def setup(args, cfg, train_ann, valid_ann):
logger = logging.getLogger('root')
train_loader = builder.build_dataloader(cfg, ann=train_ann, mode='train')
valid_loader = builder.build_dataloader(cfg, ann=valid_ann, mode='valid')
# Adjust steps per epoch if necessary (i.e., equal to 0)
# We assume if gradient accumulation is specified, then the user
# has already adjusted the steps_per_epoch accordingly in the
# config file
steps_per_epoch = cfg['train']['params']['steps_per_epoch']
gradient_accmul = cfg['train']['params']['gradient_accumulation']
if steps_per_epoch == 0:
cfg['train']['params']['steps_per_epoch'] = len(train_loader)
logger.info('Building [{}] architecture ...'.format(
cfg['model']['config']))
model = builder.build_model(cfg, args.gpu)
model = model.train().cuda()
optimizer = builder.build_optimizer(cfg['optimizer']['name'], model,
cfg['optimizer']['params'])
scheduler = builder.build_scheduler(cfg['scheduler']['name'],
optimizer,
cfg=cfg)
return cfg, \
train_loader, \
valid_loader, \
model, \
optimizer, \
scheduler
def create_model(cfg):
model = builder.build_model(cfg['model']['name'],
cfg['model']['params'])
model.load_state_dict(
torch.load(cfg['test']['checkpoint'],
map_location=lambda storage, loc: storage))
model = model.eval().cuda()
return model
def create_model(cfg, checkpoint):
model = builder.build_model(cfg['model']['name'], cfg['model']['params'])
print('Loading <{}> model from <{}> ...'.format(cfg['model']['name'], checkpoint))
weights = torch.load(checkpoint, map_location=lambda storage, loc: storage)
weights = {k.replace('module.', '') : v for k,v in weights.items()}
model.load_state_dict(weights)
model = model.eval().cuda()
return model
def test(args, cfg, test_df):
if 'csv_filename' in cfg['test'].keys():
if cfg['test']['csv_filename']:
test_df = pd.read_csv(cfg['test']['csv_filename'])
logger = logging.getLogger('root')
logger.info('TESTING : START')
logger.info('TEST: n={}'.format(len(test_df)))
if 'data_dir' in cfg['test'].keys():
if cfg['test']['data_dir']:
cfg['dataset']['data_dir'] = cfg['test']['data_dir']
test_df = test_df[test_df['part'] != 45]
test_images = [
osp.join(cfg['dataset']['data_dir'], '{}'.format(_))
for _ in test_df['vidfile']
]
test_labels = np.asarray(test_df['label'])
test_loader = builder.build_dataloader(cfg,
data_info={
'vidfiles': test_images,
'labels': test_labels
},
mode='test')
cfg['model']['params']['pretrained'] = None
model = builder.build_model(cfg['model']['name'], cfg['model']['params'])
model.load_state_dict(
torch.load(cfg['test']['checkpoint'],
map_location=lambda storage, loc: storage))
model = model.eval().cuda()
if 'params' not in cfg['test'].keys() or type(
cfg['test']['params']) == type(None):
cfg['test']['params'] = {}
predictor = getattr(factory_evaluate, cfg['test']['predictor'])
predictor = predictor(loader=test_loader, **cfg['test']['params'])
y_true, y_pred, _ = predictor.predict(model, criterion=None, epoch=None)
if not osp.exists(cfg['test']['save_preds_dir']):
os.makedirs(cfg['test']['save_preds_dir'])
with open(osp.join(cfg['test']['save_preds_dir'], 'predictions.pkl'),
'wb') as f:
pickle.dump(
{
'y_true': y_true,
'y_pred': y_pred,
'imgfiles': [im.split('/')[-1] for im in test_images]
}, f)
def predict(args, cfg):
df = pd.read_csv(cfg['predict']['csv_filename'])
logger = logging.getLogger('root')
logger.info('PREDICT : START')
logger.info('PREDICT: n={}'.format(len(df)))
images = [osp.join(cfg['predict']['data_dir'], _) for _ in df['imgfile']]
male = list(df['male'].astype('float32'))
if cfg['predict']['coords']:
coords = {k : np.asarray(df[k]) for k in ['x1','y1','x2','y2']}
else:
