This is the summary of Practical Deep Learning for Coders - part 2 of fast.ai's 2022-23 course.
In this course, we’ll explore diffusion methods such as Denoising Diffusion Probabilistic Models (DDPM) and Denoising Diffusion Implicit Models (DDIM). We’ll get our hands dirty implementing unconditional and conditional diffusion models, experimenting with different samplers, and diving into recent tricks like textual inversion and Dreambooth.
Along the way, we’ll cover essential deep learning topics like neural network architectures, data augmentation approaches, and various loss functions. We’ll build our own models from scratch, such as Multi-Layer Perceptrons (MLPs), ResNets, and Unets, while experimenting with generative architectures like autoencoders and transformers.
Throughout the course, we’ll use PyTorch to implement our models, and will create our own deep learning framework called miniai. We’ll master Python concepts like iterators, generators, and decorators to keep our code clean and efficient. We’ll also explore deep learning optimizers like stochastic gradient descent (SGD) accelerated approaches, learning rate annealing, and learning how to experiment with the impact different initialisers, batch sizes and learning rates. And of course, we’ll make use of handy tools like the Python debugger (pdb) and nbdev for building Python modules from Jupyter notebooks.
Lastly, we’ll touch on fundamental concepts like tensors, calculus, and pseudo-random number generation to provide a solid foundation for our exploration. We’ll apply these concepts to machine learning techniques like mean shift clustering and convolutional neural networks (CNNs), and will see how to use tracking with Weights and Biases (W&B).
We’ll also tackle mixed precision training using both NVIDIA’s apex library, and the Accelerate library from Hugging Face. We’ll investigate various types of normalization like Layer Normalization and Batch Normalization. By the end of the course, you’ll have a deep understanding of diffusion models and the skills to implement cutting-edge deep learning techniques.
In this video lesson, titled "Deep Learning Foundations to Stable Diffusion," the instructor introduces Part 2 of the "Practical Deep Learning for Coders" series. The lesson focuses on understanding and using Stable Diffusion, a generative model technique. The instructor emphasizes that this course is more in-depth and requires a stronger foundation in deep learning than Part 1. It is recommended that students complete Part 1 before attempting this course unless they are already comfortable with deep learning basics.
The lesson is divided into two parts: a quick run-through of using Stable Diffusion and a detailed explanation of how it works. The instructor acknowledges that the content may be challenging for those new to deep learning but aims to explain everything as clearly as possible. It is suggested that students spend about 10 hours of work on each video, with some even taking a year-long sabbatical to fully understand the material.
To get started with Stable Diffusion, the instructor recommends using Hugging Face's Diffusers library and their pre-trained pipelines. Students can save and load pipelines to and from the Hugging Face Hub, making it easy to share and collaborate on projects. The instructor demonstrates how to use a pipeline to generate an image based on a text prompt, such as "a photograph of an astronaut riding a horse." By changing the random seed, students can generate different images based on the same prompt.
The instructor also highlights various resources for students to explore, including suggested tools and examples of AI-generated artwork. These resources can help students understand the capabilities and constraints of Stable Diffusion and inspire them to create their own projects.In this section of the video lesson, the instructor demonstrates how to generate images using diffusion models. The process starts with random noise and gradually refines the image through multiple steps to make it more like the desired output. The instructor also discusses the concept of "guidance scale," which determines the degree to which the model should focus on the specific caption versus just creating an image. By adjusting the guidance scale, users can influence the model's output.
The instructor then shows how to use negative prompts to create images with specific characteristics, such as subtracting the color blue from an image. Additionally, the lesson covers how to use image-to-image pipelines to start the diffusion process with a noisy version of a drawing, allowing the model to generate images that match a particular composition.
The instructor discusses fine-tuning models using techniques like textual inversion and Dreambooth. Textual inversion involves training a single embedding to match example images, while Dreambooth fine-tunes an existing token to bring it closer to the provided images. These techniques allow users to generate novel images based on specific prompts and fine-tuned models.In this section of the video lesson, the instructor discusses the concept of using finite differencing to calculate derivatives and introduces the idea of using analytic derivatives instead. They propose creating a new endpoint that calls dot backward and gives dot grad, allowing for the calculation of gradients directly. The goal is to train a neural network to identify which pixels to change to make an image look more like a handwritten digit.
To achieve this, the instructor suggests creating training data with varying levels of noise added to real handwritten digits. Instead of trying to come up with an arbitrary score for how much an image looks like a digit, they propose predicting the amount of noise added. By predicting the noise, they can then subtract it from the input image to obtain a clearer digit. This process is repeated multiple times to improve the digit's appearance.
The instructor then introduces the concept of autoencoders, which are neural networks that can compress images by encoding them into a smaller representation called "latents." By training a U-Net on these latents instead of the full-sized images, the computation time and resources required are significantly reduced. The U-Net takes somewhat noisy latents as input and outputs the noise, which can then be subtracted to obtain the actual latents. These latents can be passed through the decoder of the VAE (Variational Autoencoder) to obtain the final image. This approach is more efficient and cost-effective than working with full-sized images.In the video lesson, the instructor discusses how to generate a specific digit by passing in a one-hot encoded version of the digit along with the noisy input to the neural network. This additional information helps the model predict noise better by knowing the original image. The instructor then explains the challenge of creating a one-hot encoded vector for more complex inputs, such as a cute teddy bear. To overcome this, they introduce the concept of creating a model that can take a sentence and return a vector of numbers representing the image. This is achieved by using two models: a text encoder and an image encoder, which are trained using a contrastive loss function.
The instructor then introduces the CLIP text encoder, which takes text input and outputs an embedding where similar sets of text with similar meanings give similar embeddings. The process of training the model involves randomly picking an image from the training set and a random amount of noise or a "t" value, which determines the amount of noise to use. This trains the model to predict noise, which can then be subtracted from the noisy image to generate the denoised image.
Finally, the instructor discusses the similarities between the diffusion-based models and deep learning optimizers, suggesting that rethinking the problem as an optimization problem rather than a differential equation solving problem could lead to better results. They also mention the possibility of using more sophisticated loss functions, such as perceptual loss, and exploring new research directions. The next lesson will delve deeper into the code behind the pipeline and build up from the foundations using only pure Python and the Python standard library.
In this section of the video lesson, the instructor showcases various student projects from the course forum, highlighting their creativity and applications of deep learning techniques. The lesson then reviews the concepts covered in the previous lesson, focusing on the Stable Diffusion process and the use of CLIP for text encoding. The instructor emphasizes the importance of understanding the math behind diffusion, even for those who may not consider themselves math-oriented.
The lesson proceeds to discuss two recent papers that have improved the Stable Diffusion process. The first paper, "Progressive Distillation for Fast Sampling of Diffusion Models," introduces a distillation process that reduces the number of steps required for denoising images from 60 to 4. This is achieved by training a student model to learn from a teacher model, which is a pre-trained Stable Diffusion model. The student model is then used to perform the denoising process more efficiently.
The second paper, "On Distillation of Guided Diffusion Models," focuses on incorporating guidance into the distillation process. This is done by passing the guidance scale as an additional input to the student model, allowing it to learn how to handle Classifier Free Guided Diffusion. The instructor recommends watching Johno's paper walkthrough video for a more in-depth understanding of these papers and their implications.In this video lesson, the presenter discusses the Imagic algorithm, which allows users to input an image and a text prompt, and the algorithm adjusts the image to match the prompt while keeping other elements as similar as possible. The presenter demonstrates various examples of the algorithm in action, such as changing a bird's pose or turning a dog from standing to sitting.
The lesson then dives into the code implementation of the Stable Diffusion model, which is used to generate images based on text prompts. The presenter explains the process of tokenizing the input text, using the CLIP encoder to create embeddings, and using a scheduler to determine the amount of noise at each step. The code is then run through a loop for a specified number of steps, updating the latents and generating the final image.
