dnn: expand refactor with cv::broadcast for onnx models

[fengyuentau]

Resolves #24300
Resolves #24308

Merge with opencv/opencv_extra#1098.

Motivation:

Current implementation of ONNX Expand is very limited and hard to maintain. It can go very unefficient, e.g. input of shape [1, 5, 1] and shape of value [1, 5, 256], it leads to 256 x [1, 5, 1] constant nodes along with a concat node. See down below for more details .

opencv/modules/dnn/src/onnx/onnx_importer.cpp

Lines 2485 to 2494 in b870ad4

	else if (broadcast_axes.size() == 1)
	{
	// FIXME: this will end up creating massive amount of Identity nodes for broadcasting,
	// for example, broadcast 1 to 256 needs 256 Identity nodes and 1 Concat node.
	// Possible improvement is to use "Scale".
	expandMid(layerParams.name, node_proto, srcName, targetShape[broadcast_axes[0]]);
	layerParams.set("axis", broadcast_axes[0]);
	layerParams.type = "Concat";
	node_proto.set_output(0, output_name);

Since now we have cv::broadcast already, it is time to do a refactor on expand.

Checklist:

• handle all-input-constant case
• fix expand_neg_batch.onnx
• take care of 0d/1d tensor

Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

• I agree to contribute to the project under Apache 2 License.
• To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
• The PR is proposed to the proper branch
• There is a reference to the original bug report and related work
• There is accuracy test, performance test and test data in opencv_extra repository, if applicable
  Patch to opencv_extra has the same branch name.
• The feature is well documented and sample code can be built with the project CMake

[asmorkalov]

@fengyuentau Could the patch unlock some "conformance" tests for ONNX?

[fengyuentau]

@asmorkalov Unfortunately, all Expand conformance tests have input shape as non-constant, which cannot be supported in the current dnn engine. However, I can still convert all of them with constant shape. Let me do it.

[fengyuentau]

CI will not pass due to #24308. Need to fix the problem in another PR first.

Decided to fix the issue here in this pull request. Basically the wrong result is given because of shape is used as input shape for Expand, which has value [2, -1, -1, -1] (See line 838):

opencv/modules/dnn/src/onnx/onnx_graph_simplifier.cpp

Lines 824 to 840 in 3889dcf

	class ExpandSubgraph : public Subgraph
	{
	public:
	ExpandSubgraph()
	{
	int input = addNodeToMatch("");
	int values = addNodeToMatch("");
	int init = addNodeToMatch("ConstantOfShape", values);
	int coeff = addNodeToMatch("Constant");
	int mul = addNodeToMatch("Mul", init, coeff);
	int shape = addNodeToMatch("Constant");
	int condition = addNodeToMatch("Equal", shape, mul);
	int where = addNodeToMatch("Where", condition, init, addNodeToMatch("Constant"));
	addNodeToMatch("Expand", input, where);
	setFusedNode("Expand", input, shape);
	}
	};

[fengyuentau]

CI is not passing again because of #24300

[fengyuentau]

All tests are green now.