torch.stack函数 axis=-1
时间: 2023-09-09 07:09:42 浏览: 65
torch.stack函数中的参数axis=-1指定了在最后一个维度上进行堆叠操作。具体来说,它将输入的张量序列沿着最后一个维度进行堆叠,并返回一个新的张量。
举个例子,假设我们有两个形状为(3, 4)的张量tensor1和tensor2。当我们使用torch.stack来堆叠这两个张量时,设置axis=-1表示沿着最后一个维度进行堆叠。即,它将tensor1和tensor2沿着列的方向进行堆叠,生成一个形状为(3, 8)的新张量。
具体实现代码如下所示:
```python
import torch
tensor1 = torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
tensor2 = torch.tensor([[13, 14, 15, 16], [17, 18, 19, 20], [21, 22, 23, 24]])
result = torch.stack((tensor1, tensor2), axis=-1)
print(result.shape) # 输出: torch.Size([3, 4, 2])
```
这里我们可以看到,torch.stack函数通过设置axis=-1将两个(3, 4)的张量堆叠为一个(3, 4, 2)的张量。
相关问题
def get_samples_ini(batch_size=100): x = torch.rand(batch_size) p0 = torch.sin(2. * np.pi * x).unsqueeze(1) return torch.stack([ x, torch.zeros(batch_size) # t=0 ], axis=-1), p0
这段代码实现了一个函数,函数名为get_samples_ini,输入参数为batch_size,默认为100。在函数内部,首先使用torch.rand生成了一个大小为batch_size的随机数x;然后使用torch.sin和np.pi对x进行一些数学运算,生成另一个张量p0;最后,将x和一个大小为(batch_size,1)的全零张量torch.zeros进行torch.stack操作,返回一个大小为(2,batch_size)的张量。
转化为paddlepaddle2.2.2代码:def safe_invert_gains(image, rgb_gain, red_gain, blue_gain): """Inverts gains while safely handling saturated pixels.""" image = image.permute(1, 2, 0) # Permute the image tensor to HxWxC format from CxHxW format gains = torch.stack((1.0 / red_gain, torch.tensor([1.0]), 1.0 / blue_gain)) / rgb_gain gains = gains.squeeze() gains = gains[None, None, :] # Prevents dimming of saturated pixels by smoothly masking gains near white. gray = torch.mean(image, dim=-1, keepdim=True) inflection = 0.9 mask = (torch.clamp(gray - inflection, min=0.0) / (1.0 - inflection)) ** 2.0 safe_gains = torch.max(mask + (1.0 - mask) * gains, gains) out = image * safe_gains out = out.permute(2, 0, 1) # Re-Permute the tensor back to CxHxW format return out
以下是将上述代码转换为PaddlePaddle 2.2.2的代码:
```
import paddle
import paddle.nn.functional as F
def safe_invert_gains(image, rgb_gain, red_gain, blue_gain):
"""Inverts gains while safely handling saturated pixels."""
image = paddle.transpose(image, perm=[1, 2, 0]) # Permute the image tensor to HxWxC format from CxHxW format
gains = paddle.stack((1.0 / red_gain, paddle.to_tensor([1.0]), 1.0 / blue_gain)) / rgb_gain
gains = paddle.squeeze(gains)
gains = gains.unsqueeze(0).unsqueeze(0) # Prevents dimming of saturated pixels by smoothly masking gains near white.
gray = paddle.mean(image, axis=-1, keepdim=True)
inflection = 0.9
mask = (F.relu(gray - inflection, inplace=True) / (1.0 - inflection)) ** 2.0
safe_gains = paddle.maximum(mask + (1.0 - mask) * gains, gains)
out = image * safe_gains
out = paddle.transpose(out, perm=[2, 0, 1]) # Re-Permute the tensor back to CxHxW format
return out
```
需要注意的是,PaddlePaddle中的API与PyTorch不完全一致,因此需要对代码做一些调整。主要包括:
1. 将torch转换为paddle。
2. 将permute函数转换为transpose函数,并调整参数格式。
3. 将torch.mean函数转换为paddle.mean,并调整参数格式。
4. 将torch.clamp函数转换为F.relu函数,并调整参数格式。
5. 将torch.max函数转换为paddle.maximum函数。
需要注意的是,在PaddlePaddle中,使用inplace=True时需要用F.relu等函数来代替。