Caffe中Loss层top[0]->cpu_diff()[0]问题

本文深入解析了Caffe框架中L2Loss层的工作原理，重点阐述了loss_weight的计算与赋值过程，以及其在反向传播中的作用。

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参考

在Caffe源码有关Loss层中在反向传播函数中，都能够看到如下代码，赋予loss_weight值为top[0]->cpu_diff()[0]的值。

    // Scale gradient
    Dtype loss_weight = top[0]->cpu_diff()[0] /
                        get_normalizer(normalization_, count);
    caffe_scal(prob_.count(), loss_weight, bottom_diff);

但是在源码中查找top[0]->cpu_diff()却发现Loss层中此值并未初始化或者被赋值。

那么，top[0]->cpu_diff()里面到底存储的是什么值呢？

其实，在Loss层中，top_diff()[0]的默认值对应于Loss层中loss_weight的值，一般设置成1，能够在caffe源码能够找到出处。
layer.cpp中的SetLossWeights函数看出，通过this->set_loss()设置对应Layer层的私有变量loss_，其值是param.loss_weight。

  /**
   * Called by SetUp to initialize the weights associated with any top blobs in
   * the loss function. Store non-zero loss weights in the diff blob.
   */
  inline void SetLossWeights(const vector<Blob<Dtype>*>& top) {
    const int num_loss_weights = layer_param_.loss_weight_size();
    if (num_loss_weights) {
      CHECK_EQ(top.size(), num_loss_weights) << "loss_weight must be "
          "unspecified or specified once per top blob.";
      for (int top_id = 0; top_id < top.size(); ++top_id) {
        // 取得loss_weight参数值
        const Dtype loss_weight = layer_param_.loss_weight(top_id);
        if (loss_weight == Dtype(0)) { continue; }
        this->set_loss(top_id, loss_weight);
        const int count = top[top_id]->count();
        Dtype* loss_multiplier = top[top_id]->mutable_cpu_diff();
        // 将loss_weight赋值给top层的cpu_diff()
        caffe_set(count, loss_weight, loss_multiplier);
      }
    }
  }

然后进入Net.cpp中找到Init()函数中如下所示代码，可以看到blob_loss_weights_层的私有变量被设置成了layer->loss(top_id)

for (int top_id = 0; top_id < top_vecs_[layer_id].size(); ++top_id) {
      if (blob_loss_weights_.size() <= top_id_vecs_[layer_id][top_id]) {
        blob_loss_weights_.resize(top_id_vecs_[layer_id][top_id] + 1, Dtype(0));
      }
      // 设置blob_loss_weights相应层的loss_weight值，其值由私有变量loss_决定
      blob_loss_weights_[top_id_vecs_[layer_id][top_id]] = layer->loss(top_id);
      LOG_IF(INFO, Caffe::root_solver())
          << "Top shape: " << top_vecs_[layer_id][top_id]->shape_string();
      if (layer->loss(top_id)) {
        LOG_IF(INFO, Caffe::root_solver())
            << "    with loss weight " << layer->loss(top_id);
      }
      memory_used_ += top_vecs_[layer_id][top_id]->count();
    }