Possible to broadcast the blob using only "Scale" layer?

[Kurt Koo]

I read the descriptions in "caffe.proto" and "scale_layer.hpp".

caffe.proto

For example, if bottom[0] is 4D with shape 100x3x40x60, the output

// top[0] will have the same shape, and bottom[1] may have any of the

// following shapes (for the given value of axis):

// (axis == 0 == -4) 100; 100x3; 100x3x40; 100x3x40x60

// (axis == 1 == -3) 3; 3x40; 3x40x60

// (axis == 2 == -2) 40; 40x60

// (axis == 3 == -1) 60

scale_layer.hpp

* @brief Computes the elementwise product of two input Blobs, with the shape of

* the latter Blob "broadcast" to match the shape of the former.

* Equivalent to tiling the latter Blob, then computing the elementwise

* product. Note: for efficiency and convenience, this layer can

* additionally perform a "broadcast" sum too when `bias_term: true`

* is set.

Then I try to compute the elementwise product of (1, 2048, 72, 72)(bottom[0]) and (1, 1, 72, 72)(bottom[1]).

Configuration of the "Scale" layer is as follows:

layer {

  name: "inception_attention_scale"

  type: "Scale"

  bottom: "inception_c2_concat"

  bottom: "inception_c1_attention"

  top: "inception_attention_scale"

  scale_param {

    axis: 0

    bias_term: false

    num_axes: -1

  }

}

Network prototxt is included in the attachment.

When I start training, it outputs as follows:

F20211208 18:22:14.814239   571 scale_layer.cpp:85] Check failed: bottom[0]->shape(axis_ + i) == scale->shape(i) (2048 vs. 1) dimension mismatch between bottom[0]->shape(1) and scale->shape(1)

Is it possible to broadcast the bottom[1] to (1, 2048, 1, 1) using only "Scale" layer?

Or is there any problem with my "Scale" layer configuration?

Any help is appreciated.

Thanks!