The presenter also shares their approach to organizing and simplifying the code, making it easier to understand and experiment with. They suggest homework assignments for viewers, such as implementing negative prompts or adding callbacks to their version of the code. This allows users to have a better understanding of the code and not rely solely on library updates.In this section of the video lesson, the instructor provides a rapid overview of Stable Diffusion and recent papers that have significantly developed the concept. The instructor then proceeds to demonstrate how to rebuild the Stable Diffusion concept from scratch, starting with basic matrix multiplication. The goal is to understand the foundations of Stable Diffusion, which include using Python, the Python standard library, Matplotlib, Jupyter notebooks, and nbdev. The instructor also introduces the "mini-ai" library that will be built throughout the course.
The instructor then demonstrates how to work with the MNIST dataset, which consists of 28x28 pixel grayscale images of handwritten digits. The instructor shows how to download the dataset, load it into Python, and convert the images into lists of lists. The instructor also introduces the concept of iterators and generators in Python, which are essential for working with large datasets efficiently.
The instructor demonstrates how to create a custom class in Python to work with matrices more conveniently. This involves defining special "dunder" methods, which have two underscores on each side and are used to define the behavior of the class. Overall, this section of the lesson provides a foundation for understanding and working with Stable Diffusion, as well as introducing essential Python concepts and techniques.In this section of the video lesson, the instructor discusses the Python data model and how to create a class that can store and index data using dunder init (init) and dunder getitem (getitem). The instructor also explains the concept of tensors, which are essentially multi-dimensional arrays, and their importance in deep learning. Tensors can be one-dimensional (vectors), two-dimensional (matrices), or higher-dimensional. The instructor then demonstrates how to create random numbers using a pseudo-random number generator, highlighting the importance of understanding random state and its implications in deep learning.
The instructor introduces the Wickman-Hill algorithm, which is a pseudo-random number generator that relies on a global random state. This algorithm is used to create a function called "rand" that generates random numbers with no obvious correlation between them and an even distribution. However, the instructor warns about the potential issues with random state when using parallel processing in deep learning, as the global state can be copied, leading to unexpected results. The instructor emphasizes the importance of properly initializing the random number generator in each process to avoid such issues.
Finally, the instructor compares the performance of their custom random number generator with Pytorch's built-in generator, showing that the Pytorch version is significantly faster. The lesson concludes with a brief discussion on creating a linear classifier using a 784 by 10 tensor, which represents the final layer of a neural network for classifying digits.
In Lesson 11, the instructor discusses various techniques and experiments shared by students on the forum. One example is John Robinson's video, which demonstrates interpolating between prompts to create a stable and visually appealing transition between seasons. Another example is Sebastian's work on improving the update process in text-to-image generation by scaling the update according to the ratio of the norms. This results in more detailed and accurate images.
The instructor also highlights Rekil Prashanth's idea of decreasing the guidance scale during the image generation process, which leads to more detailed and accurate images. Additionally, the instructor praises Alex's notes on the lesson, which serve as a great example of how to study and learn from a lesson effectively.
Lastly, the instructor introduces a new paper called DiffEdit, which focuses on semantic image editing using text-conditioned diffusion models. The paper presents a technique that allows users to edit an image based on a text query without the need for providing a mask. The instructor walks through the process of reading and understanding the paper, emphasizing the importance of grasping the main idea and not getting bogged down in every detail.In the given text, the author discusses a video lesson on understanding research papers, specifically focusing on image editing using diffusion models. The lesson covers related work, background, and the main idea of DiffEdit, a method for image editing.
The background section explains denoising diffusion probabilistic models (DDPM) and denoising diffusion implicit models (DDIM), which are the foundational papers for diffusion models. The main idea of DiffEdit involves three steps: adding noise to the input image, denoising it twice (once with the reference text and once with the query), and deriving a mask based on the difference in denoising results. This mask is then used to replace the background with pixel values during decoding.
The lesson also provides tips on understanding mathematical notation and symbols in research papers, such as using Mathpix or LaTeX to identify symbols and their meanings. The author emphasizes that understanding the limitations of the method is crucial, as it may only work on objects that are relatively similar.In this section of the lesson, the instructor demonstrates how to perform matrix multiplication using Python and highlights the limitations of using Python for this task due to its slow performance. To overcome this issue, the instructor introduces Numba, a library that can compile Python code into machine code, significantly speeding up the process. The instructor then compares APL (A Programming Language) with PyTorch and demonstrates how to perform various operations, such as element-wise addition and comparison, using both languages. APL is a mathematical notation that allows for concise and efficient code, making it easier to visualize and understand the operations being performed. The instructor also demonstrates how to create higher-rank tensors, such as matrices, using both APL and PyTorch.In this section of the video lesson, the instructor introduces the concept of Frobenius norm and demonstrates how to implement it in PyTorch. The lesson then moves on to explore the powerful concept of broadcasting, which allows for operations between tensors of different shapes. Broadcasting dates back to the programming language APL and was later adopted by NumPy, PyTorch, TensorFlow, and other libraries.
The instructor explains the rules for broadcasting, which involve comparing the shapes of tensors and checking if dimensions are compatible. Dimensions are compatible if they are equal or if one of them is 1. Broadcasting can be used to perform operations like normalizing image data, outer products, and outer Boolean operations efficiently.
The instructor demonstrates how to use broadcasting to speed up matrix multiplication. By using broadcasting, the matrix multiplication time is significantly reduced, resulting in a 5,000 times speed improvement. This makes it possible to perform matrix multiplication without the need for mini-batches.In this section of the video lesson, the instructor demonstrates the efficiency of using the whole dataset instead of a mini batch of five images. The process now takes only 656 milliseconds to complete, making it feasible to create and train simple models in a reasonable amount of time. This improvement is a significant step forward in the lesson.
The instructor emphasizes the importance of broadcasting in deep learning and machine learning code, describing it as the most critical foundational operation. They encourage students to practice and become proficient in this technique, as it is widely applicable in the field.
In Lesson 12 of Practical Deep Learning for Coders, the instructor begins by discussing the CLIP Interrogator, which has been gaining attention recently. The CLIP Interrogator is a Hugging Face Spaces Gradio app that generates text prompts for creating CLIP embeddings. However, the instructor clarifies that it does not return the exact CLIP prompt that would generate the input image. This leads to a discussion on Stable Diffusion and the concept of inverse problems.
The lesson then moves on to matrix multiplication, where the instructor demonstrates how to use Einstein summation notation to simplify the code and improve performance. The instructor shows how to define matrix multiplication using torch.einsum and compares its speed to the built-in PyTorch matmul function.
The lesson explores how to use GPUs for faster computation. The instructor introduces CUDA, a programming model for Nvidia GPUs, and Numba, a compiler that can generate GPU-accelerated CUDA code from Python. The instructor demonstrates how to write a kernel function for matrix multiplication using the @cuda.jit decorator and how to launch the kernel on the GPU. The result is then copied back to the CPU for further processing.In this section of the video lesson, the instructor demonstrates the use of GPU acceleration for matrix multiplication and introduces the concept of mean shift clustering. The instructor explains that GPU acceleration can significantly speed up computations, with the example provided showing a 5 million times speed increase compared to the original version. The instructor also discusses the use of SSH tunneling to run Jupyter Notebooks remotely.
The lesson then moves on to mean shift clustering, a technique used to identify groups of similar data points, or clusters, within a dataset. The instructor creates synthetic data with six clusters and explains the mean shift algorithm, which involves calculating the distance between data points and taking a weighted average based on their proximity. The Gaussian kernel is introduced as a method for penalizing points that are further away from the point of interest, with the bandwidth parameter determining the rate at which weights fall to zero.
Finally, the instructor demonstrates how to implement the mean shift clustering algorithm using PyTorch and discusses the importance of practicing tensor manipulation operations for efficient GPU programming. The lesson concludes with a brief discussion of alternative weighting methods, such as triangular weighting, and the use of broadcasting rules in NumPy and PyTorch.In this section of the video lesson, the instructor demonstrates the process of clustering data using the mean shift algorithm. They start by calculating the Euclidean distance between data points using NumPy broadcasting rules. The instructor then introduces the concept of norms and explains how the Euclidean distance is related to the two-norm. They proceed to calculate the weights by passing the distances into a Gaussian kernel and obtaining a weighted average of the data.
The instructor then writes a function to perform a single step of the mean shift update, which involves cloning the data, iterating a few times, and updating the data points. They also demonstrate how to create animations using Matplotlib to visualize the clustering process step by step. The instructor then discusses the limitations of the current implementation due to the loop and explores the possibility of GPU acceleration using broadcasting.
