def configurate_training_params()

in ModelConf.py [0:0]

• 42 lines of code
• 25 McCabe index (conditional complexity)

    def configurate_training_params(self):
        # optimizer
        if self.phase == 'train':
            if self.optimizer is None:
                self.raise_configuration_error('training_params.optimizer')
            if 'name' not in self.optimizer.keys():
                self.raise_configuration_error('training_params.optimizer.name')
            self.optimizer_name = self.optimizer['name']
            if 'params' not in self.optimizer.keys():
                self.raise_configuration_error('training_params.optimizer.params')
            self.optimizer_params = self.optimizer['params']
            if hasattr(self.params, 'learning_rate') and self.params.learning_rate:
                self.optimizer_params['lr'] = self.params.learning_rate
        
        # batch size
        self.batch_size_each_gpu = self.batch_size # the batch_size in conf file is the batch_size on each GPU
        if hasattr(self.params, 'batch_size') and self.params.batch_size:
            self.batch_size_each_gpu = self.params.batch_size
        if self.batch_size_each_gpu is None:
            self.raise_configuration_error('training_params.batch_size')
        self.batch_size_total = self.batch_size_each_gpu
        if torch.cuda.device_count() > 1:
            self.batch_size_total = torch.cuda.device_count() * self.batch_size_each_gpu
            self.batch_num_to_show_results = self.batch_num_to_show_results // torch.cuda.device_count()

    
        if hasattr(self.params, 'max_epoch') and self.params.max_epoch:
            self.max_epoch = self.params.max_epoch
        
        if self.valid_times_per_epoch is not None:
            logging.info("configuration[training_params][valid_times_per_epoch] is deprecated, please use configuration[training_params][steps_per_validation] instead")
        
        # sequence length
        if self.fixed_lengths:
            self.max_lengths = None
        if ProblemTypes[self.problem_type] == ProblemTypes.sequence_tagging:
            self.fixed_lengths = None
            self.max_lengths = None

        # text preprocessing
        self.__text_preprocessing = self.text_preprocessing
        self.DBC2SBC = True if 'DBC2SBC' in self.__text_preprocessing else False
        self.unicode_fix = True if 'unicode_fix' in self.__text_preprocessing else False
        self.remove_stopwords = True if 'remove_stopwords' in self.__text_preprocessing else False

        # tokenzier
        if self.tokenizer is None:
            self.tokenizer = 'jieba' if self.language == 'chinese' else 'nltk'
        
        # GPU/CPU
        if self.phase != 'cache':
            if torch.cuda.is_available() and torch.cuda.device_count() > 0 and self.use_gpu:
                logging.info("Activating GPU mode, there are %d GPUs available" % torch.cuda.device_count())
            else:
                self.use_gpu = False
                logging.info("Activating CPU mode")