Method Reshape

src/caffe/layers/scale_layer.cpp:76–114

template <typename Dtype>
void ScaleLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
      const vector<Blob<Dtype>*>& top) {
  const ScaleParameter& param = this->layer_param_.scale_param();
  Blob<Dtype>* scale = (bottom.size() > 1) ? bottom[1] : this->blobs_[0].get();
  // Always set axis_ == 0 in special case where scale is a scalar
  // (num_axes == 0). Mathematically equivalent for any choice of axis_, so the
  // actual setting can be safely ignored; and computation is most efficient
  // with axis_ == 0 and (therefore) outer_dim_ == 1. (Setting axis_ to
  // bottom[0]->num_axes() - 1, giving inner_dim_ == 1, would be equally
  // performant.)
  axis_ = (scale->num_axes() == 0) ?
      0 : bottom[0]->CanonicalAxisIndex(param.axis());
  CHECK_GE(bottom[0]->num_axes(), axis_ + scale->num_axes())
      << "scale blob's shape extends past bottom[0]'s shape when applied "
      << "starting with bottom[0] axis = " << axis_;
  for (int i = 0; i < scale->num_axes(); ++i) {
    CHECK_EQ(bottom[0]->shape(axis_ + i), scale->shape(i))
        << "dimension mismatch between bottom[0]->shape(" << axis_ + i
        << ") and scale->shape(" << i << ")";
  }
  outer_dim_ = bottom[0]->count(0, axis_);
  scale_dim_ = scale->count();
  inner_dim_ = bottom[0]->count(axis_ + scale->num_axes());
  if (bottom[0] == top[0]) {  // in-place computation
    temp_.ReshapeLike(*bottom[0]);
  } else {
    top[0]->ReshapeLike(*bottom[0]);
  }
  sum_result_.Reshape(vector<int>(1, outer_dim_ * scale_dim_));
  const int sum_mult_size = std::max(outer_dim_, inner_dim_);
  sum_multiplier_.Reshape(vector<int>(1, sum_mult_size));
  if (sum_multiplier_.cpu_data()[sum_mult_size - 1] != Dtype(1)) {
    caffe_set(sum_mult_size, Dtype(1), sum_multiplier_.mutable_cpu_data());
  }
  if (bias_layer_) {
    bias_bottom_vec_[0] = top[0];
    bias_layer_->Reshape(bias_bottom_vec_, top);
  }
}

template <typename Dtype>
void ScaleLayer<Dtype>::Forward_cpu(