fastai is a popular deep learning library that provides powerful tools for training and building state-of-the-art machine learning models. One interesting feature of fastai is its capability to generate deep dream-like images and perform image transformations. In this blog post, we will explore how to use fastai to unleash your creativity by generating fascinating deep dream images and transforming ordinary images into extraordinary artworks.

Deep Dream

Deep Dream is a technique that utilizes convolutional neural networks (CNNs) to enhance and manipulate images. It involves feeding an image into a pre-trained CNN and modifying the input image based on the activations and gradients of the CNN layers. This process creates visually stunning and surreal images that highlight the patterns and features learned by the network.

Setting up fastai and Deep Dream

Let’s start by setting up our environment. First, make sure you have fastai installed by running the following command:

!pip install fastai

Next, we need to import the necessary libraries and modules:

from fastai.vision import *

Generating Deep Dream Images

To generate deep dream images using fastai, we will use a pre-trained model and the fastai GeneralReLUExporter class. Here’s an example of how to do it:

# Load a pre-trained model
model = models.resnet34(pretrained=True)

# Create a fastai learner
learner = cnn_learner(data, model, metrics=accuracy)

# Define the dream callback
class DeepDreamCallback(LearnerCallback):
    def __init__(self, learn: Learner):
        super().__init__(learn)
    
    def on_backward_begin(self, last_input, last_output, **kwargs):
        return {'last_input': last_input}
    
    def on_backward_end(self, **kwargs):
        last_input = kwargs['last_input']
        last_input[0].grad += last_input[0].clone()

# Attach the dream callback
cb = DeepDreamCallback(learner)
learner.callbacks.append(cb)

# Generate deep dream images
learner.show_results(rows=1, figsize=(8,8))

This code loads a pre-trained ResNet34 model, creates a learner object, and defines a callback to modify the gradients during the backward pass. The show_results method then generates and displays the deep dream images.

Image Transformations

fastai also provides a convenient way to perform various image transformations using the get_transforms function. Here’s an example of how to apply image transformations to a dataset:

# Load the data
data = ImageDataBunch.from_folder(path, train="train", valid="valid", test="test", ds_tfms=get_transforms(), size=224, bs=16)

# Train the model
learner.fit_one_cycle(10)

# Apply transformations to an image
img = open_image('path/to/image.jpg')
img.apply_tfms(get_transforms())

In this code, we load the image dataset using the ImageDataBunch class and apply the get_transforms function to specify the desired transformations. We can then use the apply_tfms method to transform an individual image.

Conclusion

With fastai, you can dive into the world of deep dreams and unleash your creativity by generating stunning and surreal images. Whether you want to generate deep dream images or apply image transformations, fastai provides the tools you need to experiment with and explore the fascinating field of computer vision.

Get started with fastai and embark on an exciting journey of deep dreams and image transformations!