Comprehensive Guide to Transformers for Computer Vision Engineers

Setup Instructions for Vision Transformer Hands-on Practice

This guide provides detailed instructions for setting up your environment to work with Vision Transformers. Whether you’re using a local machine with or without a GPU, or a cloud-based solution like Google Colab, these instructions will help you get started quickly.

Environment Options

You have several options for setting up your environment:

  1. Google Colab (Recommended for beginners): Free access to GPUs with minimal setup
  2. Local setup with GPU: Fastest performance but requires compatible hardware
  3. Local setup without GPU: Limited to smaller models but works on any computer
  4. Cloud-based alternatives: Options like Kaggle Notebooks or AWS SageMaker

Option 1: Google Colab Setup

Google Colab provides free access to GPUs and comes with many pre-installed libraries, making it ideal for beginners.

Step 1: Access Google Colab

  1. Go to Google Colab
  2. Sign in with your Google account

Step 2: Create a New Notebook

  1. Click on File > New Notebook
  2. Rename the notebook by clicking on “Untitled0” at the top

Step 3: Configure GPU Runtime

Google Colab GPU Setup
Figure 1: Selecting GPU runtime in Google Colab
  1. Click on Runtime > Change runtime type
  2. Select GPU from the Hardware accelerator dropdown
  3. Click Save

Free Colab sessions have limitations:

  • Sessions timeout after 12 hours of inactivity
  • Limited GPU usage per day
  • Shared resources may affect performance

Step 4: Install Required Libraries

Run the following code in a cell to install the necessary libraries:

!pip install torch torchvision tqdm matplotlib
!pip install timm  # For Vision Transformer implementations

Step 5: Verify GPU Access

Run this code to confirm that PyTorch can access the GPU:

import torch
print(f"PyTorch version: {torch.__version__}")
print(f"CUDA available: {torch.cuda.is_available()}")
if torch.cuda.is_available():
    print(f"CUDA device: {torch.cuda.get_device_name(0)}")

Option 2: Local Setup with GPU

If you have a compatible NVIDIA GPU, setting up a local environment will provide the best performance.

Step 1: Install CUDA and cuDNN

  1. Check your GPU compatibility at NVIDIA’s CUDA GPUs list
  2. Download and install the appropriate CUDA version from NVIDIA’s CUDA download page
  3. Download and install cuDNN from NVIDIA’s cuDNN page (requires free NVIDIA developer account)

Step 2: Create a Conda Environment

  1. Install Anaconda or Miniconda
  2. Open a terminal or Anaconda prompt
  3. Create a new environment:
conda create -n vit python=3.8
conda activate vit

Step 3: Install PyTorch with GPU Support

conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch

Replace cudatoolkit=11.3 with the version that matches your installed CUDA version. Check compatibility at PyTorch’s installation page.

Step 4: Install Additional Libraries

pip install timm matplotlib tqdm jupyter

Step 5: Verify GPU Access

Launch Python and run:

import torch
print(f"PyTorch version: {torch.__version__}")
print(f"CUDA available: {torch.cuda.is_available()}")
if torch.cuda.is_available():
    print(f"CUDA device: {torch.cuda.get_device_name(0)}")

If you’re having issues with GPU detection, try the following:

  1. Ensure your GPU drivers are up to date
  2. Check that CUDA and PyTorch versions are compatible
  3. Try reinstalling PyTorch with the specific CUDA version you have installed

Option 3: Local Setup without GPU

If you don’t have a compatible GPU, you can still run smaller models on your CPU.

Step 1: Create a Conda Environment

  1. Install Anaconda or Miniconda
  2. Open a terminal or Anaconda prompt
  3. Create a new environment:
conda create -n vit python=3.8
conda activate vit

Step 2: Install PyTorch (CPU Version)

conda install pytorch torchvision torchaudio cpuonly -c pytorch

Step 3: Install Additional Libraries

pip install timm matplotlib tqdm jupyter

Running Vision Transformers on CPU will be significantly slower than on GPU. Consider:

  • Using smaller models with fewer parameters
  • Reducing batch sizes
  • Processing fewer images
  • Using pre-computed features when possible

Option 4: Cloud-based Alternatives

If Google Colab doesn’t meet your needs, consider these alternatives:

Kaggle Notebooks

  1. Create an account at Kaggle
  2. Go to “Notebooks” and click “New Notebook”
  3. Under “Settings”, select GPU accelerator
  4. Libraries like PyTorch, torchvision, and timm are pre-installed

AWS SageMaker

For more advanced users or those needing longer runtimes:

  1. Create an AWS account
  2. Navigate to SageMaker in the AWS console
  3. Create a notebook instance with GPU support (e.g., ml.p3.2xlarge)
  4. Choose a PyTorch or conda kernel

Downloading Datasets

For the hands-on exercises, we’ll use several datasets. Here’s how to download them:

The main datasets we’ll be using include:

  1. CIFAR-10: A dataset of 60,000 32x32 color images in 10 classes
  2. Flowers-102: A dataset of 102 flower categories
  3. ImageNet: A subset for inference with pre-trained models

These datasets are automatically downloaded by the code in our exercises, but you can also pre-download them if you prefer.

CIFAR-10

import torchvision
# This will download CIFAR-10 to ./data/cifar-10
train_dataset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True)
test_dataset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True)

ImageNet (Subset)

For exercises requiring ImageNet, we’ll use a subset called ImageNette:

!wget https://s3.amazonaws.com/fast-ai-imageclas/imagenette2-320.tgz
!tar -xzf imagenette2-320.tgz

Testing Your Environment

To ensure everything is set up correctly, run this simple test:

import torch
import timm

# Check PyTorch and GPU
print(f"PyTorch version: {torch.__version__}")
print(f"CUDA available: {torch.cuda.is_available()}")
if torch.cuda.is_available():
    print(f"CUDA device: {torch.cuda.get_device_name(0)}")

# Test loading a ViT model
model = timm.create_model('vit_base_patch16_224', pretrained=True)
print(f"Model loaded successfully: {model.__class__.__name__}")

# Test moving model to appropriate device
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = model.to(device)
print(f"Model moved to: {next(model.parameters()).device}")

If you see the model name printed and no errors, congratulations! Your environment is set up correctly and you’re ready to start working with Vision Transformers.

Troubleshooting Common Issues

CUDA Out of Memory

  • Reduce batch size
  • Use a smaller model variant
  • Try gradient accumulation

Package Conflicts

  • Create a fresh conda environment
  • Install packages in the recommended order
  • Check version compatibility between PyTorch and CUDA

Import Errors

  • Ensure you’ve activated the correct environment
  • Reinstall problematic packages
  • Check for missing dependencies

Slow Performance on GPU

  • Check if PyTorch is actually using the GPU (next(model.parameters()).device)
  • Update GPU drivers
  • Close other GPU-intensive applications

Next Steps

Now that your environment is set up, you’re ready to start working with Vision Transformers! Proceed to the Hands-on Practice section to begin implementing and experimenting with these powerful models.

In the hands-on practice, you’ll learn how to:

  • Load and preprocess image data
  • Implement a basic Vision Transformer from scratch
  • Fine-tune pre-trained ViT models
  • Visualize attention maps
  • Apply ViT to various computer vision tasks