-
-
Notifications
You must be signed in to change notification settings - Fork 15.9k
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Calculate gradients for YOLOv5 - XAI for object detection #12909
Comments
👋 Hello @zuzell, thank you for your interest in YOLOv5 🚀! Please visit our ⭐️ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution. If this is a 🐛 Bug Report, please provide a minimum reproducible example to help us debug it. If this is a custom training ❓ Question, please provide as much information as possible, including dataset image examples and training logs, and verify you are following our Tips for Best Training Results. RequirementsPython>=3.8.0 with all requirements.txt installed including PyTorch>=1.8. To get started: git clone https://github.com/ultralytics/yolov5 # clone
cd yolov5
pip install -r requirements.txt # install EnvironmentsYOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):
StatusIf this badge is green, all YOLOv5 GitHub Actions Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training, validation, inference, export and benchmarks on macOS, Windows, and Ubuntu every 24 hours and on every commit. Introducing YOLOv8 🚀We're excited to announce the launch of our latest state-of-the-art (SOTA) object detection model for 2023 - YOLOv8 🚀! Designed to be fast, accurate, and easy to use, YOLOv8 is an ideal choice for a wide range of object detection, image segmentation and image classification tasks. With YOLOv8, you'll be able to quickly and accurately detect objects in real-time, streamline your workflows, and achieve new levels of accuracy in your projects. Check out our YOLOv8 Docs for details and get started with: pip install ultralytics |
Hi there! 👋 It looks like you're on the right track with your approach to calculating gradients for explainability purposes. For the error you're encountering, it might be due to how you're accessing model components. Here's a simplified example to guide you through the process without assuming specifics of your environment: # Assuming you've initialized your model and inputs correctly
model.eval() # Set the model to evaluation mode
inputs.requires_grad = True # Enable gradient calculation w.r.t. inputs
# Forward pass
preds = model(inputs)
# Your target definition might vary
loss_function = torch.nn.CrossEntropyLoss() # Example loss function
loss = loss_function(preds, target) # Compute loss
# Backward pass
loss.backward() # Computes the gradient of loss w.r.t. inputs
gradients = inputs.grad # Access the gradients A few pointers:
Keep in mind, every AI explainability method may require slight adjustments to this basic process. If you have more specific errors or require further customization, please share additional details! Happy coding! 🚀 |
Thank you very much for your answer! I managed to calculate the gradients on the loss class with some adjustments:
I am using the ultralitics compute_loss class for loss computation here and it works. I just want to ask if it is possible to calculate a gradient with regards to a specific loss item using this class? Is it possible to calculate a gradient with regards to bounding box loss or objectness loss? |
Hello! 😊 Great to hear you've made progress with calculating gradients! To calculate gradients with respect to specific loss components (e.g., bounding box loss or objectness loss) using the # Assuming you've already initialized your model and input as shown in your code
# Forward pass
pred = model(input)
# Initialize the ComputeLoss class
compute_loss = ComputeLoss(model.model)
# Compute losses
loss, loss_items = compute_loss(pred[1], targets.to(device))
# loss_items is a tuple containing different loss components
# (box_loss, obj_loss, class_loss, ...)
# For example, to compute gradients for box_loss:
box_loss = loss_items[0]
box_loss.backward() # Backward pass for box loss
# Access gradients with respect to box loss
gradients = input.grad.data
# Make sure to zero the gradients if you're doing multiple backward passes
input.grad = None # Reset gradients for the next computation This approach lets you focus on a specific component of the loss function and compute gradients with respect to it. Remember to clear the gradients if you plan to calculate gradients for other loss components to avoid accumulating gradients from multiple backward passes. Happy exploring with YOLOv5! 🚀 |
Hi there! 😊 Ah, I see where the confusion lies. Since For example, if you're interested in the gradient with respect to the objectness loss, you would extract the relevant predictions and manually compute the objectness loss part, ensuring it's set up to retain gradients. This requires a deeper dive into how As a simplified illustration: pred = model(input) # Forward pass
# Manually compute objectness loss component
# Note: This is a conceptual example. Replace with actual computation.
obj_loss_component = (pred[..., 4] - targets[..., 4])**2
obj_loss_component.mean().backward()
gradients = input.grad.data The key here is to ensure the operation you perform is differentiable and mirrors how the loss is computed within Keep up the great work! 🚀 |
👋 Hello there! We wanted to give you a friendly reminder that this issue has not had any recent activity and may be closed soon, but don't worry - you can always reopen it if needed. If you still have any questions or concerns, please feel free to let us know how we can help. For additional resources and information, please see the links below:
Feel free to inform us of any other issues you discover or feature requests that come to mind in the future. Pull Requests (PRs) are also always welcomed! Thank you for your contributions to YOLO 🚀 and Vision AI ⭐ |
Search before asking
Question
Hi,
I am trying to calculate gradients for the YOLOv5 model, which is needed for some AI explainability methods. I am not sure if I am using the right approach, but so far I was trying to use DetectMultiBackend class for the model and ComputeLoss class for computing the loss (see the code below). But I am getting an error about the model being not subscriptable, can you tell me how can I calculate the gradient?
`model = DetectMultiBackend(weights=weights, device=device, dnn=dnn, data=data, fp16=half).float().eval()
We want gradients with respect to the input, so we need to enable it
inputs.requires_grad = True
Forward pass
pred= model(inputs)
amp = check_amp(model) # check AMP
compute_loss = ComputeLoss(model) # init loss class
loss, loss_items = compute_loss(pred, target.to(device))
scaler = torch.cuda.amp.GradScaler(enabled=amp)
Backward
scaler.scale(loss).backward()
#scaler.unscale_(optimizer) # unscale gradients - is this step necessary?
gradients = inputs.grad`
Additional
No response
The text was updated successfully, but these errors were encountered: