correlation代码实现
时间: 2023-11-24 16:07:37 浏览: 121
以下是一个简单的Python代码实现,用于计算两个变量之间的相关性:
```python
import numpy as np
def correlation(x, y):
# 计算均值
x_mean = np.mean(x)
y_mean = np.mean(y)
# 计算x和y的差值
x_diff = x - x_mean
y_diff = y - y_mean
# 计算协方差
covariance = np.sum(x_diff * y_diff) / len(x)
# 计算标准差
x_std = np.sqrt(np.sum(x_diff**2) / len(x))
y_std = np.sqrt(np.sum(y_diff**2) / len(y))
# 计算相关系数
correlation = covariance / (x_std * y_std)
return correlation
```
该函数接受两个数组作为输入,并输出它们之间的相关性。该函数计算均值、差值、协方差、标准差和相关系数。在计算协方差和标准差时,我们使用了NumPy库中的函数来进行计算。
相关问题
flownet中的correlation代码实现
以下是Flownet中的correlation代码实现:
```python
import tensorflow as tf
def correlation(x1, x2, kernel_size=1, max_displacement=20, stride_1=1, stride_2=2):
with tf.variable_scope('correlation'):
x1_shape = tf.shape(x1)
x2_shape = tf.shape(x2)
# Set output tensor shape
batch_size = x1_shape[0]
height = x1_shape[1]
width = x1_shape[2]
num_channels = x1_shape[3]
out_height = height
out_width = width
out_channels = kernel_size * kernel_size * num_channels
# Pad x2 to fit kernel size and max displacement
pad_size = max_displacement + (kernel_size - 1) // 2
x2_padded = tf.pad(x2, [[0, 0], [pad_size, pad_size], [pad_size, pad_size], [0, 0]], "CONSTANT")
# Create empty output tensor
output = tf.zeros(shape=[batch_size, out_height, out_width, out_channels], dtype=tf.float32)
# Compute correlation
for y in range(0, kernel_size):
for x in range(0, kernel_size):
offset = (y * kernel_size + x) * num_channels
patch = tf.slice(x1, [0, y * stride_1, x * stride_1, 0], [-1, height, width, -1])
patch = tf.reshape(patch, [batch_size, height * width, num_channels])
patch = tf.transpose(patch, [0, 2, 1])
weights = tf.slice(x2_padded, [0, y, x, 0], [-1, out_height, out_width, -1])
weights = tf.reshape(weights, [batch_size, out_height * out_width, num_channels])
weights = tf.transpose(weights, [0, 2, 1])
correlation = tf.matmul(patch, weights)
correlation = tf.transpose(correlation, [0, 2, 1])
correlation = tf.reshape(correlation, [batch_size, out_height, out_width, -1])
output += tf.slice(correlation, [0, 0, 0, offset], [-1, -1, -1, num_channels])
return output
```
该实现使用TensorFlow实现了correlation操作。输入参数x1和x2是两个张量,kernel_size是卷积核尺寸,max_displacement是最大位移量,stride_1和stride_2是步长。输出张量output是x1和x2的相关性张量。
该实现首先计算了x2_padded,即将x2张量进行填充以适应卷积核大小和最大位移量。然后创建了一个空的输出张量,将所有相关性相加到其中。最后,通过使用tf.slice将相关性张量拆分为num_channels个部分,并将它们添加到output张量中,以获得最终的相关性张量输出。
flownet中的correlation代码pytorch实现
以下是使用PyTorch实现的Flownet中的correlation代码:
```python
import torch
def correlation(x1, x2, kernel_size=1, stride=1, padding=0, dilation=1):
# x1, x2: [batch_size, channel, height, width]
# kernel_size: kernel size of cross-correlation
# stride: stride of cross-correlation
# padding: padding of cross-correlation
# dilation: dilation of cross-correlation
batch_size, channels, height, width = x1.size()
# pad input tensors
padding_size = kernel_size // 2
x1 = torch.nn.functional.pad(x1, (padding_size, padding_size, padding_size, padding_size))
x2 = torch.nn.functional.pad(x2, (padding_size, padding_size, padding_size, padding_size))
# create output tensor
out_channels = channels * kernel_size * kernel_size
out_height = (height + 2 * padding - kernel_size) // stride + 1
out_width = (width + 2 * padding - kernel_size) // stride + 1
output = torch.zeros(batch_size, out_channels, out_height, out_width).to(x1.device)
# cross-correlate
for i in range(kernel_size):
for j in range(kernel_size):
x1_shifted = x1[:, :, i:i+out_height*stride:stride, j:j+out_width*stride:stride].reshape(batch_size, channels, -1)
x2_shifted = x2[:, :, i:i+out_height*stride:stride, j:j+out_width*stride:stride].reshape(batch_size, channels, -1)
output[:, (i*kernel_size+j)*channels:(i*kernel_size+j+1)*channels, :, :] = torch.bmm(x1_shifted.permute(0,2,1), x2_shifted)
return output.reshape(batch_size, out_channels, out_height, out_width)
```
该函数接受两个输入张量x1和x2,以及卷积核大小kernel_size,步长stride,填充padding和膨胀dilation。它返回x1和x2之间的相互关系张量。
在该函数中,我们首先对输入张量进行填充以使其与卷积核大小对齐。然后,我们根据卷积核大小和步长计算输出张量的大小,并创建一个全零张量作为输出。最后,我们遍历卷积核的所有位置并计算x1和x2之间的相互关系,将结果存储在输出张量中。
我们可以使用该函数来实现Flownet中的correlation层。
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