Python models.setup() Examples

The following are 3 code examples of models.setup(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module models , or try the search function .
Example #1
Source File: train_AREL.py    From AREL with MIT License 5 votes vote down vote up 
def test(opt):
    logger = Logger(opt)
    dataset = VISTDataset(opt)
    opt.vocab_size = dataset.get_vocab_size()
    opt.seq_length = dataset.get_story_length()

    dataset.test()
    test_loader = DataLoader(dataset, batch_size=opt.batch_size, shuffle=False, num_workers=opt.workers)
    evaluator = Evaluator(opt, 'test')
    model = models.setup(opt)
    model.cuda()
    predictions, metrics = evaluator.test_story(model, dataset, test_loader, opt)
Example #2
Source File: train.py    From AREL with MIT License 5 votes vote down vote up 
def test(opt):
    logger = Logger(opt)
    dataset = VISTDataset(opt)
    opt.vocab_size = dataset.get_vocab_size()
    opt.seq_length = dataset.get_story_length()

    dataset.test()
    test_loader = DataLoader(dataset, batch_size=opt.batch_size, shuffle=False, num_workers=opt.workers)
    evaluator = Evaluator(opt, 'test')
    model = models.setup(opt)
    model.cuda()
    predictions, metrics = evaluator.test_story(model, dataset, test_loader, opt)
Example #3
Source File: main.py    From TextClassificationBenchmark with MIT License 4 votes vote down vote up 
def train(opt,train_iter, test_iter,verbose=True):
    global_start= time.time()
    logger = utils.getLogger()
    model=models.setup(opt)
    if torch.cuda.is_available():
        model.cuda()
    params = [param for param in model.parameters() if param.requires_grad] #filter(lambda p: p.requires_grad, model.parameters())
    
    model_info =";".join( [str(k)+":"+ str(v)  for k,v in opt.__dict__.items() if type(v) in (str,int,float,list,bool)])  
    logger.info("# parameters:" + str(sum(param.numel() for param in params)))
    logger.info(model_info)
    
    
    model.train()
    optimizer = utils.getOptimizer(params,name=opt.optimizer, lr=opt.learning_rate,scheduler= utils.get_lr_scheduler(opt.lr_scheduler))

    loss_fun = F.cross_entropy

    filename = None
    percisions=[]
    for i in range(opt.max_epoch):
        for epoch,batch in enumerate(train_iter):
            optimizer.zero_grad()
            start= time.time()
            
            text = batch.text[0] if opt.from_torchtext else batch.text
            predicted = model(text)
    
            loss= loss_fun(predicted,batch.label)
    
            loss.backward()
            utils.clip_gradient(optimizer, opt.grad_clip)
            optimizer.step()
            
            if verbose:
                if  torch.cuda.is_available():
                    logger.info("%d iteration %d epoch with loss : %.5f in %.4f seconds" % (i,epoch,loss.cpu().data.numpy(),time.time()-start))
                else:
                    logger.info("%d iteration %d epoch with loss : %.5f in %.4f seconds" % (i,epoch,loss.data.numpy()[0],time.time()-start))
 
        percision=utils.evaluation(model,test_iter,opt.from_torchtext)
        if verbose:
            logger.info("%d iteration with percision %.4f" % (i,percision))
        if len(percisions)==0 or percision > max(percisions):
            if filename:
                os.remove(filename)
            filename = model.save(metric=percision)
        percisions.append(percision)
            
#    while(utils.is_writeable(performance_log_file)):
    df = pd.read_csv(performance_log_file,index_col=0,sep="\t")
    df.loc[model_info,opt.dataset] =  max(percisions) 
    df.to_csv(performance_log_file,sep="\t")    
    logger.info(model_info +" with time :"+ str( time.time()-global_start)+" ->" +str( max(percisions) ) )
    print(model_info +" with time :"+ str( time.time()-global_start)+" ->" +str( max(percisions) ) )