Calculate gradients for YOLOv5 - XAI for object detection about yolov5 HOT 6 OPEN

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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

from yolov5.

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:

• Ensure your inputs and target are correctly defined and on the same device as your model.
• DetectMultiBackend and ComputeLoss usage seems correct. Make sure any custom modifications do not interfere with the grad calculation.
• The step scaler.unscale_(optimizer) is related to gradient scaling for mixed precision training. It's necessary if you're using an optimizer and want to modify gradients or apply gradient clipping before optimizer step. If you're just calculating gradients for XAI and not updating the model, it can be omitted.

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! 🚀

from yolov5.

@glenn-jocher

Thank you very much for your answer! I managed to calculate the gradients on the loss class with some adjustments:

from utils.loss import ComputeLoss
from utils.dataloaders import create_dataloader
from models.common import DetectMultiBackend

model = DetectMultiBackend(weights=weights, device=device, dnn=dnn, data=data, fp16=half).float()

val_loader = create_dataloader(
            data['val'],
            imgsz,
            batch_size,
            gs,
            shuffle=True,
            prefix=colorstr("val: ")
        )[0]

input, targets, paths, shapes = next(iter(val_loader))


if isinstance(hyp, str):
    with open(hyp, errors="ignore") as f:
        hyp = yaml.safe_load(f)  # load hyps dict


model.hyp = hyp  # attach hyperparameters to model

model.eval()
input = input.float()
input /= 255
input.requires_grad = True


pred= model(input) # Forward pass

compute_loss = ComputeLoss(model.model)  # init loss class
loss, loss_items = compute_loss(pred[1], targets.to(device))


loss.backward() # Backward


gradients = input.grad.data # Get the gradients of the input with respect to the loss

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?

from yolov5.

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 ComputeLoss class from Ultralytics, you can adjust your approach slightly. The ComputeLoss function returns several individual loss components, which you can use to compute gradients for specific losses. Here's how you could do it:

# 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! 🚀

from yolov5.

Thank you one more time for a quick response! I tried this approach and unfortunately the ´loss_items´ is a tensor without a gradient and I cant run a backward pass on its elements. Do you know if there is any way to get loss_items as a tensor with a gradient?

from yolov5.

Hi there! 😊

Ah, I see where the confusion lies. Since loss_items itself doesn't hold gradients, to focus on specific parts of the loss with gradients, you'll have to manually reconstruct these components from the outputs and target, and then perform the backward pass on these components separately.

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 ComputeLoss computes each component, and then applying the same operations on your side.

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 ComputeLoss. Given the complexity, this might require some tweaking and testing on your end.

Keep up the great work! 🚀

from yolov5.