By creating mini-batches and using broadcasting, the instructor is able to calculate the distance matrix and obtain the weights for each mini-batch. They then apply the weights to the data points and successfully cluster the data using the mean shift algorithm.In this section of the video lesson, the instructor demonstrates how to optimize the mean shift algorithm using PyTorch and GPUs. The instructor walks through the process of calculating weights, multiplying matrices, and summing up points to obtain new data points. They also discuss the use of einsum and matrix multiplication to simplify the calculations. The instructor then demonstrates how to implement the mean shift algorithm using CUDA on a GPU, resulting in significant speed improvements compared to running the algorithm without a GPU.
The instructor also explores the impact of changing batch sizes on the algorithm's performance and discovers that larger batch sizes result in faster execution times. They encourage viewers to research other clustering algorithms, such as DBSCAN and LSH, and consider how they might be optimized using similar techniques.
Finally, the instructor introduces the topic of calculus, focusing on the concept of derivatives. They explain how derivatives can be used to calculate the slope of a function and discuss the calculus of infinitesimals, which allows for treating small numbers as if they were infinitesimally small. The instructor recommends watching 3Blue1Brown's "Essence of Calculus" series for a deeper understanding of derivatives and prepares the viewers for the next lesson, which will cover backpropagation.
In Lesson 13, the instructor introduces backpropagation and discusses the creation of a simple Multi-Layer Perceptron (MLP) neural network. The lesson begins with a review of basic neural networks and their architecture, using linear models and rectified lines to create arbitrary curves. The instructor then defines variables for the number of training examples, pixels, and possible output values, and demonstrates how to create a weight matrix and biases for the neural network.
The lesson proceeds with the implementation of a simple MLP from scratch, using linear layers, ReLU activation, and a basic Mean Squared Error (MSE) loss function. The instructor explains the importance of gradients and derivatives in optimizing the neural network's weights. In more complex functions, the derivatives form a matrix, with a row for every input and a column for every output. This matrix helps determine how changing each input affects each output, ultimately leading to the optimization of the neural network.In this section of the lesson, the focus is on understanding the chain rule and backpropagation in the context of neural networks. The chain rule is essential for calculating gradients in multi-layer networks, and backpropagation is the process of applying the chain rule to compute gradients for each layer. The lesson demonstrates how to calculate derivatives using Python and the SimPy library, and then explains the chain rule using an interactive animation of spinning wheels.
The lesson proceeds to discuss the importance of the chain rule in calculating the gradient of the mean squared error (MSE) applied to a model. The model consists of multiple layers, including linear layers and ReLU activations. The chain rule is used to compute the derivatives of each layer, starting from the end and working backward. This process is called backpropagation.
The lesson also demonstrates how to use the Python debugger (pdb) to explore and understand the code interactively. The debugger is a powerful tool for examining the state of variables and expressions during code execution. The lesson shows how to set breakpoints, print variable values, and step through code using pdb.
The lesson demonstrates how to simplify gradient calculations using Einstein summation notation and matrix multiplication. This simplification makes the code more efficient and easier to understand.In this section of the lesson, the instructor demonstrates how to use PyTorch to calculate derivatives and simplify the process by creating classes for ReLU and linear functions. The Dunder call method is introduced, which allows classes to behave like functions. The instructor then shows how to store intermediate calculations in the ReLU and linear classes to make the forward and backward passes more efficient.
Next, the instructor refactors the code by creating a module class that stores inputs and outputs, making the ReLU, linear, and MSE classes simpler. The instructor also discusses the trade-off between memory usage and computational speedup when storing intermediate calculations.
The instructor demonstrates how to use PyTorch's nn.Module to create a linear layer and shows that the forward pass is the only necessary implementation since PyTorch already knows the derivatives and can handle the backward pass. The lesson then reviews softmax and log softmax calculations, as well as log and exponent rules, which are useful for simplifying neural network calculations.In this section of the video lesson, the instructor discusses the issues with floating point math and introduces the log sum exp trick to overcome these issues. The trick involves finding the maximum of all x values, subtracting it from every number, and then using exponent rules to adjust the calculations. This method ensures that the numbers involved in the calculations do not become too large for the floating point unit.
The instructor then demonstrates how to implement the log_softmax() function and the cross entropy loss using PyTorch. They also show how to calculate the accuracy of the model using the argmax function. The instructor then proceeds to create a training loop for a simple neural network, setting the loss function to cross entropy and using a batch size of 64. The training loop goes through each epoch and updates the weights and biases of the model.
Finally, the instructor encourages viewers to practice recreating the matrix multiply, forward and backward passes, and other key components of the lesson to solidify their understanding. In the next lesson, the training loop will be refactored to make it simpler, and a validation set and multi-processing data loader will be added.
In Lesson 14, the instructor discusses the implementation of the chain rule in neural network training using backpropagation. They explain how the code from the previous lesson maps to the math and recommend resources for those who need to brush up on their understanding of derivatives and the chain rule. The lesson then moves on to refactoring the code to make it more efficient and flexible. The instructor introduces PyTorch's nn.Module and demonstrates how to create custom PyTorch modules that automatically track layers and parameters. They also show how to build their own implementation of nn.Module and how to create a sequential model using PyTorch's nn.Sequential.
The instructor demonstrates how to create a custom PyTorch module by subclassing nn.Module and assigning layers as attributes. This allows the module to automatically track its layers and parameters, making the code more efficient and flexible. They also show how to create their own implementation of nn.Module using Python's dunder setattr and dunder repr methods, as well as the parameters() function. This custom implementation can then be used to create a sequential model.
The instructor demonstrates how to use PyTorch's nn.Sequential to create a sequential model by passing in a list of layers. They also show an alternative implementation using the reduce() function, which is a more general concept in computer science. The lesson concludes with the instructor showing how to use PyTorch's nn.Sequential to create a sequential model and fit it to the data.In this section of the lesson, the concept of an optimizer is introduced, which simplifies the process of updating parameters based on gradients and learning rates. The optimizer is created by passing the model parameters and learning rate. The loop is then simplified by using opt.step() and opt.zero_grad(). The lesson also demonstrates how to create a custom SGD optimizer from scratch.
The lesson proceeds to create a Dataset class, which takes in independent and dependent variables and allows for easy slicing of the data. This is followed by the creation of a DataLoader class, which takes a Dataset and a batch size, and iterates through the data in batches. The DataLoader is further improved by adding a Sampler class, which can shuffle the order of the data for each training iteration.
The lesson demonstrates how to use PyTorch's built-in DataLoader, which works similarly to the custom DataLoader created earlier. The PyTorch DataLoader can also handle multi-processing, allowing for parallel processing of data. This can be particularly useful when working with large datasets or when performing complex data transformations.In this section of the video lesson, the instructor demonstrates how to create a proper, working, and sensible training loop using PyTorch DataLoader. The instructor then introduces Hugging Face datasets and shows how to use them with the custom training loop. The lesson covers how to load a dataset called Fashion-MNIST using Hugging Face and how to create DataLoaders for it. The instructor also discusses the differences between using dictionaries and tuples in PyTorch and Hugging Face, and how to convert between them using custom collation functions.
Furthermore, the instructor introduces a Python library called nbdev, which allows users to create Python modules from Jupyter notebooks. This is used to create a library called miniai, which will be used throughout the course. The lesson also covers plotting images using matplotlib and creating custom functions to make plotting easier, such as show_image().
Overall, this section of the lesson focuses on creating a custom training loop, working with Hugging Face datasets, and visualizing data using matplotlib. The instructor emphasizes the importance of understanding the underlying code and not relying solely on other people's code, as it allows for greater flexibility and creativity in building custom solutions.In this section of the video lesson, the instructor discusses the use of **kwargs and delegates in fastcore to extend existing functions and maintain their documentation. They also demonstrate how to create subplots using matplotlib and enhance the functionality of subplots using delegates. The instructor then introduces the concept of callbacks and shows how they can be used in GUI events and slow calculations. They also cover the use of *args and **kwargs in Python functions and the importance of dunder methods in Python's data model. The instructor emphasizes the need to be familiar with these concepts as they are used throughout the course and in various frameworks.In this section of the video lesson, the instructor emphasizes that no part of the course is inherently more difficult than others, and any unfamiliarity is simply due to a lack of background in that specific area. They encourage students to spend time studying and practicing to pick up new concepts and not to stress if they don't understand something right away. The instructor also highlights the importance of asking for help, as the community is eager to assist.
