def init_environment(self):
# super().init_environment() # 先执行一次父类的该方法，这里实际上就是在父类方法的基础上进行扩展
#父类方法，直接抄过来方便查阅
self.io = torchlight.IO( #class 'torchlight.io.IO',这里的torchlight.IO就是一个容器
self.arg.work_dir, #存储结果的路径，默认为'./work_dir/tmp'
save_log=self.arg.save_log, #是否保存日志，默认true
print_log=self.arg.print_log) #是否打印日志，默认true
self.io.save_arg(self.arg)
# gpu
if self.arg.use_gpu: #如果指定了使用GPU，就默认使用0号GPU，否则使用CPU
gpus = torchlight.visible_gpu(
self.arg.device) #返回的是list(range(len(gpus)))
torchlight.occupy_gpu(gpus)
self.gpus = gpus
print('现在使用的gpu为：', self.gpus)
self.dev = "cuda:0"
else:
self.dev = "cpu"
#子类方法
#定义了几个字典对象
self.result = dict()
self.iter_info = dict()
self.epoch_info = dict()
self.meta_info = dict(epoch=0, iter=0)
def init_environment(self):
# gpu
if self.arg.use_gpu:
gpus = torchlight.visible_gpu(self.arg.device)
torchlight.occupy_gpu(gpus)
self.gpus = gpus
self.dev = "cuda:0"
else:
self.dev = "cpu"
# random seed
if self.arg.fix_random:
SEED = 0
#torch.backends.cudnn.deterministic = True
torch.manual_seed(SEED)
torch.cuda.manual_seed_all(SEED)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
np.random.seed(SEED)
random.seed(SEED)
# dir
self.work_dir = get_dir(self.arg.work_dir, None)
self.config_dir = get_dir(self.arg.work_dir, 'config')
self.checkpoint_dir = get_dir(self.arg.work_dir, 'checkpoints')
self.log_dir = get_dir(self.arg.work_dir, 'log')
self.tflog_dir = get_dir(self.arg.work_dir, 'tflog')
self.test_dir = get_dir(self.arg.work_dir, 'test_results')
def init_environment(self):
self.io = torchlight.IO(self.arg.work_dir,
save_log=self.arg.save_log,
print_log=self.arg.print_log)
self.io.save_arg(self.arg)
# gpu
gpus = torchlight.visible_gpu(self.arg.device)
torchlight.occupy_gpu(gpus)
self.gpus = gpus
self.dev = "cuda:0"
def init_environment(self):
self.io = torchlight.IO(self.arg.work_dir,
save_log=self.arg.save_log,
print_log=self.arg.print_log)
self.io.save_arg(self.arg)
print('Mid of init enviroment')
# gpu
if self.arg.use_gpu:
gpus = torchlight.visible_gpu(self.arg.device)
torchlight.occupy_gpu(gpus)
self.gpus = gpus
self.dev = "cuda:0"
else:
self.dev = "cpu"
def init_environment(self):
self.io = torchlight.IO( #class 'torchlight.io.IO',这里的torchlight.IO就是一个容器
self.arg.work_dir, #存储结果的路径，默认为'./work_dir/tmp'
save_log=self.arg.save_log, #是否保存日志，默认true
print_log=self.arg.print_log) #是否打印日志，默认true
self.io.save_arg(self.arg)
# gpu
if self.arg.use_gpu: #如果指定了使用GPU，就默认使用0号GPU，否则使用CPU
gpus = torchlight.visible_gpu(self.arg.device)
torchlight.occupy_gpu(gpus)
self.gpus = gpus
self.dev = "cuda:0"
else:
self.dev = "cpu"
def init_environment(self):
self.io = torchlight.IO(
self.arg.work_dir,
save_log=self.arg.save_log,
print_log=self.arg.print_log)
self.io.save_arg(self.arg)
# gpu
if self.arg.use_gpu:
gpus = torchlight.visible_gpu(self.arg.device)
torchlight.occupy_gpu(gpus)
self.gpus = gpus
self.dev = "cuda:0"
else:
self.dev = "cpu"
def init_environment(self):
self.save_dir = os.path.join(self.arg.work_dir, self.arg.max_hop_dir,
self.arg.lamda_act_dir)
self.io = torchlight.IO(self.save_dir,
save_log=self.arg.save_log,
print_log=self.arg.print_log)
self.io.save_arg(self.arg)
# gpu
if self.arg.use_gpu:
gpus = torchlight.visible_gpu(self.arg.device)
torchlight.occupy_gpu(gpus)
self.gpus = gpus
self.dev = "cuda:0"
else:
self.dev = "cpu"
def init_environment(self):
self.save_dir = os.path.join(self.arg.work_dir,
self.arg.fusion_layer_dir,
self.arg.learning_rate_dir,
self.arg.lamda_dir,
self.arg.crossw_dir,
self.arg.note)
self.io = torchlight.IO(self.save_dir, save_log=self.arg.save_log, print_log=self.arg.print_log)
self.io.save_arg(self.arg)
if self.arg.use_gpu:
gpus = torchlight.visible_gpu(self.arg.device)
torchlight.occupy_gpu(gpus)
self.gpus = gpus
self.dev = "cuda:0"
else:
self.dev = "cpu"
