我想创建一个形状为 (3,4,2,2) 的零张量,并在两个 (3,1) 张量给定的位置插入一个 (3,4) 张量。
示例代码:对数组的等效 numpy 操作如下:
# Existing arrays of required shapes
bbox = np.arange(3*4).reshape(3,4)
x = np.array([0,0,1])
y = np.array([1,1,1])
# Create zeros array and assign into it
output = np.zeros((3,4,2,2))
output[np.arange(3),:,x,y] = bbox
如何使用 Tensorflow 做类似的事情?
注意:我实际上想使用大小为 (32,125,32,32) 的张量。以上是复现的简单代码
最佳答案
以下是如何使用 tf.scatter_nd
做到这一点:
import tensorflow as tf
import numpy as np
bbox = np.arange(3 * 4).reshape(3, 4)
x = np.array([0, 0, 1])
y = np.array([1, 1, 1])
x_size = 2
y_size = 2
# TensorFlow calculation
with tf.Graph().as_default(), tf.Session() as sess:
bbox_t = tf.convert_to_tensor(bbox)
x_t = tf.convert_to_tensor(x)
y_t = tf.convert_to_tensor(y)
shape = tf.shape(bbox_t)
rows, cols = shape[0], shape[1]
ii, jj = tf.meshgrid(tf.range(rows), tf.range(cols), indexing='ij')
xx = tf.tile(tf.expand_dims(x_t, 1), (1, cols))
yy = tf.tile(tf.expand_dims(y_t, 1), (1, cols))
idx = tf.stack([ii, jj, xx, yy], axis=-1)
output = tf.scatter_nd(idx, bbox_t, [rows, cols, x_size, y_size])
output_tf = sess.run(output)
# Test with NumPy calculation
output_np = np.zeros((3, 4, 2, 2))
output_np[np.arange(3), :, x, y] = bbox
print(np.all(output_tf == output_np))
# True
关于python - Tensorflow 高级索引 : Assign a smaller tensor into a bigger one into a position based on two index tensors,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/54811424/