The lesson has been successful in achieving its objectives, as the students now have a well-optimized training loop, a clear understanding of DataLoaders and Datasets, and experience with an optimizer and Hugging Face datasets. These accomplishments have set the stage for creating a generic learner training loop and experimenting with various models.
In the next lesson, students can look forward to building and experimenting with different models using the foundation they have established in this lesson. The instructor encourages students to continue asking questions and seeking help as needed, fostering a supportive learning environment.
In Lesson 15, the focus is on creating a convolutional autoencoder and understanding convolutions. Convolutions allow neural networks to understand the structure of a problem, making it easier to solve. In the case of images, convolutions help identify patterns of pixels that represent the same thing, regardless of their position in the image. Convolutional Neural Networks (CNNs) are a good starting point for image processing tasks, but they can also be used for language-based tasks with one-dimensional convolutions.
The lesson demonstrates how to apply a convolution to an image using a kernel, which is a tensor that slides over the image. By applying different kernels, the network can detect various features such as top edges, left edges, and diagonal edges. The lesson also introduces the concept of im2col, a technique that converts a convolution into a matrix multiplication for faster computation. This technique is used in deep learning libraries like PyTorch, which provides optimized functions like unfold() and conv2d() for efficient convolutions.
To address the issue of losing pixels on the edges of the image during convolution, padding can be added. Padding involves starting the kernel at the edge of the image, allowing the convolution to cover the entire image without losing any pixels.In this section of the video lesson, the instructor demonstrates the use of padding and stride in convolutional neural networks (CNNs) and how they affect the output size. The instructor explains that odd-numbered edge size kernels are generally easier to deal with to ensure the output size remains the same as the input size. The instructor also introduces the concept of stride, which is the amount by which the window moves across the input. Stride 2 convolutions are useful because they reduce the dimensionality of the input by a factor of 2, which is often desired in classification architectures.
The instructor then creates a CNN from scratch using a sequential model and demonstrates how to train it on the GPU. The instructor also discusses the concept of receptive field, which is the area of the input that has an impact on a particular activation in the output. The receptive field is an important concept in understanding how different parts of the input contribute to the output of a CNN.
The instructor demonstrates how to create a convolutional neural network in Microsoft Excel, which helps visualize the receptive field and the impact of different input areas on the output.In this section of the lesson, the instructor demonstrates how to build a model using the Hugging Face library and the datasets created in a previous lesson. They create a DataLoader for training and validation and use a sequential model for classification. However, the model runs slowly and has low accuracy. To address these issues, the instructor decides to create an auto-encoder, which compresses the input image and then reconstructs it. They build a deconvolutional layer and a new fit function for the auto-encoder, but the results are still not satisfactory.
The instructor emphasizes the need for a more efficient and flexible framework to rapidly test different models and configurations. They introduce the concept of a Learner, which will be built on top of the existing model, DataLoader, loss function, learning rate, and optimizer. The Learner will allow for faster experimentation and better understanding of the model's performance. They create a simple Learner that fits on one screen and demonstrate its use with a multi-layer perceptron (MLP) model.
To make the Learner more flexible, the instructor creates a Metric class that can be subclassed to calculate different metrics, such as accuracy. They also create a basic Metric for loss calculation. The Learner is then updated to incorporate these new Metric classes, allowing for more efficient experimentation and evaluation of different models and configurations.In this section of the video lesson, the instructor explains the process of creating a Learner with the model, data loaders, loss function, learning rate, and callbacks. The fit() function is called, which goes through each epoch and calls one_epoch() for training and validation. The one_epoch() function goes through each batch in the DataLoader and calls one_batch(), which performs prediction, gets the loss, and if it's training, performs the backward() step and zero_grad(). The instructor also introduces a special decorator with callbacks, which is used to create the _fit() function.
The with_callbacks class is used to store the name of the function and is called as a decorator. When called, it receives the function (_fit) and returns a different function that calls the original function with the arguments and keyword arguments. Before calling the original function, it calls a special method called callback, passing in the string before_fit. After the original function is completed, it calls the callback method again, passing the string after_fit. The whole process is wrapped in a try-except block, looking for a CancelFitException.
The instructor emphasizes the importance of understanding Python concepts such as try-except blocks, decorators, getattr, and debugging to reduce cognitive load while learning the framework being built. Cognitive load theory suggests that learning can be difficult if there are too many things going on at the same time. The instructor encourages learners to practice and familiarize themselves with these concepts to improve their software engineering skills in data science work. The lesson concludes with the anticipation of diving deeper into these topics in the next session.
In Lesson 16, the focus is on building a flexible training framework called the learner. The lesson starts with a Basic Callbacks Learner, which is an intermediate step towards the flexible learner. The Basic Callbacks Learner is similar to the previous Learner, with a fit function that goes through each epoch, calling one_epoch with training on and off. The main difference is the addition of callbacks, which are functions or classes that are called at specific points during the training process.
The lesson demonstrates the creation of a simple callback called CompletionCB, which counts the number of batches completed during the fitting process. The concept of CancelFitException, CancelEpochException, and CancelBatchException is introduced, which allows callbacks to raise exceptions to skip certain parts of the training process.
Next, the lesson introduces metrics and demonstrates how to create a MetricsCB callback to print out metrics during training. The torcheval library is introduced as a source of pre-built metrics that can be used in the learner. A DeviceCB callback is also created to handle moving the model and data to the appropriate device, such as a GPU.
The lesson demonstrates the use of a context manager to refactor the code and reduce duplication. This simplifies the code and makes it easier to maintain and add callbacks in the future.In this section of the lesson, the focus is on looking inside the models to diagnose and fix problems during training. A set_seed function is introduced to set a reproducible seed for PyTorch, NumPy, and Python's random number generators. The same Fashion MNIST dataset is used, and a model similar to previous ones is created. MultiClassAccuracy is used again, along with the same callbacks as before. The goal is to train as fast as possible, not only to save time but also to find a more generalizable set of weights and reduce overfitting. A high learning rate of 0.6 is used to test the stability of the training. A function is created to set up the Learner with the callbacks, fit the model, and return the Learner for further use.In this section of the video lesson, the instructor discusses how to analyze the training process of a neural network by looking at the mean and standard deviation of each layer's activations. Initially, a custom SequentialModel is created to store the means and standard deviations of each layer. However, this approach is not very elegant, and the instructor introduces PyTorch hooks as a more convenient solution. Hooks allow users to add functionality to existing models without rewriting them. The instructor demonstrates how to create a Hook class and a Hooks class to simplify the process of adding hooks to a model.
The lesson then moves on to creating histograms of the activations to better visualize the training process. The instructor modifies the append_stats function to include a histogram of the absolute values of the activations. These histograms are then turned into a single column of colored pixels, with each color representing the frequency of a particular range of activation values. This creates a more visually appealing and informative representation of the training process.
The instructor emphasizes that an ideal training process should have a more even distribution of activation values, with fewer dead or nearly dead activations (close to zero). The lesson concludes with the assertion that understanding the training process and the behavior of the model's activations is crucial for building and debugging neural network models.In this section of the video lesson, the instructor has reached the deepest point of the current topic and is now ready to start building up the pieces to help train models reliably and quickly. The ultimate goal is to create high-quality generative models and other models from scratch.
In the next class, the focus will be on initialization, which is an important topic for model training. The instructor suggests that students should revise concepts like standard deviations before the next lesson, as they will be used extensively.
In Lesson 17 of Practical Deep Learning for Coders, the instructor introduces some minor changes to the miniai library and discusses the importance of weight initialization in neural networks. The instructor first explains the changes made to the Callback class and the addition of a TrainLearner subclass. They then demonstrate the use of a HooksCallback and ActivationStats to visualize the training process more easily.
