mAP calculation

[RT1991mj]

Hi,
Would you guide me on how to calculate mAP of the final logits?
I am using the below function to do so but it doesnt work:

averaged_logits = torch.mean(per_frame_logits,-1)
predictions = F.softmax(averaged_logits,-1)
print(charades_mAP(predictions,torch.max(labels,dim=2)[0]))

def charades_mAP(y_true, y_pred):

y_true = y_true.cpu().detach().numpy().astype(np.int32)
y_pred = y_pred.cpu().detach().numpy()

m_aps = []
n_classes = y_pred.shape[1]
for oc_i in range(n_classes):
    pred_row = y_pred[:, oc_i]
    sorted_idxs = np.argsort(-pred_row)
    true_row = y_true[:, oc_i]
    tp = true_row[sorted_idxs] == 1
    fp = np.invert(tp)
    n_pos = tp.sum()
    if n_pos < 0.1:
        m_aps.append(float('nan'))
        continue
    f_pcs = np.cumsum(fp)
    t_pcs = np.cumsum(tp)
    prec = t_pcs / (f_pcs + t_pcs).astype(float)
    avg_prec = 0
    for i in range(y_pred.shape[0]):
        if tp[i]:
            avg_prec += prec[i]
    m_aps.append(avg_prec / n_pos.astype(float))
m_aps = np.array(m_aps)
m_ap = np.mean(m_aps)
return m_ap

Would you please share your mAP calculation code?
Thanks a lot