def get_trainer(model, optimizer, cfg, device, **kwargs):
''' Returns the trainer object.
Args:
model (nn.Module): the Occupancy Network model
optimizer (optimizer): pytorch optimizer object
cfg (dict): imported yaml config
device (device): pytorch device
'''
threshold = cfg['test']['threshold']
out_dir = cfg['training']['out_dir']
vis_dir = os.path.join(out_dir, 'vis')
input_type = cfg['data']['input_type']
trainer = training.Trainer(model,
optimizer,
cfg,
device=device,
input_type=input_type,
vis_dir=vis_dir,
threshold=threshold,
eval_sample=cfg['training']['eval_sample'],
uda_type=cfg['training']['uda_type'],
num_epochs=cfg['training']['num_epochs'])
return trainer
def get_trainer(model, optimizer, cfg, device, **kwargs):
''' Returns the trainer object.
Args:
model (nn.Module): the Occupancy Network model
optimizer (optimizer): pytorch optimizer object
cfg (dict): imported yaml config
device (device): pytorch device
'''
threshold = cfg['test']['threshold']
out_dir = cfg['training']['out_dir']
vis_dir = os.path.join(out_dir, 'vis')
input_type = cfg['data']['input_type']
if 'surface_loss_weight' in cfg['model']:
surface_loss_weight = cfg['model']['surface_loss_weight']
else:
surface_loss_weight = 1.
if ('loss_tolerance_episolon' in cfg['training']) and (
0 in cfg['training']['loss_tolerance_episolon']):
loss_tolerance_episolon = cfg['training']['loss_tolerance_episolon'][0]
else:
loss_tolerance_episolon = 0.
if ('sign_lambda'
in cfg['training']) and (0 in cfg['training']['sign_lambda']):
sign_lambda = cfg['training']['sign_lambda'][0]
else:
sign_lambda = 0.
trainer = training.Trainer(model,
optimizer,
device=device,
input_type=input_type,
vis_dir=vis_dir,
threshold=threshold,
eval_sample=cfg['training']['eval_sample'],
surface_loss_weight=surface_loss_weight,
loss_tolerance_episolon=loss_tolerance_episolon,
sign_lambda=sign_lambda)
if 'loss_type' in cfg['training']:
trainer.loss_type = cfg['training']['loss_type']
print('loss type:', trainer.loss_type)
return trainer
def get_trainer(model, optimizer, cfg):
''' Returns the trainer object.
Args:
model (tf.keras.Model): the Occupancy Network model
optimizer (optimizer): tf.keras.optimizers object
cfg (dict): imported yaml config
'''
threshold = cfg['test']['threshold']
out_dir = cfg['training']['out_dir']
vis_dir = os.path.join(out_dir, 'vis')
input_type = cfg['data']['input_type']
trainer = training.Trainer(
model,
optimizer,
input_type=input_type,
vis_dir=vis_dir,
threshold=threshold,
eval_sample=cfg['training']['eval_sample'],
)
return trainer