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update codes

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D-X-Y
2020-01-12 01:42:17 +11:00
parent 654015bf9d
commit 33384a78af
15 changed files with 288 additions and 21 deletions
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18
exps/algos/GDAS.py
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@@ -88,12 +88,17 @@ def main(xargs):
logger.log('||||||| {:10s} ||||||| Config={:}'.format(xargs.dataset, config))
search_space = get_search_spaces('cell', xargs.search_space_name)
model_config = dict2config({'name': 'GDAS', 'C': xargs.channel, 'N': xargs.num_cells,
'max_nodes': xargs.max_nodes, 'num_classes': class_num,
'space' : search_space,
'affine' : False, 'track_running_stats': bool(xargs.track_running_stats)}, None)
if xargs.model_config is None:
model_config = dict2config({'name': 'GDAS', 'C': xargs.channel, 'N': xargs.num_cells,
'max_nodes': xargs.max_nodes, 'num_classes': class_num,
'space' : search_space,
'affine' : False, 'track_running_stats': bool(xargs.track_running_stats)}, None)
else:
model_config = load_config(xargs.model_config, {'num_classes': class_num, 'space' : search_space,
'affine' : False, 'track_running_stats': bool(xargs.track_running_stats)}, None)
search_model = get_cell_based_tiny_net(model_config)
logger.log('search-model :\n{:}'.format(search_model))
logger.log('model-config : {:}'.format(model_config))
w_optimizer, w_scheduler, criterion = get_optim_scheduler(search_model.get_weights(), config)
a_optimizer = torch.optim.Adam(search_model.get_alphas(), lr=xargs.arch_learning_rate, betas=(0.5, 0.999), weight_decay=xargs.arch_weight_decay)
@@ -104,7 +109,7 @@ def main(xargs):
flop, param = get_model_infos(search_model, xshape)
#logger.log('{:}'.format(search_model))
logger.log('FLOP = {:.2f} M, Params = {:.2f} MB'.format(flop, param))
logger.log('search-space : {:}'.format(search_space))
logger.log('search-space [{:} ops] : {:}'.format(len(search_space), search_space))
if xargs.arch_nas_dataset is None:
api = None
else:
@@ -173,7 +178,7 @@ def main(xargs):
logger.log('<<<--->>> The {:}-th epoch : find the highest validation accuracy : {:.2f}%.'.format(epoch_str, valid_a_top1))
copy_checkpoint(model_base_path, model_best_path, logger)
with torch.no_grad():
logger.log('arch-parameters :\n{:}'.format( nn.functional.softmax(search_model.arch_parameters, dim=-1).cpu() ))
logger.log('{:}'.format(search_model.show_alphas()))
if api is not None: logger.log('{:}'.format(api.query_by_arch( genotypes[epoch] )))
# measure elapsed time
epoch_time.update(time.time() - start_time)
@@ -198,6 +203,7 @@ if __name__ == '__main__':
parser.add_argument('--num_cells', type=int, help='The number of cells in one stage.')
parser.add_argument('--track_running_stats',type=int, choices=[0,1],help='Whether use track_running_stats or not in the BN layer.')
parser.add_argument('--config_path', type=str, help='The path of the configuration.')
parser.add_argument('--model_config', type=str, help='The path of the model configuration. When this arg is set, it will cover max_nodes / channels / num_cells.')
# architecture leraning rate
parser.add_argument('--arch_learning_rate', type=float, default=3e-4, help='learning rate for arch encoding')
parser.add_argument('--arch_weight_decay', type=float, default=1e-3, help='weight decay for arch encoding')