将Captum与Pytorch Lighting一起使用？

如何解决将Captum与Pytorch Lighting一起使用？

因此，我尝试将Captum与PyTorch Lightning一起使用。将模块传递给Captum时，我遇到了问题，因为它似乎对张量进行了奇怪的重塑。 例如，在下面的最小示例中，闪电代码容易工作。 但是，当我将IntegratedGradient与“ n_step> = 1”一起使用时，会遇到问题。 我想说LighningModule的代码不是那么重要，我想知道最底部的代码行有多少。

有人知道该如何解决吗？

from captum.attr import IntegratedGradients
from torch import nn,optim,rand,sum as tsum,reshape,device
import torch.nn.functional as F
from pytorch_lightning import seed_everything,LightningModule,Trainer
from torch.utils.data import DataLoader,Dataset

SAMPLE_DIM = 3


class CustomDataset(Dataset):
    def __init__(self,samples=42):
        self.dataset = rand(samples,SAMPLE_DIM).cuda().float() * 2 - 1

    def __getitem__(self,index):
        return (self.dataset[index],(tsum(self.dataset[index]) > 0).cuda().float())

    def __len__(self):
        return self.dataset.size()[0]


class OurModel(LightningModule):
    def __init__(self):
        super(OurModel,self).__init__()
        # Network layers
        self.linear = nn.Linear(SAMPLE_DIM,2048)
        self.linear2 = nn.Linear(2048,1)
        self.output = nn.Sigmoid()
        # Hyper-parameters,that we will auto-tune using lightning!
        self.lr = 0.001
        self.batch_size = 512

    def forward(self,x):
        x = self.linear(x)
        x = self.linear2(x)
        output = self.output(x)
        return reshape(output,(-1,))

    def configure_optimizers(self):
        return optim.Adam(self.parameters(),lr=self.lr)

    def train_DataLoader(self):
        loader = DataLoader(CustomDataset(samples=1000),batch_size=self.batch_size,shuffle=True)
        return loader

    def training_step(self,batch,batch_nb):
        x,y = batch
        loss = F.binary_cross_entropy(self(x),y)
        return {'loss': loss,'log': {'train_loss': loss}}


if __name__ == '__main__':
    seed_everything(42)
    device = device("cuda")
    model = OurModel().to(device)
    trainer = Trainer(max_epochs=2,min_epochs=1,auto_lr_find=False,progress_bar_refresh_rate=10)
    trainer.fit(model)
    # ok Now the Problem
    test_input = CustomDataset(samples=1).__getitem__(0)[0].requires_grad_()
    ig = IntegratedGradients(model)
    attr,delta = ig.attribute(test_input,target=1,return_convergence_delta=True)

解决方法

解决方案是包装转发功能。确保进入mode.foward（）的形状正确！

# Solution is this wrapper function
def modified_f(in_vec):
    # Shape here is wrong
    print("IN:",in_vec.size())
    x = torch.reshape(in_vec,(int(in_vec.size()[0]/SAMPLE_DIM),SAMPLE_DIM))
    print("x:",x.size())

    res = model.forward(x)
    print("res:",res.size())
    res = torch.reshape(res,(res.size()[0],1))
    print("res2:",res.size())

    return res


ig = IntegratedGradients(modified_f)
attr,delta = ig.attribute(test_input,return_convergence_delta=True,n_steps=STEP_AMOUNT)