coords = None
loader = builder.build_dataloader(cfg, data_info={'imgfiles': images, 'labels': [0]*len(images), 'male': male, 'coords': coords}, mode='predict')
model = builder.build_model(cfg['model']['name'], cfg['model']['params'])
model.load_state_dict(torch.load(cfg['predict']['checkpoint'], map_location=lambda storage, loc: storage))
model = model.eval().cuda()
if cfg['predict']['params'] is None:
cfg['predict']['params'] = {}
if 'patch' in cfg['evaluation']['params'].keys():
cfg['predict']['params']['patch'] = cfg['evaluation']['params']['patch']
predictor = getattr(factory.evaluate, cfg['predict']['predictor'])
predictor = predictor(loader=loader,
**cfg['predict']['params'])
_, y_pred, _ = predictor.predict(model, criterion=None, epoch=None)
if 'percentile' in cfg['predict'].keys() and cfg['predict']['params']['patch']:
y_pred = np.percentile(y_pred, cfg['predict']['percentile'], axis=1)
if not osp.exists(cfg['predict']['save_preds_dir']):
os.makedirs(cfg['predict']['save_preds_dir'])
with open(osp.join(cfg['predict']['save_preds_dir'], 'predictions.pkl'), 'wb') as f:
pickle.dump({
'y_pred': y_pred,
'imgfiles': [im.split('/')[-1] for im in images]
}, f)
def __init__(self):
root_path = os.path.dirname(os.path.dirname(__file__))
if torch.cuda.is_available():
self.device = torch.device("cuda")
gpu_ids = list(range(torch.cuda.device_count()))
else:
self.device = torch.device("cpu")
gpu_ids = []
with open(
os.path.join(root_path, "src", "configs",
"experiment001.yaml")) as f:
self.cfg = yaml.load(f, Loader=yaml.FullLoader)
torch.hub.set_dir("/tmp")
self.model = builder.build_model(self.cfg, 0)
self.model.load_state_dict(
torch.load(
# self.cfg["predict"]["checkpoint"],
os.path.join(root_path, "checkpoints", "experiment001",
"RET50_019_VM-0.2294.PTH"),
map_location=lambda storage, loc: storage,
))
self.model = self.model.eval()
def setup(args, cfg, train_df, valid_df):
logger = logging.getLogger('root')
if cfg['dataset']['name'] == 'FaceMaskDataset':
train_images = [
osp.join(cfg['dataset']['data_dir'], '{}'.format(_))
for _ in train_df['imgfile']
]
valid_images = [
osp.join(cfg['dataset']['data_dir'], '{}'.format(_))
for _ in valid_df['imgfile']
]
train_labels = np.asarray(train_df['label'])
valid_labels = np.asarray(valid_df['label'])
train_masks = [
osp.join(cfg['dataset']['data_dir'], '{}'.format(_))
for _ in train_df['maskfile']
]
valid_masks = [
osp.join(cfg['dataset']['data_dir'], '{}'.format(_))
for _ in valid_df['maskfile']
]
train_loader = builder.build_dataloader(cfg,
data_info={
'imgfiles': train_images,
'maskfiles': train_masks,
'labels': train_labels
},
mode='train')
valid_loader = builder.build_dataloader(cfg,
data_info={
'imgfiles': valid_images,
'maskfiles': valid_masks,
'labels': valid_labels
},
mode='valid')
else:
train_images = [
osp.join(cfg['dataset']['data_dir'], '{}'.format(_))
for _ in train_df['vidfile']
]
valid_images = [
osp.join(cfg['dataset']['data_dir'], '{}'.format(_))
for _ in valid_df['vidfile']
]
train_labels = np.asarray(train_df['label'])
valid_labels = np.asarray(valid_df['label'])
train_loader = builder.build_dataloader(cfg,
data_info={
'vidfiles': train_images,
'labels': train_labels
},
mode='train')
valid_loader = builder.build_dataloader(cfg,
data_info={
'vidfiles': valid_images,
'labels': valid_labels
},
mode='valid')
# Adjust steps per epoch if necessary (i.e., equal to 0)
# We assume if gradient accumulation is specified, then the user
# has already adjusted the steps_per_epoch accordingly in the
# config file
steps_per_epoch = cfg['train']['params']['steps_per_epoch']
gradient_accmul = cfg['train']['params']['gradient_accumulation']
