Pylint: cannot recognize some member in pytorch

[zchrissirhcz]

Steps to reproduce

1. I use Ubuntu16.04, 64bit system, python2.7.12. I also use VSCode1.23 as my text editor.
2. I use pytorch in gpu mode. it needs sudo pip install torch torchvision. I have a GPU and CUDA and CuDNN installed. I'm not sure if they are necessary.
3. I already put these content in ~/.pylintrc:

[MASTER]
extension-pkg-whitelist=numpy,torch

[TYPECHECK]
ignored-modules=numpy,torch
ignored-classes=numpy,torch
generated-members=numpy.*,torch.*

1. I use these code:

#coding:utf8
from config import opt
import os
import torch as t
import models
from data.dataset import DogCat
from torch.utils.data import DataLoader
from torch.autograd import Variable
from torchnet import meter
from utils.visualize import Visualizer
from tqdm import tqdm

def test(**kwargs):
    opt.parse(kwargs)
    # import ipdb;
    # ipdb.set_trace()
    # configure model
    model = getattr(models, opt.model)().eval()
    if opt.load_model_path:
        model.load(opt.load_model_path)
    if opt.use_gpu: model.cuda()

    # data
    train_data = DogCat(opt.test_data_root,test=True)
    test_dataloader = DataLoader(train_data,batch_size=opt.batch_size,shuffle=False,num_workers=opt.num_workers)
    results = []
    for ii,(data,path) in tqdm(enumerate(test_dataloader)):
        input = t.autograd.Variable(data,volatile = True)
        if opt.use_gpu: input = input.cuda()
        score = model(input)
        probability = t.nn.functional.softmax(score)[:,0].data.tolist()
        # label = score.max(dim = 1)[1].data.tolist()

        batch_results = [(path_,probability_) for path_,probability_ in zip(path,probability) ]

        results += batch_results
    write_csv(results,opt.result_file)

    return results

def write_csv(results,file_name):
    import csv
    with open(file_name,'w') as f:
        writer = csv.writer(f)
        writer.writerow(['id','label'])
        writer.writerows(results)

def train(**kwargs):
    opt.parse(kwargs)
    vis = Visualizer(opt.env)

    # step1: configure model
    model = getattr(models, opt.model)()
    if opt.load_model_path:
        model.load(opt.load_model_path)
    if opt.use_gpu: model.cuda()

    # step2: data
    train_data = DogCat(opt.train_data_root,train=True)
    val_data = DogCat(opt.train_data_root,train=False)
    train_dataloader = DataLoader(train_data,opt.batch_size,
                        shuffle=True,num_workers=opt.num_workers)
    val_dataloader = DataLoader(val_data,opt.batch_size,
                        shuffle=False,num_workers=opt.num_workers)

    # step3: criterion and optimizer
    criterion = t.nn.CrossEntropyLoss()
    lr = opt.lr
    optimizer = t.optim.Adam(model.parameters(),lr = lr,weight_decay = opt.weight_decay)

    # step4: meters
    loss_meter = meter.AverageValueMeter()
    confusion_matrix = meter.ConfusionMeter(2)
    previous_loss = 1e100

    # train
    for epoch in range(opt.max_epoch):

        loss_meter.reset()
        confusion_matrix.reset()

        for ii,(data,label) in tqdm(enumerate(train_dataloader)):

            # train model 
            inpu = Variable(data)  # inpu mean input
            target = Variable(label)
            if opt.use_gpu:
                inpu = inpu.cuda()
                target = target.cuda()

            optimizer.zero_grad()
            score = model(inpu)
            loss = criterion(score,target)
            loss.backward()
            optimizer.step()


            # meters update and visualize
            loss_meter.add(loss.data[0])
            confusion_matrix.add(score.data, target.data)

            if ii%opt.print_freq==opt.print_freq-1:
                vis.plot('loss', loss_meter.value()[0])

                # 进入debug模式
                if os.path.exists(opt.debug_file):
                    import ipdb
                    ipdb.set_trace()


        model.save()

        # validate and visualize
        val_cm,val_accuracy = val(model,val_dataloader)

        vis.plot('val_accuracy',val_accuracy)
        vis.log("epoch:{epoch},lr:{lr},loss:{loss},train_cm:{train_cm},val_cm:{val_cm}".format(
                    epoch = epoch,loss = loss_meter.value()[0],val_cm = str(val_cm.value()),train_cm=str(confusion_matrix.value()),lr=lr))

        # update learning rate
        if loss_meter.value()[0] > previous_loss:          
            lr = lr * opt.lr_decay
            # 第二种降低学习率的方法:不会有moment等信息的丢失
            for param_group in optimizer.param_groups:
                param_group['lr'] = lr


        previous_loss = loss_meter.value()[0]

def val(model,dataloader):
    """
    计算模型在验证集上的准确率等信息
    """
    model.eval()
    confusion_matrix = meter.ConfusionMeter(2)
    for ii, data in tqdm(enumerate(dataloader)):
        input, label = data
        val_input = Variable(input, volatile=True)
        if opt.use_gpu:
            val_input = val_input.cuda()
        score = model(val_input)
        confusion_matrix.add(score.data.squeeze(), label.type(t.LongTensor))

    model.train()
    cm_value = confusion_matrix.value()
    accuracy = 100. * (cm_value[0][0] + cm_value[1][1]) / (cm_value.sum())
    return confusion_matrix, accuracy

def help():
    """
    打印帮助的信息： python file.py help
    """

    print("""
    usage : python file.py <function> [--args=value]
    <function> := train | test | help
    example: 
            python {0} train --env='env0701' --lr=0.01
            python {0} test --dataset='path/to/dataset/root/'
            python {0} help
    avaiable args:""".format(__file__))

    from inspect import getsource
    source = (getsource(opt.__class__))
    print(source)

if __name__=='__main__':
    import fire
    fire.Fire()

Current behavior

pylint complains E1101 that Instance of 'Variable' has no 'cuda' member. Which, actually, my code can run correctly.

Expected behavior

pylint, please don't complain

pylint --version output

Using config file /home/chris/.pylintrc
pylint 1.8.4,
astroid 1.6.3
Python 2.7.12 (default, Dec 4 2017, 14:50:18)
[GCC 5.4.0 20160609]

[PCManticore]

This is probably a bug that would need an astroid brain tip to understand what pytorch is doing here.

[agajews]

Quick fix: it works if you set generated-members=numpy.*,torch.*,Variable

[Duan-JM]

Thanks, I use @agajews 's solution, it works for me.

[clouds56]

@agajews thanks for tips, this help mitigate most errors while still output

Instance of 'Variable' has no 'data' member

am I missing something?

---

update:
I'm using --generated-members=numpy.*,torch.*,*.data,*.grad to work around this