The lesson then focuses on the importance of having 0 mean and 1 standard deviation in neural networks. The instructor demonstrates the issues that can arise when the weight matrices are not scaled correctly, leading to NaNs or zeros. They introduce the Glorot (or Xavier) initialization, which scales the random numbers in the weight matrices to maintain a standard deviation of 1 and a mean of 0. This initialization helps prevent issues during training.
The instructor provides a brief explanation of variance, standard deviation, and covariance, showing how they can be calculated using code. These concepts are important for understanding the relationships between data points and the variation within and between tensors.In this section of the video lesson, the instructor discusses the importance of covariance and variance in tensors and introduces the Pearson correlation coefficient. The lesson then moves on to explain Xavier initialization (Glorot init) and its derivation. The instructor demonstrates how to create random numbers and compute the standard deviation for a matrix multiplication. The lesson highlights the importance of initializing weight matrices and input matrices with a mean of 0 and a standard deviation of 1 for proper training of deep convolutional neural networks.
The instructor then introduces the concept of General ReLU, a modified ReLU activation function that allows for a mean of 0 by subtracting a constant value and incorporating a leaky ReLU component. This new activation function is implemented in the model, and the instructor demonstrates how to modify the input data using a callback or the Hugging Face datasets library. The results show improved training and smoother graphs with the General ReLU activation function.
The instructor emphasizes the importance of proper initialization and the lack of attention it receives in the deep learning community. By creating a custom activation function and ensuring proper initialization, the model achieves better training results and higher accuracy.In this section of the video lesson, the instructor discusses the importance of initializing neural networks correctly and introduces a technique called Layer-wise Sequential Unit Variance (LSUV) based on the paper "All You Need Is a Good Init" by Dmytro Mishkin. LSUV is a general method for initializing any neural network, regardless of the activation functions used. The process involves creating a model, initializing it, and then adjusting the weight matrix and biases for each layer until the correct mean and standard deviation are achieved. This is done using hooks in Python.
The instructor then introduces normalization techniques, specifically Layer Normalization and Batch Normalization. Layer Normalization is a simpler method that normalizes the input for each layer during training, making it easier for the model to learn. It involves creating a module with a forward function that calculates the mean and variance for each input in the mini-batch and normalizes the data accordingly. Batch Normalization, on the other hand, is a more complex method that involves calculating an exponentially weighted moving average of the means and variances of the last few batches during training. This results in a smoother training process and allows for higher learning rates.
The instructor briefly mentions other normalization techniques, such as Instance Norm and Group Norm, which have different ways of averaging over channels, height, and width. These techniques can be useful in certain situations but may have their own challenges and trade-offs.In this section of the video lesson, the instructor discusses various initialization methods and their combinations. They experiment with different batch sizes and learning rates to improve performance, aiming for 90% accuracy. By using PyTorch's BatchNorm and MomentumLearner, they achieve 87.8% accuracy after three epochs, mainly due to the smaller mini-batch size. However, they still need to do more work to reach the desired 90% accuracy.
The instructor then introduces Accelerated SGD and explains the concept of momentum. Momentum helps in following the average of directions in a loss function, making the learning process smoother. They implement momentum in an optimizer and demonstrate its effectiveness by achieving 87.6% accuracy with a high learning rate of 0.4. The loss function is also smoother with momentum.
The instructor introduces RMSProp and Adam optimizers. RMSProp divides the gradient by the amount of variation, which can be useful for finicky architectures. Adam optimizer combines RMSProp and momentum, resulting in 86.5% accuracy. The instructor highlights the importance of unbiasing the gradient and square averages in the Adam optimizer for better performance.In this last section of the video lesson, the instructor discusses the performance of the current model and suggests experimenting with different values of beta1 and beta2 to potentially improve the results. However, they acknowledge that they are running out of time and decide to postpone the next part of the lesson to ensure it is covered thoroughly.
The instructor hints that in the upcoming lesson, they will demonstrate how to achieve over 90% accuracy and share some exciting techniques. They express enthusiasm for sharing this information in the next session.
In this section of the lesson, the instructor demonstrates how to use Microsoft Excel to visualize and experiment with various stochastic gradient descent (SGD) accelerated approaches, such as momentum, RMSProp, and Adam. The instructor starts by creating a simple linear regression problem in Excel and then applies basic SGD, momentum, RMSProp, and Adam to solve the problem. The instructor also introduces the concept of learning rate annealing and shows how to implement it in Excel.
The instructor then moves on to explore learning rate schedulers in PyTorch. By using the dir() function, the instructor lists all the available schedulers in the torch.optim.lr_scheduler module. The instructor decides to experiment with Cosine Annealing and demonstrates how to work with PyTorch optimizers. The instructor creates a learner with a single batch callback and fits the model to obtain an optimizer. The instructor then explores the attributes of the optimizer and explains the concept of parameter groups.
In summary, this section of the lesson provides a hands-on approach to understanding and experimenting with various SGD accelerated approaches and learning rate annealing using Microsoft Excel and PyTorch.In this section of the lesson, the instructor explains how to work with PyTorch optimizers and schedulers. The optimizer's state is stored in a dictionary where the keys are parameter tensors. The instructor also demonstrates how to create a cosine annealing scheduler and how it adjusts the learning rates of the optimizer for each set of parameters. The scheduler requires the optimizer and the number of iterations (T_max) as input.
The instructor then shows how to create a scheduler callback and a recorder callback to keep track of the learning rate during training. By using a cosine annealing scheduler and a recorder callback, the learning rate can be plotted to visualize its behavior during training. The instructor also demonstrates how to create an epoch scheduler callback, which updates the learning rate at the end of each epoch instead of each batch.
The lesson continues with the implementation of the OneCycleLR scheduler from PyTorch, which adjusts the learning rate and momentum during training. The instructor explains the benefits of using a warmup phase with low learning rates and high momentum, followed by a high learning rate phase with low momentum, and finally a phase with decreasing learning rates and increasing momentum.
Lastly, the instructor highlights some changes made to the code, such as the HasLearn callback, the addition of an lr_find method to the Learner class using fastcore's patch decorator, and the addition of new parameters to the fit method.In this section of the video lesson, the instructor discusses how to improve the architecture of a neural network by making it deeper and wider. They start by making a small change to the initial convolutional layer, changing the stride to 1 and increasing the number of channels to 128. This results in a significant improvement in accuracy, from 90.6% to 91.7%.
Next, the instructor introduces ResNets and the concept of residual connections, which allow for deeper networks without sacrificing training dynamics. They implement a ResBlock, which contains two convolutional layers and an identity connection, and replace the convolutions in the previous model with ResBlocks. This new model achieves an accuracy of 92.2%.
The instructor then explores various ResNet architectures from the PyTorch Image Models (timm) library, finding that their simple, thoughtfully designed architecture outperforms many of the pre-built models. They further improve the model by increasing the kernel size of the first ResBlock and doubling the number of channels, resulting in an accuracy of 92.7%. They create a more flexible ResNet model that can handle different input sizes by having one less layer and stopping at 256 channels.In this section of the video lesson, the instructor discusses the use of Global Average Pooling layers to reduce the dimensions of the output from the last ResBlock. This is followed by an explanation of how to calculate the number of floating point operations (FLOPs) for a model, which can be used as an approximation for the model's computational complexity. The instructor then explores different ways to reduce the number of parameters and FLOPs in the model while maintaining accuracy, such as removing certain layers or replacing ResBlocks with single convolutions.
The instructor also highlights the limitations of weight decay as a regularization technique when using BatchNorm layers and suggests using data augmentation instead. Various data augmentation techniques are demonstrated, including random erasing, which replaces a random patch in the image with Gaussian noise. Test time augmentation is also introduced, which involves averaging predictions from multiple augmented versions of the same image to improve accuracy. The instructor concludes the section by showing how to implement random erasing and test time augmentation in the model, ultimately achieving an accuracy of 94.2% in 20 epochs.In this section of the video lesson, the instructor demonstrates data augmentation techniques to improve model performance. They create a class for data augmentation, which includes random crop, random flip, and random RandErase. After running the model for 50 epochs, they achieve an accuracy of 94.6%. The instructor then explores the idea of random copying, where a part of the image is copied to another part of the same image, ensuring the correct distribution of pixels. They implement this idea manually and create a class for it. After training the model for 25 epochs, they achieve an accuracy of 94%.