if steps_per_epoch == 0:
cfg['train']['params']['steps_per_epoch'] = len(train_loader)
# if gradient_accmul > 1:
# new_steps_per_epoch = int(cfg['train']['params']['steps_per_epoch']
# / gradient_accmul)
# cfg['train']['params']['steps_per_epoch'] = new_steps_per_epoch
# Generic build function will work for model/loss
logger.info('Building [{}] architecture ...'.format(cfg['model']['name']))
if 'backbone' in cfg['model']['params'].keys():
logger.info(' Using [{}] backbone ...'.format(
cfg['model']['params']['backbone']))
if 'pretrained' in cfg['model']['params'].keys():
logger.info(' Pretrained weights : {}'.format(
cfg['model']['params']['pretrained']))
model = builder.build_model(cfg['model']['name'], cfg['model']['params'])
model = model.train().cuda()
if cfg['loss']['params'] is None:
cfg['loss']['params'] = {}
if re.search(r'^OHEM', cfg['loss']['name']):
cfg['loss']['params']['total_steps'] = cfg['train']['params'][
'num_epochs'] * cfg['train']['params']['steps_per_epoch']
criterion = builder.build_loss(cfg['loss']['name'], cfg['loss']['params'])
optimizer = builder.build_optimizer(cfg['optimizer']['name'],
model.parameters(),
cfg['optimizer']['params'])
scheduler = builder.build_scheduler(cfg['scheduler']['name'],
optimizer,
cfg=cfg)
if len(args.gpu) > 1:
model = nn.DataParallel(model, device_ids=args.gpu)
return cfg, \
train_loader, \
valid_loader, \
model, \
optimizer, \
criterion, \
scheduler
def setup(args, cfg, train_df, valid_df):
logger = logging.getLogger('root')
if isinstance(cfg['dataset']['data_dir'], list):
data_dir_dict = {
2019: cfg['dataset']['data_dir'][0],
2020: cfg['dataset']['data_dir'][1]
}
if len(cfg['dataset']['data_dir']) == 3:
data_dir_dict[2021] = cfg['dataset']['data_dir'][2]
train_images = []
for rownum, row in train_df.iterrows():
data_dir = data_dir_dict[row.isic]
imgfile = osp.join(data_dir, f'{row.image}.jpg')
train_images += [imgfile]
valid_images = []
for rownum, row in valid_df.iterrows():
data_dir = data_dir_dict[row.isic]
imgfile = osp.join(data_dir, f'{row.image}.jpg')
valid_images += [imgfile]
else:
train_images = [osp.join(cfg['dataset']['data_dir'], f'{_}.jpg') for _ in train_df['image'].values]
valid_images = [osp.join(cfg['dataset']['data_dir'], f'{_}.jpg') for _ in valid_df['image'].values]
train_data_info = {
'imgfiles': train_images,
'labels': train_df['label'].values
}
valid_data_info = {
'imgfiles': valid_images,
'labels': valid_df['label'].values
}
if cfg['dataset'].pop('meta', False):
train_data_info['meta'] = [dict(age=row['age_cat'],sex=row['sex'],ant=row['anatom_site_general_challenge']) for rownum, row in train_df.iterrows()]
valid_data_info['meta'] = [dict(age=row['age_cat'],sex=row['sex'],ant=row['anatom_site_general_challenge']) for rownum, row in valid_df.iterrows()]
train_loader = builder.build_dataloader(cfg, data_info=train_data_info, mode='train')
valid_loader = builder.build_dataloader(cfg, data_info=valid_data_info, mode='valid')
ARC = False
if cfg['model']['name'] in ('ArcNet', 'SiameseNet'):
ARC = True
if 'isic' in train_df.columns:
mel_df = train_df[(train_df['label'] == 1) & (train_df['isic'] == 2020)]
else:
mel_df = train_df[train_df['label'] == 1]
mel_df = mel_df.drop_duplicates()
arc_data_info = {
'imgfiles': [osp.join(cfg['dataset']['data_dir'], f'{_}.jpg') for _ in mel_df['image'].values],
'labels': mel_df['label'].values
}
arc_loader = builder.build_dataloader(cfg, data_info=arc_data_info, mode='predict')
print(f'{len(arc_loader)} melanoma examples will be used as reference ...')
OHEM = False
if 'ohem' in cfg['train']['params'] and cfg['train']['params']['ohem']:
print('Creating benign loader ...')
