return mae
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
if __name__ == '__main__':
tuner_params = nni.get_next_parameter()
logger.debug(tuner_params)
params = vars(merge_parameter(return_args, tuner_params))
print(params)
main(params)
type=int,
default=1,
metavar='S',
help='random seed (default: 1)')
parser.add_argument('--no_cuda',
action='store_true',
default=False,
help='disables CUDA training')
parser.add_argument(
'--log_interval',
type=int,
default=1000,
metavar='N',
help='how many batches to wait before logging training status')
args, _ = parser.parse_known_args()
return args
if __name__ == '__main__':
try:
# get parameters form tuner
tuner_params = nni.get_next_parameter()
logger.debug(tuner_params)
params = vars(merge_parameter(get_params(), tuner_params))
print(params)
main(params)
except Exception as exception:
logger.exception(exception)
raise
default='awd',
choices=['rnn', 'awd', 'transformer'])
# add model specific args
parser = LanguageModel.add_model_specific_args(parser)
# add args from trainer
parser = Trainer.add_argparse_args(parser)
hparams = parser.parse_args()
tuner_params = nni.get_next_parameter()
logger.debug(tuner_params)
hparams = merge_parameter(hparams, tuner_params)
logger.info(hparams)
if tuner_params is None:
task = Task.init(project_name="language-model",
task_name=hparams.model)
else:
task = Task.init(project_name="language-model-hp",
task_name=hparams.model,
continue_last_task=False)
task.add_tags(['hp', hparams.model])
datasets, vocab = BRTD.create(hparams.data, vocab=hparams.vocab)
hparams.num_tokens = len(vocab)
type=float,
help='ignore iou thresh')
parser.add_argument('--expand_scale',
default=None,
type=float,
help='image augmentation expand scale')
parser.add_argument('--iou_weighting',
default=None,
type=float,
help='iou loss weighting')
args = parser.parse_args()
return args
if __name__ == '__main__':
try:
# get parameters form tuner
tuner_params = nni.get_next_parameter()
#logger.debug(tuner_params)
print(tuner_params)
params = merge_parameter(get_params(), tuner_params)
id = get_sequence_id()
#params.checkpoint = 'checkpoints/%d' % id
#print(params)
main(params)
except Exception as exception:
#logger.exception(exception)
raise
image_name = '{}_test_{}.png'.format(test_config.dataset, i)
Image.fromarray(cur_input).save(
os.path.join(test_config.output_dir, 'input', image_name))
Image.fromarray(cur_label).save(
os.path.join(test_config.output_dir, 'label', image_name))
Image.fromarray(cur_output).save(
os.path.join(test_config.output_dir, 'output', image_name))
_logger.info("Images successfully saved to " + test_config.output_dir)
if __name__ == '__main__':
params_from_cl = vars(parse_args())
_, test_params = get_base_params(params_from_cl['dataset'],
params_from_cl['checkpoint'])
test_params.update(params_from_cl)
with open(test_params['parameter_cfg'], 'r') as f:
params_from_nni = json.loads(f.readline().strip())['parameters']
test_params = merge_parameter(test_params, params_from_nni)
pathlib.Path(params_from_cl['output_dir'] + '/input').mkdir(parents=True,
exist_ok=True)
pathlib.Path(params_from_cl['output_dir'] + '/label').mkdir(parents=True,
exist_ok=True)
pathlib.Path(params_from_cl['output_dir'] + '/output').mkdir(parents=True,
exist_ok=True)
main(test_params)
t_data = iter_start_time - iter_data_time
total_iters += train_config.batch_size
epoch_iter += train_config.batch_size
model.set_input(data) # unpack data from dataset and apply preprocessing
model.optimize_parameters() # calculate loss functions, get gradients, update network weights
iter_data_time = time.time()
_logger.info('End of epoch {} / {} \t Time Taken: {} sec'.format(epoch, train_config.n_epochs + train_config.n_epochs_decay, time.time() - epoch_start_time))
model.save_networks('latest')
_logger.info("Training done. Saving the final model.")
l1_score = evaluate_L1(test_config, model, test_dataset)
_logger.info("The final L1 loss the test set is {}".format(l1_score))
nni.report_final_result(l1_score)
if __name__ == '__main__':
dataset_name = 'facades'
checkpoint_dir = setup_trial_checkpoint_dir()
params_from_cl = vars(parse_args())
params_for_tuning = nni.get_next_parameter()
train_params, test_params = get_base_params(dataset_name, checkpoint_dir)
train_params.update(params_from_cl)
test_params.update(params_from_cl)
train_params = merge_parameter(train_params, params_for_tuning)
main(dataset_name, train_params, test_params)