The instructor then experiments with ensembling, where they train two separate models for 25 epochs each and combine their predictions. Although the ensemble model performs better than the individual models, it does not beat the previous best accuracy of 94.6%. The instructor encourages viewers to experiment with different techniques and data augmentation methods to improve model performance.
For homework, the instructor asks viewers to create their own cosine annealing scheduler and 1-Cycle scheduler from scratch, ensuring they work correctly with the batch scheduler callback. This exercise aims to help viewers gain a deeper understanding of the PyTorch API and the process of exploration and experimentation. Additionally, the instructor challenges viewers to beat their model's performance on the 5-epoch, 20-epoch, or 50-epoch Fashion-MNIST dataset, ideally using miniai with custom additions.
In Lesson 19, Jeremy introduces special guests Tanishq and Johno and provides a quick update on the Fashion-MNIST challenge. He discusses the use of Dropout, a simple but powerful technique that randomly deletes activations with a certain probability, improving model performance. Jeremy also mentions a test time dropout callback that can be used to measure model confidence.
Tanishq then dives into Denoising Diffusion Probabilistic Models (DDPM), a generative modeling technique that has gained popularity in recent years. He provides an overview of the goal of generative modeling, which is to obtain information about the probability distribution of data points (p(x)) to sample new points and create new generations. Tanishq highlights the key variables and equations in the DDPM paper and explains the forward and reverse processes involved in the model.
The forward process, used for training, goes from an image to pure noise, while the reverse process goes from pure noise to an image. The transition between these two processes is driven by a learned model. The lesson focuses on the code and the variables used in the math, providing a foundation for understanding DDPM and its applications in image generation.In this section of the video lesson, the instructor explains the concept of diffusion models and their application in image generation. The diffusion process involves iteratively adding noise to an image, causing it to lose its original structure and become pure noise. The reverse process uses a neural network to predict and remove the noise, bringing the image back towards its original state.
The instructor demonstrates the implementation of a diffusion model using the Fashion-MNIST dataset and a U-Net neural network architecture. The model is trained to predict the noise added to the image at each timestep. The training process involves selecting a random timestep, adding noise to the image based on that timestep, and passing the noisy image and timestep to the model. The target for the model is the actual noise added to the image.
A callback is used to set up the batch for the learner, making it easier to work with the unique training loop. The instructor also discusses the use of Greek letters in the code to match the equations from the research paper, making it easier to understand the implementation. Overall, the diffusion model allows for the generation of images by iteratively predicting and removing noise, walking towards the data distribution.In this video lesson, the middle section focuses on the implementation of a noise predicting model using a neural network. The model takes in input Xt and t and compares it to the actual epsilon. The prediction function is implemented using Hugging Face's API, which requires calling .sample to get the predictions from the model. The training loop is implemented in miniai, and the loss function calculation is performed using learn.preds and learn.batch[1]. The DDPM callback is initialized with the appropriate arguments, and an MSE loss is used.
The sampling process starts with a random image, which is not part of the data distribution. The noise predicting model is used to predict the direction to move in, and the image is updated with a weighted average of the denoised image estimate and the original noisy image, along with some additional noise. This process is repeated for each timestep, with the estimates becoming more accurate as the timesteps progress. The final generated image is obtained at the end of the sampling process.
The implementation also includes a function to visualize the noisy images at different timesteps during the sampling process. The noise schedule used in the original DDPM paper has limitations, especially when applied to smaller images. Improved DDPM papers propose alternative noise schedules that can be explored and implemented to improve the generative model's performance.In this section of the video lesson, the instructor demonstrates an alternative approach to the previous implementation by inheriting from Callback instead of TrainCB. They create a new UNet class by inheriting from UNet2DModel and replacing the forward function. This allows them to bypass the need for TrainCB and predict. They also experiment with making the model faster by dividing the channels by two and adjusting the group normalization. The instructor emphasizes the importance of visualizing the results at each step to identify and fix errors.
The instructor then discusses how to improve the training speed and learning rate. They notice that the diffusers code does not initialize anything, so they experiment with different initialization techniques, such as zeroing out every second convolutional layer and using orthogonal weights for the downsamplers. They also replace the default Adam optimizer with one that has a larger epsilon value, which helps prevent the learning rate from exploding during training.
Finally, the instructor introduces the concept of mixed precision to further speed up the training process. Mixed precision involves using 16-bit floating-point values instead of the default 32-bit values, which modern Nvidia GPUs can process much faster. However, mixed precision requires careful implementation to maintain the necessary precision for gradient calculations. The instructor plans to cover the implementation of mixed precision in the next lesson.
In this lesson, the focus is on implementing mixed precision training and experimenting with different techniques. The first part of the lesson demonstrates how to remove the DDPMCB entirely and put noisify inside a collation function. This is done by creating a DDPM data loader function and modifying the collation function. The MixedPrecision callback is then introduced, which allows for mixed precision training in PyTorch.
The lesson also explores the Accelerate library from HuggingFace, which provides a single Accelerator to speed up training loops. By adding a TrainCB subclass, the Accelerate library can be used for mixed precision training, multi-GPU training, and TPU training. The Accelerate library is used to create an Accelerator, specify the mixed precision type, and prepare the model, optimizer, and data loaders.
The lesson introduces a sneaky trick for speeding up data loading by creating a new data loader class that wraps the existing data loader and replaces dunder iter. This allows for loading and augmenting data less frequently while still providing multiple updates. This trick can be particularly useful when working with limited CPU resources, such as on Kaggle.In this video lesson, the instructor demonstrates how to optimize an image using a loss function and a pre-trained neural network. They start by creating a LengthDataset and a dummy dataset with 100 items to train for a certain number of steps without caring about the data. They then create a tensor model class that takes a tensor as its parameter and optimizes the image directly. The instructor uses the mean squared error loss function to optimize the image and shows the progress of the optimization using a logging callback.
The core idea of the lesson is to extract features from a pre-trained network, such as VGG16, to create a richer representation of the image. The instructor explains the importance of normalizing the input image to match the data used during the training of the pre-trained network. They then demonstrate how to extract features from the network by running through the layers one by one and storing the output of the target layers.
The instructor concludes the lesson by suggesting that hooks can be used to extract features from specific layers of the network more efficiently. They encourage viewers to try implementing hooks as a homework exercise.In this section of the video lesson, the instructor discusses the process of style transfer using neural networks. They begin by explaining how to extract features from different layers of a pre-trained neural network, such as VGG, to capture different aspects of an image. They introduce the concept of content loss, which is the mean squared error between the features of the input image and the target image at specific layers. This allows for a tunable way to compare two images based on their overall semantics or lower-level features.
The instructor then introduces the Gram Matrix, a technique used to measure the presence of features in an image without considering their spatial location. This is useful for style transfer, as it allows the model to capture the style of one image without being constrained by the spatial arrangement of features. The Gram Matrix is calculated by flattening the spatial dimensions of the feature map and computing the dot product of the flattened matrix with its transpose. This results in a matrix that represents the correlation between features.
The instructor demonstrates how to combine content loss and style loss to perform style transfer. They optimize an image to have the same content as the input image while incorporating the style of another image. This is achieved by calculating the Gram Matrices for both the input and style images and using the mean squared error between these matrices as the style loss. By adjusting various parameters, such as the layers used for content and style loss, the learning rate, and the balance between content and style loss, different results can be obtained, allowing for a wide range of experimentation and artistic effects.In this section of the lesson, the focus is on Neural Cellular Automata, which are inspired by Conway's Game of Life and other self-organizing systems found in nature. The idea is to replace the hardcoded update rules in traditional cellular automata with a small neural network. The training process involves starting from random initial states, applying the network over some number of steps, and comparing the final output to the target image to calculate the loss. The training process is designed to ensure that the network can maintain the desired structure indefinitely.
The authors of the paper on Neural Cellular Automata propose a pool of training examples to achieve this goal. The model starts from a random state, applies some number of updates, and then most of the time, the final output is put back into the pool to be used as a starting point for another round of training. This approach ensures that the network can maintain the desired structure even after many steps.
