from FastAutoAugment.darts.darts_micro_builder import DartsMicroBuilder
from FastAutoAugment.common.common import common_init
from FastAutoAugment.nas.evaluate import eval_arch
if __name__ == '__main__':
conf = common_init(
config_filepath='confs/darts_cifar.yaml',
param_args=['--common.experiment_name', 'darts_cifar_eval'])
conf_eval = conf['nas']['eval']
# evaluate architecture using eval settings
eval_arch(conf_eval, micro_builder=DartsMicroBuilder())
exit(0)
from FastAutoAugment.common.common import common_init
from FastAutoAugment.darts.darts_micro_builder import DartsMicroBuilder
from FastAutoAugment.nas.evaluate import eval_arch
if __name__ == '__main__':
conf = common_init(config_filepath='confs/imagenet_darts.yaml',
param_args=['--common.experiment_name', 'darts_imagenet_eval'])
conf_eval = conf['nas']['eval']
micro_builder = DartsMicroBuilder()
eval_arch(conf_eval, micro_builder=micro_builder)
exit(0)
conf_loader = conf_eval['loader']
save_filename = conf_eval['save_filename']
conf_checkpoint = conf_eval['checkpoint']
resume = conf_eval['resume']
conf_train = conf_eval['trainer']
# endregion
device = torch.device(conf_eval['device'])
checkpoint = CheckPoint(conf_checkpoint,
resume) if conf_checkpoint is not None else None
model = Net().to(device)
# get data
train_dl, _, test_dl = data.get_data(conf_loader)
assert train_dl is not None and test_dl is not None
trainer = Trainer(conf_train, model, device, checkpoint, False)
trainer.fit(train_dl, test_dl)
if __name__ == '__main__':
conf = common_init(config_filepath='confs/dawnbench.yaml',
param_args=['--common.experiment_name', 'dawn_net'])
conf_eval = conf['nas']['eval']
# evaluate architecture using eval settings
train_test(conf_eval)
exit(0)
from FastAutoAugment.common.common import common_init
from FastAutoAugment.random_arch.random_micro_builder import RandomMicroBuilder
from FastAutoAugment.nas import nas_utils
if __name__ == '__main__':
conf = common_init(config_filepath='confs/random_cifar.yaml',
param_args=['--common.experiment_name', 'random_cifar_search'])
# region config
conf_search = conf['nas']['search']
conf_model_desc = conf_search['model_desc']
final_desc_filename = conf_search['final_desc_filename']
# endregion
# create model and save it to yaml
# NOTE: there is no search here as the models are just randomly sampled
model_desc = nas_utils.create_macro_desc(conf_model_desc,
aux_tower=False,
template_model_desc=None)
macro_builder = RandomMicroBuilder()
macro_builder.build(model_desc, 0)
# save model to location specified by eval config
model_desc.save(final_desc_filename)
exit(0)
import json
import os
from FastAutoAugment.common.common import get_logger, common_init, expdir_abspath
from FastAutoAugment.data_aug.train import train_and_eval
if __name__ == '__main__':
conf = common_init(config_filepath='confs/aug_train_cifar.yaml',
param_args=[
"--autoaug.loader.aug", "fa_reduced_cifar10",
"--common.experiment_name", "autoaug_train"
])
logger = get_logger()
import time
t = time.time()
save_path = expdir_abspath('model.pth')
# result = train_and_eval(conf, val_ratio=conf['val_ratio'], val_fold=conf['val_fold'],
# save_path=save_path, only_eval=conf['only_eval'], metric='test')
# TODO: Will fail if val_ratio=0 since we are not using latest training infrastructure
# TODO: Move val_ratio, val_fold, metric to config file
result = train_and_eval(conf,
val_ratio=0.2,
val_fold=0,
save_path=save_path,
only_eval=False,
metric='test')
elapsed = time.time() - t
logger.info('training done.')
