def run_single(self):
try:
from run.runner import Runner
except ModuleNotFoundError:
from runner import Runner
run = Runner(self.agent,
self.environment,
epochs=self.args.epochs,
episodes=self.args.episodes,
horizon=self.args.horizon,
mode='train',
verbose=self.args.verbose,
model_path=self.args.model_path,
seed=92)
run.run()
run.close(save_full=self.args.eval_path + '/full_' + str(self.agent) +
'_evaluation.csv',
save_short=self.args.eval_path + '/short_' +
str(self.agent) + '_evaluation.csv')
from run.runner import Runner
if __name__ == "__main__":
run = Runner()
run.run("graph-5-hand-made", init_state_from_file=False)
bayer_pattern_size=2,
cfa_sim_out_channels=3,
correct_pixel_shift=True,
balance_labels=False,
label_percentage=1.0,
verbose=False)
run_amount = 10
eval_best_amount = 3
networks = [
NetworkIdentifier.REDUCED_UNET, NetworkIdentifier.BAYER_UNET,
NetworkIdentifier.BAYER_DENSENET, NetworkIdentifier.CONV_40_1,
NetworkIdentifier.CONV_40_3, NetworkIdentifier.CONV_40_5,
NetworkIdentifier.CONV_40_7, NetworkIdentifier.CONV_40_9
]
input_types = [
InputType.URBAN_HYPERSPECTRAL_4, InputType.URBAN_HYPERSPECTRAL_4_ONLY_RGB
]
for network in networks:
for input_type in input_types:
run_config.input_type = input_type
run_config.network_identifier = network
# use adagrad from conv_40_7
if run_config.input_type == NetworkIdentifier.CONV_40_7:
run_config.optimizer = torch.optim.adagrad
run_config.update_fingerprint()
Runner(run_config, run_amount, eval_best_amount).execute_runs()