In the code implementation, the model is set up with a small number of channels and hidden neurons to keep the parameter count low. The perception filters are hardcoded to further reduce the number of parameters. The style loss function from the previous lesson is used to evaluate the output of the cellular automata, ensuring that it matches the target style image.In this section of the video lesson, the instructor discusses the implementation of cellular automata using hard-coded filters inspired by biology. These filters include the identity filter and gradient filters. The filters are applied individually to each channel of the input. The instructor also introduces the concept of circular padding, which helps avoid issues on the edges of the input.
The instructor then demonstrates how to implement a neural network using dense linear layers and convolutional layers with a kernel size of 1x1. This approach takes advantage of the efficiency of convolutions and the parallel processing capabilities of GPUs. The cellular automata model is then put into a class, and a random update mask is introduced to add randomness and mimic biological systems.
Finally, the instructor demonstrates the training process using a style loss and an overflow loss to penalize out-of-bound values. Gradient normalization is also introduced as a technique to control the gradients during training. The resulting cellular automata model can generate patterns resembling spider webs or dragon scales, depending on the chosen style image and training parameters. The instructor encourages experimentation with different model sizes and loss functions to achieve more complex and creative results.
In the first section of the video lesson, Jeremy, Johno, and Tanishq discuss the progress of their experiments using the Fashion-MNIST dataset and the idea of moving to larger datasets and more difficult tasks. Johno introduces the CIFAR-10 dataset, a popular dataset for image classification and generative modeling. They discuss the challenges of visually inspecting the CIFAR-10 dataset due to its low-quality images.
Johno demonstrates how he used the same noisify function and UNet model with slight modifications for the CIFAR-10 dataset. He also introduces Weights and Biases (W&B), an experiment tracking and logging tool that can help manage and visualize the progress of their experiments. W&B allows users to log various metrics, save models as artifacts, and create reports for sharing results. Johno shows how he integrated W&B with the miniai library using a custom callback.
Jeremy and Tanishq discuss the benefits of using W&B for experiment tracking, such as collaboration, reproducibility, and convenience. However, Jeremy also emphasizes the importance of not relying solely on experiment tracking tools and focusing on carefully thought-out hypotheses and code changes. The discussion concludes with the acknowledgment that they will not be covering UNets in this lesson, but they have good reasons for deviating from their original plan.In this section of the video lesson, the instructor discusses new research directions and introduces the Fréchet Inception Distance (FID) metric to measure the quality of generated images. The FID metric is used to determine how similar generated images are to real images by comparing the means and covariance matrices of their features. The instructor demonstrates how to calculate the FID using a custom Fashion-MNIST model instead of the commonly used Inception model, as it is more accurate for the specific task of recognizing fashion.
The instructor explains the process of calculating the FID by first extracting features from a pre-trained model and then calculating the means and covariance matrices for both the real and generated images. The Fréchet Inception Distance is then calculated by comparing the similarity between the two covariance matrices and the two mean matrices. The instructor also discusses the Newton-Schurz method for calculating the matrix square root, which is used in the FID calculation.
The instructor highlights some caveats of the FID metric, such as its dependence on the number of samples used and the potential issues with resizing images when using the Inception model. Using a custom model trained on the specific data, like Fashion-MNIST in this case, can provide a more accurate and relevant FID metric for the task at hand.In this section of the video lesson, the presenter discusses the Fréchet Inception Distance (FID) and Kernel Inception Distance (KID) metrics for comparing image distributions. While FID is simple and automated, it has several caveats and biases. KID, on the other hand, is less biased but has high variance, making it less useful in practice. The presenter then introduces the ImageEval class for evaluating images using these metrics.
The presenter also shares an experience of fixing a bug in their code, which initially seemed to make the results worse. After further investigation and questioning the standard practices, they discovered that changing the input range of images from -1 to 1 to -0.5 to 0.5 improved the FID score. This led to questioning other standard practices, such as the linear schedule for noise addition, and experimenting with alternative schedules like the cosine schedule.
Ultimately, the presenter decides to use a linear schedule with a betamax of 0.01, which produces similar results to the cosine schedule. This allows them to keep their existing code mostly unchanged while still improving the performance of their model.In this section of the video lesson, the instructor discusses the improvements made to the DDPM_v2 model, resulting in the creation of Fashion DDPM_v3. The model's channels were doubled, and the number of epochs was increased by three, leading to better results. The Fréchet Inception Distance (FID) for the generated samples was nearly as good as real images, indicating high image quality for small, unconditional sampling.
The instructor then explores ways to make the model faster without sacrificing quality. By calling the model every third time and fine-tuning the last 50 iterations, the model becomes three times faster with only a slight increase in FID. The instructor also experiments with different schedules for how often the model is called, resulting in even faster sampling times.
The instructor introduces the Denoising Diffusion Implicit Model (DDIM) as a faster alternative to DDPM. The instructor demonstrates how to build a custom DDIM from scratch, using the existing implementation in the Diffusers library as a starting point. The DDIM approach allows for fewer steps in the sampling process while maintaining similar FID scores. The instructor concludes by discussing the benefits of using DDIM over DDPM, including faster sampling times and more concise code.In this section of the video lesson, the discussion focuses on the differences between DDPM and DDIM, as well as the benefits of using DDIM for rapid sampling. DDPM uses a fixed amount of noise, while DDIM introduces a parameter, sigma, which controls the amount of noise in the process. This allows for more control over the stochasticity of the model and even the possibility of making the process deterministic by setting sigma to zero.
The main advantage of DDIM is that it can be used with the same trained model as DDPM, making it a new sampling algorithm rather than a new training method. This is achieved by introducing a new parameter, eta, which controls the amount of noise in the process. When eta is set to one, it corresponds to regular DDPM, while setting it to zero results in a deterministic case.
Lastly, the discussion highlights the benefits of using DDIM for rapid sampling. By defining a similar distribution with a subset of diffusion steps, the same training objective can be met, allowing for faster sampling. This is particularly convenient with the cosine schedule, as it simplifies the code and allows for more flexibility with the eta parameter. The exploration of deterministic versus stochastic processes is an ongoing area of interest in this research.
In this video lesson, the instructor explores improvements to the DDPM/DDIM implementation for fashion using a Jupyter Notebook. They discuss the removal of the concept of N steps and the capital T, which previously represented the time step in the diffusion process. Instead, they now assume that the time step is between 0 and 1, representing the percentage of the way through the diffusion process. This change simplifies the process and makes it more continuous.
The instructor then explores the idea of predicting the amount of noise in an image without passing the time step (T) as input. They create a model that predicts the alpha bar T (the amount of noise) given a noisy image. After training the model, they find that it can predict the amount of noise with reasonable accuracy. However, when they attempt to use this model for sampling without passing the time step, the results are not as good as expected.
To address this issue, the instructor modifies the DDIM step to use the predicted alpha bar T for each image, clamped to be not too far away from the median. This approach updates the noise based on the amount of noise that actually seems to be left behind, rather than the assumed amount of noise that should be left behind. The results are much better, with the new approach producing similar quality images to the original method. The instructor suggests that this "no T" approach could eventually surpass the T-based approaches, as it has been developed more recently and shows promising results.In this video lesson, the instructors discuss the research process and the importance of noise scheduling for diffusion models. They highlight that the research process is not linear and involves a lot of back and forth discussions, debugging, and exploring. They also mention that they have tried different noise schedules and input scaling strategies on larger models to see if they work.
The instructors then discuss a paper on the importance of noise scheduling for diffusion models, which demonstrates that the optimal noise schedule depends on the type of data and image size. The paper also shows that scaling the input data is a good strategy for working with different noise schedules. The authors of the paper propose a method called C-skip, which predicts an interpolated version of the clean image and the noise, depending on the amount of noise present in the input. This makes the problem to be solved by the model equally difficult regardless of the noise level.
The instructors also mention that this idea of interpolating between the noise and the image is similar to the VM objectives used in other models, such as the Steeple Diffusion 2.0. This methodology has been used in practice and has produced good results.In this section of the video lesson, the presenter discusses the importance of paying attention to side notes in research papers, as they can sometimes contain valuable information. They then explain the process of scaling input and output images in the context of noise and variance. The presenter also highlights the importance of having unit variance inputs for models and demonstrates how this can be achieved.
