def main(args): # pylint:disable=redefined-outer-name
# os.environ['CUDA_VISIBLE_DEVICE'] = args.device
if args.cuda and not torch.cuda.is_available(): # cuda is not available
args.cuda = False
print("CUDA NOT AVAILABLE!")
# args.max_epoch = 1
# args.controller_max_step = 1
# args.derive_num_sample = 1
torch.manual_seed(args.random_seed)
if args.cuda:
# print("Device Num", torch.cuda.current_device())
torch.cuda.manual_seed(args.random_seed)
utils.makedirs(args.dataset)
trnr = trainer.Trainer(args)
if args.mode == 'train':
print(args)
trnr.train()
elif args.mode == 'derive':
trnr.derive()
else:
raise Exception(f"[!] Mode not found: {args.mode}")
def main(args): # pylint:disable=redefined-outer-name
if args.cuda and not torch.cuda.is_available(): # cuda is not available
args.cuda = False
# args.max_epoch = 10
# args.controller_max_step = 1
# args.derive_num_sample = 1
torch.manual_seed(args.random_seed)
if args.cuda:
torch.cuda.manual_seed(args.random_seed)
# 创建包含数据集Citeseer名称目录
utils.makedirs(args.dataset)
"""
def makedirs(path):
if not os.path.exists(path):
logger.info("[*] Make directories : {}".format(path))
os.makedirs(path)
"""
#训练入口,获取最终GNN入口
trnr = trainer.Trainer(args)
if args.mode == 'train':
print(args)
trnr.train()
elif args.mode == 'derive':
trnr.derive()
else:
raise Exception(f"[!] Mode not found: {args.mode}")
def main(args): # pylint:disable=redefined-outer-name
if args.cuda and not torch.cuda.is_available(): # cuda is not available
args.cuda = False
# args.max_epoch = 1
# args.controller_max_step = 1
# args.derive_num_sample = 1
torch.manual_seed(args.random_seed)
if args.cuda:
torch.cuda.manual_seed(args.random_seed)
utils.makedirs(args.dataset)
trnr = trainer.Trainer(args)
if args.mode == 'train':
print(args)
trnr.train()
elif args.mode == 'derive':
trnr.derive()
else:
raise Exception(f"[!] Mode not found: {args.mode}")
aggregator_type_gene_space = [0, 1, 2, 3]
activate_function_gene_space = [0, 1, 2, 3, 4, 5, 6, 7]
number_of_heads_gene_space = [1, 2, 4, 6, 8, 16]
hidden_units_gene_space = [4, 8, 16, 32, 64, 128, 256]
gene_space = [
attention_type_gene_space, aggregator_type_gene_space,
activate_function_gene_space, number_of_heads_gene_space,
hidden_units_gene_space, attention_type_gene_space,
aggregator_type_gene_space, activate_function_gene_space,
number_of_heads_gene_space
]
num_genes = len(gene_space)
mutation_type = "random"
mutation_percent_genes = 10
trnr = trainer.Trainer(args)
ga_instance = pygad.GA(num_generations=num_generations,
num_parents_mating=num_parents_mating,
gene_type=int,
fitness_func=fitness_function,
sol_per_pop=sol_per_pop,
num_genes=num_genes,
gene_space=gene_space,
parent_selection_type=parent_selection_type,
keep_parents=keep_parents,
crossover_type=crossover_type,
mutation_type=mutation_type,
on_generation=callback_gen,
mutation_percent_genes=mutation_percent_genes)
ga_instance.run()