subsample_train * prevalence)
sample_neg = kvs['args'].subsample_train - sample_pos
train_pos = oai_meta[oai_meta.Progressor > 0]
train_neg = oai_meta[oai_meta.Progressor == 0]
pos_sampled = train_pos.iloc[np.random.choice(train_pos.shape[0], sample_pos)]
neg_sampled = train_neg.iloc[np.random.choice(train_neg.shape[0], sample_neg)]
new_meta = pd.concat((pos_sampled, neg_sampled))
oai_meta = new_meta.iloc[np.random.choice(new_meta.shape[0], new_meta.shape[0])]
print(colored("==> ", 'red') + f"Train set has been sub-sampled. New # pos/neg {sample_pos}/{sample_neg}")
kvs.update('metadata', oai_meta)
kvs.update('metadata_test', most_meta)
gkf = GroupKFold(n_splits=5)
cv_split = [x for x in gkf.split(kvs['metadata'],
kvs['metadata']['Progressor'],
kvs['metadata']['ID'].astype(str))]
kvs.update('cv_split_all_folds', cv_split)
kvs.save_pkl(os.path.join(kvs['args'].snapshots, kvs['snapshot_name'], 'session.pkl'))
print(colored("==> ", 'green') +
f"Train dataset has {(kvs['metadata'].Progressor == 0).sum()} non-progressed knees")
print(colored("==> ", 'green') +
f"Train dataset has {(kvs['metadata'].Progressor > 0).sum()} progressed knees")
print(colored("==> ", 'green') +
f"Test dataset has {(kvs['metadata_test'].Progressor == 0).sum()} non-progressed knees")
print(colored("==> ", 'green') +
f"Test dataset has {(kvs['metadata_test'].Progressor > 0).sum()} progressed knees")
def init_progression_metadata():
# We get should rid of non-progressors from MOST because we can check
# non-progressors only up to 84 months
kvs = GlobalKVS()
most_meta = pd.read_csv(
os.path.join(kvs['args'].metadata_root, 'MOST_progression.csv'))
oai_meta = pd.read_csv(
os.path.join(kvs['args'].metadata_root, 'OAI_progression.csv'))
kvs.update('metadata', oai_meta)
kvs.update('metadata_test', most_meta)
gkf = GroupKFold(n_splits=5)
cv_split = [
x for x in gkf.split(kvs['metadata'], kvs['metadata']['Progressor'],
kvs['metadata']['ID'].astype(str))
]
kvs.update('cv_split_all_folds', cv_split)
kvs.save_pkl(
os.path.join(kvs['args'].snapshots, kvs['snapshot_name'],
'session.pkl'))
print(
colored("==> ", 'green') + f"Train dataset has "
f"{(kvs['metadata'].Progressor == 0).sum()} non-progressed knees")
print(
colored("==> ", 'green') + f"Train dataset has "
f"{(kvs['metadata'].Progressor > 0).sum()} progressed knees")
print(
colored("==> ", 'green') + f"Test dataset has "
f"{(kvs['metadata_test'].Progressor == 0).sum()} non-progressed knees")
print(
colored("==> ", 'green') + f"Test dataset has "
f"{(kvs['metadata_test'].Progressor > 0).sum()} progressed knees")
def init_session():
if not torch.cuda.is_available():
raise EnvironmentError('The code must be run on GPU.')
kvs = GlobalKVS()
# Getting the arguments
args = parse_args()
# Initializing the seeds
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
np.random.seed(args.seed)
# Creating the snapshot
snapshot_name = time.strftime('%Y_%m_%d_%H_%M')
os.makedirs(os.path.join(args.snapshots, snapshot_name), exist_ok=True)
res = git_info()
if res is not None:
kvs.update('git branch name', res[0])
kvs.update('git commit id', res[1])
else:
kvs.update('git branch name', None)
kvs.update('git commit id', None)
kvs.update('pytorch_version', torch.__version__)
if torch.cuda.is_available():
kvs.update('cuda', torch.version.cuda)
kvs.update('gpus', torch.cuda.device_count())
else:
kvs.update('cuda', None)
kvs.update('gpus', None)
kvs.update('snapshot_name', snapshot_name)
kvs.update('args', args)
kvs.save_pkl(os.path.join(args.snapshots, snapshot_name, 'session.pkl'))
return args, snapshot_name
def log_metrics_age_sex_bmi(boardlogger, train_loss, val_res):
kvs = GlobalKVS()
res = dict()
val_loss = val_res[0]
res['val_loss'] = val_loss,
res['epoch'] = kvs['cur_epoch']
print(colored('====> ', 'green') + f'Train loss: {train_loss:.5f}')
print(colored('====> ', 'green') + f'Validation loss: {val_loss:.5f}')
boardlogger.add_scalars('Losses', {'train': train_loss, 'val': val_loss}, kvs['cur_epoch'])
if not kvs['args'].predict_age_sex_bmi:
_, ids, gt, preds = val_res
if kvs['args'].target_var == 'SEX':
val_auc = roc_auc_score(gt.astype(int), preds)
res['sex_auc'] = val_auc
print(colored('====> ', 'green') + f'Validation AUC: {val_auc:.5f}')
boardlogger.add_scalars('AUC sex', {'val': res['sex_auc']}, kvs['cur_epoch'])
else:
val_mse = mean_squared_error(gt, preds)
val_mae = median_absolute_error(gt, preds)
res[f"{kvs['args'].target_var}_mse"] = val_mse
res[f"{kvs['args'].target_var}_mae"] = val_mae
print(colored('====> ', 'green') + f'Validation mae: {val_mae:.5f}')
print(colored('====> ', 'green') + f'Validation mse: {val_mse:.5f}')
boardlogger.add_scalars(f"MSE [{kvs['args'].target_var}]", {'val': val_mse},
kvs['cur_epoch'])
boardlogger.add_scalars(f"MAE [{kvs['args'].target_var}]", {'val': val_mae},
kvs['cur_epoch'])
else:
_, ids, gt_age, preds_age, gt_sex, preds_sex, gt_bmi, preds_bmi = val_res
val_mse_age = mean_squared_error(gt_age, preds_age)
val_mae_age = median_absolute_error(gt_age, preds_age)
val_sex_auc = roc_auc_score(gt_sex.astype(int), preds_sex)
val_mse_bmi = mean_squared_error(gt_bmi, preds_bmi)
val_mae_bmi = median_absolute_error(gt_bmi, preds_bmi)
res["AGE_mse"] = val_mse_age
res["AGE_mae"] = val_mae_age
res["BMI_mse"] = val_mse_bmi
res["BMI_mae"] = val_mae_bmi
res["SEX_auc"] = val_sex_auc
print(colored('====> ', 'green') + f'Validation mae [Age]: {val_mae_age:.5f}')
print(colored('====> ', 'green') + f'Validation mse [Age]: {val_mse_age:.5f}')
print(colored('====> ', 'green') + f'Validation val_auc [Sex]: {val_sex_auc:.5f}')
print(colored('====> ', 'green') + f'Validation mae [BMI]: {val_mae_bmi:.5f}')
print(colored('====> ', 'green') + f'Validation mse [BMI]: {val_mse_bmi:.5f}')
kvs.update(f'losses_fold_[{kvs["cur_fold"]}]', {'epoch': kvs['cur_epoch'],
'train_loss': train_loss,
'val_loss': val_loss})
kvs.update(f'val_metrics_fold_[{kvs["cur_fold"]}]', res)
kvs.save_pkl(os.path.join(kvs['args'].snapshots, kvs['snapshot_name'], 'session.pkl'))
