I'm working on object detection explainability using the Xplique library with a custom YOLOv7 model analogous to a tutorial designed for ssdlite320_mobilenet_v3_large Google Colab Tutorial.
My goal is to use Xplique's Saliency method to generate explanations for detections made by YOLOv7. However, I'm encountering a RuntimeError related to gradient computation during the explanation phase.
Approach:
To ensure the YOLOv7 model's output tensors retain the grad_fn attribute, I've wrapped the model in a custom PyTorch module, ensuring compatibility with Xplique's requirements:
import torchimport torch.nn as nnclass Ensemble(nn.ModuleList): def __init__(self): super(Ensemble, self).__init__() def forward(self, x, augment=False): y = [] for module in self: y.append(module(x, augment=augment)[0]) # Ensure augment is passed correctly y = torch.cat(y, 1) # nms ensemble return y, None # inference, train output# Load the YOLOv7 model and ensure it's ready for inferencemodel = Ensemble().to(device) # Assuming device is definedckpt = torch.load(weights_path, map_location=device) # Assuming weights_path is definedmodel.append(ckpt['ema' if ckpt.get('ema') else 'model'].float().fuse().eval())# Compatibility updatesfor m in model.modules(): if type(m) in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU]: m.inplace = True # PyTorch 1.7.0 compatibility elif type(m) is nn.Upsample: m.recompute_scale_factor = None # PyTorch 1.11.0 compatibility# Ensure your input tensor is on the same device as the modelvisualizable_torch_inputs = visualizable_torch_inputs.to(device)# Now perform inferencepredictions = model(visualizable_torch_inputs)With the model prepared, I performed inference to obtain predictions, which include the bounding boxes, confidence scores, and class IDs.
Issue:
When attempting to compute explanations using Xplique's Saliency method, the following RuntimeError is raised:
RuntimeError: RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.cuda.FloatTensor [1, 3, 20, 20, 6]], which is output 0 of SigmoidBackward0, is at version 2; expected version 0 instead. Hint: the backtrace further above shows the operation that failed to compute its gradient. The variable in question was changed in there or anywhere later. Good luck!This occurs precisely at the line:
explanation = explainer.explain(processed_tf_inputs, man_bounding_box)Questions:
How can I resolve the RuntimeError related to gradient computation when using Xplique with a YOLOv7 model?
Are there specific considerations or modifications required to make YOLOv7 compatible with Xplique's explainability methods?
Is the issue related to how YOLOv7's outputs are structured or how gradients are being computed within the model or Xplique?