如何将TensorFlow 2D卷积（tf.nn.conv2d）应用于单个（非批处理）2D图像？

Question

I would like to use the function tf.nn.conv2d() on a single image example, but the TensorFlow documentation seems to only mention applying this transformation to a batch of images.

The docs mention that the input image must be of shape [batch, in_height, in_width, in_channels] and the kernel must be of shape [filter_height, filter_width, in_channels, out_channels] . However, what is the most straightforward way to achieve 2D convolution with input shape [in_height, in_width, in_channels] ?

Here is an example of the current approach, where img has shape (height, width, channels):

img = tf.random_uniform((10,10,3))  # a single image
img = tf.nn.conv2d([img], kernel)[0] # creating a batch of 1, then indexing the single example

I am reshaping the input as follows:

[in_height, in_width, in_channels]->[1, in_height, in_width, in_channels]->[in_height, in_width, in_channels]

This feels like an unnecessary and costly operation when I am only interested in transforming one example.

Is there a simple/standard way to do this that doesn't involve reshaping?

Answer 1

AFAIK there is no way around it. It seems ( here and here ) that the first operation creates a copy (someone correct me if I'm wrong). You may use tf.expand_dims instead though, it's IMO more readable because of it's verbosity.

On the other hand, taking 0 element from the tensor should not perform a copy in this case and is almost free.

Most importantly , except for a little inconvenience with syntax (eg [0] ) those operations definitely are not costly , especially in the context of performing convolution.

BTW. Other ready alternative layers like the ones in tf.keras , require batch as first dimension as well.