I use
        with autograd.detect_anomaly():

and error detected
Function 'SymeigBackward' returned nan values in its 0th output.

I use symeig function
# ReEig Layer
def cal_rect_cov(features):
    weight1, weight2 = variable_with_orth_weight_decay(features.shape)
    features = torch.bmm(torch.bmm(weight2, features), weight1)
    result=[]
    for i in range(features.shape[0]):
        s_f,v_f=torch.symeig(features[i], eigenvectors=True)
        s_f_clamp=torch.clamp(s_f,0.0001,10000)
        s_f_clamp2=torch.diag(s_f_clamp)
        sv_m=torch.matmul(v_f,s_f_clamp2)
        features_t=torch.matmul(sv_m,v_f.t())
        result.append(features_t)
    result = torch.stack(result)
    return result

# LogEig Layer
def cal_log_cov(features):
#     features=features.detach().cpu()
    result=[]
    for i in range(features.shape[0]):
        s_f,v_f=torch.symeig(features[i], eigenvectors=True)
        s_f_log = torch.log(s_f)
        s_f_log2=torch.diag(s_f_log)
        sv_m=torch.matmul(v_f,s_f_log2)
        features_t=torch.matmul(sv_m,v_f.t())
        result.append(features_t)
    result = torch.stack(result)
    return result

when get cnn feature, and make covariance matrix using torch.symeig
but this error detected.
plz help me how to get no nan values in ‘SymeigBackward’
Thank you
-James