def __init__(self, config, model, data_feature):
self.config = config
self.device = self.config.get('device', torch.device('cpu'))
self.model = model.to(self.device)
self.evaluator = get_evaluator(config)
self.exp_id = self.config.get('exp_id', None)
self.cache_dir = './libcity/cache/{}/model_cache'.format(self.exp_id)
self.evaluate_res_dir = './libcity/cache/{}/evaluate_cache'.format(self.exp_id)
self.tmp_path = './libcity/tmp/checkpoint/'
def __init__(self, config, model, data_feature):
self.model = model
self.config = config
self.evaluator = get_evaluator(config)
self.exp_id = self.config.get('exp_id', None)
self.cache_dir = './libcity/cache/{}/model_cache'.format(self.exp_id)
self.evaluate_res_dir = './libcity/cache/{}/evaluate_cache'.format(
self.exp_id)
self._logger = getLogger()
def __init__(self, config, model, data_feature):
self.evaluator = get_evaluator(config)
self.metrics = 'Recall@{}'.format(config['topk'])
self.config = config
self.model = model.to(self.config['device'])
self.tmp_path = './libcity/tmp/checkpoint/'
self.exp_id = self.config.get('exp_id', None)
self.cache_dir = './libcity/cache/{}/model_cache'.format(self.exp_id)
self.evaluate_res_dir = './libcity/cache/{}/evaluate_cache'.format(
self.exp_id)
self.loss_func = None # TODO: 根据配置文件支持选择特定的 Loss Func 目前并未实装
self._logger = getLogger()
self.optimizer = self._build_optimizer()
self.scheduler = self._build_scheduler()
def __init__(self, config, model, data_feature):
self.evaluator = get_evaluator(config)
self.config = config
self.data_feature = data_feature
self.device = self.config.get('device', torch.device('cpu'))
self.model = model
self.exp_id = self.config.get('exp_id', None)
self.cache_dir = './libcity/cache/{}/model_cache'.format(self.exp_id)
self.evaluate_res_dir = './libcity/cache/{}/evaluate_cache'.format(self.exp_id)
ensure_dir(self.cache_dir)
ensure_dir(self.evaluate_res_dir)
self._logger = getLogger()
self._scaler = self.data_feature.get('scaler')
self.output_dim = self.config.get('output_dim', 1)
def __init__(self, config, model, data_feature):
self.evaluator = get_evaluator(config)
self.config = config
self.data_feature = data_feature
self.device = self.config.get('device', torch.device('cpu'))
self.model = model.to(self.device)
self.exp_id = self.config.get('exp_id', None)
self.cache_dir = './libcity/cache/{}/model_cache'.format(self.exp_id)
self.evaluate_res_dir = './libcity/cache/{}/evaluate_cache'.format(
self.exp_id)
self.summary_writer_dir = './libcity/cache/{}/'.format(self.exp_id)
ensure_dir(self.cache_dir)
ensure_dir(self.evaluate_res_dir)
ensure_dir(self.summary_writer_dir)
self._writer = SummaryWriter(self.summary_writer_dir)
self._logger = getLogger()
self._scaler = self.data_feature.get('scaler')
self._logger.info(self.model)
for name, param in self.model.named_parameters():
self._logger.info(
str(name) + '\t' + str(param.shape) + '\t' +
str(param.device) + '\t' + str(param.requires_grad))
total_num = sum(
[param.nelement() for param in self.model.parameters()])
self._logger.info('Total parameter numbers: {}'.format(total_num))
self.epochs = self.config.get('max_epoch', 100)
self.train_loss = self.config.get('train_loss', 'none')
self.learner = self.config.get('learner', 'adam')
self.learning_rate = self.config.get('learning_rate', 0.01)
self.weight_decay = self.config.get('weight_decay', 0)
self.lr_beta1 = self.config.get('lr_beta1', 0.9)
self.lr_beta2 = self.config.get('lr_beta2', 0.999)
self.lr_betas = (self.lr_beta1, self.lr_beta2)
self.lr_alpha = self.config.get('lr_alpha', 0.99)
self.lr_epsilon = self.config.get('lr_epsilon', 1e-8)
self.lr_momentum = self.config.get('lr_momentum', 0)
self.lr_decay = self.config.get('lr_decay', False)
self.lr_scheduler_type = self.config.get('lr_scheduler', 'multisteplr')
self.lr_decay_ratio = self.config.get('lr_decay_ratio', 0.1)
self.milestones = self.config.get('steps', [])
self.step_size = self.config.get('step_size', 10)
self.lr_lambda = self.config.get('lr_lambda', lambda x: x)
self.lr_T_max = self.config.get('lr_T_max', 30)
self.lr_eta_min = self.config.get('lr_eta_min', 0)
self.lr_patience = self.config.get('lr_patience', 10)
self.lr_threshold = self.config.get('lr_threshold', 1e-4)
self.clip_grad_norm = self.config.get('clip_grad_norm', False)
self.max_grad_norm = self.config.get('max_grad_norm', 1.)
self.use_early_stop = self.config.get('use_early_stop', False)
self.patience = self.config.get('patience', 50)
self.log_every = self.config.get('log_every', 1)
self.saved = self.config.get('saved_model', True)
self.load_best_epoch = self.config.get('load_best_epoch', True)
self.hyper_tune = self.config.get('hyper_tune', False)
self.output_dim = self.config.get('output_dim', 1)
self.optimizer = self._build_optimizer()
self.lr_scheduler = self._build_lr_scheduler()
self._epoch_num = self.config.get('epoch', 0)
if self._epoch_num > 0:
self.load_model_with_epoch(self._epoch_num)
self.loss_func = self._build_train_loss()