config.py

#coding:utf8
import time
import warnings

tfmt = '%m%d_%H%M%S'
class Config(object):
    '''
    并不是所有的配置都生效,实际运行中只根据需求获取自己需要的参数
    '''

    loss = 'multilabelloss'
    model='CNNText' 
    title_dim = 100 # 标题的卷积核数
    content_dim = 200 #描述的卷积核数
    num_classes = 1999 # 类别
    embedding_dim = 256 # embedding大小
    linear_hidden_size = 2000 # 全连接层隐藏元数目
    kmax_pooling = 2# k
    hidden_size = 256 #LSTM hidden size
    num_layers=2 #LSTM layers
    inception_dim = 512 #inception的卷积核数
    
    # vocab_size = 11973 # num of chars
    vocab_size = 411720 # num of words 
    kernel_size = 3 #单尺度卷积核
    kernel_sizes = [2,3,4] #多尺度卷积核
    title_seq_len = 50 # 标题长度,word为30 char为50
    content_seq_len = 250 #描述长度 word为120 char为250
    type_='word' #word 和char
    all=False # 模型同时训练char和word

    embedding_path = '/mnt/7/zhihu/ieee_zhihu_cup/data/char_embedding.npz' # Embedding
    train_data_path = '/mnt/7/zhihu/ieee_zhihu_cup/data/train.npz' # train
    labels_path = '/mnt/7/zhihu/ieee_zhihu_cup/data/labels.json' # labels    
    test_data_path='/mnt/7/zhihu/ieee_zhihu_cup/data/test.npz' # test
    result_path='csv/'+time.strftime(tfmt)+'.csv'
    shuffle = True # 是否需要打乱数据
    num_workers = 4 # 多线程加载所需要的线程数目
    pin_memory =  True # 数据从CPU->pin_memory—>GPU加速
    batch_size = 128

    env = time.strftime(tfmt) # Visdom env
    plot_every = 10 # 每10个batch，更新visdom等

    max_epoch=100
    lr = 5e-3 # 学习率
    lr2 = 1e-3 # embedding层的学习率
    min_lr = 1e-5 # 当学习率低于这个值，就退出训练
    lr_decay = 0.99 # 当一个epoch的损失开始上升lr = lr*lr_decay 
    weight_decay = 0 #2e-5 # 权重衰减
    weight = 1 # 正负样本的weight
    decay_every = 3000 #每多少个batch 查看一下score,并随之修改学习率

    model_path = None # 如果有 就加载
    optimizer_path='optimizer.pth' # 优化器的保存地址

    debug_file = '/tmp/debug2' #若该文件存在则进入debug模式
    debug=False
    
    gpu1 = False #如果在GPU1上运行代码,则需要修改数据存放的路径
    floyd=False # 服务如果在floyd上运行需要修改文件路径
    zhuge=False # 服务如果在zhuge上运行,修改文件路径

    ### multimode 用到的
    model_names=['MultiCNNTextBNDeep','CNNText_inception','RCNN','LSTMText','CNNText_inception']
    model_paths = ['checkpoints/MultiCNNTextBNDeep_0.37125473788','checkpoints/CNNText_tmp_0.380390420742','checkpoints/RCNN_word_0.373609030286','checkpoints/LSTMText_word_0.381833388089','checkpoints/CNNText_tmp_0.376364647145']#,'checkpoints/CNNText_tmp_0.402429167301']
    static=False # 是否训练embedding
    val=False # 跑测试集还是验证集?
    
    fold = 1 # 数据集fold, 0或1 见 data/fold_dataset.py
    augument=True # 是否进行数据增强

    ###stack
    model_num=7
    data_root="/data/text/zhihu/result/"
    labels_file="/home/a/code/pytorch/zhihu/ddd/labels.json"
    val="/home/a/code/pytorch/zhihu/ddd/val.npz"

def parse(self,kwargs,print_=True):
        '''
        根据字典kwargs 更新 config参数
        '''
        for k,v in kwargs.iteritems():
            if not hasattr(self,k):
                raise Exception("opt has not attribute <%s>" %k)
            setattr(self,k,v) 

        ###### 根据程序在哪台服务器运行,自动修正数据存放路径 ######
        if self.gpu1:
            self.train_data_path='/mnt/zhihu/data/train.npz'
            self.test_data_path='/mnt/zhihu/data/%s.npz' %('val' if self.val else 'test')
            self.labels_path='/mnt/zhihu/data/labels.json'
            self.embedding_path=self.embedding_path.replace('/mnt/7/zhihu/ieee_zhihu_cup/','/mnt/zhihu/')
        
        if self.floyd:
            self.train_data_path='/data/train.npz'
            self.test_data_path='/data/%s.npz' %('val' if self.val else 'test')
            self.labels_path='/data/labels.json'
            self.embedding_path='/data/char_embedding.npz'
        if self.zhuge:
            self.train_data_path='./ddd/train.npz'
            self.test_data_path='./ddd/%s.npz' %('val' if self.val else 'test')
            self.labels_path='./ddd/labels.json'
            self.embedding_path='./ddd/char_embedding.npz'

        ### word和char的长度不一样 ##
        if self.type_=='word':
            self.vocab_size = 411720 # num of words 
            self.title_seq_len = 30
            self.content_seq_len = 120 
            self.embedding_path=self.embedding_path.replace('char','word') if self.embedding_path is not None else None
            
        if self.type_=='char':
            self.vocab_size = 11973 # num of words
            self.title_seq_len = 50
            self.content_seq_len = 250 
        
        if self.model_path:
            self.embedding_path=None
        
        if print_:
            print('user config:')
            print('#################################')
            for k in dir(self):
                if not k.startswith('_') and k!='parse' and k!='state_dict':
                    print k,getattr(self,k)
            print('#################################')
        return self

def state_dict(self):
    return  {k:getattr(self,k) for k in dir(self) if not k.startswith('_') and k!='parse' and k!='state_dict' }


Config.parse = parse
Config.state_dict = state_dict
opt = Config()