class BaseTrainer(object):
def __init__(self,
epochs,
model,
train_dataloader,
train_loss_func,
train_metrics_func,
optimizer,
log_dir,
checkpoint_dir,
checkpoint_frequency,
checkpoint_restore=None,
val_dataloader=None,
val_metrics_func=None,
lr_scheduler=None,
lr_reduce_metric=None,
use_gpu=False,
gpu_ids=None):
# train settings
self.epochs = epochs
self.model = model
self.train_dataloader = train_dataloader
self.train_loss_func = train_loss_func
self.train_metrics_func = train_metrics_func
self.optimizer = optimizer
self.checkpoint_dir = checkpoint_dir
self.checkpoint_frequency = checkpoint_frequency
self.writer = SummaryWriter(logdir=log_dir)
# validation settings
if val_dataloader is not None:
self.validation = True
self.val_dataloader = val_dataloader
self.val_metrics_func = val_metrics_func
else:
self.validation = False
# lr scheduler settings
if lr_scheduler is not None:
self.lr_schedule = True
self.lr_scheduler = lr_scheduler
if isinstance(lr_scheduler,
torch.optim.lr_scheduler.ReduceLROnPlateau):
self.lr_reduce_metric = lr_reduce_metric
else:
self.lr_schedule = False
# multi-gpu settings
self.use_gpu = use_gpu
gpu_visible = list()
for index in range(len(gpu_ids)):
gpu_visible.append(str(gpu_ids[index]))
os.environ["CUDA_VISIBLE_DEVICES"] = ','.join(gpu_visible)
if use_gpu and torch.cuda.device_count() > 0:
self.model.cuda()
if gpu_ids is not None:
if len(gpu_ids) > 1:
self.multi_gpu = True
self.model = DataParallel(model, gpu_ids)
else:
self.multi_gpu = False
else:
if torch.cuda.device_count() > 1:
self.multi_gpu = True
self.model = DataParallel(model)
else:
self.multi_gpu = False
else:
self.multi_gpu = False
self.device = torch.device('cpu')
self.model = self.model.cpu()
# checkpoint settings
if checkpoint_restore is not None:
self.model.load_state_dict(torch.load(checkpoint_restore))
def train(self):
for epoch in range(1, self.epochs + 1):
logging.info('*' * 80)
logging.info('start epoch %d training loop' % epoch)
# train
self.model.train()
loss, metrics = self.train_epochs(epoch)
self.writer.add_scalar('train_loss', loss, epoch)
for key in metrics.keys():
self.writer.add_scalar(key, metrics[key], epoch)
if self.lr_schedule:
if isinstance(self.lr_scheduler,
torch.optim.lr_scheduler.ReduceLROnPlateau):
self.lr_scheduler.step(loss[self.lr_reduce_metric])
else:
self.lr_scheduler.step()
logging.info('train loss result: %s' % str(loss))
logging.info('train metrics result: %s' % str(metrics))
# validation
if self.validation:
logging.info('validation start ... ')
self.model.eval()
loss, metrics = self.val_epochs(epoch)
self.writer.add_scalar('val_loss', loss, epoch)
for key in metrics.keys():
self.writer.add_scalar(key, metrics[key], epoch)
logging.info('validation loss result: %s' % str(loss))
logging.info('validation metrics result: %s' % str(metrics))
# model checkpoint
if epoch % self.checkpoint_frequency == 0:
logging.info('saving model...')
checkpoint_name = 'checkpoint_%d.pth' % epoch
if self.multi_gpu:
torch.save(
self.model.module.state_dict(),
os.path.join(self.checkpoint_dir, checkpoint_name))
else:
torch.save(
self.model.state_dict(),
os.path.join(self.checkpoint_dir, checkpoint_name))
logging.info('model have saved for epoch_%d ' % epoch)
else:
logging.info('saving model skipped.')
def train_epochs(self, epoch) -> (dict, dict):
"""
:rtype: loss -> dict , metrics -> dict
"""
pass
def val_epochs(self, epoch) -> (dict, dict):
"""
:rtype: loss -> dict , metrics -> dict
"""
pass
class BaseInferencer(object):
def __init__(self,
model,
images_path,
labels_path,
patient_ids,
sample_shape,
checkpoint_restore,
inference_dir,
use_gpu=False,
gpu_ids=None):
# model settings
self.model = model
# data settings
assert len(images_path) == len(labels_path)
self.images_path = images_path
self.labels_path = labels_path
self.patient_ids = patient_ids
self.length = len(images_path)
self.sample_shape = sample_shape
self.inference_dir = inference_dir
# gpu settings
self.use_gpu = use_gpu
if use_gpu and torch.cuda.device_count() > 0:
self.model.cuda()
if gpu_ids is not None:
if len(gpu_ids) > 1:
self.multi_gpu = True
self.model = DataParallel(model, gpu_ids)
else:
self.multi_gpu = False
else:
if torch.cuda.device_count() > 1:
self.multi_gpu = True
self.model = DataParallel(model)
else:
self.multi_gpu = False
else:
self.multi_gpu = False
self.model = self.model.cpu()
self.model.load_state_dict(torch.load(checkpoint_restore))
def inference(self):
self.model.eval()
logging.info('*' * 80)
logging.info('start inference loop')
logging.info('%d patients need to be inference ' % self.length)
for index in range(self.length):
logging.info('start inference %d-th patient' % (index + 1))
self.__inference__(index)
logging.info('*' * 80)
logging.info('inference patient: %d' % self.length)
logging.info('inference result saved in: %s' % self.inference_dir)
def __inference__(self, index):
"""
:rtype: object
"""
pass
class BaseEvaluation(object):
def __init__(self,
model,
metrics,
images_path,
labels_path,
sample_shape,
checkpoint_restore,
use_gpu=False,
gpu_ids=None):
# model settings
self.model = model
# metrics settings
assert type(metrics) == dict
self.metrics = metrics
# data settings
assert len(images_path) == len(labels_path)
self.images_path = images_path
self.labels_path = labels_path
self.length = len(images_path)
self.sample_shape = sample_shape
# gpu settings
self.use_gpu = use_gpu
if use_gpu and torch.cuda.device_count() > 0:
self.model.cuda()
if gpu_ids is not None:
if len(gpu_ids) > 1:
self.multi_gpu = True
self.model = DataParallel(model, gpu_ids)
else:
self.multi_gpu = False
else:
if torch.cuda.device_count() > 1:
self.multi_gpu = True
self.model = DataParallel(model)
else:
self.multi_gpu = False
else:
self.multi_gpu = False
self.model = self.model.cpu()
self.model.load_state_dict(torch.load(checkpoint_restore))
def load_data(self, index):
"""
:rtype: image -> nd-array, label -> nd-array
"""
pass
def eval_one_patient(self, image, label):
"""
:rtype: metrics -> dict
"""
pass
def eval(self):
self.model.eval()
logging.info('*' * 80)
logging.info('start evaluation loop')
logging.info('%d patients need to be evaluated ' % self.length)
result = dict()
for index in range(self.length):
logging.info('start evaluation %d-th patient' % (index + 1))
image, label = self.load_data(index)
with torch.no_grad():
metrics = self.eval_one_patient(image, label)
for key in metrics.keys():
if key not in result.keys():
result[key] = list()
result[key].append(metrics[key])
logging.info('evaluation metrics result: %s' % str(metrics))
mean_result = dict()
for key in result.keys():
mean_result[key] = np.mean(result[key])
logging.info('*' * 80)
logging.info('evaluation report: ')
logging.info('evaluation patient: %d' % self.length)
logging.info('evaluation metrics %s' % str(mean_result))