imsitu_model.py 文件源码

def ours_train(m, x, labels, data, att_crit=None, optimizers=None):
    """
    Train the direct attribute prediction model
    :param m: Model we're using
    :param x: [batch_size, 3, 224, 224] Image input
    :param labels: [batch_size] variable with indices of the right verbs
    :param embeds: [vocab_size, 300] Variables with embeddings of all of the verbs
    :param atts_matrix: [vocab_size, att_dim] matrix with GT attributes of the verbs
    :param att_crit: AttributeLoss module that computes the loss
    :param optimizers: the decorator will use these to update parameters
    :return: 
    """
    logits = ours_logits(m, x, data, att_crit=att_crit)
    loss = m.l2_penalty
    if len(logits) == 1:
        loss += F.cross_entropy(logits[0], labels, size_average=True)
    else:
        sum_logits = sum(logits)
        for l in logits:
            loss += F.cross_entropy(l, labels, size_average=True)/(len(logits)+1)
        loss += F.cross_entropy(sum_logits, labels, size_average=True)/(len(logits)+1)
    return loss