img_order_of_axes

The img_order_of_axes function is a utility designed to reorder the axes of an image tensor for processing or visualization purposes. Its primary use case is to transform a batch of images with the format batch-height-width-channel (b, h, w, c) into a format suitable for displaying multiple images in a single row, maintaining the channel order.

This documentation provides an in-depth understanding of the img_order_of_axes function, its architecture, and the rationale behind its design. We will cover multiple usage examples, detailing the parameters, expected inputs and outputs, along with additional tips and resources.

The img_order_of_axes function plays a crucial role in scenarios where a batch of images needs to be combined into a single image with individual images laid out horizontally. This function is particularly useful when there is a need to visualize multiple similar images side by side, such as comparing different stages of image processing or visualization of input-output pairs in machine learning tasks.

Function Definition

img_order_of_axes(x)

Rearranges the axes of an image tensor from batch-height-width-channel order to height-(batch * width)-channel order.

Parameters:

Parameter	Type	Description
x	Tensor	A 4-dimensional tensor representing a batch of images with shape (b, h, w, c), where b is the batch size, h is the height, w is the width, and c is the number of channels.

Returns:

A rearranged tensor that combines the batch and width dimensions, resulting in a shape of (h, b * w, c).

Usage Example 1:

Visualizing a batch of images side by side:

import torch
from einops import rearrange

from zeta.ops import img_order_of_axes

# Create a dummy batch of images with shape (b, h, w, c)
batch_size, height, width, channels = 4, 100, 100, 3
dummy_images = torch.rand(batch_size, height, width, channels)

# Use `img_order_of_axes` to prepare the tensor for visualization
reordered_images = img_order_of_axes(dummy_images)

# `reordered_images` will have the shape (height, batch_size * width, channels)
print(reordered_images.shape)  # Expected output (100, 400, 3)

Usage Example 2:

Comparing image pairs before and after processing:

import torch
from einops import rearrange

from zeta.ops import img_order_of_axes

# Create a dummy batch of original images and processed images
batch_size, height, width, channels = 2, 100, 100, 3
original_images = torch.rand(batch_size, height, width, channels)
processed_images = torch.rand(batch_size, height, width, channels)

# Concatenate the original and processed images in the batch dimension
combined_batch = torch.cat((original_images, processed_images), dim=0)

# Reorder the axes for side by side comparison
comparison_image = img_order_of_axes(combined_batch)

# Visualize or save `comparison_image` as needed

Usage Example 3:

Preparing a batch of images for a single forward pass in a convolutional neural network (CNN):

import torch
from einops import rearrange

from zeta.ops import img_order_of_axes

# Assuming `model` is a pre-defined CNN that expects input of shape (h, w, c)
batch_size, height, width, channels = 8, 64, 64, 3
input_images = torch.rand(batch_size, height, width, channels)

# Combine all images side by side to form a single large image
large_image = img_order_of_axes(input_images)

# Now `large_image` can be fed into the CNN as a single input
output = model(large_image.unsqueeze(0))  # Add batch dimension of 1 at the beginning

Additional Information and Tips

It's important to note that the rearrange function used within img_order_of_axes is not a PyTorch built-in function. It requires the einops library which offers more flexible operations for tensor manipulation.
To install einops, use the package manager of your choice, e.g., pip install einops for Python's pip package manager.
When visualizing the rearranged tensor, ensure that the visualization tool or library you choose can handle non-standard image shapes, as the resulting tensor will have a width that is a multiple of the original width.