if __name__ == '__main__':
from train import standard_arg_paser, generate_train_cmd, run_cmds, defaults, all_models, all_output_types
parser = standard_arg_paser()
args = parser.parse_args()
# Settings
checkpoint_base_dir = "/scratch1/rwt891/data/deepfold/camara"
test_base_dir = "/scratch1/rwt891/data/deepfold/camara_test"
id_number = "01"
# Generate commands
cmds = []
data_dir_base = "/scratch1/rwt891/data/cull_pdb_pc100_entries_170602/culled_pc30_res3.0_R0.3_d170611/"
data_name = "pc30"
num_passes = "10"
for model in [
"CubedSphereModel", "SphericalModel", "CartesianHighresModel"
]:
for output_type in all_output_types:
cmd = generate_train_cmd(
args.mode,
model,
output_type,
data_name,
defaults['regularization'],
defaults['learning_rate'],
'--model', model
]
if restore_checkpoint == 'best':
best = get_best_checkpoint(checkpoint_base_dir + "/" + name + ".txt")
cmd += ["--step", best]
output_file = output_dir + '/' + ddg_name + '_' + restore_checkpoint + "_" + name + '.txt'
return cmd, output_file, name
if __name__ == '__main__':
from train import standard_arg_paser, run_cmds, defaults, all_models, all_output_types
parser = standard_arg_paser(exclude=['mode'])
args = parser.parse_args()
# Settings
checkpoint_base_dir = "/scratch1/rwt891/data/deepfold/camara"
output_dir = "/scratch1/rwt891/data/deepfold/camara_ddg"
ddg_data_dir_base = "/scratch1/rwt891/data/ddgs"
train_data_name = "pc30"
train_num_passes = "10"
train_id_number = "01"
output_type = 'aa'
# Generate commands