image = self.image.reshape((1, 28, 28, 1)).astype(np.float32) / 255.0 ValueError: cannot reshape array of size 78400 into shape (1,28,28,1)
时间: 2023-10-07 21:05:21 浏览: 59
这个错误是因为你尝试将一个长度为78400的一维数组重塑为形状为(1, 28, 28, 1)的四维数组,但是这两个形状并不兼容。
请确保输入的数组的形状为(28, 28)或(784,),然后再尝试重新调整形状。例如,对于形状为(784,)的数组,可以使用以下代码:
```
image = image.reshape((1, 28, 28, 1)).astype(np.float32) / 255.0
```
如果输入的数组是二维的,可以使用以下代码:
```
image = image.reshape((1, 28, 28, 1)).astype(np.float32) / 255.0
```
这将把输入的二维数组重塑为形状为(1, 28, 28, 1)的四维数组。
相关问题
import torch import torch.nn as nn import torch.nn.functional as F import torchvision.transforms as transforms import skimage.segmentation as seg import numpy as np # 超参数 from PIL import Image num_superpixels = 1000 compactness = 10 sigma = 1 # 定义模型 class SuperpixelSegmentation(nn.Module): def init(self): super(SuperpixelSegmentation, self).init() self.convs = nn.Sequential( nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1), nn.ReLU(inplace=True), nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1), nn.ReLU(inplace=True), nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1), nn.ReLU(inplace=True), nn.Conv2d(64, num_superpixels, kernel_size=1, stride=1) ) def forward(self, x): x = self.convs(x) return x # 加载图像 imgA = Image.open('1.png').convert('RGB') imgB = Image.open('2.jpg').convert('RGB') # 超像素分割 imgA_np = np.array(imgA) segments = seg.slic(imgA_np, n_segments=num_superpixels, compactness=compactness, sigma=sigma) segments = torch.from_numpy(segments).unsqueeze(0).unsqueeze(0).float() segments = F.interpolate(segments, size=(imgA.height, imgA.width), mode='nearest').long() # 应用超像素块范围到图像B imgB_np = np.array(imgB) for i in range(num_superpixels): mask = (segments == i) imgB_np[mask.expand(3, -1, -1)] = np.mean(imgB_np[mask.expand(3, -1, -1)], axis=1, keepdims=True) # 显示超像素分割图像 imgA_segments = np.zeros_like(imgA_np) for i in range(num_superpixels): mask = (segments == i) imgA_segments[mask.expand(3, -1, -1)] = np.random.randint(0, 255, size=(3,)) imgA_segments = Image.fromarray(imgA_segments.astype(np.uint8)) imgB_segments = Image.fromarray(imgB_np) # 显示图像 transforms.ToPILImage()(imgA).show() transforms.ToPILImage()(imgB).show() imgA_segments.show() imgB_segments.show()上述代码出现错误:RuntimeError: expand(CPUBoolType{[1, 1, 512, 512]}, size=[3, -1, -1]): the number of sizes provided (3) must be greater or equal to the number of dimensions in the tensor (4)
这个错误出现的原因是在这行代码中:
```
imgB_np[mask.expand(3, -1, -1)] = np.mean(imgB_np[mask.expand(3, -1, -1)], axis=1, keepdims=True)
```
`mask.expand(3, -1, -1)`的输出的维度是 `[3, 1, 512, 512]`,但是`imgB_np`的维度是 `[512, 512, 3]`,所以二者无法进行广播操作。你需要将`imgB_np`的维度改为`[3, 512, 512]`,然后再进行操作。修改的代码如下所示:
```
imgB_np = np.transpose(imgB_np, (2, 0, 1))
for i in range(num_superpixels):
mask = (segments == i)
imgB_np[:, mask] = np.mean(imgB_np[:, mask], axis=1, keepdims=True)
imgB_np = np.transpose(imgB_np, (1, 2, 0))
```
这里我们先对`imgB_np`进行了转置操作,将通道维度放在最前面,reshape成了`[3, 512, 512]`的维度,然后进行超像素块的操作,最后再将维度转置回来,得到了`[512, 512, 3]`的维度。
from skimage.segmentation import slic, mark_boundaries import torchvision.transforms as transforms import numpy as np from PIL import Image import matplotlib.pyplot as plt import torch.nn as nn import torch # 定义超像素池化层 class SuperpixelPooling(nn.Module): def init(self, n_segments): super(SuperpixelPooling, self).init() self.n_segments = n_segments def forward(self, x): # 使用 SLIC 算法生成超像素标记图 segments = slic(x.permute(0, 2, 3, 1).numpy(), n_segments=self.n_segments, compactness=10) # 将超像素标记图转换为张量 segments_tensor = torch.from_numpy(segments).unsqueeze(0).unsqueeze(0) # 将张量 x 与超像素标记图张量 segments_tensor 进行逐元素相乘 pooled = x * segments_tensor.float() # 在超像素维度上进行最大池化 pooled = nn.AdaptiveMaxPool2d((self.n_segments, 1))(pooled) # 压缩超像素维度 pooled = pooled.squeeze(3) # 返回池化后的特征图 return pooled # 加载图像 image = Image.open('3.jpg') # 转换为 PyTorch 张量 transform = transforms.ToTensor() img_tensor = transform(image).unsqueeze(0) # 将 PyTorch 张量转换为 Numpy 数组 img_np = img_tensor.numpy().transpose(0, 2, 3, 1)[0] # 使用 SLIC 算法生成超像素标记图 segments = slic(img_np, n_segments=60, compactness=10) # 将超像素标记图转换为张量 segments_tensor = torch.from_numpy(segments).unsqueeze(0).float() # 将超像素索引映射可视化 plt.imshow(segments, cmap='gray') plt.show() # 将 Numpy 数组转换为 PIL 图像 segment_img = Image.fromarray((mark_boundaries(img_np, segments) * 255).astype(np.uint8)) # 保存超像素索引映射可视化 segment_img.save('segment_map.jpg') # 使用超像素池化层进行池化 pooling_layer = SuperpixelPooling(n_segments=60) pooled_tensor = pooling_layer(img_tensor) # 将超像素池化后的特征图可视化 plt.imshow(pooled_tensor.squeeze().numpy().transpose(1, 0), cmap='gray') plt.show() ,上述代码出现问题:RuntimeError: adaptive_max_pool2d(): Expected 3D or 4D tensor, but got: [1, 1, 3, 512, 512],如何修改
根据报错信息,可以看到 `adaptive_max_pool2d()` 函数期望的是一个 3D 或 4D 的张量,但是实际上传入的是一个 5D 的张量,因此需要对输入张量进行维度调整。
在代码中,`pooled` 张量的维度为 `[1, 3, n_segments, h, w]`,需要将超像素维度和通道维度进行交换,然后将超像素和通道维度合并为一个维度,使得其维度变为 `[1, n_segments * 3, h, w]`,然后再传入 `adaptive_max_pool2d()` 函数进行池化。
修改代码如下:
```python
# 在超像素维度和通道维度上进行交换
pooled = pooled.permute(0, 2, 1, 3, 4)
# 将超像素维度和通道维度合并为一个维度
pooled = pooled.reshape(1, -1, pooled.size(3), pooled.size(4))
# 在超像素维度上进行最大池化
pooled = nn.AdaptiveMaxPool2d((self.n_segments, 1))(pooled)
# 压缩超像素维度
pooled = pooled.squeeze(3)
```
这样就可以避免出现 `RuntimeError` 的错误了。
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