flownet中的correlation代码实现
时间: 2023-09-24 15:13:39 浏览: 47
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。