OHEM = True
benign_df = train_df[train_df['label'] == 0]
benign_data_info = {
'imgfiles': [osp.join(cfg['dataset']['data_dir'], f'{_}.jpg') for _ in benign_df['image'].values],
'labels': benign_df['label'].values
}
benign_loader = builder.build_dataloader(cfg, data_info=benign_data_info, mode='predict')
# Adjust steps per epoch if necessary (i.e., equal to 0)
# We assume if gradient accumulation is specified, then the user
# has already adjusted the steps_per_epoch accordingly in the
# config file
steps_per_epoch = cfg['train']['params']['steps_per_epoch']
gradient_accmul = cfg['train']['params']['gradient_accumulation']
if steps_per_epoch == 0:
cfg['train']['params']['steps_per_epoch'] = len(train_loader)
# Generic build function will work for model/loss
logger.info('Building [{}] architecture ...'.format(cfg['model']['name']))
if 'backbone' in cfg['model']['params'].keys():
logger.info(' Using [{}] backbone ...'.format(cfg['model']['params']['backbone']))
if 'pretrained' in cfg['model']['params'].keys():
logger.info(' Pretrained weights : {}'.format(cfg['model']['params']['pretrained']))
model = builder.build_model(cfg['model']['name'], cfg['model']['params'])
model = model.train().cuda()
if cfg['loss']['params'] is None:
cfg['loss']['params'] = {}
if re.search(r'^OHEM', cfg['loss']['name']):
cfg['loss']['params']['total_steps'] = cfg['train']['params']['num_epochs'] * cfg['train']['params']['steps_per_epoch']
if cfg['loss']['name'] == 'CrossEntropyLoss':
weighted = cfg['loss'].pop('weighted', False)
if weighted:
wts = get_invfreq_weights(train_data_info['labels'], scale=weighted)
cfg['loss']['params']['weight'] = torch.tensor(wts)
logger.info('Using the following class weights:')
for i in range(len(wts)):
logger.info(f' Class {i} : {wts[i]:.4f}')
criterion = builder.build_loss(cfg['loss']['name'], cfg['loss']['params'])
optimizer = builder.build_optimizer(
cfg['optimizer']['name'],
model.parameters(),
cfg['optimizer']['params'])
scheduler = builder.build_scheduler(
cfg['scheduler']['name'],
optimizer,
cfg=cfg)
if len(args.gpu) > 1:
print(f'DEVICES : {args.gpu}')
model = nn.DataParallel(model, device_ids=args.gpu)
if args.gpu[0] != 0:
model.to(f'cuda:{model.device_ids[0]}')
if ARC: valid_loader = (valid_loader, arc_loader)
if OHEM: train_loader = (train_loader, benign_loader)
return cfg, \
train_loader, \
valid_loader, \
model, \
optimizer, \
criterion, \
scheduler
def predict_ensemble(args, cfg):
df = pd.read_csv(cfg['predict']['csv_filename'])
BATCH_SIZE = None
if 'batch_size' in cfg['predict'].keys():
BATCH_SIZE = cfg['predict']['batch_size']
model_cfgs = []
for cfgfile in cfg['model_configs']:
with open(cfgfile) as f:
model_cfgs.append(yaml.load(f, Loader=yaml.FullLoader))
logger = logging.getLogger('root')
logger.info('PREDICT : START')
logger.info('PREDICT: n={}'.format(len(df)))
images = [osp.join(cfg['predict']['data_dir'], _) for _ in df['imgfile']]
male = list(df['male'].astype('float32'))
if cfg['predict']['coords']:
coords = {k : np.asarray(df[k]) for k in ['x1','y1','x2','y2']}
else:
coords = None
loaders = []
models = []
for model_cfg in model_cfgs:
model_cfg = set_inference_batch_size(model_cfg)
if 'predict' not in model_cfg.keys():
model_cfg['predict'] = copy.deepcopy(model_cfg['test'])
if BATCH_SIZE:
model_cfg['predict']['batch_size'] = BATCH_SIZE
loaders.append(builder.build_dataloader(model_cfg, data_info={'imgfiles': images, 'labels': [0]*len(images), 'male': male, 'coords': coords}, mode='predict'))
model = builder.build_model(model_cfg['model']['name'], model_cfg['model']['params'])
model.load_state_dict(torch.load(model_cfg['predict']['checkpoint'], map_location=lambda storage, loc: storage))
model = model.eval().cuda()
models.append(model)
