使用PyTorch计算Shap values时绘图遇索引越界错误求助

问题描述 #

在PyTorch中使用SHAP分析MNIST模型时，运行图像可视化代码出现IndexError: index 1 is out of bounds for axis 0 with size 1错误。

运行代码 #

import numpy as np
import torch
from torch import nn, optim
from torch.nn import functional as F
from torchvision import datasets, transforms

import shap

batch_size = 128
num_epochs = 2
device = torch.device("cpu")


class Net(nn.Module):
    def __init__(self):
        super().__init__()

        self.conv_layers = nn.Sequential(
            nn.Conv2d(1, 10, kernel_size=5),
            nn.MaxPool2d(2),
            nn.ReLU(),
            nn.Conv2d(10, 20, kernel_size=5),
            nn.Dropout(),
            nn.MaxPool2d(2),
            nn.ReLU(),
        )
        self.fc_layers = nn.Sequential(
            nn.Linear(320, 50),
            nn.ReLU(),
            nn.Dropout(),
            nn.Linear(50, 10),
            nn.Softmax(dim=1),
        )

    def forward(self, x):
        x = self.conv_layers(x)
        x = x.view(-1, 320)
        x = self.fc_layers(x)
        return x


def train(model, device, train_loader, optimizer, epoch):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        data, target = data.to(device), target.to(device)
        optimizer.zero_grad()
        output = model(data)
        loss = F.nll_loss(output.log(), target)
        loss.backward()
        optimizer.step()
        if batch_idx % 100 == 0:
            print(
                f"Train Epoch: {epoch} [{batch_idx * len(data)}/{len(train_loader.dataset)}"
                f" ({100.0 * batch_idx / len(train_loader):.0f}%)]"
                f"\tLoss: {loss.item():.6f}"
            )


def test(model, device, test_loader):
    model.eval()
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for data, target in test_loader:
            data, target = data.to(device), target.to(device)
            output = model(data)
            test_loss += F.nll_loss(output.log(), target).item()  # sum up batch loss
            pred = output.max(1, keepdim=True)[
                1
            ]  # get the index of the max log-probability
            correct += pred.eq(target.view_as(pred)).sum().item()

    test_loss /= len(test_loader.dataset)
    print(
        f"\nTest set: Average loss: {test_loss:.4f},"
        f" Accuracy: {correct}/{len(test_loader.dataset)}"
        f" ({100.0 * correct / len(test_loader.dataset):.0f}%)\n"
    )


train_loader = torch.utils.data.DataLoader(
    datasets.MNIST(
        "mnist_data",
        train=True,
        download=True,
        transform=transforms.Compose([transforms.ToTensor()]),
    ),
    batch_size=batch_size,
    shuffle=True,
)

test_loader = torch.utils.data.DataLoader(
    datasets.MNIST(
        "mnist_data", train=False, transform=transforms.Compose([transforms.ToTensor()])
    ),
    batch_size=batch_size,
    shuffle=True,
)

model = Net().to(device)
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)

for epoch in range(1, num_epochs + 1):
    train(model, device, train_loader, optimizer, epoch)
    test(model, device, test_loader)

# since shuffle=True, this is a random sample of test data
batch = next(iter(test_loader))
images, _ = batch
#images = images.view(-1, 1, 28, 28)


background = images[:100]
test_images = images[100:110]

e = shap.DeepExplainer(model, background)
shap_values = e.shap_values(test_images)

shap_numpy = [np.swapaxes(np.swapaxes(s, 1, -1), 1, 2) for s in shap_values]
test_numpy = np.swapaxes(np.swapaxes(test_images.numpy(), 1, -1), 1, 2)

# plot the feature attributions
shap.image_plot(shap_numpy, -test_numpy)

报错信息 #

Traceback (most recent call last):
Cell In[5], line 5
shap.image_plot(shap_numpy, -test_numpy)
File ~\anaconda3\lib\site-packages\shap\plots_image.py:154 in image
if len(shap_values[0][row].shape) == 2:
IndexError: index 1 is out of bounds for axis 0 with size 1

错误原因及修复方案 #

1. 模型输出层适配SHAP计算 #

你的模型最后使用了Softmax(dim=1)输出概率值，但SHAP的DeepExplainer更适合处理未归一化的logits或LogSoftmax输出，概率值会导致维度计算异常。

修复步骤：

• 修改模型全连接层，将Softmax替换为LogSoftmax：

  self.fc_layers = nn.Sequential(
      nn.Linear(320, 50),
      nn.ReLU(),
      nn.Dropout(),
      nn.Linear(50, 10),
      nn.LogSoftmax(dim=1),  # 替换原Softmax层
  )
  

• 调整训练和测试的损失计算，去掉output.log()（因为LogSoftmax已输出log概率）：

  # 训练函数中
  loss = F.nll_loss(output, target)
  # 测试函数中
  test_loss += F.nll_loss(output, target).item()
  

2. 图像维度简化 #

MNIST是单通道图像，转换为numpy数组后可移除多余的通道维度，避免可视化时的维度冲突：

# 替换原维度转换代码
shap_numpy = [np.squeeze(np.swapaxes(s, 1, -1)) for s in shap_values]
test_numpy = np.squeeze(np.swapaxes(test_images.numpy(), 1, -1))

3. 强制模型进入评估模式 #

在SHAP计算前，显式设置模型为评估模式，避免Dropout等层干扰计算结果：

model.eval()  # 添加该行代码
e = shap.DeepExplainer(model, background)
shap_values = e.shap_values(test_images)

修复后关键代码片段 #

# 修改后的模型
class Net(nn.Module):
    def __init__(self):
        super().__init__()

        self.conv_layers = nn.Sequential(
            nn.Conv2d(1, 10, kernel_size=5),
            nn.MaxPool2d(2),
            nn.ReLU(),
            nn.Conv2d(10, 20, kernel_size=5),
            nn.Dropout(),
            nn.MaxPool2d(2),
            nn.ReLU(),
        )
        self.fc_layers = nn.Sequential(
            nn.Linear(320, 50),
            nn.ReLU(),
            nn.Dropout(),
            nn.Linear(50, 10),
            nn.LogSoftmax(dim=1),
        )

    def forward(self, x):
        x = self.conv_layers(x)
        x = x.view(-1, 320)
        x = self.fc_layers(x)
        return x

# 修改后的训练函数损失计算
def train(model, device, train_loader, optimizer, epoch):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        data, target = data.to(device), target.to(device)
        optimizer.zero_grad()
        output = model(data)
        loss = F.nll_loss(output, target)
        loss.backward()
        optimizer.step()
        # 其余代码不变

# 修改后的测试函数损失计算
def test(model, device, test_loader):
    model.eval()
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for data, target in test_loader:
            data, target = data.to(device), target.to(device)
            output = model(data)
            test_loss += F.nll_loss(output, target).item()
            # 其余代码不变

# 修改后的SHAP可视化部分
model.eval()
batch = next(iter(test_loader))
images, _ = batch

background = images[:100]
test_images = images[100:110]

e = shap.DeepExplainer(model, background)
shap_values = e.shap_values(test_images)

shap_numpy = [np.squeeze(np.swapaxes(s, 1, -1)) for s in shap_values]
test_numpy = np.squeeze(np.swapaxes(test_images.numpy(), 1, -1))

shap.image_plot(shap_numpy, -test_numpy)

内容的提问来源于stack exchange，提问作者Bea González