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：

import torch
import torch.nn.functional as F

2. 将 `tf.cast` 方法替换为 `torch.Tensor.to()` 方法：

# 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：

# 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：

# 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：

# 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`：

# 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：

# 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：

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