使用沿最后两个轴的索引数组索引4D数组-NumPy/Python

我想创建一批具有多个通道的零图像，每个图像有一个给定的像素，值为1

如果图像仅按通道数编制索引，则以下代码可以很好地完成此工作：

num_channels = 3
im_size = 2
images = np.zeros((num_channels, im_size, im_size))

# random locations for the ones
pixels = np.random.randint(low=0, high=im_size,
                           size=(num_channels, 2))
images[np.arange(num_channels), pixels[:, 0], pixels[:, 1]] = 1

但是，如果要考虑批处理，类似的代码也会失败：

batch_size = 4
num_channels = 3
im_size = 2
images = np.zeros((batch_size, num_channels, im_size, im_size))

# random locations for the ones
pixels = np.random.randint(low=0, high=im_size,
                           size=(batch_size, num_channels, 2))
images[np.arange(batch_size), np.arange(num_channels), pixels[:, :, 0], pixels[:, :, 1]] = 1

这就产生了错误

IndexError: shape mismatch: indexing arrays could not be broadcast together with shapes (4,) (3,) (4,3) (4,3) 

以下代码将使用低效循环完成此工作：

batch_size = 4
num_channels = 3
im_size = 2
images = np.zeros((batch_size, num_channels, im_size, im_size))

# random locations for the ones
pixels = np.random.randint(low=0, high=im_size,
                       size=(batch_size, num_channels, 2))
for k in range(batch_size):
    images[k, np.arange(num_channels), pixels[k, :, 0], pixels[k, :, 1]] = 1

如何获得矢量化解决方案？

一个简单的矢量化解决方案是-

I,J = np.arange(batch_size)[:,None],np.arange(num_channels)
images[I, J, pixels[...,0], pixels[...,1]] = 1

获取那些

I

，

J

索引器的另一种更简单的方法是-

---

非常感谢你！我不知道这个高级索引的东西的存在！对不起，我的名声太低了，我不能投你一票。

I,J = np.ogrid[:batch_size,:num_channels]