torch.log和np.log
时间: 2024-04-05 13:17:33 浏览: 16
是什么区别?
torch.log和np.log都是计算对数的函数,但是它们的输入和输出类型不同。torch.log是PyTorch中的函数,输入和输出都是tensor类型,而np.log是NumPy中的函数,输入和输出都是ndarray类型。此外,torch.log还支持自动求导,可以方便地进行反向传播。
相关问题
解释下b=torch.randa(64,256) a=torch.randn(64,256) std = torch.exp(0.5 * a) eps = torch.randn_like(std) aa=b+eps*std bb= - 0.5 * torch.sum(a + eps.pow(2) + np.log(2*np.pi), -1)
这段代码是在进行概率编码器的操作,其中b是从均匀分布中随机采样的噪声,a是从标准正态分布中采样的隐变量,std是a的标准差,eps是从标准正态分布中采样的噪声,aa是将b乘以std得到的噪声,bb是计算概率编码器的损失函数。具体来说,它是计算重构误差和KL散度的和,其中重构误差是由aa和概率编码器生成的输出之间的差异计算得到的,KL散度是衡量隐变量分布与标准正态分布之间的差异。
下面pytorch代码转化为paddlepaddle2.2.2代码: import torch import torch.distributions as tdist def random_noise_levels(): """Generates random noise levels from a log-log linear distribution.""" log_min_shot_noise = np.log(0.0001) log_max_shot_noise = np.log(0.012) log_shot_noise = torch.FloatTensor(1).uniform_(log_min_shot_noise, log_max_shot_noise) shot_noise = torch.exp(log_shot_noise) line = lambda x: 2.18 * x + 1.20 n = tdist.Normal(loc=torch.tensor([0.0]), scale=torch.tensor([0.26])) log_read_noise = line(log_shot_noise) + n.sample() read_noise = torch.exp(log_read_noise) return shot_noise, read_noise def add_noise(image, shot_noise=0.01, read_noise=0.0005): """Adds random shot (proportional to image) and read (independent) noise.""" image = image.permute(1, 2, 0) # Permute the image tensor to HxWxC format from CxHxW format variance = image * shot_noise + read_noise n = tdist.Normal(loc=torch.zeros_like(variance), scale=torch.sqrt(variance)) noise = n.sample() out = image + noise out = out.permute(2, 0, 1) # Re-Permute the tensor back to CxHxW format return out
以下是将该代码转化为PaddlePaddle2.2.2的代码:
```
import paddle
import paddle.distribution as pdist
import numpy as np
def random_noise_levels():
"""Generates random noise levels from a log-log linear distribution."""
log_min_shot_noise = np.log(0.0001)
log_max_shot_noise = np.log(0.012)
log_shot_noise = paddle.uniform(shape=[1], min=log_min_shot_noise, max=log_max_shot_noise)
shot_noise = paddle.exp(log_shot_noise)
line = lambda x: 2.18 * x + 1.20
n = pdist.Normal(loc=paddle.zeros([1]), scale=paddle.ones([1]) * 0.26)
log_read_noise = line(log_shot_noise) + n.sample()
read_noise = paddle.exp(log_read_noise)
return shot_noise, read_noise
def add_noise(image, shot_noise=0.01, read_noise=0.0005):
"""Adds random shot (proportional to image) and read (independent) noise."""
image = image.permute([1, 2, 0]) # Permute the image tensor to HxWxC format from CxHxW format
variance = image * shot_noise + read_noise
n = pdist.Normal(loc=paddle.zeros_like(variance), scale=paddle.sqrt(variance))
noise = n.sample()
out = image + noise
out = out.permute([2, 0, 1]) # Re-Permute the tensor back to CxHxW format
return out
```
请注意,PaddlePaddle的分布API(`paddle.distribution`)与PyTorch的分布API(`torch.distributions`)略有不同,需要相应地更改代码。此外,PaddlePaddle的张量API(如`paddle.exp`)与PyTorch的张量API(如`torch.exp`)也略有不同,需要相应地更改代码。