as the yolov3 is being used in inference mode with torch.no_grad() so that means no computational graph will be created for that. How it will compute the gradient for yolov3
as Loss = f(x) - f(k(g(x))) where f = yolov3 model, g = classification model I want to train, k= compression, x = original image and k(g(x)) = new compressed image
now loss gradient with respect to one parameter,
dLoss/dW = df/dk * dk/dg *dg/dW
how df/dk and dk/dg will be computed or represented in computational graph as those are not traditional differentiable operators.