def main():
# accept search and eval scripts to run
# config file can be supplied using --config
parser = argparse.ArgumentParser(description='NAS E2E Runs')
parser.add_argument('--search-script', type=str,
default='scripts/petridish/cifar_search.py',
help='Search script to run')
parser.add_argument('--eval-script', type=str,
default='scripts/petridish/cifar_eval.py',
help='Eval script to run')
parser.add_argument('--exp_prefix', type=str, default='petridish',
help='Experiment prefix to use')
args, extra_args = parser.parse_known_args()
# load config to some of the settings like logdir
conf = common_init(use_args=True)
logdir = get_conf_common()['logdir']
assert logdir
# get script, resume flag and experiment dir for search
search_script = args.search_script
resume = conf['nas']['search']['resume']
search_script = utils.full_path(search_script.strip())
experiment_name = args.exp_prefix + '_' + Path(search_script).stem
experiment_dir = os.path.join(logdir, experiment_name)
# see if search has already produced the output
final_desc_filepath = os.path.join(experiment_dir, conf['nas']['search']['final_desc_filename'])
if not resume or not os.path.exists(final_desc_filepath):
print(f'Starting {search_script}...')
result = subprocess.run(
['python', search_script,
'--config', conf.config_filepath,
'--config-defaults', conf.config_defaults_filepath,
'--common.experiment_name', experiment_name
])
print(f'Script {search_script} returned {result.returncode}')
if result.returncode != 0:
exit(result.returncode)
else:
print(f'Search is skipped because file {final_desc_filepath} already exists')
# get script, resume flag and experiment dir for eval
eval_script = args.eval_script
resume = conf['nas']['eval']['resume']
eval_script = utils.full_path(eval_script.strip())
experiment_name = args.exp_prefix + '_' + Path(eval_script).stem
experiment_dir = os.path.join(logdir, experiment_name)
# if eval has already produced the output, skip eval run
model_filepath = os.path.join(experiment_dir, conf['nas']['eval']['save_filename'])
if not resume or not os.path.exists(model_filepath):
# copy output of search to eval folder
# TODO: take final_desc_filename from eval config
os.makedirs(experiment_dir, exist_ok=True)
shutil.copy2(final_desc_filepath, experiment_dir)
print(f'Starting {eval_script}...')
result = subprocess.run(
['python', eval_script,
'--config', conf.config_filepath,
'--config-defaults', conf.config_defaults_filepath,
'--common.experiment_name', experiment_name
])
print(f'Script {eval_script} returned {result.returncode}')
if result.returncode != 0:
exit(result.returncode)
else:
print(f'Eval is skipped because file {model_filepath} already exists')
print('Search and eval done.')
exit(0)
from FastAutoAugment.common.common import common_init
from FastAutoAugment.data_aug.search import search
if __name__ == '__main__':
conf = common_init(config_filepath='confs/wresnet40x2_cifar10_b512.yaml')
search(conf)
from FastAutoAugment.nas.arch_trainer import ArchTrainer
from FastAutoAugment.common.common import common_init
from FastAutoAugment.nas import search
from FastAutoAugment.petridish.petridish_micro_builder import PetridishMicroBuilder
if __name__ == '__main__':
conf = common_init(
config_filepath='confs/petridish_cifar.yaml',
param_args=['--common.experiment_name', 'petridish_cifar_search'])
# region config
conf_search = conf['nas']['search']
# endregion
micro_builder = PetridishMicroBuilder()
trainer_class = ArchTrainer
search.search_arch(conf_search, micro_builder, trainer_class)
exit(0)
from FastAutoAugment.nas.model_desc import ModelDesc
from FastAutoAugment.common.common import common_init
from FastAutoAugment.nas.model import Model
from FastAutoAugment.petridish.petridish_micro_builder import PetridishMicroBuilder
from FastAutoAugment.nas.nas_utils import create_macro_desc
from FastAutoAugment.common.model_summary import summary
conf = common_init(
config_filepath='confs/petridish_cifar.yaml',
param_args=['--common.experiment_name', 'petridish_run2_seed42_eval'])
conf_eval = conf['nas']['eval']
conf_model_desc = conf_eval['model_desc']
conf_model_desc['n_cells'] = 14
template_model_desc = ModelDesc.load('final_model_desc.yaml')
model_desc = create_macro_desc(conf_model_desc, True, template_model_desc)
mb = PetridishMicroBuilder()
mb.register_ops()
model = Model(model_desc, droppath=False, affine=False)
#model.cuda()
summary(model, [64, 3, 32, 32])
exit(0)