The lesson then moves on to the topic of sampling, where the presenter explains the concept of reverse diffusion sigma steps and how they can be used to improve the sampling process. They discuss the Euler sampler, which is a simple and deterministic method for sampling, and show that it can achieve a good FID score. The presenter then introduces the Ancestral Euler sampler, which adds randomness to the sampling process, resulting in an even better FID score.
The presenter explains Heuns method, which calculates the average slope between the current position and the position where the Euler method would have taken the model. This method is shown to be more accurate than the Euler sampler, as it takes into account the slope at both the current position and the predicted position. Overall, the lesson emphasizes the importance of understanding the underlying concepts and techniques in research papers and demonstrates how these can be applied to improve model performance.In this section of the video lesson, the instructor discusses the performance of different samplers in the context of diffusion-based models. They mention that the Hewne sampler, which calls the model twice for a single step, performs better than the Euler sampler even with fewer steps. However, they also point out that the LMS sampler, which uses only 20 evaluations, beats Euler with 100 evaluations. The LMS sampler achieves this by storing the slope in a list and using up to the last four to estimate the curvature and take the next step.
The instructor then mentions that there is a newer sampler similar to the DDPM+ sampler, which also keeps a list of recent results and uses that for the next step. They highlight the importance of having unit variance inputs and outputs, as well as a different schedule for sampling that is unrelated to the training schedule. The instructor also appreciates the paper "Elucidating the Design Space of Diffusion-Based Models" for simplifying the code and connecting various approaches in a common framework.
In this video lesson, the instructor discusses a bug in Notebook 23 related to measuring the FID (Fréchet Inception Distance) and its impact on the results. The bug caused the model to see unusually low contrast images, leading to incorrect FID values. After fixing the bug, the FIDs were around 5.65 for generated images and 2.5 for real images.
The lesson then moves on to working with Tiny Imagenet, a dataset of 64x64 images, to create a super-resolution U-Net model. The instructor demonstrates how to create a dataset, preprocess the images, and apply data augmentation. They also discuss the challenges of overfitting and the need for data augmentation to improve the model's performance.
The instructor trains the model using AdamW optimizer and mixed precision, achieving an accuracy of nearly 60%. They also explore the potential for improvement by examining the results of other models on Tiny Imagenet from the Papers with Code website.In this section of the lesson, the focus is on super-resolution, where the goal is to scale up a low-resolution image to a higher resolution. The independent variable is a 32x32 pixel image, and the dependent variable is the original 64x64 pixel image. To ensure that the augmentation is done in the same way on both the independent and dependent variables, the augmentation is placed directly in the dataset.
The super-resolution task is challenging as the model has to learn how to draw features like eyes and whiskers from the low-resolution images. The approach taken is to create a model with a series of ResBlocks with a stride of 2, followed by an equal number of up blocks that perform up sampling and then pass through a ResBlock. This essentially undoes the stride of 2 and up samples the image. The model is trained briefly for five epochs, and the results show that it can perform super-resolution reasonably well.In this section of the video lesson, the instructor discusses the limitations of using a convolutional neural network for image super-resolution and introduces the concept of Unet, a more efficient architecture for this task. The instructor explains that using mean squared error (MSE) as a loss function can lead to blurry results, and suggests using perceptual loss as an alternative. Perceptual loss involves comparing the features of the output image and the target image at an intermediate layer of a pre-trained classifier model.
To implement perceptual loss, the instructor uses a classifier model trained on the dataset and modifies it to return the activations after the fourth residual block. The loss function is then calculated as the sum of the MSE loss between the input and target images and the MSE loss between the features obtained from the classifier model for both the target and input images. The instructor also scales the feature loss by a factor of 0.1 to balance the two components of the loss function.
After training the Unet model with the new loss function, the instructor observes that the output images are less blurry and more similar to the target images, although there is still room for improvement. The use of perceptual loss has helped the model to generate better super-resolution images compared to using MSE loss alone.In this section of the video lesson, the instructor discusses the challenges of comparing different models and their outputs. They demonstrate how perceptual loss has improved the results significantly, but also note that there isn't a clear metric to use for comparison. The instructor then moves on to gradually unfreezing pre-trained networks, a favorite trick in FastAI. They copy the weights from the pre-trained model into their model and train it for one epoch with frozen weights for the down path. This results in a significant improvement in loss.
The instructor then experiments with adding cross connections or cross cons, which are res blocks in the Unet. They create a new Unet with cross cons and train it, achieving even better results. The instructor suggests that students could try various image-to-image tasks with the Unet, such as segmentation, style transfer, colorization, or decrappification. Other ideas include watermark removal, drawing to painting, and improving the super resolution model.
In Lesson 24, the focus is on completing the unconditional stable diffusion model. The lesson begins with the creation of a 26 diffusion unit, which is based on the diffusers model. The pre-activation convolution is used, and the structure of the model is similar to what is found in diffusers. The lesson also covers the creation of a saved res block and a saved convolution, which are used to store activations during the down sampling and up sampling paths of the model.
The lesson then moves on to the implementation of the unconditional model, which is similar to the diffusers unconditional model but without attention and time embedding. The model is trained using fashion MNIST with fewer channels than the default. The lesson also discusses the importance of time embedding and introduces sine and cosine embeddings as a way to create embeddings for each time step.
The sine and cosine embeddings are used to create a res block with embeddings, where the forward function takes both the activations and the time embedding vector T. The lesson demonstrates how to create these embeddings using a range of time steps and exponents, resulting in a 100x16 matrix of embeddings.The attention mechanism in stable diffusion is designed to allow the model to take into account information from other pixels in the image, regardless of their distance. This is achieved by flattening the image into a one-dimensional sequence of tokens and applying 1D attention, which was originally developed for NLP tasks.
The attention process involves creating a weighted average of all the pixels in the flattened image. Each pixel in the image will be updated based on its original value plus the weighted average of the other pixels. The weights are designed to sum to one, ensuring that the pixel values do not change drastically.
This approach allows the model to consider information from distant pixels, such as the shape of a bunny rabbit's ear, when making decisions about the activations at a particular location in the image. Although the attention mechanism used in stable diffusion is known to be suboptimal, it serves as a starting point for understanding and implementing attention in generative models.In this section of the video lesson, the instructor discusses the concept of self-attention and multi-headed attention in the context of stable diffusion. Self-attention is a mechanism that allows a model to weigh the importance of different parts of an input sequence when making predictions. The instructor explains how to calculate the weights for self-attention using matrix products and demonstrates the implementation of self-attention in code.
The instructor then introduces multi-headed attention, which is an extension of self-attention that allows the model to focus on different aspects of the input sequence simultaneously. This is achieved by splitting the channels into multiple groups, or heads, and performing self-attention on each group separately. The instructor demonstrates how to implement multi-headed attention in code using the rearrange function from the einops library.
The instructor compares the custom implementation of self-attention and multi-headed attention with the built-in PyTorch implementation, highlighting the importance of specifying the batch_first parameter to ensure consistency with the custom implementation.In this section of the video lesson, the instructor discusses the rearrange function and its usefulness in replacing individual operations like transpose and reshape. They also mention that rearrange is becoming popular in the diffusion research community. The instructor then adds attention to the MResBlock and adjusts the down and up blocks to accommodate attention channels. They also discuss the balance of finding the right place to add attention in the network to avoid high memory usage.
The instructor briefly covers transformers and their potential use in vision tasks. They explain that transformers can approximate any convolution, but doing so requires a lot of data, layers, parameters, and compute. Pre-trained vision transformers (VITs) can perform better than convolutions when fine-tuned on ImageNet, but using them without pre-training on a large dataset would result in poor performance.
Finally, the instructor demonstrates how to create a conditional model by adding a label to the input of the UNet model. This allows the model to generate images of a specific class, such as a shirt or pants. The sampling process is then adjusted to accommodate the class ID, and the model successfully generates images of the specified class. The instructor concludes by mentioning that the next lesson will cover Variational Autoencoders (VAEs) and latent diffusion.