for model_cfg in model_cfgs:
if model_cfg['predict']['params'] is None:
model_cfg['predict']['params'] = {}
if 'patch' in model_cfg['evaluation']['params'].keys():
model_cfg['predict']['params']['patch'] = model_cfg['evaluation']['params']['patch']
predictors = []
for ind, model_cfg in enumerate(model_cfgs):
predictor = getattr(factory.evaluate, model_cfg['predict']['predictor'])
predictor = predictor(loader=loaders[ind],
**model_cfg['predict']['params'])
predictors.append(predictor)
y_pred_list = []
for ind, model_cfg in enumerate(model_cfgs):
_, y_pred, _ = predictors[ind].predict(models[ind], criterion=None, epoch=None)
if 'percentile' in model_cfg['predict'].keys() and model_cfg['predict']['params']['patch']:
y_pred = np.percentile(y_pred, model_cfg['predict']['percentile'], axis=1)
y_pred_list.append(y_pred)
y_pred = np.mean(np.asarray(y_pred_list), axis=0)
if not osp.exists(cfg['predict']['save_preds_dir']):
os.makedirs(cfg['predict']['save_preds_dir'])
with open(osp.join(cfg['predict']['save_preds_dir'], 'predictions.pkl'), 'wb') as f:
pickle.dump({
'y_pred': y_pred,
'imgfiles': [im.split('/')[-1] for im in images]
}, f)
def setup(args, cfg, train_df, valid_df):
logger = logging.getLogger('root')
train_images = [osp.join(cfg['dataset']['data_dir'], _) for _ in train_df['imgfile']]
valid_images = [osp.join(cfg['dataset']['data_dir'], _) for _ in valid_df['imgfile']]
train_labels = list(train_df['boneage'])
valid_labels = list(valid_df['boneage'])
train_male = list(train_df['male'].astype('float32'))
valid_male = list(valid_df['male'].astype('float32'))
if cfg['dataset']['coords']:
train_coords = {k : np.asarray(train_df[k]) for k in ['x1','y1','x2','y2']}
valid_coords = {k : np.asarray(valid_df[k]) for k in ['x1','y1','x2','y2']}
else:
train_coords = None
valid_coords = None
train_loader = builder.build_dataloader(cfg, data_info={'imgfiles': train_images, 'labels': train_labels, 'male': train_male, 'coords': train_coords}, mode='train')
valid_loader = builder.build_dataloader(cfg, data_info={'imgfiles': valid_images, 'labels': valid_labels, 'male': valid_male, 'coords': valid_coords}, mode='valid')
# Adjust steps per epoch if necessary (i.e., equal to 0)
# We assume if gradient accumulation is specified, then the user
# has already adjusted the steps_per_epoch accordingly in the
# config file
steps_per_epoch = cfg['train']['params']['steps_per_epoch']
gradient_accmul = cfg['train']['params']['gradient_accumulation']
if steps_per_epoch == 0:
cfg['train']['params']['steps_per_epoch'] = len(train_loader)
# Generic build function will work for model/loss
logger.info('Building [{}] architecture ...'.format(cfg['model']['name']))
logger.info(' Using [{}] backbone ...'.format(cfg['model']['params']['backbone']))
logger.info(' Pretrained weights : {}'.format(cfg['model']['params']['pretrained']))
model = builder.build_model(cfg['model']['name'], cfg['model']['params'])
model = model.train().cuda()
if cfg['loss']['name'] == 'BalancedHybridLoss':
strata_weights = pd.cut(train_df['boneage'], bins=[0,24]+list(np.arange(12*3, 12*17, 12))+[228], labels=range(16))
strata_weights = pd.DataFrame(strata_weights.value_counts()).reset_index().sort_values('index', ascending=True)
strata_weights = strata_weights['boneage'].max() / strata_weights['boneage']
strata_weights = np.asarray(strata_weights)
cfg['loss']['params']['strata_weights'] = strata_weights
criterion = builder.build_loss(cfg['loss']['name'], cfg['loss']['params'])
optimizer = builder.build_optimizer(
cfg['optimizer']['name'],
model.parameters(),
cfg['optimizer']['params'])
scheduler = builder.build_scheduler(
cfg['scheduler']['name'],
optimizer,
cfg=cfg)
return cfg, \
train_loader, \
valid_loader, \
model, \
optimizer, \
criterion, \
scheduler
