# trainnewGAN_task.execute("6", "eccv", "exp_pretrained_F_IL")
# train_da.execute("8", "eccv", "all_da_v2")
# train_da_shared.execute("1", "eccv", "all_da_shared_E1E12_l1_10")
# train_da_shared.execute("9", "eccv", "all_da_shared_E1E12_l1_25_aug")
# train_da_shared.execute("3", "eccv", "all_da_shared_E1E12_l1_25_new_nopretrained")
# train_wdgrl_withoutgen.execute("0", "eccv", "exp_wdgrl_better")
# train_da_no5.execute("8", "eccv", "all_da_aug_no5_E1E12")
# testGAN.execute("7", "eccv", "all_da_orig")
# trainnewGAN.execute("4", "eccv", "exp_reconstruct")
# trainnewGAN.execute("4", "eccv", "experiment_1")
if args.single_process == 'validation':
execute_validation("0", "eccv", "experiment_1", "SeqVal")
if args.single_process == 'drive':
execute_drive("0", "eccv", "experiment_1", 'Town02')
else:
# TODO: of course this change from gpu to gpu , but for now we just assume at least a K40
# Maybe the latest voltas will be underused
# OBS: This usage is also based on my tensorflow experiences, maybe pytorch allows more.
allocation_parameters = {
'gpu_value': 3.5,
'train_cost': 2,
'validation_cost': 1.5,
'drive_cost': 1.5
}
params = {
elif args.single_process == 'validation':
if len(args.validation_datasets) == 1:
execute_validation("0", args.folder, args.exp,
args.validation_datasets[0], args.model,
False)
else:
execute_validation("0", args.folder, args.exp,
args.validation_datasets, args.model, False)
elif args.single_process == 'drive':
driving_environments = fix_driving_environments(
list(args.driving_environments))
execute_drive("0",
args.folder,
args.exp,
driving_environments[0],
False,
no_screen=args.no_screen)
else:
raise (
" Invalid name for single process, chose from (train, validation, test)"
)
else:
# TODO: of course this change from gpu to gpu , but for now we just assume at least a K40
# Maybe the latest voltas will be underused
# OBS: This usage is also based on my tensorflow experiences, maybe pytorch allows more.
allocation_parameters = {
execute_train(gpu="0",
exp_batch=args.folder,
exp_alias=args.exp,
suppress_output=False,
number_of_workers=args.number_of_workers)
elif args.single_process == 'validation':
execute_validation(gpu="0",
exp_batch=args.folder,
exp_alias=args.exp,
dataset=args.validation_datasets[0],
suppress_output=False)
elif args.single_process == 'drive':
drive_params['suppress_output'] = False
execute_drive("0", args.folder, args.exp,
list(args.driving_environments)[0], drive_params)
else:
raise Exception(
"Invalid name for single process, chose from (train, validation, test)"
)
else:
####
# MODE 2: Folder execution. Execute train/validation/drive for all experiments on
# a certain training folder
####
# We set by default that each gpu has a value of 3.5, allowing a training and
# a driving/validation
allocation_parameters = {
'gpu_value': args.gpu_value,