masked_image[i, :, :] *= mask RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu!
时间: 2023-10-28 16:52:03 浏览: 48
这是一个运行时错误,它表示您的代码中存在与设备相关的问题。您的张量必须在同一个设备上。您可以使用 `.to()` 方法将张量尽可能地移动到同一设备上,例如将 CPU 上的所有张量移动到 GPU 上,或者将 GPU 上的所有张量移动到 CPU 上。您还可以检查代码中是否有异步计算或未正确使用 `.to()` 方法。
相关问题
if self.shift_size > 0: # calculate attention mask for SW-MSA H, W = self.input_resolution img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1 h_slices = (slice(0, -self.window_size), slice(-self.window_size, -self.shift_size), slice(-self.shift_size, None)) w_slices = (slice(0, -self.window_size), slice(-self.window_size, -self.shift_size), slice(-self.shift_size, None)) cnt = 0 for h in h_slices: for w in w_slices: img_mask[:, h, w, :] = cnt cnt += 1 mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1 mask_windows = mask_windows.view(-1, self.window_size * self.window_size) attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0)) else: attn_mask = None
这段代码是 ChitGPT 中的一部分,用于实现基于滑动窗口的多尺度自注意力机制(SW-MSA)。主要是计算用于掩盖不相关像素的注意力掩码。如果 shift_size 大于 0,就会生成一个大小为 H x W 的图像掩码,然后将其分成若干个大小为 window_size x window_size 的窗口。对于每对窗口,将它们的编号相减,并用 -100.0 填充非零元素的位置,用 0.0 填充零元素的位置,生成一个注意力掩码。如果 shift_size 等于 0,则不需要掩码。
讲一下tensorflow代码转为pytorch代码。def simulated_spectral9_camera_spectral_response_function(hyper_spectral_image,masked_response_function): masked_response_function = tf.cast(masked_response_function, dtype=hyper_spectral_image.dtype) ##9,31 hyper_spectral_image = hyper_spectral_image[:,1:-1, 1:-1,:] #裁切中心部分 batch_size,h,w,c = hyper_spectral_image.shape response3x3 = tf.reshape(masked_response_function,[3,3,31]) #padding到 h,w responsehxw = tf.tile(response3x3,[h//3,w//3,1]) response_img = hyper_spectral_image*responsehxw response_img = tf.reduce_sum(response_img,axis=-1)/ tf.reduce_sum(responsehxw,axis=-1) #,keepdims=True # 预期 shape: (batch, height, width, 1) return response_img
将 TensorFlow 代码转换为 PyTorch 代码,可以按照以下步骤进行:
1. 导入 PyTorch 库和相关模块:
```python
import torch
import torch.nn.functional as F
```
2. 将 `tf.cast` 方法替换为 `torch.Tensor.to()` 方法:
```python
# TensorFlow 代码
masked_response_function = tf.cast(masked_response_function, dtype=hyper_spectral_image.dtype)
# PyTorch 代码
masked_response_function = masked_response_function.to(hyper_spectral_image.dtype)
```
3. 将 `tf.reshape` 方法替换为 `torch.Tensor.view()` 方法:
```python
# TensorFlow 代码
response3x3 = tf.reshape(masked_response_function,[3,3,31])
# PyTorch 代码
response3x3 = masked_response_function.view(3, 3, 31)
```
4. 将 `tf.tile` 方法替换为 `torch.Tensor.repeat()` 方法:
```python
# TensorFlow 代码
responsehxw = tf.tile(response3x3,[h//3,w//3,1])
# PyTorch 代码
responsehxw = response3x3.repeat(h // 3, w // 3, 1)
```
5. 将 `tf.reduce_sum` 方法替换为 `torch.sum()` 方法:
```python
# TensorFlow 代码
response_img = tf.reduce_sum(response_img,axis=-1)/ tf.reduce_sum(responsehxw,axis=-1)
# PyTorch 代码
response_img = torch.sum(response_img, dim=-1) / torch.sum(responsehxw, dim=-1)
```
6. 将 TensorFlow 的 `None` 替换为 PyTorch 的 `None`:
```python
# TensorFlow 代码
keepdims=True
# PyTorch 代码
keepdim=True
```
7. 将 TensorFlow 的 `shape` 属性替换为 PyTorch 的 `size()` 或 `shape` 方法:
```python
# TensorFlow 代码
batch_size,h,w,c = hyper_spectral_image.shape
# PyTorch 代码
batch_size, h, w, c = hyper_spectral_image.size()
```
最终的 PyTorch 代码如下:
```python
import torch
import torch.nn.functional as F
def simulated_spectral9_camera_spectral_response_function(hyper_spectral_image, masked_response_function):
masked_response_function = masked_response_function.to(hyper_spectral_image.dtype)
hyper_spectral_image = hyper_spectral_image[:, 1:-1, 1:-1, :]
batch_size, h, w, c = hyper_spectral_image.size()
response3x3 = masked_response_function.view(3, 3, 31)
responsehxw = response3x3.repeat(h // 3, w // 3, 1)
response_img = hyper_spectral_image * responsehxw
response_img = torch.sum(response_img, dim=-1) / torch.sum(responsehxw, dim=-1, keepdim=True)
return response_img
```
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