该代码有给出报错TypeError: mean() received an invalid combination of arguments - got (axis=tuple, dtype=NoneType, out=NoneType, ), but expected one of: * (*, torch.dtype dtype) * (tuple of ints dim, bool keepdim, *, torch.dtype dtype) * (tuple of names dim, bool keepdim, *, torch.dtype dtype)该如何修改

报错 "TypeError: mean() received an invalid combination of arguments - got (axis=tuple, dtype=NoneType, out=NoneType, ), but expected one of: * (*, torch.dtype dtype) * (tuple of ints dim, bool keepdim, *, torch.dtype dtype) * (tuple of names dim, bool keepdim, *, torch.dtype dtype)" 是因为在代码的第 27 行使用了 `axis=(1,2)` 参数，但是 `features` 数组的维度不足以支持这个参数。

对于这个问题，可以使用以下代码将 `features` 变量的维度从 `(n, c, 1, 1)` 转换为 `(c, 1, 1)`：

# Reshape features to remove single-dimensional axes
features = features.squeeze()
if len(features.shape) == 3:
    features = features.unsqueeze(-1)
if len(features.shape) == 2:
    features = features.unsqueeze(-1).unsqueeze(-1)

# Calculate global average pooling of feature maps
pooled_features = torch.mean(features, dim=[1,2])

这段代码会检查 `features` 数组的维度是否为 `(n, c, 1, 1)`，如果是，会先使用 `squeeze()` 方法删除所有大小为 1 的维度。如果删除后的维度为 `(c, 1, 1)`，则不需要进一步操作。否则，会使用 `unsqueeze()` 方法添加缺少的维度。

在计算全局平均池化时，使用 `dim=[1,2]` 参数指定在第 1 和第 2 个维度上计算平均值。

这样处理之后，就可以在第 27 行使用 `axis=(0,1)` 参数，而不会出现维度错误。

完整修改后的代码如下：

import torch
import torch.nn as nn
from torchvision import models, transforms
from PIL import Image
import numpy as np
import matplotlib.pyplot as plt

# Load pre-trained ResNet-18 model
model = models.resnet18(pretrained=True)

# Remove the fully connected layer from the model
model = nn.Sequential(*list(model.children())[:-1])

# Set model to evaluation mode
model.eval()

# Define image transformation to match the pre-processing used during training
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])

# Load sample image
img = Image.open('sample_image.jpg')

# Apply transformation and convert to tensor
img_tensor = transform(img).unsqueeze(0)

# Pass image tensor through ResNet-18 to get feature maps
with torch.no_grad():
    features = model(img_tensor)

# Reshape features to remove single-dimensional axes
features = features.squeeze()
if len(features.shape) == 3:
    features = features.unsqueeze(-1)
if len(features.shape) == 2:
    features = features.unsqueeze(-1).unsqueeze(-1)

# Calculate global average pooling of feature maps
pooled_features = torch.mean(features, dim=[1,2])

# Reshape pooled features to match spatial dimensions of feature maps
pooled_features = torch.repeat_interleave(pooled_features, features.shape[1]*features.shape[2]).reshape(features.shape)

# Calculate importance of each feature map by comparing it to the global average
heatmap = torch.abs(features - pooled_features)

# Normalize heatmap to range between 0 and 1
heatmap = (heatmap - torch.min(heatmap)) / (torch.max(heatmap) - torch.min(heatmap))

# Plot heatmap on top of original image
plt.imshow(img)
plt.imshow(heatmap.sum(axis=0), alpha=0.5, cmap='jet')
plt.axis('off')
plt.show()

希望能对您有所帮助！